From 815e55a99c8fd65f0a2031d259ad380e1086902c Mon Sep 17 00:00:00 2001
From: Mitchell Victoriano <mitchell.victoriano@gmail.com>
Date: Tue, 17 Dec 2024 18:57:58 -0800
Subject: [PATCH 1/4] Added quickstart

---
 QuickStart.md | 146 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 146 insertions(+)
 create mode 100644 QuickStart.md

diff --git a/QuickStart.md b/QuickStart.md
new file mode 100644
index 00000000..972b24e6
--- /dev/null
+++ b/QuickStart.md
@@ -0,0 +1,146 @@
+# PV Validation Hub Development Environment Setup
+
+## Requirements
+
+Docker Desktop:
+<https://www.docker.com/products/docker-desktop/>
+
+Python 3.11: <https://www.python.org/downloads/release/python-31110/>
+
+Git: <https://git-scm.com/>
+
+## Repositories
+
+PV Validation Hub: <https://github.com/slacgismo/pv-validation-hub>
+
+PV Validation Hub Front-end: <https://github.com/slacgismo/pv-validation-hub-client>
+
+Analysis Markdown files: <https://github.com/kperrynrel/pvinsight_validation_hub_markdowns>
+
+Time Shift Analysis: <https://github.com/kperrynrel/time-shift-validation-hub>
+
+AZ Tilt Analysis: <https://github.com/kperrynrel/az-tilt-estimation-validator>
+
+## Outline
+
+### Pull PV Validation Hub Repository
+
+We are going to pull in the develop branch
+
+```bash
+git clone -b develop <https://github.com/slacgismo/pv-validation-hub.git>
+```
+
+### Pull Frontend Submodule
+
+```bash
+git submodule update --init --recursive
+```
+
+### Copy .env.example into a new file called .env
+
+Replace admin username and password and anything with <> to the correct file path on your machine
+
+### Create Python Virtual Environment
+
+Create a python virtual environment
+
+```bash
+ python -m venv .venv
+```
+
+then activate the virtual environment with the following commands based on your system
+
+MAC
+
+```bash
+source .venv/bin/activate
+```
+
+WINDOWS
+
+```bash
+source .venv/scripts/activate
+```
+
+### Install Python Requirements
+
+```bash
+pip install -r requirements.txt
+```
+
+### Install pre-commit
+
+```bash
+pre-commit install
+```
+
+### Run all containers
+
+**Note:** You can increase the memory allocation for Docker through the Settings > Resources > Memory Limit slider
+
+Kill all containers with ctrl + C or cmd + C and relaunch docker compose
+
+```bash
+docker compose up
+
+docker compose build api
+```
+
+### Sign Into Frontend and Django Backend
+
+Frontend: <http://localhost:3000/>
+
+Backend: <http://localhost:8005/admin/>
+
+### Upload new analysis
+
+Inserting new Analysis happens in the EC2 folder within the PV Validation Hub Repository.
+
+Pull Analysis you wish to insert into EC2 folder
+
+### Clone the markdown repo
+
+```bash
+git clone https://github.com/kperrynrel/pvinsight_validation_hub_markdowns.git
+```
+
+### Clone time shift analysis into EC2 folder
+
+```bash
+git clone https://github.com/kperrynrel/time-shift-validation-hub.git
+```
+
+### Update routes.json
+
+Depending on the analysis you may need to change the file paths stored within the routes.json depending on their location
+
+### Open a shell within the EC2 container
+
+When the containers are running within Docker Desktop, you can click on the 3 dots next to the EC2 container and select “Open in Terminal”.
+
+This will create a terminal within the container to then run the command
+
+If you have the Docker VS Code extension you can right click on the EC2 container and select “Attach Shell”
+
+### Insert the Analysis
+
+In the EC2 containers shell, you are able to manage tasks by using the manage.sh bash script with optional flags
+
+```bash
+bash manage.sh insert [--dry-run] [--force] [--prod] [--limit <number>]
+```
+
+**-–dry-run** - Test that all the files are present and validation has passed without inserting the analysis
+
+**-–force** - Forcefully create an analysis even if the exact same analysis is already present
+
+**-–limit** - Useful during development to only include the top N files for an analysis to shorten time for testing
+
+**-–prod** - If a valid AWS key exists to the production AWS, you can push an analysis to production instead of your local development environment
+
+#### REQUIRED: YOU WILL NEED TO REBUILD THE FRONTEND IMAGE AFTER INSERTING A NEW ANALYSIS FOR CHANGES TO SHOW ON FRONT END
+
+```bash
+docker compose build react-client
+```

From 3adbcd804cddcf03c04b1bdbf2b9950c7ec48744 Mon Sep 17 00:00:00 2001
From: Mitchell Victoriano <mitchell.victoriano@gmail.com>
Date: Tue, 17 Dec 2024 19:10:01 -0800
Subject: [PATCH 2/4] Remove gitignore for analysis task folders

---
 .gitignore                     |   3 -
 ec2/.gitignore                 |   3 -
 ec2/NewTaskRequirements.old.md | 117 ---------------------------------
 ec2/README.old.md              |  47 -------------
 4 files changed, 170 deletions(-)
 delete mode 100644 ec2/NewTaskRequirements.old.md
 delete mode 100644 ec2/README.old.md

diff --git a/.gitignore b/.gitignore
index 761600cf..59762a0d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -143,9 +143,6 @@ frotend/.DS_Store
 
 node-docker-ecs
 
-ec2/az-tilt-estimation-validation
-ec2/time-shift-validation-hub
-ec2/test_analysis
 ec2/evaluation_scripts/*
 
 **/.vscode
diff --git a/ec2/.gitignore b/ec2/.gitignore
index 474cb993..e69de29b 100644
--- a/ec2/.gitignore
+++ b/ec2/.gitignore
@@ -1,3 +0,0 @@
-az-tilt-submission/
-/time-shift-validation-hub/
-/pvinsight_validation_hub_markdowns/
\ No newline at end of file
diff --git a/ec2/NewTaskRequirements.old.md b/ec2/NewTaskRequirements.old.md
deleted file mode 100644
index ee6b3922..00000000
--- a/ec2/NewTaskRequirements.old.md
+++ /dev/null
@@ -1,117 +0,0 @@
-# Required files for creating a new PV Validation Hub Task
-
-## config.json
-
-Example JSON:
-
-```json
-{
-    "category_name": "Time Shift Analysis",
-    "function_name": "detect_time_shifts",
-    "comparison_type": "time_series",
-    "performance_metrics": [
-        "runtime",
-        "mean_absolute_error"
-    ],
-    "allowable_kwargs": [
-        "latitude",
-        "longitude",
-        "data_sampling_frequency"
-    ],
-    "ground_truth_compare": [
-        "time_series"
-    ],
-    "public_results_table": "time-shift-public-metrics.json",
-    "private_results_columns": [
-        "system_id",
-        "file_name",
-        "run_time",
-        "data_requirements",
-        "mean_absolute_error_time_series",
-        "data_sampling_frequency",
-        "issue"
-    ],
-    "plots": [
-        {
-            "type": "histogram",
-            "x_val": "mean_absolute_error_time_series",
-            "color_code": "issue",
-            "title": "Time Series MAE Distribution by Issue",
-            "save_file_path": "mean_absolute_error_time_series_dist.png"
-        },
-        {
-            "type": "histogram",
-            "x_val": "mean_absolute_error_time_series",
-            "color_code": "data_sampling_frequency",
-            "title": "Time Series MAE Distribution by Sampling Frequency",
-            "save_file_path": "mean_absolute_error_time_series_dist.png"
-        },
-        {
-            "type": "histogram",
-            "x_val": "run_time",
-            "title": "Run Time Distribution",
-            "save_file_path": "run_time_dist.png"
-        }
-    ]
-}
-```
-
-## system_metadata.csv
-
-Required columns:
-
-```csv
-system_id,name,azimuth,tilt,elevation,latitude,longitude,tracking,climate_type,dc_capacity
-```
-
-## file_metadata.csv
-
-Required columns:
-
-```csv
-file_id,system_id,file_name,timezone,data_sampling_frequency,issue
-```
-
-## template.py (Marimo template with cli args input)
-
-Marimo python file will need to input data from `mo.cli_args()` method
-
-Example:
-
-```python
-def create_df_from_cli_args():
-        args = mo.cli_args().to_dict()
-        data = args.get("results_df")
-        rows = []
-        for row in data:
-            rows.append(json.loads(row))
-
-        df = pd.DataFrame.from_records(rows)
-        return df
-```
-
-## csv data files
-
-file names must match what is included in the file_name in the file_metadata.csv
-
-## ground truth csv data files
-
-file names must match what is included in the data files folder
-
-## Markdown files for Task
-
-### description.md
-
-The markdown file used for the description tab in an analysis.
-
-### dataset.md
-
-The markdown file to describe the dataset in the data tab.
-
-### shortdesc.md
-
-The markdown file that is used on the card.
-
-### SubmissionInstructions.md
-
-The markdown file that is used on the Submission Instructions tab in the analysis.
diff --git a/ec2/README.old.md b/ec2/README.old.md
deleted file mode 100644
index ac88697a..00000000
--- a/ec2/README.old.md
+++ /dev/null
@@ -1,47 +0,0 @@
-This section covers the basics for administering the validation hub. It will include step-by-step instructions, as well as planned upgrades to automate these processes in the future. 
-
-# Manual Method
-
-This is to document how to manually create and update an analysis and its supporting files. While intended for maintaining the early release, use of this method should be deprecated as soon as a functional automated method is implemented, in order to reduce long-term work and human error.
-
-## Adding an Analysis
-
-An analysis is added using the following steps:
-
-* Navigate to https://api.pv-validation-hub.org/admin
-* Sign in to the API. Credentials should be shared securely from dashlane, and only to administrators.
-* Select "Analysiss"(the extra "s" is not a typo, but a Django-ism), and in the top right corner select "Add Analysis". Type in your analysis name, and hit save.
-
-Once this is complete, your new analysis will show up in the list as "Analysis object (number)". The number in the parentheses is the Analysis ID in the database. This number will be needed in the following section.
-
-## Adding markdown files and images
-
-You will need to clone this repository to complete this section. Create a new branch from develop, and give it a clear name like "add-timeshift-markdown". This lets me know to sync the updated markdown and images to s3 upon merging your PR.
-
-Markdown files are maintained in the frontend/public/assets/{analysis-id} directory. Analyses are tracked in numerical order within the postgres database, and the front-end uses the ID from the database to pull the correct markdown files for the analysis from the corresponding directory in the s3 bucket. 
-
-There is also a "development" directory that is used for local development. You can run a development instance using several simple steps:
-
-* Make sure you have node/npm installed. If you don't have it, follow the ![docs](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm)
-* Change to the frontend directory, which contains the package.json file.
-* In your terminal, Run ```npm install``` to install the node dependencies
-* Run ```npm run build``` to build the front-end
-* Run ```npm start``` to run the frontend
-
-Each filename is important. Below is the dynamic element they correspond to: 
-
-cardCover.png       ==> This is the image that will go on the analysis card, when selecting an analysis.
-headingCover.png    ==> This is the banner image across the top of the page. If too small, the image repeats to fill the space.
-dataset.md          ==> The markdown file to describle the dataset in the data tab.
-longdesc.md         ==> The markdown file used for the overview tab in an analysis.
-ruleset.md          ==> The markdown file used for the "Rules" tab, will be renamed to "instructions"
-shortdesc.md        ==> The markdown file that is used on the card. Needs work for a cleaner implementation, with defined limits due to card size.
-
-TODO: Add instructions once dynamic image method is implemented and finalized.
-
-Modify the images and text in the "development" directory until you are satisfied, and then copy the files from the development directory over to the numbered directory, corresponding to the analysis number from the last section. If the directory does not exist, create it. 
-
-## Updating markdown and images
-
-
-This development instance of the frontend can then be used for testing your updated markdown and image files. 

From ffa1e518a4224091d9d96c0ca9eacd5ba05cac5e Mon Sep 17 00:00:00 2001
From: Mitchell Victoriano <mitchell.victoriano@gmail.com>
Date: Tue, 17 Dec 2024 20:26:48 -0800
Subject: [PATCH 3/4] Added time-shift and az-tilt analysis tasks for
 validation hub repository

---
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 .../data/validation_tests.csv                 |      3 +
 .../other/generate-plots-categorization.py    |    109 +
 .../az-tilt-estimation/other/generate_map.py  |     68 +
 .../az-tilt-estimation/other/model-energy.py  |     52 +
 .../other/paper_statistics.py                 |    423 +
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 create mode 100644 ec2/analysis_taks/time-shift-detection/without.csv

diff --git a/ec2/.gitignore b/ec2/.gitignore
index e69de29b..98049ec9 100644
--- a/ec2/.gitignore
+++ b/ec2/.gitignore
@@ -0,0 +1,2 @@
+**/data/files/*
+**/data/ground-truth/*
\ No newline at end of file
diff --git a/ec2/analysis_taks/az-tilt-estimation/.gitignore b/ec2/analysis_taks/az-tilt-estimation/.gitignore
new file mode 100644
index 00000000..aedac453
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/.gitignore
@@ -0,0 +1,200 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+plots_unfiltered/
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Created by https://www.toptal.com/developers/gitignore/api/macos
+# Edit at https://www.toptal.com/developers/gitignore?templates=macos
+
+### macOS ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### macOS Patch ###
+# iCloud generated files
+*.icloud
+
+# End of https://www.toptal.com/developers/gitignore/api/macos
+
diff --git a/ec2/analysis_taks/az-tilt-estimation/Az-Tilt-PVSC-2023.pptx b/ec2/analysis_taks/az-tilt-estimation/Az-Tilt-PVSC-2023.pptx
new file mode 100644
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diff --git a/ec2/analysis_taks/az-tilt-estimation/LICENSE b/ec2/analysis_taks/az-tilt-estimation/LICENSE
new file mode 100644
index 00000000..7aa8e5d5
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Kirsten Perry
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/ec2/analysis_taks/az-tilt-estimation/README.md b/ec2/analysis_taks/az-tilt-estimation/README.md
new file mode 100644
index 00000000..b540f76c
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/README.md
@@ -0,0 +1,2 @@
+# az-tilt-estimation-validation
+PVInsight framework for the azimuth-tilt estimation algorithms (similar formatting to the time shift estimation repo).
diff --git a/ec2/analysis_taks/az-tilt-estimation/assets/SubmissionInstructions.md b/ec2/analysis_taks/az-tilt-estimation/assets/SubmissionInstructions.md
new file mode 100644
index 00000000..710a7bb9
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/assets/SubmissionInstructions.md
@@ -0,0 +1,78 @@
+# Submission Instructions
+
+### Validation Data Structure
+
+Each system can have multiple inverter-level AC power data streams that can be analyzed, with each data stream validated independently of all other data streams in the system. 
+Each data stream is a Pandas series, with a timezone-aware Pandas datetime index, and an AC power time series in kW. 
+
+An example time series is shown in Figure 1. An example dataset is provided for reference here.
+
+![image info](./time_series_data.PNG)
+
+*Figure 1: Snapshot of the structure of the time series data analyzed for azimuth-tilt estimation.*
+
+Download an example data set here.
+
+### File Submission Requirements
+
+The PV Validation Hub only accepts .zip file submissions. Each zip file must containing the following files:
+1) **submission_wrapper.py**: This Python file contains the master function for running the routines. Please refer to **Submitted Function Structure** below for more information on the associated function structure.
+2) **requirements.txt**: This text file contains the associated python packages (and versions, if applicable) to pip-install in order to run the submission_wrapper.py file.
+3) If applicable, any additional .py files containing support functions for **submission_wrapper.py**
+
+### Submitted Function Structure
+
+In the **submission_wrapper.py** file, a standard function name must be used when validating a submitted algorithm. The standard function definition, which is required to run the validation, is as follows:
+
+```
+def estimate_az_tilt(time_series, latitude=None, longitude=None, data_sampling_frequency=None):
+    """
+    This is the required function definition for running azimuth-tilt estimation.
+
+    Parameters
+    ----------
+    time_series: Required argument. Pandas series with a tz-aware datetime index.
+        This is the AC power time series that is being tested for azimuth and tilt.
+    latitude: Optional argument. Float.
+        The associated latitude coordinate of the PV site that the AC power stream is associated with.
+    longitude: Optional argument. Float.
+        The associated longitude coordinate of the PV site that the AC power stream is associated with.
+    data_sampling_frequency: Optional argument. Float.
+        The data frequency of the time series being evaluated. 
+
+    Returns
+    -------
+    azimuth: Float.
+        Predicted azimuth value.
+    tilt: Float.
+        Predicted tilt value.
+
+    """
+    [YOUR ALGORITHM HERE]
+    return azimuth, tilt
+```
+
+### Analysis Results
+
+An automated results report containing the following metrics is returned following successful analysis:
+
+1) Mean average error (MAE) across all of the test cases, for both azimuth and tilt values. To calculate MAE, the ground-truth azimuth/tilt value is compared to its associated estimated value, and error is returned in degrees.
+3) Associated graphics, including error distributions for azimuth, tilt, and run time. Additionally, a scatter plot comparing algorithm run time vs. number of days in the AC power data set is provided. Example graphics are shown in Figures 3-6, respectively.
+
+![image info](./absolute_error_az_dist.png)
+
+*Figure 3: Error distribution for system azimuth.*
+
+![image info](./absolute_error_tilt_dist.png)
+
+*Figure 4: Error distribution for system tilt.*
+
+![image info](./run_time_dist.png)
+
+*Figure 5: Distribution of algorithm run times on individual time series data streams, in seconds.*
+
+![image info](./run_time_number_days.png)
+
+*Figure 6: Algorithm run time vs. number of days present in the AC power data tested.*
+
+
diff --git a/ec2/analysis_taks/az-tilt-estimation/assets/absolute_error_az_dist.png b/ec2/analysis_taks/az-tilt-estimation/assets/absolute_error_az_dist.png
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diff --git a/ec2/analysis_taks/az-tilt-estimation/assets/config.json b/ec2/analysis_taks/az-tilt-estimation/assets/config.json
new file mode 100644
index 00000000..8d42a5f3
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/assets/config.json
@@ -0,0 +1,46 @@
+{
+	"category_name": "az_tilt_estimation",
+	"function_name": "estimate_az_tilt",
+	"comparison_type": "scalar",
+	"performance_metrics": [ "runtime", "absolute_error" ],
+	"allowable_kwargs": [ "latitude", "longitude", "data_sampling_frequency"],
+	"ground_truth_compare": [ "azimuth", "tilt" ],
+	"public_results_table": "az-tilt-public-metrics.json",
+	"private_results_columns": [
+		"system_id",
+		"file_name",
+		"run_time",
+		"data_requirements",
+		"absolute_error_azimuth",
+		"absolute_error_tilt",
+		"issue",
+		"number_days"
+	],
+	"plots": [
+		{
+			"type": "histogram",
+			"x_val": "absolute_error_azimuth",
+			"title": "Azimuth Absolute Error Distribution",
+			"save_file_path": "absolute_error_az_dist.png"
+		},
+		{
+			"type": "histogram",
+			"x_val": "absolute_error_tilt",
+			"title": "Tilt Absolute Error Distribution",
+			"save_file_path": "absolute_error_tilt_dist.png"
+		},
+		{
+			"type": "histogram",
+			"x_val": "run_time",
+			"title": "Run Time Distribution",
+			"save_file_path": "run_time_dist.png"
+		},
+		{
+			"type": "scatter_plot",
+			"x_val": "run_time",
+			"y_val": "number_days",
+			"title": "Run Time vs. Number Days in Data Set",
+			"save_file_path": "run_time_number_days.png"
+		}
+	]
+}
\ No newline at end of file
diff --git a/ec2/analysis_taks/az-tilt-estimation/assets/description.md b/ec2/analysis_taks/az-tilt-estimation/assets/description.md
new file mode 100644
index 00000000..1f56ec78
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/assets/description.md
@@ -0,0 +1,7 @@
+# Description
+It is not uncommon for PV installation metadata to be improperly labeled, including azimuth, tilt, and mounting configuration. Errors and missing data can be especially frequent in solar fleets that have been recently 
+acquired. Incorrect values for this critical metadata can affect the overall performance of PV system performance modelling. 
+
+This analysis benchmarks algorithm performance for correctly identifying azimuth and tilt for fixed tilt systems using AC power time series data as an input. Real-world AC power data from fixed-tilt systems is used to assess algorithm performance.
+These systems have been manually reviewed to ensure correct azimuth and tilt values. For each system, ground truth azimuth and tilt values are compared to algorithm-estimated azimuth and tilt values and error in degrees is calculated.
+
diff --git a/ec2/analysis_taks/az-tilt-estimation/assets/file_metadata.csv b/ec2/analysis_taks/az-tilt-estimation/assets/file_metadata.csv
new file mode 100644
index 00000000..15a8aa5e
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/assets/file_metadata.csv
@@ -0,0 +1,276 @@
+file_id,system_id,file_name,timezone,data_sampling_frequency,issue,number_days
+173,4,4_ac_power__315.csv,Etc/GMT+7,15,,1263
+176,1331,1332_inv3_ac_power__2650.csv,Etc/GMT+7,15,"clipping, shading",727
+174,1332,1332_inv1_ac_power__2642.csv,Etc/GMT+7,15,snow,1099
+175,1332,1332_inv2_ac_power__2646.csv,Etc/GMT+7,15,snow,1263
+177,1332,1332_inv_total_ac_power__2654.csv,Etc/GMT+7,15,snow,697
+178,1403,1403_ac_power__5034.csv,Etc/GMT+5,15,,675
+179,1403,1403_inv1_ac_power__4207.csv,Etc/GMT+5,15,,1129
+180,1403,1403_inv2_ac_power__4213.csv,Etc/GMT+5,15,,1135
+181,4902,4902_ac_power_inv_14538.csv,Etc/GMT+5,15,,1030
+474,5016,5016_ac_power_inv_2086.csv,Etc/GMT+7,15,clipping,1478
+475,5016,5016_ac_power_inv_2107.csv,Etc/GMT+7,15,clipping,1478
+476,5016,5016_ac_power_inv_2102.csv,Etc/GMT+7,15,clipping,1478
+477,5016,5016_ac_power_inv_2098.csv,Etc/GMT+7,15,clipping,1478
+478,5016,5016_ac_power_inv_2100.csv,Etc/GMT+7,15,clipping,1478
+479,5016,5016_ac_power_inv_2103.csv,Etc/GMT+7,15,clipping,1478
+358,5057,5057_ac_power_inv_2236.csv,Etc/GMT+5,15,,3237
+360,5061,5061_ac_power_inv_2270.csv,Etc/GMT+5,15,clipping,3682
+361,5061,5061_ac_power_inv_2269.csv,Etc/GMT+5,15,clipping,3486
+362,5070,5070_ac_power_inv_2293.csv,Etc/GMT+7,15,,3065
+364,5070,5070_ac_power_inv_2292.csv,Etc/GMT+7,15,,3064
+366,5081,5081_ac_power_inv_2321.csv,Etc/GMT+7,15,clipping,3064
+367,5099,5099_ac_power_inv_2358.csv,Etc/GMT+7,15,clipping,2984
+368,5099,5099_ac_power_inv_2357.csv,Etc/GMT+7,15,clipping,2993
+370,5114,5114_ac_power_inv_2397.csv,Etc/GMT+8,15,clipping,2787
+371,5114,5114_ac_power_inv_2405.csv,Etc/GMT+8,15,clipping,2787
+372,5114,5114_ac_power_inv_2404.csv,Etc/GMT+8,15,clipping,2787
+373,5114,5114_ac_power_inv_2401.csv,Etc/GMT+8,15,clipping,2787
+374,5114,5114_ac_power_inv_2396.csv,Etc/GMT+8,15,clipping,2787
+375,5114,5114_ac_power_inv_2403.csv,Etc/GMT+8,15,clipping,2787
+376,5129,5129_ac_power_inv_2496.csv,Etc/GMT+8,15,clipping,2710
+377,5129,5129_ac_power_inv_2493.csv,Etc/GMT+8,15,clipping,2710
+378,5129,5129_ac_power_inv_2494.csv,Etc/GMT+8,15,clipping,2697
+379,5129,5129_ac_power_inv_2495.csv,Etc/GMT+8,15,clipping,2696
+380,5129,5129_ac_power_inv_2492.csv,Etc/GMT+8,15,clipping,2696
+381,5129,5129_ac_power_inv_2497.csv,Etc/GMT+8,15,clipping,2710
+382,5133,5133_ac_power_inv_2526.csv,Etc/GMT+8,15,,2657
+384,5133,5133_ac_power_inv_2523.csv,Etc/GMT+8,15,,2657
+385,5133,5133_ac_power_inv_2524.csv,Etc/GMT+8,15,,2657
+387,5138,5138_ac_power_inv_2543.csv,Etc/GMT+8,15,clipping,2706
+388,5138,5138_ac_power_inv_2545.csv,Etc/GMT+8,15,clipping,2706
+389,5138,5138_ac_power_inv_2546.csv,Etc/GMT+8,15,clipping,2706
+390,5138,5138_ac_power_inv_2544.csv,Etc/GMT+8,15,clipping,2706
+391,5150,5150_ac_power_inv_2649.csv,Etc/GMT+7,15,clipping,2720
+392,5150,5150_ac_power_inv_2642.csv,Etc/GMT+7,15,clipping,2722
+393,5150,5150_ac_power_inv_2639.csv,Etc/GMT+7,15,clipping,2626
+394,5150,5150_ac_power_inv_2640.csv,Etc/GMT+7,15,clipping,2719
+395,5150,5150_ac_power_inv_2637.csv,Etc/GMT+7,15,clipping,2719
+396,5150,5150_ac_power_inv_2646.csv,Etc/GMT+7,15,clipping,2719
+397,5159,5159_ac_power_inv_2720.csv,Etc/GMT+7,15,clipping,2646
+398,5159,5159_ac_power_inv_2717.csv,Etc/GMT+7,15,clipping,2629
+399,5159,5159_ac_power_inv_2718.csv,Etc/GMT+7,15,clipping,2517
+400,5159,5159_ac_power_inv_2716.csv,Etc/GMT+7,15,clipping,2563
+402,5159,5159_ac_power_inv_2715.csv,Etc/GMT+7,15,clipping,2651
+403,5162,5162_ac_power_inv_2770.csv,Etc/GMT+8,15,clipping,2495
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+405,5162,5162_ac_power_inv_2762.csv,Etc/GMT+8,15,clipping,2506
+406,5162,5162_ac_power_inv_2765.csv,Etc/GMT+8,15,clipping,2481
+407,5162,5162_ac_power_inv_2760.csv,Etc/GMT+8,15,clipping,2502
+408,5162,5162_ac_power_inv_2756.csv,Etc/GMT+8,15,clipping,2480
+409,5184,5184_ac_power_inv_3059.csv,Etc/GMT+5,15,clipping,2473
+411,5184,5184_ac_power_inv_3051.csv,Etc/GMT+5,15,clipping,2492
+413,5184,5184_ac_power_inv_3052.csv,Etc/GMT+5,15,clipping,2525
+415,5196,5196_ac_power_inv_3163.csv,Etc/GMT+7,15,clipping,2519
+416,5196,5196_ac_power_inv_3157.csv,Etc/GMT+7,15,clipping,2512
+417,5196,5196_ac_power_inv_3161.csv,Etc/GMT+7,15,clipping,2518
+418,5196,5196_ac_power_inv_3162.csv,Etc/GMT+7,15,clipping,2519
+420,5196,5196_ac_power_inv_3160.csv,Etc/GMT+7,15,clipping,2505
+421,5222,5222_ac_power_inv_3332.csv,Etc/GMT+8,15,,2131
+422,5222,5222_ac_power_inv_3335.csv,Etc/GMT+8,15,,2131
+423,5222,5222_ac_power_inv_3334.csv,Etc/GMT+8,15,,2130
+424,5222,5222_ac_power_inv_3333.csv,Etc/GMT+8,15,,2130
+191,5226,5226_ac_power_inv_3355.csv,Etc/GMT+7,15,"clipping, shading",821
+192,5226,5226_ac_power_inv_3356.csv,Etc/GMT+7,15,"clipping, shading",821
+193,5226,5226_ac_power_inv_3357.csv,Etc/GMT+7,15,"clipping, shading",819
+194,5226,5226_ac_power_inv_3358.csv,Etc/GMT+7,15,"clipping, shading",821
+195,5226,5226_ac_power_inv_3359.csv,Etc/GMT+7,15,shading,821
+196,5226,5226_ac_power_inv_3360.csv,Etc/GMT+7,15,"clipping, shading",820
+197,5226,5226_ac_power_inv_3361.csv,Etc/GMT+7,15,"clipping, shading",821
+198,5226,5226_ac_power_inv_3362.csv,Etc/GMT+7,15,"clipping, shading",821
+199,5226,5226_ac_power_inv_3363.csv,Etc/GMT+7,15,"clipping, shading",821
+200,5226,5226_ac_power_inv_3364.csv,Etc/GMT+7,15,"clipping, shading",821
+201,5231,5231_ac_power_inv_3391.csv,Etc/GMT+7,15,"shading, clipping",2255
+202,5231,5231_ac_power_inv_3392.csv,Etc/GMT+7,15,"shading, clipping",2254
+203,5231,5231_ac_power_inv_3393.csv,Etc/GMT+7,15,"shading, clipping",2172
+204,5231,5231_ac_power_inv_3394.csv,Etc/GMT+7,15,"shading, clipping",2153
+205,5242,5242_ac_power_inv_3573.csv,Etc/GMT+7,15,clipping,2002
+206,5242,5242_ac_power_inv_3574.csv,Etc/GMT+7,15,clipping,1748
+207,5242,5242_ac_power_inv_3575.csv,Etc/GMT+7,15,clipping,2000
+208,5242,5242_ac_power_inv_3576.csv,Etc/GMT+7,15,clipping,1936
+209,5242,5242_ac_power_inv_3577.csv,Etc/GMT+7,15,clipping,1989
+210,5242,5242_ac_power_inv_3578.csv,Etc/GMT+7,15,clipping,1882
+211,5242,5242_ac_power_inv_3579.csv,Etc/GMT+7,15,clipping,1947
+212,5242,5242_ac_power_inv_3580.csv,Etc/GMT+7,15,clipping,1848
+213,5242,5242_ac_power_inv_3581.csv,Etc/GMT+7,15,clipping,1992
+214,5242,5242_ac_power_inv_3582.csv,Etc/GMT+7,15,clipping,1985
+215,5242,5242_ac_power_inv_3583.csv,Etc/GMT+7,15,clipping,2006
+216,5242,5242_ac_power_inv_3584.csv,Etc/GMT+7,15,clipping,2000
+217,5242,5242_ac_power_inv_3585.csv,Etc/GMT+7,15,clipping,1944
+218,5242,5242_ac_power_inv_3586.csv,Etc/GMT+7,15,clipping,1991
+219,5242,5242_ac_power_inv_3587.csv,Etc/GMT+7,15,clipping,1881
+220,5242,5242_ac_power_inv_3588.csv,Etc/GMT+7,15,clipping,1890
+221,5242,5242_ac_power_inv_3589.csv,Etc/GMT+7,15,clipping,1847
+222,5242,5242_ac_power_inv_3590.csv,Etc/GMT+7,15,clipping,2001
+223,5242,5242_ac_power_inv_3591.csv,Etc/GMT+7,15,clipping,2002
+224,5242,5242_ac_power_inv_3592.csv,Etc/GMT+7,15,clipping,2007
+225,5242,5242_ac_power_inv_3593.csv,Etc/GMT+7,15,clipping,1998
+226,5242,5242_ac_power_inv_3594.csv,Etc/GMT+7,15,clipping,1951
+227,5242,5242_ac_power_inv_3595.csv,Etc/GMT+7,15,clipping,1994
+228,5242,5242_ac_power_inv_3596.csv,Etc/GMT+7,15,clipping,1987
+229,5242,5242_ac_power_inv_3597.csv,Etc/GMT+7,15,clipping,2005
+230,5242,5242_ac_power_inv_3598.csv,Etc/GMT+7,15,clipping,1971
+231,5242,5242_ac_power_inv_3599.csv,Etc/GMT+7,15,clipping,1940
+232,5257,5257_ac_power_inv_3746.csv,Etc/GMT+7,15,"soiling, clipping",1693
+233,5257,5257_ac_power_inv_3747.csv,Etc/GMT+7,15,"soiling, clipping",1699
+234,5257,5257_ac_power_inv_3748.csv,Etc/GMT+7,15,"soiling, clipping",1698
+235,5257,5257_ac_power_inv_3749.csv,Etc/GMT+7,15,"soiling, clipping",1702
+236,5257,5257_ac_power_inv_3750.csv,Etc/GMT+7,15,"soiling, clipping",1703
+237,5257,5257_ac_power_inv_3751.csv,Etc/GMT+7,15,"soiling, clipping",1691
+238,5257,5257_ac_power_inv_3752.csv,Etc/GMT+7,15,"soiling, clipping",1695
+239,5257,5257_ac_power_inv_3753.csv,Etc/GMT+7,15,"soiling, clipping",1699
+240,5257,5257_ac_power_inv_3754.csv,Etc/GMT+7,15,"soiling, clipping",1704
+241,5257,5257_ac_power_inv_3755.csv,Etc/GMT+7,15,"soiling, clipping",1695
+242,5257,5257_ac_power_inv_3756.csv,Etc/GMT+7,15,"soiling, clipping",1698
+243,5257,5257_ac_power_inv_3757.csv,Etc/GMT+7,15,"soiling, clipping",1695
+244,5257,5257_ac_power_inv_3758.csv,Etc/GMT+7,15,"soiling, clipping",1695
+245,5257,5257_ac_power_inv_3759.csv,Etc/GMT+7,15,"soiling, clipping",1703
+246,5257,5257_ac_power_inv_3760.csv,Etc/GMT+7,15,"soiling, clipping",1693
+247,5257,5257_ac_power_inv_3761.csv,Etc/GMT+7,15,soiling,1705
+248,5257,5257_ac_power_inv_3762.csv,Etc/GMT+7,15,"soiling, clipping",1703
+249,5257,5257_ac_power_inv_3763.csv,Etc/GMT+7,15,soiling,1705
+250,5257,5257_ac_power_inv_3764.csv,Etc/GMT+7,15,"soiling, clipping",1693
+432,5273,5273_ac_power_inv_5975.csv,Etc/GMT+8,15,clipping,1819
+433,5273,5273_ac_power_inv_5973.csv,Etc/GMT+8,15,clipping,1950
+434,5273,5273_ac_power_inv_5974.csv,Etc/GMT+8,15,clipping,1950
+436,5273,5273_ac_power_inv_5972.csv,Etc/GMT+8,15,clipping,1950
+437,5273,5273_ac_power_inv_5971.csv,Etc/GMT+8,15,clipping,1950
+251,5291,5291_ac_power_inv_4104.csv,Etc/GMT+7,15,"soiling, clipping",1959
+252,5291,5291_ac_power_inv_4105.csv,Etc/GMT+7,15,"soiling, clipping",1957
+253,5291,5291_ac_power_inv_4106.csv,Etc/GMT+7,15,"soiling, clipping",1959
+254,5291,5291_ac_power_inv_4107.csv,Etc/GMT+7,15,"soiling, clipping",1929
+255,5291,5291_ac_power_inv_4108.csv,Etc/GMT+7,15,"soiling, clipping",1959
+256,5297,5297_ac_power_inv_4176.csv,Etc/GMT+4,15,clipping,1219
+257,5297,5297_ac_power_inv_4177.csv,Etc/GMT+4,15,clipping,1211
+258,5297,5297_ac_power_inv_4178.csv,Etc/GMT+4,15,clipping,1217
+259,5308,5308_ac_power_inv_4258.csv,Etc/GMT+4,15,clipping,1144
+260,5308,5308_ac_power_inv_4259.csv,Etc/GMT+4,15,clipping,1144
+261,5308,5308_ac_power_inv_4260.csv,Etc/GMT+4,15,clipping,1144
+262,5308,5308_ac_power_inv_4261.csv,Etc/GMT+4,15,clipping,1144
+263,5308,5308_ac_power_inv_4262.csv,Etc/GMT+4,15,clipping,1116
+264,5308,5308_ac_power_inv_4263.csv,Etc/GMT+4,15,clipping,1144
+265,5308,5308_ac_power_inv_4264.csv,Etc/GMT+4,15,clipping,1144
+266,5308,5308_ac_power_inv_4265.csv,Etc/GMT+4,15,clipping,1144
+267,5308,5308_ac_power_inv_4266.csv,Etc/GMT+4,15,clipping,1116
+268,5308,5308_ac_power_inv_4267.csv,Etc/GMT+4,15,clipping,1144
+269,5308,5308_ac_power_inv_4268.csv,Etc/GMT+4,15,clipping,1144
+270,5308,5308_ac_power_inv_4269.csv,Etc/GMT+4,15,clipping,1144
+271,5308,5308_ac_power_inv_4270.csv,Etc/GMT+4,15,clipping,1144
+272,5308,5308_ac_power_inv_4271.csv,Etc/GMT+4,15,clipping,1129
+273,5308,5308_ac_power_inv_4272.csv,Etc/GMT+4,15,clipping,1144
+274,5308,5308_ac_power_inv_4273.csv,Etc/GMT+4,15,clipping,1116
+275,5308,5308_ac_power_inv_4274.csv,Etc/GMT+4,15,clipping,1116
+276,5308,5308_ac_power_inv_4275.csv,Etc/GMT+4,15,clipping,1116
+277,5308,5308_ac_power_inv_4276.csv,Etc/GMT+4,15,clipping,1138
+278,5308,5308_ac_power_inv_4277.csv,Etc/GMT+4,15,clipping,1136
+279,5308,5308_ac_power_inv_4278.csv,Etc/GMT+4,15,clipping,1144
+280,5308,5308_ac_power_inv_4279.csv,Etc/GMT+4,15,clipping,1144
+281,5308,5308_ac_power_inv_4280.csv,Etc/GMT+4,15,clipping,1144
+282,5308,5308_ac_power_inv_4281.csv,Etc/GMT+4,15,clipping,1115
+283,5308,5308_ac_power_inv_4282.csv,Etc/GMT+4,15,clipping,1116
+284,5308,5308_ac_power_inv_4283.csv,Etc/GMT+4,15,clipping,1144
+285,5308,5308_ac_power_inv_4284.csv,Etc/GMT+4,15,clipping,1116
+286,5308,5308_ac_power_inv_4285.csv,Etc/GMT+4,15,clipping,1144
+287,5308,5308_ac_power_inv_4286.csv,Etc/GMT+4,15,clipping,1144
+288,5308,5308_ac_power_inv_4287.csv,Etc/GMT+4,15,clipping,1116
+438,5318,5318_ac_power_inv_6240.csv,Etc/GMT+5,15,clipping,2360
+439,5318,5318_ac_power_inv_6236.csv,Etc/GMT+5,15,clipping,2348
+440,5318,5318_ac_power_inv_6232.csv,Etc/GMT+5,15,clipping,2308
+441,5318,5318_ac_power_inv_6233.csv,Etc/GMT+5,15,clipping,2348
+442,5318,5318_ac_power_inv_6226.csv,Etc/GMT+5,15,clipping,2360
+443,5318,5318_ac_power_inv_6244.csv,Etc/GMT+5,15,clipping,2361
+444,5324,5324_ac_power_inv_4531.csv,Etc/GMT+8,15,clipping,1643
+445,5324,5324_ac_power_inv_4532.csv,Etc/GMT+8,15,clipping,1643
+446,5324,5324_ac_power_inv_4533.csv,Etc/GMT+8,15,clipping,1643
+447,5324,5324_ac_power_inv_4530.csv,Etc/GMT+8,15,clipping,1658
+289,5328,5328_ac_power_inv_4559.csv,Etc/GMT+7,15,clipping,1658
+290,5328,5328_ac_power_inv_4560.csv,Etc/GMT+7,15,clipping,1658
+291,5328,5328_ac_power_inv_4561.csv,Etc/GMT+7,15,clipping,1658
+292,5329,5329_ac_power_inv_4562.csv,Etc/GMT+7,15,clipping,1672
+293,5329,5329_ac_power_inv_4563.csv,Etc/GMT+7,15,clipping,1662
+294,5329,5329_ac_power_inv_4564.csv,Etc/GMT+7,15,clipping,1664
+295,5329,5329_ac_power_inv_4565.csv,Etc/GMT+7,15,clipping,1663
+296,5329,5329_ac_power_inv_4566.csv,Etc/GMT+7,15,clipping,1671
+297,5329,5329_ac_power_inv_4567.csv,Etc/GMT+7,15,clipping,1672
+298,5335,5335_ac_power_inv_4625.csv,Etc/GMT+7,15,clipping,1670
+299,5335,5335_ac_power_inv_4626.csv,Etc/GMT+7,15,clipping,1670
+300,5335,5335_ac_power_inv_4627.csv,Etc/GMT+7,15,clipping,1670
+301,5335,5335_ac_power_inv_4628.csv,Etc/GMT+7,15,clipping,1670
+302,5342,5342_ac_power_inv_4684.csv,Etc/GMT+7,15,clipping,1728
+303,5342,5342_ac_power_inv_4685.csv,Etc/GMT+7,15,clipping,1728
+304,5342,5342_ac_power_inv_4686.csv,Etc/GMT+7,15,clipping,1728
+305,5342,5342_ac_power_inv_4687.csv,Etc/GMT+7,15,clipping,1728
+306,5342,5342_ac_power_inv_4688.csv,Etc/GMT+7,15,clipping,1728
+307,5342,5342_ac_power_inv_4689.csv,Etc/GMT+7,15,clipping,1728
+308,5342,5342_ac_power_inv_4690.csv,Etc/GMT+7,15,clipping,1728
+309,5342,5342_ac_power_inv_4691.csv,Etc/GMT+7,15,clipping,1728
+310,5342,5342_ac_power_inv_4692.csv,Etc/GMT+7,15,clipping,1728
+311,5342,5342_ac_power_inv_4693.csv,Etc/GMT+7,15,clipping,1728
+312,5342,5342_ac_power_inv_4694.csv,Etc/GMT+7,15,clipping,1728
+313,5342,5342_ac_power_inv_4695.csv,Etc/GMT+7,15,clipping,1728
+314,5342,5342_ac_power_inv_4696.csv,Etc/GMT+7,15,clipping,1728
+315,5342,5342_ac_power_inv_4697.csv,Etc/GMT+7,15,clipping,1728
+316,5342,5342_ac_power_inv_4698.csv,Etc/GMT+7,15,clipping,1728
+317,5342,5342_ac_power_inv_4699.csv,Etc/GMT+7,15,clipping,1539
+318,5342,5342_ac_power_inv_4700.csv,Etc/GMT+7,15,clipping,1728
+319,5342,5342_ac_power_inv_4701.csv,Etc/GMT+7,15,clipping,1728
+320,5342,5342_ac_power_inv_4702.csv,Etc/GMT+7,15,clipping,1728
+321,5342,5342_ac_power_inv_4703.csv,Etc/GMT+7,15,clipping,1728
+322,5342,5342_ac_power_inv_4704.csv,Etc/GMT+7,15,clipping,1728
+323,5342,5342_ac_power_inv_4705.csv,Etc/GMT+7,15,clipping,1728
+324,5342,5342_ac_power_inv_4706.csv,Etc/GMT+7,15,clipping,1728
+325,5342,5342_ac_power_inv_4707.csv,Etc/GMT+7,15,clipping,1728
+450,5345,5345_ac_power_inv_4727.csv,Etc/GMT+8,15,clipping,1758
+451,5345,5345_ac_power_inv_4724.csv,Etc/GMT+8,15,clipping,1758
+452,5345,5345_ac_power_inv_4722.csv,Etc/GMT+8,15,clipping,1730
+453,5345,5345_ac_power_inv_4726.csv,Etc/GMT+8,15,clipping,1758
+454,5345,5345_ac_power_inv_4721.csv,Etc/GMT+8,15,clipping,1758
+456,5348,5348_ac_power_inv_4734.csv,Etc/GMT+8,15,clipping,1766
+457,5348,5348_ac_power_inv_4735.csv,Etc/GMT+8,15,clipping,1767
+326,5354,5354_ac_power_inv_4791.csv,Etc/GMT+7,15,clipping,1730
+327,5354,5354_ac_power_inv_4792.csv,Etc/GMT+7,15,clipping,1730
+328,5354,5354_ac_power_inv_4793.csv,Etc/GMT+7,15,clipping,1730
+329,5354,5354_ac_power_inv_4794.csv,Etc/GMT+7,15,,1730
+330,5354,5354_ac_power_inv_4795.csv,Etc/GMT+7,15,clipping,1730
+331,5354,5354_ac_power_inv_4796.csv,Etc/GMT+7,15,clipping,1730
+332,5354,5354_ac_power_inv_4797.csv,Etc/GMT+7,15,clipping,1730
+333,5354,5354_ac_power_inv_4798.csv,Etc/GMT+7,15,clipping,1714
+334,5354,5354_ac_power_inv_4799.csv,Etc/GMT+7,15,clipping,1718
+335,5354,5354_ac_power_inv_4800.csv,Etc/GMT+7,15,clipping,1730
+336,5354,5354_ac_power_inv_4801.csv,Etc/GMT+7,15,clipping,1730
+337,5354,5354_ac_power_inv_4802.csv,Etc/GMT+7,15,clipping,1730
+338,5354,5354_ac_power_inv_4803.csv,Etc/GMT+7,15,clipping,1730
+339,5354,5354_ac_power_inv_4804.csv,Etc/GMT+7,15,clipping,1730
+340,5354,5354_ac_power_inv_4805.csv,Etc/GMT+7,15,clipping,1730
+458,5363,5363_ac_power_inv_6395.csv,Etc/GMT+8,15,clipping,1777
+459,5363,5363_ac_power_inv_6389.csv,Etc/GMT+8,15,clipping,1777
+460,5363,5363_ac_power_inv_6386.csv,Etc/GMT+8,15,clipping,1777
+461,5363,5363_ac_power_inv_6394.csv,Etc/GMT+8,15,clipping,1777
+462,5363,5363_ac_power_inv_6387.csv,Etc/GMT+8,15,clipping,1777
+463,5363,5363_ac_power_inv_6390.csv,Etc/GMT+8,15,clipping,1777
+341,5372,5372_ac_power_inv_4955.csv,Etc/GMT+7,15,clipping,1778
+342,5372,5372_ac_power_inv_4956.csv,Etc/GMT+7,15,clipping,1779
+343,5372,5372_ac_power_inv_4957.csv,Etc/GMT+7,15,clipping,1778
+344,5372,5372_ac_power_inv_4958.csv,Etc/GMT+7,15,clipping,1778
+345,5372,5372_ac_power_inv_4959.csv,Etc/GMT+7,15,clipping,1779
+346,5372,5372_ac_power_inv_4960.csv,Etc/GMT+7,15,clipping,1779
+464,5375,5375_ac_power_inv_6304.csv,Etc/GMT+5,15,clipping,1485
+465,5375,5375_ac_power_inv_6343.csv,Etc/GMT+5,15,clipping,1422
+466,5375,5375_ac_power_inv_6309.csv,Etc/GMT+5,15,clipping,1485
+467,5375,5375_ac_power_inv_6358.csv,Etc/GMT+5,15,clipping,1498
+468,5375,5375_ac_power_inv_6327.csv,Etc/GMT+5,15,clipping,1485
+469,5375,5375_ac_power_inv_6308.csv,Etc/GMT+5,15,clipping,1485
+347,5377,5377_ac_power_inv_5055.csv,Etc/GMT+7,15,"shading, clipping",1676
+348,5377,5377_ac_power_inv_5056.csv,Etc/GMT+7,15,"shading, clipping",1676
+349,5377,5377_ac_power_inv_5057.csv,Etc/GMT+7,15,"shading, clipping",1677
+350,5377,5377_ac_power_inv_5058.csv,Etc/GMT+7,15,"shading, clipping",1658
+351,5377,5377_ac_power_inv_5059.csv,Etc/GMT+7,15,"shading, clipping",1676
+352,5377,5377_ac_power_inv_5060.csv,Etc/GMT+7,15,shading,1674
+353,5377,5377_ac_power_inv_5061.csv,Etc/GMT+7,15,"shading, clipping",1677
+354,5377,5377_ac_power_inv_5062.csv,Etc/GMT+7,15,"shading, clipping",1677
+355,5379,5379_ac_power_inv_5070.csv,Etc/GMT+7,15,clipping,1624
+356,5379,5379_ac_power_inv_5071.csv,Etc/GMT+7,15,clipping,1625
+357,5379,5379_ac_power_inv_5072.csv,Etc/GMT+7,15,clipping,1625
+471,5458,5458_ac_power_inv_6986.csv,Etc/GMT+7,15,,1261
+472,5458,5458_ac_power_inv_6985.csv,Etc/GMT+7,15,clipping,1257
+473,5458,5458_ac_power_inv_6984.csv,Etc/GMT+7,15,clipping,1261
diff --git a/ec2/analysis_taks/az-tilt-estimation/assets/run_time_dist.png b/ec2/analysis_taks/az-tilt-estimation/assets/run_time_dist.png
new file mode 100644
index 0000000000000000000000000000000000000000..df33f6875150f8346a2c084a7e664e15bae46454
GIT binary patch
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diff --git a/ec2/analysis_taks/az-tilt-estimation/assets/shortdesc.md b/ec2/analysis_taks/az-tilt-estimation/assets/shortdesc.md
new file mode 100644
index 00000000..0efe28e2
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/assets/shortdesc.md
@@ -0,0 +1 @@
+Estimate azimuth and tilt values for fixed-tilt PV systems.
\ No newline at end of file
diff --git a/ec2/analysis_taks/az-tilt-estimation/assets/system_metadata.csv b/ec2/analysis_taks/az-tilt-estimation/assets/system_metadata.csv
new file mode 100644
index 00000000..2256d34b
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/assets/system_metadata.csv
@@ -0,0 +1,45 @@
+system_id,name,azimuth,tilt,elevation,latitude,longitude,tracking
+4,Sanyo1,180,40,1795.3,39.7406,-105.1774,FALSE
+1331,NREL Parking Garage-tilt1,180,60,1770,39.7388,-105.1732,FALSE
+1332,NREL Parking Garage-tilt2,180,16.77,1770,39.7388,-105.1732,FALSE
+1403,RTC-FSEC-Baseline,180,35,12,28.405,-80.7709,FALSE
+4902,NIST_Ground_1,180,20,138,39.1319,-77.2141,FALSE
+5016,Crane Schools  Salida del Sol,180,15,121,32.710684,-114.6675958,FALSE
+5057,UMMS  Costen CRE,180,20,16,38.097551,-75.608715,FALSE
+5061,Vineland North,180,25,98,39.50479,-74.994123,FALSE
+5070,Dysart Unified  Shadow Ridge High School,147,12,1280,33.5817467,-112.4615022,FALSE
+5081,Dysart Unified  Dysart High School (SES C),180,12,1112,33.590635,-112.33999,FALSE
+5099,Peoria  Desert Harbor,180,13.5,1181,33.625383,-112.252494,FALSE
+5114,Castaic USD - Live Oak Elementary School,180,7,1040,34.4492392,-118.6201877,FALSE
+5129,Keppel  Keppel Academy,180,30,2900,34.5284066,-117.9637384,FALSE
+5133,Palmdale Cultural Center,269,7,2664,34.577755,-118.115683,FALSE
+5138,Palmdale  Marie Kerr Pool,180,7,2707,34.603666,-118.182041,FALSE
+5150,Peoria  Liberty HS,180,13.5,1296,33.6855539,-112.2661668,FALSE
+5159,TUSD Mary Meredith ES,180,12,2507,32.2312025,-110.8823651,FALSE
+5162,Chaffey - Etiwanda East,180,7.49,1375,34.128339,-117.511124,FALSE
+5184,URE Prescott,180,22,436,33.222111,-82.378167,FALSE
+5196,TUSD White ES 1,180,12,2464,32.1516678,-111.0168228,FALSE
+5222,San Gabriel Washington ES,270,7.49,0,34.1069508,-118.1036145,FALSE
+5226,Central - Cucamonga MS,180,30,0,34.1159418,-117.602299,FALSE
+5231,Central - Valle Vista ES,189,7.49,0,34.1148548,-117.6197738,FALSE
+5242,Saddleback Laguna Hills HS,150,7.49,0,33.5933833,-117.7019839,FALSE
+5257,Pomona - Ganesha HS,243,7.49,0,34.0674631,-117.7882769,FALSE
+5273,Sulphur Springs District Office,204.5,7.49,1403,34.4079181,-118.4670982,FALSE
+5291,Alta Loma Victoria Groves,188,7.49,0,34.1286731,-117.5660026,FALSE
+5297,Baltimore City I,180,25,0,39.436888,-76.2161329,FALSE
+5308,Evergreen Academy Ave,180,22,276,32.51648,-82.936657,FALSE
+5318,URE Georgia Stephens Co. HS,180,22,781,34.553116,-83.29978,FALSE
+5324,Hawthorne - Hawthorne MS,270,7.49,0,33.9148276,-118.3504147,FALSE
+5328,Hawthorne - York ES,270,7.49,0,33.9263342,-118.3444883,FALSE
+5329,Hawthorne - Zela Davis ES,90,7.49,0,33.9107563,-118.3358501,FALSE
+5335,Mountain View - Creek View ES,203,7.49,0,34.0247557,-117.5681449,FALSE
+5342,Claremont - Claremont HS,200,7.49,0,34.1129983,-117.7225368,FALSE
+5345,Claremont - El Roble IS,270,7.49,0,34.1000246,-117.7291222,FALSE
+5348,Claremont - San Antonio HS,270,7.49,0,34.0836998,-117.7153289,FALSE
+5354,Glendora - Goddard MS,195,7.49,0,34.1481396,-117.8493741,FALSE
+5363,Etiwanda - Day Creek IS,164,7.49,0,34.1539468,-117.5363846,FALSE
+5372,Etiwanda - Long ES,180,7.49,0,34.1562874,-117.4859581,FALSE
+5375,URE Dixon,180,20,288,32.604618,-82.322656,FALSE
+5377,Etiwanda - Terra Vista ES,134,7.49,0,34.1181837,-117.5516621,FALSE
+5379,Etiwanda - Windrows ES,270,7.49,0,34.1281703,-117.5299781,FALSE
+5458,TUSD III - Dietz ES,159,12,2671,32.1997354,-110.8308625,FALSE
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z#p=ro75{%MREY^M_!P{2MCm<7XA}NX=X97_>G!`#8=+K$+7GaAKX*~Pg?3$oCG4m}
ztcXcvt#<zrd&yUFr_5bK|IPgWm<D%F_EUrcp|iQR(W9ogtDq468;q5mWwC`z<X_Bx
Bnh^j1

