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Notebook","category":"Renewable Energy","sub_category":"Photovoltaics and Solar Energy","monthly_downloads":209,"total_dependent_repos":1,"total_dependent_packages":1,"readme":"# pv-system-profiler\n### Estimating PV array location and orientation from real-world power datasets.\n\n\u003ctable\u003e\n\u003ctr\u003e\n  \u003ctd\u003eLatest Release\u003c/td\u003e\n  \u003ctd\u003e\n    \u003ca href=\"https://pypi.org/project/pv-system-profiler/\"\u003e\n        \u003cimg src=\"https://img.shields.io/pypi/v/pv-system-profiler.svg\" alt=\"latest release\" /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://anaconda.org/slacgismo/pv-system-profiler\"\u003e\n        \u003cimg src=\"https://anaconda.org/slacgismo/pv-system-profiler/badges/version.svg\" /\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://anaconda.org/slacgismo/pv-system-profiler\"\u003e\n        \u003cimg src=\"https://anaconda.org/slacgismo/pv-system-profiler/badges/latest_release_date.svg\" /\u003e\n    \u003c/a\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n  \u003ctd\u003eLicense\u003c/td\u003e\n  \u003ctd\u003e\n    \u003ca href=\"https://github.com/slacgismo/pv-system-profiler/blob/master/LICENSE\"\u003e\n        \u003cimg src=\"https://img.shields.io/pypi/l/pv-system-profiler.svg\" alt=\"license\" /\u003e\n    \u003c/a\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n  \u003ctd\u003eBuild Status\u003c/td\u003e\n  \u003ctd\u003e\n    \u003ca href=\"https://app.circleci.com/pipelines/github/slacgismo/pv-system-profiler\"\u003e\n        \u003cimg src=\"https://circleci.com/gh/slacgismo/pv-system-profiler.svg?style=svg\" alt=\"CircleCi build status\" /\u003e\n    \u003c/a\u003e\n  \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003eCode Quality\u003c/td\u003e\n    \u003ctd\u003e\n        \u003ca href=\"https://lgtm.com/projects/g/slacgismo/pv-system-profiler/context:python\"\u003e\n            \u003cimg alt=\"Language grade: Python\" src=\"https://img.shields.io/lgtm/grade/python/g/slacgismo/pv-system-profiler.svg?logo=lgtm\u0026logoWidth=18\"/\u003e\n        \u003c/a\u003e\n        \u003ca href=\"https://lgtm.com/projects/g/slacgismo/pv-system-profiler/alerts/\"\u003e\n            \u003cimg alt=\"Total alerts\" src=\"https://img.shields.io/lgtm/alerts/g/slacgismo/pv-system-profiler.svg?logo=lgtm\u0026logoWidth=18\"/\u003e\n        \u003c/a\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003ePublications\u003c/td\u003e\n    \u003ctd\u003e\n      \u003ca href=\"https://zenodo.org/badge/latestdoi/183074637\"\u003e\n        \u003cimg src=\"https://zenodo.org/badge/183074637.svg\" alt=\"DOI\"\u003e\n      \u003c/a\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003ePyPI Downloads\u003c/td\u003e\n    \u003ctd\u003e\n        \u003ca href=\"https://pepy.tech/project/pv-system-profiler\"\u003e\n            \u003cimg src=\"https://img.shields.io/pypi/dm/pv-system-profiler\" alt=\"PyPI downloads\" /\u003e\n        \u003c/a\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n    \u003ctd\u003eConda Downloads\u003c/td\u003e\n    \u003ctd\u003e\n        \u003ca href=\"https://anaconda.org/slacgismo/pv-system-profiler\"\u003e\n            \u003cimg src=\"https://anaconda.org/slacgismo/pv-system-profiler/badges/downloads.svg\" alt=\"conda-forge downloads\" /\u003e\n        \u003c/a\u003e\n    \u003c/td\u003e\n\u003c/tr\u003e\n\u003c/table\u003e\n\n## Install \u0026 Setup\n\n#### 1) Recommended: Set up `conda` environment with provided `.yml` file\n\nWe recommend setting up a fresh Python virtual environment in which to use `pv-system-profiler`. We recommend using the [Conda](https://docs.conda.io/projects/conda/en/latest/index.html) package management system, and creating an environment with the environment configuration file named `pvi-user.yml`, provided in the top level of this repository. This will install the `statistical-clear-sky` and `solar-data-tools` packages as well.\n\nCreating the env:\n\n```bash\n$ conda env create -f pvi-user.yml\n```\n\nStarting the env:\n\n```bash\n$ conda activate pvi_user\n```\n\nStopping the env\n\n```bash\n$ conda deactivate\n```\n\nAdditional documentation on setting up the Conda environment is available [here](https://github.com/slacgismo/pvinsight-onboarding/blob/main/README.md).\n\n\n#### 2) PIP Package\n\n```sh\n$ pip install pv-system-profiler\n```\n\nAlternative: Clone repo from GitHub\n\nMimic the pip package by setting up locally.\n\n```bash\n$ pip install -e path/to/root/folder\n```\n\n#### 3) Anaconda Package\n\n```sh\n$ conda install -c slacgismo pv-system-profiler\n```\n\n## Solver Dependencies\n\nRefer to [solar-data-tools](https://github.com/slacgismo/solar-data-tools) documentation to get more info about solvers being used.\n\n## Usage / Run Scripts\n### Serial run\nThe `parameter_estimation_script.py` script creates a report of all systems based on the `csv` files with the system signals located in a given folder.