zenith | like top or htop but with zoom-able charts | Monitoring library

I am fairly new to python and would like to calculate sun's position (azimuth and zenith angle) from a datetime column of a dataframe.

I found Astral module for this task and also made some correct calculations from it using the individual timestamps. But when I try to process the whole datetime column, I get an error.

I also tried to localize the dataframe (datetime column) to a specific timezone, but the error remains the same.

Help appreciated.

Here is the code:

I'm trying to solve the second law of diffusion for spheres PDE using fipy. I've checked the documentation but there's no example for such a case, so I was wondering if it is actually possible to do it, as I was not successful reaching for the adequate equation definition structure. I consider azimuthal and zenith angular symmetries, so the equation I need to solve results in the following.

Of course, boundary conditions are fixed at r=0 and r=R at fixed values and the initial concentration is also known. I also tried to follow the ideas presented in here but didn't get any clear result for it. Any ideas would be welcomed.

The code I'm using at the moment is the following:

I want to use the code from Polar histogram in Python for given r, theta and z values, replaced with my dataframe columns df.azimuth, df.zenith, and df.ozone (average for each bin).

I'm having issues converting dataframe .values format, to a polar plot format.

I would like to produce a polar plot with my DataFrame columns df.azimuth, df.zenith, and df.o3 average for each bin as showed on the plot example that I attached here.

I'm having issues converting DataFrame format, to a polar plot format. I don't know how to do this from a DataFrame format. Any help is welcome.

That is my code so far, but it is not showing the plot correctly.

Dataframe (o3Pan_wff):

I applied the following function for each row in my dataframe (df), and using a Lat0/Lon0, direction(azimuth) and distance(dist) I got the new Lat/Lon in "degree minute second":

In m app I have a form with data meant to insert data into 2 tables :payers and Spouses and Payer has Many Spouse. In my PayerController i have

I'm trying to simulate radiation emitting from a point source. To do this, given the coordinates of a source and the desired length of emitted rays, I randomly generate a direction vector in spherical coordinates, convert it to cartesian, and return the correct end point. However, when I run this, and visualize the resulting point cloud (consisting of all the randomly generated end points) in Blender, I see that it's more densely populated at the "poles" of the sphere. I'd like the points to be uniformly distributed along the sphere. How can I achieve this?

The random generation function:

https://codepen.io/AnonymousCaptain/pen/MWavMWm

I am working with the Google Takeout. They give me data with json file.

Background

Traditionally I've used NREL SAM tool to estimate solar output. I've been experimenting with PVLIB which is great due to the open nature and flexibility, however I can't seem to reconcile the solar production estimates between PVLIB and NREL SAM.

What I've done

I'm modeling a hypothetical solar farm near Gympie QLD. I've gone to the climate.onebuiling website, and downloaded the zip folder / epw file for "AUS_QLD_Gympie.AP.945660_TMYx.2003-2017". I've then used that weather file in NREL's SAM tool using PVwatts, with the following specs;

• 200,000 KWdc
• Module Type = Standard
• 1.2 DC to AC Ratio
• 96% inverter efficiency
• 1 axis backtracking
• tilt = 26 degrees
• azimuth = 0 degrees
• GCR = 0.4
• losses, shading & curtailment = default

In NREL SAM i get an annual Energy Yield (AC GWh) of 415.96 GWh p.a.

I then took that same epw file and converted it to a csv, keeping just the columns for ghi, dni, dhi, temp_air & wind_speed (Google Drive link to CSV file). I've used this file as an import into PVLIB. I spec'd a PVLIB system the same specs above, with addition of albedo = 0.2 and max angle = 90 degrees (Code Below).

The result I get in PVLIB is 395.61 GWh.

Problem

The results I got are pretty different. PVLIB = ~395 GWh p.a. vs SAM = ~415 GWH p.a. I expected around a 1-2% difference, but not 5%.

Figures are even worse when I compare to a PVLIB system using clearsky.ineichen (adjusted with linke_turbidity) which yields ~475 GWh p.a.

Help Requested

Anyone know why my results are so different? is there anything I can do to narrow the gap?

PVLIB Code