literal 0
HcmV?d00001

diff --git a/ec2/analysis_taks/az-tilt-estimation/config.json b/ec2/analysis_taks/az-tilt-estimation/config.json
new file mode 100644
index 00000000..239cdd40
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/config.json
@@ -0,0 +1,101 @@
+{
+	"category_name": "Azimuth-Tilt Estimation",
+	"function_name": "estimate_az_tilt",
+	"comparison_type": "scalar",
+	"performance_metrics": [
+		"runtime",
+		"absolute_error",
+		"error"
+	],
+	"display_metrics": {
+		"mean_absolute_error_azimuth": "Azimuth Mean Absolute Error",
+		"median_absolute_error_azimuth": "Azimuth Median Absolute Error",
+		"mean_error_azimuth": "Azimuth Mean Error",
+		"median_error_azimuth": "Azimuth Median Error",
+		"mean_absolute_error_tilt": "Tilt Mean Absolute Error",
+		"median_absolute_error_tilt": "Tilt Median Absolute Error",
+		"mean_error_tilt": "Tilt Mean Error",
+		"median_error_tilt": "Tilt Median Error"
+	},
+	"metrics_operations": {
+		"absolute_error_azimuth": [
+			"mean",
+			"median"
+		],
+		"error_azimuth": [
+			"mean",
+			"median"
+		],
+		"absolute_error_tilt": [
+			"mean",
+			"median"
+		],
+		"error_tilt": [
+			"mean",
+			"median"
+		],
+		"runtime": [
+			"mean"
+		]
+	},
+	"allowable_kwargs": [
+		"latitude",
+		"longitude"
+	],
+	"ground_truth_compare": [
+		"azimuth",
+		"tilt"
+	],
+	"public_results_table": "az-tilt-public-metrics.json",
+	"private_results_columns": [
+		"system_id",
+		"file_name",
+		"run_time",
+		"data_requirements",
+		"absolute_error_azimuth",
+		"absolute_error_tilt",
+		"error_azimuth",
+		"error_tilt",
+		"issue",
+		"number_days"
+	],
+	"plots": [
+		{
+			"type": "histogram",
+			"x_val": "absolute_error_azimuth",
+			"title": "Azimuth Absolute Error Distribution",
+			"save_file_path": "absolute_error_az_dist.png"
+		},
+		{
+			"type": "histogram",
+			"x_val": "absolute_error_tilt",
+			"title": "Tilt Absolute Error Distribution",
+			"save_file_path": "absolute_error_tilt_dist.png"
+		},
+		{
+			"type": "histogram",
+			"x_val": "error_azimuth",
+			"title": "Azimuth Error Distribution",
+			"save_file_path": "absolute_error_az_dist.png"
+		},
+		{
+			"type": "histogram",
+			"x_val": "error_tilt",
+			"title": "Tilt Error Distribution",
+			"save_file_path": "error_tilt_dist.png"
+		},
+		{
+			"type": "histogram",
+			"x_val": "run_time",
+			"title": "Run Time Distribution",
+			"save_file_path": "run_time_dist.png"
+		},
+		{
+			"type": "scatter_plot",
+			"x_val": "run_time",
+			"y_val": "number_days",
+			"title": "Run Time vs. Number Days in Data Set",
+			"save_file_path": "run_time_number_days.png"
+		}
+	]
+}
\ No newline at end of file
diff --git a/ec2/analysis_taks/az-tilt-estimation/data/file_metadata.csv b/ec2/analysis_taks/az-tilt-estimation/data/file_metadata.csv
new file mode 100644
index 00000000..2d911453
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/data/file_metadata.csv
@@ -0,0 +1,276 @@
+file_id,system_id,file_name,timezone,data_sampling_frequency,issue,number_days
+173,4,4_ac_power__315.csv,Etc/GMT+7,15,,1263
+174,1332,1332_inv1_ac_power__2642.csv,Etc/GMT+7,15,snow,1099
+175,1332,1332_inv2_ac_power__2646.csv,Etc/GMT+7,15,snow,1263
+176,1331,1332_inv3_ac_power__2650.csv,Etc/GMT+7,15,"clipping, shading",727
+177,1332,1332_inv_total_ac_power__2654.csv,Etc/GMT+7,15,snow,697
+178,1403,1403_ac_power__5034.csv,Etc/GMT+5,15,,675
+179,1403,1403_inv1_ac_power__4207.csv,Etc/GMT+5,15,,1129
+180,1403,1403_inv2_ac_power__4213.csv,Etc/GMT+5,15,,1135
+181,4902,4902_ac_power_inv_14538.csv,Etc/GMT+5,15,,1030
+191,5226,5226_ac_power_inv_3355.csv,Etc/GMT+7,15,"clipping, shading",821
+192,5226,5226_ac_power_inv_3356.csv,Etc/GMT+7,15,"clipping, shading",821
+193,5226,5226_ac_power_inv_3357.csv,Etc/GMT+7,15,"clipping, shading",819
+194,5226,5226_ac_power_inv_3358.csv,Etc/GMT+7,15,"clipping, shading",821
+195,5226,5226_ac_power_inv_3359.csv,Etc/GMT+7,15,shading,821
+196,5226,5226_ac_power_inv_3360.csv,Etc/GMT+7,15,"clipping, shading",820
+197,5226,5226_ac_power_inv_3361.csv,Etc/GMT+7,15,"clipping, shading",821
+198,5226,5226_ac_power_inv_3362.csv,Etc/GMT+7,15,"clipping, shading",821
+199,5226,5226_ac_power_inv_3363.csv,Etc/GMT+7,15,"clipping, shading",821
+200,5226,5226_ac_power_inv_3364.csv,Etc/GMT+7,15,"clipping, shading",821
+201,5231,5231_ac_power_inv_3391.csv,Etc/GMT+7,15,"shading, clipping",2255
+202,5231,5231_ac_power_inv_3392.csv,Etc/GMT+7,15,"shading, clipping",2254
+203,5231,5231_ac_power_inv_3393.csv,Etc/GMT+7,15,"shading, clipping",2172
+204,5231,5231_ac_power_inv_3394.csv,Etc/GMT+7,15,"shading, clipping",2153
+205,5242,5242_ac_power_inv_3573.csv,Etc/GMT+7,15,clipping,2002
+206,5242,5242_ac_power_inv_3574.csv,Etc/GMT+7,15,clipping,1748
+207,5242,5242_ac_power_inv_3575.csv,Etc/GMT+7,15,clipping,2000
+208,5242,5242_ac_power_inv_3576.csv,Etc/GMT+7,15,clipping,1936
+209,5242,5242_ac_power_inv_3577.csv,Etc/GMT+7,15,clipping,1989
+210,5242,5242_ac_power_inv_3578.csv,Etc/GMT+7,15,clipping,1882
+211,5242,5242_ac_power_inv_3579.csv,Etc/GMT+7,15,clipping,1947
+212,5242,5242_ac_power_inv_3580.csv,Etc/GMT+7,15,clipping,1848
+213,5242,5242_ac_power_inv_3581.csv,Etc/GMT+7,15,clipping,1992
+214,5242,5242_ac_power_inv_3582.csv,Etc/GMT+7,15,clipping,1985
+215,5242,5242_ac_power_inv_3583.csv,Etc/GMT+7,15,clipping,2006
+216,5242,5242_ac_power_inv_3584.csv,Etc/GMT+7,15,clipping,2000
+217,5242,5242_ac_power_inv_3585.csv,Etc/GMT+7,15,clipping,1944
+218,5242,5242_ac_power_inv_3586.csv,Etc/GMT+7,15,clipping,1991
+219,5242,5242_ac_power_inv_3587.csv,Etc/GMT+7,15,clipping,1881
+220,5242,5242_ac_power_inv_3588.csv,Etc/GMT+7,15,clipping,1890
+221,5242,5242_ac_power_inv_3589.csv,Etc/GMT+7,15,clipping,1847
+222,5242,5242_ac_power_inv_3590.csv,Etc/GMT+7,15,clipping,2001
+223,5242,5242_ac_power_inv_3591.csv,Etc/GMT+7,15,clipping,2002
+224,5242,5242_ac_power_inv_3592.csv,Etc/GMT+7,15,clipping,2007
+225,5242,5242_ac_power_inv_3593.csv,Etc/GMT+7,15,clipping,1998
+226,5242,5242_ac_power_inv_3594.csv,Etc/GMT+7,15,clipping,1951
+227,5242,5242_ac_power_inv_3595.csv,Etc/GMT+7,15,clipping,1994
+228,5242,5242_ac_power_inv_3596.csv,Etc/GMT+7,15,clipping,1987
+229,5242,5242_ac_power_inv_3597.csv,Etc/GMT+7,15,clipping,2005
+230,5242,5242_ac_power_inv_3598.csv,Etc/GMT+7,15,clipping,1971
+231,5242,5242_ac_power_inv_3599.csv,Etc/GMT+7,15,clipping,1940
+232,5257,5257_ac_power_inv_3746.csv,Etc/GMT+7,15,"soiling, clipping",1693
+233,5257,5257_ac_power_inv_3747.csv,Etc/GMT+7,15,"soiling, clipping",1699
+234,5257,5257_ac_power_inv_3748.csv,Etc/GMT+7,15,"soiling, clipping",1698
+235,5257,5257_ac_power_inv_3749.csv,Etc/GMT+7,15,"soiling, clipping",1702
+236,5257,5257_ac_power_inv_3750.csv,Etc/GMT+7,15,"soiling, clipping",1703
+237,5257,5257_ac_power_inv_3751.csv,Etc/GMT+7,15,"soiling, clipping",1691
+238,5257,5257_ac_power_inv_3752.csv,Etc/GMT+7,15,"soiling, clipping",1695
+239,5257,5257_ac_power_inv_3753.csv,Etc/GMT+7,15,"soiling, clipping",1699
+240,5257,5257_ac_power_inv_3754.csv,Etc/GMT+7,15,"soiling, clipping",1704
+241,5257,5257_ac_power_inv_3755.csv,Etc/GMT+7,15,"soiling, clipping",1695
+242,5257,5257_ac_power_inv_3756.csv,Etc/GMT+7,15,"soiling, clipping",1698
+243,5257,5257_ac_power_inv_3757.csv,Etc/GMT+7,15,"soiling, clipping",1695
+244,5257,5257_ac_power_inv_3758.csv,Etc/GMT+7,15,"soiling, clipping",1695
+245,5257,5257_ac_power_inv_3759.csv,Etc/GMT+7,15,"soiling, clipping",1703
+246,5257,5257_ac_power_inv_3760.csv,Etc/GMT+7,15,"soiling, clipping",1693
+247,5257,5257_ac_power_inv_3761.csv,Etc/GMT+7,15,soiling,1705
+248,5257,5257_ac_power_inv_3762.csv,Etc/GMT+7,15,"soiling, clipping",1703
+249,5257,5257_ac_power_inv_3763.csv,Etc/GMT+7,15,soiling,1705
+250,5257,5257_ac_power_inv_3764.csv,Etc/GMT+7,15,"soiling, clipping",1693
+251,5291,5291_ac_power_inv_4104.csv,Etc/GMT+7,15,"soiling, clipping",1959
+252,5291,5291_ac_power_inv_4105.csv,Etc/GMT+7,15,"soiling, clipping",1957
+253,5291,5291_ac_power_inv_4106.csv,Etc/GMT+7,15,"soiling, clipping",1959
+254,5291,5291_ac_power_inv_4107.csv,Etc/GMT+7,15,"soiling, clipping",1929
+255,5291,5291_ac_power_inv_4108.csv,Etc/GMT+7,15,"soiling, clipping",1959
+256,5297,5297_ac_power_inv_4176.csv,Etc/GMT+4,15,clipping,1219
+257,5297,5297_ac_power_inv_4177.csv,Etc/GMT+4,15,clipping,1211
+258,5297,5297_ac_power_inv_4178.csv,Etc/GMT+4,15,clipping,1217
+259,5308,5308_ac_power_inv_4258.csv,Etc/GMT+4,15,clipping,1144
+260,5308,5308_ac_power_inv_4259.csv,Etc/GMT+4,15,clipping,1144
+261,5308,5308_ac_power_inv_4260.csv,Etc/GMT+4,15,clipping,1144
+262,5308,5308_ac_power_inv_4261.csv,Etc/GMT+4,15,clipping,1144
+263,5308,5308_ac_power_inv_4262.csv,Etc/GMT+4,15,clipping,1116
+264,5308,5308_ac_power_inv_4263.csv,Etc/GMT+4,15,clipping,1144
+265,5308,5308_ac_power_inv_4264.csv,Etc/GMT+4,15,clipping,1144
+266,5308,5308_ac_power_inv_4265.csv,Etc/GMT+4,15,clipping,1144
+267,5308,5308_ac_power_inv_4266.csv,Etc/GMT+4,15,clipping,1116
+268,5308,5308_ac_power_inv_4267.csv,Etc/GMT+4,15,clipping,1144
+269,5308,5308_ac_power_inv_4268.csv,Etc/GMT+4,15,clipping,1144
+270,5308,5308_ac_power_inv_4269.csv,Etc/GMT+4,15,clipping,1144
+271,5308,5308_ac_power_inv_4270.csv,Etc/GMT+4,15,clipping,1144
+272,5308,5308_ac_power_inv_4271.csv,Etc/GMT+4,15,clipping,1129
+273,5308,5308_ac_power_inv_4272.csv,Etc/GMT+4,15,clipping,1144
+274,5308,5308_ac_power_inv_4273.csv,Etc/GMT+4,15,clipping,1116
+275,5308,5308_ac_power_inv_4274.csv,Etc/GMT+4,15,clipping,1116
+276,5308,5308_ac_power_inv_4275.csv,Etc/GMT+4,15,clipping,1116
+277,5308,5308_ac_power_inv_4276.csv,Etc/GMT+4,15,clipping,1138
+278,5308,5308_ac_power_inv_4277.csv,Etc/GMT+4,15,clipping,1136
+279,5308,5308_ac_power_inv_4278.csv,Etc/GMT+4,15,clipping,1144
+280,5308,5308_ac_power_inv_4279.csv,Etc/GMT+4,15,clipping,1144
+281,5308,5308_ac_power_inv_4280.csv,Etc/GMT+4,15,clipping,1144
+282,5308,5308_ac_power_inv_4281.csv,Etc/GMT+4,15,clipping,1115
+283,5308,5308_ac_power_inv_4282.csv,Etc/GMT+4,15,clipping,1116
+284,5308,5308_ac_power_inv_4283.csv,Etc/GMT+4,15,clipping,1144
+285,5308,5308_ac_power_inv_4284.csv,Etc/GMT+4,15,clipping,1116
+286,5308,5308_ac_power_inv_4285.csv,Etc/GMT+4,15,clipping,1144
+287,5308,5308_ac_power_inv_4286.csv,Etc/GMT+4,15,clipping,1144
+288,5308,5308_ac_power_inv_4287.csv,Etc/GMT+4,15,clipping,1116
+289,5328,5328_ac_power_inv_4559.csv,Etc/GMT+7,15,clipping,1658
+290,5328,5328_ac_power_inv_4560.csv,Etc/GMT+7,15,clipping,1658
+291,5328,5328_ac_power_inv_4561.csv,Etc/GMT+7,15,clipping,1658
+292,5329,5329_ac_power_inv_4562.csv,Etc/GMT+7,15,clipping,1672
+293,5329,5329_ac_power_inv_4563.csv,Etc/GMT+7,15,clipping,1662
+294,5329,5329_ac_power_inv_4564.csv,Etc/GMT+7,15,clipping,1664
+295,5329,5329_ac_power_inv_4565.csv,Etc/GMT+7,15,clipping,1663
+296,5329,5329_ac_power_inv_4566.csv,Etc/GMT+7,15,clipping,1671
+297,5329,5329_ac_power_inv_4567.csv,Etc/GMT+7,15,clipping,1672
+298,5335,5335_ac_power_inv_4625.csv,Etc/GMT+7,15,clipping,1670
+299,5335,5335_ac_power_inv_4626.csv,Etc/GMT+7,15,clipping,1670
+300,5335,5335_ac_power_inv_4627.csv,Etc/GMT+7,15,clipping,1670
+301,5335,5335_ac_power_inv_4628.csv,Etc/GMT+7,15,clipping,1670
+302,5342,5342_ac_power_inv_4684.csv,Etc/GMT+7,15,clipping,1728
+303,5342,5342_ac_power_inv_4685.csv,Etc/GMT+7,15,clipping,1728
+304,5342,5342_ac_power_inv_4686.csv,Etc/GMT+7,15,clipping,1728
+305,5342,5342_ac_power_inv_4687.csv,Etc/GMT+7,15,clipping,1728
+306,5342,5342_ac_power_inv_4688.csv,Etc/GMT+7,15,clipping,1728
+307,5342,5342_ac_power_inv_4689.csv,Etc/GMT+7,15,clipping,1728
+308,5342,5342_ac_power_inv_4690.csv,Etc/GMT+7,15,clipping,1728
+309,5342,5342_ac_power_inv_4691.csv,Etc/GMT+7,15,clipping,1728
+310,5342,5342_ac_power_inv_4692.csv,Etc/GMT+7,15,clipping,1728
+311,5342,5342_ac_power_inv_4693.csv,Etc/GMT+7,15,clipping,1728
+312,5342,5342_ac_power_inv_4694.csv,Etc/GMT+7,15,clipping,1728
+313,5342,5342_ac_power_inv_4695.csv,Etc/GMT+7,15,clipping,1728
+314,5342,5342_ac_power_inv_4696.csv,Etc/GMT+7,15,clipping,1728
+315,5342,5342_ac_power_inv_4697.csv,Etc/GMT+7,15,clipping,1728
+316,5342,5342_ac_power_inv_4698.csv,Etc/GMT+7,15,clipping,1728
+317,5342,5342_ac_power_inv_4699.csv,Etc/GMT+7,15,clipping,1539
+318,5342,5342_ac_power_inv_4700.csv,Etc/GMT+7,15,clipping,1728
+319,5342,5342_ac_power_inv_4701.csv,Etc/GMT+7,15,clipping,1728
+320,5342,5342_ac_power_inv_4702.csv,Etc/GMT+7,15,clipping,1728
+321,5342,5342_ac_power_inv_4703.csv,Etc/GMT+7,15,clipping,1728
+322,5342,5342_ac_power_inv_4704.csv,Etc/GMT+7,15,clipping,1728
+323,5342,5342_ac_power_inv_4705.csv,Etc/GMT+7,15,clipping,1728
+324,5342,5342_ac_power_inv_4706.csv,Etc/GMT+7,15,clipping,1728
+325,5342,5342_ac_power_inv_4707.csv,Etc/GMT+7,15,clipping,1728
+326,5354,5354_ac_power_inv_4791.csv,Etc/GMT+7,15,clipping,1730
+327,5354,5354_ac_power_inv_4792.csv,Etc/GMT+7,15,clipping,1730
+328,5354,5354_ac_power_inv_4793.csv,Etc/GMT+7,15,clipping,1730
+329,5354,5354_ac_power_inv_4794.csv,Etc/GMT+7,15,,1730
+330,5354,5354_ac_power_inv_4795.csv,Etc/GMT+7,15,clipping,1730
+331,5354,5354_ac_power_inv_4796.csv,Etc/GMT+7,15,clipping,1730
+332,5354,5354_ac_power_inv_4797.csv,Etc/GMT+7,15,clipping,1730
+333,5354,5354_ac_power_inv_4798.csv,Etc/GMT+7,15,clipping,1714
+334,5354,5354_ac_power_inv_4799.csv,Etc/GMT+7,15,clipping,1718
+335,5354,5354_ac_power_inv_4800.csv,Etc/GMT+7,15,clipping,1730
+336,5354,5354_ac_power_inv_4801.csv,Etc/GMT+7,15,clipping,1730
+337,5354,5354_ac_power_inv_4802.csv,Etc/GMT+7,15,clipping,1730
+338,5354,5354_ac_power_inv_4803.csv,Etc/GMT+7,15,clipping,1730
+339,5354,5354_ac_power_inv_4804.csv,Etc/GMT+7,15,clipping,1730
+340,5354,5354_ac_power_inv_4805.csv,Etc/GMT+7,15,clipping,1730
+341,5372,5372_ac_power_inv_4955.csv,Etc/GMT+7,15,clipping,1778
+342,5372,5372_ac_power_inv_4956.csv,Etc/GMT+7,15,clipping,1779
+343,5372,5372_ac_power_inv_4957.csv,Etc/GMT+7,15,clipping,1778
+344,5372,5372_ac_power_inv_4958.csv,Etc/GMT+7,15,clipping,1778
+345,5372,5372_ac_power_inv_4959.csv,Etc/GMT+7,15,clipping,1779
+346,5372,5372_ac_power_inv_4960.csv,Etc/GMT+7,15,clipping,1779
+347,5377,5377_ac_power_inv_5055.csv,Etc/GMT+7,15,"shading, clipping",1676
+348,5377,5377_ac_power_inv_5056.csv,Etc/GMT+7,15,"shading, clipping",1676
+349,5377,5377_ac_power_inv_5057.csv,Etc/GMT+7,15,"shading, clipping",1677
+350,5377,5377_ac_power_inv_5058.csv,Etc/GMT+7,15,"shading, clipping",1658
+351,5377,5377_ac_power_inv_5059.csv,Etc/GMT+7,15,"shading, clipping",1676
+352,5377,5377_ac_power_inv_5060.csv,Etc/GMT+7,15,shading,1674
+353,5377,5377_ac_power_inv_5061.csv,Etc/GMT+7,15,"shading, clipping",1677
+354,5377,5377_ac_power_inv_5062.csv,Etc/GMT+7,15,"shading, clipping",1677
+355,5379,5379_ac_power_inv_5070.csv,Etc/GMT+7,15,clipping,1624
+356,5379,5379_ac_power_inv_5071.csv,Etc/GMT+7,15,clipping,1625
+357,5379,5379_ac_power_inv_5072.csv,Etc/GMT+7,15,clipping,1625
+358,5057,5057_ac_power_inv_2236.csv,Etc/GMT+5,15,,3237
+360,5061,5061_ac_power_inv_2270.csv,Etc/GMT+5,15,clipping,3682
+361,5061,5061_ac_power_inv_2269.csv,Etc/GMT+5,15,clipping,3486
+362,5070,5070_ac_power_inv_2293.csv,Etc/GMT+7,15,,3065
+364,5070,5070_ac_power_inv_2292.csv,Etc/GMT+7,15,,3064
+366,5081,5081_ac_power_inv_2321.csv,Etc/GMT+7,15,clipping,3064
+367,5099,5099_ac_power_inv_2358.csv,Etc/GMT+7,15,clipping,2984
+368,5099,5099_ac_power_inv_2357.csv,Etc/GMT+7,15,clipping,2993
+370,5114,5114_ac_power_inv_2397.csv,Etc/GMT+8,15,clipping,2787
+371,5114,5114_ac_power_inv_2405.csv,Etc/GMT+8,15,clipping,2787
+372,5114,5114_ac_power_inv_2404.csv,Etc/GMT+8,15,clipping,2787
+373,5114,5114_ac_power_inv_2401.csv,Etc/GMT+8,15,clipping,2787
+374,5114,5114_ac_power_inv_2396.csv,Etc/GMT+8,15,clipping,2787
+375,5114,5114_ac_power_inv_2403.csv,Etc/GMT+8,15,clipping,2787
+376,5129,5129_ac_power_inv_2496.csv,Etc/GMT+8,15,clipping,2710
+377,5129,5129_ac_power_inv_2493.csv,Etc/GMT+8,15,clipping,2710
+378,5129,5129_ac_power_inv_2494.csv,Etc/GMT+8,15,clipping,2697
+379,5129,5129_ac_power_inv_2495.csv,Etc/GMT+8,15,clipping,2696
+380,5129,5129_ac_power_inv_2492.csv,Etc/GMT+8,15,clipping,2696
+381,5129,5129_ac_power_inv_2497.csv,Etc/GMT+8,15,clipping,2710
+382,5133,5133_ac_power_inv_2526.csv,Etc/GMT+8,15,,2657
+384,5133,5133_ac_power_inv_2523.csv,Etc/GMT+8,15,,2657
+385,5133,5133_ac_power_inv_2524.csv,Etc/GMT+8,15,,2657
+387,5138,5138_ac_power_inv_2543.csv,Etc/GMT+8,15,clipping,2706
+388,5138,5138_ac_power_inv_2545.csv,Etc/GMT+8,15,clipping,2706
+389,5138,5138_ac_power_inv_2546.csv,Etc/GMT+8,15,clipping,2706
+390,5138,5138_ac_power_inv_2544.csv,Etc/GMT+8,15,clipping,2706
+391,5150,5150_ac_power_inv_2649.csv,Etc/GMT+7,15,clipping,2720
+392,5150,5150_ac_power_inv_2642.csv,Etc/GMT+7,15,clipping,2722
+393,5150,5150_ac_power_inv_2639.csv,Etc/GMT+7,15,clipping,2626
+394,5150,5150_ac_power_inv_2640.csv,Etc/GMT+7,15,clipping,2719
+395,5150,5150_ac_power_inv_2637.csv,Etc/GMT+7,15,clipping,2719
+396,5150,5150_ac_power_inv_2646.csv,Etc/GMT+7,15,clipping,2719
+397,5159,5159_ac_power_inv_2720.csv,Etc/GMT+7,15,clipping,2646
+398,5159,5159_ac_power_inv_2717.csv,Etc/GMT+7,15,clipping,2629
+399,5159,5159_ac_power_inv_2718.csv,Etc/GMT+7,15,clipping,2517
+400,5159,5159_ac_power_inv_2716.csv,Etc/GMT+7,15,clipping,2563
+402,5159,5159_ac_power_inv_2715.csv,Etc/GMT+7,15,clipping,2651
+403,5162,5162_ac_power_inv_2770.csv,Etc/GMT+8,15,clipping,2495
+404,5162,5162_ac_power_inv_2759.csv,Etc/GMT+8,15,clipping,2506
+405,5162,5162_ac_power_inv_2762.csv,Etc/GMT+8,15,clipping,2506
+406,5162,5162_ac_power_inv_2765.csv,Etc/GMT+8,15,clipping,2481
+407,5162,5162_ac_power_inv_2760.csv,Etc/GMT+8,15,clipping,2502
+408,5162,5162_ac_power_inv_2756.csv,Etc/GMT+8,15,clipping,2480
+409,5184,5184_ac_power_inv_3059.csv,Etc/GMT+5,15,clipping,2473
+411,5184,5184_ac_power_inv_3051.csv,Etc/GMT+5,15,clipping,2492
+413,5184,5184_ac_power_inv_3052.csv,Etc/GMT+5,15,clipping,2525
+415,5196,5196_ac_power_inv_3163.csv,Etc/GMT+7,15,clipping,2519
+416,5196,5196_ac_power_inv_3157.csv,Etc/GMT+7,15,clipping,2512
+417,5196,5196_ac_power_inv_3161.csv,Etc/GMT+7,15,clipping,2518
+418,5196,5196_ac_power_inv_3162.csv,Etc/GMT+7,15,clipping,2519
+420,5196,5196_ac_power_inv_3160.csv,Etc/GMT+7,15,clipping,2505
+421,5222,5222_ac_power_inv_3332.csv,Etc/GMT+8,15,,2131
+422,5222,5222_ac_power_inv_3335.csv,Etc/GMT+8,15,,2131
+423,5222,5222_ac_power_inv_3334.csv,Etc/GMT+8,15,,2130
+424,5222,5222_ac_power_inv_3333.csv,Etc/GMT+8,15,,2130
+432,5273,5273_ac_power_inv_5975.csv,Etc/GMT+8,15,clipping,1819
+433,5273,5273_ac_power_inv_5973.csv,Etc/GMT+8,15,clipping,1950
+434,5273,5273_ac_power_inv_5974.csv,Etc/GMT+8,15,clipping,1950
+436,5273,5273_ac_power_inv_5972.csv,Etc/GMT+8,15,clipping,1950
+437,5273,5273_ac_power_inv_5971.csv,Etc/GMT+8,15,clipping,1950
+438,5318,5318_ac_power_inv_6240.csv,Etc/GMT+5,15,clipping,2360
+439,5318,5318_ac_power_inv_6236.csv,Etc/GMT+5,15,clipping,2348
+440,5318,5318_ac_power_inv_6232.csv,Etc/GMT+5,15,clipping,2308
+441,5318,5318_ac_power_inv_6233.csv,Etc/GMT+5,15,clipping,2348
+442,5318,5318_ac_power_inv_6226.csv,Etc/GMT+5,15,clipping,2360
+443,5318,5318_ac_power_inv_6244.csv,Etc/GMT+5,15,clipping,2361
+444,5324,5324_ac_power_inv_4531.csv,Etc/GMT+8,15,clipping,1643
+445,5324,5324_ac_power_inv_4532.csv,Etc/GMT+8,15,clipping,1643
+446,5324,5324_ac_power_inv_4533.csv,Etc/GMT+8,15,clipping,1643
+447,5324,5324_ac_power_inv_4530.csv,Etc/GMT+8,15,clipping,1658
+450,5345,5345_ac_power_inv_4727.csv,Etc/GMT+8,15,clipping,1758
+451,5345,5345_ac_power_inv_4724.csv,Etc/GMT+8,15,clipping,1758
+452,5345,5345_ac_power_inv_4722.csv,Etc/GMT+8,15,clipping,1730
+453,5345,5345_ac_power_inv_4726.csv,Etc/GMT+8,15,clipping,1758
+454,5345,5345_ac_power_inv_4721.csv,Etc/GMT+8,15,clipping,1758
+456,5348,5348_ac_power_inv_4734.csv,Etc/GMT+8,15,clipping,1766
+457,5348,5348_ac_power_inv_4735.csv,Etc/GMT+8,15,clipping,1767
+458,5363,5363_ac_power_inv_6395.csv,Etc/GMT+8,15,clipping,1777
+459,5363,5363_ac_power_inv_6389.csv,Etc/GMT+8,15,clipping,1777
+460,5363,5363_ac_power_inv_6386.csv,Etc/GMT+8,15,clipping,1777
+461,5363,5363_ac_power_inv_6394.csv,Etc/GMT+8,15,clipping,1777
+462,5363,5363_ac_power_inv_6387.csv,Etc/GMT+8,15,clipping,1777
+463,5363,5363_ac_power_inv_6390.csv,Etc/GMT+8,15,clipping,1777
+464,5375,5375_ac_power_inv_6304.csv,Etc/GMT+5,15,clipping,1485
+465,5375,5375_ac_power_inv_6343.csv,Etc/GMT+5,15,clipping,1422
+466,5375,5375_ac_power_inv_6309.csv,Etc/GMT+5,15,clipping,1485
+467,5375,5375_ac_power_inv_6358.csv,Etc/GMT+5,15,clipping,1498
+468,5375,5375_ac_power_inv_6327.csv,Etc/GMT+5,15,clipping,1485
+469,5375,5375_ac_power_inv_6308.csv,Etc/GMT+5,15,clipping,1485
+471,5458,5458_ac_power_inv_6986.csv,Etc/GMT+7,15,,1261
+472,5458,5458_ac_power_inv_6985.csv,Etc/GMT+7,15,clipping,1257
+473,5458,5458_ac_power_inv_6984.csv,Etc/GMT+7,15,clipping,1261
+474,5016,5016_ac_power_inv_2086.csv,Etc/GMT+7,15,clipping,1478
+475,5016,5016_ac_power_inv_2107.csv,Etc/GMT+7,15,clipping,1478
+476,5016,5016_ac_power_inv_2102.csv,Etc/GMT+7,15,clipping,1478
+477,5016,5016_ac_power_inv_2098.csv,Etc/GMT+7,15,clipping,1478
+478,5016,5016_ac_power_inv_2100.csv,Etc/GMT+7,15,clipping,1478
+479,5016,5016_ac_power_inv_2103.csv,Etc/GMT+7,15,clipping,1478
diff --git a/ec2/analysis_taks/az-tilt-estimation/data/file_test_link.csv b/ec2/analysis_taks/az-tilt-estimation/data/file_test_link.csv
new file mode 100644
index 00000000..be093690
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/data/file_test_link.csv
@@ -0,0 +1,481 @@
+test_id,category_id,file_id
+0,0,0
+1,0,1
+2,0,2
+3,0,3
+4,0,4
+5,0,5
+6,0,6
+7,0,7
+8,0,8
+9,0,9
+10,0,10
+11,0,11
+12,0,12
+13,0,13
+14,0,14
+15,0,15
+16,0,16
+17,0,17
+18,0,18
+19,0,19
+20,0,20
+21,0,21
+22,0,22
+23,0,23
+24,0,24
+25,0,25
+26,0,26
+27,0,27
+28,0,28
+29,0,29
+30,0,30
+31,0,31
+32,0,32
+33,0,33
+34,0,34
+35,0,35
+36,0,36
+37,0,37
+38,0,38
+39,0,39
+40,0,40
+41,0,41
+42,0,42
+43,0,43
+44,0,44
+45,0,45
+46,0,46
+47,0,47
+48,0,48
+49,0,49
+50,0,50
+51,0,51
+52,0,52
+53,0,53
+54,0,54
+55,0,55
+56,0,56
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diff --git a/ec2/analysis_taks/az-tilt-estimation/data/merged_file_data.csv b/ec2/analysis_taks/az-tilt-estimation/data/merged_file_data.csv
new file mode 100644
index 00000000..07250107
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/data/merged_file_data.csv
@@ -0,0 +1,276 @@
+system_id,name,latitude,longitude,elevation,tracking,azimuth,tilt,file_id,file_name,timezone,data_sampling_frequency,issue,number_days
+4,Sanyo1,39.7406,-105.1774,1795.3,False,180.0,40.0,173,4_ac_power__315.csv,Etc/GMT+7,15,,1263
+1331,NREL Parking Garage-tilt1,39.7388,-105.1732,1770.0,False,180.0,60.0,176,1332_inv3_ac_power__2650.csv,Etc/GMT+7,15,clipping,727
+1332,NREL Parking Garage-tilt2,39.7388,-105.1732,1770.0,False,180.0,16.77,174,1332_inv1_ac_power__2642.csv,Etc/GMT+7,15,snow,1099
+1332,NREL Parking Garage-tilt2,39.7388,-105.1732,1770.0,False,180.0,16.77,175,1332_inv2_ac_power__2646.csv,Etc/GMT+7,15,snow,1263
+1332,NREL Parking Garage-tilt2,39.7388,-105.1732,1770.0,False,180.0,16.77,177,1332_inv_total_ac_power__2654.csv,Etc/GMT+7,15,snow,697
+1403,RTC-FSEC-Baseline,28.405,-80.7709,12.0,False,180.0,35.0,178,1403_ac_power__5034.csv,Etc/GMT+5,15,,675
+1403,RTC-FSEC-Baseline,28.405,-80.7709,12.0,False,180.0,35.0,179,1403_inv1_ac_power__4207.csv,Etc/GMT+5,15,,1129
+1403,RTC-FSEC-Baseline,28.405,-80.7709,12.0,False,180.0,35.0,180,1403_inv2_ac_power__4213.csv,Etc/GMT+5,15,,1135
+4902,NIST_Ground_1,39.1319,-77.2141,138.0,False,180.0,20.0,181,4902_ac_power_inv_14538.csv,Etc/GMT+5,15,,1030
+5016,Crane Schools  Salida del Sol,32.710684,-114.6675958,121.0,False,180.0,15.0,474,5016_ac_power_inv_2086.csv,Etc/GMT+7,15,clipping,1478
+5016,Crane Schools  Salida del Sol,32.710684,-114.6675958,121.0,False,180.0,15.0,475,5016_ac_power_inv_2107.csv,Etc/GMT+7,15,clipping,1478
+5016,Crane Schools  Salida del Sol,32.710684,-114.6675958,121.0,False,180.0,15.0,476,5016_ac_power_inv_2102.csv,Etc/GMT+7,15,clipping,1478
+5016,Crane Schools  Salida del Sol,32.710684,-114.6675958,121.0,False,180.0,15.0,477,5016_ac_power_inv_2098.csv,Etc/GMT+7,15,clipping,1478
+5016,Crane Schools  Salida del Sol,32.710684,-114.6675958,121.0,False,180.0,15.0,478,5016_ac_power_inv_2100.csv,Etc/GMT+7,15,clipping,1478
+5016,Crane Schools  Salida del Sol,32.710684,-114.6675958,121.0,False,180.0,15.0,479,5016_ac_power_inv_2103.csv,Etc/GMT+7,15,clipping,1478
+5057,UMMS  Costen CRE,38.097551,-75.608715,16.0,False,180.0,20.0,358,5057_ac_power_inv_2236.csv,Etc/GMT+5,15,,3237
+5061,Vineland North,39.50479,-74.994123,98.0,False,180.0,25.0,360,5061_ac_power_inv_2270.csv,Etc/GMT+5,15,clipping,3682
+5061,Vineland North,39.50479,-74.994123,98.0,False,180.0,25.0,361,5061_ac_power_inv_2269.csv,Etc/GMT+5,15,clipping,3486
+5070,Dysart Unified  Shadow Ridge High School,33.5817467,-112.4615022,1280.0,False,147.0,12.0,362,5070_ac_power_inv_2293.csv,Etc/GMT+7,15,,3065
+5070,Dysart Unified  Shadow Ridge High School,33.5817467,-112.4615022,1280.0,False,147.0,12.0,364,5070_ac_power_inv_2292.csv,Etc/GMT+7,15,,3064
+5081,Dysart Unified  Dysart High School (SES C),33.590635,-112.33999,1112.0,False,180.0,12.0,366,5081_ac_power_inv_2321.csv,Etc/GMT+7,15,clipping,3064
+5099,Peoria  Desert Harbor,33.625383,-112.252494,1181.0,False,180.0,13.5,367,5099_ac_power_inv_2358.csv,Etc/GMT+7,15,clipping,2984
+5099,Peoria  Desert Harbor,33.625383,-112.252494,1181.0,False,180.0,13.5,368,5099_ac_power_inv_2357.csv,Etc/GMT+7,15,clipping,2993
+5114,Castaic USD - Live Oak Elementary School,34.4492392,-118.6201877,1040.0,False,180.0,7.0,370,5114_ac_power_inv_2397.csv,Etc/GMT+8,15,clipping,2787
+5114,Castaic USD - Live Oak Elementary School,34.4492392,-118.6201877,1040.0,False,180.0,7.0,371,5114_ac_power_inv_2405.csv,Etc/GMT+8,15,clipping,2787
+5114,Castaic USD - Live Oak Elementary School,34.4492392,-118.6201877,1040.0,False,180.0,7.0,372,5114_ac_power_inv_2404.csv,Etc/GMT+8,15,clipping,2787
+5114,Castaic USD - Live Oak Elementary School,34.4492392,-118.6201877,1040.0,False,180.0,7.0,373,5114_ac_power_inv_2401.csv,Etc/GMT+8,15,clipping,2787
+5114,Castaic USD - Live Oak Elementary School,34.4492392,-118.6201877,1040.0,False,180.0,7.0,374,5114_ac_power_inv_2396.csv,Etc/GMT+8,15,clipping,2787
+5114,Castaic USD - Live Oak Elementary School,34.4492392,-118.6201877,1040.0,False,180.0,7.0,375,5114_ac_power_inv_2403.csv,Etc/GMT+8,15,clipping,2787
+5129,Keppel  Keppel Academy,34.5284066,-117.9637384,2900.0,False,180.0,30.0,376,5129_ac_power_inv_2496.csv,Etc/GMT+8,15,clipping,2710
+5129,Keppel  Keppel Academy,34.5284066,-117.9637384,2900.0,False,180.0,30.0,377,5129_ac_power_inv_2493.csv,Etc/GMT+8,15,clipping,2710
+5129,Keppel  Keppel Academy,34.5284066,-117.9637384,2900.0,False,180.0,30.0,378,5129_ac_power_inv_2494.csv,Etc/GMT+8,15,clipping,2697
+5129,Keppel  Keppel Academy,34.5284066,-117.9637384,2900.0,False,180.0,30.0,379,5129_ac_power_inv_2495.csv,Etc/GMT+8,15,clipping,2696
+5129,Keppel  Keppel Academy,34.5284066,-117.9637384,2900.0,False,180.0,30.0,380,5129_ac_power_inv_2492.csv,Etc/GMT+8,15,clipping,2696
+5129,Keppel  Keppel Academy,34.5284066,-117.9637384,2900.0,False,180.0,30.0,381,5129_ac_power_inv_2497.csv,Etc/GMT+8,15,clipping,2710
+5133,Palmdale Cultural Center,34.577755,-118.115683,2664.0,False,269.0,7.0,382,5133_ac_power_inv_2526.csv,Etc/GMT+8,15,,2657
+5133,Palmdale Cultural Center,34.577755,-118.115683,2664.0,False,269.0,7.0,384,5133_ac_power_inv_2523.csv,Etc/GMT+8,15,,2657
+5133,Palmdale Cultural Center,34.577755,-118.115683,2664.0,False,269.0,7.0,385,5133_ac_power_inv_2524.csv,Etc/GMT+8,15,,2657
+5138,Palmdale  Marie Kerr Pool,34.603666,-118.182041,2707.0,False,180.0,7.0,387,5138_ac_power_inv_2543.csv,Etc/GMT+8,15,clipping,2706
+5138,Palmdale  Marie Kerr Pool,34.603666,-118.182041,2707.0,False,180.0,7.0,388,5138_ac_power_inv_2545.csv,Etc/GMT+8,15,clipping,2706
+5138,Palmdale  Marie Kerr Pool,34.603666,-118.182041,2707.0,False,180.0,7.0,389,5138_ac_power_inv_2546.csv,Etc/GMT+8,15,clipping,2706
+5138,Palmdale  Marie Kerr Pool,34.603666,-118.182041,2707.0,False,180.0,7.0,390,5138_ac_power_inv_2544.csv,Etc/GMT+8,15,clipping,2706
+5150,Peoria  Liberty HS,33.6855539,-112.2661668,1296.0,False,180.0,13.5,391,5150_ac_power_inv_2649.csv,Etc/GMT+7,15,clipping,2720
+5150,Peoria  Liberty HS,33.6855539,-112.2661668,1296.0,False,180.0,13.5,392,5150_ac_power_inv_2642.csv,Etc/GMT+7,15,clipping,2722
+5150,Peoria  Liberty HS,33.6855539,-112.2661668,1296.0,False,180.0,13.5,393,5150_ac_power_inv_2639.csv,Etc/GMT+7,15,clipping,2626
+5150,Peoria  Liberty HS,33.6855539,-112.2661668,1296.0,False,180.0,13.5,394,5150_ac_power_inv_2640.csv,Etc/GMT+7,15,clipping,2719
+5150,Peoria  Liberty HS,33.6855539,-112.2661668,1296.0,False,180.0,13.5,395,5150_ac_power_inv_2637.csv,Etc/GMT+7,15,clipping,2719
+5150,Peoria  Liberty HS,33.6855539,-112.2661668,1296.0,False,180.0,13.5,396,5150_ac_power_inv_2646.csv,Etc/GMT+7,15,clipping,2719
+5159,TUSD Mary Meredith ES,32.2312025,-110.8823651,2507.0,False,180.0,12.0,397,5159_ac_power_inv_2720.csv,Etc/GMT+7,15,clipping,2646
+5159,TUSD Mary Meredith ES,32.2312025,-110.8823651,2507.0,False,180.0,12.0,398,5159_ac_power_inv_2717.csv,Etc/GMT+7,15,clipping,2629
+5159,TUSD Mary Meredith ES,32.2312025,-110.8823651,2507.0,False,180.0,12.0,399,5159_ac_power_inv_2718.csv,Etc/GMT+7,15,clipping,2517
+5159,TUSD Mary Meredith ES,32.2312025,-110.8823651,2507.0,False,180.0,12.0,400,5159_ac_power_inv_2716.csv,Etc/GMT+7,15,clipping,2563
+5159,TUSD Mary Meredith ES,32.2312025,-110.8823651,2507.0,False,180.0,12.0,402,5159_ac_power_inv_2715.csv,Etc/GMT+7,15,clipping,2651
+5162,Chaffey - Etiwanda East,34.128339,-117.511124,1375.0,False,180.0,7.49,403,5162_ac_power_inv_2770.csv,Etc/GMT+8,15,clipping,2495
+5162,Chaffey - Etiwanda East,34.128339,-117.511124,1375.0,False,180.0,7.49,404,5162_ac_power_inv_2759.csv,Etc/GMT+8,15,clipping,2506
+5162,Chaffey - Etiwanda East,34.128339,-117.511124,1375.0,False,180.0,7.49,405,5162_ac_power_inv_2762.csv,Etc/GMT+8,15,clipping,2506
+5162,Chaffey - Etiwanda East,34.128339,-117.511124,1375.0,False,180.0,7.49,406,5162_ac_power_inv_2765.csv,Etc/GMT+8,15,clipping,2481
+5162,Chaffey - Etiwanda East,34.128339,-117.511124,1375.0,False,180.0,7.49,407,5162_ac_power_inv_2760.csv,Etc/GMT+8,15,clipping,2502
+5162,Chaffey - Etiwanda East,34.128339,-117.511124,1375.0,False,180.0,7.49,408,5162_ac_power_inv_2756.csv,Etc/GMT+8,15,clipping,2480
+5184,URE Prescott,33.222111,-82.378167,436.0,False,180.0,22.0,409,5184_ac_power_inv_3059.csv,Etc/GMT+5,15,clipping,2473
+5184,URE Prescott,33.222111,-82.378167,436.0,False,180.0,22.0,411,5184_ac_power_inv_3051.csv,Etc/GMT+5,15,clipping,2492
+5184,URE Prescott,33.222111,-82.378167,436.0,False,180.0,22.0,413,5184_ac_power_inv_3052.csv,Etc/GMT+5,15,clipping,2525
+5196,TUSD White ES 1,32.1516678,-111.0168228,2464.0,False,180.0,12.0,415,5196_ac_power_inv_3163.csv,Etc/GMT+7,15,clipping,2519
+5196,TUSD White ES 1,32.1516678,-111.0168228,2464.0,False,180.0,12.0,416,5196_ac_power_inv_3157.csv,Etc/GMT+7,15,clipping,2512
+5196,TUSD White ES 1,32.1516678,-111.0168228,2464.0,False,180.0,12.0,417,5196_ac_power_inv_3161.csv,Etc/GMT+7,15,clipping,2518
+5196,TUSD White ES 1,32.1516678,-111.0168228,2464.0,False,180.0,12.0,418,5196_ac_power_inv_3162.csv,Etc/GMT+7,15,clipping,2519
+5196,TUSD White ES 1,32.1516678,-111.0168228,2464.0,False,180.0,12.0,420,5196_ac_power_inv_3160.csv,Etc/GMT+7,15,clipping,2505
+5222,San Gabriel Washington ES,34.1069508,-118.1036145,0.0,False,270.0,7.49,421,5222_ac_power_inv_3332.csv,Etc/GMT+8,15,,2131
+5222,San Gabriel Washington ES,34.1069508,-118.1036145,0.0,False,270.0,7.49,422,5222_ac_power_inv_3335.csv,Etc/GMT+8,15,,2131
+5222,San Gabriel Washington ES,34.1069508,-118.1036145,0.0,False,270.0,7.49,423,5222_ac_power_inv_3334.csv,Etc/GMT+8,15,,2130
+5222,San Gabriel Washington ES,34.1069508,-118.1036145,0.0,False,270.0,7.49,424,5222_ac_power_inv_3333.csv,Etc/GMT+8,15,,2130
+5226,Central - Cucamonga MS,34.1159418,-117.602299,0.0,False,180.0,30.0,191,5226_ac_power_inv_3355.csv,Etc/GMT+7,15,"clipping, shading",821
+5226,Central - Cucamonga MS,34.1159418,-117.602299,0.0,False,180.0,30.0,192,5226_ac_power_inv_3356.csv,Etc/GMT+7,15,"clipping, shading",821
+5226,Central - Cucamonga MS,34.1159418,-117.602299,0.0,False,180.0,30.0,193,5226_ac_power_inv_3357.csv,Etc/GMT+7,15,"clipping, shading",819
+5226,Central - Cucamonga MS,34.1159418,-117.602299,0.0,False,180.0,30.0,194,5226_ac_power_inv_3358.csv,Etc/GMT+7,15,"clipping, shading",821
+5226,Central - Cucamonga MS,34.1159418,-117.602299,0.0,False,180.0,30.0,195,5226_ac_power_inv_3359.csv,Etc/GMT+7,15,shading,821
+5226,Central - Cucamonga MS,34.1159418,-117.602299,0.0,False,180.0,30.0,196,5226_ac_power_inv_3360.csv,Etc/GMT+7,15,"clipping, shading",820
+5226,Central - Cucamonga MS,34.1159418,-117.602299,0.0,False,180.0,30.0,197,5226_ac_power_inv_3361.csv,Etc/GMT+7,15,"clipping, shading",821
+5226,Central - Cucamonga MS,34.1159418,-117.602299,0.0,False,180.0,30.0,198,5226_ac_power_inv_3362.csv,Etc/GMT+7,15,"clipping, shading",821
+5226,Central - Cucamonga MS,34.1159418,-117.602299,0.0,False,180.0,30.0,199,5226_ac_power_inv_3363.csv,Etc/GMT+7,15,"clipping, shading",821
+5226,Central - Cucamonga MS,34.1159418,-117.602299,0.0,False,180.0,30.0,200,5226_ac_power_inv_3364.csv,Etc/GMT+7,15,"clipping, shading",821
+5231,Central - Valle Vista ES,34.1148548,-117.6197738,0.0,False,189.0,7.49,201,5231_ac_power_inv_3391.csv,Etc/GMT+7,15,"shading, clipping",2255
+5231,Central - Valle Vista ES,34.1148548,-117.6197738,0.0,False,189.0,7.49,202,5231_ac_power_inv_3392.csv,Etc/GMT+7,15,"shading, clipping",2254
+5231,Central - Valle Vista ES,34.1148548,-117.6197738,0.0,False,189.0,7.49,203,5231_ac_power_inv_3393.csv,Etc/GMT+7,15,"shading, clipping",2172
+5231,Central - Valle Vista ES,34.1148548,-117.6197738,0.0,False,189.0,7.49,204,5231_ac_power_inv_3394.csv,Etc/GMT+7,15,"shading, clipping",2153
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,205,5242_ac_power_inv_3573.csv,Etc/GMT+7,15,clipping,2002
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,206,5242_ac_power_inv_3574.csv,Etc/GMT+7,15,clipping,1748
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,207,5242_ac_power_inv_3575.csv,Etc/GMT+7,15,clipping,2000
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,208,5242_ac_power_inv_3576.csv,Etc/GMT+7,15,clipping,1936
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,209,5242_ac_power_inv_3577.csv,Etc/GMT+7,15,clipping,1989
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,210,5242_ac_power_inv_3578.csv,Etc/GMT+7,15,clipping,1882
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,211,5242_ac_power_inv_3579.csv,Etc/GMT+7,15,clipping,1947
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,212,5242_ac_power_inv_3580.csv,Etc/GMT+7,15,clipping,1848
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,213,5242_ac_power_inv_3581.csv,Etc/GMT+7,15,clipping,1992
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,214,5242_ac_power_inv_3582.csv,Etc/GMT+7,15,clipping,1985
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,215,5242_ac_power_inv_3583.csv,Etc/GMT+7,15,clipping,2006
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,216,5242_ac_power_inv_3584.csv,Etc/GMT+7,15,clipping,2000
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,217,5242_ac_power_inv_3585.csv,Etc/GMT+7,15,clipping,1944
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,218,5242_ac_power_inv_3586.csv,Etc/GMT+7,15,clipping,1991
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,219,5242_ac_power_inv_3587.csv,Etc/GMT+7,15,clipping,1881
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,220,5242_ac_power_inv_3588.csv,Etc/GMT+7,15,clipping,1890
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,221,5242_ac_power_inv_3589.csv,Etc/GMT+7,15,clipping,1847
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,222,5242_ac_power_inv_3590.csv,Etc/GMT+7,15,clipping,2001
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,223,5242_ac_power_inv_3591.csv,Etc/GMT+7,15,clipping,2002
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,224,5242_ac_power_inv_3592.csv,Etc/GMT+7,15,clipping,2007
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,225,5242_ac_power_inv_3593.csv,Etc/GMT+7,15,clipping,1998
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,226,5242_ac_power_inv_3594.csv,Etc/GMT+7,15,clipping,1951
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,227,5242_ac_power_inv_3595.csv,Etc/GMT+7,15,clipping,1994
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,228,5242_ac_power_inv_3596.csv,Etc/GMT+7,15,clipping,1987
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,229,5242_ac_power_inv_3597.csv,Etc/GMT+7,15,clipping,2005
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,230,5242_ac_power_inv_3598.csv,Etc/GMT+7,15,clipping,1971
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0.0,False,150.0,7.49,231,5242_ac_power_inv_3599.csv,Etc/GMT+7,15,clipping,1940
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,232,5257_ac_power_inv_3746.csv,Etc/GMT+7,15,"soiling, clipping",1693
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,233,5257_ac_power_inv_3747.csv,Etc/GMT+7,15,"soiling, clipping",1699
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,234,5257_ac_power_inv_3748.csv,Etc/GMT+7,15,"soiling, clipping",1698
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,235,5257_ac_power_inv_3749.csv,Etc/GMT+7,15,"soiling, clipping",1702
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,236,5257_ac_power_inv_3750.csv,Etc/GMT+7,15,"soiling, clipping",1703
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,237,5257_ac_power_inv_3751.csv,Etc/GMT+7,15,"soiling, clipping",1691
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,238,5257_ac_power_inv_3752.csv,Etc/GMT+7,15,"soiling, clipping",1695
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,239,5257_ac_power_inv_3753.csv,Etc/GMT+7,15,"soiling, clipping",1699
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,240,5257_ac_power_inv_3754.csv,Etc/GMT+7,15,"soiling, clipping",1704
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,241,5257_ac_power_inv_3755.csv,Etc/GMT+7,15,"soiling, clipping",1695
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,242,5257_ac_power_inv_3756.csv,Etc/GMT+7,15,"soiling, clipping",1698
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,243,5257_ac_power_inv_3757.csv,Etc/GMT+7,15,"soiling, clipping",1695
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,244,5257_ac_power_inv_3758.csv,Etc/GMT+7,15,"soiling, clipping",1695
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,245,5257_ac_power_inv_3759.csv,Etc/GMT+7,15,"soiling, clipping",1703
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,246,5257_ac_power_inv_3760.csv,Etc/GMT+7,15,"soiling, clipping",1693
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,247,5257_ac_power_inv_3761.csv,Etc/GMT+7,15,soiling,1705
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,248,5257_ac_power_inv_3762.csv,Etc/GMT+7,15,"soiling, clipping",1703
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,249,5257_ac_power_inv_3763.csv,Etc/GMT+7,15,soiling,1705
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0.0,False,243.0,7.49,250,5257_ac_power_inv_3764.csv,Etc/GMT+7,15,"soiling, clipping",1693
+5273,Sulphur Springs District Office,34.4079181,-118.4670982,1403.0,False,204.5,7.49,432,5273_ac_power_inv_5975.csv,Etc/GMT+8,15,clipping,1819
+5273,Sulphur Springs District Office,34.4079181,-118.4670982,1403.0,False,204.5,7.49,433,5273_ac_power_inv_5973.csv,Etc/GMT+8,15,clipping,1950
+5273,Sulphur Springs District Office,34.4079181,-118.4670982,1403.0,False,204.5,7.49,434,5273_ac_power_inv_5974.csv,Etc/GMT+8,15,clipping,1950
+5273,Sulphur Springs District Office,34.4079181,-118.4670982,1403.0,False,204.5,7.49,436,5273_ac_power_inv_5972.csv,Etc/GMT+8,15,clipping,1950
+5273,Sulphur Springs District Office,34.4079181,-118.4670982,1403.0,False,204.5,7.49,437,5273_ac_power_inv_5971.csv,Etc/GMT+8,15,clipping,1950
+5291,Alta Loma Victoria Groves,34.1286731,-117.5660026,0.0,False,188.0,7.49,251,5291_ac_power_inv_4104.csv,Etc/GMT+7,15,"soiling, clipping",1959
+5291,Alta Loma Victoria Groves,34.1286731,-117.5660026,0.0,False,188.0,7.49,252,5291_ac_power_inv_4105.csv,Etc/GMT+7,15,"soiling, clipping",1957
+5291,Alta Loma Victoria Groves,34.1286731,-117.5660026,0.0,False,188.0,7.49,253,5291_ac_power_inv_4106.csv,Etc/GMT+7,15,"soiling, clipping",1959
+5291,Alta Loma Victoria Groves,34.1286731,-117.5660026,0.0,False,188.0,7.49,254,5291_ac_power_inv_4107.csv,Etc/GMT+7,15,"soiling, clipping",1929
+5291,Alta Loma Victoria Groves,34.1286731,-117.5660026,0.0,False,188.0,7.49,255,5291_ac_power_inv_4108.csv,Etc/GMT+7,15,"soiling, clipping",1959
+5297,Baltimore City I,39.436888,-76.2161329,0.0,False,180.0,25.0,256,5297_ac_power_inv_4176.csv,Etc/GMT+4,15,clipping,1219
+5297,Baltimore City I,39.436888,-76.2161329,0.0,False,180.0,25.0,257,5297_ac_power_inv_4177.csv,Etc/GMT+4,15,clipping,1211
+5297,Baltimore City I,39.436888,-76.2161329,0.0,False,180.0,25.0,258,5297_ac_power_inv_4178.csv,Etc/GMT+4,15,clipping,1217
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,259,5308_ac_power_inv_4258.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,260,5308_ac_power_inv_4259.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,261,5308_ac_power_inv_4260.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,262,5308_ac_power_inv_4261.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,263,5308_ac_power_inv_4262.csv,Etc/GMT+4,15,clipping,1116
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,264,5308_ac_power_inv_4263.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,265,5308_ac_power_inv_4264.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,266,5308_ac_power_inv_4265.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,267,5308_ac_power_inv_4266.csv,Etc/GMT+4,15,clipping,1116
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,268,5308_ac_power_inv_4267.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,269,5308_ac_power_inv_4268.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,270,5308_ac_power_inv_4269.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,271,5308_ac_power_inv_4270.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,272,5308_ac_power_inv_4271.csv,Etc/GMT+4,15,clipping,1129
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,273,5308_ac_power_inv_4272.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,274,5308_ac_power_inv_4273.csv,Etc/GMT+4,15,clipping,1116
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,275,5308_ac_power_inv_4274.csv,Etc/GMT+4,15,clipping,1116
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,276,5308_ac_power_inv_4275.csv,Etc/GMT+4,15,clipping,1116
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,277,5308_ac_power_inv_4276.csv,Etc/GMT+4,15,clipping,1138
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,278,5308_ac_power_inv_4277.csv,Etc/GMT+4,15,clipping,1136
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,279,5308_ac_power_inv_4278.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,280,5308_ac_power_inv_4279.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,281,5308_ac_power_inv_4280.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,282,5308_ac_power_inv_4281.csv,Etc/GMT+4,15,clipping,1115
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,283,5308_ac_power_inv_4282.csv,Etc/GMT+4,15,clipping,1116
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,284,5308_ac_power_inv_4283.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,285,5308_ac_power_inv_4284.csv,Etc/GMT+4,15,clipping,1116
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,286,5308_ac_power_inv_4285.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,287,5308_ac_power_inv_4286.csv,Etc/GMT+4,15,clipping,1144
+5308,Evergreen Academy Ave,32.51648,-82.936657,276.0,False,180.0,22.0,288,5308_ac_power_inv_4287.csv,Etc/GMT+4,15,clipping,1116
+5318,URE Georgia Stephens Co. HS,34.553116,-83.29978,781.0,False,180.0,22.0,438,5318_ac_power_inv_6240.csv,Etc/GMT+5,15,clipping,2360
+5318,URE Georgia Stephens Co. HS,34.553116,-83.29978,781.0,False,180.0,22.0,439,5318_ac_power_inv_6236.csv,Etc/GMT+5,15,clipping,2348
+5318,URE Georgia Stephens Co. HS,34.553116,-83.29978,781.0,False,180.0,22.0,440,5318_ac_power_inv_6232.csv,Etc/GMT+5,15,clipping,2308
+5318,URE Georgia Stephens Co. HS,34.553116,-83.29978,781.0,False,180.0,22.0,441,5318_ac_power_inv_6233.csv,Etc/GMT+5,15,clipping,2348
+5318,URE Georgia Stephens Co. HS,34.553116,-83.29978,781.0,False,180.0,22.0,442,5318_ac_power_inv_6226.csv,Etc/GMT+5,15,clipping,2360
+5318,URE Georgia Stephens Co. HS,34.553116,-83.29978,781.0,False,180.0,22.0,443,5318_ac_power_inv_6244.csv,Etc/GMT+5,15,clipping,2361
+5324,Hawthorne - Hawthorne MS,33.9148276,-118.3504147,0.0,False,270.0,7.49,444,5324_ac_power_inv_4531.csv,Etc/GMT+8,15,clipping,1643
+5324,Hawthorne - Hawthorne MS,33.9148276,-118.3504147,0.0,False,270.0,7.49,445,5324_ac_power_inv_4532.csv,Etc/GMT+8,15,clipping,1643
+5324,Hawthorne - Hawthorne MS,33.9148276,-118.3504147,0.0,False,270.0,7.49,446,5324_ac_power_inv_4533.csv,Etc/GMT+8,15,clipping,1643
+5324,Hawthorne - Hawthorne MS,33.9148276,-118.3504147,0.0,False,270.0,7.49,447,5324_ac_power_inv_4530.csv,Etc/GMT+8,15,clipping,1658
+5328,Hawthorne - York ES,33.9263342,-118.3444883,0.0,False,270.0,7.49,289,5328_ac_power_inv_4559.csv,Etc/GMT+7,15,clipping,1658
+5328,Hawthorne - York ES,33.9263342,-118.3444883,0.0,False,270.0,7.49,290,5328_ac_power_inv_4560.csv,Etc/GMT+7,15,clipping,1658
+5328,Hawthorne - York ES,33.9263342,-118.3444883,0.0,False,270.0,7.49,291,5328_ac_power_inv_4561.csv,Etc/GMT+7,15,clipping,1658
+5329,Hawthorne - Zela Davis ES,33.9107563,-118.3358501,0.0,False,90.0,7.49,292,5329_ac_power_inv_4562.csv,Etc/GMT+7,15,clipping,1672
+5329,Hawthorne - Zela Davis ES,33.9107563,-118.3358501,0.0,False,90.0,7.49,293,5329_ac_power_inv_4563.csv,Etc/GMT+7,15,clipping,1662
+5329,Hawthorne - Zela Davis ES,33.9107563,-118.3358501,0.0,False,90.0,7.49,294,5329_ac_power_inv_4564.csv,Etc/GMT+7,15,clipping,1664
+5329,Hawthorne - Zela Davis ES,33.9107563,-118.3358501,0.0,False,90.0,7.49,295,5329_ac_power_inv_4565.csv,Etc/GMT+7,15,clipping,1663
+5329,Hawthorne - Zela Davis ES,33.9107563,-118.3358501,0.0,False,90.0,7.49,296,5329_ac_power_inv_4566.csv,Etc/GMT+7,15,clipping,1671
+5329,Hawthorne - Zela Davis ES,33.9107563,-118.3358501,0.0,False,90.0,7.49,297,5329_ac_power_inv_4567.csv,Etc/GMT+7,15,clipping,1672
+5335,Mountain View - Creek View ES,34.0247557,-117.5681449,0.0,False,203.0,7.49,298,5335_ac_power_inv_4625.csv,Etc/GMT+7,15,clipping,1670
+5335,Mountain View - Creek View ES,34.0247557,-117.5681449,0.0,False,203.0,7.49,299,5335_ac_power_inv_4626.csv,Etc/GMT+7,15,clipping,1670
+5335,Mountain View - Creek View ES,34.0247557,-117.5681449,0.0,False,203.0,7.49,300,5335_ac_power_inv_4627.csv,Etc/GMT+7,15,clipping,1670
+5335,Mountain View - Creek View ES,34.0247557,-117.5681449,0.0,False,203.0,7.49,301,5335_ac_power_inv_4628.csv,Etc/GMT+7,15,clipping,1670
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,302,5342_ac_power_inv_4684.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,303,5342_ac_power_inv_4685.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,304,5342_ac_power_inv_4686.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,305,5342_ac_power_inv_4687.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,306,5342_ac_power_inv_4688.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,307,5342_ac_power_inv_4689.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,308,5342_ac_power_inv_4690.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,309,5342_ac_power_inv_4691.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,310,5342_ac_power_inv_4692.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,311,5342_ac_power_inv_4693.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,312,5342_ac_power_inv_4694.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,313,5342_ac_power_inv_4695.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,314,5342_ac_power_inv_4696.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,315,5342_ac_power_inv_4697.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,316,5342_ac_power_inv_4698.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,317,5342_ac_power_inv_4699.csv,Etc/GMT+7,15,clipping,1539
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,318,5342_ac_power_inv_4700.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,319,5342_ac_power_inv_4701.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,320,5342_ac_power_inv_4702.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,321,5342_ac_power_inv_4703.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,322,5342_ac_power_inv_4704.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,323,5342_ac_power_inv_4705.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,324,5342_ac_power_inv_4706.csv,Etc/GMT+7,15,clipping,1728
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0.0,False,200.0,7.49,325,5342_ac_power_inv_4707.csv,Etc/GMT+7,15,clipping,1728
+5345,Claremont - El Roble IS,34.1000246,-117.7291222,0.0,False,270.0,7.49,450,5345_ac_power_inv_4727.csv,Etc/GMT+8,15,clipping,1758
+5345,Claremont - El Roble IS,34.1000246,-117.7291222,0.0,False,270.0,7.49,451,5345_ac_power_inv_4724.csv,Etc/GMT+8,15,clipping,1758
+5345,Claremont - El Roble IS,34.1000246,-117.7291222,0.0,False,270.0,7.49,452,5345_ac_power_inv_4722.csv,Etc/GMT+8,15,clipping,1730
+5345,Claremont - El Roble IS,34.1000246,-117.7291222,0.0,False,270.0,7.49,453,5345_ac_power_inv_4726.csv,Etc/GMT+8,15,clipping,1758
+5345,Claremont - El Roble IS,34.1000246,-117.7291222,0.0,False,270.0,7.49,454,5345_ac_power_inv_4721.csv,Etc/GMT+8,15,clipping,1758
+5348,Claremont - San Antonio HS,34.0836998,-117.7153289,0.0,False,270.0,7.49,456,5348_ac_power_inv_4734.csv,Etc/GMT+8,15,clipping,1766
+5348,Claremont - San Antonio HS,34.0836998,-117.7153289,0.0,False,270.0,7.49,457,5348_ac_power_inv_4735.csv,Etc/GMT+8,15,clipping,1767
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,326,5354_ac_power_inv_4791.csv,Etc/GMT+7,15,clipping,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,327,5354_ac_power_inv_4792.csv,Etc/GMT+7,15,clipping,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,328,5354_ac_power_inv_4793.csv,Etc/GMT+7,15,clipping,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,329,5354_ac_power_inv_4794.csv,Etc/GMT+7,15,,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,330,5354_ac_power_inv_4795.csv,Etc/GMT+7,15,clipping,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,331,5354_ac_power_inv_4796.csv,Etc/GMT+7,15,clipping,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,332,5354_ac_power_inv_4797.csv,Etc/GMT+7,15,clipping,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,333,5354_ac_power_inv_4798.csv,Etc/GMT+7,15,clipping,1714
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,334,5354_ac_power_inv_4799.csv,Etc/GMT+7,15,clipping,1718
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,335,5354_ac_power_inv_4800.csv,Etc/GMT+7,15,clipping,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,336,5354_ac_power_inv_4801.csv,Etc/GMT+7,15,clipping,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,337,5354_ac_power_inv_4802.csv,Etc/GMT+7,15,clipping,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,338,5354_ac_power_inv_4803.csv,Etc/GMT+7,15,clipping,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,339,5354_ac_power_inv_4804.csv,Etc/GMT+7,15,clipping,1730
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0.0,False,195.0,7.49,340,5354_ac_power_inv_4805.csv,Etc/GMT+7,15,clipping,1730
+5363,Etiwanda - Day Creek IS,34.1539468,-117.5363846,0.0,False,164.0,7.49,458,5363_ac_power_inv_6395.csv,Etc/GMT+8,15,clipping,1777
+5363,Etiwanda - Day Creek IS,34.1539468,-117.5363846,0.0,False,164.0,7.49,459,5363_ac_power_inv_6389.csv,Etc/GMT+8,15,clipping,1777
+5363,Etiwanda - Day Creek IS,34.1539468,-117.5363846,0.0,False,164.0,7.49,460,5363_ac_power_inv_6386.csv,Etc/GMT+8,15,clipping,1777
+5363,Etiwanda - Day Creek IS,34.1539468,-117.5363846,0.0,False,164.0,7.49,461,5363_ac_power_inv_6394.csv,Etc/GMT+8,15,clipping,1777
+5363,Etiwanda - Day Creek IS,34.1539468,-117.5363846,0.0,False,164.0,7.49,462,5363_ac_power_inv_6387.csv,Etc/GMT+8,15,clipping,1777
+5363,Etiwanda - Day Creek IS,34.1539468,-117.5363846,0.0,False,164.0,7.49,463,5363_ac_power_inv_6390.csv,Etc/GMT+8,15,clipping,1777
