I'm using a PolyCollection to plot data of various sizes. Sometimes the polygons are very small. If they are too small, they don't get plotted at all. I would expect the outline at least to show up so you'd have an idea that some data is there. Is there a a setting to control this?
Here's some code to reproduce the problem, as well as the output image:
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
from matplotlib import colors
fig = plt.figure()
ax = fig.add_subplot(111)
verts = []
edge_col = colors.colorConverter.to_rgb('lime')
face_col = [(2.0 + val) / 3.0 for val in edge_col] # a little lighter
for i in range(10):
w = 0.5 * 10**(-i)
xs = [i - w, i - w, i + w, i - w]
ys = [-w, w, 0, -w]
verts.append(list(zip(xs, ys)))
ax.set_xlim(-1, 11)
ax.set_ylim(-2, 2)
ax.add_collection(PolyCollection(verts, lw=3, alpha=0.5, edgecolor=edge_col, facecolor=face_col))
plt.savefig('out.png')
Notice that only six polygons are visible, whereas there should be ten.
Edit: I understand I could zoom in to see the others, but I was hoping to see a dot or the outline or something without doing this.
Edit 2: Here's a way to get the desired effect, by plotting the faces using a PolyCollection and then the edges using a series of Line2D plots with markers, based on Patol75's answer. My application is a matplotlib animation with lots of polygons, so I'd prefer to avoid Line2D for efficiency, and it would be cleaner if I didn't need to plot things twice, so I'm still hoping for a better answer.
ax.add_collection(PolyCollection(verts, lw=3, alpha=0.5, edgecolor=None, facecolor=face_col, zorder=1))
for pts in verts:
ax.add_line(Line2D([pt[0] for pt in pts], [pt[1] for pt in pts], lw=3, alpha=0.5, color=edge_col,
marker='.', ms=1, mec=edge_col, solid_capstyle='projecting', zorder=2))
Zooming in your plotting window, you would notice that your two remaining polygons are being plotted. They are just too small for you to see them. One way to be convinced of this is to replace
ax.set_xlim(-1, 6)
ax.set_ylim(-2, 2)
by
ax.set_xlim(1e-1, 1e1)
ax.set_ylim(1e-5, 1e0)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_aspect('equal')
Your five polygons are now visible, but on the downside the log scale restrains you to the positive side of the axes.
Now to propose an answer to your problem. If you keep a linear axis, as your polygons sizes span multiple orders of magnitude, you will not be able to see them all. What you can do is add an artist on your plot which specifies their location. This can be done with a marker, an arrow, etc... If we take the example of a marker, as you said, we only want to see this marker if we cannot see the polygon. The keyword zorder in the call to plot() allows to specify which artist should have the display priority on the figure. Please consider the example below.
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
fig = plt.figure()
ax = fig.add_subplot(111)
verts = []
for i in range(5):
w = 0.5 * 10**(-i)
xs = [i - w, i - w, i + w, i + w, i - w]
ys = [-w, w, w, -w, -w]
ax.plot((xs[2] + xs[1]) / 2, (ys[1] + ys[0]) / 2, linestyle='none',
marker='o', color='xkcd:crimson', markersize=1, zorder=-1)
verts.append(list(zip(xs, ys)))
ax.set_xlim(-1, 6)
ax.set_ylim(-2, 2)
poly = PolyCollection(verts, lw=5, edgecolor='black', facecolor='gray')
ax.add_collection(poly)
plt.show()
which produces
You would notice that if you zoom on the last two dots in the matplotlib figure, you actually do not see the markers, but rather the polygons.
