diff --git a/.github/workflows/build-bench.yml b/.github/workflows/build-bench.yml index 7220d27..bb67a12 100644 --- a/.github/workflows/build-bench.yml +++ b/.github/workflows/build-bench.yml @@ -18,7 +18,7 @@ jobs: run: | python -m ensurepip python -m pip install --upgrade pip - python -m pip install poetry==1.1.13 + python -m pip install poetry==1.3.2 - name: Install dependencies shell: bash run: python -m poetry install diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 4bfdfe9..01cb724 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -18,7 +18,7 @@ jobs: run: | python -m ensurepip python -m pip install --upgrade pip - python -m pip install poetry==1.1.13 + python -m pip install poetry==1.3.2 - name: View poetry --help run: poetry --help - name: Install dependencies diff --git a/.github/workflows/test-bench.yml b/.github/workflows/test-bench.yml index 77a2cff..a1a45fa 100644 --- a/.github/workflows/test-bench.yml +++ b/.github/workflows/test-bench.yml @@ -20,7 +20,7 @@ jobs: run: | python -m ensurepip python -m pip install --upgrade pip - python -m pip install poetry==1.1.13 + python -m pip install poetry==1.3.2 - name: View poetry --help run: poetry --help diff --git a/.gitignore b/.gitignore index def6f50..daa7315 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,7 @@ __pycache__/ # Distribution / packaging .Python build/ +_build/ develop-eggs/ dist/ downloads/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 77d59f9..42288ba 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,15 +7,15 @@ repos: - id: requirements-txt-fixer - id: trailing-whitespace - id: mixed-line-ending + - repo: https://github.com/asottile/pyupgrade + rev: v3.4.0 + hooks: + - id: pyupgrade - repo: https://github.com/psf/black - rev: 22.1.0 + rev: 22.10.0 hooks: - id: black - - repo: https://github.com/pre-commit/mirrors-mypy - rev: v0.940 - hooks: - - id: mypy - - repo: https://github.com/asottile/pyupgrade - rev: v2.31.0 - hooks: - - id: pyupgrade \ No newline at end of file + # - repo: https://github.com/pycqa/flake8 + # rev: 6.0.0 + # hooks: + # - id: flake8 diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..8d2aa0b --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,17 @@ +# Required +version: 2 + +# Set the OS, Python version and other tools you might need +build: + os: "ubuntu-22.04" + tools: + python: "3.10" + jobs: + post_create_environment: + - pip install poetry + - poetry config virtualenvs.create false + post_install: + - poetry install + +sphinx: + configuration: docs/conf.py \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 2089543..0000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,24 +0,0 @@ -# How to contribute - -I'm really glad you're reading this, because we need volunteer developers to help this project come to fruition. - -## Testing - -## Submitting changes - -Please send a [GitHub Pull Request SimplePhysicalImage](https://github.com/deepskies/SimplePhysicalImage/pull/new/master) with a clear list of what you've done (read more about [pull requests](http://help.github.com/pull-requests/)). When you send a pull request, we will love you forever if you include examples. We can always use more test coverage. Please follow our coding conventions (below) and make sure all of your commits are atomic (one feature per commit). - -Always write a clear log message for your commits. One-line messages are fine for small changes, but bigger changes should look like this: - - $ git commit -m "A brief summary of the commit - > - > A paragraph describing what changed and its impact." - -## Coding conventions - -Start reading our code and you'll get the hang of it. We optimize for readability: - - * We indent using tabs - * We ALWAYS put spaces after list items and method parameters (`[1, 2, 3]`, not `[1,2,3]`), around operators (`x += 1`, not `x+=1`), and around hash arrows. - * This is open source software. Consider the people who will read your code, and make it look nice for them. It's sort of like driving a car: Perhaps you love doing donuts when you're alone, but with passengers the goal is to make the ride as smooth as possible. - diff --git a/README.md b/README.md index 7b9a450..f202660 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,14 @@ -# DeepBench +![GitHub Workflow Status](https://github.com/deepskies/DeepBench/actions/workflows/test-bench.yml/badge.svg?label=test) +[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) + [![PyPI version](https://badge.fury.io/py/deepbench.svg)](https://badge.fury.io/py/deepbench) + + + +![DeepBench Logo](docs/repository_support/DeepSkies_Logos_DeepBench.png) ### What is it? Simulation library for very simple simulations to *benchmark* machine learning algorithms. -![DeepBench Logo](/repository_support/images/DeepSkies_Logos_DeepBench.png) + ### Why do we need it? Why is it useful? @@ -10,73 +16,184 @@ Simulation library for very simple simulations to *benchmark* machine learning a 2. A very simple data set will have objects, patterns, and signals that are intuitively quanitifiable and will be fast to generate. 3. A very simple data set will be a great testing ground for new networks and for newcomers to practice with the technology. +## Documentation + +#### Readthedocs link coming soon!! + +#### To build +``` +pip install sphinx +cd docs +make html +``` + +The folder `docs/_build/html` will be populated with the documentation. Navigate to `file:////docs/_build/html/index.html` in any web browser to view. ## Requirements -1. python 3.x +* python = ">=3.8,<3.11," +* numpy = "^1.24.3" +* matplotlib = "^3.7.1" +* scikit-image = "^0.20.0" +* astropy = "^5.2.2" +* autograd = "^1.5" +* pyyaml = "^6.0" + + + +## Install -You can use conda and the 'environment.yml' file to generate a conda environment for this project. +### From PyPi +``` +pip install deepbench +``` +### From Source + +``` +git clone https://github.com/deepskies/DeepBench.git +pip install poetry +poetry shell +poetry install +poetry run pytest --cov +``` ## General Features 1. very fast to generate 2. Mimics in a very basic / toy way what is in astro images 3. Be fully controllable parametrically +![DeepBench Logo](docs/repository_support/DeepBench.png) + +### Included Simulations + +1. Astronomy Objects - simple astronomical object simulation +- Galaxy, Spiral Galaxy, Star + +2. Shapes - simple 2D geometric shapes +- Rectangle, Regular Polygon, Arc, Line, Ellipse + +3. Physics Objects - simple physics simulations +- Neutonian Pendulum, Hamiltonian Pendulum ## Example -![Example Image of pipeline](/repository_support/images/example_simplephysicalimage.png) - - -## Planned Features -1. Kinds of data to mimic - 1. Strong lenses: Arcs, circles - 2. Supernovae: light curves - 3. Quasars: Skewed Sine wave - 4. N-body simulations: Points in 2D and 3D and in lightcones - 5. Galaxy clusters: Optical - points and 2d kernels; SZ - blurred circles; X-ray - blurred circles - 6. Spectra (stellar, galactic) - 7. Noise: photon, psf -2. Dimensions of data - 1. point / graph - 2. 1D, 2D, 3D, 4D (space-time), 6D (phase space) - 3. Shapes: Polygons, Circles, squares, Polyhedrons, Spheres, cubes -3. Top Data Sets for Initial Release: - 1. Strong lenses - 2. Light curves - - -## Original Development Team + +### Standalone +* Produce 3 instance of a pendulum over 10 different times with some level of noise. +``` +import numpy as np +from deepbench.collection import Collection + +configuration = { + "object_type": "physics", + "object_name": "Pendulum", + "total_runs": 3, + "parameter_noise": 0.2, + "image_parameters": { + "pendulum_arm_length": 2, + "starting_angle_radians": 0.25, + "acceleration_due_to_gravity": 9.8, + "noise_std_percent":{ + "acceleration_due_to_gravity": 0 + }, + "object_parameters":{ + "time": np.linspace(0, 1, 10) + } +} + +phy_objects = Collection(configuration)() + +objects = phy_objects.objects +parameters = phy_objects.object_parameters +``` + +* Produce a noisy shape image with a rectangle and an arc + +``` +import numpy as np +from deepbench.collection import Collection + +configuration = { + "object_type": "shape", + "object_name": "ShapeImage", + + "total_runs": 1, + "image_parameters": { + "image_shape": (28, 28), + "object_noise_level": 0.6 + }, + + "object_parameters": { + [ + "rectangle": { + "object": { + "width": np.random.default_rng().integers(2, 28), + "height": np.random.default_rng().integers(2, 28), + "fill": True + }, + "instance": {} + }, + "arc":{ + "object": { + "radius": np.random.default_rng().integers(2, 28), + "theta1":np.random.default_rng().integers(0, 20), + "theta2":np.random.default_rng().integers(21, 180) + }, + "instance":{} + } + + ] + } +} + +shape_image = Collection(configuration)() + +objects = shape_image.objects +parameters = shape_image.object_parameters +``` + + +### Fine-Grained Control +* Make a whole bunch of stars +``` +from deepbench.astro_object import StarObject +import numpy as np + +star = StarObject( + image_dimensions = (28,28), + noise = 0.3, + radius= 0.8, + amplitude = 1.0 + ) + +generated_stars = [] +x_position, y_position = np.random.default_rng().uniform(low=1, high=27, size=(2, 50)) +for x_pos, y_pos in zip(x_position, y_position): + generated-stars.append(star.create_object(x_pos, y_pos)) +``` + + +## Contributions +### Original Team 1. Craig Brechmos 2. Renee Hlozek 3. Brian Nord +### Refactor and Deployment +1. Ashia Livaudais +2. M. Voetberg -## How to contribute -I'm really glad you're reading this, because we need volunteer developers to help this project come to fruition. +### Pendulum Team +1. Becky Nevin +2. Omari Paul -### Testing +## Contributing +[Please view the deepskies contribution guidelines before submitting a code addition](https://github.com/deepskies/.github/blob/main/CONTRIBUTING.md) -### Submitting changes +## Acknowledgement -Please send a [GitHub Pull Request to simplephysicaliage](https://github.com/deepskies/SimplePhysicalImage/pull/new/master) with a clear list of what you've done (read more about [pull requests](http://help.github.com/pull-requests/)). When you send a pull request, we will love you forever if you include examples. We can always use more test coverage. Please follow our coding conventions (below) and make sure all of your commits are atomic (one feature per commit). -Always write a clear log message for your commits. One-line messages are fine for small changes, but bigger changes should look like this: +This work was produced by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy. Publisher acknowledges the U.S. Government license to provide public access under the DOE Public Access Plan DOE Public Access Plan. +Neither the United States nor the United States Department of Energy, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any data, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. - $ git commit -m "A brief summary of the commit - > - > A paragraph describing what changed and its impact." - -### Coding conventions - -Start reading our code and you'll get the hang of it. We optimize for readability: - - * We indent using tabs - * We ALWAYS put spaces after list items and method parameters (`[1, 2, 3]`, not `[1,2,3]`), around operators (`x += 1`, not `x+=1`), and around hash arrows. - * This is open source software. Consider the people who will read your code, and make it look nice for them. It's sort of like driving a car: Perhaps you love doing donuts when you're alone, but with passengers the goal is to make the ride as smooth as possible. -![GitHub Workflow Status](https://img.shields.io/github/workflow/status/AeRabelais/DeepBenchmark/build-bench) -![GitHub Workflow Status](https://img.shields.io/github/workflow/status/AeRabelais/DeepBench/test-bench?label=test) -[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) - [![PyPI version](https://badge.fury.io/py/deepbench.svg)](https://badge.fury.io/py/deepbench) -# DeepBench - A library for the simulation of useful physics benchmark datasets. +We acknowledge the Deep Skies Lab as a community of multi-domain experts and collaborators who’ve facilitated an environment of open discussion, idea-generation, and collaboration. This community was important for the development of this project. diff --git a/archive/Catalog.ipynb b/archive/Catalog.ipynb deleted file mode 100644 index 2f651ed..0000000 --- a/archive/Catalog.ipynb +++ /dev/null @@ -1,237 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "0. This example file creates a Catalog object with series of events that \n", - " can be generated using the generate_draw module. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "## Necessary Components \n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import os\n", - "\n", - "import deepbench.module_data.generate_data as gdata\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "is_executing": false, - "name": "#%% md\n" - } - }, - "source": [ - "1. Create Individual Catalogs for Objects in Dataset\n", - " Catalog(n_obj, n_pix_side, myclass,\n", - " radius, amplitude,\n", - " id_start=0, obj_params=None)\n", - " Possible classes include 'star', 'galaxy', or 'lens'. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of Objects in galaxy_catalog: 3\n", - "Number of Objects in star_catalog: 3\n", - "Number of Objects in lens_catalog: 3\n" - ] - } - ], - "source": [ - "n_galaxies = 3\n", - "img_dim = 256\n", - "galaxy_rads = np.random.uniform(20, 100, (n_galaxies, 1))\n", - "galaxy_amps = np.random.uniform(4.5, 8.5, (n_galaxies, 1))\n", - "\n", - "#generate obj parameters if necessary (not for galaxy objects)\n", - "galaxy_catalog = gdata.Catalog(n_obj=n_galaxies, n_pix_side=img_dim,\n", - " myclass='galaxy', radius= galaxy_rads,\n", - " amplitude= galaxy_amps)\n", - "print('Number of Objects in galaxy_catalog: {}'.format(len(galaxy_catalog)))\n", - "\n", - "n_stars = 3\n", - "img_dim = 256\n", - "rad_min, rad_max = 5, 20\n", - "amp_min, amp_max = 4.5, 8.5\n", - "\n", - "star_rads = np.random.uniform(rad_min, rad_max, (n_stars, 1))\n", - "star_amps = np.random.uniform(amp_min, amp_max, (n_stars, 1))\n", - "\n", - "#generate obj parameters if necessary (not for star objects)\n", - "star_catalog = gdata.Catalog(n_obj=n_stars, n_pix_side=img_dim, myclass='star', \n", - " radius=star_rads, amplitude=star_amps)\n", - "print('Number of Objects in star_catalog: {}'.format(len(star_catalog)))\n", - "\n", - "#Gravitational Lenses require obj_params initialization.\n", - "n_gravLens = 3\n", - "lens_params = []\n", - "\n", - "for i in range(n_gravLens):\n", - " #how many individual arcs comprise the grav lens\n", - " num_arcs = np.random.randint(1, 7) \n", - " arc_list = []\n", - " arc_rad = np.random.uniform(rad_max, 50)\n", - " for j in range(num_arcs):\n", - " width = np.random.uniform(0.8, 3.)\n", - " theta1 = np.random.uniform(0., 360.)\n", - " theta2 = np.random.uniform(theta1, theta1+360.)\n", - " intensity = np.random.uniform(0.5, 1.3)\n", - " arc = [arc_rad, width, theta1, theta2, intensity]\n", - " arc_list.append(arc)\n", - " lens_params.append(arc_list)\n", - "\n", - "rad_min, rad_max = 5, 20\n", - "amp_min, amp_max = 4.5, 8.5\n", - "\n", - "lens_rads = np.random.uniform(rad_min, rad_max, (n_gravLens, 1))\n", - "lens_amps = np.random.uniform(amp_min, amp_max, (n_gravLens, 1))\n", - "\n", - "\n", - "lens_catalog = gdata.Catalog(n_obj=n_gravLens, n_pix_side=img_dim, myclass='lens', \n", - " radius=lens_rads, amplitude=lens_amps,\n", - " obj_params=lens_params)\n", - "\n", - "print('Number of Objects in lens_catalog: {}'.format(len(lens_catalog)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "2. Combine the two datasets and write out the datafile. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Length of Combined Catalog: 9\n" - ] - } - ], - "source": [ - "#Combine datasets\n", - "combined_catalog = gdata.concat_catalogs(lens_catalog, [star_catalog.data, galaxy_catalog.data])\n", - "print('Length of Combined Catalog: {}'.format(len(combined_catalog)))\n", - "\n", - "#Write out information\n", - "file_dir = r\"C:\\Users\\Tristan Paul Schefke\\Desktop\\Projects\\DeepBench\\examples\\data\\CatalogExample\"\n", - "if not os.path.exists(file_dir):\n", - " os.mkdir(file_dir)\n", - "file_name = \"training_data.csv\"\n", - "combined_catalog.to_csv(file_dir, file_name)\n", - "\n", - "#generate images\n", - "combined_catalog.generate_imageset()\n", - "combined_catalog.save_images(file_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Anaconda3\\envs\\deepBench\\lib\\site-packages\\ipykernel_launcher.py:10: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", - " # Remove the CWD from sys.path while we load stuff.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "img = combined_catalog.images\n", - "labels = combined_catalog.data['myclass']\n", - "plt.subplot(3,3,1)\n", - "total_objs = n_galaxies + n_stars + n_gravLens\n", - "data = combined_catalog.get_data()\n", - "names = ['lens', 'lens', 'lens',\n", - " 'star', 'star', 'star',\n", - " 'galaxy', 'galaxy', 'galaxy']\n", - "for i in range(len(img)):\n", - " plt.subplot(3,3,i+1)\n", - " plt.axis('off')\n", - " plt.contourf(img[i], cmap='viridis')\n", - " plt.title(names[i], y = -0.25)\n", - "plt.savefig('example_Objects.jpg')\n", - "plt.show()\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/archive/Example.ipynb b/archive/Example.ipynb deleted file mode 100644 index 9322e8c..0000000 --- a/archive/Example.ipynb +++ /dev/null @@ -1,573 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.patches import Wedge, Rectangle\n", - "from matplotlib.path import Path\n", - "from bokeh.plotting import figure, show, output_notebook, gridplot" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "2WU-r1QY8RsH" - }, - "source": [ - "# Create Methods for Structure Creation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "EJXwnaDh5o0Z" - }, - "outputs": [], - "source": [ - "def _patch2im(thepatch, imshape):\n", - " # Create meshgrid of coordinates\n", - " y = np.arange(1, imshape[0])\n", - " x = np.arange(1, imshape[1])\n", - " g = np.meshgrid(x, y)\n", - "\n", - " coords = np.array(list(zip(*(c.flat for c in g))))\n", - "\n", - " # Create path\n", - " elpath = Path(thepatch.get_verts())\n", - "\n", - " # Determine all points in the grid ithat are in the path.\n", - " validcoords = elpath.contains_points(coords)\n", - " ellipsepoints = coords[validcoords]\n", - "\n", - " outim = np.zeros(imshape)\n", - " outim[ellipsepoints[:, 0], ellipsepoints[:, 1]] = 1\n", - "\n", - " return outim\n", - "\n", - "\n", - "def create_polygon(imshape, xy, width, height, angle=0.0, fill=True):\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " rect = Rectangle(xy, width, height, angle=0.0, fill=fill)\n", - "\n", - " return _patch2im(rect, imshape)\n", - "\n", - "\n", - "def create_circle(imshape, center, radius, width):\n", - " \"\"\"\n", - " Create a cirlce.\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " return create_arc(imshape, center, radius, width=width, theta1=0, theta2=360)\n", - " \n", - "def create_line(imshape, center, radius, width):\n", - " \"\"\"\n", - " Create a cirlce.\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " # return create_line(imshape, center, radius, width=width, theta1=0, theta2=359)\n", - "\n", - "\n", - "def create_arc(imshape, center, radius, width, theta1, theta2):\n", - " \"\"\"\n", - " Create an arc (outline of a circle) with a specified\n", - " thickness (width).\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " arc = Wedge(center, radius, theta1, theta2, width)\n", - "\n", - " return _patch2im(arc, imshape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "dfiXlE9R8Wd7" - }, - "source": [ - "# Create an Example Lensing Image" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "yoKEbjT05rb8" - }, - "outputs": [], - "source": [ - "imblah = np.zeros((256, 256))\n", - "\n", - "# Example arc\n", - "arc = create_arc(imblah.shape, center=(150, 128), radius=56, width=6, theta1=27, theta2=156)\n", - "imblah += arc * 35\n", - "\n", - "# Example filled circle\n", - "circle = create_circle(imblah.shape, center=(50, 28), radius=56, width=56)\n", - "imblah += circle * 21\n", - "\n", - "# Example unfilled circle\n", - "circle = create_circle(imblah.shape, center=(28, 200), radius=50, width=8)\n", - "imblah += circle * 61\n", - "\n", - "# Example polygon\n", - "rect = create_polygon(imblah.shape, (150, 28), width=27, height=44)\n", - "imblah += rect * 13" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Make Image" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 617 - }, - "colab_type": "code", - "id": "Eiv5UqjfdufR", - "outputId": "be1714c1-ff26-4534-d894-51e0fb69fdee" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"1040\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"1040\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1040' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " \n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n", - " var css_urls = [];\n", - " \n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " function(Bokeh) {\n", - " \n", - " \n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if (root.Bokeh !== undefined || force === true) {\n", - " \n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - " if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"1040\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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- "p = figure(x_range=(1, 256), y_range=(1, 256))\n", - "\n", - "# must give a vector of image data for image parameter\n", - "p.image(image=[imblah], x=1, y=1, dw=256, dh=256, palette=\"Spectral11\")\n", - "\n", - "output_notebook()\n", - "\n", - "show(p) # open a browser" - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "include_colab_link": true, - "name": "Lensing Simulations", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/archive/deepbench/module_data/Catalog.py b/archive/deepbench/module_data/Catalog.py deleted file mode 100644 index 3dafafe..0000000 --- a/archive/deepbench/module_data/Catalog.py +++ /dev/null @@ -1,115 +0,0 @@ -import os -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import numpy.random as rand -from ..module_data.generate_data import * - - -class Catalog: - """ - The Catalog object stores objects of DeepBench in a way so that it is easy to generate - imagesets and store the data in a csv file. - - Attributes: - n_obj (int): - pix_dim (int): dimension of images stored in Catalog - data (pandas DataFrame): Data structure storing information - on Catalog objects - images (List or ListLike): images of Catalog's objects in data - - """ - def __init__(self, n_obj, n_pix_side,id_start=0): - - """ - Constructor for Catalog class. - - Parameters: - n_obj (int): number of objects to be stored - n_pix_side (int): image dimension in pixels - myclass (string): type of object being created. - Print self.poss_classes to see - all options - radius (float): radius of object drawn in the image - amplitude (float, [0, 3]): amplitude of object in image - id_start (int): start of index in data - obj_params (List): other parameters passed to DeepBench object for generating it - - - """ - - # Set parameters for DataFrame object - self.data = pd.DataFrame(n_obj) - self.data.index += id_start - - # Other catalog parameters - self.n_obj = n_obj - self.pix_dim = n_pix_side - self.images = None - - def __len__(self): - """ Return length of DataFrame in Catalog - """ - return len(self.data) - - def generate_imageset(self): - """ Populates catalog's self.images array with images of specified - objects in self.data - """ - self.images = np.zeros((self.n_obj, self.pix_dim, self.pix_dim)) - for ind, _ in enumerate(self.images): - self.images[ind] = self._generate_image(self.data.loc[ind], self.pix_dim) - - def _generate_image(self, catalog_single, n_pix_side): - pass - - - def save_images(self, path): - """ - Save images to a desired folder given. - - Parameters: - path (string or PathLike): Folder where images will be saved - - Returns: - Images saved - """ - assert self.images is not None, "Must generate images before saving. " \ - "Try calling catalog.generate_imageset() first." - if not os.path.exists(path): - os.mkdir(path) - for row, img in enumerate(self.images): - fig = plt.figure(frameon=False) - plt.contourf(img, cmap='viridis') - plt.axis('off') - name = os.path.join(path, str(row) + '.jpg') - fig.savefig(name, bbox_inches='tight', pad_inches=0) - plt.close() - - def to_csv(self, path, name): - """ - - """ - if not os.path.exists(path): - os.mkdir(path) - path = os.path.join(path, name) - return self.data.to_csv(path) - - def get_data(self): - """ - Return the data stored in the Catalog object - """ - return self.data - - def set_data(self, dataset): - """ - Set the data object in the Catalog with a new dataFrame. - Parameters: - dataset (pandas.DataFrame): object to replace Catalog's data - """ - if isinstance(dataset, pd.DataFrame): - self.data = dataset - self.images = None - else: - print("set_data must receive a DataFrame") - return diff --git a/archive/deepbench/module_data/__init__.py b/archive/deepbench/module_data/__init__.py deleted file mode 100644 index d3f5a12..0000000 --- a/archive/deepbench/module_data/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/archive/deepbench/module_data/generate_data.py b/archive/deepbench/module_data/generate_data.py deleted file mode 100644 index 950ecb0..0000000 --- a/archive/deepbench/module_data/generate_data.py +++ /dev/null @@ -1,368 +0,0 @@ -__all__ = ['Catalog', 'concat_catalogs', 'empty_imshape', 'create_meshgrid', - 'create_Sersic2D','create_Moffat2D','create_Gaussian','create_noise', - 'combine_sky_imshape','create_lensing_event','create_psf'] - -import os -import numpy as np -import numpy.random as rand -import pandas as pd -import scipy.signal -import scipy.ndimage - -from typing import Union -from matplotlib import pyplot as plt -from astropy.modeling.models import Sersic2D, Gaussian2D, Moffat2D -from module_draw.generate_draw import * - -class Catalog: - """ - The Catalog object stores objects of DeepBench in a way so that it is easy to generate - imagesets and store the data in a csv file. - - Attributes: - n_obj (int): - pix_dim (int): dimension of images stored in Catalog - data (pandas DataFrame): Data structure storing information - on Catalog objects - images (List or ListLike): images of Catalog's objects in data - - """ - column_names = ['x_center', 'y_center', - 'radius', 'amplitude', 'myclass', - 'obj_params'] - poss_classes = {'star': 0,'galaxy': 1, 'lens': 2} - - def __init__(self, n_obj, n_pix_side, myclass, - radius, amplitude, id_start=0, - obj_params=None): - - """ - Constructor for Catalog class. - - Parameters: - n_obj (int): number of objects to be stored - n_pix_side (int): image dimension in pixels - myclass (string): type of object being created. - Print self.poss_classes to see - all options - radius (float): radius of object drawn in the image - amplitude (float, [0, 3]): amplitude of object in image - id_start (int): start of index in data - obj_params (List): other parameters passed to DeepBench object for generating it - - - """ - - # Set parameters for DataFrame object - self.data = pd.DataFrame( - np.empty((n_obj, len(self.column_names))), - columns=self.column_names) - self.data.index += id_start - self.data.x_center = (n_pix_side/2) * np.ones((n_obj,1)) - self.data.y_center = (n_pix_side/2) * np.ones((n_obj,1)) - self.data.radius = radius - self.data.amplitude = amplitude - self.data.myclass = self.poss_classes[myclass] - self.data.obj_params = obj_params - - # Other catalog parameters - self.n_obj = n_obj - self.pix_dim = n_pix_side - self.images = None - - def __len__(self): - """ Return length of DataFrame in Catalog - """ - return len(self.data) - - def generate_imageset(self): - """ Populates catalog's self.images array with images of specified - objects in self.data - """ - self.images = np.zeros((self.n_obj, self.pix_dim, self.pix_dim)) - for ind, _ in enumerate(self.images): - self.images[ind] = self._generate_image(self.data.loc[ind], - self.pix_dim) - - def _generate_image(self, catalog_single, n_pix_side): - add_noise = True - imshape = empty_imshape(n_pix_side) - noise = rand.uniform(0.9, 1.3) - noise_profile = 0 - - if catalog_single.myclass == 2: - # lensing Event - imshape = create_lensing_event(imshape, - center=(catalog_single.x_center, catalog_single.y_center), - arc_obj=catalog_single.obj_params, star_radius=catalog_single.radius, - star_intensity=catalog_single.amplitude) - noise_profile = create_noise(imshape, noise) - - if catalog_single.myclass == 1: - # galaxy event - imshape = create_Sersic2D(imshape, x_0=catalog_single.x_center, - y_0=catalog_single.y_center, - amplitude=catalog_single.amplitude, - radius=catalog_single.radius, - ellip=rand.uniform(0.1, 0.9), - theta=rand.uniform(-1.5, 1.5)) - noise_profile = create_noise(imshape, noise*10.0) - - if catalog_single.myclass == 0: - # star event - imshape = create_Moffat2D(imshape, x_0=catalog_single.x_center, - y_0=catalog_single.y_center, - amplitude=catalog_single.amplitude, - radius=catalog_single.radius) - - noise_profile = create_noise(imshape, noise) - if add_noise: - imshape += noise_profile - imshape = create_psf(imshape) - return imshape - - def save_images(self, path): - """ - Save images to a desired folder given. - - Parameters: - path (string or PathLike): Folder where images will be saved - - Returns: - Images saved - """ - assert self.images is not None, "Must generate images before saving. " \ - "Try calling catalog.generate_imageset() first." - if not os.path.exists(path): - os.mkdir(path) - for row, img in enumerate(self.images): - fig = plt.figure(frameon=False) - plt.contourf(img, cmap='viridis') - plt.axis('off') - name = os.path.join(path, str(row) + '.jpg') - fig.savefig(name, bbox_inches='tight', pad_inches=0) - plt.close() - - def to_csv(self, path, name): - """ - - """ - if not os.path.exists(path): - os.mkdir(path) - path = os.path.join(path, name) - return self.data.to_csv(path) - - def get_data(self): - """ - Return the data stored in the Catalog object - """ - return self.data - - def set_data(self, dataset): - """ - Set the data object in the Catalog with a new dataFrame. - Parameters: - dataset (pandas.DataFrame): object to replace Catalog's data - """ - if isinstance(dataset, pd.DataFrame): - self.data = dataset - self.images = None - else: - print("set_data must receive a DataFrame") - return - - -def concat_catalogs(target, frames: Union[pd.DataFrame, list]): - """ - Append data to end of target. - Parameters: - target (Catalog object): Catalog object to be returned with new data. - frames (pandas.DataFrame or list): dataFrames to append to target given individually or in a list. - - Returns: - Returns target Catalog object with the appended dataFrame. - - """ - if isinstance(frames, pd.DataFrame): - frames = [frames] - - target.data = target.data.append(frames, ignore_index=True) - target.n_obj = len(target.data) - target.images = np.zeros((target.pix_dim, target.pix_dim, len(target))) - return target - - -def empty_imshape(n_pix_side): - """ - Return zeroed numpy array with square dimensions. - Parameter: - n_pix_side (unsigned int): dimension of sides of numpy array - - Return: - numpy array of size [n_pix_side, n_pix_side] initialized to zero - - """ - return np.zeros((n_pix_side, n_pix_side)) - - -def create_meshgrid(imshape): - """ - Create mesh grid used for Sersic and Moffat distributions. - Parameters: - imshape (numpy array): Used to determine the shape of returned mesh grid - - Returns: - numpy meshgrid object - """ - - return np.meshgrid(np.arange(np.shape(imshape)[0]), np.arange(np.shape(imshape)[1])) - - -def create_Sersic2D(imshape, amplitude=1, x_0=50, - y_0=50, radius=25, - n=1, ellip=.5, theta=-1): - """ - Create a galaxy profile defined by a Sersic distribution - Parameters: - imshape (numpy.array): image shape given for calculating size of distribution - amplitude (float): amplitude of the Sersic distribution - x_0 (float): horizontal position of center - y_0 (float): vertical position of center - radius (float): radius of major axis of the distribution - n (int (0.5, 10) ): Sersic number - ellip (float (0,1)): eccentricity of the Sersic distribution - theta (float): orientation angle of the major axis - Returns: - numpy.array with galaxy distribution - - """ - - x, y = create_meshgrid(imshape) - mod = Sersic2D(amplitude=amplitude, - x_0=x_0, y_0=y_0, - r_eff=radius, n=n, - ellip=ellip, theta=theta) - return mod(x, y) - - -def create_Moffat2D(imshape, amplitude=1., x_0=0., - y_0=0., radius=1., alpha=1.): - """ - Create a star profile defined by a Moffat distribution - Parameters: - imshape (numpy.array): image shape given for calculating size of distribution - amplitude (float): amplitude of the Moffat distribution - x_0 (float): horizontal position of center - y_0 (float): vertical position of center - radius (float): radius of major axis of the distribution - alpha (float): power index of the distribution - Returns: - numpy.array with star profile - """ - x, y = create_meshgrid(imshape) - mod = Moffat2D(amplitude=amplitude, x_0=x_0, - y_0=y_0, gamma=radius, alpha=alpha) - - return mod(x, y) - - -def create_Gaussian(imshape, amplitude=1, x_mean=0, y_mean=0, - x_stddev=None, y_stddev=None, - theta=None): - """ - Create a Gaussian distribution - Parameters: - imshape (numpy.array): image shape given for calculating size of distribution - amplitude (float): amplitude of the Moffat distribution - x_mean (float): average value of x - y_mean (float): average value of y - x_stddev (float): standard deviation in x - y_stddev (float): standard deviation in y - theta (float): rotation angle of distribution - - Returns: - numpy.array with Gaussian profile - """ - - x, y = create_meshgrid(imshape) - - mod = Gaussian2D(amplitude=amplitude, - x_mean=x_mean, y_mean=y_mean, - x_stddev=x_stddev, y_stddev=y_stddev, - theta=theta) - - return mod(x, y) - - -def create_noise(imshape, noise_level, seed=42): - """ - Generate Poisson noise into images. - Parameters: - imshape (numpy.array): image to generate noise for - noise_level (unsigned float): Poisson lambda parameter - seed (float): random seed for reproducibility - Return: - numpy.array to be added to image. - """ - - rs = rand.RandomState(seed) - return rs.poisson(noise_level, size=imshape.shape) - - -def create_psf(imshape): - - return scipy.ndimage.gaussian_filter(imshape, sigma=0.7) - - -def combine_sky_imshape(imshape_list, amplitude_list): - """ - Combine clean sky images. - Parameters: - imshape_list (list): image objects to combine - amplitude_list (list): amplitudes of the given imshapes - Return: - (numpy.array) Single image with imshape_list objects in it. - """ - imshape_new = np.zeros((imshape_list[0].shape[0], - imshape_list[0].shape[1])) - - for imshape, amplitude in zip(imshape_list, amplitude_list): - imshape_new += amplitude * imshape - return imshape_new - - -def create_lensing_event(canvas, center=(64., 64.), arc_obj=None, - star_radius=0.0, star_intensity=1.0): - """ - Generate an image that mimics the geometric realities of a gravitational lens. - Parameters: - canvas (numpy.array): image object that will include gravitational lens - center (Tuple(float, float)): center of lensing event - arc_obj( list(list(float)) ): list of arc objects and their parameters - arc: [radius, arc width, start_angle, end_angle] - star_radius (float): radius of star in image - star_intensity (float): intensity of star in image. - Returns: - (numpy.array) Image of gravitational lensing event. - """ - if star_radius == 0.0: - hasStar = rand.uniform(0., 1.) < 0.6 - if hasStar: - star_radius = rand.uniform(5.0, 20.0) - star_intensity = rand.uniform(0.5, 2.0) - star = create_Moffat2D(canvas, amplitude=star_intensity, - x_0=center[0], y_0=center[1], - radius=star_radius) - canvas += star - else: - star = create_Moffat2D(canvas, amplitude=star_intensity, - x_0=center[0], y_0=center[1], - radius=star_radius) - canvas += star - - for arc in arc_obj: - arcShape = create_arc(canvas, (center[0], center[1]), radius=arc[0], - width=arc[1], theta1=arc[2], - theta2=arc[3]) - canvas += arcShape * arc[4] - return canvas diff --git a/archive/deepbench/module_draw/AstroObject.py b/archive/deepbench/module_draw/AstroObject.py deleted file mode 100644 index 6f7b39e..0000000 --- a/archive/deepbench/module_draw/AstroObject.py +++ /dev/null @@ -1,36 +0,0 @@ -import numpy.random as rand -import numpy as np -import scipy.ndimage -from deepbench.module_draw.generate_draw import * - - -class AstroObject: - - def __init__(self, position, radius, amplitude, image_noise, gaussian_blur, image_dim): - """ - :param position: - :param radius: - :param amplitude: (float) magnitude of image - :param image_noise: (float) lambda of Poissonian distribution for image noise - :param gaussian_blur: (float) deviation (sigma) of Gaussian - :param image_dim: (int) image dimensions - """ - self.center = position - self.radius = radius - self.amplitude = amplitude - self.image_noise = image_noise - self.image_blur = gaussian_blur - self.image = np.zeros((image_dim, image_dim)) - - def create_psf(self): - """ - :return: - """ - - self.image = scipy.ndimage.gaussian_filter(self.image, sigma=self.image_blur) - - def create_noise(self): - """ - :return: - """ - self.image += rand.poisson(lam=self.image_noise, size=self.image.shape) \ No newline at end of file diff --git a/archive/deepbench/module_draw/Star.py b/archive/deepbench/module_draw/Star.py deleted file mode 100644 index 8995fc2..0000000 --- a/archive/deepbench/module_draw/Star.py +++ /dev/null @@ -1,46 +0,0 @@ -from deepbench.module_data.generate_data import * -from deepbench.module_draw.generate_draw import * -from deepbench.module_draw.AstroObject import * - -class Star(AstroObject): - - classname = 'Star' - - def __init__(self, center, radius, amplitude, pois_noise, gaussianBlur, img_dim): - """ - - :param center: Tuple (float, float) - :param radius: - :param amplitude: - :param pois_noise: - :param gaussianBlur: - """ - super.__init__(Star, self, position=center, radius=radius, amplitude=amplitude, - image_noise=pois_noise, gaussian_blur=gaussianBlur, image_dim=img_dim) - - def create(self, n_pix_side): - """ - Return image of specific size - :param n_pix_side: specified size - :return: imshape to be saved - """ - - imshape = empty_imshape(n_pix_side) - # star event - imshape = create_Moffat2D(imshape, x_0=round(self.center[0]), - y_0=round(self.center[1]), - amplitude=self.amplitude, - radius=self.radius) - - if self.noise is not 0.: - noise_profile = create_noise(imshape, self.noise) - imshape = create_psf(imshape) - imshape += noise_profile - return imshape - - def get_params(self): - """ - Return object labels here? - :return: parameters defining object - """ - return self.params diff --git a/archive/deepbench/module_draw/generate_draw.py b/archive/deepbench/module_draw/generate_draw.py deleted file mode 100644 index 72e6414..0000000 --- a/archive/deepbench/module_draw/generate_draw.py +++ /dev/null @@ -1,148 +0,0 @@ - -__all__ = ['create_line', 'create_arc', 'create_circle', 'create_rectangle', - 'create_regular_polygon', 'empty_imshape'] - - -import numpy as np -from math import pi, acos, cos, sin -from matplotlib.patches import Wedge, Rectangle, RegularPolygon -from matplotlib.path import Path - - - -def _patch2im(thepatch, imshape): - """ - Helper function for Patches to convert to a numpy.array - - :param thepatch: matplotlib.Patch object to be converted - :type: numpy.array - :param imshape: numpy.array to be the receiver of the patch - :return: array object representation of patch - :type: numpy.array - """ - x = np.arange(1, imshape.shape[0]) - y = np.arange(1, imshape.shape[1]) - g = np.meshgrid(x, y) - - coords = np.array(list(zip(*(c.flat for c in g)))) - - elpath = Path(thepatch.get_verts()) - - validcoords = elpath.contains_points(coords) - ellipsepoints = coords[validcoords] - - outim = np.zeros(imshape.shape) - outim[ellipsepoints[:,0],ellipsepoints[:,1]] = 1 - - return outim - - -def create_rectangle(imshape, xy, width, height, angle=0.0, fill=True): - """ - - :param imshape: image that rectangle will be drawn in - :type: numpy.array - :param xy: Lower left corner of rectangle - :type: Tuple(float,float) - :param width: x-dimension of the rectangle - :type: float - :param height: y-dimension of the rectangle - :type: float - :param angle: angle of rectangle - :type: float - :param fill: Fill rectangle shape - :type: boolean - :return: - numpy.array that includes rectangle - """ - rect = Rectangle(xy, width, height, angle=angle, fill=fill) - return _patch2im(rect,imshape) - - -def create_regular_polygon(imshape, xy, angle=0.0, vertices=3, radius=1, fill=True): - """ - Create a regular polygon with a specified number of sides. - :param imshape: Image that the polygon will be drawn in. - :type: numpy.array - :param xy: Lower-left corner of polygon. - :type: Tuple(float, float) - :param angle: Rotation of polygon - :type: float - :param vertices: number of vertices in polygon - :type: int - :param radius: The distance from the center to each of the vertices - :type: float - :param fill: Fill polygon - :type: boolean - :return: - """ - toRadians = angle*pi/180.0 - polyg = RegularPolygon(xy, vertices, radius, orientation=toRadians,fill=fill) - return _patch2im(polyg, imshape) - - -def create_arc(imshape, center, radius, width, theta1,theta2): - """ - - :param imshape: image object to receive arc object - :type: numpy.array - :param center: focal point of drawn arc. - :type: Tuple(float,float) - :param radius: distance from the center to arc - :type: float - :param width: thickness of the arc - :type: float - :param theta1: Start of subtended arc - :type: float - :param theta2: End of subtended arc - :type: float - :return: numpy.array with arc in the object. - """ - arc = Wedge(center, radius, theta1, theta2, width) - return _patch2im(arc, imshape) - - -def create_circle(imshape, center, radius, width): - """ - :param imshape: image object to receive circle object - :type: numpy.array - :param center: focal point of drawn arc. - :type: Tuple(float,float) - :param radius: distance from the center to arc - :type: float - :param width: thickness of the arc - :type: float - """ - return create_arc(imshape, center, radius, - width=width, theta1=0, theta2=360) - - -def create_line(imshape, start, end, linewidth=1): - #create a line using Rectangle, interpolate ending position - hyp = ( (end[1]-start[1])**2 + (end[0]-start[0])**2 )**0.5 - angle = acos( (end[0]-start[0]) / hyp) - - - x_shift = linewidth/2.0 * cos((pi-angle)) - y_shift = linewidth/2.0 * sin((pi-angle)) - xy_start = (start[0] + x_shift, start[1] + y_shift) - xy_end = (end[0] + x_shift, end[1]+y_shift) - - height_rect = linewidth - width_rect = ( (xy_end[1]-xy_start[1])**2 + (xy_end[0]-xy_start[0])**2 )**0.5 - angle_deg = angle*180.0/pi - rect = Rectangle(xy_start, width=width_rect,height=height_rect, angle=angle_deg) - - return _patch2im(rect, imshape) - -def empty_imshape(n_pix_side): - """ - Return zeroed numpy array with square dimensions. - Parameter: - n_pix_side (unsigned int): dimension of sides of numpy array - - Return: - numpy array of size [n_pix_side, n_pix_side] initialized to zero - - """ - return np.zeros((n_pix_side, n_pix_side)) diff --git a/archive/environment.yml b/archive/environment.yml deleted file mode 100644 index 80ed2b1..0000000 --- a/archive/environment.yml +++ /dev/null @@ -1,167 +0,0 @@ -name: SimplePhysicalImage -channels: - - conda-forge - - defaults -dependencies: - - _tflow_select=2.3.0=mkl - - absl-py=0.8.1=py36_0 - - appnope=0.1.0=py36hf537a9a_0 - - astor=0.8.0=py36_0 - - astropy=3.0.5=py36h1de35cc_0 - - attrs=19.3.0=py_0 - - autopep8=1.4.3=py36_0 - - backcall=0.1.0=py36_0 - - blas=1.0=mkl - - bleach=3.1.0=py36_0 - - bokeh=1.4.0=py36_0 - - c-ares=1.15.0=h1de35cc_1001 - - ca-certificates=2020.1.1=0 - - certifi=2019.11.28=py36_0 - - cycler=0.10.0=py36hfc81398_0 - - dbus=1.13.12=h90a0687_0 - - decorator=4.4.1=py_0 - - defusedxml=0.6.0=py_0 - - entrypoints=0.3=py36_0 - - expat=2.2.6=h0a44026_0 - - freetype=2.9.1=hb4e5f40_0 - - gast=0.2.2=py36_0 - - gettext=0.19.8.1=h15daf44_3 - - git=2.18.0=pl526h028e6c8_0 - - glib=2.63.1=hd977a24_0 - - google-pasta=0.1.8=py_0 - - grpcio=1.14.1=py36h9011c5e_0 - - h5py=2.9.0=py36h3134771_0 - - hdf5=1.10.4=hfa1e0ec_0 - - hypothesis=4.54.2=py36_0 - - icu=58.2=h4b95b61_1 - - importlib_metadata=1.3.0=py36_0 - - intel-openmp=2019.4=233 - - ipykernel=5.1.3=py36h39e3cac_1 - - ipython=7.11.1=py36h39e3cac_0 - - ipython_genutils=0.2.0=py36_0 - - ipywidgets=7.5.1=py_0 - - jedi=0.15.2=py36_0 - - jinja2=2.10.3=py_0 - - jpeg=9b=he5867d9_2 - - json5=0.8.5=py_0 - - jsonschema=3.2.0=py36_0 - - jupyter=1.0.0=py36_7 - - jupyter_client=5.3.4=py36_0 - - jupyter_console=6.0.0=py36_0 - - jupyter_contrib_core=0.3.3=py_2 - - jupyter_contrib_nbextensions=0.5.0=py36_1000 - - jupyter_core=4.6.1=py36_0 - - jupyter_highlight_selected_word=0.2.0=py36_1000 - - jupyter_latex_envs=1.4.4=py36_1000 - - jupyter_nbextensions_configurator=0.4.0=py36_1000 - - jupyterlab=1.2.5=py_0 - - jupyterlab_server=1.0.6=py_0 - - keras=2.2.4=0 - - keras-applications=1.0.8=py_0 - - keras-base=2.2.4=py36_0 - - keras-preprocessing=1.1.0=py_1 - - kiwisolver=1.1.0=py36h0a44026_0 - - libcurl=7.61.1=hf30b1f0_0 - - libcxx=4.0.1=h579ed51_0 - - libcxxabi=4.0.1=hebd6815_0 - - libedit=3.1.20170329=hb402a30_2 - - libffi=3.2.1=h475c297_4 - - libgfortran=3.0.1=h93005f0_2 - - libiconv=1.15=hdd342a3_7 - - libpng=1.6.37=ha441bb4_0 - - libprotobuf=3.11.2=hd9629dc_0 - - libsodium=1.0.16=h3efe00b_0 - - libssh2=1.8.0=h322a93b_4 - - libtiff=4.1.0=hcb84e12_0 - - libxml2=2.9.9=hf6e021a_1 - - libxslt=1.1.33=h33a18ac_0 - - lxml=4.4.2=py36hef8c89e_0 - - markdown=3.1.1=py36_0 - - markupsafe=1.1.1=py36h1de35cc_0 - - matplotlib=3.1.1=py36h54f8f79_0 - - mistune=0.8.4=py36h1de35cc_0 - - mkl=2019.4=233 - - mkl-service=2.3.0=py36hfbe908c_0 - - mkl_fft=1.0.15=py36h5e564d8_0 - - mkl_random=1.1.0=py36ha771720_0 - - more-itertools=8.0.2=py_0 - - nbconvert=5.6.1=py36_0 - - nbformat=4.4.0=py36_0 - - ncurses=6.1=h0a44026_0 - - notebook=6.0.2=py36_0 - - numpy=1.18.1=py36h7241aed_0 - - numpy-base=1.18.1=py36h6575580_0 - - olefile=0.46=py36_0 - - openssl=1.0.2u=h1de35cc_0 - - opt_einsum=3.1.0=py_0 - - packaging=20.0=py_0 - - pandas=0.25.3=py36h0a44026_0 - - pandoc=2.2.3.2=0 - - pandocfilters=1.4.2=py36_1 - - parso=0.5.2=py_0 - - patsy=0.5.1=py36_0 - - pcre=8.43=h0a44026_0 - - perl=5.26.2=h4e221da_0 - - pexpect=4.7.0=py36_0 - - pickleshare=0.7.5=py36_0 - - pillow=7.0.0=py36h4655f20_0 - - pip=10.0.1=py36_0 - - pluggy=0.13.1=py36_0 - - prometheus_client=0.7.1=py_0 - - prompt_toolkit=2.0.10=py_0 - - protobuf=3.11.2=py36h0a44026_0 - - psutil=5.6.7=py36h1de35cc_0 - - ptyprocess=0.6.0=py36_0 - - py=1.8.1=py_0 - - pycodestyle=2.5.0=py36_0 - - pygments=2.5.2=py_0 - - pyparsing=2.4.6=py_0 - - pyqt=5.9.2=py36h655552a_2 - - pyrsistent=0.15.6=py36h1de35cc_0 - - pytest=5.3.2=py36_0 - - pytest-arraydiff=0.3=py36h39e3cac_0 - - pytest-astropy=0.7.0=py_0 - - pytest-astropy-header=0.1.1=py_0 - - pytest-doctestplus=0.5.0=py_0 - - pytest-openfiles=0.4.0=py_0 - - pytest-remotedata=0.3.2=py36_0 - - python=3.6.6=hc167b69_0 - - python-dateutil=2.8.1=py_0 - - pytz=2019.3=py_0 - - pyyaml=5.2=py36h1de35cc_0 - - pyzmq=18.1.0=py36h0a44026_0 - - qt=5.9.6=h45cd832_2 - - qtconsole=4.6.0=py_1 - - readline=7.0=h1de35cc_5 - - scipy=1.3.2=py36h1410ff5_0 - - seaborn=0.9.0=py36_0 - - send2trash=1.5.0=py36_0 - - setuptools=40.2.0=py36_0 - - sip=4.19.8=py36h0a44026_0 - - six=1.13.0=py36_0 - - sqlite=3.24.0=ha441bb4_0 - - statsmodels=0.10.1=py36h1d22016_0 - - tensorboard=1.15.0=pyhb230dea_0 - - tensorflow=1.15.0=mkl_py36h975b573_0 - - tensorflow-base=1.15.0=mkl_py36h032239d_0 - - tensorflow-estimator=1.15.1=pyh2649769_0 - - termcolor=1.1.0=py36_1 - - terminado=0.8.3=py36_0 - - testpath=0.4.4=py_0 - - tk=8.6.8=ha441bb4_0 - - tornado=6.0.3=py36h1de35cc_0 - - traitlets=4.3.3=py36_0 - - wcwidth=0.1.7=py36_0 - - webencodings=0.5.1=py36_1 - - werkzeug=0.16.0=py_0 - - wheel=0.31.1=py36_0 - - widgetsnbextension=3.5.1=py36_0 - - wrapt=1.11.2=py36h1de35cc_0 - - xz=5.2.4=h1de35cc_4 - - yaml=0.1.7=hc338f04_2 - - zeromq=4.3.1=h0a44026_3 - - zipp=0.6.0=py_0 - - zlib=1.2.11=hf3cbc9b_2 - - zstd=1.3.7=h5bba6e5_0 -prefix: /anaconda3/envs/SimplePhysicalImage - diff --git a/archive/notebooks/Deprecated/SimGenerator-Copy1.ipynb b/archive/notebooks/Deprecated/SimGenerator-Copy1.ipynb deleted file mode 100644 index c803df4..0000000 --- a/archive/notebooks/Deprecated/SimGenerator-Copy1.ipynb +++ /dev/null @@ -1,2136 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "code_folding": [] - }, - "outputs": [], - "source": [ - "# packages: random\n", - "import random\n", - "\n", - "# packages: data structure\n", - "import numpy as np\n", - "import pandas as pd\n", - "import astropy.io as io\n", - "\n", - "# packages: astro image generation\n", - "from astropy.modeling.models import Sersic2D, Gaussian2D, Moffat2D\n", - "\n", - "# image manipulation\n", - "from scipy.signal import convolve2d\n", - "\n", - "# packages: image generation and plot generation\n", - "from matplotlib import pyplot as plt\n", - "import seaborn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Do's\n", - "\n", - "* Primary\n", - " * add flag for the class of object\n", - "* Secondary\n", - " * how to draw from distributions\n", - " * download real sky data\n", - " * mixing different things in one table\n", - "* Done\n", - " * efficiently create arrays for the tabular data\n", - " * function for load data\n", - " * visualization of examples\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "code_folding": [ - 0 - ] - }, - "outputs": [], - "source": [ - "# pandas\n", - "# https://pandas.pydata.org/pandas-docs/stable/tutorials.html\n", - "# https://pandas.pydata.org/pandas-docs/stable/10min.html\n", - "\n", - "# ndimage: image manipulation\n", - "# https://docs.scipy.org/doc/scipy/reference/ndimage.html#module-scipy.ndimage\n", - "\n", - "# ascii:io\n", - "# http://docs.astropy.org/en/stable/io/ascii/\n", - "\n", - "# matplotlib\n", - "# https://nickcharlton.net/posts/drawing-animating-shapes-matplotlib.html\n", - "\n", - "# astropy: modeling:\n", - "# http://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Sersic2D.html\n", - "\n", - "# data\n", - "# https://arxiv.org/pdf/1608.04369.pdf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Objects and Images" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": { - "code_folding": [ - 0, - 27, - 35, - 44, - 56, - 65, - 76, - 84, - 110, - 131, - 150, - 172 - ] - }, - "outputs": [], - "source": [ - "def _patch2im(thepatch, imshape):\n", - " \"\"\"\n", - " Convert patch to image\n", - " \"\"\"\n", - " \n", - " # Create meshgrid of coordinates\n", - " y = np.arange(1, imshape[0])\n", - " x = np.arange(1, imshape[1])\n", - " g = np.meshgrid(x, y)\n", - "\n", - " # Define coords\n", - " coords = np.array(list(zip(*(c.flat for c in g))))\n", - "\n", - " # Create path\n", - " elpath = Path(thepatch.get_verts())\n", - "\n", - " # Determine all points in the grid ithat are in the path.\n", - " validcoords = elpath.contains_points(coords)\n", - " ellipsepoints = coords[validcoords]\n", - "\n", - " # Set output\n", - " outim = np.zeros(imshape)\n", - " outim[ellipsepoints[:, 0], ellipsepoints[:, 1]] = 1\n", - "\n", - " return outim\n", - "\n", - "\n", - "def empty_imshape(n_pix_side, nb_channels=1):\n", - " \"\"\"\n", - " Create empty canvas.\n", - " \"\"\"\n", - " \n", - " return np.zeros((n_pix_side, n_pix_side))#, nb_channels))#, np.uint8)\n", - "\n", - "\n", - "def create_line(imshape, x_start, y_start, x_end, y_end, width):\n", - " \"\"\"\n", - " Create a line.\n", - " \"\"\"\n", - " \n", - " # Create the arc patch we want to add to the image\n", - " return None\n", - "\n", - "\n", - "def create_arc(imshape, center, radius, width, theta1, theta2):\n", - " \"\"\"\n", - " Create an arc (outline of a circle) with a specified\n", - " thickness (width).\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " arc = Wedge(center, radius, theta1, theta2, width)\n", - "\n", - " return _patch2im(arc, imshape)\n", - "\n", - "\n", - "def create_circle(imshape, center, radius, width):\n", - " \"\"\"\n", - " Create a circle.\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " return create_arc(imshape, center, radius, width=width, theta1=0, theta2=360)\n", - " \n", - "\n", - "def create_polygon(imshape, xy, width, height, angle=0.0, fill=True):\n", - " \"\"\"\n", - " Create a polygon\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " rect = Rectangle(xy, width, height, angle=0.0, fill=fill)\n", - "\n", - " return _patch2im(rect, imshape)\n", - "\n", - "\n", - "def create_meshgrid(imshape):\n", - " \"\"\"\n", - " Create MeshGrid\n", - " \"\"\"\n", - " \n", - " return np.meshgrid(np.arange(np.shape(imshape)[0]), np.arange(np.shape(imshape)[1]))\n", - "\n", - "\n", - "def create_Sersic2D(imshape, \n", - " amplitude=1,\n", - " x_0=50, \n", - " y_0=50,\n", - " radius=25, \n", - " n=4, \n", - " ellip=.5, \n", - " theta=-1 \n", - " ):\n", - " \"\"\"\n", - " Create Galaxy profile: Sersic\n", - " \"\"\"\n", - " \n", - " x, y = create_meshgrid(imshape)\n", - " mod = Sersic2D(amplitude=amplitude,\n", - " x_0=x_0, \n", - " y_0=y_0,\n", - " r_eff=radius, \n", - " n=n,\n", - " ellip=ellip, \n", - " theta=theta\n", - " )\n", - " \n", - " return mod(x, y)\n", - "\n", - "\n", - "def create_Moffat2D(imshape,\n", - " amplitude=1,\n", - " x_0=0, \n", - " y_0=0, \n", - " radius=1, \n", - " alpha=1\n", - " ):\n", - " \"\"\"\n", - " Create Star profile: Moffat\n", - " \"\"\"\n", - " \n", - " x, y = create_meshgrid(imshape)\n", - " mod = Moffat2D(amplitude=amplitude, \n", - " x_0=x_0, \n", - " y_0=y_0, \n", - " gamma=radius, \n", - " alpha=alpha)\n", - " \n", - " return mod(x, y) \n", - "\n", - "\n", - "def create_Gaussian(imshape, \n", - " amplitude=1, \n", - " x_mean=0, y_mean=0, \n", - " x_stddev=None, y_stddev=None, \n", - " theta=None):\n", - " \"\"\"\n", - " Create Gaussian\n", - " \"\"\"\n", - " \n", - " x, y = create_meshgrid(imshape)\n", - " \n", - " mod = Gaussian2D(amplitude=1, \n", - " x_mean=x_mean, y_mean=y_mean,\n", - " x_stddev=x_stddev, y_stddev=y_stddev, \n", - " theta=theta)\n", - " \n", - " return mod(x, y) \n", - "\n", - "\n", - "def create_psf(imshape):\n", - " \"\"\"\n", - " Add PSF to image\n", - " \"\"\"\n", - " \n", - " impsf = np.ones((np.shape(imshape)[0],np.shape(imshape)[1])) #/ 25 \n", - "\n", - " return convolve2d(imshape, impsf, 'same')\n", - " \n", - "\n", - "def create_noise(imshape, noise_level):\n", - " \"\"\"\n", - " Create noise image\n", - " https://kmdouglass.github.io/posts/modeling-noise-for-image-simulations.html\n", - " \"\"\"\n", - " \n", - " seed = 42\n", - " rs = np.random.RandomState(seed)\n", - " \n", - " return rs.poisson(noise_level, (np.shape(imshape)[0],np.shape(imshape)[1]))\n", - " \n", - " \n", - "def combine_sky_imshape(imshape_list, amplitude_list):\n", - " \"\"\"\n", - " Combine clean sky images\n", - " \"\"\"\n", - " \n", - " imshape_new = np.zeros((imshape_list[0].shape[0], imshape_list[0].shape[1]))\n", - " for imshape, amplitude in zip(imshape_list, amplitude_list):\n", - " imshape_new += amplitude*imshape\n", - " \n", - " return imshape_new " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Catalogs" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": { - "code_folding": [ - 0 - ] - }, - "outputs": [], - "source": [ - "def generate_catalog(n_obj, \n", - " n_pix_side, \n", - " myclass,\n", - " id_start=0, \n", - " amplitude=1,\n", - " amplitude_min=1,\n", - " amplitude_max=1.,\n", - " radius_min=2,\n", - " radius_max=20,\n", - " verbose=False):\n", - " \"\"\"\n", - " Generate data set: catalog\n", - " \"\"\"\n", - "\n", - " # fac = -1.0\n", - " # np.random.uniform(0 - fac* radius_max, n_pix_side + fac* radius_max, n_obj)\n", - " \n", - " # Define column names\n", - " column_names = ['id', 'x_center', 'y_center', 'radius', 'amplitude', 'myclass']\n", - "\n", - " # Data Frame: Tabular Data for Objects\n", - " catalog = pd.DataFrame(np.empty((n_obj, len(column_names))),columns=column_names)\n", - "\n", - " catalog.id = np.arange(n_obj) + id_start\n", - " catalog.x_center = n_pix_side / 2.\n", - " catalog.y_center = n_pix_side / 2.\n", - " catalog.radius = np.random.uniform(radius_min, radius_max, n_obj)\n", - " catalog.amplitude = np.random.uniform(amplitude_min, amplitude_max, n_obj)\n", - " catalog.myclass = myclass\n", - " \n", - " return catalog\n", - "\n", - " \n", - "def generate_imageset(catalog, n_pix_side, noise, verbose=False):\n", - " \"\"\"\n", - " Generate data set: images\n", - " \"\"\"\n", - "\n", - " # count objects\n", - " n_obj = len(catalog)\n", - " \n", - " # create image list\n", - " images = np.empty((n_obj, n_pix_side, n_pix_side))\n", - " \n", - " for ind, _ in enumerate(images):\n", - " images[ind] = generate_image(catalog.loc[ind], n_pix_side, noise)\n", - "\n", - " return images\n", - "\n", - "\n", - "def generate_image(catalog_single, n_pix_side, noise):\n", - " \"\"\"\n", - " Generate a single image\n", - " \"\"\"\n", - "\n", - " imshape = empty_imshape(n_pix_side)\n", - " \n", - " if catalog_single.myclass == 1:\n", - "\n", - " imshape = create_Sersic2D(imshape, \n", - " x_0=catalog_single.x_center, \n", - " y_0=catalog_single.y_center,\n", - " radius=catalog_single.radius \n", - " )\n", - " elif catalog_single.myclass == 0:\n", - " imshape = create_Moffat2D(imshape,\n", - " x_0=catalog_single.x_center, \n", - " y_0=catalog_single.y_center,\n", - " radius=catalog_single.radius\n", - " )\n", - "\n", - " # apply amplitude\n", - " imshape *= catalog_single.amplitude\n", - " \n", - " # add noise\n", - " noise = create_noise(imshape, noise)\n", - " imshape += noise\n", - " \n", - "\n", - " return imshape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare Data" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "code_folding": [ - 0, - 15, - 33, - 42 - ] - }, - "outputs": [], - "source": [ - "def combine_data(frames, data_type): \n", - " \"\"\"\n", - " Combine classes of data\n", - " \"\"\"\n", - " \n", - " if data_type=='catalog':\n", - " data = pd.concat(frames)\n", - " elif data_type=='image':\n", - " data = np.concatenate(frames)\n", - " else:\n", - " data = None\n", - " \n", - " return data\n", - "\n", - "\n", - "def randomize_data(catalog, imageset, seed=5, verbose=False):\n", - " \"\"\"\n", - " Randomize the data\n", - " \"\"\"\n", - " \n", - " # create randomized indices\n", - " random.seed(seed) \n", - " n_catalog = len(catalog)\n", - " ind_random = np.arange(n_catalog)\n", - " random.shuffle(ind_random)\n", - " \n", - " # re-order data based on randomized indices\n", - " catalog = catalog.iloc[ind_random]\n", - " imageset = imageset[ind_random]\n", - "\n", - " return catalog, imageset, ind_random\n", - "\n", - "\n", - "def recast_imageset(imageset):\n", - " \"\"\"\n", - " Recast imageset as numpy uint8\n", - " \"\"\"\n", - " imageset = imageset.astype(np.uint8)\n", - " \n", - " return imageset\n", - "\n", - "\n", - "def recast_imageset_bits(imageset):\n", - " \"\"\"\n", - " Recast imageset as numpy uint8\n", - " \"\"\"\n", - " imageset = np.unpackbits(imageset)\n", - " \n", - " return imageset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## I/O" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "code_folding": [ - 0, - 15 - ] - }, - "outputs": [], - "source": [ - "def save_data(f_data_list, data_list, data_type, verbose=False):\n", - " \"\"\"\n", - " Save Data to File\n", - " \"\"\"\n", - " \n", - " if data_type == 'catalog':\n", - " # Pandas Data Frame for tabular data: save to file\n", - " data_list.to_csv(f_data_list)\n", - " elif data_type == 'image':\n", - " # Numpy Array for image data: save to file\n", - " np.save(f_data_list, data_list)\n", - " \n", - " return \n", - "\n", - "\n", - "def load_data(f_data_list, data_type, verbose=False):\n", - " \"\"\"\n", - " Load Data from File\n", - " \"\"\"\n", - " \n", - " if data_type == 'catalog':\n", - " data_list = pd.read_csv(f_data_list)\n", - " elif data_type == 'image':\n", - " data_list = np.load(f_data_list)\n", - " \n", - " return data_list\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualize" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": { - "code_folding": [ - 29 - ] - }, - "outputs": [], - "source": [ - "def show_example(imshape, imshape_2=None, log=False):\n", - " f = plt.figure(figsize=(18, 4))\n", - "\n", - " f.add_subplot(1,2, 1)\n", - " if log:\n", - " imshape = np.log10(imshape)\n", - " plt.imshow(imshape, origin='lower', interpolation='nearest',\n", - " vmin=-1, vmax=2)\n", - " plt.xlabel('x')\n", - " plt.ylabel('y')\n", - " cbar = plt.colorbar()\n", - " cbar.set_label('Brightness', rotation=270, labelpad=25)\n", - " cbar.set_ticks([-1, 0, 1, 2], update_ticks=True)\n", - "\n", - " if imshape_2 is not None:\n", - " f.add_subplot(1,2, 2)\n", - " if log:\n", - " imshape_2 = np.log10(imshape_2)\n", - " plt.imshow(imshape_2, origin='lower', interpolation='nearest',\n", - " vmin=-1, vmax=2)\n", - " plt.xlabel('x')\n", - " plt.ylabel('y')\n", - " cbar = plt.colorbar()\n", - " cbar.set_label('Brightness', rotation=270, labelpad=25)\n", - " cbar.set_ticks([-1, 0, 1, 2], update_ticks=True)\n", - "\n", - " plt.show(block=True)\n", - " \n", - " \n", - "def show_encoded(): \n", - " \"\"\"\n", - " Show the encoded layer representation\n", - " \"\"\"\n", - " num_images = 10\n", - " np.random.seed(42)\n", - " random_test_images = np.random.randint(x_test.shape[0], size=num_images)\n", - "\n", - " encoded_imgs = encoder.predict(x_test)\n", - " decoded_imgs = autoencoder.predict(x_test)\n", - "\n", - "\n", - "\n", - " plt.figure(figsize=(18, 4))\n", - "\n", - " print(encoded_imgs[0].shape)\n", - " num_rows=4\n", - " num_pixel_x = 2\n", - " num_pixel_y = 4\n", - "\n", - " for i, image_idx in enumerate(random_test_images):\n", - " # plot original image\n", - " ax = plt.subplot(4, num_images, i + 1)\n", - " plt.imshow(x_test[image_idx].reshape(28, 28))\n", - " plt.gray()\n", - " ax.get_xaxis().set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - "\n", - " # plot encoded image\n", - " ax = plt.subplot(num_rows, num_images, num_images + i + 1)\n", - "\n", - " plt.imshow(encoded_imgs[image_idx].reshape(num_pixel_x, num_pixel_y), interpolation=None, resample=None)\n", - " plt.gray()\n", - " ax.get_xaxis().set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - "\n", - " # plot encoded image\n", - " ax = plt.subplot(num_rows, num_images, 2*num_images + i + 1)\n", - " print(np.min(encoded_imgs[image_idx].reshape(num_pixel_x, num_pixel_y)), np.max(encoded_imgs[image_idx].reshape(num_pixel_x, num_pixel_y)))\n", - "\n", - " plt.imshow(encoded_imgs[image_idx].reshape(num_pixel_x, num_pixel_y).astype('uint8'), interpolation=None, resample=None)\n", - " plt.gray()\n", - " ax.get_xaxis().set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - "\n", - "\n", - " # plot reconstructed image\n", - " ax = plt.subplot(num_rows, num_images, 3*num_images + i + 1)\n", - " plt.imshow(decoded_imgs[image_idx].reshape(28, 28))\n", - " plt.gray()\n", - " ax.get_xaxis().set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - " plt.show()\n", - " \n", - " \n", - "def plot_distributions(data, xlabel):\n", - " f = plt.figure(figsize=(12, 4))\n", - "\n", - " plt.hist(data)\n", - " plt.xlabel(xlabel)\n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": { - "code_folding": [] - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Meta data\n", - "n_obj = 200\n", - "n_pix_side = 64\n", - "noise = 0.\n", - "\n", - "# generate catalogs\n", - "catalog_gal = generate_catalog(n_obj, \n", - " n_pix_side, \n", - " 1,\n", - " id_start=0, \n", - " amplitude_min=0.8,\n", - " amplitude_max=5.0, \n", - " radius_min=24,\n", - " radius_max=26,\n", - " verbose=False)\n", - "\n", - "catalog_star = generate_catalog(n_obj, \n", - " n_pix_side, \n", - " 0,\n", - " id_start=len(catalog_gal), \n", - " amplitude_min=0.8,\n", - " amplitude_max=5.0, \n", - " radius_min=0.5,\n", - " radius_max=2,\n", - " verbose=False)\n", - "\n", - "# generate images\n", - "imageset_gal = generate_imageset(catalog_gal, n_pix_side, noise, verbose=False)\n", - "imageset_star = generate_imageset(catalog_star, n_pix_side, noise, verbose=False)\n", - "\n", - "# combine catalogs\n", - "catalog_combined = combine_data([catalog_gal, catalog_star], 'catalog')\n", - "imageset_combined = combine_data([imageset_gal, imageset_star], 'image')\n", - "\n", - "catalog_combined, imageset_combined, _ = randomize_data(catalog_combined, imageset_combined, seed=5, verbose=False)\n", - "\n", - "# imageset_combined = recast_imageset(imageset_combined)\n", - "#print(imageset_combined)\n", - "\n", - "# save \n", - "\n", - "# visualize\n", - "show_example(imageset_combined[0], imshape_2=imageset_combined[1], log=True)\n", - "\n", - "plot_distributions(catalog_combined.amplitude, 'amplitude')\n", - "plot_distributions(catalog_combined.radius, 'radius')\n", - "# save " - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": {}, - "outputs": [], - "source": [ - "# import packages for Convolutional AutoEncoder compression\n", - "import keras\n", - "from keras import regularizers, optimizers\n", - "from keras.datasets import mnist\n", - "from keras.models import Model, Sequential\n", - "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n", - "from keras.layers import Flatten, Reshape\n", - "from keras import backend as K\n", - "\n", - "\n", - "from IPython.display import Image, SVG\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [], - "source": [ - "# Prep\n", - "nb_train = 1000\n", - "nb_test = 1000\n", - "nb_epochs = 6\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [], - "source": [ - "x_train_temp = imageset_combined[0:nb_train,:,:]\n", - "x_test_temp = imageset_combined[nb_train:nb_train + nb_test,:,:]\n", - "\n", - "y_train_temp = catalog_combined[0:nb_train]\n", - "y_test_temp = catalog_combined[0:nb_train]\n", - "#print(y_train_temp)" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1000, 28, 28), (1000, 28, 28))" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(x_train_temp, _), (x_test_temp, _) = mnist.load_data()\n", - "x_train_temp = x_train_temp[0:nb_train,:,:]\n", - "x_test_temp = x_test_temp[0:nb_test,:,:]\n", - "\n", - "x_train_temp.shape, x_test_temp.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1000, 28, 28), (1000, 28, 28))" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "max_value = float(x_train_temp.max())\n", - "x_train = x_train_temp.astype('float32') / max_value\n", - "x_test = x_test_temp.astype('float32') / max_value\n", - "\n", - "x_train.shape, x_test.shape\n" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1000, 28, 28, 1)\n" - ] - } - ], - "source": [ - "n_pix_side = x_train.shape[2]\n", - "x_train = x_train.reshape((len(x_train), n_pix_side, n_pix_side, 1))\n", - "x_test = x_test.reshape((len(x_test), n_pix_side, n_pix_side, 1))\n", - "\n", - "(x_train.shape, x_test.shape)\n", - "print(x_train.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "conv2d_270 (Conv2D) (None, 28, 28, 64) 640 \n", - "_________________________________________________________________\n", - "max_pooling2d_109 (MaxPoolin (None, 14, 14, 64) 0 \n", - "_________________________________________________________________\n", - "conv2d_271 (Conv2D) (None, 14, 14, 32) 18464 \n", - "_________________________________________________________________\n", - "max_pooling2d_110 (MaxPoolin (None, 7, 7, 32) 0 \n", - "_________________________________________________________________\n", - "conv2d_272 (Conv2D) (None, 4, 4, 16) 4624 \n", - "_________________________________________________________________\n", - "max_pooling2d_111 (MaxPoolin (None, 2, 2, 16) 0 \n", - "_________________________________________________________________\n", - "conv2d_273 (Conv2D) (None, 1, 1, 8) 1160 \n", - "_________________________________________________________________\n", - "max_pooling2d_112 (MaxPoolin (None, 1, 1, 8) 0 \n", - "_________________________________________________________________\n", - "conv2d_274 (Conv2D) (None, 1, 1, 4) 292 \n", - "_________________________________________________________________\n", - "flatten_1 (Flatten) (None, 4) 0 \n", - "_________________________________________________________________\n", - "latent (Reshape) (None, 1, 1, 4) 0 \n", - "_________________________________________________________________\n", - "up_sampling2d_155 (UpSamplin (None, 2, 2, 4) 0 \n", - "_________________________________________________________________\n", - "conv2d_275 (Conv2D) (None, 2, 2, 8) 296 \n", - "_________________________________________________________________\n", - "up_sampling2d_156 (UpSamplin (None, 4, 4, 8) 0 \n", - "_________________________________________________________________\n", - "conv2d_276 (Conv2D) (None, 4, 4, 16) 1168 \n", - "_________________________________________________________________\n", - "up_sampling2d_157 (UpSamplin (None, 8, 8, 16) 0 \n", - "_________________________________________________________________\n", - "conv2d_277 (Conv2D) (None, 8, 8, 32) 4640 \n", - "_________________________________________________________________\n", - "up_sampling2d_158 (UpSamplin (None, 16, 16, 32) 0 \n", - "_________________________________________________________________\n", - "conv2d_278 (Conv2D) (None, 14, 14, 64) 18496 \n", - "_________________________________________________________________\n", - "up_sampling2d_159 (UpSamplin (None, 28, 28, 64) 0 \n", - "_________________________________________________________________\n", - "conv2d_279 (Conv2D) (None, 28, 28, 1) 577 \n", - "=================================================================\n", - "Total params: 50,357\n", - "Trainable params: 50,357\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "\n", - "======================================================================\n", - "======================================================================\n", - "======================================================================\n", - "\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "conv2d_270_input (InputLayer (None, 28, 28, 1) 0 \n", - "_________________________________________________________________\n", - "conv2d_270 (Conv2D) (None, 28, 28, 64) 640 \n", - "_________________________________________________________________\n", - "max_pooling2d_109 (MaxPoolin (None, 14, 14, 64) 0 \n", - "_________________________________________________________________\n", - "conv2d_271 (Conv2D) (None, 14, 14, 32) 18464 \n", - "_________________________________________________________________\n", - "max_pooling2d_110 (MaxPoolin (None, 7, 7, 32) 0 \n", - "_________________________________________________________________\n", - "conv2d_272 (Conv2D) (None, 4, 4, 16) 4624 \n", - "_________________________________________________________________\n", - "max_pooling2d_111 (MaxPoolin (None, 2, 2, 16) 0 \n", - "_________________________________________________________________\n", - "conv2d_273 (Conv2D) (None, 1, 1, 8) 1160 \n", - "_________________________________________________________________\n", - "max_pooling2d_112 (MaxPoolin (None, 1, 1, 8) 0 \n", - "_________________________________________________________________\n", - "conv2d_274 (Conv2D) (None, 1, 1, 4) 292 \n", - "_________________________________________________________________\n", - "flatten_1 (Flatten) (None, 4) 0 \n", - "=================================================================\n", - "Total params: 25,180\n", - "Trainable params: 25,180\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "import os\n", - "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n", - "\n", - "#keras.backend.set_floatx('float32')\n", - "\n", - "autoencoder = Sequential()\n", - "\n", - "# Encoder Layers\n", - "autoencoder.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))\n", - "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", - "autoencoder.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n", - "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", - "autoencoder.add(Conv2D(16, (3, 3), strides=(2,2), activation='relu', padding='same'))\n", - "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", - "autoencoder.add(Conv2D(8, (3, 3), strides=(2,2), activation='relu', padding='same'))\n", - "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", - "autoencoder.add(Conv2D(4, (3, 3), strides=(2,2), activation='relu', padding='same'))\n", - "\n", - "# Flatten encoding for visualization\n", - "autoencoder.add(Flatten(name='flatten_1'))\n", - "autoencoder.add(Reshape((1, 1, 4),name='latent'))\n", - "\n", - "# Decoder Layers\n", - "autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n", - "autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(Conv2D(16, (3, 3), activation='relu', padding='same'))\n", - "autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n", - "autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(Conv2D(64, (3, 3), activation='relu'))#,padding='same'))\n", - "#autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))\n", - "\n", - "autoencoder.summary()\n", - "sgd = optimizers.SGD(lr=0.0000005, decay=1e-6, momentum=0.9, nesterov=True)\n", - "\n", - "autoencoder.compile(optimizer=sgd, loss='mse', metrics=['accuracy'])\n", - "\n", - "\n", - "print()\n", - "print('======================================================================')\n", - "print('======================================================================')\n", - "print('======================================================================')\n", - "print()\n", - "\n", - "encoder = Model(inputs=autoencoder.input, outputs=autoencoder.get_layer('flatten_1').output)\n", - "encoder.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 1000 samples, validate on 1000 samples\n", - "Epoch 1/6\n", - "1000/1000 [==============================] - 11s 11ms/step - loss: 0.2313 - acc: 0.6501 - val_loss: 0.2317 - val_acc: 0.6932\n", - "Epoch 2/6\n", - "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2313 - acc: 0.7174 - val_loss: 0.2317 - val_acc: 0.7420\n", - "Epoch 3/6\n", - "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2313 - acc: 0.7530 - val_loss: 0.2317 - val_acc: 0.7687\n", - "Epoch 4/6\n", - "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2313 - acc: 0.7733 - val_loss: 0.2317 - val_acc: 0.7853\n", - "Epoch 5/6\n", - "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2313 - acc: 0.7861 - val_loss: 0.2317 - val_acc: 0.7962\n", - "Epoch 6/6\n", - "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2313 - acc: 0.7944 - val_loss: 0.2316 - val_acc: 0.8033\n" - ] - } - ], - "source": [ - "history = autoencoder.fit(x_train, x_train,\n", - " epochs=nb_epochs,\n", - " batch_size=32,\n", - " validation_data=(x_test, x_test), verbose=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - 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0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n" - ] - } - ], - "source": [ - "encoded_imgs = encoder.predict(x_test)\n", - "\n", - "encoded_imgs_16 = encoded_imgs.astype('float16')\n", - "\n", - "for img, img16 in zip(encoded_imgs, encoded_imgs_16):\n", - " print(encoded_imgs[0]) \n", - "# print(encoded_imgs_16[0])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1000, 28, 28)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "decoded_imgs = autoencoder.predict(x_test)\n", - "decoded_imgs = decoded_imgs.reshape((decoded_imgs.shape[0], n_pix_side, n_pix_side))\n", - "print(decoded_imgs.shape)\n", - "show_example(decoded_imgs[0], imshape_2=decoded_imgs[1])\n", - "#print(decoded_imgs[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# OLD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from keras import backend as K\n", - "K.floatx()\n", - "K.set_floatx('float16')\n", - "K.floatx()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://colab.research.google.com/drive/1E0URjxq_vlvrDVeLTsUIsgQOkoS9Gf94#scrollTo=yoKEbjT05rb8\n", - " https://colab.research.google.com/drive/1TfflYirHKJkVuX2jGwyjOLKplthzQP74" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - }, - "latex_envs": { - "LaTeX_envs_menu_present": true, - "autoclose": false, - "autocomplete": true, - "bibliofile": "biblio.bib", - "cite_by": "apalike", - "current_citInitial": 1, - "eqLabelWithNumbers": true, - "eqNumInitial": 1, - "hotkeys": { - "equation": "Ctrl-E", - "itemize": "Ctrl-I" - }, - "labels_anchors": false, - "latex_user_defs": false, - "report_style_numbering": false, - "user_envs_cfg": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/Deprecated/SimGenerator.ipynb b/archive/notebooks/Deprecated/SimGenerator.ipynb deleted file mode 100644 index afcc42b..0000000 --- a/archive/notebooks/Deprecated/SimGenerator.ipynb +++ /dev/null @@ -1,2137 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "code_folding": [] - }, - "outputs": [], - "source": [ - "# packages: random\n", - "import random\n", - "\n", - "# packages: data structure\n", - "import numpy as np\n", - "import pandas as pd\n", - "import astropy.io as io\n", - "\n", - "# packages: astro image generation\n", - "from astropy.modeling.models import Sersic2D, Gaussian2D, Moffat2D\n", - "\n", - "# image manipulation\n", - "from scipy.signal import convolve2d\n", - "\n", - "# packages: image generation and plot generation\n", - "from matplotlib import pyplot as plt\n", - "from matplotlib import Wedge\n", - "import seaborn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Do's\n", - "\n", - "* Primary\n", - " * add flag for the class of object\n", - "* Secondary\n", - " * how to draw from distributions\n", - " * download real sky data\n", - " * mixing different things in one table\n", - "* Done\n", - " * efficiently create arrays for the tabular data\n", - " * function for load data\n", - " * visualization of examples\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "code_folding": [ - 0 - ] - }, - "outputs": [], - "source": [ - "# pandas\n", - "# https://pandas.pydata.org/pandas-docs/stable/tutorials.html\n", - "# https://pandas.pydata.org/pandas-docs/stable/10min.html\n", - "\n", - "# ndimage: image manipulation\n", - "# https://docs.scipy.org/doc/scipy/reference/ndimage.html#module-scipy.ndimage\n", - "\n", - "# ascii:io\n", - "# http://docs.astropy.org/en/stable/io/ascii/\n", - "\n", - "# matplotlib\n", - "# https://nickcharlton.net/posts/drawing-animating-shapes-matplotlib.html\n", - "\n", - "# astropy: modeling:\n", - "# http://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Sersic2D.html\n", - "\n", - "# data\n", - "# https://arxiv.org/pdf/1608.04369.pdf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Objects and Images" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": { - "code_folding": [ - 0, - 27, - 35, - 44, - 56, - 65, - 76, - 84, - 110, - 131, - 150, - 172 - ] - }, - "outputs": [], - "source": [ - "def _patch2im(thepatch, imshape):\n", - " \"\"\"\n", - " Convert patch to image\n", - " \"\"\"\n", - " \n", - " # Create meshgrid of coordinates\n", - " y = np.arange(1, imshape[0])\n", - " x = np.arange(1, imshape[1])\n", - " g = np.meshgrid(x, y)\n", - "\n", - " # Define coords\n", - " coords = np.array(list(zip(*(c.flat for c in g))))\n", - "\n", - " # Create path\n", - " elpath = Path(thepatch.get_verts())\n", - "\n", - " # Determine all points in the grid ithat are in the path.\n", - " validcoords = elpath.contains_points(coords)\n", - " ellipsepoints = coords[validcoords]\n", - "\n", - " # Set output\n", - " outim = np.zeros(imshape)\n", - " outim[ellipsepoints[:, 0], ellipsepoints[:, 1]] = 1\n", - "\n", - " return outim\n", - "\n", - "\n", - "def empty_imshape(n_pix_side, nb_channels=1):\n", - " \"\"\"\n", - " Create empty canvas.\n", - " \"\"\"\n", - " \n", - " return np.zeros((n_pix_side, n_pix_side))#, nb_channels))#, np.uint8)\n", - "\n", - "\n", - "def create_line(imshape, x_start, y_start, x_end, y_end, width):\n", - " \"\"\"\n", - " Create a line.\n", - " \"\"\"\n", - " \n", - " # Create the arc patch we want to add to the image\n", - " return None\n", - "\n", - "\n", - "def create_arc(imshape, center, radius, width, theta1, theta2):\n", - " \"\"\"\n", - " Create an arc (outline of a circle) with a specified\n", - " thickness (width).\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " arc = Wedge(center, radius, theta1, theta2, width)\n", - "\n", - " return _patch2im(arc, imshape)\n", - "\n", - "\n", - "def create_circle(imshape, center, radius, width):\n", - " \"\"\"\n", - " Create a circle.\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " return create_arc(imshape, center, radius, width=width, theta1=0, theta2=360)\n", - " \n", - "\n", - "def create_polygon(imshape, xy, width, height, angle=0.0, fill=True):\n", - " \"\"\"\n", - " Create a polygon\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " rect = Rectangle(xy, width, height, angle=0.0, fill=fill)\n", - "\n", - " return _patch2im(rect, imshape)\n", - "\n", - "\n", - "def create_meshgrid(imshape):\n", - " \"\"\"\n", - " Create MeshGrid\n", - " \"\"\"\n", - " \n", - " return np.meshgrid(np.arange(np.shape(imshape)[0]), np.arange(np.shape(imshape)[1]))\n", - "\n", - "\n", - "def create_Sersic2D(imshape, \n", - " amplitude=1,\n", - " x_0=50, \n", - " y_0=50,\n", - " radius=25, \n", - " n=4, \n", - " ellip=.5, \n", - " theta=-1 \n", - " ):\n", - " \"\"\"\n", - " Create Galaxy profile: Sersic\n", - " \"\"\"\n", - " \n", - " x, y = create_meshgrid(imshape)\n", - " mod = Sersic2D(amplitude=amplitude,\n", - " x_0=x_0, \n", - " y_0=y_0,\n", - " r_eff=radius, \n", - " n=n,\n", - " ellip=ellip, \n", - " theta=theta\n", - " )\n", - " \n", - " return mod(x, y)\n", - "\n", - "\n", - "def create_Moffat2D(imshape,\n", - " amplitude=1,\n", - " x_0=0, \n", - " y_0=0, \n", - " radius=1, \n", - " alpha=1\n", - " ):\n", - " \"\"\"\n", - " Create Star profile: Moffat\n", - " \"\"\"\n", - " \n", - " x, y = create_meshgrid(imshape)\n", - " mod = Moffat2D(amplitude=amplitude, \n", - " x_0=x_0, \n", - " y_0=y_0, \n", - " gamma=radius, \n", - " alpha=alpha)\n", - " \n", - " return mod(x, y) \n", - "\n", - "\n", - "def create_Gaussian(imshape, \n", - " amplitude=1, \n", - " x_mean=0, y_mean=0, \n", - " x_stddev=None, y_stddev=None, \n", - " theta=None):\n", - " \"\"\"\n", - " Create Gaussian\n", - " \"\"\"\n", - " \n", - " x, y = create_meshgrid(imshape)\n", - " \n", - " mod = Gaussian2D(amplitude=1, \n", - " x_mean=x_mean, y_mean=y_mean,\n", - " x_stddev=x_stddev, y_stddev=y_stddev, \n", - " theta=theta)\n", - " \n", - " return mod(x, y) \n", - "\n", - "\n", - "def create_psf(imshape):\n", - " \"\"\"\n", - " Add PSF to image\n", - " \"\"\"\n", - " \n", - " impsf = np.ones((np.shape(imshape)[0],np.shape(imshape)[1])) #/ 25 \n", - "\n", - " return convolve2d(imshape, impsf, 'same')\n", - " \n", - "\n", - "def create_noise(imshape, noise_level):\n", - " \"\"\"\n", - " Create noise image\n", - " https://kmdouglass.github.io/posts/modeling-noise-for-image-simulations.html\n", - " \"\"\"\n", - " \n", - " seed = 42\n", - " rs = np.random.RandomState(seed)\n", - " \n", - " return rs.poisson(noise_level, (np.shape(imshape)[0],np.shape(imshape)[1]))\n", - " \n", - " \n", - "def combine_sky_imshape(imshape_list, amplitude_list):\n", - " \"\"\"\n", - " Combine clean sky images\n", - " \"\"\"\n", - " \n", - " imshape_new = np.zeros((imshape_list[0].shape[0], imshape_list[0].shape[1]))\n", - " for imshape, amplitude in zip(imshape_list, amplitude_list):\n", - " imshape_new += amplitude*imshape\n", - " \n", - " return imshape_new " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Catalogs" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": { - "code_folding": [ - 0 - ] - }, - "outputs": [], - "source": [ - "def generate_catalog(n_obj, \n", - " n_pix_side, \n", - " myclass,\n", - " id_start=0, \n", - " amplitude=1,\n", - " amplitude_min=1,\n", - " amplitude_max=1.,\n", - " radius_min=2,\n", - " radius_max=20,\n", - " verbose=False):\n", - " \"\"\"\n", - " Generate data set: catalog\n", - " \"\"\"\n", - "\n", - " # fac = -1.0\n", - " # np.random.uniform(0 - fac* radius_max, n_pix_side + fac* radius_max, n_obj)\n", - " \n", - " # Define column names\n", - " column_names = ['id', 'x_center', 'y_center', 'radius', 'amplitude', 'myclass']\n", - "\n", - " # Data Frame: Tabular Data for Objects\n", - " catalog = pd.DataFrame(np.empty((n_obj, len(column_names))),columns=column_names)\n", - "\n", - " catalog.id = np.arange(n_obj) + id_start\n", - " catalog.x_center = n_pix_side / 2.\n", - " catalog.y_center = n_pix_side / 2.\n", - " catalog.radius = np.random.uniform(radius_min, radius_max, n_obj)\n", - " catalog.amplitude = np.random.uniform(amplitude_min, amplitude_max, n_obj)\n", - " catalog.myclass = myclass\n", - " \n", - " return catalog\n", - "\n", - " \n", - "def generate_imageset(catalog, n_pix_side, noise, verbose=False):\n", - " \"\"\"\n", - " Generate data set: images\n", - " \"\"\"\n", - "\n", - " # count objects\n", - " n_obj = len(catalog)\n", - " \n", - " # create image list\n", - " images = np.empty((n_obj, n_pix_side, n_pix_side))\n", - " \n", - " for ind, _ in enumerate(images):\n", - " images[ind] = generate_image(catalog.loc[ind], n_pix_side, noise)\n", - "\n", - " return images\n", - "\n", - "\n", - "def generate_image(catalog_single, n_pix_side, noise):\n", - " \"\"\"\n", - " Generate a single image\n", - " \"\"\"\n", - "\n", - " imshape = empty_imshape(n_pix_side)\n", - " \n", - " if catalog_single.myclass == 1:\n", - "\n", - " imshape = create_Sersic2D(imshape, \n", - " x_0=catalog_single.x_center, \n", - " y_0=catalog_single.y_center,\n", - " radius=catalog_single.radius \n", - " )\n", - " elif catalog_single.myclass == 0:\n", - " imshape = create_Moffat2D(imshape,\n", - " x_0=catalog_single.x_center, \n", - " y_0=catalog_single.y_center,\n", - " radius=catalog_single.radius\n", - " )\n", - "\n", - " # apply amplitude\n", - " imshape *= catalog_single.amplitude\n", - " \n", - " # add noise\n", - " noise = create_noise(imshape, noise)\n", - " imshape += noise\n", - " \n", - "\n", - " return imshape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare Data" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "code_folding": [ - 0, - 15, - 33, - 42 - ] - }, - "outputs": [], - "source": [ - "def combine_data(frames, data_type): \n", - " \"\"\"\n", - " Combine classes of data\n", - " \"\"\"\n", - " \n", - " if data_type=='catalog':\n", - " data = pd.concat(frames)\n", - " elif data_type=='image':\n", - " data = np.concatenate(frames)\n", - " else:\n", - " data = None\n", - " \n", - " return data\n", - "\n", - "\n", - "def randomize_data(catalog, imageset, seed=5, verbose=False):\n", - " \"\"\"\n", - " Randomize the data\n", - " \"\"\"\n", - " \n", - " # create randomized indices\n", - " random.seed(seed) \n", - " n_catalog = len(catalog)\n", - " ind_random = np.arange(n_catalog)\n", - " random.shuffle(ind_random)\n", - " \n", - " # re-order data based on randomized indices\n", - " catalog = catalog.iloc[ind_random]\n", - " imageset = imageset[ind_random]\n", - "\n", - " return catalog, imageset, ind_random\n", - "\n", - "\n", - "def recast_imageset(imageset):\n", - " \"\"\"\n", - " Recast imageset as numpy uint8\n", - " \"\"\"\n", - " imageset = imageset.astype(np.uint8)\n", - " \n", - " return imageset\n", - "\n", - "\n", - "def recast_imageset_bits(imageset):\n", - " \"\"\"\n", - " Recast imageset as numpy uint8\n", - " \"\"\"\n", - " imageset = np.unpackbits(imageset)\n", - " \n", - " return imageset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## I/O" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "code_folding": [ - 0, - 15 - ] - }, - "outputs": [], - "source": [ - "def save_data(f_data_list, data_list, data_type, verbose=False):\n", - " \"\"\"\n", - " Save Data to File\n", - " \"\"\"\n", - " \n", - " if data_type == 'catalog':\n", - " # Pandas Data Frame for tabular data: save to file\n", - " data_list.to_csv(f_data_list)\n", - " elif data_type == 'image':\n", - " # Numpy Array for image data: save to file\n", - " np.save(f_data_list, data_list)\n", - " \n", - " return \n", - "\n", - "\n", - "def load_data(f_data_list, data_type, verbose=False):\n", - " \"\"\"\n", - " Load Data from File\n", - " \"\"\"\n", - " \n", - " if data_type == 'catalog':\n", - " data_list = pd.read_csv(f_data_list)\n", - " elif data_type == 'image':\n", - " data_list = np.load(f_data_list)\n", - " \n", - " return data_list\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualize" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": { - "code_folding": [ - 29 - ] - }, - "outputs": [], - "source": [ - "def show_example(imshape, imshape_2=None, log=False):\n", - " f = plt.figure(figsize=(18, 4))\n", - "\n", - " f.add_subplot(1,2, 1)\n", - " if log:\n", - " imshape = np.log10(imshape)\n", - " plt.imshow(imshape, origin='lower', interpolation='nearest',\n", - " vmin=-1, vmax=2)\n", - " plt.xlabel('x')\n", - " plt.ylabel('y')\n", - " cbar = plt.colorbar()\n", - " cbar.set_label('Brightness', rotation=270, labelpad=25)\n", - " cbar.set_ticks([-1, 0, 1, 2], update_ticks=True)\n", - "\n", - " if imshape_2 is not None:\n", - " f.add_subplot(1,2, 2)\n", - " if log:\n", - " imshape_2 = np.log10(imshape_2)\n", - " plt.imshow(imshape_2, origin='lower', interpolation='nearest',\n", - " vmin=-1, vmax=2)\n", - " plt.xlabel('x')\n", - " plt.ylabel('y')\n", - " cbar = plt.colorbar()\n", - " cbar.set_label('Brightness', rotation=270, labelpad=25)\n", - " cbar.set_ticks([-1, 0, 1, 2], update_ticks=True)\n", - "\n", - " plt.show(block=True)\n", - " \n", - " \n", - "def show_encoded(): \n", - " \"\"\"\n", - " Show the encoded layer representation\n", - " \"\"\"\n", - " num_images = 10\n", - " np.random.seed(42)\n", - " random_test_images = np.random.randint(x_test.shape[0], size=num_images)\n", - "\n", - " encoded_imgs = encoder.predict(x_test)\n", - " decoded_imgs = autoencoder.predict(x_test)\n", - "\n", - "\n", - "\n", - " plt.figure(figsize=(18, 4))\n", - "\n", - " print(encoded_imgs[0].shape)\n", - " num_rows=4\n", - " num_pixel_x = 2\n", - " num_pixel_y = 4\n", - "\n", - " for i, image_idx in enumerate(random_test_images):\n", - " # plot original image\n", - " ax = plt.subplot(4, num_images, i + 1)\n", - " plt.imshow(x_test[image_idx].reshape(28, 28))\n", - " plt.gray()\n", - " ax.get_xaxis().set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - "\n", - " # plot encoded image\n", - " ax = plt.subplot(num_rows, num_images, num_images + i + 1)\n", - "\n", - " plt.imshow(encoded_imgs[image_idx].reshape(num_pixel_x, num_pixel_y), interpolation=None, resample=None)\n", - " plt.gray()\n", - " ax.get_xaxis().set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - "\n", - " # plot encoded image\n", - " ax = plt.subplot(num_rows, num_images, 2*num_images + i + 1)\n", - " print(np.min(encoded_imgs[image_idx].reshape(num_pixel_x, num_pixel_y)), np.max(encoded_imgs[image_idx].reshape(num_pixel_x, num_pixel_y)))\n", - "\n", - " plt.imshow(encoded_imgs[image_idx].reshape(num_pixel_x, num_pixel_y).astype('uint8'), interpolation=None, resample=None)\n", - " plt.gray()\n", - " ax.get_xaxis().set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - "\n", - "\n", - " # plot reconstructed image\n", - " ax = plt.subplot(num_rows, num_images, 3*num_images + i + 1)\n", - " plt.imshow(decoded_imgs[image_idx].reshape(28, 28))\n", - " plt.gray()\n", - " ax.get_xaxis().set_visible(False)\n", - " ax.get_yaxis().set_visible(False)\n", - " plt.show()\n", - " \n", - " \n", - "def plot_distributions(data, xlabel):\n", - " f = plt.figure(figsize=(12, 4))\n", - "\n", - " plt.hist(data)\n", - " plt.xlabel(xlabel)\n", - "\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": { - "code_folding": [] - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Meta data\n", - "n_obj = 200\n", - "n_pix_side = 64\n", - "noise = 0.\n", - "\n", - "# generate catalogs\n", - "catalog_gal = generate_catalog(n_obj, \n", - " n_pix_side, \n", - " 1,\n", - " id_start=0, \n", - " amplitude_min=0.8,\n", - " amplitude_max=5.0, \n", - " radius_min=24,\n", - " radius_max=26,\n", - " verbose=False)\n", - "\n", - "catalog_star = generate_catalog(n_obj, \n", - " n_pix_side, \n", - " 0,\n", - " id_start=len(catalog_gal), \n", - " amplitude_min=0.8,\n", - " amplitude_max=5.0, \n", - " radius_min=0.5,\n", - " radius_max=2,\n", - " verbose=False)\n", - "\n", - "# generate images\n", - "imageset_gal = generate_imageset(catalog_gal, n_pix_side, noise, verbose=False)\n", - "imageset_star = generate_imageset(catalog_star, n_pix_side, noise, verbose=False)\n", - "\n", - "# combine catalogs\n", - "catalog_combined = combine_data([catalog_gal, catalog_star], 'catalog')\n", - "imageset_combined = combine_data([imageset_gal, imageset_star], 'image')\n", - "\n", - "catalog_combined, imageset_combined, _ = randomize_data(catalog_combined, imageset_combined, seed=5, verbose=False)\n", - "\n", - "# imageset_combined = recast_imageset(imageset_combined)\n", - "#print(imageset_combined)\n", - "\n", - "# save \n", - "\n", - "# visualize\n", - "show_example(imageset_combined[0], imshape_2=imageset_combined[1], log=True)\n", - "\n", - "plot_distributions(catalog_combined.amplitude, 'amplitude')\n", - "plot_distributions(catalog_combined.radius, 'radius')\n", - "# save " - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": {}, - "outputs": [], - "source": [ - "# import packages for Convolutional AutoEncoder compression\n", - "import keras\n", - "from keras import regularizers, optimizers\n", - "from keras.datasets import mnist\n", - "from keras.models import Model, Sequential\n", - "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n", - "from keras.layers import Flatten, Reshape\n", - "from keras import backend as K\n", - "\n", - "\n", - "from IPython.display import Image, SVG\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [], - "source": [ - "# Prep\n", - "nb_train = 1000\n", - "nb_test = 1000\n", - "nb_epochs = 6\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [], - "source": [ - "x_train_temp = imageset_combined[0:nb_train,:,:]\n", - "x_test_temp = imageset_combined[nb_train:nb_train + nb_test,:,:]\n", - "\n", - "y_train_temp = catalog_combined[0:nb_train]\n", - "y_test_temp = catalog_combined[0:nb_train]\n", - "#print(y_train_temp)" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1000, 28, 28), (1000, 28, 28))" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(x_train_temp, _), (x_test_temp, _) = mnist.load_data()\n", - "x_train_temp = x_train_temp[0:nb_train,:,:]\n", - "x_test_temp = x_test_temp[0:nb_test,:,:]\n", - "\n", - "x_train_temp.shape, x_test_temp.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1000, 28, 28), (1000, 28, 28))" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "max_value = float(x_train_temp.max())\n", - "x_train = x_train_temp.astype('float32') / max_value\n", - "x_test = x_test_temp.astype('float32') / max_value\n", - "\n", - "x_train.shape, x_test.shape\n" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1000, 28, 28, 1)\n" - ] - } - ], - "source": [ - "n_pix_side = x_train.shape[2]\n", - "x_train = x_train.reshape((len(x_train), n_pix_side, n_pix_side, 1))\n", - "x_test = x_test.reshape((len(x_test), n_pix_side, n_pix_side, 1))\n", - "\n", - "(x_train.shape, x_test.shape)\n", - "print(x_train.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "conv2d_270 (Conv2D) (None, 28, 28, 64) 640 \n", - "_________________________________________________________________\n", - "max_pooling2d_109 (MaxPoolin (None, 14, 14, 64) 0 \n", - "_________________________________________________________________\n", - "conv2d_271 (Conv2D) (None, 14, 14, 32) 18464 \n", - "_________________________________________________________________\n", - "max_pooling2d_110 (MaxPoolin (None, 7, 7, 32) 0 \n", - "_________________________________________________________________\n", - "conv2d_272 (Conv2D) (None, 4, 4, 16) 4624 \n", - "_________________________________________________________________\n", - "max_pooling2d_111 (MaxPoolin (None, 2, 2, 16) 0 \n", - "_________________________________________________________________\n", - "conv2d_273 (Conv2D) (None, 1, 1, 8) 1160 \n", - "_________________________________________________________________\n", - "max_pooling2d_112 (MaxPoolin (None, 1, 1, 8) 0 \n", - "_________________________________________________________________\n", - "conv2d_274 (Conv2D) (None, 1, 1, 4) 292 \n", - "_________________________________________________________________\n", - "flatten_1 (Flatten) (None, 4) 0 \n", - "_________________________________________________________________\n", - "latent (Reshape) (None, 1, 1, 4) 0 \n", - "_________________________________________________________________\n", - "up_sampling2d_155 (UpSamplin (None, 2, 2, 4) 0 \n", - "_________________________________________________________________\n", - "conv2d_275 (Conv2D) (None, 2, 2, 8) 296 \n", - "_________________________________________________________________\n", - "up_sampling2d_156 (UpSamplin (None, 4, 4, 8) 0 \n", - "_________________________________________________________________\n", - "conv2d_276 (Conv2D) (None, 4, 4, 16) 1168 \n", - "_________________________________________________________________\n", - "up_sampling2d_157 (UpSamplin (None, 8, 8, 16) 0 \n", - "_________________________________________________________________\n", - "conv2d_277 (Conv2D) (None, 8, 8, 32) 4640 \n", - "_________________________________________________________________\n", - "up_sampling2d_158 (UpSamplin (None, 16, 16, 32) 0 \n", - "_________________________________________________________________\n", - "conv2d_278 (Conv2D) (None, 14, 14, 64) 18496 \n", - "_________________________________________________________________\n", - "up_sampling2d_159 (UpSamplin (None, 28, 28, 64) 0 \n", - "_________________________________________________________________\n", - "conv2d_279 (Conv2D) (None, 28, 28, 1) 577 \n", - "=================================================================\n", - "Total params: 50,357\n", - "Trainable params: 50,357\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "\n", - "======================================================================\n", - "======================================================================\n", - "======================================================================\n", - "\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "conv2d_270_input (InputLayer (None, 28, 28, 1) 0 \n", - "_________________________________________________________________\n", - "conv2d_270 (Conv2D) (None, 28, 28, 64) 640 \n", - "_________________________________________________________________\n", - "max_pooling2d_109 (MaxPoolin (None, 14, 14, 64) 0 \n", - "_________________________________________________________________\n", - "conv2d_271 (Conv2D) (None, 14, 14, 32) 18464 \n", - "_________________________________________________________________\n", - "max_pooling2d_110 (MaxPoolin (None, 7, 7, 32) 0 \n", - "_________________________________________________________________\n", - "conv2d_272 (Conv2D) (None, 4, 4, 16) 4624 \n", - "_________________________________________________________________\n", - "max_pooling2d_111 (MaxPoolin (None, 2, 2, 16) 0 \n", - "_________________________________________________________________\n", - "conv2d_273 (Conv2D) (None, 1, 1, 8) 1160 \n", - "_________________________________________________________________\n", - "max_pooling2d_112 (MaxPoolin (None, 1, 1, 8) 0 \n", - "_________________________________________________________________\n", - "conv2d_274 (Conv2D) (None, 1, 1, 4) 292 \n", - "_________________________________________________________________\n", - "flatten_1 (Flatten) (None, 4) 0 \n", - "=================================================================\n", - "Total params: 25,180\n", - "Trainable params: 25,180\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "import os\n", - "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n", - "\n", - "#keras.backend.set_floatx('float32')\n", - "\n", - "autoencoder = Sequential()\n", - "\n", - "# Encoder Layers\n", - "autoencoder.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))\n", - "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", - "autoencoder.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n", - "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", - "autoencoder.add(Conv2D(16, (3, 3), strides=(2,2), activation='relu', padding='same'))\n", - "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", - "autoencoder.add(Conv2D(8, (3, 3), strides=(2,2), activation='relu', padding='same'))\n", - "autoencoder.add(MaxPooling2D((2, 2), padding='same'))\n", - "autoencoder.add(Conv2D(4, (3, 3), strides=(2,2), activation='relu', padding='same'))\n", - "\n", - "# Flatten encoding for visualization\n", - "autoencoder.add(Flatten(name='flatten_1'))\n", - "autoencoder.add(Reshape((1, 1, 4),name='latent'))\n", - "\n", - "# Decoder Layers\n", - "autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n", - "autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(Conv2D(16, (3, 3), activation='relu', padding='same'))\n", - "autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n", - "autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(Conv2D(64, (3, 3), activation='relu'))#,padding='same'))\n", - "#autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(UpSampling2D((2, 2)))\n", - "autoencoder.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))\n", - "\n", - "autoencoder.summary()\n", - "sgd = optimizers.SGD(lr=0.0000005, decay=1e-6, momentum=0.9, nesterov=True)\n", - "\n", - "autoencoder.compile(optimizer=sgd, loss='mse', metrics=['accuracy'])\n", - "\n", - "\n", - "print()\n", - "print('======================================================================')\n", - "print('======================================================================')\n", - "print('======================================================================')\n", - "print()\n", - "\n", - "encoder = Model(inputs=autoencoder.input, outputs=autoencoder.get_layer('flatten_1').output)\n", - "encoder.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 1000 samples, validate on 1000 samples\n", - "Epoch 1/6\n", - "1000/1000 [==============================] - 11s 11ms/step - loss: 0.2313 - acc: 0.6501 - val_loss: 0.2317 - val_acc: 0.6932\n", - "Epoch 2/6\n", - "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2313 - acc: 0.7174 - val_loss: 0.2317 - val_acc: 0.7420\n", - "Epoch 3/6\n", - "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2313 - acc: 0.7530 - val_loss: 0.2317 - val_acc: 0.7687\n", - "Epoch 4/6\n", - "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2313 - acc: 0.7733 - val_loss: 0.2317 - val_acc: 0.7853\n", - "Epoch 5/6\n", - "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2313 - acc: 0.7861 - val_loss: 0.2317 - val_acc: 0.7962\n", - "Epoch 6/6\n", - "1000/1000 [==============================] - 8s 8ms/step - loss: 0.2313 - acc: 0.7944 - val_loss: 0.2316 - val_acc: 0.8033\n" - ] - } - ], - "source": [ - "history = autoencoder.fit(x_train, x_train,\n", - " epochs=nb_epochs,\n", - " batch_size=32,\n", - " validation_data=(x_test, x_test), verbose=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - 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0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n", - "[-0. 0.01459738 -0. 0.00299369]\n" - ] - } - ], - "source": [ - "encoded_imgs = encoder.predict(x_test)\n", - "\n", - "encoded_imgs_16 = encoded_imgs.astype('float16')\n", - "\n", - "for img, img16 in zip(encoded_imgs, encoded_imgs_16):\n", - " print(encoded_imgs[0]) \n", - "# print(encoded_imgs_16[0])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1000, 28, 28)\n" - ] - }, - { - "data": { - "image/png": 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neNOB21+S5OnZP78IAFio0fOIpg+Y3g7NltXdr7/fqldXlaN8ALB0A+cRTR8wuR3bs3YfVfX1SR4xdx0AwNmNnEc0fcD0dmgjW1WfTlL5wsTOd8SsgQCwfAPnEU0fMK0dG0Pf3Q+duwYA4BwNnkc0fcD0dmgjmyRV9eQk3539v+xt3f32mUsCAA4zcB65aGNVAcOqvfWWbVBVP5Hknyb5cJLvT/I3quon560KADjMyHlE0wdwbv5Okr/a3S9L8r+7+zlJfmDmmgCAsZxTHjG8E5jejg2n6O67VzerqirJg+asBwBYw8B5xJE+YFr9hQuiHrZsibuq6itXt48nuTbJG2asBwA4zOB5xJE+YHrbswFdxw8luWd1++eTfKS73z1jPQDAOgbOI470AdPrNZft8Jh8YYfZu5M8vKq+bMZ6AIB1DJxHNH3ApCq7NVtWkl9McmdVfXmSt2R/quTXzlsSAHA2o+cRTR8wrd0bQ7/X3X+a5KlJXruaLevymWsCAM5m8Dyi6QOmt1vDKT5XVU9L8mNJfmu17tiM9QAA6xg4j2j6gOnt1kb2x5I8K8lbu/vdVXVxkn82c00AwGEGziNm7wQmt0VDJdZxW5If6u7/W1XHkjygu//D3EUBAGc3ch5xpA+Y3m7tWXtjkuNV9cAkNyR5T1W9eOaaAIDDDJxHJmv6quqaqrqjqm46sO7Sqnp7VX1k9fOSqd4fWIjeudmyHtzdf5TkO5Pc0N1fl+R7Z64JOAN5BEgyfB6Z8kjfK5I85X7rXpjkHd39mCTvWN0Hdt1u7VlLVV2a5Mokb16tuucsTwfm9YrII0AydB6ZrOnr7uuS3Hm/1U9L8srV7VcmefpU7w8sx45NkfyLSX4vySOTvKmqHprkbfOWBJyJPAKcMnIe2fRELo/o7tuTpLtvr6qHn+mJVXVVkquS5NglRl3AVtueDeihuvvXk/z6gVX3JPmpmcoBzo88AiMaOI8sdiKX7r66u09094ljx4/PXQ5wvtYdSrFDG2Jgd8gjsCMGzyObbvo+WVWXJcnq5x0bfn9gwyo7N5wC2H7yCAxm9Dyy6abv2uxfRDCrn2/c8PsDMxh5IwsskjwCAxo5j0x5yYZXJ3lPkq+tqlur6tlJXpzkyVX1kSRPXt0Hdt3AwymAeckjwOcNnEcmm8ilu688w0PfOdV7Agu1oxtQYPnkEeDzBs4ji53IBdgRaw6lWHc4RVU9pao+XFUfrSrX1gIADjd4HtH0AdM7ouEUVXUsya8m+Z4k35Dkyqr6hklqBgB2y8B5RNMHTK721lvW8PgkH+3uj3X355K8JvsXWQYAOKuR84imD5jcOQyneFhV3XBguep+L3V5kj88cP/W1ToAgLMaOY9MNpELQJJznQnrU9194iyP1xneAQDgzAbPI5o+YHpHtxm8NckVB+4/MsltR/bqAMDuGjiPGN4JTKpypLNlXZ/kMVX16Kp6UJIfzP5FlgEAzmj0POJIHzC52juaXWvdfW9VPTfJW5McS3JNd998JC8OAOy0kfOIpg+Y1rmNoT/85brfnOTNR/eKAMDOGzyPaPqAya17oVMAgKmMnEc0fcD0Bt7IAgALMXAe0fQBkxt5zxoAsAwj5xFNHzC9gTeyAMBCDJxHNH3AtDqpvbmLAACGNnge0fQBkzp1XRwAgLmMnkc0fcD0euCtLACwDAPnEU0fMLmR96wBAMswch7R9AHTOuKLoQIAnLPB84imD5jcyCdOAwDLMHIe0fQBkxt5IwsALMPIeUTTB0yrM/SJ0wDAAgyeRzR9wORGPnEaAFiGkfOIpg+Y3sAbWQA4L3WWx3yvnp+BPzdNHzCp0S+GCgDMb/Q8oukDptWd2ht4KwsAzG/wPKLpA6Y37jYWAFiKgfOIpg+Y3MjDKQCAZRg5j2j6gGl1koGHUwAACzB4HtH0AdMbdxsLACzFwHlE0wdMbuThFABwWme7JAOTGDmPzNL0VdXHk9yV5GSSe7v7xBx1AJsx8mxZwHLJIzCWkfPInEf6vqO7PzXj+wOb0Bl6OAWwePIIjGDwPGJ4JzCp/YuhDryVBQBmN3oeuWim9+0kb6uq91XVVad7QlVdVVU3VNUNJ+++e8PlAUdqb80FYLPkERjJwHlkriN9T+ju26rq4UneXlW/293XHXxCd1+d5Ook+ZIrrhi3LYcdMPKeNWDR5BEYyMh5ZJYjfd192+rnHUnekOTxc9QBbECfwwKwQfIIDGTwPLLxpq+qjlfVxaduJ/muJDdtug5gUzq1t94CsCnyCIxm7Dwyx/DORyR5Q1Wdev/f6O63zFAHsCkDD6cAFkseYV6+Gjdv4Dyy8aavuz+W5Js3/b7ATDqpHT0pGthe8ggMZvA84pINwPQG3rMGACzEwHlE0wdMb9xtLACwFAPnEU0fMLnaG3g8BQCwCCPnEU0fMK3Ozl7oFADYEoPnEU0fMKlKD30xVABgfqPnEU0fML2BN7IAwEIMnEc0fcD0Bt7IAsB5qbM8dtjX6tl+d53f31UD5xFNHzCtwcfQAwALMHge0fQBkxt5tiwAYBlGziOaPmBiPfRwCgBgCcbOI5o+YFqdoTeyAMACDJ5HNH3A9MYdTQEALMXAeUTTB0xu5OviAADLMHIe0fQB0xt4IwsALMTAeUTTB0yrOzk58HgKAMZ02LXy5nztC/39beydBs8jmj5gegPvWQMAFmLgPKLpA6Y38EYWAFiIgfOIpg+YVifZG3cjCwAswOB5RNMHTKyTHncMPQCwBGPnEU0fMK3O0CdOAwALMHge0fQB0xt4DD0AsBAD5xFNHzC9gTeyAAzqsK++KS/pwOkNnEc0fcDEeuiNLACwBGPnEU0fMK1OsjfuGHoAYAEGzyOaPmB6A+9ZAwAWYuA8oukDJtZDz5YFACzB2HlE0wdMq5Me+Lo4AMACDJ5HNH3A9PbGHU4BACzEwHlE0wdMb+Ax9ADAQgycRzR9wLS6h54tC+BI7PI13c6Www/7u5ec4Zf872zJn9tUBs8jF83xplX1lKr6cFV9tKpeOEcNwAZ1r7cAbJA8AoMZOI9s/EhfVR1L8qtJnpzk1iTXV9W13f2hTdcCbEKnT56cuwiA+5BHYDRj55E5jvQ9PslHu/tj3f25JK9J8rQZ6gA2obN/4vQ6C8DmyCMwksHzyBxN3+VJ/vDA/VtX6+6jqq6qqhuq6oaTd9+9seKACfTeegvA5sgjMJqB88gcTd/pTmv9opa6u6/u7hPdfeLY8eMbKAuYQifpvV5ruRBV9cyqurmq9qrqxNFUD+wweQQGMnoemaPpuzXJFQfuPzLJbTPUAWxC96b2rN2U5PuSXHfhRQMDkEdgJIPnkTku2XB9ksdU1aOTfCLJDyb5mzPUAWzIJk6c7u5bkqRqyXNkAwsij8BgRs4j1TNMS1pVT03yK0mOJbmmu3/ukOf/UZLfP7DqYUk+NV2FF2SptS21rkRt5+soa/vq7v5zR/Ra91FVb8l+ret4cJLPHrh/dXdffY7v964kP9ndN5zL7wHjkUdmsdS6ErWdL3nk9O/3riwoj8zS9F2oqrqhuxczRvagpda21LoStZ2vJdc2lar6T0m+4jQP/aPufuPqOe/KgjaywO5a8nZ4qbUtta5EbedrybVNZRvzyBzDOwHOS3f/1blrAADGto15ZI6JXAAAANiQbW36zmlM7YYttbal1pWo7XwtubaNq6pnVNWtSf5Skt+qqrfOXROw85a8HV5qbUutK1Hb+VpybRu31Dyylef0AQAAsJ5tPdIHAADAGjR9AAAAO2yrmr6qekpVfbiqPlpVL5y7noOq6uNV9cGqurGqZp2ataquqao7quqmA+suraq3V9VHVj8vWVBtL6qqT6w+uxtX102ao7YrquqdVXVLVd1cVc9brZ/1sztLXYv43ABGI4+sXYs8cu51LTKLHFLb7J8bh9uac/qq6liS30vy5CS3Jrk+yZXd/aFZC1upqo8nOdHds184s6q+Pclnkvy77v7G1bpfSHJnd7949QV1SXf/g4XU9qIkn+nuX9p0Pfer7bIkl3X3+6vq4iTvS/L0JD+aGT+7s9T1A1nA5wYwEnnknGqRR869rkVmkUNqk0e2wDYd6Xt8ko9298e6+3NJXpPkaTPXtEjdfV2SO++3+mlJXrm6/crs/0+6cWeobRG6+/bufv/q9l1JbklyeWb+7M5SFwCbJ4+sSR45d0vNIofUxhbYpqbv8iR/eOD+rVnWf2id5G1V9b6qumruYk7jEd19e7L/P22Sh89cz/09t6o+sBpuMctQj4Oq6lFJHpfkvVnQZ3e/upKFfW4AA5BHLsxivlPPYDHfq0vNIok8so22qemr06xb0tjUJ3T3X0jyPUl+fDVsgPX8WpKvSfLYJLcnecmcxVTVQ5K8Lsnzu/vTc9Zy0GnqWtTnBjAIeWR3LeZ7dalZJJFHttU2NX23JrniwP1HJrltplq+SHfftvp5R5I3ZH/4x5J8cjUW+9SY7DtmrufzuvuT3X2yu/eSvCwzfnZV9cDsb8he1d2vX62e/bM7XV1L+twABiKPXJjZv1PPZCnfq0vNImeqbSmfG2e3TU3f9UkeU1WPrqoHJfnBJNfOXFOSpKqOr05oTVUdT/JdSW46+29t3LVJnrW6/awkb5yxlvs4tRFbeUZm+uyqqpK8PMkt3f3SAw/N+tmdqa6lfG4Ag5FHLow8cvYaFplFzlbbEj43Drc1s3cmyWoK2F9JcizJNd39czOXlCSpqj+f/b1pSfKAJL8xZ21V9eokT0zysCSfTPKzSf5jktcm+aokf5Dkmd298ROYz1DbE7M/JKCTfDzJc06NW99wbd+W5L8k+WCSvdXqn87+ePXZPruz1HVlFvC5AYxGHlm7Hnnk3OtaZBY5pDZ5ZAtsVdMHAADAudmm4Z0AAACcI00fAADADtP0AQAA7DBNHwAAwA7T9AEAAOwwTR8AAMAO0/QBAADsME0fR6aq/mJVfaCqHlxVx6vq5qr6xrnrAgDGIY/AF3Nxdo5UVf3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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "decoded_imgs = autoencoder.predict(x_test)\n", - "decoded_imgs = decoded_imgs.reshape((decoded_imgs.shape[0], n_pix_side, n_pix_side))\n", - "print(decoded_imgs.shape)\n", - "show_example(decoded_imgs[0], imshape_2=decoded_imgs[1])\n", - "#print(decoded_imgs[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# OLD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from keras import backend as K\n", - "K.floatx()\n", - "K.set_floatx('float16')\n", - "K.floatx()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://colab.research.google.com/drive/1E0URjxq_vlvrDVeLTsUIsgQOkoS9Gf94#scrollTo=yoKEbjT05rb8\n", - " https://colab.research.google.com/drive/1TfflYirHKJkVuX2jGwyjOLKplthzQP74" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - }, - "latex_envs": { - "LaTeX_envs_menu_present": true, - "autoclose": false, - "autocomplete": true, - "bibliofile": "biblio.bib", - "cite_by": "apalike", - "current_citInitial": 1, - "eqLabelWithNumbers": true, - "eqNumInitial": 1, - "hotkeys": { - "equation": "Ctrl-E", - "itemize": "Ctrl-I" - }, - "labels_anchors": false, - "latex_user_defs": false, - "report_style_numbering": false, - "user_envs_cfg": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/archive/notebooks/Deprecated/SimGenerator_v0.ipynb b/archive/notebooks/Deprecated/SimGenerator_v0.ipynb deleted file mode 100644 index 4a11c50..0000000 --- a/archive/notebooks/Deprecated/SimGenerator_v0.ipynb +++ /dev/null @@ -1,1113 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Deprecated" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# packages: random\n", - "import random\n", - "\n", - "# packages: data structure\n", - "import numpy as np\n", - "import pandas as pd\n", - "import astropy.io as io\n", - "\n", - "# packages: image generation and plot generation\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# pandas\n", - "# https://pandas.pydata.org/pandas-docs/stable/tutorials.html\n", - "# https://pandas.pydata.org/pandas-docs/stable/10min.html\n", - "\n", - "# ascii:io\n", - "# http://docs.astropy.org/en/stable/io/ascii/\n", - "\n", - "# matplotlib\n", - "# https://nickcharlton.net/posts/drawing-animating-shapes-matplotlib.html" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# numpy: empty canvas\n", - "def empty_canvas(image_side_length=100):\n", - "\n", - " return np.indices((image_side_length, image_side_length))\n", - " \n", - " \n", - "# scikit learn: circle\n", - "def circle_sk(canvas, x_center=50, y_center=50, radius=30):\n", - "\n", - " y, x = canvas\n", - " circle = (x - x_center)**2 + (y - y_center)**2 < radius**2\n", - " img = circle.astype(float)\n", - " \n", - " return img\n", - "\n", - "\n", - "# scikit learn: rectangle\n", - "def rect_sk(canvas, x_center=50, y_center=50, radius=30):\n", - "\n", - " y, x = canvas\n", - " rect = (x < x_center + radius) & (x > x_center - radius) & (y < y_center + radius) & (y > y_center - radius)\n", - " img = rect.astype(float)\n", - " \n", - " return img\n", - " \n", - " \n", - "# scikit learn: rectangle\n", - "#def triangle_sk(canvas, x_center=50, y_center=50, radius=30):\n", - "\n", - "# y, x = canvas\n", - "# rect = (x < x_center + radius) & (x > x_center - radius) & (y < y_center + radius) & (y > y_center - radius)\n", - "# img = rect.astype(float)\n", - " \n", - "# return img\n", - " \n", - " \n", - "# plot for SPI package\n", - "def plot_spi(img):\n", - " plt.axes() \n", - " plt.imshow(img)\n", - " plt.clf()\n", - " \n", - " \n", - "# matplotlib pyplot\n", - "def circle_plt(x_center=0, y_center=0, radius=0.75, fc='r', show=False):\n", - "\n", - " plt.axes()\n", - "\n", - " circle = plt.Circle((x_center, y_center), radius=radius, fc=fc)\n", - " plt.gca().add_patch(circle)\n", - " plt.axis('scaled')\n", - " imgplot = plt.imshow(img)\n", - " imgplot = plt.savefig(\"test3.png\", dpi = (200))\n", - " #imgplot = plt.imshow()\n", - " \n", - " if show:\n", - " plt.show()\n", - "\n", - "# test each individual function\n", - "def test_individual():\n", - " #circle()\n", - " img = circle_sk()\n", - " plot_spi(img)\n", - " #star()\n", - " return\n", - "\n", - "# generate one image data set\n", - "def generate_dataset(nb_obj, \n", - " image_side_length=100, \n", - " index_start=0, \n", - " shape='rect',\n", - " x_min=32,\n", - " x_max=32,\n", - " y_min=32,\n", - " y_max=32,\n", - " radius_min=10,\n", - " radius_max=10,\n", - " show_plot=False, \n", - " verbose=False):\n", - "\n", - " # initiate image values\n", - " fac = -1.0\n", - " #x_center_list = np.random.uniform(0 + fac* radius_max, image_side_length + fac* radius_max, nb_obj)\n", - " #y_center_list = np.random.uniform(0 + fac* radius_max, image_side_length + fac* radius_max, nb_obj)\n", - " x_center_list = np.random.uniform(x_min, x_max, nb_obj)\n", - " y_center_list = np.random.uniform(y_min, y_max, nb_obj)\n", - "\n", - " radius_list = np.random.uniform(radius_min, radius_max, nb_obj)\n", - " print('x ranges', min(x_center_list), max(x_center_list))\n", - " print('y ranges', min(y_center_list), max(y_center_list))\n", - " column_names = ['ident', 'x_center', 'y_center', 'radius', 'shape']\n", - " \n", - " # create empty data structures\n", - " tab_list = np.empty((nb_obj, len(column_names)))\n", - " img_list = np.empty((nb_obj, image_side_length, image_side_length))\n", - " \n", - " # create empty canvas for a single image\n", - " canvas = empty_canvas(image_side_length=image_side_length)\n", - " \n", - " # loop over objects\n", - " icount = 0\n", - " for i_obj in np.arange(nb_obj):\n", - " \n", - " # draw object properties from list\n", - " x_center = x_center_list[i_obj]\n", - " y_center = y_center_list[i_obj] \n", - " radius = radius_list[i_obj]\n", - " \n", - " # identification value\n", - " ident = int(index_start + i_obj)\n", - " \n", - " # create object\n", - " if shape == 'rect':\n", - " img = rect_sk(canvas, x_center=x_center, y_center=y_center, radius=radius)\n", - " shape_num = 0\n", - " elif shape == 'circ':\n", - " img = circle_sk(canvas, x_center=x_center, y_center=y_center, radius=radius)\n", - " shape_num = 1 \n", - "\n", - " # add tabular data to data list structure\n", - " tab_list[i_obj] = [ident, x_center, y_center, radius, int(shape_num)]\n", - " \n", - " # add image data to image list structure\n", - " img_list[i_obj] = img\n", - "\n", - " # plot image\n", - " if show_plot and icount <20:\n", - " icount+=1\n", - " plt.figure()\n", - " plt.axes() \n", - " plt.imshow(img)\n", - " \n", - " # Data Frame: Tabular Data for Objects\n", - " tab_list = pd.DataFrame(tab_list,columns=column_names)\n", - " \n", - " # verbose\n", - " if verbose:\n", - " print(tab_list[0:10])\n", - " print(img_list[0:10])\n", - " \n", - " return tab_list, img_list\n", - "\n", - "\n", - "# save data\n", - "def save_data(f_data_list, f_img_list, data_list, img_list, verbose=False):\n", - "\n", - " # Pandas Data Frame for tabular data: save to file\n", - " data_list.to_csv(f_data_list)\n", - "\n", - " # Numpy Array for image data: save to file\n", - " np.save(f_img_list, img_list)\n", - " \n", - " # verbose\n", - " if verbose:\n", - " print(f_data_list_pd)\n", - " print(f_img_list)\n", - " \n", - " return\n", - " \n", - "\n", - "# combine data sets\n", - "def combine_data(frames, data_type='tab'): \n", - " \n", - " if data_type=='tab':\n", - " data = pd.concat(frames)\n", - " elif data_type=='img':\n", - " data = np.concatenate(frames)\n", - " \n", - " return data\n", - "\n", - "\n", - "# randomize data \n", - "def randomize_data(tab, img, seed=5, verbose=False):\n", - " \n", - " if verbose:\n", - " print('Before:', tab)\n", - " \n", - " # create randomized indices\n", - " random.seed(seed) \n", - " nb_tab = len(tab)\n", - " ind_random = np.arange(nb_tab) \n", - " random.shuffle(ind_random)\n", - " \n", - " # re-order data based on randomized indices\n", - " tab = tab.iloc[ind_random]\n", - " img = img[ind_random]\n", - " \n", - " if verbose:\n", - " print('After:', tab)\n", - "\n", - " return tab, img\n", - "\n", - "\n", - "# split data \n", - "def split_data(nb_train, nb_valid, nb_test, tab, img, printcheck=0):\n", - "\n", - " \n", - " ind_start_train = 0\n", - " ind_end_train = ind_start_valid = ind_start_train + nb_train \n", - " ind_end_valid = ind_start_test = ind_start_valid + nb_valid\n", - " ind_end_test = ind_start_test + nb_test\n", - " \n", - " if printcheck > 0:\n", - " print(tab[0:printcheck])\n", - " print(ind_start_train, ind_end_train)\n", - " # good place for unit test\n", - " \n", - " # split data in train, valid, test\n", - " tab_train = tab[ind_start_train: ind_end_train]\n", - " img_train = img[ind_start_train: ind_end_train]\n", - "\n", - " tab_valid = tab[ind_start_valid: ind_end_valid]\n", - " img_valid = img[ind_start_valid: ind_end_valid]\n", - "\n", - " tab_test = tab[ind_start_test: ind_end_test]\n", - " img_test = img[ind_start_test: ind_end_test]\n", - " \n", - " return tab_train, tab_valid, tab_test, img_train, img_valid, img_test" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x ranges 10.006670793022877 53.997868245890025\n", - "y ranges 10.012215820298476 53.995160518827696\n", - "x ranges 10.003455246206755 53.99969589009031\n", - "y ranges 10.021885568801949 53.99680971642177\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/anaconda3/envs/SimplePhysicalImage/lib/python3.6/site-packages/matplotlib/pyplot.py:537: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", - " max_open_warning, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Before: ident x_center y_center radius shape\n", - "0 0.0 53.807942 14.882025 25.918850 0.0\n", - "1 1.0 24.342952 14.469646 5.429372 0.0\n", - "2 2.0 18.198739 35.984471 15.042305 0.0\n", - "3 3.0 22.387154 38.343779 13.183867 0.0\n", - "4 4.0 36.788960 34.660622 11.280092 0.0\n", - "5 5.0 46.120199 37.327714 14.005038 0.0\n", - "6 6.0 12.225752 16.334552 25.574700 0.0\n", - "7 7.0 14.348829 47.392402 6.889213 0.0\n", - "8 8.0 47.575099 30.028321 16.538826 0.0\n", - "9 9.0 38.642397 18.855977 25.582719 0.0\n", - "10 10.0 28.898036 31.055267 18.054302 0.0\n", - "11 11.0 33.795816 53.461817 27.287078 0.0\n", - "12 12.0 30.561144 41.484994 8.953349 0.0\n", - "13 13.0 48.321409 46.195169 6.232920 0.0\n", - "14 14.0 19.113632 47.170905 26.610064 0.0\n", - "15 15.0 38.380544 49.666704 23.372532 0.0\n", - "16 16.0 30.924125 22.461703 25.493766 0.0\n", - "17 17.0 46.604287 27.557403 10.316504 0.0\n", - "18 18.0 12.942414 47.037541 23.187489 0.0\n", - "19 19.0 23.058731 50.902045 9.167487 0.0\n", - "20 20.0 36.828023 48.493008 24.641515 0.0\n", - "21 21.0 22.694921 24.721304 12.996464 0.0\n", - "22 22.0 16.935710 42.497784 19.256628 0.0\n", - "23 23.0 20.322083 21.500012 14.649496 0.0\n", - "24 24.0 23.568640 22.880119 27.021332 0.0\n", - "25 25.0 42.559653 33.061513 8.472630 0.0\n", - "26 26.0 30.238133 18.619432 28.014192 0.0\n", - "27 27.0 39.529840 12.010202 4.709973 0.0\n", - "28 28.0 17.626738 30.067770 13.020112 0.0\n", - "29 29.0 49.304624 14.913774 7.429144 0.0\n", - "... ... ... ... ... ...\n", - "4970 9970.0 51.187790 29.779559 14.655753 1.0\n", - "4971 9971.0 20.423570 44.947952 17.741139 1.0\n", - "4972 9972.0 40.867909 52.930533 21.111008 1.0\n", - "4973 9973.0 31.173566 53.210138 21.823807 1.0\n", - "4974 9974.0 35.039273 32.647294 18.073030 1.0\n", - "4975 9975.0 45.064751 18.854851 21.526037 1.0\n", - "4976 9976.0 47.481866 49.944559 16.798229 1.0\n", - "4977 9977.0 31.603136 12.999977 20.221808 1.0\n", - "4978 9978.0 40.130365 25.586251 28.490052 1.0\n", - "4979 9979.0 53.911339 21.137615 27.693974 1.0\n", - "4980 9980.0 27.520271 14.936486 10.476511 1.0\n", - "4981 9981.0 40.340422 25.406924 23.065755 1.0\n", - "4982 9982.0 37.246686 41.690740 23.072052 1.0\n", - "4983 9983.0 35.001967 30.417183 10.239100 1.0\n", - "4984 9984.0 33.872442 44.922725 26.516432 1.0\n", - "4985 9985.0 36.094603 44.708674 25.975044 1.0\n", - "4986 9986.0 24.248433 31.105424 26.332673 1.0\n", - "4987 9987.0 28.103456 53.785903 15.881829 1.0\n", - "4988 9988.0 16.207335 12.409548 28.260887 1.0\n", - "4989 9989.0 35.335368 28.993841 19.681277 1.0\n", - "4990 9990.0 33.059530 50.180718 17.780231 1.0\n", - "4991 9991.0 44.689002 23.572807 16.184068 1.0\n", - "4992 9992.0 14.745698 34.440101 8.880192 1.0\n", - "4993 9993.0 10.877891 36.560767 28.541210 1.0\n", - "4994 9994.0 33.424545 12.949527 5.252499 1.0\n", - "4995 9995.0 13.367104 11.823550 4.780767 1.0\n", - "4996 9996.0 32.654717 35.382659 11.866425 1.0\n", - "4997 9997.0 44.142243 41.243750 13.449796 1.0\n", - "4998 9998.0 27.264092 26.952294 23.457229 1.0\n", - "4999 9999.0 49.979355 25.903954 5.372476 1.0\n", - "\n", - "[10000 rows x 5 columns]\n", - "After: ident x_center y_center radius shape\n", - "4964 4964.0 50.603330 29.877418 10.791786 0.0\n", - "145 5145.0 14.372909 42.507141 10.664717 1.0\n", - "1602 6602.0 49.262425 18.641750 18.219129 1.0\n", - "3239 8239.0 38.294011 27.182801 15.627936 1.0\n", - "973 973.0 53.720754 28.748631 27.170570 0.0\n", - "133 5133.0 43.544570 22.665588 6.625489 1.0\n", - "188 5188.0 16.477941 39.975384 27.536996 1.0\n", - "760 760.0 37.158465 18.423638 14.086728 0.0\n", - "2018 2018.0 38.010363 11.735547 25.518903 0.0\n", - "1663 6663.0 27.380986 35.622920 5.197740 1.0\n", - "2390 2390.0 40.583318 35.518648 22.201665 0.0\n", - "965 965.0 14.126442 50.199392 18.402164 0.0\n", - "2 2.0 18.198739 35.984471 15.042305 0.0\n", - "4520 9520.0 14.096586 23.619115 15.673827 1.0\n", - "972 972.0 25.060561 32.601634 15.965321 0.0\n", - "2853 2853.0 42.960858 49.398808 21.642390 0.0\n", - "1853 1853.0 11.868568 44.538142 6.254827 0.0\n", - "419 5419.0 30.169153 21.157608 18.734525 1.0\n", - "2992 2992.0 20.737204 28.731228 5.076937 0.0\n", - "3154 3154.0 51.490304 33.108012 15.831810 0.0\n", - "3479 8479.0 42.707957 29.659330 26.114664 1.0\n", - "1879 1879.0 27.781478 24.584329 27.277157 0.0\n", - "1507 1507.0 11.405848 16.366881 20.419526 0.0\n", - "4297 4297.0 21.626953 39.223794 13.754427 0.0\n", - "2224 7224.0 41.191445 42.702598 24.506232 1.0\n", - "3406 3406.0 49.140108 32.566558 10.402543 0.0\n", - "1090 6090.0 46.209139 13.746842 14.831120 1.0\n", - "1210 6210.0 45.332153 16.931482 28.238771 1.0\n", - "360 5360.0 45.839057 30.142144 26.892104 1.0\n", - "848 848.0 46.659755 41.586864 17.676637 0.0\n", - "... ... ... ... ... ...\n", - "29 29.0 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27.104421 44.894385 10.856754 1.0\n", - "1091 6091.0 23.219235 48.487699 20.289381 1.0\n", - "1854 1854.0 17.921775 40.613304 24.689912 0.0\n", - "2569 2569.0 53.750934 22.338463 26.720802 0.0\n", - "849 849.0 20.230430 33.006904 7.297799 0.0\n", - "4080 4080.0 16.143105 20.466720 5.279063 0.0\n", - "2628 7628.0 44.815798 21.935492 7.458784 1.0\n", - "475 475.0 49.828479 26.147946 19.196836 0.0\n", - "3684 8684.0 16.791879 40.251527 8.221400 1.0\n", - "874 5874.0 31.189478 11.910529 29.962462 1.0\n", - "4185 4185.0 23.351629 29.399269 7.824150 0.0\n", - "\n", - "[10000 rows x 5 columns]\n", - "range 0.0 1.0\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Generate Data Parameters\n", - "nb_obj = 5000\n", - "#seed = 47283\n", - "image_side_length = 64\n", - "x_min, x_max = 10, 54\n", - "y_min, y_max = 10, 54\n", - "radius_min, radius_max = 4,30\n", - "show_plot = True\n", - "\n", - "# Generate Data\n", - "tab_a, img_a = generate_dataset(nb_obj, image_side_length=image_side_length, radius_min=radius_min, radius_max=radius_max, x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max, shape='rect', show_plot=show_plot)\n", - "tab_b, img_b = generate_dataset(nb_obj, image_side_length=image_side_length, radius_min=radius_min, radius_max=radius_max, x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max, shape='circ', show_plot=show_plot, index_start=nb_obj)\n", - "\n", - "# combine data\n", - "tab = combine_data([tab_a, tab_b])\n", - "img = combine_data([img_a, img_b], data_type='img')\n", - "\n", - "# randomize data\n", - "tab, img = randomize_data(tab, img, verbose=True)\n", - "print('range', np.min(img), np.max(img))\n", - "\n", - "# save data\n", - "f_tab = 'test_generate_pipeline_circle_data.csv'\n", - "f_img = 'test_generate_pipeline_circle_image.npy'\n", - "save_data(f_tab, f_img, tab, img, verbose=False )\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example: read data file and prepare data for network" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "range 0.0 1.0\n" - ] - } - ], - "source": [ - "# read data from file\n", - "data_list = pd.read_csv(f_tab)\n", - "img_list = np.load(f_img)\n", - "print('range', np.min(img), np.max(img))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Training parameters\n", - "batch_size = 20\n", - "num_classes = 2\n", - "epochs = 5\n", - "train_me = True\n", - "\n", - "nb_train = 1000\n", - "nb_valid = 100\n", - "nb_test = 1000\n", - "\n", - "img_rows = img_cols = img_list.shape[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0 1.0\n", - "X train, valid, test shapes: \n", - " (1000, 64, 64) \n", - " (100, 64, 64) \n", - " (1000, 64, 64)\n", - "y train, valid, test shapes: \n", - " (1000,) \n", - " (100,) \n", - " (1000,)\n" - ] - } - ], - "source": [ - "# Prepare data\n", - "\n", - "# ... split data\n", - "output = split_data(nb_train, nb_valid, nb_test, tab, img, printcheck=0)\n", - "y_train_temp, y_valid_temp, y_test_temp, x_train, x_valid, x_test = output\n", - "print(np.min(x_train), np.max(x_train))\n", - "\n", - "\n", - "# ... identify value to train on\n", - "y_train = y_train_temp['shape'].values\n", - "y_valid = y_valid_temp['shape'].values\n", - "y_test = y_test_temp['shape'].values\n", - "\n", - "\n", - "print(\"X train, valid, test shapes:\", \"\\n\", x_train.shape,\"\\n\", x_valid.shape,\"\\n\", x_test.shape)\n", - "print(\"y train, valid, test shapes:\", \"\\n\", y_train.shape,\"\\n\", y_valid.shape,\"\\n\", y_test.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "range 0.0 1.0\n", - "x_train shape: (1000, 64, 64, 1)\n", - "1000 train samples\n", - "100 valid samples\n", - "1000 test samples\n" - ] - } - ], - "source": [ - "''' MY DATA\n", - "'''\n", - "\n", - "from __future__ import print_function\n", - "import keras\n", - "from keras.datasets import mnist\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Flatten\n", - "from keras.layers import Conv2D, MaxPooling2D\n", - "from keras import backend as K\n", - "\n", - "\n", - "if K.image_data_format() == 'channels_first':\n", - " x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n", - " x_valid = x_valid.reshape(x_valid.shape[0], 1, img_rows, img_cols)\n", - " x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n", - " input_shape = (1, img_rows, img_cols)\n", - "else:\n", - " x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n", - " x_valid = x_valid.reshape(x_valid.shape[0], img_rows, img_cols, 1)\n", - " x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n", - " input_shape = (img_rows, img_cols, 1)\n", - "\n", - "\n", - "x_train = x_train.astype('float32')\n", - "x_valid = x_valid.astype('float32')\n", - "x_test = x_test.astype('float32')\n", - "print('range', np.min(x_train), np.max(x_train))\n", - "\n", - "\n", - "print('x_train shape:', x_train.shape)\n", - "print(x_train.shape[0], 'train samples')\n", - "print(x_valid.shape[0], 'valid samples')\n", - "print(x_test.shape[0], 'test samples')\n", - "\n", - "# convert class vectors to binary class matrices\n", - "y_train = keras.utils.to_categorical(y_train, num_classes)\n", - "y_valid = keras.utils.to_categorical(y_valid, num_classes)\n", - "y_test = keras.utils.to_categorical(y_test, num_classes)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 1000 samples, validate on 100 samples\n", - "Epoch 1/5\n", - "1000/1000 [==============================] - 3s 3ms/step - loss: 0.7142 - acc: 0.5270 - val_loss: 0.6753 - val_acc: 0.5100\n", - "Epoch 2/5\n", - "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6608 - acc: 0.6060 - val_loss: 0.6310 - val_acc: 0.6700\n", - "Epoch 3/5\n", - "1000/1000 [==============================] - 2s 2ms/step - loss: 0.5702 - acc: 0.7430 - val_loss: 0.5467 - val_acc: 0.7800\n", - "Epoch 4/5\n", - "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3856 - acc: 0.8790 - val_loss: 0.3321 - val_acc: 0.9300\n", - "Epoch 5/5\n", - "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1981 - acc: 0.9510 - val_loss: 0.2033 - val_acc: 0.9300\n", - "Test loss: 0.19812715935707093\n", - "Test accuracy: 0.931\n" - ] - } - ], - "source": [ - "# create model\n", - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(2, 2), activation='relu', input_shape=input_shape))\n", - "#model.add(Conv2D(64, kernel_size=(2, 2), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.3))\n", - "model.add(Flatten())\n", - "model.add(Dense(64, activation='relu'))\n", - "model.add(Dropout(0.3))\n", - "model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "# compile model\n", - "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])\n", - "if train_me:\n", - " model.fit(x_train, y_train,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " verbose=1,\n", - " validation_data=(x_valid, y_valid))\n", - " score = model.evaluate(x_test, y_test, verbose=0)\n", - " print('Test loss:', score[0])\n", - " print('Test accuracy:', score[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.array([1.])\n", - "b = a.astype('float32')\n", - "print(a, b)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "import PIL.ImageDraw as ImageDraw,PIL.Image as Image, PIL.ImageShow as ImageShow \n", - "im = Image.new(\"RGB\", (400,300))\n", - "draw = ImageDraw.Draw(im)\n", - "\n", - "draw.arc((100,100,300,200),0,270,fill=255)\n", - "\n", - "im.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/Deprecated/SimGenerator_v1.ipynb b/archive/notebooks/Deprecated/SimGenerator_v1.ipynb deleted file mode 100644 index 5db38a2..0000000 --- a/archive/notebooks/Deprecated/SimGenerator_v1.ipynb +++ /dev/null @@ -1,1116 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Deprecated" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# packages: random\n", - "import random\n", - "\n", - "# packages: data structure\n", - "import numpy as np\n", - "import pandas as pd\n", - "import astropy.io as io\n", - "\n", - "# packages: skimage\n", - "from skimage.draw import line, polygon, circle, circle_perimeter,\\\n", - " ellipse, ellipse_perimeter, bezier_curve\n", - "\n", - "# packages: image generation and plot generation\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# pandas\n", - "# https://pandas.pydata.org/pandas-docs/stable/tutorials.html\n", - "# https://pandas.pydata.org/pandas-docs/stable/10min.html\n", - "\n", - "# ascii:io\n", - "# http://docs.astropy.org/en/stable/io/ascii/\n", - "\n", - "# matplotlib\n", - "# https://nickcharlton.net/posts/drawing-animating-shapes-matplotlib.html\n", - "\n", - "# skimage\n", - "# http://scikit-image.org/docs/dev/auto_examples/edges/plot_shapes.html#sphx-glr-auto-examples-edges-plot-shapes-py" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# numpy: empty canvas\n", - "def empty_canvas(image_side_length=100):\n", - "\n", - " return np.indices((image_side_length, image_side_length))\n", - " \n", - " \n", - "# scikit learn: circle\n", - "def circle(canvas, x_center=50, y_center=50, radius=30):\n", - "\n", - " y, x = canvas\n", - " circle = (x - x_center)**2 + (y - y_center)**2 < radius**2\n", - " img = circle.astype(float)\n", - " \n", - " return img\n", - "\n", - "\n", - "# scikit learn: rectangle\n", - "def rect(canvas, x_center=50, y_center=50, radius=30):\n", - "\n", - " y, x = canvas\n", - " rect = (x < x_center + radius) & (x > x_center - radius) & (y < y_center + radius) & (y > y_center - radius)\n", - " img = rect.astype(float)\n", - " \n", - " return img\n", - " \n", - " \n", - "# plot for SPI package\n", - "def plot_spi(img):\n", - " plt.axes() \n", - " plt.imshow(img)\n", - " plt.clf()\n", - " \n", - " \n", - "# matplotlib pyplot\n", - "def circle_plt(x_center=0, y_center=0, radius=0.75, fc='r', show=False):\n", - "\n", - " plt.axes()\n", - "\n", - " circle = plt.Circle((x_center, y_center), radius=radius, fc=fc)\n", - " plt.gca().add_patch(circle)\n", - " plt.axis('scaled')\n", - " imgplot = plt.imshow(img)\n", - " imgplot = plt.savefig(\"test3.png\", dpi = (200))\n", - " #imgplot = plt.imshow()\n", - " \n", - " if show:\n", - " plt.show()\n", - "\n", - "# test each individual function\n", - "def test_individual():\n", - " #circle()\n", - " img = circle_sk()\n", - " plot_spi(img)\n", - " #star()\n", - " return\n", - "\n", - "# generate one image data set\n", - "def generate_dataset(nb_obj, \n", - " image_side_length=100, \n", - " index_start=0, \n", - " shape='rect',\n", - " x_min=32,\n", - " x_max=32,\n", - " y_min=32,\n", - " y_max=32,\n", - " radius_min=10,\n", - " radius_max=10,\n", - " show_plot=False, \n", - " verbose=False):\n", - "\n", - " # initiate image values\n", - " fac = -1.0\n", - " #x_center_list = np.random.uniform(0 + fac* radius_max, image_side_length + fac* radius_max, nb_obj)\n", - " #y_center_list = np.random.uniform(0 + fac* radius_max, image_side_length + fac* radius_max, nb_obj)\n", - " x_center_list = np.random.uniform(x_min, x_max, nb_obj)\n", - " y_center_list = np.random.uniform(y_min, y_max, nb_obj)\n", - "\n", - " radius_list = np.random.uniform(radius_min, radius_max, nb_obj)\n", - " print('x ranges', min(x_center_list), max(x_center_list))\n", - " print('y ranges', min(y_center_list), max(y_center_list))\n", - " column_names = ['ident', 'x_center', 'y_center', 'radius', 'shape']\n", - " \n", - " # create empty data structures\n", - " tab_list = np.empty((nb_obj, len(column_names)))\n", - " img_list = np.empty((nb_obj, image_side_length, image_side_length))\n", - " \n", - " # create empty canvas for a single image\n", - " canvas = empty_canvas(image_side_length=image_side_length)\n", - " \n", - " # loop over objects\n", - " icount = 0\n", - " for i_obj in np.arange(nb_obj):\n", - " \n", - " # draw object properties from list\n", - " x_center = x_center_list[i_obj]\n", - " y_center = y_center_list[i_obj] \n", - " radius = radius_list[i_obj]\n", - " \n", - " # identification value\n", - " ident = int(index_start + i_obj)\n", - " \n", - " # create object\n", - " if shape == 'rect':\n", - " img = rect_sk(canvas, x_center=x_center, y_center=y_center, radius=radius)\n", - " shape_num = 0\n", - " elif shape == 'circ':\n", - " img = circle_sk(canvas, x_center=x_center, y_center=y_center, radius=radius)\n", - " shape_num = 1 \n", - "\n", - " # add tabular data to data list structure\n", - " tab_list[i_obj] = [ident, x_center, y_center, radius, int(shape_num)]\n", - " \n", - " # add image data to image list structure\n", - " img_list[i_obj] = img\n", - "\n", - " # plot image\n", - " if show_plot and icount <20:\n", - " icount+=1\n", - " plt.figure()\n", - " plt.axes() \n", - " plt.imshow(img)\n", - " \n", - " # Data Frame: Tabular Data for Objects\n", - " tab_list = pd.DataFrame(tab_list,columns=column_names)\n", - " \n", - " # verbose\n", - " if verbose:\n", - " print(tab_list[0:10])\n", - " print(img_list[0:10])\n", - " \n", - " return tab_list, img_list\n", - "\n", - "\n", - "# save data\n", - "def save_data(f_data_list, f_img_list, data_list, img_list, verbose=False):\n", - "\n", - " # Pandas Data Frame for tabular data: save to file\n", - " data_list.to_csv(f_data_list)\n", - "\n", - " # Numpy Array for image data: save to file\n", - " np.save(f_img_list, img_list)\n", - " \n", - " # verbose\n", - " if verbose:\n", - " print(f_data_list_pd)\n", - " print(f_img_list)\n", - " \n", - " return\n", - " \n", - "\n", - "# combine data sets\n", - "def combine_data(frames, data_type='tab'): \n", - " \n", - " if data_type=='tab':\n", - " data = pd.concat(frames)\n", - " elif data_type=='img':\n", - " data = np.concatenate(frames)\n", - " \n", - " return data\n", - "\n", - "\n", - "# randomize data \n", - "def randomize_data(tab, img, seed=5, verbose=False):\n", - " \n", - " if verbose:\n", - " print('Before:', tab)\n", - " \n", - " # create randomized indices\n", - " random.seed(seed) \n", - " nb_tab = len(tab)\n", - " ind_random = np.arange(nb_tab) \n", - " random.shuffle(ind_random)\n", - " \n", - " # re-order data based on randomized indices\n", - " tab = tab.iloc[ind_random]\n", - " img = img[ind_random]\n", - " \n", - " if verbose:\n", - " print('After:', tab)\n", - "\n", - " return tab, img\n", - "\n", - "\n", - "# split data \n", - "def split_data(nb_train, nb_valid, nb_test, tab, img, printcheck=0):\n", - "\n", - " \n", - " ind_start_train = 0\n", - " ind_end_train = ind_start_valid = ind_start_train + nb_train \n", - " ind_end_valid = ind_start_test = ind_start_valid + nb_valid\n", - " ind_end_test = ind_start_test + nb_test\n", - " \n", - " if printcheck > 0:\n", - " print(tab[0:printcheck])\n", - " print(ind_start_train, ind_end_train)\n", - " # good place for unit test\n", - " \n", - " # split data in train, valid, test\n", - " tab_train = tab[ind_start_train: ind_end_train]\n", - " img_train = img[ind_start_train: ind_end_train]\n", - "\n", - " tab_valid = tab[ind_start_valid: ind_end_valid]\n", - " img_valid = img[ind_start_valid: ind_end_valid]\n", - "\n", - " tab_test = tab[ind_start_test: ind_end_test]\n", - " img_test = img[ind_start_test: ind_end_test]\n", - " \n", - " return tab_train, tab_valid, tab_test, img_train, img_valid, img_test" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x ranges 10.006670793022877 53.997868245890025\n", - "y ranges 10.012215820298476 53.995160518827696\n", - "x ranges 10.003455246206755 53.99969589009031\n", - "y ranges 10.021885568801949 53.99680971642177\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/anaconda3/envs/SimplePhysicalImage/lib/python3.6/site-packages/matplotlib/pyplot.py:537: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", - " max_open_warning, RuntimeWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Before: ident x_center y_center radius shape\n", - "0 0.0 53.807942 14.882025 25.918850 0.0\n", - "1 1.0 24.342952 14.469646 5.429372 0.0\n", - "2 2.0 18.198739 35.984471 15.042305 0.0\n", - "3 3.0 22.387154 38.343779 13.183867 0.0\n", - "4 4.0 36.788960 34.660622 11.280092 0.0\n", - "5 5.0 46.120199 37.327714 14.005038 0.0\n", - "6 6.0 12.225752 16.334552 25.574700 0.0\n", - "7 7.0 14.348829 47.392402 6.889213 0.0\n", - "8 8.0 47.575099 30.028321 16.538826 0.0\n", - "9 9.0 38.642397 18.855977 25.582719 0.0\n", - "10 10.0 28.898036 31.055267 18.054302 0.0\n", - "11 11.0 33.795816 53.461817 27.287078 0.0\n", - "12 12.0 30.561144 41.484994 8.953349 0.0\n", - "13 13.0 48.321409 46.195169 6.232920 0.0\n", - "14 14.0 19.113632 47.170905 26.610064 0.0\n", - "15 15.0 38.380544 49.666704 23.372532 0.0\n", - "16 16.0 30.924125 22.461703 25.493766 0.0\n", - "17 17.0 46.604287 27.557403 10.316504 0.0\n", - "18 18.0 12.942414 47.037541 23.187489 0.0\n", - "19 19.0 23.058731 50.902045 9.167487 0.0\n", - "20 20.0 36.828023 48.493008 24.641515 0.0\n", - "21 21.0 22.694921 24.721304 12.996464 0.0\n", - "22 22.0 16.935710 42.497784 19.256628 0.0\n", - "23 23.0 20.322083 21.500012 14.649496 0.0\n", - "24 24.0 23.568640 22.880119 27.021332 0.0\n", - "25 25.0 42.559653 33.061513 8.472630 0.0\n", - "26 26.0 30.238133 18.619432 28.014192 0.0\n", - "27 27.0 39.529840 12.010202 4.709973 0.0\n", - "28 28.0 17.626738 30.067770 13.020112 0.0\n", - "29 29.0 49.304624 14.913774 7.429144 0.0\n", - "... ... ... ... ... ...\n", - "4970 9970.0 51.187790 29.779559 14.655753 1.0\n", - "4971 9971.0 20.423570 44.947952 17.741139 1.0\n", - "4972 9972.0 40.867909 52.930533 21.111008 1.0\n", - "4973 9973.0 31.173566 53.210138 21.823807 1.0\n", - "4974 9974.0 35.039273 32.647294 18.073030 1.0\n", - "4975 9975.0 45.064751 18.854851 21.526037 1.0\n", - "4976 9976.0 47.481866 49.944559 16.798229 1.0\n", - "4977 9977.0 31.603136 12.999977 20.221808 1.0\n", - "4978 9978.0 40.130365 25.586251 28.490052 1.0\n", - "4979 9979.0 53.911339 21.137615 27.693974 1.0\n", - "4980 9980.0 27.520271 14.936486 10.476511 1.0\n", - "4981 9981.0 40.340422 25.406924 23.065755 1.0\n", - "4982 9982.0 37.246686 41.690740 23.072052 1.0\n", - "4983 9983.0 35.001967 30.417183 10.239100 1.0\n", - "4984 9984.0 33.872442 44.922725 26.516432 1.0\n", - "4985 9985.0 36.094603 44.708674 25.975044 1.0\n", - "4986 9986.0 24.248433 31.105424 26.332673 1.0\n", - "4987 9987.0 28.103456 53.785903 15.881829 1.0\n", - "4988 9988.0 16.207335 12.409548 28.260887 1.0\n", - "4989 9989.0 35.335368 28.993841 19.681277 1.0\n", - "4990 9990.0 33.059530 50.180718 17.780231 1.0\n", - "4991 9991.0 44.689002 23.572807 16.184068 1.0\n", - "4992 9992.0 14.745698 34.440101 8.880192 1.0\n", - "4993 9993.0 10.877891 36.560767 28.541210 1.0\n", - "4994 9994.0 33.424545 12.949527 5.252499 1.0\n", - "4995 9995.0 13.367104 11.823550 4.780767 1.0\n", - "4996 9996.0 32.654717 35.382659 11.866425 1.0\n", - "4997 9997.0 44.142243 41.243750 13.449796 1.0\n", - "4998 9998.0 27.264092 26.952294 23.457229 1.0\n", - "4999 9999.0 49.979355 25.903954 5.372476 1.0\n", - "\n", - "[10000 rows x 5 columns]\n", - "After: ident x_center y_center radius shape\n", - "4964 4964.0 50.603330 29.877418 10.791786 0.0\n", - "145 5145.0 14.372909 42.507141 10.664717 1.0\n", - "1602 6602.0 49.262425 18.641750 18.219129 1.0\n", - "3239 8239.0 38.294011 27.182801 15.627936 1.0\n", - "973 973.0 53.720754 28.748631 27.170570 0.0\n", - "133 5133.0 43.544570 22.665588 6.625489 1.0\n", - "188 5188.0 16.477941 39.975384 27.536996 1.0\n", - "760 760.0 37.158465 18.423638 14.086728 0.0\n", - "2018 2018.0 38.010363 11.735547 25.518903 0.0\n", - "1663 6663.0 27.380986 35.622920 5.197740 1.0\n", - "2390 2390.0 40.583318 35.518648 22.201665 0.0\n", - "965 965.0 14.126442 50.199392 18.402164 0.0\n", - "2 2.0 18.198739 35.984471 15.042305 0.0\n", - "4520 9520.0 14.096586 23.619115 15.673827 1.0\n", - "972 972.0 25.060561 32.601634 15.965321 0.0\n", - "2853 2853.0 42.960858 49.398808 21.642390 0.0\n", - "1853 1853.0 11.868568 44.538142 6.254827 0.0\n", - "419 5419.0 30.169153 21.157608 18.734525 1.0\n", - "2992 2992.0 20.737204 28.731228 5.076937 0.0\n", - "3154 3154.0 51.490304 33.108012 15.831810 0.0\n", - "3479 8479.0 42.707957 29.659330 26.114664 1.0\n", - "1879 1879.0 27.781478 24.584329 27.277157 0.0\n", - "1507 1507.0 11.405848 16.366881 20.419526 0.0\n", - "4297 4297.0 21.626953 39.223794 13.754427 0.0\n", - "2224 7224.0 41.191445 42.702598 24.506232 1.0\n", - "3406 3406.0 49.140108 32.566558 10.402543 0.0\n", - "1090 6090.0 46.209139 13.746842 14.831120 1.0\n", - "1210 6210.0 45.332153 16.931482 28.238771 1.0\n", - "360 5360.0 45.839057 30.142144 26.892104 1.0\n", - "848 848.0 46.659755 41.586864 17.676637 0.0\n", - "... ... ... ... ... ...\n", - "29 29.0 49.304624 14.913774 7.429144 0.0\n", - "2166 2166.0 20.564140 44.046026 19.766167 0.0\n", - "2075 2075.0 45.366024 42.738204 4.276316 0.0\n", - "2288 7288.0 23.510279 53.191844 21.207118 1.0\n", - "2273 2273.0 39.343601 45.732049 11.762244 0.0\n", - "1178 1178.0 45.948187 37.874446 14.894617 0.0\n", - "2614 2614.0 20.551974 15.201647 24.918978 0.0\n", - "1380 6380.0 27.398518 42.540898 16.003772 1.0\n", - "2983 2983.0 24.900526 13.592584 15.219856 0.0\n", - "4579 4579.0 23.735493 33.302646 6.852187 0.0\n", - "1687 6687.0 21.524430 44.748601 29.828658 1.0\n", - "3550 3550.0 35.682662 25.587769 27.945102 0.0\n", - "214 214.0 48.487341 49.669855 21.835414 0.0\n", - "4085 4085.0 37.872051 20.058625 4.415581 0.0\n", - "4403 9403.0 21.127704 53.992448 27.920979 1.0\n", - "1670 1670.0 37.177949 52.529147 9.691975 0.0\n", - "3908 8908.0 49.764952 20.467521 13.662351 1.0\n", - "1238 6238.0 34.582104 35.666973 27.171415 1.0\n", - "4039 4039.0 33.795622 35.553115 15.474344 0.0\n", - "2685 7685.0 27.104421 44.894385 10.856754 1.0\n", - "1091 6091.0 23.219235 48.487699 20.289381 1.0\n", - "1854 1854.0 17.921775 40.613304 24.689912 0.0\n", - "2569 2569.0 53.750934 22.338463 26.720802 0.0\n", - "849 849.0 20.230430 33.006904 7.297799 0.0\n", - "4080 4080.0 16.143105 20.466720 5.279063 0.0\n", - "2628 7628.0 44.815798 21.935492 7.458784 1.0\n", - "475 475.0 49.828479 26.147946 19.196836 0.0\n", - "3684 8684.0 16.791879 40.251527 8.221400 1.0\n", - "874 5874.0 31.189478 11.910529 29.962462 1.0\n", - "4185 4185.0 23.351629 29.399269 7.824150 0.0\n", - "\n", - "[10000 rows x 5 columns]\n", - "range 0.0 1.0\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Generate Data Parameters\n", - "nb_obj = 5000\n", - "#seed = 47283\n", - "image_side_length = 64\n", - "x_min, x_max = 10, 54\n", - "y_min, y_max = 10, 54\n", - "radius_min, radius_max = 4,30\n", - "show_plot = True\n", - "\n", - "# Generate Data\n", - "tab_a, img_a = generate_dataset(nb_obj, image_side_length=image_side_length, radius_min=radius_min, radius_max=radius_max, x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max, shape='rect', show_plot=show_plot)\n", - "tab_b, img_b = generate_dataset(nb_obj, image_side_length=image_side_length, radius_min=radius_min, radius_max=radius_max, x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max, shape='circ', show_plot=show_plot, index_start=nb_obj)\n", - "\n", - "# combine data\n", - "tab = combine_data([tab_a, tab_b])\n", - "img = combine_data([img_a, img_b], data_type='img')\n", - "\n", - "# randomize data\n", - "tab, img = randomize_data(tab, img, verbose=True)\n", - "print('range', np.min(img), np.max(img))\n", - "\n", - "# save data\n", - "f_tab = 'test_generate_pipeline_circle_data.csv'\n", - "f_img = 'test_generate_pipeline_circle_image.npy'\n", - "save_data(f_tab, f_img, tab, img, verbose=False )\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example: read data file and prepare data for network" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "range 0.0 1.0\n" - ] - } - ], - "source": [ - "# read data from file\n", - "data_list = pd.read_csv(f_tab)\n", - "img_list = np.load(f_img)\n", - "print('range', np.min(img), np.max(img))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Training parameters\n", - "batch_size = 20\n", - "num_classes = 2\n", - "epochs = 5\n", - "train_me = True\n", - "\n", - "nb_train = 1000\n", - "nb_valid = 100\n", - "nb_test = 1000\n", - "\n", - "img_rows = img_cols = img_list.shape[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0 1.0\n", - "X train, valid, test shapes: \n", - " (1000, 64, 64) \n", - " (100, 64, 64) \n", - " (1000, 64, 64)\n", - "y train, valid, test shapes: \n", - " (1000,) \n", - " (100,) \n", - " (1000,)\n" - ] - } - ], - "source": [ - "# Prepare data\n", - "\n", - "# ... split data\n", - "output = split_data(nb_train, nb_valid, nb_test, tab, img, printcheck=0)\n", - "y_train_temp, y_valid_temp, y_test_temp, x_train, x_valid, x_test = output\n", - "print(np.min(x_train), np.max(x_train))\n", - "\n", - "\n", - "# ... identify value to train on\n", - "y_train = y_train_temp['shape'].values\n", - "y_valid = y_valid_temp['shape'].values\n", - "y_test = y_test_temp['shape'].values\n", - "\n", - "\n", - "print(\"X train, valid, test shapes:\", \"\\n\", x_train.shape,\"\\n\", x_valid.shape,\"\\n\", x_test.shape)\n", - "print(\"y train, valid, test shapes:\", \"\\n\", y_train.shape,\"\\n\", y_valid.shape,\"\\n\", y_test.