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How to change background and outline color on topographic map?

I followed this excellent guide by Adam Symington and successfully created the following topographic map of Sabah (a state in Malaysia, which is a Southeast Asian nation). The awkward blob of black in the upper left corner is my attempt to plot certain coordinates on the map.
I would like to improve this diagram in the following ways:
EDIT: I have figured item (1) out and posted the solution below. (2) and (3) pending.
[SOLVED] The sch dataframe contains coordinates of all schools in the state. I would like to plot these on the map. I suspect that it is currently going wonky because the axes are not "geo-axes" (meaning, not using lat/lon scales) - you can confirm this by setting ax.axis('on'). How do I get around this? [SOLVED]
I'd like to set the portion outside the actual territory to white. Calling ax.set_facecolor('white') isn't working. I know that the specific thing setting it to grey is the ax.imshow(hillshade, cmap='Greys', alpha=0.3) line (because changing the cmap changes the background); I just don't know how to alter it while keeping the color within the map as grey.
If possible, I'd like the outline of the map to be black, but this is just pedantic.
All code to reproduce the diagram above is below. The downloadSrc function gets and saves the dependencies (a 5.7MB binary file containing the topographic data and a 0.05MB csv containing the coordinates of points to plot) in a local folder; you need only run that once.
import rasterio
from rasterio import mask as msk
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.colors import ListedColormap
import numpy as np
import pandas as pd
import geopandas as gpd
import earthpy.spatial as es
from shapely.geometry import Point
def downloadSrc(dl=1):
if dl == 1:
import os
os.mkdir('sabah')
import requests
r = requests.get('https://raw.githubusercontent.com/Thevesh/Display/master/sabah_tiff.npy')
with open('sabah/sabah_topog.npy', 'wb') as f: f.write(r.content)
df = pd.read_csv('https://raw.githubusercontent.com/Thevesh/Display/master/schools.csv')
df.to_csv('sabah/sabah_schools.csv')
# Set dl = 0 after first run; the files will be in your current working directory + /sabah
downloadSrc(dl=1)
# Load topography of Sabah, pre-saved from clipped tiff file (as per Adam Symington guide)
value_range = 4049
sabah_topography = np.load('sabah/sabah_topog.npy')
# Load coordinates of schools in Sabah
crs={'init':'epsg:4326'}
sch = pd.read_csv('sabah/sabah_schools.csv',usecols=['lat','lon'])
geometry = [Point(xy) for xy in zip(sch.lon, sch.lat)]
schools = gpd.GeoDataFrame(sch, crs=crs, geometry=geometry)
# Replicated directly from guide, with own modifications only to colours
sabah_colormap = LinearSegmentedColormap.from_list('sabah', ['lightgray', '#e6757b', '#CD212A', '#CD212A'], N=value_range)
background_color = np.array([1,1,1,1])
newcolors = sabah_colormap(np.linspace(0, 1, value_range))
newcolors = np.vstack((newcolors, background_color))
sabah_colormap = ListedColormap(newcolors)
hillshade = es.hillshade(sabah_topography[0], azimuth=180, altitude=1)
# Plot
plt.rcParams["figure.figsize"] = [5,5]
plt.rcParams["figure.autolayout"] = True
fig, ax = plt.subplots()
ax.imshow(sabah_topography[0], cmap=sabah_colormap)
ax.imshow(hillshade, cmap='Greys', alpha=0.3)
schools.plot(color='black', marker='x', markersize=10,ax=ax)
ax.axis('off')
plt.show()

As it turns out, I had given myself the hint to answering point (1), and also managed to solve (2).
For (1), the points simply needed to be rescaled, and we get this:
I did so by getting the max/min points of the map from the underlying shapefile, and then scaling it based on the max/min points of the axes, as follows:
# Get limit points
l = gpd.read_file('param_geo/sabah.shp')['geometry'].bounds
lat_min,lat_max,lon_min,lon_max = l['miny'].iloc[0], l['maxy'].iloc[0], l['minx'].iloc[0], l['maxx'].iloc[0]
xmin,xmax = ax.get_xlim()
ymin,ymax = ax.get_ylim()
# Load coordinates of schools in Sabah and rescale
crs={'init':'epsg:4326'}
sch = pd.read_csv('sabah/sabah_schools.csv',usecols=['lat','lon'])
sch.lat = ymin + (sch.lat - lat_min)/(lat_max - lat_min) * (ymax - ymin)
sch.lon = xmin + (sch.lon - lon_min)/(lon_max - lon_min) * (xmax - xmin)
For (2), the grey background is coming from the fact that the hillshade array has values outside the map area which are being mapped to grey. To remove the grey, we need to nullify these values.
In this specific case, we can leverage on the fact that we know the top right corner of this map is "outside" the map (every country in the world will have at least one corner for which this is true, because no country is a perfect square):
top_right = hillshade[0,-1]
hillshade[hillshade == top_right] = np.nan
And voila, a beautiful white background:
For (3), I suspect it requires us to rescale the Polygon from the shapefile in a manner similar to how we rescaled the coordinates.

