pyshp | library reads and writes ESRI Shapefiles | Map library

data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data

I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.

After digging I cannot manage to understand what happened with our server. I got this error when loading the web.

It was working and anyone touch anything. I have changed the ownership of the application.txt because this error. [Wed Dec 16 04:38:12.059839 2020] [wsgi:error] [pid 12343:tid 140072894818048] [remote xx.xx.xxx.xx:xxxxx] ValueError: Unable to configure handler 'file': [Errno 13] Permission denied: '/opt/yhmp-app/YHMP/eamena/logs/application.txt'

After this it is showing up the next error in the browser:

and all this is coming form the error.log when trying to access:

I know that it is possible to read a shapefile from a zipfile by extracting it in memory and then reading it: https://gis.stackexchange.com/questions/250092/using-pyshp-to-read-a-file-like-object-from-a-zipped-archive

Fiona also has ways to read a shapefile from memory: https://pypi.org/project/Fiona/1.5.0/

However, I haven't been able to find a way to read in a .gpkg (geopackage) in the same way.

How do I extract a geopackage from a zipfile and then into a geopandas geodataframe?

The shell command pip install cartopy led to several errors.

At first, the following error occurred:

I want to run a batch file in a Conda environment, not in the base env, but in another virtual environment (here pylayers). I copied the activate.bat script from F:\Anaconda3\Scripts to F:\Anaconda3\envs\pylayers\Scripts.

And my batch script (installer_win.bat) is:

I have a 2D map divided in a grid of rectangles - about 45,000 of them - and a number of polygons/multipolygons derived from shapefiles (I currently read them with the shapefile library pyshp). Unfortunately, a few of these polygons are rather complex and made up by a large number of points (one of them has 640,000 points) and can have holes in them.

What I am trying to do is to check, for each of these polygons, which cell centers (the cells of my grid) fall inside that specific polygon. However, having about 45,000 cell centers and 150 polygons is taking quite a while to check everything using shapely. This is what I am doing, more or less:

I want to generate 1000 random points within a specific ZIP Code Tabulation Area shapefile using pyshp. My code is:

I have a "seed" GeoDataFrame (GDF)(RED) which contains a 0.5 arc minutes global grid ((180*2)*(360*2) = 259200). Each cell contains an absolute population estimate. In addition, I have a "leech" GDF (GREEN) with roughly 8250 adjoining non-regular shapes of various sizes (watersheds).

I wrote a script to allocate the population estimates to the geometries in the leech GDF based on the overlapping area between grid cells (seed GDF) and the geometries in the leech GDF. The script works perfectly fine for my sample data (see below). However, once I run it on my actual data, it is very slow. I ran it overnight and the next morning only 27% of the calculations had been performed. I will have to run this script many times and waiting for two days each time, is simply not an option.

After doing a bit of literature research, I already replaced (?) for loops with for index i in df.iterrows() (or is this the same as "conventional" python for loops) but it didn't bring about the performance imporvement I had hoped for.

Any suggestion son how I can speed up my code? In twelve hours, my script only processed only ~30000 rows out of ~200000.

My expected output is the column leech_df['leeched_values'].