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "range 0.0 1.0\n", - "x_train shape: (1000, 64, 64, 1)\n", - "1000 train samples\n", - "100 valid samples\n", - "1000 test samples\n" - ] - } - ], - "source": [ - "''' MY DATA\n", - "'''\n", - "\n", - "from __future__ import print_function\n", - "import keras\n", - "from keras.datasets import mnist\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Flatten\n", - "from keras.layers import Conv2D, MaxPooling2D\n", - "from keras import backend as K\n", - "\n", - "\n", - "if K.image_data_format() == 'channels_first':\n", - " x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n", - " x_valid = x_valid.reshape(x_valid.shape[0], 1, img_rows, img_cols)\n", - " x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n", - " input_shape = (1, img_rows, img_cols)\n", - "else:\n", - " x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n", - " x_valid = x_valid.reshape(x_valid.shape[0], img_rows, img_cols, 1)\n", - " x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n", - " input_shape = (img_rows, img_cols, 1)\n", - "\n", - "\n", - "x_train = x_train.astype('float32')\n", - "x_valid = x_valid.astype('float32')\n", - "x_test = x_test.astype('float32')\n", - "print('range', np.min(x_train), np.max(x_train))\n", - "\n", - "\n", - "print('x_train shape:', x_train.shape)\n", - "print(x_train.shape[0], 'train samples')\n", - "print(x_valid.shape[0], 'valid samples')\n", - "print(x_test.shape[0], 'test samples')\n", - "\n", - "# convert class vectors to binary class matrices\n", - "y_train = keras.utils.to_categorical(y_train, num_classes)\n", - "y_valid = keras.utils.to_categorical(y_valid, num_classes)\n", - "y_test = keras.utils.to_categorical(y_test, num_classes)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 1000 samples, validate on 100 samples\n", - "Epoch 1/5\n", - "1000/1000 [==============================] - 3s 3ms/step - loss: 0.7142 - acc: 0.5270 - val_loss: 0.6753 - val_acc: 0.5100\n", - "Epoch 2/5\n", - "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6608 - acc: 0.6060 - val_loss: 0.6310 - val_acc: 0.6700\n", - "Epoch 3/5\n", - "1000/1000 [==============================] - 2s 2ms/step - loss: 0.5702 - acc: 0.7430 - val_loss: 0.5467 - val_acc: 0.7800\n", - "Epoch 4/5\n", - "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3856 - acc: 0.8790 - val_loss: 0.3321 - val_acc: 0.9300\n", - "Epoch 5/5\n", - "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1981 - acc: 0.9510 - val_loss: 0.2033 - val_acc: 0.9300\n", - "Test loss: 0.19812715935707093\n", - "Test accuracy: 0.931\n" - ] - } - ], - "source": [ - "# create model\n", - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(2, 2), activation='relu', input_shape=input_shape))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.3))\n", - "model.add(Flatten())\n", - "model.add(Dense(64, activation='relu'))\n", - "model.add(Dropout(0.3))\n", - "model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "# compile model\n", - "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])\n", - "if train_me:\n", - " model.fit(x_train, y_train,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " verbose=1,\n", - " validation_data=(x_valid, y_valid))\n", - " score = model.evaluate(x_test, y_test, verbose=0)\n", - " print('Test loss:', score[0])\n", - " print('Test accuracy:', score[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.array([1.])\n", - "b = a.astype('float32')\n", - "print(a, b)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "import PIL.ImageDraw as ImageDraw,PIL.Image as Image, PIL.ImageShow as ImageShow \n", - "im = Image.new(\"RGB\", (400,300))\n", - "draw = ImageDraw.Draw(im)\n", - "\n", - "draw.arc((100,100,300,200),0,270,fill=255)\n", - "\n", - "im.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/Deprecated/SimGenerator_vN.ipynb b/archive/notebooks/Deprecated/SimGenerator_vN.ipynb deleted file mode 100644 index bb579d5..0000000 --- a/archive/notebooks/Deprecated/SimGenerator_vN.ipynb +++ /dev/null @@ -1,896 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Deprecated" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# packages: random\n", - "import random\n", - "\n", - "# packages: data structure\n", - "import numpy as np\n", - "import pandas as pd\n", - "import astropy.io as io\n", - "\n", - "# packages: images with cv\n", - "import cv2 as cv\n", - "\n", - "# packages: image processing\n", - "# from scipy import ndimage\n", - "\n", - "# packages: pixel-level image generation\n", - "# from sklearn.feature_extraction import image\n", - "\n", - "# packages: image generation and plot generation\n", - "from matplotlib import pyplot as plt\n", - "\n", - "# packages: astro image generation\n", - "import astropy.modeling as modeling\n", - "\n", - "# packages: geometric image generation\n", - "# from shapely.geometry import Point, LineString, Polygon\n", - "\n", - "# packages: agent-based image generation\n", - "# import turtle\n", - "\n", - "# %pylab inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# pandas\n", - "# https://pandas.pydata.org/pandas-docs/stable/tutorials.html\n", - "# https://pandas.pydata.org/pandas-docs/stable/10min.html\n", - "\n", - "# ndimage: image manipulation\n", - "# https://docs.scipy.org/doc/scipy/reference/ndimage.html#module-scipy.ndimage\n", - "\n", - "# ascii:io\n", - "# http://docs.astropy.org/en/stable/io/ascii/\n", - "\n", - "# matplotlib\n", - "# https://nickcharlton.net/posts/drawing-animating-shapes-matplotlib.html\n", - "\n", - "# shapely\n", - "\n", - "# turtle\n", - "# https://docs.python.org/3/library/turtle.html\n", - "# https://michael0x2a.com/blog/turtle-examples\n", - "\n", - "# astropy: modeling:\n", - "# http://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Sersic2D.html" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# cv \n", - "\n", - "def empty_canvas_cv(image_side_length=100, nb_channels=1):\n", - " return np.zeros((image_side_length, image_side_length, nb_channels), np.uint8)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# numpy: empty canvas\n", - "def empty_canvas(image_side_length=100):\n", - "\n", - " return np.indices((image_side_length, image_side_length))\n", - " \n", - " \n", - "# scikit learn: circle\n", - "def circle_sk(canvas, x_center=50, y_center=50, radius=30):\n", - "\n", - " y, x = canvas\n", - " circle = (x - x_center)**2 + (y - y_center)**2 < radius**2\n", - " img = circle.astype(float)\n", - " \n", - " return img\n", - "\n", - "\n", - "# scikit learn: rectangle\n", - "def rect_sk(canvas, x_center=50, y_center=50, radius=30):\n", - "\n", - " y, x = canvas\n", - " rect = (x < x_center + radius) & (x > x_center - radius) & (y < y_center + radius) & (y > y_center - radius)\n", - " img = rect.astype(float)\n", - " \n", - " return img\n", - " \n", - " \n", - "# scikit learn: rectangle\n", - "def triangle_sk(canvas, x_center=50, y_center=50, radius=30):\n", - "\n", - " y, x = canvas\n", - " rect = (x < x_center + radius) & (x > x_center - radius) & (y < y_center + radius) & (y > y_center - radius)\n", - " img = rect.astype(float)\n", - " \n", - " return img\n", - " \n", - " \n", - "# plot for SPI package\n", - "def plot_spi(img):\n", - " plt.axes() \n", - " plt.imshow(img)\n", - " plt.clf()\n", - " \n", - " \n", - "# matplotlib pyplot\n", - "def circle_plt(x_center=0, y_center=0, radius=0.75, fc='r', show=False):\n", - "\n", - " plt.axes()\n", - "\n", - " circle = plt.Circle((x_center, y_center), radius=radius, fc=fc)\n", - " plt.gca().add_patch(circle)\n", - " plt.axis('scaled')\n", - " imgplot = plt.imshow(img)\n", - " imgplot = plt.savefig(\"test3.png\", dpi = (200))\n", - " #imgplot = plt.imshow()\n", - " \n", - " if show:\n", - " plt.show()\n", - "\n", - "# test each individual function\n", - "def test_individual():\n", - " #circle()\n", - " img = circle_sk()\n", - " plot_spi(img)\n", - " #star()\n", - " return\n", - "\n", - "# generate one image data set\n", - "def generate_dataset(nb_obj, \n", - " image_side_length=100, \n", - " index_start=0, \n", - " shape='rect',\n", - " radius_min=2,\n", - " radius_max=20,\n", - " show_plot=False, \n", - " verbose=False):\n", - "\n", - " # initiate image values\n", - " fac = -1.0\n", - " x_center_list = np.random.uniform(0 - fac* radius_max, image_side_length + fac* radius_max, nb_obj)\n", - " y_center_list = np.random.uniform(0 - fac* radius_max, image_side_length + fac* radius_max, nb_obj)\n", - " radius_list = np.random.uniform(radius_min, radius_max, nb_obj)\n", - " column_names = ['ident', 'x_center', 'y_center', 'radius', 'shape']\n", - " \n", - " # create empty data structures\n", - " tab_list = np.empty((nb_obj, len(column_names)))\n", - " img_list = np.empty((nb_obj, image_side_length, image_side_length))\n", - " \n", - " # create empty canvas for a single image\n", - " canvas = empty_canvas(image_side_length=image_side_length)\n", - " \n", - " # loop over objects\n", - " for i_obj in np.arange(nb_obj):\n", - " \n", - " # draw object properties from list\n", - " x_center = x_center_list[i_obj]\n", - " y_center = y_center_list[i_obj] \n", - " radius = radius_list[i_obj]\n", - " \n", - " # identification value\n", - " ident = int(index_start + i_obj)\n", - " \n", - " # create object\n", - " if shape == 'rect':\n", - " img = rect_sk(canvas, x_center=x_center, y_center=y_center, radius=radius)\n", - " shape_num = 0\n", - " elif shape == 'circle':\n", - " img = circle_sk(canvas, x_center=x_center, y_center=y_center, radius=radius)\n", - " shape_num = 1 \n", - "\n", - " # add tabular data to data list structure\n", - " tab_list[i_obj] = [ident, x_center, y_center, radius, shape_num]\n", - " \n", - " # add image data to image list structure\n", - " img_list[i_obj] = img\n", - "\n", - " # plot image\n", - " if show_plot:\n", - " plt.figure()\n", - " plt.axes() \n", - " plt.imshow(img)\n", - " \n", - " # Data Frame: Tabular Data for Objects\n", - " tab_list = pd.DataFrame(tab_list,columns=column_names)\n", - " \n", - " # verbose\n", - " if verbose:\n", - " print(tab_list)\n", - " print(img_list)\n", - " \n", - " return tab_list, img_list\n", - "\n", - "\n", - "# save data\n", - "def save_data(f_data_list, f_img_list, data_list, img_list, verbose=False):\n", - "\n", - " # Pandas Data Frame for tabular data: save to file\n", - " data_list.to_csv(f_data_list)\n", - "\n", - " # Numpy Array for image data: save to file\n", - " np.save(f_img_list, img_list)\n", - " \n", - " # verbose\n", - " if verbose:\n", - " print(f_data_list_pd)\n", - " print(f_img_list)\n", - " \n", - " return\n", - " \n", - "\n", - "# combine data sets\n", - "def combine_data(frames, data_type='tab'): \n", - " \n", - " if data_type=='tab':\n", - " data = pd.concat(frames)\n", - " elif data_type=='img':\n", - " data = np.concatenate(frames)\n", - " \n", - " return data\n", - "\n", - "\n", - "# randomize data \n", - "def randomize_data(tab, img, seed=5, verbose=False):\n", - " \n", - " if verbose:\n", - " print('Before:', tab)\n", - " \n", - " # create randomized indices\n", - " random.seed(seed) \n", - " nb_tab = len(tab)\n", - " ind_random = np.arange(nb_tab) \n", - " random.shuffle(ind_random)\n", - " \n", - " # re-order data based on randomized indices\n", - " tab = tab.iloc[ind_random]\n", - " img = img[ind_random]\n", - " \n", - " if verbose:\n", - " print('After:', tab)\n", - "\n", - " return tab, img\n", - "\n", - "\n", - "# split data \n", - "def split_data(nb_train, nb_valid, nb_test, tab, img, printcheck=0):\n", - "\n", - " \n", - " ind_start_train = 0\n", - " ind_end_train = ind_start_valid = ind_start_train + nb_train \n", - " ind_end_valid = ind_start_test = ind_start_valid + nb_valid\n", - " ind_end_test = ind_start_test + nb_test\n", - " \n", - " if printcheck > 0:\n", - " print(tab[0:printcheck])\n", - " print(ind_start_train, ind_end_train)\n", - " # good place for unit test\n", - " \n", - " # split data in train, valid, test\n", - " tab_train = tab[ind_start_train: ind_end_train]\n", - " img_train = img[ind_start_train: ind_end_train]\n", - "\n", - " tab_valid = tab[ind_start_valid: ind_end_valid]\n", - " img_valid = img[ind_start_valid: ind_end_valid]\n", - "\n", - " tab_test = tab[ind_start_test: ind_end_test]\n", - " img_test = img[ind_start_test: ind_end_test]\n", - " \n", - " return tab_train, tab_valid, tab_test, img_train, img_valid, img_test\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Before: ident x_center y_center radius shape\n", - "0 0.0 31.323323 42.746014 2.448773 0.0\n", - "1 1.0 42.732992 23.037234 14.264696 0.0\n", - "2 2.0 30.878020 25.391156 3.163774 0.0\n", - "3 3.0 36.098704 25.932020 19.172839 0.0\n", - "4 4.0 36.847619 40.204142 17.904288 0.0\n", - "5 5.0 41.543711 25.654052 17.526231 0.0\n", - "6 6.0 28.150905 26.250166 16.522191 0.0\n", - "7 7.0 27.543727 29.053957 15.362774 0.0\n", - "8 8.0 40.086332 20.002175 4.725339 0.0\n", - "9 9.0 23.250734 27.670365 7.992529 0.0\n", - "10 10.0 36.829935 34.095812 11.124781 0.0\n", - "11 11.0 35.772559 31.655348 16.028401 0.0\n", - "12 12.0 21.540188 30.013876 5.452747 0.0\n", - "13 13.0 38.572738 29.185915 2.520848 0.0\n", - "14 14.0 28.172930 36.087667 10.763983 0.0\n", - "15 15.0 24.561381 31.202893 18.042446 0.0\n", - "16 16.0 22.361247 34.039402 19.968749 0.0\n", - "17 17.0 26.766144 28.647431 11.619167 0.0\n", - "18 18.0 41.252744 36.667101 12.620202 0.0\n", - "19 19.0 32.254440 43.655383 14.892189 0.0\n", - "20 20.0 40.927516 21.816137 2.709315 0.0\n", - "21 21.0 22.356585 24.552019 16.538607 0.0\n", - "22 22.0 24.007618 30.850853 17.095598 0.0\n", - "23 23.0 25.840935 41.227749 3.477944 0.0\n", - "24 24.0 43.419306 40.409150 17.486321 0.0\n", - "25 25.0 25.190155 20.213954 19.235205 0.0\n", - "26 26.0 39.060150 31.971019 5.946691 0.0\n", - "27 27.0 24.462767 31.548428 16.027956 0.0\n", - "28 28.0 32.041023 28.773779 7.421162 0.0\n", - "29 29.0 41.415001 31.534983 18.697655 0.0\n", - ".. ... ... ... ... ...\n", - "970 1970.0 39.961747 21.937751 16.876374 1.0\n", - "971 1971.0 22.992144 41.965804 4.630396 1.0\n", - "972 1972.0 27.418658 30.000472 14.996611 1.0\n", - "973 1973.0 24.650888 42.335369 4.256731 1.0\n", - "974 1974.0 37.590383 30.347978 18.886888 1.0\n", - "975 1975.0 36.153751 31.817397 17.923976 1.0\n", - "976 1976.0 35.220053 25.411779 18.492891 1.0\n", - "977 1977.0 21.933038 35.872994 3.124716 1.0\n", - "978 1978.0 22.927703 27.631825 4.646728 1.0\n", - "979 1979.0 43.741042 20.175112 16.345838 1.0\n", - "980 1980.0 29.384802 21.106318 19.630789 1.0\n", - "981 1981.0 43.735181 43.343295 17.603924 1.0\n", - "982 1982.0 31.626312 32.359950 7.388056 1.0\n", - "983 1983.0 31.705014 42.362741 15.422934 1.0\n", - "984 1984.0 25.672793 29.757220 18.749198 1.0\n", - "985 1985.0 27.700470 32.955093 5.040123 1.0\n", - "986 1986.0 30.503950 28.176178 7.417003 1.0\n", - "987 1987.0 33.452629 26.654828 18.672937 1.0\n", - "988 1988.0 21.148093 26.583758 14.506695 1.0\n", - "989 1989.0 37.253903 33.732553 5.695627 1.0\n", - "990 1990.0 28.431587 42.342783 19.965963 1.0\n", - "991 1991.0 23.413307 24.262185 9.064301 1.0\n", - "992 1992.0 36.247812 32.156290 19.930520 1.0\n", - "993 1993.0 27.162994 27.680746 3.177151 1.0\n", - "994 1994.0 43.777477 34.297726 16.548866 1.0\n", - "995 1995.0 24.034122 31.395959 15.095484 1.0\n", - "996 1996.0 43.102711 22.222782 6.822642 1.0\n", - "997 1997.0 20.601408 35.512028 11.907216 1.0\n", - "998 1998.0 28.117702 20.603626 2.531290 1.0\n", - "999 1999.0 42.457189 30.778889 11.763249 1.0\n", - "\n", - "[2000 rows x 5 columns]\n", - "After: ident x_center y_center radius shape\n", - "764 764.0 35.324623 35.866777 19.510227 0.0\n", - "4 4.0 36.847619 40.204142 17.904288 0.0\n", - "34 1034.0 38.150434 24.913279 18.745524 1.0\n", - "755 1755.0 25.787710 33.814713 19.164580 1.0\n", - "408 1408.0 28.614813 27.276659 17.981373 1.0\n", - "814 1814.0 38.948084 25.307543 6.986260 1.0\n", - "792 1792.0 25.461741 30.799066 13.763277 1.0\n", - "556 1556.0 36.900199 29.194641 15.492603 1.0\n", - "638 1638.0 40.843954 24.403607 16.838838 1.0\n", - "452 452.0 29.529439 42.743759 4.222467 0.0\n", - "538 538.0 40.071947 21.507144 13.438985 0.0\n", - "602 602.0 31.848373 43.333032 4.247464 0.0\n", - "891 891.0 21.566832 28.834408 9.083898 0.0\n", - "842 842.0 26.131005 43.759440 4.616096 0.0\n", - "72 72.0 27.296103 37.043129 13.734974 0.0\n", - "342 1342.0 26.208766 43.011721 17.046402 1.0\n", - "600 1600.0 22.384787 27.525248 19.599696 1.0\n", - "406 1406.0 33.273553 36.570529 19.190313 1.0\n", - "121 121.0 32.385423 27.458283 9.749827 0.0\n", - "900 900.0 22.963475 35.635082 10.237318 0.0\n", - "672 672.0 41.278090 24.905281 8.987369 0.0\n", - "71 1071.0 23.670476 38.363852 19.265692 1.0\n", - "162 162.0 28.356702 37.781209 9.102103 0.0\n", - "169 169.0 37.679733 32.706235 8.812381 0.0\n", - "669 669.0 24.742136 37.839907 16.377753 0.0\n", - "754 754.0 37.679295 37.563484 13.520782 0.0\n", - "205 205.0 29.187298 26.859636 12.091397 0.0\n", - "572 1572.0 32.097200 42.250592 7.086982 1.0\n", - "870 870.0 29.522387 28.515533 3.094520 0.0\n", - "658 658.0 31.793692 25.610514 5.402138 0.0\n", - ".. ... ... ... ... ...\n", - "208 208.0 20.952158 27.213617 15.642949 0.0\n", - "113 1113.0 28.816669 33.574597 15.180854 1.0\n", - "779 779.0 37.327557 20.801743 19.513822 0.0\n", - "504 504.0 34.616709 29.050227 17.232464 0.0\n", - "778 1778.0 31.634265 39.792782 18.560779 1.0\n", - "960 960.0 21.322733 37.864290 8.287625 0.0\n", - "761 761.0 29.441176 31.444691 15.720303 0.0\n", - "231 231.0 40.263298 38.275044 9.885738 0.0\n", - "321 321.0 33.858113 35.904183 2.319553 0.0\n", - "845 1845.0 21.847323 26.812080 3.757485 1.0\n", - "106 106.0 24.398401 23.139724 8.682128 0.0\n", - "329 1329.0 21.430991 32.825269 10.511504 1.0\n", - "510 510.0 35.778735 39.422842 12.905296 0.0\n", - "931 1931.0 43.461439 37.086142 17.047584 1.0\n", - "589 1589.0 24.934461 37.552905 7.187674 1.0\n", - "953 953.0 40.818062 35.856756 2.138598 0.0\n", - "721 1721.0 33.537594 25.778214 9.432461 1.0\n", - "59 59.0 43.168261 36.004552 16.693226 0.0\n", - "85 1085.0 27.591397 23.210883 6.733797 1.0\n", - "888 1888.0 20.782159 24.590411 7.155783 1.0\n", - "335 1335.0 24.795351 30.280950 19.042611 1.0\n", - "515 1515.0 21.949374 42.860980 17.919465 1.0\n", - "723 1723.0 42.707074 42.560694 4.353266 1.0\n", - "930 1930.0 29.192857 32.433713 3.376395 1.0\n", - "414 1414.0 42.110172 26.673578 10.490774 1.0\n", - "628 1628.0 33.126973 39.804421 7.565251 1.0\n", - "734 734.0 21.465825 23.267503 16.342326 0.0\n", - "519 1519.0 26.622266 43.634553 5.365575 1.0\n", - "523 523.0 31.084991 42.155667 12.870727 0.0\n", - "275 1275.0 28.120481 22.650390 19.161178 1.0\n", - "\n", - "[2000 rows x 5 columns]\n" - ] - } - ], - "source": [ - "# Generate Data Parameters\n", - "nb_obj = 1000\n", - "image_side_length = 64\n", - "show_plot = False\n", - "\n", - "# Generate Data\n", - "tab_a, img_a = generate_dataset(nb_obj, image_side_length=image_side_length, shape='rect', show_plot=show_plot)\n", - "tab_b, img_b = generate_dataset(nb_obj, image_side_length=image_side_length, shape='circle', show_plot=show_plot, index_start=nb_obj)\n", - "\n", - "# combine data\n", - "tab = combine_data([tab_a, tab_b])\n", - "img = combine_data([img_a, img_b], data_type='img')\n", - "\n", - "# randomize data\n", - "tab, img = randomize_data(tab, img, verbose=True)\n", - "\n", - "# save data\n", - "f_tab = 'test_generate_pipeline_circle_data.csv'\n", - "f_img = 'test_generate_pipeline_circle_image.npy'\n", - "save_data(f_tab, f_img, tab, img, verbose=False )\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test Simple Neural Network" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "''' MY DATA\n", - "'''\n", - "\n", - "from __future__ import print_function\n", - "import keras\n", - "from keras.datasets import mnist\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Flatten\n", - "from keras.layers import Conv2D, MaxPooling2D\n", - "from keras import backend as K\n", - "\n", - "batch_size = 32\n", - "num_classes = 2\n", - "epochs = 5\n", - "train_me = True\n", - "\n", - "nb_train = 800\n", - "nb_valid = 100\n", - "nb_test = 100\n", - "\n", - "# the data, split between train and test sets\n", - "output = split_data(nb_train, nb_valid, nb_test, tab, img, printcheck=0)\n", - "y_train_temp, y_valid_temp, y_test_temp, x_train, x_valid, x_test = output\n", - "y_train = y_train_temp['shape']\n", - "y_test = y_test_temp['shape']\n", - "img_rows = img_cols = image_side_length\n", - "\n", - "print('x shape', x_train.shape)\n", - "\n", - "if K.image_data_format() == 'channels_first':\n", - " x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n", - " x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n", - " input_shape = (1, img_rows, img_cols)\n", - "else:\n", - " x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n", - " x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n", - " input_shape = (img_rows, img_cols, 1)\n", - "\n", - "x_train = x_train.astype('float32')\n", - "x_test = x_test.astype('float32')\n", - "x_train /= 255\n", - "x_test /= 255\n", - "print('x_train shape:', x_train.shape)\n", - "print(x_train.shape[0], 'train samples')\n", - "print(x_test.shape[0], 'test samples')\n", - "\n", - "# convert class vectors to binary class matrices\n", - "y_train = keras.utils.to_categorical(y_train, num_classes)\n", - "y_test = keras.utils.to_categorical(y_test, num_classes)\n", - "\n", - "\n", - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))\n", - "model.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25))\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])\n", - "if train_me:\n", - " model.fit(x_train, y_train,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " verbose=1,\n", - " validation_data=(x_test, y_test))\n", - " score = model.evaluate(x_test, y_test, verbose=0)\n", - " print('Test loss:', score[0])\n", - " print('Test accuracy:', score[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''ORIGINAL\n", - "'''\n", - "\n", - "from __future__ import print_function\n", - "import keras\n", - "from keras.datasets import mnist\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Flatten\n", - "from keras.layers import Conv2D, MaxPooling2D\n", - "from keras import backend as K\n", - "\n", - "\n", - "train_me = False\n", - "\n", - "batch_size = 10\n", - "num_classes = 10\n", - "epochs = 1\n", - "\n", - "# input image dimensions\n", - "img_rows, img_cols = 28, 28\n", - "\n", - "# the data, split between train and test sets\n", - "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", - "\n", - "if K.image_data_format() == 'channels_first':\n", - " x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n", - " x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n", - " input_shape = (1, img_rows, img_cols)\n", - "else:\n", - " x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n", - " x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n", - " input_shape = (img_rows, img_cols, 1)\n", - "\n", - "x_train = x_train.astype('float32')\n", - "x_test = x_test.astype('float32')\n", - "x_train /= 255\n", - "x_test /= 255\n", - "print('x_train shape:', x_train.shape)\n", - "print(x_train.shape[0], 'train samples')\n", - "print(x_test.shape[0], 'test samples')\n", - "\n", - "# convert class vectors to binary class matrices\n", - "print(y_train.shape)\n", - "y_train = keras.utils.to_categorical(y_train, num_classes)\n", - "y_test = keras.utils.to_categorical(y_test, num_classes)\n", - "print(y_train.shape)\n", - "\n", - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(3, 3),\n", - " activation='relu',\n", - " input_shape=input_shape))\n", - "model.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25))\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "model.compile(loss=keras.losses.categorical_crossentropy,\n", - " optimizer=keras.optimizers.Adadelta(),\n", - " metrics=['accuracy'])\n", - "\n", - "if train_me:\n", - "\n", - " model.fit(x_train, y_train,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " verbose=1,\n", - " validation_data=(x_test, y_test))\n", - " score = model.evaluate(x_test, y_test, verbose=0)\n", - " print('Test loss:', score[0])\n", - " print('Test accuracy:', score[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aaa = np.array([0, 1, 2, 3, 4, 5, 6])\n", - "print(aaa[0:3])\n", - "print(aaa[3:6])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# package test deep learning\n", - "import keras\n", - "from keras import backend as K\n", - "from keras import Sequential\n", - "from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# read data from file\n", - "#data_list_pd = pd.read_csv(f_data_list_pd)\n", - "#img_list = np.load(f_img_list)\n", - "#print(img_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# split data\n", - "nb_train = 80\n", - "nb_valid = 10\n", - "nb_test = 10\n", - "\n", - "num_classes = 2\n", - "\n", - "output = split_data(nb_train, nb_valid, nb_test, tab, img, printcheck=0)\n", - "tab_train, tab_valid, tab_test, img_train, img_valid, img_test = output\n", - "img_rows = img_cols = image_side_length\n", - "\n", - "if K.image_data_format() == 'channels_first':\n", - " img_train = img_train.reshape(img_train.shape[0], 1, img_rows, img_cols)\n", - " img_test = img_test.reshape(img_test.shape[0], 1, img_rows, img_cols)\n", - " input_shape = (1, img_rows, img_cols)\n", - "else:\n", - " img_train = img_train.reshape(img_train.shape[0], img_rows, img_cols, 1)\n", - " img_test = img_test.reshape(img_test.shape[0], img_rows, img_cols, 1)\n", - " input_shape = (img_rows, img_cols, 1)\n", - "\n", - "img_train = img_train.astype('float32')\n", - "img_test = img_test.astype('float32')\n", - "img_train /= 255\n", - "img_test /= 255\n", - "print('x_train shape:', img_train.shape)\n", - "print(img_train.shape[0], 'train samples')\n", - "print(img_test.shape[0], 'test samples')\n", - "\n", - "# convert class vectors to binary class matrices\n", - "print(tab_train.shape)\n", - "n = tab_train.shape[0]\n", - "categorical = np.zeros((n, num_classes), dtype=np.float32)\n", - "print(np.arange(n))\n", - "#tab_train = keras.utils.to_categorical(tab_train, num_classes) \n", - "#tab_test = keras.utils.to_categorical(tab_test, num_classes)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(512, 512, 3)\n", - "355.4\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import cv2 as cv\n", - "\n", - "img = np.ones((512,512,3), np.uint8)*355.4\n", - "\n", - "#draw a red line\n", - "img = cv.line(img, (100,100), (300,300), (100,200,255),40)\n", - "\n", - "print(img.shape)\n", - "print(max(img[0,0,:]))\n", - "#plt.imshow(img)\n", - "#plt.imshow(img[:,:,0])\n", - "plt.imshow(img[:,:,:])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'graphics'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgraphics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'graphics'" - ] - } - ], - "source": [ - "import graphics" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "from PIL import Image\n", - "import colorsys\n", - "\n", - "im = Image.new('RGB', (100,200), (255,0,0))\n", - "im.save(\"test.png\")\n", - "\n", - "c = colorsys.hsv_to_rgb(0,1,1) # h (0 - 1), s (0 - 1), v ( 0 - 1)\n", - "im2 = Image.new('RGB', (100,200), (int(255*c[0]), int(255*c[1]), int(255*c[2])))\n", - "im2.save(\"testHSV.png\")\n", - "\n", - "\n", - "c = colorsys.hls_to_rgb(0,1,1) #note HLS not HSL\n", - "im2 = Image.new('RGB', (100,200), (int(255*c[0]), int(255*c[1]), int(255*c[2])))\n", - "im2.save(\"testHSV.png\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - 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"text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#!/usr/bin/python\n", - "\n", - "from PIL import Image, ImageFont, ImageDraw\n", - "import colorsys\n", - "\n", - "im = Image.new('RGB', (100,200), (255,0,0))\n", - "dr = ImageDraw.Draw(im)\n", - "\n", - "dr.ellipse((0,0,10,10), fill=180, outline = \"blue\")\n", - "\n", - "im.save(\"circle.png\")\n", - "plt.imshow(im)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/Deprecated/SimGenerator_vtest.ipynb b/archive/notebooks/Deprecated/SimGenerator_vtest.ipynb deleted file mode 100644 index bb579d5..0000000 --- a/archive/notebooks/Deprecated/SimGenerator_vtest.ipynb +++ /dev/null @@ -1,896 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Deprecated" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# packages: random\n", - "import random\n", - "\n", - "# packages: data structure\n", - "import numpy as np\n", - "import pandas as pd\n", - "import astropy.io as io\n", - "\n", - "# packages: images with cv\n", - "import cv2 as cv\n", - "\n", - "# packages: image processing\n", - "# from scipy import ndimage\n", - "\n", - "# packages: pixel-level image generation\n", - "# from sklearn.feature_extraction import image\n", - "\n", - "# packages: image generation and plot generation\n", - "from matplotlib import pyplot as plt\n", - "\n", - "# packages: astro image generation\n", - "import astropy.modeling as modeling\n", - "\n", - "# packages: geometric image generation\n", - "# from shapely.geometry import Point, LineString, Polygon\n", - "\n", - "# packages: agent-based image generation\n", - "# import turtle\n", - "\n", - "# %pylab inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# pandas\n", - "# https://pandas.pydata.org/pandas-docs/stable/tutorials.html\n", - "# https://pandas.pydata.org/pandas-docs/stable/10min.html\n", - "\n", - "# ndimage: image manipulation\n", - "# https://docs.scipy.org/doc/scipy/reference/ndimage.html#module-scipy.ndimage\n", - "\n", - "# ascii:io\n", - "# http://docs.astropy.org/en/stable/io/ascii/\n", - "\n", - "# matplotlib\n", - "# https://nickcharlton.net/posts/drawing-animating-shapes-matplotlib.html\n", - "\n", - "# shapely\n", - "\n", - "# turtle\n", - "# https://docs.python.org/3/library/turtle.html\n", - "# https://michael0x2a.com/blog/turtle-examples\n", - "\n", - "# astropy: modeling:\n", - "# http://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Sersic2D.html" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# cv \n", - "\n", - "def empty_canvas_cv(image_side_length=100, nb_channels=1):\n", - " return np.zeros((image_side_length, image_side_length, nb_channels), np.uint8)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# numpy: empty canvas\n", - "def empty_canvas(image_side_length=100):\n", - "\n", - " return np.indices((image_side_length, image_side_length))\n", - " \n", - " \n", - "# scikit learn: circle\n", - "def circle_sk(canvas, x_center=50, y_center=50, radius=30):\n", - "\n", - " y, x = canvas\n", - " circle = (x - x_center)**2 + (y - y_center)**2 < radius**2\n", - " img = circle.astype(float)\n", - " \n", - " return img\n", - "\n", - "\n", - "# scikit learn: rectangle\n", - "def rect_sk(canvas, x_center=50, y_center=50, radius=30):\n", - "\n", - " y, x = canvas\n", - " rect = (x < x_center + radius) & (x > x_center - radius) & (y < y_center + radius) & (y > y_center - radius)\n", - " img = rect.astype(float)\n", - " \n", - " return img\n", - " \n", - " \n", - "# scikit learn: rectangle\n", - "def triangle_sk(canvas, x_center=50, y_center=50, radius=30):\n", - "\n", - " y, x = canvas\n", - " rect = (x < x_center + radius) & (x > x_center - radius) & (y < y_center + radius) & (y > y_center - radius)\n", - " img = rect.astype(float)\n", - " \n", - " return img\n", - " \n", - " \n", - "# plot for SPI package\n", - "def plot_spi(img):\n", - " plt.axes() \n", - " plt.imshow(img)\n", - " plt.clf()\n", - " \n", - " \n", - "# matplotlib pyplot\n", - "def circle_plt(x_center=0, y_center=0, radius=0.75, fc='r', show=False):\n", - "\n", - " plt.axes()\n", - "\n", - " circle = plt.Circle((x_center, y_center), radius=radius, fc=fc)\n", - " plt.gca().add_patch(circle)\n", - " plt.axis('scaled')\n", - " imgplot = plt.imshow(img)\n", - " imgplot = plt.savefig(\"test3.png\", dpi = (200))\n", - " #imgplot = plt.imshow()\n", - " \n", - " if show:\n", - " plt.show()\n", - "\n", - "# test each individual function\n", - "def test_individual():\n", - " #circle()\n", - " img = circle_sk()\n", - " plot_spi(img)\n", - " #star()\n", - " return\n", - "\n", - "# generate one image data set\n", - "def generate_dataset(nb_obj, \n", - " image_side_length=100, \n", - " index_start=0, \n", - " shape='rect',\n", - " radius_min=2,\n", - " radius_max=20,\n", - " show_plot=False, \n", - " verbose=False):\n", - "\n", - " # initiate image values\n", - " fac = -1.0\n", - " x_center_list = np.random.uniform(0 - fac* radius_max, image_side_length + fac* radius_max, nb_obj)\n", - " y_center_list = np.random.uniform(0 - fac* radius_max, image_side_length + fac* radius_max, nb_obj)\n", - " radius_list = np.random.uniform(radius_min, radius_max, nb_obj)\n", - " column_names = ['ident', 'x_center', 'y_center', 'radius', 'shape']\n", - " \n", - " # create empty data structures\n", - " tab_list = np.empty((nb_obj, len(column_names)))\n", - " img_list = np.empty((nb_obj, image_side_length, image_side_length))\n", - " \n", - " # create empty canvas for a single image\n", - " canvas = empty_canvas(image_side_length=image_side_length)\n", - " \n", - " # loop over objects\n", - " for i_obj in np.arange(nb_obj):\n", - " \n", - " # draw object properties from list\n", - " x_center = x_center_list[i_obj]\n", - " y_center = y_center_list[i_obj] \n", - " radius = radius_list[i_obj]\n", - " \n", - " # identification value\n", - " ident = int(index_start + i_obj)\n", - " \n", - " # create object\n", - " if shape == 'rect':\n", - " img = rect_sk(canvas, x_center=x_center, y_center=y_center, radius=radius)\n", - " shape_num = 0\n", - " elif shape == 'circle':\n", - " img = circle_sk(canvas, x_center=x_center, y_center=y_center, radius=radius)\n", - " shape_num = 1 \n", - "\n", - " # add tabular data to data list structure\n", - " tab_list[i_obj] = [ident, x_center, y_center, radius, shape_num]\n", - " \n", - " # add image data to image list structure\n", - " img_list[i_obj] = img\n", - "\n", - " # plot image\n", - " if show_plot:\n", - " plt.figure()\n", - " plt.axes() \n", - " plt.imshow(img)\n", - " \n", - " # Data Frame: Tabular Data for Objects\n", - " tab_list = pd.DataFrame(tab_list,columns=column_names)\n", - " \n", - " # verbose\n", - " if verbose:\n", - " print(tab_list)\n", - " print(img_list)\n", - " \n", - " return tab_list, img_list\n", - "\n", - "\n", - "# save data\n", - "def save_data(f_data_list, f_img_list, data_list, img_list, verbose=False):\n", - "\n", - " # Pandas Data Frame for tabular data: save to file\n", - " data_list.to_csv(f_data_list)\n", - "\n", - " # Numpy Array for image data: save to file\n", - " np.save(f_img_list, img_list)\n", - " \n", - " # verbose\n", - " if verbose:\n", - " print(f_data_list_pd)\n", - " print(f_img_list)\n", - " \n", - " return\n", - " \n", - "\n", - "# combine data sets\n", - "def combine_data(frames, data_type='tab'): \n", - " \n", - " if data_type=='tab':\n", - " data = pd.concat(frames)\n", - " elif data_type=='img':\n", - " data = np.concatenate(frames)\n", - " \n", - " return data\n", - "\n", - "\n", - "# randomize data \n", - "def randomize_data(tab, img, seed=5, verbose=False):\n", - " \n", - " if verbose:\n", - " print('Before:', tab)\n", - " \n", - " # create randomized indices\n", - " random.seed(seed) \n", - " nb_tab = len(tab)\n", - " ind_random = np.arange(nb_tab) \n", - " random.shuffle(ind_random)\n", - " \n", - " # re-order data based on randomized indices\n", - " tab = tab.iloc[ind_random]\n", - " img = img[ind_random]\n", - " \n", - " if verbose:\n", - " print('After:', tab)\n", - "\n", - " return tab, img\n", - "\n", - "\n", - "# split data \n", - "def split_data(nb_train, nb_valid, nb_test, tab, img, printcheck=0):\n", - "\n", - " \n", - " ind_start_train = 0\n", - " ind_end_train = ind_start_valid = ind_start_train + nb_train \n", - " ind_end_valid = ind_start_test = ind_start_valid + nb_valid\n", - " ind_end_test = ind_start_test + nb_test\n", - " \n", - " if printcheck > 0:\n", - " print(tab[0:printcheck])\n", - " print(ind_start_train, ind_end_train)\n", - " # good place for unit test\n", - " \n", - " # split data in train, valid, test\n", - " tab_train = tab[ind_start_train: ind_end_train]\n", - " img_train = img[ind_start_train: ind_end_train]\n", - "\n", - " tab_valid = tab[ind_start_valid: ind_end_valid]\n", - " img_valid = img[ind_start_valid: ind_end_valid]\n", - "\n", - " tab_test = tab[ind_start_test: ind_end_test]\n", - " img_test = img[ind_start_test: ind_end_test]\n", - " \n", - " return tab_train, tab_valid, tab_test, img_train, img_valid, img_test\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Before: ident x_center y_center radius shape\n", - "0 0.0 31.323323 42.746014 2.448773 0.0\n", - "1 1.0 42.732992 23.037234 14.264696 0.0\n", - "2 2.0 30.878020 25.391156 3.163774 0.0\n", - "3 3.0 36.098704 25.932020 19.172839 0.0\n", - "4 4.0 36.847619 40.204142 17.904288 0.0\n", - "5 5.0 41.543711 25.654052 17.526231 0.0\n", - "6 6.0 28.150905 26.250166 16.522191 0.0\n", - "7 7.0 27.543727 29.053957 15.362774 0.0\n", - "8 8.0 40.086332 20.002175 4.725339 0.0\n", - "9 9.0 23.250734 27.670365 7.992529 0.0\n", - "10 10.0 36.829935 34.095812 11.124781 0.0\n", - "11 11.0 35.772559 31.655348 16.028401 0.0\n", - "12 12.0 21.540188 30.013876 5.452747 0.0\n", - "13 13.0 38.572738 29.185915 2.520848 0.0\n", - "14 14.0 28.172930 36.087667 10.763983 0.0\n", - "15 15.0 24.561381 31.202893 18.042446 0.0\n", - "16 16.0 22.361247 34.039402 19.968749 0.0\n", - "17 17.0 26.766144 28.647431 11.619167 0.0\n", - "18 18.0 41.252744 36.667101 12.620202 0.0\n", - "19 19.0 32.254440 43.655383 14.892189 0.0\n", - "20 20.0 40.927516 21.816137 2.709315 0.0\n", - "21 21.0 22.356585 24.552019 16.538607 0.0\n", - "22 22.0 24.007618 30.850853 17.095598 0.0\n", - "23 23.0 25.840935 41.227749 3.477944 0.0\n", - "24 24.0 43.419306 40.409150 17.486321 0.0\n", - "25 25.0 25.190155 20.213954 19.235205 0.0\n", - "26 26.0 39.060150 31.971019 5.946691 0.0\n", - 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"986 1986.0 30.503950 28.176178 7.417003 1.0\n", - "987 1987.0 33.452629 26.654828 18.672937 1.0\n", - "988 1988.0 21.148093 26.583758 14.506695 1.0\n", - "989 1989.0 37.253903 33.732553 5.695627 1.0\n", - "990 1990.0 28.431587 42.342783 19.965963 1.0\n", - "991 1991.0 23.413307 24.262185 9.064301 1.0\n", - "992 1992.0 36.247812 32.156290 19.930520 1.0\n", - "993 1993.0 27.162994 27.680746 3.177151 1.0\n", - "994 1994.0 43.777477 34.297726 16.548866 1.0\n", - "995 1995.0 24.034122 31.395959 15.095484 1.0\n", - "996 1996.0 43.102711 22.222782 6.822642 1.0\n", - "997 1997.0 20.601408 35.512028 11.907216 1.0\n", - "998 1998.0 28.117702 20.603626 2.531290 1.0\n", - "999 1999.0 42.457189 30.778889 11.763249 1.0\n", - "\n", - "[2000 rows x 5 columns]\n", - "After: ident x_center y_center radius shape\n", - "764 764.0 35.324623 35.866777 19.510227 0.0\n", - "4 4.0 36.847619 40.204142 17.904288 0.0\n", - "34 1034.0 38.150434 24.913279 18.745524 1.0\n", - "755 1755.0 25.787710 33.814713 19.164580 1.0\n", - 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"tab_a, img_a = generate_dataset(nb_obj, image_side_length=image_side_length, shape='rect', show_plot=show_plot)\n", - "tab_b, img_b = generate_dataset(nb_obj, image_side_length=image_side_length, shape='circle', show_plot=show_plot, index_start=nb_obj)\n", - "\n", - "# combine data\n", - "tab = combine_data([tab_a, tab_b])\n", - "img = combine_data([img_a, img_b], data_type='img')\n", - "\n", - "# randomize data\n", - "tab, img = randomize_data(tab, img, verbose=True)\n", - "\n", - "# save data\n", - "f_tab = 'test_generate_pipeline_circle_data.csv'\n", - "f_img = 'test_generate_pipeline_circle_image.npy'\n", - "save_data(f_tab, f_img, tab, img, verbose=False )\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test Simple Neural Network" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "''' MY DATA\n", - "'''\n", - "\n", - "from __future__ import print_function\n", - "import keras\n", - "from keras.datasets import mnist\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Flatten\n", - "from keras.layers import Conv2D, MaxPooling2D\n", - "from keras import backend as K\n", - "\n", - "batch_size = 32\n", - "num_classes = 2\n", - "epochs = 5\n", - "train_me = True\n", - "\n", - "nb_train = 800\n", - "nb_valid = 100\n", - "nb_test = 100\n", - "\n", - "# the data, split between train and test sets\n", - "output = split_data(nb_train, nb_valid, nb_test, tab, img, printcheck=0)\n", - "y_train_temp, y_valid_temp, y_test_temp, x_train, x_valid, x_test = output\n", - "y_train = y_train_temp['shape']\n", - "y_test = y_test_temp['shape']\n", - "img_rows = img_cols = image_side_length\n", - "\n", - "print('x shape', x_train.shape)\n", - "\n", - "if K.image_data_format() == 'channels_first':\n", - " x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n", - " x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n", - " input_shape = (1, img_rows, img_cols)\n", - "else:\n", - " x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n", - " x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n", - " input_shape = (img_rows, img_cols, 1)\n", - "\n", - "x_train = x_train.astype('float32')\n", - "x_test = x_test.astype('float32')\n", - "x_train /= 255\n", - "x_test /= 255\n", - "print('x_train shape:', x_train.shape)\n", - "print(x_train.shape[0], 'train samples')\n", - "print(x_test.shape[0], 'test samples')\n", - "\n", - "# convert class vectors to binary class matrices\n", - "y_train = keras.utils.to_categorical(y_train, num_classes)\n", - "y_test = keras.utils.to_categorical(y_test, num_classes)\n", - "\n", - "\n", - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))\n", - "model.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25))\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])\n", - "if train_me:\n", - " model.fit(x_train, y_train,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " verbose=1,\n", - " validation_data=(x_test, y_test))\n", - " score = model.evaluate(x_test, y_test, verbose=0)\n", - " print('Test loss:', score[0])\n", - " print('Test accuracy:', score[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''ORIGINAL\n", - "'''\n", - "\n", - "from __future__ import print_function\n", - "import keras\n", - "from keras.datasets import mnist\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Flatten\n", - "from keras.layers import Conv2D, MaxPooling2D\n", - "from keras import backend as K\n", - "\n", - "\n", - "train_me = False\n", - "\n", - "batch_size = 10\n", - "num_classes = 10\n", - "epochs = 1\n", - "\n", - "# input image dimensions\n", - "img_rows, img_cols = 28, 28\n", - "\n", - "# the data, split between train and test sets\n", - "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", - "\n", - "if K.image_data_format() == 'channels_first':\n", - " x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n", - " x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n", - " input_shape = (1, img_rows, img_cols)\n", - "else:\n", - " x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n", - " x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n", - " input_shape = (img_rows, img_cols, 1)\n", - "\n", - "x_train = x_train.astype('float32')\n", - "x_test = x_test.astype('float32')\n", - "x_train /= 255\n", - "x_test /= 255\n", - "print('x_train shape:', x_train.shape)\n", - "print(x_train.shape[0], 'train samples')\n", - "print(x_test.shape[0], 'test samples')\n", - "\n", - "# convert class vectors to binary class matrices\n", - "print(y_train.shape)\n", - "y_train = keras.utils.to_categorical(y_train, num_classes)\n", - "y_test = keras.utils.to_categorical(y_test, num_classes)\n", - "print(y_train.shape)\n", - "\n", - "model = Sequential()\n", - "model.add(Conv2D(32, kernel_size=(3, 3),\n", - " activation='relu',\n", - " input_shape=input_shape))\n", - "model.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25))\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "model.compile(loss=keras.losses.categorical_crossentropy,\n", - " optimizer=keras.optimizers.Adadelta(),\n", - " metrics=['accuracy'])\n", - "\n", - "if train_me:\n", - "\n", - " model.fit(x_train, y_train,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " verbose=1,\n", - " validation_data=(x_test, y_test))\n", - " score = model.evaluate(x_test, y_test, verbose=0)\n", - " print('Test loss:', score[0])\n", - " print('Test accuracy:', score[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aaa = np.array([0, 1, 2, 3, 4, 5, 6])\n", - "print(aaa[0:3])\n", - "print(aaa[3:6])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# package test deep learning\n", - "import keras\n", - "from keras import backend as K\n", - "from keras import Sequential\n", - "from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# read data from file\n", - "#data_list_pd = pd.read_csv(f_data_list_pd)\n", - "#img_list = np.load(f_img_list)\n", - "#print(img_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# split data\n", - "nb_train = 80\n", - "nb_valid = 10\n", - "nb_test = 10\n", - "\n", - "num_classes = 2\n", - "\n", - "output = split_data(nb_train, nb_valid, nb_test, tab, img, printcheck=0)\n", - "tab_train, tab_valid, tab_test, img_train, img_valid, img_test = output\n", - "img_rows = img_cols = image_side_length\n", - "\n", - "if K.image_data_format() == 'channels_first':\n", - " img_train = img_train.reshape(img_train.shape[0], 1, img_rows, img_cols)\n", - " img_test = img_test.reshape(img_test.shape[0], 1, img_rows, img_cols)\n", - " input_shape = (1, img_rows, img_cols)\n", - "else:\n", - " img_train = img_train.reshape(img_train.shape[0], img_rows, img_cols, 1)\n", - " img_test = img_test.reshape(img_test.shape[0], img_rows, img_cols, 1)\n", - " input_shape = (img_rows, img_cols, 1)\n", - "\n", - "img_train = img_train.astype('float32')\n", - "img_test = img_test.astype('float32')\n", - "img_train /= 255\n", - "img_test /= 255\n", - "print('x_train shape:', img_train.shape)\n", - "print(img_train.shape[0], 'train samples')\n", - "print(img_test.shape[0], 'test samples')\n", - "\n", - "# convert class vectors to binary class matrices\n", - "print(tab_train.shape)\n", - "n = tab_train.shape[0]\n", - "categorical = np.zeros((n, num_classes), dtype=np.float32)\n", - "print(np.arange(n))\n", - "#tab_train = keras.utils.to_categorical(tab_train, num_classes) \n", - "#tab_test = keras.utils.to_categorical(tab_test, num_classes)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(512, 512, 3)\n", - "355.4\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import cv2 as cv\n", - "\n", - "img = np.ones((512,512,3), np.uint8)*355.4\n", - "\n", - "#draw a red line\n", - "img = cv.line(img, (100,100), (300,300), (100,200,255),40)\n", - "\n", - "print(img.shape)\n", - "print(max(img[0,0,:]))\n", - "#plt.imshow(img)\n", - "#plt.imshow(img[:,:,0])\n", - "plt.imshow(img[:,:,:])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'graphics'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgraphics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'graphics'" - ] - } - ], - "source": [ - "import graphics" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "from PIL import Image\n", - "import colorsys\n", - "\n", - "im = Image.new('RGB', (100,200), (255,0,0))\n", - "im.save(\"test.png\")\n", - "\n", - "c = colorsys.hsv_to_rgb(0,1,1) # h (0 - 1), s (0 - 1), v ( 0 - 1)\n", - "im2 = Image.new('RGB', (100,200), (int(255*c[0]), int(255*c[1]), int(255*c[2])))\n", - "im2.save(\"testHSV.png\")\n", - "\n", - "\n", - "c = colorsys.hls_to_rgb(0,1,1) #note HLS not HSL\n", - "im2 = Image.new('RGB', (100,200), (int(255*c[0]), int(255*c[1]), int(255*c[2])))\n", - "im2.save(\"testHSV.png\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - 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"text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#!/usr/bin/python\n", - "\n", - "from PIL import Image, ImageFont, ImageDraw\n", - "import colorsys\n", - "\n", - "im = Image.new('RGB', (100,200), (255,0,0))\n", - "dr = ImageDraw.Draw(im)\n", - "\n", - "dr.ellipse((0,0,10,10), fill=180, outline = \"blue\")\n", - "\n", - "im.save(\"circle.png\")\n", - "plt.imshow(im)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/Deprecated/Untitled.ipynb b/archive/notebooks/Deprecated/Untitled.ipynb deleted file mode 100644 index 2fd6442..0000000 --- a/archive/notebooks/Deprecated/Untitled.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/archive/notebooks/Example_Original.ipynb b/archive/notebooks/Example_Original.ipynb deleted file mode 100644 index 05da18f..0000000 --- a/archive/notebooks/Example_Original.ipynb +++ /dev/null @@ -1,1437 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.patches import Wedge, Rectangle\n", - "from matplotlib.path import Path\n", - "from bokeh.plotting import figure, show, output_notebook, gridplot" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "2WU-r1QY8RsH" - }, - "source": [ - "# Create Methods for Structure Creation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "EJXwnaDh5o0Z" - }, - "outputs": [], - "source": [ - "def _patch2im(thepatch, imshape):\n", - " # Create meshgrid of coordinates\n", - " y = np.arange(1, imshape[0])\n", - " x = np.arange(1, imshape[1])\n", - " g = np.meshgrid(x, y)\n", - "\n", - " coords = np.array(list(zip(*(c.flat for c in g))))\n", - "\n", - " # Create path\n", - " elpath = Path(thepatch.get_verts())\n", - "\n", - " # Determine all points in the grid ithat are in the path.\n", - " validcoords = elpath.contains_points(coords)\n", - " ellipsepoints = coords[validcoords]\n", - "\n", - " outim = np.zeros(imshape)\n", - " outim[ellipsepoints[:, 0], ellipsepoints[:, 1]] = 1\n", - "\n", - " return outim\n", - "\n", - "\n", - "def create_polygon(imshape, xy, width, height, angle=0.0, fill=True):\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " rect = Rectangle(xy, width, height, angle=0.0, fill=fill)\n", - "\n", - " return _patch2im(rect, imshape)\n", - "\n", - "\n", - "def create_circle(imshape, center, radius, width):\n", - " \"\"\"\n", - " Create a cirlce.\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " return create_arc(imshape, center, radius, width=width, theta1=0, theta2=360)\n", - " \n", - "def create_line(imshape, center, radius, width):\n", - " \"\"\"\n", - " Create a cirlce.\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " # return create_line(imshape, center, radius, width=width, theta1=0, theta2=359)\n", - "\n", - "\n", - "def create_arc(imshape, center, radius, width, theta1, theta2):\n", - " \"\"\"\n", - " Create an arc (outline of a circle) with a specified\n", - " thickness (width).\n", - " \"\"\"\n", - "\n", - " # Create the arc patch we want to add to the image\n", - " arc = Wedge(center, radius, theta1, theta2, width)\n", - "\n", - " return _patch2im(arc, imshape)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "dfiXlE9R8Wd7" - }, - "source": [ - "# Create an Example Lensing Image" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "yoKEbjT05rb8" - }, - "outputs": [], - "source": [ - "\n", - "imblah = np.zeros((256, 256))\n", - "\n", - "# Example arc\n", - "arc = create_arc(imblah.shape, center=(150, 128), radius=56, width=6, theta1=27, theta2=156)\n", - "imblah += arc * 35\n", - "\n", - "# Example filled circle\n", - "circle = create_circle(imblah.shape, center=(50, 28), radius=56, width=56)\n", - "imblah += circle * 21\n", - "\n", - "# Example unfilled circle\n", - "circle = create_circle(imblah.shape, center=(28, 200), radius=50, width=8)\n", - "imblah += circle * 61\n", - "\n", - "# Example polygon\n", - "rect = create_polygon(imblah.shape, (150, 28), width=27, height=44)\n", - "imblah += rect * 13\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 617 - }, - "colab_type": "code", - "id": "Eiv5UqjfdufR", - "outputId": "be1714c1-ff26-4534-d894-51e0fb69fdee" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"1040\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error() {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (var i = 0; i < css_urls.length; i++) {\n", - " var url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };var element = document.getElementById(\"1040\");\n", - " if (element == null) {\n", - " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1040' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " \n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n", - " var css_urls = [];\n", - " \n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " function(Bokeh) {\n", - " \n", - " \n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if (root.Bokeh !== undefined || force === true) {\n", - " \n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - " if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"1040\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1040\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"1040\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1040' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1040\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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Application\",\"version\":\"1.4.0\"}};\n", - " var render_items = [{\"docid\":\"d86f8ee1-177b-4e0c-ab69-9bba72f31b72\",\"roots\":{\"1001\":\"6acac8cf-c47b-4e0d-82d8-b0b17617b285\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " clearInterval(timer);\n", - " embed_document(root);\n", - " } else {\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " clearInterval(timer);\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", - " }\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "1001" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "p = figure(x_range=(1, 256), y_range=(1, 256))\n", - "\n", - "# must give a vector of image data for image parameter\n", - "p.image(image=[imblah], x=1, y=1, dw=256, dh=256, palette=\"Spectral11\")\n", - "\n", - "output_notebook()\n", - "\n", - "show(p) # open a browser" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "EW3WzcO68ZFD" - }, - "source": [ - "# Plot the Example Lensing Image Using Bokeh\n", - "Because I can..." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 647 - }, - "colab_type": "code", - "id": "6jTidlXM5vrV", - "outputId": "14ec7360-ad28-4b45-9343-cde0b7fd610c" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(\"ccaaa8f4-ee3c-4fdd-b605-7ee668f20294\");\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " }\n", - " finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };var element = document.getElementById(\"ccaaa8f4-ee3c-4fdd-b605-7ee668f20294\");\n", - " if (element == null) {\n", - " console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'ccaaa8f4-ee3c-4fdd-b605-7ee668f20294' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"ccaaa8f4-ee3c-4fdd-b605-7ee668f20294\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"ccaaa8f4-ee3c-4fdd-b605-7ee668f20294\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"ccaaa8f4-ee3c-4fdd-b605-7ee668f20294\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'ccaaa8f4-ee3c-4fdd-b605-7ee668f20294' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"ccaaa8f4-ee3c-4fdd-b605-7ee668f20294\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " \n", - " var docs_json = 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Application\",\"version\":\"0.13.0\"}};\n", - " var render_items = [{\"docid\":\"2c08b31e-bca7-4452-90e2-9e6060d79f38\",\"roots\":{\"7446eb27-83e8-4383-9ffa-33ff0429053b\":\"a6aa0532-1876-4797-a3e7-be1b50696b81\"}}];\n", - " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " } else {\n", - " var attempts = 0;\n", - " var timer = setInterval(function(root) {\n", - " if (root.Bokeh !== undefined) {\n", - " embed_document(root);\n", - " clearInterval(timer);\n", - " }\n", - " attempts++;\n", - " if (attempts > 100) {\n", - " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", - " clearInterval(timer);\n", - " }\n", - " }, 10, root)\n", - " }\n", - "})(window);" - ], - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "7446eb27-83e8-4383-9ffa-33ff0429053b" - }, - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "p = figure(x_range=(1, 256), y_range=(1, 256))\n", - "\n", - "# must give a vector of image data for image parameter\n", - "p.image(image=[imblah], x=1, y=1, dw=256, dh=256, palette=\"Spectral11\")\n", - "\n", - "output_notebook()\n", - "\n", - "show(p) # open a browser" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "9A1M4BO08l9l" - }, - "source": [ - "# Example of Shifted Lensing" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "_dh4BBCcWK4h" - }, - "source": [ - "## Create Positive Examples" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - }, - "colab_type": "code", - "id": "4SVxAoK47Q-o", - "outputId": "cc824db1-4750-486d-bccb-bc31afd7aa09" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100\n" - ] - } - ], - "source": [ - "# Positive examples\n", - "\n", - "nb_sample = 100\n", - "\n", - "\n", - "figures = []\n", - "lensing_images_pos = []\n", - "\n", - "\n", - "# for coord in zip(np.random.randint(32, 32, nb_sample), np.random.randint(130, 150, nb_sample)):\n", - "for ii in np.arange(nb_sample):\n", - "\n", - " imblah = np.zeros((64, 64, 3))\n", - "\n", - " # Example filled circle\n", - " circle = create_circle(imblah.shape[:2], center=(32, 32), radius=10, width=10)\n", - " imblah += np.dstack([circle.astype(np.uint8)]*3) * np.array([200, 34, 64], dtype=np.uint8)\n", - "\n", - " # Example arc\n", - " theta_mean = np.random.randint(0,360)\n", - " theta_width = np.random.randint(1,220)\n", - " theta1 = theta_mean - theta_width / 2.\n", - " theta2 = theta_mean + theta_width / 2.\n", - " arc = create_arc(imblah.shape[:2], center=(32,32), radius=np.random.randint(20, 30), width=6, theta1=theta1, theta2=theta2)\n", - " imblah += np.dstack([arc.astype(np.uint8)]*3) * np.array([34, 64, 200], dtype=np.uint8)\n", - "\n", - " lensing_images_pos.append(imblah)\n", - " \n", - " #p = figure(x_range=(1, 64), y_range=(1, 64), plot_width=128, plot_height=128)\n", - "\n", - " # must give a vector of image data for image parameter\n", - " #p.image(image=[imblah], x=1, y=1, dw=64, dh=64, palette=\"Spectral11\")\n", - "\n", - " #figures.append(p)\n", - " \n", - "#output_notebook()\n", - "\n", - "#show(gridplot([figures[:3], figures[3:6], figures[6:]]))\n", - "\n", - "lensing_labels_pos = np.ones(len(lensing_images_pos))\n", - "print(len(lensing_images_pos))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "kFi8u5LeWPpx" - }, - "source": [ - "## Create Negative Examples" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - }, - "colab_type": "code", - "id": "QZ9BzdRUVrzi", - "outputId": "e363e7b4-fbac-4ed9-91ee-ccd983de625c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100\n" - ] - } - ], - "source": [ - "# Negative examples\n", - "\n", - "figures = []\n", - "lensing_images_neg = []\n", - "\n", - "\n", - "# for coord in zip(np.random.randint(32, 32, nb_sample), np.random.randint(130, 150, nb_sample)):\n", - "for ii in np.arange(nb_sample):\n", - "\n", - " imblah = np.zeros((64, 64, 3))\n", - "\n", - " # Example filled circle\n", - " circle = create_circle(imblah.shape[:2], center=(32, 32), radius=10, width=10)\n", - " imblah += np.dstack([circle.astype(np.uint8)]*3) * np.array([200, 34, 64], dtype=np.uint8)\n", - "\n", - " # Example arc\n", - " theta_mean = np.random.randint(0,360)\n", - " theta_width = np.random.randint(1,220)\n", - " theta1 = theta_mean - theta_width / 2.\n", - " theta2 = theta_mean + theta_width / 2.\n", - " #arc = create_arc(imblah.shape[:2], center=(32,32), radius=np.random.randint(20, 30), width=6, theta1=theta1, theta2=theta2)\n", - " #imblah += np.dstack([arc.astype(np.uint8)]*3) * np.array([34, 64, 200], dtype=np.uint8)\n", - "\n", - " lensing_images_neg.append(imblah)\n", - " \n", - " # p = figure(x_range=(1, 64), y_range=(1, 64), plot_width=128, plot_height=128)\n", - "\n", - " # must give a vector of image data for image parameter\n", - " #p.image(image=[imblah], x=1, y=1, dw=64, dh=64, palette=\"Spectral11\")\n", - "\n", - " #figures.append(p)\n", - " \n", - "#output_notebook()\n", - "\n", - "#show(gridplot([figures[:3], figures[3:6], figures[6:]]))\n", - "\n", - "lensing_labels_neg = np.zeros(len(lensing_images_neg))\n", - "print(len(lensing_images_neg))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 33 - }, - "colab_type": "code", - "id": "kKcjdIxGV4pN", - "outputId": "f45b501e-3bf2-437c-d833-5e94c2659cce" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(200, 64, 64, 3) (200,)\n" - ] - } - ], - "source": [ - "#lensing_images = lensing_images_pos + lensing_images_neg \n", - "#lensing_labels = lensing_labels_pos + lensing_labels_neg\n", - "\n", - "lensing_images = np.stack(lensing_images_pos + lensing_images_neg, axis=-1)\n", - "lensing_labels = np.concatenate((lensing_labels_pos , lensing_labels_neg))\n", - "\n", - "\n", - "lensing_images = np.transpose(lensing_images, (3,0,1,2))\n", - "print(lensing_images.shape, lensing_labels.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "LtIiZoIWETEw" - }, - "source": [ - "# Keras Example: Lensing\n" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "colab_type": "code", - "id": "C3YTodDu9Np3", - "outputId": "23a9464c-ba9d-4a9a-a7f3-ab999a872ba4" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "from __future__ import print_function\n", - "import keras\n", - "from keras.datasets import mnist\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Flatten\n", - "from keras.layers import Conv2D, MaxPooling2D\n", - "from keras import backend as K\n", - "from sklearn.model_selection import train_test_split\n", - "#This example adapted from https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "9J_tuqhQFOZc" - }, - "outputs": [], - "source": [ - "# Setting some variables for the ML\n", - "batch_size = 128\n", - "num_classes = 2\n", - "epochs = 12\n", - "\n", - "X = lensing_images\n", - "y = lensing_labels\n", - "\n", - "# input image dimensions\n", - "img_rows, img_cols = imblah.shape[0], imblah.shape[1]\n", - "\n", - "# the data, split between train and test sets\n", - "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 85 - }, - "colab_type": "code", - "id": "y3CZAW1sFoTp", - "outputId": "68281cb0-90d5-4bf2-85bc-3b8669621c5e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "x_train shape: (100, 64, 64, 3)\n", - "the shape of the training data is: (num_examples, nside_x, nside_y, colour channels)\n", - "100 train samples\n", - "100 test samples\n" - ] - } - ], - "source": [ - "# Testing for formatted shape of the images and then reshaping them\n", - "\n", - "if K.image_data_format() == 'channels_first':\n", - " x_train = x_train.reshape(x_train.shape[0], 3, img_rows, img_cols)\n", - " x_test = x_test.reshape(x_test.shape[0], 3, img_rows, img_cols)\n", - " input_shape = (3, img_rows, img_cols)\n", - "else:\n", - " x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3)\n", - " x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3)\n", - " input_shape = (img_rows, img_cols, 3)\n", - "\n", - "\n", - "# Normalising the images to make them to make classification easier\n", - "x_train = x_train.astype('float32')\n", - "x_test = x_test.astype('float32')\n", - "x_train /= 255\n", - "x_test /= 255\n", - "\n", - "print('x_train shape:', x_train.shape)\n", - "print('the shape of the training data is: (num_examples, nside_x, nside_y, colour channels)')\n", - "print(x_train.shape[0], 'train samples')\n", - "print(x_test.shape[0], 'test samples')" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "M2LjwBQ8Pkiz" - }, - "outputs": [], - "source": [ - "# convert class vectors to binary class matrices\n", - "y_train = keras.utils.to_categorical(y_train, num_classes)\n", - "y_test = keras.utils.to_categorical(y_test, num_classes)\n", - "\n", - "# Pick the machine learning model you want to use to model the layers\n", - "# The Sequential model is a linear stack of layers.\n", - "model = Sequential()\n", - "\n", - "\n", - "model.add(Conv2D(32, kernel_size=(3, 3),\n", - " activation='relu',\n", - " input_shape=input_shape))\n", - "model.add(Conv2D(64, (3, 3), activation='relu'))\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25))\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(num_classes, activation='softmax'))\n", - "\n", - "model.compile(loss=keras.losses.categorical_crossentropy,\n", - " optimizer=keras.optimizers.Adadelta(),\n", - " metrics=['accuracy'])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 476 - }, - "colab_type": "code", - "id": "mAosJSX9ZkmZ", - "outputId": "772a334e-76e5-4e6b-8b68-5ddb24540517" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 100 samples, validate on 100 samples\n", - "Epoch 1/12\n", - "100/100 [==============================] - 3s 35ms/step - loss: 0.6951 - acc: 0.4500 - val_loss: 0.4403 - val_acc: 0.9900\n", - "Epoch 2/12\n", - "100/100 [==============================] - 3s 28ms/step - loss: 0.4790 - acc: 0.7900 - val_loss: 0.3826 - val_acc: 0.8200\n", - "Epoch 3/12\n", - "100/100 [==============================] - 3s 26ms/step - loss: 0.4745 - acc: 0.7900 - val_loss: 0.9370 - val_acc: 0.5100\n", - "Epoch 4/12\n", - "100/100 [==============================] - 3s 26ms/step - loss: 0.9947 - acc: 0.4900 - val_loss: 0.2932 - val_acc: 0.9100\n", - "Epoch 5/12\n", - "100/100 [==============================] - 3s 26ms/step - loss: 0.3371 - acc: 0.8800 - val_loss: 0.2312 - val_acc: 0.9500\n", - "Epoch 6/12\n", - "100/100 [==============================] - 3s 27ms/step - loss: 0.2930 - acc: 0.8800 - val_loss: 0.1858 - val_acc: 0.9500\n", - "Epoch 7/12\n", - "100/100 [==============================] - 3s 26ms/step - loss: 0.2773 - acc: 0.8900 - val_loss: 0.1660 - val_acc: 0.9700\n", - "Epoch 8/12\n", - "100/100 [==============================] - 3s 26ms/step - loss: 0.2269 - acc: 0.9200 - val_loss: 0.1442 - val_acc: 0.9700\n", - "Epoch 9/12\n", - "100/100 [==============================] - 3s 26ms/step - loss: 0.2075 - acc: 0.9300 - val_loss: 0.1223 - val_acc: 0.9700\n", - "Epoch 10/12\n", - "100/100 [==============================] - 3s 26ms/step - loss: 0.2050 - acc: 0.9500 - val_loss: 0.1096 - val_acc: 0.9800\n", - "Epoch 11/12\n", - "100/100 [==============================] - 3s 26ms/step - loss: 0.1764 - acc: 0.9400 - val_loss: 0.1031 - val_acc: 0.9900\n", - "Epoch 12/12\n", - "100/100 [==============================] - 3s 26ms/step - loss: 0.1450 - acc: 0.9700 - val_loss: 0.0843 - val_acc: 0.9800\n", - "Test loss: 0.08426426902413368\n", - "Test accuracy: 0.98\n" - ] - } - ], - "source": [ - "history = model.fit(x_train, y_train,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " verbose=1,\n", - " validation_data=(x_test, y_test))\n", - "score = model.evaluate(x_test, y_test, verbose=0)\n", - "print('Test loss:', score[0])\n", - "print('Test accuracy:', score[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 54 - }, - "colab_type": "code", - "id": "18ZWwkc3ax9u", - "outputId": "307633b7-69d1-45e0-c00b-ab1ac57098f4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'val_loss': [0.44025054574012756, 0.3825901448726654, 0.9369852542877197, 0.2932448089122772, 0.2311631590127945, 0.18580619990825653, 0.16595177352428436, 0.144161656498909, 0.12232699990272522, 0.10964975506067276, 0.10310837626457214, 0.08426427096128464], 'val_acc': [0.9900000095367432, 0.8199999928474426, 0.5099999904632568, 0.9100000262260437, 0.949999988079071, 0.949999988079071, 0.9700000286102295, 0.9700000286102295, 0.9700000286102295, 0.9800000190734863, 0.9900000095367432, 0.9800000190734863], 'loss': [0.6951392292976379, 0.4790484309196472, 0.4745144248008728, 0.9946888089179993, 0.3370739221572876, 0.29298895597457886, 0.2772583067417145, 0.22687682509422302, 0.20749539136886597, 0.20495788753032684, 0.17640440165996552, 0.14499904215335846], 'acc': [0.44999998807907104, 0.7900000214576721, 0.7900000214576721, 0.49000000953674316, 0.8799999952316284, 0.8799999952316284, 0.8899999856948853, 0.9200000166893005, 0.9300000071525574, 0.949999988079071, 0.9399999976158142, 0.9700000286102295]} hey folks\n" - ] - } - ], - "source": [ - "print(history.history, 'hey folks')" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 378 - }, - "colab_type": "code", - "id": "3MM_JkoWa6Lp", - "outputId": "1c71006d-6ddc-4335-aa91-d160da025cce" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5,0,'Time taken to do the training task (get to work)')" - ] - }, - "execution_count": 16, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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iwOUxvGfNmoWZM2e2O3bw4EGMGjUKb7/9ttcKCwSuENeX1+Hz7/TIL6zGyk8P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\n6VIcPny4y68bjUY8+uijF1TPuf75z39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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(history.history['val_loss'])\n", - "plt.ylabel('Suckyness at training')\n", - "plt.xlabel('Time taken to do the training task (get to work)')" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "s781ZJH5ntGJ" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "include_colab_link": true, - "name": "Lensing Simulations", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/archive/repository_support/images/example_simplephysicalimage.png b/archive/repository_support/images/example_simplephysicalimage.png deleted file mode 100644 index c93cb53..0000000 Binary files a/archive/repository_support/images/example_simplephysicalimage.png and /dev/null differ diff --git a/archive/repository_support/labels.json b/archive/repository_support/labels.json deleted file mode 100644 index b0b5dba..0000000 --- a/archive/repository_support/labels.json +++ /dev/null @@ -1,133 +0,0 @@ -[ - { - "name": "duplicate", - "color": "ededed" - }, - { - "name": "help wanted", - "color": "e99695" - }, - { - "name": "good first issue", - "color": "7057ff", - "aliases": [ - "beginner-friendly", - "beginner", - "good-starter-issue", - "good for beginner", - "Good for beginners", - "starter-issue", - "starter" - ] - }, - { - "name": "Priority: Critical", - "color": "ee0701" - }, - { - "name": "Priority: High", - "color": "d93f0b" - }, - { - "name": "Priority: Low", - "color": "0e8a16" - }, - { - "name": "Priority: Medium", - "color": "fbca04" - }, - { - "name": "Status: Abandoned", - "color": "000000", - "aliases": [ - "wontfix", - "invalid" - ] - }, - { - "name": "Status: Blocked", - "color": "ee0701", - "aliases": [ - "blocked" - ] - }, - { - "name": "Status: In Progress", - "color": "cccccc" - }, - { - "name": "Status: Proposal", - "color": "d4c5f9", - "aliases": [ - "idea", - "Idea", - "proposal", - "Proposal", - "discussion" - ] - }, - { - "name": "Status: PR Welcome", - "color": "c2e0c6", - "aliases": [ - "Patch Welcome", - "Status: Ready for PR" - ] - }, - { - "name": "Status: Review Needed", - "color": "fbca04" - }, - { - "name": "Type: Breaking Change", - "color": "b60205", - "aliases": [ - "breaking", - "breaking-change" - ] - }, - { - "name": "Type: Bug", - "color": "ee0701", - "aliases": [ - "bug" - ] - }, - { - "name": "Type: Documentation", - "color": "5319e7", - "aliases": [ - "documents", - "document" - ] - }, - { - "name": "Type: Feature", - "color": "1d76db", - "aliases": [ - "enhancement" - ] - }, - { - "name": "Type: Refactoring", - "color": "fbca04" - }, - { - "name": "Type: Maintenance", - "color": "0e8a16", - "aliases": [ - "greenkeeper", - "maintenance", - "refactor", - "testing", - "test" - ] - }, - { - "name": "Type: Question", - "color": "cc317c", - "aliases": [ - "question" - ] - } -] diff --git a/deepbench/__init__.py b/deepbench/__init__.py new file mode 100644 index 0000000..21a3fc2 --- /dev/null +++ b/deepbench/__init__.py @@ -0,0 +1 @@ +__version__ = "0.2.01" diff --git a/deepbench/astro_object/__init__.py b/deepbench/astro_object/__init__.py new file mode 100644 index 0000000..5033985 --- /dev/null +++ b/deepbench/astro_object/__init__.py @@ -0,0 +1,4 @@ +from deepbench.astro_object.galaxy_object import GalaxyObject +from deepbench.astro_object.n_body_object import NBodyObject +from deepbench.astro_object.spiral_galaxy import SpiralGalaxyObject +from deepbench.astro_object.star_object import StarObject diff --git a/src/deepbench/astro_object/astro_object.py b/deepbench/astro_object/astro_object.py similarity index 54% rename from src/deepbench/astro_object/astro_object.py rename to deepbench/astro_object/astro_object.py index 817647b..4bb4505 100644 --- a/src/deepbench/astro_object/astro_object.py +++ b/deepbench/astro_object/astro_object.py @@ -1,43 +1,39 @@ from abc import ABC, abstractclassmethod, abstractmethod -from typing import Union, List +from typing import Union, List, Tuple from scipy import ndimage -import numpy.random as rand import numpy as np class AstroObject(ABC): """ - Description container. + Args: + image_dimensions (Union[tuple(int,int), tuple(float,float)]): The dimension(s) of the object to be produced. + radius (Union[int, float]): The radius of the object to be produced. + amplitude (Union[int, float]): The amplitude (brightness) of the object to be produced. + noise_level (Union[float, list[float]]): The Poisson noise level (lambda, the expected seperation) to be applied to the object. + seed (Union[float, list[float]], optional): Seed to set the random state for noise in the object. Initialized at the init of the class. Default None. + + Examples: + + >>> example_obj = AstroObject(image_dimensions=28, radius=5, amplitude=3, noise_level=0.7) + """ def __init__( self, - image_dimensions: Union[int, float, List[int], List[float]], + image_dimensions: Union[Tuple[int, int], Tuple[float, float]], radius: Union[int, float], amplitude: Union[int, float], - noise: Union[float, List[float]], + noise_level: Union[float, List[float]], + seed: Union[int, None] = None, ) -> None: - """ - The initialization function for the Abstract AstroObject class. - Args: - image_dimensions (Union[int, float, List[int], List[float]]): The dimension(s) of the object to be produced. - radius (Union[int, float]): The radius of the object to be produced. - amplitude (Union[int, float]): The amplitude of the object to be produced. - noise_level (Union[float, list[float]]): The Poisson noise level to be applied to the object. - - Examples: - - >>> example_obj = AstroObject(image_dimensions=28, radius=5, amplitude=3, noise_level=0.7) - """ - - # TODO: - # - May need a position param: position=(0,0) - - self._image = np.zeros((image_dimensions, image_dimensions)) + self._image = np.zeros(image_dimensions) self._radius = radius self._amplitude = amplitude - self._noise_level = noise + self._noise_level = noise_level + + self.random_state = np.random.default_rng(seed=seed) @abstractmethod def create_object(self): @@ -49,7 +45,6 @@ def create_object(self): """ raise NotImplementedError() - @staticmethod def create_psf(self, image_shape, gaussian_blur=0.7) -> np.ndarray: """ Creates the Point Spread Function to append to the object. @@ -67,13 +62,14 @@ def create_psf(self, image_shape, gaussian_blur=0.7) -> np.ndarray: return ndimage.gaussian_filter(image_shape, sigma=gaussian_blur) # UPDATE THIS METHODS DOCSTRINGS. - @staticmethod - def create_noise(self, seed=42, galaxy=False) -> np.ndarray: + + def create_noise(self, galaxy=False) -> np.ndarray: """ Creates the Poisson noise added to the object. Args: seed (int): The random initialization seed used for reproducibility. + galaxy (bool): Scale the weight to keep with the intensity scale of a galaxy Returns: ndarray: A random sample drawn from a Poisson distribution. @@ -83,13 +79,12 @@ def create_noise(self, seed=42, galaxy=False) -> np.ndarray: >>> example_obj.create_noise(seed=5) """ if galaxy: - rs = rand.RandomState(seed) - return rs.poisson(self._noise_level * 10.0, size=self._image.shape) + return self.random_state.poisson( + self._noise_level * 10.0, size=self._image.shape + ) else: - rs = rand.RandomState(seed) - return rs.poisson(self._noise_level, size=self._image.shape) + return self.random_state.poisson(self._noise_level, size=self._image.shape) - @staticmethod def create_meshgrid(self) -> np.ndarray: """ Creates a meshgrid for the object. @@ -101,16 +96,18 @@ def create_meshgrid(self) -> np.ndarray: >>> example_obj.create_meshgrid() """ meshgrid = np.meshgrid( - np.arange(np.shape(self._image.shape)[0]), - np.arange(np.shape(self._image.shape)[1]), + np.arange(self._image.shape[0]), + np.arange(self._image.shape[1]), ) return meshgrid @abstractmethod def displayObject(self): + """ + Display the object created in a 2d plot - # Yet to implement. Will essentially just display a matplotlib image of the Object. - - print("Code Container.") + Raises: + NotImplementedError: Raised if not implimented in the child class + """ raise NotImplementedError() diff --git a/deepbench/astro_object/galaxy_object.py b/deepbench/astro_object/galaxy_object.py new file mode 100644 index 0000000..0e9fb9d --- /dev/null +++ b/deepbench/astro_object/galaxy_object.py @@ -0,0 +1,118 @@ +from typing import Union, Tuple +from deepbench.astro_object.astro_object import AstroObject +from astropy.modeling.models import Sersic2D + +import numpy as np +from numpy import random + + +class GalaxyObject(AstroObject): + + """ + + Produce a 2D galaxy using a Sersic2D distribution. + View https://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Sersic2D.html + for implimentation details. + + Args: + image_dimensions (Union[tuple(int,int), tuple(float,float)]): The dimension(s) of the object to be produced. + amplitude (Union[int, float]): The amplitude of the object to be produced, surface brightness at radius. + noise_level (Union[float, list[float]]): The Poisson noise level (lambda, the expected seperation) to be applied to the object. + radius (int, optional): Effective half-light radius of the galaxy. Defaults to 25. + n (float, optional): Sersic Index. Defaults to 1.0. + ellipse (float, optional): Galaxy Ellipticity. Defaults to random.uniform(0.1, 0.9). + theta (float, optional): The rotation of the galaxy in radians. Defaults to random.uniform(-1.5, 1.5). + seed (Union[float, list[float]], optional): Seed to set the random state for noise in the object. Initialized at the init of the class. Default None. + + Examples: + + >>> example_galaxy = GalaxyObject(image_dimensions=28) + + """ + + def __init__( + self, + image_dimensions: Union[Tuple[int, int], Tuple[float, float]], + amplitude=1, + radius=25, + n=1.0, + noise_level=0.2, + ellipse=random.uniform(0.1, 0.9), + theta=random.uniform(-1.5, 1.5), + seed: Union[int, None] = None, + ): + super().__init__( + image_dimensions=image_dimensions, + radius=radius, + amplitude=amplitude, + noise_level=noise_level, + seed=seed, + ) + + self._n = n + self._ellipse = ellipse + self._theta = theta + + def create_Sersic_profile(self, center_x, center_y): + """ + Wrapper for https://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Sersic2D.html + + Args: + center_x (float): x position of the center of the galaxy + center_y (float): y position of the center of the galaxy + + Returns: + galaxy (numpy.array): created galaxy profile + """ + + x, y = self.create_meshgrid() + profile = Sersic2D( + amplitude=self._amplitude, + x_0=center_x, + y_0=center_y, + r_eff=self._radius, + n=self._n, + ellip=self._ellipse, + theta=self._theta, + ) + + return profile(x, y) + + def create_object(self, center_x=5.0, center_y=5.0) -> np.ndarray: + """ + Create the galaxy object from a Sersic distribution and Poisson or PSF noise. + + Args: + center_x (float): The x-axis placement of the galaxy object. + center_y (float): The y-axis placement of the galaxy object. + + Returns: + ndarray: Two dimensional Galaxy object, composed of Sersic Distribution and noise appendings. + + Examples: + + >>> example_prof = example_star.create_object(center_x = 1.0, center_y = 0.0) + + """ + + # Create the empty image shape. + # Create the Poisson noise profile specific to Galaxy objects. + noise_profile = self.create_noise(galaxy=True) + + image_shape = self.create_Sersic_profile(center_x=center_x, center_y=center_y) + + # Append the noise profiles to the object. + image_shape += noise_profile + image_shape = self.create_psf(image_shape) + + return image_shape + + def displayObject(self): + """ + Display the object created in a 2d plot + + Raises: + NotImplementedError: Raised if not implimented in the child class + """ + + raise NotImplementedError() diff --git a/deepbench/astro_object/n_body_object.py b/deepbench/astro_object/n_body_object.py new file mode 100644 index 0000000..aa6c5ae --- /dev/null +++ b/deepbench/astro_object/n_body_object.py @@ -0,0 +1,42 @@ +from typing import Union, Tuple +from deepbench.astro_object.astro_object import AstroObject + + +class NBodyObject(AstroObject): + def __init__( + self, + image_dimensions: Union[Tuple[int, int], Tuple[float, float]], + t_duration: float = 2.0, + dt: float = 0.2, + dampening: float = 0, + noise_level: float = 0.02, + G: float = 9.8, + plot_real_time: bool = False, + ): + + super().__init__( + image_dimensions=image_dimensions, + radius=None, + amplitude=None, + noise_level=noise_level, + ) + self.t_duration = t_duration + self.dt = dt + self.dampening = dampening + self.G = G + self.plot_real_time = plot_real_time + + def create_object(self): + + print("Code Container.") + + def get_acceleration(self): + + print("Code Container.") + + def get_energy(self): + + print("Code Container.") + + def displayObject(self): + print("Code Container") diff --git a/deepbench/astro_object/spiral_galaxy.py b/deepbench/astro_object/spiral_galaxy.py new file mode 100644 index 0000000..be8dcad --- /dev/null +++ b/deepbench/astro_object/spiral_galaxy.py @@ -0,0 +1,131 @@ +from typing import Tuple, Union +from deepbench.astro_object.galaxy_object import GalaxyObject +import numpy as np + + +class SpiralGalaxyObject(GalaxyObject): + """ + Create a spiral galaxy object + + Args: + image_dimensions (Union[Tuple(int,int), tuple(float,float)]): The dimension(s) of the object to be produced. + amplitude (Union[int, float]): The amplitude of the object to be produced, surface brightness at the sersic radius. + noise_level (Union[float, list[float]]): The Poisson noise level to be applied to the object. + radius (int, optional): Effective half-light radius of the galaxy. Defaults to 25. + arm_thickness (float, optional): Width of each arm of the spiral. Defaults to 1.0. + winding_number (int, optional): number of arms. Defaults to 2. + spiral_pitch (float, optional): Severity of the spiral, the pitch angle. Defaults to 0.2. + seed (Union[float, list[float]], optional): Seed to set the random state for noise in the object. Initialized at the init of the class. Default None. + + Examples: + + >>> example_galaxy = SpiralGalaxyObject(image_dimensions=(28,28), winding_number=4) + + """ + + def __init__( + self, + image_dimensions: Union[Tuple[int, int], Tuple[float, float]], + amplitude=1, + radius=25, + arm_thickness=1.0, + noise_level=0.2, + winding_number: int = 2, + spiral_pitch: float = 0.2, + seed: Union[int, None] = None, + **kwargs + ): + self.pitch_angle = spiral_pitch + self.winding_number = winding_number + + super().__init__( + image_dimensions=image_dimensions, + amplitude=amplitude, + radius=radius, + n=arm_thickness, + ellipse=0.1, + theta=0.1, + noise_level=noise_level, + seed=seed, + ) + + def create_spiral_profile(self, center_x, center_y): + """ + + ref paper: https://doi.org/10.1111/j.1365-2966.2009.14950.x + Impliment a spiral galaxy profile + + Args: + center_x (float): x position of the center of the galaxy + center_y (float): y position of the center of the galaxy + + + Returns: + spiral profile (numpy array): Profile representing the spiral galaxy + """ + + # Define the grid + side_length = self._image.shape + x = np.linspace(-side_length[0] / 2, side_length[0] / 2, side_length[0]) + y = np.linspace(-side_length[1] / 2, side_length[1] / 2, side_length[1]) + X, Y = np.meshgrid(x, y) + + # Calculate the distance from the center + R = np.sqrt(X**2 + Y**2) # + np.sqrt(center_x**2 + center_y**2) + + # Calculate the angle from the x-axis + theta = np.arctan2(Y, X) + np.pi + + # Create the spiral pattern + spiral = np.zeros_like(R) + for arm in range(self.winding_number): + arm_angle = 2 * np.pi * arm / self.winding_number + # Calculate logarithmic spiral + r_spiral = ((self._radius * 2) / (2 * np.pi)) * np.exp( + (theta - arm_angle) / np.tan(self.pitch_angle) + ) + + # Calculate distance from each point to the spiral arm + distance = np.abs(R - r_spiral) + + # Add arm intensity to the spiral pattern + spiral += self._amplitude * np.exp(-(distance**2) / (2 * self._n**2)) + + return spiral + + def create_object(self, center_x, center_y) -> np.ndarray: + """ + + ref paper: https://doi.org/10.1111/j.1365-2966.2009.14950.x + Create a spiral galaxy image + + Args: + center_x (float): x position of the center of the galaxy + center_y (float): y position of the center of the galaxy + + + Returns: + spiral profile (numpy array): Profile representing the spiral galaxy + """ + + # Create the Poisson noise profile specific to Galaxy objects. + noise_profile = self.create_noise(galaxy=True) + + # Create the spiral galaxy profile + image_shape = self.create_spiral_profile(center_x, center_y) + + # Append the noise profiles to the object. + image_shape += noise_profile + image_shape = self.create_psf(image_shape) + + return image_shape + + def displayObject(self): + """ + Display the object created in a 2d plot + + Raises: + NotImplementedError: Raised if not implimented in the child class + """ + + raise NotImplementedError() diff --git a/src/deepbench/astro_object/star_object.py b/deepbench/astro_object/star_object.py similarity index 63% rename from src/deepbench/astro_object/star_object.py rename to deepbench/astro_object/star_object.py index cfe10c1..79bcb27 100644 --- a/src/deepbench/astro_object/star_object.py +++ b/deepbench/astro_object/star_object.py @@ -1,6 +1,5 @@ -from astro_object import AstroObject +from deepbench.astro_object.astro_object import AstroObject from astropy.modeling.models import Moffat2D -from shape_generator.shape_generator import create_empty_shape from typing import Union, List, Tuple @@ -9,39 +8,37 @@ class StarObject(AstroObject): """ - Description Container. + Create Stars + + Args: + image_dimensions (Union[int, float, List[int], List[float]]): The dimension(s) of the object to be produced. + radius (Union[int, float]): The radius of the object to be produced. + amplitude (Union[int, float]): The amplitude (brightness) of the object to be produced. + noise_level (Union[float, list[float]]): The Poisson noise level (lambda, the expected seperation) to be applied to the object. + seed (Union[float, list[float]], optional): Seed to set the random state for noise in the object. Initialized at the init of the class. Default None. + + Examples: + + >>> example_star = StarObject(image_dimensions=28, noise=5.0, center=3, radius=0.7, amplitude=1.2) + >>> example_star = StarObject(image_dimensions=(28,28), noise=5.0) """ def __init__( self, - img_dim: Union[int, float, List[int], List[float]], - noise: Union[float, List[float]], - center: Tuple[float, float] = (0.0, 0.0), + image_dimensions: Union[int, float, List[int], List[float]], + noise_level: Union[float, List[float]] = 0.0, radius: Union[int, float] = 1.0, amplitude: Union[int, float] = 1.0, + seed: Union[int, None] = None, ) -> None: - """ - The initialization function for the StarObject. - - Args: - img_dim (Union[int, float, List[int], List[float]]): The dimension(s) of the Star to be produced. - noise_level (Union[float, list[float]]): The Poisson noise level to be applied to the object. - center (Tuple[float], Tuple[int]): The center of the Star object to be produced. - radius (Union[int, float]): The radius of the object to be produced. - amplitude (Union[int, float]): The amplitude of the object to be produced. - - Examples: - >>> example_star = StarObject(img_dim=28, noise=5.0, center=3, radius=0.7, amplitude=1.2) - >>> example_star = StarObject(img_dim=(28,28), noise=5.0) - """ super().__init__( - image_dimensions=img_dim, + image_dimensions=image_dimensions, radius=radius, amplitude=amplitude, - noise_level=noise, + noise_level=noise_level, + seed=seed, ) - self._center = center def create_Moffat_profile( self, center_x: float, center_y: float, alpha=1.0 @@ -66,8 +63,8 @@ def create_Moffat_profile( x, y = self.create_meshgrid() profile = Moffat2D( amplitude=self._amplitude, - x_center=self._center[0], - y_center=self._center[1], + x_0=center_x, + y_0=center_y, gamma=self._radius, alpha=alpha, ) @@ -94,12 +91,14 @@ def create_object(self, center_x: float, center_y: float, alpha=1.0) -> np.ndarr """ # Create the empty image shape. - image_shape = create_empty_shape(self._image) + image_shape = self._image.copy() # Create the Poisson noise profile. noise_profile = self.create_noise() - image_shape = self.create_Moffat_profile(center_x=center_x, center_y=center_y) + image_shape = self.create_Moffat_profile( + center_x=center_x, center_y=center_y, alpha=alpha + ) # Append the noise profiles to the object. image_shape += noise_profile @@ -107,7 +106,7 @@ def create_object(self, center_x: float, center_y: float, alpha=1.0) -> np.ndarr return image_shape - def displayImage(self): + def displayObject(self): # To be implemented. Check parent for details. diff --git a/deepbench/collection/__init__.py b/deepbench/collection/__init__.py new file mode 100644 index 0000000..f349eed --- /dev/null +++ b/deepbench/collection/__init__.py @@ -0,0 +1,2 @@ +from deepbench.collection.save import Save +from deepbench.collection.collection import Collection diff --git a/deepbench/collection/collection.py b/deepbench/collection/collection.py new file mode 100644 index 0000000..2c3c7f5 --- /dev/null +++ b/deepbench/collection/collection.py @@ -0,0 +1,252 @@ +import inspect +import deepbench.image as image +import deepbench.astro_object as astro +import deepbench.physics_object as physics +from deepbench.collection import Save + +import numpy as np +import yaml +import os + +class Collection: + """ + + Take a configuration file (dictionary) and produce the simulation output, + automatically passing arguments where they need to be. + + Handles both compositional images (ones with multiple objects) + and single object images + + Holds onto all the parameters used to make these files, including the default parameters, for replication. + + Args: + object_config (dict, optional): dictionary containing the parameters for the simulation output. Required fields: + * object_type: [sky, shape, physics, astro] : overall type of image + * object_name: Name of the class used in the image generation (e.g. - Pendulum, Star) + * total_runs: Number of times the simulation will be executed + * image_parameters: parameters for the image itself. In single object images, this is the parameters for the parent class. + * object parameters: list of objects that will be included in each image and their parameters + Defaults to None. + + """ + + def __init__(self, object_config: dict=None): + + self.object_type = None + self.object_name = None + + self.total_objects = None + self.included_params = None + self.object_rules = None + + + self.object_engine_classes = None + self.object_engine = None + + self.n_objects = 0 + self.objects = {} + self.object_params = {} + + if object_config is not None: + self._set_parameters(object_config) + + + def from_config(self, config_path:str): + """ + Read an external configuration file and initalize the dataset. Must be run if a configuration is not supplied at initalization. + + Args: + config_path (str): Path to yaml file containing an object dictionary. Object dictionary must have the parameters: "object_type","object_name",total_runs",image_parameters","object_parameters" + """ + config = yaml.safe_load(open(config_path)) + + self._set_parameters(config) + + + def _set_parameters(self, object_config): + + self.object_type = object_config["object_type"] + self.object_name = object_config["object_name"] + + self.total_objects = object_config["total_runs"] + self.included_params = object_config["image_parameters"] + self.object_rules = object_config["object_parameters"] + + object_parent_class = { + "sky": image.sky_image.SkyImage, + "shape": image.shape_image.ShapeImage, + "physics": physics.physics_object.PhysicsObject, + "astro": astro.astro_object.AstroObject, + }[self.object_type] + + self.object_engine_classes = { + cls.__name__: cls for cls in object_parent_class.__subclasses__() + } + self.object_engine_classes[object_parent_class.__name__] = object_parent_class + + self.object_engine = self.object_engine_classes[self.object_name]( + **self.included_params + ) + + self.n_objects = 0 + self.objects = {} + self.object_params = {} + + if "seed" in object_config: + self.seed = object_config["seed"] + + if "parameter_noise" in object_config: + self.parameter_noise = object_config["parameter_noise"] + + if "name" in object_config: + self.save_path = object_config["name"] + if not os.path.exists: + os.makedirs(self.save_path) + + + def add_parameter_noise(self, seed, params): + """ + Add noise to the image wide parameters + + Args: + seed (int): integer stored by the program to denote the noise seed added to the object + params (dict): parameters that have added noise. + + Returns: + dict: parameters with added uniform noise + """ + if hasattr(self, "parameter_noise"): + noisy_object_parameters = { + key: params[key] + + np.random.default_rng(seed=seed).uniform( + high=self.parameter_noise, size=len(params[key]) + ) + for key in params.keys() + } + return noisy_object_parameters + + else: + return params + + def engine_defaults(self): + """ + Locate the default parameters for any simulation being called, via the `inspect.signature` method + + Returns: + dict: all the parameters either default to the called object or modified by the program + """ + init_signature = inspect.signature( + self.object_engine_classes[self.object_name].__init__ + ) + init_signature_defaults = { + k: v.default + for k, v in init_signature.parameters.items() + if v.default is not inspect.Parameter.empty + } + if self.object_type in ["sky", "shape"]: + create = self.object_engine.combine_objects + + elif self.object_type in ["physics", "astro"]: + create = self.object_engine.create_object + + create_signature = inspect.signature(create) + create_signature_defaults = { + k: v.default + for k, v in create_signature.parameters.items() + if v.default is not inspect.Parameter.empty + } + return {**init_signature_defaults, **create_signature_defaults} + + def add_object(self): + """ + Use the parameters set by the configuration file to create an object and store that and its associated parameters + Adds noise to parameters if set by the program perviously + + If the specified object is a composite image, it will only find the default values for the compositor method, not the indivual simulations + + """ + assert self.object_type is not None, "Collection parameters not initialized, please run collection.from_config(your_configuration_path)" + + random_seed = ( + np.random.default_rng().integers(1, 10**6, size=1)[0] + if not hasattr(self, "seed") + else self.seed + ) + + if self.object_type in ["sky", "shape"]: + instance_parameters = [ + self.add_parameter_noise( + random_seed, self.object_rules[key]["instance"] + ) + for key in self.object_rules.keys() + ] + + object_parameters = [ + self.add_parameter_noise(random_seed, self.object_rules[key]["object"]) + for key in self.object_rules.keys() + ] + + object = self.object_engine.combine_objects( + self.object_rules.keys(), instance_parameters, object_parameters + ) + + object_parameters = { + list(self.object_rules.keys())[key_index]: { + "object": object_parameters[key_index], + "instance": instance_parameters[key_index], + } + for key_index in range(len(self.object_rules.keys())) + } + + elif self.object_type in ["physics", "astro"]: + object_parameters = self.add_parameter_noise(random_seed, self.object_rules) + object_parameters["seed"] = random_seed + + object = self.object_engine.create_object(**object_parameters) + + self.objects[self.n_objects] = object + + self.object_params[self.n_objects] = { + **self.engine_defaults(), + **object_parameters, + } + self.object_params[self.n_objects] = { + **self.object_params[self.n_objects], + **self.included_params, + } + self.object_params[self.n_objects]["seed"] = random_seed + + self.n_objects += 1 + + def __call__(self): + """ + Create N objects and add them to the `objects` variable. + """ + for _ in range(self.total_objects): + self.add_object() + + if hasattr(self, "save_path"): + self.save() + + + def save(self, save_path:str=None, format:str='h5'): + """ + Save generated dataset to path of your choosing. + If the path is not specified, the program will look for a save path to be specified by the configation_file + + Args: + save_path (str, optional): directory, location to save a file. Will be created if does not already exist. Defaults to None. + format (str, optional): Format to save the file in. Defaults to h5. + """ + + if save_path is None: + assert hasattr(self, "save_path"), "Could not parse save path from config, please supply it manually" + save_path = self.save_path + + if not os.path.exists(save_path): + os.makedirs(save_path) + + Save(self, save_path)(format=format) + + + \ No newline at end of file diff --git a/src/deepbench/astro_object/__init__.py b/deepbench/collection/read_config.py similarity index 100% rename from src/deepbench/astro_object/__init__.py rename to deepbench/collection/read_config.py diff --git a/deepbench/collection/save.py b/deepbench/collection/save.py new file mode 100644 index 0000000..4a05bbe --- /dev/null +++ b/deepbench/collection/save.py @@ -0,0 +1,45 @@ +import yaml +import h5py +import numpy as np + +class Save: + """ + _summary_ + + Args: + collection_instance (deepbench.collection.Collection): Instance of a collection to save + save_path (str): Directory to save to + """ + def __init__(self, collection_instance, save_path) -> None: + self.objects = collection_instance.objects + self.params = collection_instance.object_params + self._clean_params() + self.save_path = save_path + + + def _save_parameters(self): + with open(f"{self.save_path.rstrip('/')}/dataset_parameters.yaml", 'w') as f: + yaml.safe_dump(self.params, f) + + def _clean_params(self): + for key in self.params: + for subkey in self.params[key]: + if "tolist" in dir(self.params[key][subkey]): + self.params[key][subkey] = self.params[key][subkey].tolist() + + def _save_h5(self): + object_array = np.array(list(self.objects.values()), dtype=np.float32) + f = h5py.File(f"{self.save_path.rstrip('/')}/dataset.h5",'w') + + f.create_dataset('data',data=object_array,dtype=np.float32) + f.close() + + def __call__(self, format): + options={ + "h5":self._save_h5 + } + if format not in options.keys(): + raise NotImplementedError + + options[format]() + self._save_parameters() \ No newline at end of file diff --git a/deepbench/image/__init__.py b/deepbench/image/__init__.py new file mode 100644 index 0000000..a0016fe --- /dev/null +++ b/deepbench/image/__init__.py @@ -0,0 +1,2 @@ +from deepbench.image.shape_image import ShapeImage +from deepbench.image.sky_image import SkyImage diff --git a/deepbench/image/image.py b/deepbench/image/image.py new file mode 100644 index 0000000..d7f4884 --- /dev/null +++ b/deepbench/image/image.py @@ -0,0 +1,85 @@ +from abc import abstractmethod, ABC +from typing import Tuple +from PIL import Image as PILImage +import os +import numpy as np + + +class Image(ABC): + @abstractmethod + def __init__( + self, + image_shape: Tuple[int, int], + object_noise_type: str = "gaussian", + object_noise_level: float = 0.0, + ): + assert len(image_shape) >= 2 + "All images must be in at least 2d." + + self.image_shape = image_shape + self.image = np.zeros(self.image_shape) + + self.object_noise_type = object_noise_type + self.object_noise_level = object_noise_level + + def create_empty_shape(self): + return np.zeros(self.image_shape) + + def __len__(self): + return len(self.objects) + + def _image_parameters(self): + return self.image_shape, self.objects + + def combine_objects(self, objects, object_params, seed=42): + raise NotImplementedError + + def generate_noise(self, seed=42): + """ + Add noise to an image + + """ + noise_map = { + "gaussian": self._generate_gaussian_noise, + "poisson": self._generate_poisson_noise, + } + + if self.object_noise_type not in noise_map.keys(): + raise NotImplementedError(f"{self.object_noise_type} noise not available") + + noise = noise_map[self.object_noise_type](seed) + return noise + + def save_image(self, save_dir="results", image_name="image_1", image_format="jpg"): + """ + Save the generated image into the specified directory. + Will create directory if it does not already exist. + + Args: + save_dir (str): Directory to save + param image_name (str): base name of the saved image + param image_format (str): file format. Recommended jpg + """ + + assert self.image is not None, "Image not instantiated" + + save_dir = save_dir.rstrip("/") + + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + # TODO Check with multiple image shapes + image = PILImage.fromarray(self.image, "RGB") + save_path = f"{save_dir}/{image_name}.{image_format}" + + image.save(save_path) + + def _generate_gaussian_noise(self, seed=42): + return np.random.default_rng(seed=seed).normal( + scale=self.object_noise_level, size=self.image_shape + ) + + def _generate_poisson_noise(self, seed=42): + return np.random.default_rng(seed=seed).poisson( + lam=self.object_noise_level, size=self.image.shape + ) diff --git a/deepbench/image/shape_image.py b/deepbench/image/shape_image.py new file mode 100644 index 0000000..9d15b09 --- /dev/null +++ b/deepbench/image/shape_image.py @@ -0,0 +1,86 @@ +import inspect +from typing import Tuple +from deepbench.shapes import Shapes as ShapeGenerator +from deepbench.image.image import Image + + +class ShapeImage(Image): + """ + Create an image that is a composition of multiple shapes + + Args: + image_shape (Tuple[int, int]): Dimensions of the shape image. + object_noise_type (str, optional): Noise distribution applied to image. Defaults to "gaussian". + object_noise_level (float, optional): Relative noise level (scale 0 to 1). Defaults to 0.0. + + """ + + def __init__( + self, + image_shape: Tuple[int, int], + object_noise_type: str = "gaussian", + object_noise_level: float = 0.0, + ): + + self.shapes = ShapeGenerator(image_shape=image_shape) + self.method_map = self._get_methods() + super().__init__( + image_shape=image_shape, + object_noise_level=object_noise_level, + object_noise_type=object_noise_type, + ) + + def _get_methods(self): + + methods = [ + method + for method in inspect.getmembers( + ShapeGenerator, predicate=inspect.isfunction + ) + if "create_" in method[0] + ] + + return {method[0].split("_")[-1]: method[1] for method in methods} + + def _create_object(self, shape, shape_params): + + if shape not in self.method_map.keys(): + raise NotImplementedError() + return self.method_map[shape](self.shapes, **shape_params) + + def combine_objects(self, objects, object_params, instance_params=None, seed=42): + """ + Utilize Image._generate_astro_objects to overlay all selected astro objects into one image + If object parameters are not included in object list, defaults are used. + Updates SkyImage.image. + + Current input parameter assumptions (totally up to change): + For a single image: + [{ + "object_type":"", + "object_parameters":{} + }] + + Args: + objects (list): str discriptors of the included object + object_params (list): Parameters of each object (ie, position in frame) + seed (int, optional): random seed for noise. Defaults to 42. + + Returns: + ndarray : image with objects and noise + + """ + image = self.shapes.create_empty_shape() + + if type(objects) == str: + objects = [objects] + + if type(object_params) == dict: + object_params = [object_params] + + for shape, params in zip(objects, object_params): + image += self._create_object(shape, params) + + noise = self.generate_noise(seed) + image += noise + return image diff --git a/deepbench/image/sky_image.py b/deepbench/image/sky_image.py new file mode 100644 index 0000000..8b26452 --- /dev/null +++ b/deepbench/image/sky_image.py @@ -0,0 +1,109 @@ +from typing import Union +from deepbench.image.image import Image +from deepbench import astro_object +import numpy as np + + +class SkyImage(Image): + def __init__( + self, + image_shape, + object_noise_level=0, + object_noise_type="gaussian", + scale=True, + ): + """ + Create an image that is a composition of multiple astronomy objects + + Args: + image_shape (tuple(int, int)): Shape of the output image + object_noise_level (int, optional): Level of noise added to the full image. Defaults to 0. + object_noise_type (str, optional): Type of noise added. Defaults to "gaussian". + scale (bool, optional): Scale objects between 0 and 1 before adding to the composition + + """ + self.scale = scale + assert len(image_shape) >= 2, "Image must be 2D or higher." + super().__init__(image_shape, object_noise_type, object_noise_level) + + def _generate_astro_object(self, object_type, object_parameters): + + astro_object_map = { + "star": astro_object.star_object.StarObject, + "galaxy": astro_object.galaxy_object.GalaxyObject, + "spiral_galaxy": astro_object.spiral_galaxy.SpiralGalaxyObject, + } + + if object_type not in astro_object_map.keys(): + raise NotImplementedError( + f"Object type {object_type}, is not available. " + f"Please select object from {astro_object_map.keys()}" + ) + + return astro_object_map[object_type](**object_parameters) + + def _scale(self, input_image): + image = input_image.copy() + image = (image - input_image.min()) / (input_image.max() - input_image.min()) + return image + + def combine_objects( + self, + objects: Union[list, str], + instance_params: Union[list, dict], + object_params: Union[list, dict], + seed: int = 42, + ): + """ + Utilize Image._generate_astro_objects to overlay all selected astro objects into one image + If object parameters are not included in object list, defaults are used. + Updates SkyImage.image. + + Current input parameter assumptions (totally up to change): + For a single image: + [{ + "object_type":"", + "object_parameters":{} + }] + + Args: + objects (list): str discriptors of the included object + instance_params (list): Parameters for the instance of the object (ei, overall noise) + object_params (list): Parameters of each object (ei: position in frame) + seed (int, optional): random seed for noise. Defaults to 42. + + Returns: + ndarray : image with objects and noise + + """ + object_images = [] + + if type(objects) == str: + objects = [objects] + if type(instance_params) == dict: + instance_params = [instance_params] + if type(object_params) == dict: + object_params = [object_params] + + for sky_object, sky_params, object in zip( + objects, instance_params, object_params + ): + import matplotlib.pyplot as plt + + sky_params["image_dimensions"] = self.image_shape + if "noise_level" not in sky_params: + sky_params["noise_level"] = 0 + + additional_sky_object = self._generate_astro_object(sky_object, sky_params) + + object_image = additional_sky_object.create_object(**object) + if self.scale: + object_image = self._scale(object_image) + + object_images.append(object_image) + + noise = self.generate_noise(seed) + object_images.append(noise) + image = np.sum(object_images, axis=0) + + return image diff --git a/deepbench/physics_object/__init__.py b/deepbench/physics_object/__init__.py new file mode 100644 index 0000000..2bc7ef2 --- /dev/null +++ b/deepbench/physics_object/__init__.py @@ -0,0 +1,2 @@ +from deepbench.physics_object.pendulum import Pendulum +from deepbench.physics_object.hamiltonian_pendulum import HamiltonianPendulum diff --git a/deepbench/physics_object/hamiltonian_pendulum.py b/deepbench/physics_object/hamiltonian_pendulum.py new file mode 100644 index 0000000..fb3b0a4 --- /dev/null +++ b/deepbench/physics_object/hamiltonian_pendulum.py @@ -0,0 +1,184 @@ +from deepbench.physics_object import Pendulum + +import autograd +import autograd.numpy as np +from scipy.integrate import solve_ivp +from typing import Union, Optional + + +class HamiltonianPendulum(Pendulum): + """ + The Hamiltonian Pendulum class. + + Args: + pendulum_arm_length (float): The length of the pendulum arm + starting_angle_radians (float): The starting angle of the pendulum + (angle from the 'ceiling') + noise_std_percent (dict): A dictionary of the Gaussian noise + level to be applied to each parameter. The default is no + noise. Each number is the standard deviation when + multiplied by the parameter. See create_noise(). + acceleration_due_to_gravity (float): little g, local gravity + coefficient + mass_pendulum_bob (float): Mass of the pendulum bob, + this is optional if calculation_type is position only. + + Examples: + + >>> pendulum_obj = HamiltonianPendulum(pendulum_arm_length=10., + starting_angle_radians=np.pi/4, + acceleration_due_to_gravity=9.8, + noise_std_percent= + {'pendulum_arm_length': 0.1, + 'starting_angle_radians': 0.1, + 'acceleration_due_to_gravity': 0.1} + ) + """ + + def __init__( + self, + pendulum_arm_length: float, + starting_angle_radians: float, + acceleration_due_to_gravity: Union[float, None] = None, + mass_pendulum_bob: Optional[float] = 10.0, + noise_std_percent: dict = { + "pendulum_arm_length": 0.0, + "starting_angle_radians": 0.0, + "mass_pendulum_bob": 0.0, + "acceleration_due_to_gravity": None, + }, + ): + + super().__init__( + pendulum_arm_length=pendulum_arm_length, + starting_angle_radians=starting_angle_radians, + noise_std_percent=noise_std_percent, + acceleration_due_to_gravity=acceleration_due_to_gravity, + mass_pendulum_bob=mass_pendulum_bob, + ) + + def _hamiltonian_fn(self, coords, m, L, g): + + q, p = np.split(coords, 2) + + H = (m * g * L) * (1 - np.cos(q)) + ((L**2) * (p**2)) / (2 * m) + return H + + def dynamics_fn(self, t, coords): + """ + derives the gradient of the hamiltonian function + + Args: + coords (np.ndarray): coordinates of the pendulum + + Returns: + np.ndarray:time derivates of p and q. + """ + # + dcoords = autograd.grad(self._hamiltonian_fn)( + coords, + self.mass_pendulum_bob, + self.pendulum_arm_length, + self.acceleration_due_to_gravity, + ) + + dqdt, dpdt = np.split(dcoords, 2) + S = np.concatenate([dpdt, -dqdt], axis=-1) + return S + + def create_object( + self, time: Union[float, np.array], noiseless: bool = True, seed: int = 42 + ): + """ + Given a single or array of times, simulates the pendulum position at + each of these times and optionally adds Gaussian noise to each + parameter. + + Args: + time (Union[float, np.array]): A single moment in time, or + an array of times (s) + noiseless (bool): Add noise to the pendulum parameters + seed (int): Random seed for parameters + + Returns: + tuple + q (np.ndarray): position. + p (np.ndarray): momentum. + dqdt (np.ndarray): velocity. + dpdt (np.ndarray) - force. + t_eval (np.ndarray) - times. + """ + + if not noiseless: + raise NotImplementedError + + return super().create_object(time, noiseless, seed=seed) + + def simulate_pendulum_dynamics(self, time, **kwargs): + """ + Evaulate the hamilitonian at times `time` and return the position, momentum and time derviates + + Args: + time (np.ndarray): Times to simulate + + Returns: + tuple + q (np.ndarray): position. + p (np.ndarray): momentum. + dqdt (np.ndarray): velocity. + dpdt (np.ndarray) - force. + t_eval (np.ndarray) - times. + """ + + assert time.size > 1, "you must enter more than one point in time" + + t_eval = np.array(time) + t_span = np.array([time[0], time[-1]]) + + radius = np.random.rand() + 1.3 + + # get initial state + if self.starting_angle_radians is None: + self.starting_angle_radians = np.random.rand(2) * 2.0 - 1 + + y0 = np.array([2, 0]) + y0 = y0 / np.sqrt((y0**2).sum()) * radius + + spring_ivp = solve_ivp( + fun=self.dynamics_fn, + t_span=t_span, + y0=y0, + t_eval=t_eval, + rtol=1e-10, + **kwargs + ) + q, p = spring_ivp["y"][0], spring_ivp["y"][1] + dydt = [self.dynamics_fn(None, y) for y in spring_ivp["y"].T] + dydt = np.stack(dydt).T + dqdt, dpdt = np.split(dydt, 2) # split the dydt into dqdt and dpdt + + # add noise + noise_std = 0.1 + q += ( + np.random.randn(*q.shape) * noise_std + ) # creates a random array of size q.shape and is scaled with noise_std then adds to q for noise + p += ( + np.random.randn(*p.shape) * noise_std + ) # creates a random array of size p.shape and is scaled with noise_std then adds to p for noise + return q, p, dqdt, dpdt, t_eval + + def _get_field(self, xmin=-1.2, xmax=1.2, ymin=-1.2, ymax=1.2, gridsize=20): + """get_field() is used to visualize the the gradiant vector field.""" + field = {"meta": locals()} + + b, a = np.meshgrid( + np.linspace(xmin, xmax, gridsize), np.linspace(ymin, ymax, gridsize) + ) + ys = np.stack([b.flatten(), a.flatten()]) + + # get vector directions + dydt = [self.dynamics_fn(None, y) for y in ys.T] + dydt = np.stack(dydt).T + field["x"] = ys.T + field["dx"] = dydt.T + return field diff --git a/deepbench/physics_object/pendulum.py b/deepbench/physics_object/pendulum.py new file mode 100644 index 0000000..05f7c84 --- /dev/null +++ b/deepbench/physics_object/pendulum.py @@ -0,0 +1,352 @@ +from deepbench.physics_object.physics_object import PhysicsObject +import numpy as np +import numpy.random as rand +import matplotlib.pyplot as plt +from matplotlib.animation import FuncAnimation +from typing import Union, Optional, Tuple +import logging +import os + + +class Pendulum(PhysicsObject): + def __init__( + self, + pendulum_arm_length: float, + starting_angle_radians: float, + noise_std_percent: dict = { + "pendulum_arm_length": 0.0, + "starting_angle_radians": 0.0, + "acceleration_due_to_gravity": 0.0, + "big_G_newton": None, + "phi_planet": None, + }, + acceleration_due_to_gravity: Optional[float] = None, + big_G_newton: Optional[float] = None, + phi_planet: Optional[float] = None, + mass_pendulum_bob: Optional[float] = 10.0, + coefficient_friction: Optional[float] = 0.0, + ): + """ + The initialization function for the Pendulum class. + + Args: + pendulum_arm_length (float): The length of the pendulum arm + starting_angle_radians (float): The starting angle of the pendulum + (angle from the 'ceiling') + noise_std_percent (dict): A dictionary of the Gaussian noise + level to be applied to each parameter. The default is no + noise. Each number is the standard deviation when + multiplied by the parameter. See create_noise(). + acceleration_due_to_gravity (float): little g, local gravity + coefficient, optional if G and phi are defined, + g = G * phi + big_G_newton (float): Big G, the gravitational constant, + optional if g is defined + phi_planet (float): M/r^2, this changes based on the planet, + optional if g is defined + mass_pendulum_bob (float): Mass of the pendulum bob, + this is optional if calculation_type is position only. + coefficient_friction (float): Coefficient of friction, + optional argument. + + Examples: + + >>> pendulum_obj = Pendulum(pendulum_arm_length=10., + starting_angle_radians=np.pi/4, + acceleration_due_to_gravity=9.8, + noise_std_percent= + {'pendulum_arm_length': 0.1, + 'starting_angle_radians': 0.1, + 'acceleration_due_to_gravity': 0.1} + ) + """ + super().__init__( + noise_level=noise_std_percent, + ) + if acceleration_due_to_gravity is not None and not isinstance( + acceleration_due_to_gravity, float + ): + raise TypeError("acceleration_due_to_gravity should be a float") + + self.pendulum_arm_length = pendulum_arm_length + self.starting_angle_radians = starting_angle_radians + assert ( + self.starting_angle_radians < np.pi + ), "The angle better not be in degrees or else" + self.big_G_newton = big_G_newton + self.phi_planet = phi_planet + if acceleration_due_to_gravity is None: + assert ( + self.big_G_newton is not None and self.phi_planet is not None + ), "must define big_G_newton and phi_planet if \ + acceleration_due_to_gravity is not provided" + + self.acceleration_due_to_gravity = self.big_G_newton * self.phi_planet + self.initial_parameters = { + "pendulum_arm_length": self.pendulum_arm_length, + "starting_angle_radians": self.starting_angle_radians, + "acceleration_due_to_gravity": self.acceleration_due_to_gravity, + "big_G_newton": self.big_G_newton, + "phi_planet": self.phi_planet, + } + else: + self.acceleration_due_to_gravity = acceleration_due_to_gravity + self.initial_parameters = { + "pendulum_arm_length": self.pendulum_arm_length, + "starting_angle_radians": self.starting_angle_radians, + "acceleration_due_to_gravity": self.acceleration_due_to_gravity, + } + self.mass_pendulum_bob = mass_pendulum_bob + self.coefficient_friction = coefficient_friction + + # Verify the requested noise parameters are variables you can use + for key, item in noise_std_percent.items(): + assert key in [key for key in self.__dict__.keys()] + # If the acceleration_due_to_gravity is None, + # then the accompanying noise parameter also needs to be none + # otherwise the noise module will be confused + # about if we're doing the hierarchical case + if acceleration_due_to_gravity is None: + assert ( + noise_std_percent["acceleration_due_to_gravity"] is None + ), "if acceleration due to gravity is not defined (None) then \ + the accompanying noise term must be None as well so that \ + the noise model knows this is the hierarchical case" + + # Finally define the parameter map used to later modify parameters + # in create_noise and destroy_noise: + self.parameter_map = { + "pendulum_arm_length": self.pendulum_arm_length, + "starting_angle_radians": self.starting_angle_radians, + "acceleration_due_to_gravity": self.acceleration_due_to_gravity, + "big_G_newton": self.big_G_newton, + "phi_planet": self.phi_planet, + } + # create logger to store the random seeds generated by noise + # Erase log if already exists: + log_file = "randomseeds.log" + if os.path.exists(log_file): + os.remove(log_file) + # this will overwrite logger each time a new pendulum class + # is initialized + logger = logging.getLogger("randomseeds") + logger.setLevel(logging.DEBUG) + # create file handler which logs even debug messages + fh = logging.FileHandler("randomseeds.log") + logger.addHandler(fh) + self.logfile = logger + + def create_noise( + self, + noiseless: bool = False, + seed: int = None, + n_steps: Union[int, Tuple[int, int]] = 10, + verbose: bool = False, + ) -> np.array: + """ + Creates noise on top of simulate_pendulum_dynamics + Also deals with the hierarchical case, where + acceleration_due_to_gravity is defined via + big_G_newton and phi_planet + + Args: + seed (int): Random seed used to generate Gaussian noise + n_steps (int or Tuple[int,int]): The shape of the noise to be + created. This is specified in create_object using the shape + of the input time array (or float). + + Examples (see create_object) + """ + if seed: + rs = rand.RandomState(seed) + else: + rs = rand.RandomState() + # Save the random state only if noisy + if noiseless is False: + self.logfile.info(str(rs.get_state()[1][0])) + for key in self._noise_level.keys(): + if key not in self.parameter_map: + raise ValueError(f"Invalid parameter name: {key}") + + attribute = self.parameter_map[key] + noise_level = self._noise_level[key] + if verbose: + print("key", key, "attribute", attribute, "noise level", noise_level) + if noise_level is not None: + attribute = rs.normal( + loc=attribute, scale=attribute * noise_level, size=n_steps + ) + setattr(self, key, attribute) + # Now, if this is the hierarchical case, we can redefine + # the acceleration_due_to_gravity term + if self._noise_level["acceleration_due_to_gravity"] is None: + assert ( + self.big_G_newton is not None and self.phi_planet is not None + ), "must define big_G_newton and phi_planet if \ + acceleration_due_to_gravity is not provided" + if self._noise_level["big_G_newton"] is not None: + self.big_G_newton = rs.normal( + loc=self.big_G_newton, + scale=self.big_G_newton * self._noise_level["big_G_newton"], + size=n_steps, + ) + if self._noise_level["phi_planet"] is not None: + self.phi_planet = rs.normal( + loc=self.phi_planet, + scale=self.phi_planet * self._noise_level["phi_planet"], + size=n_steps, + ) + # redefine: + # acceleration_due_to_gravity = multiple of noisy G and phi + if self.big_G_newton is not None and self.phi_planet is not None: + self.acceleration_due_to_gravity = self.big_G_newton * self.phi_planet + else: + assert f"Either big G or phi_planet is None \ + (G = {self.big_G_newton}, ø = {self.phi_planet}); \ + this is not allowed with hierarchical noise" + + def destroy_noise(self): + """ + Remove noise from the parameters + + """ + # Re-modify the global parameters to + # have the original value + for key in self._noise_level.keys(): + if key not in self.parameter_map: + raise ValueError(f"Invalid parameter name: {key}") + attribute = self.initial_parameters[key] + setattr(self, key, attribute) + + def create_object( + self, + time: Union[float, np.array], + noiseless: bool = False, + seed: int = None, + verbose: bool = False, + ): + """ + Given a single or array of times, simulates the pendulum position at + each of these times and optionally adds Gaussian noise to each + parameter. + + Args: + time (Union[float, np.array]): A single moment in time, or + an array of times (s) + noiseless (bool): Enables a noise realization if True. + Default is set to False + seed (int): Random seed used to generate Gaussian noise + + Example: + >>> pendulum = Pendulum() + >>> time = np.array(np.linspace(0, 10, 20)) + >>> pend_position = pendulum.create_object(time, noiseless=True) + """ + time = np.asarray(time) + assert time.size > 0, "you must enter one or more points in time" + if isinstance(time, (float, int)): + n_steps = 1 + else: + time = np.asarray(time) + n_steps = time.shape + self.create_noise(seed=seed, noiseless=noiseless, n_steps=n_steps) + if noiseless: + self.destroy_noise() + pendulum = self.simulate_pendulum_dynamics(time) + self.destroy_noise() + return pendulum + + def simulate_pendulum_dynamics(self, time: Union[float, np.array]): + """ + Simulate a pendulum with Neutonian physics + + Args: + time (Union[float, np.array]): times to simulate + + Returns: + np.ndarray: position of the pendulum. + """ + time = np.asarray(time) + assert time.size > 0, "you must enter one or more points in time" + # Check if parameters are single values + # or arrays with the same length as time + if isinstance(self.pendulum_arm_length, (float, int)): + pendulum_arm_length_values = np.full_like( + np.asarray(time), self.pendulum_arm_length + ) + else: + pendulum_arm_length_values = np.asarray(self.pendulum_arm_length) + + if isinstance(self.starting_angle_radians, (float, int)): + starting_angle_values = np.full_like( + np.asarray(time), self.starting_angle_radians + ) + else: + starting_angle_values = np.asarray(self.starting_angle_radians) + + if isinstance(self.acceleration_due_to_gravity, (float, int)): + acceleration_values = np.full_like( + np.asarray(time), self.acceleration_due_to_gravity + ) + else: + acceleration_values = np.asarray(self.acceleration_due_to_gravity) + + # Calculate theta_time based on the parameters + assert ( + acceleration_values.any() > 0 + ), "f{acceleration_values} not greater than zero" + assert ( + pendulum_arm_length_values.any() > 0 + ), "f{pendulum_arm_length_values} not greater than zero" + theta_time = starting_angle_values * np.cos( + np.sqrt(acceleration_values / pendulum_arm_length_values) * time + ) + + # Calculate x using the modified parameters and time + return pendulum_arm_length_values * np.sin(theta_time) + + def displayObject(self, time: Union[float, np.array]): + """ + Display the pendulum over times. + + Args: + time (Union[float, np.array]): times to display the pendulum position + + Returns: + tuple(np.ndarray, np.ndarray): noiseless, noisy arrays at times "time" + """ + noisy = self.create_object(time) + noise_free = self.create_object(time, noiseless=True) + plt.clf() + plt.plot(time, noisy, color="#EF5D60") + plt.scatter(time, noisy, label="noisy", color="#EF5D60") + plt.plot(time, noise_free, color="#0E131F") + plt.scatter(time, noise_free, label="noise free", color="#0E131F") + plt.legend() + plt.show() + + # plot multiple noise draws and show the standard + # deviation: + noise_free = self.create_object(time, noiseless=True) + plt.clf() + num_random = 10 + noisy_ys = np.zeros((num_random, len(time))) + for i in range(num_random): + noisy = self.create_object(time) + noisy_ys[i, :] = noisy + ci_list = [np.std(abs(noisy_ys[:, t])) for t, _ in enumerate(time)] + plt.fill_between( + time, + (noise_free - ci_list), + (noise_free + ci_list), + color="#EF5D60", + alpha=0.5, + edgecolor="None", + ) + plt.plot(time, noise_free, color="#0E131F", zorder=100) + plt.scatter(time, noise_free, label="noise free", color="#0E131F", zorder=100) + plt.legend() + plt.ylabel("x position") + plt.xlabel("time [s]") + plt.show() + return noise_free, noisy_ys diff --git a/deepbench/physics_object/physics_object.py b/deepbench/physics_object/physics_object.py new file mode 100644 index 0000000..315a584 --- /dev/null +++ b/deepbench/physics_object/physics_object.py @@ -0,0 +1,52 @@ +from abc import ABC, abstractclassmethod, abstractmethod +from typing import Union, List, Tuple +from scipy import ndimage +import numpy.random as rand +import numpy as np + + +class PhysicsObject(ABC): + def __init__(self, noise_level: float) -> None: + """Abstract method for a genetic physics sim object + + Args: + noise_level (float): Level of noise to be generated by the "create_noise" method + """ + self._noise_level = noise_level + + @abstractmethod + def create_object(self) -> np.array: + """ + Creates the object + Code which is called to make the object during execution + """ + raise NotImplementedError() + + def create_noise( + self, seed: int = 42, n_steps: Union[int, Tuple[int, int]] = 10 + ) -> np.array: + """ + Create guassian noise + + Args: + seed (int, optional): random seed for generation Defaults to 42. + n_steps (Union[int, tuple[int, int]], optional): Number of steps noise is generated for. Corresponds to the shape of the created object. Defaults to 10. + + Returns: + np.array: Gaussian noise with the self.noise_level, of size (n_steps) + """ + rs = rand.RandomState(seed) + normal = rs.normal(scale=self._noise_level, size=n_steps) + + if type(n_steps) == tuple: + return normal.reshape(normal) + else: + return normal + + @abstractmethod + def displayObject(self): + """ + Object container + """ + + raise NotImplementedError() diff --git a/src/deepbench/image/__init__.py b/deepbench/settings/default_astro_object.yaml similarity index 100% rename from src/deepbench/image/__init__.py rename to deepbench/settings/default_astro_object.yaml diff --git a/deepbench/settings/default_physics_object.yaml b/deepbench/settings/default_physics_object.yaml new file mode 100644 index 0000000..e694053 --- /dev/null +++ b/deepbench/settings/default_physics_object.yaml @@ -0,0 +1,15 @@ +object_type: "physics" +object_name: "Pendulum" + +total_runs: 3 +parameter_noise: 0.2 + +image_parameters: + pendulum_arm_length: 2 + starting_angle_radians: 0.25 + acceleration_due_to_gravity: 9.8 + noise_std_percent: + acceleration_due_to_gravity: 0 + +object_parameters: + time: [0,1,2,3,4,5,6,7,8,9] \ No newline at end of file diff --git a/deepbench/settings/default_shapes.yaml b/deepbench/settings/default_shapes.yaml new file mode 100644 index 0000000..9f5cc11 --- /dev/null +++ b/deepbench/settings/default_shapes.yaml @@ -0,0 +1,18 @@ +object_type: "shape" +object_name: "ShapeImage" + +total_runs: 3 +image_parameters: + image_shape: + - 28 + - 28 + + +object_parameters: { + rectangle: { + object: { + fill: True + }, + instance: {} + } +} \ No newline at end of file diff --git a/deepbench/settings/default_sky_object.yaml b/deepbench/settings/default_sky_object.yaml new file mode 100644 index 0000000..5794fce --- /dev/null +++ b/deepbench/settings/default_sky_object.yaml @@ -0,0 +1,24 @@ +object_type: "sky" +object_name: "SkyImage" + +total_runs: 3 +image_parameters: + image_shape: + - 28 + - 28 + + +object_parameters: { + star: { + object: { + center_x: 14, + center_y: 14, + alpha: 1.0 + }, + instance: { + noise_level: 0, + radius: 1.0, + amplitude: 1.0 + } + } +} \ No newline at end of file diff --git a/deepbench/shapes/__init__.py b/deepbench/shapes/__init__.py new file mode 100644 index 0000000..145e304 --- /dev/null +++ b/deepbench/shapes/__init__.py @@ -0,0 +1 @@ +from deepbench.shapes.shape_generator import ShapeGenerator as Shapes diff --git a/deepbench/shapes/shape_generator.py b/deepbench/shapes/shape_generator.py new file mode 100644 index 0000000..81370ba --- /dev/null +++ b/deepbench/shapes/shape_generator.py @@ -0,0 +1,327 @@ +from typing import Union +import numpy as np +from matplotlib.path import Path +from matplotlib import patches +from skimage import transform + + +class ShapeGenerator: + def __init__(self, image_shape: tuple = (28, 28)): + self.image_shape = image_shape + self.n_dimensions = len(self.image_shape) + + def resize(self, image: np.ndarray, resize_dimensions: tuple = (28, 28)): + """ + Resize an array-like + + Args: + image (np.ndarray): shape to resize + resize_dimensions (tuple, optional): resize to shape. Defaults to (28, 28). + + Raises: + ValueError: invalid size, either too small (0,0) or having the number of incorrect dimensions + + Returns: + np.ndarray: array of size (resize_dimensions) + """ + resize_dimensions = tuple(map(lambda dim: int(np.ceil(dim)), resize_dimensions)) + if len(image.shape) != len(resize_dimensions): + raise ValueError( + f"Number of dimensions of source image ({image.shape} do not make target dimensions ({resize_dimensions})" + ) + if 0 in resize_dimensions: + raise ValueError( + "Attempting to resize to an invalid size, please increase the size" + ) + + resized_image = transform.resize(image, resize_dimensions) + return resized_image + + def _convert_patch_to_image( + self, image: patches.Patch, cutout: patches.Path = None + ): + + n_dim = len(self.image_shape) + self.image_shape = tuple(map(lambda dim: int(np.ceil(dim)), self.image_shape)) + + if n_dim < 2: + raise ValueError( + f"Image shape input of length {n_dim}; but input must be length >=2" + ) + + if 0 in self.image_shape: + raise ValueError(f"Image size must be greater than 0") + + x = np.arange(0, self.image_shape[0]) + y = np.arange(0, self.image_shape[1]) + meshgrid = np.meshgrid(x, y) + + coordinates = np.array(list(zip(*(c.flat for c in meshgrid)))) + + valid_coordinates = Path(image.get_verts()).contains_points(coordinates) + shape_points = coordinates[valid_coordinates] + + out_array = np.zeros(self.image_shape) + out_array[shape_points[:, 0], shape_points[:, 1]] = 1.0 + + if cutout is not None: + cutout_coords = Path(cutout.get_verts()).contains_points(coordinates) + cutout_points = coordinates[cutout_coords] + out_array[cutout_points[:, 0], cutout_points[:, 1]] = 0.0 + + return out_array + + def create_rectangle( + self, + center: tuple = (np.random.randint(10, 16), np.random.randint(10, 16)), + width: int = np.random.randint(10, 16), + height: int = np.random.randint(10, 16), + angle: Union[float, int] = 0, + line_width: int = 1, + fill: bool = False, + ): + + """ + Make a rectangle. + + Args: + center (tuple(*int), optional): Center of the rectangle (coordinate). Defaults to (14, 14). + width (int, optional): Horizontal width of the rectangle (in pixels). Defaults to random int. + height (int, optional): Vertical height of the rectangle (in pixels). Defaults to random int. + angle (Union[float, int], optional): tilt the rectangle (degrees). Defaults to 0. + line_width (int, optional): line width of the outline. Defaults to 1. + fill (bool, optional): Fill in the rectangle. Defaults to False. + + + Returns: + np.ndarray: A Rectangle image + """ + + n_center_dim = len(center) + if self.n_dimensions != n_center_dim: + raise ValueError( + f"Image shape input of length {self.n_dimensions}; but supplied center coordinates with dimension f{n_center_dim}" + ) + + width += line_width + height += line_width + + xy = (center[0] - (width / 2), center[1] - (height / 2)) + rectangle = patches.Rectangle(xy=xy, width=width, height=height, angle=angle) + + cutout = None + if not fill: + + cutout_h = height - (2 * line_width) + cutout_w = width - (2 * line_width) + xy_cutout = (center[0] - (cutout_w / 2), center[1] - (cutout_h / 2)) + cutout = patches.Rectangle( + xy=xy_cutout, width=cutout_w, height=cutout_h, angle=angle + ) + + rectangle_array = self._convert_patch_to_image(rectangle, cutout=cutout) + + return rectangle_array + + def create_regular_polygon( + self, + center: tuple = (np.random.randint(10, 16), np.random.randint(10, 16)), + angle: Union[int, float] = np.random.uniform(20, 90), + vertices: int = 3, + radius: Union[int, float] = np.random.uniform(8, 12), + line_width=1, + fill=False, + ): + """ + Create a polygon with equal length sides + + Args: + center (tuple[*int], optional): Where to center the object. + angle (Union[int, float], optional): Angle of rotation (degrees) + vertices (int, optional): Number of verticies. Defaults to 3, a triangle. + radius (int, optional): distance from vertex to vertex Defaults to 10. + line_width (int, optional): line width of the outline. Defaults to 1. + fill (bool, optional): Fill in the rectangle. Defaults to False. + + + Returns: + np.ndarray: A polygon image + """ + + radius = abs(radius) + + n_center_dim = len(center) + if self.n_dimensions != n_center_dim: + raise ValueError( + f"Image shape input of length {n_center_dim}; but supplied center coordinates with dimension f{n_center_dim}" + ) + if vertices <= 0: + raise ValueError(f"Cannot plot a polygon with {vertices}") + + xy = (center[0] - (radius / vertices) / 2, center[1] - (radius / vertices) / 2) + polygon = patches.RegularPolygon( + xy=xy, numVertices=vertices, radius=radius, orientation=angle + ) + cutout = None + if not fill: + cutout_radius = radius - line_width + xy_cutout = ( + center[0] - (cutout_radius / vertices) / 2, + center[1] - (cutout_radius / vertices) / 2, + ) + cutout = patches.RegularPolygon( + xy=xy_cutout, + numVertices=vertices, + radius=cutout_radius, + orientation=angle, + ) + + polygon_array = self._convert_patch_to_image(polygon, cutout=cutout) + + return polygon_array + + def create_arc( + self, + center: tuple = (np.random.randint(10, 16), np.random.randint(10, 16)), + radius: Union[int, float] = np.random.uniform(8, 12), + theta1: Union[int, float] = np.random.uniform(0, 45), + theta2: Union[int, float] = np.random.uniform(85, 120), + line_width: int = 1, + ): + """ + Create an arc with radius "radius" arcing from theta1 to theta2 counter-clockwise + + Args: + center (tuple, optional): Center point of the arc. Defaults to (np.random.randint(10, 16), np.random.randint(10, 16)). + radius (Union[int, float], optional): distance from the arc to the center point. Defaults to np.random.random(8, 12). + theta1 (Union[int, float], optional): starting point of the arc (degrees). Defaults to np.random.random(0, 45). + theta2 (Union[int, float], optional): ending point of the arc (degrees). Defaults to np.random.random(85, 120). + line_width (int, optional): thickness of the arc (pixels) Defaults to 1. + + + Returns: + np.ndarray: The arc image + """ + + arc = patches.Wedge( + center=center, r=radius, theta1=theta1, theta2=theta2, width=line_width + ) + arc_array = self._convert_patch_to_image(arc) + + return arc_array + + def create_line( + self, + start: tuple = (np.random.randint(0, 10), np.random.randint(0, 10)), + end: tuple = (np.random.randint(12, 28), np.random.randint(12, 28)), + line_width: int = 1, + ): + + """ + Generate a numpy array of a line + + Args: + start (tuple, optional): Starting corner of the line. Defaults to (np.random.randint(0, 10), np.random.randint(0, 10)). + end (tuple, optional): Ending corner of the line. Defaults to (np.random.randint(12, 28), np.random.randint(12, 28)). + line_width (int, optional): Thickness of the line (pixels). Defaults to 1. + + Returns: + np.ndarray + """ + + if len(start) != len(end): + raise ValueError( + f"Dimension mismatch, start point had dimensions of {len(start)}, but end point had dimensions of {len(end)}" + ) + + if start == end: + raise ValueError(f"Start point and end point must be different") + + hyp = ((end[1] - start[1]) ** 2 + (end[0] - start[0]) ** 2) ** 0.5 + angle = np.arccos((end[0] - start[0]) / hyp) + + x_shift = line_width / 2.0 * np.cos(np.pi - angle) + y_shift = line_width / 2.0 * np.sin(np.pi - angle) + x_start, y_start = start[0] + x_shift, start[1] + y_shift + x_end, y_end = end[0] + x_shift, end[1] + y_shift + + height_rect = line_width + width_rect = ((y_end - y_start) ** 2 + (x_end - x_start) ** 2) ** 0.5 + + angle_degrees = angle * 180.0 / np.pi + line = patches.Rectangle( + (x_start, y_start), + width=width_rect, + height=height_rect, + angle=angle_degrees, + ) + line_array = self._convert_patch_to_image(line) + + return line_array + + def create_ellipse( + self, + center: tuple = (np.random.randint(10, 16), np.random.randint(10, 16)), + width: int = np.random.randint(10, 16), + height: int = np.random.randint(10, 16), + angle: Union[float, int] = 0, + line_width: int = 1, + fill: bool = False, + ): + """ + Create an ellipse/circle (where width/height are the same) + + Args: + center (tuple, optional): Center point of the ellipse. Defaults to (np.random.randint(10, 16), np.random.randint(10, 16)). + width (int, optional): Horizontal length of the ellipse (pixels). Defaults to np.random.randint(10, 16). + height (int, optional): Vertical height of the ellipse (pixels). Defaults to np.random.randint(10, 16). + angle (Union[float, int], optional): Rotation angle of the ellipse (degrees). Defaults to 0. + line_width (int, optional): Width of the ellipse's border (pixels). Defaults to 1. + fill (bool, optional): Fill the center of the ellipse. Defaults to False. + + + Returns: + np.ndarray + + """ + + if self.n_dimensions != len(center): + raise ValueError( + f"Dimension mismatch, image had dimensions of {self.n_dimensions}, " + f"but center point had dimensions of {len(center)}" + ) + + height, width = int(np.ceil(height)), int(np.ceil(width)) + + ellipse = patches.Ellipse(xy=center, width=width, height=height, angle=angle) + + cutout = None + if not fill: + xy_cutout = center + width_cutout = width - (2 * line_width) if width != 0 else 0 + height_cutout = height - (2 * line_width) if height != 0 else 0 + + cutout = patches.Ellipse( + xy=xy_cutout, width=width_cutout, height=height_cutout, angle=angle + ) + + ellipse_array = self._convert_patch_to_image(ellipse, cutout=cutout) + + return ellipse_array + + def create_empty_shape(self): + """ + Create an array of 0s with shape self.image_shape + + Returns: + np.ndarray + """ + if self.n_dimensions < 2: + raise ValueError( + f"Image shape input of length {self.n_dimensions}; but input must be length >=2" + ) + + if 0 in self.image_shape: + raise ValueError(f"Image size must be greater than 0") + + return np.zeros(self.image_shape) diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d4bb2cb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/_build/html/.DS_Store b/docs/_build/html/.DS_Store new file mode 100644 index 0000000..0cc435d Binary files /dev/null and b/docs/_build/html/.DS_Store differ diff --git a/docs/_build/html/.buildinfo b/docs/_build/html/.buildinfo new file mode 100644 index 0000000..4c34121 --- /dev/null +++ b/docs/_build/html/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: 0ea69721f570b11a732d0cc9f31cb19e +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/_sources/astro_object.rst.txt b/docs/_build/html/_sources/astro_object.rst.txt new file mode 100644 index 0000000..3e46955 --- /dev/null +++ b/docs/_build/html/_sources/astro_object.rst.txt @@ -0,0 +1,21 @@ +Astronomy Objects +=================== + + +Galaxies +--------- + +.. autoclass:: deepbench.astro_object.GalaxyObject + :members: + +Spiral Galaxies +---------------- + +.. autoclass:: deepbench.astro_object.SpiralGalaxyObject + :members: + +Stars +---------------- + +.. autoclass:: deepbench.astro_object.StarObject + :members: diff --git a/docs/_build/html/_sources/collection.rst.txt b/docs/_build/html/_sources/collection.rst.txt new file mode 100644 index 0000000..a8b6cc2 --- /dev/null +++ b/docs/_build/html/_sources/collection.rst.txt @@ -0,0 +1,5 @@ +Collection +========== + +.. autoclass:: deepbench.collection.Collection + :members: \ No newline at end of file diff --git a/docs/_build/html/_sources/deepbench.collection.collection.rst.txt b/docs/_build/html/_sources/deepbench.collection.collection.rst.txt new file mode 100644 index 0000000..073429d --- /dev/null +++ b/docs/_build/html/_sources/deepbench.collection.collection.rst.txt @@ -0,0 +1,29 @@ +deepbench.collection.collection +=============================== + +.. automodule:: deepbench.collection.collection + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + Collection + + + + + + + + + diff --git a/docs/_build/html/_sources/example.rst.txt b/docs/_build/html/_sources/example.rst.txt new file mode 100644 index 0000000..5622685 --- /dev/null +++ b/docs/_build/html/_sources/example.rst.txt @@ -0,0 +1,26 @@ +Examples and Quickstart +========================= + +Astro Objects +-------------- + +.. raw:: html + :file: examples/astro_object.html + +Physics Objects +---------------- + + +.. raw:: html + :file: examples/Pendulum Example.html + + +.. raw:: html + :file: examples/HamiltonianPendulumExample.html + + +Shape Objects +-------------- + +.. raw:: html + :file: examples/Shape Object Example.html \ No newline at end of file diff --git a/docs/_build/html/_sources/examples/astro_example.rst.txt b/docs/_build/html/_sources/examples/astro_example.rst.txt new file mode 100644 index 0000000..0be4910 --- /dev/null +++ b/docs/_build/html/_sources/examples/astro_example.rst.txt @@ -0,0 +1,15609 @@ +Astronomy Object Example +========================= + +.. raw:: html + + + + + + + + +Astro Object Example + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_sources/image.rst.txt b/docs/_build/html/_sources/image.rst.txt new file mode 100644 index 0000000..249295a --- /dev/null +++ b/docs/_build/html/_sources/image.rst.txt @@ -0,0 +1,8 @@ +Images +========= + +.. autoclass:: deepbench.image.ShapeImage + :members: + +.. autoclass:: deepbench.image.SkyImage + :members: \ No newline at end of file diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt new file mode 100644 index 0000000..8067070 --- /dev/null +++ b/docs/_build/html/_sources/index.rst.txt @@ -0,0 +1,25 @@ +.. DeepBench documentation master file, created by + sphinx-quickstart on Tue Jun 20 13:50:51 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to DeepBench's documentation! +===================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + collection + image + astro_object + physics_object + shapes + example + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/_build/html/_sources/physics_object.rst.txt b/docs/_build/html/_sources/physics_object.rst.txt new file mode 100644 index 0000000..a456fd5 --- /dev/null +++ b/docs/_build/html/_sources/physics_object.rst.txt @@ -0,0 +1,31 @@ +Physics Simulation Objects +========================== + + +Newtonian Pendulum +------------------ + + +.. autoclass:: deepbench.physics_object.Pendulum + :members: + + +Example +^^^^^^^^^^ + +.. raw:: html + :file: examples/Pendulum Example.html + + +Hamiltonian Pendulum +---------------------- + +.. autoclass:: deepbench.physics_object.HamiltonianPendulum + :members: + + +Example +^^^^^^^^^^ + +.. raw:: html + :file: examples/HamiltonianPendulumExample.html \ No newline at end of file diff --git a/docs/_build/html/_sources/shapes.rst.txt b/docs/_build/html/_sources/shapes.rst.txt new file mode 100644 index 0000000..4502938 --- /dev/null +++ b/docs/_build/html/_sources/shapes.rst.txt @@ -0,0 +1,11 @@ +Shapes +======== + + +Shapes +-------- +.. autoclass:: deepbench.shapes.Shapes + :members: + +.. autoclass:: deepbench.shapes.shape_generator.ShapeGenerator + :members: diff --git a/docs/_build/html/_static/alabaster.css b/docs/_build/html/_static/alabaster.css new file mode 100644 index 0000000..517d0b2 --- /dev/null +++ b/docs/_build/html/_static/alabaster.css @@ -0,0 +1,703 @@ +@import url("basic.css"); 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+} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + + +/* -- other body styles ----------------------------------------------------- */ + +ol.arabic { + list-style: decimal; +} + +ol.loweralpha { + list-style: lower-alpha; +} + +ol.upperalpha { + list-style: upper-alpha; +} + +ol.lowerroman { + list-style: lower-roman; +} + +ol.upperroman { + list-style: upper-roman; +} + +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/docs/_build/html/_static/custom.css b/docs/_build/html/_static/custom.css new file mode 100644 index 0000000..2a924f1 --- /dev/null +++ b/docs/_build/html/_static/custom.css @@ -0,0 +1 @@ +/* This file intentionally left blank. */ diff --git a/docs/_build/html/_static/doctools.js b/docs/_build/html/_static/doctools.js new file mode 100644 index 0000000..d06a71d --- /dev/null +++ b/docs/_build/html/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js new file mode 100644 index 0000000..3462e59 --- /dev/null +++ b/docs/_build/html/_static/documentation_options.js @@ -0,0 +1,14 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '0.1.1', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/docs/_build/html/_static/file.png b/docs/_build/html/_static/file.png new file mode 100644 index 0000000..a858a41 Binary files /dev/null and b/docs/_build/html/_static/file.png differ diff --git a/docs/_build/html/_static/language_data.js b/docs/_build/html/_static/language_data.js new file mode 100644 index 0000000..250f566 --- /dev/null +++ b/docs/_build/html/_static/language_data.js @@ -0,0 +1,199 @@ +/* + * language_data.js + * ~~~~~~~~~~~~~~~~ + * + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, is available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/docs/_build/html/_static/minus.png b/docs/_build/html/_static/minus.png new file mode 100644 index 0000000..d96755f Binary files /dev/null and b/docs/_build/html/_static/minus.png differ diff --git a/docs/_build/html/_static/plus.png b/docs/_build/html/_static/plus.png new file mode 100644 index 0000000..7107cec Binary files /dev/null and b/docs/_build/html/_static/plus.png differ diff --git a/docs/_build/html/_static/pygments.css b/docs/_build/html/_static/pygments.css new file mode 100644 index 0000000..9abe04b --- /dev/null +++ b/docs/_build/html/_static/pygments.css @@ -0,0 +1,83 @@ +pre { line-height: 125%; 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = docUrlRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = docUrlRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/docs/_build/html/_static/sphinx_highlight.js b/docs/_build/html/_static/sphinx_highlight.js new file mode 100644 index 0000000..aae669d --- /dev/null +++ b/docs/_build/html/_static/sphinx_highlight.js @@ -0,0 +1,144 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(SphinxHighlight.highlightSearchWords); +_ready(SphinxHighlight.initEscapeListener); diff --git a/docs/_build/html/astro_object.html b/docs/_build/html/astro_object.html new file mode 100644 index 0000000..75c78a9 --- /dev/null +++ b/docs/_build/html/astro_object.html @@ -0,0 +1,382 @@ + + + + + + + + Astronomy Objects — DeepBench 0.1.1 documentation + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Astronomy Objects