Creating similar spectrogram in continues wavelet transform compared to discret wavelet transform

Using PyWavelets and Matplotbib.Specgram on a signal gives more detailed plots with pywt.dwt then pywt.cwt. How can I get a pywt.cwt specgram in a similar way?
With dwt:
import pywt
import pywt.data
import matplotlib.pyplot as plot
from scipy import signal
from scipy.io import wavfile
bA, bD = pywt.dwt(datamean, 'db2')
powerSpectrum, freqenciesFound, time, imageAxis = plot.specgram(bA, NFFT = 387, Fs=100)
plot.xlabel('Time')
plot.ylabel('Frequency')
plot.show()
with this spectrogram plot:
https://imgur.com/a/bYb8bBS
With cwt:
widths = np.arange(1,5)
coef, freqs = pywt.cwt(datamean, widths,'morl')
powerSpectrum, freqenciesFound, time, imageAxis = plot.specgram(coef, NFFT = 129, Fs=100)
plot.xlabel('Time')
plot.ylabel('Frequency')
plot.show()
with this spectrogram plot:
https://imgur.com/a/GIINzJp
and for better results:
sig = datamean
widths = np.arange(1, 31)
cwtmatr = signal.cwt(sig, signal.ricker, widths)
plt.imshow(cwtmatr, extent=[-1, 1, 1, 5], cmap='PRGn', aspect='auto',
vmax=abs(cwtmatr).max(), vmin=-abs(cwtmatr).max())
plt.show()
with this spectrogram plot:
https://imgur.com/a/TnXqgGR
How can I get for cwt (spectrogram plot 2 and 3) a similar spectogram plot and style like in the first one?
It seems like the 1st spectrogram plot compared to the 3rd has much more details.

This would be better as a comment, but since I lack the Karma to do that:
You don't want to make a spectrogram with wavelets, but a scalogram instead. What it looks like you're doing above is projecting your data in a scale subspace (that correlates to frequency), then taking those scales and finding the frequency content of them which is not what you probably want.
The detail and approximation coefficients are what you would want to use directly. Unfortunately, PyWavelets doesn't have a simple plotting function to do this for you, AFAIK. Matlab does, and their help page may be illuminating if I fail.
def scalogram(data):
wave='db4'
coeff=pywt.wavedec(data,wave)
levels=len(coeff)
lengths=[len(co) for co in coeff]
col=np.max(lengths)
im=np.ones([levels,col])
col=col.astype(float)
for level in range(levels):
#print [lengths[level],col]
y=coeff[level]
if lengths[1+level]<col:
x=col/(lengths[1+level]+1)*np.arange(1,len(y)+1)
xi=np.linspace(0,int(col),int(col))
yi=griddata(points=x,values=y,xi=xi,method='nearest')
else:
yi=y
im[level,:]=yi
im[im==0]=np.nan
tiles=sum(lengths)-lengths[0]
return im,tiles
Wxx,tiles=scalogram(data)
IM=plt.imshow(np.log10(abs(Wxx)),aspect='auto')
plt.show()
There are better ways of doing that, but it works. This produces a square matrix similar to spectrogram in "Wxx", and tiles is simply a counter of the number of time-frequency tilings to compare to the number used in a SFFT.
I've attached a picture of what these tilings look like

