Weather.get is one of the most important methods, it need to have a lot of error checking. Specifically, if you use PSM3 it should first check to see if the gid or the lat/lon are valid and in the range of testable area. If not, an error message should come up with a link to where to determine the applicable area or how to properly get the gid. A similar thing should be in place for the other methods using the NSRDB or PVGIS.

We need to create a geospatial down-selection function that will be comprehensive.

The input would be a list of, GID, lat/lon, altitude and altitude. The output would be the shortened list with just the GID or other identifier as appropriate/selected.

We want to be able to select the number of points to include in the final list.

To get more useful data, we would want to account for topology where preference for data next to or in mountains would be preferentially selected. This could be accomplished by a nearest neighbor search where a weighted number is calculated based on the altitude difference between the nearest neighbors. Then all the points are randomly included with this weighted probability. This method does rely on there being a statistically large enough number of data points.

We would also want to determine the perimeter locations or locations near an ocean or large lake and try to make sure there is a good outline. This could be done through a point search that looks for a direction where there are no data points in a ~150 degree cone for a specified number of miles. The number of miles would be determined by looking at the typical spacing (e.g. 4 km) determined by a few random tests of nearest neighbors, and then just multiplying that distance by say a factor of 10. Then you would, for example, look for points where there is a direction with nothing for 40 km. Then you put all the edge points into a sublist and down select with half the rate of exclusion.

These calculations may take some time, but would create nice lists to make the subsequent calculations much better.

For the method descriptions we should make this look very professional. This includes adding in units for clarity and completeness. Also, you can get things like the degree sign or superscript to be included by pulling them up in Word or Excel and pasting them into your document, e.g. ±²³₉₈ [°C]. This looks more professional as opposed to ^2 or just [C].

We need to differentiate between Jupyter Notebook tutorials and calculation tools. I'm creating a tool to do a complete standoff analysis which is much more than a tutorial. Tutorials should be just how to use some common and important functions. So I think we need a new folder called something like "Calculation_Tools" or just "Tools".

Standoff tutorial notebook will break post merge, Kempe_Standoff -> main and dev_montecarlo -> main

We need to create a a function that will allow you to create a stress series of data which can be subsequently used to either calculate degradation in the chamber or calculate an acceleration factor using another tool.

The entry would be a list of **kwargs where there are standardized cycles such as TC, HF, DH... and some preprogrammed sequences such as that found in IEC 61730 or IEC 61215. Additionally, one could specify an arbitray sequence with calling parameters such as T, RH, Irr, Irr-spectrum, light source, chamber equilibration time, sample equilibration time...

The output would be a time series of data with of an arbitrary time step including T, RH, Irr, V data, and meta data for the spectrum and other relevant parameters.

Once the function is created, we would want there to be a tool that will simplify the creation of an *.csv file that can be read as if it was a weather file with weather.read().

Currently the temperature.py method will only run the sapm model. It should be able to run all of them.

There should be a single mapping function to handle all of the variable mapping needs.

-) rename NREL_HPC to dataset_path and have predefined paths for AWS and NREL
-) include function to select a satellite based on lat, lon
-) pvlib uses 'TMY' as default for names. We could do the same and if no TMY file exists we could fall back to the latest available non-leap year.

We want to create a tool for LETID that will provide some sort of interface to the degradation database, allow calculations for a single site for a specified location and number of years, produce a performance plot vs time, calculate total power losses vs time, do a present value of losses calculation (and any other similar useful calculations), if we get ambitious do a Monte Carlo simulation (that would be worthy of a paper publication), and lastly run regional calculations similar to what Martin did.

We would like to be able to pull in UV spectral data.

I wrote the code but it doesn't have proper testing yet. This is for both the Juypter notebook and the standards.py code.

There are a thousand or so sites from the older data sets that are based on ground based sources in Alaska and elsewhere. I think we should tackle the single site access through NSRDB first. What this would look like is when you call the .get method there could be a boolean pass through that allows for the use of this dataset, with a default setting of true. Then if the selected lat/lon is not in the dataset it would look at the ground based sites that were identified as being outside of all the satellites. The big issue to how to store it on AWS. Maybe the NSRDB people could help.

