I am trying to create a 2D log chromaticity plot in python with OpenCV. The same question was asked here
How to compute 2D log-chromaticity?
but it was never answered.
(ASIDE: A guess was made that the axes must be log instead of linear, but this is incorrect as the paper uses negative coordinates, and log axes cannot be negative. Also, I was desparate and tried plt.xscale('log') and plt.yscale('log'), but it didn't work).
This work is based off this paper:
https://www.cs.sfu.ca/~mark/ftp/Eccv04/
(I re-mention it below)
My Code:
import numpy as np
import cv2
import os
import matplotlib.pyplot as plt
root = r'.\path\to\root'
root = r'my_img.jpg'
if __name__ == '__main__':
img = cv2.imread(os.path.join(root, fl))
cv2.imshow('Original', img)
cv2.waitKey(0)
b, g, r = cv2.split(img)
img_sum = np.sum(img, axis = 2) # NOTE: This dtype will be uint32.
# Each channel can be up to
# 255 (dtype = uint8), but
# since uint8 can only go up
# to 255, sum naturally uint32
# "Normalized" channels
# NOTE: np.ma is the masked array library. It automatically masks
# inf and nan answers from result
n_r = np.ma.divide(1.*r, g)
n_b = np.ma.divide(1.*b, g)
log_rg = np.ma.log( n_r )
log_bg = np.ma.log( n_b )
plt.scatter(l_rg, l_bg, s = 2)
plt.xlabel('Log(R/G)')
plt.ylabel('Log(B/G)')
plt.title('2D Log Chromaticity')
plt.show()
Input:
Color Checker Chart
Result:
My Log Chromaticity Plot
Expected Result:
Finlayson Log Chromaticity Plot
The expected result was taken from this paper ("Intrinsic Images by Entropy Minimization", by: Finlayson, G., et. al.):
https://www.cs.sfu.ca/~mark/ftp/Eccv04/
(Paper also mentioned above)
Can you help me please?!
This is the closest I can figure. Reading through this:
http://www2.cmp.uea.ac.uk/Research/compvis/Papers/DrewFinHor_ICCV03.pdf
I came across the sentence:
"Fig. 2(a) shows log-chromaticities for the 24 surfaces of a Macbeth ColorChecker Chart, (the six neutral patches all belong to the same
cluster). If we now vary the lighting and plot median values
for each patch, we see the curves in Fig. 2(b)."
If you look closely at the log-chromaticity plot, you see 19 blobs, corresponding to each of the 18 colors in the Macbeth chart, plus the sum of all the 6 grayscale targets in the bottom row:
Explanation of Log Chromaticities
With 1 picture, we can only get 1 point of each blob: We take the median value inside each target and plot it. To get plot from the paper, we would have to create multiple images with different lighting. We might be able to do this by varying the temperature of the image in an image editor.
For now, I just looked at the color patches in the original image and plotted the points:
Input:
Output:
The graph dots are not all in the same place as the paper, but I figure it's fairly close. Would someone please check my work to see if this makes sense?
I have a lab colorspace
And I want to "bin" the colorspace in a grid of 10x10 squares.
So the first bin might be (-110,-110) to (-100,-100) then the next one might be (-100,-110) to (-90,-100) and so on. These bins could be bin 1 and bin 2
I have seen np.digitize() but it appears that you have to pass it 1-dimensional bins.
A rudimentary approach that I have tried is this:
for fn in filenames:
image = color.rgb2lab(io.imread(fn))
ab = image[:,:,1:]
width,height,d = ab.shape
reshaped_ab = np.reshape(ab,(width*height,d))
print reshaped_ab.shape
images.append(reshaped_ab)
all_abs = np.vstack(images)
all_abs = shuffle(all_abs,random_state=0)
sns
df = pd.DataFrame(all_abs[:3000],columns=["a","b"])
top_a,top_b = df.max()
bottom_a,bottom_b = df.min()
range_a = top_a-bottom_a
range_b = top_b-bottom_b
corner_a = bottom_a
corner_b = bottom_b
bins = []
for i in xrange(int(range_a/10)):
for j in xrange(int(range_b/10)):
bins.append([corner_a,corner_b,corner_a+10,corner_b+10])
corner_b = bottom_b+10
corner_a = corner_a+10
but the "bins" that results seem kinda sketchy. For one thing there are many empty bins as the color space does have values in a square arrangement and that code pretty much just boxes off from the max and min values. Additionally, the rounding might cause issues. I am wondering if there is a better way to do this? I have heard of color histograms which count the values in each "bin". I don't need the values but the bins are I think what I am looking for here.
