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|---|---|---|
| @@ -0,0 +1,318 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "55a1b889-7fb4-4d73-a9d0-23ab1bdb4dcb", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pandas as pd\n", | ||
| "import sqlite3\n", | ||
| "\n", | ||
| "from adler.dataclasses.AdlerData import AdlerData\n", | ||
| "from adler.dataclasses.AdlerPlanetoid import AdlerPlanetoid\n", | ||
| "from adler.utilities.tests_utilities import get_test_data_filepath" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "5264c132-e86d-4415-bfd4-cb1856d2fc33", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "This is a quick notebook demonstrating how Adler's calculated values can be stored and then retrieved for later." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "8baa263b-5f45-4f5a-b13e-565a5e2d181b", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "First, let's make our AdlerPlanetoid object. In this case, we're populating it from a testing SQL database." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "b04683c9-36db-4320-b6b7-7ef487aaf02e", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "ssoid = \"8268570668335894776\"\n", | ||
| "test_db_path = get_test_data_filepath(\"testing_database.db\")\n", | ||
| "test_planetoid = AdlerPlanetoid.construct_from_SQL(ssoid, test_db_path, filter_list=[\"g\", \"r\"])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "e1de656a-1a10-418a-8646-f0e57d811dc6", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Now let's make up some pretend Adler calculated values, and populate the AdlerData object stored in AdlerPlanetoid." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "b6e1a766-56fb-4682-8466-b42d5aa80ad2", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "g_model_1 = {\n", | ||
| " \"model_name\": \"model_1\",\n", | ||
| " \"phaseAngle_min\": 31.0,\n", | ||
| " \"phaseAngle_range\": 32.0,\n", | ||
| " \"nobs\": 33,\n", | ||
| " \"arc\": 34.0,\n", | ||
| " \"H\": 35.0,\n", | ||
| " \"H_err\": 36.0,\n", | ||
| " \"phase_parameter_1\": 37.0,\n", | ||
| " \"phase_parameter_1_err\": 38.0,\n", | ||
| "}\n", | ||
| "\n", | ||
| "r_model_2 = {\n", | ||
| " \"model_name\": \"model_2\",\n", | ||
| " \"phaseAngle_min\": 41.0,\n", | ||
| " \"phaseAngle_range\": 42.0,\n", | ||
| " \"nobs\": 43,\n", | ||
| " \"arc\": 44.0,\n", | ||
| " \"H\": 45.0,\n", | ||
| " \"H_err\": 46.0,\n", | ||
| " \"phase_parameter_1\": 47.0,\n", | ||
| " \"phase_parameter_1_err\": 48.0,\n", | ||
| " \"phase_parameter_2\": 49.0,\n", | ||
| " \"phase_parameter_2_err\": 50.0,\n", | ||
| "}" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "63635480-5b9f-49a4-97f8-a70cce410829", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "test_planetoid.AdlerData.populate_phase_parameters(\"g\", **g_model_1)\n", | ||
| "test_planetoid.AdlerData.populate_phase_parameters(\"r\", **r_model_2)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "1c5b2ebe-094f-4dbc-b5ef-594d60ec1b28", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Now we can write these out." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "1e795fe1-afa7-4921-91f7-3dfc38240f80", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "database_filepath = \"./gen_test_data/example_AdlerData_database.db\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "b7637fbc-2b03-46fa-95f7-0e760b54d9e2", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "test_planetoid.AdlerData.write_row_to_database(database_filepath)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "4e9eebdd-d844-4261-8521-c04688d3813a", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "We'll use Pandas to look at what we just wrote out." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "5bc42334-e6ad-451a-a3f3-17794206c82b", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "con = sqlite3.connect(database_filepath)\n", | ||
| "adler_data_out = pd.read_sql(\"SELECT * from AdlerData\", con)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "b4cb5d43-33b3-4fca-9a9b-52b781de7fb1", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "adler_data_out" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "3e77e0ab-1310-40f6-9380-499705849960", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Note that write_row_to_database() method always appends. So:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "810379ff-75bd-4c81-8ad9-66334e6ff9c5", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "test_planetoid.AdlerData.write_row_to_database(database_filepath)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "9e89559b-9500-4416-9d11-92bec442406a", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "con = sqlite3.connect(database_filepath)\n", | ||
| "adler_data_out = pd.read_sql(\"SELECT * from AdlerData\", con)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "894623fa-7128-4518-9ca6-1b2a94d175d1", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "adler_data_out" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "49b89142-765f-4115-a011-d93c7c12737d", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Now we have added two rows." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "65cefda8-ac98-4b04-ad4e-2175fb22b37c", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "So perhaps we have an AdlerPlanetoid object and this time, we want to load in some previously calculated values for comparison. This is extremely easy. We'll do it on the AdlerPlanetoid object we already made." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "5604ea13-4a37-44e2-bb2e-59ec2461e805", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "test_planetoid.attach_previous_adler_data(database_filepath)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "8139a2c1-aa8c-4a57-86f6-5d3ac5923063", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "This can be more easily accessed and read:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "a926d90a-5d7d-4185-9f00-f06a51e06739", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "test_planetoid.PreviousAdlerData.print_data()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "45f810b5-4d9f-46d5-8fcc-d6dddc8e6b0e", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "test_planetoid.PreviousAdlerData.get_phase_parameters_in_filter(\"g\", \"model_1\").__dict__" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "f0455add-ca30-417b-92c6-6cceb6f67363", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Or, if you don't want to work with an existing AdlerPlanetoid object, you can directly populate an AdlerData object from a database." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "6ded7e1d-1016-4eb7-8442-3ff474c0d715", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "adler_data_object = AdlerData(ssoid, [\"g\", \"r\"])\n", | ||
| "adler_data_object.populate_from_database(database_filepath)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "66a87ed1-3d99-4698-9c1f-5e6888989f0c", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "adler_data_object.print_data()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "41dc5ab2-961e-497a-8dad-c8fb35043923", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "adler_data_object.get_phase_parameters_in_filter(\"g\", \"model_1\").__dict__" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "8a7ada08-dc87-4f5e-b72f-32caa164c210", | ||
| "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": "3.10.13" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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