+5372,Etiwanda - Long ES,34.1562874,-117.4859581,0.0,False,180.0,7.49,341,5372_ac_power_inv_4955.csv,Etc/GMT+7,15,clipping,1778
+5372,Etiwanda - Long ES,34.1562874,-117.4859581,0.0,False,180.0,7.49,342,5372_ac_power_inv_4956.csv,Etc/GMT+7,15,clipping,1779
+5372,Etiwanda - Long ES,34.1562874,-117.4859581,0.0,False,180.0,7.49,343,5372_ac_power_inv_4957.csv,Etc/GMT+7,15,clipping,1778
+5372,Etiwanda - Long ES,34.1562874,-117.4859581,0.0,False,180.0,7.49,344,5372_ac_power_inv_4958.csv,Etc/GMT+7,15,clipping,1778
+5372,Etiwanda - Long ES,34.1562874,-117.4859581,0.0,False,180.0,7.49,345,5372_ac_power_inv_4959.csv,Etc/GMT+7,15,clipping,1779
+5372,Etiwanda - Long ES,34.1562874,-117.4859581,0.0,False,180.0,7.49,346,5372_ac_power_inv_4960.csv,Etc/GMT+7,15,clipping,1779
+5375,URE Dixon,32.604618,-82.322656,288.0,False,180.0,20.0,464,5375_ac_power_inv_6304.csv,Etc/GMT+5,15,clipping,1485
+5375,URE Dixon,32.604618,-82.322656,288.0,False,180.0,20.0,465,5375_ac_power_inv_6343.csv,Etc/GMT+5,15,clipping,1422
+5375,URE Dixon,32.604618,-82.322656,288.0,False,180.0,20.0,466,5375_ac_power_inv_6309.csv,Etc/GMT+5,15,clipping,1485
+5375,URE Dixon,32.604618,-82.322656,288.0,False,180.0,20.0,467,5375_ac_power_inv_6358.csv,Etc/GMT+5,15,clipping,1498
+5375,URE Dixon,32.604618,-82.322656,288.0,False,180.0,20.0,468,5375_ac_power_inv_6327.csv,Etc/GMT+5,15,clipping,1485
+5375,URE Dixon,32.604618,-82.322656,288.0,False,180.0,20.0,469,5375_ac_power_inv_6308.csv,Etc/GMT+5,15,clipping,1485
+5377,Etiwanda - Terra Vista ES,34.1181837,-117.5516621,0.0,False,134.0,7.49,347,5377_ac_power_inv_5055.csv,Etc/GMT+7,15,"shading, clipping",1676
+5377,Etiwanda - Terra Vista ES,34.1181837,-117.5516621,0.0,False,134.0,7.49,348,5377_ac_power_inv_5056.csv,Etc/GMT+7,15,"shading, clipping",1676
+5377,Etiwanda - Terra Vista ES,34.1181837,-117.5516621,0.0,False,134.0,7.49,349,5377_ac_power_inv_5057.csv,Etc/GMT+7,15,"shading, clipping",1677
+5377,Etiwanda - Terra Vista ES,34.1181837,-117.5516621,0.0,False,134.0,7.49,350,5377_ac_power_inv_5058.csv,Etc/GMT+7,15,"shading, clipping",1658
+5377,Etiwanda - Terra Vista ES,34.1181837,-117.5516621,0.0,False,134.0,7.49,351,5377_ac_power_inv_5059.csv,Etc/GMT+7,15,"shading, clipping",1676
+5377,Etiwanda - Terra Vista ES,34.1181837,-117.5516621,0.0,False,134.0,7.49,352,5377_ac_power_inv_5060.csv,Etc/GMT+7,15,shading,1674
+5377,Etiwanda - Terra Vista ES,34.1181837,-117.5516621,0.0,False,134.0,7.49,353,5377_ac_power_inv_5061.csv,Etc/GMT+7,15,"shading, clipping",1677
+5377,Etiwanda - Terra Vista ES,34.1181837,-117.5516621,0.0,False,134.0,7.49,354,5377_ac_power_inv_5062.csv,Etc/GMT+7,15,"shading, clipping",1677
+5379,Etiwanda - Windrows ES,34.1281703,-117.5299781,0.0,False,270.0,7.49,355,5379_ac_power_inv_5070.csv,Etc/GMT+7,15,clipping,1624
+5379,Etiwanda - Windrows ES,34.1281703,-117.5299781,0.0,False,270.0,7.49,356,5379_ac_power_inv_5071.csv,Etc/GMT+7,15,clipping,1625
+5379,Etiwanda - Windrows ES,34.1281703,-117.5299781,0.0,False,270.0,7.49,357,5379_ac_power_inv_5072.csv,Etc/GMT+7,15,clipping,1625
+5458,TUSD III - Dietz ES,32.1997354,-110.8308625,2671.0,False,159.0,12.0,471,5458_ac_power_inv_6986.csv,Etc/GMT+7,15,,1261
+5458,TUSD III - Dietz ES,32.1997354,-110.8308625,2671.0,False,159.0,12.0,472,5458_ac_power_inv_6985.csv,Etc/GMT+7,15,clipping,1257
+5458,TUSD III - Dietz ES,32.1997354,-110.8308625,2671.0,False,159.0,12.0,473,5458_ac_power_inv_6984.csv,Etc/GMT+7,15,clipping,1261
diff --git a/ec2/analysis_taks/az-tilt-estimation/data/system_metadata.csv b/ec2/analysis_taks/az-tilt-estimation/data/system_metadata.csv
new file mode 100644
index 00000000..81cdce4d
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/data/system_metadata.csv
@@ -0,0 +1,45 @@
+system_id,name,latitude,longitude,elevation,tracking,azimuth,tilt
+4,Sanyo1,39.7406,-105.1774,1795.3,FALSE,180,40
+1331,NREL Parking Garage-tilt1,39.7388,-105.1732,1770,FALSE,180,60
+1332,NREL Parking Garage-tilt2,39.7388,-105.1732,1770,FALSE,180,16.77
+1403,RTC-FSEC-Baseline,28.405,-80.7709,12,FALSE,180,35
+4902,NIST_Ground_1,39.1319,-77.2141,138,FALSE,180,20
+5016,Crane Schools  Salida del Sol,32.710684,-114.6675958,121,FALSE,180,15
+5057,UMMS  Costen CRE,38.097551,-75.608715,16,FALSE,180,20
+5061,Vineland North,39.50479,-74.994123,98,FALSE,180,25
+5070,Dysart Unified  Shadow Ridge High School,33.5817467,-112.4615022,1280,FALSE,147,12
+5081,Dysart Unified  Dysart High School (SES C),33.590635,-112.33999,1112,FALSE,180,12
+5099,Peoria  Desert Harbor,33.625383,-112.252494,1181,FALSE,180,13.5
+5114,Castaic USD - Live Oak Elementary School,34.4492392,-118.6201877,1040,FALSE,180,7
+5129,Keppel  Keppel Academy,34.5284066,-117.9637384,2900,FALSE,180,30
+5133,Palmdale Cultural Center,34.577755,-118.115683,2664,FALSE,269,7
+5138,Palmdale  Marie Kerr Pool,34.603666,-118.182041,2707,FALSE,180,7
+5150,Peoria  Liberty HS,33.6855539,-112.2661668,1296,FALSE,180,13.5
+5159,TUSD Mary Meredith ES,32.2312025,-110.8823651,2507,FALSE,180,12
+5162,Chaffey - Etiwanda East,34.128339,-117.511124,1375,FALSE,180,7.49
+5184,URE Prescott,33.222111,-82.378167,436,FALSE,180,22
+5196,TUSD White ES 1,32.1516678,-111.0168228,2464,FALSE,180,12
+5222,San Gabriel Washington ES,34.1069508,-118.1036145,0,FALSE,270,7.49
+5226,Central - Cucamonga MS,34.1159418,-117.602299,0,FALSE,180,30
+5231,Central - Valle Vista ES,34.1148548,-117.6197738,0,FALSE,189,7.49
+5242,Saddleback Laguna Hills HS,33.5933833,-117.7019839,0,FALSE,150,7.49
+5257,Pomona - Ganesha HS,34.0674631,-117.7882769,0,FALSE,243,7.49
+5273,Sulphur Springs District Office,34.4079181,-118.4670982,1403,FALSE,204.5,7.49
+5291,Alta Loma Victoria Groves,34.1286731,-117.5660026,0,FALSE,188,7.49
+5297,Baltimore City I,39.436888,-76.2161329,0,FALSE,180,25
+5308,Evergreen Academy Ave,32.51648,-82.936657,276,FALSE,180,22
+5318,URE Georgia Stephens Co. HS,34.553116,-83.29978,781,FALSE,180,22
+5324,Hawthorne - Hawthorne MS,33.9148276,-118.3504147,0,FALSE,270,7.49
+5328,Hawthorne - York ES,33.9263342,-118.3444883,0,FALSE,270,7.49
+5329,Hawthorne - Zela Davis ES,33.9107563,-118.3358501,0,FALSE,90,7.49
+5335,Mountain View - Creek View ES,34.0247557,-117.5681449,0,FALSE,203,7.49
+5342,Claremont - Claremont HS,34.1129983,-117.7225368,0,FALSE,200,7.49
+5345,Claremont - El Roble IS,34.1000246,-117.7291222,0,FALSE,270,7.49
+5348,Claremont - San Antonio HS,34.0836998,-117.7153289,0,FALSE,270,7.49
+5354,Glendora - Goddard MS,34.1481396,-117.8493741,0,FALSE,195,7.49
+5363,Etiwanda - Day Creek IS,34.1539468,-117.5363846,0,FALSE,164,7.49
+5372,Etiwanda - Long ES,34.1562874,-117.4859581,0,FALSE,180,7.49
+5375,URE Dixon,32.604618,-82.322656,288,FALSE,180,20
+5377,Etiwanda - Terra Vista ES,34.1181837,-117.5516621,0,FALSE,134,7.49
+5379,Etiwanda - Windrows ES,34.1281703,-117.5299781,0,FALSE,270,7.49
+5458,TUSD III - Dietz ES,32.1997354,-110.8308625,2671,FALSE,159,12
diff --git a/ec2/analysis_taks/az-tilt-estimation/data/validation_tests.csv b/ec2/analysis_taks/az-tilt-estimation/data/validation_tests.csv
new file mode 100644
index 00000000..2a178668
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/data/validation_tests.csv
@@ -0,0 +1,3 @@
+category_id,category_name,performance_metrics,function_name
+0,time_shifts,"['mean_absolute_error', 'run_time', 'data_requirements']",detect_time_shifts
+1,az_tilt_estimation,"['mean_absolute_error', 'run_time', 'data_requirements']",estimate_az_tilt
diff --git a/ec2/analysis_taks/az-tilt-estimation/other/generate-plots-categorization.py b/ec2/analysis_taks/az-tilt-estimation/other/generate-plots-categorization.py
new file mode 100644
index 00000000..1cfd4c70
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/other/generate-plots-categorization.py
@@ -0,0 +1,109 @@
+"""
+Generated unfiltered plots so we can identify issues: clipping, capacity shifts, etc.
+"""
+
+import glob
+import pandas as pd
+import pvlib
+from pvanalytics import system
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics import r2_score
+from pvanalytics.quality import gaps
+from pvanalytics.quality import data_shifts as ds
+import matplotlib.pyplot as plt
+from pvanalytics.quality.outliers import hampel, zscore, tukey
+import rdtools
+import plotly.express as px
+
+
+def generateDSTHeatMapPlot(
+    time_series, time_series_name, system_id, heatmap_file_path
+):
+    """
+    This function creates a heat map of a time series, which shows if there are
+    shifts due to daylight savings time or sensor drift.
+    """
+    plt.figure()
+    # Get time of day from the associated datetime column
+    time_of_day = pd.Series(
+        time_series.index.hour + time_series.index.minute / 60,
+        index=time_series.index,
+    )
+    # Pivot the dataframe
+    dataframe = pd.DataFrame(pd.concat([time_series, time_of_day], axis=1))
+    dataframe.columns = ["values", "time_of_day"]
+    dataframe = dataframe.dropna()
+    dataframe_pivoted = dataframe.pivot_table(
+        index="time_of_day", columns=dataframe.index, values="values"
+    )
+    plt.pcolormesh(
+        dataframe_pivoted.columns,
+        dataframe_pivoted.index,
+        dataframe_pivoted,
+        shading="auto",
+    )
+    plt.ylabel("Time of day [0-24]")
+    plt.xlabel("Date")
+    plt.xticks(rotation=60)
+    plt.title("Datetime signal - " + str(system_id) + " - " + time_series_name)
+    plt.colorbar()
+    plt.tight_layout()
+    plt.savefig(heatmap_file_path)
+    plt.clf()
+    plt.close("all")
+    return
+
+
+stream_files = glob.glob(
+    "C:/Users/kperry/Documents/source/repos/az-tilt-estimation-validation/data/file_data/*.csv"
+)
+sys_meta = pd.read_csv(
+    "C:/Users/kperry/Documents/source/repos/az-tilt-estimation-validation/data/system_metadata.csv"
+)
+master_pred_df = pd.DataFrame(
+    columns=[
+        "system_id",
+        "year",
+        "data_stream",
+        "predicted_azimuth",
+        "predicted_tilt",
+        "actual_azimuth",
+        "actual_tilt",
+    ]
+)
+# pd.read_csv("./results_new/pvwatts_model_resultsv2.csv",index_col=0)
+
+file_linker_list = list()
+master_predictions = list()
+for file in stream_files:
+    if file.split("\\")[-1].split(".csv")[0] not in list(
+        master_pred_df["data_stream"]
+    ):
+        df = pd.read_csv(file, index_col=0, parse_dates=True)
+        # Get the associated system details
+        sys = int(file.split("\\")[-1].split("_")[0])
+        stream = file.split("\\")[-1].split(".csv")[0]
+        time_series = df.iloc[:, 0]
+        # Detect any data shifts and remove them
+        time_series_daily = time_series.resample("D").sum()
+        shift_mask = ds.detect_data_shifts(time_series_daily)
+        shift_list = list(time_series_daily[shift_mask].index)
+        # Apply clipping filter from Rdtools.
+        time_series = time_series.asfreq("15T")
+        clip_mask = rdtools.logic_clip_filter(time_series)
+        # # Heatmap
+        # heatmap_file_path = "C:/Users/kperry/Documents/source/repos/az-tilt-estimation-validation/plots_unfiltered/" + stream + ".png"
+        # generateDSTHeatMapPlot(time_series,
+        #                        time_series.name,
+        #                        sys,
+        #                        heatmap_file_path)
+        # Plot it
+        fig = px.scatter(x=time_series.index, y=time_series, color=clip_mask)
+        for shift in shift_list:
+            fig.add_hline(y=shift)
+        fig.write_html(
+            "C:/Users/kperry/Documents/source/repos/az-tilt-estimation-validation/plots_unfiltered/"
+            + stream
+            + ".html"
+        )
diff --git a/ec2/analysis_taks/az-tilt-estimation/other/generate_map.py b/ec2/analysis_taks/az-tilt-estimation/other/generate_map.py
new file mode 100644
index 00000000..3298d525
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/other/generate_map.py
@@ -0,0 +1,68 @@
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import matplotlib.pyplot as plt
+import rioxarray
+import numpy as np
+import pandas as pd
+
+# written for:
+# - cartopy==0.18.0
+# - rioxarray==0.13.4
+# - matplotlib==3.4.3
+
+# example locations.  replace these with your coordinates
+df = pd.read_csv("./data/system_metadata.csv")
+lats = df["latitude"]
+lons = df["longitude"]
+
+# read PSM3 raster file
+# download rasters from: https://www.nrel.gov/gis/solar-resource-maps.html
+# this uses the annual file
+fn = "./data/nsrdb3_ghi.tif"
+psm3 = rioxarray.open_rasterio(fn)
+
+psm3 = psm3.squeeze("band")  # drop useless `band` dimension
+psm3 = psm3.where(psm3 > 0, np.nan)  # replace zero values w/ nan (ocean)
+psm3 = psm3.sel(x=slice(-130, -60), y=slice(55, 24))  # bounds order matters
+
+# %%
+
+fig = plt.figure(figsize=(6.5, 3), dpi=225)
+ax = fig.add_subplot(1, 1, 1, projection=ccrs.LambertConformal())
+ax.set_extent([-120, -72, 23, 49.5], ccrs.Geodetic())
+
+ax.add_feature(cfeature.COASTLINE)
+ax.add_feature(cfeature.LAKES)
+ax.add_feature(cfeature.BORDERS)
+ax.add_feature(cfeature.STATES, linewidth=0.25)
+
+psm3.plot.pcolormesh(
+    ax=ax,
+    transform=ccrs.PlateCarree(),
+    cmap="YlOrRd",
+    cbar_kwargs=dict(label="Average GHI [kWh/m$^2$/day]"),
+)
+ax.set_title(None)  # hide irksome xarray default title
+
+ax.scatter(
+    lons,
+    lats,
+    transform=ccrs.PlateCarree(),
+    zorder=100,
+    c="white",
+    edgecolors="black",
+    s=50,
+)
+
+# gl = ax.gridlines(draw_labels=True, x_inline=False, y_inline=False, linestyle=':', color='black')
+# gl.top_labels = False
+# gl.right_labels = False
+
+# gl.xlabel_style = {'rotation': 0}
+# gl.ylabel_style = {'rotation': 0}
+
+# # hide irksome label on the right side:
+# plt.pause(1)
+# gl.bottom_label_artists[-1].set_text('')
+
+fig.savefig("./results/validation_location_resource_map.png")
diff --git a/ec2/analysis_taks/az-tilt-estimation/other/model-energy.py b/ec2/analysis_taks/az-tilt-estimation/other/model-energy.py
new file mode 100644
index 00000000..a4bf3043
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/other/model-energy.py
@@ -0,0 +1,52 @@
+# -*- coding: utf-8 -*-
+"""
+PVWatts v5 modeled energy using real az-tilt vs predicted az-tilt
+"""
+
+import pvlib
+import pandas as pd
+
+# Read in the metadata, which contains the ground-truth az-tilt estimates
+metadata = pd.read_csv("./data/system_metadata.csv")
+
+for index, row in metadata.iterrows():
+    sys_id = row["system_id"]
+    tilt = row["tilt"]
+    az = row["azimuth"]
+    latitude = row["latitude"]
+    longitude = row["longitude"]
+    # Read in TMY from NSRDB (one year only so we're consistent??)
+
+    location = pvlib.location.Location(latitude, longitude)
+
+    tracker_data = pvlib.tracking.singleaxis(
+        solar_position["apparent_zenith"],
+        solar_position["azimuth"],
+        axis_azimuth=180,
+    )
+    tilt = tracker_data["surface_tilt"].fillna(0)
+    azimuth = tracker_data["surface_azimuth"].fillna(0)
+
+    df_poa_tracker = pvlib.irradiance.get_total_irradiance(
+        surface_tilt=tilt,
+        surface_azimuth=azimuth,
+        dni=df_tmy["DNI"],
+        ghi=df_tmy["GHI"],
+        dhi=df_tmy["DHI"],
+        solar_zenith=solar_position["apparent_zenith"],
+        solar_azimuth=solar_position["azimuth"],
+    )
+    tracker_poa = df_poa_tracker["poa_global"]
+
+    parameters = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS["sapm"][
+        "open_rack_glass_polymer"
+    ]
+    cell_temperature = pvlib.temperature.sapm_cell(
+        tracker_poa, df_tmy["DryBulb"], df_tmy["Wspd"], **parameters
+    )
+
+    pvlib.pvsystem.pvwatts_dc(
+        g_poa_effective, temp_cell, pdc0, gamma_pdc, temp_ref=25.0
+    )
+
+    pvlib.inverter.pvwatts(pdc, pdc0, eta_inv_nom=0.96, eta_inv_ref=0.9637)
diff --git a/ec2/analysis_taks/az-tilt-estimation/other/paper_statistics.py b/ec2/analysis_taks/az-tilt-estimation/other/paper_statistics.py
new file mode 100644
index 00000000..51d532df
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/other/paper_statistics.py
@@ -0,0 +1,423 @@
+# -*- coding: utf-8 -*-
+"""
+Post-processing results (for paper statistics + graphics)
+"""
+
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import rdtools
+
+
+def generateDSTHeatMapPlot(time_series):
+    """
+    This function creates a heat map of a time series, which shows if there are
+    shifts due to daylight savings time or sensor drift.
+    """
+    plt.figure(figsize=(7, 6))
+    time_series = (time_series - time_series.min()) / (
+        time_series.max() - time_series.min()
+    )
+    # Get time of day from the associated datetime column
+    time_of_day = pd.Series(
+        time_series.index.hour + time_series.index.minute / 60,
+        index=time_series.index,
+    )
+    # Pivot the dataframe
+    dataframe = pd.DataFrame(pd.concat([time_series, time_of_day], axis=1))
+    dataframe.columns = ["values", "time_of_day"]
+    dataframe = dataframe.dropna()
+    dataframe_pivoted = dataframe.pivot_table(
+        index="time_of_day", columns=dataframe.index, values="values"
+    )
+    plt.pcolormesh(
+        dataframe_pivoted.columns,
+        dataframe_pivoted.index,
+        dataframe_pivoted,
+        shading="auto",
+    )
+    plt.ylabel("Time of day [0-24 hours]")
+    plt.xlabel("Date")
+    plt.xticks(rotation=60)
+    plt.colorbar()
+    plt.tight_layout()
+    plt.savefig("./results/heavily-shaded-system-heatmap.png")
+    plt.close()
+    plt.clf()
+    return
+
+
+# Read in the SDT lat-lon known data
+sdt_lat_lon = pd.read_csv(
+    "./results/sdt-module-latlon_full_results.csv", index_col=0
+)
+pvwatts = pd.read_csv(
+    "./results/pvwatts-5-module_full_results.csv", index_col=0
+)
+sdt_no_lat_lon = pd.read_csv(
+    "./results/sdt-module-no-latlon_full_results.csv", index_col=0
+)
+
+pv_peak = pd.read_csv(
+    "./results/pv-peak-module-matt-redo_full_results.csv", index_col=0
+)
+
+# File metadata
+file_df = pd.read_csv("./data/file_metadata.csv")
+
+sdt_lat_lon = pd.merge(
+    file_df[["file_name", "number_days"]], sdt_lat_lon, on="file_name"
+)
+
+pvwatts = pd.merge(
+    file_df[["file_name", "number_days"]], pvwatts, on="file_name"
+)
+
+sdt_no_lat_lon = pd.merge(
+    file_df[["file_name", "number_days"]], sdt_no_lat_lon, on="file_name"
+)
+
+pv_peak = pd.merge(
+    file_df[["file_name", "number_days"]], pv_peak, on="file_name"
+)
+
+
+# median run time statistics
+
+print("SDT- lat-long median run time:")
+print(sdt_lat_lon["run_time"].median())
+
+print("SDT- no lat-long median run time:")
+print(sdt_no_lat_lon["run_time"].median())
+
+print("PVWatts median run time:")
+print(pvwatts["run_time"].median())
+
+print("PV Peak median run time:")
+print(pv_peak["run_time"].median())
+
+
+# median MAE for azimuth
+
+print("SDT- lat-long median az MAE:")
+print(sdt_lat_lon["mean_absolute_error_azimuth"].median())
+
+print("SDT- no lat-long median az MAE:")
+print(sdt_no_lat_lon["mean_absolute_error_azimuth"].median())
+
+print("PVWatts median az MAE:")
+print(pvwatts["mean_absolute_error_azimuth"].median())
+
+print("PV Peak median az MAE:")
+print(pv_peak["mean_absolute_error_azimuth"].median())
+
+# median MAE for tilt
+
+print("SDT- lat-long median tilt MAE:")
+print(sdt_lat_lon["mean_absolute_error_tilt"].median())
+
+print("SDT- no lat-long median tilt MAE:")
+print(sdt_no_lat_lon["mean_absolute_error_tilt"].median())
+
+print("PVWatts median tilt MAE:")
+print(pvwatts["mean_absolute_error_tilt"].median())
+
+print("PV-Peak median tilt MAE:")
+print(pv_peak["mean_absolute_error_tilt"].median())
+
+# Build out some graphics for each case
+sdt_lat_lon["algorithm"] = "SDT-lat-lon known"
+sdt_no_lat_lon["algorithm"] = "SDT-lat-lon unknown"
+pvwatts["algorithm"] = "PVWatts-5"
+pv_peak["algorithm"] = "PV-Peak"
+
+df_master = pd.concat([sdt_lat_lon, sdt_no_lat_lon, pvwatts, pv_peak])
+
+
+# Convert to subplots
+plt.rcParams.update({"font.size": 18})
+fig, axes = plt.subplots(3, 1)
+fig.set_figheight(15)
+fig.set_figwidth(8)
+
+# Azimuth error distribution
+ax0 = sns.boxplot(
+    x=df_master["algorithm"],
+    y=df_master["mean_absolute_error_azimuth"],
+    ax=axes[0],
+).set(
+    title="Azimuth Absolute Error",
+    ylabel="Error (degrees)",
+    xlabel=None,
+    xticklabels=[],
+)
+# Tilt error distribution
+ax1 = sns.boxplot(
+    y=df_master["mean_absolute_error_tilt"],
+    x=df_master["algorithm"],
+    ax=axes[1],
+).set(
+    title="Tilt Absolute Error",
+    ylabel="Error (degrees)",
+    xlabel=None,
+    xticklabels=[],
+)
+# Run time distribution
+sns.boxplot(y=df_master["run_time"], x=df_master["algorithm"], ax=axes[2]).set(
+    title="Run Time", ylabel="Run Time (s)", xlabel=None
+)
+for ax in fig.axes:
+    plt.sca(ax)
+    plt.xticks(rotation=20)
+plt.tight_layout()
+# Save to a folder
+plt.savefig("./results/combined-error-runtime-distribution.pdf")
+plt.close()
+plt.clf()
+
+
+# Compare time series length to run time
+plt.rcParams.update({"font.size": 18})
+fig, (ax1, ax2, ax3) = plt.subplots(3, 1)
+fig.set_figheight(12)
+fig.set_figwidth(8)
+# SDT-lat-lon
+ax1.set_title("SDT-Lat-Long Known")
+sns.scatterplot(
+    x=sdt_lat_lon["run_time"], y=sdt_lat_lon["number_days"], ax=ax1
+)
+sns.regplot(
+    x=sdt_lat_lon["run_time"],
+    y=sdt_lat_lon["number_days"],
+    scatter=False,
+    ax=ax1,
+)
+ax1.set_ylabel("Number days")
+ax1.set_xlabel(None)
+# PVWatts
+ax2.set_title("PVWatts")
+sns.scatterplot(x=pvwatts["run_time"], y=pvwatts["number_days"], ax=ax2).set(
+    xlabel="Run Time (s)", ylabel="Number Days"
+)
+sns.regplot(
+    x=pvwatts["run_time"], y=pvwatts["number_days"], scatter=False, ax=ax2
+)
+ax2.set_ylabel("Number days")
+ax2.set_xlabel(None)
+# PV-Peak
+ax3.set_title("PV-Peak")
+sns.scatterplot(x=pv_peak["run_time"], y=pv_peak["number_days"], ax=ax3).set(
+    xlabel="Run Time (s)", ylabel="Number Days"
+)
+sns.regplot(
+    x=pv_peak["run_time"], y=pv_peak["number_days"], scatter=False, ax=ax3
+)
+ax3.set_ylabel("Number days")
+ax3.set_xlabel("Run Time (s)")
+plt.tight_layout()
+# Save to a folder
+plt.savefig("./results/run-time-number-days-comparison.pdf")
+plt.close()
+plt.clf()
+
+
+# Look at MAE vs data set size
+# PVWatts
+sns.scatterplot(
+    x=pvwatts["number_days"], y=pvwatts["mean_absolute_error_tilt"]
+)
+
+plt.title("PVWatts: Absolute Error Tilt vs Number Days")
+plt.ylabel("Absolute Error Tilt")
+plt.xlabel("Number Days")
+plt.tight_layout()
+# Save to a folder
+plt.savefig("./results/mae-tilt-number-days-pvwatts.pdf")
+plt.close()
+plt.clf()
+
+#
+sns.scatterplot(
+    x=pvwatts["number_days"], y=pvwatts["mean_absolute_error_azimuth"]
+)
+plt.ylabel("Absolute Error Tilt")
+plt.xlabel("Number Days")
+plt.tight_layout()
+# Save to a folder
+plt.savefig("./results/mae-azimuth-number-days-pvwatts.pdf")
+plt.close()
+plt.clf()
+
+# Data set issue analysis
+
+# Soiling-azimuth
+df_master.loc[df_master["issue"].isna(), "issue"] = ""
+df_master["has_soiling"] = False
+df_master.loc[df_master["issue"].str.contains("soiling"), "has_soiling"] = True
+sns.boxplot(
+    data=df_master,
+    x="algorithm",
+    y="mean_absolute_error_azimuth",
+    hue="has_soiling",
+)
+plt.ylabel("Absolute Error Azimuth")
+plt.xlabel("Algorithm")
+plt.tight_layout()
+# Save to a folder
+plt.savefig("./results/mae-azimuth-soiling-detected.pdf")
+plt.close()
+plt.clf()
+
+# Soiling-tilt
+sns.boxplot(
+    data=df_master,
+    x="algorithm",
+    y="mean_absolute_error_tilt",
+    hue="has_soiling",
+)
+plt.title("Tilt Absolute Error Color-Coded by Presence of Soiling")
+plt.ylabel("Absolute Error Tilt")
+plt.xlabel("Algorithm")
+plt.tight_layout()
+# Save to a folder
+plt.savefig("./results/mae-tilt-soiling-detected.pdf")
+plt.close()
+plt.clf()
+
+# Clipping-azimuth
+df_master.loc[df_master["issue"].isna(), "issue"] = ""
+df_master["has_clipping"] = False
+df_master.loc[df_master["issue"].str.contains("clipping"), "has_clipping"] = (
+    True
+)
+sns.boxplot(
+    data=df_master,
+    x="algorithm",
+    y="mean_absolute_error_azimuth",
+    hue="has_clipping",
+)
+plt.ylabel("Absolute Error Azimuth")
+plt.xlabel("Algorithm")
+plt.tight_layout()
+# Save to a folder
+plt.savefig("./results/mae-azimuth-clipping-detected.pdf")
+plt.close()
+plt.clf()
+
+# Clipping-tilt
+sns.boxplot(
+    data=df_master,
+    x="algorithm",
+    y="mean_absolute_error_tilt",
+    hue="has_clipping",
+)
+plt.ylabel("Absolute Error Tilt")
+plt.xlabel("Algorithm")
+plt.tight_layout()
+# Save to a folder
+plt.savefig("./results/mae-tilt-clipping-detected.pdf")
+plt.close()
+plt.clf()
+
+# Shading-azimuth
+df_master.loc[df_master["issue"].isna(), "issue"] = ""
+df_master["has_shading"] = False
+df_master.loc[df_master["issue"].str.contains("shading"), "has_shading"] = True
+sns.boxplot(
+    data=df_master,
+    x="algorithm",
+    y="mean_absolute_error_azimuth",
+    hue="has_shading",
+)
+plt.ylabel("Absolute Error Azimuth")
+plt.xlabel("Algorithm")
+plt.tight_layout()
+# Save to a folder
+plt.savefig("./results/mae-azimuth-shading-detected.pdf")
+plt.close()
+plt.clf()
+
+# Shading-tilt
+sns.boxplot(
+    data=df_master,
+    x="algorithm",
+    y="mean_absolute_error_tilt",
+    hue="has_shading",
+)
+plt.ylabel("Absolute Error Tilt")
+plt.xlabel("Algorithm")
+plt.tight_layout()
+# Save to a folder
+plt.savefig("./results/mae-tilt-shading-detected.pdf")
+plt.close()
+plt.clf()
+
+
+# Isolate specific test cases for certain issues
+
+systems_no_issues = df_master[df_master["issue"] == ""]
+systems_no_issues["median_error_az"] = systems_no_issues.groupby("algorithm")[
+    "mean_absolute_error_azimuth"
+].transform("median")
+systems_no_issues["median_error_tilt"] = systems_no_issues.groupby(
+    "algorithm"
+)["mean_absolute_error_tilt"].transform("median")
+
+
+# Clipped system
+clip_file_names = [
+    "5184_ac_power_inv_3051.csv"
+]  # ["5016_ac_power_inv_2100.csv","5159_ac_power_inv_2718.csv",
+# "5184_ac_power_inv_3051.csv", "5226_ac_power_inv_3356.csv"]
+clip_df = df_master[df_master["file_name"].isin(clip_file_names)]
+
+
+# Shaded system
+clip_file_names = [
+    "5184_ac_power_inv_3051.csv"
+]  # ["5016_ac_power_inv_2100.csv","5159_ac_power_inv_2718.csv",
+# "5184_ac_power_inv_3051.csv", "5226_ac_power_inv_3356.csv"]
+clip_df = df_master[df_master["file_name"].isin(clip_file_names)]
+
+
+shade_file_names = ["1332_inv3_ac_power__2650.csv"]
+shade_df = df_master[df_master["file_name"].isin(shade_file_names)]
+
+# Generate a time heatmap for the system
+
+shaded_sys = pd.read_csv(
+    "./data/file_data/1332_inv3_ac_power__2650.csv",
+    index_col=0,
+    parse_dates=True,
+)
+generateDSTHeatMapPlot(shaded_sys)
+
+
+# Plot a clipped system
+clipped_sys = pd.read_csv(
+    "./data/file_data/5184_ac_power_inv_3051.csv",
+    index_col=0,
+    parse_dates=True,
+)
+
+clipped_sys = (clipped_sys - clipped_sys.min()) / (
+    clipped_sys.max() - clipped_sys.min()
+)
+
+time_series = clipped_sys.iloc[:, 0]
+time_series = time_series.asfreq("15T")
+clip_mask = rdtools.logic_clip_filter(time_series)
+
+sns.lineplot(
+    x=time_series[
+        "2015-01-01 00:00:00-05:00":"2016-01-01 00:00:00-05:00"
+    ].index,
+    y=time_series["2015-01-01 00:00:00-05:00":"2016-01-01 00:00:00-05:00"],
+)
+plt.xticks(rotation=30)
+plt.xlabel("Date")
+plt.ylabel("Normalized Power")
+plt.tight_layout()
+plt.savefig("./results/clipped-system.pdf")
+plt.close()
+plt.clf()
diff --git a/ec2/analysis_taks/az-tilt-estimation/other/tilt-az-file-viewer-SDT.py b/ec2/analysis_taks/az-tilt-estimation/other/tilt-az-file-viewer-SDT.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ec2/analysis_taks/az-tilt-estimation/pvinsight-az-tilt-runner.py b/ec2/analysis_taks/az-tilt-estimation/pvinsight-az-tilt-runner.py
new file mode 100644
index 00000000..fa8a6d28
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/pvinsight-az-tilt-runner.py
@@ -0,0 +1,312 @@
+"""
+Runner script for assessing the azimuth-tilt estimation algorithm. In this
+script, the following occurs:
+    1. Pull down all of the metadata associated with the data sets
+    2. Loop through all metadata cases, pull down the associated data, and
+    run the associated submission on it
+    3. Aggregate the results for the entire data set and generate assessment 
+    metrics. Assessment metrics will vary based on the type of analysis being
+    run. For this analysis, the following is calculated:
+        1. Mean Absolute Error between predicted time shift series and ground
+        truth time series (in minutes)
+        2. Average run time for each data set (in seconds)
+    4. Further aggregate performance metrics into visualizations:
+        -Distribution Histograms
+        -Scatter plots
+      This section will be dependent on the type of analysis being run.
+"""
+
+import pandas as pd
+import ast
+import os
+from importlib import import_module
+import inspect
+import time
+from collections import ChainMap
+import seaborn as sns
+import matplotlib.pyplot as plt
+import json
+import numpy as np
+
+from utilities import progress
+
+MODULE_NAME = "pvwatts-5-module"
+
+
+def generate_histogram(
+    dataframe, x_axis, title, color_code=None, number_bins=30
+):
+    """
+    Generate a histogram for a distribution. Option to color code the
+    histogram by the color_code column parameter.
+    """
+    sns.displot(
+        dataframe, x=x_axis, hue=color_code, multiple="stack", bins=number_bins
+    )
+    plt.title(title)
+    plt.tight_layout()
+    return plt
+
+
+def generate_scatter_plot(dataframe, x_axis, y_axis, title):
+    """
+    Generate a scatterplot between an x- and a y-variable.
+    """
+    sns.scatterplot(data=dataframe, x=x_axis, y=y_axis)
+    plt.title(title)
+    plt.tight_layout()
+    return plt
+
+
+if __name__ == "__main__":
+    # Read in the JSON associated with the run
+    with open("config.json") as f:
+        config_data = json.load(f)
+    # Load in the module that we're going to test on.
+    module_to_import = MODULE_NAME
+    # Generate list for us to store all of our results for the module
+    results_list = list()
+    # Load in data set that we're going to analyze.
+    # System metadata: This CSV represents the system_metadata table, which is
+    # a master table for associated system metadata (system_id, name, azimuth,
+    # tilt, etc.)
+    system_metadata = pd.read_csv("./data/system_metadata.csv")
+    # File metadata: This file represents the file_metadata table, which is
+    # the master table for files associated with different tests (az-tilt,
+    # time shifts, degradation, etc.). Contains file name, associated file
+    # information (sampling frequency, specific test, timezone, etc) as well
+    # as ground truth information to test against in the validation_dictionary
+    # field
+    file_metadata = pd.read_csv("./data/file_metadata.csv")
+    # Validation tests: This file represents the validation_tests table,
+    # which is the master table associated with each of the tests run on the
+    # PVInsight Validation Hub. This table contains information on test type
+    # (example: time_shifts, az_tilt_detection, etc), as well as function name
+    # for each test type, performance metrics outputs (how to assess
+    # test performance), as well as expected function outputs and function
+    # output types (in order). This provides a standardized template of
+    # what to expect (input, output, naming conventions) for each test
+    validation_tests = pd.read_csv("./data/validation_tests.csv")
+    # File category link: This file represents the file_category_link table,
+    # which links specific files in the file_metadata table to categories in
+    # the validation_tests table. This table exists specifically to allow for
+    # many-to-many relationships, where one file can link to multiple
+    # categories/tests, and multiple categories/tests can link to multiple
+    # files. This table exists solely to link these two tables together
+    # when performing testing.
+    file_category_link = pd.read_csv("./data/file_test_link.csv")
+    # Link the above tables together to get all of the files associated
+    # with the time_shift category in the validation_tests table.
+    time_shift_test_information = dict(
+        validation_tests[
+            validation_tests["category_name"] == config_data["category_name"]
+        ].iloc[0]
+    )
+    # Get the associated metrics we're supposed to calculate
+    performance_metrics = ast.literal_eval(
+        time_shift_test_information["performance_metrics"]
+    )
+    # Get all of the linked files for time shift analysis via a series
+    # of dataframe joins
+    associated_file_ids = list(
+        file_category_link[
+            file_category_link["category_id"]
+            == time_shift_test_information["category_id"]
+        ]["file_id"]
+    )
+    associated_files = file_metadata[
+        file_metadata["file_id"].isin(associated_file_ids)
+    ]
+    associated_files = associated_files
+    # Get the information associated with the module to run the tests
+    # Get the name of the function we want to import associated with this
+    # test
+    function_name = time_shift_test_information["function_name"]
+    # Import designated module via importlib
+    module = import_module(module_to_import)
+    function = getattr(module, function_name)
+    function_parameters = list(inspect.signature(function).parameters)
+    # Loop through each file and generate predictions
+    total = len(associated_files)
+    t_start = time.time()
+    for index, row in associated_files.iterrows():
+        progress(
+            index,
+            total,
+            status=f"elapsed time: {(time.time() - t_start) / 60:.2f} minutes",
+        )
+        # Get file_name, which will be pulled from database or S3 for
+        # each analysis
+        file_name = row["file_name"]
+        # Get associated system ID
+        system_id = row["system_id"]
+        # Get all of the associated metadata for the particular file based
+        # on its system ID. This metadata will be passed in via kwargs for
+        # any necessary arguments
+        associated_metadata = dict(
+            system_metadata[system_metadata["system_id"] == system_id].iloc[0]
+        )
+        # Get the ground truth scalars that we will compare to
+        ground_truth_dict = dict()
+        if config_data["comparison_type"] == "scalar":
+            for val in config_data["ground_truth_compare"]:
+                ground_truth_dict[val] = associated_metadata[val]
+        if config_data["comparison_type"] == "time_series":
+            ground_truth_series = pd.read_csv(
+                os.path.join("./data/validation_data/", file_name),
+                index_col=0,
+                parse_dates=True,
+            ).squeeze()
+            ground_truth_dict["time_series"] = ground_truth_series
+        # Create master dictionary of all possible function kwargs
+        kwargs_dict = dict(ChainMap(dict(row), associated_metadata))
+        # Filter out to only allowable args for the function
+        kwargs_dict = {
+            key: kwargs_dict[key] for key in config_data["allowable_kwargs"]
+        }
+        # Now that we've collected all of the information associated with the
+        # test, let's read in the file as a pandas dataframe (this data
+        # would most likely be stored in an S3 bucket)
+        time_series = pd.read_csv(
+            os.path.join("./data/file_data/", file_name),
+            index_col=0,
+            parse_dates=True,
+        ).squeeze()
+        time_series = time_series.asfreq(
+            str(row["data_sampling_frequency"]) + "T"
+        )
+        # Filter the kwargs dictionary based on required function params
+        kwargs = dict(
+            (k, kwargs_dict[k])
+            for k in function_parameters
+            if k in kwargs_dict
+        )
+        # Get the performance metrics that we want to quantify
+        performance_metrics = config_data["performance_metrics"]
+        # Run the routine (timed)
+        start_time = time.time()
+        data_outputs = function(time_series, **kwargs)
+        end_time = time.time()
+        function_run_time = end_time - start_time
+        # Convert the data outputs to a dictionary identical to the
+        # ground truth dictionary
+        output_dictionary = dict()
+        if config_data["comparison_type"] == "scalar":
+            for idx in range(len(config_data["ground_truth_compare"])):
+                output_dictionary[config_data["ground_truth_compare"][idx]] = (
+                    data_outputs[idx]
+                )
+        if config_data["comparison_type"] == "time_series":
+            output_dictionary["time_series"] = data_outputs
+        # Run routine for all of the performance metrics and append
+        # results to the dictionary
+        results_dictionary = dict()
+        results_dictionary["file_name"] = file_name
+        # Set the runtime in the results dictionary
+        results_dictionary["run_time"] = function_run_time
+        # Set the data requirements in the dictionary
+        results_dictionary["data_requirements"] = function_parameters
+        # Loop through the rest of the performance metrics and calculate them
+        # (this predominantly applies to error metrics)
+        for metric in performance_metrics:
+            if metric == "absolute_error":
+                # Loop through the input and the output dictionaries,
+                # and calculate the absolute error
+                for val in config_data["ground_truth_compare"]:
+                    error = np.abs(
+                        output_dictionary[val] - ground_truth_dict[val]
+                    )
+                    results_dictionary[metric + "_" + val] = error
+            elif metric == "mean_absolute_error":
+                for val in config_data["ground_truth_compare"]:
+                    error = np.mean(
+                        np.abs(output_dictionary[val] - ground_truth_dict[val])
+                    )
+                    results_dictionary[metric + "_" + val] = error
+        results_list.append(results_dictionary)
+    progress(
+        total,
+        total,
+        status=f"elapsed time: {(time.time() - t_start) / 60:.2f} minutes",
+    )
+    # Convert the results to a pandas dataframe and perform all of the
+    # post-processing in the script
+    results_df = pd.DataFrame(results_list)
+    # Build out the final processed results:
+    #   1) Public reporting: mean MAE, mean run time, etc.
+    #   2) Private reporting: graphics and tables split by different factors
+    # First get mean value for all the performance metrics and save (this will
+    # be saved to a public metrics dictionary)
+    public_metrics_dict = dict()
+    public_metrics_dict["module"] = module_to_import
+    # Get the mean and median run times
+    public_metrics_dict["mean_run_time"] = results_df["run_time"].mean()
+    public_metrics_dict["median_run_time"] = results_df["run_time"].median()
+    public_metrics_dict["function_parameters"] = function_parameters
+    for metric in performance_metrics:
+        if "absolute_error" in metric:
+            for val in config_data["ground_truth_compare"]:
+                public_metrics_dict["mean_" + metric + "_" + val] = results_df[
+                    metric + "_" + val
+                ].mean()
+                public_metrics_dict["median_" + metric + "_" + val] = (
+                    results_df[metric + "_" + val].median()
+                )
+    # TODO: Write public metric information to a public results table. here we
+    # just write a json to illustrate that final outputs.
+    with open(config_data["public_results_table"], "w") as fp:
+        json.dump(public_metrics_dict, fp)
+    # Now generate private results. These will be more specific to the
+    # type of analysis being run as results will be color-coded by certain
+    # parameters. These params will be available as columns in the
+    # 'associated_files' dataframe
+    results_df_private = pd.merge(results_df, associated_files, on="file_name")
+    # Filter to only the necessary columns (available via the config)
+    results_df_private = results_df_private[
+        config_data["private_results_columns"]
+    ]
+    results_df_private.to_csv(
+        os.path.join("./results", module_to_import + "_full_results.csv")
+    )
+    # Loop through all of the plot dictionaries and generate plots and
+    # associated tables for reporting
+    for plot in config_data["plots"]:
+        if plot["type"] == "histogram":
+            if "color_code" in plot:
+                color_code = plot["color_code"]
+            else:
+                color_code = None
+            gen_plot = generate_histogram(
+                results_df_private, plot["x_val"], plot["title"], color_code
+            )
+            # Save the plot
+            gen_plot.savefig(plot["save_file_path"])
+            plt.close()
+            plt.clf()
+            # Write the stratified results to a table for private reporting
+            # (if color_code param is not None)
+            if color_code:
+                stratified_results_tbl = pd.DataFrame(
+                    results_df_private.groupby(color_code)[
+                        plot["x_val"]
+                    ].mean()
+                )
+                stratified_results_tbl.to_csv(
+                    os.path.join(
+                        "./results",
+                        module_to_import
+                        + "_"
+                        + str(color_code)
+                        + "_"
+                        + plot["x_val"]
+                        + ".csv",
+                    )
+                )
+        if plot["type"] == "scatter_plot":
+            gen_plot = generate_scatter_plot(
+                results_df_private, plot["x_val"], plot["y_val"], plot["title"]
+            )
+            # Save the plot
+            gen_plot.savefig(plot["save_file_path"])
+            plt.close()
+            plt.clf()
diff --git a/ec2/analysis_taks/az-tilt-estimation/pvwatts-5-module.py b/ec2/analysis_taks/az-tilt-estimation/pvwatts-5-module.py
new file mode 100644
index 00000000..a7636d22
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/pvwatts-5-module.py
@@ -0,0 +1,158 @@
+"""
+PVWatts-based model submission.
+"""
+
+import pvlib
+import pandas as pd
+from pvanalytics import system
+from pvanalytics.quality import gaps
+from pvanalytics.quality import data_shifts as ds
+from pvanalytics.quality.outliers import hampel, zscore
+import numpy as np
+import rdtools
+
+# Suppress warnings here (just because we've already fixed script up)
+import warnings
+
+warnings.filterwarnings("ignore")
+
+
+def estimate_az_tilt(time_series, latitude, longitude):
+    """
+    Estimate the azimuth and tilt of a system based on its time series data,
+    using the PVAnalytics PVWatts-based method.
+
+    Parameters
+    ----------
+    time_series : Pandas series
+        Time series of an AC power stream associated with the system.
+    latitude: Float.
+        Latitude coordinate of the site.
+
+    longitude: Float.
+        Longitude coordinate of the site.
+
+    Returns
+    -------
+    azimuth: Float.
+        Predicted azimuth value.
+    tilt: Float.
+        Predicted tilt value.
+
+    """
+    try:
+        psm3s = []
+        years = list(time_series.index.year.drop_duplicates())
+        years = [x for x in years if x < 2021]
+        for year in years:
+            psm3 = None
+            tries = 0
+            while (psm3 is None) | (tries < 3):
+                try:
+                    # connect
+                    psm3, _ = pvlib.iotools.get_psm3(
+                        latitude,
+                        longitude,
+                        "ZrX1lhgpYuHm2LZhWGcvHrdc1kvP7aMfIitZDD5T",
+                        "kirsten.perry@nrel.gov",
+                        year,
+                        attributes=[
+                            "air_temperature",
+                            "ghi",
+                            "clearsky_ghi",
+                            "clearsky_dni",
+                            "clearsky_dhi",
+                        ],
+                        map_variables=True,
+                        interval=30,
+                        leap_day=True,
+                    )
+                    tries = tries + 1
+                except:
+                    pass
+            psm3s.append(psm3)
+        psm3 = pd.concat(psm3s)
+        psm3 = psm3.reindex(
+            pd.date_range(psm3.index[0], psm3.index[-1], freq="15T")
+        ).interpolate()
+        psm3 = psm3.reindex(time_series.index)
+        # psm3[(psm3.index.year == year) | (psm3.index.year == year+1)]
+        psm3_sub = psm3
+        is_clear = psm3_sub.ghi_clear == psm3_sub.ghi
+        is_daytime = psm3_sub.ghi > 0
+        # Run the associated data-cleaning routines on the time series:
+        # 1) Data shift detection + removal
+        # 2) Removal of frozen/stuck data
+        # 3) Removal of data periods with low data 'completeness'
+        # 4) Removal of negative data
+        # 5) Removal of outliers via Hampel + outlier filter
+        # 6) Removal of clipped values via clipping filter
+        # 7) Filter to clearsky data only, as determined by PSM3
+        # try:
+        # Detect any data shifts and remove them
+        time_series_daily = time_series.resample("D").sum()
+        start_date, end_date = ds.get_longest_shift_segment_dates(
+            time_series_daily
+        )
+        time_series = time_series[start_date:end_date]
+        # Trim based on frozen data values
+        stale_data_mask = gaps.stale_values_diff(time_series)
+        time_series = time_series.asfreq("15T")
+        time_series = time_series[~stale_data_mask]
+        time_series = time_series.asfreq("15T")
+        # Remove negative data
+        time_series = time_series[(time_series >= 0) | (time_series.isna())]
+        time_series = time_series.asfreq("15T")
+        # Remove any outliers via Hampel and z-score filters
+        hampel_outlier_mask = hampel(time_series, window=5)
+        zscore_outlier_mask = zscore(time_series, zmax=2, nan_policy="omit")
+        time_series = time_series[
+            (~hampel_outlier_mask) & (~zscore_outlier_mask)
+        ]
+        time_series = time_series.asfreq("15T")
+        # Apply clipping filter from Rdtools.
+        clip_mask = rdtools.logic_clip_filter(time_series)
+        time_series = time_series[clip_mask]
+        # Reindex the time series after all of the filtering so it
+        # has the same index as PSM3
+        time_series = time_series.reindex(is_clear.index)
+        # Trim based on clearsky values
+        time_series_clearsky = time_series[(is_clear) & (is_daytime)]
+        # Remove any incomplete days (less than 25% data)
+        time_series_clearsky = time_series_clearsky.asfreq("15T")
+        completeness = gaps.complete(
+            time_series_clearsky, minimum_completeness=0.25
+        )
+        time_series_clearsky = time_series_clearsky[completeness]
+        # Get the peak value for each day, and if it's less than 25% of the
+        # monthly average peak value, then throw it out
+        daily_max = time_series_clearsky.groupby(
+            time_series_clearsky.index.date
+        ).transform("max")
+        average_max_day_month = daily_max.groupby(
+            time_series_clearsky.index.month
+        ).transform("median")
+        time_series_clearsky = time_series_clearsky[
+            daily_max > 0.25 * average_max_day_month
+        ]
+        time_series_clearsky = time_series_clearsky.dropna()
+        # Get the clearsky data
+        psm3_clearsky = psm3.loc[time_series_clearsky.index]
+        solpos_clearsky = pvlib.solarposition.get_solarposition(
+            time_series_clearsky.index, latitude, longitude
+        )
+        # Run PVWatts estimator
+        best_tilt, best_azimuth, r2 = system.infer_orientation_fit_pvwatts(
+            time_series_clearsky,
+            psm3_clearsky.ghi_clear,
+            psm3_clearsky.dhi_clear,
+            psm3_clearsky.dni_clear,
+            solpos_clearsky.zenith,
+            solpos_clearsky.azimuth,
+            temperature=psm3_clearsky.temp_air,
+            azimuth_min=85,
+            azimuth_max=275,
+        )
+        return best_azimuth, best_tilt
+    except:
+        return np.nan, np.nan
diff --git a/ec2/analysis_taks/az-tilt-estimation/results/absolute_error_az_dist.png b/ec2/analysis_taks/az-tilt-estimation/results/absolute_error_az_dist.png
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/absolute_error_tilt_dist.png b/ec2/analysis_taks/az-tilt-estimation/results/absolute_error_tilt_dist.png
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/az-tilt-public-metrics.json b/ec2/analysis_taks/az-tilt-estimation/results/az-tilt-public-metrics.json
new file mode 100644
index 00000000..97b2928b
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/az-tilt-public-metrics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/ec2/analysis_taks/az-tilt-estimation/results/azimuth-error-distribution.pdf b/ec2/analysis_taks/az-tilt-estimation/results/azimuth-error-distribution.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/clipped-system.pdf b/ec2/analysis_taks/az-tilt-estimation/results/clipped-system.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/combined-error-runtime-distribution.pdf b/ec2/analysis_taks/az-tilt-estimation/results/combined-error-runtime-distribution.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/error-distribution-heavily-clipped.pdf b/ec2/analysis_taks/az-tilt-estimation/results/error-distribution-heavily-clipped.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/mae-azimuth-clipping-detected.pdf b/ec2/analysis_taks/az-tilt-estimation/results/mae-azimuth-clipping-detected.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/mae-azimuth-number-days-pvwatts.pdf b/ec2/analysis_taks/az-tilt-estimation/results/mae-azimuth-number-days-pvwatts.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/mae-azimuth-shading-detected.pdf b/ec2/analysis_taks/az-tilt-estimation/results/mae-azimuth-shading-detected.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/mae-azimuth-soiling-detected.pdf b/ec2/analysis_taks/az-tilt-estimation/results/mae-azimuth-soiling-detected.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/mae-tilt-clipping-detected.pdf b/ec2/analysis_taks/az-tilt-estimation/results/mae-tilt-clipping-detected.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/mae-tilt-number-days-pvwatts.pdf b/ec2/analysis_taks/az-tilt-estimation/results/mae-tilt-number-days-pvwatts.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/mae-tilt-shading-detected.pdf b/ec2/analysis_taks/az-tilt-estimation/results/mae-tilt-shading-detected.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module-matt-redo_full_results.csv b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module-matt-redo_full_results.csv
new file mode 100644
index 00000000..8230eef7
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module-matt-redo_full_results.csv
@@ -0,0 +1,276 @@
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module-matt-redo_issue_mean_absolute_error_results_azimuth.csv b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module-matt-redo_issue_mean_absolute_error_results_azimuth.csv
new file mode 100644
index 00000000..5d120eda
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module-matt-redo_issue_mean_absolute_error_results_azimuth.csv
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module-matt-redo_issue_mean_absolute_error_results_tilt.csv b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module-matt-redo_issue_mean_absolute_error_results_tilt.csv
new file mode 100644
index 00000000..61a2c215
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+++ b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module-matt-redo_issue_mean_absolute_error_results_tilt.csv
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module-matt-redo_issue_mean_absolute_error_tilt.png b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module-matt-redo_issue_mean_absolute_error_tilt.png
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HcmV?d00001

diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_full_results.csv b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_full_results.csv
new file mode 100644
index 00000000..9e650848
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_full_results.csv
@@ -0,0 +1,139 @@
+,file_name,mean_absolute_error_azimuth,mean_absolute_error_tilt,run_time,data_requirements,issue
+0,5291_ac_power_inv_4105.csv,,,17.516422986984253,"['time_series', 'latitude', 'longitude']","soiling, clipping"
+1,5291_ac_power_inv_4106.csv,,,19.86812138557434,"['time_series', 'latitude', 'longitude']","soiling, clipping"
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_issue_mean_absolute_error_azimuth.png b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_issue_mean_absolute_error_azimuth.png
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_issue_mean_absolute_error_results_azimuth.csv b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_issue_mean_absolute_error_results_azimuth.csv
new file mode 100644
index 00000000..60f2ec16
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_issue_mean_absolute_error_results_azimuth.csv
@@ -0,0 +1,5 @@
+issue,mean_absolute_error_azimuth
+clipping,26.846774193548388
+shading,21.0
+"shading, clipping",23.285714285714285
+"soiling, clipping",
diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_issue_mean_absolute_error_results_tilt.csv b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_issue_mean_absolute_error_results_tilt.csv
new file mode 100644
index 00000000..b35dd977
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_issue_mean_absolute_error_results_tilt.csv
@@ -0,0 +1,5 @@
+issue,mean_absolute_error_tilt
+clipping,14.724274193548387
+shading,7.51
+"shading, clipping",6.075714285714286
+"soiling, clipping",
diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_issue_mean_absolute_error_tilt.png b/ec2/analysis_taks/az-tilt-estimation/results/pv-peak-module_issue_mean_absolute_error_tilt.png
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pvwatts-5-module_full_results.csv b/ec2/analysis_taks/az-tilt-estimation/results/pvwatts-5-module_full_results.csv
new file mode 100644
index 00000000..1fea593b
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/pvwatts-5-module_full_results.csv
@@ -0,0 +1,276 @@
+,system_id,file_name,run_time,data_requirements,absolute_error_azimuth,absolute_error_tilt,issue,number_days
+0,4,4_ac_power__315.csv,80.43217039108276,"['time_series', 'latitude', 'longitude']",0.41430250120058076,11.19271800171392,,1263
+1,1331,1332_inv3_ac_power__2650.csv,49.27906894683838,"['time_series', 'latitude', 'longitude']",0.41037463891294124,22.443769662084847,"clipping, shading",727
+2,1332,1332_inv1_ac_power__2642.csv,51.17948341369629,"['time_series', 'latitude', 'longitude']",8.667099711234641,15.096779131144405,snow,1099
+3,1332,1332_inv2_ac_power__2646.csv,49.654431104660034,"['time_series', 'latitude', 'longitude']",,,snow,1263
+4,1332,1332_inv_total_ac_power__2654.csv,36.74013662338257,"['time_series', 'latitude', 'longitude']",3.708569389261612,11.420865654345972,snow,697
+5,1403,1403_ac_power__5034.csv,62.915255069732666,"['time_series', 'latitude', 'longitude']",0.18771768772498376,7.228502348856811,,675
+6,1403,1403_inv1_ac_power__4207.csv,55.45541048049927,"['time_series', 'latitude', 'longitude']",0.12210760766015483,5.599629316047007,,1129
+7,1403,1403_inv2_ac_power__4213.csv,55.89111065864563,"['time_series', 'latitude', 'longitude']",0.35480498489332035,5.869561643550455,,1135
+8,4902,4902_ac_power_inv_14538.csv,55.570924043655396,"['time_series', 'latitude', 'longitude']",1.8476476042960428,3.0506890985858988,,1030
+9,5016,5016_ac_power_inv_2086.csv,81.88263583183289,"['time_series', 'latitude', 'longitude']",3.7039704642718903,0.347782477587824,clipping,1478
+10,5016,5016_ac_power_inv_2107.csv,77.24004435539246,"['time_series', 'latitude', 'longitude']",15.176702072486535,4.515093068973927,clipping,1478
+11,5016,5016_ac_power_inv_2102.csv,76.26622271537781,"['time_series', 'latitude', 'longitude']",1.4181583617374542,0.45834665242770534,clipping,1478
+12,5016,5016_ac_power_inv_2098.csv,74.57975220680237,"['time_series', 'latitude', 'longitude']",2.0798460136953736,0.13584235826781743,clipping,1478
+13,5016,5016_ac_power_inv_2100.csv,71.924320936203,"['time_series', 'latitude', 'longitude']",3.1880467424321353,1.400059709166582,clipping,1478
+14,5016,5016_ac_power_inv_2103.csv,78.52074980735779,"['time_series', 'latitude', 'longitude']",1.09625825120483,1.6741062880982724,clipping,1478
+15,5057,5057_ac_power_inv_2236.csv,187.1871337890625,"['time_series', 'latitude', 'longitude']",4.134863848143823,1.4359626936342664,,3237
+16,5061,5061_ac_power_inv_2270.csv,214.57569551467896,"['time_series', 'latitude', 'longitude']",5.092543120665226,6.641224113569347,clipping,3682
+17,5061,5061_ac_power_inv_2269.csv,178.8279116153717,"['time_series', 'latitude', 'longitude']",8.204678595124676,6.656703766178012,clipping,3486
+18,5070,5070_ac_power_inv_2293.csv,146.84006667137146,"['time_series', 'latitude', 'longitude']",31.48246438207967,1.8406788957890416,,3065
+19,5070,5070_ac_power_inv_2292.csv,176.1664228439331,"['time_series', 'latitude', 'longitude']",10.023368486864172,0.16940328249395264,,3064
+20,5081,5081_ac_power_inv_2321.csv,196.4062521457672,"['time_series', 'latitude', 'longitude']",10.384839081003719,1.352380501779317,clipping,3064
+21,5099,5099_ac_power_inv_2358.csv,191.57604122161865,"['time_series', 'latitude', 'longitude']",6.218298900120828,1.754419899879105,clipping,2984
+22,5099,5099_ac_power_inv_2357.csv,122.81897258758545,"['time_series', 'latitude', 'longitude']",5.705079914922351,0.903758695619894,clipping,2993
+23,5114,5114_ac_power_inv_2397.csv,157.88907980918884,"['time_series', 'latitude', 'longitude']",5.185231495252054,1.750265620151854,clipping,2787
+24,5114,5114_ac_power_inv_2405.csv,97.07818150520325,"['time_series', 'latitude', 'longitude']",7.258320417061611,0.8574130279325303,clipping,2787
+25,5114,5114_ac_power_inv_2404.csv,140.6894247531891,"['time_series', 'latitude', 'longitude']",7.997704212123551,1.3233694110600052,clipping,2787
+26,5114,5114_ac_power_inv_2401.csv,127.31053948402405,"['time_series', 'latitude', 'longitude']",4.548772521023153,1.2398705280508153,clipping,2787
+27,5114,5114_ac_power_inv_2396.csv,160.28472113609314,"['time_series', 'latitude', 'longitude']",9.373339696943475,0.581016247805632,clipping,2787
+28,5114,5114_ac_power_inv_2403.csv,133.90272617340088,"['time_series', 'latitude', 'longitude']",8.189534717536532,1.0790870114580002,clipping,2787
+29,5129,5129_ac_power_inv_2496.csv,127.69111490249634,"['time_series', 'latitude', 'longitude']",2.877852385686964,1.0137035698998247,clipping,2710
+30,5129,5129_ac_power_inv_2493.csv,105.10608983039856,"['time_series', 'latitude', 'longitude']",4.839898058407783,4.529813680104535,clipping,2710
+31,5129,5129_ac_power_inv_2494.csv,144.2631983757019,"['time_series', 'latitude', 'longitude']",5.315136487537586,2.256001052333989,clipping,2697
+32,5129,5129_ac_power_inv_2495.csv,148.63006281852722,"['time_series', 'latitude', 'longitude']",3.7562756676993274,0.7563178608057086,clipping,2696
+33,5129,5129_ac_power_inv_2492.csv,129.99217534065247,"['time_series', 'latitude', 'longitude']",5.6062589355777845,2.0935963869786107,clipping,2696
+34,5129,5129_ac_power_inv_2497.csv,136.01115107536316,"['time_series', 'latitude', 'longitude']",6.158236006380008,1.212036715579135,clipping,2710
+35,5133,5133_ac_power_inv_2526.csv,166.55939888954163,"['time_series', 'latitude', 'longitude']",5.642033253258262,1.3426969893980303,,2657
+36,5133,5133_ac_power_inv_2523.csv,125.07191133499146,"['time_series', 'latitude', 'longitude']",5.183183614711027,1.0593098779521046,,2657
+37,5133,5133_ac_power_inv_2524.csv,100.8910460472107,"['time_series', 'latitude', 'longitude']",5.9999999881131885,1.1911038406791077,,2657
+38,5138,5138_ac_power_inv_2543.csv,152.27928495407104,"['time_series', 'latitude', 'longitude']",5.15265338986606,0.7015347127567502,clipping,2706
+39,5138,5138_ac_power_inv_2545.csv,120.66537427902222,"['time_series', 'latitude', 'longitude']",6.099739773106137,0.31298322800189293,clipping,2706
+40,5138,5138_ac_power_inv_2546.csv,124.06284141540527,"['time_series', 'latitude', 'longitude']",4.909338612847193,0.43657933460943,clipping,2706
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pvwatts-5-module_issue_mean_absolute_error_results_azimuth.csv b/ec2/analysis_taks/az-tilt-estimation/results/pvwatts-5-module_issue_mean_absolute_error_results_azimuth.csv
new file mode 100644
index 00000000..dacf2007
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/pvwatts-5-module_issue_mean_absolute_error_results_azimuth.csv
@@ -0,0 +1,8 @@
+issue,mean_absolute_error_azimuth
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+"clipping, shading",4.154557057949088
+shading,10.37556230435083
+"shading, clipping",21.706776542505235
+snow,5.270291790995988
+soiling,19.3205775595724
+"soiling, clipping",20.709580831843116
diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pvwatts-5-module_issue_mean_absolute_error_results_tilt.csv b/ec2/analysis_taks/az-tilt-estimation/results/pvwatts-5-module_issue_mean_absolute_error_results_tilt.csv
new file mode 100644
index 00000000..32d0800b
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/pvwatts-5-module_issue_mean_absolute_error_results_tilt.csv
@@ -0,0 +1,8 @@
+issue,mean_absolute_error_tilt
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/pvwatts-5-module_issue_mean_absolute_error_tilt.png b/ec2/analysis_taks/az-tilt-estimation/results/pvwatts-5-module_issue_mean_absolute_error_tilt.png
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/run-time-distribution.pdf b/ec2/analysis_taks/az-tilt-estimation/results/run-time-distribution.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/run-time-number-days-comparison.pdf b/ec2/analysis_taks/az-tilt-estimation/results/run-time-number-days-comparison.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/run-time-number-days-pvwatts.pdf b/ec2/analysis_taks/az-tilt-estimation/results/run-time-number-days-pvwatts.pdf
new file mode 100644
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/run-time-number-days-sdt-lat-lon.pdf b/ec2/analysis_taks/az-tilt-estimation/results/run-time-number-days-sdt-lat-lon.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/run-time-number-days-sdt-no-lat-lon.pdf b/ec2/analysis_taks/az-tilt-estimation/results/run-time-number-days-sdt-no-lat-lon.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_full_results.csv b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_full_results.csv
new file mode 100644
index 00000000..2072ef5b
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_full_results.csv
@@ -0,0 +1,276 @@
+,file_name,mean_absolute_error_azimuth,mean_absolute_error_tilt,run_time,data_requirements,issue
+0,4_ac_power__315.csv,1.63796651836617,2.8398994676204126,3.834057092666626,"['time_series', 'latitude', 'longitude']",
+1,1332_inv3_ac_power__2650.csv,0.8778557385103909,7.992075772584641,4.42669415473938,"['time_series', 'latitude', 'longitude']",clipping
+2,1332_inv1_ac_power__2642.csv,5.106120823884822,6.191673785775702,4.254189968109131,"['time_series', 'latitude', 'longitude']",snow
+3,1332_inv2_ac_power__2646.csv,5.356227118180783,5.933522424990084,4.403230905532837,"['time_series', 'latitude', 'longitude']",snow
+4,1332_inv_total_ac_power__2654.csv,2.4849781340579398,0.616357044564456,3.6367738246917725,"['time_series', 'latitude', 'longitude']",snow
+5,1403_ac_power__5034.csv,0.019320552784989786,4.2121725499008065,3.528177261352539,"['time_series', 'latitude', 'longitude']",
+6,1403_inv1_ac_power__4207.csv,0.0591964924114734,0.6795037314110246,4.222646951675415,"['time_series', 'latitude', 'longitude']",
+7,1403_inv2_ac_power__4213.csv,0.2928506020345196,0.7966364849140177,3.9297640323638916,"['time_series', 'latitude', 'longitude']",
+8,4902_ac_power_inv_14538.csv,2.273136466112163,5.932147057986381,4.146368980407715,"['time_series', 'latitude', 'longitude']",
+9,5016_ac_power_inv_2086.csv,3.5972740935391982,7.489730075449373,5.813232898712158,"['time_series', 'latitude', 'longitude']",clipping
+10,5016_ac_power_inv_2107.csv,7.533073660867927,5.78332669929285,5.887441873550415,"['time_series', 'latitude', 'longitude']",clipping
+11,5016_ac_power_inv_2102.csv,3.32634702159271,7.723783902777729,5.431228160858154,"['time_series', 'latitude', 'longitude']",clipping
+12,5016_ac_power_inv_2098.csv,3.709168907188058,7.732203340397096,4.937665700912476,"['time_series', 'latitude', 'longitude']",clipping
+13,5016_ac_power_inv_2100.csv,3.772970116154312,8.763175264965035,5.769911766052246,"['time_series', 'latitude', 'longitude']",clipping
+14,5016_ac_power_inv_2103.csv,3.172541633311596,8.517456196440339,5.734786033630371,"['time_series', 'latitude', 'longitude']",clipping
+15,5057_ac_power_inv_2236.csv,3.5222627824563233,5.090458824335933,7.480131149291992,"['time_series', 'latitude', 'longitude']",
+16,5061_ac_power_inv_2270.csv,4.989220037234247,0.781659503609184,7.9781434535980225,"['time_series', 'latitude', 'longitude']",clipping
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_issue_mean_absolute_error_azimuth.png b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_issue_mean_absolute_error_azimuth.png
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HcmV?d00001