How to remove/omit smaller contour lines using matplotlib

I am trying to plot contour lines of pressure level. I am using a netCDF file which contain the higher resolution data (ranges from 3 km to 27 km). Due to higher resolution data set, I get lot of pressure values which are not required to be plotted (rather I don't mind omitting certain contour line of insignificant values). I have written some plotting script based on the examples given in this link http://matplotlib.org/basemap/users/examples.html.
After plotting the image looks like this
From the image I have encircled the contours which are small and not required to be plotted. Also, I would like to plot all the contour lines smoother as mentioned in the above image. Overall I would like to get the contour image like this:-
Possible solution I think of are
Find out the number of points required for plotting contour and mask/omit those lines if they are small in number.
or
Find the area of the contour (as I want to omit only circled contour) and omit/mask those are smaller.
or
Reduce the resolution (only contour) by increasing the distance to 50 km - 100 km.
I am able to successfully get the points using SO thread Python: find contour lines from matplotlib.pyplot.contour()
But I am not able to implement any of the suggested solution above using those points.
Any solution to implement the above suggested solution is really appreciated.
Edit:-
# Andras Deak
I used print 'diameter is ', diameter line just above del(level.get_paths()[kp]) line to check if the code filters out the required diameter. Here is the filterd messages when I set if diameter < 15000::
diameter is 9099.66295612
diameter is 13264.7838257
diameter is 445.574234531
diameter is 1618.74618114
diameter is 1512.58974168
However the resulting image does not have any effect. All look same as posed image above. I am pretty sure that I have saved the figure (after plotting the wind barbs).
Regarding the solution for reducing the resolution, plt.contour(x[::2,::2],y[::2,::2],mslp[::2,::2]) it works. I have to apply some filter to make the curve smooth.
Full working example code for removing lines:-
Here is the example code for your review
#!/usr/bin/env python
from netCDF4 import Dataset
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import numpy as np
import scipy.ndimage
from mpl_toolkits.basemap import interp
from mpl_toolkits.basemap import Basemap
# Set default map
west_lon = 68
east_lon = 93
south_lat = 7
north_lat = 23
nc = Dataset('ncfile.nc')
# Get this variable for later calucation
temps = nc.variables['T2']
time = 0 # We will take only first interval for this example
# Draw basemap
m = Basemap(projection='merc', llcrnrlat=south_lat, urcrnrlat=north_lat,
llcrnrlon=west_lon, urcrnrlon=east_lon, resolution='l')
m.drawcoastlines()
m.drawcountries(linewidth=1.0)
# This sets the standard grid point structure at full resolution
x, y = m(nc.variables['XLONG'][0], nc.variables['XLAT'][0])
# Set figure margins
width = 10
height = 8
plt.figure(figsize=(width, height))
plt.rc("figure.subplot", left=.001)
plt.rc("figure.subplot", right=.999)
plt.rc("figure.subplot", bottom=.001)
plt.rc("figure.subplot", top=.999)
plt.figure(figsize=(width, height), frameon=False)
# Convert Surface Pressure to Mean Sea Level Pressure
stemps = temps[time] + 6.5 * nc.variables['HGT'][time] / 1000.
mslp = nc.variables['PSFC'][time] * np.exp(9.81 / (287.0 * stemps) * nc.variables['HGT'][time]) * 0.01 + (
6.7 * nc.variables['HGT'][time] / 1000)
# Contour only at 2 hpa interval
level = []
for i in range(mslp.min(), mslp.max(), 1):
if i % 2 == 0:
if i >= 1006 and i <= 1018:
level.append(i)
# Save mslp values to upload to SO thread
# np.savetxt('mslp.txt', mslp, fmt='%.14f', delimiter=',')
P = plt.contour(x, y, mslp, V=2, colors='b', linewidths=2, levels=level)
# Solution suggested by Andras Deak
for level in P.collections:
for kp,path in enumerate(level.get_paths()):
# include test for "smallness" of your choice here:
# I'm using a simple estimation for the diameter based on the
# x and y diameter...
verts = path.vertices # (N,2)-shape array of contour line coordinates
diameter = np.max(verts.max(axis=0) - verts.min(axis=0))
if diameter < 15000: # threshold to be refined for your actual dimensions!
#print 'diameter is ', diameter
del(level.get_paths()[kp]) # no remove() for Path objects:(
#level.remove() # This does not work. produces ValueError: list.remove(x): x not in list
plt.gcf().canvas.draw()
plt.savefig('dummy', bbox_inches='tight')
plt.close()
After the plot is saved I get the same image
You can see that the lines are not removed yet. Here is the link to mslp array which we are trying to play with http://www.mediafire.com/download/7vi0mxqoe0y6pm9/mslp.txt
If you want x and y data which are being used in the above code, I can upload for your review.
Smooth line
You code to remove the smaller circles working perfectly. However the other question I have asked in the original post (smooth line) does not seems to work. I have used your code to slice the array to get minimal values and contoured it. I have used the following code to reduce the array size:-
slice = 15
CS = plt.contour(x[::slice,::slice],y[::slice,::slice],mslp[::slice,::slice], colors='b', linewidths=1, levels=levels)
The result is below.
After searching for few hours I found this SO thread having simmilar issue:-
Regridding regular netcdf data
But none of the solution provided over there works.The questions similar to mine above does not have proper solutions. If this issue is solved then the code is perfect and complete.