+
+

Galaxies

+
+
+class deepbench.astro_object.GalaxyObject(image_dimensions, amplitude=1, radius=25, n=1.0, noise_level=0.2, ellipse=0.5078568292083924, theta=-0.10251866271635413)
+

Produce a 2D galaxy using a Sersic2D distribution. +View https://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Sersic2D.html +for implimentation details.

+
+
Parameters:
+
    +

  • image_dimensions (Union[int, float, List[int], List[float]]) – The dimension(s) of the object to be produced.

    • +

  • amplitude (Union[int, float]) – The amplitude of the object to be produced, surface brightness at radius.

    • +

  • noise_level (Union[float, list[float]]) – The Poisson noise level (lambda, the expected seperation) to be applied to the object.

    • +

  • radius (int, optional) – Effective half-light radius of the galaxy. Defaults to 25.

    • +

  • n (float, optional) – Sersic Index. Defaults to 1.0.

    • +

  • ellipse (float, optional) – Galaxy Ellipticity. Defaults to random.uniform(0.1, 0.9).

    • +

  • theta (float, optional) – The rotation of the galaxy in radians. Defaults to random.uniform(-1.5, 1.5).

    • +
+
+
+

Examples

+
>>> example_galaxy = GalaxyObject(image_dimensions=28)
+
+
+
+
+create_Sersic_profile(center_x, center_y)
+

Wrapper for https://docs.astropy.org/en/stable/api/astropy.modeling.functional_models.Sersic2D.html

+
+
Parameters:
+
    +

  • center_x (float) – x position of the center of the galaxy

    • +

  • center_y (float) – y position of the center of the galaxy

    • +
+
+
Returns:
+

created galaxy profile

+
+
Return type:
+

galaxy (numpy.array)

+
+
+
+ +
+
+create_object(center_x=5.0, center_y=5.0)
+

Create the galaxy object from a Sersic distribution and Poisson or PSF noise.

+
+
Parameters:
+
    +

  • center_x (float) – The x-axis placement of the galaxy object.

    • +

  • center_y (float) – The y-axis placement of the galaxy object.

    • +
+
+
Returns:
+

Two dimensional Galaxy object, composed of Sersic Distribution and noise appendings.

+
+
Return type:
+

ndarray

+
+
+

Examples

+
>>> example_prof = example_star.create_object(center_x = 1.0, center_y = 0.0)
+
+
+
+ +
+
+displayObject()
+

Display the object created in a 2d plot

+
+
Raises:
+

NotImplementedError – Raised if not implimented in the child class

+
+
+
+ +
+ +
+
+

Spiral Galaxies

+
+
+class deepbench.astro_object.SpiralGalaxyObject(image_dimensions, amplitude=1, radius=25, n=1.0, noise_level=0.2, ellipse=0.30940371983445336, theta=-0.5479092479402858, winding_number=2, spiral_pitch=0.2)
+

Create a spiral galaxy object

+
+
Parameters:
+
    +

  • image_dimensions (Union[int, float, List[int], List[float]]) – The dimension(s) of the object to be produced.

    • +

  • amplitude (Union[int, float]) – The amplitude of the object to be produced, surface brightness at the sersic radius.

    • +

  • noise_level (Union[float, list[float]]) – The Poisson noise level to be applied to the object.

    • +

  • radius (int, optional) – Effective half-light radius of the galaxy. Defaults to 25.

    • +

  • n (float, optional) – Sersic Index. Defaults to 1.0.

    • +

  • ellipse (float, optional) – Galaxy Ellipticity. Defaults to random.uniform(0.1, 0.9).

    • +

  • theta (float, optional) – The rotation of the galaxy in radians. Defaults to random.uniform(-1.5, 1.5).

    • +

  • winding_number (int, optional) – number of arms. Defaults to 2.

    • +

  • spiral_pitch (float, optional) – Severity of the spiral, the pitch angle. Defaults to 0.2.

    • +
+
+
+

Examples

+
>>> example_galaxy = SpiralGalaxyObject(image_dimensions=28, winding_number=4)
+
+
+
+
+create_object(center_x, center_y)
+

Create the galaxy object from a Sersic distribution and Poisson and PSF noise.

+
+
Parameters:
+
    +

  • center_x (float) – The x-axis placement of the galaxy object.

    • +

  • center_y (float) – The y-axis placement of the galaxy object.

    • +
+
+
Returns:
+

Two dimensional Spiral object, composed of Spiral Distribution and noise appendings.

+
+
Return type:
+

ndarray

+
+
+

Examples

+
>>> example_prof = example_spiral.create_object(center_x = 1.0, center_y = 0.0)
+
+
+
+ +
+
+create_spiral_profile(center_x, center_y)
+

ref paper: https://doi.org/10.1111/j.1365-2966.2009.14950.x +Impliment a spiral galaxy profile

+
+
Parameters:
+
    +

  • center_x (float) – x position of the center of the galaxy

    • +

  • center_y (float) – y position of the center of the galaxy

    • +
+
+
Returns:
+

Profile representing the spiral galaxy

+
+
Return type:
+

spiral profile (numpy array)

+
+
+
+ +
+
+displayObject()
+

Display the object created in a 2d plot

+
+
Raises:
+

NotImplementedError – Raised if not implimented in the child class

+
+
+
+ +
+ +
+
+

Stars

+
+
+class deepbench.astro_object.StarObject(image_dimensions, noise=0.0, radius=1.0, amplitude=1.0)
+
+

Create Stars

+

Args:

+
+
+
image_dimensions (Union[int, float, List[int], List[float]]): The dimension(s) of the object to be produced.
+

radius (Union[int, float]): The radius of the object to be produced. +amplitude (Union[int, float]): The amplitude (brightness) of the object to be produced. +noise_level (Union[float, list[float]]): The Poisson noise level (lambda, the expected seperation) to be applied to the object.

+
+

Examples:

+
>>> example_star = StarObject(image_dimensions=28, noise=5.0, center=3, radius=0.7, amplitude=1.2)
+>>> example_star = StarObject(image_dimensions=(28,28), noise=5.0)
+
+
+
+
+
+
Parameters:
+
    +

  • image_dimensions (int | float | List[int] | List[float]) –

    • +

  • noise (float | List[float]) –

    • +

  • radius (int | float) –

    • +

  • amplitude (int | float) –

    • +
+
+
+
+
+create_Moffat_profile(center_x, center_y, alpha=1.0)
+

Create the Moffat distribution used to simulate star objects.

+
+
Parameters:
+
    +

  • center_x (float) – The x-axis placement of the star object.

    • +

  • center_y (float) – The y-axis placement of the star object.

    • +

  • alpha (float) – The luminosity of the Moffat distribution.

    • +
+
+
Returns:
+

Two dimensional Moffat distribution.

+
+
Return type:
+

ndarray

+
+
+

Examples

+
>>> example_prof = example_star.create_Moffat_profile(center_x = 1.0, center_y = 0.0, alpha=200.0)
+>>> example_prof = example_star.create_Moffat_profile(center_x = 1.0, center_y = 0.0)
+
+
+
+ +
+
+create_object(center_x, center_y, alpha=1.0)
+

Create the star object from a Moffat distribution and Poisson and PSF noise.

+
+
Parameters:
+
    +

  • center_x (float) – The x-axis placement of the star object.

    • +

  • center_y (float) – The y-axis placement of the star object.

    • +

  • alpha (float) – The luminosity of the Moffat distribution.

    • +
+
+
Returns:
+

Two dimensional Star object, composed of Moffat Distribution and noise appendings.

+
+
Return type:
+

ndarray

+
+
+

Examples

+
>>> example_prof = example_star.create_object(center_x = 1.0, center_y = 0.0, alpha=200.0)
+>>> example_prof = example_star.create_object(center_x = 1.0, center_y = 0.0)
+
+
+
+ +
+
+displayObject()
+

Display the object created in a 2d plot

+
+
Raises:
+

NotImplementedError – Raised if not implimented in the child class

+
+
+
+ +
+ +
+
+ + +
+ +
+
+ +
+
+
+ ©2023, Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord. + + | + Powered by Sphinx 7.0.1 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/docs/_build/html/collection.html b/docs/_build/html/collection.html new file mode 100644 index 0000000..5321f5c --- /dev/null +++ b/docs/_build/html/collection.html @@ -0,0 +1,176 @@ + + + + + + + + Collection — DeepBench 0.1.1 documentation + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Collection

+
+
+class deepbench.collection.Collection(object_config)
+

Take a configuration file (dictionary) and produce the simulation output, +automatically passing arguments where they need to be.

+

Handles both compositional images (ones with multiple objects) +and single object images

+

Holds onto all the parameters used to make these files, including the default parameters, for replication.

+
+
Parameters:
+

object_config (dict) – dictionary containing the parameters for the simulation output. Required fields: +- object_type: [sky, shape, physics, astro] : overall type of image +- object_name: Name of the class used in the image generation (e.g. - Pendulum, Star) +- total_runs: Number of times the simulation will be executed +- image_parameters: parameters for the image itself. In single object images, this is the parameters for the parent class. +- object parameters: list of objects that will be included in each image and their parameters

+
+
+
+
+add_object()
+

Use the parameters set by the configuration file to create an object and store that and its associated parameters +Adds noise to parameters if set by the program perviously

+

If the specified object is a composite image, it will only find the default values for the compositor method, not the indivual simulations

+
+ +
+
+add_parameter_noise(seed, params)
+

Add noise to the image wide parameters

+
+
Parameters:
+
    +

  • seed (int) – integer stored by the program to denote the noise seed added to the object

    • +

  • params (dict) – parameters that have added noise.

    • +
+
+
Returns:
+

parameters with added uniform noise

+
+
Return type:
+

dict

+
+
+
+ +
+
+engine_defaults()
+

Locate the default parameters for any simulation being called, via the inspect.signature method

+
+
Returns:
+

all the parameters either default to the called object or modified by the program

+
+
Return type:
+

dict

+
+
+
+ +
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord. + + | + Powered by Sphinx 7.0.1 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/docs/_build/html/deepbench.collection.collection.html b/docs/_build/html/deepbench.collection.collection.html new file mode 100644 index 0000000..5450396 --- /dev/null +++ b/docs/_build/html/deepbench.collection.collection.html @@ -0,0 +1,107 @@ + + + + + + + + deepbench.collection.collection — DeepBench 0.1.1 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

deepbench.collection.collection

+

Classes

+ + + + + + +

Collection(object_config)

Take a configuration file (dictionary) and produce the simulation output, automatically passing arguments where they need to be.

+
+ + +
+ +
+
+ +
+
+
+ ©2023, Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord. + + | + Powered by Sphinx 7.0.1 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/docs/_build/html/example.html b/docs/_build/html/example.html new file mode 100644 index 0000000..b200a1c --- /dev/null +++ b/docs/_build/html/example.html @@ -0,0 +1,60941 @@ + + + + + + + + Examples and Quickstart — DeepBench 0.1.1 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Examples and Quickstart

+
+

Astro Objects

+ + + + + +Astro Object Example + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+

Physics Objects

+ + + + + +Pendulum Example + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +HamiltonianPendulumExample + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+

Shape Objects

+ + + + + +Shape Object Example + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+
+ +
+
+
+ ©2023, Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord. + + | + Powered by Sphinx 7.0.1 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/docs/_build/html/examples/astro_example.html b/docs/_build/html/examples/astro_example.html new file mode 100644 index 0000000..b38d1a5 --- /dev/null +++ b/docs/_build/html/examples/astro_example.html @@ -0,0 +1,19417 @@ + + + + + + + + Astronomy Object Example — DeepBench 0.1.1 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Astronomy Object Example

+

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}

+

/* <DEPRECATED> / +.p-ScrollBar[data-orientation=’horizontal’], +/ </DEPRECATED> */ +.lm-ScrollBar[data-orientation=’horizontal’] {

+
+

flex-direction: row;

+
+

}

+

/* <DEPRECATED> / +.p-ScrollBar[data-orientation=’vertical’], +/ </DEPRECATED> */ +.lm-ScrollBar[data-orientation=’vertical’] {

+
+

flex-direction: column;

+
+

}

+

/* <DEPRECATED> / +.p-ScrollBar-button, / </DEPRECATED> */ +.lm-ScrollBar-button {

+
+

box-sizing: border-box; +flex: 0 0 auto;

+
+

}

+

/* <DEPRECATED> / +.p-ScrollBar-track, / </DEPRECATED> */ +.lm-ScrollBar-track {

+
+

box-sizing: border-box; +position: relative; +overflow: hidden; +flex: 1 1 auto;

+
+

}

+

/* <DEPRECATED> / +.p-ScrollBar-thumb, / </DEPRECATED> */ +.lm-ScrollBar-thumb {

+
+

box-sizing: border-box; +position: absolute;

+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Copyright (c) 2014-2017, PhosphorJS Contributors +| +| Distributed under the terms of the BSD 3-Clause License. +| +| The full license is in the file LICENSE, distributed with this software. +|—————————————————————————-/

+

/* <DEPRECATED> / +.p-SplitPanel-child, / </DEPRECATED> */ +.lm-SplitPanel-child {

+
+

z-index: 0;

+
+

}

+

/* <DEPRECATED> / +.p-SplitPanel-handle, / </DEPRECATED> */ +.lm-SplitPanel-handle {

+
+

z-index: 1;

+
+

}

+

/* <DEPRECATED> / +.p-SplitPanel-handle.p-mod-hidden, / </DEPRECATED> */ +.lm-SplitPanel-handle.lm-mod-hidden {

+
+

display: none !important;

+
+

}

+

/* <DEPRECATED> / +.p-SplitPanel-handle:after, / </DEPRECATED> */ +.lm-SplitPanel-handle:after {

+
+

position: absolute; +top: 0; +left: 0; +width: 100%; +height: 100%; +content: ‘’;

+
+

}

+

/* <DEPRECATED> / +.p-SplitPanel[data-orientation=’horizontal’] > .p-SplitPanel-handle, +/ </DEPRECATED> */ +.lm-SplitPanel[data-orientation=’horizontal’] > .lm-SplitPanel-handle {

+
+

cursor: ew-resize;

+
+

}

+

/* <DEPRECATED> / +.p-SplitPanel[data-orientation=’vertical’] > .p-SplitPanel-handle, +/ </DEPRECATED> */ +.lm-SplitPanel[data-orientation=’vertical’] > .lm-SplitPanel-handle {

+
+

cursor: ns-resize;

+
+

}

+

/* <DEPRECATED> / +.p-SplitPanel[data-orientation=’horizontal’] > .p-SplitPanel-handle:after, +/ </DEPRECATED> */ +.lm-SplitPanel[data-orientation=’horizontal’] > .lm-SplitPanel-handle:after {

+
+

left: 50%; +min-width: 8px; +transform: translateX(-50%);

+
+

}

+

/* <DEPRECATED> / +.p-SplitPanel[data-orientation=’vertical’] > .p-SplitPanel-handle:after, +/ </DEPRECATED> */ +.lm-SplitPanel[data-orientation=’vertical’] > .lm-SplitPanel-handle:after {

+
+

top: 50%; +min-height: 8px; +transform: translateY(-50%);

+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Copyright (c) 2014-2017, PhosphorJS Contributors +| +| Distributed under the terms of the BSD 3-Clause License. +| +| The full license is in the file LICENSE, distributed with this software. +|—————————————————————————-/

+

/* <DEPRECATED> / +.p-TabBar, / </DEPRECATED> */ +.lm-TabBar {

+
+

display: flex; +-webkit-user-select: none; +-moz-user-select: none; +-ms-user-select: none; +user-select: none;

+
+

}

+

/* <DEPRECATED> / +.p-TabBar[data-orientation=’horizontal’], / </DEPRECATED> */ +.lm-TabBar[data-orientation=’horizontal’] {

+
+

flex-direction: row; +align-items: flex-end;

+
+

}

+

/* <DEPRECATED> / +.p-TabBar[data-orientation=’vertical’], / </DEPRECATED> */ +.lm-TabBar[data-orientation=’vertical’] {

+
+

flex-direction: column; +align-items: flex-end;

+
+

}

+

/* <DEPRECATED> / +.p-TabBar-content, / </DEPRECATED> */ +.lm-TabBar-content {

+
+

margin: 0; +padding: 0; +display: flex; +flex: 1 1 auto; +list-style-type: none;

+
+

}

+

/* <DEPRECATED> / +.p-TabBar[data-orientation=’horizontal’] > .p-TabBar-content, +/ </DEPRECATED> */ +.lm-TabBar[data-orientation=’horizontal’] > .lm-TabBar-content {

+
+

flex-direction: row;

+
+

}

+

/* <DEPRECATED> / +.p-TabBar[data-orientation=’vertical’] > .p-TabBar-content, +/ </DEPRECATED> */ +.lm-TabBar[data-orientation=’vertical’] > .lm-TabBar-content {

+
+

flex-direction: column;

+
+

}

+

/* <DEPRECATED> / +.p-TabBar-tab, / </DEPRECATED> */ +.lm-TabBar-tab {

+
+

display: flex; +flex-direction: row; +box-sizing: border-box; +overflow: hidden; +touch-action: none; /* Disable native Drag/Drop */

+
+

}

+

/* <DEPRECATED> / +.p-TabBar-tabIcon, +.p-TabBar-tabCloseIcon, +/ </DEPRECATED> */ +.lm-TabBar-tabIcon, +.lm-TabBar-tabCloseIcon {

+
+

flex: 0 0 auto;

+
+

}

+

/* <DEPRECATED> / +.p-TabBar-tabLabel, / </DEPRECATED> */ +.lm-TabBar-tabLabel {

+
+

flex: 1 1 auto; +overflow: hidden; +white-space: nowrap;

+
+

}

+
+
.lm-TabBar-tabInput {

user-select: all; +width: 100%; +box-sizing: border-box;

+
+
+

}

+

/* <DEPRECATED> / +.p-TabBar-tab.p-mod-hidden, / </DEPRECATED> */ +.lm-TabBar-tab.lm-mod-hidden {

+
+

display: none !important;

+
+

}

+
+
.lm-TabBar-addButton.lm-mod-hidden {

display: none !important;

+
+
+

}

+

/* <DEPRECATED> / +.p-TabBar.p-mod-dragging .p-TabBar-tab, / </DEPRECATED> */ +.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {

+
+

position: relative;

+
+

}

+

/* <DEPRECATED> / +.p-TabBar.p-mod-dragging[data-orientation=’horizontal’] .p-TabBar-tab, +/ </DEPRECATED> */ +.lm-TabBar.lm-mod-dragging[data-orientation=’horizontal’] .lm-TabBar-tab {

+
+

left: 0; +transition: left 150ms ease;

+
+

}

+

/* <DEPRECATED> / +.p-TabBar.p-mod-dragging[data-orientation=’vertical’] .p-TabBar-tab, +/ </DEPRECATED> */ +.lm-TabBar.lm-mod-dragging[data-orientation=’vertical’] .lm-TabBar-tab {

+
+

top: 0; +transition: top 150ms ease;

+
+

}

+

/* <DEPRECATED> / +.p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging, +/ </DEPRECATED> */ +.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {

+
+

transition: none;

+
+

}

+
+
.lm-TabBar-tabLabel .lm-TabBar-tabInput {

user-select: all; +width: 100%; +box-sizing: border-box; +background: inherit;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Copyright (c) 2014-2017, PhosphorJS Contributors +| +| Distributed under the terms of the BSD 3-Clause License. +| +| The full license is in the file LICENSE, distributed with this software. +|—————————————————————————-/

+

/* <DEPRECATED> / +.p-TabPanel-tabBar, / </DEPRECATED> */ +.lm-TabPanel-tabBar {

+
+

z-index: 1;

+
+

}

+

/* <DEPRECATED> / +.p-TabPanel-stackedPanel, / </DEPRECATED> */ +.lm-TabPanel-stackedPanel {

+
+

z-index: 0;

+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Copyright (c) 2014-2017, PhosphorJS Contributors +| +| Distributed under the terms of the BSD 3-Clause License. +| +| The full license is in the file LICENSE, distributed with this software. +|—————————————————————————-/

+

@charset “UTF-8”; +html{

+
+
+
-webkit-box-sizing:border-box;

box-sizing:border-box; }

+
+
+
+

*, +*::before, +*::after{

+
+
+
-webkit-box-sizing:inherit;

box-sizing:inherit; }

+
+
+
+
+
body{

font-size:14px; +font-weight:400; +letter-spacing:0; +line-height:1.28581; +text-transform:none; +color:#182026; +font-family:-apple-system, “BlinkMacSystemFont”, “Segoe UI”, “Roboto”, “Oxygen”, “Ubuntu”, “Cantarell”, “Open Sans”, “Helvetica Neue”, “Icons16”, sans-serif; }

+
+
p{

margin-bottom:10px; +margin-top:0; }

+
+
small{

font-size:12px; }

+
+
strong{

font-weight:600; }

+
+
::-moz-selection{

background:rgba(125, 188, 255, 0.6); }

+
+
::selection{

background:rgba(125, 188, 255, 0.6); }

+
+
.bp3-heading{

color:#182026; +font-weight:600; +margin:0 0 10px; +padding:0; } +.bp3-dark .bp3-heading{

+
+

color:#f5f8fa; }

+
+
+
h1.bp3-heading, .bp3-running-text h1{

font-size:36px; +line-height:40px; }

+
+
h2.bp3-heading, .bp3-running-text h2{

font-size:28px; +line-height:32px; }

+
+
h3.bp3-heading, .bp3-running-text h3{

font-size:22px; +line-height:25px; }

+
+
h4.bp3-heading, .bp3-running-text h4{

font-size:18px; +line-height:21px; }

+
+
h5.bp3-heading, .bp3-running-text h5{

font-size:16px; +line-height:19px; }

+
+
h6.bp3-heading, .bp3-running-text h6{

font-size:14px; +line-height:16px; }

+
+
.bp3-ui-text{

font-size:14px; +font-weight:400; +letter-spacing:0; +line-height:1.28581; +text-transform:none; }

+
+
.bp3-monospace-text{

font-family:monospace; +text-transform:none; }

+
+
.bp3-text-muted{

color:#5c7080; } +.bp3-dark .bp3-text-muted{

+
+

color:#a7b6c2; }

+
+
+
.bp3-text-disabled{

color:rgba(92, 112, 128, 0.6); } +.bp3-dark .bp3-text-disabled{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
.bp3-text-overflow-ellipsis{

overflow:hidden; +text-overflow:ellipsis; +white-space:nowrap; +word-wrap:normal; }

+
+
.bp3-running-text{

font-size:14px; +line-height:1.5; } +.bp3-running-text h1{

+
+

color:#182026; +font-weight:600; +margin-bottom:20px; +margin-top:40px; } +.bp3-dark .bp3-running-text h1{

+
+

color:#f5f8fa; }

+
+
+
+
.bp3-running-text h2{

color:#182026; +font-weight:600; +margin-bottom:20px; +margin-top:40px; } +.bp3-dark .bp3-running-text h2{

+
+

color:#f5f8fa; }

+
+
+
.bp3-running-text h3{

color:#182026; +font-weight:600; +margin-bottom:20px; +margin-top:40px; } +.bp3-dark .bp3-running-text h3{

+
+

color:#f5f8fa; }

+
+
+
.bp3-running-text h4{

color:#182026; +font-weight:600; +margin-bottom:20px; +margin-top:40px; } +.bp3-dark .bp3-running-text h4{

+
+

color:#f5f8fa; }

+
+
+
.bp3-running-text h5{

color:#182026; +font-weight:600; +margin-bottom:20px; +margin-top:40px; } +.bp3-dark .bp3-running-text h5{

+
+

color:#f5f8fa; }

+
+
+
.bp3-running-text h6{

color:#182026; +font-weight:600; +margin-bottom:20px; +margin-top:40px; } +.bp3-dark .bp3-running-text h6{

+
+

color:#f5f8fa; }

+
+
+
.bp3-running-text hr{

border:none; +border-bottom:1px solid rgba(16, 22, 26, 0.15); +margin:20px 0; } +.bp3-dark .bp3-running-text hr{

+
+

border-color:rgba(255, 255, 255, 0.15); }

+
+
+
.bp3-running-text p{

margin:0 0 10px; +padding:0; }

+
+
+
+
.bp3-text-large{

font-size:16px; }

+
+
.bp3-text-small{

font-size:12px; }

+
+
a{

color:#106ba3; +text-decoration:none; } +a:hover{

+
+

color:#106ba3; +cursor:pointer; +text-decoration:underline; }

+
+
+
a .bp3-icon, a .bp3-icon-standard, a .bp3-icon-large{

color:inherit; }

+
+
+

a code, +.bp3-dark a code{

+
+

color:inherit; }

+
+

.bp3-dark a, +.bp3-dark a:hover{

+
+

color:#48aff0; } +.bp3-dark a .bp3-icon, .bp3-dark a .bp3-icon-standard, .bp3-dark a .bp3-icon-large, +.bp3-dark a:hover .bp3-icon, +.bp3-dark a:hover .bp3-icon-standard, +.bp3-dark a:hover .bp3-icon-large{

+
+

color:inherit; }

+
+
+
+
.bp3-running-text code, .bp3-code{

font-family:monospace; +text-transform:none; +background:rgba(255, 255, 255, 0.7); +border-radius:3px; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2);

+
+

color:#5c7080; +font-size:smaller; +padding:2px 5px; } +.bp3-dark .bp3-running-text code, .bp3-running-text .bp3-dark code, .bp3-dark .bp3-code{

+
+

background:rgba(16, 22, 26, 0.3); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

color:#a7b6c2; }

+
+
+
.bp3-running-text a > code, a > .bp3-code{

color:#137cbd; } +.bp3-dark .bp3-running-text a > code, .bp3-running-text .bp3-dark a > code, .bp3-dark a > .bp3-code{

+
+

color:inherit; }

+
+
+
+
+
.bp3-running-text pre, .bp3-code-block{

font-family:monospace; +text-transform:none; +background:rgba(255, 255, 255, 0.7); +border-radius:3px; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);

+
+

color:#182026; +display:block; +font-size:13px; +line-height:1.4; +margin:10px 0; +padding:13px 15px 12px; +word-break:break-all; +word-wrap:break-word; } +.bp3-dark .bp3-running-text pre, .bp3-running-text .bp3-dark pre, .bp3-dark .bp3-code-block{

+
+

background:rgba(16, 22, 26, 0.3); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

color:#f5f8fa; }

+
+
+
.bp3-running-text pre > code, .bp3-code-block > code{

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:inherit; +font-size:inherit; +padding:0; }

+
+
+
+
.bp3-running-text kbd, .bp3-key{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

background:#ffffff; +border-radius:3px; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

color:#5c7080; +display:-webkit-inline-box; +display:-ms-inline-flexbox; +display:inline-flex; +font-family:inherit; +font-size:12px; +height:24px; +-webkit-box-pack:center;

+
+
+
-ms-flex-pack:center;

justify-content:center;

+
+
+
+

line-height:24px; +min-width:24px; +padding:3px 6px; +vertical-align:middle; } +.bp3-running-text kbd .bp3-icon, .bp3-key .bp3-icon, .bp3-running-text kbd .bp3-icon-standard, .bp3-key .bp3-icon-standard, .bp3-running-text kbd .bp3-icon-large, .bp3-key .bp3-icon-large{

+
+

margin-right:5px; }

+
+
+
.bp3-dark .bp3-running-text kbd, .bp3-running-text .bp3-dark kbd, .bp3-dark .bp3-key{

background:#394b59; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);

+
+

color:#a7b6c2; }

+
+
+
+
.bp3-running-text blockquote, .bp3-blockquote{

border-left:solid 4px rgba(167, 182, 194, 0.5); +margin:0 0 10px; +padding:0 20px; } +.bp3-dark .bp3-running-text blockquote, .bp3-running-text .bp3-dark blockquote, .bp3-dark .bp3-blockquote{

+
+

border-color:rgba(115, 134, 148, 0.5); }

+
+
+
+

.bp3-running-text ul, +.bp3-running-text ol, .bp3-list{

+
+

margin:10px 0; +padding-left:30px; } +.bp3-running-text ul li:not(:last-child), .bp3-running-text ol li:not(:last-child), .bp3-list li:not(:last-child){

+
+

margin-bottom:5px; }

+
+

.bp3-running-text ul ol, .bp3-running-text ol ol, .bp3-list ol, +.bp3-running-text ul ul, +.bp3-running-text ol ul, +.bp3-list ul{

+
+

margin-top:5px; }

+
+
+
+
.bp3-list-unstyled{

list-style:none; +margin:0; +padding:0; } +.bp3-list-unstyled li{

+
+

padding:0; }

+
+
+
.bp3-rtl{

text-align:right; }

+
+
.bp3-dark{

color:#f5f8fa; }

+
+
:focus{

outline:rgba(19, 124, 189, 0.6) auto 2px; +outline-offset:2px; +-moz-outline-radius:6px; }

+
+
.bp3-focus-disabled :focus{

outline:none !important; } +.bp3-focus-disabled :focus ~ .bp3-control-indicator{

+
+

outline:none !important; }

+
+
+
.bp3-alert{

max-width:400px; +padding:20px; }

+
+
.bp3-alert-body{

display:-webkit-box; +display:-ms-flexbox; +display:flex; } +.bp3-alert-body .bp3-icon{

+
+

font-size:40px; +margin-right:20px; +margin-top:0; }

+
+
+
.bp3-alert-contents{

word-break:break-word; }

+
+
.bp3-alert-footer{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:horizontal; +-webkit-box-direction:reverse;

+
+
+
-ms-flex-direction:row-reverse;

flex-direction:row-reverse;

+
+
+
+

margin-top:10px; } +.bp3-alert-footer .bp3-button{

+
+

margin-left:10px; }

+
+
+
.bp3-breadcrumbs{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

cursor:default; +display:-webkit-box; +display:-ms-flexbox; +display:flex; +-ms-flex-wrap:wrap;

+
+

flex-wrap:wrap;

+
+

height:30px; +list-style:none; +margin:0; +padding:0; } +.bp3-breadcrumbs > li{

+
+
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; } +.bp3-breadcrumbs > li::after{

+
+

background:url(”data:image/svg+xml,%3csvg xmlns=’http://www.w3.org/2000/svg’ viewBox=’0 0 16 16’%3e%3cpath fill-rule=’evenodd’ clip-rule=’evenodd’ d=’M10.71 7.29l-4-4a1.003 1.003 0 00-1.42 1.42L8.59 8 5.3 11.29c-.19.18-.3.43-.3.71a1.003 1.003 0 001.71.71l4-4c.18-.18.29-.43.29-.71 0-.28-.11-.53-.29-.71z’ fill=’%235C7080’/%3e%3c/svg%3e”); +content:””; +display:block; +height:16px; +margin:0 5px; +width:16px; }

+
+
+
.bp3-breadcrumbs > li:last-of-type::after{

display:none; }

+
+
+
+
+
+

.bp3-breadcrumb, +.bp3-breadcrumb-current, +.bp3-breadcrumbs-collapsed{

+
+
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

display:-webkit-inline-box; +display:-ms-inline-flexbox; +display:inline-flex; +font-size:16px; }

+
+

.bp3-breadcrumb, +.bp3-breadcrumbs-collapsed{

+
+

color:#5c7080; }

+
+
+
.bp3-breadcrumb:hover{

text-decoration:none; }

+
+
.bp3-breadcrumb.bp3-disabled{

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; }

+
+
.bp3-breadcrumb .bp3-icon{

margin-right:5px; }

+
+
.bp3-breadcrumb-current{

color:inherit; +font-weight:600; } +.bp3-breadcrumb-current .bp3-input{

+
+

font-size:inherit; +font-weight:inherit; +vertical-align:baseline; }

+
+
+
.bp3-breadcrumbs-collapsed{

background:#ced9e0; +border:none; +border-radius:3px; +cursor:pointer; +margin-right:2px; +padding:1px 5px; +vertical-align:text-bottom; } +.bp3-breadcrumbs-collapsed::before{

+
+

background:url(”data:image/svg+xml,%3csvg xmlns=’http://www.w3.org/2000/svg’ viewBox=’0 0 16 16’%3e%3cg fill=’%235C7080’%3e%3ccircle cx=’2’ cy=’8.03’ r=’2’/%3e%3ccircle cx=’14’ cy=’8.03’ r=’2’/%3e%3ccircle cx=’8’ cy=’8.03’ r=’2’/%3e%3c/g%3e%3c/svg%3e”) center no-repeat; +content:””; +display:block; +height:16px; +width:16px; }

+
+
+
.bp3-breadcrumbs-collapsed:hover{

background:#bfccd6; +color:#182026; +text-decoration:none; }

+
+
+
+
+

.bp3-dark .bp3-breadcrumb, +.bp3-dark .bp3-breadcrumbs-collapsed{

+
+

color:#a7b6c2; }

+
+
+
.bp3-dark .bp3-breadcrumbs > li::after{

color:#a7b6c2; }

+
+
.bp3-dark .bp3-breadcrumb.bp3-disabled{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-breadcrumb-current{

color:#f5f8fa; }

+
+
.bp3-dark .bp3-breadcrumbs-collapsed{

background:rgba(16, 22, 26, 0.4); } +.bp3-dark .bp3-breadcrumbs-collapsed:hover{

+
+

background:rgba(16, 22, 26, 0.6); +color:#f5f8fa; }

+
+
+
.bp3-button{

display:-webkit-inline-box; +display:-ms-inline-flexbox; +display:inline-flex; +-webkit-box-orient:horizontal; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:row;

flex-direction:row;

+
+
+
+
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

border:none; +border-radius:3px; +cursor:pointer; +font-size:14px; +-webkit-box-pack:center;

+
+
+
-ms-flex-pack:center;

justify-content:center;

+
+
+
+

padding:5px 10px; +text-align:left; +vertical-align:middle; +min-height:30px; +min-width:30px; } +.bp3-button > *{

+
+
+
-webkit-box-flex:0;
+
-ms-flex-positive:0;

flex-grow:0;

+
+
+
+
-ms-flex-negative:0;

flex-shrink:0; }

+
+
+
+
+
.bp3-button > .bp3-fill{
+
-webkit-box-flex:1;
+
-ms-flex-positive:1;

flex-grow:1;

+
+
+
+
-ms-flex-negative:1;

flex-shrink:1; }

+
+
+
+
+

.bp3-button::before, +.bp3-button > *{

+
+

margin-right:7px; }

+
+

.bp3-button:empty::before, +.bp3-button > :last-child{

+
+

margin-right:0; }

+
+
+
.bp3-button:empty{

padding:0 !important; }

+
+
.bp3-button:disabled, .bp3-button.bp3-disabled{

cursor:not-allowed; }

+
+
.bp3-button.bp3-fill{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +width:100%; }

+
+
+

.bp3-button.bp3-align-right, +.bp3-align-right .bp3-button{

+
+

text-align:right; }

+
+

.bp3-button.bp3-align-left, +.bp3-align-left .bp3-button{

+
+

text-align:left; }

+
+
+
.bp3-button:not([class*=”bp3-intent-“]){

background-color:#f5f8fa; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0)); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

color:#182026; } +.bp3-button:not([class*=”bp3-intent-“]):hover{

+
+

background-clip:padding-box; +background-color:#ebf1f5; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }

+
+
+
+
.bp3-button:not([class*=”bp3-intent-“]):active, .bp3-button:not([class*=”bp3-intent-“]).bp3-active{

background-color:#d8e1e8; +background-image:none; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-button:not([class*=”bp3-intent-“]):disabled, .bp3-button:not([class*=”bp3-intent-“]).bp3-disabled{

background-color:rgba(206, 217, 224, 0.5); +background-image:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; +outline:none; } +.bp3-button:not([class*=”bp3-intent-“]):disabled.bp3-active, .bp3-button:not([class*=”bp3-intent-“]):disabled.bp3-active:hover, .bp3-button:not([class*=”bp3-intent-“]).bp3-disabled.bp3-active, .bp3-button:not([class*=”bp3-intent-“]).bp3-disabled.bp3-active:hover{

+
+

background:rgba(206, 217, 224, 0.7); }

+
+
+
+
+
.bp3-button.bp3-intent-primary{

background-color:#137cbd; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

color:#ffffff; } +.bp3-button.bp3-intent-primary:hover, .bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{

+
+

color:#ffffff; }

+
+
+
.bp3-button.bp3-intent-primary:hover{

background-color:#106ba3; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{

background-color:#0e5a8a; +background-image:none; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-button.bp3-intent-primary:disabled, .bp3-button.bp3-intent-primary.bp3-disabled{

background-color:rgba(19, 124, 189, 0.5); +background-image:none; +border-color:transparent; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(255, 255, 255, 0.6); }

+
+
+
+
.bp3-button.bp3-intent-success{

background-color:#0f9960; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

color:#ffffff; } +.bp3-button.bp3-intent-success:hover, .bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{

+
+

color:#ffffff; }

+
+
+
.bp3-button.bp3-intent-success:hover{

background-color:#0d8050; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{

background-color:#0a6640; +background-image:none; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-button.bp3-intent-success:disabled, .bp3-button.bp3-intent-success.bp3-disabled{

background-color:rgba(15, 153, 96, 0.5); +background-image:none; +border-color:transparent; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(255, 255, 255, 0.6); }

+
+
+
+
.bp3-button.bp3-intent-warning{

background-color:#d9822b; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

color:#ffffff; } +.bp3-button.bp3-intent-warning:hover, .bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{

+
+

color:#ffffff; }

+
+
+
.bp3-button.bp3-intent-warning:hover{

background-color:#bf7326; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{

background-color:#a66321; +background-image:none; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-button.bp3-intent-warning:disabled, .bp3-button.bp3-intent-warning.bp3-disabled{

background-color:rgba(217, 130, 43, 0.5); +background-image:none; +border-color:transparent; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(255, 255, 255, 0.6); }

+
+
+
+
.bp3-button.bp3-intent-danger{

background-color:#db3737; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

color:#ffffff; } +.bp3-button.bp3-intent-danger:hover, .bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{

+
+

color:#ffffff; }

+
+
+
.bp3-button.bp3-intent-danger:hover{

background-color:#c23030; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{

background-color:#a82a2a; +background-image:none; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-button.bp3-intent-danger:disabled, .bp3-button.bp3-intent-danger.bp3-disabled{

background-color:rgba(219, 55, 55, 0.5); +background-image:none; +border-color:transparent; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(255, 255, 255, 0.6); }

+
+
+
+
.bp3-button[class*=”bp3-intent-”] .bp3-button-spinner .bp3-spinner-head{

stroke:#ffffff; }

+
+
+

.bp3-button.bp3-large, +.bp3-large .bp3-button{

+
+

min-height:40px; +min-width:40px; +font-size:16px; +padding:5px 15px; } +.bp3-button.bp3-large::before, +.bp3-button.bp3-large > *, +.bp3-large .bp3-button::before, +.bp3-large .bp3-button > *{

+
+

margin-right:10px; }

+
+

.bp3-button.bp3-large:empty::before, +.bp3-button.bp3-large > :last-child, +.bp3-large .bp3-button:empty::before, +.bp3-large .bp3-button > :last-child{

+
+

margin-right:0; }

+
+
+

.bp3-button.bp3-small, +.bp3-small .bp3-button{

+
+

min-height:24px; +min-width:24px; +padding:0 7px; }

+
+
+
.bp3-button.bp3-loading{

position:relative; } +.bp3-button.bp3-loading[class*=”bp3-icon-“]::before{

+
+

visibility:hidden; }

+
+
+
.bp3-button.bp3-loading .bp3-button-spinner{

margin:0; +position:absolute; }

+
+
.bp3-button.bp3-loading > :not(.bp3-button-spinner){

visibility:hidden; }

+
+
+
+
.bp3-button[class*=”bp3-icon-“]::before{

font-family:”Icons16”, sans-serif; +font-size:16px; +font-style:normal; +font-weight:400; +line-height:1; +-moz-osx-font-smoothing:grayscale; +-webkit-font-smoothing:antialiased; +color:#5c7080; }

+
+
.bp3-button .bp3-icon, .bp3-button .bp3-icon-standard, .bp3-button .bp3-icon-large{

color:#5c7080; } +.bp3-button .bp3-icon.bp3-align-right, .bp3-button .bp3-icon-standard.bp3-align-right, .bp3-button .bp3-icon-large.bp3-align-right{

+
+

margin-left:7px; }

+
+
+
+

.bp3-button .bp3-icon:first-child:last-child, +.bp3-button .bp3-spinner + .bp3-icon:last-child{

+
+

margin:0 -7px; }

+
+
+
.bp3-dark .bp3-button:not([class*=”bp3-intent-“]){

background-color:#394b59; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

color:#f5f8fa; } +.bp3-dark .bp3-button:not([class*=”bp3-intent-“]):hover, .bp3-dark .bp3-button:not([class*=”bp3-intent-“]):active, .bp3-dark .bp3-button:not([class*=”bp3-intent-“]).bp3-active{

+
+

color:#f5f8fa; }

+
+
+
.bp3-dark .bp3-button:not([class*=”bp3-intent-“]):hover{

background-color:#30404d; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-button:not([class*=”bp3-intent-“]):active, .bp3-dark .bp3-button:not([class*=”bp3-intent-“]).bp3-active{

background-color:#202b33; +background-image:none; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-dark .bp3-button:not([class*=”bp3-intent-“]):disabled, .bp3-dark .bp3-button:not([class*=”bp3-intent-“]).bp3-disabled{

background-color:rgba(57, 75, 89, 0.5); +background-image:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(167, 182, 194, 0.6); } +.bp3-dark .bp3-button:not([class*=”bp3-intent-“]):disabled.bp3-active, .bp3-dark .bp3-button:not([class*=”bp3-intent-“]).bp3-disabled.bp3-active{

+
+

background:rgba(57, 75, 89, 0.7); }

+
+
+
.bp3-dark .bp3-button:not([class*=”bp3-intent-“]) .bp3-button-spinner .bp3-spinner-head{

background:rgba(16, 22, 26, 0.5); +stroke:#8a9ba8; }

+
+
.bp3-dark .bp3-button:not([class*=”bp3-intent-“])[class*=”bp3-icon-“]::before{

color:#a7b6c2; }

+
+
.bp3-dark .bp3-button:not([class*=”bp3-intent-“]) .bp3-icon, .bp3-dark .bp3-button:not([class*=”bp3-intent-“]) .bp3-icon-standard, .bp3-dark .bp3-button:not([class*=”bp3-intent-“]) .bp3-icon-large{

color:#a7b6c2; }

+
+
+
+
.bp3-dark .bp3-button[class*=”bp3-intent-“]{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
.bp3-dark .bp3-button[class*=”bp3-intent-“]:hover{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-button[class*=”bp3-intent-“]:active, .bp3-dark .bp3-button[class*=”bp3-intent-“].bp3-active{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-dark .bp3-button[class*=”bp3-intent-“]:disabled, .bp3-dark .bp3-button[class*=”bp3-intent-“].bp3-disabled{

background-image:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(255, 255, 255, 0.3); }

+
+
.bp3-dark .bp3-button[class*=”bp3-intent-”] .bp3-button-spinner .bp3-spinner-head{

stroke:#8a9ba8; }

+
+
+
+
+

.bp3-button:disabled::before, +.bp3-button:disabled .bp3-icon, .bp3-button:disabled .bp3-icon-standard, .bp3-button:disabled .bp3-icon-large, .bp3-button.bp3-disabled::before, +.bp3-button.bp3-disabled .bp3-icon, .bp3-button.bp3-disabled .bp3-icon-standard, .bp3-button.bp3-disabled .bp3-icon-large, .bp3-button[class*=”bp3-intent-“]::before, +.bp3-button[class*=”bp3-intent-”] .bp3-icon, .bp3-button[class*=”bp3-intent-”] .bp3-icon-standard, .bp3-button[class*=”bp3-intent-”] .bp3-icon-large{

+
+

color:inherit !important; }

+
+
+
.bp3-button.bp3-minimal{

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+
.bp3-button.bp3-minimal:hover{

background:rgba(167, 182, 194, 0.3); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#182026; +text-decoration:none; }

+
+
.bp3-button.bp3-minimal:active, .bp3-button.bp3-minimal.bp3-active{

background:rgba(115, 134, 148, 0.3); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#182026; }

+
+
.bp3-button.bp3-minimal:disabled, .bp3-button.bp3-minimal:disabled:hover, .bp3-button.bp3-minimal.bp3-disabled, .bp3-button.bp3-minimal.bp3-disabled:hover{

background:none; +color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; } +.bp3-button.bp3-minimal:disabled.bp3-active, .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{

+
+

background:rgba(115, 134, 148, 0.3); }

+
+
+
.bp3-dark .bp3-button.bp3-minimal{

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:inherit; } +.bp3-dark .bp3-button.bp3-minimal:hover, .bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+
+
.bp3-dark .bp3-button.bp3-minimal:hover{

background:rgba(138, 155, 168, 0.15); }

+
+
.bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{

background:rgba(138, 155, 168, 0.3); +color:#f5f8fa; }

+
+
.bp3-dark .bp3-button.bp3-minimal:disabled, .bp3-dark .bp3-button.bp3-minimal:disabled:hover, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover{

background:none; +color:rgba(167, 182, 194, 0.6); +cursor:not-allowed; } +.bp3-dark .bp3-button.bp3-minimal:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{