diff --git a/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_issue_mean_absolute_error_results_azimuth.csv b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_issue_mean_absolute_error_results_azimuth.csv
new file mode 100644
index 00000000..3d74cce9
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_issue_mean_absolute_error_results_azimuth.csv
@@ -0,0 +1,8 @@
+issue,mean_absolute_error_azimuth
+clipping,12.120500488549862
+"clipping, shading",3.1195518575140557
+shading,11.265429768130304
+"shading, clipping",22.637253260886087
+snow,4.315775358707849
+soiling,28.689026697924987
+"soiling, clipping",26.546402880262224
diff --git a/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_issue_mean_absolute_error_results_tilt.csv b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_issue_mean_absolute_error_results_tilt.csv
new file mode 100644
index 00000000..cbf3a54a
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_issue_mean_absolute_error_results_tilt.csv
@@ -0,0 +1,8 @@
+issue,mean_absolute_error_tilt
+clipping,6.37550477452159
+"clipping, shading",1.004870406989145
+shading,1.5652318040259576
+"shading, clipping",5.329709375641285
+snow,4.247184418443414
+soiling,0.29993940828921284
+"soiling, clipping",2.7031285360765467
diff --git a/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_issue_mean_absolute_error_tilt.png b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_issue_mean_absolute_error_tilt.png
new file mode 100644
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_run_time.png b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-latlon_run_time.png
new file mode 100644
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_full_results.csv b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_full_results.csv
new file mode 100644
index 00000000..78020bdf
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_full_results.csv
@@ -0,0 +1,276 @@
+,file_name,mean_absolute_error_azimuth,mean_absolute_error_tilt,run_time,data_requirements,issue
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+1,1332_inv3_ac_power__2650.csv,0.4530742782872892,5.72224723853671,5.942371845245361,['time_series'],clipping
+2,1332_inv1_ac_power__2642.csv,4.947506648778756,7.320021102337098,6.2425620555877686,['time_series'],snow
+3,1332_inv2_ac_power__2646.csv,5.848248418733306,7.0620534367052095,6.493535041809082,['time_series'],snow
+4,1332_inv_total_ac_power__2654.csv,1.2764457683947228,2.4967788588577147,5.3329479694366455,['time_series'],snow
+5,1403_ac_power__5034.csv,0.5106760198307825,5.062492456834221,5.084675073623657,['time_series'],
+6,1403_inv1_ac_power__4207.csv,1.042070561999708,5.253708950122174,6.216132879257202,['time_series'],
+7,1403_inv2_ac_power__4213.csv,0.7401785565651835,5.381332317823407,6.0303120613098145,['time_series'],
+8,4902_ac_power_inv_14538.csv,3.6535273173126086,0.37179738604687174,6.15214991569519,['time_series'],
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_issue_mean_absolute_error_results_azimuth.csv b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_issue_mean_absolute_error_results_azimuth.csv
new file mode 100644
index 00000000..4c6f651d
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_issue_mean_absolute_error_results_azimuth.csv
@@ -0,0 +1,8 @@
+issue,mean_absolute_error_azimuth
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+shading,8.559349547282963
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+soiling,22.4857445590375
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_issue_mean_absolute_error_results_tilt.csv b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_issue_mean_absolute_error_results_tilt.csv
new file mode 100644
index 00000000..f683934c
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_issue_mean_absolute_error_results_tilt.csv
@@ -0,0 +1,8 @@
+issue,mean_absolute_error_tilt
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_issue_mean_absolute_error_tilt.png b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_issue_mean_absolute_error_tilt.png
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_run_time.png b/ec2/analysis_taks/az-tilt-estimation/results/sdt-module-no-latlon_run_time.png
new file mode 100644
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diff --git a/ec2/analysis_taks/az-tilt-estimation/results/tilt-error-distribution.pdf b/ec2/analysis_taks/az-tilt-estimation/results/tilt-error-distribution.pdf
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diff --git a/ec2/analysis_taks/az-tilt-estimation/sdt-module-latlon.py b/ec2/analysis_taks/az-tilt-estimation/sdt-module-latlon.py
new file mode 100644
index 00000000..05629e77
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/sdt-module-latlon.py
@@ -0,0 +1,24 @@
+"""
+PVWatts-based model submission.
+"""
+
+import pvlib
+import pandas as pd
+from solardatatools import DataHandler
+from pvsystemprofiler.estimator import ConfigurationEstimator
+
+# Suppress warnings here (just because we've already fixed script up)
+import warnings
+
+warnings.filterwarnings("ignore")
+
+
+def estimate_az_tilt(time_series, latitude, longitude):
+    df = time_series.to_frame(name="power")
+    dh = DataHandler(df)
+    dh.run_pipeline(verbose=False)
+    ts = time_series.index[0]
+    tz_offset = int(str(ts)[-6:-3])
+    est = ConfigurationEstimator(dh, gmt_offset=int(tz_offset))
+    est.estimate_orientation(longitude=longitude, latitude=latitude)
+    return est.azimuth + 180, est.tilt
diff --git a/ec2/analysis_taks/az-tilt-estimation/sdt-module-no-latlon.py b/ec2/analysis_taks/az-tilt-estimation/sdt-module-no-latlon.py
new file mode 100644
index 00000000..e8b78a70
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/sdt-module-no-latlon.py
@@ -0,0 +1,24 @@
+"""
+PVWatts-based model submission.
+"""
+
+import pvlib
+import pandas as pd
+from solardatatools import DataHandler
+from pvsystemprofiler.estimator import ConfigurationEstimator
+
+# Suppress warnings here (just because we've already fixed script up)
+import warnings
+
+warnings.filterwarnings("ignore")
+
+
+def estimate_az_tilt(time_series):
+    df = time_series.to_frame(name="power")
+    dh = DataHandler(df)
+    dh.run_pipeline(verbose=False)
+    ts = time_series.index[0]
+    tz_offset = int(str(ts)[-6:-3])
+    est = ConfigurationEstimator(dh, gmt_offset=int(tz_offset))
+    est.estimate_orientation()
+    return est.azimuth + 180, est.tilt
diff --git a/ec2/analysis_taks/az-tilt-estimation/submissions/1332_inv_total_ac_power__2654.csv b/ec2/analysis_taks/az-tilt-estimation/submissions/1332_inv_total_ac_power__2654.csv
new file mode 100644
index 00000000..2541431c
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/submissions/1332_inv_total_ac_power__2654.csv
@@ -0,0 +1,79164 @@
+measured_on,inv_total_ac_power__2654
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diff --git a/ec2/analysis_taks/az-tilt-estimation/submissions/pvwatts-5-module.py b/ec2/analysis_taks/az-tilt-estimation/submissions/pvwatts-5-module.py
new file mode 100644
index 00000000..d9b0ebbb
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/submissions/pvwatts-5-module.py
@@ -0,0 +1,144 @@
+"""
+PVWatts-based model submission.
+"""
+
+import pvlib
+import pandas as pd
+from pvanalytics import system
+from pvanalytics.quality import gaps
+from pvanalytics.quality import data_shifts as ds
+from pvanalytics.quality.outliers import hampel, zscore
+import rdtools
+
+
+def estimate_az_tilt(time_series, latitude, longitude):
+    """
+    Estimate the azimuth and tilt of a system based on its time series data,
+    using the PVAnalytics PVWatts-based method.
+
+    Parameters
+    ----------
+    time_series : Pandas series
+        Time series of an AC power stream associated with the system.
+    latitude: Float.
+        Latitude coordinate of the site.
+
+    longitude: Float.
+        Longitude coordinate of the site.
+
+    Returns
+    -------
+    azimuth: Float.
+        Predicted azimuth value.
+    tilt: Float.
+        Predicted tilt value.
+
+    """
+    psm3s = []
+    years = list(time_series.index.year.drop_duplicates())
+    years = [x for x in years if x < 2021]
+    for year in years:
+        psm3 = None
+        try:
+            # connect
+            psm3, _ = pvlib.iotools.get_psm3(
+                latitude,
+                longitude,
+                "4z5fRAXbGB3qldVVd3c6WH5CuhtY5mhgC2DyD952",
+                "kirsten.perry@nrel.gov",
+                year,
+                attributes=[
+                    "air_temperature",
+                    "ghi",
+                    "clearsky_ghi",
+                    "clearsky_dni",
+                    "clearsky_dhi",
+                ],
+                map_variables=True,
+                interval=30,
+                leap_day=True,
+            )
+        except Exception as e:
+            print(e)
+        psm3s.append(psm3)
+    psm3 = pd.concat(psm3s)
+    psm3 = psm3.reindex(
+        pd.date_range(psm3.index[0], psm3.index[-1], freq="15T")
+    ).interpolate()
+    psm3 = psm3.reindex(time_series.index)
+    # psm3[(psm3.index.year == year) | (psm3.index.year == year+1)]
+    psm3_sub = psm3
+    is_clear = psm3_sub.ghi_clear == psm3_sub.ghi
+    is_daytime = psm3_sub.ghi > 0
+    # Run the associated data-cleaning routines on the time series:
+    # 1) Data shift detection + removal
+    # 2) Removal of frozen/stuck data
+    # 3) Removal of data periods with low data 'completeness'
+    # 4) Removal of negative data
+    # 5) Removal of outliers via Hampel + outlier filter
+    # 6) Removal of clipped values via clipping filter
+    # 7) Filter to clearsky data only, as determined by PSM3
+    # try:
+    # Detect any data shifts and remove them
+    time_series_daily = time_series.resample("D").sum()
+    start_date, end_date = ds.get_longest_shift_segment_dates(
+        time_series_daily
+    )
+    time_series = time_series[start_date:end_date]
+    # Trim based on frozen data values
+    stale_data_mask = gaps.stale_values_diff(time_series)
+    time_series = time_series.asfreq("15T")
+    time_series = time_series[~stale_data_mask]
+    time_series = time_series.asfreq("15T")
+    # Remove negative data
+    time_series = time_series[(time_series >= 0) | (time_series.isna())]
+    time_series = time_series.asfreq("15T")
+    # Remove any outliers via Hampel and z-score filters
+    hampel_outlier_mask = hampel(time_series, window=5)
+    zscore_outlier_mask = zscore(time_series, zmax=2, nan_policy="omit")
+    time_series = time_series[(~hampel_outlier_mask) & (~zscore_outlier_mask)]
+    time_series = time_series.asfreq("15T")
+    # Apply clipping filter from Rdtools.
+    clip_mask = rdtools.logic_clip_filter(time_series)
+    time_series = time_series[clip_mask]
+    # Reindex the time series after all of the filtering so it
+    # has the same index as PSM3
+    time_series = time_series.reindex(is_clear.index)
+    # Trim based on clearsky values
+    time_series_clearsky = time_series[(is_clear) & (is_daytime)]
+    # Remove any incomplete days (less than 25% data)
+    time_series_clearsky = time_series_clearsky.asfreq("15T")
+    completeness = gaps.complete(
+        time_series_clearsky, minimum_completeness=0.25
+    )
+    time_series_clearsky = time_series_clearsky[completeness]
+    # Get the peak value for each day, and if it's less than 25% of the
+    # monthly average peak value, then throw it out
+    daily_max = time_series_clearsky.groupby(
+        time_series_clearsky.index.date
+    ).transform("max")
+    average_max_day_month = daily_max.groupby(
+        time_series_clearsky.index.month
+    ).transform("median")
+    time_series_clearsky = time_series_clearsky[
+        daily_max > 0.25 * average_max_day_month
+    ]
+    time_series_clearsky = time_series_clearsky.dropna()
+    # Get the clearsky data
+    psm3_clearsky = psm3.loc[time_series_clearsky.index]
+    solpos_clearsky = pvlib.solarposition.get_solarposition(
+        time_series_clearsky.index, latitude, longitude
+    )
+    # Run PVWatts estimator
+    best_tilt, best_azimuth, r2 = system.infer_orientation_fit_pvwatts(
+        time_series_clearsky,
+        psm3_clearsky.ghi_clear,
+        psm3_clearsky.dhi_clear,
+        psm3_clearsky.dni_clear,
+        solpos_clearsky.zenith,
+        solpos_clearsky.azimuth,
+        temperature=psm3_clearsky.temp_air,
+        azimuth_min=85,
+        azimuth_max=275,
+    )
+    return best_azimuth, best_tilt
diff --git a/ec2/analysis_taks/az-tilt-estimation/submissions/requirements.txt b/ec2/analysis_taks/az-tilt-estimation/submissions/requirements.txt
new file mode 100644
index 00000000..33cdfb92
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/submissions/requirements.txt
@@ -0,0 +1,6 @@
+numpy==1.18.5
+pandas==1.0.5
+pvanalytics==0.2.0
+pvlib==0.10.3
+rdtools==2.1.8
+ruptures==1.1.9
\ No newline at end of file
diff --git a/ec2/analysis_taks/az-tilt-estimation/submissions/submission.zip b/ec2/analysis_taks/az-tilt-estimation/submissions/submission.zip
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diff --git a/ec2/analysis_taks/az-tilt-estimation/submissions/tester.py b/ec2/analysis_taks/az-tilt-estimation/submissions/tester.py
new file mode 100644
index 00000000..a710c876
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/submissions/tester.py
@@ -0,0 +1,163 @@
+"""
+PVWatts-based model submission.
+"""
+
+import pvlib
+import pandas as pd
+from pvanalytics import system
+from pvanalytics.quality import gaps
+from pvanalytics.quality import data_shifts as ds
+from pvanalytics.quality.outliers import hampel, zscore
+import numpy as np
+import rdtools
+
+
+def estimate_az_tilt(time_series, latitude, longitude):
+    """
+    Estimate the azimuth and tilt of a system based on its time series data,
+    using the PVAnalytics PVWatts-based method.
+
+    Parameters
+    ----------
+    time_series : Pandas series
+        Time series of an AC power stream associated with the system.
+    latitude: Float.
+        Latitude coordinate of the site.
+
+    longitude: Float.
+        Longitude coordinate of the site.
+
+    Returns
+    -------
+    azimuth: Float.
+        Predicted azimuth value.
+    tilt: Float.
+        Predicted tilt value.
+
+    """
+    psm3s = []
+    years = list(time_series.index.year.drop_duplicates())
+    years = [x for x in years if x < 2021]
+    for year in years:
+        psm3 = None
+        try:
+            # connect
+            psm3, _ = pvlib.iotools.get_psm3(
+                latitude,
+                longitude,
+                "4z5fRAXbGB3qldVVd3c6WH5CuhtY5mhgC2DyD952",
+                "kirsten.perry@nrel.gov",
+                year,
+                attributes=[
+                    "air_temperature",
+                    "ghi",
+                    "clearsky_ghi",
+                    "clearsky_dni",
+                    "clearsky_dhi",
+                ],
+                map_variables=True,
+                interval=30,
+                leap_day=True,
+            )
+        except Exception as e:
+            print(e)
+        psm3s.append(psm3)
+    psm3 = pd.concat(psm3s)
+    psm3 = psm3.reindex(
+        pd.date_range(psm3.index[0], psm3.index[-1], freq="15T")
+    ).interpolate()
+    psm3 = psm3.reindex(time_series.index)
+    # psm3[(psm3.index.year == year) | (psm3.index.year == year+1)]
+    psm3_sub = psm3
+    is_clear = psm3_sub.ghi_clear == psm3_sub.ghi
+    is_daytime = psm3_sub.ghi > 0
+    # Run the associated data-cleaning routines on the time series:
+    # 1) Data shift detection + removal
+    # 2) Removal of frozen/stuck data
+    # 3) Removal of data periods with low data 'completeness'
+    # 4) Removal of negative data
+    # 5) Removal of outliers via Hampel + outlier filter
+    # 6) Removal of clipped values via clipping filter
+    # 7) Filter to clearsky data only, as determined by PSM3
+    # try:
+    # Detect any data shifts and remove them
+    time_series_daily = time_series.resample("D").sum()
+    start_date, end_date = ds.get_longest_shift_segment_dates(
+        time_series_daily
+    )
+    time_series = time_series[start_date:end_date]
+    # Trim based on frozen data values
+    stale_data_mask = gaps.stale_values_diff(time_series)
+    time_series = time_series.asfreq("15T")
+    time_series = time_series[~stale_data_mask]
+    time_series = time_series.asfreq("15T")
+    # Remove negative data
+    time_series = time_series[(time_series >= 0) | (time_series.isna())]
+    time_series = time_series.asfreq("15T")
+    # Remove any outliers via Hampel and z-score filters
+    hampel_outlier_mask = hampel(time_series, window=5)
+    zscore_outlier_mask = zscore(time_series, zmax=2, nan_policy="omit")
+    time_series = time_series[(~hampel_outlier_mask) & (~zscore_outlier_mask)]
+    time_series = time_series.asfreq("15T")
+    # Apply clipping filter from Rdtools.
+    clip_mask = rdtools.logic_clip_filter(time_series)
+    time_series = time_series[clip_mask]
+    # Reindex the time series after all of the filtering so it
+    # has the same index as PSM3
+    time_series = time_series.reindex(is_clear.index)
+    # Trim based on clearsky values
+    time_series_clearsky = time_series[(is_clear) & (is_daytime)]
+    # Remove any incomplete days (less than 25% data)
+    time_series_clearsky = time_series_clearsky.asfreq("15T")
+    completeness = gaps.complete(
+        time_series_clearsky, minimum_completeness=0.25
+    )
+    time_series_clearsky = time_series_clearsky[completeness]
+    # Get the peak value for each day, and if it's less than 25% of the
+    # monthly average peak value, then throw it out
+    daily_max = time_series_clearsky.groupby(
+        time_series_clearsky.index.date
+    ).transform("max")
+    average_max_day_month = daily_max.groupby(
+        time_series_clearsky.index.month
+    ).transform("median")
+    time_series_clearsky = time_series_clearsky[
+        daily_max > 0.25 * average_max_day_month
+    ]
+    time_series_clearsky = time_series_clearsky.dropna()
+    # Get the clearsky data
+    psm3_clearsky = psm3.loc[time_series_clearsky.index]
+    solpos_clearsky = pvlib.solarposition.get_solarposition(
+        time_series_clearsky.index, latitude, longitude
+    )
+    # Run PVWatts estimator
+    best_tilt, best_azimuth, r2 = system.infer_orientation_fit_pvwatts(
+        time_series_clearsky,
+        psm3_clearsky.ghi_clear,
+        psm3_clearsky.dhi_clear,
+        psm3_clearsky.dni_clear,
+        solpos_clearsky.zenith,
+        solpos_clearsky.azimuth,
+        temperature=psm3_clearsky.temp_air,
+        azimuth_min=85,
+        azimuth_max=275,
+    )
+    return best_azimuth, best_tilt
+
+
+if __name__ == "__main__":
+    # Test the function
+    print("Reading data")
+    file_name = "1332_inv_total_ac_power__2654.csv"
+
+    time_series = pd.read_csv(file_name, index_col=0, parse_dates=True)
+    print(time_series.head())
+
+    time_series = time_series.asfreq("15min").squeeze()
+
+    print(time_series.head())
+    print("Estimating azimuth and tilt...")
+    results = estimate_az_tilt(
+        time_series, latitude=40.0150, longitude=-105.2705
+    )
+    print(results)
diff --git a/ec2/analysis_taks/az-tilt-estimation/template.py b/ec2/analysis_taks/az-tilt-estimation/template.py
new file mode 100644
index 00000000..3199610f
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/template.py
@@ -0,0 +1,264 @@
+"""
+Az-tilt Marimo prototype.
+"""
+
+import marimo
+
+__generated_with = "0.1.18"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md("# Private Report: Azimuth-Tilt Results")
+    return
+
+
+@app.cell
+def __(create_df_from_cli_args, generatePlots):
+    results_df = create_df_from_cli_args()
+    plotting = generatePlots(results_df)
+    return plotting, results_df
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        median_run_time = round(plotting.results_df.run_time.median(), 2)
+        mean_run_time = round(plotting.results_df.run_time.mean(), 2)
+        max_run_time = round(plotting.results_df.run_time.max(), 2)
+        min_run_time = round(plotting.results_df.run_time.min(), 2)
+        _fig = mo.md(
+            f"""
+                     First, we visualize the distribution of run times. 
+                     
+                     Median run time: """
+            + str(median_run_time)
+            + """ seconds
+                     
+                     Mean run time: """
+            + str(mean_run_time)
+            + """ seconds
+                     
+                     Max run time: """
+            + str(max_run_time)
+            + """ seconds
+                     
+                     Min run time: """
+            + str(min_run_time)
+            + """ seconds
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting):
+    if plotting is not None:
+        _fig = plotting.plot_run_times()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        az_mae = round(plotting.results_df["absolute_error_azimuth"].mean(), 2)
+        tilt_mae = round(plotting.results_df["absolute_error_tilt"].mean(), 2)
+        _fig = mo.md(
+            f"""
+                     Next, we visualize the mean absolute error distribution.
+                     
+                     Azimuth MAE: """
+            + str(az_mae)
+            + """ degrees
+                     
+                     Tilt MAE: """
+            + str(tilt_mae)
+            + """ degrees
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        _fig = mo.md(
+            f"""
+                     We visualize the absolute error distribution for azimuth.
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        _fig = plotting.plot_azimuth_error()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        _fig = mo.md(
+            f"""
+                     We then visualize the absolute error distribution for tilt.
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting):
+    if plotting is not None:
+        _fig = plotting.plot_tilt_error()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        _fig = mo.md(
+            f"""
+                     We then visualize algorithm run time (in seconds) vs. the number of days in the associated AC power data set.
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting):
+    if plotting is not None:
+        _fig = plotting.plot_number_days_runtime()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(
+    mo,
+    np,
+    sns,
+    pd,
+    plt,
+):
+
+    class generatePlots:
+
+        def __init__(self, results_df):
+            """Create plotting class."""
+            self.results_df = results_df
+            self.results_df = self.results_df.rename(
+                columns={
+                    "issue": "Data Issue Type",
+                    "absolute_error_azimuth": "Azimuth Absolute Error",
+                    "absolute_error_tilt": "Tilt Absolute Error",
+                    "run_time": "Run Time",
+                    "number_days": "Number Days",
+                }
+            )
+
+        def plot_run_times(self):
+            fig = sns.histplot(self.results_df, x="run_time", bins=40)
+            fig.set(xlabel="Run Time (seconds)", ylabel="Number Instances")
+            return fig
+
+        def plot_azimuth_error(self):
+            fig = sns.histplot(
+                self.results_df,
+                x="Azimuth Absolute Error",
+                hue="Data Issue Type",
+                bins=30,
+            )
+            fig.set(
+                xlabel="Azimuth Absolute Error (deg)",
+                ylabel="Number Instances",
+            )
+            return fig
+
+        def plot_tilt_error(self):
+            fig = sns.histplot(
+                self.results_df,
+                x="Tilt Absolute Error",
+                hue="Data Issue Type",
+                bins=30,
+            )
+            fig.set(
+                xlabel="Tilt Absolute Error (deg)", ylabel="Number Instances"
+            )
+            return fig
+
+        def plot_number_days_runtime(self):
+            fig = sns.scatterplot(
+                data=self.results_df, x="Run Time", y="Number Days"
+            )
+            fig.set(xlabel="Run Time (s)", ylabel="Number Days in Dataset")
+            return fig
+
+    return (generatePlots,)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    import numpy as np
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+    import json
+
+    return (
+        json,
+        mo,
+        np,
+        sns,
+        pd,
+        plt,
+    )
+
+
+@app.cell
+def __(json, mo, pd):
+
+    def create_df_from_cli_args():
+        args = mo.cli_args().to_dict()
+        data = args.get("results_df")
+        rows = []
+        for row in data:
+            rows.append(json.loads(row))
+
+        df = pd.DataFrame.from_records(rows)
+        return df
+
+    return (create_df_from_cli_args,)
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/ec2/analysis_taks/az-tilt-estimation/utilities.py b/ec2/analysis_taks/az-tilt-estimation/utilities.py
new file mode 100644
index 00000000..1a012ba7
--- /dev/null
+++ b/ec2/analysis_taks/az-tilt-estimation/utilities.py
@@ -0,0 +1,20 @@
+import sys
+
+
+def progress(count, total, status="", bar_length=60):
+    """
+    Python command line progress bar in less than 10 lines of code. · GitHub
+    https://gist.github.com/vladignatyev/06860ec2040cb497f0f3
+    :param count: the current count, int
+    :param total: to total count, int
+    :param status: a message to display
+    :return:
+    """
+    bar_len = bar_length
+    filled_len = int(round(bar_len * count / float(total)))
+
+    percents = round(100.0 * count / float(total), 1)
+    bar = "=" * filled_len + "-" * (bar_len - filled_len)
+
+    sys.stdout.write("[%s] %s%s ...%s\r" % (bar, percents, "%", status))
+    sys.stdout.flush()
diff --git a/ec2/analysis_taks/example-task/README.md b/ec2/analysis_taks/example-task/README.md
new file mode 100644
index 00000000..e69de29b
diff --git a/ec2/analysis_taks/example-task/config.json b/ec2/analysis_taks/example-task/config.json
new file mode 100644
index 00000000..e69de29b
diff --git a/ec2/analysis_taks/example-task/data/file_metadata.csv b/ec2/analysis_taks/example-task/data/file_metadata.csv
new file mode 100644
index 00000000..e69de29b
diff --git a/ec2/analysis_taks/example-task/data/system_metadata.csv b/ec2/analysis_taks/example-task/data/system_metadata.csv
new file mode 100644
index 00000000..e69de29b
diff --git a/ec2/analysis_taks/example-task/template.py b/ec2/analysis_taks/example-task/template.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ec2/analysis_taks/time-shift-detection/.gitignore b/ec2/analysis_taks/time-shift-detection/.gitignore
new file mode 100644
index 00000000..14ba7857
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/.gitignore
@@ -0,0 +1,230 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Created by https://www.toptal.com/developers/gitignore/api/macos,windows,linux,microsoftoffice
+# Edit at https://www.toptal.com/developers/gitignore?templates=macos,windows,linux,microsoftoffice
+
+### Linux ###
+*~
+
+# temporary files which can be created if a process still has a handle open of a deleted file
+.fuse_hidden*
+
+# KDE directory preferences
+.directory
+
+# Linux trash folder which might appear on any partition or disk
+.Trash-*
+
+# .nfs files are created when an open file is removed but is still being accessed
+.nfs*
+
+### macOS ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### macOS Patch ###
+# iCloud generated files
+*.icloud
+
+### MicrosoftOffice ###
+*.tmp
+
+# Word temporary
+~$*.doc*
+
+# Word Auto Backup File
+Backup of *.doc*
+
+# Excel temporary
+~$*.xls*
+
+# Excel Backup File
+*.xlk
+
+# PowerPoint temporary
+~$*.ppt*
+
+# Visio autosave temporary files
+*.~vsd*
+
+### Windows ###
+# Windows thumbnail cache files
+Thumbs.db
+Thumbs.db:encryptable
+ehthumbs.db
+ehthumbs_vista.db
+
+# Dump file
+*.stackdump
+
+# Folder config file
+[Dd]esktop.ini
+
+# Recycle Bin used on file shares
+$RECYCLE.BIN/
+
+# Windows Installer files
+*.cab
+*.msi
+*.msix
+*.msm
+*.msp
+
+# Windows shortcuts
+*.lnk
+
+# End of https://www.toptal.com/developers/gitignore/api/macos,windows,linux,microsoftoffice
+
diff --git a/ec2/analysis_taks/time-shift-detection/LICENSE b/ec2/analysis_taks/time-shift-detection/LICENSE
new file mode 100644
index 00000000..7aa8e5d5
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Kirsten Perry
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/ec2/analysis_taks/time-shift-detection/README.md b/ec2/analysis_taks/time-shift-detection/README.md
new file mode 100644
index 00000000..a9428086
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/README.md
@@ -0,0 +1,46 @@
+# time-shift-validation-hub
+
+This repo is an example pipeline for running a validation test in the PV Validation Hub. Specifically,
+this test covers time shift estimation & validation. The explanation for the scripts are as follows:
+
+pvinsight-time-shift-runner.py: This the main runner script for executing a particular validation test.
+It aggregates all of the information from the tables, loads in the developer-submitted module to test against,
+and runs the module against the validation data. Finally, it takes the results of the validation run,
+aggregates them, and creates public and private reports for feeding back into the database and
+disseminating to the developer, respectively.
+
+pvanalytics_cpd_module.py: This is an example of a user-submitted module. This module would be loaded into
+the pvinsight-time-shift-runner and tested against with the validation data.
+
+pvanalytics_osd_module.py: This is an example of a user-submitted module. This module would be loaded into
+the pvinsight-time-shift-runner and tested against with the validation data.
+
+sdt_module.py: This is an example of a user-submitted module. This module would be loaded into
+the pvinsight-time-shift-runner and tested against with the validation data.
+
+proposed-test-infrastructure-erd.PNG: This file shows the relationships between the proposed tables in
+the test structure. Examples of these tables are shown in the /data/ folder.
+
+/data/ folder: This folder contains all of the information for running the validation test. In particular,
+the folder contains the following:
+	- /file_data/ csv's: These are the raw time series input files that are passed to the module for
+		each individual test. 
+	- /validation_data/ csv's: These are the output files that we compare to (ground-truth files) when
+		we benchmark each module output (this allows us to calculate error metrics for each example)
+	- system_metadata.csv: This is a representation of what could site in the system_metadata table. This
+		file contains information of system metadata (latitude, longitude, elevation, etc). A system
+		represents a physical solar site. Multiple files can be associated with a single system.
+	- file_metadata.csv: This is a representation of what could sit in the file_metadata table. This
+		contains metadata information associated with each file. It also links to the system_metadata
+		table via the system_id
+	- validation_tests.csv: This file represents the validation_tests table. It includes information
+		on specific validation tests run the Valdiation Hub, such as time shift detection or
+		azimuth-tilt estimation. Each validation tests has its own unique row.
+	- file_test_link.csv: This file links unique file IDs to a particular validation_test ID (links files
+		to the validation test that we want to run)
+
+/results/ folder: This folder contains files related to public and private reporting. Specifically, the data
+in time-shift-public-metrics.json would feed into a final 'public results' table with general information
+on the module run (average run speed, average MAE, requirements for running function, etc). The associated .png
+files in this folder would be used in private reporting and sent directly to the developer who submitted the
+module.
diff --git a/ec2/analysis_taks/time-shift-detection/Time_shift_algorithm_study_PVRW.pptx b/ec2/analysis_taks/time-shift-detection/Time_shift_algorithm_study_PVRW.pptx
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+<h1 class="code-line" data-line-start="0" data-line-end="1"><a id="Submission_Instructions_0"></a>Submission Instructions</h1>
+<h3 class="code-line" data-line-start="2" data-line-end="3"><a id="Validation_Data_Structure_2"></a>Validation Data Structure</h3>
+<p class="has-line-data" data-line-start="4" data-line-end="5">For each AC power stream analyzed, there are two sets of data:</p>
+<ol>
+<li class="has-line-data" data-line-start="5" data-line-end="6">The associated time series data for the AC power, in kW, and an index for timezone-aware datetime.</li>
+<li class="has-line-data" data-line-start="6" data-line-end="8">The time shift validation file, which gives the associated time shift amount on a daily basis. A daily timezone-unaware timestamp is used as the associated index.</li>
+</ol>
+<p class="has-line-data" data-line-start="8" data-line-end="9">An example time series and its associated time shift data are shown in Figures XX and XX, respectively. An example dataset is provided for reference here.</p>
+<h3 class="code-line" data-line-start="10" data-line-end="11"><a id="File_Submission_Requirements_10"></a>File Submission Requirements</h3>
+<p class="has-line-data" data-line-start="12" data-line-end="13">The PV Validation Hub only accepts .zip file submissions. Each zip file must containing the following files:</p>
+<ol>
+<li class="has-line-data" data-line-start="13" data-line-end="14"><strong>submission_wrapper.py</strong>: This Python file contains the master function for running the routines. Please refer to <strong>Submitted Function Structure</strong> below for more information on the associated function structure.</li>
+<li class="has-line-data" data-line-start="14" data-line-end="15"><strong>requirements.txt</strong>: This text file contains the associated python packages (and versions, if applicable) to pip-install in order to run the submission_wrapper.py file.</li>
+<li class="has-line-data" data-line-start="15" data-line-end="17">If applicable, any additional .py files containing support functions for <strong>submission_wrapper.py</strong></li>
+</ol>
+<h3 class="code-line" data-line-start="17" data-line-end="18"><a id="Submitted_Function_Structure_17"></a>Submitted Function Structure</h3>
+<p class="has-line-data" data-line-start="19" data-line-end="20">In the <strong>submission_wrapper.py</strong> file, a standard function name must be used when validating a submitted algorithm. The standard function definition, which is required to run the validation, is as follows:</p>
+<pre><code class="has-line-data" data-line-start="22" data-line-end="47">def detect_time_shifts(time_series,
+                       latitude=None, longitude=None,
+                       data_sampling_frequency=None):
+    &quot;&quot;&quot;
+    This is the required function definition for running time shift validation.
+    
+    Parameters 
+    ----------
+    time_series: Required argument. Pandas series with a tz-aware datetime index.
+        This is the AC power time series that is being tested for time shifts.
+    latitude: Optional argument. Float.
+        The associated latitude coordinate of the PV site that the AC power stream is associated with.
+    longitude: Optional argument. Float.
+        The associated longitude coordinate of the PV site that the AC power stream is associated with.
+    data_sampling_frequency: Optional argument. Float.
+        The data frequency of the time series being evaluated. 
+    
+    Returns
+    -------
+    time_shift_series: A pandas series with a tz-unaware date index of daily time shift amounts in minutes.
+    See validation data above as reference.
+    &quot;&quot;&quot;
+    [YOUR ALGORITHM HERE]
+    return time_shift_series
+</code></pre>
+<h3 class="code-line" data-line-start="48" data-line-end="49"><a id="Analysis_Results_48"></a>Analysis Results</h3>
+</body></html>
\ No newline at end of file
diff --git a/ec2/analysis_taks/time-shift-detection/assets/SubmissionInstructions.md b/ec2/analysis_taks/time-shift-detection/assets/SubmissionInstructions.md
new file mode 100644
index 00000000..d6c344d9
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/assets/SubmissionInstructions.md
@@ -0,0 +1,77 @@
+# Submission Instructions
+
+### Validation Data Structure
+
+For each AC power stream analyzed, there are two sets of data:
+1) The associated time series data for the AC power, in kW, and an index for timezone-aware datetime.
+2) The time shift validation file, which gives the associated time shift amount on a daily basis. A daily timezone-unaware timestamp is used as the associated index. 
+
+An example time series and its associated time shift data are shown in Figures 1 and 2, respectively. An example dataset is provided for reference here.
+
+![image info](./time_series_data.PNG)
+
+*Figure 1: Snapshot of the structure of the time series data analyzed for time shifts.*
+
+![image info](./time_series_validation_data.PNG)
+
+*Figure 2: Snapshot of the structure of the ground truth/validation data that time shift results are compared against.*
+
+Download an example data set here.
+
+### File Submission Requirements
+
+The PV Validation Hub only accepts .zip file submissions. Each zip file must containing the following files:
+1) **submission_wrapper.py**: This Python file contains the master function for running the routines. Please refer to **Submitted Function Structure** below for more information on the associated function structure.
+2) **requirements.txt**: This text file contains the associated python packages (and versions, if applicable) to pip-install in order to run the submission_wrapper.py file.
+3) If applicable, any additional .py files containing support functions for **submission_wrapper.py**
+
+### Submitted Function Structure
+
+In the **submission_wrapper.py** file, a standard function name must be used when validating a submitted algorithm. The standard function definition, which is required to run the validation, is as follows:
+
+```
+def detect_time_shifts(time_series,
+                       latitude=None, longitude=None,
+                       data_sampling_frequency=None):
+    """
+    This is the required function definition for running time shift validation.
+    
+    Parameters 
+    ----------
+    time_series: Required argument. Pandas series with a tz-aware datetime index.
+        This is the AC power time series that is being tested for time shifts.
+    latitude: Optional argument. Float.
+        The associated latitude coordinate of the PV site that the AC power stream is associated with.
+    longitude: Optional argument. Float.
+        The associated longitude coordinate of the PV site that the AC power stream is associated with.
+    data_sampling_frequency: Optional argument. Float.
+        The data frequency of the time series being evaluated. 
+    
+    Returns
+    -------
+    time_shift_series: A pandas series with a tz-unaware date index of daily time shift amounts in minutes.
+    See validation data above as reference.
+    """
+    [YOUR ALGORITHM HERE]
+    return time_shift_series
+```
+
+### Analysis Results
+
+An automated results report containing the following metrics is returned following successful analysis:
+
+1) Mean average error (MAE) across all of the test cases. MAE is calculated at the time stamp level. The predicted time shift at each time stamp is compared to the ground-truth time shift, and error across all time shifts is calculated.
+2) Average run time per instance (in this case, by data stream), calculated in seconds
+3) Associated graphics, including error distributions color-coded by issue type (DST, partial DST, random shifts) and frequency of the time series evaluated (1 minute, 5 minute, 15 minute, etc.), and a distribution of instance run times. Some example graphic outputs are shown in Figures 3, 4, and 5.
+
+![image info](./histogram-mae-issue.png)
+
+*Figure 3: Error distribution of algorithm results, color-coded by issue type.*
+
+![image info](./histogram-mae-data-freq.png)
+
+*Figure 4: Error distribution of algorithm results, color-coded by time frequency of the input time series.*
+
+![image info](./run_time_dist.png)
+
+*Figure 5: Distribution of algorithm run times on individual time series data streams, in seconds.*
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+<h1 class="code-line" data-line-start="1" data-line-end="2"><a id="Description_1"></a>Description</h1>
+<p class="has-line-data" data-line-start="2" data-line-end="4">Measured photovolataic (PV) data from fielded systems may be incorrectly labeled by time zone, erroneously<br>
+contain daylight savings time (DST), or have time shift issues due to operator error. Correcting time shifts in PV data is essential for accurate solar performance analysis, including degradation analysis. To illustrate this associated issue, Figures 1, 2, and 3, respectively, show AC power data streams heatmapped by hour of the day for the following scenarios:</p>
+<ol>
+<li class="has-line-data" data-line-start="4" data-line-end="5">No issues</li>
+<li class="has-line-data" data-line-start="5" data-line-end="6">DST is present</li>
+<li class="has-line-data" data-line-start="6" data-line-end="8">A random time shift is present</li>
+</ol>
+<p class="has-line-data" data-line-start="8" data-line-end="10"><img src="time_shift_no_issues.png" alt="image info"><br>
+<em>Heatmapped AC power stream with no time shift issues.</em></p>
+<p class="has-line-data" data-line-start="11" data-line-end="13"><img src="daylight_savings_time.png" alt="image info"><br>
+<em>Heatmapped AC power stream with daylight savings time.</em></p>
+<p class="has-line-data" data-line-start="14" data-line-end="16"><img src="random_time_shift.png" alt="image info"><br>
+<em>Heatmapped AC power stream with a random time shift.</em></p>
+<p class="has-line-data" data-line-start="17" data-line-end="18">Ideally, we want to run analysis on an AC power data stream like Figure 1, where no random time shifts or DST is present.</p>
+<p class="has-line-data" data-line-start="19" data-line-end="20">This analysis benchmarks algorithm performance for finding time shifts in PV data and correcting them accordingly. To do this, real-world PV data sets are manipulated to create common time shift issues, such as incorrect time zone, partial DST, full DST, and random time shifts. All ground truth time shift issues for each data set are recorded, and used here to validate submitted time shift detection algorithms. In particular, the associated time shift amount at each value in the predicted time series will be compared to the associated ground-truth time shift value.</p>
+</body></html>
\ No newline at end of file
diff --git a/ec2/analysis_taks/time-shift-detection/assets/description.md b/ec2/analysis_taks/time-shift-detection/assets/description.md
new file mode 100644
index 00000000..aabca293
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/assets/description.md
@@ -0,0 +1,21 @@
+
+# Description
+Measured photovolataic (PV) data from fielded systems may be incorrectly labeled by time zone, erroneously
+contain daylight savings time (DST), or have time shift issues due to operator error. Correcting time shifts in PV data is essential for accurate solar performance analysis, including degradation analysis. To illustrate this associated issue, Figures 1, 2, and 3, respectively, show AC power data streams heatmapped by hour of the day for the following scenarios: 
+1) No issues
+2) DST is present
+3) A random time shift is present
+
+![image info](./time_shift_no_issues.png)
+*Heatmapped AC power stream with no time shift issues.*
+
+![image info](./daylight_savings_time.png)
+*Heatmapped AC power stream with daylight savings time.*
+
+![image info](./random_time_shift.png)
+*Heatmapped AC power stream with a random time shift.*
+
+Ideally, we want to run analysis on an AC power data stream like Figure 1, where no random time shifts or DST is present.
+
+This analysis benchmarks algorithm performance for finding time shifts in PV data and correcting them accordingly. To do this, real-world PV data sets are manipulated to create common time shift issues, such as incorrect time zone, partial DST, full DST, and random time shifts. All ground truth time shift issues for each data set are recorded, and used here to validate submitted time shift detection algorithms. In particular, the associated time shift amount at each value in the predicted time series will be compared to the associated ground-truth time shift value. 
+
diff --git a/ec2/analysis_taks/time-shift-detection/assets/histogram-mae-data-freq.png b/ec2/analysis_taks/time-shift-detection/assets/histogram-mae-data-freq.png
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diff --git a/ec2/analysis_taks/time-shift-detection/assets/shortdesc.md b/ec2/analysis_taks/time-shift-detection/assets/shortdesc.md
new file mode 100644
index 00000000..7140b50b
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/assets/shortdesc.md
@@ -0,0 +1 @@
+# To edit the contents of this element you will need to modify the shortdesc.md asset file
diff --git a/ec2/analysis_taks/time-shift-detection/assets/time-shift-example.zip b/ec2/analysis_taks/time-shift-detection/assets/time-shift-example.zip
new file mode 100644
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diff --git a/ec2/analysis_taks/time-shift-detection/bennet-error-ts.csv b/ec2/analysis_taks/time-shift-detection/bennet-error-ts.csv
new file mode 100644
index 00000000..c7dedd5c
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/bennet-error-ts.csv
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+2017-01-15 09:00:00-07:00,126068.71384615384
diff --git a/ec2/analysis_taks/time-shift-detection/config.json b/ec2/analysis_taks/time-shift-detection/config.json
new file mode 100644
index 00000000..b0bfff66
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/config.json
@@ -0,0 +1,62 @@
+{
+  "category_name": "Time Shift Detection",
+  "function_name": "detect_time_shifts",
+  "comparison_type": "time_series",
+  "display_metrics": {
+    "mean_mean_absolute_error_time_series": "Mean Mean Absolute Error",
+    "median_mean_absolute_error_time_series": "Median Mean Absolute Error"
+  },
+  "performance_metrics": [
+    "runtime",
+    "mean_absolute_error"
+  ],
+  "metrics_operations": {
+    "mean_absolute_error_time_series": [
+      "mean",
+      "median"
+    ],
+    "runtime": [
+      "mean"
+    ]
+  },
+  "allowable_kwargs": [
+    "latitude",
+    "longitude",
+    "data_sampling_frequency"
+  ],
+  "ground_truth_compare": [
+    "time_series"
+  ],
+  "public_results_table": "time-shift-public-metrics.json",
+  "private_results_columns": [
+    "system_id",
+    "file_name",
+    "runtime",
+    "data_requirements",
+    "mean_absolute_error_time_series",
+    "data_sampling_frequency",
+    "issue"
+  ],
+  "plots": [
+    {
+      "type": "histogram",
+      "x_val": "mean_absolute_error_time_series",
+      "color_code": "issue",
+      "title": "Time Series MAE Distribution by Issue",
+      "save_file_path": "mean_absolute_error_time_series_dist.png"
+    },
+    {
+      "type": "histogram",
+      "x_val": "mean_absolute_error_time_series",
+      "color_code": "data_sampling_frequency",
+      "title": "Time Series MAE Distribution by Sampling Frequency",
+      "save_file_path": "mean_absolute_error_time_series_dist.png"
+    },
+    {
+      "type": "histogram",
+      "x_val": "run_time",
+      "title": "Run Time Distribution",
+      "save_file_path": "run_time_dist.png"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/ec2/analysis_taks/time-shift-detection/data/file_metadata.csv b/ec2/analysis_taks/time-shift-detection/data/file_metadata.csv
new file mode 100644
index 00000000..cea9202d
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/data/file_metadata.csv
@@ -0,0 +1,144 @@
+file_id,system_id,file_name,timezone,data_sampling_frequency,issue
+0,4,4_ac_power__315_full_DST.csv,Etc/GMT+7,15,Full DST
+1,4,4_ac_power__315_30min_wrong_timezone.csv,Etc/GMT+7,30,Wrong Timezone
+2,4,4_ac_power__315_60min_wrong_timezone.csv,Etc/GMT+7,60,Wrong Timezone
+3,4,4_ac_power__315_60min.csv,Etc/GMT+7,60,No Issue
+4,4,4_ac_power__315_30min.csv,Etc/GMT+7,30,No Issue
+5,4,4_ac_power__315_15min.csv,Etc/GMT+7,15,No Issue
+6,4,4_ac_power__315_15min_full_DST.csv,Etc/GMT+7,15,Full DST
+7,50,50_ac_power_wrong_timezone.csv,Etc/GMT+7,1,Wrong Timezone
+8,50,50_ac_power_1_random_time_shifts.csv,Etc/GMT+7,1,Random Time Shifts
+9,50,50_ac_power_15min_wrong_timezone.csv,Etc/GMT+7,15,Wrong Timezone
+10,50,50_ac_power_1_15min_random_time_shifts.csv,Etc/GMT+7,15,Random Time Shifts
+11,50,50_ac_power_1_30min_wrong_timezone.csv,Etc/GMT+7,30,Wrong Timezone
+12,50,50_ac_power_1_60min_wrong_timezone.csv,Etc/GMT+7,60,Wrong Timezone
+13,50,50_ac_power_2_full_DST.csv,Etc/GMT+7,1,Full DST
+14,50,50_ac_power_30min.csv,Etc/GMT+7,30,No Issue
+15,50,50_ac_power_2_30min_random_time_shifts.csv,Etc/GMT+7,30,Random Time Shifts
+16,50,50_ac_power_2_15min_wrong_timezone.csv,Etc/GMT+7,15,Wrong Timezone
+17,50,50_ac_power_30min_wrong_timezone.csv,Etc/GMT+7,30,Wrong Timezone
+18,50,50_ac_power.csv,Etc/GMT+7,1,No Issue
+19,50,50_ac_power_1.csv,Etc/GMT+7,1,No Issue
+20,50,50_ac_power_15min.csv,Etc/GMT+7,15,No Issue
+21,50,50_ac_power_1_15min.csv,Etc/GMT+7,15,No Issue
+22,50,50_ac_power_1_30min.csv,Etc/GMT+7,30,No Issue
+23,50,50_ac_power_1_60min.csv,Etc/GMT+7,60,No Issue
+24,50,50_ac_power_2.csv,Etc/GMT+7,1,No Issue
+25,50,50_ac_power_2_15min.csv,Etc/GMT+7,15,No Issue
+26,50,50_ac_power_2_30min.csv,Etc/GMT+7,30,No Issue
+27,50,50_ac_power_2_60min.csv,Etc/GMT+7,60,No Issue
+28,50,50_ac_power_2_60min_random_time_shifts.csv,Etc/GMT+7,60,Random Time Shifts
+29,50,50_ac_power_60min_random_time_shifts.csv,Etc/GMT+7,60,Random Time Shifts
+30,50,50_ac_power_60min.csv,Etc/GMT+7,60,No Issue
+31,1229,1229_poa_irradiance_west_array_60min.csv,Etc/GMT+5,60,No Issue
+32,1229,1229_gh_irradiance_15min.csv,Etc/GMT+5,15,No Issue
+33,1229,1229_gh_irradiance_30min.csv,Etc/GMT+5,30,No Issue
+34,1229,1229_gh_irradiance_60min.csv,Etc/GMT+5,60,No Issue
+35,1229,1229_poa_irradiance_15min.csv,Etc/GMT+5,15,No Issue
+36,1229,1229_poa_irradiance_30min.csv,Etc/GMT+5,30,No Issue
+37,1229,1229_poa_irradiance_60min.csv,Etc/GMT+5,60,No Issue
+38,1229,1229_poa_irradiance_south_array_15min.csv,Etc/GMT+5,15,No Issue
+39,1229,1229_poa_irradiance_south_array_30min.csv,Etc/GMT+5,30,No Issue
+40,1229,1229_poa_irradiance_south_array_60min.csv,Etc/GMT+5,60,No Issue
+41,1229,1229_poa_irradiance_west_array_15min.csv,Etc/GMT+5,15,No Issue
+42,1229,1229_poa_irradiance_west_array_30min.csv,Etc/GMT+5,30,No Issue
+43,1229,1229_poa_irradiance_west_array_30min_partial_DST.csv,Etc/GMT+5,30,Partial DST
+44,1229,1229_poa_irradiance_west_array_15min_wrong_timezone.csv,Etc/GMT+5,15,Wrong Timezone
+45,1229,1229_poa_irradiance_south_array_60min_wrong_timezone.csv,Etc/GMT+5,60,Wrong Timezone
+46,1229,1229_poa_irradiance_south_array_30min_random_time_shifts.csv,Etc/GMT+5,30,Random Time Shifts
+47,1229,1229_poa_irradiance_south_array_15min_random_time_shifts.csv,Etc/GMT+5,15,Random Time Shifts
+48,1229,1229_poa_irradiance_south_array_full_DST.csv,Etc/GMT+5,15,Full DST
+49,1229,1229_poa_irradiance_60min_wrong_timezone.csv,Etc/GMT+5,60,Wrong Timezone
+50,1229,1229_poa_irradiance_30min_wrong_timezone.csv,Etc/GMT+5,30,Wrong Timezone
+51,1229,1229_poa_irradiance_15min_random_time_shifts.csv,Etc/GMT+5,15,Random Time Shifts
+52,1229,1229_poa_irradiance_random_time_shifts.csv,Etc/GMT+5,15,Random Time Shifts
+53,1229,1229_gh_irradiance_60min_random_time_shifts.csv,Etc/GMT+5,60,Random Time Shifts
+54,1229,1229_gh_irradiance_30min_random_time_shifts.csv,Etc/GMT+5,30,Random Time Shifts
+55,1229,1229_gh_irradiance_15min_random_time_shifts.csv,Etc/GMT+5,15,Random Time Shifts
+56,1229,1229_poa_irradiance_west_array_full_DST.csv,Etc/GMT+5,15,Full DST
+57,1229,1229_poa_irradiance_west_array_60min_random_time_shifts.csv,Etc/GMT+5,60,Random Time Shifts
+58,1229,1229_gh_irradiance_random_time_shifts.csv,Etc/GMT+5,15,Random Time Shifts
+59,1333,1333_inv3_ac_power__2650_15min.csv,Etc/GMT+7,15,No Issue
+60,1332,1332_inv_total_ac_power__2654_30min.csv,Etc/GMT+7,30,No Issue
+61,1332,1332_inv_total_ac_power__2654_15min.csv,Etc/GMT+7,15,No Issue
+62,1333,1333_inv3_ac_power__2650_60min.csv,Etc/GMT+7,60,No Issue
+63,1332,1332_inv2_ac_power__2646_60min.csv,Etc/GMT+7,60,No Issue
+64,1333,1333_inv3_ac_power__2650_30min.csv,Etc/GMT+7,30,No Issue
+65,1332,1332_inv2_ac_power__2646_partial_DST.csv,Etc/GMT+7,15,Partial DST
+66,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,Etc/GMT+7,15,Full DST
+67,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,Etc/GMT+7,30,Wrong Timezone
+68,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,Etc/GMT+7,60,Wrong Timezone
+69,1333,1333_inv3_ac_power__2650_random_time_shifts.csv,Etc/GMT+7,15,Random Time Shifts
+70,1333,1333_inv3_ac_power__2650_15min_random_time_shifts.csv,Etc/GMT+7,15,Random Time Shifts
+71,1333,1333_inv3_ac_power__2650_30min_random_time_shifts.csv,Etc/GMT+7,30,Random Time Shifts
+72,1333,1333_inv3_ac_power__2650_60min_random_time_shifts.csv,Etc/GMT+7,60,Random Time Shifts
+73,1332,1332_inv_total_ac_power__2654_partial_DST.csv,Etc/GMT+7,15,Partial DST
+74,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,Etc/GMT+7,15,Random Time Shifts
+75,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,Etc/GMT+7,30,Partial DST
+76,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,Etc/GMT+7,60,Partial DST
+77,1332,1332_inv2_ac_power__2646_15min.csv,Etc/GMT+7,15,No Issue
+78,1332,1332_inv2_ac_power__2646_30min.csv,Etc/GMT+7,30,No Issue
+79,1332,1332_inv_total_ac_power__2654_60min.csv,Etc/GMT+7,60,No Issue
+80,1422,1422_inv1_ac_power.csv,Etc/GMT+5,1,No Issue
+81,1422,1422_inv2_ac_power.csv,Etc/GMT+5,1,No Issue
+82,1422,1422_inv1_ac_power_30min.csv,Etc/GMT+5,30,No Issue
+83,1422,1422_inv1_ac_power_60min.csv,Etc/GMT+5,60,No Issue
+84,1422,1422_inv2_ac_power_15min.csv,Etc/GMT+5,15,No Issue
+85,1422,1422_inv2_ac_power_30min.csv,Etc/GMT+5,30,No Issue
+86,1422,1422_inv2_ac_power_60min.csv,Etc/GMT+5,60,No Issue
+87,1422,1422_inv2_ac_power_30min_full_DST.csv,Etc/GMT+5,30,Full DST
+88,1422,1422_inv2_ac_power_15min_wrong_timezone.csv,Etc/GMT+5,15,Wrong Timezone
+89,1422,1422_inv2_ac_power_partial_DST.csv,Etc/GMT+5,1,Partial DST
+90,1422,1422_inv1_ac_power_15min.csv,Etc/GMT+5,15,No Issue
+91,1422,1422_inv2_ac_power_60min_wrong_timezone.csv,Etc/GMT+5,60,Wrong Timezone
+92,1422,1422_inv1_ac_power_60min_random_time_shifts.csv,Etc/GMT+5,60,Random Time Shifts
+93,1422,1422_inv1_ac_power_15min_wrong_timezone.csv,Etc/GMT+5,15,Wrong Timezone
+94,1422,1422_inv1_ac_power_random_time_shifts.csv,Etc/GMT+5,1,Random Time Shifts
+95,1422,1422_inv1_ac_power_30min_random_time_shifts.csv,Etc/GMT+5,30,Random Time Shifts
+96,1423,1423_inv1_ac_power_15min.csv,Etc/GMT+8,15,No Issue
+97,1423,1423_inv2_ac_power_30min.csv,Etc/GMT+8,30,No Issue
+98,1423,1423_inv2_ac_power_15min.csv,Etc/GMT+8,15,No Issue
+99,1423,1423_inv2_ac_power.csv,Etc/GMT+8,1,No Issue
+100,1423,1423_inv1_ac_power_60min.csv,Etc/GMT+8,60,No Issue
+101,1423,1423_inv1_ac_power_30min.csv,Etc/GMT+8,30,No Issue
+102,1423,1423_inv1_ac_power.csv,Etc/GMT+8,1,No Issue
+103,1423,1423_inv2_ac_power_30min_wrong_timezone.csv,Etc/GMT+8,30,Wrong Timezone
+104,1423,1423_inv2_ac_power_15min_wrong_timezone.csv,Etc/GMT+8,15,Wrong Timezone
+105,1423,1423_inv2_ac_power_wrong_timezone.csv,Etc/GMT+8,1,Wrong Timezone
+106,1423,1423_inv1_ac_power_60min_wrong_timezone.csv,Etc/GMT+8,60,Wrong Timezone
+107,1423,1423_inv1_ac_power_30min_random_time_shifts.csv,Etc/GMT+8,30,Random Time Shifts
+108,1423,1423_inv1_ac_power_15min_wrong_timezone.csv,Etc/GMT+8,15,Wrong Timezone
+109,1423,1423_inv1_ac_power_full_DST.csv,Etc/GMT+8,1,Full DST
+110,1423,1423_inv2_ac_power_60min_full_DST.csv,Etc/GMT+8,60,Full DST
+111,1423,1423_inv2_ac_power_60min.csv,Etc/GMT+8,60,No Issue
+112,4901,4901_InvPAC_kW_Avg_60min.csv,Etc/GMT+5,60,No Issue
+113,4901,4901_InvPAC_kW_Avg_30min.csv,Etc/GMT+5,30,No Issue
+114,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,Etc/GMT+5,15,Random Time Shifts
+115,4901,4901_InvPAC_kW_Avg.csv,Etc/GMT+5,1,No Issue
+116,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,Etc/GMT+5,60,Random Time Shifts
+117,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,Etc/GMT+5,30,Full DST
+118,4901,4901_InvPAC_kW_Avg_15min.csv,Etc/GMT+5,15,No Issue
+119,4901,4901_InvPAC_kW_Avg_partial_DST.csv,Etc/GMT+5,1,Partial DST
+120,4902,4902_ac_power_inv_14538_15min.csv,Etc/GMT+5,15,No Issue
+121,4902,4902_ac_power_inv_14538_random_time_shifts.csv,Etc/GMT+5,1,Random Time Shifts
+122,4902,4902_ac_power_inv_14538_15min_wrong_timezone.csv,Etc/GMT+5,15,Wrong Timezone
+123,4902,4902_ac_power_inv_14538_30min_random_time_shifts.csv,Etc/GMT+5,30,Random Time Shifts
+124,4902,4902_ac_power_inv_14538_60min_partial_DST.csv,Etc/GMT+5,60,Partial DST
+125,4902,4902_ac_power_inv_14538_30min.csv,Etc/GMT+5,30,No Issue
+126,4902,4902_ac_power_inv_14538.csv,Etc/GMT+5,1,No Issue
+127,4902,4902_ac_power_inv_14538_60min.csv,Etc/GMT+5,60,No Issue
+128,4903,4903_ac_power_inv_14539.csv,Etc/GMT+5,1,No Issue
+129,4903,4903_ac_power_other_inv_14539_2_15min_partial_DST.csv,Etc/GMT+5,15,Partial DST
+130,4903,4903_ac_power_other_inv_14539_2_random_time_shifts.csv,Etc/GMT+5,15,Random Time Shifts
+131,4903,4903_ac_power_inv_14539_wrong_timezone.csv,Etc/GMT+5,1,Wrong Timezone
+132,4903,4903_ac_power_other_inv_14539_2_30min_wrong_timezone.csv,Etc/GMT+5,30,Wrong Timezone
+133,4903,4903_ac_power_inv_14539_30min_random_time_shifts.csv,Etc/GMT+5,30,Random Time Shifts
+134,4903,4903_ac_power_inv_14539_60min_random_time_shifts.csv,Etc/GMT+5,60,Random Time Shifts
+135,4903,4903_ac_power_other_inv_14539_2_60min.csv,Etc/GMT+5,60,No Issue
+136,4903,4903_ac_power_other_inv_14539_2_30min.csv,Etc/GMT+5,30,No Issue
+137,4903,4903_ac_power_other_inv_14539_2_15min.csv,Etc/GMT+5,15,No Issue
+138,4903,4903_ac_power_inv_14539_60min.csv,Etc/GMT+5,60,No Issue
+139,4903,4903_ac_power_inv_14539_30min.csv,Etc/GMT+5,30,No Issue
+140,4903,4903_ac_power_inv_14539_15min.csv,Etc/GMT+5,15,No Issue
+141,4903,4903_ac_power_inv_14539_15min_random_time_shifts.csv,Etc/GMT+5,15,Random Time Shifts
+142,4903,4903_ac_power_other_inv_14539_2_60min_wrong_timezone.csv,Etc/GMT+5,60,Wrong Timezone
diff --git a/ec2/analysis_taks/time-shift-detection/data/file_test_link.csv b/ec2/analysis_taks/time-shift-detection/data/file_test_link.csv
new file mode 100644
index 00000000..326afa2d
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/data/file_test_link.csv
@@ -0,0 +1,144 @@
+test_id,category_id,file_id
+0,0,0
+1,0,1
+2,0,2
+3,0,3
+4,0,4
+5,0,5
+6,0,6
+7,0,7
+8,0,8
+9,0,9
+10,0,10
+11,0,11
+12,0,12
+13,0,13
+14,0,14
+15,0,15
+16,0,16
+17,0,17
+18,0,18
+19,0,19
+20,0,20
+21,0,21
+22,0,22
+23,0,23
+24,0,24
+25,0,25
+26,0,26
+27,0,27
+28,0,28
+29,0,29
+30,0,30
+31,0,31
+32,0,32
+33,0,33
+34,0,34
+35,0,35
+36,0,36
+37,0,37
+38,0,38
+39,0,39
+40,0,40
+41,0,41
+42,0,42
+43,0,43
+44,0,44
+45,0,45
+46,0,46
+47,0,47
+48,0,48
+49,0,49
+50,0,50
+51,0,51
+52,0,52
+53,0,53
+54,0,54
+55,0,55
+56,0,56
+57,0,57
+58,0,58
+59,0,59
+60,0,60
+61,0,61
+62,0,62
+63,0,63
+64,0,64
+65,0,65
+66,0,66
+67,0,67
+68,0,68
+69,0,69
+70,0,70
+71,0,71
+72,0,72
+73,0,73
+74,0,74
+75,0,75
+76,0,76
+77,0,77
+78,0,78
+79,0,79
+80,0,80
+81,0,81
+82,0,82
+83,0,83
+84,0,84
+85,0,85
+86,0,86
+87,0,87
+88,0,88
+89,0,89
+90,0,90
+91,0,91
+92,0,92
+93,0,93
+94,0,94
+95,0,95
+96,0,96
+97,0,97
+98,0,98
+99,0,99
+100,0,100
+101,0,101
+102,0,102
+103,0,103
+104,0,104
+105,0,105
+106,0,106
+107,0,107
+108,0,108
+109,0,109
+110,0,110
+111,0,111
+112,0,112
+113,0,113
+114,0,114
+115,0,115
+116,0,116
+117,0,117
+118,0,118
+119,0,119
+120,0,120
+121,0,121
+122,0,122
+123,0,123
+124,0,124
+125,0,125
+126,0,126
+127,0,127
+128,0,128
+129,0,129
+130,0,130
+131,0,131
+132,0,132
+133,0,133
+134,0,134
+135,0,135
+136,0,136
+137,0,137
+138,0,138
+139,0,139
+140,0,140
+141,0,141
+142,0,142
diff --git a/ec2/analysis_taks/time-shift-detection/data/fix_files.py b/ec2/analysis_taks/time-shift-detection/data/fix_files.py
new file mode 100644
index 00000000..0716e45a
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/data/fix_files.py
@@ -0,0 +1,72 @@
+# Clean up any duplicated data sets
+
+from statistics import mode
+import pandas as pd
+import glob
+import os
+
+file_metadata = pd.read_csv(
+    "C:/Users/kperry/Documents/source/repos/time-shift-validation-hub/data/file_metadata.csv"
+)
+
+# file_metadata_filter = file_metadata.drop_duplicates(subset=['file_name', 'system_id', 'file_name', 'timezone',
+#        'data_sampling_frequency', 'issue', 'subissue'], keep="last")
+
+# file_metadata_filter['file_id'] = file_metadata_filter.reset_index(drop=True).index
+
+# file_metadata_filter.to_csv("C:/Users/kperry/Documents/source/repos/time-shift-validation-hub/data/file_metadata.csv",
+#                             index=False)
+
+file_test_link = pd.read_csv(
+    "C:/Users/kperry/Documents/source/repos/time-shift-validation-hub/data/file_test_link.csv"
+)
+
+
+system_metadata = pd.read_csv(
+    "C:/Users/kperry/Documents/source/repos/time-shift-validation-hub/data/system_metadata.csv"
+)
+
+
+files = glob.glob(
+    "C:/Users/kperry/Documents/source/repos/time-shift-validation-hub/data/file_data/*"
+)
+
+error_freq_list = list()
+
+# for file in files:
+#     df = pd.read_csv(file, index_col=0, parse_dates=True)
+#     # Double verify our sampling frequency is right
+#     freq_minutes = mode(df.index.to_series().diff().dt.seconds / 60)
+#     # Look up the file metadata to double verify
+#     data_info = dict(file_metadata[file_metadata['file_name'] == os.path.basename(file)].iloc[0])
+#     if data_info['data_sampling_frequency'] != freq_minutes:
+#         print("Data frequency discrepancy for file: " + file)
+#         print("Frequency should be: " + str(freq_minutes))
+#         error_freq_list.append({"file_name": os.path.basename(file),
+#                                 "data_frequency": freq_minutes})
+
+
+# error_list = pd.DataFrame(error_freq_list)
+
+file_test_link_new = pd.DataFrame()
+
+file_test_link_new["file_id"] = file_metadata["file_id"]
+file_test_link_new["test_id"] = file_metadata["file_id"]
+file_test_link_new["category_id"] = 0
+
+file_test_link_new = file_test_link_new[["test_id", "category_id", "file_id"]]
+
+file_test_link_new.to_csv(
+    "C:/Users/kperry/Documents/source/repos/time-shift-validation-hub/data/file_test_link.csv",
+    index=False,
+)
+
+
+val_files = glob.glob(
+    "C:/Users/kperry/Documents/source/repos/time-shift-validation-hub/data/validation_data/*"
+)
+
+val_files_base = [os.path.basename(val) for val in val_files]
+files_base = [os.path.basename(file) for file in files]
+
+main_list = [item for item in val_files_base if item not in files_base]
diff --git a/ec2/analysis_taks/time-shift-detection/data/system_metadata.csv b/ec2/analysis_taks/time-shift-detection/data/system_metadata.csv
new file mode 100644
index 00000000..88c3ff52
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/data/system_metadata.csv
@@ -0,0 +1,11 @@
+system_id,name,azimuth,tilt,elevation,latitude,longitude,tracking,climate_type,dc_capacity
+4,Sanyo1,180,40,1795.3,39.7406,-105.1774,FALSE,BSk,1
+50,SERFEast,158,45,1994.7,39.742,-105.1727,FALSE,BSk,6
+1229,FSEC - Zero Energy House - Lakeland,240,33.3,58,28.0392,-81.95,FALSE,Cfa,4.05
+1332,NREL Parking Garage mnt 1,180,16.77,1770,39.7388,-105.1732,FALSE,BSk,1153.488
+1333,NREL Parking Garage mnt 2,180,60,1770,39.7388,-105.1732,FALSE,BSk,1153.488
+1422,RTC-VT-Baseline,180,35,109,44.4665,-73.1014,FALSE,Dfb,6
+1423,RTC-NV-Baseline,180,35,697,36.0275,-114.9215,FALSE,BWh,6
+4901,NIST_Canopy_1,90,5,137,39.1385,-77.2155,FALSE,Cfa,242.5
+4902,NIST_Ground_1,180,20,138,39.1319,-77.2141,FALSE,Cfa,270.7
+4903,NIST_Roof_1,180,10,149,39.1354,-77.2156,FALSE,Cfa,73.7
diff --git a/ec2/analysis_taks/time-shift-detection/data/test-size.py b/ec2/analysis_taks/time-shift-detection/data/test-size.py
new file mode 100644
index 00000000..f02fbd92
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/data/test-size.py
@@ -0,0 +1,26 @@
+import pandas as pd
+
+orig_df = pd.read_csv(
+    "./file_data/1332_inv_total_ac_power__2654_15min_random_time_shifts.csv",
+    index_col=0,
+    parse_dates=True,
+)
+
+
+valid_df = pd.read_csv(
+    "./validation_data/1332_inv_total_ac_power__2654_15min_random_time_shifts.csv",
+    index_col=0,
+    parse_dates=True,
+)
+
+valid_df.index = valid_df.index.tz_localize(orig_df.index.tz)
+
+valid_df = valid_df.reindex(orig_df.index).ffill()
+
+print("Number dates:")
+print(len(pd.Series(orig_df.index.date).drop_duplicates()))
+print(len(pd.Series(valid_df.index.date).drop_duplicates()))
+
+print("Number timestamps:")
+print(len(pd.Series(orig_df.index).drop_duplicates()))
+print(len(pd.Series(valid_df.index).drop_duplicates()))
diff --git a/ec2/analysis_taks/time-shift-detection/data/time-shift-example.zip b/ec2/analysis_taks/time-shift-detection/data/time-shift-example.zip
new file mode 100644
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diff --git a/ec2/analysis_taks/time-shift-detection/data/time-shift-example/4_ac_power__315_15min_full_DST_data.csv b/ec2/analysis_taks/time-shift-detection/data/time-shift-example/4_ac_power__315_15min_full_DST_data.csv
new file mode 100644
index 00000000..56702388
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/data/time-shift-example/4_ac_power__315_15min_full_DST_data.csv
@@ -0,0 +1,122577 @@
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diff --git a/ec2/analysis_taks/time-shift-detection/data/time-shift-example/4_ac_power__315_15min_full_DST_validation.csv b/ec2/analysis_taks/time-shift-detection/data/time-shift-example/4_ac_power__315_15min_full_DST_validation.csv
new file mode 100644
index 00000000..8698ae46
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/data/time-shift-example/4_ac_power__315_15min_full_DST_validation.csv
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diff --git a/ec2/analysis_taks/time-shift-detection/data/time-shift-example/metadata.csv b/ec2/analysis_taks/time-shift-detection/data/time-shift-example/metadata.csv
new file mode 100644
index 00000000..5a675563
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/data/time-shift-example/metadata.csv
@@ -0,0 +1,2 @@
+system_id,file_name,timezone,data_sampling_frequency,issue,subissue,name,azimuth,tilt,elevation,latitude,longitude,tracking,climate_type,dc_capacity
+4,4_ac_power__315_15min_full_DST.csv,Etc/GMT+7,15,Full DST,,Sanyo1,180,40,1795.3,39.7406,-105.1774,FALSE,BSk,1
diff --git a/ec2/analysis_taks/time-shift-detection/data/time-shift-example/runner.py b/ec2/analysis_taks/time-shift-detection/data/time-shift-example/runner.py
new file mode 100644
index 00000000..fca5a1dd
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/data/time-shift-example/runner.py
@@ -0,0 +1,420 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar  4 15:34:30 2024
+
+@author: kperry
+"""
+
+"""
+Runner script for assessing validation algorithms. In this
+script, the following occurs:
+    1. Pull down all of the metadata associated with the data sets
+    2. Loop through all metadata cases, pull down the associated data, and
+    run the associated submission on it
+    3. Aggregate the results for the entire data set and generate assessment 
+    metrics. Assessment metrics will vary based on the type of analysis being
+    run. Some examples include:
+        1. Mean Absolute Error between predicted time shift series and ground
+        truth time series (in minutes)
+        2. Average run time for each data set (in seconds)
+    4. Further aggregate performance metrics into visualizations:
+        -Distribution Histograms
+        -Scatter plots
+      This section will be dependent on the type of analysis being run.
+"""
+
+import pandas as pd
+import ast
+import os
+from importlib import import_module
+import inspect
+import time
+from collections import ChainMap
+from sklearn.metrics import mean_absolute_error
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+import json
+import requests
+import tarfile
+import shutil
+import sys
+import zipfile
+import subprocess
+import logging
+import boto3
+
+
+def convert_compressed_file_path_to_directory(compressed_file_path):
+    path_components = compressed_file_path.split("/")
+    path_components[-1] = path_components[-1].split(".")[0]
+    path_components = "/".join(path_components)
+    return path_components
+
+
+def get_file_extension(path):
+    return path.split("/")[-1].split(".")[-1]
+
+
+def decompress_file(path):
+    if get_file_extension(path) == "gz":
+        with tarfile.open(path, "r:gz") as tar:
+            tar.extractall(convert_compressed_file_path_to_directory(path))
+    else:
+        with zipfile.ZipFile(path, "r") as zip_ref:
+            zip_ref.extractall(convert_compressed_file_path_to_directory(path))
+    return convert_compressed_file_path_to_directory(path)
+
+
+def get_module_file_name(module_dir):
+    for root, _, files in os.walk(module_dir, topdown=True):
+        for name in files:
+            if name.endswith(".py"):
+                return name.split("/")[-1]
+
+
+def get_module_name(module_dir):
+    return get_module_file_name(module_dir)[:-3]
+
+
+def generate_histogram(
+    dataframe, x_axis, title, color_code=None, number_bins=30
+):
+    """
+    Generate a histogram for a distribution. Option to color code the
+    histogram by the color_code column parameter.
+    """
+    sns.displot(
+        dataframe, x=x_axis, hue=color_code, multiple="stack", bins=number_bins
+    )
+    plt.title(title)
+    plt.tight_layout()
+    return plt
+
+
+def generate_scatter_plot(dataframe, x_axis, y_axis, title):
+    """
+    Generate a scatterplot between an x- and a y-variable.
+    """
+    sns.scatterplot(data=dataframe, x=x_axis, y=y_axis)
+    plt.title(title)
+    plt.tight_layout()
+    return plt
+
+
+def run(module_to_import, current_evaluation_dir=None):
+    # If a path is provided, set the directories to that path, otherwise use default
+    if current_evaluation_dir is not None:
+        results_dir = (
+            current_evaluation_dir + "/results"
+            if not current_evaluation_dir.endswith("/")
+            else current_evaluation_dir + "results"
+        )
+        data_dir = (
+            current_evaluation_dir + "/data"
+            if not current_evaluation_dir.endswith("/")
+            else current_evaluation_dir + "data"
+        )
+    else:
+        results_dir = "./results"
+        data_dir = "./data"
+
+    if current_evaluation_dir is not None:
+        sys.path.append(
+            current_evaluation_dir
+        )  # append current_evaluation_dir to sys.path
+
+    # Ensure results directory exists
+    os.makedirs(results_dir, exist_ok=True)
+
+    # Ensure results directory exists
+    os.makedirs(data_dir, exist_ok=True)
+
+    # Load in the module that we're going to test on.
+
+    target_module_compressed_file_path = pull_from_s3(module_to_import_s3_path)
+
+    target_module_path = decompress_file(target_module_compressed_file_path)
+
+    # get current directory, i.e. directory of runner.py file
+    new_dir = os.path.dirname(os.path.abspath(__file__))
+
+    file_name = get_module_file_name(target_module_path)
+    module_name = get_module_name(target_module_path)
+
+    # install submission dependency
+    try:
+        subprocess.check_call(
+            [
+                "python",
+                "-m",
+                "pip",
+                "install",
+                "-r",
+                os.path.join(target_module_path, "requirements.txt"),
+            ]
+        )
+        print("submission dependencies installed successfully.")
+    except subprocess.CalledProcessError as e:
+        print("error installing submission dependencies:", e)
+    shutil.move(
+        os.path.join(target_module_path, file_name),
+        os.path.join(new_dir, file_name),
+    )
+
+    # Generate list for us to store all of our results for the module
+    results_list = list()
+    # Load in data set that we're going to analyze.
+
+    # Make GET requests to the Django API to get the system metadata
+    # http://api.pv-validation-hub.org/system_metadata/systemmetadata/
+    smd_url = f"http://{api_base_url}/system_metadata/systemmetadata/"
+    system_metadata_response = requests.get(smd_url)
+
+    # Convert the responses to DataFrames
+
+    # System metadata: This CSV represents the system_metadata table, which is
+    # a master table for associated system metadata (system_id, name, azimuth,
+    # tilt, etc.)
+    system_metadata = pd.DataFrame(system_metadata_response.json())
+
+    # File category link: This file represents the file_category_link table,
+    # which links specific files in the file_metadata table.
+    # This table exists specifically to allow for
+    # many-to-many relationships, where one file can link to multiple
+    # categories/tests, and multiple categories/tests can link to multiple
+    # files.
+    file_test_link = pd.read_csv(
+        os.path.join(current_evaluation_dir, "file_test_link.csv")
+    )
+
+    # Get the unique file ids
+    unique_file_ids = file_test_link["file_id"].unique()
+
+    # File metadata: This file represents the file_metadata table, which is
+    # the master table for files associated with different tests (az-tilt,
+    # time shifts, degradation, etc.). Contains file name, associated file
+    # information (sampling frequency, specific test, timezone, etc) as well
+    # as ground truth information to test against in the validation_dictionary
+    # field
+    # For each unique file id, make a GET request to the Django API to get the corresponding file metadata
+    file_metadata_list = []
+    for file_id in unique_file_ids:
+        fmd_url = (
+            f"http://{api_base_url}/file_metadata/filemetadata/{file_id}/"
+        )
+        response = requests.get(fmd_url)
+        file_metadata_list.append(response.json())
+
+    # Convert the list of file metadata to a DataFrame
+    file_metadata = pd.DataFrame(file_metadata_list)
+
+    # Read in the configuration JSON for the particular run
+    with open(os.path.join(current_evaluation_dir, "config.json")) as f:
+        config_data = json.load(f)
+
+    # Get the associated metrics we're supposed to calculate
+    performance_metrics = config_data["performance_metrics"]
+    logger.info(f"performance_metrics: {performance_metrics}")
+
+    # Get the name of the function we want to import associated with this
+    # test
+    function_name = config_data["function_name"]
+    # Import designated module via importlib
+    module = import_module(module_name)
+    function = getattr(module, function_name)
+    function_parameters = list(inspect.signature(function).parameters)
+    # Loop through each file and generate predictions
+    for index, row in file_metadata.iterrows():
+        # Get file_name, which will be pulled from database or S3 for
+        # each analysis
+        file_name = row["file_name"]
+        # Get associated system ID
+        system_id = row["system_id"]
+        # Get all of the associated metadata for the particular file based
+        # on its system ID. This metadata will be passed in via kwargs for
+        # any necessary arguments
+        associated_metadata = dict(
+            system_metadata[system_metadata["system_id"] == system_id].iloc[0]
+        )
+        # Get the ground truth scalars that we will compare to
+        ground_truth_dict = dict()
+        if config_data["comparison_type"] == "scalar":
+            for val in config_data["ground_truth_compare"]:
+                ground_truth_dict[val] = associated_metadata[val]
+        if config_data["comparison_type"] == "time_series":
+            ground_truth_series = pd.read_csv(
+                os.path.join(data_dir + "/validation_data/", file_name),
+                index_col=0,
+                parse_dates=True,
+            ).squeeze()
+            ground_truth_dict["time_series"] = ground_truth_series
+        # Create master dictionary of all possible function kwargs
+        kwargs_dict = dict(ChainMap(dict(row), associated_metadata))
+        # Filter out to only allowable args for the function
+        kwargs_dict = {
+            key: kwargs_dict[key] for key in config_data["allowable_kwargs"]
+        }
+        # Now that we've collected all of the information associated with the
+        # test, let's read in the file as a pandas dataframe (this data
+        # would most likely be stored in an S3 bucket)
+        time_series = pd.read_csv(
+            os.path.join(data_dir + "/file_data/", file_name),
+            index_col=0,
+            parse_dates=True,
+        ).squeeze()
+        time_series = time_series.asfreq(
+            str(row["data_sampling_frequency"]) + "T"
+        )
+        # Filter the kwargs dictionary based on required function params
+        kwargs = dict(
+            (k, kwargs_dict[k])
+            for k in function_parameters
+            if k in kwargs_dict
+        )
+        # Run the routine (timed)
+        start_time = time.time()
+        data_outputs = function(time_series, **kwargs)
+        end_time = time.time()
+        function_run_time = end_time - start_time
+        # Convert the data outputs to a dictionary identical to the
+        # ground truth dictionary
+        output_dictionary = dict()
+        if config_data["comparison_type"] == "scalar":
+            for idx in range(len(config_data["ground_truth_compare"])):
+                output_dictionary[config_data["ground_truth_compare"][idx]] = (
+                    data_outputs[idx]
+                )
+        if config_data["comparison_type"] == "time_series":
+            output_dictionary["time_series"] = data_outputs
+        # Run routine for all of the performance metrics and append
+        # results to the dictionary
+        results_dictionary = dict()
+        results_dictionary["file_name"] = file_name
+        # Set the runtime in the results dictionary
+        results_dictionary["run_time"] = function_run_time
+        # Set the data requirements in the dictionary
+        results_dictionary["data_requirements"] = function_parameters
+        # Loop through the rest of the performance metrics and calculate them
+        # (this predominantly applies to error metrics)
+        for metric in performance_metrics:
+            if metric == "absolute_error":
+                # Loop through the input and the output dictionaries,
+                # and calculate the absolute error
+                for val in config_data["ground_truth_compare"]:
+                    error = np.abs(
+                        output_dictionary[val] - ground_truth_dict[val]
+                    )
+                    results_dictionary[metric + "_" + val] = error
+            elif metric == "mean_absolute_error":
+                for val in config_data["ground_truth_compare"]:
+                    error = np.mean(
+                        np.abs(output_dictionary[val] - ground_truth_dict[val])
+                    )
+                    results_dictionary[metric + "_" + val] = error
+        results_list.append(results_dictionary)
+        logger.info(f"results_dictionary: {results_dictionary}")
+    # Convert the results to a pandas dataframe and perform all of the
+    # post-processing in the script
+    results_df = pd.DataFrame(results_list)
+    logger.info(f"results_df: {results_df}")
+    # Build out the final processed results:
+    #   1) Public reporting: mean MAE, mean run time, etc.
+    #   2) Private reporting: graphics and tables split by different factors
+    # First get mean value for all the performance metrics and save (this will
+    # be saved to a public metrics dictionary)
+    public_metrics_dict = dict()
+    public_metrics_dict["module"] = module_name
+    # Get the mean and median run times
+    public_metrics_dict["mean_run_time"] = results_df["run_time"].mean()
+    public_metrics_dict["median_run_time"] = results_df["run_time"].median()
+    public_metrics_dict["function_parameters"] = function_parameters
+
+    # Get the mean and median absolute errors
+    # when combining the metric and name for the public metrics dictionary,
+    # do not add anything to them. mean_mean_average_error and median_mean_average_error
+    # are valid keys, anything else breaks our results processing
+    for metric in performance_metrics:
+        if "absolute_error" in metric:
+            for val in config_data["ground_truth_compare"]:
+                logger.info(
+                    f"metric: {metric}, val: {val}, combined: {'mean_' + metric}"
+                )
+                public_metrics_dict["mean_" + metric] = results_df[
+                    metric + "_" + val
+                ].mean()
+                public_metrics_dict["median_" + metric] = results_df[
+                    metric + "_" + val
+                ].median()
+    # Write public metric information to a public results table.