\nThe script takes all input parameters as `kwargs`. The example below illustrates the use of report_script:\n```shell\npython 'repository location of run script'/parameter_estimation_script.py report None all \ns3://s3_bucket_with_signals/ 'repeating_part_of label' /home/results.csv True False \nFalse False s3://'s3_path_to_file_containing_metadata/metadata.csv' None s3\n```\nIn the example above the full path to `parameter_estimation_script.py` is specified to run a\n`report`. The script allows to provide a `csv` file with list of sites to be analyzed. In this case no list is provided \nand therefore the `kwarg` `None` is entered. The script also allows to run an analysis on the first `n_files` containing \ninput signals in the `s3` repository. In this, case the `all` `kwarg` specifies that all input signals are to be analyzed. \nIn this example, all `csv` files containing the input signals are located in the `s3` bucket with the name \n`s3://s3_bucket_with_signals/`. Usually these `csv` files are of the form `ID_repeating_part_of_label.csv`, for example:\n`1_composite_10.csv`, `2_composite_10.csv`, where `_composite_10` is the repeating part of the label. The repeating part \nof the label is either None or a string as in the example above. Next, an absolute path to the desired location of the \nresults file is provided, in this case `/home/results.csv`. The two following `kwargs` are type Boolean and are used to set the \nvalues of the `correct_tz` and `fix_shifts` pipeline `kwargs`. The next  `kwarg`,  `check_json` is also Boolean. It \nis used to indicate if  there is a `json` file present in `s3://s3_bucket_with_signals/` with additional site information \nthat is  to be analyzed. The next Boolean `kwarg` is used to set the `convert_to_ts` `kwarg` when instantiating the data \nhandler.  The next `kawrg` contains the full path to the `csv` file containing site metadata, here called `metadata.csv`. \nThe information that this file should contain varies depending on the `estimation` to be performed. This file is \noptional and the `kwarg` can be set to `None`. For the case of a `report`, a `csv` file with columns labeled `site`, \n`system` and `gmt_offset` and their respective values need to be provided. Alternatively, if the `gmt_offset` `kwarg`, \nthe next `kwarg` (in the example above set to `None`), has a numeric \nvalue different to `None`, all sites will use that single value when running the report. For the case of the `report` \nestimation, the metadata file should contain `site`, `system` and `gmt_offset` columns with the respective\nvalues for each system. For the case of the `longitude` estimation, the metadata file should contain `site`, `system` \nand `latitude` columns with the respective values for each system. For the case of the `tilt_azimuth` estimation, the \nmetadata file should contain `site`, `system`, `gmt_offset`, `estimated_longitude` and `estimated_latitude`, `tilt`, \n`azimuth` columns and with the respective values for each system. Additionally, if a manual inspection for time shifts \nwas performed, another \ncolumn labeled `time_shift_manual` having a zero for systems with no time shift and ones for systems with time shift\nmay be included. If a `time_shift_manual` column is included, it will be used to determine whether the `fix_dst()` \nmethod is run after instantiating the data handler. The next `karg is` `gmt_offset` and in this case it is set to None. \nThe last kwarg corresponds to the `data_source`. In this case the value is `s3` since files with the input signals are\nlocated in an `s3` bucket.\n ## Partitioned run\nA script that runs the site report, the longitude, latitude and tilt and azimuth scripts using a number of prescribed \nAmazon Web Services (AWS), instances is provided. The script reads the folder containing the system signals and \npartitions these signals to run in  a `n` user prescribed AWS instances in parallel. Here is an example shell command \nfor a partitioned run:\n```shell\npython 'repository location of run script'/run_partition_script.py parameter_estimation_script.py report None all \ns3://s3_bucket_with_signals/ 'repeating_part_of label' /home/results.csv True False \nFalse False s3://'s3_path_to_file_containing_metadata/metadata.csv' None s3\n'repository location of run script'/parameter_estimation_script.py pvi-dev my_instance\n ```\nwhere the individual value of each kwarg are defined in run_partition_script.py. This script takes the same inputs as\nthe `parameter_estimation_script.py` plus three additional parameters. Note that the first kwarg is the partitioning \nscript repository location of run script `/run_partition_script.py parameter_estimation_script.py`. The estimation run\nscript `/parameter_estimation_script.p` is specified as the third to last kwarg. The second to last kwarg is the conda\nenviroment to be used to run the estimation, in this case `pvi-dev`. The last kwarg is the name of the AWS instances to\nbe used to run `run_partition_script.py`, in this case `my_instance`. Previous to running this command it is necessary to create `n` identical AWS \ninstances that correspond to the number of desired partitions. These instances need to have the same \n`Name='instance name'` AWS tag. The simplest way to accomplish this is by parting from an AWS  image of a  previously \nconfigured instance. This image needs to have all the  repositories and conda environments that \nwould be  needed in a serial run. Once each partitioned run is finished, results will be automatically collected in the \nlocal folder where `run_partition_script.py` was run. \n## Unit tests\n\nIn order to run unit tests:\n```\npython -m unittest -v\n```\n\n## Test Coverage\n\nIn order to view the current test coverage metrics:\n```\ncoverage run --source pvsystemprofiler -m unittest discover \u0026\u0026 coverage html\nopen htmlcov/index.html\n```\n\n## Versioning\n\nWe use [Semantic Versioning](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/slacgismo/pv-system-profiler/tags).\n\n## License\n\nThis project is licensed under the BSD 2-Clause License - see the [LICENSE](LICENSE) file for details\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/19802","html_url":"https://ost.ecosyste.ms/projects/19802"}