+
+

background:rgba(138, 155, 168, 0.3); }

+
+
+
+
+
.bp3-button.bp3-minimal.bp3-intent-primary{

color:#106ba3; } +.bp3-button.bp3-minimal.bp3-intent-primary:hover, .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#106ba3; }

+
+
+
.bp3-button.bp3-minimal.bp3-intent-primary:hover{

background:rgba(19, 124, 189, 0.15); +color:#106ba3; }

+
+
.bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{

background:rgba(19, 124, 189, 0.3); +color:#106ba3; }

+
+
.bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{

background:none; +color:rgba(16, 107, 163, 0.5); } +.bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{

+
+

background:rgba(19, 124, 189, 0.3); }

+
+
+
.bp3-button.bp3-minimal.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{

stroke:#106ba3; }

+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary{

color:#48aff0; } +.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:hover{

+
+

background:rgba(19, 124, 189, 0.2); +color:#48aff0; }

+
+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{

background:rgba(19, 124, 189, 0.3); +color:#48aff0; }

+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{

background:none; +color:rgba(72, 175, 240, 0.5); } +.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{

+
+

background:rgba(19, 124, 189, 0.3); }

+
+
+
+
+
+
+
.bp3-button.bp3-minimal.bp3-intent-success{

color:#0d8050; } +.bp3-button.bp3-minimal.bp3-intent-success:hover, .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#0d8050; }

+
+
+
.bp3-button.bp3-minimal.bp3-intent-success:hover{

background:rgba(15, 153, 96, 0.15); +color:#0d8050; }

+
+
.bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{

background:rgba(15, 153, 96, 0.3); +color:#0d8050; }

+
+
.bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{

background:none; +color:rgba(13, 128, 80, 0.5); } +.bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{

+
+

background:rgba(15, 153, 96, 0.3); }

+
+
+
.bp3-button.bp3-minimal.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{

stroke:#0d8050; }

+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success{

color:#3dcc91; } +.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:hover{

+
+

background:rgba(15, 153, 96, 0.2); +color:#3dcc91; }

+
+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{

background:rgba(15, 153, 96, 0.3); +color:#3dcc91; }

+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{

background:none; +color:rgba(61, 204, 145, 0.5); } +.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{

+
+

background:rgba(15, 153, 96, 0.3); }

+
+
+
+
+
+
+
.bp3-button.bp3-minimal.bp3-intent-warning{

color:#bf7326; } +.bp3-button.bp3-minimal.bp3-intent-warning:hover, .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#bf7326; }

+
+
+
.bp3-button.bp3-minimal.bp3-intent-warning:hover{

background:rgba(217, 130, 43, 0.15); +color:#bf7326; }

+
+
.bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{

background:rgba(217, 130, 43, 0.3); +color:#bf7326; }

+
+
.bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{

background:none; +color:rgba(191, 115, 38, 0.5); } +.bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{

+
+

background:rgba(217, 130, 43, 0.3); }

+
+
+
.bp3-button.bp3-minimal.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{

stroke:#bf7326; }

+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning{

color:#ffb366; } +.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:hover{

+
+

background:rgba(217, 130, 43, 0.2); +color:#ffb366; }

+
+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{

background:rgba(217, 130, 43, 0.3); +color:#ffb366; }

+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{

background:none; +color:rgba(255, 179, 102, 0.5); } +.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{

+
+

background:rgba(217, 130, 43, 0.3); }

+
+
+
+
+
+
+
.bp3-button.bp3-minimal.bp3-intent-danger{

color:#c23030; } +.bp3-button.bp3-minimal.bp3-intent-danger:hover, .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#c23030; }

+
+
+
.bp3-button.bp3-minimal.bp3-intent-danger:hover{

background:rgba(219, 55, 55, 0.15); +color:#c23030; }

+
+
.bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{

background:rgba(219, 55, 55, 0.3); +color:#c23030; }

+
+
.bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{

background:none; +color:rgba(194, 48, 48, 0.5); } +.bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{

+
+

background:rgba(219, 55, 55, 0.3); }

+
+
+
.bp3-button.bp3-minimal.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{

stroke:#c23030; }

+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger{

color:#ff7373; } +.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:hover{

+
+

background:rgba(219, 55, 55, 0.2); +color:#ff7373; }

+
+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{

background:rgba(219, 55, 55, 0.3); +color:#ff7373; }

+
+
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{

background:none; +color:rgba(255, 115, 115, 0.5); } +.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{

+
+

background:rgba(219, 55, 55, 0.3); }

+
+
+
+
+
+
+
+
+
.bp3-button.bp3-outlined{

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

border:1px solid rgba(24, 32, 38, 0.2); +-webkit-box-sizing:border-box;

+
+

box-sizing:border-box; }

+
+
+
.bp3-button.bp3-outlined:hover{

background:rgba(167, 182, 194, 0.3); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#182026; +text-decoration:none; }

+
+
.bp3-button.bp3-outlined:active, .bp3-button.bp3-outlined.bp3-active{

background:rgba(115, 134, 148, 0.3); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#182026; }

+
+
.bp3-button.bp3-outlined:disabled, .bp3-button.bp3-outlined:disabled:hover, .bp3-button.bp3-outlined.bp3-disabled, .bp3-button.bp3-outlined.bp3-disabled:hover{

background:none; +color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; } +.bp3-button.bp3-outlined:disabled.bp3-active, .bp3-button.bp3-outlined:disabled:hover.bp3-active, .bp3-button.bp3-outlined.bp3-disabled.bp3-active, .bp3-button.bp3-outlined.bp3-disabled:hover.bp3-active{

+
+

background:rgba(115, 134, 148, 0.3); }

+
+
+
.bp3-dark .bp3-button.bp3-outlined{

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:inherit; } +.bp3-dark .bp3-button.bp3-outlined:hover, .bp3-dark .bp3-button.bp3-outlined:active, .bp3-dark .bp3-button.bp3-outlined.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+
+
.bp3-dark .bp3-button.bp3-outlined:hover{

background:rgba(138, 155, 168, 0.15); }

+
+
.bp3-dark .bp3-button.bp3-outlined:active, .bp3-dark .bp3-button.bp3-outlined.bp3-active{

background:rgba(138, 155, 168, 0.3); +color:#f5f8fa; }

+
+
.bp3-dark .bp3-button.bp3-outlined:disabled, .bp3-dark .bp3-button.bp3-outlined:disabled:hover, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover{

background:none; +color:rgba(167, 182, 194, 0.6); +cursor:not-allowed; } +.bp3-dark .bp3-button.bp3-outlined:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined:disabled:hover.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover.bp3-active{

+
+

background:rgba(138, 155, 168, 0.3); }

+
+
+
+
+
.bp3-button.bp3-outlined.bp3-intent-primary{

color:#106ba3; } +.bp3-button.bp3-outlined.bp3-intent-primary:hover, .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#106ba3; }

+
+
+
.bp3-button.bp3-outlined.bp3-intent-primary:hover{

background:rgba(19, 124, 189, 0.15); +color:#106ba3; }

+
+
.bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{

background:rgba(19, 124, 189, 0.3); +color:#106ba3; }

+
+
.bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{

background:none; +color:rgba(16, 107, 163, 0.5); } +.bp3-button.bp3-outlined.bp3-intent-primary:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled.bp3-active{

+
+

background:rgba(19, 124, 189, 0.3); }

+
+
+
.bp3-button.bp3-outlined.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{

stroke:#106ba3; }

+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary{

color:#48aff0; } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:hover{

+
+

background:rgba(19, 124, 189, 0.2); +color:#48aff0; }

+
+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{

background:rgba(19, 124, 189, 0.3); +color:#48aff0; }

+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{

background:none; +color:rgba(72, 175, 240, 0.5); } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled.bp3-active{

+
+

background:rgba(19, 124, 189, 0.3); }

+
+
+
+
+
+
+
.bp3-button.bp3-outlined.bp3-intent-success{

color:#0d8050; } +.bp3-button.bp3-outlined.bp3-intent-success:hover, .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#0d8050; }

+
+
+
.bp3-button.bp3-outlined.bp3-intent-success:hover{

background:rgba(15, 153, 96, 0.15); +color:#0d8050; }

+
+
.bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{

background:rgba(15, 153, 96, 0.3); +color:#0d8050; }

+
+
.bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{

background:none; +color:rgba(13, 128, 80, 0.5); } +.bp3-button.bp3-outlined.bp3-intent-success:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled.bp3-active{

+
+

background:rgba(15, 153, 96, 0.3); }

+
+
+
.bp3-button.bp3-outlined.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{

stroke:#0d8050; }

+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success{

color:#3dcc91; } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:hover{

+
+

background:rgba(15, 153, 96, 0.2); +color:#3dcc91; }

+
+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{

background:rgba(15, 153, 96, 0.3); +color:#3dcc91; }

+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{

background:none; +color:rgba(61, 204, 145, 0.5); } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled.bp3-active{

+
+

background:rgba(15, 153, 96, 0.3); }

+
+
+
+
+
+
+
.bp3-button.bp3-outlined.bp3-intent-warning{

color:#bf7326; } +.bp3-button.bp3-outlined.bp3-intent-warning:hover, .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#bf7326; }

+
+
+
.bp3-button.bp3-outlined.bp3-intent-warning:hover{

background:rgba(217, 130, 43, 0.15); +color:#bf7326; }

+
+
.bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{

background:rgba(217, 130, 43, 0.3); +color:#bf7326; }

+
+
.bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{

background:none; +color:rgba(191, 115, 38, 0.5); } +.bp3-button.bp3-outlined.bp3-intent-warning:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled.bp3-active{

+
+

background:rgba(217, 130, 43, 0.3); }

+
+
+
.bp3-button.bp3-outlined.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{

stroke:#bf7326; }

+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning{

color:#ffb366; } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:hover{

+
+

background:rgba(217, 130, 43, 0.2); +color:#ffb366; }

+
+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{

background:rgba(217, 130, 43, 0.3); +color:#ffb366; }

+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{

background:none; +color:rgba(255, 179, 102, 0.5); } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled.bp3-active{

+
+

background:rgba(217, 130, 43, 0.3); }

+
+
+
+
+
+
+
.bp3-button.bp3-outlined.bp3-intent-danger{

color:#c23030; } +.bp3-button.bp3-outlined.bp3-intent-danger:hover, .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#c23030; }

+
+
+
.bp3-button.bp3-outlined.bp3-intent-danger:hover{

background:rgba(219, 55, 55, 0.15); +color:#c23030; }

+
+
.bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{

background:rgba(219, 55, 55, 0.3); +color:#c23030; }

+
+
.bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{

background:none; +color:rgba(194, 48, 48, 0.5); } +.bp3-button.bp3-outlined.bp3-intent-danger:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled.bp3-active{

+
+

background:rgba(219, 55, 55, 0.3); }

+
+
+
.bp3-button.bp3-outlined.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{

stroke:#c23030; }

+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger{

color:#ff7373; } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:hover{

+
+

background:rgba(219, 55, 55, 0.2); +color:#ff7373; }

+
+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{

background:rgba(219, 55, 55, 0.3); +color:#ff7373; }

+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{

background:none; +color:rgba(255, 115, 115, 0.5); } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled.bp3-active{

+
+

background:rgba(219, 55, 55, 0.3); }

+
+
+
+
+
+
+
.bp3-button.bp3-outlined:disabled, .bp3-button.bp3-outlined.bp3-disabled, .bp3-button.bp3-outlined:disabled:hover, .bp3-button.bp3-outlined.bp3-disabled:hover{

border-color:rgba(92, 112, 128, 0.1); }

+
+
.bp3-dark .bp3-button.bp3-outlined{

border-color:rgba(255, 255, 255, 0.4); } +.bp3-dark .bp3-button.bp3-outlined:disabled, .bp3-dark .bp3-button.bp3-outlined:disabled:hover, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover{

+
+

border-color:rgba(255, 255, 255, 0.2); }

+
+
+
.bp3-button.bp3-outlined.bp3-intent-primary{

border-color:rgba(16, 107, 163, 0.6); } +.bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{

+
+

border-color:rgba(16, 107, 163, 0.2); }

+
+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary{

border-color:rgba(72, 175, 240, 0.6); } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{

+
+

border-color:rgba(72, 175, 240, 0.2); }

+
+
+
+
+
.bp3-button.bp3-outlined.bp3-intent-success{

border-color:rgba(13, 128, 80, 0.6); } +.bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{

+
+

border-color:rgba(13, 128, 80, 0.2); }

+
+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success{

border-color:rgba(61, 204, 145, 0.6); } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{

+
+

border-color:rgba(61, 204, 145, 0.2); }

+
+
+
+
+
.bp3-button.bp3-outlined.bp3-intent-warning{

border-color:rgba(191, 115, 38, 0.6); } +.bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{

+
+

border-color:rgba(191, 115, 38, 0.2); }

+
+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning{

border-color:rgba(255, 179, 102, 0.6); } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{

+
+

border-color:rgba(255, 179, 102, 0.2); }

+
+
+
+
+
.bp3-button.bp3-outlined.bp3-intent-danger{

border-color:rgba(194, 48, 48, 0.6); } +.bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{

+
+

border-color:rgba(194, 48, 48, 0.2); }

+
+
+
.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger{

border-color:rgba(255, 115, 115, 0.6); } +.bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{

+
+

border-color:rgba(255, 115, 115, 0.2); }

+
+
+
+
+
+
+
+
+
a.bp3-button{

text-align:center; +text-decoration:none; +-webkit-transition:none; +transition:none; } +a.bp3-button, a.bp3-button:hover, a.bp3-button:active{

+
+

color:#182026; }

+
+
+
a.bp3-button.bp3-disabled{

color:rgba(92, 112, 128, 0.6); }

+
+
+
+
.bp3-button-text{
+
-webkit-box-flex:0;
+
-ms-flex:0 1 auto;

flex:0 1 auto; }

+
+
+
+
+
+
+

.bp3-button.bp3-align-left .bp3-button-text, .bp3-button.bp3-align-right .bp3-button-text, +.bp3-button-group.bp3-align-left .bp3-button-text, +.bp3-button-group.bp3-align-right .bp3-button-text{

+
+
+
-webkit-box-flex:1;
+
-ms-flex:1 1 auto;

flex:1 1 auto; }

+
+
+
+
+
+
+
.bp3-button-group{

display:-webkit-inline-box; +display:-ms-inline-flexbox; +display:inline-flex; } +.bp3-button-group .bp3-button{

+
+
+
-webkit-box-flex:0;
+
-ms-flex:0 0 auto;

flex:0 0 auto;

+
+
+
+
+

position:relative; +z-index:4; } +.bp3-button-group .bp3-button:focus{

+
+

z-index:5; }

+
+
+
.bp3-button-group .bp3-button:hover{

z-index:6; }

+
+
.bp3-button-group .bp3-button:active, .bp3-button-group .bp3-button.bp3-active{

z-index:7; }

+
+
.bp3-button-group .bp3-button:disabled, .bp3-button-group .bp3-button.bp3-disabled{

z-index:3; }

+
+
.bp3-button-group .bp3-button[class*=”bp3-intent-“]{

z-index:9; } +.bp3-button-group .bp3-button[class*=”bp3-intent-“]:focus{

+
+

z-index:10; }

+
+
+
.bp3-button-group .bp3-button[class*=”bp3-intent-“]:hover{

z-index:11; }

+
+
.bp3-button-group .bp3-button[class*=”bp3-intent-“]:active, .bp3-button-group .bp3-button[class*=”bp3-intent-“].bp3-active{

z-index:12; }

+
+
.bp3-button-group .bp3-button[class*=”bp3-intent-“]:disabled, .bp3-button-group .bp3-button[class*=”bp3-intent-“].bp3-disabled{

z-index:8; }

+
+
+
+
+
+

.bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:first-child) .bp3-button, +.bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:first-child){

+
+

border-bottom-left-radius:0; +border-top-left-radius:0; }

+
+

.bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button, +.bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){

+
+

border-bottom-right-radius:0; +border-top-right-radius:0; +margin-right:-1px; }

+
+
+
.bp3-button-group.bp3-minimal .bp3-button{

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+
.bp3-button-group.bp3-minimal .bp3-button:hover{

background:rgba(167, 182, 194, 0.3); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#182026; +text-decoration:none; }

+
+
.bp3-button-group.bp3-minimal .bp3-button:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-active{

background:rgba(115, 134, 148, 0.3); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#182026; }

+
+
.bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{

background:none; +color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; } +.bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{

+
+

background:rgba(115, 134, 148, 0.3); }

+
+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button{

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:inherit; } +.bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover{

background:rgba(138, 155, 168, 0.15); }

+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{

background:rgba(138, 155, 168, 0.3); +color:#f5f8fa; }

+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{

background:none; +color:rgba(167, 182, 194, 0.6); +cursor:not-allowed; } +.bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{

+
+

background:rgba(138, 155, 168, 0.3); }

+
+
+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{

color:#106ba3; } +.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#106ba3; }

+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{

background:rgba(19, 124, 189, 0.15); +color:#106ba3; }

+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{

background:rgba(19, 124, 189, 0.3); +color:#106ba3; }

+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{

background:none; +color:rgba(16, 107, 163, 0.5); } +.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{

+
+

background:rgba(19, 124, 189, 0.3); }

+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{

stroke:#106ba3; }

+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{

color:#48aff0; } +.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{

+
+

background:rgba(19, 124, 189, 0.2); +color:#48aff0; }

+
+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{

background:rgba(19, 124, 189, 0.3); +color:#48aff0; }

+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{

background:none; +color:rgba(72, 175, 240, 0.5); } +.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{

+
+

background:rgba(19, 124, 189, 0.3); }

+
+
+
+
+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{

color:#0d8050; } +.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#0d8050; }

+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{

background:rgba(15, 153, 96, 0.15); +color:#0d8050; }

+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{

background:rgba(15, 153, 96, 0.3); +color:#0d8050; }

+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{

background:none; +color:rgba(13, 128, 80, 0.5); } +.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{

+
+

background:rgba(15, 153, 96, 0.3); }

+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{

stroke:#0d8050; }

+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{

color:#3dcc91; } +.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{

+
+

background:rgba(15, 153, 96, 0.2); +color:#3dcc91; }

+
+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{

background:rgba(15, 153, 96, 0.3); +color:#3dcc91; }

+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{

background:none; +color:rgba(61, 204, 145, 0.5); } +.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{

+
+

background:rgba(15, 153, 96, 0.3); }

+
+
+
+
+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{

color:#bf7326; } +.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#bf7326; }

+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{

background:rgba(217, 130, 43, 0.15); +color:#bf7326; }

+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{

background:rgba(217, 130, 43, 0.3); +color:#bf7326; }

+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{

background:none; +color:rgba(191, 115, 38, 0.5); } +.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{

+
+

background:rgba(217, 130, 43, 0.3); }

+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{

stroke:#bf7326; }

+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{

color:#ffb366; } +.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{

+
+

background:rgba(217, 130, 43, 0.2); +color:#ffb366; }

+
+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{

background:rgba(217, 130, 43, 0.3); +color:#ffb366; }

+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{

background:none; +color:rgba(255, 179, 102, 0.5); } +.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{

+
+

background:rgba(217, 130, 43, 0.3); }

+
+
+
+
+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{

color:#c23030; } +.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#c23030; }

+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{

background:rgba(219, 55, 55, 0.15); +color:#c23030; }

+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{

background:rgba(219, 55, 55, 0.3); +color:#c23030; }

+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{

background:none; +color:rgba(194, 48, 48, 0.5); } +.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{

+
+

background:rgba(219, 55, 55, 0.3); }

+
+
+
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{

stroke:#c23030; }

+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{

color:#ff7373; } +.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{

+
+

background:rgba(219, 55, 55, 0.2); +color:#ff7373; }

+
+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{

background:rgba(219, 55, 55, 0.3); +color:#ff7373; }

+
+
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{

background:none; +color:rgba(255, 115, 115, 0.5); } +.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{

+
+

background:rgba(219, 55, 55, 0.3); }

+
+
+
+
+
+
+
+
+
+

.bp3-button-group .bp3-popover-wrapper, +.bp3-button-group .bp3-popover-target{

+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-flex:1;

+
+
+
-ms-flex:1 1 auto;

flex:1 1 auto; }

+
+
+
+
+
+
.bp3-button-group.bp3-fill{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +width:100%; }

+
+
+

.bp3-button-group .bp3-button.bp3-fill, +.bp3-button-group.bp3-fill .bp3-button:not(.bp3-fixed){

+
+
+
-webkit-box-flex:1;
+
-ms-flex:1 1 auto;

flex:1 1 auto; }

+
+
+
+
+
+
+
.bp3-button-group.bp3-vertical{
+
-webkit-box-align:stretch;
+
-ms-flex-align:stretch;

align-items:stretch;

+
+
+
+
+

-webkit-box-orient:vertical; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:column;

flex-direction:column;

+
+
+
+

vertical-align:top; } +.bp3-button-group.bp3-vertical.bp3-fill{

+
+

height:100%; +width:unset; }

+
+
+
.bp3-button-group.bp3-vertical .bp3-button{

margin-right:0 !important; +width:100%; }

+
+
+

.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:first-child .bp3-button, +.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:first-child{

+
+

border-radius:3px 3px 0 0; }

+
+

.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:last-child .bp3-button, +.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:last-child{

+
+

border-radius:0 0 3px 3px; }

+
+

.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button, +.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:not(:last-child){

+
+

margin-bottom:-1px; }

+
+
+
.bp3-button-group.bp3-align-left .bp3-button{

text-align:left; }

+
+
+

.bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button, +.bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){

+
+

margin-right:1px; }

+
+

.bp3-dark .bp3-button-group.bp3-vertical > .bp3-popover-wrapper:not(:last-child) .bp3-button, +.bp3-dark .bp3-button-group.bp3-vertical > .bp3-button:not(:last-child){

+
+

margin-bottom:1px; }

+
+
+
.bp3-callout{

font-size:14px; +line-height:1.5; +background-color:rgba(138, 155, 168, 0.15); +border-radius:3px; +padding:10px 12px 9px; +position:relative; +width:100%; } +.bp3-callout[class*=”bp3-icon-“]{

+
+

padding-left:40px; } +.bp3-callout[class*=”bp3-icon-“]::before{

+
+

font-family:”Icons20”, sans-serif; +font-size:20px; +font-style:normal; +font-weight:400; +line-height:1; +-moz-osx-font-smoothing:grayscale; +-webkit-font-smoothing:antialiased; +color:#5c7080; +left:10px; +position:absolute; +top:10px; }

+
+
+
+
.bp3-callout.bp3-callout-icon{

padding-left:40px; } +.bp3-callout.bp3-callout-icon > .bp3-icon:first-child{

+
+

color:#5c7080; +left:10px; +position:absolute; +top:10px; }

+
+
+
.bp3-callout .bp3-heading{

line-height:20px; +margin-bottom:5px; +margin-top:0; } +.bp3-callout .bp3-heading:last-child{

+
+

margin-bottom:0; }

+
+
+
.bp3-dark .bp3-callout{

background-color:rgba(138, 155, 168, 0.2); } +.bp3-dark .bp3-callout[class*=”bp3-icon-“]::before{

+
+

color:#a7b6c2; }

+
+
+
.bp3-callout.bp3-intent-primary{

background-color:rgba(19, 124, 189, 0.15); } +.bp3-callout.bp3-intent-primary[class*=”bp3-icon-“]::before, +.bp3-callout.bp3-intent-primary > .bp3-icon:first-child, +.bp3-callout.bp3-intent-primary .bp3-heading{

+
+

color:#106ba3; }

+
+
+
.bp3-dark .bp3-callout.bp3-intent-primary{

background-color:rgba(19, 124, 189, 0.25); } +.bp3-dark .bp3-callout.bp3-intent-primary[class*=”bp3-icon-“]::before, +.bp3-dark .bp3-callout.bp3-intent-primary > .bp3-icon:first-child, +.bp3-dark .bp3-callout.bp3-intent-primary .bp3-heading{

+
+

color:#48aff0; }

+
+
+
+
+
.bp3-callout.bp3-intent-success{

background-color:rgba(15, 153, 96, 0.15); } +.bp3-callout.bp3-intent-success[class*=”bp3-icon-“]::before, +.bp3-callout.bp3-intent-success > .bp3-icon:first-child, +.bp3-callout.bp3-intent-success .bp3-heading{

+
+

color:#0d8050; }

+
+
+
.bp3-dark .bp3-callout.bp3-intent-success{

background-color:rgba(15, 153, 96, 0.25); } +.bp3-dark .bp3-callout.bp3-intent-success[class*=”bp3-icon-“]::before, +.bp3-dark .bp3-callout.bp3-intent-success > .bp3-icon:first-child, +.bp3-dark .bp3-callout.bp3-intent-success .bp3-heading{

+
+

color:#3dcc91; }

+
+
+
+
+
.bp3-callout.bp3-intent-warning{

background-color:rgba(217, 130, 43, 0.15); } +.bp3-callout.bp3-intent-warning[class*=”bp3-icon-“]::before, +.bp3-callout.bp3-intent-warning > .bp3-icon:first-child, +.bp3-callout.bp3-intent-warning .bp3-heading{

+
+

color:#bf7326; }

+
+
+
.bp3-dark .bp3-callout.bp3-intent-warning{

background-color:rgba(217, 130, 43, 0.25); } +.bp3-dark .bp3-callout.bp3-intent-warning[class*=”bp3-icon-“]::before, +.bp3-dark .bp3-callout.bp3-intent-warning > .bp3-icon:first-child, +.bp3-dark .bp3-callout.bp3-intent-warning .bp3-heading{

+
+

color:#ffb366; }

+
+
+
+
+
.bp3-callout.bp3-intent-danger{

background-color:rgba(219, 55, 55, 0.15); } +.bp3-callout.bp3-intent-danger[class*=”bp3-icon-“]::before, +.bp3-callout.bp3-intent-danger > .bp3-icon:first-child, +.bp3-callout.bp3-intent-danger .bp3-heading{

+
+

color:#c23030; }

+
+
+
.bp3-dark .bp3-callout.bp3-intent-danger{

background-color:rgba(219, 55, 55, 0.25); } +.bp3-dark .bp3-callout.bp3-intent-danger[class*=”bp3-icon-“]::before, +.bp3-dark .bp3-callout.bp3-intent-danger > .bp3-icon:first-child, +.bp3-dark .bp3-callout.bp3-intent-danger .bp3-heading{

+
+

color:#ff7373; }

+
+
+
+
+
.bp3-running-text .bp3-callout{

margin:20px 0; }

+
+
+
+
.bp3-card{

background-color:#ffffff; +border-radius:3px; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);

+
+

padding:20px; +-webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9); } +.bp3-card.bp3-dark, +.bp3-dark .bp3-card{

+
+

background-color:#30404d; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }

+
+
+
+
.bp3-elevation-0{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }

+
+
+

.bp3-elevation-0.bp3-dark, +.bp3-dark .bp3-elevation-0{

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); }

+
+
+
+
+
.bp3-elevation-1{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+

.bp3-elevation-1.bp3-dark, +.bp3-dark .bp3-elevation-1{

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
+
.bp3-elevation-2{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2); }

+
+
+

.bp3-elevation-2.bp3-dark, +.bp3-dark .bp3-elevation-2{

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4); }

+
+
+
+
+
.bp3-elevation-3{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); }

+
+
+

.bp3-elevation-3.bp3-dark, +.bp3-dark .bp3-elevation-3{

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }

+
+
+
+
+
.bp3-elevation-4{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); }

+
+
+

.bp3-elevation-4.bp3-dark, +.bp3-dark .bp3-elevation-4{

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); }

+
+
+
+
+
.bp3-card.bp3-interactive:hover{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

+
+
+

cursor:pointer; } +.bp3-card.bp3-interactive:hover.bp3-dark, +.bp3-dark .bp3-card.bp3-interactive:hover{

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }

+
+
+
+
+
.bp3-card.bp3-interactive:active{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);

+
+
+

opacity:0.9; +-webkit-transition-duration:0;

+
+

transition-duration:0; }

+
+

.bp3-card.bp3-interactive:active.bp3-dark, +.bp3-dark .bp3-card.bp3-interactive:active{

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
+
.bp3-collapse{

height:0; +overflow-y:hidden; +-webkit-transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9); } +.bp3-collapse .bp3-collapse-body{

+
+

-webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); } +.bp3-collapse .bp3-collapse-body[aria-hidden=”true”]{

+
+

display:none; }

+
+
+
+
.bp3-context-menu .bp3-popover-target{

display:block; }

+
+
.bp3-context-menu-popover-target{

position:fixed; }

+
+
.bp3-divider{

border-bottom:1px solid rgba(16, 22, 26, 0.15); +border-right:1px solid rgba(16, 22, 26, 0.15); +margin:5px; } +.bp3-dark .bp3-divider{

+
+

border-color:rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dialog-container{

opacity:1; +-webkit-transform:scale(1);

+
+

transform:scale(1);

+
+
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-pack:center;

+
+
+
-ms-flex-pack:center;

justify-content:center;

+
+
+
+

min-height:100%; +pointer-events:none; +-webkit-user-select:none;

+
+
+
-moz-user-select:none;
+
-ms-user-select:none;

user-select:none;

+
+
+
+
+
+

width:100%; } +.bp3-dialog-container.bp3-overlay-enter > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear > .bp3-dialog{

+
+

opacity:0; +-webkit-transform:scale(0.5);

+
+

transform:scale(0.5); }

+
+
+
+
.bp3-dialog-container.bp3-overlay-enter-active > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear-active > .bp3-dialog{

opacity:1; +-webkit-transform:scale(1);

+
+

transform:scale(1);

+
+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:300ms;

transition-duration:300ms;

+
+
+

-webkit-transition-property:opacity, -webkit-transform; +transition-property:opacity, -webkit-transform; +transition-property:opacity, transform; +transition-property:opacity, transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);

+
+

transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }

+
+
+
.bp3-dialog-container.bp3-overlay-exit > .bp3-dialog{

opacity:1; +-webkit-transform:scale(1);

+
+

transform:scale(1); }

+
+
+
.bp3-dialog-container.bp3-overlay-exit-active > .bp3-dialog{

opacity:0; +-webkit-transform:scale(0.5);

+
+

transform:scale(0.5);

+
+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:300ms;

transition-duration:300ms;

+
+
+

-webkit-transition-property:opacity, -webkit-transform; +transition-property:opacity, -webkit-transform; +transition-property:opacity, transform; +transition-property:opacity, transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);

+
+

transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }

+
+
+
+
+
.bp3-dialog{

background:#ebf1f5; +border-radius:6px; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);

+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:vertical; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:column;

flex-direction:column;

+
+
+
+

margin:30px 0; +padding-bottom:20px; +pointer-events:all; +-webkit-user-select:text;

+
+
+
-moz-user-select:text;
+
-ms-user-select:text;

user-select:text;

+
+
+
+
+
+

width:500px; } +.bp3-dialog:focus{

+
+

outline:0; }

+
+

.bp3-dialog.bp3-dark, +.bp3-dark .bp3-dialog{

+
+

background:#293742; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);

+
+

color:#f5f8fa; }

+
+
+
.bp3-dialog-header{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

background:#ffffff; +border-radius:6px 6px 0 0; +-webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);

+
+

box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);

+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-flex:0;

+
+
+
-ms-flex:0 0 auto;

flex:0 0 auto;

+
+
+
+

min-height:40px; +padding-left:20px; +padding-right:5px; +z-index:30; } +.bp3-dialog-header .bp3-icon-large, +.bp3-dialog-header .bp3-icon{

+
+

color:#5c7080; +-webkit-box-flex:0;

+
+
+
-ms-flex:0 0 auto;

flex:0 0 auto;

+
+
+
+

margin-right:10px; }

+
+
+
.bp3-dialog-header .bp3-heading{

overflow:hidden; +text-overflow:ellipsis; +white-space:nowrap; +word-wrap:normal; +-webkit-box-flex:1;

+
+
+
-ms-flex:1 1 auto;

flex:1 1 auto;

+
+
+
+

line-height:inherit; +margin:0; } +.bp3-dialog-header .bp3-heading:last-child{

+
+

margin-right:20px; }

+
+
+
.bp3-dark .bp3-dialog-header{

background:#30404d; +-webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); }

+
+

.bp3-dark .bp3-dialog-header .bp3-icon-large, +.bp3-dark .bp3-dialog-header .bp3-icon{

+
+

color:#a7b6c2; }

+
+
+
+
+
.bp3-dialog-body{
+
-webkit-box-flex:1;
+
-ms-flex:1 1 auto;

flex:1 1 auto;

+
+
+
+
+

line-height:18px; +margin:20px; }

+
+
.bp3-dialog-footer{
+
-webkit-box-flex:0;
+
-ms-flex:0 0 auto;

flex:0 0 auto;

+
+
+
+
+

margin:0 20px; }

+
+
.bp3-dialog-footer-actions{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-pack:end;

+
+
+
-ms-flex-pack:end;

justify-content:flex-end; }

+
+
+
+
+
.bp3-dialog-footer-actions .bp3-button{

margin-left:10px; }

+
+
+
+
.bp3-multistep-dialog-panels{

display:-webkit-box; +display:-ms-flexbox; +display:flex; }

+
+
.bp3-multistep-dialog-left-panel{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-flex:1;

+
+
+
-ms-flex:1;

flex:1;

+
+
+
+

-webkit-box-orient:vertical; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:column;

flex-direction:column; }

+
+
+
+
+
.bp3-dark .bp3-multistep-dialog-left-panel{

background:#202b33; }

+
+
+
+
.bp3-multistep-dialog-right-panel{

background-color:#f5f8fa; +border-left:1px solid rgba(16, 22, 26, 0.15); +border-radius:0 0 6px 0; +-webkit-box-flex:3;

+
+
+
-ms-flex:3;

flex:3;

+
+
+
+

min-width:0; } +.bp3-dark .bp3-multistep-dialog-right-panel{

+
+

background-color:#293742; +border-left:1px solid rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-multistep-dialog-footer{

background-color:#ffffff; +border-radius:0 0 6px 0; +border-top:1px solid rgba(16, 22, 26, 0.15); +padding:10px; } +.bp3-dark .bp3-multistep-dialog-footer{

+
+

background:#30404d; +border-top:1px solid rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dialog-step-container{

background-color:#f5f8fa; +border-bottom:1px solid rgba(16, 22, 26, 0.15); } +.bp3-dark .bp3-dialog-step-container{

+
+

background:#293742; +border-bottom:1px solid rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dialog-step-container.bp3-dialog-step-viewed{

background-color:#ffffff; } +.bp3-dark .bp3-dialog-step-container.bp3-dialog-step-viewed{

+
+

background:#30404d; }

+
+
+
+
+
.bp3-dialog-step{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

background-color:#f5f8fa; +border-radius:6px; +cursor:not-allowed; +display:-webkit-box; +display:-ms-flexbox; +display:flex; +margin:4px; +padding:6px 14px; } +.bp3-dark .bp3-dialog-step{

+
+

background:#293742; }

+
+
+
.bp3-dialog-step-viewed .bp3-dialog-step{

background-color:#ffffff; +cursor:pointer; } +.bp3-dark .bp3-dialog-step-viewed .bp3-dialog-step{

+
+

background:#30404d; }

+
+
+
.bp3-dialog-step:hover{

background-color:#f5f8fa; } +.bp3-dark .bp3-dialog-step:hover{

+
+

background:#293742; }

+
+
+
+
+
.bp3-dialog-step-icon{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

background-color:rgba(92, 112, 128, 0.6); +border-radius:50%; +color:#ffffff; +display:-webkit-box; +display:-ms-flexbox; +display:flex; +height:25px; +-webkit-box-pack:center;

+
+
+
-ms-flex-pack:center;

justify-content:center;

+
+
+
+

width:25px; } +.bp3-dark .bp3-dialog-step-icon{

+
+

background-color:rgba(167, 182, 194, 0.6); }

+
+
+
.bp3-active.bp3-dialog-step-viewed .bp3-dialog-step-icon{

background-color:#2b95d6; }

+
+
.bp3-dialog-step-viewed .bp3-dialog-step-icon{

background-color:#8a9ba8; }

+
+
+
+
.bp3-dialog-step-title{

color:rgba(92, 112, 128, 0.6); +-webkit-box-flex:1;

+
+
+
-ms-flex:1;

flex:1;

+
+
+
+

padding-left:10px; } +.bp3-dark .bp3-dialog-step-title{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
.bp3-active.bp3-dialog-step-viewed .bp3-dialog-step-title{

color:#2b95d6; }

+
+
.bp3-dialog-step-viewed:not(.bp3-active) .bp3-dialog-step-title{

color:#182026; } +.bp3-dark .bp3-dialog-step-viewed:not(.bp3-active) .bp3-dialog-step-title{

+
+

color:#f5f8fa; }

+
+
+
+
+
.bp3-drawer{

background:#ffffff; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);

+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:vertical; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:column;

flex-direction:column;

+
+
+
+

margin:0; +padding:0; } +.bp3-drawer:focus{

+
+

outline:0; }

+
+
+
.bp3-drawer.bp3-position-top{

height:50%; +left:0; +right:0; +top:0; } +.bp3-drawer.bp3-position-top.bp3-overlay-enter, .bp3-drawer.bp3-position-top.bp3-overlay-appear{

+
+
+
-webkit-transform:translateY(-100%);

transform:translateY(-100%); }

+
+
+
+
+
.bp3-drawer.bp3-position-top.bp3-overlay-enter-active, .bp3-drawer.bp3-position-top.bp3-overlay-appear-active{
+
-webkit-transform:translateY(0);

transform:translateY(0);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:200ms;

transition-duration:200ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-drawer.bp3-position-top.bp3-overlay-exit{
+
-webkit-transform:translateY(0);

transform:translateY(0); }

+
+
+
+
.bp3-drawer.bp3-position-top.bp3-overlay-exit-active{
+
-webkit-transform:translateY(-100%);

transform:translateY(-100%);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
+
+
.bp3-drawer.bp3-position-bottom{

bottom:0; +height:50%; +left:0; +right:0; } +.bp3-drawer.bp3-position-bottom.bp3-overlay-enter, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear{

+
+
+
-webkit-transform:translateY(100%);

transform:translateY(100%); }

+
+
+
+
+
.bp3-drawer.bp3-position-bottom.bp3-overlay-enter-active, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear-active{
+
-webkit-transform:translateY(0);

transform:translateY(0);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:200ms;

transition-duration:200ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-drawer.bp3-position-bottom.bp3-overlay-exit{
+
-webkit-transform:translateY(0);

transform:translateY(0); }

+
+
+
+
.bp3-drawer.bp3-position-bottom.bp3-overlay-exit-active{
+
-webkit-transform:translateY(100%);

transform:translateY(100%);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
+
+
.bp3-drawer.bp3-position-left{

bottom:0; +left:0; +top:0; +width:50%; } +.bp3-drawer.bp3-position-left.bp3-overlay-enter, .bp3-drawer.bp3-position-left.bp3-overlay-appear{

+
+
+
-webkit-transform:translateX(-100%);

transform:translateX(-100%); }

+
+
+
+
+
.bp3-drawer.bp3-position-left.bp3-overlay-enter-active, .bp3-drawer.bp3-position-left.bp3-overlay-appear-active{
+
-webkit-transform:translateX(0);

transform:translateX(0);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:200ms;

transition-duration:200ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-drawer.bp3-position-left.bp3-overlay-exit{
+
-webkit-transform:translateX(0);

transform:translateX(0); }

+
+
+
+
.bp3-drawer.bp3-position-left.bp3-overlay-exit-active{
+
-webkit-transform:translateX(-100%);

transform:translateX(-100%);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
+
+
.bp3-drawer.bp3-position-right{

bottom:0; +right:0; +top:0; +width:50%; } +.bp3-drawer.bp3-position-right.bp3-overlay-enter, .bp3-drawer.bp3-position-right.bp3-overlay-appear{

+
+
+
-webkit-transform:translateX(100%);

transform:translateX(100%); }

+
+
+
+
+
.bp3-drawer.bp3-position-right.bp3-overlay-enter-active, .bp3-drawer.bp3-position-right.bp3-overlay-appear-active{
+
-webkit-transform:translateX(0);

transform:translateX(0);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:200ms;

transition-duration:200ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-drawer.bp3-position-right.bp3-overlay-exit{
+
-webkit-transform:translateX(0);

transform:translateX(0); }

+
+
+
+
.bp3-drawer.bp3-position-right.bp3-overlay-exit-active{
+
-webkit-transform:translateX(100%);

transform:translateX(100%);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
+
+
+

.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right):not(.bp3-vertical){

+
+

bottom:0; +right:0; +top:0; +width:50%; } +.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right):not(.bp3-vertical).bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right):not(.bp3-vertical).bp3-overlay-appear{

+
+
+
-webkit-transform:translateX(100%);

transform:translateX(100%); }

+
+
+
+

.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right):not(.bp3-vertical).bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right):not(.bp3-vertical).bp3-overlay-appear-active{

+
+
+
-webkit-transform:translateX(0);

transform:translateX(0);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:200ms;

transition-duration:200ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+

.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right):not(.bp3-vertical).bp3-overlay-exit{

+
+
+
-webkit-transform:translateX(0);

transform:translateX(0); }

+
+
+
+

.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right):not(.bp3-vertical).bp3-overlay-exit-active{

+
+
+
-webkit-transform:translateX(100%);

transform:translateX(100%);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
+

.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right).bp3-vertical{

+
+

bottom:0; +height:50%; +left:0; +right:0; } +.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right).bp3-vertical.bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right).bp3-vertical.bp3-overlay-appear{

+
+
+
-webkit-transform:translateY(100%);

transform:translateY(100%); }

+
+
+
+

.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right).bp3-vertical.bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right).bp3-vertical.bp3-overlay-appear-active{

+
+
+
-webkit-transform:translateY(0);

transform:translateY(0);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:200ms;

transition-duration:200ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+

.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right).bp3-vertical.bp3-overlay-exit{

+
+
+
-webkit-transform:translateY(0);

transform:translateY(0); }

+
+
+
+

.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( +.bp3-position-right).bp3-vertical.bp3-overlay-exit-active{

+
+
+
-webkit-transform:translateY(100%);

transform:translateY(100%);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
+

.bp3-drawer.bp3-dark, +.bp3-dark .bp3-drawer{

+
+

background:#30404d; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);

+
+

color:#f5f8fa; }

+
+
+
.bp3-drawer-header{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

border-radius:0; +-webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);

+
+

box-shadow:0 1px 0 rgba(16, 22, 26, 0.15);

+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-flex:0;

+
+
+
-ms-flex:0 0 auto;

flex:0 0 auto;

+
+
+
+

min-height:40px; +padding:5px; +padding-left:20px; +position:relative; } +.bp3-drawer-header .bp3-icon-large, +.bp3-drawer-header .bp3-icon{

+
+

color:#5c7080; +-webkit-box-flex:0;

+
+
+
-ms-flex:0 0 auto;

flex:0 0 auto;

+
+
+
+

margin-right:10px; }

+
+
+
.bp3-drawer-header .bp3-heading{

overflow:hidden; +text-overflow:ellipsis; +white-space:nowrap; +word-wrap:normal; +-webkit-box-flex:1;

+
+
+
-ms-flex:1 1 auto;

flex:1 1 auto;

+
+
+
+

line-height:inherit; +margin:0; } +.bp3-drawer-header .bp3-heading:last-child{

+
+

margin-right:20px; }

+
+
+
.bp3-dark .bp3-drawer-header{
+
-webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);

box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); }

+
+
+

.bp3-dark .bp3-drawer-header .bp3-icon-large, +.bp3-dark .bp3-drawer-header .bp3-icon{

+
+

color:#a7b6c2; }

+
+
+
+
+
.bp3-drawer-body{
+
-webkit-box-flex:1;
+
-ms-flex:1 1 auto;

flex:1 1 auto;

+
+
+
+
+

line-height:18px; +overflow:auto; }

+
+
.bp3-drawer-footer{
+
-webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);

box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);

+
+
-webkit-box-flex:0;
+
-ms-flex:0 0 auto;

flex:0 0 auto;

+
+
+
+
+

padding:10px 20px; +position:relative; } +.bp3-dark .bp3-drawer-footer{

+
+
+
-webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4);

box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4); }

+
+
+
+
+
.bp3-editable-text{

cursor:text; +display:inline-block; +max-width:100%; +position:relative; +vertical-align:top; +white-space:nowrap; } +.bp3-editable-text::before{

+
+

bottom:-3px; +left:-3px; +position:absolute; +right:-3px; +top:-3px; +border-radius:3px; +content:””; +-webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-editable-text:hover::before{
+
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15);

box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15); }

+
+
+
+
.bp3-editable-text.bp3-editable-text-editing::before{

background-color:#ffffff; +-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-editable-text.bp3-disabled::before{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+

.bp3-editable-text.bp3-intent-primary .bp3-editable-text-input, +.bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{

+
+

color:#137cbd; }

+
+
+
.bp3-editable-text.bp3-intent-primary:hover::before{
+
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(19, 124, 189, 0.4);

box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(19, 124, 189, 0.4); }

+
+
+
+
.bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
+
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
+

.bp3-editable-text.bp3-intent-success .bp3-editable-text-input, +.bp3-editable-text.bp3-intent-success .bp3-editable-text-content{

+
+

color:#0f9960; }

+
+
+
.bp3-editable-text.bp3-intent-success:hover::before{
+
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4);

box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4); }

+
+
+
+
.bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
+
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
+

.bp3-editable-text.bp3-intent-warning .bp3-editable-text-input, +.bp3-editable-text.bp3-intent-warning .bp3-editable-text-content{

+
+

color:#d9822b; }

+
+
+
.bp3-editable-text.bp3-intent-warning:hover::before{
+
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4);

box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4); }

+
+
+
+
.bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
+
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
+

.bp3-editable-text.bp3-intent-danger .bp3-editable-text-input, +.bp3-editable-text.bp3-intent-danger .bp3-editable-text-content{

+
+

color:#db3737; }

+
+
+
.bp3-editable-text.bp3-intent-danger:hover::before{
+
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4);

box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4); }

+
+
+
+
.bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
+
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-dark .bp3-editable-text:hover::before{
+
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15);

box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15); }

+
+
+
+
.bp3-dark .bp3-editable-text.bp3-editable-text-editing::before{

background-color:rgba(16, 22, 26, 0.3); +-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-editable-text.bp3-disabled::before{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
.bp3-dark .bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{

color:#48aff0; }

+
+
.bp3-dark .bp3-editable-text.bp3-intent-primary:hover::before{
+
-webkit-box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4);

box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4); }

+
+
+
+
.bp3-dark .bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
+
-webkit-box-shadow:0 0 0 1px #48aff0, 0 0 0 3px rgba(72, 175, 240, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #48aff0, 0 0 0 3px rgba(72, 175, 240, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-editable-text.bp3-intent-success .bp3-editable-text-content{

color:#3dcc91; }

+
+
.bp3-dark .bp3-editable-text.bp3-intent-success:hover::before{
+
-webkit-box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4);

box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4); }

+
+
+
+
.bp3-dark .bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
+
-webkit-box-shadow:0 0 0 1px #3dcc91, 0 0 0 3px rgba(61, 204, 145, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #3dcc91, 0 0 0 3px rgba(61, 204, 145, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-editable-text.bp3-intent-warning .bp3-editable-text-content{

color:#ffb366; }

+
+
.bp3-dark .bp3-editable-text.bp3-intent-warning:hover::before{
+
-webkit-box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4);

box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4); }

+
+
+
+
.bp3-dark .bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
+
-webkit-box-shadow:0 0 0 1px #ffb366, 0 0 0 3px rgba(255, 179, 102, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #ffb366, 0 0 0 3px rgba(255, 179, 102, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-editable-text.bp3-intent-danger .bp3-editable-text-content{

color:#ff7373; }

+
+
.bp3-dark .bp3-editable-text.bp3-intent-danger:hover::before{
+
-webkit-box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4);

box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4); }

+
+
+
+
.bp3-dark .bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
+
-webkit-box-shadow:0 0 0 1px #ff7373, 0 0 0 3px rgba(255, 115, 115, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #ff7373, 0 0 0 3px rgba(255, 115, 115, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
+
+
+

.bp3-editable-text-input, +.bp3-editable-text-content{

+
+

color:inherit; +display:inherit; +font:inherit; +letter-spacing:inherit; +max-width:inherit; +min-width:inherit; +position:relative; +resize:none; +text-transform:inherit; +vertical-align:top; }

+
+
+
.bp3-editable-text-input{

background:none; +border:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

padding:0; +white-space:pre-wrap; +width:100%; } +.bp3-editable-text-input::-webkit-input-placeholder{

+
+

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
+
.bp3-editable-text-input::-moz-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-editable-text-input:-ms-input-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-editable-text-input::-ms-input-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-editable-text-input::placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-editable-text-input:focus{

outline:none; }

+
+
.bp3-editable-text-input::-ms-clear{

display:none; }

+
+
+
+
.bp3-editable-text-content{

overflow:hidden; +padding-right:2px; +text-overflow:ellipsis; +white-space:pre; } +.bp3-editable-text-editing > .bp3-editable-text-content{

+
+

left:0; +position:absolute; +visibility:hidden; }

+
+
+
.bp3-editable-text-placeholder > .bp3-editable-text-content{

color:rgba(92, 112, 128, 0.6); } +.bp3-dark .bp3-editable-text-placeholder > .bp3-editable-text-content{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
+
+
.bp3-editable-text.bp3-multiline{

display:block; } +.bp3-editable-text.bp3-multiline .bp3-editable-text-content{

+
+

overflow:auto; +white-space:pre-wrap; +word-wrap:break-word; }

+
+
+
.bp3-divider{

border-bottom:1px solid rgba(16, 22, 26, 0.15); +border-right:1px solid rgba(16, 22, 26, 0.15); +margin:5px; } +.bp3-dark .bp3-divider{

+
+

border-color:rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-control-group{
+
-webkit-transform:translateZ(0);

transform:translateZ(0);

+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:horizontal; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:row;

flex-direction:row;

+
+
+
+
+
-webkit-box-align:stretch;
+
-ms-flex-align:stretch;

align-items:stretch; }

+
+
+
+
.bp3-control-group > *{
+
-webkit-box-flex:0;
+
-ms-flex-positive:0;

flex-grow:0;

+
+
+
+
-ms-flex-negative:0;

flex-shrink:0; }

+
+
+
+
.bp3-control-group > .bp3-fill{
+
-webkit-box-flex:1;
+
-ms-flex-positive:1;

flex-grow:1;

+
+
+
+
-ms-flex-negative:1;

flex-shrink:1; }

+
+
+
+
+

.bp3-control-group .bp3-button, +.bp3-control-group .bp3-html-select, +.bp3-control-group .bp3-input, +.bp3-control-group .bp3-select{

+
+

position:relative; }

+
+
+
.bp3-control-group .bp3-input{

border-radius:inherit; +z-index:2; } +.bp3-control-group .bp3-input:focus{

+
+

border-radius:3px; +z-index:14; }

+
+
+
.bp3-control-group .bp3-input[class*=”bp3-intent”]{

z-index:13; } +.bp3-control-group .bp3-input[class*=”bp3-intent”]:focus{

+
+

z-index:15; }

+
+
+
.bp3-control-group .bp3-input[readonly], .bp3-control-group .bp3-input:disabled, .bp3-control-group .bp3-input.bp3-disabled{

z-index:1; }

+
+
+
+
.bp3-control-group .bp3-input-group[class*=”bp3-intent”] .bp3-input{

z-index:13; } +.bp3-control-group .bp3-input-group[class*=”bp3-intent”] .bp3-input:focus{

+
+

z-index:15; }

+
+
+
+

.bp3-control-group .bp3-button, +.bp3-control-group .bp3-html-select select, +.bp3-control-group .bp3-select select{

+
+
+
-webkit-transform:translateZ(0);

transform:translateZ(0);

+
+
+

border-radius:inherit; +z-index:4; } +.bp3-control-group .bp3-button:focus, +.bp3-control-group .bp3-html-select select:focus, +.bp3-control-group .bp3-select select:focus{

+
+

z-index:5; }

+
+

.bp3-control-group .bp3-button:hover, +.bp3-control-group .bp3-html-select select:hover, +.bp3-control-group .bp3-select select:hover{

+
+

z-index:6; }

+
+

.bp3-control-group .bp3-button:active, +.bp3-control-group .bp3-html-select select:active, +.bp3-control-group .bp3-select select:active{

+
+

z-index:7; }

+
+

.bp3-control-group .bp3-button[readonly], .bp3-control-group .bp3-button:disabled, .bp3-control-group .bp3-button.bp3-disabled, +.bp3-control-group .bp3-html-select select[readonly], +.bp3-control-group .bp3-html-select select:disabled, +.bp3-control-group .bp3-html-select select.bp3-disabled, +.bp3-control-group .bp3-select select[readonly], +.bp3-control-group .bp3-select select:disabled, +.bp3-control-group .bp3-select select.bp3-disabled{

+
+

z-index:3; }

+
+

.bp3-control-group .bp3-button[class*=”bp3-intent”], +.bp3-control-group .bp3-html-select select[class*=”bp3-intent”], +.bp3-control-group .bp3-select select[class*=”bp3-intent”]{

+
+

z-index:9; } +.bp3-control-group .bp3-button[class*=”bp3-intent”]:focus, +.bp3-control-group .bp3-html-select select[class*=”bp3-intent”]:focus, +.bp3-control-group .bp3-select select[class*=”bp3-intent”]:focus{

+
+

z-index:10; }

+
+

.bp3-control-group .bp3-button[class*=”bp3-intent”]:hover, +.bp3-control-group .bp3-html-select select[class*=”bp3-intent”]:hover, +.bp3-control-group .bp3-select select[class*=”bp3-intent”]:hover{

+
+

z-index:11; }

+
+

.bp3-control-group .bp3-button[class*=”bp3-intent”]:active, +.bp3-control-group .bp3-html-select select[class*=”bp3-intent”]:active, +.bp3-control-group .bp3-select select[class*=”bp3-intent”]:active{

+
+

z-index:12; }

+
+

.bp3-control-group .bp3-button[class*=”bp3-intent”][readonly], .bp3-control-group .bp3-button[class*=”bp3-intent”]:disabled, .bp3-control-group .bp3-button[class*=”bp3-intent”].bp3-disabled, +.bp3-control-group .bp3-html-select select[class*=”bp3-intent”][readonly], +.bp3-control-group .bp3-html-select select[class*=”bp3-intent”]:disabled, +.bp3-control-group .bp3-html-select select[class*=”bp3-intent”].bp3-disabled, +.bp3-control-group .bp3-select select[class*=”bp3-intent”][readonly], +.bp3-control-group .bp3-select select[class*=”bp3-intent”]:disabled, +.bp3-control-group .bp3-select select[class*=”bp3-intent”].bp3-disabled{

+
+

z-index:8; }

+
+
+
+

.bp3-control-group .bp3-input-group > .bp3-icon, +.bp3-control-group .bp3-input-group > .bp3-button, +.bp3-control-group .bp3-input-group > .bp3-input-left-container, +.bp3-control-group .bp3-input-group > .bp3-input-action{

+
+

z-index:16; }

+
+

.bp3-control-group .bp3-select::after, +.bp3-control-group .bp3-html-select::after, +.bp3-control-group .bp3-select > .bp3-icon, +.bp3-control-group .bp3-html-select > .bp3-icon{

+
+

z-index:17; }

+
+
+
.bp3-control-group .bp3-select:focus-within{

z-index:5; }

+
+
.bp3-control-group:not(.bp3-vertical) > *:not(.bp3-divider){

margin-right:-1px; }

+
+
.bp3-control-group:not(.bp3-vertical) > .bp3-divider:not(:first-child){

margin-left:6px; }

+
+
.bp3-dark .bp3-control-group:not(.bp3-vertical) > *:not(.bp3-divider){

margin-right:0; }

+
+
.bp3-dark .bp3-control-group:not(.bp3-vertical) > .bp3-button + .bp3-button{

margin-left:1px; }

+
+
+

.bp3-control-group .bp3-popover-wrapper, +.bp3-control-group .bp3-popover-target{

+
+

border-radius:inherit; }

+
+
+
.bp3-control-group > :first-child{

border-radius:3px 0 0 3px; }

+
+
.bp3-control-group > :last-child{

border-radius:0 3px 3px 0; +margin-right:0; }

+
+
.bp3-control-group > :only-child{

border-radius:3px; +margin-right:0; }

+
+
.bp3-control-group .bp3-input-group .bp3-button{

border-radius:3px; }

+
+
.bp3-control-group .bp3-numeric-input:not(:first-child) .bp3-input-group{

border-bottom-left-radius:0; +border-top-left-radius:0; }

+
+
.bp3-control-group.bp3-fill{

width:100%; }

+
+
.bp3-control-group > .bp3-fill{
+
-webkit-box-flex:1;
+
-ms-flex:1 1 auto;

flex:1 1 auto; }

+
+
+
+
+
+
.bp3-control-group.bp3-fill > *:not(.bp3-fixed){
+
-webkit-box-flex:1;
+
-ms-flex:1 1 auto;

flex:1 1 auto; }

+
+
+
+
+
+
.bp3-control-group.bp3-vertical{

-webkit-box-orient:vertical; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:column;

flex-direction:column; }

+
+
+
+
+
.bp3-control-group.bp3-vertical > *{

margin-top:-1px; }

+
+
.bp3-control-group.bp3-vertical > :first-child{

border-radius:3px 3px 0 0; +margin-top:0; }

+
+
.bp3-control-group.bp3-vertical > :last-child{

border-radius:0 0 3px 3px; }

+
+
+
+
+
+
.bp3-control{

cursor:pointer; +display:block; +margin-bottom:10px; +position:relative; +text-transform:none; } +.bp3-control input:checked ~ .bp3-control-indicator{

+
+

background-color:#137cbd; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

color:#ffffff; }

+
+
+
.bp3-control:hover input:checked ~ .bp3-control-indicator{

background-color:#106ba3; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{

background:#0e5a8a; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-control input:disabled:checked ~ .bp3-control-indicator{

background:rgba(19, 124, 189, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+
.bp3-dark .bp3-control input:checked ~ .bp3-control-indicator{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-control:hover input:checked ~ .bp3-control-indicator{

background-color:#106ba3; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{

background-color:#0e5a8a; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-dark .bp3-control input:disabled:checked ~ .bp3-control-indicator{

background:rgba(14, 90, 138, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+
.bp3-control:not(.bp3-align-right){

padding-left:26px; } +.bp3-control:not(.bp3-align-right) .bp3-control-indicator{

+
+

margin-left:-26px; }

+
+
+
.bp3-control.bp3-align-right{

padding-right:26px; } +.bp3-control.bp3-align-right .bp3-control-indicator{

+
+

margin-right:-26px; }

+
+
+
.bp3-control.bp3-disabled{

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; }

+
+
.bp3-control.bp3-inline{

display:inline-block; +margin-right:20px; }

+
+
.bp3-control input{

left:0; +opacity:0; +position:absolute; +top:0; +z-index:-1; }

+
+
.bp3-control .bp3-control-indicator{

background-clip:padding-box; +background-color:#f5f8fa; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0)); +border:none; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

cursor:pointer; +display:inline-block; +font-size:16px; +height:1em; +margin-right:10px; +margin-top:-3px; +position:relative; +-webkit-user-select:none;

+
+
+
-moz-user-select:none;
+
-ms-user-select:none;

user-select:none;

+
+
+
+
+
+

vertical-align:middle; +width:1em; } +.bp3-control .bp3-control-indicator::before{

+
+

content:””; +display:block; +height:1em; +width:1em; }

+
+
+
.bp3-control:hover .bp3-control-indicator{

background-color:#ebf1f5; }

+
+
.bp3-control input:not(:disabled):active ~ .bp3-control-indicator{

background:#d8e1e8; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-control input:disabled ~ .bp3-control-indicator{

background:rgba(206, 217, 224, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

cursor:not-allowed; }

+
+
.bp3-control input:focus ~ .bp3-control-indicator{

outline:rgba(19, 124, 189, 0.6) auto 2px; +outline-offset:2px; +-moz-outline-radius:6px; }

+
+
.bp3-control.bp3-align-right .bp3-control-indicator{

float:right; +margin-left:10px; +margin-top:1px; }

+
+
.bp3-control.bp3-large{

font-size:16px; } +.bp3-control.bp3-large:not(.bp3-align-right){

+
+

padding-left:30px; } +.bp3-control.bp3-large:not(.bp3-align-right) .bp3-control-indicator{

+
+

margin-left:-30px; }

+
+
+
+
.bp3-control.bp3-large.bp3-align-right{

padding-right:30px; } +.bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{

+
+

margin-right:-30px; }

+
+
+
.bp3-control.bp3-large .bp3-control-indicator{

font-size:20px; }

+
+
.bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{

margin-top:0; }

+
+
+
+
.bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{

background-color:#137cbd; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

color:#ffffff; }

+
+
.bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{

background-color:#106ba3; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{

background:#0e5a8a; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{

background:rgba(19, 124, 189, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+
.bp3-dark .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{

background-color:#106ba3; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{

background-color:#0e5a8a; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{

background:rgba(14, 90, 138, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+
.bp3-control.bp3-checkbox .bp3-control-indicator{

border-radius:3px; }

+
+
.bp3-control.bp3-checkbox input:checked ~ .bp3-control-indicator::before{

background-image:url(”data:image/svg+xml,%3csvg xmlns=’http://www.w3.org/2000/svg’ viewBox=’0 0 16 16’%3e%3cpath fill-rule=’evenodd’ clip-rule=’evenodd’ d=’M12 5c-.28 0-.53.11-.71.29L7 9.59l-2.29-2.3a1.003 1.003 0 00-1.42 1.42l3 3c.18.18.43.29.71.29s.53-.11.71-.29l5-5A1.003 1.003 0 0012 5z’ fill=’white’/%3e%3c/svg%3e”); }

+
+
.bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator::before{

background-image:url(”data:image/svg+xml,%3csvg xmlns=’http://www.w3.org/2000/svg’ viewBox=’0 0 16 16’%3e%3cpath fill-rule=’evenodd’ clip-rule=’evenodd’ d=’M11 7H5c-.55 0-1 .45-1 1s.45 1 1 1h6c.55 0 1-.45 1-1s-.45-1-1-1z’ fill=’white’/%3e%3c/svg%3e”); }

+
+
.bp3-control.bp3-radio .bp3-control-indicator{

border-radius:50%; }

+
+
.bp3-control.bp3-radio input:checked ~ .bp3-control-indicator::before{

background-image:radial-gradient(#ffffff, #ffffff 28%, transparent 32%); }

+
+
.bp3-control.bp3-radio input:checked:disabled ~ .bp3-control-indicator::before{

opacity:0.5; }

+
+
.bp3-control.bp3-radio input:focus ~ .bp3-control-indicator{

-moz-outline-radius:16px; }

+
+
.bp3-control.bp3-switch input ~ .bp3-control-indicator{

background:rgba(167, 182, 194, 0.5); }

+
+
.bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{

background:rgba(115, 134, 148, 0.5); }

+
+
.bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{

background:rgba(92, 112, 128, 0.5); }

+
+
.bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{

background:rgba(206, 217, 224, 0.5); } +.bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{

+
+

background:rgba(255, 255, 255, 0.8); }

+
+
+
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{

background:#137cbd; }

+
+
.bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{

background:#106ba3; }

+
+
.bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{

background:#0e5a8a; }

+
+
.bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{

background:rgba(19, 124, 189, 0.5); } +.bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{

+
+

background:rgba(255, 255, 255, 0.8); }

+
+
+
.bp3-control.bp3-switch:not(.bp3-align-right){

padding-left:38px; } +.bp3-control.bp3-switch:not(.bp3-align-right) .bp3-control-indicator{

+
+

margin-left:-38px; }

+
+
+
.bp3-control.bp3-switch.bp3-align-right{

padding-right:38px; } +.bp3-control.bp3-switch.bp3-align-right .bp3-control-indicator{

+
+

margin-right:-38px; }

+
+
+
.bp3-control.bp3-switch .bp3-control-indicator{

border:none; +border-radius:1.75em; +-webkit-box-shadow:none !important;

+
+

box-shadow:none !important;

+
+

min-width:1.75em; +-webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +width:auto; } +.bp3-control.bp3-switch .bp3-control-indicator::before{

+
+

background:#ffffff; +border-radius:50%; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

height:calc(1em - 4px); +left:0; +margin:2px; +position:absolute; +-webkit-transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +width:calc(1em - 4px); }

+
+
+
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{

left:calc(100% - 1em); }

+
+
.bp3-control.bp3-switch.bp3-large:not(.bp3-align-right){

padding-left:45px; } +.bp3-control.bp3-switch.bp3-large:not(.bp3-align-right) .bp3-control-indicator{

+
+

margin-left:-45px; }

+
+
+
.bp3-control.bp3-switch.bp3-large.bp3-align-right{

padding-right:45px; } +.bp3-control.bp3-switch.bp3-large.bp3-align-right .bp3-control-indicator{

+
+

margin-right:-45px; }

+
+
+
.bp3-dark .bp3-control.bp3-switch input ~ .bp3-control-indicator{

background:rgba(16, 22, 26, 0.5); }

+
+
.bp3-dark .bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{

background:rgba(16, 22, 26, 0.7); }

+
+
.bp3-dark .bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{

background:rgba(16, 22, 26, 0.9); }

+
+
.bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{

background:rgba(57, 75, 89, 0.5); } +.bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{

+
+

background:rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{

background:#137cbd; }

+
+
.bp3-dark .bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{

background:#106ba3; }

+
+
.bp3-dark .bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{

background:#0e5a8a; }

+
+
.bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{

background:rgba(14, 90, 138, 0.5); } +.bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{

+
+

background:rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-control.bp3-switch .bp3-control-indicator::before{

background:#394b59; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{
+
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-control.bp3-switch .bp3-switch-inner-text{

font-size:0.7em; +text-align:center; }

+
+
.bp3-control.bp3-switch .bp3-control-indicator-child:first-child{

line-height:0; +margin-left:0.5em; +margin-right:1.2em; +visibility:hidden; }

+
+
.bp3-control.bp3-switch .bp3-control-indicator-child:last-child{

line-height:1em; +margin-left:1.2em; +margin-right:0.5em; +visibility:visible; }

+
+
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:first-child{

line-height:1em; +visibility:visible; }

+
+
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:last-child{

line-height:0; +visibility:hidden; }

+
+
.bp3-dark .bp3-control{

color:#f5f8fa; } +.bp3-dark .bp3-control.bp3-disabled{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
.bp3-dark .bp3-control .bp3-control-indicator{

background-color:#394b59; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-control:hover .bp3-control-indicator{

background-color:#30404d; }

+
+
.bp3-dark .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{

background:#202b33; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-dark .bp3-control input:disabled ~ .bp3-control-indicator{

background:rgba(57, 75, 89, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

cursor:not-allowed; }

+
+
.bp3-dark .bp3-control.bp3-checkbox input:disabled:checked ~ .bp3-control-indicator, .bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{

color:rgba(167, 182, 194, 0.6); }

+
+
+
+
+
+
.bp3-file-input{

cursor:pointer; +display:inline-block; +height:30px; +position:relative; } +.bp3-file-input input{

+
+

margin:0; +min-width:200px; +opacity:0; } +.bp3-file-input input:disabled + .bp3-file-upload-input, +.bp3-file-input input.bp3-disabled + .bp3-file-upload-input{

+
+

background:rgba(206, 217, 224, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; +resize:none; } +.bp3-file-input input:disabled + .bp3-file-upload-input::after, +.bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{

+
+

background-color:rgba(206, 217, 224, 0.5); +background-image:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; +outline:none; } +.bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active:hover, +.bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active, +.bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active:hover{

+
+

background:rgba(206, 217, 224, 0.7); }

+
+
+

.bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input, .bp3-dark +.bp3-file-input input.bp3-disabled + .bp3-file-upload-input{

+
+

background:rgba(57, 75, 89, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(167, 182, 194, 0.6); } +.bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after, .bp3-dark +.bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{

+
+

background-color:rgba(57, 75, 89, 0.5); +background-image:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(167, 182, 194, 0.6); } +.bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-dark +.bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active{

+
+

background:rgba(57, 75, 89, 0.7); }

+
+
+
+
+
+
+
.bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{

color:#182026; }

+
+
.bp3-dark .bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{

color:#f5f8fa; }

+
+
.bp3-file-input.bp3-fill{

width:100%; }

+
+
+

.bp3-file-input.bp3-large, +.bp3-large .bp3-file-input{

+
+

height:40px; }

+
+
+
.bp3-file-input .bp3-file-upload-input-custom-text::after{

content:attr(bp3-button-text); }

+
+
+
+
.bp3-file-upload-input{
+
-webkit-appearance:none;
+
-moz-appearance:none;

appearance:none;

+
+
+
+
+

background:#ffffff; +border:none; +border-radius:3px; +-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

color:#182026; +font-size:14px; +font-weight:400; +height:30px; +line-height:30px; +outline:none; +padding:0 10px; +-webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +vertical-align:middle; +overflow:hidden; +text-overflow:ellipsis; +white-space:nowrap; +word-wrap:normal; +color:rgba(92, 112, 128, 0.6); +left:0; +padding-right:80px; +position:absolute; +right:0; +top:0; +-webkit-user-select:none;

+
+
+
-moz-user-select:none;
+
-ms-user-select:none;

user-select:none; }

+
+
+
+
+
+
+
.bp3-file-upload-input::-webkit-input-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-file-upload-input::-moz-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-file-upload-input:-ms-input-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-file-upload-input::-ms-input-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-file-upload-input::placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-file-upload-input:focus, .bp3-file-upload-input.bp3-active{
+
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-file-upload-input[type=”search”], .bp3-file-upload-input.bp3-round{

border-radius:30px; +-webkit-box-sizing:border-box;

+
+

box-sizing:border-box;

+
+

padding-left:10px; }

+
+
.bp3-file-upload-input[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); }

+
+
+
+
.bp3-file-upload-input:disabled, .bp3-file-upload-input.bp3-disabled{

background:rgba(206, 217, 224, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; +resize:none; }

+
+
.bp3-file-upload-input::after{

background-color:#f5f8fa; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0)); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

color:#182026; +min-height:24px; +min-width:24px; +overflow:hidden; +text-overflow:ellipsis; +white-space:nowrap; +word-wrap:normal; +border-radius:3px; +content:”Browse”; +line-height:24px; +margin:3px; +position:absolute; +right:0; +text-align:center; +top:0; +width:70px; } +.bp3-file-upload-input::after:hover{

+
+

background-clip:padding-box; +background-color:#ebf1f5; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }

+
+
+
+
.bp3-file-upload-input::after:active, .bp3-file-upload-input::after.bp3-active{

background-color:#d8e1e8; +background-image:none; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-file-upload-input::after:disabled, .bp3-file-upload-input::after.bp3-disabled{

background-color:rgba(206, 217, 224, 0.5); +background-image:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; +outline:none; } +.bp3-file-upload-input::after:disabled.bp3-active, .bp3-file-upload-input::after:disabled.bp3-active:hover, .bp3-file-upload-input::after.bp3-disabled.bp3-active, .bp3-file-upload-input::after.bp3-disabled.bp3-active:hover{

+
+

background:rgba(206, 217, 224, 0.7); }

+
+
+
+
+
.bp3-file-upload-input:hover::after{

background-clip:padding-box; +background-color:#ebf1f5; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }

+
+
+
.bp3-file-upload-input:active::after{

background-color:#d8e1e8; +background-image:none; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-large .bp3-file-upload-input{

font-size:16px; +height:40px; +line-height:40px; +padding-right:95px; } +.bp3-large .bp3-file-upload-input[type=”search”], .bp3-large .bp3-file-upload-input.bp3-round{

+
+

padding:0 15px; }

+
+
+
.bp3-large .bp3-file-upload-input::after{

min-height:30px; +min-width:30px; +line-height:30px; +margin:5px; +width:85px; }

+
+
+
+
.bp3-dark .bp3-file-upload-input{

background:rgba(16, 22, 26, 0.3); +-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

+
+

color:#f5f8fa; +color:rgba(167, 182, 194, 0.6); } +.bp3-dark .bp3-file-upload-input::-webkit-input-placeholder{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
.bp3-dark .bp3-file-upload-input::-moz-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-file-upload-input:-ms-input-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-file-upload-input::-ms-input-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-file-upload-input::placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-file-upload-input:focus{
+
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-file-upload-input[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-file-upload-input:disabled, .bp3-dark .bp3-file-upload-input.bp3-disabled{

background:rgba(57, 75, 89, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-file-upload-input::after{

background-color:#394b59; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

color:#f5f8fa; } +.bp3-dark .bp3-file-upload-input::after:hover, .bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{

+
+

color:#f5f8fa; }

+
+
+
.bp3-dark .bp3-file-upload-input::after:hover{

background-color:#30404d; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{

background-color:#202b33; +background-image:none; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-dark .bp3-file-upload-input::after:disabled, .bp3-dark .bp3-file-upload-input::after.bp3-disabled{

background-color:rgba(57, 75, 89, 0.5); +background-image:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(167, 182, 194, 0.6); } +.bp3-dark .bp3-file-upload-input::after:disabled.bp3-active, .bp3-dark .bp3-file-upload-input::after.bp3-disabled.bp3-active{

+
+

background:rgba(57, 75, 89, 0.7); }

+
+
+
.bp3-dark .bp3-file-upload-input::after .bp3-button-spinner .bp3-spinner-head{

background:rgba(16, 22, 26, 0.5); +stroke:#8a9ba8; }

+
+
+
+
.bp3-dark .bp3-file-upload-input:hover::after{

background-color:#30404d; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-file-upload-input:active::after{

background-color:#202b33; +background-image:none; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
+
+
+
+
.bp3-file-upload-input::after{
+
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }

+
+
+
+
.bp3-form-group{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:vertical; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:column;

flex-direction:column;

+
+
+
+

margin:0 0 15px; } +.bp3-form-group label.bp3-label{

+
+

margin-bottom:5px; }

+
+
+
.bp3-form-group .bp3-control{

margin-top:7px; }

+
+
.bp3-form-group .bp3-form-helper-text{

color:#5c7080; +font-size:12px; +margin-top:5px; }

+
+
.bp3-form-group.bp3-intent-primary .bp3-form-helper-text{

color:#106ba3; }

+
+
.bp3-form-group.bp3-intent-success .bp3-form-helper-text{

color:#0d8050; }

+
+
.bp3-form-group.bp3-intent-warning .bp3-form-helper-text{

color:#bf7326; }

+
+
.bp3-form-group.bp3-intent-danger .bp3-form-helper-text{

color:#c23030; }

+
+
.bp3-form-group.bp3-inline{
+
-webkit-box-align:start;
+
-ms-flex-align:start;

align-items:flex-start;

+
+
+
+
+

-webkit-box-orient:horizontal; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:row;

flex-direction:row; }

+
+
+
+
+
.bp3-form-group.bp3-inline.bp3-large label.bp3-label{

line-height:40px; +margin:0 10px 0 0; }

+
+
.bp3-form-group.bp3-inline label.bp3-label{

line-height:30px; +margin:0 10px 0 0; }

+
+
+
+
+

.bp3-form-group.bp3-disabled .bp3-label, +.bp3-form-group.bp3-disabled .bp3-text-muted, +.bp3-form-group.bp3-disabled .bp3-form-helper-text{

+
+

color:rgba(92, 112, 128, 0.6) !important; }

+
+
+
.bp3-dark .bp3-form-group.bp3-intent-primary .bp3-form-helper-text{

color:#48aff0; }

+
+
.bp3-dark .bp3-form-group.bp3-intent-success .bp3-form-helper-text{

color:#3dcc91; }

+
+
.bp3-dark .bp3-form-group.bp3-intent-warning .bp3-form-helper-text{

color:#ffb366; }

+
+
.bp3-dark .bp3-form-group.bp3-intent-danger .bp3-form-helper-text{

color:#ff7373; }

+
+
.bp3-dark .bp3-form-group .bp3-form-helper-text{

color:#a7b6c2; }

+
+
+

.bp3-dark .bp3-form-group.bp3-disabled .bp3-label, +.bp3-dark .bp3-form-group.bp3-disabled .bp3-text-muted, +.bp3-dark .bp3-form-group.bp3-disabled .bp3-form-helper-text{

+
+

color:rgba(167, 182, 194, 0.6) !important; }

+
+
+
.bp3-input-group{

display:block; +position:relative; } +.bp3-input-group .bp3-input{

+
+

position:relative; +width:100%; } +.bp3-input-group .bp3-input:not(:first-child){

+
+

padding-left:30px; }

+
+
+
.bp3-input-group .bp3-input:not(:last-child){

padding-right:30px; }

+
+
+
+

.bp3-input-group .bp3-input-action, +.bp3-input-group > .bp3-input-left-container, +.bp3-input-group > .bp3-button, +.bp3-input-group > .bp3-icon{

+
+

position:absolute; +top:0; } +.bp3-input-group .bp3-input-action:first-child, +.bp3-input-group > .bp3-input-left-container:first-child, +.bp3-input-group > .bp3-button:first-child, +.bp3-input-group > .bp3-icon:first-child{

+
+

left:0; }

+
+

.bp3-input-group .bp3-input-action:last-child, +.bp3-input-group > .bp3-input-left-container:last-child, +.bp3-input-group > .bp3-button:last-child, +.bp3-input-group > .bp3-icon:last-child{

+
+

right:0; }

+
+
+
+
.bp3-input-group .bp3-button{

min-height:24px; +min-width:24px; +margin:3px; +padding:0 7px; } +.bp3-input-group .bp3-button:empty{

+
+

padding:0; }

+
+
+
+

.bp3-input-group > .bp3-input-left-container, +.bp3-input-group > .bp3-icon{

+
+

z-index:1; }

+
+

.bp3-input-group > .bp3-input-left-container > .bp3-icon, +.bp3-input-group > .bp3-icon{

+
+

color:#5c7080; } +.bp3-input-group > .bp3-input-left-container > .bp3-icon:empty, +.bp3-input-group > .bp3-icon:empty{

+
+

font-family:”Icons16”, sans-serif; +font-size:16px; +font-style:normal; +font-weight:400; +line-height:1; +-moz-osx-font-smoothing:grayscale; +-webkit-font-smoothing:antialiased; }

+
+
+

.bp3-input-group > .bp3-input-left-container > .bp3-icon, +.bp3-input-group > .bp3-icon, +.bp3-input-group .bp3-input-action > .bp3-spinner{

+
+

margin:7px; }

+
+
+
.bp3-input-group .bp3-tag{

margin:5px; }

+
+
+

.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus), +.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){

+
+

color:#5c7080; } +.bp3-dark .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus), .bp3-dark +.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){

+
+

color:#a7b6c2; }

+
+

.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large, +.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon, +.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard, +.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large{

+
+

color:#5c7080; }

+
+
+

.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled, +.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled{

+
+

color:rgba(92, 112, 128, 0.6) !important; } +.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-large, +.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon, +.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-standard, +.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-large{

+
+

color:rgba(92, 112, 128, 0.6) !important; }

+
+
+
+
.bp3-input-group.bp3-disabled{

cursor:not-allowed; } +.bp3-input-group.bp3-disabled .bp3-icon{

+
+

color:rgba(92, 112, 128, 0.6); }

+
+
+
.bp3-input-group.bp3-large .bp3-button{

min-height:30px; +min-width:30px; +margin:5px; }

+
+
+

.bp3-input-group.bp3-large > .bp3-input-left-container > .bp3-icon, +.bp3-input-group.bp3-large > .bp3-icon, +.bp3-input-group.bp3-large .bp3-input-action > .bp3-spinner{

+
+

margin:12px; }

+
+
+
.bp3-input-group.bp3-large .bp3-input{

font-size:16px; +height:40px; +line-height:40px; } +.bp3-input-group.bp3-large .bp3-input[type=”search”], .bp3-input-group.bp3-large .bp3-input.bp3-round{

+
+

padding:0 15px; }

+
+
+
.bp3-input-group.bp3-large .bp3-input:not(:first-child){

padding-left:40px; }

+
+
.bp3-input-group.bp3-large .bp3-input:not(:last-child){

padding-right:40px; }

+
+
+
+
.bp3-input-group.bp3-small .bp3-button{

min-height:20px; +min-width:20px; +margin:2px; }

+
+
.bp3-input-group.bp3-small .bp3-tag{

min-height:20px; +min-width:20px; +margin:2px; }

+
+
+

.bp3-input-group.bp3-small > .bp3-input-left-container > .bp3-icon, +.bp3-input-group.bp3-small > .bp3-icon, +.bp3-input-group.bp3-small .bp3-input-action > .bp3-spinner{

+
+

margin:4px; }

+
+
+
.bp3-input-group.bp3-small .bp3-input{

font-size:12px; +height:24px; +line-height:24px; +padding-left:8px; +padding-right:8px; } +.bp3-input-group.bp3-small .bp3-input[type=”search”], .bp3-input-group.bp3-small .bp3-input.bp3-round{

+
+

padding:0 12px; }

+
+
+
.bp3-input-group.bp3-small .bp3-input:not(:first-child){

padding-left:24px; }

+
+
.bp3-input-group.bp3-small .bp3-input:not(:last-child){

padding-right:24px; }

+
+
+
+
.bp3-input-group.bp3-fill{
+
-webkit-box-flex:1;
+
-ms-flex:1 1 auto;

flex:1 1 auto;

+
+
+
+
+

width:100%; }

+
+
+

.bp3-input-group.bp3-round .bp3-button, +.bp3-input-group.bp3-round .bp3-input, +.bp3-input-group.bp3-round .bp3-tag{

+
+

border-radius:30px; }

+
+
+
.bp3-dark .bp3-input-group .bp3-icon{

color:#a7b6c2; }

+
+
.bp3-dark .bp3-input-group.bp3-disabled .bp3-icon{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-input-group.bp3-intent-primary .bp3-input{
+
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
.bp3-input-group.bp3-intent-primary .bp3-input:focus{
+
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-input-group.bp3-intent-primary .bp3-input[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #137cbd;

box-shadow:inset 0 0 0 1px #137cbd; }

+
+
+
+
.bp3-input-group.bp3-intent-primary .bp3-input:disabled, .bp3-input-group.bp3-intent-primary .bp3-input.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+
+
.bp3-input-group.bp3-intent-primary > .bp3-icon{

color:#106ba3; } +.bp3-dark .bp3-input-group.bp3-intent-primary > .bp3-icon{

+
+

color:#48aff0; }

+
+
+
.bp3-input-group.bp3-intent-success .bp3-input{
+
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
.bp3-input-group.bp3-intent-success .bp3-input:focus{
+
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-input-group.bp3-intent-success .bp3-input[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #0f9960;

box-shadow:inset 0 0 0 1px #0f9960; }

+
+
+
+
.bp3-input-group.bp3-intent-success .bp3-input:disabled, .bp3-input-group.bp3-intent-success .bp3-input.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+
+
.bp3-input-group.bp3-intent-success > .bp3-icon{

color:#0d8050; } +.bp3-dark .bp3-input-group.bp3-intent-success > .bp3-icon{

+
+

color:#3dcc91; }

+
+
+
.bp3-input-group.bp3-intent-warning .bp3-input{
+
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
.bp3-input-group.bp3-intent-warning .bp3-input:focus{
+
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-input-group.bp3-intent-warning .bp3-input[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #d9822b;

box-shadow:inset 0 0 0 1px #d9822b; }

+
+
+
+
.bp3-input-group.bp3-intent-warning .bp3-input:disabled, .bp3-input-group.bp3-intent-warning .bp3-input.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+
+
.bp3-input-group.bp3-intent-warning > .bp3-icon{

color:#bf7326; } +.bp3-dark .bp3-input-group.bp3-intent-warning > .bp3-icon{

+
+

color:#ffb366; }

+
+
+
.bp3-input-group.bp3-intent-danger .bp3-input{
+
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
.bp3-input-group.bp3-intent-danger .bp3-input:focus{
+
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-input-group.bp3-intent-danger .bp3-input[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #db3737;

box-shadow:inset 0 0 0 1px #db3737; }

+
+
+
+
.bp3-input-group.bp3-intent-danger .bp3-input:disabled, .bp3-input-group.bp3-intent-danger .bp3-input.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+
+
.bp3-input-group.bp3-intent-danger > .bp3-icon{

color:#c23030; } +.bp3-dark .bp3-input-group.bp3-intent-danger > .bp3-icon{

+
+

color:#ff7373; }

+
+
+
+
+
.bp3-input{
+
-webkit-appearance:none;
+
-moz-appearance:none;

appearance:none;

+
+
+
+
+

background:#ffffff; +border:none; +border-radius:3px; +-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

color:#182026; +font-size:14px; +font-weight:400; +height:30px; +line-height:30px; +outline:none; +padding:0 10px; +-webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); +vertical-align:middle; } +.bp3-input::-webkit-input-placeholder{

+
+

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
+
.bp3-input::-moz-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-input:-ms-input-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-input::-ms-input-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-input::placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-input:focus, .bp3-input.bp3-active{
+
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-input[type=”search”], .bp3-input.bp3-round{

border-radius:30px; +-webkit-box-sizing:border-box;

+
+

box-sizing:border-box;

+
+

padding-left:10px; }

+
+
.bp3-input[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15);

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); }

+
+
+
+
.bp3-input:disabled, .bp3-input.bp3-disabled{

background:rgba(206, 217, 224, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; +resize:none; }

+
+
.bp3-input.bp3-large{

font-size:16px; +height:40px; +line-height:40px; } +.bp3-input.bp3-large[type=”search”], .bp3-input.bp3-large.bp3-round{

+
+

padding:0 15px; }

+
+
+
.bp3-input.bp3-small{

font-size:12px; +height:24px; +line-height:24px; +padding-left:8px; +padding-right:8px; } +.bp3-input.bp3-small[type=”search”], .bp3-input.bp3-small.bp3-round{

+
+

padding:0 12px; }

+
+
+
.bp3-input.bp3-fill{
+
-webkit-box-flex:1;
+
-ms-flex:1 1 auto;

flex:1 1 auto;

+
+
+
+
+

width:100%; }

+
+
.bp3-dark .bp3-input{

background:rgba(16, 22, 26, 0.3); +-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

+
+

color:#f5f8fa; } +.bp3-dark .bp3-input::-webkit-input-placeholder{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
.bp3-dark .bp3-input::-moz-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-input:-ms-input-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-input::-ms-input-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-input::placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-input:focus{
+
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-input[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-input:disabled, .bp3-dark .bp3-input.bp3-disabled{

background:rgba(57, 75, 89, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
+
.bp3-input.bp3-intent-primary{
+
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
.bp3-input.bp3-intent-primary:focus{
+
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-input.bp3-intent-primary[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #137cbd;

box-shadow:inset 0 0 0 1px #137cbd; }

+
+
+
+
.bp3-input.bp3-intent-primary:disabled, .bp3-input.bp3-intent-primary.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-primary{
+
-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
.bp3-dark .bp3-input.bp3-intent-primary:focus{
+
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-primary[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #137cbd;

box-shadow:inset 0 0 0 1px #137cbd; }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-primary:disabled, .bp3-dark .bp3-input.bp3-intent-primary.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+
+
+
+
.bp3-input.bp3-intent-success{
+
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
.bp3-input.bp3-intent-success:focus{
+
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-input.bp3-intent-success[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #0f9960;

box-shadow:inset 0 0 0 1px #0f9960; }

+
+
+
+
.bp3-input.bp3-intent-success:disabled, .bp3-input.bp3-intent-success.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-success{
+
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
.bp3-dark .bp3-input.bp3-intent-success:focus{
+
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-success[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #0f9960;

box-shadow:inset 0 0 0 1px #0f9960; }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-success:disabled, .bp3-dark .bp3-input.bp3-intent-success.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+
+
+
+
.bp3-input.bp3-intent-warning{
+
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
.bp3-input.bp3-intent-warning:focus{
+
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-input.bp3-intent-warning[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #d9822b;

box-shadow:inset 0 0 0 1px #d9822b; }

+
+
+
+
.bp3-input.bp3-intent-warning:disabled, .bp3-input.bp3-intent-warning.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-warning{
+
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
.bp3-dark .bp3-input.bp3-intent-warning:focus{
+
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-warning[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #d9822b;

box-shadow:inset 0 0 0 1px #d9822b; }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-warning:disabled, .bp3-dark .bp3-input.bp3-intent-warning.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+
+
+
+
.bp3-input.bp3-intent-danger{
+
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
.bp3-input.bp3-intent-danger:focus{
+
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-input.bp3-intent-danger[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #db3737;

box-shadow:inset 0 0 0 1px #db3737; }

+
+
+
+
.bp3-input.bp3-intent-danger:disabled, .bp3-input.bp3-intent-danger.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-danger{
+
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
.bp3-dark .bp3-input.bp3-intent-danger:focus{
+
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-danger[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px #db3737;

box-shadow:inset 0 0 0 1px #db3737; }

+
+
+
+
.bp3-dark .bp3-input.bp3-intent-danger:disabled, .bp3-dark .bp3-input.bp3-intent-danger.bp3-disabled{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+
+
+
+
.bp3-input::-ms-clear{

display:none; }

+
+
+
+
textarea.bp3-input{

max-width:100%; +padding:10px; } +textarea.bp3-input, textarea.bp3-input.bp3-large, textarea.bp3-input.bp3-small{

+
+

height:auto; +line-height:inherit; }

+
+
+
textarea.bp3-input.bp3-small{

padding:8px; }

+
+
.bp3-dark textarea.bp3-input{

background:rgba(16, 22, 26, 0.3); +-webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

+
+

color:#f5f8fa; } +.bp3-dark textarea.bp3-input::-webkit-input-placeholder{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
.bp3-dark textarea.bp3-input::-moz-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark textarea.bp3-input:-ms-input-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark textarea.bp3-input::-ms-input-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark textarea.bp3-input::placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark textarea.bp3-input:focus{
+
-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark textarea.bp3-input[readonly]{
+
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4);

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark textarea.bp3-input:disabled, .bp3-dark textarea.bp3-input.bp3-disabled{

background:rgba(57, 75, 89, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
+
+
+
label.bp3-label{

display:block; +margin-bottom:15px; +margin-top:0; } +label.bp3-label .bp3-html-select, +label.bp3-label .bp3-input, +label.bp3-label .bp3-select, +label.bp3-label .bp3-slider, +label.bp3-label .bp3-popover-wrapper{

+
+

display:block; +margin-top:5px; +text-transform:none; }

+
+
+
label.bp3-label .bp3-button-group{

margin-top:5px; }

+
+
+

label.bp3-label .bp3-select select, +label.bp3-label .bp3-html-select select{

+
+

font-weight:400; +vertical-align:top; +width:100%; }

+
+

label.bp3-label.bp3-disabled, +label.bp3-label.bp3-disabled .bp3-text-muted{

+
+

color:rgba(92, 112, 128, 0.6); }

+
+
+
label.bp3-label.bp3-inline{

line-height:30px; } +label.bp3-label.bp3-inline .bp3-html-select, +label.bp3-label.bp3-inline .bp3-input, +label.bp3-label.bp3-inline .bp3-input-group, +label.bp3-label.bp3-inline .bp3-select, +label.bp3-label.bp3-inline .bp3-popover-wrapper{

+
+

display:inline-block; +margin:0 0 0 5px; +vertical-align:top; }

+
+
+
label.bp3-label.bp3-inline .bp3-button-group{

margin:0 0 0 5px; }

+
+
label.bp3-label.bp3-inline .bp3-input-group .bp3-input{

margin-left:0; }

+
+
label.bp3-label.bp3-inline.bp3-large{

line-height:40px; }

+
+
+
+
label.bp3-label:not(.bp3-inline) .bp3-popover-target{

display:block; }

+
+
.bp3-dark label.bp3-label{

color:#f5f8fa; } +.bp3-dark label.bp3-label.bp3-disabled, +.bp3-dark label.bp3-label.bp3-disabled .bp3-text-muted{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
+
+
.bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button{
+
-webkit-box-flex:1;
+
-ms-flex:1 1 14px;

flex:1 1 14px;

+
+
+
+
+

min-height:0; +padding:0; +width:30px; } +.bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:first-child{

+
+

border-radius:0 3px 0 0; }

+
+
+
.bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:last-child{

border-radius:0 0 3px 0; }

+
+
+
+
.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:first-child{

border-radius:3px 0 0 0; }

+
+
.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:last-child{

border-radius:0 0 0 3px; }

+
+
.bp3-numeric-input.bp3-large .bp3-button-group.bp3-vertical > .bp3-button{

width:40px; }

+
+
form{

display:block; }

+
+
+

.bp3-html-select select, +.bp3-select select{

+
+

display:-webkit-inline-box; +display:-ms-inline-flexbox; +display:inline-flex; +-webkit-box-orient:horizontal; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:row;

flex-direction:row;

+
+
+
+
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

border:none; +border-radius:3px; +cursor:pointer; +font-size:14px; +-webkit-box-pack:center;

+
+
+
-ms-flex-pack:center;

justify-content:center;

+
+
+
+

padding:5px 10px; +text-align:left; +vertical-align:middle; +background-color:#f5f8fa; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0)); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

color:#182026; +-moz-appearance:none; +-webkit-appearance:none; +border-radius:3px; +height:30px; +padding:0 25px 0 10px; +width:100%; } +.bp3-html-select select > *, .bp3-select select > *{

+
+
+
-webkit-box-flex:0;
+
-ms-flex-positive:0;

flex-grow:0;

+
+
+
+
-ms-flex-negative:0;

flex-shrink:0; }

+
+
+
+
+
.bp3-html-select select > .bp3-fill, .bp3-select select > .bp3-fill{
+
-webkit-box-flex:1;
+
-ms-flex-positive:1;

flex-grow:1;

+
+
+
+
-ms-flex-negative:1;

flex-shrink:1; }

+
+
+
+
+

.bp3-html-select select::before, +.bp3-select select::before, .bp3-html-select select > *, .bp3-select select > *{

+
+

margin-right:7px; }

+
+

.bp3-html-select select:empty::before, +.bp3-select select:empty::before, +.bp3-html-select select > :last-child, +.bp3-select select > :last-child{

+
+

margin-right:0; }

+
+

.bp3-html-select select:hover, +.bp3-select select:hover{

+
+

background-clip:padding-box; +background-color:#ebf1f5; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }

+
+
+

.bp3-html-select select:active, +.bp3-select select:active, .bp3-html-select select.bp3-active, +.bp3-select select.bp3-active{

+
+

background-color:#d8e1e8; +background-image:none; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+

.bp3-html-select select:disabled, +.bp3-select select:disabled, .bp3-html-select select.bp3-disabled, +.bp3-select select.bp3-disabled{

+
+

background-color:rgba(206, 217, 224, 0.5); +background-image:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; +outline:none; } +.bp3-html-select select:disabled.bp3-active, +.bp3-select select:disabled.bp3-active, .bp3-html-select select:disabled.bp3-active:hover, +.bp3-select select:disabled.bp3-active:hover, .bp3-html-select select.bp3-disabled.bp3-active, +.bp3-select select.bp3-disabled.bp3-active, .bp3-html-select select.bp3-disabled.bp3-active:hover, +.bp3-select select.bp3-disabled.bp3-active:hover{

+
+

background:rgba(206, 217, 224, 0.7); }

+
+
+
+

.bp3-html-select.bp3-minimal select, +.bp3-select.bp3-minimal select{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+

.bp3-html-select.bp3-minimal select:hover, +.bp3-select.bp3-minimal select:hover{

+
+

background:rgba(167, 182, 194, 0.3); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#182026; +text-decoration:none; }

+
+

.bp3-html-select.bp3-minimal select:active, +.bp3-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal select.bp3-active, +.bp3-select.bp3-minimal select.bp3-active{

+
+

background:rgba(115, 134, 148, 0.3); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#182026; }

+
+

.bp3-html-select.bp3-minimal select:disabled, +.bp3-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal select:disabled:hover, +.bp3-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal select.bp3-disabled, +.bp3-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal select.bp3-disabled:hover, +.bp3-select.bp3-minimal select.bp3-disabled:hover{

+
+

background:none; +color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; } +.bp3-html-select.bp3-minimal select:disabled.bp3-active, +.bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active, +.bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active, +.bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active, +.bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active{

+
+

background:rgba(115, 134, 148, 0.3); }

+
+
+

.bp3-dark .bp3-html-select.bp3-minimal select, .bp3-html-select.bp3-minimal .bp3-dark select, +.bp3-dark .bp3-select.bp3-minimal select, .bp3-select.bp3-minimal .bp3-dark select{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:inherit; } +.bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover, +.bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover, .bp3-dark .bp3-html-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal .bp3-dark select:active, +.bp3-dark .bp3-select.bp3-minimal select:active, .bp3-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+

.bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover, +.bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover{

+
+

background:rgba(138, 155, 168, 0.15); }

+
+

.bp3-dark .bp3-html-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal .bp3-dark select:active, +.bp3-dark .bp3-select.bp3-minimal select:active, .bp3-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-active{

+
+

background:rgba(138, 155, 168, 0.3); +color:#f5f8fa; }

+
+

.bp3-dark .bp3-html-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal .bp3-dark select:disabled, +.bp3-dark .bp3-select.bp3-minimal select:disabled, .bp3-select.bp3-minimal .bp3-dark select:disabled, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover, +.bp3-dark .bp3-select.bp3-minimal select:disabled:hover, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled, +.bp3-dark .bp3-select.bp3-minimal select.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover, +.bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover{

+
+

background:none; +color:rgba(167, 182, 194, 0.6); +cursor:not-allowed; } +.bp3-dark .bp3-html-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active{

+
+

background:rgba(138, 155, 168, 0.3); }

+
+
+
+

.bp3-html-select.bp3-minimal select.bp3-intent-primary, +.bp3-select.bp3-minimal select.bp3-intent-primary{

+
+

color:#106ba3; } +.bp3-html-select.bp3-minimal select.bp3-intent-primary:hover, +.bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal select.bp3-intent-primary:active, +.bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#106ba3; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-primary:hover, +.bp3-select.bp3-minimal select.bp3-intent-primary:hover{

+
+

background:rgba(19, 124, 189, 0.15); +color:#106ba3; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-primary:active, +.bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active{

+
+

background:rgba(19, 124, 189, 0.3); +color:#106ba3; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled, +.bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, +.bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled{

+
+

background:none; +color:rgba(16, 107, 163, 0.5); } +.bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active{

+
+

background:rgba(19, 124, 189, 0.3); }

+
+
+
+
.bp3-html-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{

stroke:#106ba3; }

+
+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary{

+
+

color:#48aff0; } +.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover{

+
+

background:rgba(19, 124, 189, 0.2); +color:#48aff0; }

+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active{

+
+

background:rgba(19, 124, 189, 0.3); +color:#48aff0; }

+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled{

+
+

background:none; +color:rgba(72, 175, 240, 0.5); } +.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active{

+
+

background:rgba(19, 124, 189, 0.3); }

+
+
+
+
+

.bp3-html-select.bp3-minimal select.bp3-intent-success, +.bp3-select.bp3-minimal select.bp3-intent-success{

+
+

color:#0d8050; } +.bp3-html-select.bp3-minimal select.bp3-intent-success:hover, +.bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal select.bp3-intent-success:active, +.bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-success.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#0d8050; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-success:hover, +.bp3-select.bp3-minimal select.bp3-intent-success:hover{

+
+

background:rgba(15, 153, 96, 0.15); +color:#0d8050; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-success:active, +.bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-success.bp3-active{

+
+

background:rgba(15, 153, 96, 0.3); +color:#0d8050; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-success:disabled, +.bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled, +.bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled{

+
+

background:none; +color:rgba(13, 128, 80, 0.5); } +.bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active{

+
+

background:rgba(15, 153, 96, 0.3); }

+
+
+
+
.bp3-html-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{

stroke:#0d8050; }

+
+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success{

+
+

color:#3dcc91; } +.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover{

+
+

background:rgba(15, 153, 96, 0.2); +color:#3dcc91; }

+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active{

+
+

background:rgba(15, 153, 96, 0.3); +color:#3dcc91; }

+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled{

+
+

background:none; +color:rgba(61, 204, 145, 0.5); } +.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active{

+
+

background:rgba(15, 153, 96, 0.3); }

+
+
+
+
+

.bp3-html-select.bp3-minimal select.bp3-intent-warning, +.bp3-select.bp3-minimal select.bp3-intent-warning{

+
+

color:#bf7326; } +.bp3-html-select.bp3-minimal select.bp3-intent-warning:hover, +.bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal select.bp3-intent-warning:active, +.bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#bf7326; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-warning:hover, +.bp3-select.bp3-minimal select.bp3-intent-warning:hover{

+
+

background:rgba(217, 130, 43, 0.15); +color:#bf7326; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-warning:active, +.bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active{

+
+

background:rgba(217, 130, 43, 0.3); +color:#bf7326; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled, +.bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, +.bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled{

+
+

background:none; +color:rgba(191, 115, 38, 0.5); } +.bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active{

+
+

background:rgba(217, 130, 43, 0.3); }

+
+
+
+
.bp3-html-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{

stroke:#bf7326; }

+
+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning{

+
+

color:#ffb366; } +.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover{

+
+

background:rgba(217, 130, 43, 0.2); +color:#ffb366; }

+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active{

+
+

background:rgba(217, 130, 43, 0.3); +color:#ffb366; }

+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled{

+
+

background:none; +color:rgba(255, 179, 102, 0.5); } +.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active{

+
+

background:rgba(217, 130, 43, 0.3); }

+
+
+
+
+

.bp3-html-select.bp3-minimal select.bp3-intent-danger, +.bp3-select.bp3-minimal select.bp3-intent-danger{

+
+

color:#c23030; } +.bp3-html-select.bp3-minimal select.bp3-intent-danger:hover, +.bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal select.bp3-intent-danger:active, +.bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active{

+
+

background:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#c23030; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-danger:hover, +.bp3-select.bp3-minimal select.bp3-intent-danger:hover{

+
+

background:rgba(219, 55, 55, 0.15); +color:#c23030; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-danger:active, +.bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active{

+
+

background:rgba(219, 55, 55, 0.3); +color:#c23030; }

+
+

.bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled, +.bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, +.bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled{

+
+

background:none; +color:rgba(194, 48, 48, 0.5); } +.bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, +.bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active{

+
+

background:rgba(219, 55, 55, 0.3); }

+
+
+
+
.bp3-html-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{

stroke:#c23030; }

+
+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger{

+
+

color:#ff7373; } +.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover{

+
+

background:rgba(219, 55, 55, 0.2); +color:#ff7373; }

+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active{

+
+

background:rgba(219, 55, 55, 0.3); +color:#ff7373; }

+
+

.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled{

+
+

background:none; +color:rgba(255, 115, 115, 0.5); } +.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active, +.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active{

+
+

background:rgba(219, 55, 55, 0.3); }

+
+
+
+
+
+

.bp3-html-select.bp3-large select, +.bp3-select.bp3-large select{

+
+

font-size:16px; +height:40px; +padding-right:35px; }

+
+
+
.bp3-dark .bp3-html-select select, .bp3-dark .bp3-select select{

background-color:#394b59; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

color:#f5f8fa; } +.bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover, .bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{

+
+

color:#f5f8fa; }

+
+
+
.bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover{

background-color:#30404d; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{

background-color:#202b33; +background-image:none; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-dark .bp3-html-select select:disabled, .bp3-dark .bp3-select select:disabled, .bp3-dark .bp3-html-select select.bp3-disabled, .bp3-dark .bp3-select select.bp3-disabled{

background-color:rgba(57, 75, 89, 0.5); +background-image:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(167, 182, 194, 0.6); } +.bp3-dark .bp3-html-select select:disabled.bp3-active, .bp3-dark .bp3-select select:disabled.bp3-active, .bp3-dark .bp3-html-select select.bp3-disabled.bp3-active, .bp3-dark .bp3-select select.bp3-disabled.bp3-active{

+
+

background:rgba(57, 75, 89, 0.7); }

+
+
+
.bp3-dark .bp3-html-select select .bp3-button-spinner .bp3-spinner-head, .bp3-dark .bp3-select select .bp3-button-spinner .bp3-spinner-head{

background:rgba(16, 22, 26, 0.5); +stroke:#8a9ba8; }

+
+
+
+
+

.bp3-html-select select:disabled, +.bp3-select select:disabled{

+
+

background-color:rgba(206, 217, 224, 0.5); +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; }

+
+

.bp3-html-select .bp3-icon, +.bp3-select .bp3-icon, .bp3-select::after{

+
+

color:#5c7080; +pointer-events:none; +position:absolute; +right:7px; +top:7px; } +.bp3-html-select .bp3-disabled.bp3-icon, +.bp3-select .bp3-disabled.bp3-icon, .bp3-disabled.bp3-select::after{

+
+

color:rgba(92, 112, 128, 0.6); }

+
+
+

.bp3-html-select, +.bp3-select{

+
+

display:inline-block; +letter-spacing:normal; +position:relative; +vertical-align:middle; } +.bp3-html-select select::-ms-expand, +.bp3-select select::-ms-expand{

+
+

display:none; }

+
+

.bp3-html-select .bp3-icon, +.bp3-select .bp3-icon{

+
+

color:#5c7080; } +.bp3-html-select .bp3-icon:hover, +.bp3-select .bp3-icon:hover{

+
+

color:#182026; }

+
+

.bp3-dark .bp3-html-select .bp3-icon, .bp3-dark +.bp3-select .bp3-icon{

+
+

color:#a7b6c2; } +.bp3-dark .bp3-html-select .bp3-icon:hover, .bp3-dark +.bp3-select .bp3-icon:hover{

+
+

color:#f5f8fa; }

+
+
+
+

.bp3-html-select.bp3-large::after, +.bp3-html-select.bp3-large .bp3-icon, +.bp3-select.bp3-large::after, +.bp3-select.bp3-large .bp3-icon{

+
+

right:12px; +top:12px; }

+
+

.bp3-html-select.bp3-fill, +.bp3-html-select.bp3-fill select, +.bp3-select.bp3-fill, +.bp3-select.bp3-fill select{

+
+

width:100%; }

+
+

.bp3-dark .bp3-html-select option, .bp3-dark +.bp3-select option{

+
+

background-color:#30404d; +color:#f5f8fa; }

+
+

.bp3-dark .bp3-html-select option:disabled, .bp3-dark +.bp3-select option:disabled{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+

.bp3-dark .bp3-html-select::after, .bp3-dark +.bp3-select::after{

+
+

color:#a7b6c2; }

+
+
+
+
.bp3-select::after{

font-family:”Icons16”, sans-serif; +font-size:16px; +font-style:normal; +font-weight:400; +line-height:1; +-moz-osx-font-smoothing:grayscale; +-webkit-font-smoothing:antialiased; +content:””; }

+
+
.bp3-running-text table, table.bp3-html-table{

border-spacing:0; +font-size:14px; } +.bp3-running-text table th, table.bp3-html-table th, +.bp3-running-text table td, +table.bp3-html-table td{

+
+

padding:11px; +text-align:left; +vertical-align:top; }

+
+
+
.bp3-running-text table th, table.bp3-html-table th{

color:#182026; +font-weight:600; }

+
+
+

.bp3-running-text table td, +table.bp3-html-table td{

+
+

color:#182026; }

+
+

.bp3-running-text table tbody tr:first-child th, table.bp3-html-table tbody tr:first-child th, +.bp3-running-text table tbody tr:first-child td, +table.bp3-html-table tbody tr:first-child td, +.bp3-running-text table tfoot tr:first-child th, +table.bp3-html-table tfoot tr:first-child th, +.bp3-running-text table tfoot tr:first-child td, +table.bp3-html-table tfoot tr:first-child td{

+
+
+
-webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);

box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }

+
+
+
+
+
.bp3-dark .bp3-running-text table th, .bp3-running-text .bp3-dark table th, .bp3-dark table.bp3-html-table th{

color:#f5f8fa; }

+
+
.bp3-dark .bp3-running-text table td, .bp3-running-text .bp3-dark table td, .bp3-dark table.bp3-html-table td{

color:#f5f8fa; }

+
+
+

.bp3-dark .bp3-running-text table tbody tr:first-child th, .bp3-running-text .bp3-dark table tbody tr:first-child th, .bp3-dark table.bp3-html-table tbody tr:first-child th, +.bp3-dark .bp3-running-text table tbody tr:first-child td, +.bp3-running-text .bp3-dark table tbody tr:first-child td, +.bp3-dark table.bp3-html-table tbody tr:first-child td, +.bp3-dark .bp3-running-text table tfoot tr:first-child th, +.bp3-running-text .bp3-dark table tfoot tr:first-child th, +.bp3-dark table.bp3-html-table tfoot tr:first-child th, +.bp3-dark .bp3-running-text table tfoot tr:first-child td, +.bp3-running-text .bp3-dark table tfoot tr:first-child td, +.bp3-dark table.bp3-html-table tfoot tr:first-child td{

+
+
+
-webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15);

box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); }

+
+
+
+
+
+

table.bp3-html-table.bp3-html-table-condensed th, +table.bp3-html-table.bp3-html-table-condensed td, table.bp3-html-table.bp3-small th, +table.bp3-html-table.bp3-small td{

+
+

padding-bottom:6px; +padding-top:6px; }

+
+
+
table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{

background:rgba(191, 204, 214, 0.15); }

+
+
table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){
+
-webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);

box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }

+
+
+
+
+

table.bp3-html-table.bp3-html-table-bordered tbody tr td, +table.bp3-html-table.bp3-html-table-bordered tfoot tr td{

+
+
+
-webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);

box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }

+
+
+

table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child), +table.bp3-html-table.bp3-html-table-bordered tfoot tr td:not(:first-child){

+
+
+
-webkit-box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15);

box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15); }

+
+
+
+
+
+
table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:not(:first-child){
+
-webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);

box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }

+
+
+
+
+
+
table.bp3-html-table.bp3-interactive tbody tr:hover td{

background-color:rgba(191, 204, 214, 0.3); +cursor:pointer; }

+
+
table.bp3-html-table.bp3-interactive tbody tr:active td{

background-color:rgba(191, 204, 214, 0.4); }

+
+
.bp3-dark table.bp3-html-table{ }
+
.bp3-dark table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{

background:rgba(92, 112, 128, 0.15); }

+
+
.bp3-dark table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){
+
-webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15);

box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); }

+
+
+
+
+

.bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td, +.bp3-dark table.bp3-html-table.bp3-html-table-bordered tfoot tr td{

+
+
+
-webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15);

box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); }

+
+
+

.bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child), +.bp3-dark table.bp3-html-table.bp3-html-table-bordered tfoot tr td:not(:first-child){

+
+
+
-webkit-box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15);

box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15); }

+
+
+
+
+
+
.bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{
+
-webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15);

box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); }

+
+
.bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:first-child{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+
+
.bp3-dark table.bp3-html-table.bp3-interactive tbody tr:hover td{

background-color:rgba(92, 112, 128, 0.3); +cursor:pointer; }

+
+
.bp3-dark table.bp3-html-table.bp3-interactive tbody tr:active td{

background-color:rgba(92, 112, 128, 0.4); }

+
+
+
+
.bp3-key-combo{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:horizontal; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:row;

flex-direction:row;

+
+
+
+
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center; }

+
+
+
+
.bp3-key-combo > *{
+
-webkit-box-flex:0;
+
-ms-flex-positive:0;

flex-grow:0;

+
+
+
+
-ms-flex-negative:0;

flex-shrink:0; }

+
+
+
+
.bp3-key-combo > .bp3-fill{
+
-webkit-box-flex:1;
+
-ms-flex-positive:1;

flex-grow:1;

+
+
+
+
-ms-flex-negative:1;

flex-shrink:1; }

+
+
+
+
+

.bp3-key-combo::before, +.bp3-key-combo > *{

+
+

margin-right:5px; }

+
+

.bp3-key-combo:empty::before, +.bp3-key-combo > :last-child{

+
+

margin-right:0; }

+
+
+
.bp3-hotkey-dialog{

padding-bottom:0; +top:40px; } +.bp3-hotkey-dialog .bp3-dialog-body{

+
+

margin:0; +padding:0; }

+
+
+
.bp3-hotkey-dialog .bp3-hotkey-label{
+
-webkit-box-flex:1;
+
-ms-flex-positive:1;

flex-grow:1; }

+
+
+
+
+
+
+
+
.bp3-hotkey-column{

margin:auto; +max-height:80vh; +overflow-y:auto; +padding:30px; } +.bp3-hotkey-column .bp3-heading{

+
+

margin-bottom:20px; } +.bp3-hotkey-column .bp3-heading:not(:first-child){

+
+

margin-top:40px; }

+
+
+
+
.bp3-hotkey{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-pack:justify;

+
+
+
-ms-flex-pack:justify;

justify-content:space-between;

+
+
+
+

margin-left:0; +margin-right:0; } +.bp3-hotkey:not(:last-child){

+
+

margin-bottom:10px; }

+
+
+
.bp3-icon{

display:inline-block; +-webkit-box-flex:0;

+
+
+
-ms-flex:0 0 auto;

flex:0 0 auto;

+
+
+
+

vertical-align:text-bottom; } +.bp3-icon:not(:empty)::before{

+
+

content:”” !important; +content:unset !important; }

+
+
+
.bp3-icon > svg{

display:block; } +.bp3-icon > svg:not([fill]){

+
+

fill:currentColor; }

+
+
+
+
+
.bp3-icon.bp3-intent-primary, .bp3-icon-standard.bp3-intent-primary, .bp3-icon-large.bp3-intent-primary{

color:#106ba3; } +.bp3-dark .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-icon-large.bp3-intent-primary{

+
+

color:#48aff0; }

+
+
+
.bp3-icon.bp3-intent-success, .bp3-icon-standard.bp3-intent-success, .bp3-icon-large.bp3-intent-success{

color:#0d8050; } +.bp3-dark .bp3-icon.bp3-intent-success, .bp3-dark .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-icon-large.bp3-intent-success{

+
+

color:#3dcc91; }

+
+
+
.bp3-icon.bp3-intent-warning, .bp3-icon-standard.bp3-intent-warning, .bp3-icon-large.bp3-intent-warning{

color:#bf7326; } +.bp3-dark .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-icon-large.bp3-intent-warning{

+
+

color:#ffb366; }

+
+
+
.bp3-icon.bp3-intent-danger, .bp3-icon-standard.bp3-intent-danger, .bp3-icon-large.bp3-intent-danger{

color:#c23030; } +.bp3-dark .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-icon-large.bp3-intent-danger{

+
+

color:#ff7373; }

+
+
+
span.bp3-icon-standard{

font-family:”Icons16”, sans-serif; +font-size:16px; +font-style:normal; +font-weight:400; +line-height:1; +-moz-osx-font-smoothing:grayscale; +-webkit-font-smoothing:antialiased; +display:inline-block; }

+
+
span.bp3-icon-large{

font-family:”Icons20”, sans-serif; +font-size:20px; +font-style:normal; +font-weight:400; +line-height:1; +-moz-osx-font-smoothing:grayscale; +-webkit-font-smoothing:antialiased; +display:inline-block; }

+
+
span.bp3-icon:empty{

font-family:”Icons20”; +font-size:inherit; +font-style:normal; +font-weight:400; +line-height:1; } +span.bp3-icon:empty::before{

+
+

-moz-osx-font-smoothing:grayscale; +-webkit-font-smoothing:antialiased; }

+
+
+
.bp3-icon-add::before{

content:””; }

+
+
.bp3-icon-add-column-left::before{

content:””; }

+
+
.bp3-icon-add-column-right::before{

content:””; }

+
+
.bp3-icon-add-row-bottom::before{

content:””; }

+
+
.bp3-icon-add-row-top::before{

content:””; }

+
+
.bp3-icon-add-to-artifact::before{

content:””; }

+
+
.bp3-icon-add-to-folder::before{

content:””; }

+
+
.bp3-icon-airplane::before{

content:””; }

+
+
.bp3-icon-align-center::before{

content:””; }

+
+
.bp3-icon-align-justify::before{

content:””; }

+
+
.bp3-icon-align-left::before{

content:””; }

+
+
.bp3-icon-align-right::before{

content:””; }

+
+
.bp3-icon-alignment-bottom::before{

content:””; }

+
+
.bp3-icon-alignment-horizontal-center::before{

content:””; }

+
+
.bp3-icon-alignment-left::before{

content:””; }

+
+
.bp3-icon-alignment-right::before{

content:””; }

+
+
.bp3-icon-alignment-top::before{

content:””; }

+
+
.bp3-icon-alignment-vertical-center::before{

content:””; }

+
+
.bp3-icon-annotation::before{

content:””; }

+
+
.bp3-icon-application::before{

content:””; }

+
+
.bp3-icon-applications::before{

content:””; }

+
+
.bp3-icon-archive::before{

content:””; }

+
+
.bp3-icon-arrow-bottom-left::before{

content:”↙”; }

+
+
.bp3-icon-arrow-bottom-right::before{

content:”↘”; }

+
+
.bp3-icon-arrow-down::before{

content:”↓”; }

+
+
.bp3-icon-arrow-left::before{

content:”←”; }

+
+
.bp3-icon-arrow-right::before{

content:”→”; }

+
+
.bp3-icon-arrow-top-left::before{

content:”↖”; }

+
+
.bp3-icon-arrow-top-right::before{

content:”↗”; }

+
+
.bp3-icon-arrow-up::before{

content:”↑”; }

+
+
.bp3-icon-arrows-horizontal::before{

content:”↔”; }

+
+
.bp3-icon-arrows-vertical::before{

content:”↕”; }

+
+
.bp3-icon-asterisk::before{

content:”*”; }

+
+
.bp3-icon-automatic-updates::before{

content:””; }

+
+
.bp3-icon-badge::before{

content:””; }

+
+
.bp3-icon-ban-circle::before{

content:””; }

+
+
.bp3-icon-bank-account::before{

content:””; }

+
+
.bp3-icon-barcode::before{

content:””; }

+
+
.bp3-icon-blank::before{

content:””; }

+
+
.bp3-icon-blocked-person::before{

content:””; }

+
+
.bp3-icon-bold::before{

content:””; }

+
+
.bp3-icon-book::before{

content:””; }

+
+
.bp3-icon-bookmark::before{

content:””; }

+
+
.bp3-icon-box::before{

content:””; }

+
+
.bp3-icon-briefcase::before{

content:””; }

+
+
.bp3-icon-bring-data::before{

content:””; }

+
+
.bp3-icon-build::before{

content:””; }

+
+
.bp3-icon-calculator::before{

content:””; }

+
+
.bp3-icon-calendar::before{

content:””; }

+
+
.bp3-icon-camera::before{

content:””; }

+
+
.bp3-icon-caret-down::before{

content:”⌄”; }

+
+
.bp3-icon-caret-left::before{

content:”〈”; }

+
+
.bp3-icon-caret-right::before{

content:”〉”; }

+
+
.bp3-icon-caret-up::before{

content:”⌃”; }

+
+
.bp3-icon-cell-tower::before{

content:””; }

+
+
.bp3-icon-changes::before{

content:””; }

+
+
.bp3-icon-chart::before{

content:””; }

+
+
.bp3-icon-chat::before{

content:””; }

+
+
.bp3-icon-chevron-backward::before{

content:””; }

+
+
.bp3-icon-chevron-down::before{

content:””; }

+
+
.bp3-icon-chevron-forward::before{

content:””; }

+
+
.bp3-icon-chevron-left::before{

content:””; }

+
+
.bp3-icon-chevron-right::before{

content:””; }

+
+
.bp3-icon-chevron-up::before{

content:””; }

+
+
.bp3-icon-circle::before{

content:””; }

+
+
.bp3-icon-circle-arrow-down::before{

content:””; }

+
+
.bp3-icon-circle-arrow-left::before{

content:””; }

+
+
.bp3-icon-circle-arrow-right::before{

content:””; }

+
+
.bp3-icon-circle-arrow-up::before{

content:””; }

+
+
.bp3-icon-citation::before{

content:””; }

+
+
.bp3-icon-clean::before{

content:””; }

+
+
.bp3-icon-clipboard::before{

content:””; }

+
+
.bp3-icon-cloud::before{

content:”☁”; }

+
+
.bp3-icon-cloud-download::before{

content:””; }

+
+
.bp3-icon-cloud-upload::before{

content:””; }

+
+
.bp3-icon-code::before{

content:””; }

+
+
.bp3-icon-code-block::before{

content:””; }

+
+
.bp3-icon-cog::before{

content:””; }

+
+
.bp3-icon-collapse-all::before{

content:””; }

+
+
.bp3-icon-column-layout::before{

content:””; }

+
+
.bp3-icon-comment::before{

content:””; }

+
+
.bp3-icon-comparison::before{

content:””; }

+
+
.bp3-icon-compass::before{

content:””; }

+
+
.bp3-icon-compressed::before{

content:””; }

+
+
.bp3-icon-confirm::before{

content:””; }

+
+
.bp3-icon-console::before{

content:””; }

+
+
.bp3-icon-contrast::before{

content:””; }

+
+
.bp3-icon-control::before{

content:””; }

+
+
.bp3-icon-credit-card::before{

content:””; }

+
+
.bp3-icon-cross::before{

content:”✗”; }

+
+
.bp3-icon-crown::before{

content:””; }

+
+
.bp3-icon-cube::before{

content:””; }

+
+
.bp3-icon-cube-add::before{

content:””; }

+
+
.bp3-icon-cube-remove::before{

content:””; }

+
+
.bp3-icon-curved-range-chart::before{

content:””; }

+
+
.bp3-icon-cut::before{

content:””; }

+
+
.bp3-icon-dashboard::before{

content:””; }

+
+
.bp3-icon-data-lineage::before{

content:””; }

+
+
.bp3-icon-database::before{

content:””; }

+
+
.bp3-icon-delete::before{

content:””; }

+
+
.bp3-icon-delta::before{

content:”Δ”; }

+
+
.bp3-icon-derive-column::before{

content:””; }

+
+
.bp3-icon-desktop::before{

content:””; }

+
+
.bp3-icon-diagnosis::before{

content:””; }

+
+
.bp3-icon-diagram-tree::before{

content:””; }

+
+
.bp3-icon-direction-left::before{

content:””; }

+
+
.bp3-icon-direction-right::before{

content:””; }

+
+
.bp3-icon-disable::before{

content:””; }

+
+
.bp3-icon-document::before{

content:””; }

+
+
.bp3-icon-document-open::before{

content:””; }

+
+
.bp3-icon-document-share::before{

content:””; }

+
+
.bp3-icon-dollar::before{

content:”$”; }

+
+
.bp3-icon-dot::before{

content:”•”; }

+
+
.bp3-icon-double-caret-horizontal::before{

content:””; }

+
+
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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:”✎”; }