Ideally the bins would be an object that each have a label. So I could do bins.indices[0] and it would return the bounding box I gave it. Then also I could bin each observation, like if a new color was color = [15.342,-6.534], color.bin would return 15 or the 15th bin.
I realize this is a lot to ask for, but I think it must be a somewhat common need for people working with color spaces. So is there any python module or tool that can accomplish what I'm asking? How would you approach this? thanks!
Use the standard numpy 2D-histogram function: numpy.histogram2d:
import numpy as np
# a and b are arrays representing your color points
H, a_edges, b_edges = np.histogram2d(a, b, bins=10)
If you want to discard the empty bins, you'd have to do some work from here. But I don't see why you'd want that, because assigning future colors to existing nonempty bins will be much more work if they are not on a rectangular grid.
You are probably trying to repeat what Richard Zhang did in "Colorful Image Colorization" research: http://richzhang.github.io/colorization/
Here, author himself discuss this problem: https://github.com/richzhang/colorization/issues/23
Fortunately Zhang provides .npy file, that contains those quantized values. It is under: https://github.com/richzhang/colorization/blob/master/resources/pts_in_hull.npy
The only thing, you have to do now, is to load this file in your python script:
import numpy as np
pts_in_hull = np.load("pts_in_hull.npy")
It is numpy array of shape 313x2 containing values from your image.
I know this answer comes few years too late, but maybe it will help someone else.
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.
I am trying to create four gabor patches, very similar to those below.
I don't need them to be identical to the pictures below, but similar.
Despite a bit of tinkering, I have been unable to reproduce these images...
I believe they were created in MATLAB originally. I don't have access to the original MATLAB code.
I have the following code in python (2.7.10):
import numpy as np
from scipy.misc import toimage # One can also use matplotlib*
data = gabor_fn(sigma = ???, theta = 0, Lambda = ???, psi = ???, gamma = ???)
toimage(data).show()
*graphing a numpy array with matplotlib
gabor_fn, from here, is defined below:
def gabor_fn(sigma,theta,Lambda,psi,gamma):
sigma_x = sigma;
sigma_y = float(sigma)/gamma;
# Bounding box
nstds = 3;
xmax = max(abs(nstds*sigma_x*numpy.cos(theta)),abs(nstds*sigma_y*numpy.sin(theta)));
xmax = numpy.ceil(max(1,xmax));
ymax = max(abs(nstds*sigma_x*numpy.sin(theta)),abs(nstds*sigma_y*numpy.cos(theta)));
ymax = numpy.ceil(max(1,ymax));
xmin = -xmax; ymin = -ymax;
(x,y) = numpy.meshgrid(numpy.arange(xmin,xmax+1),numpy.arange(ymin,ymax+1 ));
(y,x) = numpy.meshgrid(numpy.arange(ymin,ymax+1),numpy.arange(xmin,xmax+1 ));
# Rotation
x_theta=x*numpy.cos(theta)+y*numpy.sin(theta);
y_theta=-x*numpy.sin(theta)+y*numpy.cos(theta);
gb= numpy.exp(-.5*(x_theta**2/sigma_x**2+y_theta**2/sigma_y**2))*numpy.cos(2*numpy.pi/Lambda*x_theta+psi);
return gb
As you may be able to tell, the only difference (I believe) between the images is contrast. So, gabor_fn would likely needed to be altered to do allow for this (unless I misunderstand one of the params)...I'm just not sure how.
UPDATE:
from math import pi
from matplotlib import pyplot as plt
data = gabor_fn(sigma=5.,theta=pi/2.,Lambda=12.5,psi=90,gamma=1.)
unit = #From left to right, unit was set to 1, 3, 7 and 9.
bound = 0.0009/unit
fig = plt.imshow(
data
,cmap = 'gray'
,interpolation='none'
,vmin = -bound
,vmax = bound
)
plt.axis('off')
The problem you are having is a visualization problem (although, I think you are chossing too large parameters).