We need to go through all the code and where there are For-Next loops or intensive calculations on the whole series of weather data (e.g. calculating the total degradation using the Arrhenius equation).

For the LETID, this probably requires some combinations of subroutines to make it work, but it could help it run 10x faster.

Describe the bug
import pvdeg causes an error message on some installations

To Reproduce
Not sure which systems/versions are affected.

Expected behavior
DataLibrary should be found.

Screenshots

Desktop (please complete the following information):

• OS: unkown
• pvdeg version: 0.2.x

Additional context
reported by Yili

We want to have a world map with country outlines that you can zoom into. It will have a slider where you can adjust the Xeff value which will then change the map to indicate whether level 0, 1 or 2 module certification is needed. How difficult is this?

Describe the bug
on [PyPi][https://pypi.org/project/pvdeg/] there is a blank box where the logo should be.

To Reproduce
Visit https://pypi.org/project/pvdeg/

Screenshot

New tutorials including

• Van't Hoff
• Monte Carlo
• Other demos

That are live on main are not present on Jupyterbook.

Hello PVdeg team and thanks for this effort!

I might be able to suggest a significantly more streamlined way of configuring, building, and testing this repo using a pyproject.toml file. I just want to check if the team is open to this if I can show that it would work.

We need to have a customized filed for the anemometer height in the satellite data.

Lines of code -
import os
import pandas as pd

import pvdeg
from pvdeg import DATA_DIR

PSM_FILE = os.path.join(DATA_DIR,'psm3_demo.csv')

WEATHER, META = pvdeg.weather.read(PSM_FILE,'psm')

Error-
Traceback (most recent call last):

File ~\anaconda3\lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec
exec(code, globals, locals)

File c:\users\saikatghosh\degradation rates\degradation.py:11
import pvdeg

File ~\anaconda3\lib\site-packages\pvdeg_init_.py:5
from . import cli

File ~\anaconda3\lib\site-packages\pvdeg\cli.py:1
from pvdeg.standards import run_calc_standoff

File ~\anaconda3\lib\site-packages\pvdeg\standards.py:18
from . import weather

File ~\anaconda3\lib\site-packages\pvdeg\weather.py:10
from pvdeg import humidity

File ~\anaconda3\lib\site-packages\pvdeg\humidity.py:7
from numba import jit

File ~\anaconda3\lib\site-packages\numba_init_.py:55
_ensure_critical_deps()

File ~\anaconda3\lib\site-packages\numba_init_.py:42 in _ensure_critical_deps
raise ImportError("Numba needs NumPy 1.24 or less")

ImportError: Numba needs NumPy 1.24 or less

As a tutorial, the instructions should be sufficient on this to be able to get it to work. Specifically item 1 needs better instructions or a link to better instructions.

When not doing a geospatial analysis get the city, state, country, prefecture, county, or other meta data fields filled in.

(https://www.geeksforgeeks.org/get-the-city-state-and-country-names-from-latitude-and-longitude-using-python/)

pip install geopy

import module

from geopy.geocoders import Nominatim

initialize Nominatim API

geolocator = Nominatim(user_agent="geoapiExercises")

Latitude & Longitude input

Latitude = "25.594095"
Longitude = "85.137566"

location = geolocator.reverse(Latitude+","+Longitude)

Display

print(location)

address = location.raw['address']
print(address)

city = address.get('city', '')
state = address.get('state', '')
country = address.get('country', '')
code = address.get('country_code')
zipcode = address.get('postcode')
print('City : ',city)
print('State : ',state)
print('Country : ',country)
print('Zip Code : ', zipcode)

Create a basic object oriented simulation object, to keep track of things like:

-Database being used. If NSRDB, this will set elements like -wind_speed_factor for some of the functions, or calculated humidity instead of provided humidity, etc.

Currently temperature variables use capital T. Should be updated to confirm with PEP8 naming conventions.

We need to incoporate more pytests into the document. All major methods must have a pytest. To start, we should go through and make a list of the inadequacies.

I want to predict the future degradation for an upcoming pv plant.

Do a review of the variable names used to make sure they conform to pv-terms.

https://duramat.github.io/pv-terms/