cbur24

def random_sampling(da,
                    n,
                    sampling='stratified_random',
                    manual_class_ratios=None,
                    out_fname=None
                   ):
    
    """
    Creates randomly sampled points for post-classification
    accuracy assessment.

MuhsinFatih

I am assuming you are here because like me, you installed a bazillion different python interpreters on mac and the whole thing is a spagetti. Today, I finally fixed my python installation. Whatever I install for python2 or python3 using pip JUST.WORKS.. My god! finally.

What the hell?

Here is what I had messed up, which you also probably did:

• I had too many different python interpreters
• Too many different symlinks which I lost track of
• almost no package I installed with pip worked without a headache
• any attempt to fix using online resources made it worse.

Kukanani

import numpy as np
from sklearn import mixture

# make a GMM
gmm = mixture.GaussianMixture(n_components=n_components, covariance_type=cv_type)
# fit the GMM however you'd like

# save to file
gmm_name = 'new_gmm'
np.save(gmm_name + '_weights', gmm.weights_, allow_pickle=False)

braaannigan

# The following must be executed in a jupyter notebook rather than a shell environment.
# Use numpy to work with arrays
import numpy as np
# Use scipy.stats to do plot some statistical data
import scipy.stats as stats

import holoviews as hv
# Holoviews builds on top of either the Matplotlib or Bokeh plotting library. Choose to use Bokeh 
# in these plots as interactive plots work very well with Bokeh
hv.notebook_extension('bokeh')

anttilipp

#
# Hi,
# this is the Python code I used to make the sea surface temperature anomaly
# visualization https://twitter.com/anttilip/status/899005930141921280.
# This is a version in which the AREA of the circle represents the temperature
# anomaly (not radius).
# The data is included in this file but I plan to write some kind of tutorial
# in the near future how the average temperatures were computed (using GDAL
# and suitable masks).
# Feel free to improve, modify, do whatever you want with this code. If you decide