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content:”⏏”; }

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content:””; }

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content:”✉”; }

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content:””; }

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content:””; }

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content:”€”; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:”?”; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:”☰”; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:”−”; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:”☎”; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:”+”; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:”⎙”; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:”⦿”; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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content:””; }

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.bp3-icon-shop::before{

content:””; }

+
+
.bp3-icon-shopping-cart::before{

content:””; }

+
+
.bp3-icon-signal-search::before{

content:””; }

+
+
.bp3-icon-sim-card::before{

content:””; }

+
+
.bp3-icon-slash::before{

content:””; }

+
+
.bp3-icon-small-cross::before{

content:””; }

+
+
.bp3-icon-small-minus::before{

content:””; }

+
+
.bp3-icon-small-plus::before{

content:””; }

+
+
.bp3-icon-small-tick::before{

content:””; }

+
+
.bp3-icon-snowflake::before{

content:””; }

+
+
.bp3-icon-social-media::before{

content:””; }

+
+
.bp3-icon-sort::before{

content:””; }

+
+
.bp3-icon-sort-alphabetical::before{

content:””; }

+
+
.bp3-icon-sort-alphabetical-desc::before{

content:””; }

+
+
.bp3-icon-sort-asc::before{

content:””; }

+
+
.bp3-icon-sort-desc::before{

content:””; }

+
+
.bp3-icon-sort-numerical::before{

content:””; }

+
+
.bp3-icon-sort-numerical-desc::before{

content:””; }

+
+
.bp3-icon-split-columns::before{

content:””; }

+
+
.bp3-icon-square::before{

content:””; }

+
+
.bp3-icon-stacked-chart::before{

content:””; }

+
+
.bp3-icon-star::before{

content:”★”; }

+
+
.bp3-icon-star-empty::before{

content:”☆”; }

+
+
.bp3-icon-step-backward::before{

content:””; }

+
+
.bp3-icon-step-chart::before{

content:””; }

+
+
.bp3-icon-step-forward::before{

content:””; }

+
+
.bp3-icon-stop::before{

content:””; }

+
+
.bp3-icon-stopwatch::before{

content:””; }

+
+
.bp3-icon-strikethrough::before{

content:””; }

+
+
.bp3-icon-style::before{

content:””; }

+
+
.bp3-icon-swap-horizontal::before{

content:””; }

+
+
.bp3-icon-swap-vertical::before{

content:””; }

+
+
.bp3-icon-symbol-circle::before{

content:””; }

+
+
.bp3-icon-symbol-cross::before{

content:””; }

+
+
.bp3-icon-symbol-diamond::before{

content:””; }

+
+
.bp3-icon-symbol-square::before{

content:””; }

+
+
.bp3-icon-symbol-triangle-down::before{

content:””; }

+
+
.bp3-icon-symbol-triangle-up::before{

content:””; }

+
+
.bp3-icon-tag::before{

content:””; }

+
+
.bp3-icon-take-action::before{

content:””; }

+
+
.bp3-icon-taxi::before{

content:””; }

+
+
.bp3-icon-text-highlight::before{

content:””; }

+
+
.bp3-icon-th::before{

content:””; }

+
+
.bp3-icon-th-derived::before{

content:””; }

+
+
.bp3-icon-th-disconnect::before{

content:””; }

+
+
.bp3-icon-th-filtered::before{

content:””; }

+
+
.bp3-icon-th-list::before{

content:””; }

+
+
.bp3-icon-thumbs-down::before{

content:””; }

+
+
.bp3-icon-thumbs-up::before{

content:””; }

+
+
.bp3-icon-tick::before{

content:”✓”; }

+
+
.bp3-icon-tick-circle::before{

content:””; }

+
+
.bp3-icon-time::before{

content:”⏲”; }

+
+
.bp3-icon-timeline-area-chart::before{

content:””; }

+
+
.bp3-icon-timeline-bar-chart::before{

content:””; }

+
+
.bp3-icon-timeline-events::before{

content:””; }

+
+
.bp3-icon-timeline-line-chart::before{

content:””; }

+
+
.bp3-icon-tint::before{

content:””; }

+
+
.bp3-icon-torch::before{

content:””; }

+
+
.bp3-icon-tractor::before{

content:””; }

+
+
.bp3-icon-train::before{

content:””; }

+
+
.bp3-icon-translate::before{

content:””; }

+
+
.bp3-icon-trash::before{

content:””; }

+
+
.bp3-icon-tree::before{

content:””; }

+
+
.bp3-icon-trending-down::before{

content:””; }

+
+
.bp3-icon-trending-up::before{

content:””; }

+
+
.bp3-icon-truck::before{

content:””; }

+
+
.bp3-icon-two-columns::before{

content:””; }

+
+
.bp3-icon-unarchive::before{

content:””; }

+
+
.bp3-icon-underline::before{

content:”⎁”; }

+
+
.bp3-icon-undo::before{

content:”⎌”; }

+
+
.bp3-icon-ungroup-objects::before{

content:””; }

+
+
.bp3-icon-unknown-vehicle::before{

content:””; }

+
+
.bp3-icon-unlock::before{

content:””; }

+
+
.bp3-icon-unpin::before{

content:””; }

+
+
.bp3-icon-unresolve::before{

content:””; }

+
+
.bp3-icon-updated::before{

content:””; }

+
+
.bp3-icon-upload::before{

content:””; }

+
+
.bp3-icon-user::before{

content:””; }

+
+
.bp3-icon-variable::before{

content:””; }

+
+
.bp3-icon-vertical-bar-chart-asc::before{

content:””; }

+
+
.bp3-icon-vertical-bar-chart-desc::before{

content:””; }

+
+
.bp3-icon-vertical-distribution::before{

content:””; }

+
+
.bp3-icon-video::before{

content:””; }

+
+
.bp3-icon-volume-down::before{

content:””; }

+
+
.bp3-icon-volume-off::before{

content:””; }

+
+
.bp3-icon-volume-up::before{

content:””; }

+
+
.bp3-icon-walk::before{

content:””; }

+
+
.bp3-icon-warning-sign::before{

content:””; }

+
+
.bp3-icon-waterfall-chart::before{

content:””; }

+
+
.bp3-icon-widget::before{

content:””; }

+
+
.bp3-icon-widget-button::before{

content:””; }

+
+
.bp3-icon-widget-footer::before{

content:””; }

+
+
.bp3-icon-widget-header::before{

content:””; }

+
+
.bp3-icon-wrench::before{

content:””; }

+
+
.bp3-icon-zoom-in::before{

content:””; }

+
+
.bp3-icon-zoom-out::before{

content:””; }

+
+
.bp3-icon-zoom-to-fit::before{

content:””; }

+
+
.bp3-submenu > .bp3-popover-wrapper{

display:block; }

+
+
.bp3-submenu .bp3-popover-target{

display:block; } +.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{ }

+
+
.bp3-submenu.bp3-popover{
+
-webkit-box-shadow:none;

box-shadow:none;

+
+
+

padding:0 5px; } +.bp3-submenu.bp3-popover > .bp3-popover-content{

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); }

+
+
+
+
+
.bp3-dark .bp3-submenu.bp3-popover, .bp3-submenu.bp3-popover.bp3-dark{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
.bp3-dark .bp3-submenu.bp3-popover > .bp3-popover-content, .bp3-submenu.bp3-popover.bp3-dark > .bp3-popover-content{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }

+
+
+
+
+
+
+
+
.bp3-menu{

background:#ffffff; +border-radius:3px; +color:#182026; +list-style:none; +margin:0; +min-width:180px; +padding:5px; +text-align:left; }

+
+
.bp3-menu-divider{

border-top:1px solid rgba(16, 22, 26, 0.15); +display:block; +margin:5px; } +.bp3-dark .bp3-menu-divider{

+
+

border-top-color:rgba(255, 255, 255, 0.15); }

+
+
+
.bp3-menu-item{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:horizontal; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:row;

flex-direction:row;

+
+
+
+
+
-webkit-box-align:start;
+
-ms-flex-align:start;

align-items:flex-start;

+
+
+
+
+

border-radius:2px; +color:inherit; +line-height:20px; +padding:5px 7px; +text-decoration:none; +-webkit-user-select:none;

+
+
+
-moz-user-select:none;
+
-ms-user-select:none;

user-select:none; }

+
+
+
+
+
+
+
.bp3-menu-item > *{
+
-webkit-box-flex:0;
+
-ms-flex-positive:0;

flex-grow:0;

+
+
+
+
-ms-flex-negative:0;

flex-shrink:0; }

+
+
+
+
.bp3-menu-item > .bp3-fill{
+
-webkit-box-flex:1;
+
-ms-flex-positive:1;

flex-grow:1;

+
+
+
+
-ms-flex-negative:1;

flex-shrink:1; }

+
+
+
+
+

.bp3-menu-item::before, +.bp3-menu-item > *{

+
+

margin-right:7px; }

+
+

.bp3-menu-item:empty::before, +.bp3-menu-item > :last-child{

+
+

margin-right:0; }

+
+
+
.bp3-menu-item > .bp3-fill{

word-break:break-word; }

+
+
.bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{

background-color:rgba(167, 182, 194, 0.3); +cursor:pointer; +text-decoration:none; }

+
+
.bp3-menu-item.bp3-disabled{

background-color:inherit; +color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; }

+
+
.bp3-dark .bp3-menu-item{

color:inherit; } +.bp3-dark .bp3-menu-item:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-menu-item{

+
+

background-color:rgba(138, 155, 168, 0.15); +color:inherit; }

+
+
+
.bp3-dark .bp3-menu-item.bp3-disabled{

background-color:inherit; +color:rgba(167, 182, 194, 0.6); }

+
+
+
+
.bp3-menu-item.bp3-intent-primary{

color:#106ba3; } +.bp3-menu-item.bp3-intent-primary .bp3-icon{

+
+

color:inherit; }

+
+

.bp3-menu-item.bp3-intent-primary::before, .bp3-menu-item.bp3-intent-primary::after, +.bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{

+
+

color:#106ba3; }

+
+
+
.bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary.bp3-active{

background-color:#137cbd; }

+
+
.bp3-menu-item.bp3-intent-primary:active{

background-color:#106ba3; }

+
+
+

.bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after, +.bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label, +.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary:active, .bp3-menu-item.bp3-intent-primary:active::before, .bp3-menu-item.bp3-intent-primary:active::after, +.bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-menu-item.bp3-intent-primary.bp3-active::after, +.bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{

+
+

color:#ffffff; }

+
+
+
.bp3-menu-item.bp3-intent-success{

color:#0d8050; } +.bp3-menu-item.bp3-intent-success .bp3-icon{

+
+

color:inherit; }

+
+

.bp3-menu-item.bp3-intent-success::before, .bp3-menu-item.bp3-intent-success::after, +.bp3-menu-item.bp3-intent-success .bp3-menu-item-label{

+
+

color:#0d8050; }

+
+
+
.bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success.bp3-active{

background-color:#0f9960; }

+
+
.bp3-menu-item.bp3-intent-success:active{

background-color:#0d8050; }

+
+
+

.bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-menu-item.bp3-intent-success:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after, +.bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label, +.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success:active, .bp3-menu-item.bp3-intent-success:active::before, .bp3-menu-item.bp3-intent-success:active::after, +.bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-menu-item.bp3-intent-success.bp3-active::after, +.bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{

+
+

color:#ffffff; }

+
+
+
.bp3-menu-item.bp3-intent-warning{

color:#bf7326; } +.bp3-menu-item.bp3-intent-warning .bp3-icon{

+
+

color:inherit; }

+
+

.bp3-menu-item.bp3-intent-warning::before, .bp3-menu-item.bp3-intent-warning::after, +.bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{

+
+

color:#bf7326; }

+
+
+
.bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning.bp3-active{

background-color:#d9822b; }

+
+
.bp3-menu-item.bp3-intent-warning:active{

background-color:#bf7326; }

+
+
+

.bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after, +.bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label, +.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning:active, .bp3-menu-item.bp3-intent-warning:active::before, .bp3-menu-item.bp3-intent-warning:active::after, +.bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-menu-item.bp3-intent-warning.bp3-active::after, +.bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{

+
+

color:#ffffff; }

+
+
+
.bp3-menu-item.bp3-intent-danger{

color:#c23030; } +.bp3-menu-item.bp3-intent-danger .bp3-icon{

+
+

color:inherit; }

+
+

.bp3-menu-item.bp3-intent-danger::before, .bp3-menu-item.bp3-intent-danger::after, +.bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{

+
+

color:#c23030; }

+
+
+
.bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger.bp3-active{

background-color:#db3737; }

+
+
.bp3-menu-item.bp3-intent-danger:active{

background-color:#c23030; }

+
+
+

.bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after, +.bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label, +.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger:active, .bp3-menu-item.bp3-intent-danger:active::before, .bp3-menu-item.bp3-intent-danger:active::after, +.bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-menu-item.bp3-intent-danger.bp3-active::after, +.bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{

+
+

color:#ffffff; }

+
+
+
.bp3-menu-item::before{

font-family:”Icons16”, sans-serif; +font-size:16px; +font-style:normal; +font-weight:400; +line-height:1; +-moz-osx-font-smoothing:grayscale; +-webkit-font-smoothing:antialiased; +margin-right:7px; }

+
+
+

.bp3-menu-item::before, +.bp3-menu-item > .bp3-icon{

+
+

color:#5c7080; +margin-top:2px; }

+
+
+
.bp3-menu-item .bp3-menu-item-label{

color:#5c7080; }

+
+
.bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{

color:inherit; }

+
+
.bp3-menu-item.bp3-active, .bp3-menu-item:active{

background-color:rgba(115, 134, 148, 0.3); }

+
+
.bp3-menu-item.bp3-disabled{

background-color:inherit !important; +color:rgba(92, 112, 128, 0.6) !important; +cursor:not-allowed !important; +outline:none !important; } +.bp3-menu-item.bp3-disabled::before, +.bp3-menu-item.bp3-disabled > .bp3-icon, +.bp3-menu-item.bp3-disabled .bp3-menu-item-label{

+
+

color:rgba(92, 112, 128, 0.6) !important; }

+
+
+
.bp3-large .bp3-menu-item{

font-size:16px; +line-height:22px; +padding:9px 7px; } +.bp3-large .bp3-menu-item .bp3-icon{

+
+

margin-top:3px; }

+
+
+
.bp3-large .bp3-menu-item::before{

font-family:”Icons20”, sans-serif; +font-size:20px; +font-style:normal; +font-weight:400; +line-height:1; +-moz-osx-font-smoothing:grayscale; +-webkit-font-smoothing:antialiased; +margin-right:10px; +margin-top:1px; }

+
+
+
+
+
+
button.bp3-menu-item{

background:none; +border:none; +text-align:left; +width:100%; }

+
+
.bp3-menu-header{

border-top:1px solid rgba(16, 22, 26, 0.15); +display:block; +margin:5px; +cursor:default; +padding-left:2px; } +.bp3-dark .bp3-menu-header{

+
+

border-top-color:rgba(255, 255, 255, 0.15); }

+
+
+
.bp3-menu-header:first-of-type{

border-top:none; }

+
+
.bp3-menu-header > h6{

color:#182026; +font-weight:600; +overflow:hidden; +text-overflow:ellipsis; +white-space:nowrap; +word-wrap:normal; +line-height:17px; +margin:0; +padding:10px 7px 0 1px; } +.bp3-dark .bp3-menu-header > h6{

+
+

color:#f5f8fa; }

+
+
+
.bp3-menu-header:first-of-type > h6{

padding-top:0; }

+
+
.bp3-large .bp3-menu-header > h6{

font-size:18px; +padding-bottom:5px; +padding-top:15px; }

+
+
.bp3-large .bp3-menu-header:first-of-type > h6{

padding-top:0; }

+
+
+
+
.bp3-dark .bp3-menu{

background:#30404d; +color:#f5f8fa; }

+
+
.bp3-dark .bp3-menu-item{ }
+
.bp3-dark .bp3-menu-item.bp3-intent-primary{

color:#48aff0; } +.bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-icon{

+
+

color:inherit; }

+
+

.bp3-dark .bp3-menu-item.bp3-intent-primary::before, .bp3-dark .bp3-menu-item.bp3-intent-primary::after, +.bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{

+
+

color:#48aff0; }

+
+
+
.bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active{

background-color:#137cbd; }

+
+
.bp3-dark .bp3-menu-item.bp3-intent-primary:active{

background-color:#106ba3; }

+
+
+

.bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after, +.bp3-dark .bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label, +.bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, +.bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary:active, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::after, +.bp3-dark .bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::after, +.bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{

+
+

color:#ffffff; }

+
+
+
.bp3-dark .bp3-menu-item.bp3-intent-success{

color:#3dcc91; } +.bp3-dark .bp3-menu-item.bp3-intent-success .bp3-icon{

+
+

color:inherit; }

+
+

.bp3-dark .bp3-menu-item.bp3-intent-success::before, .bp3-dark .bp3-menu-item.bp3-intent-success::after, +.bp3-dark .bp3-menu-item.bp3-intent-success .bp3-menu-item-label{

+
+

color:#3dcc91; }

+
+
+
.bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active{

background-color:#0f9960; }

+
+
.bp3-dark .bp3-menu-item.bp3-intent-success:active{

background-color:#0d8050; }

+
+
+

.bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after, +.bp3-dark .bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label, +.bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, +.bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success:active, .bp3-dark .bp3-menu-item.bp3-intent-success:active::before, .bp3-dark .bp3-menu-item.bp3-intent-success:active::after, +.bp3-dark .bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::after, +.bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{

+
+

color:#ffffff; }

+
+
+
.bp3-dark .bp3-menu-item.bp3-intent-warning{

color:#ffb366; } +.bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-icon{

+
+

color:inherit; }

+
+

.bp3-dark .bp3-menu-item.bp3-intent-warning::before, .bp3-dark .bp3-menu-item.bp3-intent-warning::after, +.bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{

+
+

color:#ffb366; }

+
+
+
.bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active{

background-color:#d9822b; }

+
+
.bp3-dark .bp3-menu-item.bp3-intent-warning:active{

background-color:#bf7326; }

+
+
+

.bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after, +.bp3-dark .bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label, +.bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, +.bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning:active, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::after, +.bp3-dark .bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::after, +.bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{

+
+

color:#ffffff; }

+
+
+
.bp3-dark .bp3-menu-item.bp3-intent-danger{

color:#ff7373; } +.bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-icon{

+
+

color:inherit; }

+
+

.bp3-dark .bp3-menu-item.bp3-intent-danger::before, .bp3-dark .bp3-menu-item.bp3-intent-danger::after, +.bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{

+
+

color:#ff7373; }

+
+
+
.bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active{

background-color:#db3737; }

+
+
.bp3-dark .bp3-menu-item.bp3-intent-danger:active{

background-color:#c23030; }

+
+
+

.bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after, +.bp3-dark .bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label, +.bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, +.bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger:active, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::after, +.bp3-dark .bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::after, +.bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{

+
+

color:#ffffff; }

+
+
+
+

.bp3-dark .bp3-menu-item::before, +.bp3-dark .bp3-menu-item > .bp3-icon{

+
+

color:#a7b6c2; }

+
+
+
.bp3-dark .bp3-menu-item .bp3-menu-item-label{

color:#a7b6c2; }

+
+
.bp3-dark .bp3-menu-item.bp3-active, .bp3-dark .bp3-menu-item:active{

background-color:rgba(138, 155, 168, 0.3); }

+
+
.bp3-dark .bp3-menu-item.bp3-disabled{

color:rgba(167, 182, 194, 0.6) !important; } +.bp3-dark .bp3-menu-item.bp3-disabled::before, +.bp3-dark .bp3-menu-item.bp3-disabled > .bp3-icon, +.bp3-dark .bp3-menu-item.bp3-disabled .bp3-menu-item-label{

+
+

color:rgba(167, 182, 194, 0.6) !important; }

+
+
+
+
+
+

.bp3-dark .bp3-menu-divider, +.bp3-dark .bp3-menu-header{

+
+

border-color:rgba(255, 255, 255, 0.15); }

+
+
+
.bp3-dark .bp3-menu-header > h6{

color:#f5f8fa; }

+
+
.bp3-label .bp3-menu{

margin-top:5px; }

+
+
.bp3-navbar{

background-color:#ffffff; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

height:50px; +padding:0 15px; +position:relative; +width:100%; +z-index:10; } +.bp3-navbar.bp3-dark, +.bp3-dark .bp3-navbar{

+
+

background-color:#394b59; }

+
+
+
.bp3-navbar.bp3-dark{
+
-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-navbar{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-navbar.bp3-fixed-top{

left:0; +position:fixed; +right:0; +top:0; }

+
+
+
+
.bp3-navbar-heading{

font-size:16px; +margin-right:15px; }

+
+
.bp3-navbar-group{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +height:50px; } +.bp3-navbar-group.bp3-align-left{

+
+

float:left; }

+
+
+
.bp3-navbar-group.bp3-align-right{

float:right; }

+
+
+
+
.bp3-navbar-divider{

border-left:1px solid rgba(16, 22, 26, 0.15); +height:20px; +margin:0 10px; } +.bp3-dark .bp3-navbar-divider{

+
+

border-left-color:rgba(255, 255, 255, 0.15); }

+
+
+
.bp3-non-ideal-state{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:vertical; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:column;

flex-direction:column;

+
+
+
+
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

height:100%; +-webkit-box-pack:center;

+
+
+
-ms-flex-pack:center;

justify-content:center;

+
+
+
+

text-align:center; +width:100%; } +.bp3-non-ideal-state > *{

+
+
+
-webkit-box-flex:0;
+
-ms-flex-positive:0;

flex-grow:0;

+
+
+
+
-ms-flex-negative:0;

flex-shrink:0; }

+
+
+
+
+
.bp3-non-ideal-state > .bp3-fill{
+
-webkit-box-flex:1;
+
-ms-flex-positive:1;

flex-grow:1;

+
+
+
+
-ms-flex-negative:1;

flex-shrink:1; }

+
+
+
+
+

.bp3-non-ideal-state::before, +.bp3-non-ideal-state > *{

+
+

margin-bottom:20px; }

+
+

.bp3-non-ideal-state:empty::before, +.bp3-non-ideal-state > :last-child{

+
+

margin-bottom:0; }

+
+
+
.bp3-non-ideal-state > *{

max-width:400px; }

+
+
+
+
.bp3-non-ideal-state-visual{

color:rgba(92, 112, 128, 0.6); +font-size:60px; } +.bp3-dark .bp3-non-ideal-state-visual{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
.bp3-overflow-list{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-ms-flex-wrap:nowrap;

+
+

flex-wrap:nowrap;

+
+

min-width:0; }

+
+
.bp3-overflow-list-spacer{
+
-ms-flex-negative:1;

flex-shrink:1;

+
+
+

width:1px; }

+
+
body.bp3-overlay-open{

overflow:hidden; }

+
+
.bp3-overlay{

bottom:0; +left:0; +position:static; +right:0; +top:0; +z-index:20; } +.bp3-overlay:not(.bp3-overlay-open){

+
+

pointer-events:none; }

+
+
+
.bp3-overlay.bp3-overlay-container{

overflow:hidden; +position:fixed; } +.bp3-overlay.bp3-overlay-container.bp3-overlay-inline{

+
+

position:absolute; }

+
+
+
.bp3-overlay.bp3-overlay-scroll-container{

overflow:auto; +position:fixed; } +.bp3-overlay.bp3-overlay-scroll-container.bp3-overlay-inline{

+
+

position:absolute; }

+
+
+
.bp3-overlay.bp3-overlay-inline{

display:inline; +overflow:visible; }

+
+
+
+
.bp3-overlay-content{

position:fixed; +z-index:20; } +.bp3-overlay-inline .bp3-overlay-content, +.bp3-overlay-scroll-container .bp3-overlay-content{

+
+

position:absolute; }

+
+
+
.bp3-overlay-backdrop{

bottom:0; +left:0; +position:fixed; +right:0; +top:0; +opacity:1; +background-color:rgba(16, 22, 26, 0.7); +overflow:auto; +-webkit-user-select:none;

+
+
+
-moz-user-select:none;
+
-ms-user-select:none;

user-select:none;

+
+
+
+
+
+

z-index:20; } +.bp3-overlay-backdrop.bp3-overlay-enter, .bp3-overlay-backdrop.bp3-overlay-appear{

+
+

opacity:0; }

+
+
+
.bp3-overlay-backdrop.bp3-overlay-enter-active, .bp3-overlay-backdrop.bp3-overlay-appear-active{

opacity:1; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:200ms;

transition-duration:200ms;

+
+
+

-webkit-transition-property:opacity; +transition-property:opacity; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-overlay-backdrop.bp3-overlay-exit{

opacity:1; }

+
+
.bp3-overlay-backdrop.bp3-overlay-exit-active{

opacity:0; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:200ms;

transition-duration:200ms;

+
+
+

-webkit-transition-property:opacity; +transition-property:opacity; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-overlay-backdrop:focus{

outline:none; }

+
+
.bp3-overlay-inline .bp3-overlay-backdrop{

position:absolute; }

+
+
+
+
.bp3-panel-stack{

overflow:hidden; +position:relative; }

+
+
.bp3-panel-stack-header{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
-webkit-box-shadow:0 1px rgba(16, 22, 26, 0.15);

box-shadow:0 1px rgba(16, 22, 26, 0.15);

+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-ms-flex-negative:0;

+
+

flex-shrink:0;

+
+

height:30px; +z-index:1; } +.bp3-dark .bp3-panel-stack-header{

+
+
+
-webkit-box-shadow:0 1px rgba(255, 255, 255, 0.15);

box-shadow:0 1px rgba(255, 255, 255, 0.15); }

+
+
+
+
+
.bp3-panel-stack-header > span{
+
-webkit-box-align:stretch;
+
-ms-flex-align:stretch;

align-items:stretch;

+
+
+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-flex:1;

+
+
+
-ms-flex:1;

flex:1; }

+
+
+
+
+
.bp3-panel-stack-header .bp3-heading{

margin:0 5px; }

+
+
+
+
.bp3-button.bp3-panel-stack-header-back{

margin-left:5px; +padding-left:0; +white-space:nowrap; } +.bp3-button.bp3-panel-stack-header-back .bp3-icon{

+
+

margin:0 2px; }

+
+
+
.bp3-panel-stack-view{

bottom:0; +left:0; +position:absolute; +right:0; +top:0; +background-color:#ffffff; +border-right:1px solid rgba(16, 22, 26, 0.15); +display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:vertical; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:column;

flex-direction:column;

+
+
+
+

margin-right:-1px; +overflow-y:auto; +z-index:1; } +.bp3-dark .bp3-panel-stack-view{

+
+

background-color:#30404d; }

+
+
+
.bp3-panel-stack-view:nth-last-child(n + 4){

display:none; }

+
+
+
+
.bp3-panel-stack-push .bp3-panel-stack-enter, .bp3-panel-stack-push .bp3-panel-stack-appear{
+
-webkit-transform:translateX(100%);

transform:translateX(100%);

+
+
+

opacity:0; }

+
+
.bp3-panel-stack-push .bp3-panel-stack-enter-active, .bp3-panel-stack-push .bp3-panel-stack-appear-active{
+
-webkit-transform:translate(0%);

transform:translate(0%);

+
+
+

opacity:1; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:400ms;

transition-duration:400ms;

+
+
+

-webkit-transition-property:opacity, -webkit-transform; +transition-property:opacity, -webkit-transform; +transition-property:transform, opacity; +transition-property:transform, opacity, -webkit-transform; +-webkit-transition-timing-function:ease;

+
+

transition-timing-function:ease; }

+
+
+
.bp3-panel-stack-push .bp3-panel-stack-exit{
+
-webkit-transform:translate(0%);

transform:translate(0%);

+
+
+

opacity:1; }

+
+
.bp3-panel-stack-push .bp3-panel-stack-exit-active{
+
-webkit-transform:translateX(-50%);

transform:translateX(-50%);

+
+
+

opacity:0; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:400ms;

transition-duration:400ms;

+
+
+

-webkit-transition-property:opacity, -webkit-transform; +transition-property:opacity, -webkit-transform; +transition-property:transform, opacity; +transition-property:transform, opacity, -webkit-transform; +-webkit-transition-timing-function:ease;

+
+

transition-timing-function:ease; }

+
+
+
.bp3-panel-stack-pop .bp3-panel-stack-enter, .bp3-panel-stack-pop .bp3-panel-stack-appear{
+
-webkit-transform:translateX(-50%);

transform:translateX(-50%);

+
+
+

opacity:0; }

+
+
.bp3-panel-stack-pop .bp3-panel-stack-enter-active, .bp3-panel-stack-pop .bp3-panel-stack-appear-active{
+
-webkit-transform:translate(0%);

transform:translate(0%);

+
+
+

opacity:1; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:400ms;

transition-duration:400ms;

+
+
+

-webkit-transition-property:opacity, -webkit-transform; +transition-property:opacity, -webkit-transform; +transition-property:transform, opacity; +transition-property:transform, opacity, -webkit-transform; +-webkit-transition-timing-function:ease;

+
+

transition-timing-function:ease; }

+
+
+
.bp3-panel-stack-pop .bp3-panel-stack-exit{
+
-webkit-transform:translate(0%);

transform:translate(0%);

+
+
+

opacity:1; }

+
+
.bp3-panel-stack-pop .bp3-panel-stack-exit-active{
+
-webkit-transform:translateX(100%);

transform:translateX(100%);

+
+
+

opacity:0; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:400ms;

transition-duration:400ms;

+
+
+

-webkit-transition-property:opacity, -webkit-transform; +transition-property:opacity, -webkit-transform; +transition-property:transform, opacity; +transition-property:transform, opacity, -webkit-transform; +-webkit-transition-timing-function:ease;

+
+

transition-timing-function:ease; }

+
+
+
.bp3-panel-stack2{

overflow:hidden; +position:relative; }

+
+
.bp3-panel-stack2-header{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
-webkit-box-shadow:0 1px rgba(16, 22, 26, 0.15);

box-shadow:0 1px rgba(16, 22, 26, 0.15);

+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-ms-flex-negative:0;

+
+

flex-shrink:0;

+
+

height:30px; +z-index:1; } +.bp3-dark .bp3-panel-stack2-header{

+
+
+
-webkit-box-shadow:0 1px rgba(255, 255, 255, 0.15);

box-shadow:0 1px rgba(255, 255, 255, 0.15); }

+
+
+
+
+
.bp3-panel-stack2-header > span{
+
-webkit-box-align:stretch;
+
-ms-flex-align:stretch;

align-items:stretch;

+
+
+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-flex:1;

+
+
+
-ms-flex:1;

flex:1; }

+
+
+
+
+
.bp3-panel-stack2-header .bp3-heading{

margin:0 5px; }

+
+
+
+
.bp3-button.bp3-panel-stack2-header-back{

margin-left:5px; +padding-left:0; +white-space:nowrap; } +.bp3-button.bp3-panel-stack2-header-back .bp3-icon{

+
+

margin:0 2px; }

+
+
+
.bp3-panel-stack2-view{

bottom:0; +left:0; +position:absolute; +right:0; +top:0; +background-color:#ffffff; +border-right:1px solid rgba(16, 22, 26, 0.15); +display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:vertical; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:column;

flex-direction:column;

+
+
+
+

margin-right:-1px; +overflow-y:auto; +z-index:1; } +.bp3-dark .bp3-panel-stack2-view{

+
+

background-color:#30404d; }

+
+
+
.bp3-panel-stack2-view:nth-last-child(n + 4){

display:none; }

+
+
+
+
.bp3-panel-stack2-push .bp3-panel-stack2-enter, .bp3-panel-stack2-push .bp3-panel-stack2-appear{
+
-webkit-transform:translateX(100%);

transform:translateX(100%);

+
+
+

opacity:0; }

+
+
.bp3-panel-stack2-push .bp3-panel-stack2-enter-active, .bp3-panel-stack2-push .bp3-panel-stack2-appear-active{
+
-webkit-transform:translate(0%);

transform:translate(0%);

+
+
+

opacity:1; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:400ms;

transition-duration:400ms;

+
+
+

-webkit-transition-property:opacity, -webkit-transform; +transition-property:opacity, -webkit-transform; +transition-property:transform, opacity; +transition-property:transform, opacity, -webkit-transform; +-webkit-transition-timing-function:ease;

+
+

transition-timing-function:ease; }

+
+
+
.bp3-panel-stack2-push .bp3-panel-stack2-exit{
+
-webkit-transform:translate(0%);

transform:translate(0%);

+
+
+

opacity:1; }

+
+
.bp3-panel-stack2-push .bp3-panel-stack2-exit-active{
+
-webkit-transform:translateX(-50%);

transform:translateX(-50%);

+
+
+

opacity:0; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:400ms;

transition-duration:400ms;

+
+
+

-webkit-transition-property:opacity, -webkit-transform; +transition-property:opacity, -webkit-transform; +transition-property:transform, opacity; +transition-property:transform, opacity, -webkit-transform; +-webkit-transition-timing-function:ease;

+
+

transition-timing-function:ease; }

+
+
+
.bp3-panel-stack2-pop .bp3-panel-stack2-enter, .bp3-panel-stack2-pop .bp3-panel-stack2-appear{
+
-webkit-transform:translateX(-50%);

transform:translateX(-50%);

+
+
+

opacity:0; }

+
+
.bp3-panel-stack2-pop .bp3-panel-stack2-enter-active, .bp3-panel-stack2-pop .bp3-panel-stack2-appear-active{
+
-webkit-transform:translate(0%);

transform:translate(0%);

+
+
+

opacity:1; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:400ms;

transition-duration:400ms;

+
+
+

-webkit-transition-property:opacity, -webkit-transform; +transition-property:opacity, -webkit-transform; +transition-property:transform, opacity; +transition-property:transform, opacity, -webkit-transform; +-webkit-transition-timing-function:ease;

+
+

transition-timing-function:ease; }

+
+
+
.bp3-panel-stack2-pop .bp3-panel-stack2-exit{
+
-webkit-transform:translate(0%);

transform:translate(0%);

+
+
+

opacity:1; }

+
+
.bp3-panel-stack2-pop .bp3-panel-stack2-exit-active{
+
-webkit-transform:translateX(100%);

transform:translateX(100%);

+
+
+

opacity:0; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:400ms;

transition-duration:400ms;

+
+
+

-webkit-transition-property:opacity, -webkit-transform; +transition-property:opacity, -webkit-transform; +transition-property:transform, opacity; +transition-property:transform, opacity, -webkit-transform; +-webkit-transition-timing-function:ease;

+
+

transition-timing-function:ease; }

+
+
+
.bp3-popover{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

+
+
-webkit-transform:scale(1);

transform:scale(1);

+
+
+

border-radius:3px; +display:inline-block; +z-index:20; } +.bp3-popover .bp3-popover-arrow{

+
+

height:30px; +position:absolute; +width:30px; } +.bp3-popover .bp3-popover-arrow::before{

+
+

height:20px; +margin:5px; +width:20px; }

+
+
+
+
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover{

margin-bottom:17px; +margin-top:-17px; } +.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{

+
+

bottom:-11px; } +.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow svg{

+
+
+
-webkit-transform:rotate(-90deg);

transform:rotate(-90deg); }

+
+
+
+
+
+
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover{

margin-left:17px; } +.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{

+
+

left:-11px; } +.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow svg{

+
+
+
-webkit-transform:rotate(0);

transform:rotate(0); }

+
+
+
+
+
+
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover{

margin-top:17px; } +.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{

+
+

top:-11px; } +.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow svg{

+
+
+
-webkit-transform:rotate(90deg);

transform:rotate(90deg); }

+
+
+
+
+
+
.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover{

margin-left:-17px; +margin-right:17px; } +.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{

+
+

right:-11px; } +.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow svg{

+
+
+
-webkit-transform:rotate(180deg);

transform:rotate(180deg); }

+
+
+
+
+
+
.bp3-tether-element-attached-middle > .bp3-popover > .bp3-popover-arrow{

top:50%; +-webkit-transform:translateY(-50%);

+
+

transform:translateY(-50%); }

+
+
+
.bp3-tether-element-attached-center > .bp3-popover > .bp3-popover-arrow{

right:50%; +-webkit-transform:translateX(50%);

+
+

transform:translateX(50%); }

+
+
+
.bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{

top:-0.3934px; }

+
+
.bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{

right:-0.3934px; }

+
+
.bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{

left:-0.3934px; }

+
+
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{

bottom:-0.3934px; }

+
+
.bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-popover{
+
-webkit-transform-origin:top left;

transform-origin:top left; }

+
+
+
+
.bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-popover{
+
-webkit-transform-origin:top center;

transform-origin:top center; }

+
+
+
+
.bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-popover{
+
-webkit-transform-origin:top right;

transform-origin:top right; }

+
+
+
+
.bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-popover{
+
-webkit-transform-origin:center left;

transform-origin:center left; }

+
+
+
+
.bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-popover{
+
-webkit-transform-origin:center center;

transform-origin:center center; }

+
+
+
+
.bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-popover{
+
-webkit-transform-origin:center right;

transform-origin:center right; }

+
+
+
+
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-popover{
+
-webkit-transform-origin:bottom left;

transform-origin:bottom left; }

+
+
+
+
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-popover{
+
-webkit-transform-origin:bottom center;

transform-origin:bottom center; }

+
+
+
+
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-popover{
+
-webkit-transform-origin:bottom right;

transform-origin:bottom right; }

+
+
+
+
.bp3-popover .bp3-popover-content{

background:#ffffff; +color:inherit; }

+
+
.bp3-popover .bp3-popover-arrow::before{
+
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);

box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-popover .bp3-popover-arrow-border{

fill:#10161a; +fill-opacity:0.1; }

+
+
.bp3-popover .bp3-popover-arrow-fill{

fill:#ffffff; }

+
+
.bp3-popover-enter > .bp3-popover, .bp3-popover-appear > .bp3-popover{
+
-webkit-transform:scale(0.3);

transform:scale(0.3); }

+
+
+
+
.bp3-popover-enter-active > .bp3-popover, .bp3-popover-appear-active > .bp3-popover{
+
-webkit-transform:scale(1);

transform:scale(1);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:300ms;

transition-duration:300ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);

+
+

transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }

+
+
+
.bp3-popover-exit > .bp3-popover{
+
-webkit-transform:scale(1);

transform:scale(1); }

+
+
+
+
.bp3-popover-exit-active > .bp3-popover{
+
-webkit-transform:scale(0.3);

transform:scale(0.3);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:300ms;

transition-duration:300ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);

+
+

transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }

+
+
+
.bp3-popover .bp3-popover-content{

border-radius:3px; +position:relative; }

+
+
.bp3-popover.bp3-popover-content-sizing .bp3-popover-content{

max-width:350px; +padding:20px; }

+
+
.bp3-popover-target + .bp3-overlay .bp3-popover.bp3-popover-content-sizing{

width:350px; }

+
+
.bp3-popover.bp3-minimal{

margin:0 !important; } +.bp3-popover.bp3-minimal .bp3-popover-arrow{

+
+

display:none; }

+
+
+
.bp3-popover.bp3-minimal.bp3-popover{
+
-webkit-transform:scale(1);

transform:scale(1); }

+
+
.bp3-popover-enter > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear > .bp3-popover.bp3-minimal.bp3-popover{
+
-webkit-transform:scale(1);

transform:scale(1); }

+
+
+
+
.bp3-popover-enter-active > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear-active > .bp3-popover.bp3-minimal.bp3-popover{
+
-webkit-transform:scale(1);

transform:scale(1);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-popover-exit > .bp3-popover.bp3-minimal.bp3-popover{
+
-webkit-transform:scale(1);

transform:scale(1); }

+
+
+
+
.bp3-popover-exit-active > .bp3-popover.bp3-minimal.bp3-popover{
+
-webkit-transform:scale(1);

transform:scale(1);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
+
+
+
+
+

.bp3-popover.bp3-dark, +.bp3-dark .bp3-popover{

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }

+
+
+

.bp3-popover.bp3-dark .bp3-popover-content, +.bp3-dark .bp3-popover .bp3-popover-content{

+
+

background:#30404d; +color:inherit; }

+
+

.bp3-popover.bp3-dark .bp3-popover-arrow::before, +.bp3-dark .bp3-popover .bp3-popover-arrow::before{

+
+
+
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);

box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }

+
+
+
+

.bp3-popover.bp3-dark .bp3-popover-arrow-border, +.bp3-dark .bp3-popover .bp3-popover-arrow-border{

+
+

fill:#10161a; +fill-opacity:0.2; }

+
+

.bp3-popover.bp3-dark .bp3-popover-arrow-fill, +.bp3-dark .bp3-popover .bp3-popover-arrow-fill{

+
+

fill:#30404d; }

+
+
+
+
.bp3-popover-arrow::before{

border-radius:2px; +content:””; +display:block; +position:absolute; +-webkit-transform:rotate(45deg);

+
+

transform:rotate(45deg); }

+
+
+
.bp3-tether-pinned .bp3-popover-arrow{

display:none; }

+
+
.bp3-popover-backdrop{

background:rgba(255, 255, 255, 0); }

+
+
.bp3-transition-container{

opacity:1; +display:-webkit-box; +display:-ms-flexbox; +display:flex; +z-index:20; } +.bp3-transition-container.bp3-popover-enter, .bp3-transition-container.bp3-popover-appear{

+
+

opacity:0; }

+
+
+
.bp3-transition-container.bp3-popover-enter-active, .bp3-transition-container.bp3-popover-appear-active{

opacity:1; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:opacity; +transition-property:opacity; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-transition-container.bp3-popover-exit{

opacity:1; }

+
+
.bp3-transition-container.bp3-popover-exit-active{

opacity:0; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:opacity; +transition-property:opacity; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-transition-container:focus{

outline:none; }

+
+
.bp3-transition-container.bp3-popover-leave .bp3-popover-content{

pointer-events:none; }

+
+
.bp3-transition-container[data-x-out-of-boundaries]{

display:none; }

+
+
+
+
span.bp3-popover-target{

display:inline-block; }

+
+
.bp3-popover-wrapper.bp3-fill{

width:100%; }

+
+
.bp3-portal{

left:0; +position:absolute; +right:0; +top:0; }

+
+
@-webkit-keyframes linear-progress-bar-stripes{
+
from{

background-position:0 0; }

+
+
to{

background-position:30px 0; } }

+
+
+
+
@keyframes linear-progress-bar-stripes{
+
from{

background-position:0 0; }

+
+
to{

background-position:30px 0; } }

+
+
+
+
.bp3-progress-bar{

background:rgba(92, 112, 128, 0.2); +border-radius:40px; +display:block; +height:8px; +overflow:hidden; +position:relative; +width:100%; } +.bp3-progress-bar .bp3-progress-meter{

+
+

background:linear-gradient(-45deg, rgba(255, 255, 255, 0.2) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.2) 50%, rgba(255, 255, 255, 0.2) 75%, transparent 75%); +background-color:rgba(92, 112, 128, 0.8); +background-size:30px 30px; +border-radius:40px; +height:100%; +position:absolute; +-webkit-transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +width:100%; }

+
+
+
.bp3-progress-bar:not(.bp3-no-animation):not(.bp3-no-stripes) .bp3-progress-meter{

animation:linear-progress-bar-stripes 300ms linear infinite reverse; }

+
+
.bp3-progress-bar.bp3-no-stripes .bp3-progress-meter{

background-image:none; }

+
+
+
+
.bp3-dark .bp3-progress-bar{

background:rgba(16, 22, 26, 0.5); } +.bp3-dark .bp3-progress-bar .bp3-progress-meter{

+
+

background-color:#8a9ba8; }

+
+
+
.bp3-progress-bar.bp3-intent-primary .bp3-progress-meter{

background-color:#137cbd; }

+
+
.bp3-progress-bar.bp3-intent-success .bp3-progress-meter{

background-color:#0f9960; }

+
+
.bp3-progress-bar.bp3-intent-warning .bp3-progress-meter{

background-color:#d9822b; }

+
+
.bp3-progress-bar.bp3-intent-danger .bp3-progress-meter{

background-color:#db3737; }

+
+
@-webkit-keyframes skeleton-glow{
+
from{

background:rgba(206, 217, 224, 0.2); +border-color:rgba(206, 217, 224, 0.2); }

+
+
to{

background:rgba(92, 112, 128, 0.2); +border-color:rgba(92, 112, 128, 0.2); } }

+
+
+
+
@keyframes skeleton-glow{
+
from{

background:rgba(206, 217, 224, 0.2); +border-color:rgba(206, 217, 224, 0.2); }

+
+
to{

background:rgba(92, 112, 128, 0.2); +border-color:rgba(92, 112, 128, 0.2); } }

+
+
+
+
.bp3-skeleton{
+
-webkit-animation:1000ms linear infinite alternate skeleton-glow;

animation:1000ms linear infinite alternate skeleton-glow;

+
+
+

background:rgba(206, 217, 224, 0.2); +background-clip:padding-box !important; +border-color:rgba(206, 217, 224, 0.2) !important; +border-radius:2px; +-webkit-box-shadow:none !important;

+
+

box-shadow:none !important;

+
+

color:transparent !important; +cursor:default; +pointer-events:none; +-webkit-user-select:none;

+
+
+
-moz-user-select:none;
+
-ms-user-select:none;

user-select:none; }

+
+
+
+
+
+

.bp3-skeleton::before, .bp3-skeleton::after, +.bp3-skeleton *{

+
+

visibility:hidden !important; }

+
+
+
.bp3-slider{

height:40px; +min-width:150px; +width:100%; +cursor:default; +outline:none; +position:relative; +-webkit-user-select:none;

+
+
+
-moz-user-select:none;
+
-ms-user-select:none;

user-select:none; }

+
+
+
+
+
+
+
.bp3-slider:hover{

cursor:pointer; }

+
+
.bp3-slider:active{

cursor:-webkit-grabbing; +cursor:grabbing; }

+
+
.bp3-slider.bp3-disabled{

cursor:not-allowed; +opacity:0.5; }

+
+
.bp3-slider.bp3-slider-unlabeled{

height:16px; }

+
+
+
+
+

.bp3-slider-track, +.bp3-slider-progress{

+
+

height:6px; +left:0; +right:0; +top:5px; +position:absolute; }

+
+
+
.bp3-slider-track{

border-radius:3px; +overflow:hidden; }

+
+
.bp3-slider-progress{

background:rgba(92, 112, 128, 0.2); } +.bp3-dark .bp3-slider-progress{

+
+

background:rgba(16, 22, 26, 0.5); }

+
+
+
.bp3-slider-progress.bp3-intent-primary{

background-color:#137cbd; }

+
+
.bp3-slider-progress.bp3-intent-success{

background-color:#0f9960; }

+
+
.bp3-slider-progress.bp3-intent-warning{

background-color:#d9822b; }

+
+
.bp3-slider-progress.bp3-intent-danger{

background-color:#db3737; }

+
+
+
+
.bp3-slider-handle{

background-color:#f5f8fa; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0)); +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

color:#182026; +border-radius:3px; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

cursor:pointer; +height:16px; +left:0; +position:absolute; +top:0; +width:16px; } +.bp3-slider-handle:hover{

+
+

background-clip:padding-box; +background-color:#ebf1f5; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); }

+
+
+
+
.bp3-slider-handle:active, .bp3-slider-handle.bp3-active{

background-color:#d8e1e8; +background-image:none; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-slider-handle:disabled, .bp3-slider-handle.bp3-disabled{

background-color:rgba(206, 217, 224, 0.5); +background-image:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; +outline:none; } +.bp3-slider-handle:disabled.bp3-active, .bp3-slider-handle:disabled.bp3-active:hover, .bp3-slider-handle.bp3-disabled.bp3-active, .bp3-slider-handle.bp3-disabled.bp3-active:hover{

+
+

background:rgba(206, 217, 224, 0.7); }

+
+
+
.bp3-slider-handle:focus{

z-index:1; }

+
+
.bp3-slider-handle:hover{

background-clip:padding-box; +background-color:#ebf1f5; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1);

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2);

+
+
+

cursor:-webkit-grab; +cursor:grab; +z-index:2; }

+
+
.bp3-slider-handle.bp3-active{

background-color:#d8e1e8; +background-image:none; +-webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1);

+
+
+

cursor:-webkit-grabbing; +cursor:grabbing; }

+
+
.bp3-disabled .bp3-slider-handle{

background:#bfccd6; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

pointer-events:none; }

+
+
.bp3-dark .bp3-slider-handle{

background-color:#394b59; +background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0))); +background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

color:#f5f8fa; } +.bp3-dark .bp3-slider-handle:hover, .bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{

+
+

color:#f5f8fa; }

+
+
+
.bp3-dark .bp3-slider-handle:hover{

background-color:#30404d; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{

background-color:#202b33; +background-image:none; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-dark .bp3-slider-handle:disabled, .bp3-dark .bp3-slider-handle.bp3-disabled{

background-color:rgba(57, 75, 89, 0.5); +background-image:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:rgba(167, 182, 194, 0.6); } +.bp3-dark .bp3-slider-handle:disabled.bp3-active, .bp3-dark .bp3-slider-handle.bp3-disabled.bp3-active{

+
+

background:rgba(57, 75, 89, 0.7); }

+
+
+
.bp3-dark .bp3-slider-handle .bp3-button-spinner .bp3-spinner-head{

background:rgba(16, 22, 26, 0.5); +stroke:#8a9ba8; }

+
+
.bp3-dark .bp3-slider-handle, .bp3-dark .bp3-slider-handle:hover{

background-color:#394b59; }

+
+
.bp3-dark .bp3-slider-handle.bp3-active{

background-color:#293742; }

+
+
+
+
.bp3-dark .bp3-disabled .bp3-slider-handle{

background:#5c7080; +border-color:#5c7080; +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+
.bp3-slider-handle .bp3-slider-label{

background:#394b59; +border-radius:3px; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

+
+

color:#f5f8fa; +margin-left:8px; } +.bp3-dark .bp3-slider-handle .bp3-slider-label{

+
+

background:#e1e8ed; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);

+
+

color:#394b59; }

+
+
+
.bp3-disabled .bp3-slider-handle .bp3-slider-label{
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+
+
.bp3-slider-handle.bp3-start, .bp3-slider-handle.bp3-end{

width:8px; }

+
+
.bp3-slider-handle.bp3-start{

border-bottom-right-radius:0; +border-top-right-radius:0; }

+
+
.bp3-slider-handle.bp3-end{

border-bottom-left-radius:0; +border-top-left-radius:0; +margin-left:8px; } +.bp3-slider-handle.bp3-end .bp3-slider-label{

+
+

margin-left:0; }

+
+
+
+
+
.bp3-slider-label{
+
-webkit-transform:translate(-50%, 20px);

transform:translate(-50%, 20px);

+
+
+

display:inline-block; +font-size:12px; +line-height:1; +padding:2px 5px; +position:absolute; +vertical-align:top; }

+
+
.bp3-slider.bp3-vertical{

height:150px; +min-width:40px; +width:40px; } +.bp3-slider.bp3-vertical .bp3-slider-track, +.bp3-slider.bp3-vertical .bp3-slider-progress{

+
+

bottom:0; +height:auto; +left:5px; +top:0; +width:6px; }

+
+
+
.bp3-slider.bp3-vertical .bp3-slider-progress{

top:auto; }

+
+
.bp3-slider.bp3-vertical .bp3-slider-label{
+
-webkit-transform:translate(20px, 50%);

transform:translate(20px, 50%); }

+
+
+
+
.bp3-slider.bp3-vertical .bp3-slider-handle{

top:auto; } +.bp3-slider.bp3-vertical .bp3-slider-handle .bp3-slider-label{

+
+

margin-left:0; +margin-top:-8px; }

+
+
+
.bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end, .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{

height:8px; +margin-left:0; +width:16px; }

+
+
.bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{

border-bottom-right-radius:3px; +border-top-left-radius:0; } +.bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start .bp3-slider-label{

+
+
+
-webkit-transform:translate(20px);

transform:translate(20px); }

+
+
+
+
+
.bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end{

border-bottom-left-radius:0; +border-bottom-right-radius:0; +border-top-left-radius:3px; +margin-bottom:8px; }

+
+
+
+
+
+
@-webkit-keyframes pt-spinner-animation{
+
from{
+
-webkit-transform:rotate(0deg);

transform:rotate(0deg); }

+
+
+
+
to{
+
-webkit-transform:rotate(360deg);

transform:rotate(360deg); } }

+
+
+
+
+
+
@keyframes pt-spinner-animation{
+
from{
+
-webkit-transform:rotate(0deg);

transform:rotate(0deg); }

+
+
+
+
to{
+
-webkit-transform:rotate(360deg);

transform:rotate(360deg); } }

+
+
+
+
+
+
.bp3-spinner{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-pack:center;

+
+
+
-ms-flex-pack:center;

justify-content:center;

+
+
+
+

overflow:visible; +vertical-align:middle; } +.bp3-spinner svg{

+
+

display:block; }

+
+
+
.bp3-spinner path{

fill-opacity:0; }

+
+
.bp3-spinner .bp3-spinner-head{

stroke:rgba(92, 112, 128, 0.8); +stroke-linecap:round; +-webkit-transform-origin:center;

+
+

transform-origin:center;

+
+

-webkit-transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
.bp3-spinner .bp3-spinner-track{

stroke:rgba(92, 112, 128, 0.2); }

+
+
+
+
.bp3-spinner-animation{
+
-webkit-animation:pt-spinner-animation 500ms linear infinite;

animation:pt-spinner-animation 500ms linear infinite; }

+
+
.bp3-no-spin > .bp3-spinner-animation{
+
-webkit-animation:none;

animation:none; }

+
+
+
+
+
+
.bp3-dark .bp3-spinner .bp3-spinner-head{

stroke:#8a9ba8; }

+
+
.bp3-dark .bp3-spinner .bp3-spinner-track{

stroke:rgba(16, 22, 26, 0.5); }

+
+
.bp3-spinner.bp3-intent-primary .bp3-spinner-head{

stroke:#137cbd; }

+
+
.bp3-spinner.bp3-intent-success .bp3-spinner-head{

stroke:#0f9960; }

+
+
.bp3-spinner.bp3-intent-warning .bp3-spinner-head{

stroke:#d9822b; }

+
+
.bp3-spinner.bp3-intent-danger .bp3-spinner-head{

stroke:#db3737; }

+
+
.bp3-tabs.bp3-vertical{

display:-webkit-box; +display:-ms-flexbox; +display:flex; } +.bp3-tabs.bp3-vertical > .bp3-tab-list{

+
+
+
-webkit-box-align:start;
+
-ms-flex-align:start;

align-items:flex-start;

+
+
+
+
+

-webkit-box-orient:vertical; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:column;

flex-direction:column; }

+
+
+
+
+
.bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab{

border-radius:3px; +padding:0 10px; +width:100%; } +.bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab[aria-selected=”true”]{

+
+

background-color:rgba(19, 124, 189, 0.2); +-webkit-box-shadow:none;

+
+

box-shadow:none; }

+
+
+
+
.bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab-indicator-wrapper .bp3-tab-indicator{

background-color:rgba(19, 124, 189, 0.2); +border-radius:3px; +bottom:0; +height:auto; +left:0; +right:0; +top:0; }

+
+
+
+
+
.bp3-tabs.bp3-vertical > .bp3-tab-panel{

margin-top:0; +padding-left:20px; }

+
+
+
+
.bp3-tab-list{
+
-webkit-box-align:end;
+
-ms-flex-align:end;

align-items:flex-end;

+
+
+
+
+

border:none; +display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-flex:0;

+
+
+
-ms-flex:0 0 auto;

flex:0 0 auto;

+
+
+
+

list-style:none; +margin:0; +padding:0; +position:relative; } +.bp3-tab-list > *:not(:last-child){

+
+

margin-right:20px; }

+
+
+
.bp3-tab{

overflow:hidden; +text-overflow:ellipsis; +white-space:nowrap; +word-wrap:normal; +color:#182026; +cursor:pointer; +-webkit-box-flex:0;

+
+
+
-ms-flex:0 0 auto;

flex:0 0 auto;

+
+
+
+

font-size:14px; +line-height:30px; +max-width:100%; +position:relative; +vertical-align:top; } +.bp3-tab a{

+
+

color:inherit; +display:block; +text-decoration:none; }

+
+
+
.bp3-tab-indicator-wrapper ~ .bp3-tab{

background-color:transparent !important; +-webkit-box-shadow:none !important;

+
+

box-shadow:none !important; }

+
+
+
.bp3-tab[aria-disabled=”true”]{

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; }

+
+
.bp3-tab[aria-selected=”true”]{

border-radius:0; +-webkit-box-shadow:inset 0 -3px 0 #106ba3;

+
+

box-shadow:inset 0 -3px 0 #106ba3; }

+
+
+
.bp3-tab[aria-selected=”true”], .bp3-tab:not([aria-disabled=”true”]):hover{

color:#106ba3; }

+
+
.bp3-tab:focus{

-moz-outline-radius:0; }

+
+
.bp3-large > .bp3-tab{

font-size:16px; +line-height:40px; }

+
+
+
+
.bp3-tab-panel{

margin-top:20px; } +.bp3-tab-panel[aria-hidden=”true”]{

+
+

display:none; }

+
+
+
.bp3-tab-indicator-wrapper{

left:0; +pointer-events:none; +position:absolute; +top:0; +-webkit-transform:translateX(0), translateY(0);

+
+

transform:translateX(0), translateY(0);

+
+

-webkit-transition:height, width, -webkit-transform; +transition:height, width, -webkit-transform; +transition:height, transform, width; +transition:height, transform, width, -webkit-transform; +-webkit-transition-duration:200ms;

+
+

transition-duration:200ms;

+
+
+
-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
.bp3-tab-indicator-wrapper .bp3-tab-indicator{

background-color:#106ba3; +bottom:0; +height:3px; +left:0; +position:absolute; +right:0; }

+
+
.bp3-tab-indicator-wrapper.bp3-no-animation{

-webkit-transition:none; +transition:none; }

+
+
+
+
.bp3-dark .bp3-tab{

color:#f5f8fa; } +.bp3-dark .bp3-tab[aria-disabled=”true”]{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
.bp3-dark .bp3-tab[aria-selected=”true”]{
+
-webkit-box-shadow:inset 0 -3px 0 #48aff0;

box-shadow:inset 0 -3px 0 #48aff0; }

+
+
+
+
.bp3-dark .bp3-tab[aria-selected=”true”], .bp3-dark .bp3-tab:not([aria-disabled=”true”]):hover{

color:#48aff0; }

+
+
+
+
.bp3-dark .bp3-tab-indicator{

background-color:#48aff0; }

+
+
.bp3-flex-expander{
+
-webkit-box-flex:1;
+
-ms-flex:1 1;

flex:1 1; }

+
+
+
+
+
+
.bp3-tag{

display:-webkit-inline-box; +display:-ms-inline-flexbox; +display:inline-flex; +-webkit-box-orient:horizontal; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:row;

flex-direction:row;

+
+
+
+
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

background-color:#5c7080; +border:none; +border-radius:3px; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

color:#f5f8fa; +font-size:12px; +line-height:16px; +max-width:100%; +min-height:20px; +min-width:20px; +padding:2px 6px; +position:relative; } +.bp3-tag.bp3-interactive{

+
+

cursor:pointer; } +.bp3-tag.bp3-interactive:hover{

+
+

background-color:rgba(92, 112, 128, 0.85); }

+
+
+
.bp3-tag.bp3-interactive.bp3-active, .bp3-tag.bp3-interactive:active{

background-color:rgba(92, 112, 128, 0.7); }

+
+
+
+
+
.bp3-tag > *{
+
-webkit-box-flex:0;
+
-ms-flex-positive:0;

flex-grow:0;

+
+
+
+
-ms-flex-negative:0;

flex-shrink:0; }

+
+
+
+
.bp3-tag > .bp3-fill{
+
-webkit-box-flex:1;
+
-ms-flex-positive:1;

flex-grow:1;

+
+
+
+
-ms-flex-negative:1;

flex-shrink:1; }

+
+
+
+
+

.bp3-tag::before, +.bp3-tag > *{

+
+

margin-right:4px; }

+
+

.bp3-tag:empty::before, +.bp3-tag > :last-child{

+
+

margin-right:0; }

+
+
+
.bp3-tag:focus{

outline:rgba(19, 124, 189, 0.6) auto 2px; +outline-offset:0; +-moz-outline-radius:6px; }

+
+
.bp3-tag.bp3-round{

border-radius:30px; +padding-left:8px; +padding-right:8px; }

+
+
.bp3-dark .bp3-tag{

background-color:#bfccd6; +color:#182026; } +.bp3-dark .bp3-tag.bp3-interactive{

+
+

cursor:pointer; } +.bp3-dark .bp3-tag.bp3-interactive:hover{

+
+

background-color:rgba(191, 204, 214, 0.85); }

+
+
+
.bp3-dark .bp3-tag.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-interactive:active{

background-color:rgba(191, 204, 214, 0.7); }

+
+
+
+
+
.bp3-dark .bp3-tag > .bp3-icon, .bp3-dark .bp3-tag .bp3-icon-standard, .bp3-dark .bp3-tag .bp3-icon-large{

fill:currentColor; }

+
+
+
+
.bp3-tag > .bp3-icon, .bp3-tag .bp3-icon-standard, .bp3-tag .bp3-icon-large{

fill:#ffffff; }

+
+
+

.bp3-tag.bp3-large, +.bp3-large .bp3-tag{

+
+

font-size:14px; +line-height:20px; +min-height:30px; +min-width:30px; +padding:5px 10px; } +.bp3-tag.bp3-large::before, +.bp3-tag.bp3-large > *, +.bp3-large .bp3-tag::before, +.bp3-large .bp3-tag > *{

+
+

margin-right:7px; }

+
+

.bp3-tag.bp3-large:empty::before, +.bp3-tag.bp3-large > :last-child, +.bp3-large .bp3-tag:empty::before, +.bp3-large .bp3-tag > :last-child{

+
+

margin-right:0; }

+
+

.bp3-tag.bp3-large.bp3-round, +.bp3-large .bp3-tag.bp3-round{

+
+

padding-left:12px; +padding-right:12px; }

+
+
+
+
.bp3-tag.bp3-intent-primary{

background:#137cbd; +color:#ffffff; } +.bp3-tag.bp3-intent-primary.bp3-interactive{

+
+

cursor:pointer; } +.bp3-tag.bp3-intent-primary.bp3-interactive:hover{

+
+

background-color:rgba(19, 124, 189, 0.85); }

+
+
+
.bp3-tag.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-primary.bp3-interactive:active{

background-color:rgba(19, 124, 189, 0.7); }

+
+
+
+
+
.bp3-tag.bp3-intent-success{

background:#0f9960; +color:#ffffff; } +.bp3-tag.bp3-intent-success.bp3-interactive{

+
+

cursor:pointer; } +.bp3-tag.bp3-intent-success.bp3-interactive:hover{

+
+

background-color:rgba(15, 153, 96, 0.85); }

+
+
+
.bp3-tag.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-success.bp3-interactive:active{

background-color:rgba(15, 153, 96, 0.7); }

+
+
+
+
+
.bp3-tag.bp3-intent-warning{

background:#d9822b; +color:#ffffff; } +.bp3-tag.bp3-intent-warning.bp3-interactive{

+
+

cursor:pointer; } +.bp3-tag.bp3-intent-warning.bp3-interactive:hover{

+
+

background-color:rgba(217, 130, 43, 0.85); }

+
+
+
.bp3-tag.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-warning.bp3-interactive:active{

background-color:rgba(217, 130, 43, 0.7); }

+
+
+
+
+
.bp3-tag.bp3-intent-danger{

background:#db3737; +color:#ffffff; } +.bp3-tag.bp3-intent-danger.bp3-interactive{

+
+

cursor:pointer; } +.bp3-tag.bp3-intent-danger.bp3-interactive:hover{

+
+

background-color:rgba(219, 55, 55, 0.85); }

+
+
+
.bp3-tag.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-danger.bp3-interactive:active{

background-color:rgba(219, 55, 55, 0.7); }

+
+
+
+
+
.bp3-tag.bp3-fill{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +width:100%; }

+
+
.bp3-tag.bp3-minimal > .bp3-icon, .bp3-tag.bp3-minimal .bp3-icon-standard, .bp3-tag.bp3-minimal .bp3-icon-large{

fill:#5c7080; }

+
+
.bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]){

background-color:rgba(138, 155, 168, 0.2); +color:#182026; } +.bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]).bp3-interactive{

+
+

cursor:pointer; } +.bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]).bp3-interactive:hover{

+
+

background-color:rgba(92, 112, 128, 0.3); }

+
+
+
.bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]).bp3-interactive.bp3-active, .bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]).bp3-interactive:active{

background-color:rgba(92, 112, 128, 0.4); }

+
+
+
+
+
.bp3-dark .bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]){

color:#f5f8fa; } +.bp3-dark .bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]).bp3-interactive{

+
+

cursor:pointer; } +.bp3-dark .bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]).bp3-interactive:hover{

+
+

background-color:rgba(191, 204, 214, 0.3); }

+
+
+
.bp3-dark .bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]).bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]).bp3-interactive:active{

background-color:rgba(191, 204, 214, 0.4); }

+
+
+
+
+
.bp3-dark .bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]) > .bp3-icon, .bp3-dark .bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]) .bp3-icon-standard, .bp3-dark .bp3-tag.bp3-minimal:not([class*=”bp3-intent-“]) .bp3-icon-large{

fill:#a7b6c2; }

+
+
+
+
+
+
.bp3-tag.bp3-minimal.bp3-intent-primary{

background-color:rgba(19, 124, 189, 0.15); +color:#106ba3; } +.bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{

+
+

cursor:pointer; } +.bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{

+
+

background-color:rgba(19, 124, 189, 0.25); }

+
+
+
.bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{

background-color:rgba(19, 124, 189, 0.35); }

+
+
+
+
+
.bp3-tag.bp3-minimal.bp3-intent-primary > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-large{

fill:#137cbd; }

+
+
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary{

background-color:rgba(19, 124, 189, 0.25); +color:#48aff0; } +.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{

+
+

cursor:pointer; } +.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{

+
+

background-color:rgba(19, 124, 189, 0.35); }

+
+
+
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{

background-color:rgba(19, 124, 189, 0.45); }

+
+
+
+
+
+
+
.bp3-tag.bp3-minimal.bp3-intent-success{

background-color:rgba(15, 153, 96, 0.15); +color:#0d8050; } +.bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{

+
+

cursor:pointer; } +.bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{

+
+

background-color:rgba(15, 153, 96, 0.25); }

+
+
+
.bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{

background-color:rgba(15, 153, 96, 0.35); }

+
+
+
+
+
.bp3-tag.bp3-minimal.bp3-intent-success > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-large{

fill:#0f9960; }

+
+
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success{

background-color:rgba(15, 153, 96, 0.25); +color:#3dcc91; } +.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{

+
+

cursor:pointer; } +.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{

+
+

background-color:rgba(15, 153, 96, 0.35); }

+
+
+
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{

background-color:rgba(15, 153, 96, 0.45); }

+
+
+
+
+
+
+
.bp3-tag.bp3-minimal.bp3-intent-warning{

background-color:rgba(217, 130, 43, 0.15); +color:#bf7326; } +.bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{

+
+

cursor:pointer; } +.bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{

+
+

background-color:rgba(217, 130, 43, 0.25); }

+
+
+
.bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{

background-color:rgba(217, 130, 43, 0.35); }

+
+
+
+
+
.bp3-tag.bp3-minimal.bp3-intent-warning > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-large{

fill:#d9822b; }

+
+
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning{

background-color:rgba(217, 130, 43, 0.25); +color:#ffb366; } +.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{

+
+

cursor:pointer; } +.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{

+
+

background-color:rgba(217, 130, 43, 0.35); }

+
+
+
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{

background-color:rgba(217, 130, 43, 0.45); }

+
+
+
+
+
+
+
.bp3-tag.bp3-minimal.bp3-intent-danger{

background-color:rgba(219, 55, 55, 0.15); +color:#c23030; } +.bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{

+
+

cursor:pointer; } +.bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{

+
+

background-color:rgba(219, 55, 55, 0.25); }

+
+
+
.bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{

background-color:rgba(219, 55, 55, 0.35); }

+
+
+
+
+
.bp3-tag.bp3-minimal.bp3-intent-danger > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-large{

fill:#db3737; }

+
+
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger{

background-color:rgba(219, 55, 55, 0.25); +color:#ff7373; } +.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{

+
+

cursor:pointer; } +.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{

+
+

background-color:rgba(219, 55, 55, 0.35); }

+
+
+
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{

background-color:rgba(219, 55, 55, 0.45); }

+
+
+
+
+
+
+
+
+
.bp3-tag-remove{

background:none; +border:none; +color:inherit; +cursor:pointer; +display:-webkit-box; +display:-ms-flexbox; +display:flex; +margin-bottom:-2px; +margin-right:-6px !important; +margin-top:-2px; +opacity:0.5; +padding:2px; +padding-left:0; } +.bp3-tag-remove:hover{

+
+

background:none; +opacity:0.8; +text-decoration:none; }

+
+
+
.bp3-tag-remove:active{

opacity:1; }

+
+
.bp3-tag-remove:empty::before{

font-family:”Icons16”, sans-serif; +font-size:16px; +font-style:normal; +font-weight:400; +line-height:1; +-moz-osx-font-smoothing:grayscale; +-webkit-font-smoothing:antialiased; +content:””; }

+
+
.bp3-large .bp3-tag-remove{

margin-right:-10px !important; +padding:0 5px 0 0; } +.bp3-large .bp3-tag-remove:empty::before{

+
+

font-family:”Icons20”, sans-serif; +font-size:20px; +font-style:normal; +font-weight:400; +line-height:1; }

+
+
+
+
+
.bp3-tag-input{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:horizontal; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:row;

flex-direction:row;

+
+
+
+
+
-webkit-box-align:start;
+
-ms-flex-align:start;

align-items:flex-start;

+
+
+
+
+

cursor:text; +height:auto; +line-height:inherit; +min-height:30px; +padding-left:5px; +padding-right:0; } +.bp3-tag-input > *{

+
+
+
-webkit-box-flex:0;
+
-ms-flex-positive:0;

flex-grow:0;

+
+
+
+
-ms-flex-negative:0;

flex-shrink:0; }

+
+
+
+
+
.bp3-tag-input > .bp3-tag-input-values{
+
-webkit-box-flex:1;
+
-ms-flex-positive:1;

flex-grow:1;

+
+
+
+
-ms-flex-negative:1;

flex-shrink:1; }

+
+
+
+
.bp3-tag-input .bp3-tag-input-icon{

color:#5c7080; +margin-left:2px; +margin-right:7px; +margin-top:7px; }

+
+
.bp3-tag-input .bp3-tag-input-values{

display:-webkit-box; +display:-ms-flexbox; +display:flex; +-webkit-box-orient:horizontal; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:row;

flex-direction:row;

+
+
+
+
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
-ms-flex-item-align:stretch;

align-self:stretch;

+
+
-ms-flex-wrap:wrap;

flex-wrap:wrap;

+
+
+

margin-right:7px; +margin-top:5px; +min-width:0; } +.bp3-tag-input .bp3-tag-input-values > *{

+
+
+
-webkit-box-flex:0;
+
-ms-flex-positive:0;

flex-grow:0;

+
+
+
+
-ms-flex-negative:0;

flex-shrink:0; }

+
+
+
+
+
.bp3-tag-input .bp3-tag-input-values > .bp3-fill{
+
-webkit-box-flex:1;
+
-ms-flex-positive:1;

flex-grow:1;

+
+
+
+
-ms-flex-negative:1;

flex-shrink:1; }

+
+
+
+
+

.bp3-tag-input .bp3-tag-input-values::before, +.bp3-tag-input .bp3-tag-input-values > *{

+
+

margin-right:5px; }

+
+

.bp3-tag-input .bp3-tag-input-values:empty::before, +.bp3-tag-input .bp3-tag-input-values > :last-child{

+
+

margin-right:0; }

+
+
+
.bp3-tag-input .bp3-tag-input-values:first-child .bp3-input-ghost:first-child{

padding-left:5px; }

+
+
.bp3-tag-input .bp3-tag-input-values > *{

margin-bottom:5px; }

+
+
+
+
.bp3-tag-input .bp3-tag{

overflow-wrap:break-word; } +.bp3-tag-input .bp3-tag.bp3-active{

+
+

outline:rgba(19, 124, 189, 0.6) auto 2px; +outline-offset:0; +-moz-outline-radius:6px; }

+
+
+
.bp3-tag-input .bp3-input-ghost{
+
-webkit-box-flex:1;
+
-ms-flex:1 1 auto;

flex:1 1 auto;

+
+
+
+
+

line-height:20px; +width:80px; } +.bp3-tag-input .bp3-input-ghost:disabled, .bp3-tag-input .bp3-input-ghost.bp3-disabled{

+
+

cursor:not-allowed; }

+
+
+
+

.bp3-tag-input .bp3-button, +.bp3-tag-input .bp3-spinner{

+
+

margin:3px; +margin-left:0; }

+
+
+
.bp3-tag-input .bp3-button{

min-height:24px; +min-width:24px; +padding:0 7px; }

+
+
.bp3-tag-input.bp3-large{

height:auto; +min-height:40px; } +.bp3-tag-input.bp3-large::before, +.bp3-tag-input.bp3-large > *{

+
+

margin-right:10px; }

+
+

.bp3-tag-input.bp3-large:empty::before, +.bp3-tag-input.bp3-large > :last-child{

+
+

margin-right:0; }

+
+
+
.bp3-tag-input.bp3-large .bp3-tag-input-icon{

margin-left:5px; +margin-top:10px; }

+
+
.bp3-tag-input.bp3-large .bp3-input-ghost{

line-height:30px; }

+
+
.bp3-tag-input.bp3-large .bp3-button{

min-height:30px; +min-width:30px; +padding:5px 10px; +margin:5px; +margin-left:0; }

+
+
.bp3-tag-input.bp3-large .bp3-spinner{

margin:8px; +margin-left:0; }

+
+
+
+
.bp3-tag-input.bp3-active{

background-color:#ffffff; +-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
.bp3-tag-input.bp3-active.bp3-intent-primary{
+
-webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-tag-input.bp3-active.bp3-intent-success{
+
-webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-tag-input.bp3-active.bp3-intent-warning{
+
-webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-tag-input.bp3-active.bp3-intent-danger{
+
-webkit-box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }

+
+
+
+
+
+
.bp3-dark .bp3-tag-input .bp3-tag-input-icon, .bp3-tag-input.bp3-dark .bp3-tag-input-icon{

color:#a7b6c2; }

+
+
.bp3-dark .bp3-tag-input .bp3-input-ghost, .bp3-tag-input.bp3-dark .bp3-input-ghost{

color:#f5f8fa; } +.bp3-dark .bp3-tag-input .bp3-input-ghost::-webkit-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-webkit-input-placeholder{

+
+

color:rgba(167, 182, 194, 0.6); }

+
+
+
.bp3-dark .bp3-tag-input .bp3-input-ghost::-moz-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-moz-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-tag-input .bp3-input-ghost:-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost:-ms-input-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-tag-input .bp3-input-ghost::-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-ms-input-placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
.bp3-dark .bp3-tag-input .bp3-input-ghost::placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::placeholder{

color:rgba(167, 182, 194, 0.6); }

+
+
+
+
.bp3-dark .bp3-tag-input.bp3-active, .bp3-tag-input.bp3-dark.bp3-active{

background-color:rgba(16, 22, 26, 0.3); +-webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
.bp3-dark .bp3-tag-input.bp3-active.bp3-intent-primary, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-primary{
+
-webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-tag-input.bp3-active.bp3-intent-success, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-success{
+
-webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-tag-input.bp3-active.bp3-intent-warning, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-warning{
+
-webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
.bp3-dark .bp3-tag-input.bp3-active.bp3-intent-danger, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-danger{
+
-webkit-box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }

+
+
+
+
+
+
+
+
.bp3-input-ghost{

background:none; +border:none; +-webkit-box-shadow:none;

+
+

box-shadow:none;

+
+

padding:0; } +.bp3-input-ghost::-webkit-input-placeholder{

+
+

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
+
.bp3-input-ghost::-moz-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-input-ghost:-ms-input-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-input-ghost::-ms-input-placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-input-ghost::placeholder{

color:rgba(92, 112, 128, 0.6); +opacity:1; }

+
+
.bp3-input-ghost:focus{

outline:none !important; }

+
+
+
+
.bp3-toast{
+
-webkit-box-align:start;
+
-ms-flex-align:start;

align-items:flex-start;

+
+
+
+
+

background-color:#ffffff; +border-radius:3px; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +margin:20px 0 0; +max-width:500px; +min-width:300px; +pointer-events:all; +position:relative !important; } +.bp3-toast.bp3-toast-enter, .bp3-toast.bp3-toast-appear{

+
+
+
-webkit-transform:translateY(-40px);

transform:translateY(-40px); }

+
+
+
+
+
.bp3-toast.bp3-toast-enter-active, .bp3-toast.bp3-toast-appear-active{
+
-webkit-transform:translateY(0);

transform:translateY(0);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:300ms;

transition-duration:300ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);

+
+

transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }

+
+
+
.bp3-toast.bp3-toast-enter ~ .bp3-toast, .bp3-toast.bp3-toast-appear ~ .bp3-toast{
+
-webkit-transform:translateY(-40px);

transform:translateY(-40px); }

+
+
+
+
.bp3-toast.bp3-toast-enter-active ~ .bp3-toast, .bp3-toast.bp3-toast-appear-active ~ .bp3-toast{
+
-webkit-transform:translateY(0);

transform:translateY(0);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:300ms;

transition-duration:300ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11);

+
+

transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); }

+
+
+
.bp3-toast.bp3-toast-exit{

opacity:1; +-webkit-filter:blur(0);

+
+

filter:blur(0); }

+
+
+
.bp3-toast.bp3-toast-exit-active{

opacity:0; +-webkit-filter:blur(10px);

+
+

filter:blur(10px);

+
+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:300ms;

transition-duration:300ms;

+
+
+

-webkit-transition-property:opacity, -webkit-filter; +transition-property:opacity, -webkit-filter; +transition-property:opacity, filter; +transition-property:opacity, filter, -webkit-filter; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-toast.bp3-toast-exit ~ .bp3-toast{
+
-webkit-transform:translateY(0);

transform:translateY(0); }

+
+
+
+
.bp3-toast.bp3-toast-exit-active ~ .bp3-toast{
+
-webkit-transform:translateY(-40px);

transform:translateY(-40px);

+
+
-webkit-transition-delay:50ms;

transition-delay:50ms;

+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-toast .bp3-button-group{
+
-webkit-box-flex:0;
+
-ms-flex:0 0 auto;

flex:0 0 auto;

+
+
+
+
+

padding:5px; +padding-left:0; }

+
+
.bp3-toast > .bp3-icon{

color:#5c7080; +margin:12px; +margin-right:0; }

+
+
+

.bp3-toast.bp3-dark, +.bp3-dark .bp3-toast{

+
+

background-color:#394b59; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }

+
+

.bp3-toast.bp3-dark > .bp3-icon, +.bp3-dark .bp3-toast > .bp3-icon{

+
+

color:#a7b6c2; }

+
+
+
+
.bp3-toast[class*=”bp3-intent-”] a{

color:rgba(255, 255, 255, 0.7); } +.bp3-toast[class*=”bp3-intent-”] a:hover{

+
+

color:#ffffff; }

+
+
+
.bp3-toast[class*=”bp3-intent-”] > .bp3-icon{

color:#ffffff; }

+
+
+

.bp3-toast[class*=”bp3-intent-”] .bp3-button, .bp3-toast[class*=”bp3-intent-”] .bp3-button::before, +.bp3-toast[class*=”bp3-intent-”] .bp3-button .bp3-icon, .bp3-toast[class*=”bp3-intent-”] .bp3-button:active{

+
+

color:rgba(255, 255, 255, 0.7) !important; }

+
+
+
.bp3-toast[class*=”bp3-intent-”] .bp3-button:focus{

outline-color:rgba(255, 255, 255, 0.5); }

+
+
.bp3-toast[class*=”bp3-intent-”] .bp3-button:hover{

background-color:rgba(255, 255, 255, 0.15) !important; +color:#ffffff !important; }

+
+
.bp3-toast[class*=”bp3-intent-”] .bp3-button:active{

background-color:rgba(255, 255, 255, 0.3) !important; +color:#ffffff !important; }

+
+
.bp3-toast[class*=”bp3-intent-”] .bp3-button::after{

background:rgba(255, 255, 255, 0.3) !important; }

+
+
.bp3-toast.bp3-intent-primary{

background-color:#137cbd; +color:#ffffff; }

+
+
.bp3-toast.bp3-intent-success{

background-color:#0f9960; +color:#ffffff; }

+
+
.bp3-toast.bp3-intent-warning{

background-color:#d9822b; +color:#ffffff; }

+
+
.bp3-toast.bp3-intent-danger{

background-color:#db3737; +color:#ffffff; }

+
+
+
+
.bp3-toast-message{
+
-webkit-box-flex:1;
+
-ms-flex:1 1 auto;

flex:1 1 auto;

+
+
+
+
+

padding:11px; +word-break:break-word; }

+
+
.bp3-toast-container{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

display:-webkit-box !important; +display:-ms-flexbox !important; +display:flex !important; +-webkit-box-orient:vertical; +-webkit-box-direction:normal;

+
+
+
-ms-flex-direction:column;

flex-direction:column;

+
+
+
+

left:0; +overflow:hidden; +padding:0 20px 20px; +pointer-events:none; +right:0; +z-index:40; } +.bp3-toast-container.bp3-toast-container-in-portal{

+
+

position:fixed; }

+
+
+
.bp3-toast-container.bp3-toast-container-inline{

position:absolute; }

+
+
.bp3-toast-container.bp3-toast-container-top{

top:0; }

+
+
.bp3-toast-container.bp3-toast-container-bottom{

bottom:0; +-webkit-box-orient:vertical; +-webkit-box-direction:reverse;

+
+
+
-ms-flex-direction:column-reverse;

flex-direction:column-reverse;

+
+
+
+

top:auto; }

+
+
.bp3-toast-container.bp3-toast-container-left{
+
-webkit-box-align:start;
+
-ms-flex-align:start;

align-items:flex-start; }

+
+
+
+
+
+
.bp3-toast-container.bp3-toast-container-right{
+
-webkit-box-align:end;
+
-ms-flex-align:end;

align-items:flex-end; }

+
+
+
+
+
+
+
+
+

.bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active), +.bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active) ~ .bp3-toast, .bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active), +.bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active) ~ .bp3-toast, +.bp3-toast-container-bottom .bp3-toast.bp3-toast-exit-active ~ .bp3-toast, +.bp3-toast-container-bottom .bp3-toast.bp3-toast-leave-active ~ .bp3-toast{

+
+
+
-webkit-transform:translateY(60px);

transform:translateY(60px); }

+
+
+
+
+
.bp3-tooltip{
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2);

+
+
-webkit-transform:scale(1);

transform:scale(1); }

+
+
.bp3-tooltip .bp3-popover-arrow{

height:22px; +position:absolute; +width:22px; } +.bp3-tooltip .bp3-popover-arrow::before{

+
+

height:14px; +margin:4px; +width:14px; }

+
+
+
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip{

margin-bottom:11px; +margin-top:-11px; } +.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{

+
+

bottom:-8px; } +.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow svg{

+
+
+
-webkit-transform:rotate(-90deg);

transform:rotate(-90deg); }

+
+
+
+
+
+
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip{

margin-left:11px; } +.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{

+
+

left:-8px; } +.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow svg{

+
+
+
-webkit-transform:rotate(0);

transform:rotate(0); }

+
+
+
+
+
+
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip{

margin-top:11px; } +.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{

+
+

top:-8px; } +.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow svg{

+
+
+
-webkit-transform:rotate(90deg);

transform:rotate(90deg); }

+
+
+
+
+
+
.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip{

margin-left:-11px; +margin-right:11px; } +.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{

+
+

right:-8px; } +.bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow svg{

+
+
+
-webkit-transform:rotate(180deg);

transform:rotate(180deg); }

+
+
+
+
+
+
.bp3-tether-element-attached-middle > .bp3-tooltip > .bp3-popover-arrow{

top:50%; +-webkit-transform:translateY(-50%);

+
+

transform:translateY(-50%); }

+
+
+
.bp3-tether-element-attached-center > .bp3-tooltip > .bp3-popover-arrow{

right:50%; +-webkit-transform:translateX(50%);

+
+

transform:translateX(50%); }

+
+
+
.bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{

top:-0.22183px; }

+
+
.bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{

right:-0.22183px; }

+
+
.bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{

left:-0.22183px; }

+
+
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{

bottom:-0.22183px; }

+
+
.bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-tooltip{
+
-webkit-transform-origin:top left;

transform-origin:top left; }

+
+
+
+
.bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-tooltip{
+
-webkit-transform-origin:top center;

transform-origin:top center; }

+
+
+
+
.bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-tooltip{
+
-webkit-transform-origin:top right;

transform-origin:top right; }

+
+
+
+
.bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-tooltip{
+
-webkit-transform-origin:center left;

transform-origin:center left; }

+
+
+
+
.bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-tooltip{
+
-webkit-transform-origin:center center;

transform-origin:center center; }

+
+
+
+
.bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-tooltip{
+
-webkit-transform-origin:center right;

transform-origin:center right; }

+
+
+
+
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-tooltip{
+
-webkit-transform-origin:bottom left;

transform-origin:bottom left; }

+
+
+
+
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-tooltip{
+
-webkit-transform-origin:bottom center;

transform-origin:bottom center; }

+
+
+
+
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-tooltip{
+
-webkit-transform-origin:bottom right;

transform-origin:bottom right; }

+
+
+
+
.bp3-tooltip .bp3-popover-content{

background:#394b59; +color:#f5f8fa; }

+
+
.bp3-tooltip .bp3-popover-arrow::before{
+
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);

box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }

+
+
+
+
.bp3-tooltip .bp3-popover-arrow-border{

fill:#10161a; +fill-opacity:0.1; }

+
+
.bp3-tooltip .bp3-popover-arrow-fill{

fill:#394b59; }

+
+
.bp3-popover-enter > .bp3-tooltip, .bp3-popover-appear > .bp3-tooltip{
+
-webkit-transform:scale(0.8);

transform:scale(0.8); }

+
+
+
+
.bp3-popover-enter-active > .bp3-tooltip, .bp3-popover-appear-active > .bp3-tooltip{
+
-webkit-transform:scale(1);

transform:scale(1);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-popover-exit > .bp3-tooltip{
+
-webkit-transform:scale(1);

transform:scale(1); }

+
+
+
+
.bp3-popover-exit-active > .bp3-tooltip{
+
-webkit-transform:scale(0.8);

transform:scale(0.8);

+
+
-webkit-transition-delay:0;

transition-delay:0;

+
+
-webkit-transition-duration:100ms;

transition-duration:100ms;

+
+
+

-webkit-transition-property:-webkit-transform; +transition-property:-webkit-transform; +transition-property:transform; +transition-property:transform, -webkit-transform; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-tooltip .bp3-popover-content{

padding:10px 12px; }

+
+
+

.bp3-tooltip.bp3-dark, +.bp3-dark .bp3-tooltip{

+
+
+
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4);

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); }

+
+
+

.bp3-tooltip.bp3-dark .bp3-popover-content, +.bp3-dark .bp3-tooltip .bp3-popover-content{

+
+

background:#e1e8ed; +color:#394b59; }

+
+

.bp3-tooltip.bp3-dark .bp3-popover-arrow::before, +.bp3-dark .bp3-tooltip .bp3-popover-arrow::before{

+
+
+
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);

box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }

+
+
+
+

.bp3-tooltip.bp3-dark .bp3-popover-arrow-border, +.bp3-dark .bp3-tooltip .bp3-popover-arrow-border{

+
+

fill:#10161a; +fill-opacity:0.2; }

+
+

.bp3-tooltip.bp3-dark .bp3-popover-arrow-fill, +.bp3-dark .bp3-tooltip .bp3-popover-arrow-fill{

+
+

fill:#e1e8ed; }

+
+
+
+
.bp3-tooltip.bp3-intent-primary .bp3-popover-content{

background:#137cbd; +color:#ffffff; }

+
+
.bp3-tooltip.bp3-intent-primary .bp3-popover-arrow-fill{

fill:#137cbd; }

+
+
.bp3-tooltip.bp3-intent-success .bp3-popover-content{

background:#0f9960; +color:#ffffff; }

+
+
.bp3-tooltip.bp3-intent-success .bp3-popover-arrow-fill{

fill:#0f9960; }

+
+
.bp3-tooltip.bp3-intent-warning .bp3-popover-content{

background:#d9822b; +color:#ffffff; }

+
+
.bp3-tooltip.bp3-intent-warning .bp3-popover-arrow-fill{

fill:#d9822b; }

+
+
.bp3-tooltip.bp3-intent-danger .bp3-popover-content{

background:#db3737; +color:#ffffff; }

+
+
.bp3-tooltip.bp3-intent-danger .bp3-popover-arrow-fill{

fill:#db3737; }

+
+
+
+
.bp3-tooltip-indicator{

border-bottom:dotted 1px; +cursor:help; }

+
+
.bp3-tree .bp3-icon, .bp3-tree .bp3-icon-standard, .bp3-tree .bp3-icon-large{

color:#5c7080; } +.bp3-tree .bp3-icon.bp3-intent-primary, .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-tree .bp3-icon-large.bp3-intent-primary{

+
+

color:#137cbd; }

+
+
+
.bp3-tree .bp3-icon.bp3-intent-success, .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-tree .bp3-icon-large.bp3-intent-success{

color:#0f9960; }

+
+
.bp3-tree .bp3-icon.bp3-intent-warning, .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-tree .bp3-icon-large.bp3-intent-warning{

color:#d9822b; }

+
+
.bp3-tree .bp3-icon.bp3-intent-danger, .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-tree .bp3-icon-large.bp3-intent-danger{

color:#db3737; }

+
+
+
+
.bp3-tree-node-list{

list-style:none; +margin:0; +padding-left:0; }

+
+
.bp3-tree-root{

background-color:transparent; +cursor:default; +padding-left:0; +position:relative; }

+
+
.bp3-tree-node-content-0{

padding-left:0px; }

+
+
.bp3-tree-node-content-1{

padding-left:23px; }

+
+
.bp3-tree-node-content-2{

padding-left:46px; }

+
+
.bp3-tree-node-content-3{

padding-left:69px; }

+
+
.bp3-tree-node-content-4{

padding-left:92px; }

+
+
.bp3-tree-node-content-5{

padding-left:115px; }

+
+
.bp3-tree-node-content-6{

padding-left:138px; }

+
+
.bp3-tree-node-content-7{

padding-left:161px; }

+
+
.bp3-tree-node-content-8{

padding-left:184px; }

+
+
.bp3-tree-node-content-9{

padding-left:207px; }

+
+
.bp3-tree-node-content-10{

padding-left:230px; }

+
+
.bp3-tree-node-content-11{

padding-left:253px; }

+
+
.bp3-tree-node-content-12{

padding-left:276px; }

+
+
.bp3-tree-node-content-13{

padding-left:299px; }

+
+
.bp3-tree-node-content-14{

padding-left:322px; }

+
+
.bp3-tree-node-content-15{

padding-left:345px; }

+
+
.bp3-tree-node-content-16{

padding-left:368px; }

+
+
.bp3-tree-node-content-17{

padding-left:391px; }

+
+
.bp3-tree-node-content-18{

padding-left:414px; }

+
+
.bp3-tree-node-content-19{

padding-left:437px; }

+
+
.bp3-tree-node-content-20{

padding-left:460px; }

+
+
.bp3-tree-node-content{
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; +height:30px; +padding-right:5px; +width:100%; } +.bp3-tree-node-content:hover{

+
+

background-color:rgba(191, 204, 214, 0.4); }

+
+
+
+

.bp3-tree-node-caret, +.bp3-tree-node-caret-none{

+
+

min-width:30px; }

+
+
+
.bp3-tree-node-caret{

color:#5c7080; +cursor:pointer; +padding:7px; +-webkit-transform:rotate(0deg);

+
+

transform:rotate(0deg);

+
+

-webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); +transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); } +.bp3-tree-node-caret:hover{

+
+

color:#182026; }

+
+
+
.bp3-dark .bp3-tree-node-caret{

color:#a7b6c2; } +.bp3-dark .bp3-tree-node-caret:hover{

+
+

color:#f5f8fa; }

+
+
+
.bp3-tree-node-caret.bp3-tree-node-caret-open{
+
-webkit-transform:rotate(90deg);

transform:rotate(90deg); }

+
+
+
+
.bp3-tree-node-caret.bp3-icon-standard::before{

content:””; }

+
+
+
+
.bp3-tree-node-icon{

margin-right:7px; +position:relative; }

+
+
.bp3-tree-node-label{

overflow:hidden; +text-overflow:ellipsis; +white-space:nowrap; +word-wrap:normal; +-webkit-box-flex:1;

+
+
+
-ms-flex:1 1 auto;

flex:1 1 auto;

+
+
+
+

position:relative; +-webkit-user-select:none;

+
+
+
-moz-user-select:none;
+
-ms-user-select:none;

user-select:none; }

+
+
+
+
+
+
+
.bp3-tree-node-label span{

display:inline; }

+
+
+
+
.bp3-tree-node-secondary-label{

padding:0 5px; +-webkit-user-select:none;

+
+
+
-moz-user-select:none;
+
-ms-user-select:none;

user-select:none; }

+
+
+
+
+
+

.bp3-tree-node-secondary-label .bp3-popover-wrapper, +.bp3-tree-node-secondary-label .bp3-popover-target{

+
+
+
-webkit-box-align:center;
+
-ms-flex-align:center;

align-items:center;

+
+
+
+
+

display:-webkit-box; +display:-ms-flexbox; +display:flex; }

+
+
+
.bp3-tree-node.bp3-disabled .bp3-tree-node-content{

background-color:inherit; +color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; }

+
+
+

.bp3-tree-node.bp3-disabled .bp3-tree-node-caret, +.bp3-tree-node.bp3-disabled .bp3-tree-node-icon{

+
+

color:rgba(92, 112, 128, 0.6); +cursor:not-allowed; }

+
+
+
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{

background-color:#137cbd; } +.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content, +.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-standard, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-large{

+
+

color:#ffffff; }

+
+
+
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret::before{

color:rgba(255, 255, 255, 0.7); }

+
+
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret:hover::before{

color:#ffffff; }

+
+
+
+
.bp3-dark .bp3-tree-node-content:hover{

background-color:rgba(92, 112, 128, 0.3); }

+
+
.bp3-dark .bp3-tree .bp3-icon, .bp3-dark .bp3-tree .bp3-icon-standard, .bp3-dark .bp3-tree .bp3-icon-large{

color:#a7b6c2; } +.bp3-dark .bp3-tree .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-primary{

+
+

color:#137cbd; }

+
+
+
.bp3-dark .bp3-tree .bp3-icon.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-success{

color:#0f9960; }

+
+
.bp3-dark .bp3-tree .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-warning{

color:#d9822b; }

+
+
.bp3-dark .bp3-tree .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-danger{

color:#db3737; }

+
+
+
+
.bp3-dark .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{

background-color:#137cbd; }

+
+
.bp3-omnibar{
+
-webkit-filter:blur(0);

filter:blur(0);

+
+
+

opacity:1; +background-color:#ffffff; +border-radius:3px; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2);

+
+

left:calc(50% - 250px); +top:20vh; +width:500px; +z-index:21; } +.bp3-omnibar.bp3-overlay-enter, .bp3-omnibar.bp3-overlay-appear{

+
+
+
-webkit-filter:blur(20px);

filter:blur(20px);

+
+
+

opacity:0.2; }

+
+
+
.bp3-omnibar.bp3-overlay-enter-active, .bp3-omnibar.bp3-overlay-appear-active{
+
-webkit-filter:blur(0);

filter:blur(0);

+
+
+

opacity:1; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:200ms;

transition-duration:200ms;

+
+
+

-webkit-transition-property:opacity, -webkit-filter; +transition-property:opacity, -webkit-filter; +transition-property:filter, opacity; +transition-property:filter, opacity, -webkit-filter; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-omnibar.bp3-overlay-exit{
+
-webkit-filter:blur(0);

filter:blur(0);

+
+
+

opacity:1; }

+
+
.bp3-omnibar.bp3-overlay-exit-active{
+
-webkit-filter:blur(20px);

filter:blur(20px);

+
+
+

opacity:0.2; +-webkit-transition-delay:0;

+
+

transition-delay:0;

+
+
+
-webkit-transition-duration:200ms;

transition-duration:200ms;

+
+
+

-webkit-transition-property:opacity, -webkit-filter; +transition-property:opacity, -webkit-filter; +transition-property:filter, opacity; +transition-property:filter, opacity, -webkit-filter; +-webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9);

+
+

transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); }

+
+
+
.bp3-omnibar .bp3-input{

background-color:transparent; +border-radius:0; } +.bp3-omnibar .bp3-input, .bp3-omnibar .bp3-input:focus{

+
+
+
-webkit-box-shadow:none;

box-shadow:none; }

+
+
+
+
+
.bp3-omnibar .bp3-menu{

background-color:transparent; +border-radius:0; +-webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);

+
+

box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15);

+
+

max-height:calc(60vh - 40px); +overflow:auto; } +.bp3-omnibar .bp3-menu:empty{

+
+

display:none; }

+
+
+
.bp3-dark .bp3-omnibar, .bp3-omnibar.bp3-dark{

background-color:#30404d; +-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4);

+
+

box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); }

+
+
+
+
+
.bp3-omnibar-overlay .bp3-overlay-backdrop{

background-color:rgba(16, 22, 26, 0.2); }

+
+
.bp3-select-popover .bp3-popover-content{

padding:5px; }

+
+
.bp3-select-popover .bp3-input-group{

margin-bottom:0; }

+
+
.bp3-select-popover .bp3-menu{

max-height:300px; +max-width:400px; +overflow:auto; +padding:0; } +.bp3-select-popover .bp3-menu:not(:first-child){

+
+

padding-top:5px; }

+
+
+
.bp3-multi-select{

min-width:150px; }

+
+
.bp3-multi-select-popover .bp3-menu{

max-height:300px; +max-width:400px; +overflow:auto; }

+
+
.bp3-select-popover .bp3-popover-content{

padding:5px; }

+
+
.bp3-select-popover .bp3-input-group{

margin-bottom:0; }

+
+
.bp3-select-popover .bp3-menu{

max-height:300px; +max-width:400px; +overflow:auto; +padding:0; } +.bp3-select-popover .bp3-menu:not(:first-child){

+
+

padding-top:5px; }

+
+
+
+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */

+
+
/**
    +

  • (DEPRECATED) Support for consuming icons as CSS background images

    • +
+

*/

+
+
+

/* Icons urls */

+
+
:root {

–jp-icon-add-above: 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+–jp-icon-add-below: 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+–jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=); +–jp-icon-bell: 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+–jp-icon-bug: url(data:image/svg+xml;base64,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); +–jp-icon-build: 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+–jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtdGV4dC1lZGl0b3ItaWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xNSAxNUgzdjJoMTJ2LTJ6bTAtOEgzdjJoMTJWN3pNMyAxM2gxOHYtMkgzdjJ6bTAgOGgxOHYtMkgzdjJ6TTMgM3YyaDE4VjNIM3oiLz4KPC9zdmc+Cg==); +–jp-icon-toc: url(data:image/svg+xml;base64,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); +–jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4=); +–jp-icon-trusted: url(data:image/svg+xml;base64,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); +–jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==); +–jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+); +–jp-icon-users: url(data:image/svg+xml;base64,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); +–jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==); +–jp-icon-yaml: url(data:image/svg+xml;base64,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);

+
+
+

}

+

/* Icon CSS class declarations */

+
+
.jp-AddAboveIcon {

background-image: var(–jp-icon-add-above);

+
+
+

} +.jp-AddBelowIcon {

+
+

background-image: var(–jp-icon-add-below);

+
+

} +.jp-AddIcon {

+
+

background-image: var(–jp-icon-add);

+
+

} +.jp-BellIcon {

+
+

background-image: var(–jp-icon-bell);

+
+

} +.jp-BugDotIcon {

+
+

background-image: var(–jp-icon-bug-dot);

+
+

} +.jp-BugIcon {

+
+

background-image: var(–jp-icon-bug);

+
+

} +.jp-BuildIcon {

+
+

background-image: var(–jp-icon-build);

+
+

} +.jp-CaretDownEmptyIcon {

+
+

background-image: var(–jp-icon-caret-down-empty);

+
+

} +.jp-CaretDownEmptyThinIcon {

+
+

background-image: var(–jp-icon-caret-down-empty-thin);

+
+

} +.jp-CaretDownIcon {

+
+

background-image: var(–jp-icon-caret-down);

+
+

} +.jp-CaretLeftIcon {

+
+

background-image: var(–jp-icon-caret-left);

+
+

} +.jp-CaretRightIcon {

+
+

background-image: var(–jp-icon-caret-right);

+
+

} +.jp-CaretUpEmptyThinIcon {

+
+

background-image: var(–jp-icon-caret-up-empty-thin);

+
+

} +.jp-CaretUpIcon {

+
+

background-image: var(–jp-icon-caret-up);

+
+

} +.jp-CaseSensitiveIcon {

+
+

background-image: var(–jp-icon-case-sensitive);

+
+

} +.jp-CheckIcon {

+
+

background-image: var(–jp-icon-check);

+
+

} +.jp-CircleEmptyIcon {

+
+

background-image: var(–jp-icon-circle-empty);

+
+

} +.jp-CircleIcon {

+
+

background-image: var(–jp-icon-circle);

+
+

} +.jp-ClearIcon {

+
+

background-image: var(–jp-icon-clear);

+
+

} +.jp-CloseIcon {

+
+

background-image: var(–jp-icon-close);

+
+

} +.jp-CodeIcon {

+
+

background-image: var(–jp-icon-code);

+
+

} +.jp-ConsoleIcon {

+
+

background-image: var(–jp-icon-console);

+
+

} +.jp-CopyIcon {

+
+

background-image: var(–jp-icon-copy);

+
+

} +.jp-CopyrightIcon {

+
+

background-image: var(–jp-icon-copyright);

+
+

} +.jp-CutIcon {

+
+

background-image: var(–jp-icon-cut);

+
+

} +.jp-DeleteIcon {

+
+

background-image: var(–jp-icon-delete);

+
+

} +.jp-DownloadIcon {

+
+

background-image: var(–jp-icon-download);

+
+

} +.jp-DuplicateIcon {

+
+

background-image: var(–jp-icon-duplicate);

+
+

} +.jp-EditIcon {

+
+

background-image: var(–jp-icon-edit);

+
+

} +.jp-EllipsesIcon {

+
+

background-image: var(–jp-icon-ellipses);

+
+

} +.jp-ExtensionIcon {

+
+

background-image: var(–jp-icon-extension);

+
+

} +.jp-FastForwardIcon {

+
+

background-image: var(–jp-icon-fast-forward);

+
+

} +.jp-FileIcon {

+
+

background-image: var(–jp-icon-file);

+
+

} +.jp-FileUploadIcon {

+
+

background-image: var(–jp-icon-file-upload);

+
+

} +.jp-FilterListIcon {

+
+

background-image: var(–jp-icon-filter-list);

+
+

} +.jp-FolderFavoriteIcon {

+
+

background-image: var(–jp-icon-folder-favorite);

+
+

} +.jp-FolderIcon {

+
+

background-image: var(–jp-icon-folder);

+
+

} +.jp-HomeIcon {

+
+

background-image: var(–jp-icon-home);

+
+

} +.jp-Html5Icon {

+
+

background-image: var(–jp-icon-html5);

+
+

} +.jp-ImageIcon {

+
+

background-image: var(–jp-icon-image);

+
+

} +.jp-InspectorIcon {

+
+

background-image: var(–jp-icon-inspector);

+
+

} +.jp-JsonIcon {

+
+

background-image: var(–jp-icon-json);

+
+

} +.jp-JuliaIcon {

+
+

background-image: var(–jp-icon-julia);

+
+

} +.jp-JupyterFaviconIcon {

+
+

background-image: var(–jp-icon-jupyter-favicon);

+
+

} +.jp-JupyterIcon {

+
+

background-image: var(–jp-icon-jupyter);

+
+

} +.jp-JupyterlabWordmarkIcon {

+
+

background-image: var(–jp-icon-jupyterlab-wordmark);

+
+

} +.jp-KernelIcon {

+
+

background-image: var(–jp-icon-kernel);

+
+

} +.jp-KeyboardIcon {

+
+

background-image: var(–jp-icon-keyboard);

+
+

} +.jp-LaunchIcon {

+
+

background-image: var(–jp-icon-launch);

+
+

} +.jp-LauncherIcon {

+
+

background-image: var(–jp-icon-launcher);

+
+

} +.jp-LineFormIcon {

+
+

background-image: var(–jp-icon-line-form);

+
+

} +.jp-LinkIcon {

+
+

background-image: var(–jp-icon-link);

+
+

} +.jp-ListIcon {

+
+

background-image: var(–jp-icon-list);

+
+

} +.jp-ListingsInfoIcon {

+
+

background-image: var(–jp-icon-listings-info);

+
+

} +.jp-MarkdownIcon {

+
+

background-image: var(–jp-icon-markdown);

+
+

} +.jp-MoveDownIcon {

+
+

background-image: var(–jp-icon-move-down);

+
+

} +.jp-MoveUpIcon {

+
+

background-image: var(–jp-icon-move-up);

+
+

} +.jp-NewFolderIcon {

+
+

background-image: var(–jp-icon-new-folder);

+
+

} +.jp-NotTrustedIcon {

+
+

background-image: var(–jp-icon-not-trusted);

+
+

} +.jp-NotebookIcon {

+
+

background-image: var(–jp-icon-notebook);

+
+

} +.jp-NumberingIcon {

+
+

background-image: var(–jp-icon-numbering);

+
+

} +.jp-OfflineBoltIcon {

+
+

background-image: var(–jp-icon-offline-bolt);

+
+

} +.jp-PaletteIcon {

+
+

background-image: var(–jp-icon-palette);

+
+

} +.jp-PasteIcon {

+
+

background-image: var(–jp-icon-paste);

+
+

} +.jp-PdfIcon {

+
+

background-image: var(–jp-icon-pdf);

+
+

} +.jp-PythonIcon {

+
+

background-image: var(–jp-icon-python);

+
+

} +.jp-RKernelIcon {

+
+

background-image: var(–jp-icon-r-kernel);

+
+

} +.jp-ReactIcon {

+
+

background-image: var(–jp-icon-react);

+
+

} +.jp-RedoIcon {

+
+

background-image: var(–jp-icon-redo);

+
+

} +.jp-RefreshIcon {

+
+

background-image: var(–jp-icon-refresh);

+
+

} +.jp-RegexIcon {

+
+

background-image: var(–jp-icon-regex);

+
+

} +.jp-RunIcon {

+
+

background-image: var(–jp-icon-run);

+
+

} +.jp-RunningIcon {

+
+

background-image: var(–jp-icon-running);

+
+

} +.jp-SaveIcon {

+
+

background-image: var(–jp-icon-save);

+
+

} +.jp-SearchIcon {

+
+

background-image: var(–jp-icon-search);

+
+

} +.jp-SettingsIcon {

+
+

background-image: var(–jp-icon-settings);

+
+

} +.jp-ShareIcon {

+
+

background-image: var(–jp-icon-share);

+
+

} +.jp-SpreadsheetIcon {

+
+

background-image: var(–jp-icon-spreadsheet);

+
+

} +.jp-StopIcon {

+
+

background-image: var(–jp-icon-stop);

+
+

} +.jp-TabIcon {

+
+

background-image: var(–jp-icon-tab);

+
+

} +.jp-TableRowsIcon {

+
+

background-image: var(–jp-icon-table-rows);

+
+

} +.jp-TagIcon {

+
+

background-image: var(–jp-icon-tag);

+
+

} +.jp-TerminalIcon {

+
+

background-image: var(–jp-icon-terminal);

+
+

} +.jp-TextEditorIcon {

+
+

background-image: var(–jp-icon-text-editor);

+
+

} +.jp-TocIcon {

+
+

background-image: var(–jp-icon-toc);

+
+

} +.jp-TreeViewIcon {

+
+

background-image: var(–jp-icon-tree-view);

+
+

} +.jp-TrustedIcon {

+
+

background-image: var(–jp-icon-trusted);

+
+

} +.jp-UndoIcon {

+
+

background-image: var(–jp-icon-undo);

+
+

} +.jp-UserIcon {

+
+

background-image: var(–jp-icon-user);

+
+

} +.jp-UsersIcon {

+
+

background-image: var(–jp-icon-users);

+
+

} +.jp-VegaIcon {

+
+

background-image: var(–jp-icon-vega);

+
+

} +.jp-YamlIcon {

+
+

background-image: var(–jp-icon-yaml);

+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
/**
    +

  • (DEPRECATED) Support for consuming icons as CSS background images

    • +
+

*/

+
+
+

.jp-Icon, +.jp-MaterialIcon {

+
+

background-position: center; +background-repeat: no-repeat; +background-size: 16px; +min-width: 16px; +min-height: 16px;

+
+

}

+
+
.jp-Icon-cover {

background-position: center; +background-repeat: no-repeat; +background-size: cover;

+
+
+

}

+
+
/**
    +

  • (DEPRECATED) Support for specific CSS icon sizes

    • +
+

*/

+
+
.jp-Icon-16 {

background-size: 16px; +min-width: 16px; +min-height: 16px;

+
+
+

}

+
+
.jp-Icon-18 {

background-size: 18px; +min-width: 18px; +min-height: 18px;

+
+
+

}

+
+
.jp-Icon-20 {

background-size: 20px; +min-width: 20px; +min-height: 20px;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.lm-TabBar .lm-TabBar-addButton {

align-items: center; +display: flex; +padding: 4px; +padding-bottom: 5px; +margin-right: 1px; +background-color: var(–jp-layout-color2);

+
+
+

}

+
+
.lm-TabBar .lm-TabBar-addButton:hover {

background-color: var(–jp-layout-color1);

+
+
+

}

+
+
.lm-DockPanel-tabBar .lm-TabBar-tab {

width: var(–jp-private-horizontal-tab-width);

+
+
+

}

+
+
.lm-DockPanel-tabBar .lm-TabBar-content {

flex: unset;

+
+
+

}

+
+
.lm-DockPanel-tabBar[data-orientation=’horizontal’] {

flex: 1 1 auto;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
/**
    +

  • Support for icons as inline SVG HTMLElements

    • +
+

*/

+
+
+

/* recolor the primary elements of an icon */ +.jp-icon0[fill] {

+
+

fill: var(–jp-inverse-layout-color0);

+
+

} +.jp-icon1[fill] {

+
+

fill: var(–jp-inverse-layout-color1);

+
+

} +.jp-icon2[fill] {

+
+

fill: var(–jp-inverse-layout-color2);

+
+

} +.jp-icon3[fill] {

+
+

fill: var(–jp-inverse-layout-color3);

+
+

} +.jp-icon4[fill] {

+
+

fill: var(–jp-inverse-layout-color4);

+
+

}

+
+
.jp-icon0[stroke] {

stroke: var(–jp-inverse-layout-color0);

+
+
+

} +.jp-icon1[stroke] {

+
+

stroke: var(–jp-inverse-layout-color1);

+
+

} +.jp-icon2[stroke] {

+
+

stroke: var(–jp-inverse-layout-color2);

+
+

} +.jp-icon3[stroke] {

+
+

stroke: var(–jp-inverse-layout-color3);

+
+

} +.jp-icon4[stroke] {

+
+

stroke: var(–jp-inverse-layout-color4);

+
+

} +/* recolor the accent elements of an icon */ +.jp-icon-accent0[fill] {

+
+

fill: var(–jp-layout-color0);

+
+

} +.jp-icon-accent1[fill] {

+
+

fill: var(–jp-layout-color1);

+
+

} +.jp-icon-accent2[fill] {

+
+

fill: var(–jp-layout-color2);

+
+

} +.jp-icon-accent3[fill] {

+
+

fill: var(–jp-layout-color3);

+
+

} +.jp-icon-accent4[fill] {

+
+

fill: var(–jp-layout-color4);

+
+

}

+
+
.jp-icon-accent0[stroke] {

stroke: var(–jp-layout-color0);

+
+
+

} +.jp-icon-accent1[stroke] {

+
+

stroke: var(–jp-layout-color1);

+
+

} +.jp-icon-accent2[stroke] {

+
+

stroke: var(–jp-layout-color2);

+
+

} +.jp-icon-accent3[stroke] {

+
+

stroke: var(–jp-layout-color3);

+
+

} +.jp-icon-accent4[stroke] {

+
+

stroke: var(–jp-layout-color4);

+
+

} +/* set the color of an icon to transparent */ +.jp-icon-none[fill] {

+
+

fill: none;

+
+

}

+
+
.jp-icon-none[stroke] {

stroke: none;

+
+
+

} +/* brand icon colors. Same for light and dark */ +.jp-icon-brand0[fill] {

+
+

fill: var(–jp-brand-color0);

+
+

} +.jp-icon-brand1[fill] {

+
+

fill: var(–jp-brand-color1);

+
+

} +.jp-icon-brand2[fill] {

+
+

fill: var(–jp-brand-color2);

+
+

} +.jp-icon-brand3[fill] {

+
+

fill: var(–jp-brand-color3);

+
+

} +.jp-icon-brand4[fill] {

+
+

fill: var(–jp-brand-color4);

+
+

}

+
+
.jp-icon-brand0[stroke] {

stroke: var(–jp-brand-color0);

+
+
+

} +.jp-icon-brand1[stroke] {

+
+

stroke: var(–jp-brand-color1);

+
+

} +.jp-icon-brand2[stroke] {

+
+

stroke: var(–jp-brand-color2);

+
+

} +.jp-icon-brand3[stroke] {

+
+

stroke: var(–jp-brand-color3);

+
+

} +.jp-icon-brand4[stroke] {

+
+

stroke: var(–jp-brand-color4);

+
+

} +/* warn icon colors. Same for light and dark */ +.jp-icon-warn0[fill] {

+
+

fill: var(–jp-warn-color0);

+
+

} +.jp-icon-warn1[fill] {

+
+

fill: var(–jp-warn-color1);

+
+

} +.jp-icon-warn2[fill] {

+
+

fill: var(–jp-warn-color2);

+
+

} +.jp-icon-warn3[fill] {

+
+

fill: var(–jp-warn-color3);

+
+

}

+
+
.jp-icon-warn0[stroke] {

stroke: var(–jp-warn-color0);

+
+
+

} +.jp-icon-warn1[stroke] {

+
+

stroke: var(–jp-warn-color1);

+
+

} +.jp-icon-warn2[stroke] {

+
+

stroke: var(–jp-warn-color2);

+
+

} +.jp-icon-warn3[stroke] {

+
+

stroke: var(–jp-warn-color3);

+
+

} +/* icon colors that contrast well with each other and most backgrounds */ +.jp-icon-contrast0[fill] {

+
+

fill: var(–jp-icon-contrast-color0);

+
+

} +.jp-icon-contrast1[fill] {

+
+

fill: var(–jp-icon-contrast-color1);

+
+

} +.jp-icon-contrast2[fill] {

+
+

fill: var(–jp-icon-contrast-color2);

+
+

} +.jp-icon-contrast3[fill] {

+
+

fill: var(–jp-icon-contrast-color3);

+
+

}

+
+
.jp-icon-contrast0[stroke] {

stroke: var(–jp-icon-contrast-color0);

+
+
+

} +.jp-icon-contrast1[stroke] {

+
+

stroke: var(–jp-icon-contrast-color1);

+
+

} +.jp-icon-contrast2[stroke] {

+
+

stroke: var(–jp-icon-contrast-color2);

+
+

} +.jp-icon-contrast3[stroke] {

+
+

stroke: var(–jp-icon-contrast-color3);

+
+

}

+
+
.jp-jupyter-icon-color[fill] {

fill: var(–jp-jupyter-icon-color, var(–jp-warn-color0));

+
+
+

}

+
+
.jp-notebook-icon-color[fill] {

fill: var(–jp-notebook-icon-color, var(–jp-warn-color0));

+
+
+

}

+
+
.jp-json-icon-color[fill] {

fill: var(–jp-json-icon-color, var(–jp-warn-color1));

+
+
+

}

+
+
.jp-console-icon-color[fill] {

fill: var(–jp-console-icon-color, white);

+
+
+

}

+
+
.jp-console-icon-background-color[fill] {

fill: var(–jp-console-icon-background-color, var(–jp-brand-color1));

+
+
+

}

+
+
.jp-terminal-icon-color[fill] {

fill: var(–jp-terminal-icon-color, var(–jp-layout-color2));

+
+
+

}

+
+
.jp-terminal-icon-background-color[fill] {

fill: var(–jp-terminal-icon-background-color, var(–jp-inverse-layout2));