You may introduce some minimal unit minw, which is the smallest size a shape can have.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
from matplotlib import colors
fig = plt.figure()
ax = fig.add_subplot(111)
verts = []
edge_col = colors.colorConverter.to_rgb('lime')
face_col = [(2.0 + val) / 3.0 for val in edge_col] # a little lighter
ax.set_xlim(-1, 11)
ax.set_ylim(-2, 2)
u = np.diff(np.array([ax.get_xlim(), ax.get_ylim()]), axis=1).min()
px = np.max(fig.get_size_inches())*fig.dpi
minw = u/px/2
for i in range(10):
w = 0.5 * 10**(-i)
if w < minw:
w = minw
xs = [i - w, i - w, i + w, i - w]
ys = [-w, w, 0, -w]
verts.append(list(zip(xs, ys)))
ax.add_collection(PolyCollection(verts, lw=3, alpha=0.5, edgecolor=edge_col, facecolor=face_col))
plt.savefig('out.png')
plt.show()
Related
So let's say I have a vector of numbers.
np.random.randn(5).round(2).tolist()
[2.05, -1.57, 1.07, 1.37, 0.32]
I want a draw a rectangle that shows this elements as numbers in a rectangle.
Something like this:
Is there an easy way to do this in matplotlib?
A bit convoluted but you could take advantage of seaborn.heatmap, creating a white colormap:
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
data = np.random.randn(5).round(2).tolist()
linewidth = 2
ax = sns.heatmap([data], annot=True, cmap=LinearSegmentedColormap.from_list('', ['w', 'w'], N=1),
linewidths=linewidth, linecolor='black', square=True,
cbar=False, xticklabels=False, yticklabels=False)
plt.tight_layout()
plt.show()
In this case, the external lines won't be as thick as the internal ones. If needed, this can be fixed with:
ax.axhline(y=0, color='black', lw=linewidth*2)
ax.axhline(y=1, color='black', lw=linewidth*2)
ax.axvline(x=0, color='black', lw=linewidth*2)
ax.axvline(x=len(data), color='black', lw=linewidth*2)
Edit: avoid these lines and add clip_on=False to sns.heatmap (thanks/credit #JohanC)
Output:
We can add rectangles , and annotate them in a for loop.
from matplotlib import pyplot as plt
import numpy as np
# Our numbers
nums = np.random.randn(5).round(2).tolist()
# rectangle_size
rectangle_size = 2
# We want rectangles look squared, you can change if you want
plt.rcParams["figure.figsize"] = [rectangle_size * len(nums), rectangle_size]
plt.rcParams["figure.autolayout"] = True
fig = plt.figure()
ax = fig.add_subplot(111)
for i in range(len(nums)):
# We are adding rectangles
# You can change colors as you wish
plt.broken_barh([(rectangle_size * i, rectangle_size)], (0, rectangle_size), facecolors='white', edgecolor='black'
,linewidth = 1)
# We are calculating where to annotate numbers
cy = rectangle_size / 2.0
cx = rectangle_size * i + cy
# Annotation You can change color,font, etc ..
ax.annotate(str(nums[i]), (cx, cy), color='black', weight='bold', fontsize=20, ha='center', va='center')
# For squared look
plt.xlim([0, rectangle_size*len(nums)])
plt.ylim([0, rectangle_size])
# We dont want to show ticks
plt.axis('off')
plt.show()
One way using the Rectangle patch is:
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.patches import Rectangle
x = np.random.randn(5).round(2).tolist()
fig, ax = plt.subplots(figsize=(9, 2)) # make figure
dx = 0.15 # edge size of box
buf = dx / 10 # buffer around edges
# set x and y limits
ax.set_xlim([0 - buf, len(x) * dx + buf])
ax.set_ylim([0 - buf, dx + buf])
# set axes as equal and turn off axis lines
ax.set_aspect("equal")
ax.axis("off")
# draw plot
for i in range(len(x)):
# create rectangle with linewidth=4
rect = Rectangle((dx * i, 0), dx, dx, facecolor="none", edgecolor="black", lw=4)
ax.add_patch(rect)
# get text position
x0, y0 = dx * i + dx / 2, dx / 2
# add text
ax.text(
x0, y0, f"{x[i]}", color="black", ha="center", va="center", fontsize=28, fontweight="bold"
)
fig.tight_layout()
fig.show()
which gives:
I want to plot colored pie charts at specific positions without distorting their circular aspect ratio. I'm using Wedge patches because I could not find a better solution. Here is the code
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import patches, collections
fig, axes = plt.subplots()
for i in range(20):
x = np.random.uniform(low=0, high=1, size=10).cumsum()
axes.scatter(x=x, y=np.repeat(i, x.shape[0]), c='gray', s=1)
pies = []
N = 4
cmap = plt.cm.get_cmap("hsv", N + 1)
colors = list(map(cmap, range(N)))
print(colors)
for i in range(2, 2 + N):
thetas = np.linspace(0, 360, num=i)
assert len(thetas) - 1 <= len(colors)
for theta1, theta2, c in zip(thetas[:-1], thetas[1:], colors):
wedge = patches.Wedge((i, i), r=i / 10, theta1=theta1, theta2=theta2,
color=c)
pies.append(wedge)
axes.add_collection(collections.PatchCollection(pies,
match_original=True))
plt.show()
How to preserve the aspect ratio of pie charts? Setting axes.set_aspect("equal") is NOT an option because it squeezes the plot completely when I have more data points.