General idea
Your question seems to have 2 very different halves: one about omitting small contours, and another one about smoothing the contour lines. The latter is simpler, since I can't really think of anything else other than decreasing the resolution of your contour() call, just like you said.
As for removing a few contour lines, here's a solution which is based on directly removing contour lines individually. You have to loop over the collections of the object returned by contour(), and for each element check each Path, and delete the ones you don't need. Redrawing the figure's canvas will get rid of the unnecessary lines:
# dummy example based on matplotlib.pyplot.clabel example:
import matplotlib
import numpy as np
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
# difference of Gaussians
Z = 10.0 * (Z2 - Z1)
plt.figure()
CS = plt.contour(X, Y, Z)
for level in CS.collections:
for kp,path in reversed(list(enumerate(level.get_paths()))):
# go in reversed order due to deletions!
# include test for "smallness" of your choice here:
# I'm using a simple estimation for the diameter based on the
# x and y diameter...
verts = path.vertices # (N,2)-shape array of contour line coordinates
diameter = np.max(verts.max(axis=0) - verts.min(axis=0))
if diameter<1: # threshold to be refined for your actual dimensions!
del(level.get_paths()[kp]) # no remove() for Path objects:(
# this might be necessary on interactive sessions: redraw figure
plt.gcf().canvas.draw()
Here's the original(left) and the removed version(right) for a diameter threshold of 1 (note the little piece of the 0 level at the top):
Note that the top little line is removed while the huge cyan one in the middle doesn't, even though both correspond to the same collections element i.e. the same contour level. If we didn't want to allow this, we could've called CS.collections[k].remove(), which would probably be a much safer way of doing the same thing (but it wouldn't allow us to differentiate between multiple lines corresponding to the same contour level).
To show that fiddling around with the cut-off diameter works as expected, here's the result for a threshold of 2:
All in all it seems quite reasonable.
Your actual case
Since you've added your actual data, here's the application to your case. Note that you can directly generate the levels in a single line using np, which will almost give you the same result. The exact same can be achieved in 2 lines (generating an arange, then selecting those that fall between p1 and p2). Also, since you're setting levels in the call to contour, I believe the V=2 part of the function call has no effect.
import numpy as np
import matplotlib.pyplot as plt
# insert actual data here...
Z = np.loadtxt('mslp.txt',delimiter=',')
X,Y = np.meshgrid(np.linspace(0,300000,Z.shape[1]),np.linspace(0,200000,Z.shape[0]))
p1,p2 = 1006,1018
# this is almost the same as the original, although it will produce
# [p1, p1+2, ...] instead of `[Z.min()+n, Z.min()+n+2, ...]`
levels = np.arange(np.maximum(Z.min(),p1),np.minimum(Z.max(),p2),2)
#control
plt.figure()
CS = plt.contour(X, Y, Z, colors='b', linewidths=2, levels=levels)
#modified
plt.figure()
CS = plt.contour(X, Y, Z, colors='b', linewidths=2, levels=levels)
for level in CS.collections:
for kp,path in reversed(list(enumerate(level.get_paths()))):
# go in reversed order due to deletions!
# include test for "smallness" of your choice here:
# I'm using a simple estimation for the diameter based on the
# x and y diameter...
verts = path.vertices # (N,2)-shape array of contour line coordinates
diameter = np.max(verts.max(axis=0) - verts.min(axis=0))
if diameter<15000: # threshold to be refined for your actual dimensions!
del(level.get_paths()[kp]) # no remove() for Path objects:(
# this might be necessary on interactive sessions: redraw figure
plt.gcf().canvas.draw()
plt.show()
Results, original(left) vs new(right):
Smoothing by resampling
I've decided to tackle the smoothing problem as well. All I could come up with is downsampling your original data, then upsampling again using griddata (interpolation). The downsampling part could also be done with interpolation, although the small-scale variation in your input data might make this problem ill-posed. So here's the crude version:
import scipy.interpolate as interp #the new one
# assume you have X,Y,Z,levels defined as before
# start resampling stuff
dN = 10 # use every dN'th element of the gridded input data
my_slice = [slice(None,None,dN),slice(None,None,dN)]
# downsampled data
X2,Y2,Z2 = X[my_slice],Y[my_slice],Z[my_slice]
# same as X2 = X[::dN,::dN] etc.
# upsampling with griddata over original mesh
Zsmooth = interp.griddata(np.array([X2.ravel(),Y2.ravel()]).T,Z2.ravel(),(X,Y),method='cubic')
# plot
plt.figure()
CS = plt.contour(X, Y, Zsmooth, colors='b', linewidths=2, levels=levels)
You can freely play around with the grids used for interpolation, in this case I just used the original mesh, as it was at hand. You can also play around with different kinds of interpolation: the default 'linear' one will be faster, but less smooth.
Result after downsampling(left) and upsampling(right):
Of course you should still apply the small-line-removal algorithm after this resampling business, and keep in mind that this heavily distorts your input data (since if it wasn't distorted, then it wouldn't be smooth). Also, note that due to the crude method used in the downsampling step, we introduce some missing values near the top/right edges of the region under consideraton. If this is a problem, you should consider doing the downsampling based on griddata as I've noted earlier.