+
+
+

}

+
+
.jp-text-editor-icon-color[fill] {

fill: var(–jp-text-editor-icon-color, var(–jp-inverse-layout3));

+
+
+

}

+
+
.jp-inspector-icon-color[fill] {

fill: var(–jp-inspector-icon-color, var(–jp-inverse-layout3));

+
+
+

}

+

/* CSS for icons in selected filebrowser listing items */ +.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {

+
+

fill: #fff;

+
+

} +.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {

+
+

fill: var(–jp-brand-color1);

+
+

}

+

/* CSS for icons in selected tabs in the sidebar tab manager */ +#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable[fill] {

+
+

fill: #fff;

+
+

}

+
+
#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable-inverse[fill] {

fill: var(–jp-brand-color1);

+
+
+

} +#tab-manager

+
+

.lm-TabBar-tab.jp-mod-active +.jp-icon-hover +:hover +.jp-icon-selectable[fill] { +fill: var(–jp-brand-color1);

+
+

}

+
+
#tab-manager

.lm-TabBar-tab.jp-mod-active +.jp-icon-hover +:hover +.jp-icon-selectable-inverse[fill] { +fill: #fff;

+
+
+

}

+
+
/**
    +

  • TODO: come up with non css-hack solution for showing the busy icon on top

    • +

  • of the close icon

    • +

  • CSS for complex behavior of close icon of tabs in the sidebar tab manager

    • +
+

*/

+
+
#tab-manager

.lm-TabBar-tab.jp-mod-dirty +> .lm-TabBar-tabCloseIcon +> :not(:hover) +> .jp-icon3[fill] { +fill: none;

+
+
+

} +#tab-manager

+
+

.lm-TabBar-tab.jp-mod-dirty +> .lm-TabBar-tabCloseIcon +> :not(:hover) +> .jp-icon-busy[fill] { +fill: var(–jp-inverse-layout-color3);

+
+

}

+
+
#tab-manager

.lm-TabBar-tab.jp-mod-dirty.jp-mod-active +> .lm-TabBar-tabCloseIcon +> :not(:hover) +> .jp-icon-busy[fill] { +fill: #fff;

+
+
+

}

+

/** +* TODO: come up with non css-hack solution for showing the busy icon on top +* of the close icon +* CSS for complex behavior of close icon of tabs in the main area tabbar +*/ +.lm-DockPanel-tabBar

+
+

.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty +> .lm-TabBar-tabCloseIcon +> :not(:hover) +> .jp-icon3[fill] { +fill: none;

+
+

} +.lm-DockPanel-tabBar

+
+

.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty +> .lm-TabBar-tabCloseIcon +> :not(:hover) +> .jp-icon-busy[fill] { +fill: var(–jp-inverse-layout-color3);

+
+

}

+

/* CSS for icons in status bar */ +#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {

+
+

fill: #fff;

+
+

}

+
+
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {

fill: var(–jp-brand-color1);

+
+
+

} +/* special handling for splash icon CSS. While the theme CSS reloads during

+
+

splash, the splash icon can loose theming. To prevent that, we set a +default for its color variable */

+
+
+
:root {

–jp-warn-color0: var(–md-orange-700);

+
+
+

}

+

/* not sure what to do with this one, used in filebrowser listing */ +.jp-DragIcon {

+
+

margin-right: 4px;

+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
/**
    +

  • Support for alt colors for icons as inline SVG HTMLElements

    • +
+

*/

+
+
+

/* alt recolor the primary elements of an icon */ +.jp-icon-alt .jp-icon0[fill] {

+
+

fill: var(–jp-layout-color0);

+
+

} +.jp-icon-alt .jp-icon1[fill] {

+
+

fill: var(–jp-layout-color1);

+
+

} +.jp-icon-alt .jp-icon2[fill] {

+
+

fill: var(–jp-layout-color2);

+
+

} +.jp-icon-alt .jp-icon3[fill] {

+
+

fill: var(–jp-layout-color3);

+
+

} +.jp-icon-alt .jp-icon4[fill] {

+
+

fill: var(–jp-layout-color4);

+
+

}

+
+
.jp-icon-alt .jp-icon0[stroke] {

stroke: var(–jp-layout-color0);

+
+
+

} +.jp-icon-alt .jp-icon1[stroke] {

+
+

stroke: var(–jp-layout-color1);

+
+

} +.jp-icon-alt .jp-icon2[stroke] {

+
+

stroke: var(–jp-layout-color2);

+
+

} +.jp-icon-alt .jp-icon3[stroke] {

+
+

stroke: var(–jp-layout-color3);

+
+

} +.jp-icon-alt .jp-icon4[stroke] {

+
+

stroke: var(–jp-layout-color4);

+
+

}

+

/* alt recolor the accent elements of an icon */ +.jp-icon-alt .jp-icon-accent0[fill] {

+
+

fill: var(–jp-inverse-layout-color0);

+
+

} +.jp-icon-alt .jp-icon-accent1[fill] {

+
+

fill: var(–jp-inverse-layout-color1);

+
+

} +.jp-icon-alt .jp-icon-accent2[fill] {

+
+

fill: var(–jp-inverse-layout-color2);

+
+

} +.jp-icon-alt .jp-icon-accent3[fill] {

+
+

fill: var(–jp-inverse-layout-color3);

+
+

} +.jp-icon-alt .jp-icon-accent4[fill] {

+
+

fill: var(–jp-inverse-layout-color4);

+
+

}

+
+
.jp-icon-alt .jp-icon-accent0[stroke] {

stroke: var(–jp-inverse-layout-color0);

+
+
+

} +.jp-icon-alt .jp-icon-accent1[stroke] {

+
+

stroke: var(–jp-inverse-layout-color1);

+
+

} +.jp-icon-alt .jp-icon-accent2[stroke] {

+
+

stroke: var(–jp-inverse-layout-color2);

+
+

} +.jp-icon-alt .jp-icon-accent3[stroke] {

+
+

stroke: var(–jp-inverse-layout-color3);

+
+

} +.jp-icon-alt .jp-icon-accent4[stroke] {

+
+

stroke: var(–jp-inverse-layout-color4);

+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {

display: none !important;

+
+
+

}

+
+
/**
    +

  • Support for hover colors for icons as inline SVG HTMLElements

    • +
+

*/

+
+
/**
    +

  • regular colors

    • +
+

*/

+
+
+

/* recolor the primary elements of an icon */ +.jp-icon-hover :hover .jp-icon0-hover[fill] {

+
+

fill: var(–jp-inverse-layout-color0);

+
+

} +.jp-icon-hover :hover .jp-icon1-hover[fill] {

+
+

fill: var(–jp-inverse-layout-color1);

+
+

} +.jp-icon-hover :hover .jp-icon2-hover[fill] {

+
+

fill: var(–jp-inverse-layout-color2);

+
+

} +.jp-icon-hover :hover .jp-icon3-hover[fill] {

+
+

fill: var(–jp-inverse-layout-color3);

+
+

} +.jp-icon-hover :hover .jp-icon4-hover[fill] {

+
+

fill: var(–jp-inverse-layout-color4);

+
+

}

+
+
.jp-icon-hover :hover .jp-icon0-hover[stroke] {

stroke: var(–jp-inverse-layout-color0);

+
+
+

} +.jp-icon-hover :hover .jp-icon1-hover[stroke] {

+
+

stroke: var(–jp-inverse-layout-color1);

+
+

} +.jp-icon-hover :hover .jp-icon2-hover[stroke] {

+
+

stroke: var(–jp-inverse-layout-color2);

+
+

} +.jp-icon-hover :hover .jp-icon3-hover[stroke] {

+
+

stroke: var(–jp-inverse-layout-color3);

+
+

} +.jp-icon-hover :hover .jp-icon4-hover[stroke] {

+
+

stroke: var(–jp-inverse-layout-color4);

+
+

}

+

/* recolor the accent elements of an icon */ +.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {

+
+

fill: var(–jp-layout-color0);

+
+

} +.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {

+
+

fill: var(–jp-layout-color1);

+
+

} +.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {

+
+

fill: var(–jp-layout-color2);

+
+

} +.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {

+
+

fill: var(–jp-layout-color3);

+
+

} +.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {

+
+

fill: var(–jp-layout-color4);

+
+

}

+
+
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {

stroke: var(–jp-layout-color0);

+
+
+

} +.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {

+
+

stroke: var(–jp-layout-color1);

+
+

} +.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {

+
+

stroke: var(–jp-layout-color2);

+
+

} +.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {

+
+

stroke: var(–jp-layout-color3);

+
+

} +.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {

+
+

stroke: var(–jp-layout-color4);

+
+

}

+

/* set the color of an icon to transparent */ +.jp-icon-hover :hover .jp-icon-none-hover[fill] {

+
+

fill: none;

+
+

}

+
+
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {

stroke: none;

+
+
+

}

+
+
/**
    +

  • inverse colors

    • +
+

*/

+
+
+

/* inverse recolor the primary elements of an icon */ +.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {

+
+

fill: var(–jp-layout-color0);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {

+
+

fill: var(–jp-layout-color1);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {

+
+

fill: var(–jp-layout-color2);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {

+
+

fill: var(–jp-layout-color3);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {

+
+

fill: var(–jp-layout-color4);

+
+

}

+
+
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {

stroke: var(–jp-layout-color0);

+
+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {

+
+

stroke: var(–jp-layout-color1);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {

+
+

stroke: var(–jp-layout-color2);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {

+
+

stroke: var(–jp-layout-color3);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {

+
+

stroke: var(–jp-layout-color4);

+
+

}

+

/* inverse recolor the accent elements of an icon */ +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {

+
+

fill: var(–jp-inverse-layout-color0);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {

+
+

fill: var(–jp-inverse-layout-color1);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {

+
+

fill: var(–jp-inverse-layout-color2);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {

+
+

fill: var(–jp-inverse-layout-color3);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {

+
+

fill: var(–jp-inverse-layout-color4);

+
+

}

+
+
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {

stroke: var(–jp-inverse-layout-color0);

+
+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {

+
+

stroke: var(–jp-inverse-layout-color1);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {

+
+

stroke: var(–jp-inverse-layout-color2);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {

+
+

stroke: var(–jp-inverse-layout-color3);

+
+

} +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {

+
+

stroke: var(–jp-inverse-layout-color4);

+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-switch {

display: flex; +align-items: center; +padding-left: 4px; +padding-right: 4px; +font-size: var(–jp-ui-font-size1); +background-color: transparent; +color: var(–jp-ui-font-color1); +border: none; +height: 20px;

+
+
+

}

+
+
.jp-switch:hover {

background-color: var(–jp-layout-color2);

+
+
+

}

+
+
.jp-switch-label {

margin-right: 5px;

+
+
+

}

+
+
.jp-switch-track {

cursor: pointer; +background-color: var(–jp-switch-color, var(–jp-border-color1)); +-webkit-transition: 0.4s; +transition: 0.4s; +border-radius: 34px; +height: 16px; +width: 35px; +position: relative;

+
+
+

}

+
+
.jp-switch-track::before {

content: ‘’; +position: absolute; +height: 10px; +width: 10px; +margin: 3px; +left: 0px; +background-color: var(–jp-ui-inverse-font-color1); +-webkit-transition: 0.4s; +transition: 0.4s; +border-radius: 50%;

+
+
+

}

+
+
.jp-switch[aria-checked=’true’] .jp-switch-track {

background-color: var(–jp-switch-true-position-color, var(–jp-warn-color0));

+
+
+

}

+
+
.jp-switch[aria-checked=’true’] .jp-switch-track::before {

/* track width (35) - margins (3 + 3) - thumb width (10) */ +left: 19px;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/* Sibling imports */

+

/* Override Blueprint’s _reset.scss styles */ +html {

+
+

box-sizing: unset;

+
+

}

+

*, +*::before, +*::after {

+
+

box-sizing: unset;

+
+

}

+
+
body {

color: unset; +font-family: var(–jp-ui-font-family);

+
+
+

}

+
+
p {

margin-top: unset; +margin-bottom: unset;

+
+
+

}

+
+
small {

font-size: unset;

+
+
+

}

+
+
strong {

font-weight: unset;

+
+
+

}

+

/* Override Blueprint’s _typography.scss styles */ +a {

+
+

text-decoration: unset; +color: unset;

+
+

} +a:hover {

+
+

text-decoration: unset; +color: unset;

+
+

}

+

/* Override Blueprint’s _accessibility.scss styles */ +:focus {

+
+

outline: unset; +outline-offset: unset; +-moz-outline-radius: unset;

+
+

}

+

/* Styles for ui-components */ +.jp-Button {

+
+

border-radius: var(–jp-border-radius); +padding: 0px 12px; +font-size: var(–jp-ui-font-size1);

+
+

}

+

/* Use our own theme for hover styles */ +button.jp-Button.bp3-button.bp3-minimal:hover {

+
+

background-color: var(–jp-layout-color2);

+
+

} +.jp-Button.minimal {

+
+

color: unset !important;

+
+

}

+
+
.jp-Button.jp-ToolbarButtonComponent {

text-transform: none;

+
+
+

}

+
+
.jp-InputGroup input {

box-sizing: border-box; +border-radius: 0; +background-color: transparent; +color: var(–jp-ui-font-color0); +box-shadow: inset 0 0 0 var(–jp-border-width) var(–jp-input-border-color);

+
+
+

}

+
+
.jp-InputGroup input:focus {
+
box-shadow: inset 0 0 0 var(–jp-border-width)
+

var(–jp-input-active-box-shadow-color),

+
+

inset 0 0 0 3px var(–jp-input-active-box-shadow-color);

+
+
+
+
+

}

+

.jp-InputGroup input::placeholder, +input::placeholder {

+
+

color: var(–jp-ui-font-color3);

+
+

}

+
+
.jp-BPIcon {

display: inline-block; +vertical-align: middle; +margin: auto;

+
+
+

}

+

/* Stop blueprint futzing with our icon fills */ +.bp3-icon.jp-BPIcon > svg:not([fill]) {

+
+

fill: var(–jp-inverse-layout-color3);

+
+

}

+
+
.jp-InputGroupAction {

padding: 6px;

+
+
+

}

+
+
.jp-HTMLSelect.jp-DefaultStyle select {

background-color: initial; +border: none; +border-radius: 0; +box-shadow: none; +color: var(–jp-ui-font-color0); +display: block; +font-size: var(–jp-ui-font-size1); +height: 24px; +line-height: 14px; +padding: 0 25px 0 10px; +text-align: left; +-moz-appearance: none; +-webkit-appearance: none;

+
+
+

}

+

/* Use our own theme for hover and option styles */ +.jp-HTMLSelect.jp-DefaultStyle select:hover, +.jp-HTMLSelect.jp-DefaultStyle select > option {

+
+

background-color: var(–jp-layout-color2); +color: var(–jp-ui-font-color0);

+
+

} +select {

+
+

box-sizing: border-box;

+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-Collapse {

display: flex; +flex-direction: column; +align-items: stretch; +border-top: 1px solid var(–jp-border-color2); +border-bottom: 1px solid var(–jp-border-color2);

+
+
+

}

+
+
.jp-Collapse-header {

padding: 1px 12px; +color: var(–jp-ui-font-color1); +background-color: var(–jp-layout-color1); +font-size: var(–jp-ui-font-size2);

+
+
+

}

+
+
.jp-Collapse-header:hover {

background-color: var(–jp-layout-color2);

+
+
+

}

+
+
.jp-Collapse-contents {

padding: 0px 12px 0px 12px; +background-color: var(–jp-layout-color1); +color: var(–jp-ui-font-color1); +overflow: auto;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Variables +|—————————————————————————-/

+
+
:root {

–jp-private-commandpalette-search-height: 28px;

+
+
+

}

+

/—————————————————————————– +| Overall styles +|—————————————————————————-/

+
+
.lm-CommandPalette {

padding-bottom: 0px; +color: var(–jp-ui-font-color1); +background: var(–jp-layout-color1); +/* This is needed so that all font sizing of children done in ems is

+
+
    +

  • relative to this base size */

    • +
+
+

font-size: var(–jp-ui-font-size1);

+
+
+

}

+

/—————————————————————————– +| Modal variant +|—————————————————————————-/

+
+
.jp-ModalCommandPalette {

position: absolute; +z-index: 10000; +top: 38px; +left: 30%; +margin: 0; +padding: 4px; +width: 40%; +box-shadow: var(–jp-elevation-z4); +border-radius: 4px; +background: var(–jp-layout-color0);

+
+
+

}

+
+
.jp-ModalCommandPalette .lm-CommandPalette {

max-height: 40vh;

+
+
+

}

+
+
.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {

display: none;

+
+
+

}

+
+
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {

display: none;

+
+
+

}

+
+
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {

margin-left: 4px; +margin-right: 4px;

+
+
+

}

+
+
.jp-ModalCommandPalette

.lm-CommandPalette +.lm-CommandPalette-item.lm-mod-disabled { +display: none;

+
+
+

}

+

/—————————————————————————– +| Search +|—————————————————————————-/

+
+
.lm-CommandPalette-search {

padding: 4px; +background-color: var(–jp-layout-color1); +z-index: 2;

+
+
+

}

+
+
.lm-CommandPalette-wrapper {

overflow: overlay; +padding: 0px 9px; +background-color: var(–jp-input-active-background); +height: 30px; +box-shadow: inset 0 0 0 var(–jp-border-width) var(–jp-input-border-color);

+
+
+

}

+
+
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
+
box-shadow: inset 0 0 0 1px var(–jp-input-active-box-shadow-color),

inset 0 0 0 3px var(–jp-input-active-box-shadow-color);

+
+
+
+
+

}

+
+
.jp-SearchIconGroup {

color: white; +background-color: var(–jp-brand-color1); +position: absolute; +top: 4px; +right: 4px; +padding: 5px 5px 1px 5px;

+
+
+

}

+
+
.jp-SearchIconGroup svg {

height: 20px; +width: 20px;

+
+
+

}

+
+
.jp-SearchIconGroup .jp-icon3[fill] {

fill: var(–jp-layout-color0);

+
+
+

}

+
+
.lm-CommandPalette-input {

background: transparent; +width: calc(100% - 18px); +float: left; +border: none; +outline: none; +font-size: var(–jp-ui-font-size1); +color: var(–jp-ui-font-color0); +line-height: var(–jp-private-commandpalette-search-height);

+
+
+

}

+

.lm-CommandPalette-input::-webkit-input-placeholder, +.lm-CommandPalette-input::-moz-placeholder, +.lm-CommandPalette-input:-ms-input-placeholder {

+
+

color: var(–jp-ui-font-color2); +font-size: var(–jp-ui-font-size1);

+
+

}

+

/—————————————————————————– +| Results +|—————————————————————————-/

+
+
.lm-CommandPalette-header:first-child {

margin-top: 0px;

+
+
+

}

+
+
.lm-CommandPalette-header {

border-bottom: solid var(–jp-border-width) var(–jp-border-color2); +color: var(–jp-ui-font-color1); +cursor: pointer; +display: flex; +font-size: var(–jp-ui-font-size0); +font-weight: 600; +letter-spacing: 1px; +margin-top: 8px; +padding: 8px 0 8px 12px; +text-transform: uppercase;

+
+
+

}

+
+
.lm-CommandPalette-header.lm-mod-active {

background: var(–jp-layout-color2);

+
+
+

}

+
+
.lm-CommandPalette-header > mark {

background-color: transparent; +font-weight: bold; +color: var(–jp-ui-font-color1);

+
+
+

}

+
+
.lm-CommandPalette-item {

padding: 4px 12px 4px 4px; +color: var(–jp-ui-font-color1); +font-size: var(–jp-ui-font-size1); +font-weight: 400; +display: flex;

+
+
+

}

+
+
.lm-CommandPalette-item.lm-mod-disabled {

color: var(–jp-ui-font-color2);

+
+
+

}

+
+
.lm-CommandPalette-item.lm-mod-active {

color: var(–jp-ui-inverse-font-color1); +background: var(–jp-brand-color1);

+
+
+

}

+
+
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {

color: var(–jp-ui-inverse-font-color0);

+
+
+

}

+
+
.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {

fill: var(–jp-layout-color0);

+
+
+

}

+
+
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {

color: var(–jp-ui-inverse-font-color0);

+
+
+

}

+
+
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {

color: var(–jp-ui-inverse-font-color1); +background: var(–jp-brand-color1);

+
+
+

}

+
+
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {

background: var(–jp-layout-color2);

+
+
+

}

+
+
.lm-CommandPalette-itemContent {

overflow: hidden;

+
+
+

}

+
+
.lm-CommandPalette-itemLabel > mark {

color: var(–jp-ui-font-color0); +background-color: transparent; +font-weight: bold;

+
+
+

}

+
+
.lm-CommandPalette-item.lm-mod-disabled mark {

color: var(–jp-ui-font-color2);

+
+
+

}

+
+
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {

margin: 0 4px 0 0; +position: relative; +width: 16px; +top: 2px; +flex: 0 0 auto;

+
+
+

}

+
+
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {

opacity: 0.6;

+
+
+

}

+
+
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {

flex: 0 0 auto;

+
+
+

}

+
+
.lm-CommandPalette-itemCaption {

display: none;

+
+
+

}

+
+
.lm-CommandPalette-content {

background-color: var(–jp-layout-color1);

+
+
+

}

+
+
.lm-CommandPalette-content:empty:after {

content: ‘No results’; +margin: auto; +margin-top: 20px; +width: 100px; +display: block; +font-size: var(–jp-ui-font-size2); +font-family: var(–jp-ui-font-family); +font-weight: lighter;

+
+
+

}

+
+
.lm-CommandPalette-emptyMessage {

text-align: center; +margin-top: 24px; +line-height: 1.32; +padding: 0px 8px; +color: var(–jp-content-font-color3);

+
+
+

}

+

/—————————————————————————– +| Copyright (c) 2014-2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-Dialog {

position: absolute; +z-index: 10000; +display: flex; +flex-direction: column; +align-items: center; +justify-content: center; +top: 0px; +left: 0px; +margin: 0; +padding: 0; +width: 100%; +height: 100%; +background: var(–jp-dialog-background);

+
+
+

}

+
+
.jp-Dialog-content {

display: flex; +flex-direction: column; +margin-left: auto; +margin-right: auto; +background: var(–jp-layout-color1); +padding: 24px 24px 12px 24px; +min-width: 300px; +min-height: 150px; +max-width: 1000px; +max-height: 500px; +box-sizing: border-box; +box-shadow: var(–jp-elevation-z20); +word-wrap: break-word; +border-radius: var(–jp-border-radius); +/* This is needed so that all font sizing of children done in ems is

+
+
    +

  • relative to this base size */

    • +
+
+

font-size: var(–jp-ui-font-size1); +color: var(–jp-ui-font-color1); +resize: both;

+
+
+

}

+
+
.jp-Dialog-content.jp-Dialog-content-small {

max-width: 500px;

+
+
+

}

+
+
.jp-Dialog-button {

overflow: visible;

+
+
+

}

+
+
button.jp-Dialog-button:focus {

outline: 1px solid var(–jp-brand-color1); +outline-offset: 4px; +-moz-outline-radius: 0px;

+
+
+

}

+
+
button.jp-Dialog-button:focus::-moz-focus-inner {

border: 0;

+
+
+

}

+

button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus, +button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus, +button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {

+
+

outline-offset: 4px; +-moz-outline-radius: 0px;

+
+

}

+
+
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {

outline: 1px solid var(–md-blue-700);

+
+
+

}

+
+
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {

outline: 1px solid var(–md-red-600);

+
+
+

}

+
+
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {

outline: 1px solid var(–md-grey-700);

+
+
+

}

+
+
button.jp-Dialog-close-button {

padding: 0; +height: 100%; +min-width: unset; +min-height: unset;

+
+
+

}

+
+
.jp-Dialog-header {

display: flex; +justify-content: space-between; +flex: 0 0 auto; +padding-bottom: 12px; +font-size: var(–jp-ui-font-size3); +font-weight: 400; +color: var(–jp-ui-font-color0);

+
+
+

}

+
+
.jp-Dialog-body {

display: flex; +flex-direction: column; +flex: 1 1 auto; +font-size: var(–jp-ui-font-size1); +background: var(–jp-layout-color1); +overflow: auto;

+
+
+

}

+
+
.jp-Dialog-footer {

display: flex; +flex-direction: row; +justify-content: flex-end; +align-items: center; +flex: 0 0 auto; +margin-left: -12px; +margin-right: -12px; +padding: 12px;

+
+
+

}

+
+
.jp-Dialog-checkbox {

padding-right: 5px;

+
+
+

}

+
+
.jp-Dialog-checkbox > input:focus-visible {

outline: 1px solid var(–jp-input-active-border-color); +outline-offset: 1px;

+
+
+

}

+
+
.jp-Dialog-spacer {

flex: 1 1 auto;

+
+
+

}

+
+
.jp-Dialog-title {

overflow: hidden; +white-space: nowrap; +text-overflow: ellipsis;

+
+
+

}

+
+
.jp-Dialog-body > .jp-select-wrapper {

width: 100%;

+
+
+

}

+
+
.jp-Dialog-body > button {

padding: 0px 16px;

+
+
+

}

+
+
.jp-Dialog-body > label {

line-height: 1.4; +color: var(–jp-ui-font-color0);

+
+
+

}

+
+
.jp-Dialog-button.jp-mod-styled:not(:last-child) {

margin-right: 12px;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-HoverBox {

position: fixed;

+
+
+

}

+
+
.jp-HoverBox.jp-mod-outofview {

display: none;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-IFrame {

width: 100%; +height: 100%;

+
+
+

}

+
+
.jp-IFrame > iframe {

border: none;

+
+
+

}

+

/* +When drag events occur, p-mod-override-cursor is added to the body. +Because iframes steal all cursor events, the following two rules are necessary +to suppress pointer events while resize drags are occurring. There may be a +better solution to this problem. +*/ +body.lm-mod-override-cursor .jp-IFrame {

+
+

position: relative;

+
+

}

+
+
body.lm-mod-override-cursor .jp-IFrame:before {

content: ‘’; +position: absolute; +top: 0; +left: 0; +right: 0; +bottom: 0; +background: transparent;

+
+
+

}

+
+
.jp-Input-Boolean-Dialog {

flex-direction: row-reverse; +align-items: end; +width: 100%;

+
+
+

}

+
+
.jp-Input-Boolean-Dialog > label {

flex: 1 1 auto;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-MainAreaWidget > :focus {

outline: none;

+
+
+

}

+
+
.jp-MainAreaWidget .jp-MainAreaWidget-error {

padding: 6px;

+
+
+

}

+
+
.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {

width: auto; +padding: 10px; +background: var(–jp-error-color3); +border: var(–jp-border-width) solid var(–jp-error-color1); +border-radius: var(–jp-border-radius); +color: var(–jp-ui-font-color1); +font-size: var(–jp-ui-font-size1); +white-space: pre-wrap; +word-wrap: break-word;

+
+
+

}

+
+
.jp-MainAreaWidget {

contain: strict;

+
+
+

}

+
+
/**
+

*/

+
+
:root {

–md-red-50: #ffebee; +–md-red-100: #ffcdd2; +–md-red-200: #ef9a9a; +–md-red-300: #e57373; +–md-red-400: #ef5350; +–md-red-500: #f44336; +–md-red-600: #e53935; +–md-red-700: #d32f2f; +–md-red-800: #c62828; +–md-red-900: #b71c1c; +–md-red-A100: #ff8a80; +–md-red-A200: #ff5252; +–md-red-A400: #ff1744; +–md-red-A700: #d50000;

+

–md-pink-50: #fce4ec; +–md-pink-100: #f8bbd0; +–md-pink-200: #f48fb1; +–md-pink-300: #f06292; +–md-pink-400: #ec407a; +–md-pink-500: #e91e63; +–md-pink-600: #d81b60; +–md-pink-700: #c2185b; +–md-pink-800: #ad1457; +–md-pink-900: #880e4f; +–md-pink-A100: #ff80ab; +–md-pink-A200: #ff4081; +–md-pink-A400: #f50057; +–md-pink-A700: #c51162;

+

–md-purple-50: #f3e5f5; +–md-purple-100: #e1bee7; +–md-purple-200: #ce93d8; +–md-purple-300: #ba68c8; +–md-purple-400: #ab47bc; +–md-purple-500: #9c27b0; +–md-purple-600: #8e24aa; +–md-purple-700: #7b1fa2; +–md-purple-800: #6a1b9a; +–md-purple-900: #4a148c; +–md-purple-A100: #ea80fc; +–md-purple-A200: #e040fb; +–md-purple-A400: #d500f9; +–md-purple-A700: #aa00ff;

+

–md-deep-purple-50: #ede7f6; +–md-deep-purple-100: #d1c4e9; +–md-deep-purple-200: #b39ddb; +–md-deep-purple-300: #9575cd; +–md-deep-purple-400: #7e57c2; +–md-deep-purple-500: #673ab7; +–md-deep-purple-600: #5e35b1; +–md-deep-purple-700: #512da8; +–md-deep-purple-800: #4527a0; +–md-deep-purple-900: #311b92; +–md-deep-purple-A100: #b388ff; +–md-deep-purple-A200: #7c4dff; +–md-deep-purple-A400: #651fff; +–md-deep-purple-A700: #6200ea;

+

–md-indigo-50: #e8eaf6; +–md-indigo-100: #c5cae9; +–md-indigo-200: #9fa8da; +–md-indigo-300: #7986cb; +–md-indigo-400: #5c6bc0; +–md-indigo-500: #3f51b5; +–md-indigo-600: #3949ab; +–md-indigo-700: #303f9f; +–md-indigo-800: #283593; +–md-indigo-900: #1a237e; +–md-indigo-A100: #8c9eff; +–md-indigo-A200: #536dfe; +–md-indigo-A400: #3d5afe; +–md-indigo-A700: #304ffe;

+

–md-blue-50: #e3f2fd; +–md-blue-100: #bbdefb; +–md-blue-200: #90caf9; +–md-blue-300: #64b5f6; +–md-blue-400: #42a5f5; +–md-blue-500: #2196f3; +–md-blue-600: #1e88e5; +–md-blue-700: #1976d2; +–md-blue-800: #1565c0; +–md-blue-900: #0d47a1; +–md-blue-A100: #82b1ff; +–md-blue-A200: #448aff; +–md-blue-A400: #2979ff; +–md-blue-A700: #2962ff;

+

–md-light-blue-50: #e1f5fe; +–md-light-blue-100: #b3e5fc; +–md-light-blue-200: #81d4fa; +–md-light-blue-300: #4fc3f7; +–md-light-blue-400: #29b6f6; +–md-light-blue-500: #03a9f4; +–md-light-blue-600: #039be5; +–md-light-blue-700: #0288d1; +–md-light-blue-800: #0277bd; +–md-light-blue-900: #01579b; +–md-light-blue-A100: #80d8ff; +–md-light-blue-A200: #40c4ff; +–md-light-blue-A400: #00b0ff; +–md-light-blue-A700: #0091ea;

+

–md-cyan-50: #e0f7fa; +–md-cyan-100: #b2ebf2; +–md-cyan-200: #80deea; +–md-cyan-300: #4dd0e1; +–md-cyan-400: #26c6da; +–md-cyan-500: #00bcd4; +–md-cyan-600: #00acc1; +–md-cyan-700: #0097a7; +–md-cyan-800: #00838f; +–md-cyan-900: #006064; +–md-cyan-A100: #84ffff; +–md-cyan-A200: #18ffff; +–md-cyan-A400: #00e5ff; +–md-cyan-A700: #00b8d4;

+

–md-teal-50: #e0f2f1; +–md-teal-100: #b2dfdb; +–md-teal-200: #80cbc4; +–md-teal-300: #4db6ac; +–md-teal-400: #26a69a; +–md-teal-500: #009688; +–md-teal-600: #00897b; +–md-teal-700: #00796b; +–md-teal-800: #00695c; +–md-teal-900: #004d40; +–md-teal-A100: #a7ffeb; +–md-teal-A200: #64ffda; +–md-teal-A400: #1de9b6; +–md-teal-A700: #00bfa5;

+

–md-green-50: #e8f5e9; +–md-green-100: #c8e6c9; +–md-green-200: #a5d6a7; +–md-green-300: #81c784; +–md-green-400: #66bb6a; +–md-green-500: #4caf50; +–md-green-600: #43a047; +–md-green-700: #388e3c; +–md-green-800: #2e7d32; +–md-green-900: #1b5e20; +–md-green-A100: #b9f6ca; +–md-green-A200: #69f0ae; +–md-green-A400: #00e676; +–md-green-A700: #00c853;

+

–md-light-green-50: #f1f8e9; +–md-light-green-100: #dcedc8; +–md-light-green-200: #c5e1a5; +–md-light-green-300: #aed581; +–md-light-green-400: #9ccc65; +–md-light-green-500: #8bc34a; +–md-light-green-600: #7cb342; +–md-light-green-700: #689f38; +–md-light-green-800: #558b2f; +–md-light-green-900: #33691e; +–md-light-green-A100: #ccff90; +–md-light-green-A200: #b2ff59; +–md-light-green-A400: #76ff03; +–md-light-green-A700: #64dd17;

+

–md-lime-50: #f9fbe7; +–md-lime-100: #f0f4c3; +–md-lime-200: #e6ee9c; +–md-lime-300: #dce775; +–md-lime-400: #d4e157; +–md-lime-500: #cddc39; +–md-lime-600: #c0ca33; +–md-lime-700: #afb42b; +–md-lime-800: #9e9d24; +–md-lime-900: #827717; +–md-lime-A100: #f4ff81; +–md-lime-A200: #eeff41; +–md-lime-A400: #c6ff00; +–md-lime-A700: #aeea00;

+

–md-yellow-50: #fffde7; +–md-yellow-100: #fff9c4; +–md-yellow-200: #fff59d; +–md-yellow-300: #fff176; +–md-yellow-400: #ffee58; +–md-yellow-500: #ffeb3b; +–md-yellow-600: #fdd835; +–md-yellow-700: #fbc02d; +–md-yellow-800: #f9a825; +–md-yellow-900: #f57f17; +–md-yellow-A100: #ffff8d; +–md-yellow-A200: #ffff00; +–md-yellow-A400: #ffea00; +–md-yellow-A700: #ffd600;

+

–md-amber-50: #fff8e1; +–md-amber-100: #ffecb3; +–md-amber-200: #ffe082; +–md-amber-300: #ffd54f; +–md-amber-400: #ffca28; +–md-amber-500: #ffc107; +–md-amber-600: #ffb300; +–md-amber-700: #ffa000; +–md-amber-800: #ff8f00; +–md-amber-900: #ff6f00; +–md-amber-A100: #ffe57f; +–md-amber-A200: #ffd740; +–md-amber-A400: #ffc400; +–md-amber-A700: #ffab00;

+

–md-orange-50: #fff3e0; +–md-orange-100: #ffe0b2; +–md-orange-200: #ffcc80; +–md-orange-300: #ffb74d; +–md-orange-400: #ffa726; +–md-orange-500: #ff9800; +–md-orange-600: #fb8c00; +–md-orange-700: #f57c00; +–md-orange-800: #ef6c00; +–md-orange-900: #e65100; +–md-orange-A100: #ffd180; +–md-orange-A200: #ffab40; +–md-orange-A400: #ff9100; +–md-orange-A700: #ff6d00;

+

–md-deep-orange-50: #fbe9e7; +–md-deep-orange-100: #ffccbc; +–md-deep-orange-200: #ffab91; +–md-deep-orange-300: #ff8a65; +–md-deep-orange-400: #ff7043; +–md-deep-orange-500: #ff5722; +–md-deep-orange-600: #f4511e; +–md-deep-orange-700: #e64a19; +–md-deep-orange-800: #d84315; +–md-deep-orange-900: #bf360c; +–md-deep-orange-A100: #ff9e80; +–md-deep-orange-A200: #ff6e40; +–md-deep-orange-A400: #ff3d00; +–md-deep-orange-A700: #dd2c00;

+

–md-brown-50: #efebe9; +–md-brown-100: #d7ccc8; +–md-brown-200: #bcaaa4; +–md-brown-300: #a1887f; +–md-brown-400: #8d6e63; +–md-brown-500: #795548; +–md-brown-600: #6d4c41; +–md-brown-700: #5d4037; +–md-brown-800: #4e342e; +–md-brown-900: #3e2723;

+

–md-grey-50: #fafafa; +–md-grey-100: #f5f5f5; +–md-grey-200: #eeeeee; +–md-grey-300: #e0e0e0; +–md-grey-400: #bdbdbd; +–md-grey-500: #9e9e9e; +–md-grey-600: #757575; +–md-grey-700: #616161; +–md-grey-800: #424242; +–md-grey-900: #212121;

+

–md-blue-grey-50: #eceff1; +–md-blue-grey-100: #cfd8dc; +–md-blue-grey-200: #b0bec5; +–md-blue-grey-300: #90a4ae; +–md-blue-grey-400: #78909c; +–md-blue-grey-500: #607d8b; +–md-blue-grey-600: #546e7a; +–md-blue-grey-700: #455a64; +–md-blue-grey-800: #37474f; +–md-blue-grey-900: #263238;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) 2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-Spinner {

position: absolute; +display: flex; +justify-content: center; +align-items: center; +z-index: 10; +left: 0; +top: 0; +width: 100%; +height: 100%; +background: var(–jp-layout-color0); +outline: none;

+
+
+

}

+
+
.jp-SpinnerContent {

font-size: 10px; +margin: 50px auto; +text-indent: -9999em; +width: 3em; +height: 3em; +border-radius: 50%; +background: var(–jp-brand-color3); +background: linear-gradient(

+
+

to right, +#f37626 10%, +rgba(255, 255, 255, 0) 42%

+
+

); +position: relative; +animation: load3 1s infinite linear, fadeIn 1s;

+
+
+

}

+
+
.jp-SpinnerContent:before {

width: 50%; +height: 50%; +background: #f37626; +border-radius: 100% 0 0 0; +position: absolute; +top: 0; +left: 0; +content: ‘’;

+
+
+

}

+
+
.jp-SpinnerContent:after {

background: var(–jp-layout-color0); +width: 75%; +height: 75%; +border-radius: 50%; +content: ‘’; +margin: auto; +position: absolute; +top: 0; +left: 0; +bottom: 0; +right: 0;

+
+
+

}

+
+
@keyframes fadeIn {
+
0% {

opacity: 0;

+
+
+

} +100% {

+
+

opacity: 1;

+
+

}

+
+
+

}

+
+
@keyframes load3 {
+
0% {

transform: rotate(0deg);

+
+
+

} +100% {

+
+

transform: rotate(360deg);

+
+

}

+
+
+

}

+

/—————————————————————————– +| Copyright (c) 2014-2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
button.jp-mod-styled {

font-size: var(–jp-ui-font-size1); +color: var(–jp-ui-font-color0); +border: none; +box-sizing: border-box; +text-align: center; +line-height: 32px; +height: 32px; +padding: 0px 12px; +letter-spacing: 0.8px; +outline: none; +appearance: none; +-webkit-appearance: none; +-moz-appearance: none;

+
+
+

}

+
+
input.jp-mod-styled {

background: var(–jp-input-background); +height: 28px; +box-sizing: border-box; +border: var(–jp-border-width) solid var(–jp-border-color1); +padding-left: 7px; +padding-right: 7px; +font-size: var(–jp-ui-font-size2); +color: var(–jp-ui-font-color0); +outline: none; +appearance: none; +-webkit-appearance: none; +-moz-appearance: none;

+
+
+

}

+
+
input[type=’checkbox’].jp-mod-styled {

appearance: checkbox; +-webkit-appearance: checkbox; +-moz-appearance: checkbox; +height: auto;

+
+
+

}

+
+
input.jp-mod-styled:focus {

border: var(–jp-border-width) solid var(–md-blue-500); +box-shadow: inset 0 0 4px var(–md-blue-300);

+
+
+

}

+
+
.jp-FileDialog-Checkbox {

margin-top: 35px; +display: flex; +flex-direction: row; +align-items: end; +width: 100%;

+
+
+

}

+
+
.jp-FileDialog-Checkbox > label {

flex: 1 1 auto;

+
+
+

}

+
+
.jp-select-wrapper {

display: flex; +position: relative; +flex-direction: column; +padding: 1px; +background-color: var(–jp-layout-color1); +height: 28px; +box-sizing: border-box; +margin-bottom: 12px;

+
+
+

}

+
+
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {

border: var(–jp-border-width) solid var(–jp-input-active-border-color); +box-shadow: var(–jp-input-box-shadow); +background-color: var(–jp-input-active-background);

+
+
+

}

+
+
select.jp-mod-styled:hover {

background-color: var(–jp-layout-color1); +cursor: pointer; +color: var(–jp-ui-font-color0); +background-color: var(–jp-input-hover-background); +box-shadow: inset 0 0px 1px rgba(0, 0, 0, 0.5);

+
+
+

}

+
+
select.jp-mod-styled {

flex: 1 1 auto; +height: 32px; +width: 100%; +font-size: var(–jp-ui-font-size2); +background: var(–jp-input-background); +color: var(–jp-ui-font-color0); +padding: 0 25px 0 8px; +border: var(–jp-border-width) solid var(–jp-input-border-color); +border-radius: 0px; +outline: none; +appearance: none; +-webkit-appearance: none; +-moz-appearance: none;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
:root {
+
–jp-private-toolbar-height: calc(

28px + var(–jp-border-width)

+
+
+

); /* leave 28px for content */

+
+
+

}

+
+
.jp-Toolbar {

color: var(–jp-ui-font-color1); +flex: 0 0 auto; +display: flex; +flex-direction: row; +border-bottom: var(–jp-border-width) solid var(–jp-toolbar-border-color); +box-shadow: var(–jp-toolbar-box-shadow); +background: var(–jp-toolbar-background); +min-height: var(–jp-toolbar-micro-height); +padding: 2px; +z-index: 8; +overflow-x: hidden;

+
+
+

}

+

/* Toolbar items */

+
+
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {

flex-grow: 1; +flex-shrink: 1;

+
+
+

}

+
+
.jp-Toolbar-item.jp-Toolbar-kernelStatus {

display: inline-block; +width: 32px; +background-repeat: no-repeat; +background-position: center; +background-size: 16px;

+
+
+

}

+
+
.jp-Toolbar > .jp-Toolbar-item {

flex: 0 0 auto; +display: flex; +padding-left: 1px; +padding-right: 1px; +font-size: var(–jp-ui-font-size1); +line-height: var(–jp-private-toolbar-height); +height: 100%;

+
+
+

}

+

/* Toolbar buttons */

+

/* This is the div we use to wrap the react component into a Widget */ +div.jp-ToolbarButton {

+
+

color: transparent; +border: none; +box-sizing: border-box; +outline: none; +appearance: none; +-webkit-appearance: none; +-moz-appearance: none; +padding: 0px; +margin: 0px;

+
+

}

+
+
button.jp-ToolbarButtonComponent {

background: var(–jp-layout-color1); +border: none; +box-sizing: border-box; +outline: none; +appearance: none; +-webkit-appearance: none; +-moz-appearance: none; +padding: 0px 6px; +margin: 0px; +height: 24px; +border-radius: var(–jp-border-radius); +display: flex; +align-items: center; +text-align: center; +font-size: 14px; +min-width: unset; +min-height: unset;

+
+
+

}

+
+
button.jp-ToolbarButtonComponent:disabled {

opacity: 0.4;

+
+
+

}

+
+
button.jp-ToolbarButtonComponent span {

padding: 0px; +flex: 0 0 auto;

+
+
+

}

+
+
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {

font-size: var(–jp-ui-font-size1); +line-height: 100%; +padding-left: 2px; +color: var(–jp-ui-font-color1);

+
+
+

}

+
+
#jp-main-dock-panel[data-mode=’single-document’]

.jp-MainAreaWidget +> .jp-Toolbar.jp-Toolbar-micro { +padding: 0; +min-height: 0;

+
+
+

}

+
+
#jp-main-dock-panel[data-mode=’single-document’]

.jp-MainAreaWidget +> .jp-Toolbar { +border: none; +box-shadow: none;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) 2014-2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Copyright (c) 2014-2017, PhosphorJS Contributors +| +| Distributed under the terms of the BSD 3-Clause License. +| +| The full license is in the file LICENSE, distributed with this software. +|—————————————————————————-/

+

/* <DEPRECATED> / +body.p-mod-override-cursor *, / </DEPRECATED> */ +body.lm-mod-override-cursor * {

+
+

cursor: inherit !important;

+
+

}

+

/—————————————————————————– +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-JSONEditor {

display: flex; +flex-direction: column; +width: 100%;

+
+
+

}

+
+
.jp-JSONEditor-host {

flex: 1 1 auto; +border: var(–jp-border-width) solid var(–jp-input-border-color); +border-radius: 0px; +background: var(–jp-layout-color0); +min-height: 50px; +padding: 1px;

+
+
+

}

+
+
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {

border-color: red; +outline-color: red;

+
+
+

}

+
+
.jp-JSONEditor-header {

display: flex; +flex: 1 0 auto; +padding: 0 0 0 12px;

+
+
+

}

+
+
.jp-JSONEditor-header label {

flex: 0 0 auto;

+
+
+

}

+
+
.jp-JSONEditor-commitButton {

height: 16px; +width: 16px; +background-size: 18px; +background-repeat: no-repeat; +background-position: center;

+
+
+

}

+
+
.jp-JSONEditor-host.jp-mod-focused {

background-color: var(–jp-input-active-background); +border: 1px solid var(–jp-input-active-border-color); +box-shadow: var(–jp-input-box-shadow);

+
+
+

}

+
+
.jp-Editor.jp-mod-dropTarget {

border: var(–jp-border-width) solid var(–jp-input-active-border-color); +box-shadow: var(–jp-input-box-shadow);

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Variables +|—————————————————————————-/

+

/*—————————————————————————–

+

/—————————————————————————– +| Styles +|—————————————————————————-/

+
+
.jp-Statusbar-ProgressCircle svg {

display: block; +margin: 0 auto; +width: 16px; +height: 24px; +align-self: normal;

+
+
+

} +.jp-Statusbar-ProgressCircle path {

+
+

fill: var(–jp-inverse-layout-color3);

+
+

}

+
+
.jp-Statusbar-ProgressBar-progress-bar {

height: 10px; +width: 100px; +border: solid 0.25px var(–jp-brand-color2); +border-radius: 3px; +overflow: hidden; +align-self: center;

+
+
+

} +.jp-Statusbar-ProgressBar-progress-bar > div {

+
+

background-color: var(–jp-brand-color2); +background-image: linear-gradient(

+
+

-45deg, +rgba(255, 255, 255, 0.2) 25%, +transparent 25%, +transparent 50%, +rgba(255, 255, 255, 0.2) 50%, +rgba(255, 255, 255, 0.2) 75%, +transparent 75%, +transparent

+
+

); +background-size: 40px 40px; +float: left; +width: 0%; +height: 100%; +font-size: 12px; +line-height: 14px; +color: #ffffff; +text-align: center; +animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;

+
+

}

+
+
.jp-Statusbar-ProgressBar-progress-bar p {

color: var(–jp-ui-font-color1); +font-family: var(–jp-ui-font-family); +font-size: var(–jp-ui-font-size1); +line-height: 10px; +width: 100px;

+
+
+

}

+
+
@keyframes jp-Statusbar-ExecutionTime-progress-bar {
+
0% {

background-position: 0 0;

+
+
+

} +100% {

+
+

background-position: 40px 40px;

+
+

}

+
+
+

}

+

/* BASICS */

+
+
.CodeMirror {

/* Set height, width, borders, and global font properties here */ +font-family: monospace; +height: 300px; +color: black; +direction: ltr;

+
+
+

}

+

/* PADDING */

+
+
.CodeMirror-lines {

padding: 4px 0; /* Vertical padding around content */

+
+
+

} +.CodeMirror pre.CodeMirror-line, +.CodeMirror pre.CodeMirror-line-like {

+
+

padding: 0 4px; /* Horizontal padding of content */

+
+

}

+
+
.CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {

background-color: white; /* The little square between H and V scrollbars */

+
+
+

}

+

/* GUTTER */

+
+
.CodeMirror-gutters {

border-right: 1px solid #ddd; +background-color: #f7f7f7; +white-space: nowrap;

+
+
+

} +.CodeMirror-linenumbers {} +.CodeMirror-linenumber {

+
+

padding: 0 3px 0 5px; +min-width: 20px; +text-align: right; +color: #999; +white-space: nowrap;

+
+

}

+

.CodeMirror-guttermarker { color: black; } +.CodeMirror-guttermarker-subtle { color: #999; }

+

/* CURSOR */

+
+
.CodeMirror-cursor {

border-left: 1px solid black; +border-right: none; +width: 0;

+
+
+

} +/* Shown when moving in bi-directional text */ +.CodeMirror div.CodeMirror-secondarycursor {

+
+

border-left: 1px solid silver;

+
+

} +.cm-fat-cursor .CodeMirror-cursor {

+
+

width: auto; +border: 0 !important; +background: #7e7;

+
+

} +.cm-fat-cursor div.CodeMirror-cursors {

+
+

z-index: 1;

+
+

} +.cm-fat-cursor-mark {

+
+

background-color: rgba(20, 255, 20, 0.5); +-webkit-animation: blink 1.06s steps(1) infinite; +-moz-animation: blink 1.06s steps(1) infinite; +animation: blink 1.06s steps(1) infinite;

+
+

} +.cm-animate-fat-cursor {

+
+

width: auto; +border: 0; +-webkit-animation: blink 1.06s steps(1) infinite; +-moz-animation: blink 1.06s steps(1) infinite; +animation: blink 1.06s steps(1) infinite; +background-color: #7e7;

+
+

} +@-moz-keyframes blink {

+
+

0% {} +50% { background-color: transparent; } +100% {}

+
+

} +@-webkit-keyframes blink {

+
+

0% {} +50% { background-color: transparent; } +100% {}

+
+

} +@keyframes blink {

+
+

0% {} +50% { background-color: transparent; } +100% {}

+
+

}

+

/* Can style cursor different in overwrite (non-insert) mode */ +.CodeMirror-overwrite .CodeMirror-cursor {}

+

.cm-tab { display: inline-block; text-decoration: inherit; }

+
+
.CodeMirror-rulers {

position: absolute; +left: 0; right: 0; top: -50px; bottom: 0; +overflow: hidden;

+
+
+

} +.CodeMirror-ruler {

+
+

border-left: 1px solid #ccc; +top: 0; bottom: 0; +position: absolute;

+
+

}

+

/* DEFAULT THEME */

+

.cm-s-default .cm-header {color: blue;} +.cm-s-default .cm-quote {color: #090;} +.cm-negative {color: #d44;} +.cm-positive {color: #292;} +.cm-header, .cm-strong {font-weight: bold;} +.cm-em {font-style: italic;} +.cm-link {text-decoration: underline;} +.cm-strikethrough {text-decoration: line-through;}

+

.cm-s-default .cm-keyword {color: #708;} +.cm-s-default .cm-atom {color: #219;} +.cm-s-default .cm-number {color: #164;} +.cm-s-default .cm-def {color: #00f;} +.cm-s-default .cm-variable, +.cm-s-default .cm-punctuation, +.cm-s-default .cm-property, +.cm-s-default .cm-operator {} +.cm-s-default .cm-variable-2 {color: #05a;} +.cm-s-default .cm-variable-3, .cm-s-default .cm-type {color: #085;} +.cm-s-default .cm-comment {color: #a50;} +.cm-s-default .cm-string {color: #a11;} +.cm-s-default .cm-string-2 {color: #f50;} +.cm-s-default .cm-meta {color: #555;} +.cm-s-default .cm-qualifier {color: #555;} +.cm-s-default .cm-builtin {color: #30a;} +.cm-s-default .cm-bracket {color: #997;} +.cm-s-default .cm-tag {color: #170;} +.cm-s-default .cm-attribute {color: #00c;} +.cm-s-default .cm-hr {color: #999;} +.cm-s-default .cm-link {color: #00c;}

+

.cm-s-default .cm-error {color: #f00;} +.cm-invalidchar {color: #f00;}

+

.CodeMirror-composing { border-bottom: 2px solid; }

+

/* Default styles for common addons */

+

div.CodeMirror span.CodeMirror-matchingbracket {color: #0b0;} +div.CodeMirror span.CodeMirror-nonmatchingbracket {color: #a22;} +.CodeMirror-matchingtag { background: rgba(255, 150, 0, .3); } +.CodeMirror-activeline-background {background: #e8f2ff;}

+

/* STOP */

+
+
/* The rest of this file contains styles related to the mechanics of

the editor. You probably shouldn’t touch them. */

+
+
.CodeMirror {

position: relative; +overflow: hidden; +background: white;

+
+
+

}

+
+
.CodeMirror-scroll {

overflow: scroll !important; /* Things will break if this is overridden / +/ 50px is the magic margin used to hide the element’s real scrollbars / +/ See overflow: hidden in .CodeMirror / +margin-bottom: -50px; margin-right: -50px; +padding-bottom: 50px; +height: 100%; +outline: none; / Prevent dragging from highlighting the element */ +position: relative;

+
+
+

} +.CodeMirror-sizer {

+
+

position: relative; +border-right: 50px solid transparent;

+
+

}

+
+
/* The fake, visible scrollbars. Used to force redraw during scrolling

before actual scrolling happens, thus preventing shaking and +flickering artifacts. */

+
+
.CodeMirror-vscrollbar, .CodeMirror-hscrollbar, .CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {

position: absolute; +z-index: 6; +display: none; +outline: none;

+
+
+

} +.CodeMirror-vscrollbar {

+
+

right: 0; top: 0; +overflow-x: hidden; +overflow-y: scroll;

+
+

} +.CodeMirror-hscrollbar {

+
+

bottom: 0; left: 0; +overflow-y: hidden; +overflow-x: scroll;

+
+

} +.CodeMirror-scrollbar-filler {

+
+

right: 0; bottom: 0;

+
+

} +.CodeMirror-gutter-filler {

+
+

left: 0; bottom: 0;

+
+

}

+
+
.CodeMirror-gutters {

position: absolute; left: 0; top: 0; +min-height: 100%; +z-index: 3;

+
+
+

} +.CodeMirror-gutter {

+
+

white-space: normal; +height: 100%; +display: inline-block; +vertical-align: top; +margin-bottom: -50px;

+
+

} +.CodeMirror-gutter-wrapper {

+
+

position: absolute; +z-index: 4; +background: none !important; +border: none !important;

+
+

} +.CodeMirror-gutter-background {

+
+

position: absolute; +top: 0; bottom: 0; +z-index: 4;

+
+

} +.CodeMirror-gutter-elt {

+
+

position: absolute; +cursor: default; +z-index: 4;

+
+

} +.CodeMirror-gutter-wrapper ::selection { background-color: transparent } +.CodeMirror-gutter-wrapper ::-moz-selection { background-color: transparent }

+
+
.CodeMirror-lines {

cursor: text; +min-height: 1px; /* prevents collapsing before first draw */

+
+
+

} +.CodeMirror pre.CodeMirror-line, +.CodeMirror pre.CodeMirror-line-like {

+
+

/* Reset some styles that the rest of the page might have set */ +-moz-border-radius: 0; -webkit-border-radius: 0; border-radius: 0; +border-width: 0; +background: transparent; +font-family: inherit; +font-size: inherit; +margin: 0; +white-space: pre; +word-wrap: normal; +line-height: inherit; +color: inherit; +z-index: 2; +position: relative; +overflow: visible; +-webkit-tap-highlight-color: transparent; +-webkit-font-variant-ligatures: contextual; +font-variant-ligatures: contextual;

+
+

} +.CodeMirror-wrap pre.CodeMirror-line, +.CodeMirror-wrap pre.CodeMirror-line-like {

+
+

word-wrap: break-word; +white-space: pre-wrap; +word-break: normal;

+
+

}

+
+
.CodeMirror-linebackground {

position: absolute; +left: 0; right: 0; top: 0; bottom: 0; +z-index: 0;

+
+
+

}

+
+
.CodeMirror-linewidget {

position: relative; +z-index: 2; +padding: 0.1px; /* Force widget margins to stay inside of the container */

+
+
+

}

+

.CodeMirror-widget {}

+

.CodeMirror-rtl pre { direction: rtl; }

+
+
.CodeMirror-code {

outline: none;

+
+
+

}

+

/* Force content-box sizing for the elements where we expect it */ +.CodeMirror-scroll, +.CodeMirror-sizer, +.CodeMirror-gutter, +.CodeMirror-gutters, +.CodeMirror-linenumber {

+
+

-moz-box-sizing: content-box; +box-sizing: content-box;

+
+

}

+
+
.CodeMirror-measure {

position: absolute; +width: 100%; +height: 0; +overflow: hidden; +visibility: hidden;

+
+
+

}

+
+
.CodeMirror-cursor {

position: absolute; +pointer-events: none;

+
+
+

} +.CodeMirror-measure pre { position: static; }

+
+
div.CodeMirror-cursors {

visibility: hidden; +position: relative; +z-index: 3;

+
+
+

} +div.CodeMirror-dragcursors {

+
+

visibility: visible;

+
+

}

+
+
.CodeMirror-focused div.CodeMirror-cursors {

visibility: visible;

+
+
+

}

+

.CodeMirror-selected { background: #d9d9d9; } +.CodeMirror-focused .CodeMirror-selected { background: #d7d4f0; } +.CodeMirror-crosshair { cursor: crosshair; } +.CodeMirror-line::selection, .CodeMirror-line > span::selection, .CodeMirror-line > span > span::selection { background: #d7d4f0; } +.CodeMirror-line::-moz-selection, .CodeMirror-line > span::-moz-selection, .CodeMirror-line > span > span::-moz-selection { background: #d7d4f0; }

+
+
.cm-searching {

background-color: #ffa; +background-color: rgba(255, 255, 0, .4);

+
+
+

}

+

/* Used to force a border model for a node */ +.cm-force-border { padding-right: .1px; }

+
+
@media print {

/* Hide the cursor when printing */ +.CodeMirror div.CodeMirror-cursors {

+
+

visibility: hidden;

+
+

}

+
+
+

}

+

/* See issue #2901 */ +.cm-tab-wrap-hack:after { content: ‘’; }

+

/* Help users use markselection to safely style text background */ +span.CodeMirror-selectedtext { background: none; }

+
+
.CodeMirror-dialog {

position: absolute; +left: 0; right: 0; +background: inherit; +z-index: 15; +padding: .1em .8em; +overflow: hidden; +color: inherit;

+
+
+

}

+
+
.CodeMirror-dialog-top {

border-bottom: 1px solid #eee; +top: 0;

+
+
+

}

+
+
.CodeMirror-dialog-bottom {

border-top: 1px solid #eee; +bottom: 0;

+
+
+

}

+
+
.CodeMirror-dialog input {

border: none; +outline: none; +background: transparent; +width: 20em; +color: inherit; +font-family: monospace;

+
+
+

}

+
+
.CodeMirror-dialog button {

font-size: 70%;

+
+
+

}

+
+
.CodeMirror-foldmarker {

color: blue; +text-shadow: #b9f 1px 1px 2px, #b9f -1px -1px 2px, #b9f 1px -1px 2px, #b9f -1px 1px 2px; +font-family: arial; +line-height: .3; +cursor: pointer;

+
+
+

} +.CodeMirror-foldgutter {

+
+

width: .7em;

+
+

} +.CodeMirror-foldgutter-open, +.CodeMirror-foldgutter-folded {

+
+

cursor: pointer;

+
+

} +.CodeMirror-foldgutter-open:after {

+
+

content: “25BE”;

+
+

} +.CodeMirror-foldgutter-folded:after {

+
+

content: “25B8”;

+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.CodeMirror {

line-height: var(–jp-code-line-height); +font-size: var(–jp-code-font-size); +font-family: var(–jp-code-font-family); +border: 0; +border-radius: 0; +height: auto; +/* Changed to auto to autogrow */

+
+
+

}

+
+
.CodeMirror pre {

padding: 0 var(–jp-code-padding);

+
+
+

}

+
+
.CodeMirror.cm-fat-cursor .cm-overlay.cm-searching {

opacity: 0.5;

+
+
+

}

+
+
.jp-CodeMirrorEditor[data-type=’inline’] .CodeMirror-dialog {

background-color: var(–jp-layout-color0); +color: var(–jp-content-font-color1);

+
+
+

}

+

/* This causes https://github.com/jupyter/jupyterlab/issues/522 / +/ May not cause it not because we changed it! */ +.CodeMirror-lines {

+
+

padding: var(–jp-code-padding) 0;

+
+

}

+
+
.CodeMirror-linenumber {

padding: 0 8px;

+
+
+

}

+
+
.jp-CodeMirrorEditor {

cursor: text;

+
+
+

}

+
+
.jp-CodeMirrorEditor[data-type=’inline’] .CodeMirror-cursor {

border-left: var(–jp-code-cursor-width0) solid var(–jp-editor-cursor-color);

+
+
+

}

+

/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */ +@media screen and (min-width: 2138px) and (max-width: 4319px) {

+
+
+
.jp-CodeMirrorEditor[data-type=’inline’] .CodeMirror-cursor {
+
border-left: var(–jp-code-cursor-width1) solid

var(–jp-editor-cursor-color);

+
+
+
+
+

}

+
+

}

+

/* When zoomed out less than 33% */ +@media screen and (min-width: 4320px) {

+
+
+
.jp-CodeMirrorEditor[data-type=’inline’] .CodeMirror-cursor {
+
border-left: var(–jp-code-cursor-width2) solid

var(–jp-editor-cursor-color);

+
+
+
+
+

}

+
+

}

+
+
.CodeMirror.jp-mod-readOnly .CodeMirror-cursor {

display: none;

+
+
+

}

+
+
.CodeMirror-gutters {

border-right: 1px solid var(–jp-border-color2); +background-color: var(–jp-layout-color0);

+
+
+

}

+
+
.jp-CollaboratorCursor {

border-left: 5px solid transparent; +border-right: 5px solid transparent; +border-top: none; +border-bottom: 3px solid; +background-clip: content-box; +margin-left: -5px; +margin-right: -5px;

+
+
+

}

+
+
.CodeMirror-selectedtext.cm-searching {

background-color: var(–jp-search-selected-match-background-color) !important; +color: var(–jp-search-selected-match-color) !important;

+
+
+

}

+
+
.cm-searching {
+
background-color: var(

–jp-search-unselected-match-background-color

+
+
+

) !important; +color: var(–jp-search-unselected-match-color) !important;

+
+
+

}

+
+
.cm-trailingspace {

background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=); +background-position: center left; +background-repeat: repeat-x;

+
+
+

}

+
+
.CodeMirror-focused .CodeMirror-selected {

background-color: var(–jp-editor-selected-focused-background);

+
+
+

}

+
+
.CodeMirror-selected {

background-color: var(–jp-editor-selected-background);

+
+
+

}

+
+
.jp-CollaboratorCursor-hover {

position: absolute; +z-index: 1; +transform: translateX(-50%); +color: white; +border-radius: 3px; +padding-left: 4px; +padding-right: 4px; +padding-top: 1px; +padding-bottom: 1px; +text-align: center; +font-size: var(–jp-ui-font-size1); +white-space: nowrap;

+
+
+

}

+
+
.jp-CodeMirror-ruler {

border-left: 1px dashed var(–jp-border-color2);

+
+
+

}

+
+
/**
    +

  • Here is our jupyter theme for CodeMirror syntax highlighting

    • +

  • This is used in our marked.js syntax highlighting and CodeMirror itself

    • +

  • The string “jupyter” is set in ../codemirror/widget.DEFAULT_CODEMIRROR_THEME

    • +

  • This came from the classic notebook, which came form highlight.js/GitHub

    • +
+

*/

+
+
/**
    +

  • CodeMirror themes are handling the background/color in this way. This works

    • +

  • fine for CodeMirror editors outside the notebook, but the notebook styles

    • +

  • these things differently.

    • +
+

*/

+
+
.CodeMirror.cm-s-jupyter {

background: var(–jp-layout-color0); +color: var(–jp-content-font-color1);

+
+
+

}

+

/* In the notebook, we want this styling to be handled by its container */ +.jp-CodeConsole .CodeMirror.cm-s-jupyter, +.jp-Notebook .CodeMirror.cm-s-jupyter {

+
+

background: transparent;

+
+

}

+
+
.cm-s-jupyter .CodeMirror-cursor {

border-left: var(–jp-code-cursor-width0) solid var(–jp-editor-cursor-color);

+
+
+

} +.cm-s-jupyter span.cm-keyword {

+
+

color: var(–jp-mirror-editor-keyword-color); +font-weight: bold;

+
+

} +.cm-s-jupyter span.cm-atom {

+
+

color: var(–jp-mirror-editor-atom-color);

+
+

} +.cm-s-jupyter span.cm-number {

+
+

color: var(–jp-mirror-editor-number-color);

+
+

} +.cm-s-jupyter span.cm-def {

+
+

color: var(–jp-mirror-editor-def-color);

+
+

} +.cm-s-jupyter span.cm-variable {

+
+

color: var(–jp-mirror-editor-variable-color);

+
+

} +.cm-s-jupyter span.cm-variable-2 {

+
+

color: var(–jp-mirror-editor-variable-2-color);

+
+

} +.cm-s-jupyter span.cm-variable-3 {

+
+

color: var(–jp-mirror-editor-variable-3-color);

+
+

} +.cm-s-jupyter span.cm-punctuation {

+
+

color: var(–jp-mirror-editor-punctuation-color);

+
+

} +.cm-s-jupyter span.cm-property {

+
+

color: var(–jp-mirror-editor-property-color);

+
+

} +.cm-s-jupyter span.cm-operator {

+
+

color: var(–jp-mirror-editor-operator-color); +font-weight: bold;

+
+

} +.cm-s-jupyter span.cm-comment {

+
+

color: var(–jp-mirror-editor-comment-color); +font-style: italic;

+
+

} +.cm-s-jupyter span.cm-string {

+
+

color: var(–jp-mirror-editor-string-color);

+
+

} +.cm-s-jupyter span.cm-string-2 {

+
+

color: var(–jp-mirror-editor-string-2-color);

+
+

} +.cm-s-jupyter span.cm-meta {

+
+

color: var(–jp-mirror-editor-meta-color);

+
+

} +.cm-s-jupyter span.cm-qualifier {

+
+

color: var(–jp-mirror-editor-qualifier-color);

+
+

} +.cm-s-jupyter span.cm-builtin {

+
+

color: var(–jp-mirror-editor-builtin-color);

+
+

} +.cm-s-jupyter span.cm-bracket {

+
+

color: var(–jp-mirror-editor-bracket-color);

+
+

} +.cm-s-jupyter span.cm-tag {

+
+

color: var(–jp-mirror-editor-tag-color);

+
+

} +.cm-s-jupyter span.cm-attribute {

+
+

color: var(–jp-mirror-editor-attribute-color);

+
+

} +.cm-s-jupyter span.cm-header {

+
+

color: var(–jp-mirror-editor-header-color);

+
+

} +.cm-s-jupyter span.cm-quote {

+
+

color: var(–jp-mirror-editor-quote-color);

+
+

} +.cm-s-jupyter span.cm-link {

+
+

color: var(–jp-mirror-editor-link-color);

+
+

} +.cm-s-jupyter span.cm-error {

+
+

color: var(–jp-mirror-editor-error-color);

+
+

} +.cm-s-jupyter span.cm-hr {

+
+

color: #999;

+
+

}

+
+
.cm-s-jupyter span.cm-tab {

background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=); +background-position: right; +background-repeat: no-repeat;

+
+
+

}

+

.cm-s-jupyter .CodeMirror-activeline-background, +.cm-s-jupyter .CodeMirror-gutter {

+
+

background-color: var(–jp-layout-color2);

+
+

}

+

/* Styles for shared cursors (remote cursor locations and selected ranges) */ +.jp-CodeMirrorEditor .remote-caret {

+
+

position: relative; +border-left: 2px solid black; +margin-left: -1px; +margin-right: -1px; +box-sizing: border-box;

+
+

}

+
+
.jp-CodeMirrorEditor .remote-caret > div {

white-space: nowrap; +position: absolute; +top: -1.15em; +padding-bottom: 0.05em; +left: -2px; +font-size: 0.95em; +background-color: rgb(250, 129, 0); +font-family: var(–jp-ui-font-family); +font-weight: bold; +line-height: normal; +user-select: none; +color: white; +padding-left: 2px; +padding-right: 2px; +z-index: 3; +transition: opacity 0.3s ease-in-out;

+
+
+

}

+
+
.jp-CodeMirrorEditor .remote-caret.hide-name > div {

transition-delay: 0.7s; +opacity: 0;

+
+
+

}

+
+
/* Use div[style] as more specific selector on 3.4.x to reduce the impact of
    +

  • Chromium style invalidation strategy on performance when many divs are present.

    • +
+

*/

+
+
.jp-CodeMirrorEditor .remote-caret:hover > div[style] {

opacity: 1; +transition-delay: 0s;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| RenderedText +|—————————————————————————-/

+
+
:root {

/* This is the padding value to fill the gaps between lines containing spans with background color. */ +–jp-private-code-span-padding: calc(

+
+

(var(–jp-code-line-height) - 1) * var(–jp-code-font-size) / 2

+
+

);

+
+
+

}

+
+
.jp-RenderedText {

text-align: left; +padding-left: var(–jp-code-padding); +line-height: var(–jp-code-line-height); +font-family: var(–jp-code-font-family);

+
+
+

}

+

.jp-RenderedText pre, +.jp-RenderedJavaScript pre, +.jp-RenderedHTMLCommon pre {

+
+

color: var(–jp-content-font-color1); +font-size: var(–jp-code-font-size); +border: none; +margin: 0px; +padding: 0px;

+
+

}

+
+
.jp-RenderedText pre a:link {

text-decoration: none; +color: var(–jp-content-link-color);

+
+
+

} +.jp-RenderedText pre a:hover {

+
+

text-decoration: underline; +color: var(–jp-content-link-color);

+
+

} +.jp-RenderedText pre a:visited {

+
+

text-decoration: none; +color: var(–jp-content-link-color);

+
+

}

+

/* console foregrounds and backgrounds */ +.jp-RenderedText pre .ansi-black-fg {

+
+

color: #3e424d;

+
+

} +.jp-RenderedText pre .ansi-red-fg {

+
+

color: #e75c58;

+
+

} +.jp-RenderedText pre .ansi-green-fg {

+
+

color: #00a250;

+
+

} +.jp-RenderedText pre .ansi-yellow-fg {

+
+

color: #ddb62b;

+
+

} +.jp-RenderedText pre .ansi-blue-fg {

+
+

color: #208ffb;

+
+

} +.jp-RenderedText pre .ansi-magenta-fg {

+
+

color: #d160c4;

+
+

} +.jp-RenderedText pre .ansi-cyan-fg {

+
+

color: #60c6c8;

+
+

} +.jp-RenderedText pre .ansi-white-fg {

+
+

color: #c5c1b4;

+
+

}

+
+
.jp-RenderedText pre .ansi-black-bg {

background-color: #3e424d; +padding: var(–jp-private-code-span-padding) 0;

+
+
+

} +.jp-RenderedText pre .ansi-red-bg {

+
+

background-color: #e75c58; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-green-bg {

+
+

background-color: #00a250; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-yellow-bg {

+
+

background-color: #ddb62b; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-blue-bg {

+
+

background-color: #208ffb; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-magenta-bg {

+
+

background-color: #d160c4; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-cyan-bg {

+
+

background-color: #60c6c8; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-white-bg {

+
+

background-color: #c5c1b4; +padding: var(–jp-private-code-span-padding) 0;

+
+

}

+
+
.jp-RenderedText pre .ansi-black-intense-fg {

color: #282c36;

+
+
+

} +.jp-RenderedText pre .ansi-red-intense-fg {

+
+

color: #b22b31;

+
+

} +.jp-RenderedText pre .ansi-green-intense-fg {

+
+

color: #007427;

+
+

} +.jp-RenderedText pre .ansi-yellow-intense-fg {

+
+

color: #b27d12;

+
+

} +.jp-RenderedText pre .ansi-blue-intense-fg {

+
+

color: #0065ca;

+
+

} +.jp-RenderedText pre .ansi-magenta-intense-fg {

+
+

color: #a03196;

+
+

} +.jp-RenderedText pre .ansi-cyan-intense-fg {

+
+

color: #258f8f;

+
+

} +.jp-RenderedText pre .ansi-white-intense-fg {

+
+

color: #a1a6b2;

+
+

}

+
+
.jp-RenderedText pre .ansi-black-intense-bg {

background-color: #282c36; +padding: var(–jp-private-code-span-padding) 0;

+
+
+

} +.jp-RenderedText pre .ansi-red-intense-bg {

+
+

background-color: #b22b31; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-green-intense-bg {

+
+

background-color: #007427; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-yellow-intense-bg {

+
+

background-color: #b27d12; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-blue-intense-bg {

+
+

background-color: #0065ca; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-magenta-intense-bg {

+
+

background-color: #a03196; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-cyan-intense-bg {

+
+

background-color: #258f8f; +padding: var(–jp-private-code-span-padding) 0;

+
+

} +.jp-RenderedText pre .ansi-white-intense-bg {

+
+

background-color: #a1a6b2; +padding: var(–jp-private-code-span-padding) 0;

+
+

}

+
+
.jp-RenderedText pre .ansi-default-inverse-fg {

color: var(–jp-ui-inverse-font-color0);

+
+
+

} +.jp-RenderedText pre .ansi-default-inverse-bg {

+
+

background-color: var(–jp-inverse-layout-color0); +padding: var(–jp-private-code-span-padding) 0;

+
+

}

+
+
.jp-RenderedText pre .ansi-bold {

font-weight: bold;

+
+
+

} +.jp-RenderedText pre .ansi-underline {

+
+

text-decoration: underline;

+
+

}

+
+
.jp-RenderedText[data-mime-type=’application/vnd.jupyter.stderr’] {

background: var(–jp-rendermime-error-background); +padding-top: var(–jp-code-padding);

+
+
+

}

+

/—————————————————————————– +| RenderedLatex +|—————————————————————————-/

+
+
.jp-RenderedLatex {

color: var(–jp-content-font-color1); +font-size: var(–jp-content-font-size1); +line-height: var(–jp-content-line-height);

+
+
+

}

+

/* Left-justify outputs.*/ +.jp-OutputArea-output.jp-RenderedLatex {

+
+

padding: var(–jp-code-padding); +text-align: left;

+
+

}

+

/—————————————————————————– +| RenderedHTML +|—————————————————————————-/

+
+
.jp-RenderedHTMLCommon {

color: var(–jp-content-font-color1); +font-family: var(–jp-content-font-family); +font-size: var(–jp-content-font-size1); +line-height: var(–jp-content-line-height); +/* Give a bit more R padding on Markdown text to keep line lengths reasonable */ +padding-right: 20px;

+
+
+

}

+
+
.jp-RenderedHTMLCommon em {

font-style: italic;

+
+
+

}

+
+
.jp-RenderedHTMLCommon strong {

font-weight: bold;

+
+
+

}

+
+
.jp-RenderedHTMLCommon u {

text-decoration: underline;

+
+
+

}

+
+
.jp-RenderedHTMLCommon a:link {

text-decoration: none; +color: var(–jp-content-link-color);

+
+
+

}

+
+
.jp-RenderedHTMLCommon a:hover {

text-decoration: underline; +color: var(–jp-content-link-color);

+
+
+

}

+
+
.jp-RenderedHTMLCommon a:visited {

text-decoration: none; +color: var(–jp-content-link-color);

+
+
+

}

+

/* Headings */

+

.jp-RenderedHTMLCommon h1, +.jp-RenderedHTMLCommon h2, +.jp-RenderedHTMLCommon h3, +.jp-RenderedHTMLCommon h4, +.jp-RenderedHTMLCommon h5, +.jp-RenderedHTMLCommon h6 {

+
+

line-height: var(–jp-content-heading-line-height); +font-weight: var(–jp-content-heading-font-weight); +font-style: normal; +margin: var(–jp-content-heading-margin-top) 0

+
+

var(–jp-content-heading-margin-bottom) 0;

+
+
+

}

+

.jp-RenderedHTMLCommon h1:first-child, +.jp-RenderedHTMLCommon h2:first-child, +.jp-RenderedHTMLCommon h3:first-child, +.jp-RenderedHTMLCommon h4:first-child, +.jp-RenderedHTMLCommon h5:first-child, +.jp-RenderedHTMLCommon h6:first-child {

+
+

margin-top: calc(0.5 * var(–jp-content-heading-margin-top));

+
+

}

+

.jp-RenderedHTMLCommon h1:last-child, +.jp-RenderedHTMLCommon h2:last-child, +.jp-RenderedHTMLCommon h3:last-child, +.jp-RenderedHTMLCommon h4:last-child, +.jp-RenderedHTMLCommon h5:last-child, +.jp-RenderedHTMLCommon h6:last-child {

+
+

margin-bottom: calc(0.5 * var(–jp-content-heading-margin-bottom));

+
+

}

+
+
.jp-RenderedHTMLCommon h1 {

font-size: var(–jp-content-font-size5);

+
+
+

}

+
+
.jp-RenderedHTMLCommon h2 {

font-size: var(–jp-content-font-size4);

+
+
+

}

+
+
.jp-RenderedHTMLCommon h3 {

font-size: var(–jp-content-font-size3);

+
+
+

}

+
+
.jp-RenderedHTMLCommon h4 {

font-size: var(–jp-content-font-size2);

+
+
+

}

+
+
.jp-RenderedHTMLCommon h5 {

font-size: var(–jp-content-font-size1);

+
+
+

}

+
+
.jp-RenderedHTMLCommon h6 {

font-size: var(–jp-content-font-size0);

+
+
+

}

+

/* Lists */

+

.jp-RenderedHTMLCommon ul:not(.list-inline), +.jp-RenderedHTMLCommon ol:not(.list-inline) {

+
+

padding-left: 2em;

+
+

}

+
+
.jp-RenderedHTMLCommon ul {

list-style: disc;

+
+
+

}

+
+
.jp-RenderedHTMLCommon ul ul {

list-style: square;

+
+
+

}

+
+
.jp-RenderedHTMLCommon ul ul ul {

list-style: circle;

+
+
+

}

+
+
.jp-RenderedHTMLCommon ol {

list-style: decimal;

+
+
+

}

+
+
.jp-RenderedHTMLCommon ol ol {

list-style: upper-alpha;

+
+
+

}

+
+
.jp-RenderedHTMLCommon ol ol ol {

list-style: lower-alpha;

+
+
+

}

+
+
.jp-RenderedHTMLCommon ol ol ol ol {

list-style: lower-roman;

+
+
+

}

+
+
.jp-RenderedHTMLCommon ol ol ol ol ol {

list-style: decimal;

+
+
+

}

+

.jp-RenderedHTMLCommon ol, +.jp-RenderedHTMLCommon ul {

+
+

margin-bottom: 1em;

+
+

}

+

.jp-RenderedHTMLCommon ul ul, +.jp-RenderedHTMLCommon ul ol, +.jp-RenderedHTMLCommon ol ul, +.jp-RenderedHTMLCommon ol ol {

+
+

margin-bottom: 0em;

+
+

}

+
+
.jp-RenderedHTMLCommon hr {

color: var(–jp-border-color2); +background-color: var(–jp-border-color1); +margin-top: 1em; +margin-bottom: 1em;

+
+
+

}

+
+
.jp-RenderedHTMLCommon > pre {

margin: 1.5em 2em;

+
+
+

}

+

.jp-RenderedHTMLCommon pre, +.jp-RenderedHTMLCommon code {

+
+

border: 0; +background-color: var(–jp-layout-color0); +color: var(–jp-content-font-color1); +font-family: var(–jp-code-font-family); +font-size: inherit; +line-height: var(–jp-code-line-height); +padding: 0; +white-space: pre-wrap;

+
+

}

+
+
.jp-RenderedHTMLCommon :not(pre) > code {

background-color: var(–jp-layout-color2); +padding: 1px 5px;

+
+
+

}

+

/* Tables */

+
+
.jp-RenderedHTMLCommon table {

border-collapse: collapse; +border-spacing: 0; +border: none; +color: var(–jp-ui-font-color1); +font-size: var(–jp-ui-font-size1); +table-layout: fixed; +margin-left: auto; +margin-right: auto;

+
+
+

}

+
+
.jp-RenderedHTMLCommon thead {

border-bottom: var(–jp-border-width) solid var(–jp-border-color1); +vertical-align: bottom;

+
+
+

}

+

.jp-RenderedHTMLCommon td, +.jp-RenderedHTMLCommon th, +.jp-RenderedHTMLCommon tr {

+
+

vertical-align: middle; +padding: 0.5em 0.5em; +line-height: normal; +white-space: normal; +max-width: none; +border: none;

+
+

}

+

.jp-RenderedMarkdown.jp-RenderedHTMLCommon td, +.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {

+
+

max-width: none;

+
+

}

+

:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td, +:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th, +:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {

+
+

text-align: right;

+
+

}

+
+
.jp-RenderedHTMLCommon th {

font-weight: bold;

+
+
+

}

+
+
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {

background: var(–jp-layout-color0);

+
+
+

}

+
+
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {

background: var(–jp-rendermime-table-row-background);

+
+
+

}

+
+
.jp-RenderedHTMLCommon tbody tr:hover {

background: var(–jp-rendermime-table-row-hover-background);

+
+
+

}

+
+
.jp-RenderedHTMLCommon table {

margin-bottom: 1em;

+
+
+

}

+
+
.jp-RenderedHTMLCommon p {

text-align: left; +margin: 0px;

+
+
+

}

+
+
.jp-RenderedHTMLCommon p {

margin-bottom: 1em;

+
+
+

}

+
+
.jp-RenderedHTMLCommon img {

-moz-force-broken-image-icon: 1;

+
+
+

}

+

/* Restrict to direct children as other images could be nested in other content. */ +.jp-RenderedHTMLCommon > img {

+
+

display: block; +margin-left: 0; +margin-right: 0; +margin-bottom: 1em;

+
+

}

+

/* Change color behind transparent images if they need it… */ +[data-jp-theme-light=’false’] .jp-RenderedImage img.jp-needs-light-background {

+
+

background-color: var(–jp-inverse-layout-color1);

+
+

} +[data-jp-theme-light=’true’] .jp-RenderedImage img.jp-needs-dark-background {

+
+

background-color: var(–jp-inverse-layout-color1);

+
+

} +/* …or leave it untouched if they don’t */ +[data-jp-theme-light=’false’] .jp-RenderedImage img.jp-needs-dark-background { +} +[data-jp-theme-light=’true’] .jp-RenderedImage img.jp-needs-light-background { +}

+

.jp-RenderedHTMLCommon img, +.jp-RenderedImage img, +.jp-RenderedHTMLCommon svg, +.jp-RenderedSVG svg {

+
+

max-width: 100%; +height: auto;

+
+

}

+

.jp-RenderedHTMLCommon img.jp-mod-unconfined, +.jp-RenderedImage img.jp-mod-unconfined, +.jp-RenderedHTMLCommon svg.jp-mod-unconfined, +.jp-RenderedSVG svg.jp-mod-unconfined {

+
+

max-width: none;

+
+

}

+
+
.jp-RenderedHTMLCommon .alert {

padding: var(–jp-notebook-padding); +border: var(–jp-border-width) solid transparent; +border-radius: var(–jp-border-radius); +margin-bottom: 1em;

+
+
+

}

+
+
.jp-RenderedHTMLCommon .alert-info {

color: var(–jp-info-color0); +background-color: var(–jp-info-color3); +border-color: var(–jp-info-color2);

+
+
+

} +.jp-RenderedHTMLCommon .alert-info hr {

+
+

border-color: var(–jp-info-color3);

+
+

} +.jp-RenderedHTMLCommon .alert-info > p:last-child, +.jp-RenderedHTMLCommon .alert-info > ul:last-child {

+
+

margin-bottom: 0;

+
+

}

+
+
.jp-RenderedHTMLCommon .alert-warning {

color: var(–jp-warn-color0); +background-color: var(–jp-warn-color3); +border-color: var(–jp-warn-color2);

+
+
+

} +.jp-RenderedHTMLCommon .alert-warning hr {

+
+

border-color: var(–jp-warn-color3);

+
+

} +.jp-RenderedHTMLCommon .alert-warning > p:last-child, +.jp-RenderedHTMLCommon .alert-warning > ul:last-child {

+
+

margin-bottom: 0;

+
+

}

+
+
.jp-RenderedHTMLCommon .alert-success {

color: var(–jp-success-color0); +background-color: var(–jp-success-color3); +border-color: var(–jp-success-color2);

+
+
+

} +.jp-RenderedHTMLCommon .alert-success hr {

+
+

border-color: var(–jp-success-color3);

+
+

} +.jp-RenderedHTMLCommon .alert-success > p:last-child, +.jp-RenderedHTMLCommon .alert-success > ul:last-child {

+
+

margin-bottom: 0;

+
+

}

+
+
.jp-RenderedHTMLCommon .alert-danger {

color: var(–jp-error-color0); +background-color: var(–jp-error-color3); +border-color: var(–jp-error-color2);

+
+
+

} +.jp-RenderedHTMLCommon .alert-danger hr {

+
+

border-color: var(–jp-error-color3);

+
+

} +.jp-RenderedHTMLCommon .alert-danger > p:last-child, +.jp-RenderedHTMLCommon .alert-danger > ul:last-child {

+
+

margin-bottom: 0;

+
+

}

+
+
.jp-RenderedHTMLCommon blockquote {

margin: 1em 2em; +padding: 0 1em; +border-left: 5px solid var(–jp-border-color2);

+
+
+

}

+
+
a.jp-InternalAnchorLink {

visibility: hidden; +margin-left: 8px; +color: var(–md-blue-800);

+
+
+

}

+

h1:hover .jp-InternalAnchorLink, +h2:hover .jp-InternalAnchorLink, +h3:hover .jp-InternalAnchorLink, +h4:hover .jp-InternalAnchorLink, +h5:hover .jp-InternalAnchorLink, +h6:hover .jp-InternalAnchorLink {

+
+

visibility: visible;

+
+

}

+
+
.jp-RenderedHTMLCommon kbd {

background-color: var(–jp-rendermime-table-row-background); +border: 1px solid var(–jp-border-color0); +border-bottom-color: var(–jp-border-color2); +border-radius: 3px; +box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); +display: inline-block; +font-size: var(–jp-ui-font-size0); +line-height: 1em; +padding: 0.2em 0.5em;

+
+
+

}

+
+
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
    +

  • At the bottom of cells this is a bit too much as there is also spacing

    • +

  • between cells. Going all the way to 0 gets too tight between markdown and

    • +

  • code cells.

    • +
+

*/

+
+
.jp-RenderedHTMLCommon > *:last-child {

margin-bottom: 0.5em;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-MimeDocument {

outline: none;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Variables +|—————————————————————————-/

+
+
:root {

–jp-private-filebrowser-button-height: 28px; +–jp-private-filebrowser-button-width: 48px;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-FileBrowser {

display: flex; +flex-direction: column; +color: var(–jp-ui-font-color1); +background: var(–jp-layout-color1); +/* This is needed so that all font sizing of children done in ems is

+
+
    +

  • relative to this base size */

    • +
+
+

font-size: var(–jp-ui-font-size1);

+
+
+

}

+
+
.jp-FileBrowser-toolbar.jp-Toolbar {

border-bottom: none; +height: auto; +margin: 8px 12px 0px 12px; +padding: 0px; +box-shadow: none; +justify-content: flex-start;

+
+
+

}

+
+
.jp-BreadCrumbs {

flex: 0 0 auto; +margin: 8px 12px 8px 12px;

+
+
+

}

+
+
.jp-BreadCrumbs-item {

margin: 0px 2px; +padding: 0px 2px; +border-radius: var(–jp-border-radius); +cursor: pointer;

+
+
+

}

+
+
.jp-BreadCrumbs-item:hover {

background-color: var(–jp-layout-color2);

+
+
+

}

+
+
.jp-BreadCrumbs-item:first-child {

margin-left: 0px;

+
+
+

}

+
+
.jp-BreadCrumbs-item.jp-mod-dropTarget {

background-color: var(–jp-brand-color2); +opacity: 0.7;

+
+
+

}

+

/—————————————————————————– +| Buttons +|—————————————————————————-/

+
+
.jp-FileBrowser-toolbar > .jp-Toolbar-item {

flex: 0 0 auto; +padding-left: 0px; +padding-right: 2px;

+
+
+

}

+
+
.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {

width: 40px;

+
+
+

}

+
+
.jp-FileBrowser-toolbar

.jp-ToolbarButtonComponent[data-command=’filebrowser:create-main-launcher’] { +width: 72px; +background: var(–jp-brand-color1);

+
+
+

}

+
+
.jp-FileBrowser-toolbar

.jp-ToolbarButtonComponent[data-command=’filebrowser:create-main-launcher’]:hover,

+
+
.jp-FileBrowser-toolbar

.jp-ToolbarButtonComponent[data-command=’filebrowser:create-main-launcher’]:focus-visible { +background-color: var(–jp-brand-color0) !important;

+
+
+

}

+
+
.jp-FileBrowser-toolbar

.jp-ToolbarButtonComponent[data-command=’filebrowser:create-main-launcher’] +.jp-icon3 { +fill: var(–jp-layout-color1);

+
+
+

}

+

/—————————————————————————– +| Other styles +|—————————————————————————-/

+
+
.jp-FileDialog.jp-mod-conflict input {

color: var(–jp-error-color1);

+
+
+

}

+
+
.jp-FileDialog .jp-new-name-title {

margin-top: 12px;

+
+
+

}

+
+
.jp-LastModified-hidden {

display: none;

+
+
+

}

+
+
.jp-FileBrowser-filterBox {

padding: 0px; +flex: 0 0 auto; +margin: 8px 12px 0px 12px;

+
+
+

}

+

/—————————————————————————– +| DirListing +|—————————————————————————-/

+
+
.jp-DirListing {

flex: 1 1 auto; +display: flex; +flex-direction: column; +outline: 0;

+
+
+

}

+
+
.jp-DirListing:focus-visible {

outline: 1px solid var(–jp-brand-color1); +outline-offset: -2px;

+
+
+

}

+
+
.jp-DirListing-header {

flex: 0 0 auto; +display: flex; +flex-direction: row; +overflow: hidden; +border-top: var(–jp-border-width) solid var(–jp-border-color2); +border-bottom: var(–jp-border-width) solid var(–jp-border-color1); +box-shadow: var(–jp-toolbar-box-shadow); +z-index: 2;

+
+
+

}

+
+
.jp-DirListing-headerItem {

padding: 4px 12px 2px 12px; +font-weight: 500;

+
+
+

}

+
+
.jp-DirListing-headerItem:hover {

background: var(–jp-layout-color2);

+
+
+

}

+
+
.jp-DirListing-headerItem.jp-id-name {

flex: 1 0 84px;

+
+
+

}

+
+
.jp-DirListing-headerItem.jp-id-modified {

flex: 0 0 112px; +border-left: var(–jp-border-width) solid var(–jp-border-color2); +text-align: right;

+
+
+

}

+
+
.jp-id-narrow {

display: none; +flex: 0 0 5px; +padding: 4px 4px; +border-left: var(–jp-border-width) solid var(–jp-border-color2); +text-align: right; +color: var(–jp-border-color2);

+
+
+

}

+
+
.jp-DirListing-narrow .jp-id-narrow {

display: block;

+
+
+

}

+

.jp-DirListing-narrow .jp-id-modified, +.jp-DirListing-narrow .jp-DirListing-itemModified {

+
+

display: none;

+
+

}

+
+
.jp-DirListing-headerItem.jp-mod-selected {

font-weight: 600;

+
+
+

}

+

/* increase specificity to override bundled default */ +.jp-DirListing-content {

+
+

flex: 1 1 auto; +margin: 0; +padding: 0; +list-style-type: none; +overflow: auto; +background-color: var(–jp-layout-color1);

+
+

}

+
+
.jp-DirListing-content mark {

color: var(–jp-ui-font-color0); +background-color: transparent; +font-weight: bold;

+
+
+

}

+
+
.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {

color: var(–jp-ui-inverse-font-color0);

+
+
+

}

+

/* Style the directory listing content when a user drops a file to upload */ +.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {

+
+

outline: 5px dashed rgba(128, 128, 128, 0.5); +outline-offset: -10px; +cursor: copy;

+
+

}

+
+
.jp-DirListing-item {

display: flex; +flex-direction: row; +padding: 4px 12px; +-webkit-user-select: none; +-moz-user-select: none; +-ms-user-select: none; +user-select: none;

+
+
+

}

+
+
.jp-DirListing-item[data-is-dot] {

opacity: 75%;

+
+
+

}

+
+
.jp-DirListing-item.jp-mod-selected {

color: var(–jp-ui-inverse-font-color1); +background: var(–jp-brand-color1);

+
+
+

}

+
+
.jp-DirListing-item.jp-mod-dropTarget {

background: var(–jp-brand-color3);

+
+
+

}

+
+
.jp-DirListing-item:hover:not(.jp-mod-selected) {

background: var(–jp-layout-color2);

+
+
+

}

+
+
.jp-DirListing-itemIcon {

flex: 0 0 20px; +margin-right: 4px;

+
+
+

}

+
+
.jp-DirListing-itemText {

flex: 1 0 64px; +white-space: nowrap; +overflow: hidden; +text-overflow: ellipsis; +user-select: none;

+
+
+

}

+
+
.jp-DirListing-itemModified {

flex: 0 0 125px; +text-align: right;

+
+
+

}

+
+
.jp-DirListing-editor {

flex: 1 0 64px; +outline: none; +border: none; +color: var(–jp-ui-font-color1); +background-color: var(–jp-layout-color1);

+
+
+

}

+
+
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon:before {

color: var(–jp-success-color1); +content: ‘25CF’; +font-size: 8px; +position: absolute; +left: -8px;

+
+
+

}

+
+
.jp-DirListing-item.jp-mod-running.jp-mod-selected

.jp-DirListing-itemIcon:before { +color: var(–jp-ui-inverse-font-color1);

+
+
+

}

+

.jp-DirListing-item.lm-mod-drag-image, +.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {

+
+

font-size: var(–jp-ui-font-size1); +padding-left: 4px; +margin-left: 4px; +width: 160px; +background-color: var(–jp-ui-inverse-font-color2); +box-shadow: var(–jp-elevation-z2); +border-radius: 0px; +color: var(–jp-ui-font-color1); +transform: translateX(-40%) translateY(-58%);

+
+

}

+
+
.jp-Document {

min-width: 120px; +min-height: 120px; +outline: none;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Private CSS variables +|—————————————————————————-/

+

:root { +}

+

/—————————————————————————– +| Main OutputArea +| OutputArea has a list of Outputs +|—————————————————————————-/

+
+
.jp-OutputArea {

overflow-y: auto;

+
+
+

}

+
+
.jp-OutputArea-child {

display: flex; +flex-direction: row;

+
+
+

}

+
+
body[data-format=’mobile’] .jp-OutputArea-child {

flex-direction: column;

+
+
+

}

+
+
.jp-OutputPrompt {

flex: 0 0 var(–jp-cell-prompt-width); +color: var(–jp-cell-outprompt-font-color); +font-family: var(–jp-cell-prompt-font-family); +padding: var(–jp-code-padding); +letter-spacing: var(–jp-cell-prompt-letter-spacing); +line-height: var(–jp-code-line-height); +font-size: var(–jp-code-font-size); +border: var(–jp-border-width) solid transparent; +opacity: var(–jp-cell-prompt-opacity); +/* Right align prompt text, don’t wrap to handle large prompt numbers / +text-align: right; +white-space: nowrap; +overflow: hidden; +text-overflow: ellipsis; +/ Disable text selection */ +-webkit-user-select: none; +-moz-user-select: none; +-ms-user-select: none; +user-select: none;

+
+
+

}

+
+
body[data-format=’mobile’] .jp-OutputPrompt {

flex: 0 0 auto; +text-align: left;

+
+
+

}

+
+
.jp-OutputArea-output {

height: auto; +overflow: auto; +user-select: text; +-moz-user-select: text; +-webkit-user-select: text; +-ms-user-select: text;

+
+
+

}

+
+
.jp-OutputArea-child .jp-OutputArea-output {

flex-grow: 1; +flex-shrink: 1;

+
+
+

}

+
+
body[data-format=’mobile’] .jp-OutputArea-child .jp-OutputArea-output {

margin-left: var(–jp-notebook-padding);

+
+
+

}

+
+
/**
    +

  • Isolated output.

    • +
+

*/

+
+
.jp-OutputArea-output.jp-mod-isolated {

width: 100%; +display: block;

+
+
+

}

+

/* +When drag events occur, p-mod-override-cursor is added to the body. +Because iframes steal all cursor events, the following two rules are necessary +to suppress pointer events while resize drags are occurring. There may be a +better solution to this problem. +*/ +body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {

+
+

position: relative;

+
+

}

+
+
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {

content: ‘’; +position: absolute; +top: 0; +left: 0; +right: 0; +bottom: 0; +background: transparent;

+
+
+

}

+

/* pre */

+
+
.jp-OutputArea-output pre {

border: none; +margin: 0px; +padding: 0px; +overflow-x: auto; +overflow-y: auto; +word-break: break-all; +word-wrap: break-word; +white-space: pre-wrap;

+
+
+

}

+

/* tables */

+
+
.jp-OutputArea-output.jp-RenderedHTMLCommon table {

margin-left: 0; +margin-right: 0;

+
+
+

}

+

/* description lists */

+

.jp-OutputArea-output dl, +.jp-OutputArea-output dt, +.jp-OutputArea-output dd {

+
+

display: block;

+
+

}

+
+
.jp-OutputArea-output dl {

width: 100%; +overflow: hidden; +padding: 0; +margin: 0;

+
+
+

}

+
+
.jp-OutputArea-output dt {

font-weight: bold; +float: left; +width: 20%; +padding: 0; +margin: 0;

+
+
+

}

+
+
.jp-OutputArea-output dd {

float: left; +width: 80%; +padding: 0; +margin: 0;

+
+
+

}

+
+
.jp-TrimmedOutputs a {

margin: 10px; +text-decoration: none; +cursor: pointer;

+
+
+

}

+
+
/* Hide the gutter in case of
    +
    • +

    • nested output areas (e.g. in the case of output widgets)

      • +
    +
    • +
    • +

    • mirrored output areas

      • +
    +
    • +
+

*/

+
+
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {

display: none;

+
+
+

}

+

/* Hide empty lines in the output area, for instance due to cleared widgets */ +.jp-OutputArea-prompt:empty {

+
+

padding: 0; +border: 0;

+
+

}

+

/—————————————————————————– +| executeResult is added to any Output-result for the display of the object +| returned by a cell +|—————————————————————————-/

+
+
.jp-OutputArea-output.jp-OutputArea-executeResult {

margin-left: 0px; +flex: 1 1 auto;

+
+
+

}

+
+
/* Text output with the Out[] prompt needs a top padding to match the
    +

  • alignment of the Out[] prompt itself.

    • +
+

*/

+
+
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {

padding-top: var(–jp-code-padding); +border-top: var(–jp-border-width) solid transparent;

+
+
+

}

+

/—————————————————————————– +| The Stdin output +|—————————————————————————-/

+
+
.jp-Stdin-prompt {

color: var(–jp-content-font-color0); +padding-right: var(–jp-code-padding); +vertical-align: baseline; +flex: 0 0 auto;

+
+
+

}

+
+
.jp-Stdin-input {

font-family: var(–jp-code-font-family); +font-size: inherit; +color: inherit; +background-color: inherit; +width: 42%; +min-width: 200px; +/* make sure input baseline aligns with prompt / +vertical-align: baseline; +/ padding + margin = 0.5em between prompt and cursor */ +padding: 0em 0.25em; +margin: 0em 0.25em; +flex: 0 0 70%;

+
+
+

}

+
+
.jp-Stdin-input::placeholder {

opacity: 0;

+
+
+

}

+
+
.jp-Stdin-input:focus {

box-shadow: none;

+
+
+

}

+
+
.jp-Stdin-input:focus::placeholder {

opacity: 1;

+
+
+

}

+

/—————————————————————————– +| Output Area View +|—————————————————————————-/

+
+
.jp-LinkedOutputView .jp-OutputArea {

height: 100%; +display: block;

+
+
+

}

+
+
.jp-LinkedOutputView .jp-OutputArea-output:only-child {

height: 100%;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+
+
.jp-Collapser {

flex: 0 0 var(–jp-cell-collapser-width); +padding: 0px; +margin: 0px; +border: none; +outline: none; +background: transparent; +border-radius: var(–jp-border-radius); +opacity: 1;

+
+
+

}

+
+
.jp-Collapser-child {

display: block; +width: 100%; +box-sizing: border-box; +/* height: 100% doesn’t work because the height of its parent is computed from content */ +position: absolute; +top: 0px; +bottom: 0px;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Header/Footer +|—————————————————————————-/

+

/* Hidden by zero height by default */ +.jp-CellHeader, +.jp-CellFooter {

+
+

height: 0px; +width: 100%; +padding: 0px; +margin: 0px; +border: none; +outline: none; +background: transparent;

+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Input +|—————————————————————————-/

+

/* All input areas */ +.jp-InputArea {

+
+

display: flex; +flex-direction: row; +overflow: hidden;

+
+

}

+
+
body[data-format=’mobile’] .jp-InputArea {

flex-direction: column;

+
+
+

}

+
+
.jp-InputArea-editor {

flex: 1 1 auto; +overflow: hidden;

+
+
+

}

+
+
.jp-InputArea-editor {

/* This is the non-active, default styling */ +border: var(–jp-border-width) solid var(–jp-cell-editor-border-color); +border-radius: 0px; +background: var(–jp-cell-editor-background);

+
+
+

}

+
+
body[data-format=’mobile’] .jp-InputArea-editor {

margin-left: var(–jp-notebook-padding);

+
+
+

}

+
+
.jp-InputPrompt {

flex: 0 0 var(–jp-cell-prompt-width); +color: var(–jp-cell-inprompt-font-color); +font-family: var(–jp-cell-prompt-font-family); +padding: var(–jp-code-padding); +letter-spacing: var(–jp-cell-prompt-letter-spacing); +opacity: var(–jp-cell-prompt-opacity); +line-height: var(–jp-code-line-height); +font-size: var(–jp-code-font-size); +border: var(–jp-border-width) solid transparent; +opacity: var(–jp-cell-prompt-opacity); +/* Right align prompt text, don’t wrap to handle large prompt numbers / +text-align: right; +white-space: nowrap; +overflow: hidden; +text-overflow: ellipsis; +/ Disable text selection */ +-webkit-user-select: none; +-moz-user-select: none; +-ms-user-select: none; +user-select: none;

+
+
+

}

+
+
body[data-format=’mobile’] .jp-InputPrompt {

flex: 0 0 auto; +text-align: left;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Placeholder +|—————————————————————————-/

+
+
.jp-Placeholder {

display: flex; +flex-direction: row; +flex: 1 1 auto;

+
+
+

}

+
+
.jp-Placeholder-prompt {

box-sizing: border-box;

+
+
+

}

+
+
.jp-Placeholder-content {

flex: 1 1 auto; +border: none; +background: transparent; +height: 20px; +box-sizing: border-box;

+
+
+

}

+
+
.jp-Placeholder-content .jp-MoreHorizIcon {

width: 32px; +height: 16px; +border: 1px solid transparent; +border-radius: var(–jp-border-radius);

+
+
+

}

+
+
.jp-Placeholder-content .jp-MoreHorizIcon:hover {

border: 1px solid var(–jp-border-color1); +box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.25); +background-color: var(–jp-layout-color0);

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Private CSS variables +|—————————————————————————-/

+
+
:root {

–jp-private-cell-scrolling-output-offset: 5px;

+
+
+

}

+

/—————————————————————————– +| Cell +|—————————————————————————-/

+
+
.jp-Cell {

padding: var(–jp-cell-padding); +margin: 0px; +border: none; +outline: none; +background: transparent;

+
+
+

}

+

/—————————————————————————– +| Common input/output +|—————————————————————————-/

+

.jp-Cell-inputWrapper, +.jp-Cell-outputWrapper {

+
+

display: flex; +flex-direction: row; +padding: 0px; +margin: 0px; +/* Added to reveal the box-shadow on the input and output collapsers. */ +overflow: visible;

+
+

}

+

/* Only input/output areas inside cells */ +.jp-Cell-inputArea, +.jp-Cell-outputArea {

+
+

flex: 1 1 auto;

+
+

}

+

/—————————————————————————– +| Collapser +|—————————————————————————-/

+
+
/* Make the output collapser disappear when there is not output, but do so
    +

  • in a manner that leaves it in the layout and preserves its width.

    • +
+

*/

+
+
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {

border: none !important; +background: transparent !important;

+
+
+

}

+
+
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {

min-height: var(–jp-cell-collapser-min-height);

+
+
+

}

+

/—————————————————————————– +| Output +|—————————————————————————-/

+

/* Put a space between input and output when there IS output */ +.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {

+
+

margin-top: 5px;

+
+

}

+
+
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {

overflow-y: auto; +max-height: 24em; +margin-left: var(–jp-private-cell-scrolling-output-offset);

+
+
+

}

+
+
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {

content: ‘ ‘; +box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%); +width: 100%; +height: 100%; +position: sticky; +bottom: 0; +top: 0; +margin-top: -50%; +float: left; +display: block; +pointer-events: none;

+
+
+

}

+
+
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {

padding-top: 6px;

+
+
+

}

+
+
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
+
flex: 0 0
+
calc(
+
var(–jp-cell-prompt-width) -

var(–jp-private-cell-scrolling-output-offset)

+
+
+
+
+

);

+
+
+
+
+

}

+

/—————————————————————————– +| CodeCell +|—————————————————————————-/

+

/—————————————————————————– +| MarkdownCell +|—————————————————————————-/

+
+
.jp-MarkdownOutput {

flex: 1 1 auto; +margin-top: 0; +margin-bottom: 0; +padding-left: var(–jp-code-padding);

+
+
+

}

+
+
.jp-MarkdownOutput.jp-RenderedHTMLCommon {

overflow: auto;

+
+
+

}

+

/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */ +.jp-collapseHeadingButton {

+
+

display: none; +min-height: var(–jp-cell-collapser-min-height); +font-size: var(–jp-code-font-size); +position: absolute; +right: 0; +top: 0; +bottom: 0; +background-color: transparent; +background-size: 25px; +background-repeat: no-repeat; +background-position-x: center; +background-position-y: top; +background-image: var(–jp-icon-caret-down); +border: none; +cursor: pointer;

+
+

}

+
+
.jp-collapseHeadingButton:hover {

background-color: var(–jp-layout-color2);

+
+
+

}

+
+
.jp-collapseHeadingButton.jp-mod-collapsed {

background-image: var(–jp-icon-caret-right);

+
+
+

}

+
+
:is(.jp-MarkdownCell:hover, .jp-mod-active) .jp-collapseHeadingButton {

display: flex;

+
+
+

}

+
+
/*

set the container font size to match that of content +so that the nested collapse buttons have the right size

+
+
+

*/ +.jp-MarkdownCell .jp-InputPrompt {

+
+

font-size: var(–jp-content-font-size1);

+
+

}

+
+
/*

Align collapseHeadingButton with cell top header +The font sizes are identical to the ones in packages/rendermime/style/base.css

+
+
+

*/ +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level=’1’] {

+
+

font-size: var(–jp-content-font-size5); +background-position-y: calc(0.3 * var(–jp-content-font-size5));

+
+

}

+
+
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level=’2’] {

font-size: var(–jp-content-font-size4); +background-position-y: calc(0.3 * var(–jp-content-font-size4));

+
+
+

}

+
+
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level=’3’] {

font-size: var(–jp-content-font-size3); +background-position-y: calc(0.3 * var(–jp-content-font-size3));

+
+
+

}

+
+
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level=’4’] {

font-size: var(–jp-content-font-size2); +background-position-y: calc(0.3 * var(–jp-content-font-size2));

+
+
+

}

+
+
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level=’5’] {

font-size: var(–jp-content-font-size1); +background-position-y: top;

+
+
+

}

+
+
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level=’6’] {

font-size: var(–jp-content-font-size0); +background-position-y: top;

+
+
+

}

+
+
.jp-showHiddenCellsButton {

margin-left: calc(var(–jp-cell-prompt-width) + 2 * var(–jp-code-padding)); +margin-top: var(–jp-code-padding); +border: 1px solid var(–jp-border-color2); +background-color: var(–jp-border-color3) !important; +color: var(–jp-content-font-color0) !important;

+
+
+

}

+
+
.jp-showHiddenCellsButton:hover {

background-color: var(–jp-border-color2) !important;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Variables +|—————————————————————————-/

+
+
:root {

–jp-notebook-toolbar-padding: 2px 5px 2px 2px;

+
+
+

}

+

/*—————————————————————————–

+

/—————————————————————————– +| Styles +|—————————————————————————-/

+
+
.jp-NotebookPanel-toolbar {

padding: var(–jp-notebook-toolbar-padding);

+
+
+

}

+
+
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {

border: none; +box-shadow: none;

+
+
+

}

+
+
.jp-Notebook-toolbarCellTypeDropdown select {

height: 24px; +font-size: var(–jp-ui-font-size1); +line-height: 14px; +border-radius: 0; +display: block;

+
+
+

}

+
+
.jp-Notebook-toolbarCellTypeDropdown span {

top: 5px !important;

+
+
+

}

+
+
.jp-Toolbar-responsive-popup {

position: absolute; +height: fit-content; +display: flex; +flex-direction: row; +flex-wrap: wrap; +justify-content: flex-end; +border-bottom: var(–jp-border-width) solid var(–jp-toolbar-border-color); +box-shadow: var(–jp-toolbar-box-shadow); +background: var(–jp-toolbar-background); +min-height: var(–jp-toolbar-micro-height); +padding: var(–jp-notebook-toolbar-padding); +z-index: 1; +right: 0px; +top: 0px;

+
+
+

}

+
+
.jp-Toolbar > .jp-Toolbar-responsive-opener {

margin-left: auto;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Variables +|—————————————————————————-/

+

/*—————————————————————————–

+

/—————————————————————————– +| Styles +|—————————————————————————-/

+
+
.jp-Notebook-ExecutionIndicator {

position: relative; +display: inline-block; +height: 100%; +z-index: 9997;

+
+
+

}

+
+
.jp-Notebook-ExecutionIndicator-tooltip {

visibility: hidden; +height: auto; +width: max-content; +width: -moz-max-content; +background-color: var(–jp-layout-color2); +color: var(–jp-ui-font-color1); +text-align: justify; +border-radius: 6px; +padding: 0 5px; +position: fixed; +display: table;

+
+
+

}

+
+
.jp-Notebook-ExecutionIndicator-tooltip.up {

transform: translateX(-50%) translateY(-100%) translateY(-32px);

+
+
+

}

+
+
.jp-Notebook-ExecutionIndicator-tooltip.down {

transform: translateX(calc(-100% + 16px)) translateY(5px);

+
+
+

}

+
+
.jp-Notebook-ExecutionIndicator-tooltip.hidden {

display: none;

+
+
+

}

+
+
.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {

visibility: visible;

+
+
+

}

+
+
.jp-Notebook-ExecutionIndicator span {

font-size: var(–jp-ui-font-size1); +font-family: var(–jp-ui-font-family); +color: var(–jp-ui-font-color1); +line-height: 24px; +display: block;

+
+
+

}

+
+
.jp-Notebook-ExecutionIndicator-progress-bar {

display: flex; +justify-content: center; +height: 100%;

+
+
+

}

+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/—————————————————————————– +| Private CSS variables +|—————————————————————————-/

+
+
:root {

–jp-private-notebook-dragImage-width: 304px; +–jp-private-notebook-dragImage-height: 36px; +–jp-private-notebook-selected-color: var(–md-blue-400); +–jp-private-notebook-active-color: var(–md-green-400);

+
+
+

}

+

/—————————————————————————– +| Imports +|—————————————————————————-/

+

/—————————————————————————– +| Notebook +|—————————————————————————-/

+
+
.jp-NotebookPanel {

display: block; +height: 100%;

+
+
+

}

+
+
.jp-NotebookPanel.jp-Document {

min-width: 240px; +min-height: 120px;

+
+
+

}

+
+
.jp-Notebook {

padding: var(–jp-notebook-padding); +outline: none; +overflow: auto; +background: var(–jp-layout-color0);

+
+
+

}

+
+
.jp-Notebook.jp-mod-scrollPastEnd::after {

display: block; +content: ‘’; +min-height: var(–jp-notebook-scroll-padding);

+
+
+

}

+
+
.jp-MainAreaWidget-ContainStrict .jp-Notebook * {

contain: strict;

+
+
+

}

+
+
.jp-Notebook .jp-Cell {

overflow: visible;

+
+
+

}

+
+
.jp-Notebook .jp-Cell .jp-InputPrompt {

cursor: move; +float: left;

+
+
+

}

+

/—————————————————————————– +| Notebook state related styling +| +| The notebook and cells each have states, here are the possibilities: +| +| - Notebook +| - Command +| - Edit +| - Cell +| - None +| - Active (only one can be active) +| - Selected (the cells actions are applied to) +| - Multiselected (when multiple selected, the cursor) +| - No outputs +|—————————————————————————-/

+

/* Command or edit modes */

+
+
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {

opacity: var(–jp-cell-prompt-not-active-opacity); +color: var(–jp-cell-prompt-not-active-font-color);

+
+
+

}

+
+
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {

opacity: var(–jp-cell-prompt-not-active-opacity); +color: var(–jp-cell-prompt-not-active-font-color);

+
+
+

}

+

/* cell is active */ +.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {

+
+

background: var(–jp-brand-color1);

+
+

}

+

/* cell is dirty */ +.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {

+
+

color: var(–jp-warn-color1);

+
+

} +.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt:before {

+
+

color: var(–jp-warn-color1); +content: ‘•’;

+
+

}

+
+
.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {

background: var(–jp-warn-color1);

+
+
+

}

+

/* collapser is hovered */ +.jp-Notebook .jp-Cell .jp-Collapser:hover {

+
+

box-shadow: var(–jp-elevation-z2); +background: var(–jp-brand-color1); +opacity: var(–jp-cell-collapser-not-active-hover-opacity);

+
+

}

+

/* cell is active and collapser is hovered */ +.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {

+
+

background: var(–jp-brand-color0); +opacity: 1;

+
+

}

+

/* Command mode */

+
+
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {

background: var(–jp-notebook-multiselected-color);

+
+
+

}

+
+
.jp-Notebook.jp-mod-commandMode

.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) { +background: transparent;

+
+
+

}

+

/* Edit mode */

+
+
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {

border: var(–jp-border-width) solid var(–jp-cell-editor-active-border-color); +box-shadow: var(–jp-input-box-shadow); +background-color: var(–jp-cell-editor-active-background);

+
+
+

}

+

/—————————————————————————– +| Notebook drag and drop +|—————————————————————————-/

+
+
.jp-Notebook-cell.jp-mod-dropSource {

opacity: 0.5;

+
+
+

}

+

.jp-Notebook-cell.jp-mod-dropTarget, +.jp-Notebook.jp-mod-commandMode

+
+

.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget { +border-top-color: var(–jp-private-notebook-selected-color); +border-top-style: solid; +border-top-width: 2px;

+
+

}

+
+
.jp-dragImage {

display: block; +flex-direction: row; +width: var(–jp-private-notebook-dragImage-width); +height: var(–jp-private-notebook-dragImage-height); +border: var(–jp-border-width) solid var(–jp-cell-editor-border-color); +background: var(–jp-cell-editor-background); +overflow: visible;

+
+
+

}

+
+
.jp-dragImage-singlePrompt {

box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);

+
+
+

}

+
+
.jp-dragImage .jp-dragImage-content {

flex: 1 1 auto; +z-index: 2; +font-size: var(–jp-code-font-size); +font-family: var(–jp-code-font-family); +line-height: var(–jp-code-line-height); +padding: var(–jp-code-padding); +border: var(–jp-border-width) solid var(–jp-cell-editor-border-color); +background: var(–jp-cell-editor-background-color); +color: var(–jp-content-font-color3); +text-align: left; +margin: 4px 4px 4px 0px;

+
+
+

}

+
+
.jp-dragImage .jp-dragImage-prompt {

flex: 0 0 auto; +min-width: 36px; +color: var(–jp-cell-inprompt-font-color); +padding: var(–jp-code-padding); +padding-left: 12px; +font-family: var(–jp-cell-prompt-font-family); +letter-spacing: var(–jp-cell-prompt-letter-spacing); +line-height: 1.9; +font-size: var(–jp-code-font-size); +border: var(–jp-border-width) solid transparent;

+
+
+

}

+
+
.jp-dragImage-multipleBack {

z-index: -1; +position: absolute; +height: 32px; +width: 300px; +top: 8px; +left: 8px; +background: var(–jp-layout-color2); +border: var(–jp-border-width) solid var(–jp-input-border-color); +box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);

+
+
+

}

+

/—————————————————————————– +| Cell toolbar +|—————————————————————————-/

+
+
.jp-NotebookTools {

display: block; +min-width: var(–jp-sidebar-min-width); +color: var(–jp-ui-font-color1); +background: var(–jp-layout-color1); +/* This is needed so that all font sizing of children done in ems is

+
+
    +

  • relative to this base size */

    • +
+
+

font-size: var(–jp-ui-font-size1); +overflow: auto;

+
+
+

}

+
+
.jp-NotebookTools-tool {

padding: 0px 12px 0 12px;

+
+
+

}

+
+
.jp-ActiveCellTool {

padding: 12px; +background-color: var(–jp-layout-color1); +border-top: none !important;

+
+
+

}

+
+
.jp-ActiveCellTool .jp-InputArea-prompt {

flex: 0 0 auto; +padding-left: 0px;

+
+
+

}

+
+
.jp-ActiveCellTool .jp-InputArea-editor {

flex: 1 1 auto; +background: var(–jp-cell-editor-background); +border-color: var(–jp-cell-editor-border-color);

+
+
+

}

+
+
.jp-ActiveCellTool .jp-InputArea-editor .CodeMirror {

background: transparent;

+
+
+

}

+
+
.jp-MetadataEditorTool {

flex-direction: column; +padding: 12px 0px 12px 0px;

+
+
+

}

+
+
.jp-RankedPanel > :not(:first-child) {

margin-top: 12px;

+
+
+

}

+
+
.jp-KeySelector select.jp-mod-styled {

font-size: var(–jp-ui-font-size1); +color: var(–jp-ui-font-color0); +border: var(–jp-border-width) solid var(–jp-border-color1);

+
+
+

}

+

.jp-KeySelector label, +.jp-MetadataEditorTool label {

+
+

line-height: 1.4;

+
+

}

+
+
.jp-NotebookTools .jp-select-wrapper {

margin-top: 4px; +margin-bottom: 0px;

+
+
+

}

+
+
.jp-NotebookTools .jp-Collapse {

margin-top: 16px;

+
+
+

}

+

/—————————————————————————– +| Presentation Mode (.jp-mod-presentationMode) +|—————————————————————————-/

+
+
.jp-mod-presentationMode .jp-Notebook {

–jp-content-font-size1: var(–jp-content-presentation-font-size1); +–jp-code-font-size: var(–jp-code-presentation-font-size);

+
+
+

}

+

.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt, +.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {

+
+

flex: 0 0 110px;

+
+

}

+

/—————————————————————————– +| Side-by-side Mode (.jp-mod-sideBySide) +|—————————————————————————-/ +:root {

+
+

–jp-side-by-side-output-size: 1fr; +–jp-side-by-side-resized-cell: var(–jp-side-by-side-output-size);

+
+

}

+
+
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {

margin-top: 3em; +margin-bottom: 3em; +margin-left: 5%; +margin-right: 5%;

+
+
+

}

+
+
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {

display: grid; +grid-template-columns: minmax(0, 1fr) min-content minmax(

+
+
+

0, +var(–jp-side-by-side-output-size)

+
+

);

+
+

grid-template-rows: auto minmax(0, 1fr) auto; +grid-template-areas:

+
+

‘header header header’ +‘input handle output’ +‘footer footer footer’;

+
+
+
+

}

+
+
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
+
grid-template-columns: minmax(0, 1fr) min-content minmax(
+

0, +var(–jp-side-by-side-resized-cell)

+
+

);

+
+
+
+
+

}

+
+
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {

grid-area: header;

+
+
+

}

+
+
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {

grid-area: input;

+
+
+

}

+
+
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {

/* overwrite the default margin (no vertical separation needed in side by side move */ +margin-top: 0; +grid-area: output;

+
+
+

}

+
+
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {

grid-area: footer;

+
+
+

}

+
+
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {

grid-area: handle; +user-select: none; +display: block; +height: 100%; +cursor: ew-resize; +padding: 0 var(–jp-cell-padding);

+
+
+

}

+
+
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {

content: ‘’; +display: block; +background: var(–jp-border-color2); +height: 100%; +width: 5px;

+
+
+

}

+
+
.jp-mod-sideBySide.jp-Notebook

.jp-CodeCell.jp-mod-resizedCell +.jp-CellResizeHandle::after { +background: var(–jp-border-color0);

+
+
+

}

+
+
.jp-CellResizeHandle {

display: none;

+
+
+

}

+

/—————————————————————————– +| Placeholder +|—————————————————————————-/

+
+
.jp-Cell-Placeholder {

padding-left: 55px;

+
+
+

}

+
+
.jp-Cell-Placeholder-wrapper {

background: #fff; +border: 1px solid; +border-color: #e5e6e9 #dfe0e4 #d0d1d5; +border-radius: 4px; +-webkit-border-radius: 4px; +margin: 10px 15px;

+
+
+

}

+
+
.jp-Cell-Placeholder-wrapper-inner {

padding: 15px; +position: relative;

+
+
+

}

+
+
.jp-Cell-Placeholder-wrapper-body {

background-repeat: repeat; +background-size: 50% auto;

+
+
+

}

+
+
.jp-Cell-Placeholder-wrapper-body div {

background: #f6f7f8; +background-image: -webkit-linear-gradient(

+
+

left, +#f6f7f8 0%, +#edeef1 20%, +#f6f7f8 40%, +#f6f7f8 100%

+
+

); +background-repeat: no-repeat; +background-size: 800px 104px; +height: 104px; +position: relative;

+
+
+

}

+
+
.jp-Cell-Placeholder-wrapper-body div {

position: absolute; +right: 15px; +left: 15px; +top: 15px;

+
+
+

}

+
+
div.jp-Cell-Placeholder-h1 {

top: 20px; +height: 20px; +left: 15px; +width: 150px;

+
+
+

}

+
+
div.jp-Cell-Placeholder-h2 {

left: 15px; +top: 50px; +height: 10px; +width: 100px;

+
+
+

}

+

div.jp-Cell-Placeholder-content-1, +div.jp-Cell-Placeholder-content-2, +div.jp-Cell-Placeholder-content-3 {

+
+

left: 15px; +right: 15px; +height: 10px;

+
+

}

+
+
div.jp-Cell-Placeholder-content-1 {

top: 100px;

+
+
+

}

+
+
div.jp-Cell-Placeholder-content-2 {

top: 120px;

+
+
+

}

+
+
div.jp-Cell-Placeholder-content-3 {

top: 140px;

+
+
+

}

+

</style>

+
+

<style type=”text/css”>

+
+

/—————————————————————————– +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|—————————————————————————-/

+

/* +The following CSS variables define the main, public API for styling JupyterLab. +These variables should be used by all plugins wherever possible. In other +words, plugins should not define custom colors, sizes, etc unless absolutely +necessary. This enables users to change the visual theme of JupyterLab +by changing these variables.