+    with open(
+        os.path.join(results_dir, config_data["public_results_table"]), "w"
+    ) as fp:
+        json.dump(public_metrics_dict, fp)
+
+    logger.info(f"public_metrics_dict: {public_metrics_dict}")
+    # Now generate private results. These will be more specific to the
+    # type of analysis being run as results will be color-coded by certain
+    # parameters. These params will be available as columns in the
+    # 'associated_files' dataframe
+    results_df_private = pd.merge(results_df, file_metadata, on="file_name")
+    # Filter to only the necessary columns (available via the config)
+    results_df_private = results_df_private[
+        config_data["private_results_columns"]
+    ]
+    results_df_private.to_csv(
+        os.path.join(results_dir, module_name + "_full_results.csv")
+    )
+    # Loop through all of the plot dictionaries and generate plots and
+    # associated tables for reporting
+    for plot in config_data["plots"]:
+        if plot["type"] == "histogram":
+            if "color_code" in plot:
+                color_code = plot["color_code"]
+            else:
+                color_code = None
+            gen_plot = generate_histogram(
+                results_df_private, plot["x_val"], plot["title"], color_code
+            )
+            # Save the plot
+            gen_plot.savefig(os.path.join(results_dir, plot["save_file_path"]))
+            plt.close()
+            plt.clf()
+            # Write the stratified results to a table for private reporting
+            # (if color_code param is not None)
+            if color_code:
+                stratified_results_tbl = pd.DataFrame(
+                    results_df_private.groupby(color_code)[
+                        plot["x_val"]
+                    ].mean()
+                )
+                stratified_results_tbl.to_csv(
+                    os.path.join(
+                        results_dir,
+                        module_name
+                        + "_"
+                        + str(color_code)
+                        + "_"
+                        + plot["x_val"]
+                        + ".csv",
+                    )
+                )
+        if plot["type"] == "scatter_plot":
+            gen_plot = generate_scatter_plot(
+                results_df_private, plot["x_val"], plot["y_val"], plot["title"]
+            )
+            # Save the plot
+            gen_plot.savefig(os.path.join(results_dir, plot["save_file_path"]))
+            plt.close()
+            plt.clf()
+    return public_metrics_dict
+
+
+if __name__ == "__main__":
+    run(
+        "pv-validation-hub-bucket/submission_files/submission_user_1/submission_1/archive.tar.gz"
+    )
+    push_to_s3(
+        "/pv-validation-hub-bucket/submission_files/submission_user_1/submission_1/results/time-shift-public-metrics.json",
+        "pv-validation-hub-bucket/test_bucket/test_subfolder/res.json",
+    )
diff --git a/ec2/analysis_taks/time-shift-detection/data/validation_tests.csv b/ec2/analysis_taks/time-shift-detection/data/validation_tests.csv
new file mode 100644
index 00000000..2a178668
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/data/validation_tests.csv
@@ -0,0 +1,3 @@
+category_id,category_name,performance_metrics,function_name
+0,time_shifts,"['mean_absolute_error', 'run_time', 'data_requirements']",detect_time_shifts
+1,az_tilt_estimation,"['mean_absolute_error', 'run_time', 'data_requirements']",estimate_az_tilt
diff --git a/ec2/analysis_taks/time-shift-detection/daytime.py b/ec2/analysis_taks/time-shift-detection/daytime.py
new file mode 100644
index 00000000..5779125b
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/daytime.py
@@ -0,0 +1,388 @@
+"""Functions for identifying daytime"""
+
+import numpy as np
+import pandas as pd
+from pvanalytics import util
+from pandas.tseries.frequencies import to_offset
+
+
+def _rolling_by_minute(data, days, f):
+    # apply `f` to a rolling window of length `days` at each minute of
+    # the day.
+    rolling = data.groupby(data.index.hour * 60 + data.index.minute).rolling(
+        min_periods=1, center=True, window=days
+    )
+    result = f(rolling).reset_index(0, drop=True)
+    return result.sort_index()
+
+
+def _run_lengths(series):
+    # Count the number of equal values adjacent to each value.
+    #
+    # Examples
+    # --------
+    # >>> _run_lengths(pd.Series([True, True, True]))
+    # 0    3
+    # 1    3
+    # 2    3
+    #
+    # >>> _run_lengths(
+    # ...     pd.Series([True, False, False, True, True, False]
+    # ... ))
+    # 0    1
+    # 1    2
+    # 2    2
+    # 3    2
+    # 4    2
+    # 5    1
+    runs = (series != series.shift(1)).cumsum()
+    return runs.groupby(runs).transform("count")
+
+
+def _correct_if_invalid(series, invalid, correction_window):
+    # For every time marked `invalid` replace the truth value in `series`
+    # with the truth value at the same time in the majority of the
+    # surrounding days. The number of surrounding days to examine is indicated
+    # by `correction_window`.
+    rolling_majority = _rolling_by_minute(
+        series, days=correction_window, f=lambda x: x.sum() / x.count() > 0.5
+    )
+    return (~invalid & series) | (invalid & rolling_majority)
+
+
+def _correct_midday_errors(
+    night, minutes_per_value, hours_min, correction_window
+):
+    # identify periods of time that appear to switch from night to day
+    # (or day to night) on too short a time scale to be reasonable.
+    invalid = _run_lengths(night) * minutes_per_value <= hours_min * 60
+    return _correct_if_invalid(night, invalid, correction_window)
+
+
+def _correct_edge_of_day_errors(
+    night,
+    minutes_per_value,
+    day_length_difference_max,
+    day_length_window,
+    correction_window,
+):
+    # Identify day-time periods that are "too short" and replace
+    # values with the majority truth-value for the same time in the
+    # surrounding days.
+    #
+    # Because daylight savings shifts happen at night we cannot look
+    # at night-length directly. Instead we look for too-short days and
+    # flag the full day for correction. This may result in slightly
+    # reduced accuracy for sunrise/sunset times on these days (even if
+    # the day/night boundary at one end of the day - sunrise or sunset
+    # - was correctly marked, it will be replaced with the rolling
+    # median for that minute).
+    day_periods = (~night).astype(int)
+    day_length = (
+        1 + day_periods.groupby(night.cumsum()).transform("sum")
+    ) * minutes_per_value
+    # remove night time values so they don't interfere with the median
+    # day length.
+    day_length.loc[night] = np.nan
+    day_length_median = day_length.rolling(
+        window=str(day_length_window) + "D"
+    ).median()
+    # flag days that are more than 30 minutes shorter than the median
+    short_days = day_length < (day_length_median - day_length_difference_max)
+    invalid = short_days.groupby(short_days.index.date).transform(
+        lambda day: any(day)
+    )
+    return _correct_if_invalid(night, invalid, correction_window)
+
+
+def _ffill_short_periods(night, minutes_per_value, hours_min):
+    # identify periods of time that appear to switch from night to day
+    # (or day to night) on too short a time scale to be reasonable.
+    invalid = _run_lengths(night) * minutes_per_value <= hours_min * 60
+    # Throw out anything on the first or last 2 day period, as only part of
+    # this period may be represented. 2 days is used here as the first or
+    # last day may only have one timestamp present (which is not uncommon)
+    invalid.loc[
+        invalid.index.date <= (invalid.index.date.min() + pd.Timedelta(days=1))
+    ] = False
+    invalid.loc[
+        invalid.index.date >= (invalid.index.date.max() - pd.Timedelta(days=1))
+    ] = False
+    # Set those invalid periods to NaN, and then forward fill them.
+    # This is a final step for picking up any cases that weren't caught
+    # in _correct_midday_errors() or _correct_edge_of_day_errors()
+    night[invalid] = np.nan
+    return night.ffill()
+
+
+def _filter_and_normalize(series, outliers):
+    # filter a series by removing outliers and clamping the minimum to
+    # 0. Then normalize the series by the maximum deviation.
+    if outliers is not None:
+        series.loc[outliers] = np.nan
+    series.loc[series < 0] = 0
+    return (series - series.min()) / (series.max() - series.min())
+
+
+def _freqstr_to_minutes(freqstr):
+    return util.freq_to_timedelta(freqstr).seconds / 60
+
+
+def power_or_irradiance(
+    series,
+    outliers=None,
+    low_value_threshold=0.003,
+    low_median_threshold=0.0015,
+    low_diff_threshold=0.0005,
+    median_days=7,
+    clipping=None,
+    freq=None,
+    correction_window=31,
+    hours_min=5,
+    day_length_difference_max=30,
+    day_length_window=14,
+    nullify_repeat_count=None,
+):
+    """Return True for values that are during the day.
+
+    After removing outliers and normalizing the data, a time is
+    classified as night when two of the following three criteria are
+    satisfied:
+
+    - near-zero value
+    - near-zero first-order derivative
+    - near-zero rolling median at the same time over the surrounding
+      week (see `median_days`)
+
+    Mid-day times where power goes near zero or
+    stops changing may be incorrectly classified as night. To correct
+    these errors, night or day periods with duration that is too long or
+    too short are identified, and times in these periods are re-classified
+    to have the majority value at the same time on preceding and
+    following days (as set by `correction_window`).
+
+    Finally any values that are True in `clipping` are marked as day.
+
+    Parameters
+    ----------
+    series : Series
+        Time series of power or irradiance.
+    outliers : Series, optional
+        Boolean time series with True for values in `series` that are
+        outliers.
+    low_value_threshold : float, default 0.003
+        Maximum normalized power or irradiance value for a time to be
+        considered night.
+    low_median_threshold : float, default 0.0015
+        Maximum rolling median of power or irradiance for a time to be
+        considered night.
+    low_diff_threshold : float, default 0.0005
+        Maximum derivative of normalized power or irradiance for a time
+        to be considered night.
+    median_days : int, default 7
+        Number of days to use to calculate the rolling median at each
+        minute. [days]
+    clipping : Series, optional
+        True when clipping indicated. Any values where clipping is
+        indicated are automatically considered 'daytime'.
+    freq : str, optional
+        A pandas freqstr specifying the expected timestamp spacing for
+        the series. If None, the frequency will be inferred from the index.
+    correction_window : int, default 31
+        Number of adjacent days to examine when correcting
+        day/night classification errors. [days]
+    hours_min : float, default 5
+        Minimum number of hours in a contiguous period of day or
+        night. A day/night period shorter than `hours_min` is
+        flagged for error correction. [hours]
+    day_length_difference_max : float, default 30
+        Days with length that is `day_length_difference_max` minutes less
+        than the median length of surrounding days are flagged for
+        corrections.
+    day_length_window : int, default 14
+        The length of the rolling window used for calculating the
+        median length of the day when correcting errors in the morning
+        or afternoon. [days]
+
+
+    Returns
+    -------
+    Series
+        Boolean time series with True for times that are during the
+        day.
+
+    Notes
+    -----
+
+    ``NA`` values are treated like zeros.
+
+    Derived from the PVFleets QA Analysis project.
+
+    """
+    series = series.fillna(value=0)
+    series_norm = _filter_and_normalize(series, outliers).fillna(value=0)
+    minutes_per_value = _freqstr_to_minutes(
+        freq or pd.infer_freq(series.index)
+    )
+    first_order_diff = series_norm.diff() / minutes_per_value
+    rolling_median = _rolling_by_minute(
+        series_norm, days=median_days, f=pd.core.window.RollingGroupby.median
+    )
+    # Night-time if two of the following are satisfied:
+    # - Near-zero value
+    # - Near-zero first-order derivative
+    # - Near-zero rolling median
+    low_value = series_norm <= low_value_threshold
+    low_median = rolling_median <= low_median_threshold
+    low_diff = abs(first_order_diff) <= low_diff_threshold
+
+    night = (
+        (low_value & low_diff)
+        | (low_value & low_median)
+        | (low_diff & low_median)
+    )
+    # Fix erroneous classifications (e.g. midday outages where power
+    # goes to 0 and stays there for several hours, clipping classified
+    # as night, and night-time periods that are too long)
+    night_corrected_midday = _correct_midday_errors(
+        night, minutes_per_value, hours_min, correction_window
+    )
+    night_corrected_clipping = ~(
+        (clipping or False) | (~night_corrected_midday)
+    )
+    night_corrected_edges = _correct_edge_of_day_errors(
+        night_corrected_clipping,
+        minutes_per_value,
+        day_length_difference_max,
+        day_length_window,
+        correction_window,
+    )
+    return ~night_corrected_edges
+
+
+def get_sunrise(daytime_mask, freq=None, data_alignment="L"):
+    """
+    Using the outputs of power_or_irradiance(), derive sunrise values for
+    each day in the associated time series.
+    Parameters
+    ----------
+    daytime_mask_series  : Series
+        Boolean series delineating night periods from day periods, where
+        day is True and night is False.
+    freq : str, optional
+        A pandas freqstr specifying the expected timestamp spacing for
+        the series. If None, the frequency will be inferred from the index.
+    data_alignment  : String, default 'L'
+        The data alignment of the series (left-aligned or right-aligned). Data
+        alignment affects the value selected as sunrise. Options are 'L' (left-
+        aligned), 'R' (right-aligned), or 'C' (center-aligned)
+    Returns
+    -------
+    Series
+        Series of daily sunrise times, based on the daytime_mask series.
+        This series has the same index as the passed daytime_mask series.
+    """
+    # Get the first day period for each day
+    sunrise_series = (
+        daytime_mask.index[daytime_mask]
+        .to_series()
+        .groupby(daytime_mask[daytime_mask].index.date)
+        .transform("first")
+        .reindex(daytime_mask.index)
+    )
+    sunrise_series = (
+        sunrise_series.groupby(sunrise_series.index.date).ffill().bfill()
+    )
+    # Backfilling and front filling fills all NaN's, so we set cases not in
+    # the right day to NaN
+    sunrise_series.loc[sunrise_series.index.date != sunrise_series.dt.date] = (
+        np.nan
+    )
+    # If there's no frequency value, infer it from the daytime_mask series
+    if not freq:
+        freq = pd.infer_freq(daytime_mask.index)
+    # For left-aligned data, we want the first 'day' mask for
+    # each day in the series; this will act as a proxy for sunrise.
+    # Because of this, we will just return the sunrise_series with
+    # no modifications
+    if data_alignment == "L":
+        return sunrise_series
+    # For center-aligned data, we want the mid-point between the last night
+    # mask and the first day mask. To do this, we subtract freq / 2 from
+    # each sunrise time in the sunrise_series.
+    elif data_alignment == "C":
+        return sunrise_series - (to_offset(freq) / 2)
+    # For right-aligned data, get the last nighttime mask datetime
+    # before the first 'day' mask in the series. To do this, we subtract freq
+    # from each sunrise time in the sunrise_series.
+    elif data_alignment == "R":
+        return sunrise_series - to_offset(freq)
+    else:
+        # Throw an error if right,left, or center-alignment are not declared
+        raise ValueError(
+            "No valid data alignment given. Please pass 'L'"
+            " for left-aligned data, 'R' for right-aligned data,"
+            " or 'C' for center-aligned data."
+        )
+
+
+def get_sunset(daytime_mask, freq=None, data_alignment="L"):
+    """
+    Using the outputs of power_or_irradiance(), derive sunset values for
+    each day in the associated time series.
+    Parameters
+    ----------
+    daytime_mask  : Series
+        Boolean series delineating night periods from day periods, where
+        day is True and night is False.
+    freq : str, optional
+        A pandas freqstr specifying the expected timestamp spacing for
+        the series. If None, the frequency will be inferred from the index.
+    data_alignment  : String, default 'L'
+        The data alignment of the series (left-aligned or right-aligned). Data
+        alignment affects the value selected as sunrise. Options are 'L' (left-
+        aligned), 'R' (right-aligned), or 'C' (center-aligned)
+    Returns
+    -------
+    Series
+        Series of daily sunset times, based on the daytime_mask series.
+        This series has the same index as the passed daytime_mask series.
+    """
+    sunset_series = (
+        daytime_mask.index[daytime_mask]
+        .to_series()
+        .groupby(daytime_mask[daytime_mask].index.date)
+        .transform("last")
+        .reindex(daytime_mask.index)
+    )
+    sunset_series = (
+        sunset_series.groupby(sunset_series.index.date).ffill().bfill()
+    )
+    # Backfilling and front filling fills all NaN's, so we set cases not in
+    # the right day to NaN
+    sunset_series.loc[sunset_series.index.date != sunset_series.dt.date] = (
+        np.nan
+    )
+    # If there's no frequency value, infer it from the daytime_mask series
+    if not freq:
+        freq = pd.infer_freq(daytime_mask.index)
+    #  For left-aligned data, sunset is the first nighttime period
+    # after the day mask. To get this, we add freq to each sunset time in
+    # the sunset time series.
+    if data_alignment == "L":
+        return sunset_series + to_offset(freq)
+    # For center-aligned data, sunset is the midpoint between the last day
+    # mask and the first nighttime mask. We calculate this by adding (freq / 2)
+    # to each sunset time in the sunset_series.
+    elif data_alignment == "C":
+        return sunset_series + (to_offset(freq) / 2)
+    # For right-aligned data, the last 'day' mask time stamp is sunset.
+    elif data_alignment == "R":
+        return sunset_series
+    else:
+        # Throw an error if right, left, or center-alignment are not declared
+        raise ValueError(
+            "No valid data alignment given. Please pass 'L'"
+            " for left-aligned data, 'R' for right-aligned data,"
+            " or 'C' for center-aligned data."
+        )
diff --git a/ec2/analysis_taks/time-shift-detection/environment.yml b/ec2/analysis_taks/time-shift-detection/environment.yml
new file mode 100644
index 00000000..2a83a960
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/environment.yml
@@ -0,0 +1,17 @@
+name: time-shift-validation
+channels:
+    - defaults
+    - anaconda
+    - conda-forge
+dependencies:
+    - python= 3.10.11
+    - pip
+    - pvlib=0.9.4
+    - numpy
+    - seaborn=0.11.1
+    - matplotlib=3.7.1
+    - ruptures=1.1.7
+    - pandas=1.5.3
+    - pip:
+        - solar_data_tools==0.7.0
+        - pvanalytics==0.1.3
\ No newline at end of file
diff --git a/ec2/analysis_taks/time-shift-detection/marimo-prototype.py b/ec2/analysis_taks/time-shift-detection/marimo-prototype.py
new file mode 100644
index 00000000..7d99b217
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/marimo-prototype.py
@@ -0,0 +1,262 @@
+import marimo
+
+__generated_with = "0.1.18"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md("# Private Report: Time Shift Data Results")
+    return
+
+
+@app.cell
+def __(pd, generatePlots):
+    results_df = pd.read_csv("time_shifts_full_results.csv")
+    plotting = generatePlots(results_df)
+    return results_df, plotting
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        median_run_time = round(plotting.results_df.run_time.median(), 2)
+        mean_run_time = round(plotting.results_df.run_time.mean(), 2)
+        max_run_time = round(plotting.results_df.run_time.max(), 2)
+        min_run_time = round(plotting.results_df.run_time.min(), 2)
+        _fig = mo.md(
+            f"""
+                     First, we visualize the distribution of run times. 
+                     
+                     Median run time: """
+            + str(median_run_time)
+            + """ seconds
+                     
+                     Mean run time: """
+            + str(mean_run_time)
+            + """ seconds
+                     
+                     Max run time: """
+            + str(max_run_time)
+            + """ seconds
+                     
+                     Min run time: """
+            + str(min_run_time)
+            + """ seconds
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting):
+    if plotting is not None:
+        _fig = plotting.plot_run_times()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        median_mae = round(
+            plotting.results_df["Time Series-Level MAE"].median(), 2
+        )
+        mean_mae = round(
+            plotting.results_df["Time Series-Level MAE"].mean(), 2
+        )
+        _fig = mo.md(
+            f"""
+                     Next, we visualize the mean absolute error distribution, color-coded by issues present in the time series.
+                     
+                     Median time series MAE: """
+            + str(median_mae)
+            + """ minutes
+                     
+                     Mean time series MAE: """
+            + str(mean_mae)
+            + """ minutes
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting):
+    if plotting is not None:
+        _fig = plotting.plot_mae_by_issue()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        _fig = mo.md(
+            f"""
+                     Mean of Time Series-Level MAE, by Issue Type
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        _fig = plotting.dataframe_mae_by_issue_type()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        _fig = mo.md(
+            f"""
+                     We then visualize the mean absolute error distribution, color-coded by data sampling frequency.
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting):
+    if plotting is not None:
+        _fig = plotting.plot_mae_by_sampling_frequency()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting, mo):
+    if plotting is not None:
+        _fig = mo.md(
+            f"""
+                     Mean of Time Series-Level MAE, by Data Sampling Frequency
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting, mo, pd):
+    if plotting is not None:
+        _fig = plotting.dataframe_mae_by_sampling()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(
+    mo,
+    np,
+    sns,
+    pd,
+    plt,
+):
+
+    class generatePlots:
+
+        def __init__(self, results_df):
+            """Create plotting class."""
+            self.results_df = results_df
+            self.results_df = self.results_df.rename(
+                columns={
+                    "issue": "Data Issue Type",
+                    "mean_absolute_error_time_series": "Time Series-Level MAE",
+                    "data_sampling_frequency": "Data Sampling Frequency (minutes)",
+                }
+            )
+
+        def plot_run_times(self):
+            fig = sns.histplot(self.results_df, x="run_time", bins=40)
+            fig.set(xlabel="Run Time (seconds)", ylabel="Number Instances")
+            return fig
+
+        def plot_mae_by_issue(self):
+            fig = sns.histplot(
+                self.results_df,
+                x="Time Series-Level MAE",
+                hue="Data Issue Type",
+                bins=30,
+            )
+            fig.set(
+                xlabel="Time Series-Level MAE (minutes)",
+                ylabel="Number Instances",
+            )
+            return fig
+
+        def plot_mae_by_sampling_frequency(self):
+            fig = sns.histplot(
+                self.results_df,
+                x="Time Series-Level MAE",
+                hue="Data Sampling Frequency (minutes)",
+                bins=30,
+            )
+            fig.set(
+                xlabel="Time Series-Level MAE (minutes)",
+                ylabel="Number Instances",
+            )
+            return fig
+
+        def dataframe_mae_by_issue_type(self):
+            df = self.results_df.groupby("Data Issue Type")[
+                "Time Series-Level MAE"
+            ].mean()
+            df = pd.DataFrame(df.reset_index())
+            return df
+
+        def dataframe_mae_by_sampling(self):
+            df = self.results_df.groupby("Data Sampling Frequency (minutes)")[
+                "Time Series-Level MAE"
+            ].mean()
+            df = pd.DataFrame(df.reset_index())
+            return df
+
+    return (generatePlots,)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    import numpy as np
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+
+    return (
+        mo,
+        np,
+        sns,
+        pd,
+        plt,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/ec2/analysis_taks/time-shift-detection/marimo_wrapper.py b/ec2/analysis_taks/time-shift-detection/marimo_wrapper.py
new file mode 100644
index 00000000..29b50919
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/marimo_wrapper.py
@@ -0,0 +1,188 @@
+import os
+from typing import Any, TypeVar
+import subprocess
+import json
+import pandas as pd
+import logging
+from logging import Logger
+
+
+logger = logging.getLogger(__name__)
+
+T = TypeVar("T")
+
+
+def logger_if_able(
+    message: str, logger: Logger | None = None, level: str = "INFO"
+):
+    if logger is not None:
+        levels_dict = {
+            "DEBUG": logging.DEBUG,
+            "INFO": logging.INFO,
+            "WARNING": logging.WARNING,
+            "ERROR": logging.ERROR,
+            "CRITICAL": logging.CRITICAL,
+        }
+
+        level = level.upper()
+
+        if level not in levels_dict:
+            raise Exception(f"Invalid log level: {level}")
+
+        log_level = levels_dict[level]
+
+        logger.log(log_level, message)
+    else:
+        print(message)
+
+
+def flatten_list(items: list[T]) -> list[T]:
+    flat_list: list[T] = []
+    for item in items:
+        if isinstance(item, list):
+            flat_list.extend(flatten_list(item))
+        else:
+            flat_list.append(item)
+    return flat_list
+
+
+def format_tuple(
+    t: tuple[str, Any], logger: Logger | None = None
+) -> str | list[str]:
+    key, value = t
+
+    logger_if_able(
+        f"key: {key}, value: {value}, type: {type(value)}", logger, "DEBUG"
+    )
+
+    if isinstance(value, (int, float)):
+        return f"--{key}={value}"
+
+    if isinstance(value, str):
+        if " " in [value]:
+            return f'--{key}="{value}"'
+        return f"--{key}={value}"
+
+    if isinstance(value, (dict)):
+        try:
+            json_str = json.dumps(value)
+        except Exception as e:
+            raise ValueError(f"Failed to convert to JSON: {e}")
+
+        return f"--{key}={json_str}"
+
+    if isinstance(value, bool):
+        return f"--{key}={str(value).lower()}"
+
+    if isinstance(value, list):
+        list_args: list[str] = []
+        for item in value:
+            formatted_item = format_tuple((key, item))
+            if isinstance(formatted_item, list):
+                list_args.extend(flatten_list(formatted_item))
+            if isinstance(formatted_item, str):
+                list_args.append(formatted_item)
+        return list_args
+
+    raise ValueError(f"Unsupported type: {type(value)}")
+
+
+def prepare_json_for_marimo_args(json_data: dict[str, Any]):
+
+    args_list: list[str] = []
+
+    for key, value in json_data.items():
+
+        args = format_tuple((key, value))
+
+        if isinstance(args, list):
+            args_list.extend(flatten_list(args))
+        if isinstance(args, str):
+            args_list.append(args)
+
+    return args_list
+
+
+def generate_private_report_for_submission(
+    df: pd.DataFrame,
+    action: str,
+    template_file_path: str,
+    html_file_path: str,
+    logger: Logger | None = None,
+):
+    json_data: dict[str, Any] = {}
+    json_data["results_df"] = df.to_dict(orient="records")
+
+    data_args_list = prepare_json_for_marimo_args(json_data)
+
+    if not data_args_list or len(data_args_list) == 0:
+        raise ValueError("No data to pass to marimo")
+
+    logger_if_able(f"Data as args: {data_args_list}", logger, "DEBUG")
+
+    cli_commands = {
+        "export": [
+            "marimo",
+            "export",
+            "html",
+            f"{template_file_path}",
+            "-o",
+            f"{html_file_path}",
+            "--no-include-code",
+            "--",
+            *data_args_list,
+        ],
+        "edit": [
+            "marimo",
+            "edit",
+            f"{template_file_path}",
+            "--",
+            *data_args_list,
+        ],
+        "run": [
+            "marimo",
+            "run",
+            f"{template_file_path}",
+            "--",
+            *data_args_list,
+        ],
+    }
+
+    if action not in cli_commands.keys():
+        raise ValueError("Unsupported command")
+
+    try:
+        subprocess.run(
+            cli_commands[action],
+            check=True,
+            # stdout=subprocess.PIPE,
+            # stderr=subprocess.PIPE,
+        )
+    except Exception as e:
+        logger_if_able(f"Error: {e}", logger, "ERROR")
+        return
+
+
+def main(action: str = "export"):
+    action = action.lower()
+
+    data_file_path = os.path.join(
+        os.path.dirname(__file__), "time_shifts_full_results.csv"
+    )
+
+    html_file_path = os.path.join(os.path.dirname(__file__), "template.html")
+    template_file_path = os.path.join(os.path.dirname(__file__), "template.py")
+
+    df = pd.DataFrame()
+
+    with open(data_file_path, "r") as data_file:
+        df = pd.read_csv(data_file)
+
+    generate_private_report_for_submission(
+        df, action, template_file_path, html_file_path
+    )
+
+
+if __name__ == "__main__":
+
+    main(action="export")
diff --git a/ec2/analysis_taks/time-shift-detection/proposed-test-infrastructure-erd.PNG b/ec2/analysis_taks/time-shift-detection/proposed-test-infrastructure-erd.PNG
new file mode 100644
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diff --git a/ec2/analysis_taks/time-shift-detection/pvanalytics-cpd-module.py b/ec2/analysis_taks/time-shift-detection/pvanalytics-cpd-module.py
new file mode 100644
index 00000000..6b69956a
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/pvanalytics-cpd-module.py
@@ -0,0 +1,151 @@
+"""
+PVAnalytics-CPD based module. This module will be uploaded by the user
+and tested using the data sets accordingly. 
+"""
+
+import pandas as pd
+from pvanalytics.quality.time import shifts_ruptures
+from pvanalytics.features import daytime
+from pvanalytics.quality.outliers import zscore
+from pvanalytics.quality import gaps
+import pvlib
+import matplotlib.pyplot as plt
+import pvanalytics
+import ruptures
+
+
+def detect_time_shifts(
+    time_series, latitude, longitude, data_sampling_frequency
+):
+    """
+    Master function for testing for time shifts in a series and returning
+    time-shifted periods
+
+    """
+    # Save the dates of the time series index for reindexing at the end
+    date_index = pd.Series(time_series.index.date).drop_duplicates()
+    # Data pre-processing:
+    # 1) Removal of frozen/stuck data
+    # 2) Removal of data periods with low data 'completeness'
+    # 3) Removal of negative data
+    # 4) Removal of outliers via Hampel + outlier filter
+    # Trim based on frozen data values
+    # REMOVE STALE DATA (that isn't during nighttime periods)
+    # Day/night mask
+    daytime_mask = daytime.power_or_irradiance(time_series)
+    # Stale data mask
+    stale_data_mask = gaps.stale_values_round(
+        time_series, window=3, decimals=2
+    )
+    stale_data_mask = stale_data_mask & daytime_mask
+
+    # REMOVE NEGATIVE DATA
+    negative_mask = time_series < 0
+
+    # FIND ABNORMAL PERIODS
+    daily_min = time_series.resample("D").min()
+    series_min = 0.1 * time_series.mean()
+    erroneous_mask = daily_min >= series_min
+    erroneous_mask = erroneous_mask.reindex(
+        index=time_series.index, method="ffill", fill_value=False
+    )
+    # FIND OUTLIERS (Z-SCORE FILTER)
+    zscore_outlier_mask = zscore(time_series, zmax=3.5, nan_policy="omit")
+
+    # Filter the time series, taking out all of the issues
+    issue_mask = (
+        (~stale_data_mask)
+        & (~negative_mask)
+        & (~erroneous_mask)
+        & (~zscore_outlier_mask)
+    )
+
+    time_series = time_series[issue_mask]
+    time_series = time_series.asfreq(str(data_sampling_frequency) + "T")
+    # Data completeness
+    # Trim the series based on daily completeness score
+    trim_series_mask = pvanalytics.quality.gaps.trim_incomplete(
+        time_series,
+        minimum_completeness=0.25,
+        freq=str(data_sampling_frequency) + "T",
+    )
+
+    time_series = time_series[trim_series_mask]
+    if len(time_series) > 0:
+        # Calculate a nighttime offset
+        # Mask daytime periods for the time series
+        daytime_mask = daytime.power_or_irradiance(
+            time_series,
+            freq=str(data_sampling_frequency) + "T",
+            low_value_threshold=0.005,
+        )
+        # Get the modeled sunrise and sunset time series based on the system's
+        # latitude-longitude coordinates
+        modeled_sunrise_sunset_df = (
+            pvlib.solarposition.sun_rise_set_transit_spa(
+                time_series.index, latitude, longitude
+            )
+        )
+
+        # Calculate the midday point between sunrise and sunset for each day
+        # in the modeled irradiance series
+        modeled_midday_series = (
+            modeled_sunrise_sunset_df["sunrise"]
+            + (
+                modeled_sunrise_sunset_df["sunset"]
+                - modeled_sunrise_sunset_df["sunrise"]
+            )
+            / 2
+        )
+
+        # Generate the sunrise, sunset, and halfway pts for the data stream
+        sunrise_series = daytime.get_sunrise(daytime_mask)
+        sunset_series = daytime.get_sunset(daytime_mask)
+        midday_series = sunrise_series + ((sunset_series - sunrise_series) / 2)
+        # Convert the midday and modeled midday series to daily values
+        midday_series_daily, modeled_midday_series_daily = (
+            midday_series.resample("D").mean(),
+            modeled_midday_series.resample("D").mean(),
+        )
+
+        # Set midday value series as minutes since midnight, from midday datetime
+        # values
+        midday_series_daily = (
+            midday_series_daily.dt.hour * 60
+            + midday_series_daily.dt.minute
+            + midday_series_daily.dt.second / 60
+        )
+        modeled_midday_series_daily = (
+            modeled_midday_series_daily.dt.hour * 60
+            + modeled_midday_series_daily.dt.minute
+            + modeled_midday_series_daily.dt.second / 60
+        )
+
+        # Estimate the time shifts by comparing the modelled midday point to the
+        # measured midday point.
+        is_shifted, time_shift_series = shifts_ruptures(
+            midday_series_daily,
+            modeled_midday_series_daily,
+            period_min=15,
+            shift_min=15,
+            zscore_cutoff=0.75,
+        )
+        time_shift_series = -1 * time_shift_series
+
+        # Create a midday difference series between modeled and measured midday, to
+        # visualize time shifts. First, resample each time series to daily frequency,
+        # and compare the data stream's daily halfway point to the modeled halfway
+        # point
+        midday_diff_series = (
+            modeled_midday_series.resample("D").mean()
+            - midday_series.resample("D").mean()
+        ).dt.total_seconds() / 60
+
+        midday_diff_series.plot()
+        time_shift_series.plot()
+        plt.show()
+        plt.close()
+        time_shift_series.index = time_shift_series.index.date
+        return time_shift_series
+    else:
+        return pd.Series(0, index=date_index)
diff --git a/ec2/analysis_taks/time-shift-detection/pvanalytics-osd-module.py b/ec2/analysis_taks/time-shift-detection/pvanalytics-osd-module.py
new file mode 100644
index 00000000..72325228
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/pvanalytics-osd-module.py
@@ -0,0 +1,159 @@
+"""
+PVAnalytics-OSD based module. This module will be uploaded by the user
+and tested using the data sets accordingly. 
+"""
+
+import pandas as pd
+from pvanalytics.features import daytime
+from pvanalytics.quality.outliers import zscore, hampel
+from pvanalytics.quality import gaps
+import pvlib
+from gfosd import Problem
+from gfosd.components import SumSquare, SumCard
+
+
+def get_sunrise_sunset_timestamps(time_series, daytime_mask):
+    """
+    Get the timestamp for sunrise/sunset for the time series.
+
+    Parameters
+    ----------
+    time_series: Pandas datetime series of measured data (can be irradiance,
+                                                          power, or energy)
+    daytime_mask: Pandas series of boolean masks for day/night periods.
+        Same datetime index as time_series object.
+
+    Returns
+    ---------
+    sunrise_series: Pandas series of the sunrise
+        datetimes for each day in the time series.
+    sunset_series: Pandas series of the sunset
+        datetimes for each day in the time series.
+    midday_series: Pandas series of the midway
+        point datetimes (halfway between sunrise and sunset) for each day in
+        the time series.
+    """
+    day_night_changes = daytime_mask.groupby(daytime_mask.index.date).apply(
+        lambda x: x.ne(x.shift().ffill())
+    )
+    # Get the first 'day' mask for each day in the series; proxy for sunrise
+    sunrise_series = pd.Series(
+        daytime_mask[(daytime_mask) & (day_night_changes)].index
+    )
+    sunrise_series = pd.Series(
+        sunrise_series.groupby(sunrise_series.dt.date).min(),
+        index=sunrise_series.dt.date,
+    ).drop_duplicates()
+    # Get the sunset value for each day; this is the first nighttime period
+    # after sunrise
+    sunset_series = pd.Series(
+        daytime_mask[~(daytime_mask) & (day_night_changes)].index
+    )
+    sunset_series = pd.Series(
+        sunset_series.groupby(sunset_series.dt.date).max(),
+        index=sunset_series.dt.date,
+    ).drop_duplicates()
+    # Generate a 'midday' series, which is the midpoint between sunrise
+    # and sunset for every day in the data set
+    midday_series = sunrise_series + ((sunset_series - sunrise_series) / 2)
+    # Return the pandas series associated with the sunrise, sunset, and midday points
+    return sunrise_series, sunset_series, midday_series
+
+
+def time_shift_estimation_osd(midday_diff_series, weight=10):
+    """
+    Run OSD-based variation on the midday difference series to estimate
+    time shifts.
+    """
+    c1 = SumSquare(weight=1 / len(midday_diff_series))
+    c2 = SumCard(weight, diff=1)
+    problem = Problem(midday_diff_series, [c1, c2])
+    problem.decompose(verbose=True)
+    # Get the estimated denoised series
+    estimated_series = pd.Series(
+        problem.decomposition[1], index=midday_diff_series.index
+    )
+    hampel_outlier_mask = hampel(estimated_series, window=20, max_deviation=2)
+    estimated_series = estimated_series[~hampel_outlier_mask]
+    estimated_series = estimated_series.ffill()
+    return estimated_series
+
+
+def detect_time_shifts(
+    time_series, latitude, longitude, data_sampling_frequency
+):
+    """
+    Master function for testing for time shifts in a series and returning
+    time-shifted periods
+
+    """
+    # Save the dates of the time series index for reindexing at the end
+    date_index = pd.Series(time_series.index.date).drop_duplicates()
+    # Data pre-processing:
+    # 1) Removal of frozen/stuck data
+    # 2) Removal of data periods with low data 'completeness'
+    # 3) Removal of negative data
+    # 4) Removal of outliers via Hampel + outlier filter
+    # Trim based on frozen data values
+    stale_data_mask = gaps.stale_values_diff(time_series)
+    time_series = time_series[~stale_data_mask]
+    time_series = time_series.asfreq(str(data_sampling_frequency) + "T")
+    # Remove negative data
+    time_series = time_series[(time_series >= 0) | (time_series.isna())]
+    time_series = time_series.asfreq(str(data_sampling_frequency) + "T")
+    # Remove any outliers via z-score filter
+    zscore_outlier_mask = zscore(time_series, zmax=2, nan_policy="omit")
+    time_series = time_series[(~zscore_outlier_mask)]
+    time_series = time_series.asfreq(str(data_sampling_frequency) + "T")
+    # Remove any incomplete days (less than 33% data)
+    completeness = gaps.complete(time_series, minimum_completeness=0.33)
+    time_series = time_series[completeness]
+    time_series = time_series.asfreq(str(data_sampling_frequency) + "T")
+    if len(time_series) > 0:
+        # Calculate a nighttime offset
+        # Mask daytime periods for the time series
+        daytime_mask = daytime.power_or_irradiance(
+            time_series,
+            freq=str(data_sampling_frequency) + "T",
+            low_value_threshold=0.005,
+        )
+        # Get the modeled sunrise and sunset time series based on the system's
+        # latitude-longitude coordinates
+        modeled_sunrise_sunset_df = (
+            pvlib.solarposition.sun_rise_set_transit_spa(
+                time_series.index, latitude, longitude
+            )
+        )
+        modeled_sunrise_sunset_df.index = modeled_sunrise_sunset_df.index.date
+        modeled_sunrise_sunset_df = modeled_sunrise_sunset_df.drop_duplicates()
+        # Calculate the midday point between sunrise and sunset for each day
+        # in the modeled irradiance series
+        modeled_midday_series = (
+            modeled_sunrise_sunset_df["sunrise"]
+            + (
+                modeled_sunrise_sunset_df["sunset"]
+                - modeled_sunrise_sunset_df["sunrise"]
+            )
+            / 2
+        )
+        # Generate the sunrise, sunset, and halfway pts for the data stream
+        sunrise_series, sunset_series, midday_series = (
+            get_sunrise_sunset_timestamps(time_series, daytime_mask)
+        )
+        # Compare the data stream's daily halfway point to the modeled halfway point
+        midday_diff_series = (
+            modeled_midday_series - midday_series
+        ).dt.total_seconds() / 60
+        # Run a secondary z-score outlier filter to clean up the midday series
+        zscore_outlier_mask = zscore(
+            midday_diff_series, zmax=3, nan_policy="omit"
+        )
+        midday_diff_series = midday_diff_series[~zscore_outlier_mask]
+        midday_diff_series = midday_diff_series.dropna().dropna()
+        time_shift_series = time_shift_estimation_osd(midday_diff_series)
+        time_shift_series = (
+            time_shift_series.asfreq("D").reindex(date_index).ffill().bfill()
+        )
+        return time_shift_series
+    else:
+        return pd.Series(0, index=date_index)
diff --git a/ec2/analysis_taks/time-shift-detection/pvinsight-time-shift-runner.py b/ec2/analysis_taks/time-shift-detection/pvinsight-time-shift-runner.py
new file mode 100644
index 00000000..1a686f7a
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/pvinsight-time-shift-runner.py
@@ -0,0 +1,312 @@
+"""
+Runner script for assessing the time shift validation algorithm. In this
+script, the following occurs:
+    1. Pull down all of the metadata associated with the data sets
+    2. Loop through all metadata cases, pull down the associated data, and
+    run the associated submission on it
+    3. Aggregate the results for the entire data set and generate assessment 
+    metrics. Assessment metrics will vary based on the type of analysis being
+    run. For this analysis, the following is calculated:
+        1. Mean Absolute Error between predicted time shift series and ground
+        truth time series (in minutes)
+        2. Average run time for each data set (in seconds)
+    4. Further aggregate performance metrics into visualizations:
+        -Distribution Histograms
+        -Scatter plots
+      This section will be dependent on the type of analysis being run.
+"""
+
+import pandas as pd
+import ast
+import os
+from importlib import import_module
+import inspect
+import time
+from collections import ChainMap
+import seaborn as sns
+import matplotlib.pyplot as plt
+import json
+import numpy as np
+
+from utilities import progress
+
+MODULE_NAME = "pvanalytics-cpd-module-pva"
+
+
+def generate_histogram(
+    dataframe, x_axis, title, color_code=None, number_bins=30
+):
+    """
+    Generate a histogram for a distribution. Option to color code the
+    histogram by the color_code column parameter.
+    """
+    sns.displot(
+        dataframe, x=x_axis, hue=color_code, multiple="stack", bins=number_bins
+    )
+    plt.title(title)
+    plt.tight_layout()
+    return plt
+
+
+def generate_scatter_plot(dataframe, x_axis, y_axis, title):
+    """
+    Generate a scatterplot between an x- and a y-variable.
+    """
+    sns.scatterplot(data=dataframe, x=x_axis, y=y_axis)
+    plt.title(title)
+    plt.tight_layout()
+    return plt
+
+
+if __name__ == "__main__":
+    # Read in the JSON associated with the run
+    with open("config.json") as f:
+        config_data = json.load(f)
+    # Load in the module that we're going to test on.
+    module_to_import = MODULE_NAME
+    # Generate list for us to store all of our results for the module
+    results_list = list()
+    # Load in data set that we're going to analyze.
+    # System metadata: This CSV represents the system_metadata table, which is
+    # a master table for associated system metadata (system_id, name, azimuth,
+    # tilt, etc.)
+    system_metadata = pd.read_csv("./data/system_metadata.csv")
+    # File metadata: This file represents the file_metadata table, which is
+    # the master table for files associated with different tests (az-tilt,
+    # time shifts, degradation, etc.). Contains file name, associated file
+    # information (sampling frequency, specific test, timezone, etc) as well
+    # as ground truth information to test against in the validation_dictionary
+    # field
+    file_metadata = pd.read_csv("./data/file_metadata.csv")
+    # Validation tests: This file represents the validation_tests table,
+    # which is the master table associated with each of the tests run on the
+    # PVInsight Validation Hub. This table contains information on test type
+    # (example: time_shifts, az_tilt_detection, etc), as well as function name
+    # for each test type, performance metrics outputs (how to assess
+    # test performance), as well as expected function outputs and function
+    # output types (in order). This provides a standardized template of
+    # what to expect (input, output, naming conventions) for each test
+    validation_tests = pd.read_csv("./data/validation_tests.csv")
+    # File category link: This file represents the file_category_link table,
+    # which links specific files in the file_metadata table to categories in
+    # the validation_tests table. This table exists specifically to allow for
+    # many-to-many relationships, where one file can link to multiple
+    # categories/tests, and multiple categories/tests can link to multiple
+    # files. This table exists solely to link these two tables together
+    # when performing testing.
+    file_category_link = pd.read_csv("./data/file_test_link.csv")
+    # Link the above tables together to get all of the files associated
+    # with the time_shift category in the validation_tests table.
+    time_shift_test_information = dict(
+        validation_tests[
+            validation_tests["category_name"] == config_data["category_name"]
+        ].iloc[0]
+    )
+    # Get the associated metrics we're supposed to calculate
+    performance_metrics = ast.literal_eval(
+        time_shift_test_information["performance_metrics"]
+    )
+    # Get all of the linked files for time shift analysis via a series
+    # of dataframe joins
+    associated_file_ids = list(
+        file_category_link[
+            file_category_link["category_id"]
+            == time_shift_test_information["category_id"]
+        ]["file_id"]
+    )
+    associated_files = file_metadata[
+        file_metadata["file_id"].isin(associated_file_ids)
+    ]
+    associated_files = associated_files
+    # Get the information associated with the module to run the tests
+    # Get the name of the function we want to import associated with this
+    # test
+    function_name = time_shift_test_information["function_name"]
+    # Import designated module via importlib
+    module = import_module(module_to_import)
+    function = getattr(module, function_name)
+    function_parameters = list(inspect.signature(function).parameters)
+    # Loop through each file and generate predictions
+    total = len(associated_files)
+    t_start = time.time()
+    for index, row in associated_files.iterrows():
+        progress(
+            index,
+            total,
+            status=f"elapsed time: {(time.time() - t_start) / 60:.2f} minutes",
+        )
+        # Get file_name, which will be pulled from database or S3 for
+        # each analysis
+        file_name = row["file_name"]
+        # Get associated system ID
+        system_id = row["system_id"]
+        # Get all of the associated metadata for the particular file based
+        # on its system ID. This metadata will be passed in via kwargs for
+        # any necessary arguments
+        associated_metadata = dict(
+            system_metadata[system_metadata["system_id"] == system_id].iloc[0]
+        )
+        # Get the ground truth scalars that we will compare to
+        ground_truth_dict = dict()
+        if config_data["comparison_type"] == "scalar":
+            for val in config_data["ground_truth_compare"]:
+                ground_truth_dict[val] = associated_metadata[val]
+        if config_data["comparison_type"] == "time_series":
+            ground_truth_series = pd.read_csv(
+                os.path.join("./data/validation_data/", file_name),
+                index_col=0,
+                parse_dates=True,
+            ).squeeze()
+            ground_truth_dict["time_series"] = ground_truth_series
+        # Create master dictionary of all possible function kwargs
+        kwargs_dict = dict(ChainMap(dict(row), associated_metadata))
+        # Filter out to only allowable args for the function
+        kwargs_dict = {
+            key: kwargs_dict[key] for key in config_data["allowable_kwargs"]
+        }
+        # Now that we've collected all of the information associated with the
+        # test, let's read in the file as a pandas dataframe (this data
+        # would most likely be stored in an S3 bucket)
+        time_series = pd.read_csv(
+            os.path.join("./data/file_data/", file_name),
+            index_col=0,
+            parse_dates=True,
+        ).squeeze()
+        time_series = time_series.asfreq(
+            str(row["data_sampling_frequency"]) + "T"
+        )
+        # Filter the kwargs dictionary based on required function params
+        kwargs = dict(
+            (k, kwargs_dict[k])
+            for k in function_parameters
+            if k in kwargs_dict
+        )
+        # Get the performance metrics that we want to quantify
+        performance_metrics = config_data["performance_metrics"]
+        # Run the routine (timed)
+        start_time = time.time()
+        data_outputs = function(time_series, **kwargs).tz_localize(None)
+        end_time = time.time()
+        function_run_time = end_time - start_time
+        # Convert the data outputs to a dictionary identical to the
+        # ground truth dictionary
+        output_dictionary = dict()
+        if config_data["comparison_type"] == "scalar":
+            for idx in range(len(config_data["ground_truth_compare"])):
+                output_dictionary[config_data["ground_truth_compare"][idx]] = (
+                    data_outputs[idx]
+                )
+        if config_data["comparison_type"] == "time_series":
+            output_dictionary["time_series"] = data_outputs
+        # Run routine for all of the performance metrics and append
+        # results to the dictionary
+        results_dictionary = dict()
+        results_dictionary["file_name"] = file_name
+        # Set the runtime in the results dictionary
+        results_dictionary["run_time"] = function_run_time
+        # Set the data requirements in the dictionary
+        results_dictionary["data_requirements"] = function_parameters
+        # Loop through the rest of the performance metrics and calculate them
+        # (this predominantly applies to error metrics)
+        for metric in performance_metrics:
+            if metric == "absolute_error":
+                # Loop through the input and the output dictionaries,
+                # and calculate the absolute error
+                for val in config_data["ground_truth_compare"]:
+                    error = np.abs(
+                        output_dictionary[val] - ground_truth_dict[val]
+                    )
+                    results_dictionary[metric + "_" + val] = error
+            elif metric == "mean_absolute_error":
+                for val in config_data["ground_truth_compare"]:
+                    error = np.mean(
+                        np.abs(output_dictionary[val] - ground_truth_dict[val])
+                    )
+                    results_dictionary[metric + "_" + val] = error
+        results_list.append(results_dictionary)
+    progress(
+        total,
+        total,
+        status=f"elapsed time: {(time.time() - t_start) / 60:.2f} minutes",
+    )
+    # Convert the results to a pandas dataframe and perform all of the
+    # post-processing in the script
+    results_df = pd.DataFrame(results_list)
+    # Build out the final processed results:
+    #   1) Public reporting: mean MAE, mean run time, etc.
+    #   2) Private reporting: graphics and tables split by different factors
+    # First get mean value for all the performance metrics and save (this will
+    # be saved to a public metrics dictionary)
+    public_metrics_dict = dict()
+    public_metrics_dict["module"] = module_to_import
+    # Get the mean and median run times
+    public_metrics_dict["mean_run_time"] = results_df["run_time"].mean()
+    public_metrics_dict["median_run_time"] = results_df["run_time"].median()
+    public_metrics_dict["function_parameters"] = function_parameters
+    for metric in performance_metrics:
+        if "absolute_error" in metric:
+            for val in config_data["ground_truth_compare"]:
+                public_metrics_dict["mean_" + metric + "_" + val] = results_df[
+                    metric + "_" + val
+                ].mean()
+                public_metrics_dict["median_" + metric + "_" + val] = (
+                    results_df[metric + "_" + val].median()
+                )
+    # TODO: Write public metric information to a public results table. here we
+    # just write a json to illustrate that final outputs.
+    with open(config_data["public_results_table"], "w") as fp:
+        json.dump(public_metrics_dict, fp)
+    # Now generate private results. These will be more specific to the
+    # type of analysis being run as results will be color-coded by certain
+    # parameters. These params will be available as columns in the
+    # 'associated_files' dataframe
+    results_df_private = pd.merge(results_df, associated_files, on="file_name")
+    # Filter to only the necessary columns (available via the config)
+    results_df_private = results_df_private[
+        config_data["private_results_columns"]
+    ]
+    results_df_private.to_csv(
+        os.path.join("./results", module_to_import + "_full_results.csv")
+    )
+    # Loop through all of the plot dictionaries and generate plots and
+    # associated tables for reporting
+    for plot in config_data["plots"]:
+        if plot["type"] == "histogram":
+            if "color_code" in plot:
+                color_code = plot["color_code"]
+            else:
+                color_code = None
+            gen_plot = generate_histogram(
+                results_df_private, plot["x_val"], plot["title"], color_code
+            )
+            # Save the plot
+            gen_plot.savefig(plot["save_file_path"])
+            plt.close()
+            plt.clf()
+            # Write the stratified results to a table for private reporting
+            # (if color_code param is not None)
+            if color_code:
+                stratified_results_tbl = pd.DataFrame(
+                    results_df_private.groupby(color_code)[
+                        plot["x_val"]
+                    ].mean()
+                )
+                stratified_results_tbl.to_csv(
+                    os.path.join(
+                        "./results",
+                        module_to_import
+                        + "_"
+                        + str(color_code)
+                        + "_"
+                        + plot["x_val"]
+                        + ".csv",
+                    )
+                )
+        if plot["type"] == "scatter_plot":
+            gen_plot = generate_scatter_plot(
+                results_df_private, plot["x_val"], plot["y_val"], plot["title"]
+            )
+            # Save the plot
+            gen_plot.savefig(plot["save_file_path"])
+            plt.close()
+            plt.clf()
diff --git a/ec2/analysis_taks/time-shift-detection/pvinsight-validation-runner-tester.py b/ec2/analysis_taks/time-shift-detection/pvinsight-validation-runner-tester.py
new file mode 100644
index 00000000..7f0af229
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/pvinsight-validation-runner-tester.py
@@ -0,0 +1,448 @@
+"""
+Runner script for assessing validation algorithms. In this
+script, the following occurs:
+    1. Pull down all of the metadata associated with the data sets
+    2. Loop through all metadata cases, pull down the associated data, and
+    run the associated submission on it
+    3. Aggregate the results for the entire data set and generate assessment 
+    metrics. Assessment metrics will vary based on the type of analysis being
+    run. Some examples include:
+        1. Mean Absolute Error between predicted time shift series and ground
+        truth time series (in minutes)
+        2. Average run time for each data set (in seconds)
+    4. Further aggregate performance metrics into visualizations:
+        -Distribution Histograms
+        -Scatter plots
+      This section will be dependent on the type of analysis being run.
+"""
+
+import pandas as pd
+import ast
+import os
+from importlib import import_module
+import inspect
+import time
+from collections import ChainMap
+from sklearn.metrics import mean_absolute_error
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+import json
+import requests
+import tarfile
+import shutil
+import sys
+import zipfile
+import subprocess
+import logging
+
+
+# Basic logging configuration
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+# Create a logger
+logger = logging.getLogger(__name__)
+
+
+def is_local():
+    """
+    Checks if the application is running locally or in an Amazon ECS environment.
+
+    Returns:
+        bool: True if the application is running locally, False otherwise.
+    """
+    return (
+        "AWS_EXECUTION_ENV" not in os.environ
+        and "ECS_CONTAINER_METADATA_URI" not in os.environ
+        and "ECS_CONTAINER_METADATA_URI_V4" not in os.environ
+    )
+
+
+is_s3_emulation = is_local()
+
+
+def pull_from_s3(s3_file_path):
+    if s3_file_path.startswith("/"):
+        s3_file_path = s3_file_path[1:]
+
+    if is_s3_emulation:
+        s3_file_full_path = "http://s3:5000/get_object/" + s3_file_path
+    else:
+        s3_file_full_path = "s3://" + s3_file_path
+
+    target_file_path = os.path.join("/tmp/", s3_file_full_path.split("/")[-1])
+
+    if is_s3_emulation:
+        r = requests.get(s3_file_full_path, stream=True)
+        if r.status_code != 200:
+            print(
+                f"error get file {s3_file_path} from s3, status code {r.status_code} {r.content}",
+                file=sys.stderr,
+            )
+        with open(target_file_path, "wb") as f:
+            f.write(r.content)
+    else:
+        raise NotImplementedError("real s3 mode not implemented")
+
+    return target_file_path
+
+
+def push_to_s3(local_file_path, s3_file_path):
+    if s3_file_path.startswith("/"):
+        s3_file_path = s3_file_path[1:]
+
+    if is_s3_emulation:
+        s3_file_full_path = "http://s3:5000/put_object/" + s3_file_path
+    else:
+        s3_file_full_path = "s3://" + s3_file_path
+
+    if is_s3_emulation:
+        with open(local_file_path, "rb") as f:
+            file_content = f.read()
+            r = requests.put(s3_file_full_path, data=file_content)
+            if r.status_code != 204:
+                print(
+                    f"error put file {s3_file_path} to s3, status code {r.status_code} {r.content}",
+                    file=sys.stderr,
+                )
+    else:
+        raise NotImplementedError("real s3 mode not implemented")
+
+
+def convert_compressed_file_path_to_directory(compressed_file_path):
+    path_components = compressed_file_path.split("/")
+    path_components[-1] = path_components[-1].split(".")[0]
+    path_components = "/".join(path_components)
+    return path_components
+
+
+def get_file_extension(path):
+    return path.split("/")[-1].split(".")[-1]
+
+
+def decompress_file(path):
+    if get_file_extension(path) == "gz":
+        with tarfile.open(path, "r:gz") as tar:
+            tar.extractall(convert_compressed_file_path_to_directory(path))
+    else:
+        with zipfile.ZipFile(path, "r") as zip_ref:
+            zip_ref.extractall(convert_compressed_file_path_to_directory(path))
+    return convert_compressed_file_path_to_directory(path)
+
+
+def get_module_file_name(module_dir):
+    for root, _, files in os.walk(module_dir, topdown=True):
+        for name in files:
+            if name.endswith(".py"):
+                return name.split("/")[-1]
+
+
+def get_module_name(module_dir):
+    return get_module_file_name(module_dir)[:-3]
+
+
+def generate_histogram(
+    dataframe, x_axis, title, color_code=None, number_bins=30
+):
+    """
+    Generate a histogram for a distribution. Option to color code the
+    histogram by the color_code column parameter.
+    """
+    sns.displot(
+        dataframe, x=x_axis, hue=color_code, multiple="stack", bins=number_bins
+    )
+    plt.title(title)
+    plt.tight_layout()
+    return plt
+
+
+def generate_scatter_plot(dataframe, x_axis, y_axis, title):
+    """
+    Generate a scatterplot between an x- and a y-variable.
+    """
+    sns.scatterplot(data=dataframe, x=x_axis, y=y_axis)
+    plt.title(title)
+    plt.tight_layout()
+    return plt
+
+
+def run(module_to_import_s3_path, current_evaluation_dir=None):
+    # # If a path is provided, set the directories to that path, otherwise use default
+    # if current_evaluation_dir is not None:
+    #     results_dir = current_evaluation_dir + "/results" if not current_evaluation_dir.endswith('/') else current_evaluation_dir + "results"
+    #     data_dir = current_evaluation_dir + "/data" if not current_evaluation_dir.endswith('/') else current_evaluation_dir + "data"
+    # else:
+    #     results_dir = "./results"
+    #     data_dir = "./data"
+
+    # if current_evaluation_dir is not None:
+    #     sys.path.append(current_evaluation_dir)  # append current_evaluation_dir to sys.path
+
+    # # Ensure results directory exists
+    # os.makedirs(results_dir, exist_ok=True)
+
+    # # Ensure results directory exists
+    # os.makedirs(data_dir, exist_ok=True)
+
+    # # Load in the module that we're going to test on.
+
+    # target_module_compressed_file_path = pull_from_s3(module_to_import_s3_path)
+
+    # target_module_path = decompress_file(target_module_compressed_file_path)
+
+    # # get current directory, i.e. directory of runner.py file
+    # new_dir = os.path.dirname(os.path.abspath(__file__))
+
+    # file_name = get_module_file_name(target_module_path)
+    # module_name = get_module_name(target_module_path)
+
+    # # install submission dependency
+    # try:
+    #     subprocess.check_call(["python", "-m", "pip", "install", "-r", os.path.join(target_module_path, 'requirements.txt')])
+    #     print("submission dependencies installed successfully.")
+    # except subprocess.CalledProcessError as e:
+    #     print("error installing submission dependencies:", e)
+    # shutil.move(os.path.join(target_module_path, file_name), os.path.join(new_dir, file_name))
+    module_name = "pvanalytics-cpd-module"
+    data_dir = "C:/Users/kperry/Documents/source/repos/time-shift-validation-hub/data/"
+    # Generate list for us to store all of our results for the module
+    results_list = list()
+    # Load in data set that we're going to analyze.
+
+    # Make GET requests to the Django API to get the system metadata
+    # system_metadata_response = requests.get('http://api:8005/system_metadata/systemmetadata/')
+
+    # Convert the responses to DataFrames
+
+    # System metadata: This CSV represents the system_metadata table, which is
+    # a master table for associated system metadata (system_id, name, azimuth,
+    # tilt, etc.)
+    system_metadata = pd.read_csv(
+        "C:/Users/kperry/Documents/source/repos/time-shift-validation-hub/data/system_metadata.csv"
+    )  # pd.DataFrame(system_metadata_response.json())
+
+    # File category link: This file represents the file_category_link table,
+    # which links specific files in the file_metadata table.
+    # This table exists specifically to allow for
+    # many-to-many relationships, where one file can link to multiple
+    # categories/tests, and multiple categories/tests can link to multiple
+    # files.
+    file_test_link = pd.read_csv(
+        "C:/Users/kperry/Documents/source/repos/time-shift-validation-hub/data/file_test_link.csv"
+    )
+    # pd.read_csv(os.path.join(current_evaluation_dir,
+    #                                          "file_test_link.csv"))
+
+    # Get the unique file ids
+    unique_file_ids = file_test_link["file_id"].unique()
+
+    # File metadata: This file represents the file_metadata table, which is
+    # the master table for files associated with different tests (az-tilt,
+    # time shifts, degradation, etc.). Contains file name, associated file
+    # information (sampling frequency, specific test, timezone, etc) as well
+    # as ground truth information to test against in the validation_dictionary
+    # field
+    # For each unique file id, make a GET request to the Django API to get the corresponding file metadata
+    # file_metadata_list = []
+    # for file_id in unique_file_ids:
+    #     response = requests.get(f'http://api:8005/file_metadata/filemetadata/{file_id}/')
+    #     file_metadata_list.append(response.json())
+
+    # Convert the list of file metadata to a DataFrame
+    file_metadata = pd.read_csv(
+        "C:/Users/kperry/Documents/source/repos/time-shift-validation-hub/data/file_metadata.csv"
+    )  # pd.DataFrame(file_metadata_list)
+    current_evaluation_dir = (
+        "C:/Users/kperry/Documents/source/repos/time-shift-validation-hub"
+    )
+    # Read in the configuration JSON for the particular run
+    with open(os.path.join(current_evaluation_dir, "config.json")) as f:
+        config_data = json.load(f)
+
+    # Get the associated metrics we're supposed to calculate
+    performance_metrics = config_data["performance_metrics"]
+
+    # Get the name of the function we want to import associated with this
+    # test
+    function_name = config_data["function_name"]
+    # Import designated module via importlib
+    module = import_module(module_name)
+    function = getattr(module, function_name)
+    function_parameters = list(inspect.signature(function).parameters)
+    # Loop through each file and generate predictions
+    for index, row in file_metadata.iterrows():
+        # Get file_name, which will be pulled from database or S3 for
+        # each analysis
+        file_name = row["file_name"]
+        # Get associated system ID
+        system_id = row["system_id"]
+        # Get all of the associated metadata for the particular file based
+        # on its system ID. This metadata will be passed in via kwargs for
+        # any necessary arguments
+        associated_metadata = dict(
+            system_metadata[system_metadata["system_id"] == system_id].iloc[0]
+        )
+        # Get the ground truth scalars that we will compare to
+        ground_truth_dict = dict()
+        if config_data["comparison_type"] == "scalar":
+            for val in config_data["ground_truth_compare"]:
+                ground_truth_dict[val] = associated_metadata[val]
+        if config_data["comparison_type"] == "time_series":
+            ground_truth_series = pd.read_csv(
+                os.path.join(data_dir + "/validation_data/", file_name),
+                index_col=0,
+                parse_dates=True,
+            ).squeeze()
+            ground_truth_dict["time_series"] = ground_truth_series
+        # Create master dictionary of all possible function kwargs
+        kwargs_dict = dict(ChainMap(dict(row), associated_metadata))
+        # Filter out to only allowable args for the function
+        kwargs_dict = {
+            key: kwargs_dict[key] for key in config_data["allowable_kwargs"]
+        }
+        # Now that we've collected all of the information associated with the
+        # test, let's read in the file as a pandas dataframe (this data
+        # would most likely be stored in an S3 bucket)
+        time_series = pd.read_csv(
+            os.path.join(data_dir + "/file_data/", file_name),
+            index_col=0,
+            parse_dates=True,
+        ).squeeze()
+        time_series = time_series.asfreq(
+            str(row["data_sampling_frequency"]) + "T"
+        )
+        # Filter the kwargs dictionary based on required function params
+        kwargs = dict(
+            (k, kwargs_dict[k])
+            for k in function_parameters
+            if k in kwargs_dict
+        )
+        # Run the routine (timed)
+        start_time = time.time()
+        data_outputs = function(time_series, **kwargs)
+        end_time = time.time()
+        function_run_time = end_time - start_time
+        # Convert the data outputs to a dictionary identical to the
+        # ground truth dictionary
+        output_dictionary = dict()
+        if config_data["comparison_type"] == "scalar":
+            for idx in range(len(config_data["ground_truth_compare"])):
+                output_dictionary[config_data["ground_truth_compare"][idx]] = (
+                    data_outputs[idx]
+                )
+        if config_data["comparison_type"] == "time_series":
+            output_dictionary["time_series"] = data_outputs
+        # Run routine for all of the performance metrics and append
+        # results to the dictionary
+        results_dictionary = dict()
+        results_dictionary["file_name"] = file_name
+        # Set the runtime in the results dictionary
+        results_dictionary["run_time"] = function_run_time
+        # Set the data requirements in the dictionary
+        results_dictionary["data_requirements"] = function_parameters
+        # Loop through the rest of the performance metrics and calculate them
+        # (this predominantly applies to error metrics)
+        for metric in performance_metrics:
+            if metric == "absolute_error":
+                # Loop through the input and the output dictionaries,
+                # and calculate the absolute error
+                for val in config_data["ground_truth_compare"]:
+                    error = np.abs(
+                        output_dictionary[val] - ground_truth_dict[val]
+                    )
+                    results_dictionary[metric + "_" + val] = error
+            elif metric == "mean_absolute_error":
+                for val in config_data["ground_truth_compare"]:
+                    error = np.mean(
+                        np.abs(output_dictionary[val] - ground_truth_dict[val])
+                    )
+                    results_dictionary[metric + "_" + val] = error
+        results_list.append(results_dictionary)
+    # Convert the results to a pandas dataframe and perform all of the
+    # post-processing in the script
+    results_df = pd.DataFrame(results_list)
+    # # Build out the final processed results:
+    # #   1) Public reporting: mean MAE, mean run time, etc.
+    # #   2) Private reporting: graphics and tables split by different factors
+    # # First get mean value for all the performance metrics and save (this will
+    # # be saved to a public metrics dictionary)
+    # public_metrics_dict = dict()
+    # public_metrics_dict['module'] = module_name
+    # # Get the mean and median run times
+    # public_metrics_dict['mean_run_time'] = results_df['run_time'].mean()
+    # public_metrics_dict['median_run_time'] = results_df['run_time'].median()
+    # public_metrics_dict['function_parameters'] = function_parameters
+    # for metric in performance_metrics:
+    #     if 'absolute_error' in metric:
+    #         for val in config_data['ground_truth_compare']:
+    #             public_metrics_dict['mean_' + metric + '_' + val] = \
+    #                 results_df[metric + "_" + val].mean()
+    #             public_metrics_dict['median_' + metric + '_' + val] = \
+    #                 results_df[metric + "_" + val].median()
+    # # Write public metric information to a public results table.
+    # with open(os.path.join(results_dir, config_data['public_results_table']),
+    #           'w') as fp:
+    #     json.dump(public_metrics_dict, fp)
+    # # Now generate private results. These will be more specific to the
+    # # type of analysis being run as results will be color-coded by certain
+    # # parameters. These params will be available as columns in the
+    # # 'associated_files' dataframe
+    # results_df_private = pd.merge(results_df,
+    #                               file_metadata,
+    #                               on='file_name')
+    # # Filter to only the necessary columns (available via the config)
+    # results_df_private = results_df_private[config_data
+    #                                         ["private_results_columns"]]
+    # results_df_private.to_csv(
+    #     os.path.join(results_dir,
+    #                   module_name + "_full_results.csv"))
+    # # Loop through all of the plot dictionaries and generate plots and
+    # # associated tables for reporting
+    # for plot in config_data['plots']:
+    #     if plot['type'] == 'histogram':
+    #         if 'color_code' in plot:
+    #             color_code = plot['color_code']
+    #         else:
+    #             color_code = None
+    #         gen_plot = generate_histogram(results_df_private,
+    #                                       plot['x_val'],
+    #                                       plot['title'],
+    #                                       color_code)
+    #         # Save the plot
+    #         gen_plot.savefig(os.path.join(results_dir,
+    #                                       plot['save_file_path']))
+    #         plt.close()
+    #         plt.clf()
+    #         # Write the stratified results to a table for private reporting
+    #         # (if color_code param is not None)
+    #         if color_code:
+    #             stratified_results_tbl = pd.DataFrame(
+    #                 results_df_private.groupby(color_code)[
+    #                     plot['x_val']].mean())
+    #             stratified_results_tbl.to_csv(
+    #                 os.path.join(results_dir,
+    #                              module_name + '_' + str(color_code) +
+    #                               '_' + plot['x_val'] + '.csv'))
+    #     if plot['type'] == 'scatter_plot':
+    #         gen_plot = generate_scatter_plot(results_df_private,
+    #                                          plot['x_val'],
+    #                                          plot['y_val'],
+    #                                          plot['title'])
+    #         # Save the plot
+    #         gen_plot.savefig(os.path.join(results_dir,
+    #                                       plot['save_file_path']))
+    #         plt.close()
+    #         plt.clf()
+    # return public_metrics_dict
+
+
+if __name__ == "__main__":
+    run(
+        "pv-validation-hub-bucket/submission_files/submission_user_1/submission_1/archive.tar.gz"
+    )
+    push_to_s3(
+        "/pv-validation-hub-bucket/submission_files/submission_user_1/submission_1/results/time-shift-public-metrics.json",
+        "pv-validation-hub-bucket/test_bucket/test_subfolder/res.json",
+    )
diff --git a/ec2/analysis_taks/time-shift-detection/requirements.txt b/ec2/analysis_taks/time-shift-detection/requirements.txt
new file mode 100644
index 00000000..519c3aeb
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/requirements.txt
@@ -0,0 +1,6 @@
+matplotlib==3.9.1
+numpy==1.26.4
+pandas==2.2.2
+pvanalytics==0.2.0
+pvlib==0.10.5
+ruptures==1.1.8
\ No newline at end of file
diff --git a/ec2/analysis_taks/time-shift-detection/results/data_sampling_frequency_mean_absolute_error.png b/ec2/analysis_taks/time-shift-detection/results/data_sampling_frequency_mean_absolute_error.png
new file mode 100644
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diff --git a/ec2/analysis_taks/time-shift-detection/results/data_sampling_frequency_mean_absolute_error_results.csv b/ec2/analysis_taks/time-shift-detection/results/data_sampling_frequency_mean_absolute_error_results.csv
new file mode 100644
index 00000000..d32d09a5
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/results/data_sampling_frequency_mean_absolute_error_results.csv
@@ -0,0 +1,5 @@
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+1,0.0
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+30,0.0
+60,0.0
diff --git a/ec2/analysis_taks/time-shift-detection/results/data_sampling_frequency_run_time.png b/ec2/analysis_taks/time-shift-detection/results/data_sampling_frequency_run_time.png
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diff --git a/ec2/analysis_taks/time-shift-detection/results/data_sampling_frequency_run_time_results.csv b/ec2/analysis_taks/time-shift-detection/results/data_sampling_frequency_run_time_results.csv
new file mode 100644
index 00000000..34c3d341
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/results/data_sampling_frequency_run_time_results.csv
@@ -0,0 +1,5 @@
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diff --git a/ec2/analysis_taks/time-shift-detection/results/issue_mean_absolute_error.png b/ec2/analysis_taks/time-shift-detection/results/issue_mean_absolute_error.png
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diff --git a/ec2/analysis_taks/time-shift-detection/results/issue_mean_absolute_error_results.csv b/ec2/analysis_taks/time-shift-detection/results/issue_mean_absolute_error_results.csv
new file mode 100644
index 00000000..a8434a71
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/results/issue_mean_absolute_error_results.csv
@@ -0,0 +1,4 @@
+issue,mean_absolute_error
+Full DST,11.934220830070478
+No Issue,0.0
+Wrong Timezone,0.0
diff --git a/ec2/analysis_taks/time-shift-detection/results/issue_run_time.png b/ec2/analysis_taks/time-shift-detection/results/issue_run_time.png
new file mode 100644
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diff --git a/ec2/analysis_taks/time-shift-detection/results/issue_run_time_results.csv b/ec2/analysis_taks/time-shift-detection/results/issue_run_time_results.csv
new file mode 100644
index 00000000..8c907134
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/results/issue_run_time_results.csv
@@ -0,0 +1,4 @@
+issue,run_time
+Full DST,24.848682761192322
+No Issue,14.000648657480875
+Wrong Timezone,107.26202821731567
diff --git a/ec2/analysis_taks/time-shift-detection/results/mean_absolute_error_time_series_dist.png b/ec2/analysis_taks/time-shift-detection/results/mean_absolute_error_time_series_dist.png
new file mode 100644
index 0000000000000000000000000000000000000000..f34a598124ad188d60d194342a80968451db478b
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literal 0
HcmV?d00001