This is a pretty bad solution, but it's the only one that I've come up with. Use the get_contour_verts function in this solution you linked to, possibly with the matplotlib._cntr module so that nothing gets plotted initially. That gives you a list of contour lines, sections, vertices, etc. Then you have to go through that list and pop the contours you don't want. You could do this by calculating a minimum diameter, for example; if the max distance between points is less than some cutoff, throw it out.
That leaves you with a list of LineCollection objects. Now if you make a Figure and Axes instance, you can use Axes.add_collection to add all of the LineCollections in the list.
I checked this out really quick, but it seemed to work. I'll come back with a minimum working example if I get a chance. Hope it helps!
Edit: Here's an MWE of the basic idea. I wasn't familiar with plt._cntr.Cntr, so I ended up using plt.contour to get the initial contour object. As a result, you end up making two figures; you just have to close the first one. You can replace checkDiameter with whatever function works. I think you could turn the line segments into a Polygon and calculate areas, but you'd have to figure that out on your own. Let me know if you run into problems with this code, but it at least works for me.
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
def checkDiameter(seg, tol=.3):
# Function for screening line segments. NB: Not actually a proper diameter.
diam = (seg[:,0].max() - seg[:,0].min(),
seg[:,1].max() - seg[:,1].min())
return not (diam[0] < tol or diam[1] < tol)
# Create testing data
x = np.linspace(-1,1, 21)
xx, yy = np.meshgrid(x,x)
z = np.exp(-(xx**2 + .5*yy**2))
# Original plot with plt.contour
fig0, ax0 = plt.subplots()
# Make sure this contour object actually has a tiny contour to remove
cntrObj = ax0.contour(xx,yy,z, levels=[.2,.4,.6,.8,.9,.95,.99,.999])
# Primary loop: Copy contours into a new LineCollection
lineNew = list()
for lineOriginal in cntrObj.collections:
# Get properties of the original LineCollection
segments = lineOriginal.get_segments()
propDict = lineOriginal.properties()
propDict = {key: value for (key,value) in propDict.items()
if key in ['linewidth','color','linestyle']} # Whatever parameters you want to carry over
# Filter out the lines with small diameters
segments = [seg for seg in segments if checkDiameter(seg)]
# Create new LineCollection out of the OK segments
if len(segments) > 0:
lineNew.append(mpl.collections.LineCollection(segments, **propDict))
# Make new plot with only these line collections; display results
fig1, ax1 = plt.subplots()
ax1.set_xlim(ax0.get_xlim())
ax1.set_ylim(ax0.get_ylim())
for line in lineNew:
ax1.add_collection(line)
plt.show()
FYI: The bit with propDict is just to automate bringing over some of the line properties from the original plot. You can't use the whole dictionary at once, though. First, it contains the old plot's line segments, but you can just swap those for the new ones. But second, it appears to contain a number of parameters that are in conflict with each other: multiple linewidths, facecolors, etc. The {key for key in propDict if I want key} workaround is my way to bypass that, but I'm sure someone else can do it more cleanly.