+

Many variables appear in an ordered sequence (0,1,2,3). These sequences +are designed to work well together, so for example, –jp-border-color1 should +be used with –jp-layout-color1. The numbers have the following meanings:

+
    +

  • 0: super-primary, reserved for special emphasis

    • +

  • 1: primary, most important under normal situations

    • +

  • 2: secondary, next most important under normal situations

    • +

  • 3: tertiary, next most important under normal situations

    • +
+

Throughout JupyterLab, we are mostly following principles from Google’s +Material Design when selecting colors. We are not, however, following +all of MD as it is not optimized for dense, information rich UIs. +*/

+
+
:root {
+
/* Elevation
+

*/

+
+
+

–jp-shadow-base-lightness: 0; +–jp-shadow-umbra-color: rgba(

+
+

var(–jp-shadow-base-lightness), +var(–jp-shadow-base-lightness), +var(–jp-shadow-base-lightness), +0.2

+
+

); +–jp-shadow-penumbra-color: rgba(

+
+

var(–jp-shadow-base-lightness), +var(–jp-shadow-base-lightness), +var(–jp-shadow-base-lightness), +0.14

+
+

); +–jp-shadow-ambient-color: rgba(

+
+

var(–jp-shadow-base-lightness), +var(–jp-shadow-base-lightness), +var(–jp-shadow-base-lightness), +0.12

+
+

); +–jp-elevation-z0: none; +–jp-elevation-z1: 0px 2px 1px -1px var(–jp-shadow-umbra-color),

+
+

0px 1px 1px 0px var(–jp-shadow-penumbra-color), +0px 1px 3px 0px var(–jp-shadow-ambient-color);

+
+
+
–jp-elevation-z2: 0px 3px 1px -2px var(–jp-shadow-umbra-color),

0px 2px 2px 0px var(–jp-shadow-penumbra-color), +0px 1px 5px 0px var(–jp-shadow-ambient-color);

+
+
–jp-elevation-z4: 0px 2px 4px -1px var(–jp-shadow-umbra-color),

0px 4px 5px 0px var(–jp-shadow-penumbra-color), +0px 1px 10px 0px var(–jp-shadow-ambient-color);

+
+
–jp-elevation-z6: 0px 3px 5px -1px var(–jp-shadow-umbra-color),

0px 6px 10px 0px var(–jp-shadow-penumbra-color), +0px 1px 18px 0px var(–jp-shadow-ambient-color);

+
+
–jp-elevation-z8: 0px 5px 5px -3px var(–jp-shadow-umbra-color),

0px 8px 10px 1px var(–jp-shadow-penumbra-color), +0px 3px 14px 2px var(–jp-shadow-ambient-color);

+
+
–jp-elevation-z12: 0px 7px 8px -4px var(–jp-shadow-umbra-color),

0px 12px 17px 2px var(–jp-shadow-penumbra-color), +0px 5px 22px 4px var(–jp-shadow-ambient-color);

+
+
–jp-elevation-z16: 0px 8px 10px -5px var(–jp-shadow-umbra-color),

0px 16px 24px 2px var(–jp-shadow-penumbra-color), +0px 6px 30px 5px var(–jp-shadow-ambient-color);

+
+
–jp-elevation-z20: 0px 10px 13px -6px var(–jp-shadow-umbra-color),

0px 20px 31px 3px var(–jp-shadow-penumbra-color), +0px 8px 38px 7px var(–jp-shadow-ambient-color);

+
+
–jp-elevation-z24: 0px 11px 15px -7px var(–jp-shadow-umbra-color),

0px 24px 38px 3px var(–jp-shadow-penumbra-color), +0px 9px 46px 8px var(–jp-shadow-ambient-color);

+
+
/* Borders
    +
  • +

  • The following variables, specify the visual styling of borders in JupyterLab.

    • +
+

*/

+
+
+

–jp-border-width: 1px; +–jp-border-color0: var(–md-grey-400); +–jp-border-color1: var(–md-grey-400); +–jp-border-color2: var(–md-grey-300); +–jp-border-color3: var(–md-grey-200); +–jp-inverse-border-color: var(–md-grey-600); +–jp-border-radius: 2px;

+
+
/* UI Fonts
    +
  • +

  • The UI font CSS variables are used for the typography all of the JupyterLab

    • +

  • user interface elements that are not directly user generated content.

    • +
  • +

  • The font sizing here is done assuming that the body font size of –jp-ui-font-size1

    • +

  • is applied to a parent element. When children elements, such as headings, are sized

    • +

  • in em all things will be computed relative to that body size.

    • +
+

*/

+
+
+

–jp-ui-font-scale-factor: 1.2; +–jp-ui-font-size0: 0.83333em; +–jp-ui-font-size1: 13px; /* Base font size */ +–jp-ui-font-size2: 1.2em; +–jp-ui-font-size3: 1.44em;

+
+
–jp-ui-font-family: -apple-system, BlinkMacSystemFont, ‘Segoe UI’, Helvetica,

Arial, sans-serif, ‘Apple Color Emoji’, ‘Segoe UI Emoji’, ‘Segoe UI Symbol’;

+
+
/*
    +

  • Use these font colors against the corresponding main layout colors.

    • +

  • In a light theme, these go from dark to light.

    • +
+

*/

+
+
+

/* Defaults use Material Design specification */ +–jp-ui-font-color0: rgba(0, 0, 0, 1); +–jp-ui-font-color1: rgba(0, 0, 0, 0.87); +–jp-ui-font-color2: rgba(0, 0, 0, 0.54); +–jp-ui-font-color3: rgba(0, 0, 0, 0.38);

+
+
/*
    +

  • Use these against the brand/accent/warn/error colors.

    • +

  • These will typically go from light to darker, in both a dark and light theme.

    • +
+

*/

+
+
+

–jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); +–jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); +–jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); +–jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);

+
+
/* Content Fonts
    +
  • +

  • Content font variables are used for typography of user generated content.

    • +
  • +

  • The font sizing here is done assuming that the body font size of –jp-content-font-size1

    • +

  • is applied to a parent element. When children elements, such as headings, are sized

    • +

  • in em all things will be computed relative to that body size.

    • +
+

*/

+
+
+

–jp-content-line-height: 1.6; +–jp-content-font-scale-factor: 1.2; +–jp-content-font-size0: 0.83333em; +–jp-content-font-size1: 14px; /* Base font size */ +–jp-content-font-size2: 1.2em; +–jp-content-font-size3: 1.44em; +–jp-content-font-size4: 1.728em; +–jp-content-font-size5: 2.0736em;

+

/* This gives a magnification of about 125% in presentation mode over normal. */ +–jp-content-presentation-font-size1: 17px;

+

–jp-content-heading-line-height: 1; +–jp-content-heading-margin-top: 1.2em; +–jp-content-heading-margin-bottom: 0.8em; +–jp-content-heading-font-weight: 500;

+

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</style> +<!– End of mermaid configuration –></head> +<body class=”jp-Notebook” data-jp-theme-light=”true” data-jp-theme-name=”JupyterLab Light”>

+

<div id=”cell-id=55d6bd7b” class=”jp-Cell jp-MarkdownCell jp-Notebook-cell”> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”><div class=”jp-InputPrompt jp-InputArea-prompt”> +</div><div class=”jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput “ data-mime-type=”text/markdown”> +<h1 id=”Create-a-single-image-with-a-star”>Create a single image with a star<a class=”anchor-link” href=”#Create-a-single-image-with-a-star”>&#182;</a></h1><p>The astro module contain a number of astronomy objects. In this notebook, we will go over creating an instance of a star object, how to generate a star image with and without noise, and display the results.</p>

+

</div> +</div> +</div> +</div><div id=”cell-id=bd463d6c” class=”jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs “> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”> +<div class=”jp-InputPrompt jp-InputArea-prompt”>In&nbsp;[3]:</div> +<div class=”jp-CodeMirrorEditor jp-Editor jp-InputArea-editor” data-type=”inline”>

+
+

<div class=”CodeMirror cm-s-jupyter”>

+
+

<div class=” highlight hl-ipython3”><pre><span></span><span class=”kn”>import</span> <span class=”nn”>numpy</span> <span class=”k”>as</span> <span class=”nn”>np</span> +<span class=”kn”>import</span> <span class=”nn”>matplotlib.pyplot</span> <span class=”k”>as</span> <span class=”nn”>plt</span>

+

<span class=”kn”>from</span> <span class=”nn”>deepbench.astro_object</span> <span class=”kn”>import</span> <span class=”n”>StarObject</span> +<span class=”kn”>from</span> <span class=”nn”>deepbench.image</span> <span class=”kn”>import</span> <span class=”n”>SkyImage</span> +<span class=”kn”>from</span> <span class=”nn”>deepbench.collection</span> <span class=”kn”>import</span> <span class=”n”>Collection</span> +</pre></div>

+
+

</div>

+
+

</div> +</div> +</div>

+

</div> +<div id=”cell-id=1252a0d0” class=”jp-Cell jp-MarkdownCell jp-Notebook-cell”> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”><div class=”jp-InputPrompt jp-InputArea-prompt”> +</div><div class=”jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput “ data-mime-type=”text/markdown”> +<h2 id=”The-first-step-is-to-initilize-the-star-class”>The first step is to initilize the star class<a class=”anchor-link” href=”#The-first-step-is-to-initilize-the-star-class”>&#182;</a></h2><p>Inputs include the dimension of the image (a square), noise, radius, and amplitude. Here we introduce no noise:</p>

+

</div> +</div> +</div> +</div><div id=”cell-id=35a86e75” class=”jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs “> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”> +<div class=”jp-InputPrompt jp-InputArea-prompt”>In&nbsp;[65]:</div> +<div class=”jp-CodeMirrorEditor jp-Editor jp-InputArea-editor” data-type=”inline”>

+
+

<div class=”CodeMirror cm-s-jupyter”>

+
+
+
<div class=” highlight hl-ipython3”><pre><span></span><span class=”n”>star</span> <span class=”o”>=</span> <span class=”n”>StarObject</span><span class=”p”>(</span>
+

<span class=”n”>image_dimensions</span> <span class=”o”>=</span> <span class=”mi”>28</span><span class=”p”>,</span> +<span class=”n”>noise</span> <span class=”o”>=</span> <span class=”mf”>0.0</span><span class=”p”>,</span> +<span class=”n”>radius</span><span class=”o”>=</span> <span class=”mf”>0.2</span><span class=”p”>,</span> +<span class=”n”>amplitude</span> <span class=”o”>=</span> <span class=”mf”>5.0</span>

+
+

<span class=”p”>)</span>

+
+
+

</pre></div>

+
+

</div>

+
+

</div> +</div> +</div>

+

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<div class=” highlight hl-ipython3”><pre><span></span><span class=”n”>x_position</span><span class=”p”>,</span> <span class=”n”>y_position</span> <span class=”o”>=</span> <span class=”n”>np</span><span class=”o”>.</span><span class=”n”>random</span><span class=”o”>.</span><span class=”n”>default_rng</span><span class=”p”>()</span><span class=”o”>.</span><span class=”n”>uniform</span><span class=”p”>(</span><span class=”n”>low</span><span class=”o”>=</span><span class=”mi”>1</span><span class=”p”>,</span> <span class=”n”>high</span><span class=”o”>=</span><span class=”mi”>27</span><span class=”p”>,</span> <span class=”n”>size</span><span class=”o”>=</span><span class=”p”>(</span><span class=”mi”>2</span><span class=”p”>,</span> <span class=”mi”>5</span><span class=”p”>))</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>clf</span><span class=”p”>()</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>scatter</span><span class=”p”>(</span><span class=”n”>x_position</span><span class=”p”>,</span> <span class=”n”>y_position</span><span class=”p”>)</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>xlabel</span><span class=”p”>(</span><span class=”s1”>'x_position'</span><span class=”p”>)</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>ylabel</span><span class=”p”>(</span><span class=”s1”>'y_position'</span><span class=”p”>)</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>title</span><span class=”p”>(</span><span class=”s1”>'5 randomly generated x and y positions'</span><span class=”p”>)</span> +<span class=”c1”>#plt.aspect_ratio('square')</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>show</span><span class=”p”>()</span> +</pre></div>

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” +class=” +” +> +</div>

+

</div>

+

</div>

+

</div>

+

</div> +<div id=”cell-id=c61b0e42” class=”jp-Cell jp-MarkdownCell jp-Notebook-cell”> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”><div class=”jp-InputPrompt jp-InputArea-prompt”> +</div><div class=”jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput “ data-mime-type=”text/markdown”> +<h2 id=”Generate-stars-for-each-randonly-generated-x-and-y-position”>Generate stars for each randonly generated x and y position<a class=”anchor-link” href=”#Generate-stars-for-each-randonly-generated-x-and-y-position”>&#182;</a></h2> +</div> +</div> +</div> +</div><div id=”cell-id=a12087d8” class=”jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs “> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”> +<div class=”jp-InputPrompt jp-InputArea-prompt”>In&nbsp;[67]:</div> +<div class=”jp-CodeMirrorEditor jp-Editor jp-InputArea-editor” data-type=”inline”>

+
+

<div class=”CodeMirror cm-s-jupyter”>

+
+

<div class=” highlight hl-ipython3”><pre><span></span><span class=”n”>generated_stars</span> <span class=”o”>=</span> <span class=”p”>[]</span> +<span class=”k”>for</span> <span class=”n”>x_pos</span><span class=”p”>,</span> <span class=”n”>y_pos</span> <span class=”ow”>in</span> <span class=”nb”>zip</span><span class=”p”>(</span><span class=”n”>x_position</span><span class=”p”>,</span> <span class=”n”>y_position</span><span class=”p”>):</span>

+
+

<span class=”n”>generated_stars</span><span class=”o”>.</span><span class=”n”>append</span><span class=”p”>(</span><span class=”n”>star</span><span class=”o”>.</span><span class=”n”>create_object</span><span class=”p”>(</span><span class=”n”>x_pos</span><span class=”p”>,</span> <span class=”n”>y_pos</span><span class=”p”>))</span>

+
+

</pre></div>

+
+

</div>

+
+

</div> +</div> +</div>

+

</div> +<div id=”cell-id=788e53ed” class=”jp-Cell jp-MarkdownCell jp-Notebook-cell”> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”><div class=”jp-InputPrompt jp-InputArea-prompt”> +</div><div class=”jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput “ data-mime-type=”text/markdown”> +<h2 id=”Sum-and-plot-the-resulting-images”>Sum and plot the resulting images<a class=”anchor-link” href=”#Sum-and-plot-the-resulting-images”>&#182;</a></h2><p>With overlayed original x and y positions.</p>

+

</div> +</div> +</div> +</div><div id=”cell-id=5b933976” class=”jp-Cell jp-CodeCell jp-Notebook-cell “> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”> +<div class=”jp-InputPrompt jp-InputArea-prompt”>In&nbsp;[68]:</div> +<div class=”jp-CodeMirrorEditor jp-Editor jp-InputArea-editor” data-type=”inline”>

+
+

<div class=”CodeMirror cm-s-jupyter”>

+
+

<div class=” highlight hl-ipython3”><pre><span></span><span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>clf</span><span class=”p”>()</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>imshow</span><span class=”p”>(</span><span class=”n”>np</span><span class=”o”>.</span><span class=”n”>sum</span><span class=”p”>(</span><span class=”n”>generated_stars</span><span class=”p”>,</span> <span class=”n”>axis</span> <span class=”o”>=</span> <span class=”mi”>0</span><span class=”p”>),</span> <span class=”n”>origin</span> <span class=”o”>=</span> <span class=”s1”>'lower'</span><span class=”p”>)</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>scatter</span><span class=”p”>(</span><span class=”n”>x_position</span><span class=”p”>,</span> <span class=”n”>y_position</span><span class=”p”>,</span> <span class=”n”>color</span> <span class=”o”>=</span> <span class=”s1”>'red'</span><span class=”p”>)</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>show</span><span class=”p”>()</span> +</pre></div>

+
+

</div>

+
+

</div> +</div> +</div>

+

<div class=”jp-Cell-outputWrapper”> +<div class=”jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser”> +</div>

+

<div class=”jp-OutputArea jp-Cell-outputArea”> +<div class=”jp-OutputArea-child”>

+
+

<div class=”jp-OutputPrompt jp-OutputArea-prompt”></div>

+
+

<div class=”jp-RenderedImage jp-OutputArea-output “> +<img 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” +class=” +” +> +</div>

+

</div>

+

</div>

+

</div>

+

</div> +<div id=”cell-id=3d2d49df” class=”jp-Cell jp-MarkdownCell jp-Notebook-cell”> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”><div class=”jp-InputPrompt jp-InputArea-prompt”> +</div><div class=”jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput “ data-mime-type=”text/markdown”> +<h2 id=”Repeat-the-analysis-with-noise”>Repeat the analysis with noise<a class=”anchor-link” href=”#Repeat-the-analysis-with-noise”>&#182;</a></h2> +</div> +</div> +</div> +</div><div id=”cell-id=02df8423” class=”jp-Cell jp-CodeCell jp-Notebook-cell “> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”> +<div class=”jp-InputPrompt jp-InputArea-prompt”>In&nbsp;[78]:</div> +<div class=”jp-CodeMirrorEditor jp-Editor jp-InputArea-editor” data-type=”inline”>

+
+

<div class=”CodeMirror cm-s-jupyter”>

+
+
+
<div class=” highlight hl-ipython3”><pre><span></span><span class=”n”>star_noisy</span> <span class=”o”>=</span> <span class=”n”>StarObject</span><span class=”p”>(</span>
+

<span class=”n”>image_dimensions</span> <span class=”o”>=</span> <span class=”mi”>28</span><span class=”p”>,</span> +<span class=”n”>noise</span> <span class=”o”>=</span> <span class=”mf”>0.1</span><span class=”p”>,</span> +<span class=”n”>radius</span><span class=”o”>=</span> <span class=”mf”>0.8</span><span class=”p”>,</span> +<span class=”n”>amplitude</span> <span class=”o”>=</span> <span class=”mf”>5.0</span>

+
+

<span class=”p”>)</span>

+
+
+

<span class=”n”>generated_stars_noisy</span> <span class=”o”>=</span> <span class=”p”>[]</span> +<span class=”k”>for</span> <span class=”n”>x_pos</span><span class=”p”>,</span> <span class=”n”>y_pos</span> <span class=”ow”>in</span> <span class=”nb”>zip</span><span class=”p”>(</span><span class=”n”>x_position</span><span class=”p”>,</span> <span class=”n”>y_position</span><span class=”p”>):</span>

+
+

<span class=”n”>generated_stars_noisy</span><span class=”o”>.</span><span class=”n”>append</span><span class=”p”>(</span><span class=”n”>star_noisy</span><span class=”o”>.</span><span class=”n”>create_object</span><span class=”p”>(</span><span class=”n”>x_pos</span><span class=”p”>,</span> <span class=”n”>y_pos</span><span class=”p”>))</span>

+
+

<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>clf</span><span class=”p”>()</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>imshow</span><span class=”p”>(</span><span class=”n”>np</span><span class=”o”>.</span><span class=”n”>sum</span><span class=”p”>(</span><span class=”n”>generated_stars_noisy</span><span class=”p”>,</span> <span class=”n”>axis</span> <span class=”o”>=</span> <span class=”mi”>0</span><span class=”p”>),</span> <span class=”n”>origin</span> <span class=”o”>=</span> <span class=”s1”>'lower'</span><span class=”p”>)</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>scatter</span><span class=”p”>(</span><span class=”n”>x_position</span><span class=”p”>,</span> <span class=”n”>y_position</span><span class=”p”>,</span> <span class=”n”>color</span> <span class=”o”>=</span> <span class=”s1”>'red'</span><span class=”p”>)</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>show</span><span class=”p”>()</span> +</pre></div>

+
+

</div>

+
+

</div> +</div> +</div>

+

<div class=”jp-Cell-outputWrapper”> +<div class=”jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser”> +</div>

+

<div class=”jp-OutputArea jp-Cell-outputArea”> +<div class=”jp-OutputArea-child”>

+
+

<div class=”jp-OutputPrompt jp-OutputArea-prompt”></div>

+
+

<div class=”jp-RenderedImage jp-OutputArea-output “> +<img 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” +class=” +” +> +</div>

+

</div>

+

</div>

+

</div>

+

</div><div id=”cell-id=11e7f670” class=”jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs “> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”> +<div class=”jp-InputPrompt jp-InputArea-prompt”>In&nbsp;[21]:</div> +<div class=”jp-CodeMirrorEditor jp-Editor jp-InputArea-editor” data-type=”inline”>

+
+

<div class=”CodeMirror cm-s-jupyter”>

+
+

<div class=” highlight hl-ipython3”><pre><span></span><span class=”c1”># Now create a sky image with a combination of astro objects</span> +<span class=”n”>sky_image</span> <span class=”o”>=</span> <span class=”n”>SkyImage</span><span class=”p”>(</span>

+
+

<span class=”n”>image_shape</span><span class=”o”>=</span><span class=”p”>(</span><span class=”mi”>28</span><span class=”p”>,</span><span class=”mi”>28</span><span class=”p”>),</span> +<span class=”n”>object_noise_level</span><span class=”o”>=</span><span class=”mf”>0.1</span> +<span class=”p”>)</span> <span class=”c1”># Add .2 sigma of guassian noise </span>

+
+

</pre></div>

+
+

</div>

+
+

</div> +</div> +</div>

+

</div><div id=”cell-id=3fda89bf” class=”jp-Cell jp-CodeCell jp-Notebook-cell “> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”> +<div class=”jp-InputPrompt jp-InputArea-prompt”>In&nbsp;[26]:</div> +<div class=”jp-CodeMirrorEditor jp-Editor jp-InputArea-editor” data-type=”inline”>

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<div class=”CodeMirror cm-s-jupyter”>

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<div class=” highlight hl-ipython3”><pre><span></span><span class=”c1”># A single star in a noisy image</span> +<span class=”n”>noisy_star</span> <span class=”o”>=</span> <span class=”n”>sky_image</span><span class=”o”>.</span><span class=”n”>combine_objects</span><span class=”p”>(</span>

+
+

<span class=”n”>objects</span><span class=”o”>=</span><span class=”p”>[</span><span class=”s2”>&quot;star&quot;</span><span class=”p”>,</span><span class=”s2”>&quot;galaxy&quot;</span><span class=”p”>],</span> +<span class=”n”>instance_params</span><span class=”o”>=</span><span class=”p”>[{</span><span class=”s1”>'noise'</span><span class=”p”>:</span><span class=”mf”>0.2</span><span class=”p”>,</span> <span class=”s1”>'radius'</span><span class=”p”>:</span><span class=”mf”>0.8</span><span class=”p”>,</span> <span class=”s1”>'amplitude'</span><span class=”p”>:</span><span class=”mi”>1</span><span class=”p”>},</span>

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+

<span class=”p”>{</span><span class=”s1”>'radius'</span><span class=”p”>:</span><span class=”mi”>5</span><span class=”p”>,</span> <span class=”s1”>'amplitude'</span><span class=”p”>:</span><span class=”mi”>10</span><span class=”p”>}],</span>

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+
<span class=”n”>object_params</span><span class=”o”>=</span><span class=”p”>[{</span><span class=”s1”>'center_x'</span><span class=”p”>:</span><span class=”mi”>14</span><span class=”p”>,</span> <span class=”s1”>'center_y'</span><span class=”p”>:</span><span class=”mi”>14</span><span class=”p”>},</span>

<span class=”p”>{</span><span class=”s1”>'center_x'</span><span class=”p”>:</span><span class=”mi”>10</span><span class=”p”>,</span> <span class=”s1”>'center_y'</span><span class=”p”>:</span><span class=”mi”>5</span><span class=”p”>}]</span>

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+
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+

<span class=”p”>)</span> +<span class=”n”>plt</span><span class=”o”>.</span><span class=”n”>imshow</span><span class=”p”>(</span><span class=”n”>noisy_star</span><span class=”p”>)</span> +</pre></div>

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+

</div>

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</div> +</div> +</div>

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<div class=”jp-Cell-outputWrapper”> +<div class=”jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser”> +</div>

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<div class=”jp-OutputPrompt jp-OutputArea-prompt”>Out[26]:</div>

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<div class=”jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult” data-mime-type=”text/plain”> +<pre>&lt;matplotlib.image.AxesImage at 0x12b315e10&gt;</pre> +</div>

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</div> +<div class=”jp-OutputArea-child”>

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<div class=”jp-OutputPrompt jp-OutputArea-prompt”></div>

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<div class=”jp-RenderedImage jp-OutputArea-output “> +<img 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” +class=” +” +> +</div>

+

</div>

+

</div>

+

</div>

+

</div><div id=”cell-id=cb1b6ed4” class=”jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs “> +<div class=”jp-Cell-inputWrapper”> +<div class=”jp-Collapser jp-InputCollapser jp-Cell-inputCollapser”> +</div> +<div class=”jp-InputArea jp-Cell-inputArea”> +<div class=”jp-InputPrompt jp-InputArea-prompt”>In&nbsp;[&nbsp;]:</div> +<div class=”jp-CodeMirrorEditor jp-Editor jp-InputArea-editor” data-type=”inline”>

+
+

<div class=”CodeMirror cm-s-jupyter”>

+
+

<div class=” highlight hl-ipython3”><pre><span></span> +</pre></div>

+
+

</div>

+
+

</div> +</div> +</div>

+

</div> +</body>

+

</html> +</embed>

+
+ + +
+ +
+
+ +
+
+
+ ©2023, Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord. + + | + Powered by Sphinx 7.0.1 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html new file mode 100644 index 0000000..7f6b01c --- /dev/null +++ b/docs/_build/html/genindex.html @@ -0,0 +1,264 @@ + + + + + + + Index — DeepBench 0.1.1 documentation + + + + + + + + + + + + + + + + +
+
+
+ + +
+ + +

Index

+ +
+ A + | C + | D + | E + | G + | H + | P + | R + | S + +
+

A

+ + + +
+ +

C

+ + + +
+ +

D

+ + + +
+ +

E

+ + +
+ +

G

+ + +
+ +

H

+ + +
+ +

P

+ + +
+ +

R

+ + +
+ +

S

+ + + +
+ + + +
+ +
+
+ +
+
+
+ ©2023, Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord. + + | + Powered by Sphinx 7.0.1 + & Alabaster 0.7.13 + +
+ + + + + + \ No newline at end of file diff --git a/docs/_build/html/image.html b/docs/_build/html/image.html new file mode 100644 index 0000000..e5bdfc9 --- /dev/null +++ b/docs/_build/html/image.html @@ -0,0 +1,199 @@ + + + + + + + + Images — DeepBench 0.1.1 documentation + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Images

+
+
+class deepbench.image.ShapeImage(image_shape, object_noise_type='gaussian', object_noise_level=0.0)
+

Create an image that is a composition of multiple shapes

+
+
Parameters:
+
    +

  • image_shape (Tuple[int, int]) – Dimensions of the shape image.

    • +

  • object_noise_type (str, optional) – Noise distribution applied to image. Defaults to “gaussian”.

    • +

  • object_noise_level (float, optional) – Relative noise level (scale 0 to 1). Defaults to 0.0.

    • +
+
+
+
+
+combine_objects(objects, object_params, instance_params=None, seed=42)
+

Utilize Image._generate_astro_objects to overlay all selected astro objects into one image +If object parameters are not included in object list, defaults are used. +Updates SkyImage.image.

+

Current input parameter assumptions (totally up to change): +For a single image: +[{

+
+

“object_type”:”<object_type>”, +“object_parameters”:{<parameters for that object>}

+
+

}]

+
+
Parameters:
+
    +

  • objects (list) – str discriptors of the included object

    • +

  • object_params (list) – Parameters of each object (ie, position in frame)

    • +

  • seed (int, optional) – random seed for noise. Defaults to 42.

    • +
+
+
Returns:
+

image with objects and noise

+
+
Return type:
+

ndarray

+
+
+
+ +
+ +
+
+class deepbench.image.SkyImage(image_shape, object_noise_level=0, object_noise_type='gaussian')
+
+
+combine_objects(objects, instance_params, object_params, seed=42)
+

Utilize Image._generate_astro_objects to overlay all selected astro objects into one image +If object parameters are not included in object list, defaults are used. +Updates SkyImage.image.

+

Current input parameter assumptions (totally up to change): +For a single image: +[{

+
+

“object_type”:”<object_type>”, +“object_parameters”:{<parameters for that object>}

+
+

}]

+
+
Parameters:
+
    +

  • objects (list) – str discriptors of the included object

    • +

  • instance_params (list) – Parameters for the instance of the object (ei, overall noise)

    • +

  • object_params (list) – Parameters of each object (ei: position in frame)

    • +

  • seed (int, optional) – random seed for noise. Defaults to 42.

    • +
+
+
Returns:
+

image with objects and noise

+
+
Return type:
+

ndarray

+
+
+
+ +
+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord. + + | + Powered by Sphinx 7.0.1 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html new file mode 100644 index 0000000..0e203b9 --- /dev/null +++ b/docs/_build/html/index.html @@ -0,0 +1,153 @@ + + + + + + + + Welcome to DeepBench’s documentation! — DeepBench 0.1.1 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Welcome to DeepBench’s documentation!

+
+

Contents:

+ +
+
+
+

Indices and tables

+ +
+ + +
+ +
+
+ +
+
+
+ ©2023, Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord. + + | + Powered by Sphinx 7.0.1 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv new file mode 100644 index 0000000..f1b10a0 Binary files /dev/null and b/docs/_build/html/objects.inv differ diff --git a/docs/_build/html/physics_object.html b/docs/_build/html/physics_object.html new file mode 100644 index 0000000..16558c3 --- /dev/null +++ b/docs/_build/html/physics_object.html @@ -0,0 +1,31090 @@ + + + + + + + + Physics Simulation Objects — DeepBench 0.1.1 documentation + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Physics Simulation Objects

+
+

Newtonian Pendulum

+
+
+class deepbench.physics_object.Pendulum(pendulum_arm_length, starting_angle_radians, noise_std_percent={'acceleration_due_to_gravity': 0.0, 'big_G_newton': None, 'pendulum_arm_length': 0.0, 'phi_planet': None, 'starting_angle_radians': 0.0}, acceleration_due_to_gravity=None, big_G_newton=None, phi_planet=None, mass_pendulum_bob=10.0, coefficient_friction=0.0)
+
+
Parameters:
+
    +

  • pendulum_arm_length (float) –

    • +

  • starting_angle_radians (float) –

    • +

  • noise_std_percent (dict) –

    • +

  • acceleration_due_to_gravity (float | None) –

    • +

  • big_G_newton (float | None) –

    • +

  • phi_planet (float | None) –

    • +

  • mass_pendulum_bob (float | None) –

    • +

  • coefficient_friction (float | None) –

    • +
+
+
+
+
+create_noise(noiseless=False, seed=None, n_steps=10, verbose=False)
+

Creates noise on top of simulate_pendulum_dynamics +Also deals with the hierarchical case, where +acceleration_due_to_gravity is defined via +big_G_newton and phi_planet

+
+
Parameters:
+
    +

  • seed (int) – Random seed used to generate Gaussian noise

    • +

  • n_steps (int or Tuple[int,int]) – The shape of the noise to be +created. This is specified in create_object using the shape +of the input time array (or float).

    • +

  • noiseless (bool) –

    • +

  • verbose (bool) –

    • +
+
+
Return type:
+

array

+
+
+

Examples (see create_object)

+
+ +
+
+create_object(time, noiseless=False, seed=None, verbose=False)
+

Given a single or array of times, simulates the pendulum position at +each of these times and optionally adds Gaussian noise to each +parameter.

+
+
Parameters:
+
    +

  • time (Union[float, np.array]) – A single moment in time, or +an array of times (s)

    • +

  • noiseless (bool) – Enables a noise realization if True. +Default is set to False

    • +

  • seed (int) – Random seed used to generate Gaussian noise

    • +

  • verbose (bool) –

    • +
+
+
+

Example

+
>>> pendulum = Pendulum()
+>>> time = np.array(np.linspace(0, 10, 20))
+>>> pend_position = pendulum.create_object(time, noiseless=True)
+
+
+
+ +
+
+destroy_noise()
+

Remove noise from the parameters

+
+ +
+
+displayObject(time)
+

Display the pendulum over times.

+
+
Parameters:
+

time (Union[float, np.array]) – times to display the pendulum position

+
+
Returns:
+

noiseless, noisy arrays at times “time”

+
+
Return type:
+

tuple(np.ndarray, np.ndarray)

+
+
+
+ +
+
+simulate_pendulum_dynamics(time)
+

Simulate a pendulum with Neutonian physics

+
+
Parameters:
+

time (Union[float, np.array]) – times to simulate

+
+
Returns:
+

position of the pendulum.

+
+
Return type:
+

np.ndarray

+
+
+
+ +
+ +
+

Example

+ + + + + +Pendulum Example + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+

Hamiltonian Pendulum

+
+
+class deepbench.physics_object.HamiltonianPendulum(pendulum_arm_length, starting_angle_radians, acceleration_due_to_gravity=None, mass_pendulum_bob=10.0, noise_std_percent={'acceleration_due_to_gravity': None, 'mass_pendulum_bob': 0.0, 'pendulum_arm_length': 0.0, 'starting_angle_radians': 0.0})
+

The Hamiltonian Pendulum class.

+
+
Parameters:
+
    +

  • pendulum_arm_length (float) – The length of the pendulum arm

    • +

  • starting_angle_radians (float) – The starting angle of the pendulum +(angle from the ‘ceiling’)

    • +

  • noise_std_percent (dict) – A dictionary of the Gaussian noise +level to be applied to each parameter. The default is no +noise. Each number is the standard deviation when +multiplied by the parameter. See create_noise().

    • +

  • acceleration_due_to_gravity (float) – little g, local gravity +coefficient

    • +

  • mass_pendulum_bob (float) – Mass of the pendulum bob, +this is optional if calculation_type is position only.

    • +
+
+
+

Examples

+
>>> pendulum_obj = HamiltonianPendulum(pendulum_arm_length=10.,
+                            starting_angle_radians=np.pi/4,
+                            acceleration_due_to_gravity=9.8,
+                            noise_std_percent=
+                            {'pendulum_arm_length': 0.1,
+                             'starting_angle_radians': 0.1,
+                             'acceleration_due_to_gravity': 0.1}
+                            )
+
+
+
+
+create_object(time, noiseless=True, seed=42)
+

Given a single or array of times, simulates the pendulum position at +each of these times and optionally adds Gaussian noise to each +parameter.

+
+
Parameters:
+
    +

  • time (Union[float, np.array]) – A single moment in time, or +an array of times (s)

    • +

  • noiseless (bool) – Add noise to the pendulum parameters

    • +

  • seed (int) – Random seed for parameters

    • +
+
+
Returns:
+

tuple +q (np.ndarray): position. +p (np.ndarray): momentum. +dqdt (np.ndarray): velocity. +dpdt (np.ndarray) - force. +t_eval (np.ndarray) - times.

+
+
+
+ +
+
+dynamics_fn(t, coords)
+

derives the gradient of the hamiltonian function

+
+
Parameters:
+

coords (np.ndarray) – coordinates of the pendulum

+
+
Returns:
+

time derivates of p and q.

+
+
Return type:
+

np.ndarray

+
+
+
+ +
+
+simulate_pendulum_dynamics(time, **kwargs)
+

Evaulate the hamilitonian at times time and return the position, momentum and time derviates

+
+
Parameters:
+

time (np.ndarray) – Times to simulate

+
+
Returns:
+

tuple +q (np.ndarray): position. +p (np.ndarray): momentum. +dqdt (np.ndarray): velocity. +dpdt (np.ndarray) - force. +t_eval (np.ndarray) - times.

+
+
+
+ +
+ +
+

Example

+ + + + + +HamiltonianPendulumExample + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+
+ +
+
+
+ ©2023, Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord. + + | + Powered by Sphinx 7.0.1 + & Alabaster 0.7.13 + + | + Page source +
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diff --git a/docs/_build/html/shapes.html b/docs/_build/html/shapes.html new file mode 100644 index 0000000..dbb930f --- /dev/null +++ b/docs/_build/html/shapes.html @@ -0,0 +1,278 @@ + + + + + + + + Shapes — DeepBench 0.1.1 documentation + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Shapes

+
+

Shapes

+
+
+deepbench.shapes.Shapes
+

alias of ShapeGenerator

+
+ +
+
+class deepbench.shapes.shape_generator.ShapeGenerator(image_shape=(28, 28))
+
+
Parameters:
+

image_shape (tuple) –

+
+
+
+
+create_arc(center=(12, 14), radius=9.444508656341222, theta1=9.753398997365007, theta2=95.23967568027233, line_width=1)
+

Create an arc with radius “radius” arcing from theta1 to theta2 counter-clockwise

+
+
Parameters:
+
    +

  • center (tuple, optional) – Center point of the arc. Defaults to (np.random.randint(10, 16), np.random.randint(10, 16)).

    • +

  • radius (Union[int, float], optional) – distance from the arc to the center point. Defaults to np.random.random(8, 12).

    • +

  • theta1 (Union[int, float], optional) – starting point of the arc (degrees). Defaults to np.random.random(0, 45).

    • +

  • theta2 (Union[int, float], optional) – ending point of the arc (degrees). Defaults to np.random.random(85, 120).

    • +

  • line_width (int, optional) – thickness of the arc (pixels) Defaults to 1.

    • +
+
+
Returns:
+

The arc image

+
+
Return type:
+

np.ndarray

+
+
+
+ +
+
+create_ellipse(center=(13, 11), width=13, height=14, angle=0, line_width=1, fill=False)
+

Create an ellipse/circle (where width/height are the same)

+
+
Parameters:
+
    +

  • center (tuple, optional) – Center point of the ellipse. Defaults to (np.random.randint(10, 16), np.random.randint(10, 16)).

    • +

  • width (int, optional) – Horizontal length of the ellipse (pixels). Defaults to np.random.randint(10, 16).

    • +

  • height (int, optional) – Vertical height of the ellipse (pixels). Defaults to np.random.randint(10, 16).

    • +

  • angle (Union[float, int], optional) – Rotation angle of the ellipse (degrees). Defaults to 0.

    • +

  • line_width (int, optional) – Width of the ellipse’s border (pixels). Defaults to 1.

    • +

  • fill (bool, optional) – Fill the center of the ellipse. Defaults to False.

    • +
+
+
Returns:
+

np.ndarray

+
+
+
+ +
+
+create_empty_shape()
+

Create an array of 0s with shape self.image_shape

+
+
Returns:
+

np.ndarray

+
+
+
+ +
+
+create_line(start=(0, 5), end=(15, 12), line_width=1)
+

Generate a numpy array of a line

+
+
Parameters:
+
    +

  • start (tuple, optional) – Starting corner of the line. Defaults to (np.random.randint(0, 10), np.random.randint(0, 10)).

    • +

  • end (tuple, optional) – Ending corner of the line. Defaults to (np.random.randint(12, 28), np.random.randint(12, 28)).

    • +

  • line_width (int, optional) – Thickness of the line (pixels). Defaults to 1.

    • +
+
+
Returns:
+

np.ndarray

+
+
+
+ +
+
+create_rectangle(center=(13, 14), width=13, height=10, angle=0, line_width=1, fill=False)
+

Make a rectangle.

+
+
Parameters:
+
    +

  • center (tuple(*int), optional) – Center of the rectangle (coordinate). Defaults to (14, 14).

    • +

  • width (int, optional) – Horizontal width of the rectangle (in pixels). Defaults to random int.

    • +

  • height (int, optional) – Vertical height of the rectangle (in pixels). Defaults to random int.

    • +

  • angle (Union[float, int], optional) – tilt the rectangle (degrees). Defaults to 0.

    • +

  • line_width (int, optional) – line width of the outline. Defaults to 1.

    • +

  • fill (bool, optional) – Fill in the rectangle. Defaults to False.

    • +
+
+
Returns:
+

A Rectangle image

+
+
Return type:
+

np.ndarray

+
+
+
+ +
+
+create_regular_polygon(center=(14, 11), angle=28.891335073449582, vertices=3, radius=10.716248649411046, line_width=1, fill=False)
+

Create a polygon with equal length sides

+
+
Parameters:
+
    +

  • center (tuple[*int], optional) – Where to center the object.

    • +

  • angle (Union[int, float], optional) – Angle of rotation (degrees)

    • +

  • vertices (int, optional) – Number of verticies. Defaults to 3, a triangle.

    • +

  • radius (int, optional) – distance from vertex to vertex Defaults to 10.

    • +

  • line_width (int, optional) – line width of the outline. Defaults to 1.

    • +

  • fill (bool, optional) – Fill in the rectangle. Defaults to False.

    • +
+
+
Returns:
+

A polygon image

+
+
Return type:
+

np.ndarray

+
+
+
+ +
+
+resize(image, resize_dimensions=(28, 28))
+

Resize an array-like

+
+
Parameters:
+
    +

  • image (np.ndarray) – shape to resize

    • +

  • resize_dimensions (tuple, optional) – resize to shape. Defaults to (28, 28).

    • +
+
+
Raises:
+

ValueError – invalid size, either too small (0,0) or having the number of incorrect dimensions

+
+
Returns:
+

array of size (resize_dimensions)