diff --git a/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module-pva_data_sampling_frequency_mean_absolute_error_time_series.csv b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module-pva_data_sampling_frequency_mean_absolute_error_time_series.csv
new file mode 100644
index 00000000..afaca500
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module-pva_data_sampling_frequency_mean_absolute_error_time_series.csv
@@ -0,0 +1,5 @@
+data_sampling_frequency,mean_absolute_error_time_series
+1,1.8984743514134148
+15,1.729425573654835
+30,1.7010007977468313
+60,4.734657405015671
diff --git a/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module-pva_full_results.csv b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module-pva_full_results.csv
new file mode 100644
index 00000000..c5d14b94
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module-pva_full_results.csv
@@ -0,0 +1,226 @@
+,system_id,file_name,run_time,data_requirements,mean_absolute_error_time_series,data_sampling_frequency,issue
+0,4,4_ac_power__315_full_DST.csv,18.830113172531128,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.40407204385278,15,Full DST
+1,4,4_ac_power__315_30min_wrong_timezone.csv,9.96409273147583,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",37.676332288401255,30,Wrong Timezone
+2,4,4_ac_power__315_60min_wrong_timezone.csv,6.543065309524536,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.29365700861394,60,Wrong Timezone
+3,4,4_ac_power__315_60min.csv,6.601576328277588,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",2.8778386844166013,60,No Issue
+4,4,4_ac_power__315_30min.csv,14.413450717926025,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.8574784651527017,30,No Issue
+5,4,4_ac_power__315_15min.csv,19.629616498947144,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",2.137823022709475,15,No Issue
+6,4,4_ac_power__315_15min_full_DST.csv,27.506799459457397,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.40407204385278,15,Full DST
+7,50,50_ac_power_wrong_timezone.csv,365.19071793556213,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,Wrong Timezone
+8,50,50_ac_power_1_random_time_shifts.csv,376.5482888221741,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.12096774193548387,1,Random Time Shifts
+9,50,50_ac_power_15min_wrong_timezone.csv,25.03329300880432,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+10,50,50_ac_power_1_15min_random_time_shifts.csv,24.982542753219604,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.3326612903225806,15,Random Time Shifts
+11,50,50_ac_power_1_30min_wrong_timezone.csv,13.90609073638916,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+12,50,50_ac_power_1_60min_wrong_timezone.csv,4.574662923812866,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.92641129032258,60,Wrong Timezone
+13,50,50_ac_power_2_full_DST.csv,368.22898745536804,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.12096774193548387,1,Full DST
+14,50,50_ac_power_30min.csv,12.097249746322632,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+15,50,50_ac_power_2_30min_random_time_shifts.csv,14.361329317092896,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.329637096774194,30,Random Time Shifts
+16,50,50_ac_power_2_15min_wrong_timezone.csv,25.421506643295288,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+17,50,50_ac_power_30min_wrong_timezone.csv,12.185845851898193,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+18,50,50_ac_power.csv,207.22214818000793,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+19,50,50_ac_power_1.csv,208.75459551811218,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+20,50,50_ac_power_15min.csv,12.34924840927124,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+21,50,50_ac_power_1_15min.csv,12.39419174194336,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+22,50,50_ac_power_1_30min.csv,6.9678709506988525,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+23,50,50_ac_power_1_60min.csv,4.159549713134766,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.92641129032258,60,No Issue
+24,50,50_ac_power_2.csv,228.48227071762085,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+25,50,50_ac_power_2_15min.csv,15.239885807037354,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+26,50,50_ac_power_2_30min.csv,7.395113706588745,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,30,No Issue
+27,50,50_ac_power_2_60min.csv,6.804012298583984,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,No Issue
+28,50,50_ac_power_2_60min_random_time_shifts.csv,4.644904851913452,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.090725806451612,60,Random Time Shifts
+29,50,50_ac_power_60min_random_time_shifts.csv,4.830859422683716,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",11.930443548387096,60,Random Time Shifts
+30,50,50_ac_power_60min.csv,5.1325109004974365,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.92641129032258,60,No Issue
+31,1229,1229_poa_irradiance_west_array_60min.csv,6.955023288726807,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+32,1229,1229_gh_irradiance_15min.csv,20.539912223815918,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+33,1229,1229_gh_irradiance_30min.csv,11.597663879394531,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+34,1229,1229_gh_irradiance_60min.csv,7.080140113830566,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+35,1229,1229_poa_irradiance_15min.csv,25.168532133102417,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+36,1229,1229_poa_irradiance_30min.csv,16.446418046951294,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+37,1229,1229_poa_irradiance_60min.csv,10.786420345306396,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+38,1229,1229_poa_irradiance_south_array_15min.csv,21.43882656097412,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+39,1229,1229_poa_irradiance_south_array_30min.csv,12.985199213027954,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+40,1229,1229_poa_irradiance_south_array_60min.csv,7.553039789199829,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+41,1229,1229_poa_irradiance_west_array_15min.csv,19.650710344314575,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+42,1229,1229_poa_irradiance_west_array_30min.csv,11.386460304260254,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+43,1229,1229_poa_irradiance_west_array_30min_partial_DST.csv,13.315558671951294,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Partial DST
+44,1229,1229_poa_irradiance_west_array_15min_wrong_timezone.csv,26.307789087295532,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+45,1229,1229_poa_irradiance_south_array_60min_wrong_timezone.csv,5.828544616699219,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+46,1229,1229_poa_irradiance_south_array_30min_random_time_shifts.csv,12.609826564788818,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.04633204633204633,30,Random Time Shifts
+47,1229,1229_poa_irradiance_south_array_15min_random_time_shifts.csv,24.809378623962402,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+48,1229,1229_poa_irradiance_south_array_full_DST.csv,22.43193507194519,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Full DST
+49,1229,1229_poa_irradiance_60min_wrong_timezone.csv,7.151170253753662,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+50,1229,1229_poa_irradiance_30min_wrong_timezone.csv,13.485921382904053,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+51,1229,1229_poa_irradiance_15min_random_time_shifts.csv,21.55881118774414,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09266409266409266,15,Random Time Shifts
+52,1229,1229_poa_irradiance_random_time_shifts.csv,19.81346082687378,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+53,1229,1229_gh_irradiance_60min_random_time_shifts.csv,7.132343292236328,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09266409266409266,60,Random Time Shifts
+54,1229,1229_gh_irradiance_30min_random_time_shifts.csv,12.957679986953735,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09266409266409266,30,Random Time Shifts
+55,1229,1229_gh_irradiance_15min_random_time_shifts.csv,22.91370129585266,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+56,1229,1229_poa_irradiance_west_array_full_DST.csv,24.72825598716736,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Full DST
+57,1229,1229_poa_irradiance_west_array_60min_random_time_shifts.csv,9.602325201034546,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",11.305019305019306,60,Random Time Shifts
+58,1229,1229_gh_irradiance_random_time_shifts.csv,19.308517456054688,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.04633204633204633,15,Random Time Shifts
+59,1332,1332_inv3_ac_power__2650_15min.csv,8.57125973701477,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+60,1332,1332_inv3_ac_power__2650_15min.csv,8.57125973701477,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+61,1332,1332_inv3_ac_power__2650_15min.csv,9.856366157531738,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+62,1332,1332_inv3_ac_power__2650_15min.csv,9.856366157531738,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+63,1332,1332_inv_total_ac_power__2654_30min.csv,4.580288410186768,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+64,1332,1332_inv_total_ac_power__2654_30min.csv,4.580288410186768,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+65,1332,1332_inv_total_ac_power__2654_30min.csv,4.630299806594849,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+66,1332,1332_inv_total_ac_power__2654_30min.csv,4.630299806594849,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+67,1332,1332_inv_total_ac_power__2654_15min.csv,7.520233869552612,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+68,1332,1332_inv_total_ac_power__2654_15min.csv,7.520233869552612,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+69,1332,1332_inv_total_ac_power__2654_15min.csv,8.588587522506714,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+70,1332,1332_inv_total_ac_power__2654_15min.csv,8.588587522506714,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+71,1332,1332_inv3_ac_power__2650_60min.csv,2.522028923034668,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+72,1332,1332_inv3_ac_power__2650_60min.csv,2.522028923034668,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+73,1332,1332_inv3_ac_power__2650_60min.csv,2.6538469791412354,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+74,1332,1332_inv3_ac_power__2650_60min.csv,2.6538469791412354,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+75,1332,1332_inv2_ac_power__2646_60min.csv,6.364583730697632,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+76,1332,1332_inv2_ac_power__2646_60min.csv,6.364583730697632,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+77,1332,1332_inv2_ac_power__2646_60min.csv,6.048230409622192,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+78,1332,1332_inv2_ac_power__2646_60min.csv,6.048230409622192,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+79,1332,1332_inv3_ac_power__2650_30min.csv,6.019947528839111,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+80,1332,1332_inv3_ac_power__2650_30min.csv,6.019947528839111,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+81,1332,1332_inv3_ac_power__2650_30min.csv,7.071189880371094,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+82,1332,1332_inv3_ac_power__2650_30min.csv,7.071189880371094,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+83,1332,1332_inv2_ac_power__2646_partial_DST.csv,18.859569549560547,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.2745869394177813,15,Partial DST
+84,1332,1332_inv2_ac_power__2646_partial_DST.csv,18.859569549560547,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.2745869394177813,15,Partial DST
+85,1332,1332_inv2_ac_power__2646_partial_DST.csv,16.14967155456543,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.2745869394177813,15,Partial DST
+86,1332,1332_inv2_ac_power__2646_partial_DST.csv,16.14967155456543,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.2745869394177813,15,Partial DST
+87,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,17.403905630111694,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.14162077104642015,15,Full DST
+88,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,17.403905630111694,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.14162077104642015,15,Full DST
+89,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,15.580873966217041,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.14162077104642015,15,Full DST
+90,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,15.580873966217041,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.14162077104642015,15,Full DST
+91,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,7.699001789093018,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+92,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,7.699001789093018,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+93,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,8.896512031555176,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+94,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,8.896512031555176,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+95,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,10.036876916885376,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+96,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,10.036876916885376,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+97,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,4096.849172115326,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+98,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,4096.849172115326,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+99,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,16.917093515396118,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,15,Random Time Shifts
+100,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,16.917093515396118,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,15,Random Time Shifts
+101,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,17.20373511314392,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,15,Random Time Shifts
+102,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,17.20373511314392,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,15,Random Time Shifts
+103,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,14.497919082641602,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+104,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,14.497919082641602,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+105,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,14.653126001358032,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+106,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,14.653126001358032,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+107,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,7.2034080028533936,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,30,Random Time Shifts
+108,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,7.2034080028533936,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,30,Random Time Shifts
+109,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,6.963090896606445,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,30,Random Time Shifts
+110,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,6.963090896606445,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,30,Random Time Shifts
+111,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,4.308997869491577,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.803475935828876,60,Random Time Shifts
+112,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,4.308997869491577,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.803475935828876,60,Random Time Shifts
+113,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,3.9570000171661377,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.803475935828876,60,Random Time Shifts
+114,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,3.9570000171661377,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.803475935828876,60,Random Time Shifts
+115,1332,1332_inv_total_ac_power__2654_partial_DST.csv,13.751193046569824,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.959677419354838,15,Partial DST
+116,1332,1332_inv_total_ac_power__2654_partial_DST.csv,13.751193046569824,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.959677419354838,15,Partial DST
+117,1332,1332_inv_total_ac_power__2654_partial_DST.csv,14.115089654922485,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.959677419354838,15,Partial DST
+118,1332,1332_inv_total_ac_power__2654_partial_DST.csv,14.115089654922485,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.959677419354838,15,Partial DST
+119,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,13.98009204864502,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.6129032258064515,15,Random Time Shifts
+120,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,13.98009204864502,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.6129032258064515,15,Random Time Shifts
+121,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,13.552443504333496,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.6129032258064515,15,Random Time Shifts
+122,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,13.552443504333496,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.6129032258064515,15,Random Time Shifts
+123,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,7.583796977996826,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Partial DST
+124,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,7.583796977996826,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Partial DST
+125,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,7.625634670257568,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Partial DST
+126,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,7.625634670257568,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Partial DST
+127,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,5.084035396575928,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+128,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,5.084035396575928,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+129,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,4.25100040435791,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+130,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,4.25100040435791,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+131,1332,1332_inv2_ac_power__2646_15min.csv,23.738205671310425,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+132,1332,1332_inv2_ac_power__2646_15min.csv,23.738205671310425,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+133,1332,1332_inv2_ac_power__2646_15min.csv,23.68103265762329,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+134,1332,1332_inv2_ac_power__2646_15min.csv,23.68103265762329,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+135,1332,1332_inv2_ac_power__2646_30min.csv,14.349507570266724,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+136,1332,1332_inv2_ac_power__2646_30min.csv,14.349507570266724,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+137,1332,1332_inv2_ac_power__2646_30min.csv,14.228468418121338,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+138,1332,1332_inv2_ac_power__2646_30min.csv,14.228468418121338,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+139,1332,1332_inv_total_ac_power__2654_60min.csv,4.628998517990112,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+140,1332,1332_inv_total_ac_power__2654_60min.csv,4.628998517990112,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+141,1332,1332_inv_total_ac_power__2654_60min.csv,3.899998903274536,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+142,1332,1332_inv_total_ac_power__2654_60min.csv,3.899998903274536,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+143,1422,1422_inv1_ac_power.csv,329.5113568305969,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+144,1422,1422_inv2_ac_power.csv,276.51875734329224,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+145,1422,1422_inv1_ac_power_30min.csv,9.502019166946411,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+146,1422,1422_inv1_ac_power_60min.csv,4.4046790599823,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+147,1422,1422_inv2_ac_power_15min.csv,15.02925729751587,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+148,1422,1422_inv2_ac_power_30min.csv,7.256957530975342,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+149,1422,1422_inv2_ac_power_60min.csv,4.3679726123809814,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+150,1422,1422_inv2_ac_power_30min_full_DST.csv,8.729579210281372,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.6678082191780822,30,Full DST
+151,1422,1422_inv2_ac_power_15min_wrong_timezone.csv,17.959052324295044,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+152,1422,1422_inv2_ac_power_partial_DST.csv,243.38549995422363,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.040797824116048,1,Partial DST
+153,1422,1422_inv1_ac_power_15min.csv,12.34145975112915,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+154,1422,1422_inv2_ac_power_60min_wrong_timezone.csv,3.491999864578247,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+155,1422,1422_inv1_ac_power_60min_random_time_shifts.csv,3.661188840866089,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",4.700342465753424,60,Random Time Shifts
+156,1422,1422_inv1_ac_power_15min_wrong_timezone.csv,11.894993543624878,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+157,1422,1422_inv1_ac_power_random_time_shifts.csv,151.00323152542114,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",22.188090737240074,1,Random Time Shifts
+158,1422,1422_inv1_ac_power_30min_random_time_shifts.csv,7.745015621185303,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",4.674657534246576,30,Random Time Shifts
+159,1423,1423_inv1_ac_power_15min.csv,31.405709981918335,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+160,1423,1423_inv2_ac_power_30min.csv,18.66277027130127,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+161,1423,1423_inv2_ac_power_15min.csv,34.78596329689026,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+162,1423,1423_inv2_ac_power.csv,516.8010802268982,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+163,1423,1423_inv1_ac_power_60min.csv,21.185796976089478,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5824039653035935,60,No Issue
+164,1423,1423_inv1_ac_power_30min.csv,28.17335605621338,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+165,1423,1423_inv1_ac_power.csv,531.9869368076324,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+166,1423,1423_inv2_ac_power_30min_wrong_timezone.csv,22.578685522079468,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+167,1423,1423_inv2_ac_power_15min_wrong_timezone.csv,41.03016257286072,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+168,1423,1423_inv2_ac_power_wrong_timezone.csv,826.2154247760773,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,Wrong Timezone
+169,1423,1423_inv1_ac_power_60min_wrong_timezone.csv,19.967979907989502,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5824039653035935,60,Wrong Timezone
+170,1423,1423_inv1_ac_power_30min_random_time_shifts.csv,19.724013566970825,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.04956629491945477,30,Random Time Shifts
+171,1423,1423_inv1_ac_power_15min_wrong_timezone.csv,38.35711598396301,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+172,1423,1423_inv1_ac_power_full_DST.csv,453.54144167900085,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.442379182156134,1,Full DST
+173,1423,1423_inv2_ac_power_60min_full_DST.csv,26.845848321914673,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",7.060546875,60,Full DST
+174,1423,1423_inv2_ac_power_60min.csv,30.554606437683105,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.2421875,60,No Issue
+175,4901,4901_InvPAC_kW_Avg_60min.csv,5.16202974319458,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+176,4901,4901_InvPAC_kW_Avg_60min.csv,5.16202974319458,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+177,4901,4901_InvPAC_kW_Avg_60min.csv,5.475994825363159,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+178,4901,4901_InvPAC_kW_Avg_60min.csv,5.475994825363159,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+179,4901,4901_InvPAC_kW_Avg_30min.csv,9.602619171142578,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+180,4901,4901_InvPAC_kW_Avg_30min.csv,9.602619171142578,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+181,4901,4901_InvPAC_kW_Avg_30min.csv,8.822126865386963,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+182,4901,4901_InvPAC_kW_Avg_30min.csv,8.822126865386963,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+183,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,16.472388982772827,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.045317220543806644,15,Random Time Shifts
+184,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,16.472388982772827,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.045317220543806644,15,Random Time Shifts
+185,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,17.365429878234863,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.045317220543806644,15,Random Time Shifts
+186,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,17.365429878234863,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.045317220543806644,15,Random Time Shifts
+187,4901,4901_InvPAC_kW_Avg.csv,255.33652305603027,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+188,4901,4901_InvPAC_kW_Avg.csv,255.33652305603027,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+189,4901,4901_InvPAC_kW_Avg.csv,259.7840850353241,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+190,4901,4901_InvPAC_kW_Avg.csv,259.7840850353241,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+191,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,5.847094297409058,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Random Time Shifts
+192,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,5.847094297409058,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Random Time Shifts
+193,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,6.241239547729492,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Random Time Shifts
+194,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,6.241239547729492,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Random Time Shifts
+195,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,11.682185173034668,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.45317220543806647,30,Full DST
+196,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,11.682185173034668,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.45317220543806647,30,Full DST
+197,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,11.35204792022705,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.45317220543806647,30,Full DST
+198,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,11.35204792022705,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.45317220543806647,30,Full DST
+199,4901,4901_InvPAC_kW_Avg_15min.csv,18.148995876312256,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+200,4901,4901_InvPAC_kW_Avg_15min.csv,18.148995876312256,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+201,4901,4901_InvPAC_kW_Avg_15min.csv,17.037468671798706,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+202,4901,4901_InvPAC_kW_Avg_15min.csv,17.037468671798706,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+203,4901,4901_InvPAC_kW_Avg_partial_DST.csv,263.1433343887329,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5891238670694864,1,Partial DST
+204,4901,4901_InvPAC_kW_Avg_partial_DST.csv,263.1433343887329,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5891238670694864,1,Partial DST
+205,4901,4901_InvPAC_kW_Avg_partial_DST.csv,261.2420082092285,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5891238670694864,1,Partial DST
+206,4901,4901_InvPAC_kW_Avg_partial_DST.csv,261.2420082092285,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5891238670694864,1,Partial DST
+207,4902,4902_ac_power_inv_14538_15min.csv,15.979040622711182,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+208,4902,4902_ac_power_inv_14538_random_time_shifts.csv,250.62322282791138,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09063444108761329,1,Random Time Shifts
+209,4902,4902_ac_power_inv_14538_15min_wrong_timezone.csv,15.961017608642578,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+210,4902,4902_ac_power_inv_14538_30min_random_time_shifts.csv,9.46309757232666,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Random Time Shifts
+211,4902,4902_ac_power_inv_14538_60min_partial_DST.csv,6.163048982620239,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",7.182779456193353,60,Partial DST
+212,4902,4902_ac_power_inv_14538_30min.csv,9.555004358291626,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+213,4902,4902_ac_power_inv_14538.csv,248.62708568572998,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+214,4902,4902_ac_power_inv_14538_60min.csv,4.957221031188965,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+215,4903,4903_ac_power_inv_14539.csv,254.58531260490417,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+216,4903,4903_ac_power_other_inv_14539_2_15min_partial_DST.csv,10.887035608291626,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",17.72965879265092,15,Partial DST
+217,4903,4903_ac_power_other_inv_14539_2_random_time_shifts.csv,9.339041709899902,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",21.614173228346456,15,Random Time Shifts
+218,4903,4903_ac_power_inv_14539_wrong_timezone.csv,256.68192052841187,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,Wrong Timezone
+219,4903,4903_ac_power_other_inv_14539_2_30min_wrong_timezone.csv,5.459724426269531,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,30,Wrong Timezone
+220,4903,4903_ac_power_inv_14539_30min_random_time_shifts.csv,8.778224229812622,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",2.9121725731895225,30,Random Time Shifts
+221,4903,4903_ac_power_inv_14539_60min_random_time_shifts.csv,6.10811972618103,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",42.27272727272727,60,Random Time Shifts
+222,4903,4903_ac_power_other_inv_14539_2_60min.csv,4.603696823120117,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.931034482758621,60,No Issue
+223,4903,4903_ac_power_other_inv_14539_2_30min.csv,6.315152406692505,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,30,No Issue
+224,4903,4903_ac_power_other_inv_14539_2_15min.csv,9.607762813568115,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,15,No Issue
diff --git a/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module-pva_issue_mean_absolute_error_time_series.csv b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module-pva_issue_mean_absolute_error_time_series.csv
new file mode 100644
index 00000000..2d3073fb
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module-pva_issue_mean_absolute_error_time_series.csv
@@ -0,0 +1,6 @@
+issue,mean_absolute_error_time_series
+Full DST,2.7799386257445757
+No Issue,1.625467193777191
+Partial DST,3.6352828740136975
+Random Time Shifts,3.734672320497822
+Wrong Timezone,2.660606598472302
diff --git a/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module_data_sampling_frequency_mean_absolute_error_time_series.csv b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module_data_sampling_frequency_mean_absolute_error_time_series.csv
new file mode 100644
index 00000000..c5497c82
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module_data_sampling_frequency_mean_absolute_error_time_series.csv
@@ -0,0 +1,5 @@
+data_sampling_frequency,mean_absolute_error_time_series
+1,1.70548173884654
+15,2.499753369818256
+30,1.3458919104359777
+60,4.566173445393444
diff --git a/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module_full_results.csv b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module_full_results.csv
new file mode 100644
index 00000000..b6c0cf9a
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module_full_results.csv
@@ -0,0 +1,232 @@
+,system_id,file_name,run_time,data_requirements,mean_absolute_error_time_series,data_sampling_frequency,issue
+0,4,4_ac_power__315_full_DST.csv,36.417848348617554,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",11.981205951448707,15,Full DST
+1,4,4_ac_power__315_30min_wrong_timezone.csv,18.03521180152893,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+2,4,4_ac_power__315_60min_wrong_timezone.csv,10.219050407409668,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+3,4,4_ac_power__315_60min.csv,10.2231764793396,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+4,4,4_ac_power__315_30min.csv,18.94883108139038,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+5,4,4_ac_power__315_15min.csv,31.129875898361206,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+6,4,4_ac_power__315_15min_full_DST.csv,31.580842971801758,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",11.981205951448707,15,Full DST
+7,50,50_ac_power_wrong_timezone.csv,408.5517942905426,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,Wrong Timezone
+8,50,50_ac_power_1_random_time_shifts.csv,371.49825406074524,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.4838709677419355,1,Random Time Shifts
+9,50,50_ac_power_15min_wrong_timezone.csv,26.458285331726074,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+10,50,50_ac_power_1_15min_random_time_shifts.csv,23.055137634277344,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",2.555443548387097,15,Random Time Shifts
+11,50,50_ac_power_1_30min_wrong_timezone.csv,9.767218589782715,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+12,50,50_ac_power_1_60min_wrong_timezone.csv,7.712303161621094,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,Wrong Timezone
+13,50,50_ac_power_2_full_DST.csv,411.6130156517029,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.6048387096774194,1,Full DST
+14,50,50_ac_power_30min.csv,10.528113842010498,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+15,50,50_ac_power_2_30min_random_time_shifts.csv,9.33000659942627,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.423387096774194,30,Random Time Shifts
+16,50,50_ac_power_2_15min_wrong_timezone.csv,23.886648178100586,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+17,50,50_ac_power_30min_wrong_timezone.csv,13.87597942352295,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+18,50,50_ac_power.csv,348.6959400177002,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+19,50,50_ac_power_1.csv,344.8544075489044,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+20,50,50_ac_power_15min.csv,21.28752064704895,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+21,50,50_ac_power_1_15min.csv,20.840614318847656,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+22,50,50_ac_power_1_30min.csv,12.457207202911377,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+23,50,50_ac_power_1_60min.csv,6.285996675491333,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,No Issue
+24,50,50_ac_power_2.csv,448.7007393836975,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+25,50,50_ac_power_2_15min.csv,52.74507212638855,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+26,50,50_ac_power_2_30min.csv,28.522862672805786,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,30,No Issue
+27,50,50_ac_power_2_60min.csv,14.212299108505249,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,No Issue
+28,50,50_ac_power_2_60min_random_time_shifts.csv,20.003239631652832,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.211693548387096,60,Random Time Shifts
+29,50,50_ac_power_60min_random_time_shifts.csv,15.606981039047241,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.883064516129032,60,Random Time Shifts
+30,50,50_ac_power_60min.csv,13.44467568397522,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,No Issue
+31,1229,1229_poa_irradiance_west_array_60min.csv,18.006192684173584,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+32,1229,1229_gh_irradiance_15min.csv,62.75973129272461,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+33,1229,1229_gh_irradiance_30min.csv,36.643842697143555,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+34,1229,1229_gh_irradiance_60min.csv,21.340290307998657,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+35,1229,1229_poa_irradiance_15min.csv,55.13626194000244,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+36,1229,1229_poa_irradiance_30min.csv,23.97901153564453,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+37,1229,1229_poa_irradiance_60min.csv,13.065289735794067,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+38,1229,1229_poa_irradiance_south_array_15min.csv,44.3490424156189,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+39,1229,1229_poa_irradiance_south_array_30min.csv,23.744961261749268,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+40,1229,1229_poa_irradiance_south_array_60min.csv,15.981892347335815,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+41,1229,1229_poa_irradiance_west_array_15min.csv,50.4371018409729,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+42,1229,1229_poa_irradiance_west_array_30min.csv,21.48188543319702,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+43,1229,1229_poa_irradiance_west_array_30min_partial_DST.csv,26.210114240646362,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.18532818532818532,30,Partial DST
+44,1229,1229_poa_irradiance_west_array_15min_wrong_timezone.csv,51.2076952457428,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+45,1229,1229_poa_irradiance_south_array_60min_wrong_timezone.csv,16.22228717803955,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+46,1229,1229_poa_irradiance_south_array_30min_random_time_shifts.csv,25.43772268295288,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Random Time Shifts
+47,1229,1229_poa_irradiance_south_array_15min_random_time_shifts.csv,55.07992625236511,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+48,1229,1229_poa_irradiance_south_array_full_DST.csv,54.60927391052246,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.37065637065637064,15,Full DST
+49,1229,1229_poa_irradiance_60min_wrong_timezone.csv,15.880443811416626,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+50,1229,1229_poa_irradiance_30min_wrong_timezone.csv,26.2710702419281,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+51,1229,1229_poa_irradiance_15min_random_time_shifts.csv,39.37988543510437,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.18532818532818532,15,Random Time Shifts
+52,1229,1229_poa_irradiance_random_time_shifts.csv,39.721118688583374,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+53,1229,1229_gh_irradiance_60min_random_time_shifts.csv,12.523155689239502,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.277992277992278,60,Random Time Shifts
+54,1229,1229_gh_irradiance_30min_random_time_shifts.csv,24.76369881629944,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.44015444015444016,30,Random Time Shifts
+55,1229,1229_gh_irradiance_15min_random_time_shifts.csv,41.26914572715759,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.05791505791505792,15,Random Time Shifts
+56,1229,1229_poa_irradiance_west_array_full_DST.csv,44.592097759246826,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.138996138996139,15,Full DST
+57,1229,1229_poa_irradiance_west_array_60min_random_time_shifts.csv,12.570943593978882,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",11.328185328185327,60,Random Time Shifts
+58,1229,1229_gh_irradiance_random_time_shifts.csv,44.348517656326294,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09266409266409266,15,Random Time Shifts
+59,1332,1332_inv3_ac_power__2650_15min.csv,20.450513124465942,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+60,1332,1332_inv3_ac_power__2650_15min.csv,20.450513124465942,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+61,1332,1332_inv3_ac_power__2650_15min.csv,19.453051328659058,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+62,1332,1332_inv3_ac_power__2650_15min.csv,19.453051328659058,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+63,1332,1332_inv_total_ac_power__2654_30min.csv,9.50999641418457,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+64,1332,1332_inv_total_ac_power__2654_30min.csv,9.50999641418457,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+65,1332,1332_inv_total_ac_power__2654_30min.csv,9.575424909591675,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+66,1332,1332_inv_total_ac_power__2654_30min.csv,9.575424909591675,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+67,1332,1332_inv_total_ac_power__2654_15min.csv,17.5021653175354,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+68,1332,1332_inv_total_ac_power__2654_15min.csv,17.5021653175354,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+69,1332,1332_inv_total_ac_power__2654_15min.csv,18.584660530090332,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+70,1332,1332_inv_total_ac_power__2654_15min.csv,18.584660530090332,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+71,1332,1332_inv3_ac_power__2650_60min.csv,5.890956163406372,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+72,1332,1332_inv3_ac_power__2650_60min.csv,5.890956163406372,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+73,1332,1332_inv3_ac_power__2650_60min.csv,5.169487714767456,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+74,1332,1332_inv3_ac_power__2650_60min.csv,5.169487714767456,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+75,1332,1332_inv2_ac_power__2646_60min.csv,10.643221855163574,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+76,1332,1332_inv2_ac_power__2646_60min.csv,10.643221855163574,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+77,1332,1332_inv2_ac_power__2646_60min.csv,9.77955174446106,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+78,1332,1332_inv2_ac_power__2646_60min.csv,9.77955174446106,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+79,1332,1332_inv3_ac_power__2650_30min.csv,9.321442127227783,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+80,1332,1332_inv3_ac_power__2650_30min.csv,9.321442127227783,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+81,1332,1332_inv3_ac_power__2650_30min.csv,8.98558235168457,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+82,1332,1332_inv3_ac_power__2650_30min.csv,8.98558235168457,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+83,1332,1332_inv2_ac_power__2646_partial_DST.csv,29.847739458084106,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.457680250783699,15,Partial DST
+84,1332,1332_inv2_ac_power__2646_partial_DST.csv,29.847739458084106,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.457680250783699,15,Partial DST
+85,1332,1332_inv2_ac_power__2646_partial_DST.csv,30.038100957870483,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.457680250783699,15,Partial DST
+86,1332,1332_inv2_ac_power__2646_partial_DST.csv,30.038100957870483,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.457680250783699,15,Partial DST
+87,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,29.893580198287964,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.4702194357366771,15,Full DST
+88,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,29.893580198287964,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.4702194357366771,15,Full DST
+89,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,30.970849990844727,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.4702194357366771,15,Full DST
+90,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,30.970849990844727,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.4702194357366771,15,Full DST
+91,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,17.316962480545044,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+92,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,17.316962480545044,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+93,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,14.985536575317383,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+94,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,14.985536575317383,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+95,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,9.155675172805786,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+96,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,9.155675172805786,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+97,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,8.918974161148071,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+98,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,8.918974161148071,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+99,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,16.723313808441162,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.32085561497326204,15,Random Time Shifts
+100,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,16.723313808441162,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.32085561497326204,15,Random Time Shifts
+101,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,17.957117080688477,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.32085561497326204,15,Random Time Shifts
+102,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,17.957117080688477,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.32085561497326204,15,Random Time Shifts
+103,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,17.089725255966187,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+104,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,17.089725255966187,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+105,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,23.445382356643677,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+106,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,23.445382356643677,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+107,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,11.953073740005493,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.48128342245989303,30,Random Time Shifts
+108,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,11.953073740005493,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.48128342245989303,30,Random Time Shifts
+109,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,11.332341432571411,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.48128342245989303,30,Random Time Shifts
+110,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,11.332341432571411,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.48128342245989303,30,Random Time Shifts
+111,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,6.06907844543457,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.863636363636363,60,Random Time Shifts
+112,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,6.06907844543457,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.863636363636363,60,Random Time Shifts
+113,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,7.915778398513794,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.863636363636363,60,Random Time Shifts
+114,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,7.915778398513794,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.863636363636363,60,Random Time Shifts
+115,1332,1332_inv_total_ac_power__2654_partial_DST.csv,14.050724506378174,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.669902912621358,15,Partial DST
+116,1332,1332_inv_total_ac_power__2654_partial_DST.csv,14.050724506378174,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.669902912621358,15,Partial DST
+117,1332,1332_inv_total_ac_power__2654_partial_DST.csv,21.366387844085693,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.669902912621358,15,Partial DST
+118,1332,1332_inv_total_ac_power__2654_partial_DST.csv,21.366387844085693,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.669902912621358,15,Partial DST
+119,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,19.33788228034973,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.106796116504855,15,Random Time Shifts
+120,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,19.33788228034973,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.106796116504855,15,Random Time Shifts
+121,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,25.657039642333984,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.106796116504855,15,Random Time Shifts
+122,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,25.657039642333984,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.106796116504855,15,Random Time Shifts
+123,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,12.054098129272461,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.674757281553398,30,Partial DST
+124,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,12.054098129272461,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.674757281553398,30,Partial DST
+125,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,16.372033834457397,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.674757281553398,30,Partial DST
+126,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,16.372033834457397,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.674757281553398,30,Partial DST
+127,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,8.855716705322266,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+128,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,8.855716705322266,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+129,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,7.571859359741211,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+130,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,7.571859359741211,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+131,1332,1332_inv2_ac_power__2646_15min.csv,38.56131410598755,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+132,1332,1332_inv2_ac_power__2646_15min.csv,38.56131410598755,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+133,1332,1332_inv2_ac_power__2646_15min.csv,37.76382231712341,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+134,1332,1332_inv2_ac_power__2646_15min.csv,37.76382231712341,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+135,1332,1332_inv2_ac_power__2646_30min.csv,19.42027497291565,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+136,1332,1332_inv2_ac_power__2646_30min.csv,19.42027497291565,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+137,1332,1332_inv2_ac_power__2646_30min.csv,19.55843472480774,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+138,1332,1332_inv2_ac_power__2646_30min.csv,19.55843472480774,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+139,1332,1332_inv_total_ac_power__2654_60min.csv,5.966779947280884,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+140,1332,1332_inv_total_ac_power__2654_60min.csv,5.966779947280884,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+141,1332,1332_inv_total_ac_power__2654_60min.csv,10.600020170211792,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+142,1332,1332_inv_total_ac_power__2654_60min.csv,10.600020170211792,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+143,1422,1422_inv1_ac_power.csv,577.8746733665466,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+144,1422,1422_inv2_ac_power.csv,537.2379961013794,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+145,1422,1422_inv1_ac_power_30min.csv,16.792707204818726,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+146,1422,1422_inv1_ac_power_60min.csv,10.231870889663696,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+147,1422,1422_inv2_ac_power_15min.csv,33.672709465026855,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+148,1422,1422_inv2_ac_power_30min.csv,27.794044733047485,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+149,1422,1422_inv2_ac_power_60min.csv,11.766140937805176,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+150,1422,1422_inv2_ac_power_30min_full_DST.csv,15.486412525177002,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.4897260273972603,30,Full DST
+151,1422,1422_inv2_ac_power_15min_wrong_timezone.csv,31.73692297935486,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+152,1422,1422_inv2_ac_power_partial_DST.csv,655.1986155509949,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",14.215938303341902,1,Partial DST
+153,1422,1422_inv1_ac_power_15min.csv,36.883973121643066,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+154,1422,1422_inv2_ac_power_60min_wrong_timezone.csv,13.50630259513855,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+155,1422,1422_inv1_ac_power_60min_random_time_shifts.csv,12.902904987335205,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",4.803082191780822,60,Random Time Shifts
+156,1422,1422_inv1_ac_power_15min_wrong_timezone.csv,45.2496337890625,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+157,1422,1422_inv1_ac_power_random_time_shifts.csv,456.92638182640076,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",21.041131105398456,1,Random Time Shifts
+158,1422,1422_inv1_ac_power_30min_random_time_shifts.csv,16.89690065383911,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",4.52054794520548,30,Random Time Shifts
+159,1423,1423_inv1_ac_power_15min.csv,73.17849850654602,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+160,1423,1423_inv2_ac_power_30min.csv,40.60102868080139,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+161,1423,1423_inv2_ac_power_15min.csv,80.4278450012207,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+162,1423,1423_inv2_ac_power.csv,1095.530221939087,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+163,1423,1423_inv1_ac_power_60min.csv,23.131810426712036,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5762081784386617,60,No Issue
+164,1423,1423_inv1_ac_power_30min.csv,29.373534440994263,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+165,1423,1423_inv1_ac_power.csv,834.4265019893646,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+166,1423,1423_inv2_ac_power_30min_wrong_timezone.csv,36.75889205932617,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+167,1423,1423_inv2_ac_power_15min_wrong_timezone.csv,63.378474950790405,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+168,1423,1423_inv2_ac_power_wrong_timezone.csv,1100.3713946342468,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,Wrong Timezone
+169,1423,1423_inv1_ac_power_60min_wrong_timezone.csv,21.448011875152588,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5762081784386617,60,Wrong Timezone
+170,1423,1423_inv1_ac_power_30min_random_time_shifts.csv,32.1729896068573,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.14869888475836432,30,Random Time Shifts
+171,1423,1423_inv1_ac_power_15min_wrong_timezone.csv,68.88969779014587,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+172,1423,1423_inv1_ac_power_full_DST.csv,879.8789784908295,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",7.980173482032218,1,Full DST
+173,1423,1423_inv2_ac_power_60min_full_DST.csv,22.642247915267944,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.908203125,60,Full DST
+174,1423,1423_inv2_ac_power_60min.csv,17.49142551422119,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+175,4901,4901_InvPAC_kW_Avg_60min.csv,9.740148544311523,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+176,4901,4901_InvPAC_kW_Avg_60min.csv,9.740148544311523,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+177,4901,4901_InvPAC_kW_Avg_60min.csv,8.290334224700928,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+178,4901,4901_InvPAC_kW_Avg_60min.csv,8.290334224700928,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+179,4901,4901_InvPAC_kW_Avg_30min.csv,17.5408935546875,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+180,4901,4901_InvPAC_kW_Avg_30min.csv,17.5408935546875,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+181,4901,4901_InvPAC_kW_Avg_30min.csv,14.604455709457397,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+182,4901,4901_InvPAC_kW_Avg_30min.csv,14.604455709457397,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+183,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,31.679872512817383,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.22658610271903323,15,Random Time Shifts
+184,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,31.679872512817383,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.22658610271903323,15,Random Time Shifts
+185,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,29.76544451713562,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.22658610271903323,15,Random Time Shifts
+186,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,29.76544451713562,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.22658610271903323,15,Random Time Shifts
+187,4901,4901_InvPAC_kW_Avg.csv,441.58872842788696,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+188,4901,4901_InvPAC_kW_Avg.csv,441.58872842788696,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+189,4901,4901_InvPAC_kW_Avg.csv,475.8439338207245,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+190,4901,4901_InvPAC_kW_Avg.csv,475.8439338207245,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+191,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,8.733614444732666,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09063444108761329,60,Random Time Shifts
+192,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,8.733614444732666,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09063444108761329,60,Random Time Shifts
+193,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,9.741227865219116,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09063444108761329,60,Random Time Shifts
+194,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,9.741227865219116,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09063444108761329,60,Random Time Shifts
+195,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,15.957876443862915,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.8157099697885196,30,Full DST
+196,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,15.957876443862915,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.8157099697885196,30,Full DST
+197,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,18.682955741882324,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.8157099697885196,30,Full DST
+198,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,18.682955741882324,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.8157099697885196,30,Full DST
+199,4901,4901_InvPAC_kW_Avg_15min.csv,28.899012088775635,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+200,4901,4901_InvPAC_kW_Avg_15min.csv,28.899012088775635,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+201,4901,4901_InvPAC_kW_Avg_15min.csv,30.038745880126953,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+202,4901,4901_InvPAC_kW_Avg_15min.csv,30.038745880126953,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+203,4901,4901_InvPAC_kW_Avg_partial_DST.csv,458.1782805919647,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.36253776435045315,1,Partial DST
+204,4901,4901_InvPAC_kW_Avg_partial_DST.csv,458.1782805919647,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.36253776435045315,1,Partial DST
+205,4901,4901_InvPAC_kW_Avg_partial_DST.csv,391.50052785873413,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.36253776435045315,1,Partial DST
+206,4901,4901_InvPAC_kW_Avg_partial_DST.csv,391.50052785873413,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.36253776435045315,1,Partial DST
+207,4902,4902_ac_power_inv_14538_15min.csv,15.702306270599365,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+208,4902,4902_ac_power_inv_14538_random_time_shifts.csv,277.6441948413849,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.2719033232628399,1,Random Time Shifts
+209,4902,4902_ac_power_inv_14538_15min_wrong_timezone.csv,15.724353790283203,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+210,4902,4902_ac_power_inv_14538_30min_random_time_shifts.csv,8.826139211654663,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.2719033232628399,30,Random Time Shifts
+211,4902,4902_ac_power_inv_14538_60min_partial_DST.csv,5.6981425285339355,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",7.2960725075528705,60,Partial DST
+212,4902,4902_ac_power_inv_14538_30min.csv,9.54916262626648,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+213,4902,4902_ac_power_inv_14538.csv,272.93376898765564,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+214,4902,4902_ac_power_inv_14538_60min.csv,4.931900501251221,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+215,4903,4903_ac_power_inv_14539.csv,279.4164824485779,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+216,4903,4903_ac_power_other_inv_14539_2_15min_partial_DST.csv,10.615003824234009,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",17.964071856287426,15,Partial DST
+217,4903,4903_ac_power_other_inv_14539_2_random_time_shifts.csv,11.153998851776123,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",24.238665526090674,15,Random Time Shifts
+218,4903,4903_ac_power_inv_14539_wrong_timezone.csv,276.3200762271881,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,Wrong Timezone
+219,4903,4903_ac_power_other_inv_14539_2_30min_wrong_timezone.csv,6.178351879119873,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,30,Wrong Timezone
+220,4903,4903_ac_power_inv_14539_30min_random_time_shifts.csv,9.207152128219604,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",4.078549848942598,30,Random Time Shifts
+221,4903,4903_ac_power_inv_14539_60min_random_time_shifts.csv,6.055145263671875,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",42.97205438066465,60,Random Time Shifts
+222,4903,4903_ac_power_other_inv_14539_2_60min.csv,4.106168270111084,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.844311377245509,60,No Issue
+223,4903,4903_ac_power_other_inv_14539_2_30min.csv,6.19269871711731,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,30,No Issue
+224,4903,4903_ac_power_other_inv_14539_2_15min.csv,11.342822790145874,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,15,No Issue
+225,4903,4903_ac_power_other_inv_14539_2.csv,3.0011162757873535,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+226,4903,4903_ac_power_inv_14539_60min.csv,5.026315689086914,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+227,4903,4903_ac_power_inv_14539_30min.csv,8.479468584060669,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+228,4903,4903_ac_power_inv_14539_15min.csv,17.408210277557373,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+229,4903,4903_ac_power_inv_14539_15min_random_time_shifts.csv,16.919278383255005,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",2.0619335347432024,15,Random Time Shifts
+230,4903,4903_ac_power_other_inv_14539_2_60min_wrong_timezone.csv,5.619644403457642,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.842465753424657,60,Wrong Timezone
diff --git a/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module_issue_mean_absolute_error_time_series.csv b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module_issue_mean_absolute_error_time_series.csv
new file mode 100644
index 00000000..f4050e5f
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/results/pvanalytics-cpd-module_issue_mean_absolute_error_time_series.csv
@@ -0,0 +1,6 @@
+issue,mean_absolute_error_time_series
+Full DST,3.0999202111723507
+No Issue,1.4976179096250255
+Partial DST,4.346705153739418
+Random Time Shifts,4.92406459629677
+Wrong Timezone,1.323756443435245
diff --git a/ec2/analysis_taks/time-shift-detection/results/run_time_dist.png b/ec2/analysis_taks/time-shift-detection/results/run_time_dist.png
new file mode 100644
index 0000000000000000000000000000000000000000..1a04e5d4e03605b642475dd7a6c199def5090240
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diff --git a/ec2/analysis_taks/time-shift-detection/results/time-shift-public-metrics.json b/ec2/analysis_taks/time-shift-detection/results/time-shift-public-metrics.json
new file mode 100644
index 00000000..833b12fb
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/results/time-shift-public-metrics.json
@@ -0,0 +1 @@
+{"module": "pvanalytics-cpd-module-pva", "mean_run_time": 78.4254280592867, "median_run_time": 13.315558671951294, "function_parameters": ["time_series", "latitude", "longitude", "data_sampling_frequency"], "mean_mean_absolute_error_time_series": 2.967270219394481, "median_mean_absolute_error_time_series": 0.0}
\ No newline at end of file
diff --git a/ec2/analysis_taks/time-shift-detection/sdt-module.py b/ec2/analysis_taks/time-shift-detection/sdt-module.py
new file mode 100644
index 00000000..cae54ba0
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/sdt-module.py
@@ -0,0 +1,19 @@
+"""
+Solar-data-tools based module. This module will be uploaded by the user
+and tested using the data sets accordingly. 
+"""
+
+import numpy as np
+from solardatatools import DataHandler
+
+
+def detect_time_shifts(
+    time_series, latitude=None, longitude=None, data_sampling_frequency=None
+):
+    dh = DataHandler(time_series.to_frame())
+    dh.run_pipeline(fix_shifts=True, verbose=False)
+    estimated = np.copy(dh.time_shift_analysis.s1)
+    estimated -= dh.time_shift_analysis.baseline
+    estimated -= dh.tz_correction
+    estimated *= -60
+    return estimated
diff --git a/ec2/analysis_taks/time-shift-detection/submission.zip b/ec2/analysis_taks/time-shift-detection/submission.zip
new file mode 100644
index 0000000000000000000000000000000000000000..aa443209888751dfd80953e7cd2ac346907883b2
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diff --git a/ec2/analysis_taks/time-shift-detection/submissions/pvanalytics-submission.zip b/ec2/analysis_taks/time-shift-detection/submissions/pvanalytics-submission.zip
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diff --git a/ec2/analysis_taks/time-shift-detection/template.py b/ec2/analysis_taks/time-shift-detection/template.py
new file mode 100644
index 00000000..f0cfd3a2
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/template.py
@@ -0,0 +1,266 @@
+import marimo
+
+__generated_with = "0.6.16"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md("# Private Report: Time Shift Data Results")
+    return
+
+
+@app.cell
+def __(create_df_from_cli_args, generatePlots):
+    results_df = create_df_from_cli_args()
+    plotting = generatePlots(results_df)
+    return plotting, results_df
+
+
+@app.cell
+def __(mo, plotting):
+    if plotting is not None:
+        median_runtime = round(plotting.results_df.runtime.median(), 2)
+        mean_runtime = round(plotting.results_df.runtime.mean(), 2)
+        max_runtime = round(plotting.results_df.runtime.max(), 2)
+        min_runtime = round(plotting.results_df.runtime.min(), 2)
+        _fig = mo.md(
+            f"""
+                     First, we visualize the distribution of run times. 
+
+                     Median run time: """
+            + str(median_runtime)
+            + """ seconds
+
+                     Mean run time: """
+            + str(mean_runtime)
+            + """ seconds
+
+                     Max run time: """
+            + str(max_runtime)
+            + """ seconds
+
+                     Min run time: """
+            + str(min_runtime)
+            + """ seconds
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return max_runtime, mean_runtime, median_runtime, min_runtime
+
+
+@app.cell
+def __(plotting):
+    if plotting is not None:
+        _fig = plotting.plot_runtimes()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(mo, plotting):
+    if plotting is not None:
+        median_mae = round(
+            plotting.results_df["Time Series-Level MAE"].median(), 2
+        )
+        mean_mae = round(
+            plotting.results_df["Time Series-Level MAE"].mean(), 2
+        )
+        _fig = mo.md(
+            f"""
+                     Next, we visualize the mean absolute error distribution, color-coded by issues present in the time series.
+
+                     Median time series MAE: """
+            + str(median_mae)
+            + """ minutes
+
+                     Mean time series MAE: """
+            + str(mean_mae)
+            + """ minutes
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return mean_mae, median_mae
+
+
+@app.cell
+def __(plotting):
+    if plotting is not None:
+        _fig = plotting.plot_mae_by_issue()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(mo, plotting):
+    if plotting is not None:
+        _fig = mo.md(
+            f"""
+                     Mean of Time Series-Level MAE, by Issue Type
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting):
+    if plotting is not None:
+        _fig = plotting.dataframe_mae_by_issue_type()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(mo, plotting):
+    if plotting is not None:
+        _fig = mo.md(
+            f"""
+                     We then visualize the mean absolute error distribution, color-coded by data sampling frequency.
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting):
+    if plotting is not None:
+        _fig = plotting.plot_mae_by_sampling_frequency()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(mo, plotting):
+    if plotting is not None:
+        _fig = mo.md(
+            f"""
+                     Mean of Time Series-Level MAE, by Data Sampling Frequency
+                     """
+        )
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(plotting):
+    if plotting is not None:
+        _fig = plotting.dataframe_mae_by_sampling()
+    else:
+        _fig = None
+    _fig
+    return
+
+
+@app.cell
+def __(pd, sns):
+    class generatePlots:
+
+        def __init__(self, results_df):
+            """Create plotting class."""
+            self.results_df = results_df
+            self.results_df = self.results_df.rename(
+                columns={
+                    "issue": "Data Issue Type",
+                    "mean_absolute_error_time_series": "Time Series-Level MAE",
+                    "data_sampling_frequency": "Data Sampling Frequency (minutes)",
+                }
+            )
+
+        def plot_runtimes(self):
+            fig = sns.histplot(self.results_df, x="runtime", bins=40)
+            fig.set(xlabel="Run Time (seconds)", ylabel="Number Instances")
+            return fig
+
+        def plot_mae_by_issue(self):
+            fig = sns.histplot(
+                self.results_df,
+                x="Time Series-Level MAE",
+                hue="Data Issue Type",
+                bins=30,
+            )
+            fig.set(
+                xlabel="Time Series-Level MAE (minutes)",
+                ylabel="Number Instances",
+            )
+            return fig
+
+        def plot_mae_by_sampling_frequency(self):
+            fig = sns.histplot(
+                self.results_df,
+                x="Time Series-Level MAE",
+                hue="Data Sampling Frequency (minutes)",
+                bins=30,
+            )
+            fig.set(
+                xlabel="Time Series-Level MAE (minutes)",
+                ylabel="Number Instances",
+            )
+            return fig
+
+        def dataframe_mae_by_issue_type(self):
+            df = self.results_df.groupby("Data Issue Type")[
+                "Time Series-Level MAE"
+            ].mean()
+            df = pd.DataFrame(df.reset_index())
+            return df
+
+        def dataframe_mae_by_sampling(self):
+            df = self.results_df.groupby("Data Sampling Frequency (minutes)")[
+                "Time Series-Level MAE"
+            ].mean()
+            df = pd.DataFrame(df.reset_index())
+            return df
+
+    return (generatePlots,)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    import numpy as np
+    import pandas as pd
+    import json
+    import matplotlib.pyplot as plt
+    import seaborn as sns
+
+    return json, mo, np, pd, plt, sns
+
+
+@app.cell
+def __(json, mo, pd):
+
+    def create_df_from_cli_args():
+        args = mo.cli_args().to_dict()
+        data = args.get("results_df")
+        rows = []
+        for row in data:
+            rows.append(json.loads(row))
+
+        df = pd.DataFrame.from_records(rows)
+        return df
+
+    return (create_df_from_cli_args,)
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/ec2/analysis_taks/time-shift-detection/time_shifts_full_results.csv b/ec2/analysis_taks/time-shift-detection/time_shifts_full_results.csv
new file mode 100644
index 00000000..c5d14b94
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/time_shifts_full_results.csv
@@ -0,0 +1,226 @@
+,system_id,file_name,run_time,data_requirements,mean_absolute_error_time_series,data_sampling_frequency,issue
+0,4,4_ac_power__315_full_DST.csv,18.830113172531128,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.40407204385278,15,Full DST
+1,4,4_ac_power__315_30min_wrong_timezone.csv,9.96409273147583,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",37.676332288401255,30,Wrong Timezone
+2,4,4_ac_power__315_60min_wrong_timezone.csv,6.543065309524536,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.29365700861394,60,Wrong Timezone
+3,4,4_ac_power__315_60min.csv,6.601576328277588,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",2.8778386844166013,60,No Issue
+4,4,4_ac_power__315_30min.csv,14.413450717926025,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.8574784651527017,30,No Issue
+5,4,4_ac_power__315_15min.csv,19.629616498947144,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",2.137823022709475,15,No Issue
+6,4,4_ac_power__315_15min_full_DST.csv,27.506799459457397,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.40407204385278,15,Full DST
+7,50,50_ac_power_wrong_timezone.csv,365.19071793556213,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,Wrong Timezone
+8,50,50_ac_power_1_random_time_shifts.csv,376.5482888221741,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.12096774193548387,1,Random Time Shifts
+9,50,50_ac_power_15min_wrong_timezone.csv,25.03329300880432,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+10,50,50_ac_power_1_15min_random_time_shifts.csv,24.982542753219604,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.3326612903225806,15,Random Time Shifts
+11,50,50_ac_power_1_30min_wrong_timezone.csv,13.90609073638916,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+12,50,50_ac_power_1_60min_wrong_timezone.csv,4.574662923812866,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.92641129032258,60,Wrong Timezone
+13,50,50_ac_power_2_full_DST.csv,368.22898745536804,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.12096774193548387,1,Full DST
+14,50,50_ac_power_30min.csv,12.097249746322632,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+15,50,50_ac_power_2_30min_random_time_shifts.csv,14.361329317092896,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.329637096774194,30,Random Time Shifts
+16,50,50_ac_power_2_15min_wrong_timezone.csv,25.421506643295288,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+17,50,50_ac_power_30min_wrong_timezone.csv,12.185845851898193,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+18,50,50_ac_power.csv,207.22214818000793,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+19,50,50_ac_power_1.csv,208.75459551811218,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+20,50,50_ac_power_15min.csv,12.34924840927124,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+21,50,50_ac_power_1_15min.csv,12.39419174194336,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+22,50,50_ac_power_1_30min.csv,6.9678709506988525,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+23,50,50_ac_power_1_60min.csv,4.159549713134766,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.92641129032258,60,No Issue
+24,50,50_ac_power_2.csv,228.48227071762085,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+25,50,50_ac_power_2_15min.csv,15.239885807037354,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+26,50,50_ac_power_2_30min.csv,7.395113706588745,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,30,No Issue
+27,50,50_ac_power_2_60min.csv,6.804012298583984,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,No Issue
+28,50,50_ac_power_2_60min_random_time_shifts.csv,4.644904851913452,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.090725806451612,60,Random Time Shifts
+29,50,50_ac_power_60min_random_time_shifts.csv,4.830859422683716,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",11.930443548387096,60,Random Time Shifts
+30,50,50_ac_power_60min.csv,5.1325109004974365,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.92641129032258,60,No Issue
+31,1229,1229_poa_irradiance_west_array_60min.csv,6.955023288726807,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+32,1229,1229_gh_irradiance_15min.csv,20.539912223815918,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+33,1229,1229_gh_irradiance_30min.csv,11.597663879394531,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+34,1229,1229_gh_irradiance_60min.csv,7.080140113830566,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+35,1229,1229_poa_irradiance_15min.csv,25.168532133102417,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+36,1229,1229_poa_irradiance_30min.csv,16.446418046951294,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+37,1229,1229_poa_irradiance_60min.csv,10.786420345306396,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+38,1229,1229_poa_irradiance_south_array_15min.csv,21.43882656097412,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+39,1229,1229_poa_irradiance_south_array_30min.csv,12.985199213027954,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+40,1229,1229_poa_irradiance_south_array_60min.csv,7.553039789199829,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+41,1229,1229_poa_irradiance_west_array_15min.csv,19.650710344314575,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+42,1229,1229_poa_irradiance_west_array_30min.csv,11.386460304260254,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+43,1229,1229_poa_irradiance_west_array_30min_partial_DST.csv,13.315558671951294,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Partial DST
+44,1229,1229_poa_irradiance_west_array_15min_wrong_timezone.csv,26.307789087295532,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+45,1229,1229_poa_irradiance_south_array_60min_wrong_timezone.csv,5.828544616699219,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+46,1229,1229_poa_irradiance_south_array_30min_random_time_shifts.csv,12.609826564788818,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.04633204633204633,30,Random Time Shifts
+47,1229,1229_poa_irradiance_south_array_15min_random_time_shifts.csv,24.809378623962402,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+48,1229,1229_poa_irradiance_south_array_full_DST.csv,22.43193507194519,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Full DST
+49,1229,1229_poa_irradiance_60min_wrong_timezone.csv,7.151170253753662,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+50,1229,1229_poa_irradiance_30min_wrong_timezone.csv,13.485921382904053,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+51,1229,1229_poa_irradiance_15min_random_time_shifts.csv,21.55881118774414,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09266409266409266,15,Random Time Shifts
+52,1229,1229_poa_irradiance_random_time_shifts.csv,19.81346082687378,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+53,1229,1229_gh_irradiance_60min_random_time_shifts.csv,7.132343292236328,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09266409266409266,60,Random Time Shifts
+54,1229,1229_gh_irradiance_30min_random_time_shifts.csv,12.957679986953735,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09266409266409266,30,Random Time Shifts
+55,1229,1229_gh_irradiance_15min_random_time_shifts.csv,22.91370129585266,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+56,1229,1229_poa_irradiance_west_array_full_DST.csv,24.72825598716736,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Full DST
+57,1229,1229_poa_irradiance_west_array_60min_random_time_shifts.csv,9.602325201034546,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",11.305019305019306,60,Random Time Shifts
+58,1229,1229_gh_irradiance_random_time_shifts.csv,19.308517456054688,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.04633204633204633,15,Random Time Shifts
+59,1332,1332_inv3_ac_power__2650_15min.csv,8.57125973701477,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+60,1332,1332_inv3_ac_power__2650_15min.csv,8.57125973701477,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+61,1332,1332_inv3_ac_power__2650_15min.csv,9.856366157531738,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+62,1332,1332_inv3_ac_power__2650_15min.csv,9.856366157531738,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+63,1332,1332_inv_total_ac_power__2654_30min.csv,4.580288410186768,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+64,1332,1332_inv_total_ac_power__2654_30min.csv,4.580288410186768,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+65,1332,1332_inv_total_ac_power__2654_30min.csv,4.630299806594849,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+66,1332,1332_inv_total_ac_power__2654_30min.csv,4.630299806594849,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+67,1332,1332_inv_total_ac_power__2654_15min.csv,7.520233869552612,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+68,1332,1332_inv_total_ac_power__2654_15min.csv,7.520233869552612,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+69,1332,1332_inv_total_ac_power__2654_15min.csv,8.588587522506714,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+70,1332,1332_inv_total_ac_power__2654_15min.csv,8.588587522506714,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+71,1332,1332_inv3_ac_power__2650_60min.csv,2.522028923034668,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+72,1332,1332_inv3_ac_power__2650_60min.csv,2.522028923034668,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+73,1332,1332_inv3_ac_power__2650_60min.csv,2.6538469791412354,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+74,1332,1332_inv3_ac_power__2650_60min.csv,2.6538469791412354,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,60,No Issue
+75,1332,1332_inv2_ac_power__2646_60min.csv,6.364583730697632,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+76,1332,1332_inv2_ac_power__2646_60min.csv,6.364583730697632,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+77,1332,1332_inv2_ac_power__2646_60min.csv,6.048230409622192,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+78,1332,1332_inv2_ac_power__2646_60min.csv,6.048230409622192,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+79,1332,1332_inv3_ac_power__2650_30min.csv,6.019947528839111,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+80,1332,1332_inv3_ac_power__2650_30min.csv,6.019947528839111,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+81,1332,1332_inv3_ac_power__2650_30min.csv,7.071189880371094,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+82,1332,1332_inv3_ac_power__2650_30min.csv,7.071189880371094,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+83,1332,1332_inv2_ac_power__2646_partial_DST.csv,18.859569549560547,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.2745869394177813,15,Partial DST
+84,1332,1332_inv2_ac_power__2646_partial_DST.csv,18.859569549560547,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.2745869394177813,15,Partial DST
+85,1332,1332_inv2_ac_power__2646_partial_DST.csv,16.14967155456543,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.2745869394177813,15,Partial DST
+86,1332,1332_inv2_ac_power__2646_partial_DST.csv,16.14967155456543,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.2745869394177813,15,Partial DST
+87,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,17.403905630111694,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.14162077104642015,15,Full DST
+88,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,17.403905630111694,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.14162077104642015,15,Full DST
+89,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,15.580873966217041,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.14162077104642015,15,Full DST
+90,1332,1332_inv2_ac_power__2646_15min_full_DST.csv,15.580873966217041,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.14162077104642015,15,Full DST
+91,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,7.699001789093018,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+92,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,7.699001789093018,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+93,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,8.896512031555176,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+94,1332,1332_inv2_ac_power__2646_30min_wrong_timezone.csv,8.896512031555176,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+95,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,10.036876916885376,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+96,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,10.036876916885376,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+97,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,4096.849172115326,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+98,1332,1332_inv2_ac_power__2646_60min_wrong_timezone.csv,4096.849172115326,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+99,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,16.917093515396118,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,15,Random Time Shifts
+100,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,16.917093515396118,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,15,Random Time Shifts
+101,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,17.20373511314392,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,15,Random Time Shifts
+102,1332,1332_inv3_ac_power__2650_random_time_shifts.csv,17.20373511314392,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,15,Random Time Shifts
+103,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,14.497919082641602,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+104,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,14.497919082641602,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+105,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,14.653126001358032,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+106,1332,1332_inv3_ac_power__2650_15min_random_time_shifts.csv,14.653126001358032,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.08021390374331551,15,Random Time Shifts
+107,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,7.2034080028533936,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,30,Random Time Shifts
+108,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,7.2034080028533936,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,30,Random Time Shifts
+109,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,6.963090896606445,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,30,Random Time Shifts
+110,1332,1332_inv3_ac_power__2650_30min_random_time_shifts.csv,6.963090896606445,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.16042780748663102,30,Random Time Shifts
+111,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,4.308997869491577,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.803475935828876,60,Random Time Shifts
+112,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,4.308997869491577,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.803475935828876,60,Random Time Shifts
+113,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,3.9570000171661377,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.803475935828876,60,Random Time Shifts
+114,1332,1332_inv3_ac_power__2650_60min_random_time_shifts.csv,3.9570000171661377,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",8.803475935828876,60,Random Time Shifts
+115,1332,1332_inv_total_ac_power__2654_partial_DST.csv,13.751193046569824,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.959677419354838,15,Partial DST
+116,1332,1332_inv_total_ac_power__2654_partial_DST.csv,13.751193046569824,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.959677419354838,15,Partial DST
+117,1332,1332_inv_total_ac_power__2654_partial_DST.csv,14.115089654922485,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.959677419354838,15,Partial DST
+118,1332,1332_inv_total_ac_power__2654_partial_DST.csv,14.115089654922485,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.959677419354838,15,Partial DST
+119,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,13.98009204864502,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.6129032258064515,15,Random Time Shifts
+120,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,13.98009204864502,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.6129032258064515,15,Random Time Shifts
+121,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,13.552443504333496,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.6129032258064515,15,Random Time Shifts
+122,1332,1332_inv_total_ac_power__2654_15min_random_time_shifts.csv,13.552443504333496,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.6129032258064515,15,Random Time Shifts
+123,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,7.583796977996826,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Partial DST
+124,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,7.583796977996826,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Partial DST
+125,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,7.625634670257568,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Partial DST
+126,1332,1332_inv_total_ac_power__2654_30min_partial_DST.csv,7.625634670257568,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Partial DST
+127,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,5.084035396575928,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+128,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,5.084035396575928,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+129,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,4.25100040435791,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+130,1332,1332_inv_total_ac_power__2654_60min_partial_DST.csv,4.25100040435791,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Partial DST
+131,1332,1332_inv2_ac_power__2646_15min.csv,23.738205671310425,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+132,1332,1332_inv2_ac_power__2646_15min.csv,23.738205671310425,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+133,1332,1332_inv2_ac_power__2646_15min.csv,23.68103265762329,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+134,1332,1332_inv2_ac_power__2646_15min.csv,23.68103265762329,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+135,1332,1332_inv2_ac_power__2646_30min.csv,14.349507570266724,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+136,1332,1332_inv2_ac_power__2646_30min.csv,14.349507570266724,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+137,1332,1332_inv2_ac_power__2646_30min.csv,14.228468418121338,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+138,1332,1332_inv2_ac_power__2646_30min.csv,14.228468418121338,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+139,1332,1332_inv_total_ac_power__2654_60min.csv,4.628998517990112,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+140,1332,1332_inv_total_ac_power__2654_60min.csv,4.628998517990112,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+141,1332,1332_inv_total_ac_power__2654_60min.csv,3.899998903274536,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+142,1332,1332_inv_total_ac_power__2654_60min.csv,3.899998903274536,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+143,1422,1422_inv1_ac_power.csv,329.5113568305969,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+144,1422,1422_inv2_ac_power.csv,276.51875734329224,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+145,1422,1422_inv1_ac_power_30min.csv,9.502019166946411,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+146,1422,1422_inv1_ac_power_60min.csv,4.4046790599823,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+147,1422,1422_inv2_ac_power_15min.csv,15.02925729751587,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+148,1422,1422_inv2_ac_power_30min.csv,7.256957530975342,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+149,1422,1422_inv2_ac_power_60min.csv,4.3679726123809814,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+150,1422,1422_inv2_ac_power_30min_full_DST.csv,8.729579210281372,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.6678082191780822,30,Full DST
+151,1422,1422_inv2_ac_power_15min_wrong_timezone.csv,17.959052324295044,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+152,1422,1422_inv2_ac_power_partial_DST.csv,243.38549995422363,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.040797824116048,1,Partial DST
+153,1422,1422_inv1_ac_power_15min.csv,12.34145975112915,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+154,1422,1422_inv2_ac_power_60min_wrong_timezone.csv,3.491999864578247,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+155,1422,1422_inv1_ac_power_60min_random_time_shifts.csv,3.661188840866089,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",4.700342465753424,60,Random Time Shifts
+156,1422,1422_inv1_ac_power_15min_wrong_timezone.csv,11.894993543624878,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+157,1422,1422_inv1_ac_power_random_time_shifts.csv,151.00323152542114,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",22.188090737240074,1,Random Time Shifts
+158,1422,1422_inv1_ac_power_30min_random_time_shifts.csv,7.745015621185303,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",4.674657534246576,30,Random Time Shifts
+159,1423,1423_inv1_ac_power_15min.csv,31.405709981918335,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+160,1423,1423_inv2_ac_power_30min.csv,18.66277027130127,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+161,1423,1423_inv2_ac_power_15min.csv,34.78596329689026,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+162,1423,1423_inv2_ac_power.csv,516.8010802268982,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+163,1423,1423_inv1_ac_power_60min.csv,21.185796976089478,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5824039653035935,60,No Issue
+164,1423,1423_inv1_ac_power_30min.csv,28.17335605621338,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+165,1423,1423_inv1_ac_power.csv,531.9869368076324,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+166,1423,1423_inv2_ac_power_30min_wrong_timezone.csv,22.578685522079468,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+167,1423,1423_inv2_ac_power_15min_wrong_timezone.csv,41.03016257286072,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+168,1423,1423_inv2_ac_power_wrong_timezone.csv,826.2154247760773,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,Wrong Timezone
+169,1423,1423_inv1_ac_power_60min_wrong_timezone.csv,19.967979907989502,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5824039653035935,60,Wrong Timezone
+170,1423,1423_inv1_ac_power_30min_random_time_shifts.csv,19.724013566970825,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.04956629491945477,30,Random Time Shifts
+171,1423,1423_inv1_ac_power_15min_wrong_timezone.csv,38.35711598396301,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+172,1423,1423_inv1_ac_power_full_DST.csv,453.54144167900085,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",9.442379182156134,1,Full DST
+173,1423,1423_inv2_ac_power_60min_full_DST.csv,26.845848321914673,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",7.060546875,60,Full DST
+174,1423,1423_inv2_ac_power_60min.csv,30.554606437683105,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",1.2421875,60,No Issue
+175,4901,4901_InvPAC_kW_Avg_60min.csv,5.16202974319458,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+176,4901,4901_InvPAC_kW_Avg_60min.csv,5.16202974319458,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+177,4901,4901_InvPAC_kW_Avg_60min.csv,5.475994825363159,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+178,4901,4901_InvPAC_kW_Avg_60min.csv,5.475994825363159,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+179,4901,4901_InvPAC_kW_Avg_30min.csv,9.602619171142578,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+180,4901,4901_InvPAC_kW_Avg_30min.csv,9.602619171142578,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+181,4901,4901_InvPAC_kW_Avg_30min.csv,8.822126865386963,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+182,4901,4901_InvPAC_kW_Avg_30min.csv,8.822126865386963,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+183,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,16.472388982772827,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.045317220543806644,15,Random Time Shifts
+184,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,16.472388982772827,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.045317220543806644,15,Random Time Shifts
+185,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,17.365429878234863,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.045317220543806644,15,Random Time Shifts
+186,4901,4901_InvPAC_kW_Avg_15min_random_time_shifts.csv,17.365429878234863,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.045317220543806644,15,Random Time Shifts
+187,4901,4901_InvPAC_kW_Avg.csv,255.33652305603027,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+188,4901,4901_InvPAC_kW_Avg.csv,255.33652305603027,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+189,4901,4901_InvPAC_kW_Avg.csv,259.7840850353241,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+190,4901,4901_InvPAC_kW_Avg.csv,259.7840850353241,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+191,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,5.847094297409058,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Random Time Shifts
+192,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,5.847094297409058,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Random Time Shifts
+193,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,6.241239547729492,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Random Time Shifts
+194,4901,4901_InvPAC_kW_Avg_60min_random_time_shifts.csv,6.241239547729492,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Random Time Shifts
+195,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,11.682185173034668,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.45317220543806647,30,Full DST
+196,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,11.682185173034668,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.45317220543806647,30,Full DST
+197,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,11.35204792022705,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.45317220543806647,30,Full DST
+198,4901,4901_InvPAC_kW_Avg_30min_full_DST.csv,11.35204792022705,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.45317220543806647,30,Full DST
+199,4901,4901_InvPAC_kW_Avg_15min.csv,18.148995876312256,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+200,4901,4901_InvPAC_kW_Avg_15min.csv,18.148995876312256,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+201,4901,4901_InvPAC_kW_Avg_15min.csv,17.037468671798706,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+202,4901,4901_InvPAC_kW_Avg_15min.csv,17.037468671798706,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+203,4901,4901_InvPAC_kW_Avg_partial_DST.csv,263.1433343887329,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5891238670694864,1,Partial DST
+204,4901,4901_InvPAC_kW_Avg_partial_DST.csv,263.1433343887329,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5891238670694864,1,Partial DST
+205,4901,4901_InvPAC_kW_Avg_partial_DST.csv,261.2420082092285,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5891238670694864,1,Partial DST
+206,4901,4901_InvPAC_kW_Avg_partial_DST.csv,261.2420082092285,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.5891238670694864,1,Partial DST
+207,4902,4902_ac_power_inv_14538_15min.csv,15.979040622711182,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+208,4902,4902_ac_power_inv_14538_random_time_shifts.csv,250.62322282791138,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09063444108761329,1,Random Time Shifts
+209,4902,4902_ac_power_inv_14538_15min_wrong_timezone.csv,15.961017608642578,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+210,4902,4902_ac_power_inv_14538_30min_random_time_shifts.csv,9.46309757232666,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Random Time Shifts
+211,4902,4902_ac_power_inv_14538_60min_partial_DST.csv,6.163048982620239,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",7.182779456193353,60,Partial DST
+212,4902,4902_ac_power_inv_14538_30min.csv,9.555004358291626,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+213,4902,4902_ac_power_inv_14538.csv,248.62708568572998,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+214,4902,4902_ac_power_inv_14538_60min.csv,4.957221031188965,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+215,4903,4903_ac_power_inv_14539.csv,254.58531260490417,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+216,4903,4903_ac_power_other_inv_14539_2_15min_partial_DST.csv,10.887035608291626,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",17.72965879265092,15,Partial DST
+217,4903,4903_ac_power_other_inv_14539_2_random_time_shifts.csv,9.339041709899902,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",21.614173228346456,15,Random Time Shifts
+218,4903,4903_ac_power_inv_14539_wrong_timezone.csv,256.68192052841187,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,Wrong Timezone
+219,4903,4903_ac_power_other_inv_14539_2_30min_wrong_timezone.csv,5.459724426269531,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,30,Wrong Timezone
+220,4903,4903_ac_power_inv_14539_30min_random_time_shifts.csv,8.778224229812622,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",2.9121725731895225,30,Random Time Shifts
+221,4903,4903_ac_power_inv_14539_60min_random_time_shifts.csv,6.10811972618103,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",42.27272727272727,60,Random Time Shifts
+222,4903,4903_ac_power_other_inv_14539_2_60min.csv,4.603696823120117,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.931034482758621,60,No Issue
+223,4903,4903_ac_power_other_inv_14539_2_30min.csv,6.315152406692505,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,30,No Issue
+224,4903,4903_ac_power_other_inv_14539_2_15min.csv,9.607762813568115,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,15,No Issue
diff --git a/ec2/analysis_taks/time-shift-detection/utilities.py b/ec2/analysis_taks/time-shift-detection/utilities.py
new file mode 100644
index 00000000..1a012ba7
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/utilities.py
@@ -0,0 +1,20 @@
+import sys
+
+
+def progress(count, total, status="", bar_length=60):
+    """
+    Python command line progress bar in less than 10 lines of code. · GitHub
+    https://gist.github.com/vladignatyev/06860ec2040cb497f0f3
+    :param count: the current count, int
+    :param total: to total count, int
+    :param status: a message to display
+    :return:
+    """
+    bar_len = bar_length
+    filled_len = int(round(bar_len * count / float(total)))
+
+    percents = round(100.0 * count / float(total), 1)
+    bar = "=" * filled_len + "-" * (bar_len - filled_len)
+
+    sys.stdout.write("[%s] %s%s ...%s\r" % (bar, percents, "%", status))
+    sys.stdout.flush()
diff --git a/ec2/analysis_taks/time-shift-detection/with.csv b/ec2/analysis_taks/time-shift-detection/with.csv
new file mode 100644
index 00000000..e7754c3f
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/with.csv
@@ -0,0 +1,61 @@
+,system_id,file_name,run_time,data_requirements,mean_absolute_error_time_series,data_sampling_frequency,issue
+0,4,4_ac_power__315_full_DST.csv,34.90727496147156,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.075176194205168,15,Full DST
+1,4,4_ac_power__315_30min_wrong_timezone.csv,21.706873655319214,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+2,4,4_ac_power__315_60min_wrong_timezone.csv,12.961978197097778,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+3,4,4_ac_power__315_60min.csv,8.706212282180786,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+4,4,4_ac_power__315_30min.csv,20.87249493598938,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+5,4,4_ac_power__315_15min.csv,51.23320746421814,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+6,4,4_ac_power__315_15min_full_DST.csv,39.87471890449524,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.075176194205168,15,Full DST
+7,50,50_ac_power_wrong_timezone.csv,470.50916957855225,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,Wrong Timezone
+8,50,50_ac_power_1_random_time_shifts.csv,470.3852791786194,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.3629032258064516,1,Random Time Shifts
+9,50,50_ac_power_15min_wrong_timezone.csv,25.348291397094727,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+10,50,50_ac_power_1_15min_random_time_shifts.csv,29.842859029769897,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",2.3588709677419355,15,Random Time Shifts
+11,50,50_ac_power_1_30min_wrong_timezone.csv,12.436638832092285,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+12,50,50_ac_power_1_60min_wrong_timezone.csv,7.088029623031616,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,Wrong Timezone
+13,50,50_ac_power_2_full_DST.csv,551.6944043636322,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.6048387096774194,1,Full DST
+14,50,50_ac_power_30min.csv,19.68176817893982,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+15,50,50_ac_power_2_30min_random_time_shifts.csv,19.21680498123169,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.423387096774194,30,Random Time Shifts
+16,50,50_ac_power_2_15min_wrong_timezone.csv,32.974079847335815,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+17,50,50_ac_power_30min_wrong_timezone.csv,20.471663236618042,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+18,50,50_ac_power.csv,505.6583662033081,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+19,50,50_ac_power_1.csv,496.39406847953796,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+20,50,50_ac_power_15min.csv,40.655024766922,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+21,50,50_ac_power_1_15min.csv,38.98139786720276,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+22,50,50_ac_power_1_30min.csv,18.4488627910614,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+23,50,50_ac_power_1_60min.csv,10.706721305847168,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,No Issue
+24,50,50_ac_power_2.csv,598.0040035247803,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+25,50,50_ac_power_2_15min.csv,34.650712728500366,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+26,50,50_ac_power_2_30min.csv,16.348299741744995,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,30,No Issue
+27,50,50_ac_power_2_60min.csv,9.09198522567749,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,No Issue
+28,50,50_ac_power_2_60min_random_time_shifts.csv,10.366418600082397,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.211693548387096,60,Random Time Shifts
+29,50,50_ac_power_60min_random_time_shifts.csv,13.183933973312378,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.883064516129032,60,Random Time Shifts
+30,50,50_ac_power_60min.csv,9.885443210601807,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,No Issue
+31,1229,1229_poa_irradiance_west_array_60min.csv,13.181412696838379,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+32,1229,1229_gh_irradiance_15min.csv,50.77135729789734,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+33,1229,1229_gh_irradiance_30min.csv,24.83160376548767,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+34,1229,1229_gh_irradiance_60min.csv,14.268895626068115,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+35,1229,1229_poa_irradiance_15min.csv,48.397486209869385,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+36,1229,1229_poa_irradiance_30min.csv,24.948834896087646,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+37,1229,1229_poa_irradiance_60min.csv,14.745826244354248,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+38,1229,1229_poa_irradiance_south_array_15min.csv,49.68852996826172,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+39,1229,1229_poa_irradiance_south_array_30min.csv,25.149044513702393,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+40,1229,1229_poa_irradiance_south_array_60min.csv,14.14261794090271,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+41,1229,1229_poa_irradiance_west_array_15min.csv,46.037270307540894,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+42,1229,1229_poa_irradiance_west_array_30min.csv,22.053478002548218,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+43,1229,1229_poa_irradiance_west_array_30min_partial_DST.csv,24.50466227531433,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.18532818532818532,30,Partial DST
+44,1229,1229_poa_irradiance_west_array_15min_wrong_timezone.csv,46.35543656349182,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+45,1229,1229_poa_irradiance_south_array_60min_wrong_timezone.csv,12.530768632888794,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+46,1229,1229_poa_irradiance_south_array_30min_random_time_shifts.csv,21.880296230316162,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Random Time Shifts
+47,1229,1229_poa_irradiance_south_array_15min_random_time_shifts.csv,42.61737036705017,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+48,1229,1229_poa_irradiance_south_array_full_DST.csv,41.356683015823364,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.37065637065637064,15,Full DST
+49,1229,1229_poa_irradiance_60min_wrong_timezone.csv,10.77398943901062,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+50,1229,1229_poa_irradiance_30min_wrong_timezone.csv,19.2072114944458,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+51,1229,1229_poa_irradiance_15min_random_time_shifts.csv,37.76782274246216,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.18532818532818532,15,Random Time Shifts
+52,1229,1229_poa_irradiance_random_time_shifts.csv,42.43992233276367,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+53,1229,1229_gh_irradiance_60min_random_time_shifts.csv,10.847017765045166,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.277992277992278,60,Random Time Shifts
+54,1229,1229_gh_irradiance_30min_random_time_shifts.csv,20.605677843093872,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.44015444015444016,30,Random Time Shifts
+55,1229,1229_gh_irradiance_15min_random_time_shifts.csv,42.68475604057312,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.05791505791505792,15,Random Time Shifts
+56,1229,1229_poa_irradiance_west_array_full_DST.csv,42.196202516555786,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.138996138996139,15,Full DST
+57,1229,1229_poa_irradiance_west_array_60min_random_time_shifts.csv,11.308561563491821,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",11.328185328185327,60,Random Time Shifts
+58,1229,1229_gh_irradiance_random_time_shifts.csv,42.37387180328369,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.138996138996139,15,Random Time Shifts
+59,1332,1332_inv3_ac_power__2650_15min.csv,17.747118949890137,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
diff --git a/ec2/analysis_taks/time-shift-detection/without.csv b/ec2/analysis_taks/time-shift-detection/without.csv
new file mode 100644
index 00000000..56638e6d
--- /dev/null
+++ b/ec2/analysis_taks/time-shift-detection/without.csv
@@ -0,0 +1,61 @@
+,system_id,file_name,run_time,data_requirements,mean_absolute_error_time_series,data_sampling_frequency,issue
+0,4,4_ac_power__315_full_DST.csv,43.131714820861816,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",11.981205951448707,15,Full DST
+1,4,4_ac_power__315_30min_wrong_timezone.csv,21.339045524597168,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+2,4,4_ac_power__315_60min_wrong_timezone.csv,12.286386251449585,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+3,4,4_ac_power__315_60min.csv,18.16911220550537,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+4,4,4_ac_power__315_30min.csv,21.68332290649414,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+5,4,4_ac_power__315_15min.csv,40.56131839752197,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+6,4,4_ac_power__315_15min_full_DST.csv,39.251957416534424,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",11.981205951448707,15,Full DST
+7,50,50_ac_power_wrong_timezone.csv,466.54888129234314,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,Wrong Timezone
+8,50,50_ac_power_1_random_time_shifts.csv,465.28292322158813,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.4838709677419355,1,Random Time Shifts
+9,50,50_ac_power_15min_wrong_timezone.csv,29.609864234924316,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+10,50,50_ac_power_1_15min_random_time_shifts.csv,25.847919702529907,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",2.555443548387097,15,Random Time Shifts
+11,50,50_ac_power_1_30min_wrong_timezone.csv,14.555633068084717,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+12,50,50_ac_power_1_60min_wrong_timezone.csv,8.735718488693237,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,Wrong Timezone
+13,50,50_ac_power_2_full_DST.csv,559.1163897514343,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.6048387096774194,1,Full DST
+14,50,50_ac_power_30min.csv,16.766238927841187,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+15,50,50_ac_power_2_30min_random_time_shifts.csv,17.78877305984497,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.423387096774194,30,Random Time Shifts
+16,50,50_ac_power_2_15min_wrong_timezone.csv,37.54592728614807,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+17,50,50_ac_power_30min_wrong_timezone.csv,18.23801350593567,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+18,50,50_ac_power.csv,500.0403046607971,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+19,50,50_ac_power_1.csv,513.4839479923248,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+20,50,50_ac_power_15min.csv,37.770044803619385,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+21,50,50_ac_power_1_15min.csv,36.75596904754639,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+22,50,50_ac_power_1_30min.csv,20.25804829597473,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+23,50,50_ac_power_1_60min.csv,9.776930093765259,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,No Issue
+24,50,50_ac_power_2.csv,590.8388669490814,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,1,No Issue
+25,50,50_ac_power_2_15min.csv,30.5111083984375,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+26,50,50_ac_power_2_30min.csv,20.45271062850952,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.0,30,No Issue
+27,50,50_ac_power_2_60min.csv,11.085412740707397,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,No Issue
+28,50,50_ac_power_2_60min_random_time_shifts.csv,10.332018613815308,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",15.211693548387096,60,Random Time Shifts
+29,50,50_ac_power_60min_random_time_shifts.csv,10.418641805648804,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",12.883064516129032,60,Random Time Shifts
+30,50,50_ac_power_60min.csv,11.776971340179443,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",13.941532258064516,60,No Issue
+31,1229,1229_poa_irradiance_west_array_60min.csv,14.391368389129639,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+32,1229,1229_gh_irradiance_15min.csv,49.97129726409912,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+33,1229,1229_gh_irradiance_30min.csv,21.043630599975586,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+34,1229,1229_gh_irradiance_60min.csv,13.676306009292603,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+35,1229,1229_poa_irradiance_15min.csv,50.5355122089386,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+36,1229,1229_poa_irradiance_30min.csv,27.32389807701111,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+37,1229,1229_poa_irradiance_60min.csv,13.503303050994873,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+38,1229,1229_poa_irradiance_south_array_15min.csv,52.63319754600525,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+39,1229,1229_poa_irradiance_south_array_30min.csv,25.26525115966797,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+40,1229,1229_poa_irradiance_south_array_60min.csv,13.598221778869629,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,No Issue
+41,1229,1229_poa_irradiance_west_array_15min.csv,45.359179973602295,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue
+42,1229,1229_poa_irradiance_west_array_30min.csv,23.937071800231934,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,No Issue
+43,1229,1229_poa_irradiance_west_array_30min_partial_DST.csv,22.870121955871582,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.18532818532818532,30,Partial DST
+44,1229,1229_poa_irradiance_west_array_15min_wrong_timezone.csv,48.04014801979065,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,Wrong Timezone
+45,1229,1229_poa_irradiance_south_array_60min_wrong_timezone.csv,13.688610553741455,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+46,1229,1229_poa_irradiance_south_array_30min_random_time_shifts.csv,23.75854206085205,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Random Time Shifts
+47,1229,1229_poa_irradiance_south_array_15min_random_time_shifts.csv,43.02658200263977,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+48,1229,1229_poa_irradiance_south_array_full_DST.csv,36.77186036109924,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.37065637065637064,15,Full DST
+49,1229,1229_poa_irradiance_60min_wrong_timezone.csv,12.38723611831665,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,60,Wrong Timezone
+50,1229,1229_poa_irradiance_30min_wrong_timezone.csv,19.83583950996399,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,30,Wrong Timezone
+51,1229,1229_poa_irradiance_15min_random_time_shifts.csv,43.09074068069458,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.18532818532818532,15,Random Time Shifts
+52,1229,1229_poa_irradiance_random_time_shifts.csv,39.24523878097534,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.011583011583011582,15,Random Time Shifts
+53,1229,1229_gh_irradiance_60min_random_time_shifts.csv,15.330822229385376,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.277992277992278,60,Random Time Shifts
+54,1229,1229_gh_irradiance_30min_random_time_shifts.csv,20.18026375770569,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.44015444015444016,30,Random Time Shifts
+55,1229,1229_gh_irradiance_15min_random_time_shifts.csv,39.37149238586426,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.05791505791505792,15,Random Time Shifts
+56,1229,1229_poa_irradiance_west_array_full_DST.csv,42.48407316207886,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.138996138996139,15,Full DST
+57,1229,1229_poa_irradiance_west_array_60min_random_time_shifts.csv,12.98689889907837,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",11.328185328185327,60,Random Time Shifts
+58,1229,1229_gh_irradiance_random_time_shifts.csv,43.2609806060791,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.09266409266409266,15,Random Time Shifts
+59,1332,1332_inv3_ac_power__2650_15min.csv,17.080054759979248,"['time_series', 'latitude', 'longitude', 'data_sampling_frequency']",0.0,15,No Issue