By default matplotlib, and scipy's (toimage) use bilinear (or trilinear) interpolation, depending on your matplotlib's configuration script. That's why your image looks so smooth. It is because your pixels values are being interpolated, and you are not displaying the raw kernel you have just calculated.
Try using matplotlib with no interpolation:
from matplotlib import pyplot as plt
plt.imshow(data, 'gray', interpolation='none')
plt.show()
For the following parameters:
data = gabor_fn(sigma=5.,theta=pi/2.,Lambda=25.,psi=90,gamma=1.)
You get this output:
If you reduce lamda to 15, you get something like this:
Additionally, the sigma you choose changes the strength of the smoothing, adding parameters vmin=-1 and vmax=1 to imshow (similar to what #kazemakase) suggested, will give you the desired contrast.
Check this guide for sensible values (and ways to use) gabor kernels:
http://scikit-image.org/docs/dev/auto_examples/plot_gabor.html
It seems like toimage scales the input data so that the min/max values are mapped to black/white.
I do not know what amplitudes to reasonably expect from gabor patches, but you should try something like this:
toimage(data, cmin=-1, cmax=1).show()
This tells toimage what range your data is in. You can try to play around with cmin and cmax, but make sure they are symmetric (i.e. cmin=-x, cmax=x) so that a value of 0 maps to grey.
I'm trying to plot some data to analyze them.
My data is defined as below:
class Data(object):
def __init__(self, rows=200, cols=300):
"""
The Data constructor
"""
# The data grid
self.cols = cols
self.rows = rows
# The 2D data structure
self.data = numpy.zeros((rows, cols), float)
At first, I had this method:
def generate_data_heat_map(data, x_axis_label, y_axis_label, plot_title, file_path):
plt.figure()
plt.title(plot_title)
fig = plt.imshow(data.data, extent=[0, data.cols, data.rows, 0])
plt.xlabel(x_axis_label)
plt.ylabel(y_axis_label)
plt.colorbar(fig)
plt.savefig(file_path + '.png')
plt.close()
This gives me something as a heat map image (second figure), 'cause I'm passing to it an MxN [luminance (grayscale, float array only)]. And don't know why this doesn't generate a grayscale image, but so far I didn't worry about it 'cause that is the result I wanted.
After some more calculation, I had this method to visualize my data, using the data_property as RGB and data_uncertaity as alpha:
def generate_data_uncertainty_heat_map(data_property, data_uncertainty, x_axis_label, y_axis_label, plot_title, file_path):
plt.figure()
uncertainty = numpy.zeros((data_property.rows, data_property.cols, 4))
uncertainty[..., :3] = data_property.data[..., numpy.newaxis]
uncertainty[..., 3] = data_uncertainty.data
plt.title(plot_title)
fig = plt.imshow(uncertainty.data, extent=[0, data_property.cols, data_property.rows, 0])
plt.xlabel(x_axis_label)
plt.ylabel(y_axis_label)
plt.colorbar(fig)
plt.savefig(file_path + '.png')
plt.close()
But, of course, this give me a grayscale image with alpha values, since I am repeating the same values for R, G and B. But what I really would like to have was the first method result (colored) with some alpha values calculated as uncertainty about the data.
I've noticed that my color bar has nothing about my data too (it's in RGB, I can't use it to analyze my data)
I don't know how to achieve the result that I want, which is to a have a "heat map" plot with merged the alpha values defined with my uncertainty_data and a color bar representing this uncertainty. Like merging this two images above:
This as my color:
This as my alpha:
With the conversion presented by #BlazBratanic, I guess I can see a little bit of color (not sure about it), but its far of what I was expecting.
All my values is between 0.0 and 1.0.
Thank you in advance.
Use Matplotlib cm module to map your grayscale to color values. If i remember correctly "jet" is the default colormap. So you would do something like:
uncertainty = plt.cm.jet(data_property.data)
uncertainty[..., 3] = data_uncertainty.data