+
+
Return type:
+

np.ndarray

+
+
+
+ +
+ +
+
+ + +
+ +
+
+ +
+
+
+ ©2023, Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord. + + | + Powered by Sphinx 7.0.1 + & Alabaster 0.7.13 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/docs/_empty.txt b/docs/_empty.txt deleted file mode 100644 index 5a93946..0000000 --- a/docs/_empty.txt +++ /dev/null @@ -1 +0,0 @@ -I'm so lonely ... diff --git a/docs/astro_object.rst b/docs/astro_object.rst new file mode 100644 index 0000000..3e46955 --- /dev/null +++ b/docs/astro_object.rst @@ -0,0 +1,21 @@ +Astronomy Objects +=================== + + +Galaxies +--------- + +.. autoclass:: deepbench.astro_object.GalaxyObject + :members: + +Spiral Galaxies +---------------- + +.. autoclass:: deepbench.astro_object.SpiralGalaxyObject + :members: + +Stars +---------------- + +.. autoclass:: deepbench.astro_object.StarObject + :members: diff --git a/docs/collection.rst b/docs/collection.rst new file mode 100644 index 0000000..a8b6cc2 --- /dev/null +++ b/docs/collection.rst @@ -0,0 +1,5 @@ +Collection +========== + +.. autoclass:: deepbench.collection.Collection + :members: \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..7778219 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,41 @@ +import sys + +sys.path.append("..") + +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = "DeepBench" +copyright = "2023, Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord" +author = "Ashia Livaudais, M. Voetberg, Becky Nevin, Omari Paul, Brian Nord" +release = "0.1.1" + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.napoleon", + "sphinx_autodoc_typehints", +] +napoleon_use_param = True +autodoc_default_options = { + "members": True, +} +autodoc_typehints = "description" + +templates_path = ["_templates"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = "alabaster" +html_static_path = ["_static"] diff --git a/docs/example.rst b/docs/example.rst new file mode 100644 index 0000000..5622685 --- /dev/null +++ b/docs/example.rst @@ -0,0 +1,26 @@ +Examples and Quickstart +========================= + +Astro Objects +-------------- + +.. raw:: html + :file: examples/astro_object.html + +Physics Objects +---------------- + + +.. raw:: html + :file: examples/Pendulum Example.html + + +.. raw:: html + :file: examples/HamiltonianPendulumExample.html + + +Shape Objects +-------------- + +.. raw:: html + :file: examples/Shape Object Example.html \ No newline at end of file diff --git a/docs/examples/HamiltonianPendulumExample.html b/docs/examples/HamiltonianPendulumExample.html new file mode 100644 index 0000000..88fa156 --- /dev/null +++ b/docs/examples/HamiltonianPendulumExample.html @@ -0,0 +1,15383 @@ + + + + + +HamiltonianPendulumExample + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/examples/Pendulum Example.html b/docs/examples/Pendulum Example.html new file mode 100644 index 0000000..3ce2220 --- /dev/null +++ b/docs/examples/Pendulum Example.html @@ -0,0 +1,15374 @@ + + + + + +Pendulum Example + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/examples/Shape Object Example.html b/docs/examples/Shape Object Example.html new file mode 100644 index 0000000..7535cba --- /dev/null +++ b/docs/examples/Shape Object Example.html @@ -0,0 +1,14458 @@ + + + + + +Shape Object Example + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/examples/astro_object.html b/docs/examples/astro_object.html new file mode 100644 index 0000000..e2125fe --- /dev/null +++ b/docs/examples/astro_object.html @@ -0,0 +1,15602 @@ + + + + + +Astro Object Example + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/image.rst b/docs/image.rst new file mode 100644 index 0000000..249295a --- /dev/null +++ b/docs/image.rst @@ -0,0 +1,8 @@ +Images +========= + +.. autoclass:: deepbench.image.ShapeImage + :members: + +.. autoclass:: deepbench.image.SkyImage + :members: \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..b5c11cf --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,31 @@ +.. DeepBench documentation master file, created by + sphinx-quickstart on Tue Jun 20 13:50:51 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to DeepBench's documentation! + +.. image:: repository_support/DeepSkies_Logos_DeepBench.png + :width: 800 +===================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + collection + image + astro_object + physics_object + shapes + example + +.. image:: repository_support/DeepBench.png + :width: 800 + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..32bb245 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/physics_object.rst b/docs/physics_object.rst new file mode 100644 index 0000000..a456fd5 --- /dev/null +++ b/docs/physics_object.rst @@ -0,0 +1,31 @@ +Physics Simulation Objects +========================== + + +Newtonian Pendulum +------------------ + + +.. autoclass:: deepbench.physics_object.Pendulum + :members: + + +Example +^^^^^^^^^^ + +.. raw:: html + :file: examples/Pendulum Example.html + + +Hamiltonian Pendulum +---------------------- + +.. autoclass:: deepbench.physics_object.HamiltonianPendulum + :members: + + +Example +^^^^^^^^^^ + +.. raw:: html + :file: examples/HamiltonianPendulumExample.html \ No newline at end of file diff --git a/docs/repository_support/DeepBench.png b/docs/repository_support/DeepBench.png new file mode 100644 index 0000000..1854878 Binary files /dev/null and b/docs/repository_support/DeepBench.png differ diff --git a/archive/repository_support/images/DeepSkies_Logos_DeepBench.png b/docs/repository_support/DeepSkies_Logos_DeepBench.png similarity index 100% rename from archive/repository_support/images/DeepSkies_Logos_DeepBench.png rename to docs/repository_support/DeepSkies_Logos_DeepBench.png diff --git a/docs/repository_support/hamiltonian.png b/docs/repository_support/hamiltonian.png new file mode 100644 index 0000000..ac02314 Binary files /dev/null and b/docs/repository_support/hamiltonian.png differ diff --git a/docs/repository_support/noiseless_circle.png b/docs/repository_support/noiseless_circle.png new file mode 100644 index 0000000..1ef35ae Binary files /dev/null and b/docs/repository_support/noiseless_circle.png differ diff --git a/docs/repository_support/noiseless_line.png b/docs/repository_support/noiseless_line.png new file mode 100644 index 0000000..558d2c6 Binary files /dev/null and b/docs/repository_support/noiseless_line.png differ diff --git a/docs/repository_support/noiseless_square.png b/docs/repository_support/noiseless_square.png new file mode 100644 index 0000000..ad1ca3e Binary files /dev/null and b/docs/repository_support/noiseless_square.png differ diff --git a/docs/repository_support/noisy_square_rectangle.png b/docs/repository_support/noisy_square_rectangle.png new file mode 100644 index 0000000..22b0f4a Binary files /dev/null and b/docs/repository_support/noisy_square_rectangle.png differ diff --git a/docs/repository_support/noisy_stars.png b/docs/repository_support/noisy_stars.png new file mode 100644 index 0000000..9b3bddb Binary files /dev/null and b/docs/repository_support/noisy_stars.png differ diff --git a/docs/repository_support/pendulum_draws.png b/docs/repository_support/pendulum_draws.png new file mode 100644 index 0000000..c810fb7 Binary files /dev/null and b/docs/repository_support/pendulum_draws.png differ diff --git a/docs/shapes.rst b/docs/shapes.rst new file mode 100644 index 0000000..4502938 --- /dev/null +++ b/docs/shapes.rst @@ -0,0 +1,11 @@ +Shapes +======== + + +Shapes +-------- +.. autoclass:: deepbench.shapes.Shapes + :members: + +.. autoclass:: deepbench.shapes.shape_generator.ShapeGenerator + :members: diff --git a/notebooks/Astro Object Example.html b/notebooks/Astro Object Example.html new file mode 100644 index 0000000..0ed8fd1 --- /dev/null +++ b/notebooks/Astro Object Example.html @@ -0,0 +1,15601 @@ + + + + + +Astro Object Example + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/notebooks/Astro Object Example.ipynb b/notebooks/Astro Object Example.ipynb new file mode 100644 index 0000000..a631768 --- /dev/null +++ b/notebooks/Astro Object Example.ipynb @@ -0,0 +1,268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "55d6bd7b", + "metadata": {}, + "source": [ + "# Create a single image with a star \n", + "\n", + "The astro module contain a number of astronomy objects. In this notebook, we will go over creating an instance of a star object, how to generate a star image with and without noise, and display the results. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bd463d6c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from deepbench.astro_object import StarObject\n", + "from deepbench.image import SkyImage\n", + "from deepbench.collection import Collection" + ] + }, + { + "cell_type": "markdown", + "id": "1252a0d0", + "metadata": {}, + "source": [ + "## The first step is to initilize the star class\n", + "Inputs include the dimension of the image (a square), noise, radius, and amplitude. Here we introduce no noise:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "35a86e75", + "metadata": {}, + "outputs": [], + "source": [ + "star = StarObject(\n", + " image_dimensions = (28,28),\n", + " noise = 0.0,\n", + " radius= 0.2,\n", + " amplitude = 5.0\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "a1615e32", + "metadata": {}, + "source": [ + "## Generate multiple stars\n", + "The input is x and y positions. The output is an image of a star at that position.\n", + "\n", + "First, generate random x and y positions." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "38ee326d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Z+sMf/qBJkybl2bdRo0Yuz/Pa78uWLdPw4cPVv39/TZw4UWFhYfLy8tLMmTN19OjRAtcppwZJeuKJJ9SjR488+3h6mIXPPvtM0m8h5OTJk6patWqh8zz66KNaunSpxo0bp/bt28vhcMhms+mee+7J8+Lq0nh/33PPPRo/fryWL1+uyZMna9myZbrxxhuLFQQ9pXPnzjp69KjWrFmjf/3rX/rrX/+qV155RYsXL9bIkSNLvZ6ibP/f/e53OnTokNavX6+4uDj9/e9/1+uvv67nnnvOOfQCyieCDzzihx9+kK+vr6pUqeLW/Js3b9bZs2e1atUqde7c2dkeHx/vdk3R0dE6cOCAjDEu/3s7dOhQrn457V27dnWZdujQIef0knTfffepX79++vLLL7V8+XK1atVKTZs2LXCenLqOHDmSa9rVbfXr19fGjRvVsWPHPAOIO+rXr6/z5887j/C4Y+XKlapXr55WrVrlso9yjtbkyO/IV7169SRJ3t7ehdYRHR2tw4cP52q/+v1QkLi4OP31r3/VpEmTtHz5cg0bNkw7d+4s9DbrlStXatiwYXrppZecbenp6bnuKCqqnH1/+PBh5zaQpDNnzhR6hDFHSEiI+vTpo+XLl+vee+/Vtm3bNG/evCLXkNe2/N///id/f39nkPb3989z+37//feqVKmSoqKiXOq5//77df/99+v8+fPq3Lmzpk6dWmDwKepIzld+xq/cXpmZmYqPj3f7PZxzR9+gQYOUmZmpO++8U3/5y1/09NNPy9fX161louRxqgvFktdt119//bXWrl2r7t27q1Il995SOf/DuvJ/VJmZmXr99dfdK1RS7969derUKa1cudLZdvHiRb3xxhsu/W688UaFhYVp8eLFLre6//Of/9R3332nPn36uF1DUfXq1UvVq1fX7NmztWXLliId7YmMjFRsbKzee+89nT9/3tm+ZcsWffPNNy59Bw4cqKysLE2fPj3Xci5fvuzWF/DAgQO1fft25xGQKyUnJxdpqIO89vvOnTu1fft2l3454zxdXWdYWJhuvvlmLVmyRAkJCbmWf+X7tXfv3tqxY4d27drlMj2/o2BXS05O1siRI9W2bVvNmDFDf/3rX7Vnzx7NmDGj0Hm9vLxyHa1ZsGBBkU4N56Vbt27y9vbWggULXJZbnOAi/TbQ38GDBzVx4kR5eXnpnnvuKfK827dv1549e5zPT5w4oTVr1qh79+7OcXS6d++uNWvWuNxWfvr0aX3wwQfq1KmT8+jw2bNnXZZdpUoVNWjQIN+hJ3IEBAQU6b3brVs3+fj4aP78+S7b66233lJKSopbn/Gra/bx8VFMTIyMMbp06VKxl4fSwxEfFMugQYPk5+enDh06KCwsTAcPHtQbb7whf39/zZo1y+3ldujQQcHBwRo2bJjGjh0rm82m999//5oO7T/44INauHCh7rvvPn311VeKiIjQ+++/n2uwRG9vb82ePVv333+/unTposGDB+v06dN69dVXVadOHY0fP97tGorK29tb99xzjxYuXCgvLy8NHjy4SPPNmDFD/fr1U8eOHXX//ffrl19+0cKFCxUbG+sShrp06aKHH35YM2fO1L59+9S9e3d5e3vr8OHDWrFihV599dViD0g5ceJErV27Vn379tXw4cPVunVrXbhwQd98841WrlypH3/8UdWrVy9wGX379tWqVat0xx13qE+fPoqPj9fixYsVExPjUr+fn59iYmL00UcfqVGjRgoJCVFsbKxiY2P12muvqVOnTmrWrJkefPBB1atXT6dPn9b27dt18uRJff3115KkSZMm6f3331fPnj312GOPKSAgQG+88Yaio6OLdD3JY489prNnz2rjxo3y8vJSz549NXLkSL3wwgvq16+fWrRoUeB6vv/++3I4HIqJidH27du1ceNGVatWrYhb21VoaKieeOIJzZw5U3379lXv3r21d+9e/fOf/yx0m1+pT58+qlatmlasWKFevXopLCysyPPGxsaqR48eGjt2rOx2u/M/KVee5nnhhRe0YcMGderUSY888ogqV66sJUuWKCMjw2VcnJiYGN18881q3bq1QkJCtHv3bq1cuVJjxowpsIbWrVtr48aNevnllxUZGam6deuqXbt2ufqFhobq6aef1rRp09SzZ0/dfvvtOnTokF5//XW1adPGrdPK3bt3V3h4uDp27KgaNWrou+++08KFC9WnTx8FBgYWe3koRWVxRTUqrldffdW0bdvWhISEmMqVK5uIiAgzZMgQc/jw4SLN36VLl3xvAd22bZv5/e9/b/z8/ExkZKSZNGmS+eyzz3LdBZXfMoYNG5Zr7Jpjx46Z22+/3fj7+5vq1aubxx57zHkL99W3in/00UemVatWxm63m5CQEHPvvfc67wi68jUCAgKKvF7R0dEut+nmdVdXjl27dhlJpnv37nlsnfx9+OGHpkmTJsZut5vY2Fizdu1aM2DAANOkSZNcfd944w3TunVr4+fnZwIDA02zZs3MpEmTzKlTp/Kt+cp17NKli0tbWlqaefrpp02DBg2Mj4+PqV69uunQoYOZO3euc2yUnLu6XnzxxVzLzM7ONjNmzDDR0dHGbrebVq1amfXr1+e5L7/44gvTunVr4+Pjk+sOr6NHj5r77rvPhIeHG29vb1OzZk3Tt29fs3LlSpdl7N+/33Tp0sX4+vqamjVrmunTp5u33nqr0Lu61qxZYySZl156yaU9NTXVREdHmxYtWriMBXO1X375xdx///2mevXqpkqVKqZHjx7m+++/N9HR0S53JeXcjXT1uFh5vW+ysrLMtGnTTEREhPHz8zM333yzOXDgQK5lFibnrrYPPvigyPNIMqNHjzbLli0zDRs2dO67vN7Xe/bsMT169DBVqlQx/v7+5pZbbnGOt5PjhRdeMG3btjVVq1Y1fn5+pkmTJuYvf/mLyzbN666u77//3nTu3Nn4+fm53M2W1zg+xvx2+3qTJk2Mt7e3qVGjhhk1apTLOF3GFP3vy5IlS0znzp1NtWrVjN1uN/Xr1zcTJ0503qmG8stmTClcDQqgUF9//bVatmyp9957r8h31uSnZcuWCg0NdRn5FsjL+PHj9dZbbykxMbHIPx1js9k0evRoLVy4sISrAzyPa3yAcuLNN99UlSpVdOeddxZ5nkuXLuW6lmbz5s36+uuv+TFPFCo9PV3Lli3TgAED+L08WAbX+ABlbN26dc5rpcaMGaOAgIAiz/vTTz+pW7duGjJkiCIjI/X9999r8eLFCg8P15/+9KcSrBoVWVJSkjZu3KiVK1fq7Nmzeuyxx8q6JKDUEHyAMvboo4/q9OnT6t27d7HH/wgODlbr1q3117/+VWfOnFFAQID69OmjWbNmuX3hLK5/Bw8e1L333quwsDDNnz9fLVu2LOuSgFLDNT4AAMAyuMYHAABYBsEHAABYxnV/jU92drZOnTqlwMDAIg9vDgAAypYxRmlpaYqMjHT7VwHyW3CZmTFjhrnxxhtNlSpVTGhoqOnXr5/5/vvvXfp06dLFSHJ5PPzww0V+jRMnTuSanwcPHjx48OBRMR4nTpzwaPYo0yM+W7Zs0ejRo9WmTRtdvnxZkydPVvfu3XXw4EGXW3offPBBPf/8887nxRlvImfo8BMnThT6q+EAAKB8SE1NVVRUlMd/AqRMg09cXJzL83feeUdhYWH66quvXH6h29/fX+Hh4W69Rs7praCgIIIPAAAVjKcvUylXFzenpKRIkkJCQlzaly9frurVqys2NlZPP/20Ll68mO8yMjIylJqa6vIAAACQytHFzdnZ2Ro3bpw6duyo2NhYZ/sf//hHRUdHKzIyUvv379eTTz6pQ4cOadWqVXkuZ+bMmcUeBA4AAFhDuRnAcNSoUfrnP/+p//73v6pVq1a+/f7973/r1ltv1ZEjR1S/fv1c0zMyMpSRkeF8nnOOMCUlhVNdAABUEKmpqXI4HB7//i4XR3zGjBmj9evXa+vWrQWGHklq166dJOUbfOx2u+x2e4nUCQAAKrYyDT7GGD366KP65JNPtHnzZtWtW7fQefbt2ydJioiIKOHqAADA9aZMg8/o0aP1wQcfaM2aNQoMDFRiYqIkyeFwyM/PT0ePHtUHH3yg3r17q1q1atq/f7/Gjx+vzp07q3nz5mVZOgAAqIDK9Bqf/G5RW7p0qYYPH64TJ05oyJAhOnDggC5cuKCoqCjdcccdeuaZZ4p8vq+kzhECAICSc11e41NY5oqKitKWLVtKqRoAAHC9K1fj+AAAAJSkcnFXF65vWdlGu+LPKSktXWGBvmpbN0RelfjBWABA6SP4oETFHUjQtHUHlZCS7myLcPhqym0x6hnLnXkAgNLFqS6UmLgDCRq1bI9L6JGkxJR0jVq2R3EHEsqoMgCAVRF8UCKyso2mrTuovC5fz2mbtu6gsrLLxcDhAACLIPigROyKP5frSM+VjKSElHTtij9XekUBACyP4IMSkZSWf+hxpx8AAJ5A8EGJCAv09Wg/AAA8geCDEtG2bogiHL7K76Z1m367u6tt3ZDSLAsAYHEEH5QIr0o2TbktRpJyhZ+c51Nui2E8HwBAqSL4oMT0jI3QoiE3KNzhejor3OGrRUNuYBwfAECpYwBDlKiesRH6Q0w4IzcDAMoFgg9KnFclm9rXr1bWZQAAwKkuAABgHQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGQQfAABgGWUafGbOnKk2bdooMDBQYWFh6t+/vw4dOuTSJz09XaNHj1a1atVUpUoVDRgwQKdPny6jigEAQEVWpsFny5YtGj16tHbs2KENGzbo0qVL6t69uy5cuODsM378eK1bt04rVqzQli1bdOrUKd15551lWDUAAKiobMYYU9ZF5Dhz5ozCwsK0ZcsWde7cWSkpKQoNDdUHH3ygu+66S5L0/fff63e/+522b9+u3//+94UuMzU1VQ6HQykpKQoKCirpVQAAAB5QUt/f5eoan5SUFElSSEiIJOmrr77SpUuX1K1bN2efJk2aqHbt2tq+fXuey8jIyFBqaqrLAwAAQCpHwSc7O1vjxo1Tx44dFRsbK0lKTEyUj4+Pqlat6tK3Ro0aSkxMzHM5M2fOlMPhcD6ioqJKunQAAFBBlJvgM3r0aB04cEAffvjhNS3n6aefVkpKivNx4sQJD1UIAAAqusplXYAkjRkzRuvXr9fWrVtVq1YtZ3t4eLgyMzOVnJzsctTn9OnTCg8Pz3NZdrtddru9pEsGAAAVUJke8THGaMyYMfrkk0/073//W3Xr1nWZ3rp1a3l7e+vzzz93th06dEjHjx9X+/btS7tcAABQwZXpEZ/Ro0frgw8+0Jo1axQYGOi8bsfhcMjPz08Oh0MPPPCAJkyYoJCQEAUFBenRRx9V+/bti3RHFwAAwJXK9HZ2m82WZ/vSpUs1fPhwSb8NYPj444/rb3/7mzIyMtSjRw+9/vrr+Z7quhq3swMAUPGU1Pd3uRrHpyQQfAAAqHgsMY4PAABASSL4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAyyD4AAAAy6h8LTNnZmYqKSlJ2dnZLu21a9e+pqIAAABKglvB5/DhwxoxYoS++OILl3ZjjGw2m7KysjxSHAAAgCe5FXyGDx+uypUra/369YqIiJDNZvN0XQAAAB7nVvDZt2+fvvrqKzVp0sTT9QAAAJQYty5ujomJ0c8//+zpWgAAAEqUW8Fn9uzZmjRpkjZv3qyzZ88qNTXV5QEAAFAeuRV8unXrph07dujWW29VWFiYgoODFRwcrKpVqyo4OLjIy9m6datuu+02RUZGymazafXq1S7Thw8fLpvN5vLo2bOnOyUDAAC4d43Ppk2bPPLiFy5cUIsWLTRixAjdeeedefbp2bOnli5d6nxut9s98toAAMB63Ao+Xbp08ciL9+rVS7169Sqwj91uV3h4uEdeDwAAWJvbAxgmJyfrrbfe0nfffSdJatq0qUaMGCGHw+Gx4iRp8+bNztNpXbt21QsvvKBq1arl2z8jI0MZGRnO51xzBAAAcrh1jc/u3btVv359vfLKKzp37pzOnTunl19+WfXr19eePXs8VlzPnj313nvv6fPPP9fs2bO1ZcsW9erVq8ABEmfOnCmHw+F8REVFeaweAABQsdmMMaa4M910001q0KCB3nzzTVWu/NtBo8uXL2vkyJH64YcftHXr1uIXYrPpk08+Uf/+/fPt88MPP6h+/frauHGjbr311jz75HXEJyoqSikpKQoKCip2XQAAoPSlpqbK4XB4/Pvb7SM+Tz75pDP0SFLlypU1adIk7d6922PFXa1evXqqXr26jhw5km8fu92uoKAglwcAAIDkZvAJCgrS8ePHc7WfOHFCgYGB11xUfk6ePKmzZ88qIiKixF4DAABcv9y6uHnQoEF64IEHNHfuXHXo0EGStG3bNk2cOFGDBw8u8nLOnz/vcvQmPj5e+/btU0hIiEJCQjRt2jQNGDBA4eHhOnr0qCZNmqQGDRqoR48e7pQNAAAszq3gM3fuXNlsNt133326fPmyJMnb21ujRo3SrFmziryc3bt365ZbbnE+nzBhgiRp2LBhWrRokfbv3693331XycnJioyMVPfu3TV9+nTG8gEAAG5x6+LmHBcvXtTRo0clSfXr15e/v7/HCvOUkro4CgAAlJyS+v52exwfSfL391ezZs08VQsAAECJKnLwufPOO/XOO+8oKCgo35+XyLFq1aprLgwAAMDTihx8HA6HbDabpN/u6sr5NwAAQEVxTdf4VARc4wMAQMVTrgYw7Nq1q5KTk3O1p6amqmvXrtdaEwAAQIlwK/hs3rxZmZmZudrT09P1n//855qLAgAAKAnFuqtr//79zn8fPHhQiYmJzudZWVmKi4tTzZo1PVcdAACABxUr+LRs2VI2m002my3PU1p+fn5asGCBx4oDAADwpGIFn/j4eBljVK9ePe3atUuhoaHOaT4+PgoLC5OXl5fHiwQAAPCEYgWf6OhoSVJ2dnaJFAMAAFCSihx81q5dq169esnb21tr164tsO/tt99+zYUBAAB4WpHH8alUqZISExMVFhamSpXyvxnMZrMpKyvLYwVeK8bxAQCg4inz3+q68vQWp7oAAEBF5NY4PnnJa0BDAACA8sSt4DN79mx99NFHzud33323QkJCVLNmTX399dceKw4AAMCT3Ao+ixcvVlRUlCRpw4YN2rhxo+Li4tSrVy9NnDjRowUCAAB4SrFuZ8+RmJjoDD7r16/XwIED1b17d9WpU0ft2rXzaIEAAACe4tYRn+DgYJ04cUKSFBcXp27dukmSjDHl6o4uAACAK7l1xOfOO+/UH//4RzVs2FBnz55Vr169JEl79+5VgwYNPFogAACAp7gVfF555RXVqVNHJ06c0Jw5c1SlShVJUkJCgh555BGPFggAAOApRR7AsKJiAEMAACqeMh/A8GpHjx7VvHnz9N1330mSYmJiNG7cONWrV89jxQEAAHiSWxc3f/bZZ4qJidGuXbvUvHlzNW/eXDt37lRMTIw2bNjg6RoBAAA8wq1TXa1atVKPHj00a9Ysl/annnpK//rXv7Rnzx6PFXitONUFAEDFU1Lf324d8fnuu+/0wAMP5GofMWKEDh48eM1FAQAAlAS3gk9oaKj27duXq33fvn0KCwu71poAAABKhFsXNz/44IN66KGH9MMPP6hDhw6SpG3btmn27NmaMGGCRwsEAADwFLeu8THGaN68eXrppZd06tQpSVJkZKQmTpyosWPHymazebxQd3GNDwAAFU9JfX9f8zg+aWlpkqTAwECPFORpBB8AACqecjeOjyQlJSXp0KFDkqQmTZooNDTUI0UBAACUBLcubk5LS9PQoUMVGRmpLl26qEuXLoqMjNSQIUOUkpLi6RoBAAA8wq3gM3LkSO3cuVOffvqpkpOTlZycrPXr12v37t16+OGHPV0jAACAR7h1jU9AQIA+++wzderUyaX9P//5j3r27KkLFy54rMBrxTU+AABUPOVqAMNq1arJ4XDkanc4HAoODr7mogAAAEqCW8HnmWee0YQJE5SYmOhsS0xM1MSJE/Xss896rDgAAABPcvu3uo4cOaKMjAzVrl1bknT8+HHZ7XY1bNjQpW9Z/24Xp7oAAKh4ytXt7P379/dYAQAAAKXFreAzZcqUIvX729/+pgsXLiggIMCdlwEAAPAot67xKaqHH35Yp0+fLsmXAAAAKLISDT7X+GsYAAAAHlWiwQcAAKA8IfgAAADLIPgAAADLIPgAAADLcCv4DBs2TFu3bi20X3R0tLy9vd15CQAAAI9zK/ikpKSoW7duatiwoWbMmKGffvopz34HDhxQVFTUNRUIAADgKW4Fn9WrV+unn37SqFGj9NFHH6lOnTrq1auXVq5cqUuXLnm6RgAAAI9w+xqf0NBQTZgwQV9//bV27typBg0aaOjQoYqMjNT48eN1+PBhT9YJAABwza754uaEhARt2LBBGzZskJeXl3r37q1vvvlGMTExeuWVVzxRIwAAgEe4FXwuXbqkv//97+rbt6+io6O1YsUKjRs3TqdOndK7776rjRs36uOPP9bzzz/v6XoBAADc5taPlEZERCg7O1uDBw/Wrl271LJly1x9brnlFlWtWvUaywMAAPAct4LPK6+8orvvvlu+vr759qlatari4+PdLgwAAMDT3Ao+Q4cO9XQdAAAAJY6RmwEAgGUQfAAAgGUQfAAAgGUQfAAAgGUQfAAAgGUQfAAAgGUQfAAAgGUQfAAAgGUQfAAAgGUQfAAAgGW49ZMVAADA2rKyjXbFn1NSWrrCAn3Vtm6IvCrZyrqsQhF8AABAscQdSNC0dQeVkJLubItw+GrKbTHqGRtRhpUVjlNdAACgyOIOJGjUsj0uoUeSElPSNWrZHsUdSCijyoqG4AMAAIokK9to2rqDMnlMy2mbtu6gsrLz6lE+lGnw2bp1q2677TZFRkbKZrNp9erVLtONMXruuecUEREhPz8/devWTYcPHy6bYgEAsLhd8edyHem5kpGUkJKuXfHnSq+oYirT4HPhwgW1aNFCr732Wp7T58yZo/nz52vx4sXauXOnAgIC1KNHD6Wn57/RAQBAyUhKK9r3b1H7lYUyvbi5V69e6tWrV57TjDGaN2+ennnmGfXr10+S9N5776lGjRpavXq17rnnntIsFQAAywsL9PVov7JQbq/xiY+PV2Jiorp16+ZsczgcateunbZv316GlQEAYE1t64YowuGr/G5at+m3u7va1g0pzbKKpdwGn8TERElSjRo1XNpr1KjhnJaXjIwMpaamujwAAMC186pk05TbYiQpV/jJeT7ltphyPZ5PuQ0+7po5c6YcDofzERUVVdYlAQBw3egZG6FFQ25QuMP1dFa4w1eLhtxQ7sfxKbcDGIaHh0uSTp8+rYiI/9uIp0+fVsuWLfOd7+mnn9aECROcz1NTUwk/AAB4UM/YCP0hJpyRmz2pbt26Cg8P1+eff+4MOqmpqdq5c6dGjRqV73x2u112u72UqgQAwJq8KtnUvn61si6j2Mo0+Jw/f15HjhxxPo+Pj9e+ffsUEhKi2rVra9y4cXrhhRfUsGFD1a1bV88++6wiIyPVv3//sisaAABUWGUafHbv3q1bbrnF+TznFNWwYcP0zjvvaNKkSbpw4YIeeughJScnq1OnToqLi5Ovb/m9TQ4AAJRfNmNM+R1X2gNSU1PlcDiUkpKioKCgsi4HAAAUQUl9f193d3UBAADkh+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAso3JZF1BRZWUb7Yo/p6S0dIUF+qpt3RB5VbKVdVkAAKAABB83xB1I0LR1B5WQku5si3D4asptMeoZG1GGlQEAgIJwqquY4g4kaNSyPS6hR5ISU9I1atkexR1IKKPKAABAYQg+xZCVbTRt3UGZPKbltE1bd1BZ2Xn1AAAAZY3gUwy74s/lOtJzJSMpISVdu+LPlV5RAACgyAg+xZCUln/ocacfAAAoXQSfYggL9PVoPwAAULoIPsXQtm6IIhy+yu+mdZt+u7urbd2Q0iwLAAAUEcGnGLwq2TTlthhJyhV+cp5PuS2G8XwAACinCD7F1DM2QouG3KBwh+vprHCHrxYNuYFxfAAAKMcYwNANPWMj9IeYcEZuBgCggiH4uMmrkk3t61cr6zIAAEAxcKoLAABYBsEHAABYBsEHAABYBsEHAABYBsEHAABYBsEHAABYBsEHAABYBsEHAABYBsEHAABYBsEHAABYBsEHAABYBsEHAABYBsEHAABYBsEHAABYRuWyLgAor7KyjXbFn1NSWrrCAn3Vtm6IvCrZyrosAMA1IPgAeYg7kKBp6w4qISXd2Rbh8NWU22LUMzaiDCsDAFwLTnUBV4k7kKBRy/a4hB5JSkxJ16hlexR3IKGMKgMAXCuCD3CFrGyjaesOyuQxLadt2rqDysrOqwcAoLwj+ABX2BV/LteRnisZSQkp6doVf670igIAeAzBB7hCUlr+ocedfgCA8oXgA1whLNDXo/0AAOULwQe4Qtu6IYpw+Cq/m9Zt+u3urrZ1Q0qzLACAhxB8gCt4VbJpym0xkpQr/OQ8n3JbDOP5AEAFVe6Dz9SpU2Wz2VweTZo0KeuycB3rGRuhRUNuULjD9XRWuMNXi4bcwDg+AFCBVYgBDJs2baqNGzc6n1euXCHKRgXWMzZCf4gJZ+RmALjOVIgEUblyZYWHh5d1GbAYr0o2ta9frazLAAB4ULk/1SVJhw8fVmRkpOrVq6d7771Xx48fz7dvRkaGUlNTXR4AAABSBQg+7dq10zvvvKO4uDgtWrRI8fHxuummm5SWlpZn/5kzZ8rhcDgfUVFRpVwxAAAor2zGmAo19n5ycrKio6P18ssv64EHHsg1PSMjQxkZGc7nqampioqKUkpKioKCgkqzVAAA4KbU1FQ5HA6Pf39XiGt8rlS1alU1atRIR44cyXO63W6X3W4v5aoAAEBFUO5PdV3t/PnzOnr0qCIiuKUYAAAUT7kPPk888YS2bNmiH3/8UV988YXuuOMOeXl5afDgwWVdGgAAqGDK/amukydPavDgwTp79qxCQ0PVqVMn7dixQ6GhoWVdGgAAqGDKffD58MMPy7oEAABwnSj3p7oAAAA8heADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAsg+ADAAAso3JZF4DrQ1a20a74c0pKS1dYoK/a1g2RVyVbWZcFAIALgg+uWdyBBE1bd1AJKenOtgiHr6bcFqOesRFlWBkAAK441YVrEncgQaOW7XEJPZKUmJKuUcv2KO5AQhlVBgBAbgQfuC0r22jauoMyeUzLaZu27qCysvPqAQBA6SP4wG274s/lOtJzJSMpISVdu+LPlV5RAAAUgOADtyWl5R963OkHAEBJI/jAbWGBvh7tBwBASSP4wG1t64YowuGr/G5at+m3u7va1g0pzbIAAMgXwQdu86pk05TbYiQpV/jJeT7lthjG8wEAlBsEH1yTnrERWjTkBoU7XE9nhTt8tWjIDYzjAwAoVxjAENesZ2yE/hATzsjNAIByj+ADj/CqZFP7+tXKugwAAArEqS4AAGAZBB8AAGAZBB8AAGAZBB8AAGAZBB8AAGAZBB8AAGAZBB8AAGAZBB8AAGAZBB8AAGAZ1/3IzcYYSVJqamoZVwIAAIoq53s753vcU6774JOWliZJioqKKuNKAABAcaWlpcnhcHhseTbj6ShVzmRnZ+vUqVMKDAyUzVZ+fzQzNTVVUVFROnHihIKCgsq6nDJh9W3A+rP+Vl5/iW3A+ruuvzFGaWlpioyMVKVKnrsy57o/4lOpUiXVqlWrrMsosqCgIEu+4a9k9W3A+rP+Vl5/iW3A+v/f+nvySE8OLm4GAACWQfABAACWQfApJ+x2u6ZMmSK73V7WpZQZq28D1p/1t/L6S2wD1r901v+6v7gZAAAgB0d8AACAZRB8AACAZRB8AACAZRB8AACAZRB8SsHUqVNls9lcHk2aNClwnhUrVqhJkyby9fVVs2bN9I9//KOUqi0ZderUybUNbDabRo8enWf/d955J1dfX1/fUq7afVu3btVtt92myMhI2Ww2rV692mW6MUbPPfecIiIi5Ofnp27duunw4cOFLve1115TnTp15Ovrq3bt2mnXrl0ltAbXpqD1v3Tpkp588kk1a9ZMAQEBioyM1H333adTp04VuEx3PkdlpbD9P3z48Fzr0rNnz0KXW1H2v1T4Nsjr74HNZtOLL76Y7zIryntg5syZatOmjQIDAxUWFqb+/fvr0KFDLn3S09M1evRoVatWTVWqVNGAAQN0+vTpApfr7t+NslDYNjh37pweffRRNW7cWH5+fqpdu7bGjh2rlJSUApfr7mfnSgSfUtK0aVMlJCQ4H//973/z7fvFF19o8ODBeuCBB7R37171799f/fv314EDB0qxYs/68ssvXdZ/w4YNkqS7774733mCgoJc5jl27FhplXvNLly4oBYtWui1117Lc/qcOXM0f/58LV68WDt37lRAQIB69Oih9PT0fJf50UcfacKECZoyZYr27NmjFi1aqEePHkpKSiqp1XBbQet/8eJF7dmzR88++6z27NmjVatW6dChQ7r99tsLXW5xPkdlqbD9L0k9e/Z0WZe//e1vBS6zIu1/qfBtcOW6JyQk6O2335bNZtOAAQMKXG5FeA9s2bJFo0eP1o4dO7RhwwZdunRJ3bt314ULF5x9xo8fr3Xr1mnFihXasmWLTp06pTvvvLPA5brzd6OsFLYNTp06pVOnTmnu3Lk6cOCA3nnnHcXFxemBBx4odNnF/ezkYlDipkyZYlq0aFHk/gMHDjR9+vRxaWvXrp15+OGHPVxZ2XnsscdM/fr1TXZ2dp7Tly5dahwOR+kWVUIkmU8++cT5PDs724SHh5sXX3zR2ZacnGzsdrv529/+lu9y2rZta0aPHu18npWVZSIjI83MmTNLpG5PuXr987Jr1y4jyRw7dizfPsX9HJUXea3/sGHDTL9+/Yq1nIq6/40p2nugX79+pmvXrgX2qajvgaSkJCPJbNmyxRjz2+fd29vbrFixwtnnu+++M5LM9u3b81yGu383yourt0FePv74Y+Pj42MuXbqUbx93PjtX44hPKTl8+LAiIyNVr1493XvvvTp+/Hi+fbdv365u3bq5tPXo0UPbt28v6TJLRWZmppYtW6YRI0YU+MOx58+fV3R0tKKiotSvXz99++23pVhlyYmPj1diYqLLPnY4HGrXrl2++zgzM1NfffWVyzyVKlVSt27drov3RUpKimw2m6pWrVpgv+J8jsq7zZs3KywsTI0bN9aoUaN09uzZfPte7/v/9OnT+vTTT4v0v/2K+B7IOX0TEhIiSfrqq6906dIll/3ZpEkT1a5dO9/96c7fjfLk6m2QX5+goCBVrlzwz4gW57OTF4JPKWjXrp3zMN6iRYsUHx+vm266SWlpaXn2T0xMVI0aNVzaatSoocTExNIot8StXr1aycnJGj58eL59GjdurLfffltr1qzRsmXLlJ2drQ4dOujkyZOlV2gJydmPxdnHP//8s7Kysq7L90V6erqefPJJDR48uMAfZizu56g869mzp9577z19/vnnmj17trZs2aJevXopKysrz/7X8/6XpHfffVeBgYGFnuqpiO+B7OxsjRs3Th07dlRsbKyk3/4G+Pj45Ar6Be1Pd/5ulBd5bYOr/fzzz5o+fboeeuihApdV3M9OXq77X2cvD3r16uX8d/PmzdWuXTtFR0fr448/LtL/cK43b731lnr16qXIyMh8+7Rv317t27d3Pu/QoYN+97vfacmSJZo+fXpplIlScOnSJQ0cOFDGGC1atKjAvtfT5+iee+5x/rtZs2Zq3ry56tevr82bN+vWW28tw8rKxttvv61777230BsYKuJ7YPTo0Tpw4EC5vBaptBS2DVJTU9WnTx/FxMRo6tSpBS7LE58djviUgapVq6pRo0Y6cuRIntPDw8NzXd1/+vRphYeHl0Z5JerYsWPauHGjRo4cWaz5vL291apVq3y3WUWSsx+Ls4+rV68uLy+v6+p9kRN6jh07pg0bNhR4tCcvhX2OKpJ69eqpevXq+a7L9bj/c/znP//RoUOHiv03QSr/74ExY8Zo/fr12rRpk2rVquVsDw8PV2ZmppKTk136F7Q/3fm7UR7ktw1ypKWlqWfPngoMDNQnn3wib2/vYi2/sM9OXgg+ZeD8+fM6evSoIiIi8pzevn17ff755y5tGzZscDkCUlEtXbpUYWFh6tOnT7Hmy8rK0jfffJPvNqtI6tatq/DwcJd9nJqaqp07d+a7j318fNS6dWuXebKzs/X5559XyPdFTug5fPiwNm7cqGrVqhV7GYV9jiqSkydP6uzZs/muy/W2/6/01ltvqXXr1mrRokWx5y2v7wFjjMaMGaNPPvlE//73v1W3bl2X6a1bt5a3t7fL/jx06JCOHz+e7/505+9GWSpsG0i/1d+9e3f5+Pho7dq1bg1ZUthnJ7/iUMIef/xxs3nzZhMfH2+2bdtmunXrZqpXr26SkpKMMcYMHTrUPPXUU87+27ZtM5UrVzZz58413333nZkyZYrx9vY233zzTVmtgkdkZWWZ2rVrmyeffDLXtKu3wbRp08xnn31mjh49ar766itzzz33GF9fX/Ptt9+WZsluS0tLM3v37jV79+41kszLL79s9u7d67xradasWaZq1apmzZo1Zv/+/aZfv36mbt265tdff3Uuo2vXrmbBggXO5x9++KGx2+3mnXfeMQcPHjQPPfSQqVq1qklMTCz19StMQeufmZlpbr/9dlOrVi2zb98+k5CQ4HxkZGQ4l3H1+hf2OSpPClr/tLQ088QTT5jt27eb+Ph4s3HjRnPDDTeYhg0bmvT0dOcyKvL+N6bwz4AxxqSkpBh/f3+zaNGiPJdRUd8Do0aNMg6Hw2zevNnl/X3x4kVnnz/96U+mdu3a5t///rfZvXu3ad++vWnfvr3Lcho3bmxWrVrlfF6UvxvlRWHbICUlxbRr1840a9bMHDlyxKXP5cuXncu5chsU9bNTGIJPKRg0aJCJiIgwPj4+pmbNmmbQoEHmyJEjzuldunQxw4YNc5nn448/No0aNTI+Pj6madOm5tNPPy3lqj3vs88+M5LMoUOHck27ehuMGzfO1K5d2/j4+JgaNWqY3r17mz179pRitddm06ZNRlKuR846Zmdnm2effdbUqFHD2O12c+utt+baLtHR0WbKlCkubQsWLHBul7Zt25odO3aU0hoVT0HrHx8fn+c0SWbTpk3OZVy9/oV9jsqTgtb/4sWLpnv37iY0NNR4e3ub6Oho8+CDD+YKMBV5/xtT+GfAGGOWLFli/Pz8THJycp7LqKjvgfze30uXLnX2+fXXX80jjzxigoODjb+/v7njjjtMQkJCruVcOU9R/m6UF4Vtg/zeH5JMfHy8y3Jy5inqZ6cwtv+/YAAAgOse1/gAAADLIPgAAADLIPgAAADLIPgAAADLIPgAAADLIPgAAADLIPgAAADLIPgAuO7YbDatXr26wD7Dhw9X//79S6UeAOUHAxgCuO4kJiYqODhYdrtdP/74o+rWrau9e/eqZcuWzj4pKSkyxqhq1aplVieA0le5rAsAAE8ryq9VOxyOUqgEQHnDqS4AxXbmzBmFh4drxowZzrYvvvhCPj4+Lr8enZepU6eqZcuWWrJkiaKiouTv76+BAwcqJSXF2Sc7O1vPP/+8atWqJbvdrpYtWyouLs45PTMzU2PGjFFERIR8fX0VHR2tmTNnOqdfeaor51ehW7VqJZvNpptvvllS7lNdGRkZGjt2rMLCwuTr66tOnTrpyy+/dE7fvHmzbDabPv/8c914443y9/dXhw4ddOjQoWJvPwBlh+ADoNhCQ0P19ttva+rUqdq9e7fS0tI0dOhQjRkzRrfeemuh8x85ckQff/yx1q1bp7i4OO3du1ePPPKIc/qrr76ql156SXPnztX+/fvVo0cP3X777Tp8+LAkaf78+Vq7dq0+/vhjHTp0SMuXL1edOnXyfK1du3ZJkjZu3KiEhAStWrUqz36TJk3S3//+d7377rvas2ePGjRooB49eujcuXMu/f785z/rpZde0u7du1W5cmWNGDGiKJsMQHlx7b/BCsCqHnnkEdOoUSPzxz/+0TRr1sykp6cXOs+UKVOMl5eXOXnypLPtn//8p6lUqZLz16kjIyPNX/7yF5f52rRpYx555BFjjDGPPvqo6dq1q8nOzs7zNSSZTz75xBhjnL8Gv3fvXpc+w4YNM/369TPGGHP+/Hnj7e1tli9f7pyemZlpIiMjzZw5c4wx//dr0hs3bnT2+fTTT40k8+uvvxa63gDKB474AHDb3LlzdfnyZa1YsULLly+X3W4v0ny1a9dWzZo1nc/bt2+v7OxsHTp0SKmpqTp16pQ6duzoMk/Hjh313XffSfrtNNW+ffvUuHFjjR07Vv/617+uaT2OHj2qS5cuubymt7e32rZt63zNHM2bN3f+OyIiQpKUlJR0Ta8PoPQQfAC47ejRozp16pSys7P1448/ltrr3nDDDYqPj9f06dP166+/auDAgbrrrrtK5bW9vb2d/7bZbJJ+uyYJQMVA8AHglszMTA0ZMkSDBg3S9OnTNXLkyCIf+Th+/LhOnTrlfL5jxw5VqlRJjRs3VlBQkCIjI7Vt2zaXebZt26aYmBjn86CgIA0aNEhvvvmmPvroI/3973/PdT2OJPn4+EiSsrKy8q2nfv368vHxcXnNS5cu6csvv3R5TQAVH7ezA3DLn//8Z6WkpGj+/PmqUqWK/vGPf2jEiBFav359ofP6+vpq2LBhmjt3rlJTUzV27FgNHDjQeRv6xIkTNWXKFNWvX18tW7bU0qVLtW/fPi1fvlyS9PLLLysiIkKtWrVSpUqVtGLFCoWHh+c5Jk9YWJj8/PwUFxenWrVqydfXN9et7AEBARo1apQmTpyokJAQ1a5dW3PmzNHFixf1wAMPXPvGAlBuEHwAFNvmzZs1b948bdq0SUFBQZKk999/Xy1atNCiRYs0atSoAudv0KCB7rzzTvXu3Vvnzp1T37599frrrzunjx07VikpKXr88ceVlJSkmJgYrV27Vg0bNpQkBQYGas6cOTp8+LC8vLzUpk0b/eMf/1ClSrkPYleuXFnz58/X888/r+eee0433XSTNm/enKvfrFmzlJ2draFDhyotLU033nijPvvsMwUHB1/DlgJQ3jByM4BSNXXqVK1evVr79u0r61IAWBDX+AAAAMvgVBcAj2ratKmOHTuW57QlS5aUcjUA4IpTXQA86tixY7p06VKe02rUqKHAwMBSrggA/g/BBwAAWAbX+AAAAMsg+AAAAMsg+AAAAMsg+AAAAMsg+AAAAMsg+AAAAMsg+AAAAMsg+AAAAMv4f5syEGUW+ES8AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_position, y_position = np.random.default_rng().uniform(low=1, high=27, size=(2, 5))\n", + "plt.clf()\n", + "plt.scatter(x_position, y_position)\n", + "plt.xlabel('x_position')\n", + "plt.ylabel('y_position')\n", + "plt.title('5 randomly generated x and y positions')\n", + "#plt.aspect_ratio('square')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c61b0e42", + "metadata": {}, + "source": [ + "## Generate stars for each randonly generated x and y position" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "a12087d8", + "metadata": {}, + "outputs": [], + "source": [ + "generated_stars = []\n", + "for x_pos, y_pos in zip(x_position, y_position):\n", + " generated_stars.append(star.create_object(x_pos, y_pos))" + ] + }, + { + "cell_type": "markdown", + "id": "788e53ed", + "metadata": {}, + "source": [ + "## Sum and plot the resulting images\n", + "With overlayed original x and y positions." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "5b933976", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "plt.imshow(np.sum(generated_stars, axis = 0), origin = 'lower')\n", + "plt.scatter(x_position, y_position, color = 'red')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3d2d49df", + "metadata": {}, + "source": [ + "## Repeat the analysis with noise" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "02df8423", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "star_noisy = StarObject(\n", + " image_dimensions = (28,28),\n", + " noise = 0.1,\n", + " radius= 0.8,\n", + " amplitude = 5.0\n", + " )\n", + "generated_stars_noisy = []\n", + "for x_pos, y_pos in zip(x_position, y_position):\n", + " generated_stars_noisy.append(star_noisy.create_object(x_pos, y_pos))\n", + "plt.clf()\n", + "plt.imshow(np.sum(generated_stars_noisy, axis = 0), origin = 'lower')\n", + "plt.scatter(x_position, y_position, color = 'red')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "11e7f670", + "metadata": {}, + "outputs": [], + "source": [ + "# Now create a sky image with a combination of astro objects\n", + "sky_image = SkyImage(\n", + " image_shape=(28,28), \n", + " object_noise_level=0.1\n", + " ) # Add .2 sigma of guassian noise \n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3fda89bf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AQRBAAIAgCCAAQBAEEAAgCAIIABCEvdb5YkmmpNjQGDxmbzF2gx6Nv5J86sPj8gbzTL62yjwTTdhXF+XsjcSSpNjj65eL1L0e+xV8K0raG76vajltnhlssLej9+TnmWfKM/YmcUmqtRcmq/w9j8dTZD9QXFtjP44nl8mYZyKPXz2Tv8zjNuXt56okRVl7e3tcU227fn5MKuFphVdAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABDE9C0jHR/3K7s0cBN+jZWRR4GiF4/b73y2LFnuMSQ5j7l41F6EWNU7Zp4Zq7aXfUpS5ir7fbuo5ox5Zk3dQfPMg8O3mmdGjqfNM5JUnrHvX9nAqHkmytrvW6/nhXK/p7rI4xx3HsdyZfbblPj9sHlGkuSx5y6RsA1EpV2fV0AAgCAIIABAEAQQACAIAggAEAQBBAAIggACAARBAAEAgiCAAABBEEAAgCAIIABAEAQQACAIAggAEMS0LSONkklFcbL0gZThun+USNeYZyRJE3m/Oatx+3GisZx9ZiRrnjk7aC/ujDwKYMs9Zioa/ApWK35rP4/+q22ueWbDp4bMM/+n6Xfmmber/MpIE1lnnomy9qJZOftxVGYsxpTkYt8CYY9z3OMxGPd77IPP3kle+6dKYzntRJl0+uOvxisgAEAQBBAAIAgCCAAQBAEEAAiCAAIABEEAAQCCIIAAAEEQQACAIAggAEAQBBAAIAgCCAAQBAEEAAhi2paRurExOUM8RhXGsjxJyl+kUlFJbihjnolHPUpCEx5Fgx5ln5IU5eynT762yn4gj9LFsozffVt52v41WffhFvPMk02fMs/0jVWaZyK/u1bxuH3P81X2x2Cc8PgaeNx+o3wLd121fc+91ucx44ZHzDOSpHF7aWyUtJX0Rvmxkq7HKyAAQBAEEAAgCHMA7dmzR7fccotaWloURZFefPHFos/fddddiqKo6LJu3brJWi8AYJYwB1Amk9Hy5cu1bdu2C15n3bp1OnXqVOHy9NNPf6JFAgBmH/N3kdevX6/169d/5HVSqZSampq8FwUAmP2m5HtAu3fv1rx583T11Vfrvvvu05kzZy543Ww2q4GBgaILAGD2m/QAWrdunX7yk59o165d+td//Vd1dXVp/fr1mrjAW307OzuVTqcLl9bW1sleEgBgGpr0nwO64447Cn9eunSpli1bpiuuuEK7d+/W6tWrz7n+1q1btWXLlsLfBwYGCCEAuARM+duwFy1apIaGBh0+fPi8n0+lUqqtrS26AABmvykPoBMnTujMmTNqbm6e6kMBAGYQ83/BDQ0NFb2aOXr0qN59913V19ervr5ejzzyiDZu3KimpiYdOXJE3/zmN3XllVdq7dq1k7pwAMDMZg6gt956SzfffHPh7x98/2bTpk164okndODAAf34xz9WX1+fWlpatGbNGv3TP/2TUimPrjYAwKxlDqCbbrpJ7iPKIX/5y19+ogV9IKqtVhQbQsunWDRvL1z0PpZHAWDeo2wwqqk2z6jWY0aSyjyKTz32ziXt75UpH7LvtySlBuy3qeJ35eaZxw5+3jwTxx4FoZ5f941X2v93vmyOfR98xDmPx1Jfv9exohGPwk+fL7Z9HhceBcdnB+3nkfl5pcTnVrrgAABBEEAAgCAIIABAEAQQACAIAggAEAQBBAAIggACAARBAAEAgiCAAABBEEAAgCAIIABAEAQQACAIAggAEMSk/0ruyeIqK+QSpbfKRgP2Zlg3PGyekSR5tPH6NORGPjMV9hlX7nca+Oy5cjnzSFxZYZ6J0nPMM5KU6rPvRer39q/jRg7ZG8hHq+0txhV+peAar4jsM9X2Nux43KMFeiJpnokSfl9ru5FRrzmrqMx+3kWej1s3mjXPTPyux3Z9V9rjnFdAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABDEtC0jVW5cyidKv76zFzX6lPL5ipP2okb5zIzZyz6jQb9SVjcwaB+K7SWXbmLCfhifwlhJFWX2r8nSsb34tHzIcG7/0VjavrbyIfvjQpKSQ/aS0ETW434atd9PLmHfu7jaXv4q+ZWYupS9LNWn4NineFiSNGQvEc6/f8Z0fUcZKQBgOiOAAABBEEAAgCAIIABAEAQQACAIAggAEAQBBAAIggACAARBAAEAgiCAAABBEEAAgCAIIABAENO3jNQ5r4JRi8ij1FCSVFlhn/EoFvUq+/Qp4fQoCJUk53H/xHPspZBudNQ8k/fZO0mJyL4XFR6FlROpKvNM5OzHKRvxewwlRuzFomV99nLfuG/IPKPY4+vmcs+nunH7PrgK+2M9yo6ZZ7xF9v2LK2zPebGLpRIetrwCAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIIggAAAQRBAAIAgpm0ZaZTPK1K+9AGPssGoxl6MeXbQXljpqjwKTPsG7DM++2AsGizM5Q33zx/lL6s1z8T9Pl8nDXvMSBq3l7km+kfMM+WXpcwzkX27lRj1GJJUlrHvQ5yxl8Z6lX3m7MeJPB6zkpTP2M+jaMyjWNSnYNWnFFlS5FGMrArb+Rq5iDJSAMD0RQABAIIwBVBnZ6euu+461dTUaN68edqwYYO6u7uLrjM6OqqOjg7NnTtX1dXV2rhxo3p7eyd10QCAmc8UQF1dXero6NC+ffv0yiuvKJfLac2aNcpkMoXrPPDAA/r5z3+u5557Tl1dXTp58qRuu+22SV84AGBmM33HeufOnUV/37Fjh+bNm6f9+/dr1apV6u/v1w9/+EM99dRT+vznPy9J2r59u/7sz/5M+/bt01/91V9N3soBADPaJ/oeUH9/vySpvr5ekrR//37lcjm1t7cXrrN48WItWLBAe/fuPe+/kc1mNTAwUHQBAMx+3gGUz+d1//3364YbbtCSJUskST09PUomk6qrqyu6bmNjo3p6es7773R2diqdThcura2tvksCAMwg3gHU0dGhgwcP6plnnvlEC9i6dav6+/sLl+PHj3+ifw8AMDN4/SDq5s2b9fLLL2vPnj2aP39+4eNNTU0aGxtTX19f0aug3t5eNTU1nfffSqVSSqXsP5QHAJjZTK+AnHPavHmzXnjhBb322mtqa2sr+vy1116r8vJy7dq1q/Cx7u5uHTt2TCtXrpycFQMAZgXTK6COjg499dRTeumll1RTU1P4vk46nVZlZaXS6bS++tWvasuWLaqvr1dtba2+9rWvaeXKlbwDDgBQxBRATzzxhCTppptuKvr49u3bddddd0mS/u3f/k1xHGvjxo3KZrNau3atfvCDH0zKYgEAs0fknHOhF/GnBgYGlE6ntXre/1ZZnCx5LjKW5UnyKkKU5FccmLDPuBGP0sUy+7f1nEcBp++x5FGE6Dz2zqcwVpJXmaty9v2bmGsvws3OtZ/jZRm/czwx6lFG6jPzXp95xmWz5hlfLmsvFo1ra6ZgJecx4XffulzOPmN8Lhp3Y3pt+Bn19/ertvbCBcR0wQEAgiCAAABBEEAAgCAIIABAEAQQACAIAggAEAQBBAAIggACAARBAAEAgiCAAABBEEAAgCAIIABAEAQQACAIr9+IelGMj9tapxNV5kM4n+ZjSc6jeTvK5+0H8miOVmbEPuPZqqvKCvuMxz64mkr7TMpj7yTFQx4N5B6t6ol++/2U9GkF9+TTbC2fYv1U6Y33H4g8ZnxFPrfJp2Xfo5He+xcZeDTFx/WX2a6fz0rDJVzPvBIAACYBAQQACIIAAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIKYtmWkUXWVorj00k8X2wv2vMo+5VksOmGfiXIeBYXDHmWkzuP2SF6lhhrNmkfiAY/jWIps/4RXianH+RCN2PehrNejINTnPpLf+ny4SnuxrxffffApFs3l7DPlHufdcAltn5PEej+5EreNV0AAgCAIIABAEAQQACAIAggAEAQBBAAIggACAARBAAEAgiCAAABBEEAAgCAIIABAEAQQACAIAggAEMS0LSN1gxm5yFDql7BnaeRTACj5FV1OeJQappL2GQ9RdbXnoEfBo89t8tnvkVH7jPwKYDXuURo7p9I8E2XsRbPOpzhX8rtvfR5PPsdxzj7juQ+u3P4UGZV5PK36rC9O2GckKW9/LopGx2zXz5d2fV4BAQCCIIAAAEEQQACAIAggAEAQBBAAIAgCCAAQBAEEAAiCAAIABEEAAQCCIIAAAEEQQACAIAggAEAQ07aMVHMvkxKpkq8ejWTNh3BDGfOMJK/iU014lA3W2ktCo3SNecancNFbhUcZ6ZihlPaPooRfUaMbs5UuSlKUtN8mV1X6uV04jk9R6rBfKavzOMUjn/PIp4TT5zjjHmXAkl+JsMfzg8sMexzH7xyPqu3PEebS2BILT3kFBAAIggACAARhCqDOzk5dd911qqmp0bx587RhwwZ1d3cXXeemm25SFEVFl3vvvXdSFw0AmPlMAdTV1aWOjg7t27dPr7zyinK5nNasWaNMpvh7KXfffbdOnTpVuDz66KOTumgAwMxn+m7ezp07i/6+Y8cOzZs3T/v379eqVasKH6+qqlJTU9PkrBAAMCt9ou8B9ff3S5Lq6+uLPv7Tn/5UDQ0NWrJkibZu3arh4Qu/wyObzWpgYKDoAgCY/bzff5vP53X//ffrhhtu0JIlSwofv/POO7Vw4UK1tLTowIED+ta3vqXu7m49//zz5/13Ojs79cgjj/guAwAwQ3kHUEdHhw4ePKg33nij6OP33HNP4c9Lly5Vc3OzVq9erSNHjuiKK64459/ZunWrtmzZUvj7wMCAWltbfZcFAJghvAJo8+bNevnll7Vnzx7Nnz//I6+7YsUKSdLhw4fPG0CpVEqplP2H8gAAM5spgJxz+trXvqYXXnhBu3fvVltb28fOvPvuu5Kk5uZmrwUCAGYnUwB1dHToqaee0ksvvaSamhr19PRIktLptCorK3XkyBE99dRT+sIXvqC5c+fqwIEDeuCBB7Rq1SotW7ZsSm4AAGBmMgXQE088IensD5v+qe3bt+uuu+5SMpnUq6++qscee0yZTEatra3auHGjHnzwwUlbMABgdjD/F9xHaW1tVVdX1ydaEADg0jBt27BdRblcotww8NHheD6RTxOvJDfu0UocefzIlc/6rK218mxZlqTY4zaNjZhHnE+jc9Jw7vyJqMLjDTEe+xD3ezSxZ+1N3fI5VyWpzOOpweM8imL7+erT3h6Neuyd5NW87XzOvSH785dPS7wkOY/GfPNzRInPx5SRAgCCIIAAAEEQQACAIAggAEAQBBAAIAgCCAAQBAEEAAiCAAIABEEAAQCCIIAAAEEQQACAIAggAEAQ07aMdKIqqais9GLIsqy9CNGrVFRSVG4vG3Q1Vfbj9A3ajzOatR/Hp4BTkjzmfIpFXcZe3Jnv87xvE/avyaJk0j5Tay+E/Lg2+vPJDw+bZyQprqk2z7gKj30YnzDP+JSeuto59uNIks/6Tp8xj7gRe0mvVxmwpDjjcSyrfGlFqbwCAgAEQQABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIIggAAAQUy7LrgP+q7GJ4ydZtbrS1J+zD4jKcrnzTNuIuFxHPv6nLPPRPnIPCNJsm+D3EW6Tc55dHhJipx9LyJ7RZuivP18dXl7B1reldbJ9WGxz2PD4zEY5e33k/P5unnC406SpAmP88jrfPXYb+fZBedx7lmN//H8+bj+wsj5NBxOoRMnTqi1tTX0MgAAn9Dx48c1f/78C35+2gVQPp/XyZMnVVNToygq/mp0YGBAra2tOn78uGprawOtMDz24Sz24Sz24Sz24azpsA/OOQ0ODqqlpUXxR7R2T7v/govj+CMTU5Jqa2sv6RPsA+zDWezDWezDWezDWaH3IZ1Of+x1eBMCACAIAggAEMSMCqBUKqWHH35YqZTnb/CcJdiHs9iHs9iHs9iHs2bSPky7NyEAAC4NM+oVEABg9iCAAABBEEAAgCAIIABAEDMmgLZt26ZPf/rTqqio0IoVK/TrX/869JIuuu985zuKoqjosnjx4tDLmnJ79uzRLbfcopaWFkVRpBdffLHo8845PfTQQ2publZlZaXa29t16NChMIudQh+3D3fdddc558e6devCLHaKdHZ26rrrrlNNTY3mzZunDRs2qLu7u+g6o6Oj6ujo0Ny5c1VdXa2NGzeqt7c30IqnRin7cNNNN51zPtx7772BVnx+MyKAfvazn2nLli16+OGH9fbbb2v58uVau3atTp8+HXppF90111yjU6dOFS5vvPFG6CVNuUwmo+XLl2vbtm3n/fyjjz6qxx9/XE8++aTefPNNzZkzR2vXrtXo6OhFXunU+rh9kKR169YVnR9PP/30RVzh1Ovq6lJHR4f27dunV155RblcTmvWrFEmkylc54EHHtDPf/5zPffcc+rq6tLJkyd12223BVz15CtlHyTp7rvvLjofHn300UArvgA3A1x//fWuo6Oj8PeJiQnX0tLiOjs7A67q4nv44Yfd8uXLQy8jKEnuhRdeKPw9n8+7pqYm993vfrfwsb6+PpdKpdzTTz8dYIUXx4f3wTnnNm3a5G699dYg6wnl9OnTTpLr6upyzp2978vLy91zzz1XuM5vfvMbJ8nt3bs31DKn3If3wTnn/vqv/9p9/etfD7eoEkz7V0BjY2Pav3+/2tvbCx+L41jt7e3au3dvwJWFcejQIbW0tGjRokX68pe/rGPHjoVeUlBHjx5VT09P0fmRTqe1YsWKS/L82L17t+bNm6err75a9913n86cORN6SVOqv79fklRfXy9J2r9/v3K5XNH5sHjxYi1YsGBWnw8f3ocP/PSnP1VDQ4OWLFmirVu3anh4OMTyLmjalZF+2Pvvv6+JiQk1NjYWfbyxsVH/+Z//GWhVYaxYsUI7duzQ1VdfrVOnTumRRx7RjTfeqIMHD6qmpib08oLo6emRpPOeHx987lKxbt063XbbbWpra9ORI0f0D//wD1q/fr327t2rRML++6imu3w+r/vvv1833HCDlixZIuns+ZBMJlVXV1d03dl8PpxvHyTpzjvv1MKFC9XS0qIDBw7oW9/6lrq7u/X8888HXG2xaR9A+B/r168v/HnZsmVasWKFFi5cqGeffVZf/epXA64M08Edd9xR+PPSpUu1bNkyXXHFFdq9e7dWr14dcGVTo6OjQwcPHrwkvg/6US60D/fcc0/hz0uXLlVzc7NWr16tI0eO6IorrrjYyzyvaf9fcA0NDUokEue8i6W3t1dNTU2BVjU91NXV6TOf+YwOHz4ceinBfHAOcH6ca9GiRWpoaJiV58fmzZv18ssv6/XXXy/69S1NTU0aGxtTX19f0fVn6/lwoX04nxUrVkjStDofpn0AJZNJXXvttdq1a1fhY/l8Xrt27dLKlSsDriy8oaEhHTlyRM3NzaGXEkxbW5uampqKzo+BgQG9+eabl/z5ceLECZ05c2ZWnR/OOW3evFkvvPCCXnvtNbW1tRV9/tprr1V5eXnR+dDd3a1jx47NqvPh4/bhfN59911Jml7nQ+h3QZTimWeecalUyu3YscP9x3/8h7vnnntcXV2d6+npCb20i+rv/u7v3O7du93Ro0fdv//7v7v29nbX0NDgTp8+HXppU2pwcNC988477p133nGS3Pe+9z33zjvvuN/+9rfOOef+5V/+xdXV1bmXXnrJHThwwN16662ura3NjYyMBF755PqofRgcHHTf+MY33N69e93Ro0fdq6++6v7iL/7CXXXVVW50dDT00ifNfffd59LptNu9e7c7depU4TI8PFy4zr333usWLFjgXnvtNffWW2+5lStXupUrVwZc9eT7uH04fPiw+8d//Ef31ltvuaNHj7qXXnrJLVq0yK1atSrwyovNiAByzrnvf//7bsGCBS6ZTLrrr7/e7du3L/SSLrrbb7/dNTc3u2Qy6T71qU+522+/3R0+fDj0sqbc66+/7iSdc9m0aZNz7uxbsb/97W+7xsZGl0ql3OrVq113d3fYRU+Bj9qH4eFht2bNGnf55Ze78vJyt3DhQnf33XfPui/Sznf7Jbnt27cXrjMyMuL+9m//1l122WWuqqrKffGLX3SnTp0Kt+gp8HH7cOzYMbdq1SpXX1/vUqmUu/LKK93f//3fu/7+/rAL/xB+HQMAIIhp/z0gAMDsRAABAIIggAAAQRBAAIAgCCAAQBAEEAAgCAIIABAEAQQACIIAAgAEQQABAIIggAAAQRBAAIAg/j9+kwbILoDW3AAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# A single star in a noisy image\n", + "noisy_star = sky_image.combine_objects(\n", + " objects=[\"star\",\"galaxy\"],\n", + " instance_params=[{'noise':0.2, 'radius':0.8, 'amplitude':1},\n", + " {'radius':5, 'amplitude':10}],\n", + " object_params=[{'center_x':14, 'center_y':14},\n", + " {'center_x':10, 'center_y':5}]\n", + ")\n", + "plt.imshow(noisy_star)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb1b6ed4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/HamiltonianPendulumExample.html b/notebooks/HamiltonianPendulumExample.html new file mode 100644 index 0000000..88fa156 --- /dev/null +++ b/notebooks/HamiltonianPendulumExample.html @@ -0,0 +1,15383 @@ + + + + + +HamiltonianPendulumExample + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/notebooks/HamiltonianPendulumExample.ipynb b/notebooks/HamiltonianPendulumExample.ipynb new file mode 100644 index 0000000..66cf0de --- /dev/null +++ b/notebooks/HamiltonianPendulumExample.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hamiltonian Pendulum\n", + "The Hamiltonian pendulum object is one of the physics objects. This notebook runs through a couple of examples in initilizing the pendulum class and plotting the result." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sys, os \n", + "\n", + "MAIN_DIR = os.path.dirname(os.path.abspath('')) \n", + "sys.path.insert(0, f'{MAIN_DIR}') \n", + "\n", + "from deepbench.physics_object.hamiltonian_pendulum import HamiltonianPendulum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The first step is to initilize the pendulum class\n", + "Inputs include the arm length, the starting angle, and the acceleration due to gravity.\n", + "The pendulum class also requires 'noise_std_percent', which is a dictionary with the percent error for each parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "pendulum = HamiltonianPendulum(\n", + " pendulum_arm_length=10.0,\n", + " starting_angle_radians=np.pi / 4,\n", + " acceleration_due_to_gravity=9.8,\n", + " noise_std_percent={\n", + " \"pendulum_arm_length\": 0.0,\n", + " \"starting_angle_radians\": 0.0,\n", + " \"acceleration_due_to_gravity\": 0.0,\n", + " },\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Draw Diagrams\n", + "Next, create an array of times and use the 'create_object' returning an array containing q, p, dq/dt, dp/dt, and time steps respectively. Observe the phase space and position and time plots." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "time = np.array(np.linspace(0, 50, 200))\n", + "pendulum_data = pendulum.create_object(time)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(15, 5), facecolor='white')\n", + "ax = fig.add_subplot(1,2,1)\n", + "plt.scatter(pendulum_data[0], pendulum_data[1], label='Data', color = '#832161')\n", + "plt.xlabel(\"Position (q)\", fontsize=14, labelpad=10)\n", + "plt.ylabel(\"Momentun (p)\", fontsize=14, labelpad=10)\n", + "plt.title(\"Phase Space\")\n", + "plt.legend(loc='upper right')\n", + "\n", + "ax = fig.add_subplot(1,2,2)\n", + "plt.plot(pendulum_data[4], pendulum_data[0], label='Data', color = '#832161')\n", + "plt.ylabel(\"Position (q)\", fontsize=14, labelpad=10)\n", + "plt.xlabel(\"Time\", fontsize=14, labelpad=10)\n", + "plt.title(\"Position Vs. Time\")\n", + "plt.legend(loc='upper right')\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/Pendulum Example.html b/notebooks/Pendulum Example.html new file mode 100644 index 0000000..3ce2220 --- /dev/null +++ b/notebooks/Pendulum Example.html @@ -0,0 +1,15374 @@ + + + + + +Pendulum Example + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/notebooks/Pendulum Example.ipynb b/notebooks/Pendulum Example.ipynb new file mode 100644 index 0000000..962d31e --- /dev/null +++ b/notebooks/Pendulum Example.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "55939ac7", + "metadata": {}, + "source": [ + "# Newtonian Pendulum\n", + "The pendulum object is one of the physics objects. This notebook runs through a couple of examples of initilizing the pendulum class, creating realizations with and without noise, and plotting the result." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f708efe8", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from deepbench.physics_object import Pendulum" + ] + }, + { + "cell_type": "markdown", + "id": "954311c6", + "metadata": {}, + "source": [ + "## The first step is to initilize the pendulum class\n", + "Inputs include the arm length, the starting angle, and the acceleration due to gravity. The acceleration due to gravity is not required if newton's gravitational constant and phi planet are provided. (We give an example of a hierarchical pendulum below.)\n", + "The pendulum class also requires 'noise_std_percent', which is a dictionary with the percent error for each parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "df3fff86", + "metadata": {}, + "outputs": [], + "source": [ + "pendulum = Pendulum(\n", + " pendulum_arm_length=10.0,\n", + " starting_angle_radians=np.pi / 4,\n", + " acceleration_due_to_gravity=9.8,\n", + " noise_std_percent={\n", + " \"pendulum_arm_length\": 0.0,\n", + " \"starting_angle_radians\": 0.1,\n", + " \"acceleration_due_to_gravity\": 0.1,\n", + " },\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "9b141762", + "metadata": {}, + "source": [ + "## Generate positions\n", + "Next, create an array of times and use the 'create_object' to draw the position at all these points in time. The noiseless parameter determines whether noise is added to the positions in time." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "eba82f42", + "metadata": {}, + "outputs": [], + "source": [ + "time = np.array(np.linspace(0, 50, 200))\n", + "pendulum_noisy = pendulum.create_object(time, noiseless=False)\n", + "pendulum_noiseless = pendulum.create_object(time, noiseless=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6c239b5c", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "plt.plot(time, pendulum_noisy, color = '#832161')\n", + "plt.scatter(time, pendulum_noisy, label = 'Noisy draws', color = '#832161')\n", + "plt.plot(time, pendulum_noiseless, color = '#EDCBB1')\n", + "plt.scatter(time, pendulum_noiseless, label = 'Noise free draws', color = '#EDCBB1')\n", + "legend = plt.legend(loc=\"upper right\", edgecolor=\"black\")\n", + "legend.get_frame().set_alpha(1.0)\n", + "plt.xlabel('time [s]')\n", + "plt.ylabel('x position of pendulum')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Shape Object Example.html b/notebooks/Shape Object Example.html new file mode 100644 index 0000000..7535cba --- /dev/null +++ b/notebooks/Shape Object Example.html @@ -0,0 +1,14458 @@ + + + + + +Shape Object Example + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/notebooks/Shape Object Example.ipynb b/notebooks/Shape Object Example.ipynb new file mode 100644 index 0000000..dca9cb2 --- /dev/null +++ b/notebooks/Shape Object Example.ipynb @@ -0,0 +1,351 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create a single image with shapes \n", + "\n", + "The shape module contain a number of geometric shapes. In this notebook, we will go over creating a instance of `Shapes` and how to generate shapes with and without noise, and display the results. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "from deepbench.shapes import Shapes\n", + "from deepbench.image import ShapeImage\n", + "from deepbench.collection import Collection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Noiseless Image \n", + "\n", + "The first example shows how to create multiple objects without noise added to the image. We'll look at a couple of different objects and their parameters. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "shapes = Shapes(image_shape=(28,28)) # Make an instance of the Shapes class " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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YVIB27dolSVqwYEHE43v27NHq1aslSb/5zW80atQoLV++XD09PSopKdFvf/vbmAwLAEgeHuecsx7ii0KhkHw+nxZoicZ4UqzHATCCRbuI6WAl2wKmt1yvanVIwWBQ6enpA+7HWnAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwEdVvRAWAkSCaVaqjWUE7mtckwwraXAEBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACZYjBQAYmg4L2AqDa9FTLkCAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMsBgpABgbqgVMo31dvBYw5QoIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADDBYqQAkICiXSA0msVIB/uaUFefvvb4/ffjCggAYIIAAQBMDCpAVVVVmjNnjtLS0pSdna2lS5eqqakpYp8FCxbI4/FEbOvXr4/p0ACAxDeoANXV1am8vFwNDQ06evSoent7tWjRInV3d0fst3btWrW1tYW37du3x3RoAEDiG9RNCEeOHIn4urq6WtnZ2WpsbNT8+fPDj48bN05+vz82EwIAktIDfQYUDAYlSZmZmRGPv/vuu8rKytKMGTNUWVmp69evD/g9enp6FAqFIjYAQPKL+jbsvr4+bdq0SfPmzdOMGTPCjz/33HOaPHmyAoGAzp49q1deeUVNTU364IMP+v0+VVVV2rZtW7RjAAASlMc556J54YYNG/SnP/1JH3/8sSZOnDjgfsePH9fChQvV3NysqVOn3vV8T0+Penp6wl+HQiHl5eVpgZZojCclmtEAAAOI5ueABuvOzwH9j4LBoNLT0wfcL6oroI0bN+rw4cM6ceLEPeMjSYWFhZI0YIC8Xq+8Xm80YwAAEtigAuSc04svvqgDBw6otrZW+fn5933NmTNnJEm5ublRDQgASE6DClB5ebn27t2rQ4cOKS0tTe3t7ZIkn8+nsWPHqqWlRXv37tUPf/hDjR8/XmfPntXmzZs1f/58zZo1Ky7/AQCAxDSoAO3atUvSnR82/aI9e/Zo9erVSk1N1bFjx/Tmm2+qu7tbeXl5Wr58uV599dWYDQwASA6D/ie4e8nLy1NdXd0DDQQAGBlYDRsARpBoVtGO151zLEYKADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJhgMVIAwD0NdgHTW65X0v/cdz+ugAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJgYdmvBOeckSbfUKznjYQAAg3ZLvZL+8/f5QIZdgLq6uiRJH+u/jCcBADyIrq4u+Xy+AZ/3uPslaoj19fXp8uXLSktLk8fjiXguFAopLy9PFy9eVHp6utGE9jgOd3Ac7uA43MFxuGM4HAfnnLq6uhQIBDRq1MCf9Ay7K6BRo0Zp4sSJ99wnPT19RJ9gn+M43MFxuIPjcAfH4Q7r43CvK5/PcRMCAMAEAQIAmEioAHm9Xm3dulVer9d6FFMchzs4DndwHO7gONyRSMdh2N2EAAAYGRLqCggAkDwIEADABAECAJggQAAAEwkToJ07d+ob3/iGHnroIRUWFuqvf/2r9UhD7vXXX5fH44nYpk+fbj1W3J04cUKLFy9WIBCQx+PRwYMHI553zmnLli3Kzc3V2LFjVVxcrPPnz9sMG0f3Ow6rV6++6/woLS21GTZOqqqqNGfOHKWlpSk7O1tLly5VU1NTxD43btxQeXm5xo8fr0ceeUTLly9XR0eH0cTx8VWOw4IFC+46H9avX280cf8SIkDvv/++KioqtHXrVn3yyScqKChQSUmJrly5Yj3akHviiSfU1tYW3j7++GPrkeKuu7tbBQUF2rlzZ7/Pb9++XW+99ZZ2796tkydP6uGHH1ZJSYlu3LgxxJPG1/2OgySVlpZGnB/79u0bwgnjr66uTuXl5WpoaNDRo0fV29urRYsWqbu7O7zP5s2b9eGHH2r//v2qq6vT5cuXtWzZMsOpY++rHAdJWrt2bcT5sH37dqOJB+ASwNy5c115eXn469u3b7tAIOCqqqoMpxp6W7dudQUFBdZjmJLkDhw4EP66r6/P+f1+98Ybb4Qf6+zsdF6v1+3bt89gwqHx5ePgnHOrVq1yS5YsMZnHypUrV5wkV1dX55y78799SkqK279/f3ifv//9706Sq6+vtxoz7r58HJxz7vvf/7778Y9/bDfUVzDsr4Bu3rypxsZGFRcXhx8bNWqUiouLVV9fbziZjfPnzysQCGjKlCl6/vnndeHCBeuRTLW2tqq9vT3i/PD5fCosLByR50dtba2ys7M1bdo0bdiwQVevXrUeKa6CwaAkKTMzU5LU2Nio3t7eiPNh+vTpmjRpUlKfD18+Dp979913lZWVpRkzZqiyslLXr1+3GG9Aw24x0i/79NNPdfv2beXk5EQ8npOTo3/84x9GU9koLCxUdXW1pk2bpra2Nm3btk1PPfWUzp07p7S0NOvxTLS3t0tSv+fH58+NFKWlpVq2bJny8/PV0tKin/3sZyorK1N9fb1Gjx5tPV7M9fX1adOmTZo3b55mzJgh6c75kJqaqoyMjIh9k/l86O84SNJzzz2nyZMnKxAI6OzZs3rllVfU1NSkDz74wHDaSMM+QPiPsrKy8J9nzZqlwsJCTZ48WX/84x+1Zs0aw8kwHKxcuTL855kzZ2rWrFmaOnWqamtrtXDhQsPJ4qO8vFznzp0bEZ+D3stAx2HdunXhP8+cOVO5ublauHChWlpaNHXq1KEes1/D/p/gsrKyNHr06LvuYuno6JDf7zeaanjIyMjQ448/rubmZutRzHx+DnB+3G3KlCnKyspKyvNj48aNOnz4sD766KOIX9/i9/t18+ZNdXZ2RuyfrOfDQMehP4WFhZI0rM6HYR+g1NRUzZ49WzU1NeHH+vr6VFNTo6KiIsPJ7F27dk0tLS3Kzc21HsVMfn6+/H5/xPkRCoV08uTJEX9+XLp0SVevXk2q88M5p40bN+rAgQM6fvy48vPzI56fPXu2UlJSIs6HpqYmXbhwIanOh/sdh/6cOXNGkobX+WB9F8RX8d577zmv1+uqq6vd3/72N7du3TqXkZHh2tvbrUcbUj/5yU9cbW2ta21tdX/+859dcXGxy8rKcleuXLEeLa66urrc6dOn3enTp50kt2PHDnf69Gn3r3/9yznn3K9+9SuXkZHhDh065M6ePeuWLFni8vPz3WeffWY8eWzd6zh0dXW5l156ydXX17vW1lZ37Ngx953vfMc99thj7saNG9ajx8yGDRucz+dztbW1rq2tLbxdv349vM/69evdpEmT3PHjx92pU6dcUVGRKyoqMpw69u53HJqbm93Pf/5zd+rUKdfa2uoOHTrkpkyZ4ubPn288eaSECJBzzr399ttu0qRJLjU11c2dO9c1NDRYjzTkVqxY4XJzc11qaqr7+te/7lasWOGam5utx4q7jz76yEm6a1u1apVz7s6t2K+99prLyclxXq/XLVy40DU1NdkOHQf3Og7Xr193ixYtchMmTHApKSlu8uTJbu3atUn3f9L6+++X5Pbs2RPe57PPPnM/+tGP3Ne+9jU3btw498wzz7i2tja7oePgfsfhwoULbv78+S4zM9N5vV736KOPup/+9KcuGAzaDv4l/DoGAICJYf8ZEAAgOREgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJv4XVqiIegQkldsAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "line = shapes.create_line() # Create a line using the random defaults\n", + "\n", + "plt.imshow(line)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a rectangle specifying the specific center and width/length\n", + "\n", + "rectange = shapes.create_rectangle(\n", + " center=(10,10), \n", + " width=2, \n", + " height=8)\n", + "\n", + "plt.imshow(rectange)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAY90lEQVR4nO3dfUyV9/3/8ddR4VRbOAwRDmeiQ9vqVivNnDJi62gkAkuMd0vszRJtjEaHzZR1bVxardsSNpt0TRunf03XpGpnUiU139koFkw3dNFqjNlKhLGJ4cbWhHMQ6xHl8/vDX896FLTgOb45h+cjuRLPua5zzturV3z28lxcepxzTgAA3GcjrAcAAAxPBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJgYZT3ArXp7e9Xa2qq0tDR5PB7rcQAAA+ScU1dXlwKBgEaM6P88Z8gFqLW1VXl5edZjAADuUUtLi8aPH9/v+iEXoLS0NEnSk/qxRinFeBoAwEBdV48+0f9F/jzvT9wCtHXrVr3xxhtqb29XQUGB3nnnHc2aNeuur/vqr91GKUWjPAQIABLO/7/D6N2+RonLRQjvv/++KisrtWnTJn366acqKChQaWmpLl68GI+PAwAkoLgE6M0339TKlSv1wgsv6Hvf+562b9+uMWPG6E9/+lM8Pg4AkIBiHqBr167p5MmTKikp+d+HjBihkpIS1dfX37Z9OBxWKBSKWgAAyS/mAfriiy9048YN5eTkRD2fk5Oj9vb227avqqqSz+eLLFwBBwDDg/kPom7YsEHBYDCytLS0WI8EALgPYn4VXFZWlkaOHKmOjo6o5zs6OuT3+2/b3uv1yuv1xnoMAMAQF/MzoNTUVM2YMUM1NTWR53p7e1VTU6OioqJYfxwAIEHF5eeAKisrtWzZMv3gBz/QrFmz9NZbb6m7u1svvPBCPD4OAJCA4hKgpUuX6vPPP9fGjRvV3t6uJ554QgcPHrztwgQAwPDlcc456yG+LhQKyefzqVgLuBMCACSg665HtapWMBhUenp6v9uZXwUHABieCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATcbkbNux81Hp6wK8pDTwR8zkA4G44AwIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJ7oY9CIO54/RQNtR/P9ytG0hOnAEBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACaG9c1Ih/pNOHHTYP47cQNTYOjjDAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMJE0NyPlxqL4usEeD9zEFLh/OAMCAJggQAAAEzEP0Ouvvy6PxxO1TJ06NdYfAwBIcHH5Duixxx7T4cOH//cho5LmqyYAQIzEpQyjRo2S3++Px1sDAJJEXL4DOnfunAKBgCZNmqTnn39e58+f73fbcDisUCgUtQAAkl/MA1RYWKidO3fq4MGD2rZtm5qbm/XUU0+pq6urz+2rqqrk8/kiS15eXqxHAgAMQR7nnIvnB3R2dmrixIl68803tWLFitvWh8NhhcPhyONQKKS8vDwVa4FGeVK+8efwc0CIBX4OCLh3112PalWtYDCo9PT0freL+9UBGRkZevTRR9XY2Njneq/XK6/XG+8xAABDTNx/Dujy5ctqampSbm5uvD8KAJBAYh6gl156SXV1dfrPf/6jv//971q0aJFGjhypZ599NtYfBQBIYDH/K7gLFy7o2Wef1aVLlzRu3Dg9+eSTOnbsmMaNGxfrjwIAJLCYB2jPnj2xfksAQBLiXnAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgYpT1AMBQ8lHr6QG/pjTwRMznAIYDzoAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABPcjBT4Gm4sCtw/nAEBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwMO0NGjRzV//nwFAgF5PB7t378/ar1zThs3blRubq5Gjx6tkpISnTt3LlbzAgCSxIAD1N3drYKCAm3durXP9Vu2bNHbb7+t7du36/jx43rwwQdVWlqqq1ev3vOwAIDkMeB/EbW8vFzl5eV9rnPO6a233tKrr76qBQsWSJLeffdd5eTkaP/+/XrmmWfubVoAQNKI6XdAzc3Nam9vV0lJSeQ5n8+nwsJC1dfX9/macDisUCgUtQAAkl9MA9Te3i5JysnJiXo+Jycnsu5WVVVV8vl8kSUvLy+WIwEAhijzq+A2bNigYDAYWVpaWqxHAgDcBzENkN/vlyR1dHREPd/R0RFZdyuv16v09PSoBQCQ/GIaoPz8fPn9ftXU1ESeC4VCOn78uIqKimL5UQCABDfgq+AuX76sxsbGyOPm5madPn1amZmZmjBhgtatW6ff/va3euSRR5Sfn6/XXntNgUBACxcujOXcAIAEN+AAnThxQk8//XTkcWVlpSRp2bJl2rlzp15++WV1d3dr1apV6uzs1JNPPqmDBw/qgQceiN3UAICE53HOOeshvi4UCsnn86lYCzTKk/KNX/dR6+n4DYVhozTwhPUIQMK77npUq2oFg8E7fq9vfhUcAGB4IkAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgIkB/3MMQ9Vg7mLMHbSTF3e1BoY+zoAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABNJczPSwRjsDSu5ien9xY1FgeTEGRAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYGJY34x0sIbyzTEHc6PUofz7AZC8OAMCAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAExwM9Ikw41FASQKzoAAACYIEADAxIADdPToUc2fP1+BQEAej0f79++PWr98+XJ5PJ6opaysLFbzAgCSxIAD1N3drYKCAm3durXfbcrKytTW1hZZdu/efU9DAgCSz4AvQigvL1d5efkdt/F6vfL7/YMeCgCQ/OLyHVBtba2ys7M1ZcoUrVmzRpcuXep323A4rFAoFLUAAJJfzANUVlamd999VzU1Nfr973+vuro6lZeX68aNG31uX1VVJZ/PF1ny8vJiPRIAYAjyOOfcoF/s8Wjfvn1auHBhv9v8+9//1uTJk3X48GHNnTv3tvXhcFjhcDjyOBQKKS8vT8VaoFGelMGOBgAwct31qFbVCgaDSk9P73e7uF+GPWnSJGVlZamxsbHP9V6vV+np6VELACD5xT1AFy5c0KVLl5SbmxvvjwIAJJABXwV3+fLlqLOZ5uZmnT59WpmZmcrMzNTmzZu1ZMkS+f1+NTU16eWXX9bDDz+s0tLSmA4OAEhsAw7QiRMn9PTTT0ceV1ZWSpKWLVumbdu26cyZM/rzn/+szs5OBQIBzZs3T7/5zW/k9XpjNzUAIOENOEDFxcW603ULH3300T0NBAAYHrgXHADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMDGgAFVVVWnmzJlKS0tTdna2Fi5cqIaGhqhtrl69qoqKCo0dO1YPPfSQlixZoo6OjpgODQBIfAMKUF1dnSoqKnTs2DEdOnRIPT09mjdvnrq7uyPbrF+/Xh9++KH27t2ruro6tba2avHixTEfHACQ2DzOOTfYF3/++efKzs5WXV2d5syZo2AwqHHjxmnXrl36yU9+Ikn67LPP9N3vflf19fX64Q9/eNf3DIVC8vl8KtYCjfKkDHY0AICR665HtapWMBhUenp6v9vd03dAwWBQkpSZmSlJOnnypHp6elRSUhLZZurUqZowYYLq6+v7fI9wOKxQKBS1AACS36AD1Nvbq3Xr1mn27NmaNm2aJKm9vV2pqanKyMiI2jYnJ0ft7e19vk9VVZV8Pl9kycvLG+xIAIAEMugAVVRU6OzZs9qzZ889DbBhwwYFg8HI0tLSck/vBwBIDKMG86K1a9fqwIEDOnr0qMaPHx953u/369q1a+rs7Iw6C+ro6JDf7+/zvbxer7xe72DGAAAksAGdATnntHbtWu3bt09HjhxRfn5+1PoZM2YoJSVFNTU1kecaGhp0/vx5FRUVxWZiAEBSGNAZUEVFhXbt2qXq6mqlpaVFvtfx+XwaPXq0fD6fVqxYocrKSmVmZio9PV0vvviiioqKvtEVcACA4WNAAdq2bZskqbi4OOr5HTt2aPny5ZKkP/zhDxoxYoSWLFmicDis0tJS/fGPf4zJsACA5HFPPwcUD/wcEAAktvvyc0AAAAwWAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwMaAAVVVVaebMmUpLS1N2drYWLlyohoaGqG2Ki4vl8XiiltWrV8d0aABA4htQgOrq6lRRUaFjx47p0KFD6unp0bx589Td3R213cqVK9XW1hZZtmzZEtOhAQCJb9RANj548GDU4507dyo7O1snT57UnDlzIs+PGTNGfr8/NhMCAJLSPX0HFAwGJUmZmZlRz7/33nvKysrStGnTtGHDBl25cqXf9wiHwwqFQlELACD5DegM6Ot6e3u1bt06zZ49W9OmTYs8/9xzz2nixIkKBAI6c+aMXnnlFTU0NOiDDz7o832qqqq0efPmwY4BAEhQHuecG8wL16xZo7/+9a/65JNPNH78+H63O3LkiObOnavGxkZNnjz5tvXhcFjhcDjyOBQKKS8vT8VaoFGelMGMBgAwdN31qFbVCgaDSk9P73e7QZ0BrV27VgcOHNDRo0fvGB9JKiwslKR+A+T1euX1egczBgAggQ0oQM45vfjii9q3b59qa2uVn59/19ecPn1akpSbmzuoAQEAyWlAAaqoqNCuXbtUXV2ttLQ0tbe3S5J8Pp9Gjx6tpqYm7dq1Sz/+8Y81duxYnTlzRuvXr9ecOXM0ffr0uPwGAACJaUDfAXk8nj6f37Fjh5YvX66Wlhb99Kc/1dmzZ9Xd3a28vDwtWrRIr7766h3/HvDrQqGQfD4f3wEBQIKKy3dAd2tVXl6e6urqBvKWAIBhinvBAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMjLIe4FbOOUnSdfVIzngYAMCAXVePpP/9ed6fIRegrq4uSdIn+j/jSQAA96Krq0s+n6/f9R53t0TdZ729vWptbVVaWpo8Hk/UulAopLy8PLW0tCg9Pd1oQnvsh5vYDzexH25iP9w0FPaDc05dXV0KBAIaMaL/b3qG3BnQiBEjNH78+Dtuk56ePqwPsK+wH25iP9zEfriJ/XCT9X6405nPV7gIAQBgggABAEwkVIC8Xq82bdokr9drPYop9sNN7Ieb2A83sR9uSqT9MOQuQgAADA8JdQYEAEgeBAgAYIIAAQBMECAAgImECdDWrVv1ne98Rw888IAKCwv1j3/8w3qk++7111+Xx+OJWqZOnWo9VtwdPXpU8+fPVyAQkMfj0f79+6PWO+e0ceNG5ebmavTo0SopKdG5c+dsho2ju+2H5cuX33Z8lJWV2QwbJ1VVVZo5c6bS0tKUnZ2thQsXqqGhIWqbq1evqqKiQmPHjtVDDz2kJUuWqKOjw2ji+Pgm+6G4uPi242H16tVGE/ctIQL0/vvvq7KyUps2bdKnn36qgoIClZaW6uLFi9aj3XePPfaY2traIssnn3xiPVLcdXd3q6CgQFu3bu1z/ZYtW/T2229r+/btOn78uB588EGVlpbq6tWr93nS+LrbfpCksrKyqONj9+7d93HC+Kurq1NFRYWOHTumQ4cOqaenR/PmzVN3d3dkm/Xr1+vDDz/U3r17VVdXp9bWVi1evNhw6tj7JvtBklauXBl1PGzZssVo4n64BDBr1ixXUVEReXzjxg0XCARcVVWV4VT336ZNm1xBQYH1GKYkuX379kUe9/b2Or/f7954443Ic52dnc7r9brdu3cbTHh/3LofnHNu2bJlbsGCBSbzWLl48aKT5Orq6pxzN//bp6SkuL1790a2+de//uUkufr6eqsx4+7W/eCccz/60Y/cz3/+c7uhvoEhfwZ07do1nTx5UiUlJZHnRowYoZKSEtXX1xtOZuPcuXMKBAKaNGmSnn/+eZ0/f956JFPNzc1qb2+POj58Pp8KCwuH5fFRW1ur7OxsTZkyRWvWrNGlS5esR4qrYDAoScrMzJQknTx5Uj09PVHHw9SpUzVhwoSkPh5u3Q9fee+995SVlaVp06Zpw4YNunLlisV4/RpyNyO91RdffKEbN24oJycn6vmcnBx99tlnRlPZKCws1M6dOzVlyhS1tbVp8+bNeuqpp3T27FmlpaVZj2eivb1dkvo8Pr5aN1yUlZVp8eLFys/PV1NTk371q1+pvLxc9fX1GjlypPV4Mdfb26t169Zp9uzZmjZtmqSbx0NqaqoyMjKitk3m46Gv/SBJzz33nCZOnKhAIKAzZ87olVdeUUNDgz744APDaaMN+QDhf8rLyyO/nj59ugoLCzVx4kT95S9/0YoVKwwnw1DwzDPPRH79+OOPa/r06Zo8ebJqa2s1d+5cw8nio6KiQmfPnh0W34PeSX/7YdWqVZFfP/7448rNzdXcuXPV1NSkyZMn3+8x+zTk/wouKytLI0eOvO0qlo6ODvn9fqOphoaMjAw9+uijamxstB7FzFfHAMfH7SZNmqSsrKykPD7Wrl2rAwcO6OOPP47651v8fr+uXbumzs7OqO2T9Xjobz/0pbCwUJKG1PEw5AOUmpqqGTNmqKamJvJcb2+vampqVFRUZDiZvcuXL6upqUm5ubnWo5jJz8+X3++POj5CoZCOHz8+7I+PCxcu6NKlS0l1fDjntHbtWu3bt09HjhxRfn5+1PoZM2YoJSUl6nhoaGjQ+fPnk+p4uNt+6Mvp06claWgdD9ZXQXwTe/bscV6v1+3cudP985//dKtWrXIZGRmuvb3derT76he/+IWrra11zc3N7m9/+5srKSlxWVlZ7uLFi9ajxVVXV5c7deqUO3XqlJPk3nzzTXfq1Cn33//+1znn3O9+9zuXkZHhqqur3ZkzZ9yCBQtcfn6++/LLL40nj6077Yeuri730ksvufr6etfc3OwOHz7svv/977tHHnnEXb161Xr0mFmzZo3z+XyutrbWtbW1RZYrV65Etlm9erWbMGGCO3LkiDtx4oQrKipyRUVFhlPH3t32Q2Njo/v1r3/tTpw44Zqbm111dbWbNGmSmzNnjvHk0RIiQM45984777gJEya41NRUN2vWLHfs2DHrke67pUuXutzcXJeamuq+/e1vu6VLl7rGxkbrseLu448/dpJuW5YtW+acu3kp9muvveZycnKc1+t1c+fOdQ0NDbZDx8Gd9sOVK1fcvHnz3Lhx41xKSoqbOHGiW7lyZdL9T1pfv39JbseOHZFtvvzyS/ezn/3Mfetb33JjxoxxixYtcm1tbXZDx8Hd9sP58+fdnDlzXGZmpvN6ve7hhx92v/zlL10wGLQd/Bb8cwwAABND/jsgAEByIkAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBM/D+hbS+PRvGzsgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a filled-in circle \n", + "circle = shapes.create_ellipse(center=(8, 6), width=12, height=12, fill=True)\n", + "plt.imshow(circle)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Noisy Image \n", + "\n", + "Now, we'll add noise to all those images using the `ShapeImage` class. \n", + "The `ShapeImage` both combines shapes and adds noise to the image. \n", + "You can make either single shape images, or add many together. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "shape_image = ShapeImage(\n", + " image_shape=(28,28), \n", + " object_noise_level=0.2\n", + " ) # Add .2 sigma of guassian noise \n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# A single rectangle in a noisy image\n", + "noisy_rectangle = shape_image.combine_objects(\n", + " objects=[\"rectangle\"], \n", + " instance_params={}, \n", + " object_params={} # Using all the default values\n", + ")\n", + "plt.imshow(noisy_rectangle)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# An image with both noise and multiple shapes\n", + " \n", + "noisy_rectange = shape_image.combine_objects(\n", + " objects=[\"ellipse\", \"rectangle\"], \n", + " instance_params=[{},{}], \n", + " object_params=[\n", + " {\"center\":(14,10), \"fill\":True}, # The circle parameters\n", + " {\"center\":(14,18)} # The rectangle parameters\n", + " ]\n", + ")\n", + "plt.imshow(noisy_rectange)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using a collection \n", + "\n", + "If you want to do this same thing, but keep track of the parameters and the default values, we recommend you use a `Collection`.\n", + "\n", + "This has a similar interface to the `Image` type objects, but work through initializing the `Collection` with the full parameters of your dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "shape_config = {\n", + " \"object_type\": \"shape\", # Defines that this is a shape module\n", + " \"object_name\": \"ShapeImage\", # Class used to generate the image\n", + "\n", + " \"total_runs\": 1,\n", + " \"image_parameters\": {\n", + " \"image_shape\": (28, 28),\n", + " \"object_noise_level\": 0.2 # Collection noise level\n", + " },\n", + " \"object_parameters\": {\n", + " \"rectangle\": { # First object in the image\n", + " \"object\": {\n", + " \"width\": np.random.default_rng().integers(2, 28),\n", + " \"height\": np.random.default_rng().integers(2, 28),\n", + " \"fill\": True\n", + " },\n", + " \"instance\": {}\n", + " },\n", + " \"arc\":{ # Second object in the image\n", + " \"object\": {\n", + " \"radius\": np.random.default_rng().integers(2, 6),\n", + " \"theta1\":np.random.default_rng().integers(0, 20),\n", + " \"theta2\":np.random.default_rng().integers(60, 180)\n", + " },\n", + " \"instance\":{}\n", + " }}\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "shape_collection = Collection(object_config=shape_config) # initalize\n", + "shape_collection() # generate the images \n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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"sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"}, -] +lock-version = "2.0" +python-versions = ">=3.8,<4.0," +content-hash = "a140764a9e9b208dd63dba585bf3e300d62cab53f179b3b9a8153153d1d1381f" diff --git a/pyproject.toml b/pyproject.toml index a5a8f9b..82c61ac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,21 +1,24 @@ [tool.poetry] name = "DeepBench" -version = "0.1.0" +version = "0.2.2" description = "Physics Benchmark Dataset Generator" license = "Apache 2.0" -authors = ["Ashia Lewis ", "Maggie Voetberg "] -maintainers = ["Ashia Lewis ", "Maggie Voetberg "] +authors = ["M. Voetberg ", "Ashia Livaudais", "Becky Nevin", "Omari Paul", "Brian Nord"] +maintainers = ["M. Voetberg "] readme = "README.md" -homepage = "https://github.com/AeRabelais/DeepBenchmark" -repository = "https://github.com/AeRabelais/DeepBenchmark" -documentation = "https://github.com/AeRabelais/DeepBenchmark/tree/main/docs" +homepage = "https://github.com/deepskies/DeepBenchmark" +repository = "https://github.com/deepskies/DeepBenchmark" +documentation = "https://github.com/deepskies/DeepBenchmark/tree/main/docs" [tool.poetry.dependencies] -python = ">=3.8,<3.11," +python = ">=3.8,<4.0," numpy = "^1.24.3" matplotlib = "^3.7.1" scikit-image = "^0.20.0" astropy = "^5.2.2" +autograd = "^1.5" +pyyaml = "^6.0" +h5py = ">=3.9.0" [tool.poetry.dev-dependencies] @@ -24,6 +27,10 @@ pytest = "^7.3.1" pre-commit = "^3.2.2" +[tool.poetry.group.dev.dependencies] +sphinx = "^7.0.1" +sphinx-autodoc-typehints = "^1.23.2" + [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" diff --git a/src/deepbench/__init__.py b/src/deepbench/__init__.py deleted file mode 100644 index 3dc1f76..0000000 --- a/src/deepbench/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = "0.1.0" diff --git a/src/deepbench/astro_object/galaxy_object.py b/src/deepbench/astro_object/galaxy_object.py deleted file mode 100644 index c510d72..0000000 --- a/src/deepbench/astro_object/galaxy_object.py +++ /dev/null @@ -1,82 +0,0 @@ -from astro_object import AstroObject -from shape_generator.shape_generator import create_empty_shape -from astropy.modeling.models import Sersic2D - -import numpy as np -from numpy import random - - -class GalaxyObject(AstroObject): - - # BOUNDS: ellipse -> (0,1), n -> (0.5, 1.0) - def __init__( - self, - img_dim, - amplitude=1, - center=(50, 50), - radius=25, - n=1.0, - ellipse=random.uniform(0.1, 0.9), - theta=random.uniform(-1.5, 1.5), - ): - - super().__init__(image_dimensions=img_dim, radius=radius, amplitude=amplitude) - - self._center = center - self._n = n - self._ellipse = ellipse - self._theta = theta - - def create_Sersic_profile(self, center_x, center_y): - - x, y = self.create_meshgrid() - profile = Sersic2D( - amplitude=self._amplitude, - x_0=center_x, - y_0=center_y, - r_eff=self._radius, - n=self._n, - ellip=self._ellipse, - theta=self._theta, - ) - - return profile(x, y) - - def create_object(self, center_x: float, center_y: float) -> np.ndarray: - """ - Create the star object from a Moffat distribution and Poisson and PSF noise. - - Args: - center_x (float): The x-axis placement of the star object. - center_y (float): The y-axis placement of the star object. - - Returns: - ndarray: Two dimensional Galaxy object, composed of Sersic Distribution and noise appendings. - - Examples: - - >>> example_prof = example_star.create_object(center_x = 1.0, center_y = 0.0) - - """ - - # Create the empty image shape. - image_shape = create_empty_shape(self._image) - - # Create the Poisson noise profile specific to Galaxy objects. - noise_profile = self.create_noise(galaxy=True) - - image_shape = self.create_Sersic_profile( - center_x=self._center[0], center_y=self._center[1] - ) - - # Append the noise profiles to the object. - image_shape += noise_profile - image_shape = self.create_psf(image_shape) - - return image_shape - - def displayImage(self): - - # To be implemented. Check parent for details. - - print("Code Container.") diff --git a/src/deepbench/astro_object/n_body_object.py b/src/deepbench/astro_object/n_body_object.py deleted file mode 100644 index b09273b..0000000 --- a/src/deepbench/astro_object/n_body_object.py +++ /dev/null @@ -1,22 +0,0 @@ -from astro_object import AstroObject -from shape_generator.shape_generator import create_empty_shape -import numpy as np -from numpy import random - - -class NBodyObject(AstroObject): - def __init__(self, img_dim, N, t_duration, dt, softening, G, plot_real_time): - - print("Code Container.") - - def create_object(self): - - print("Code Container.") - - def get_acceleration(self): - - print("Code Container.") - - def get_energy(self): - - print("Code Container.") diff --git a/src/deepbench/astro_object/spiral_galaxy.py b/src/deepbench/astro_object/spiral_galaxy.py deleted file mode 100644 index b775019..0000000 --- a/src/deepbench/astro_object/spiral_galaxy.py +++ /dev/null @@ -1,36 +0,0 @@ -from galaxy_object import GalaxyObject - -from numpy import random - - -class SpiralGalaxyObject(GalaxyObject): - def __init__( - self, - img_dim, - amplitude=1, - center=(50, 50), - radius=25, - n=1.0, - ellipse=random.uniform(0.1, 0.9), - theta=random.uniform(-1.5, 1.5), - ): - - super().__init__( - img_dim=img_dim, - amplitude=amplitude, - center=center, - radius=radius, - n=n, - ellipse=ellipse, - theta=theta, - ) - - def create_sprial_profile(self): - - # TO BE IMPLEMENTED. - print("Code Container.") - - def create_object(self): - - # TO BE IMPLEMENTED. - print("Code Container.") diff --git a/src/deepbench/image/image.py b/src/deepbench/image/image.py deleted file mode 100644 index 30783e0..0000000 --- a/src/deepbench/image/image.py +++ /dev/null @@ -1,95 +0,0 @@ -from abc import abstractmethod, ABC -from PIL import Image as PILImage -import os -import numpy as np - -from src.deepbench import astro_object - - -class Image(ABC): - @abstractmethod - def __init__(self, object_list, image_shape): - # TODO Change to something less ambiguous than 'objects' - self.objects = object_list - self.image_shape = image_shape - self.image = None - - def __len__(self): - return len(self.objects) - - def _image_parameters(self): - return self.image_shape, self.objects - - def combine_objects(self): - raise NotImplementedError - - def generate_noise(self, noise_type): - raise NotImplementedError - - def _generate_astro_object(self, object_type, object_parameters): - """ - Utilize the astro_object module and generate instances of the classes - - :param object_type: String identifier of the class. - Pick from ["star", "strong_lens”, "galaxy", "spiral_galaxy", "n_body"] - :param object_parameters: Dictionary of the parameters required for the selected class - Any passed image_shape will be overwritten into Image.image_shape. - :return: Instance of the selected class initialized with passed object parameters. - """ - - if object_type == "test_object": - astro_object_map = {"test_object": ObjectForTesting} - - else: - # TODO Replace with real class names and verify naming scheme - # TODO Check where Sin objects are/if they're included - # TODO Remove this if/else once astro objects are implimented - astro_object_map = { - "star": astro_object.star_object.StarObject, - "strong_lens": astro_object.strong_lens_object.StrongLensObject, - "galaxy": astro_object.galaxy_object.GalaxyObject, - "spiral_galaxy": astro_object.spiral_galaxy_object.SpiralGalaxyObject, - "n_body": astro_object.n_body_object.NBodyObject, - } - - if object_type not in astro_object_map.keys(): - raise NotImplementedError( - f"Object type {object_type}, is not available. " - f"Please select object from {astro_object_map.keys()}" - ) - - object_parameters["image_shape"] = self.image_shape - - return astro_object_map[object_type](**object_parameters) - - def save_image(self, save_dir="results", image_name="image_1", image_format="jpg"): - """ - Save the generated image into the specified directory. - Will create directory if it does not already exist. - - :param save_dir: Directory to save - :param image_name: base name of the saved image - :param image_format: file format. Recommended jpg - :return: None - """ - - assert self.image is not None, "Image not instantiated" - - save_dir = save_dir.rstrip("/") - - if not os.path.exists(save_dir): - os.makedirs(save_dir) - - # TODO Check with multiple image shapes - image = PILImage.fromarray(self.image, "RGB") - save_path = f"{save_dir}/{image_name}.{image_format}" - - image.save(save_path) - - -class ObjectForTesting: - def __init__(self, image_shape): - self.image = np.zeros(image_shape) - - def create_object(self): - return self.image diff --git a/src/deepbench/image/sky_image.py b/src/deepbench/image/sky_image.py deleted file mode 100644 index 92f4ce4..0000000 --- a/src/deepbench/image/sky_image.py +++ /dev/null @@ -1,84 +0,0 @@ -from src.deepbench.image.image import Image -from scipy import ndimage -import numpy as np - -import warnings - - -class SkyImage(Image): - def __init__(self, objects, image_shape): - - assert len(image_shape) >= 2, "Image must be 2D or higher." - assert len(objects) >= 1, "Please pass at least one object per image" - - super().__init__(objects, image_shape) - - def combine_objects(self): - self.image = np.zeros(self.image_shape) - - """ - Utilize Image._generate_astro_objects to overlay all selected astro objects into one image - If object parameters are not included in object list, defaults are used. - Updates SkyImage.image. - - Current input parameter assumptions (totally up to change): - For a single image: - [{ - "object_type":"", - "object_parameters":{} - }] - - """ - - for sky_object in self.objects: - - if "object_type" in sky_object.keys(): - object_type = sky_object["object_type"] - - object_parameters = ( - {} - if "object_parameters" not in sky_object.keys() - else sky_object["object_parameters"] - ) - additional_sky_object = self._generate_astro_object( - object_type, object_parameters - ) - - object_image = additional_sky_object.create_object() - - self.image += object_image - - else: - # TODO Test for this check - warnings.warn( - "Parameter 'object_type' is needed to generate sky objects" - ) - - def generate_noise(self, noise_type, **kwargs): - """ - Add noise to an image - Updates SkyImage.image - - :param noise_type: Type of noise add. Select from [“gaussian”,“poisson”] - :param kwargs: arg required for the noise. ["gaussian"->sigma, "poisson"->lam] - """ - noise_map = { - "gaussian": self._generate_gaussian_noise, - "poisson": self._generate_poisson_noise, - } - - if noise_type not in noise_map.keys(): - raise NotImplementedError(f"{noise_type} noise type not available") - - assert ( - self.image is not None - ), "Image not generated, please run SkyImage.combine_objects" - - noise = noise_map[noise_type](**kwargs) - self.image += noise - - def _generate_gaussian_noise(self, sigma=1): - return ndimage.gaussian_filter(self.image, sigma=sigma) - - def _generate_poisson_noise(self, image_noise=1): - return np.random.poisson(lam=image_noise, size=self.image.shape) diff --git a/src/deepbench/image/time_series_image.py b/src/deepbench/image/time_series_image.py deleted file mode 100644 index 629cf51..0000000 --- a/src/deepbench/image/time_series_image.py +++ /dev/null @@ -1,80 +0,0 @@ -from src.deepbench.image.image import Image -from src.deepbench import astro_object - -import numpy as np -from scipy import ndimage -import warnings - - -class TimeSeriesImage(Image): - def __init__(self, objects, image_shape): - - # Requires the object to be a single dictionary - assert len(image_shape) >= 2, "Image shape must be >=2" - assert len(objects) == 1, "Must pass exactly one image" - - super().__init__(objects, image_shape) - - def combine_objects(self): - - """ - Utilize Image._generate_astro_objects to overlay all selected astro objects into one image - If object parameters are not included in object list, defaults are used. - Updates SkyImage.image. - - Current input parameter assumptions (totally up to change): - [{ - "object_type":"", - "object_parameters":{} - }] - - """ - - generative_object = self.objects[0] - if "object_type" in generative_object.keys(): - object_type = generative_object["object_type"] - - object_parameters = ( - {} - if "object_parameters" not in generative_object.keys() - else generative_object["object_parameters"] - ) - - additional_object = self._generate_astro_object( - object_type, object_parameters - ) - - self.image = additional_object.create_object() - - else: - # TODO Test for this check - warnings.warn("Parameters 'object_type' needed to generate") - - def generate_noise(self, noise_type, **kwargs): - """ - Add noise to an image - Updates SkyImage.image - - :param noise_type: Type of noise add. Select from [“gaussian”,“poisson”] - :param kwargs: arg required for the noise. ["gaussian"->sigma, "poisson"->lam] - """ - noise_map = { - "gaussian": self._generate_gaussian_noise, - "poisson": self._generate_poisson_noise, - } - - if noise_type not in noise_map.keys(): - raise NotImplementedError(f"{noise_type} noise type not available") - - assert ( - self.image is not None - ), "Image not generated, please run SkyImage.combine_objects" - - noise = noise_map[noise_type](**kwargs) - self.image += noise - - def _generate_gaussian_noise(self, sigma=1): - return ndimage.gaussian_filter(self.image, sigma=sigma) - - def _generate_poisson_noise(self, image_noise=1): - return np.random.poisson(lam=image_noise, size=self.image.shape) diff --git a/src/deepbench/image_generator/image.py b/src/deepbench/image_generator/image.py deleted file mode 100644 index 669747e..0000000 --- a/src/deepbench/image_generator/image.py +++ /dev/null @@ -1,40 +0,0 @@ -from abc import ABC, abstractmethod - - -class Image(ABC): - def __init__(self, object_dict, image_shape): - - self._object_dict = object_dict - self._image_shape = image_shape - - print( - "Initialize image with a list of objects to add to canvas and the shape of the image." - ) - - def __len__(self): - - return len(self._object_dict.keys()) - print("Return the length of the object list.") - - @abstractmethod - def create_image(self): # formerly known as 'combine_objects' - - print("Creates the canvas of the concatenated objects.") - - @abstractmethod - def generate_noise(self, noise_type, **noise_parameters): - - print("Generates pixel noise on the image.") - - def get_image_parameters(self): - - return tuple(self._image_shape, self._object_dict.keys()) - print("Returns the image shape and object_dict keys in multidimensional tuple.") - - def generate_astro_objects(self, **astro_parameters): - - print("Generates all of the specified AstroObjects to be added to canvas.") - - def save_image(self, file_path, format=".jpeg"): - - print("Saves the resultant image as a .jpeg or .h5 file.") diff --git a/src/deepbench/image_generator/strong_lens_image.py b/src/deepbench/image_generator/strong_lens_image.py deleted file mode 100644 index 564e03c..0000000 --- a/src/deepbench/image_generator/strong_lens_image.py +++ /dev/null @@ -1,29 +0,0 @@ -from image import Image -from shape_generator import shape_generator - -from numpy import random - - -class StrongLensImage(Image): - """ - Description Container. - - Methods: - """ - - __strong_lens_dict = {"star": {"img_dim": 28.0, "noise": 0.7}, "arc": {}} - - def __init__(self, strong_lens_dict, image_shape=(128, 128)): - - if strong_lens_dict: - super().__init__(strong_lens_dict, image_shape) - else: - super().__init__(self.__strong_lens_dict, image_shape) - - def create_object(self): - - print("Code container.") - - def hasStar(self): - - print("Code container.") diff --git a/src/deepbench/shape_generator/shape_generator.py b/src/deepbench/shape_generator/shape_generator.py deleted file mode 100644 index 6d4f172..0000000 --- a/src/deepbench/shape_generator/shape_generator.py +++ /dev/null @@ -1,322 +0,0 @@ -import numpy as np -from matplotlib.path import Path -from matplotlib import patches -from skimage import transform - - -class ShapeGenerator: - def __init__(self): - pass - - @staticmethod - def resize(image, resize_dimensions=(28, 28)): - """ - Resize a numpy array - :param image: Numpy array to resize - :param resize_dimensions: tuple(*int) Dimensions to reshape the image to - :return: numpy array of shape resize_dimensions - """ - resize_dimensions = tuple(map(lambda dim: int(np.ceil(dim)), resize_dimensions)) - if len(image.shape) != len(resize_dimensions): - raise ValueError( - f"Number of dimensions of source image ({image.shape} do not make target dimensions ({resize_dimensions})" - ) - if 0 in resize_dimensions: - raise ValueError( - "Attempting to resize to an invalid size, please increase the size" - ) - - resized_image = transform.resize(image, resize_dimensions) - return resized_image - - @staticmethod - def _convert_patch_to_image(image, image_shape=(28, 28), cutout=None): - """ - Converts a matplot path or patch object into a numpy array of the designated size - :param image: Matplotlib image object to convert - :param image_shape: [tuple(*int)] Size of the desired image - :return: numpy array of shape image_shape, containing the image - """ - n_dim = len(image_shape) - image_shape = tuple(map(lambda dim: int(np.ceil(dim)), image_shape)) - - if n_dim < 2: - raise ValueError( - f"Image shape input of length {n_dim}; but input must be length >=2" - ) - - if 0 in image_shape: - raise ValueError(f"Image size must be greater than 0") - - x = np.arange(0, image_shape[0]) - y = np.arange(0, image_shape[1]) - meshgrid = np.meshgrid(x, y) - - coordinates = np.array(list(zip(*(c.flat for c in meshgrid)))) - - valid_coordinates = Path(image.get_verts()).contains_points(coordinates) - shape_points = coordinates[valid_coordinates] - - out_array = np.zeros(image_shape) - out_array[shape_points[:, 0], shape_points[:, 1]] = 1.0 - - if cutout is not None: - cutout_coords = Path(cutout.get_verts()).contains_points(coordinates) - cutout_points = coordinates[cutout_coords] - out_array[cutout_points[:, 0], cutout_points[:, 1]] = 0.0 - - return out_array - - @staticmethod - def create_rectangle( - image_shape=(28, 28), - center=(14, 14), - width=10, - height=10, - angle=0, - line_width=1, - fill=False, - ): - """ - :param image_shape: [tuple(*int)] Shape of the image output - :param center: [tuple(*int)] Center of the rectangle (coordinate) - :param width: [int] Horizontal width of the rectangle (in pixels) - :param height: [int] Vertical height of the rectangle (in pixels) - :param angle: [float] Angle to rotate, in degrees - :param line_width: [int] Width (in pixels) - :param fill: [bool] to color in the center of the rectangle - :return: Numpy array of size image_shape containing a rectangle - """ - - n_dim = len(image_shape) - n_center_dim = len(center) - if n_dim != n_center_dim: - raise ValueError( - f"Image shape input of length {n_dim}; but supplied center coordinates with dimension f{n_center_dim}" - ) - - width += line_width - height += line_width - - xy = (center[0] - (width / 2), center[1] - (height / 2)) - rectangle = patches.Rectangle(xy=xy, width=width, height=height, angle=angle) - - cutout = None - if not fill: - - cutout_h = height - (2 * line_width) - cutout_w = width - (2 * line_width) - xy_cutout = (center[0] - (cutout_w / 2), center[1] - (cutout_h / 2)) - cutout = patches.Rectangle( - xy=xy_cutout, width=cutout_w, height=cutout_h, angle=angle - ) - - rectangle_array = ShapeGenerator._convert_patch_to_image( - rectangle, image_shape=image_shape, cutout=cutout - ) - - if rectangle_array.ravel().sum() == 0.0: - raise UserWarning("Image out of bounds, no shape displayed") - - return rectangle_array - - @staticmethod - def create_regular_polygon( - image_shape=(28, 28), - center=(14, 14), - angle=45, - vertices=3, - radius=10, - line_width=2, - fill=False, - ): - """ - Create a polygon with equal length sides - :param image_shape: tuple[*int] Size of the output image (pixels) - :param center: tuple[*int] Where to center the object - :param angle: float Angle of rotation (degrees) - :param vertices: int number of vertices (3=triangle, 4=square, etc) - :param radius: int Distance from vertex to vertex - :param line_width: int Width of lines (pixels) - :param fill: bool Fill center of the object - :return: Numpy array of shape image_shape containing the polygon - """ - - radius = abs(radius) - - n_dim = len(image_shape) - n_center_dim = len(center) - if n_dim != n_center_dim: - raise ValueError( - f"Image shape input of length {n_dim}; but supplied center coordinates with dimension f{n_center_dim}" - ) - if vertices <= 0: - raise ValueError(f"Cannot plot a polygon with {vertices}") - - xy = (center[0] - (radius / vertices) / 2, center[1] - (radius / vertices) / 2) - polygon = patches.RegularPolygon( - xy=xy, numVertices=vertices, radius=radius, orientation=angle - ) - cutout = None - if not fill: - cutout_radius = radius - line_width - xy_cutout = ( - center[0] - (cutout_radius / vertices) / 2, - center[1] - (cutout_radius / vertices) / 2, - ) - cutout = patches.RegularPolygon( - xy=xy_cutout, - numVertices=vertices, - radius=cutout_radius, - orientation=angle, - ) - - polygon_array = ShapeGenerator._convert_patch_to_image( - polygon, image_shape=image_shape, cutout=cutout - ) - - if polygon_array.ravel().sum() == 0.0: - raise UserWarning("Image out of bounds, no shape displayed") - - return polygon_array - - @staticmethod - def create_arc( - image_shape=(28, 28), - center=(14, 14), - radius=10, - theta1=0, - theta2=90, - line_width=1, - ): - """ - Create an arc with radius "radius" arcing from theta1 to theta2 - :param image_shape: Shape of the output array (pixels) - :param center: tuple[*int]Center point of the arc - :param radius: int distance from the arc to the center point - :param theta1: float starting point of the arc (degrees) - :param theta2: float ending point of the arc (degrees) - :param line_width: int thickness of the arc (pixels) - :return: Numpy array of the arc - """ - - arc = patches.Wedge( - center=center, r=radius, theta1=theta1, theta2=theta2, width=line_width - ) - arc_array = ShapeGenerator._convert_patch_to_image(arc, image_shape=image_shape) - - if arc_array.ravel().sum() == 0.0: - raise UserWarning("Image out of bounds, no shape displayed") - - return arc_array - - @staticmethod - def create_line(image_shape=(28, 28), start=(0, 0), end=(28, 28), line_width=1): - """ - Generate a numpy array of a line - :param image_shape: tuple(*int) Shape of the output arrray (pixels) - :param start: tuple(*int) Starting corner of the line - :param end: tuple(*int) Ending corner of the line - :param line_width: int Thickness of the line (pixels) - :return: Numpy array containing the line - """ - - if len(start) != len(end): - raise ValueError( - f"Dimension mismatch, start point had dimensions of {len(start)}, but end point had dimensions of {len(end)}" - ) - - if start == end: - raise ValueError(f"Start point and end point must be different") - - hyp = ((end[1] - start[1]) ** 2 + (end[0] - start[0]) ** 2) ** 0.5 - angle = np.arccos((end[0] - start[0]) / hyp) - - x_shift = line_width / 2.0 * np.cos(np.pi - angle) - y_shift = line_width / 2.0 * np.sin(np.pi - angle) - x_start, y_start = start[0] + x_shift, start[1] + y_shift - x_end, y_end = end[0] + x_shift, end[1] + y_shift - - height_rect = line_width - width_rect = ((y_end - y_start) ** 2 + (x_end - x_start) ** 2) ** 0.5 - - angle_degrees = angle * 180.0 / np.pi - line = patches.Rectangle( - (x_start, y_start), - width=width_rect, - height=height_rect, - angle=angle_degrees, - ) - line_array = ShapeGenerator._convert_patch_to_image( - line, image_shape=image_shape - ) - - if line_array.ravel().sum() == 0.0: - raise UserWarning("Image out of bounds, no shape displayed") - - return line_array - - @staticmethod - def create_ellipse( - image_shape=(28, 28), - center=(14, 14), - width=10, - height=10, - angle=0, - line_width=1, - fill=False, - ): - """ - :param image_shape: tuple(*int) Shape of the output arrray (pixels) - :param center: tuple(*int) Center point of the ellipse - :param width: int Horizontal length of the ellipse (pixels) - :param height: int Vertical height of the ellipse (pixels) - :param angle: float Rotation angle of the ellipse (degrees) - :param line_width: int Width of the ellipse's border (pixels) - :param fill: bool Fill the center of the ellipse - :return: Numpy array containing the ellipse - """ - - if len(image_shape) != len(center): - raise ValueError( - f"Dimension mismatch, image had dimensions of {len(image_shape)}, " - f"but center point had dimensions of {len(center)}" - ) - - height, width = int(np.ceil(height)), int(np.ceil(width)) - - ellipse = patches.Ellipse(xy=center, width=width, height=height, angle=angle) - - cutout = None - if not fill: - xy_cutout = center - width_cutout = width - (2 * line_width) if width != 0 else 0 - height_cutout = height - (2 * line_width) if height != 0 else 0 - - cutout = patches.Ellipse( - xy=xy_cutout, width=width_cutout, height=height_cutout, angle=angle - ) - - ellipse_array = ShapeGenerator._convert_patch_to_image( - ellipse, image_shape=image_shape, cutout=cutout - ) - - if ellipse_array.ravel().sum() == 0.0: - raise UserWarning("Image out of bounds, no shape displayed") - - return ellipse_array - - @staticmethod - def create_empty_shape(image_shape=(28, 28)): - n_dim = len(image_shape) - image_shape = tuple(map(lambda dim: int(np.ceil(dim)), image_shape)) - - if n_dim < 2: - raise ValueError( - f"Image shape input of length {n_dim}; but input must be length >=2" - ) - - if 0 in image_shape: - raise ValueError(f"Image size must be greater than 0") - - return np.zeros(image_shape) diff --git a/tests/astro_objects_test.py b/tests/astro_objects_test.py new file mode 100644 index 0000000..8e5a6e3 --- /dev/null +++ b/tests/astro_objects_test.py @@ -0,0 +1,67 @@ +import pytest +from deepbench.astro_object.astro_object import AstroObject + +# Checking all the child classes work +from deepbench.astro_object import GalaxyObject +from deepbench.astro_object import NBodyObject +from deepbench.astro_object import SpiralGalaxyObject +from deepbench.astro_object import StarObject + + +def test_astro_init(): + with pytest.raises(TypeError): + AstroObject(image_dimensions=(1, 1), radius=1, amplitude=1, noise_level=1) + + +def test_galaxy_init(): + GalaxyObject(image_dimensions=(1, 1)) + assert issubclass(GalaxyObject, AstroObject) + + +def test_nbody_init(): + NBodyObject(image_dimensions=(1, 1)) + assert issubclass(NBodyObject, AstroObject) + + +def test_spiral_galaxy_init(): + SpiralGalaxyObject(image_dimensions=(1, 1)) + assert issubclass(SpiralGalaxyObject, AstroObject) + assert issubclass(SpiralGalaxyObject, GalaxyObject) + + +def test_spiral_galaxy_contents(): + spiral = SpiralGalaxyObject( + image_dimensions=(100, 100), + radius=30, + winding_number=4, + spiral_pitch=0.8, + arm_thickness=5, + noise_level=0, + ) + spiral = spiral.create_object(center_x=25, center_y=50) + + assert spiral.sum(axis=-1).sum(axis=0) != 0.0 + + +def test_star_init(): + StarObject(image_dimensions=(1, 1), noise_level=1) + assert issubclass(StarObject, AstroObject) + + +def test_object_contains(): + + radius = 5 + centerx = centery = 14 + star = StarObject( + image_dimensions=(28, 28), noise_level=0, radius=radius + ).create_object(centerx, centery) + galaxy = GalaxyObject(image_dimensions=(28, 28), noise_level=0).create_object( + centerx, centery + ) + spiral = SpiralGalaxyObject( + image_dimensions=(28, 28), noise_level=0, radius=radius + ).create_object(centerx, centery) + + assert (star != galaxy).all() + assert (star != spiral).all() + assert (galaxy != spiral).all() diff --git a/tests/collection_test.py b/tests/collection_test.py new file mode 100644 index 0000000..ce5c322 --- /dev/null +++ b/tests/collection_test.py @@ -0,0 +1,237 @@ +import pytest +import os +import yaml +import numpy as np + +from deepbench.collection import Collection + + +@pytest.fixture() +def default_physics(): + return yaml.safe_load( + open( + f"{os.path.dirname(__file__)}/../deepbench/settings/default_physics_object.yaml" + ) + ) + + +@pytest.fixture() +def default_sky(): + return yaml.safe_load( + open( + f"{os.path.dirname(__file__)}/../deepbench/settings/default_sky_object.yaml" + ) + ) + + +@pytest.fixture() +def default_shape(): + return yaml.safe_load( + open(f"{os.path.dirname(__file__)}/../deepbench/settings/default_shapes.yaml") + ) + + +def test_default_init(default_physics, default_shape, default_sky): + physics = Collection(default_physics) + + assert physics.n_objects == 0 + assert physics.object_type == "physics" + assert physics.object_name == "Pendulum" + + from deepbench.physics_object import Pendulum + + assert isinstance(physics.object_engine, Pendulum) + + physics = Collection(default_shape) + assert physics.n_objects == 0 + assert physics.object_type == "shape" + assert physics.object_name == "ShapeImage" + + from deepbench.image import ShapeImage + + assert isinstance(physics.object_engine, ShapeImage) + + physics = Collection(default_sky) + assert physics.n_objects == 0 + assert physics.object_type == "sky" + assert physics.object_name == "SkyImage" + + from deepbench.image import SkyImage + + assert isinstance(physics.object_engine, SkyImage) + +def test_load_from_config(): + config_path = f"{os.path.dirname(__file__)}/../deepbench/settings/default_physics_object.yaml" + physics = Collection() + physics.from_config(config_path) + + physics.add_object() + + assert len(physics.objects) == len(physics.object_params) == 1 + assert physics.n_objects == 1 + +def test_run_without_config(): + physics = Collection() + with pytest.raises(AssertionError): + physics.add_object() + + +def test_add_single_item_phy(default_physics): + physics = Collection(default_physics) + physics.add_object() + + assert len(physics.objects) == len(physics.object_params) == 1 + assert physics.n_objects == 1 + + +def test_add_single_item_image(default_sky): + sky = Collection(default_sky) + sky.add_object() + + assert len(sky.objects) == len(sky.object_params) == 1 + assert sky.n_objects == 1 + + +def test_add_seed(default_physics): + phy = Collection(default_physics) + phy.add_object() + assert "seed" in phy.object_params[0].keys() + + +def test_make_image(default_sky): + sky = Collection(default_sky) + sky.add_object() + sky_object = sky.objects[0] + sky_object_param = sky.object_params[0] + + assert sky_object.shape[0] == sky_object_param["image_shape"][0] + + +def test_make_shape(default_shape): + shape = Collection(default_shape) + shape.add_object() + shape_object = shape.objects[0] + shpe_object_param = shape.object_params[0] + + assert shape_object.shape[0] == shpe_object_param["image_shape"][0] + + +def test_make_physics_obj(default_physics): + phy = Collection(default_physics) + phy.add_object() + phy_object = phy.objects[0] + phy_object_param = phy.object_params[0] + + assert phy_object.shape[0] == len(phy_object_param["time"]) + + +def test_included_seed(default_physics): + default_physics["seed"] = 56 + physics = Collection(default_physics) + physics() + for key in physics.object_params: + assert physics.object_params[key]["seed"] == 56 + + +def test_generate_seed(default_physics): + physics = Collection(default_physics) + physics() + seeds = [ + physics.object_params[obj_index]["seed"] + for obj_index in physics.object_params.keys() + ] + assert len(seeds) == len(set(seeds)) + + +def test_find_default_params(default_physics): + physics = Collection(default_physics) + default_object_params_keys = { + "time", + "seed", + "pendulum_arm_length", + "starting_angle_radians", + "acceleration_due_to_gravity", + "noise_std_percent", + "big_G_newton", + "phi_planet", + "mass_pendulum_bob", + "coefficient_friction", + "verbose", + "noiseless", + } + + physics.add_object() + assert default_object_params_keys == { + key for key in physics.object_params[0].keys() + } + + +def test_number_object_created(default_physics): + n_objects = 5 + default_physics["total_runs"] = n_objects + physics = Collection(default_physics) + physics() + + assert len(physics.objects) == len(physics.object_params) == n_objects + + +def test_add_parameter_noise(default_physics): + default_physics["parameter_noise"] = 0.2 + physics = Collection(default_physics) + + physics() + + assert len( + np.unique( + np.asarray( + [ + physics.object_params[obj_index]["time"] + for obj_index in physics.object_params.keys() + ] + ).flatten() + ) + ) == len( + np.asarray( + [ + physics.object_params[obj_index]["time"] + for obj_index in physics.object_params.keys() + ] + ).flatten() + ) + + +def test_no_added_noise(default_physics): + default_physics.pop("parameter_noise", None) + physics = Collection(default_physics) + + physics() + arm_lengths = np.unique( + np.asarray( + [ + physics.object_params[obj_index]["time"] + for obj_index in physics.object_params.keys() + ] + ).flatten() + ) + assert len(arm_lengths) == len(default_physics["object_parameters"]["time"]) + + +def test_save_results_no_path(default_physics): + collection = Collection(default_physics) + collection.add_object() + with pytest.raises(AssertionError): + collection.save() + +def test_save_supplied_path(default_physics): + collection = Collection(default_physics) + collection.add_object() + collection.save("./custom_path/") + + assert os.path.exists("./custom_path/dataset.h5") + assert os.path.exists("./custom_path/dataset_parameters.yaml") + +def test_save_results_yes_path(default_physics): + default_physics['name'] = "./from_config_dataset/" + collection = Collection(default_physics) + collection.add_object() + collection.save() diff --git a/tests/deepbench_test.py b/tests/deepbench_test.py new file mode 100644 index 0000000..3fbfe90 --- /dev/null +++ b/tests/deepbench_test.py @@ -0,0 +1,32 @@ + +from deepbench import __version__ + +def test_version(): + assert __version__ == "0.2.01" + +def test_astroobj_module_import(): + from deepbench import astro_object + + from deepbench.astro_object import GalaxyObject + from deepbench.astro_object import NBodyObject + from deepbench.astro_object import SpiralGalaxyObject + from deepbench.astro_object import StarObject + + astro_object.GalaxyObject + astro_object.NBodyObject + astro_object.SpiralGalaxyObject + astro_object.StarObject + + +def test_collection_import(): + from deepbench.collection import Collection + + +def test_phyobj_import(): + from deepbench.physics_object import Pendulum + from deepbench.physics_object import HamiltonianPendulum + + +def test_image_import(): + from deepbench.image import SkyImage + from deepbench.image import ShapeImage diff --git a/tests/hamiltonian_pendulum_test.py b/tests/hamiltonian_pendulum_test.py new file mode 100644 index 0000000..07fbecd --- /dev/null +++ b/tests/hamiltonian_pendulum_test.py @@ -0,0 +1,250 @@ +import numpy as np +import pytest +from unittest import TestCase +from deepbench.physics_object import HamiltonianPendulum + + +class TestHamiltonianPendulum(TestCase): + def test_init(self): + # make sure people are passing all of + # the required arguments: + with self.assertRaises(AssertionError): + # this raises an error for missing a_g: + HamiltonianPendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + + # better raise an error if angle is too big + with self.assertRaises(AssertionError): + HamiltonianPendulum( + pendulum_arm_length=10.0, + starting_angle_radians=45, + acceleration_due_to_gravity=10.0, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + + # raise error if missing required noise arg + with self.assertRaises(AssertionError): + HamiltonianPendulum( + pendulum_arm_length=10.0, + starting_angle_radians=2 * np.pi, + acceleration_due_to_gravity=10.0, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + }, + ) + # raise error if misspelled required noise arg + with self.assertRaises(AssertionError): + HamiltonianPendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=10.0, + noise_std_percent={ + "ppendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + + def test_array_input_args(self): + with self.assertRaises(TypeError): + HamiltonianPendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=[10.0, 11.0], + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + + def test_zero_time(self): + # it better not produce something + # when you give it no time + time = [] + with self.assertRaises(AssertionError): + pendulum = HamiltonianPendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=9.8, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + pendulum.create_object(time) + + def test_one_time(self): + with pytest.raises(AssertionError): + # testing if it produces a one item output + # when you only give it one moment in time + time = 0.0 + pendulum = HamiltonianPendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=9.8, + noise_std_percent={ + "pendulum_arm_length": 0.5, + "starting_angle_radians": 0.5, + "acceleration_due_to_gravity": 0.5, + }, + ) + pendulum.create_object(time) + # self.assertIsNotNone(output) + # self.assertEqual(np.shape(time), np.shape(output)) + + # @pytest.mark.skip(reason="Noise not included for gradients") + def test_array_time(self): + # output shape better match that of input time + # when time is an array + time = np.linspace(0, 3, 45) + pendulum = HamiltonianPendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=9.8, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + q, p, dqdt, dpdt, t_eval = pendulum.create_object(time) + # [var is not None for var in [q, p, dqdt, dpdt, t_eval]] + for var in [q, p, dqdt, dpdt, t_eval]: + assert var.any() != None + self.assertEqual(np.shape(time), np.shape(np.squeeze(var))) + + """ + def test_noise_one_time(self): + # does noise work + time = 0. + pendulum = Pendulum(pendulum_arm_length=10., + starting_angle_radians=np.pi/4, + acceleration_due_to_gravity=9.8, + noise_std_percent= + {'pendulum_arm_length': 0.5, + 'starting_angle_radians': 0.5, + 'acceleration_due_to_gravity': 0.5} + ) + out1 = pendulum.create_object(time) + out2 = pendulum.create_object(time, destroynoise=False) + print('out1', out1) + print('out2', out2) + assert out1 != out2, "not equal" + pendulum.displayObject(time, destroynoise=False) + #self.assertIsNotNone(output) + #self.assertEqual(np.shape(time), np.shape(output)) + pendulum.displayObject(time, destroynoise=True,) + """ + + @pytest.mark.skip(reason="Noise not included for gradients") + def test_noise_array(self): + # does noise work for an array of times? + time = np.array(np.linspace(0, 10, 20)) + pendulum = HamiltonianPendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=9.8, + noise_std_percent={ + "pendulum_arm_length": 0.0, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.1, + }, + ) + # pendulum.displayObject(time) + q, p, dqdt, dpdt, t_eval = pendulum.create_object(time, noiseless=False) + q_noise, p_noise, dqdt_noise, dpdt_noise, t_eval_noise = pendulum.create_object( + time, noiseless=True + ) + # [var is not None for var in [q, p, dqdt, dpdt, t_eval]] + for var in [ + q, + p, + dqdt, + dpdt, + t_eval, + q_noise, + p_noise, + dqdt_noise, + dpdt_noise, + t_eval_noise, + ]: + assert var.any() != None + self.assertEqual(np.shape(time), np.shape(np.squeeze(var))) + + +""" + def test_noise_hierarchical(self): + # does noise work for an array of times? + time = np.array(np.linspace(0, 10, 20)) + pendulum = HamiltonianPendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=None, + big_G_newton=10.0, + phi_planet=1.0, + noise_std_percent={ + "pendulum_arm_length": 0.0, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": None, + "big_G_newton": 0.0, + "phi_planet": 0.0, + }, + ) + # pendulum.displayObject(time) + pendulum_noisy = pendulum.create_object(time, noiseless=False) + pendulum_noiseless = pendulum.create_object(time, noiseless=True) + assert len(pendulum_noisy) == len(pendulum_noiseless) == len(time) + assert pendulum_noisy.any() == pendulum_noiseless.any() + + + def test_generate_gaussian_noise(self): + object_params = [{"object_type": "test_object"}] + image_shape = (14, 14) + one_image_sky = TimeSeriesImage(object_params, image_shape) + one_image_sky.combine_objects() + one_image_sky.generate_noise("gaussian") + + self.assertIsNotNone(one_image_sky.image) + self.assertEqual(image_shape, one_image_sky.image.shape) + + def test_generate_poisson_noise(self): + object_params = [{"object_type": "test_object"}] + image_shape = (14, 14) + one_image_sky = TimeSeriesImage(object_params, image_shape) + one_image_sky.combine_objects() + one_image_sky.generate_noise("poisson") + + self.assertIsNotNone(one_image_sky.image) + self.assertEqual(image_shape, one_image_sky.image.shape) + + def test_add_fake_noise(self): + with self.assertRaises(NotImplementedError): + object_params = [{"object_type": "test_object"}] + image_shape = (14, 14) + one_image_sky = TimeSeriesImage(object_params, image_shape) + one_image_sky.combine_objects() + one_image_sky.generate_noise("Fake Noise") + + def test_image_not_made(self): + with self.assertRaises(AssertionError): + object_params = [{"object_type": "test_object"}] + image_shape = (14, 14) + one_image_sky = TimeSeriesImage(object_params, image_shape) + + ##Go straight to noise instead of making objects first + one_image_sky.generate_noise("gaussian") +""" diff --git a/tests/image_test.py b/tests/image_test.py deleted file mode 100644 index 55c6652..0000000 --- a/tests/image_test.py +++ /dev/null @@ -1,222 +0,0 @@ -from unittest import TestCase -import numpy as np -import os -from src.deepbench.image.image import Image -from src.deepbench.image.image import ObjectForTesting -from src.deepbench import astro_object - - -class InitTestClass(Image): - def __init__(self, objects, image_shape): - super().__init__(objects, image_shape) - - -class TestImage(TestCase): - def setUp(self): - self.test_image_instance = InitTestClass([], ()) - - def test_direct_init(self): - with self.assertRaises(TypeError): - objects = [] - image_shape = () - Image(objects, image_shape) - - def test_image_init(self): - test_image = InitTestClass([], ()) - self.assertIsNone(test_image.image) - - def test_init_within_other_class(self): - assert issubclass(InitTestClass, Image) - - def test_superinit_shape(self): - objects = [] - image_shape = (14, 14) - test_image = InitTestClass(objects, image_shape) - - self.assertEqual(image_shape, test_image.image_shape) - - def test_superinit_objects(self): - - objects = [] - image_shape = (14, 14) - test_image = InitTestClass(objects, image_shape) - - self.assertEqual(objects, test_image.objects) - - def test_object_len(self): - objects = [] - image_shape = (14, 14) - test_image = InitTestClass(objects, image_shape) - - image_class_len = test_image.__len__() - self.assertEqual(0, image_class_len) - - def test_multi_objects_len(self): - objects = [{}, {}, {}] - image_shape = (14, 14) - test_image = InitTestClass(objects, image_shape) - - image_class_len = test_image.__len__() - self.assertEqual(3, image_class_len) - - def test_image_parameters_no_init(self): - with self.assertRaises(TypeError): - objects = [] - image_shape = (14, 14) - test_image = Image(objects, image_shape) - test_image._image_parameters() - - def test_image_parameters_alt_init(self): - objects = [] - image_shape = (14, 14) - test_image = InitTestClass(objects, image_shape) - - image_class_parameters = test_image._image_parameters() - self.assertEqual((image_shape, objects), image_class_parameters) - - def test_combined_not_init(self): - with self.assertRaises(NotImplementedError): - test_image = InitTestClass([], ()) - test_image.combine_objects() - - def test_combined_other_class(self): - class TestCombined(Image): - def __init__(self): - super().__init__([1, 1, 1], ()) - - def combine_objects(self): - return sum(self.objects) - - expected_sum = 3 - actual_sum = TestCombined().combine_objects() - self.assertEqual(expected_sum, actual_sum) - - def test_generate_noise_no_init(self): - with self.assertRaises(NotImplementedError): - test_image = InitTestClass([], ()) - test_image.generate_noise("noise") - - def test_generate_noise_init(self): - class TestCombined(Image): - def __init__(self): - super().__init__([1, 1, 1], ()) - - def generate_noise(self, noise_type): - return noise_type - - expected_noise = "guassian" - actual_noise = TestCombined().generate_noise(noise_type="guassian") - self.assertEqual(expected_noise, actual_noise) - - def test_save_image_defaults(self): - test_image = InitTestClass([], ()) - empty_image = np.zeros((10, 10)) - test_image.image = empty_image - - test_image.save_image() - out_path = "results/image_1.jpg" - - image_exists = os.path.exists(out_path) - - if image_exists: - os.remove(out_path) - os.rmdir("results/") - - self.assertTrue(image_exists) - - def test_save_image_no_image(self): - test_image = InitTestClass([], ()) - with self.assertRaises(AssertionError): - test_image.save_image() - - def test_save_image_nodir(self): - test_image = InitTestClass([], ()) - empty_image = np.zeros((10, 10)) - test_image.image = empty_image - - new_folder = "test_save_dir" - test_image.save_image(save_dir=new_folder) - out_path = f"{new_folder}/image_1.jpg" - - image_exists = os.path.exists(out_path) - - if image_exists: - os.remove(out_path) - os.rmdir(new_folder) - - self.assertTrue(image_exists) - - def test_save_existing_dir(self): - test_image = InitTestClass([], ()) - empty_image = np.zeros((10, 10)) - test_image.image = empty_image - new_save_dir = "test_save_dir" - os.makedirs(new_save_dir) - out_path = f"{new_save_dir}/image_1.jpg" - test_image.save_image(save_dir=new_save_dir) - - image_exists = os.path.exists(out_path) - - if image_exists: - os.remove(out_path) - os.rmdir(new_save_dir) - - self.assertTrue(image_exists) - - def test_generate_astro_object_test_object(self): - object_params = [{"object_type": "test_object", "object_parameters": {}}] - image_shape = (14, 14) - test_object = InitTestClass(object_params, image_shape) - obj = test_object._generate_astro_object( - "test_object", object_params[0]["object_parameters"] - ) - - # Test object is a debug object that "create_object" just makes a empty array for - self.assertIsInstance(obj, ObjectForTesting) - - # def test_generate_astro_object_star(self): - # object_params = [{}] - # image_shape = (14, 14) - # one_image_sky = TestClass(object_params, image_shape) - # obj = one_image_sky.generate_astro_object("star", {}) - # - # self.assertIsInstance(obj, astro_object.Star) - # - # def test_generate_astro_object_strong_lens(self): - # object_params = [{}] - # image_shape = (14, 14) - # one_image_sky = TestClass(object_params, image_shape) - # obj = one_image_sky._generate_astro_object("strong_lens", {}) - # - # self.assertIsInstance(obj, astro_object.StrongLens) - # - # def test_generate_galaxy_object(self): - # object_params = [{}] - # image_shape = (14, 14) - # one_image_sky = TestClass(object_params, image_shape) - # obj = one_image_sky._generate_astro_object("galaxy", {}) - # - # self.assertIsInstance(obj, astro_object.Galaxy) - # - # def test_generate_spiral_galaxy_object(self): - # object_params = [{}] - # image_shape = (14, 14) - # one_image_sky = TestClass(object_params, image_shape) - # obj = one_image_sky._generate_astro_object("spiral_galaxy", {}) - # - # self.assertIsInstance(obj, astro_object.SpiralGalaxy) - # - # def test_generate_n_body_object(self): - # object_params = [{}] - # image_shape = (14, 14) - # one_image_sky = TestClass(object_params, image_shape) - # obj = one_image_sky._generate_astro_object("n_body", {}) - # - # self.assertIsInstance(obj, astro_object.NBody) - # - # def test_not_included_object(self): - # with self.assertRaises(NotImplementedError): - # object_params = [{}] - # image_shape = (14, 14) - # one_image_sky = TestClass(object_params, image_shape) - # one_image_sky._generate_astro_object("Fake Object", {}) diff --git a/tests/pendulum_test.py b/tests/pendulum_test.py new file mode 100644 index 0000000..adee452 --- /dev/null +++ b/tests/pendulum_test.py @@ -0,0 +1,269 @@ +import numpy as np +from unittest import TestCase +from deepbench.physics_object import Pendulum +import os +import pytest + +""" +@pytest.fixture() +def resource(request): + print("setup") + os.remove('randomseeds.log') + def teardown(): + + print("teardown") + request.addfinalizer(teardown) + return "resource" +""" + + +class TestPendulum(TestCase): + def test_init(self): + # make sure people are passing all of + # the required arguments: + with self.assertRaises(AssertionError): + # this raises an error for missing a_g: + Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + # should raise an error if missing phi_planet but + # you have big_G_newton + with self.assertRaises(AssertionError): + Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + big_G_newton=10.0, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + # better raise an error if angle is too big + with self.assertRaises(AssertionError): + Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=45, + acceleration_due_to_gravity=10.0, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + # raise error if missing required noise arg + with self.assertRaises(AssertionError): + Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=2 * np.pi, + acceleration_due_to_gravity=10.0, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + }, + ) + # raise error if misspelled required noise arg + with self.assertRaises(AssertionError): + Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=10.0, + noise_std_percent={ + "ppendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + + def test_hierarchical_fail(self): + with self.assertRaises(AssertionError): + Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=None, + big_G_newton=10.0, + phi_planet=1.0, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + + def test_array_input_args(self): + with self.assertRaises(TypeError): + Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=[10.0, 11.0], + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + + def test_zero_time(self): + # it better not produce something + # when you give it no time + time = [] + with self.assertRaises(AssertionError): + pendulum = Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=9.8, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + pendulum.create_object(time) + + def test_one_time(self): + # testing if it produces a one item output + # when you only give it one moment in time + time = 0.0 + pendulum = Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=9.8, + noise_std_percent={ + "pendulum_arm_length": 0.5, + "starting_angle_radians": 0.5, + "acceleration_due_to_gravity": 0.5, + }, + ) + output = pendulum.create_object(time) + self.assertIsNotNone(output) + self.assertEqual(np.shape(time), np.shape(output)) + + def test_array_time(self): + # output shape better match that of input time + # when time is an array + time = np.array(np.linspace(0, 100, 100)) + pendulum = Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=9.8, + noise_std_percent={ + "pendulum_arm_length": 0.1, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.0, + }, + ) + output = pendulum.create_object(time) + self.assertIsNotNone(output) + self.assertEqual(np.shape(time), np.shape(output)) + + """ + def test_noise_one_time(self): + # does noise work + time = 0. + pendulum = Pendulum(pendulum_arm_length=10., + starting_angle_radians=np.pi/4, + acceleration_due_to_gravity=9.8, + noise_std_percent= + {'pendulum_arm_length': 0.5, + 'starting_angle_radians': 0.5, + 'acceleration_due_to_gravity': 0.5} + ) + out1 = pendulum.create_object(time) + out2 = pendulum.create_object(time, destroynoise=False) + print('out1', out1) + print('out2', out2) + assert out1 != out2, "not equal" + pendulum.displayObject(time, destroynoise=False) + #self.assertIsNotNone(output) + #self.assertEqual(np.shape(time), np.shape(output)) + pendulum.displayObject(time, destroynoise=True,) + """ + + def test_noise_array(self): + # does noise work for an array of times? + time = np.array(np.linspace(0, 50, 200)) + pendulum = Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=9.8, + noise_std_percent={ + "pendulum_arm_length": 0.0, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.1, + }, + ) + pendulum_noisy = pendulum.create_object(time, noiseless=False) + pendulum_noiseless = pendulum.create_object(time, noiseless=True) + assert len(pendulum_noisy) == len(pendulum_noiseless) == len(time) + assert pendulum_noisy.any() == pendulum_noiseless.any() + + def test_noise_hierarchical(self): + # does noise work for an array of times? + time = np.array(np.linspace(0, 10, 20)) + pendulum = Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=None, + big_G_newton=10.0, + phi_planet=1.0, + noise_std_percent={ + "pendulum_arm_length": 0.0, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": None, + "big_G_newton": 0.0, + "phi_planet": 0.0, + }, + ) + pendulum_noisy = pendulum.create_object(time, noiseless=False, seed=42) + pendulum_noiseless = pendulum.create_object(time, noiseless=True) + assert len(pendulum_noisy) == len(pendulum_noiseless) == len(time) + assert pendulum_noisy.any() == pendulum_noiseless.any() + + def test_displayobject(self): + # does the plotting work + time = np.array(np.linspace(0, 50, 200)) + pendulum = Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=9.8, + noise_std_percent={ + "pendulum_arm_length": 0.0, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.1, + }, + ) + # Sorry these prints are really annoying when I'm running all files + # y, noisy_y = pendulum.displayObject(time) + # assert np.shape(y)[0] == np.shape(noisy_y)[1] + + def test_logfile(self): + # is the logfile getting overwritten for each init? + # first remove it to later see if its there + time = np.array(np.linspace(0, 50, 200)) + pendulum = Pendulum( + pendulum_arm_length=10.0, + starting_angle_radians=np.pi / 4, + acceleration_due_to_gravity=9.8, + noise_std_percent={ + "pendulum_arm_length": 0.0, + "starting_angle_radians": 0.1, + "acceleration_due_to_gravity": 0.1, + }, + ) + # Is the logfile there? + assert os.path.exists("randomseeds.log") + # Is it empty? + assert os.stat("randomseeds.log").st_size == 0, "randomseeds is not empty" + # Now add noise: + pendulum.create_object(time, seed=23, noiseless=False) + assert ( + os.stat("randomseeds.log").st_size != 0 + ), "randomseeds is empty after noise added, incorrect" + file = open("randomseeds.log") + assert file.read() == "23\n" diff --git a/tests/shape_generator_test.py b/tests/shape_generator_test.py new file mode 100644 index 0000000..1051e6d --- /dev/null +++ b/tests/shape_generator_test.py @@ -0,0 +1,341 @@ +import pytest +from deepbench.shapes import Shapes as ShapeGenerator + +import numpy as np +import matplotlib.patches as patch + + +def test_patch_conversion_defaults(): + # simple patch object. Convert to a numpy array + simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) + converted_object = ShapeGenerator((28, 28))._convert_patch_to_image( + image=simple_patch + ) + expected_obj_type = np.array([0, 0]) + assert type(converted_object) == type(expected_obj_type) + + +def test_patch_conversion_default_dimensions(): + simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) + converted_object = ShapeGenerator()._convert_patch_to_image(image=simple_patch) + converted_object_shape_x, converted_object_shape_y = converted_object.shape + expected_x_dim, expected_y_dim = 28, 28 + + assert converted_object_shape_x == expected_x_dim + assert converted_object_shape_y == expected_y_dim + + +def test_patch_conversion_single_image_dimension(): + with pytest.raises(TypeError): + simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) + ShapeGenerator(tuple(28))._convert_patch_to_image(image=simple_patch) + + +def test_patch_conversion_N_dimension(): + simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) + converted_object = ShapeGenerator((28, 28, 28))._convert_patch_to_image( + image=simple_patch + ) + ( + converted_object_shape_x, + converted_object_shape_y, + converted_object_shape_z, + ) = converted_object.shape + expected_x_dim, expected_y_dim, expected_z_dim = 28, 28, 28 + + assert converted_object_shape_x == expected_x_dim + assert converted_object_shape_y == expected_y_dim + assert converted_object_shape_z == expected_z_dim + + +def test_patch_conversion_size_0(): + with pytest.raises(ValueError): + simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) + ShapeGenerator((0, 0))._convert_patch_to_image(image=simple_patch) + + +def test_patch_conversion_non_int_size(): + simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) + converted_object = ShapeGenerator((55.5, 55.5))._convert_patch_to_image( + image=simple_patch + ) + converted_object_shape_x, converted_object_shape_y = converted_object.shape + expected_x_dim, expected_y_dim = 56, 56 + + assert converted_object_shape_x == expected_x_dim + assert converted_object_shape_y == expected_y_dim + + +def test_patch_conversion_non_int_size_round(): + simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) + converted_object = ShapeGenerator(image_shape=(55.1, 55.6))._convert_patch_to_image( + image=simple_patch + ) + converted_object_shape_x, converted_object_shape_y = converted_object.shape + expected_x_dim, expected_y_dim = 56, 56 + assert converted_object_shape_x == expected_x_dim + assert converted_object_shape_y == expected_y_dim + + +def test_resize_default(): + simple_image = np.zeros((32, 32)) + resized_image = ShapeGenerator().resize(image=simple_image) + resized_image_x, resized_image_y = resized_image.shape + default_new_size_x, default_new_size_y = 28, 28 + + assert resized_image_x == default_new_size_x + assert resized_image_y == default_new_size_y + + +def test_resize_dim_mismatch(): + with pytest.raises(ValueError): + simple_image = np.zeros((32, 32)) + resize_dimensions = (1, 1, 1) + ShapeGenerator().resize(simple_image, resize_dimensions=resize_dimensions) + + +def test_resize_zero_size_source(): + with pytest.raises(ValueError): + simple_image = np.zeros((0, 0)) + resize_dimensions = (1, 1) + ShapeGenerator().resize(image=simple_image, resize_dimensions=resize_dimensions) + + +def test_resize_zero_size_target(): + with pytest.raises(ValueError): + simple_image = np.zeros((1, 1)) + resize_dimensions = (0, 0) + ShapeGenerator().resize(image=simple_image, resize_dimensions=resize_dimensions) + + +def test_resize_upscale(): + simple_image = np.zeros((32, 32)) + resize = (50, 50) + resized_image = ShapeGenerator().resize( + image=simple_image, resize_dimensions=resize + ) + resized_image_x, resized_image_y = resized_image.shape + new_size_x, new_size_y = resize + + assert resized_image_x == new_size_x + assert resized_image_y == new_size_y + + +def test_resize_negative(): + with pytest.raises(ValueError): + simple_image = np.zeros((80, 80)) + resize = (-1, -1) + ShapeGenerator().resize(image=simple_image, resize_dimensions=resize) + + +def test_rectangle_default(): + rectangle = ShapeGenerator().create_rectangle() + + x, y = rectangle.shape + expected_x, expected_y = (28, 28) + assert x == expected_x + assert y == expected_y + + +def test_rectangle_size_center_dim_mismatch(): + with pytest.raises(ValueError): + ShapeGenerator().create_rectangle(center=(1, 1, 1)) + + +def test_rectangle_xy_out_of_range(): + rectangle = ShapeGenerator((10, 10)).create_rectangle(center=(100, 100)) + rectangle_contents = rectangle.sum().sum() + assert 0 == rectangle_contents + + +def test_rectangle_angle_in_bounds_no_change(): + angle = 90 + rectangle_rotated = ShapeGenerator().create_rectangle(angle=angle) + + rectangle_non_rotated = ShapeGenerator().create_rectangle(angle=0) + + # But it's a square so it looks the same + assert rectangle_rotated.all() == rectangle_non_rotated.all() + + +def test_rectangle_angle_oob_positive(): + angle = 45 + rectangle_rotated = ShapeGenerator().create_rectangle(angle=angle + 360) + rectangle_non_rotated = ShapeGenerator().create_rectangle(angle=angle) + + assert rectangle_rotated.all() == rectangle_non_rotated.all() + + +def test_rectangle_angle_oob_negative(): + angle = 45 + rectangle_rotated = ShapeGenerator().create_rectangle(angle=angle - 360) + rectangle_non_rotated = ShapeGenerator().create_rectangle(angle=angle) + + assert rectangle_rotated.all() == rectangle_non_rotated.all() + + +def test_rectangle_fill(): + # This should fill the whole screen. + rectangle = ShapeGenerator().create_rectangle(width=80, height=80, fill=True) + ideal_rectangle = np.ones((28, 28)) + + assert ideal_rectangle.all() == rectangle.all() + + +def test_polygon_default(): + triangle = ShapeGenerator().create_regular_polygon() + + shape_x, shape_y = triangle.shape + expected_x, expected_y = 28, 28 + + assert shape_x == expected_x + assert shape_y == expected_y + + +def test_polygon_size_center_mismatch(): + with pytest.raises(ValueError): + ShapeGenerator().create_regular_polygon(center=(14, 14, 14)) + + +def test_polygon_positive_oob_angle(): + angle = 45 + triangle_rotated = ShapeGenerator().create_regular_polygon(angle=angle + 360) + triangle_non_rotated = ShapeGenerator().create_regular_polygon(angle=angle) + + assert (triangle_rotated.all(), triangle_non_rotated.all()) + + +def test_polygon_negative_oob_angle(): + angle = 45 + triangle_rotated = ShapeGenerator().create_regular_polygon(angle=angle - 360) + triangle_non_rotated = ShapeGenerator().create_regular_polygon(angle=angle) + + assert (triangle_rotated.all(), triangle_non_rotated.all()) + + +def test_polygon_negative_vertices(): + with pytest.raises(ValueError): + ShapeGenerator((28, 28)).create_regular_polygon(vertices=-3) + + +def test_arc_default(): + arc = ShapeGenerator((28, 28)).create_arc() + + x, y = arc.shape + expected_x, expected_y = (28, 28) + assert (x, expected_x) + assert (y, expected_y) + + +def test_arc_size_center_dim_mismatch(): + with pytest.raises(ValueError): + ShapeGenerator().create_arc(center=(0, 0, 0)) + + +def test_arc_oob_negative_theta1(): + angle = 45 + arc_rotated = ShapeGenerator().create_arc(theta1=angle - 360) + arc_non_rotated = ShapeGenerator().create_arc(theta1=angle) + + assert arc_rotated.all() == arc_non_rotated.all() + + +def test_arc_oob_positive_theta1(): + angle = 45 + arc_rotated = ShapeGenerator().create_arc(theta1=angle + 360) + arc_non_rotated = ShapeGenerator().create_arc(theta1=angle) + + assert arc_rotated.all() == arc_non_rotated.all() + + +def test_arc_oob_negative_theta2(): + angle = 45 + arc_rotated = ShapeGenerator().create_arc(theta2=angle - 360) + arc_non_rotated = ShapeGenerator().create_arc(theta2=angle) + + assert (arc_rotated.all(), arc_non_rotated.all()) + + +def test_arc_oob_positive_theta2(): + angle = 45 + arc_rotated = ShapeGenerator().create_arc(theta2=angle + 360) + arc_non_rotated = ShapeGenerator().create_arc(theta2=angle) + + assert (arc_rotated.all(), arc_non_rotated.all()) + + +def test_arc_oob_width(): + arc = ShapeGenerator((14, 14)).create_arc( + radius=28, line_width=100, theta1=0, theta2=360 + ) + + size = arc.size + n_black_pixels = int(arc.sum().sum()) + assert size == n_black_pixels + + +def test_line_defaults(): + line = ShapeGenerator().create_line(start=(0, 0), end=(28, 28)) + + x, y = line.shape + expected_x, expected_y = (28, 28) + assert x == expected_x + assert y == expected_y + + # Bottom left top right are black. Opposite are white + assert (1.0 == line[1, 2], "line start incorrect") + assert (1.0 == line[26, 27], "line end incorrect") + + assert (0.0 == line[1, 27], "line corner incorrect") + assert (0.0 == line[27, 1], "line corner incorrect") + + +def test_line_size_start_dim_mismatch(): + with pytest.raises(ValueError): + ShapeGenerator().create_line(start=(0, 0, 0)) + + +def test_line_size_end_dim_mismatch(): + with pytest.raises(ValueError): + ShapeGenerator().create_line(end=(0, 0, 0)) + + +def test_line_start_oob(): + line = ShapeGenerator().create_line(start=(-200, -100), end=(28, 28)) + + # Bottom left top right are black. Opposite are white + assert 1.0 == line[1, 14] + assert 1.0 == line[25, 27] + + +def test_line_end_oob(): + line = ShapeGenerator().create_line(start=(0, 0), end=(200, 100)) + + assert 1.0 == line[1, 1] + assert 1.0 == line[27, 14] + + +def test_line_same_start_end(): + with pytest.raises(ValueError): + ShapeGenerator().create_line(start=(0, 0), end=(0, 0)) + + +def test_ellipse_default(): + circle = ShapeGenerator().create_ellipse() + + shape_x, shape_y = circle.shape + expected_x, expected_y = 28, 28 + + assert shape_x == expected_x + assert shape_y == expected_y + + +def test_ellipse_size_xy_mismatch(): + with pytest.raises(ValueError): + ShapeGenerator().create_ellipse(center=(14, 14, 14)) + + +def test_ellipse_oob_center(): + circle = ShapeGenerator((10, 10)).create_ellipse(center=(100, 100)) + contents = circle.sum().sum() + assert 0.0 == contents diff --git a/tests/shape_image_test.py b/tests/shape_image_test.py new file mode 100644 index 0000000..337b50a --- /dev/null +++ b/tests/shape_image_test.py @@ -0,0 +1,96 @@ +import pytest +from deepbench.image import ShapeImage + + +@pytest.fixture() +def rectangle(): + return { + "objects": "rectangle", + "object_params": {"fill": True}, + "instance_params": {}, + } + + +def test_init(): + test_sky = ShapeImage((14, 14)) + assert (14, 14) == test_sky.image_shape + + +def test_1dim_size(): + with pytest.raises(AssertionError): + im_shape = (12,) + ShapeImage(im_shape) + + +def test_combine_one_image(rectangle): + image_shape = (14, 14) + shapes_image = ShapeImage(image_shape) + + # # Not testing that they're the right ones, only that they're made + single_image = shapes_image.combine_objects(**rectangle) + assert image_shape == single_image.shape + + +def combine_all_images(): + image_shape = (14, 14) + shapes_image = ShapeImage(image_shape) + + all_shapes = shapes_image.method_map.keys() + multiple_objects = { + "objects": [shape for shape in all_shapes], + "instance_params": [{} for _ in all_shapes], + "object_params": [{} for _ in all_shapes], + } + # Not testing that they're the right ones, only that they're made + out_image = shapes_image.combine_objects(**multiple_objects) + + assert image_shape == out_image.shape + + +def test_combine_no_images(): + image_shape = (14, 14) + shapes_image = ShapeImage(image_shape) + + assert 0 == shapes_image.image.sum() + + +def test_generate_noise(rectangle): + image_shape = (14, 14) + shapes_image_guass = ShapeImage( + image_shape, object_noise_level=0.8, object_noise_type="gaussian" + ) + noiseless_image = shapes_image_guass._create_object( + rectangle["objects"], rectangle["object_params"] + ) + + guass_noise_image = shapes_image_guass.combine_objects(**rectangle) + + assert guass_noise_image.all() != noiseless_image.all() + + shapes_image_poisson = ShapeImage( + image_shape, object_noise_level=8, object_noise_type="poisson" + ) + poisson_noise_image = shapes_image_poisson.combine_objects(**rectangle) + assert poisson_noise_image.all() != noiseless_image.all() + + assert poisson_noise_image[0][0] != guass_noise_image[0][0] + + +def test_add_fake_noise(rectangle): + with pytest.raises(NotImplementedError): + + fake_noise = "Not A Noise Type" + image_shape = (14, 14) + shapes_image = ShapeImage(image_shape, object_noise_type=fake_noise) + shapes_image.combine_objects(**rectangle) + + +def test_not_an_astro_object(rectangle): + fake_object = "I wanted to write a funny joke here but my brain is gone" + with pytest.raises(NotImplementedError): + + image_shape = (14, 14) + shapes_image = ShapeImage(image_shape) + shapes_image.combine_objects( + fake_object, rectangle["instance_params"], rectangle["object_params"] + ) diff --git a/tests/sky_image_test.py b/tests/sky_image_test.py index afafb3a..8caa05d 100644 --- a/tests/sky_image_test.py +++ b/tests/sky_image_test.py @@ -1,106 +1,164 @@ -from unittest import TestCase -from src.deepbench.image.sky_image import SkyImage +import pytest +from deepbench.image import SkyImage -class TestSkyImage(TestCase): - def test_init(self): - test_sky = SkyImage([{}], (14, 14)) +@pytest.fixture() +def star(): + return { + "objects": "star", + "object_params": {"center_x": 14, "center_y": 14, "alpha": 1.0}, + "instance_params": {"noise_level": 0, "radius": 1.0, "amplitude": 1.0}, + } - self.assertIsNone(test_sky.image) - self.assertEqual([{}], test_sky.objects) - self.assertEqual((14, 14), test_sky.image_shape) - def test_1dim_size(self): - with self.assertRaises(AssertionError): - im_shape = (12,) - SkyImage([{}], im_shape) +def test_init(): + test_sky = SkyImage((14, 14)) + assert (14, 14) == test_sky.image_shape - def test_0dim_size(self): - with self.assertRaises(AssertionError): - im_shape = () - SkyImage([{}], im_shape) - def test_3dim_size(self): - im_shape = (14, 14, 3) - test_sky = SkyImage([{}], im_shape) +def test_1dim_size(): + with pytest.raises(AssertionError): + im_shape = (12,) + SkyImage(im_shape) - self.assertIsNone(test_sky.image) - self.assertEqual([{}], test_sky.objects) - self.assertEqual(im_shape, test_sky.image_shape) - def test_combine_one_image(self): - object_params = [{"object_type": "test_object"}] - image_shape = (14, 14) - one_image_sky = SkyImage(object_params, image_shape) +def test_3dim_size(): + im_shape = (14, 14, 3) + test_sky = SkyImage(im_shape) - # Not testing that they're the right ones, only that they're made - one_image_sky.combine_objects() + assert test_sky.image.shape == im_shape - self.assertEqual(image_shape, one_image_sky.image.shape) - def test_combine_2_images(self): - object_params = [{"object_type": "test_object"}, {"object_type": "test_object"}] - image_shape = (14, 14) - one_image_sky = SkyImage(object_params, image_shape) +def test_combine_one_image(star): + image_shape = (14, 14) + one_image_sky = SkyImage(image_shape) - # Not testing that they're the right ones, only that they're made - one_image_sky.combine_objects() + # # Not testing that they're the right ones, only that they're made + one_image_sky.combine_objects(**star) - self.assertEqual(image_shape, one_image_sky.image.shape) + assert image_shape == one_image_sky.image.shape - def test_combine_no_images(self): - object_params = [{}] - image_shape = (14, 14) - one_image_sky = SkyImage(object_params, image_shape) - # Not testing that they're the right ones, only that they're made - with self.assertWarns(UserWarning): - one_image_sky.combine_objects() +def test_combine_2_images(star): + image_shape = (14, 14) + one_image_sky = SkyImage(image_shape) - self.assertEqual(image_shape, one_image_sky.image.shape) - self.assertEqual(0, one_image_sky.image.sum()) + multiple_objects = { + "objects": [star["objects"], star["objects"]], + "instance_params": [star["instance_params"], star["instance_params"]], + "object_params": [star["object_params"], star["object_params"]], + } + # Not testing that they're the right ones, only that they're made + one_image_sky.combine_objects(**multiple_objects) - def test_generate_gaussian_noise(self): + assert image_shape == one_image_sky.image.shape - object_params = [{"object_type": "test_object", "object_parameters": {}}] - image_shape = (14, 14) - one_image_sky = SkyImage(object_params, image_shape) - one_image_sky.combine_objects() - one_image_sky.generate_noise("gaussian") +def test_combine_no_images(): + image_shape = (14, 14) + one_image_sky = SkyImage(image_shape) + + assert 0 == one_image_sky.image.sum() + + +def test_generate_gaussian_noise(star): + image_shape = (14, 14) + one_image_sky = SkyImage( + image_shape, object_noise_level=0.1, object_noise_type="guassian" + ) + one_image_sky.combine_objects(**star) + + assert image_shape == one_image_sky.image.shape + assert ( + one_image_sky.image.all() + != one_image_sky._generate_astro_object( + star["objects"], star["instance_params"] + ) + .create_object(**star["object_params"]) + .all() + ) + + +def test_generate_gaussian_noise(star): + image_shape = (14, 14) + one_image_sky = SkyImage( + image_shape, object_noise_level=0.1, object_noise_type="poisson" + ) + one_image_sky.combine_objects(**star) - self.assertIsNotNone(one_image_sky.image) - self.assertEqual(image_shape, one_image_sky.image.shape) + assert image_shape == one_image_sky.image.shape + assert ( + one_image_sky.image.all() + != one_image_sky._generate_astro_object( + star["objects"], star["instance_params"] + ) + .create_object(**star["object_params"]) + .all() + ) - def test_generate_poisson_noise(self): - object_params = [{"object_type": "test_object", "object_parameters": {}}] +def test_add_fake_noise(star): + with pytest.raises(NotImplementedError): + fake_noise = "Not A Noise Type" image_shape = (14, 14) - one_image_sky = SkyImage(object_params, image_shape) - one_image_sky.combine_objects() - one_image_sky.generate_noise("poisson") + one_image_sky = SkyImage(image_shape, object_noise_type=fake_noise) + one_image_sky.combine_objects(**star) + + +def test_not_an_astro_object(star): + fake_object = "I wanted to write a funny joke here but my brain is gone" + with pytest.raises(NotImplementedError): + + image_shape = (14, 14) + one_image_sky = SkyImage(image_shape) + one_image_sky.combine_objects( + fake_object, star["instance_params"], star["object_params"] + ) + + +def test_make_all_astro_objects(): + sky_objects = ["star", "galaxy", "spiral_galaxy"] + sky_params = [{"noise_level": 0, "radius": 1.0, "amplitude": 1.0}, {}, {}] + object_params = [ + {"center_x": 14, "center_y": 14}, + {"center_x": 14, "center_y": 14}, + {"center_x": 14, "center_y": 14}, + ] + + image_shape = (14, 14) + one_image_sky = SkyImage(image_shape) + one_image_sky.combine_objects(sky_objects, sky_params, object_params) + + +def test_make_different_images(): + + sky_params = {"noise_level": 0, "radius": 1.0, "amplitude": 1.0} + object_params = {"center_x": 14, "center_y": 14} - self.assertIsNotNone(one_image_sky.image) - self.assertEqual(image_shape, one_image_sky.image.shape) + image_shape = (14, 14) + one_image_sky = SkyImage(image_shape, scale=False) + star_image = one_image_sky.combine_objects(["star"], sky_params, object_params) + galaxy_image = one_image_sky.combine_objects(["galaxy"], sky_params, object_params) - def test_add_fake_noise(self): - with self.assertRaises(NotImplementedError): + assert (star_image != galaxy_image).all() - object_params = [{"object_type": "test_object", "object_parameters": {}}] - image_shape = (14, 14) - one_image_sky = SkyImage(object_params, image_shape) - one_image_sky.combine_objects() - one_image_sky.generate_noise("Fake Noise") +def test_make_the_same_things(): - def test_image_not_made(self): - with self.assertRaises(AssertionError): + sky_params = {"noise_level": 0, "radius": 1.0, "amplitude": 1.0} + object_params = {"center_x": 14, "center_y": 14} - object_params = [{"object_type": "test_object", "object_parameters": {}}] + image_shape = (14, 14) + one_image_sky = SkyImage(image_shape, scale=True) + star_image = one_image_sky.combine_objects(["star"], sky_params, object_params) + galaxy_image = one_image_sky.combine_objects(["galaxy"], sky_params, object_params) - image_shape = (14, 14) - one_image_sky = SkyImage(object_params, image_shape) + combined_image = star_image + galaxy_image + generated_combined_image = one_image_sky.combine_objects( + ["star", "galaxy"], + instance_params=[sky_params, sky_params], + object_params=[object_params, object_params], + ) - ##Go straight to noise instead of making objects first - one_image_sky.generate_noise("gaussian") + assert (combined_image == generated_combined_image).all() diff --git a/tests/test_deepbench.py b/tests/test_deepbench.py deleted file mode 100644 index b6c8994..0000000 --- a/tests/test_deepbench.py +++ /dev/null @@ -1,5 +0,0 @@ -from src.deepbench import __version__ - - -def test_version(): - assert __version__ == "0.1.0" diff --git a/tests/test_shape_generator.py b/tests/test_shape_generator.py deleted file mode 100644 index 11ca63e..0000000 --- a/tests/test_shape_generator.py +++ /dev/null @@ -1,446 +0,0 @@ -from unittest import TestCase -from src.deepbench.shape_generator.shape_generator import ShapeGenerator - -import numpy as np -import matplotlib.patches as patch - - -class TestShapeGenerator(TestCase): - def test_patch_conversion_defaults(self): - # simple patch object. Convert to a numpy array - simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) - converted_object = ShapeGenerator._convert_patch_to_image(image=simple_patch) - expected_obj_type = np.array([0, 0]) - self.assertEqual(type(converted_object), type(expected_obj_type)) - - def test_patch_conversion_default_dimensions(self): - simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) - converted_object = ShapeGenerator._convert_patch_to_image(image=simple_patch) - converted_object_shape_x, converted_object_shape_y = converted_object.shape - expected_x_dim, expected_y_dim = 28, 28 - - self.assertEqual(converted_object_shape_x, expected_x_dim) - self.assertEqual(converted_object_shape_y, expected_y_dim) - - def test_patch_conversion_single_image_dimension(self): - with self.assertRaises(ValueError): - simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) - ShapeGenerator._convert_patch_to_image( - image=simple_patch, image_shape=tuple([28]) - ) - - def test_patch_conversion_N_dimension(self): - simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) - converted_object = ShapeGenerator._convert_patch_to_image( - image=simple_patch, image_shape=(28, 28, 28) - ) - ( - converted_object_shape_x, - converted_object_shape_y, - converted_object_shape_z, - ) = converted_object.shape - expected_x_dim, expected_y_dim, expected_z_dim = 28, 28, 28 - - self.assertEqual(converted_object_shape_x, expected_x_dim) - self.assertEqual(converted_object_shape_y, expected_y_dim) - self.assertEqual(converted_object_shape_z, expected_z_dim) - - def test_patch_conversion_size_0(self): - with self.assertRaises(ValueError): - simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) - ShapeGenerator._convert_patch_to_image( - image=simple_patch, image_shape=(0, 0) - ) - - def test_patch_conversion_non_int_size(self): - simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) - converted_object = ShapeGenerator._convert_patch_to_image( - image=simple_patch, image_shape=(55.5, 55.5) - ) - converted_object_shape_x, converted_object_shape_y = converted_object.shape - expected_x_dim, expected_y_dim = 56, 56 - - self.assertEqual(converted_object_shape_x, expected_x_dim) - self.assertEqual(converted_object_shape_y, expected_y_dim) - - def test_patch_conversion_non_int_size_round(self): - simple_patch = patch.Rectangle(xy=(0, 0), width=1, height=1) - converted_object = ShapeGenerator._convert_patch_to_image( - image=simple_patch, image_shape=(55.1, 55.6) - ) - converted_object_shape_x, converted_object_shape_y = converted_object.shape - expected_x_dim, expected_y_dim = 56, 56 - - self.assertEqual(converted_object_shape_x, expected_x_dim) - self.assertEqual(converted_object_shape_y, expected_y_dim) - - def test_resize_default(self): - simple_image = np.zeros((32, 32)) - resized_image = ShapeGenerator.resize(image=simple_image) - resized_image_x, resized_image_y = resized_image.shape - default_new_size_x, default_new_size_y = 28, 28 - - self.assertEqual(resized_image_x, default_new_size_x) - self.assertEqual(resized_image_y, default_new_size_y) - - def test_resize_dim_mismatch(self): - with self.assertRaises(ValueError): - simple_image = np.zeros((32, 32)) - resize_dimensions = (1, 1, 1) - ShapeGenerator.resize( - image=simple_image, resize_dimensions=resize_dimensions - ) - - def test_resize_zero_size_source(self): - with self.assertRaises(ValueError): - simple_image = np.zeros((0, 0)) - resize_dimensions = (1, 1) - ShapeGenerator.resize( - image=simple_image, resize_dimensions=resize_dimensions - ) - - def test_resize_zero_size_target(self): - with self.assertRaises(ValueError): - simple_image = np.zeros((1, 1)) - resize_dimensions = (0, 0) - ShapeGenerator.resize( - image=simple_image, resize_dimensions=resize_dimensions - ) - - def test_resize_upscale(self): - simple_image = np.zeros((32, 32)) - resize = (50, 50) - resized_image = ShapeGenerator.resize( - image=simple_image, resize_dimensions=resize - ) - resized_image_x, resized_image_y = resized_image.shape - new_size_x, new_size_y = resize - - self.assertEqual(resized_image_x, new_size_x) - self.assertEqual(resized_image_y, new_size_y) - - def test_resize_downscale(self): - simple_image = np.zeros((80, 80)) - resize = (50, 50) - resized_image = ShapeGenerator.resize( - image=simple_image, resize_dimensions=resize - ) - resized_image_x, resized_image_y = resized_image.shape - new_size_x, new_size_y = resize - - self.assertEqual(resized_image_x, new_size_x) - self.assertEqual(resized_image_y, new_size_y) - - def test_resize_non_int(self): - simple_image = np.zeros((80, 80)) - resize = (49.1, 49.6) - resized_image = ShapeGenerator.resize( - image=simple_image, resize_dimensions=resize - ) - resized_image_x, resized_image_y = resized_image.shape - new_size_x, new_size_y = (50, 50) - - self.assertEqual(resized_image_x, new_size_x) - self.assertEqual(resized_image_y, new_size_y) - - def test_resize_negative(self): - with self.assertRaises(ValueError): - simple_image = np.zeros((80, 80)) - resize = (-1, -1) - ShapeGenerator.resize(image=simple_image, resize_dimensions=resize) - - def test_rectangle_default(self): - rectangle = ShapeGenerator.create_rectangle() - - x, y = rectangle.shape - expected_x, expected_y = (28, 28) - self.assertEqual(x, expected_x) - self.assertEqual(y, expected_y) - - # Each corner should have a black pixel - self.assertEqual(1.0, rectangle[9, 10], "corner 1 failed") - self.assertEqual(1.0, rectangle[19, 19], "corner 4 failed") - - # Center needs to be white (default is unfilled) - self.assertEqual(0.0, rectangle[14, 14], "Center Filled") - - def test_rectangle_size_center_dim_mismatch(self): - with self.assertRaises(ValueError): - ShapeGenerator.create_rectangle(center=(1, 1, 1)) - - def test_rectangle_xy_out_of_range(self): - with self.assertRaises(UserWarning): - rectangle = ShapeGenerator.create_rectangle( - image_shape=(10, 10), center=(100, 100) - ) - - rectangle_contents = rectangle.sum().sum() - self.assertEqual(0, rectangle_contents) - - def test_rectangle_angle_in_bounds_no_change(self): - angle = 90 - rectangle_rotated = ShapeGenerator.create_rectangle(angle=angle) - - rectangle_non_rotated = ShapeGenerator.create_rectangle(angle=0) - - # But it's a square so it looks the same - self.assertEqual(rectangle_rotated.all(), rectangle_non_rotated.all()) - - def test_rectangle_angle_in_bounds_change(self): - angle = 360 - rectangle = ShapeGenerator.create_rectangle(width=10, height=10, angle=angle) - - # Each corner should have a black pixel - self.assertEqual(1.0, rectangle[9, 10], "corner 1 failed") - self.assertEqual(1.0, rectangle[19, 19], "corner 4 failed") - - # Center needs to be white (default is unfilled) - self.assertEqual(0.0, rectangle[14, 14], "Center Filled") - - # TODO Test to check odd n on width and height - # TODO Checks on line width validity - - def test_rectangle_angle_oob_positive(self): - angle = 45 - rectangle_rotated = ShapeGenerator.create_rectangle(angle=angle + 360) - rectangle_non_rotated = ShapeGenerator.create_rectangle(angle=angle) - - self.assertEqual(rectangle_rotated.all(), rectangle_non_rotated.all()) - - def test_rectangle_angle_oob_negative(self): - angle = 45 - rectangle_rotated = ShapeGenerator.create_rectangle(angle=angle - 360) - rectangle_non_rotated = ShapeGenerator.create_rectangle(angle=angle) - - self.assertEqual(rectangle_rotated.all(), rectangle_non_rotated.all()) - - def test_rectangle_fill(self): - # This should fill the whole screen. - rectangle = ShapeGenerator.create_rectangle(width=80, height=80, fill=True) - ideal_rectangle = np.ones((28, 28)) - - self.assertEqual(ideal_rectangle.all(), rectangle.all()) - - def test_polygon_default(self): - triangle = ShapeGenerator.create_regular_polygon() - - shape_x, shape_y = triangle.shape - expected_x, expected_y = 28, 28 - - self.assertEqual(shape_x, expected_x) - self.assertEqual(shape_y, expected_y) - - # Center should be white - self.assertEqual(0.0, triangle[14, 14]) - - # top point should be black - self.assertEqual(1.0, triangle[21, 17]) - - # TODO the trig to check the other points here - - def test_polygon_size_center_mismatch(self): - with self.assertRaises(ValueError): - ShapeGenerator.create_regular_polygon(center=(14, 14, 14)) - - def test_polygon_oob_xy(self): - with self.assertRaises(UserWarning): - triangle = ShapeGenerator.create_regular_polygon( - image_shape=(10, 10), center=(100, 100) - ) - - contents = triangle.sum().sum() - self.assertEqual(0.0, contents) - - def test_polygon_positive_oob_angle(self): - angle = 45 - triangle_rotated = ShapeGenerator.create_regular_polygon(angle=angle + 360) - triangle_non_rotated = ShapeGenerator.create_regular_polygon(angle=angle) - - self.assertEqual(triangle_rotated.all(), triangle_non_rotated.all()) - - def test_polygon_negative_oob_angle(self): - angle = 45 - triangle_rotated = ShapeGenerator.create_regular_polygon(angle=angle - 360) - triangle_non_rotated = ShapeGenerator.create_regular_polygon(angle=angle) - - self.assertEqual(triangle_rotated.all(), triangle_non_rotated.all()) - - def test_polygon_negative_radius(self): - triangle = ShapeGenerator.create_regular_polygon(radius=-10) - self.assertRaises(UserWarning) - - # Center should be white - self.assertEqual(0.0, triangle[14, 14]) - - # top point should be black - # The radius will just be abs - self.assertEqual(1.0, triangle[21, 17]) - - def test_polygon_negative_vertices(self): - with self.assertRaises(ValueError): - ShapeGenerator.create_regular_polygon(vertices=-3) - - def test_arc_default(self): - arc = ShapeGenerator.create_arc() - - x, y = arc.shape - expected_x, expected_y = (28, 28) - self.assertEqual(x, expected_x) - self.assertEqual(y, expected_y) - - self.assertEqual(0.0, arc[14, 14], "Empty Center") - self.assertEqual(1.0, arc[14, 24], "Bottom Point") - self.assertEqual(1.0, arc[23, 15], "Starting Point") - - def test_arc_size_center_dim_mismatch(self): - with self.assertRaises(ValueError): - ShapeGenerator.create_arc(center=(0, 0, 0)) - - def test_arc_oob_center(self): - with self.assertRaises(UserWarning): - triangle = ShapeGenerator.create_arc(center=(100, 100)) - - contents = triangle.sum().sum() - self.assertEqual(0.0, contents) - - def test_arc_oob_negative_theta1(self): - angle = 45 - arc_rotated = ShapeGenerator.create_arc(theta1=angle - 360) - arc_non_rotated = ShapeGenerator.create_arc(theta1=angle) - - self.assertEqual(arc_rotated.all(), arc_non_rotated.all()) - - def test_arc_oob_positive_theta1(self): - angle = 45 - arc_rotated = ShapeGenerator.create_arc(theta1=angle + 360) - arc_non_rotated = ShapeGenerator.create_arc(theta1=angle) - - self.assertEqual(arc_rotated.all(), arc_non_rotated.all()) - - def test_arc_oob_negative_theta2(self): - angle = 45 - arc_rotated = ShapeGenerator.create_arc(theta2=angle - 360) - arc_non_rotated = ShapeGenerator.create_arc(theta2=angle) - - self.assertEqual(arc_rotated.all(), arc_non_rotated.all()) - - def test_arc_oob_positive_theta2(self): - angle = 45 - arc_rotated = ShapeGenerator.create_arc(theta2=angle + 360) - arc_non_rotated = ShapeGenerator.create_arc(theta2=angle) - - self.assertEqual(arc_rotated.all(), arc_non_rotated.all()) - - def test_arc_theta2_less_than_theta1(self): - - arc_negative_sweep = ShapeGenerator.create_arc(theta1=90, theta2=0) - arc_positive_sweep = ShapeGenerator.create_arc(theta1=0, theta2=90) - - self.assertEqual(arc_negative_sweep.all(), arc_positive_sweep.all()) - - def test_arc_oob_width(self): - arc = ShapeGenerator.create_arc( - image_shape=(14, 14), radius=28, line_width=100, theta1=0, theta2=360 - ) - - size = arc.size - n_black_pixels = int(arc.sum().sum()) - self.assertEqual(size, n_black_pixels) - - def test_line_defaults(self): - line = ShapeGenerator.create_line() - - x, y = line.shape - expected_x, expected_y = (28, 28) - self.assertEqual(x, expected_x) - self.assertEqual(y, expected_y) - - # Bottom left top right are black. Opposite are white - self.assertEqual(1.0, line[1, 2], "line start incorrect") - self.assertEqual(1.0, line[26, 27], "line end incorrect") - - self.assertEqual(0.0, line[1, 27], "line corner incorrect") - self.assertEqual(0.0, line[27, 1], "line corner incorrect") - - def test_line_size_start_dim_mismatch(self): - with self.assertRaises(ValueError): - ShapeGenerator.create_line(start=(0, 0, 0)) - - def test_line_size_end_dim_mismatch(self): - with self.assertRaises(ValueError): - ShapeGenerator.create_line(end=(0, 0, 0)) - - def test_line_start_oob(self): - line = ShapeGenerator.create_line(start=(-200, -100)) - - # Bottom left top right are black. Opposite are white - self.assertEqual(1.0, line[1, 14]) - self.assertEqual(1.0, line[25, 27]) - - def test_line_end_oob(self): - line = ShapeGenerator.create_line(end=(200, 100)) - self.assertRaises(UserWarning) - - self.assertEqual(1.0, line[1, 1], "line start incorrect") - self.assertEqual(1.0, line[27, 14], "line end incorrect") - - def test_line_same_start_end(self): - with self.assertRaises(ValueError): - ShapeGenerator.create_line(end=(0, 0)) - - def test_ellipse_default(self): - circle = ShapeGenerator.create_ellipse() - - shape_x, shape_y = circle.shape - expected_x, expected_y = 28, 28 - - self.assertEqual(shape_x, expected_x) - self.assertEqual(shape_y, expected_y) - - # Center should be white - self.assertEqual(0.0, circle[14, 14]) - - # Default is a circle - self.assertEqual(1.0, circle[11, 10]) - self.assertEqual(1.0, circle[14, 19]) - self.assertEqual(1.0, circle[19, 14]) - self.assertEqual(1.0, circle[9, 14]) - - def test_ellipse_size_xy_mismatch(self): - with self.assertRaises(ValueError): - ShapeGenerator.create_ellipse(center=(14, 14, 14)) - - def test_ellipse_0_radius(self): - ## Nothing is displayed - with self.assertRaises(UserWarning): - ShapeGenerator.create_ellipse(width=0, height=0) - - def test_ellipse_non_int_radius(self): - # Just round up - circle = ShapeGenerator.create_ellipse(height=9.4) - - # Center should be white - self.assertEqual(0.0, circle[14, 14]) - - # top and bottom points should be black - self.assertEqual(1.0, circle[14, 19]) - self.assertEqual(1.0, circle[19, 14]) - - def test_ellipse_oob_center(self): - with self.assertRaises(UserWarning): - circle = ShapeGenerator.create_ellipse( - image_shape=(10, 10), center=(100, 100) - ) - contents = circle.sum().sum() - self.assertEqual(0.0, contents) - - def test_ellipse_non_int_width(self): - # Just round up - circle = ShapeGenerator.create_ellipse(width=9.4) - - # Center should be white - self.assertEqual(0.0, circle[14, 14]) - - # top and bottom points should be black - self.assertEqual(1.0, circle[14, 19]) - self.assertEqual(1.0, circle[19, 14]) diff --git a/tests/time_series_image_test.py b/tests/time_series_image_test.py deleted file mode 100644 index a4fd0b3..0000000 --- a/tests/time_series_image_test.py +++ /dev/null @@ -1,98 +0,0 @@ -from unittest import TestCase -from src.deepbench.image.time_series_image import TimeSeriesImage - - -class TestTimeSeriesImage(TestCase): - def test_1d_init(self): - with self.assertRaises(AssertionError): - image_shape = (12,) - object_params = [{"object_type": "test_object"}] - TimeSeriesImage(object_params, image_shape) - - def test_2d_init(self): - image_shape = (12, 12) - object_params = [{"object_type": "test_object"}] - time_series = TimeSeriesImage(object_params, image_shape) - - self.assertEqual(image_shape, time_series.image_shape) - - def test_3d_init(self): - image_shape = (12, 12, 3) - object_params = [{"object_type": "test_object"}] - time_series = TimeSeriesImage(object_params, image_shape) - - self.assertEqual(image_shape, time_series.image_shape) - - def test_0d_init(self): - with self.assertRaises(AssertionError): - image_shape = () - object_params = [{"object_type": "test_object"}] - TimeSeriesImage(object_params, image_shape) - - def test_no_object_init(self): - with self.assertRaises(AssertionError): - image_shape = (12, 12) - object_params = [] - TimeSeriesImage(object_params, image_shape) - - def test_one_object_init(self): - image_shape = (12, 12) - object_params = [{"object_type": "test_object"}] - time_series = TimeSeriesImage(object_params, image_shape) - - self.assertEqual(image_shape, time_series.image_shape) - self.assertEqual(object_params, time_series.objects) - - def test_two_object_init(self): - with self.assertRaises(AssertionError): - image_shape = (12, 12) - object_params = [ - {"object_type": "test_object"}, - [{"object_type": "test_object"}], - ] - TimeSeriesImage(object_params, image_shape) - - def test_combine_one_object(self): - image_shape = (12, 12) - object_params = [{"object_type": "test_object"}] - time_series = TimeSeriesImage(object_params, image_shape) - time_series.combine_objects() - - self.assertEqual(image_shape, time_series.image.shape) - - def test_generate_gaussian_noise(self): - object_params = [{"object_type": "test_object"}] - image_shape = (14, 14) - one_image_sky = TimeSeriesImage(object_params, image_shape) - one_image_sky.combine_objects() - one_image_sky.generate_noise("gaussian") - - self.assertIsNotNone(one_image_sky.image) - self.assertEqual(image_shape, one_image_sky.image.shape) - - def test_generate_poisson_noise(self): - object_params = [{"object_type": "test_object"}] - image_shape = (14, 14) - one_image_sky = TimeSeriesImage(object_params, image_shape) - one_image_sky.combine_objects() - one_image_sky.generate_noise("poisson") - - self.assertIsNotNone(one_image_sky.image) - self.assertEqual(image_shape, one_image_sky.image.shape) - - def test_add_fake_noise(self): - with self.assertRaises(NotImplementedError): - object_params = [{"object_type": "test_object"}] - image_shape = (14, 14) - one_image_sky = TimeSeriesImage(object_params, image_shape) - one_image_sky.combine_objects() - one_image_sky.generate_noise("Fake Noise") - - def test_image_not_made(self): - with self.assertRaises(AssertionError): - object_params = [{"object_type": "test_object"}] - image_shape = (14, 14) - one_image_sky = TimeSeriesImage(object_params, image_shape) - - ##Go straight to noise instead of making objects first - one_image_sky.generate_noise("gaussian")