How I can overcome the _geoslib problem in this code? - matplotlib-basemap

Question

This code is specified to visualize the CALIPSO satellite atmospheric profiles
The input files are .HDF
The code is copyrighted to the HDF group.
In the begining, I struggled with installing the basemap,
finally I installed it using .whl file on my windows10.
Now, this error is reached when I run the script:
SystemError:
execution of module _geoslib raised unreported exception.
I have looked a lot in google, but nothing done.
Can you please help me?
Cheers
"Copyright (C) 2014-2019 The HDF Group
Copyright (C) 2014 John Evans
This example code illustrates how to access and visualize a LaRC CALIPSO file
in file in Python.
If you have any questions, suggestions, or comments on this example, please use
the HDF-EOS Forum (http://hdfeos.org/forums). If you would like to see an
example of any other NASA HDF/HDF-EOS data product that is not listed in the
HDF-EOS Comprehensive Examples page (http://hdfeos.org/zoo), feel free to
contact us at eoshelp#hdfgroup.org or post it at the HDF-EOS Forum
(http://hdfeos.org/forums).
Usage: save this script and run
$python CAL_LID_L2_VFM-ValStage1-V3-02.2011-12-31T23-18-11ZD.hdf.py
The HDF file must either be in your current working directory
or in a directory specified by the environment variable HDFEOS_ZOO_DIR.
Tested under: Python 2.7.15::Anaconda custom (64-bit)
Last updated: 2019-01-25
"""
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.basemap import Basemap
from matplotlib import colors
USE_NETCDF4 = False
def run(FILE_NAME):
# Identify the data field.
DATAFIELD_NAME = 'Feature_Classification_Flags'
if USE_NETCDF4:
from netCDF4 import Dataset
nc = Dataset(FILE_NAME)
# Subset the data to match the size of the swath geolocation fields.
# Turn off autoscaling, we'll handle that ourselves due to presence of
# a valid range.
var = nc.variables[DATAFIELD_NAME]
data = var[:,1256]
# Read geolocation datasets.
lat = nc.variables['Latitude'][:]
lon = nc.variables['Longitude'][:]
else:
from pyhdf.SD import SD, SDC
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset.
data2D = hdf.select(DATAFIELD_NAME)
data = data2D[:,1256]
# Read geolocation datasets.
latitude = hdf.select('Latitude')
lat = latitude[:]
longitude = hdf.select('Longitude')
lon = longitude[:]
# Subset data. Otherwise, all points look black.
lat = lat[::10]
lon = lon[::10]
data = data[::10]
# Extract Feature Type only through bitmask.
data = data & 7
# Make a color map of fixed colors.
cmap = colors.ListedColormap(['black', 'blue', 'yellow', 'green', 'red', 'purple', 'gray', 'white'])
# The data is global, so render in a global projection.
m = Basemap(projection='cyl', resolution='l',
llcrnrlat=-90, urcrnrlat=90,
llcrnrlon=-180, urcrnrlon=180)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(-90.,90,45))
m.drawmeridians(np.arange(-180.,180,45), labels=[True,False,False,True])
x,y = m(lon, lat)
i = 0
for feature in data:
m.plot(x[i], y[i], 'o', color=cmap(feature), markersize=3)
i = i+1
long_name = 'Feature Type at Altitude = 2500m'
basename = os.path.basename(FILE_NAME)
plt.title('{0}\n{1}'.format(basename, long_name))
fig = plt.gcf()
# define the bins and normalize
bounds = np.linspace(0,8,9)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
# create a second axes for the colorbar
ax2 = fig.add_axes([0.93, 0.2, 0.01, 0.6])
cb = mpl.colorbar.ColorbarBase(ax2, cmap=cmap, norm=norm, spacing='proportional', ticks=bounds, boundaries=bounds, format='%1i')
cb.ax.set_yticklabels(['invalid', 'clear', 'cloud', 'aerosol', 'strato', 'surface', 'subsurf', 'no signal'], fontsize=5)
# plt.show()
pngfile = "{0}.py.png".format(basename)
fig.savefig(pngfile)
if __name__ == "__main__":
# If a certain environment variable is set, look there for the input
# file, otherwise look in the current directory.
hdffile = 'CAL_LID_L2_VFM-ValStage1-V3-02.2011-12-31T23-18-11ZD.hdf'
try:
fname = os.path.join(os.environ['HDFEOS_ZOO_DIR'], ncfile)
except KeyError:
fname = hdffile
run(fname)

Answer 1

Please try miniconda and use basemap from conda-forge:
conda install -c conda-forge basemap