Numpy inpaint nans interpolate AND extrapolate

I'm working on a project using numpy and scipy and I need to fill in nanvalues. Currently I use scipy.interpolate.rbf, but it keeps causing python to crash so severely try/except won't even save it. However, after running it a few times, it seems as if it may keep failing in cases where there is data in the middle surrounded by all nans, like an island. Is there a better solution to this that won't keep crashing?
By the way, this is a LOT of data I need to extrapolate. Sometimes as much as half the image (70x70, greyscale), but it doesn't need to be perfect. It's part of an image stitching program, so as long as it's similar to the actual data, it'll work. I've tried nearest neighbor to fill in the nans, but the results are too different.
EDIT:
The image it always seems to fail on. Isolating this image allowed it to pass the image ONCE before crashing.
I'm using at least version NumPy 1.8.0 and SciPy 0.13.2.

Using SciPy's LinearNDInterpolator. If all images are of the same size, grid coordinates can be pre-computed and re-used.
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
x = np.linspace(0, 1, 500)
y = x[:, None]
image = x + y
# Destroy some values
mask = np.random.random(image.shape) > 0.7
image[mask] = np.nan
valid_mask = ~np.isnan(image)
coords = np.array(np.nonzero(valid_mask)).T
values = image[valid_mask]
it = interpolate.LinearNDInterpolator(coords, values, fill_value=0)
filled = it(list(np.ndindex(image.shape))).reshape(image.shape)
f, (ax0, ax1) = plt.subplots(1, 2)
ax0.imshow(image, cmap='gray', interpolation='nearest')
ax0.set_title('Input image')
ax1.imshow(filled, cmap='gray', interpolation='nearest')
ax1.set_title('Interpolated data')
plt.show()

This proved sufficient for my needs. It is actually quite fast and produces reasonable results:
ipn_kernel = np.array([[1,1,1],[1,0,1],[1,1,1]]) # kernel for inpaint_nans
def inpaint_nans(im):
nans = np.isnan(im)
while np.sum(nans)>0:
im[nans] = 0
vNeighbors = scipy.signal.convolve2d((nans==False),ipn_kernel,mode='same',boundary='symm')
im2 = scipy.signal.convolve2d(im,ipn_kernel,mode='same',boundary='symm')
im2[vNeighbors>0] = im2[vNeighbors>0]/vNeighbors[vNeighbors>0]
im2[vNeighbors==0] = np.nan
im2[(nans==False)] = im[(nans==False)]
im = im2
nans = np.isnan(im)
return im

Can anyone please explain how this python code works line by line?

I am working in image processing right now in python using numpy and scipy all the time. I have one piece of code that can enlarge an image, but not sure how this works.
So please some expert in scipy/numpy in python can explain to me line by line. I am always eager to learn.
import numpy as N
import os.path
import scipy.signal
import scipy.interpolate
import matplotlib.pyplot as plt
import matplotlib.cm as cm
def enlarge(img, rowscale, colscale, method='linear'):
x, y = N.meshgrid(N.arange(img.shape[1]), N.arange(img.shape[0]))
pts = N.column_stack((x.ravel(), y.ravel()))
xx, yy = N.mgrid[0.:float(img.shape[1]):1/float(colscale),
0.:float(img.shape[0]):1/float(rowscale)]
large = scipy.interpolate.griddata(pts, img.flatten(), (xx, yy), method).T
large[-1,:] = large[-2,:]
large[:,-1] = large[:,-2]
return large
Thanks a lot.

First, a grid of empty points is created with point per pixel.
x, y = N.meshgrid(N.arange(img.shape[1]), N.arange(img.shape[0]))
The actual image pixels are placed into the variable pts which will be needed later.
pts = N.column_stack((x.ravel(), y.ravel()))
After that, it creates a mesh grid with one point per pixel for the enlarged image; if the original image was 200x400, the colscale set to 4 and rowscale set to 2, the mesh grid would have (200*4)x(400*2) or 800x800 points.
xx, yy = N.mgrid[0.:float(img.shape[1]):1/float(colscale),
0.:float(img.shape[0]):1/float(rowscale)]
Using scipy, the points in pts variable are interpolated into the larger grid. Interpolation is the manner in which missing points are filled or estimated usually when going from a smaller set of points to a larger set of points.
large = scipy.interpolate.griddata(pts, img.flatten(), (xx, yy), method).T
I am not 100% certain what the last two lines do without going back and looking at what the griddata method returns. It appears to be throwing out some additional data that isn't needed for the image or performing a translation.
large[-1,:] = large[-2,:]
large[:,-1] = large[:,-2]
return large