pv-system-profiler 0.1.0

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pv-system-profiler

Estimating PV array location and orientation from real-world power datasets.

Run Scripts

Serial run

The site_report script creates a report of all systems based on the csv files with the sytem signals located in a given folder. The script takes all input parameters as kwargs. The example below illustrates the use of report_script:

python /"repository location"/pv-system-profiler/pvsystemprofiler/scripts/site_report.py
None all s3://"s3 bucket location with csv signals"/ None power_inv /"output folder location"/results.csv
True False False False None

where the individual value of each kwarg are defined in site_report.py. In this example, the folder with the csv input signals is located in an Amazon Web Services (AWS) bucket. It can also be located in a local folder. Results are written to the file results.csv in a local directory Similar kwargs are entered for the longitude_script, latitude_script and the tilt_azimuth script. Here is an example:

python /"repository location"/pv-system-profiler/pvsystemprofiler/scripts/tilt_azimuth_script.py
None all s3://"s3 bucket location with csv signals"/ None power_inv_  /"output folder location"/results.csv
 True False False False s3://"s3 bucket location with longitude and latitude precalculates"/lon_lat_precalculates.csv

here, the last kwarg provides the location of a site list with previously calculated values of longitude and latitude and the true values. In the case of the longitude and latitude scripts, a list with the true values of longitude and latitude can be provided. It is also possible to provide no input list at all.

Partitioned run

A script that runs the site report and the longitude, latitude and tilt and azimuth scripts using a number of prescribed AWS instances is provided. The script reads the folder containing the system signals and partitions these signals to run in a n user prescribed AWS instances in parallel. Here is an example shell command for a partitioned run:

python /"repository location"/pv-system-profiler/pvsystemprofiler/scripts/run_partition_script.py
None all /"repository location"/pv-system-profiler/pvsystemprofiler/scripts/tilt_azimuth_script.py conda_environment
None power_inv_ True False True False  "instance_name"
s3://"s3 bucket location with longitude and latitude precalculates"/lon_lat_precalculates.csv

where the individual value of each kwarg are defined in run_partition_script.py. Previous to running this command it is necessary to create n identical AWS instances that correspond to the number of desired partitions. These instances need to have the same Name="instance name" AWS tag. The simplest way to accomplish this is by parting from an AWS image of a previously configured instance. This image needs to have all the repositories and conda environments that would be needed in a serial run. Once each partitioned run is finished, results will be automatically collected in the local folder where run_script.py was run. The kwarg "instance_name" corresponds to the conda environment that is to be used in the partitioned run.

Unit tests

In order to run unit tests:

python -m unittest -v

Test Coverage

In order to view the current test coverage metrics:

coverage run --source pvsystemprofiler -m unittest discover && coverage html
open htmlcov/index.html