From 663cec523c2e5c1c1b73b0000502ec9497b31600 Mon Sep 17 00:00:00 2001
From: Mitchell Victoriano <mitchell.victoriano@gmail.com>
Date: Wed, 18 Dec 2024 20:24:56 -0800
Subject: [PATCH 4/4] Modified bash script to reference internal tasks without
 route.json parameters

---
 docker-compose.yml                            |   1 +
 ec2/Dockerfile                                |   2 +-
 .../az-tilt-estimation/.gitignore             |   0
 .../az-tilt-estimation/Az-Tilt-PVSC-2023.pptx | Bin
 .../az-tilt-estimation/LICENSE                |   0
 .../az-tilt-estimation/README.md              |   0
 .../assets/SubmissionInstructions.md          |   0
 .../assets/absolute_error_az_dist.png         | Bin
 .../assets/absolute_error_tilt_dist.png       | Bin
 .../az-tilt-estimation/assets/banner.png      | Bin
 .../az-tilt-estimation/assets/cardCover.png   | Bin
 .../az-tilt-estimation/assets/config.json     |   0
 .../az-tilt-estimation/assets/description.md  |   0
 .../assets/file_metadata.csv                  |   0
 .../assets/run_time_dist.png                  | Bin
 .../assets/run_time_number_days.png           | Bin
 .../az-tilt-estimation/assets/shortdesc.md    |   0
 .../assets/system_metadata.csv                |   0
 .../assets/time_series_data.PNG               | Bin
 .../az-tilt-estimation/config.json            |   0
 .../az-tilt-estimation/data/file_metadata.csv |   0
 .../data/file_test_link.csv                   |   0
 .../data/merged_file_data.csv                 |   0
 .../data/system_metadata.csv                  |   0
 .../data/validation_tests.csv                 |   0
 .../other/generate-plots-categorization.py    |   0
 .../az-tilt-estimation/other/generate_map.py  |   0
 .../az-tilt-estimation/other/model-energy.py  |   0
 .../other/paper_statistics.py                 |   0
 .../other/tilt-az-file-viewer-SDT.py          |   0
 .../pvinsight-az-tilt-runner.py               |   0
 .../az-tilt-estimation/pvwatts-5-module.py    |   0
 .../results/absolute_error_az_dist.png        | Bin
 .../results/absolute_error_tilt_dist.png      | Bin
 .../results/az-tilt-public-metrics.json       |   0
 .../results/azimuth-error-distribution.pdf    | Bin
 .../results/azimuth-error-distribution.svg    |   0
 .../results/clipped-system.pdf                | Bin
 .../combined-error-runtime-distribution.pdf   | Bin
 .../error-distribution-heavily-clipped.pdf    | Bin
 .../results/heavily-shaded-system-heatmap.png | Bin
 .../results/mae-azimuth-clipping-detected.pdf | Bin
 .../results/mae-azimuth-clipping-detected.svg |   0
 .../mae-azimuth-number-days-pvwatts.pdf       | Bin
 .../mae-azimuth-number-days-pvwatts.svg       |   0
 .../results/mae-azimuth-number-days.svg       |   0
 .../results/mae-azimuth-shading-detected.pdf  | Bin
 .../results/mae-azimuth-shading-detected.svg  |   0
 .../results/mae-azimuth-soiling-detected.pdf  | Bin
 .../results/mae-azimuth-soiling-detected.svg  |   0
 .../results/mae-tilt-clipping-detected.pdf    | Bin
 .../results/mae-tilt-clipping-detected.svg    |   0
 .../results/mae-tilt-number-days-pvwatts.pdf  | Bin
 .../results/mae-tilt-number-days-pvwatts.svg  |   0
 .../results/mae-tilt-number-days.svg          |   0
 .../results/mae-tilt-run-time.svg             |   0
 .../results/mae-tilt-shading-detected.pdf     | Bin
 .../results/mae-tilt-shading-detected.svg     |   0
 .../results/mae-tilt-soiling-detected.pdf     | Bin
 .../results/mae-tilt-soiling-detected.svg     |   0
 .../pv-peak-module-matt-redo_full_results.csv |   0
 ...redo_issue_mean_absolute_error_azimuth.png | Bin
 ...ue_mean_absolute_error_results_azimuth.csv |   0
 ...issue_mean_absolute_error_results_tilt.csv |   0
 ...tt-redo_issue_mean_absolute_error_tilt.png | Bin
 .../pv-peak-module-matt-redo_run_time.png     | Bin
 .../results/pv-peak-module_full_results.csv   |   0
 ...dule_issue_mean_absolute_error_azimuth.png | Bin
 ...ue_mean_absolute_error_results_azimuth.csv |   0
 ...issue_mean_absolute_error_results_tilt.csv |   0
 ...-module_issue_mean_absolute_error_tilt.png | Bin
 .../results/pv-peak-module_run_time.png       | Bin
 .../results/pvwatts-5-module_full_results.csv |   0
 ...dule_issue_mean_absolute_error_azimuth.png | Bin
 ...ue_mean_absolute_error_results_azimuth.csv |   0
 ...issue_mean_absolute_error_results_tilt.csv |   0
 ...-module_issue_mean_absolute_error_tilt.png | Bin
 .../results/pvwatts-5-module_run_time.png     | Bin
 .../results/run-time-distribution.pdf         | Bin
 .../results/run-time-distribution.svg         |   0
 .../run-time-number-days-comparison.pdf       | Bin
 .../results/run-time-number-days-pvwatts.pdf  | Bin
 .../results/run-time-number-days-pvwatts.svg  |   0
 .../run-time-number-days-sdt-lat-lon.pdf      | Bin
 .../run-time-number-days-sdt-lat-lon.svg      |   0
 .../run-time-number-days-sdt-no-lat-lon.pdf   | Bin
 .../run-time-number-days-sdt-no-lat-lon.svg   |   0
 .../results/run_time_dist.png                 | Bin
 .../results/run_time_number_days.png          | Bin
 .../sdt-module-latlon_full_results.csv        |   0
 ...tlon_issue_mean_absolute_error_azimuth.png | Bin
 ...ue_mean_absolute_error_results_azimuth.csv |   0
 ...issue_mean_absolute_error_results_tilt.csv |   0
 ...-latlon_issue_mean_absolute_error_tilt.png | Bin
 .../results/sdt-module-latlon_run_time.png    | Bin
 .../sdt-module-no-latlon_full_results.csv     |   0
 ...tlon_issue_mean_absolute_error_azimuth.png | Bin
 ...ue_mean_absolute_error_results_azimuth.csv |   0
 ...issue_mean_absolute_error_results_tilt.csv |   0
 ...-latlon_issue_mean_absolute_error_tilt.png | Bin
 .../results/sdt-module-no-latlon_run_time.png | Bin
 .../results/tilt-error-distribution.pdf       | Bin
 .../results/tilt-error-distribution.svg       |   0
 .../validation_location_resource_map.png      | Bin
 .../az-tilt-estimation/sdt-module-latlon.py   |   0
 .../sdt-module-no-latlon.py                   |   0
 .../1332_inv_total_ac_power__2654.csv         |   0
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 rename ec2/{analysis_taks => analysis-tasks}/time-shift-detection/submission.zip (100%)
 rename ec2/{analysis_taks => analysis-tasks}/time-shift-detection/submissions/pvanalytics-submission.zip (100%)
 rename ec2/{analysis_taks => analysis-tasks}/time-shift-detection/template.py (100%)
 rename ec2/{analysis_taks => analysis-tasks}/time-shift-detection/time_shifts_full_results.csv (100%)
 rename ec2/{analysis_taks => analysis-tasks}/time-shift-detection/utilities.py (100%)
 rename ec2/{analysis_taks => analysis-tasks}/time-shift-detection/with.csv (100%)
 rename ec2/{analysis_taks => analysis-tasks}/time-shift-detection/without.csv (100%)
 mode change 100644 => 100755 ec2/manage.sh

diff --git a/docker-compose.yml b/docker-compose.yml
index fa0bd2cb..93bf2254 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -66,6 +66,7 @@ services:
       - "7000:7000"
     depends_on:
       - api
+      - s3
     volumes:
       - type: bind
         source: ./ec2
diff --git a/ec2/Dockerfile b/ec2/Dockerfile
index 54c8360c..e3c4e8d0 100755
--- a/ec2/Dockerfile
+++ b/ec2/Dockerfile
@@ -1,4 +1,4 @@
-FROM python:3.10-slim
+FROM python:3.11-slim
 
 # docker build --progress=plain -t "hmm:Dockerfile" -f valhub/Dockerfile .
 
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rename from ec2/analysis_taks/time-shift-detection/results/time-shift-public-metrics.json
rename to ec2/analysis-tasks/time-shift-detection/results/time-shift-public-metrics.json
diff --git a/ec2/analysis_taks/time-shift-detection/sdt-module.py b/ec2/analysis-tasks/time-shift-detection/sdt-module.py
similarity index 100%
rename from ec2/analysis_taks/time-shift-detection/sdt-module.py
rename to ec2/analysis-tasks/time-shift-detection/sdt-module.py
diff --git a/ec2/analysis_taks/time-shift-detection/submission.zip b/ec2/analysis-tasks/time-shift-detection/submission.zip
similarity index 100%
rename from ec2/analysis_taks/time-shift-detection/submission.zip
rename to ec2/analysis-tasks/time-shift-detection/submission.zip
diff --git a/ec2/analysis_taks/time-shift-detection/submissions/pvanalytics-submission.zip b/ec2/analysis-tasks/time-shift-detection/submissions/pvanalytics-submission.zip
similarity index 100%
rename from ec2/analysis_taks/time-shift-detection/submissions/pvanalytics-submission.zip
rename to ec2/analysis-tasks/time-shift-detection/submissions/pvanalytics-submission.zip
diff --git a/ec2/analysis_taks/time-shift-detection/template.py b/ec2/analysis-tasks/time-shift-detection/template.py
similarity index 100%
rename from ec2/analysis_taks/time-shift-detection/template.py
rename to ec2/analysis-tasks/time-shift-detection/template.py
diff --git a/ec2/analysis_taks/time-shift-detection/time_shifts_full_results.csv b/ec2/analysis-tasks/time-shift-detection/time_shifts_full_results.csv
similarity index 100%
rename from ec2/analysis_taks/time-shift-detection/time_shifts_full_results.csv
rename to ec2/analysis-tasks/time-shift-detection/time_shifts_full_results.csv
diff --git a/ec2/analysis_taks/time-shift-detection/utilities.py b/ec2/analysis-tasks/time-shift-detection/utilities.py
similarity index 100%
rename from ec2/analysis_taks/time-shift-detection/utilities.py
rename to ec2/analysis-tasks/time-shift-detection/utilities.py
diff --git a/ec2/analysis_taks/time-shift-detection/with.csv b/ec2/analysis-tasks/time-shift-detection/with.csv
similarity index 100%
rename from ec2/analysis_taks/time-shift-detection/with.csv
rename to ec2/analysis-tasks/time-shift-detection/with.csv
diff --git a/ec2/analysis_taks/time-shift-detection/without.csv b/ec2/analysis-tasks/time-shift-detection/without.csv
similarity index 100%
rename from ec2/analysis_taks/time-shift-detection/without.csv
rename to ec2/analysis-tasks/time-shift-detection/without.csv
diff --git a/ec2/insert_analysis.py b/ec2/insert_analysis.py
index 4cb68f15..8d40ee77 100755
--- a/ec2/insert_analysis.py
+++ b/ec2/insert_analysis.py
@@ -109,8 +109,7 @@ def upload_to_s3_bucket(
             r = requests.put(s3_file_full_path, data=file_content)
             if r.status_code != 204:
                 logger.info(
-                    f"error put file {upload_path} to s3, status code {r.status_code} {r.content}",
-                    file=sys.stderr,
+                    f"error put file {upload_path} to s3, status code {r.status_code} {r.content}"
                 )
                 return None
             else:
@@ -1004,6 +1003,10 @@ def insertData(self, force=False):
         help="Set to production mode",
         default=False,
     )
+    parser.add_argument(
+        "--dir",
+        help="Directory of the task",
+    )
 
     args, unknown = parser.parse_known_args()
     logger.info(dict(args._get_kwargs()))
@@ -1030,32 +1033,39 @@ def convert_int(val) -> int:
         is_force = convert_bool(args.force)
         limit = convert_int(args.limit)
         is_dry_run = convert_bool(args.dry_run)
+        task_dir = args.dir
         ########################################################################
 
         logger.info("is_dry_run", is_dry_run, type(is_dry_run))
         logger.info("is_force", is_force, type(is_force))
         logger.info("limit", limit, type(limit))
         logger.info("is_local", is_local, type(is_local))
+        logger.info("task_dir", task_dir, type(task_dir))
+
+        if not os.path.exists(task_dir):
+            raise ValueError(f"Directory {task_dir} does not exist")
 
         api_url = config["local"]["api"] if is_local else config["prod"]["api"]
         s3_url = config["local"]["s3"] if is_local else config["prod"]["s3"]
 
-        config_file_path = config["config_file_path"]
-        file_data_folder_path = config["file_data_folder_path"]
-        evaluation_scripts_folder_path = config[
-            "evaluation_scripts_folder_path"
-        ]
-        sys_metadata_file_path = config["sys_metadata_file_path"]
-        file_metadata_file_path = config["file_metadata_file_path"]
-        validation_data_folder_path = config["validation_data_folder_path"]
-        private_report_template_file_path = config[
-            "private_report_template_file_path"
-        ]
+        config_file_path = os.path.join(task_dir, "config.json")
+        file_data_folder_path = os.path.join(task_dir, "data/files/")
+        evaluation_scripts_folder_path = "./evaluation_scripts/"
+        sys_metadata_file_path = os.path.join(
+            task_dir, "data/system_metadata.csv"
+        )
+        file_metadata_file_path = os.path.join(
+            task_dir, "data/file_metadata.csv"
+        )
+        validation_data_folder_path = os.path.join(
+            task_dir, "data/ground-truth/"
+        )
+        private_report_template_file_path = os.path.join(
+            task_dir, "template.py"
+        )
 
-        analysis_markdown_files_folder_path = config[
-            "analysis_markdown_files_folder_path"
-        ]
-        front_end_assets_folder_path = config["front_end_assets_folder_path"]
+        analysis_markdown_files_folder_path = os.path.join(task_dir, "assets")
+        front_end_assets_folder_path = "./front_end_assets"
 
         r = InsertAnalysis(
             markdown_files_folder_path=analysis_markdown_files_folder_path,
diff --git a/ec2/manage.sh b/ec2/manage.sh
old mode 100644
new mode 100755
index a274218a..c7babecf
--- a/ec2/manage.sh
+++ b/ec2/manage.sh
@@ -1,9 +1,10 @@
-#!/bin/bash
+#!/usr/bin/bash
 
-set -e  # Exit immediately if a command exits with a non-zero status
+set -euo pipefail
+IFS=$'\n\t'
 
 function usage() {
-    echo "Usage: $0 {insert} [--dry-run] [--force] [--prod] [--limit <number>]"
+    echo "Usage: $0 {insert} {time-shift-detection|az-tilt-estimation} [--dry-run] [--force] [--prod] [--limit <number>]"
     exit 1
 }
 
@@ -11,14 +12,19 @@ if [ $# -lt 1 ]; then
     usage
 fi
 
-COMMAND=$1
-shift
-
 DRY_RUN=False
 FORCE=False
 LIMIT=0
 PROD=False
 
+TASK_DIR="./analysis-tasks"
+TIME_SHIFT_DIR="$TASK_DIR/time-shift-detection"
+AZ_TILT_DIR="$TASK_DIR/az-tilt-estimation"
+
+COMMAND=$1
+TASK=$2
+shift 2
+
 while [[ "$#" -gt 0 ]]; do
     case $1 in
         --dry-run) DRY_RUN=True ;;
@@ -32,10 +38,19 @@ done
 
 case "$COMMAND" in
     insert)
-        echo "Inserting analysis..."
-        echo "DRY_RUN=$DRY_RUN, FORCE=$FORCE, LIMIT=$LIMIT, PROD=$PROD"
-        python3 insert_analysis.py \
-            --dry-run $DRY_RUN --force $FORCE --limit $LIMIT --prod $PROD
+        case "$TASK" in
+            time-shift-detection)
+                echo "Inserting time-shift-detection task"
+                python3 insert_analysis.py --dry-run $DRY_RUN --force $FORCE --limit $LIMIT --prod $PROD --dir $TIME_SHIFT_DIR
+                ;;
+            az-tilt-estimation)
+                echo "Inserting az-tilt-estimation task"
+                python3 insert_analysis.py --dry-run $DRY_RUN --force $FORCE --limit $LIMIT --prod $PROD --dir $AZ_TILT_DIR
+                ;;
+            *)
+                usage
+                ;;
+        esac
         ;;
     *)
         usage