I've been looking at how to draw circles and preserve the aspect ratio but the solution cannot be adopted here - I'm plotting Wedges/pie charts, not Circles.
I also looked at matplotlib transforms but couldn't find the answer there either.
I tried the same thing, and matplotlib really doesn't try to make this easy for you, but I found a solution that you should be able to use.
You need to separate the centers from the wedges and add them to the PatchCollection as offsets. Then you can apply different transforms to the offsets (transOffset) and shape (transform).
Notice that I have changed the r-value (radius). This value is no longer in data coordinates, so it should always be the same size, regardless of how much you zoom, but it is too small to be visible at i/10.
from matplotlib import patches, collections, transforms
offsets = []
for i in range(2, 2 + N):
thetas = np.linspace(0, 360, num=i)
assert len(thetas) - 1 <= len(colors)
for theta1, theta2, c in zip(thetas[:-1], thetas[1:], colors):
wedge = patches.Wedge((0, 0), r=10, theta1=theta1, theta2=theta2,
color=c)
offsets.append((i, i))
pies.append(wedge)
coll = collections.PatchCollection(
pies, match_original=True, offsets=offsets,
transform=transforms.IdentityTransform(),
transOffset=axes.transData
)
It works fine for me when I set set_aspect('equal'):
Image is narrowed because y-range is longer than x-range I think.
If you'd set y_lim between 0 and a number lower than y_max, you'd see it better:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import patches, collections
fig, axes = plt.subplots()
for i in range(20):
x = np.random.uniform(low=0, high=1, size=10).cumsum()
axes.scatter(x=x, y=np.repeat(i, x.shape[0]), c='gray', s=1)
pies = []
N = 4
cmap = plt.cm.get_cmap("hsv", N + 1)
colors = list(map(cmap, range(N)))
print(colors)
for i in range(2, 2 + N):
thetas = np.linspace(0, 360, num=i)
assert len(thetas) - 1 <= len(colors)
for theta1, theta2, c in zip(thetas[:-1], thetas[1:], colors):
wedge = patches.Wedge((i, i), r=i / 10, theta1=theta1, theta2=theta2,
color=c)
pies.append(wedge)
axes.add_collection(collections.PatchCollection(pies,
match_original=True))
axes.set_aspect('equal')
axes.set_ylim(0,7.5)
plt.show()
When dealing with overlapping high density scatter or line plots of different colors it can be convenient to implement additive blending schemes, where the RGB colors of each marker add together to produce the final color in the canvas. This is a common operation in 2D and 3D render engines.
However, in Matplotlib I've only found support for alpha/opacity blending. Is there any roundabout way of doing it or am I stuck with rendering to bitmap and then blending them in some paint program?
Edit: Here's some example code and a manual solution.
This will produce two partially overlapping random distributions:
x1 = randn(1000)
y1 = randn(1000)
x2 = randn(1000) * 5
y2 = randn(1000)
scatter(x1,y1,c='b',edgecolors='none')
scatter(x2,y2,c='r',edgecolors='none')
This will produce in matplotlib the following:
As you can see, there are some overlapping blue points that are occluded by red points and we would like to see them. By using alpha/opacity blending in matplotlib, you can do:
scatter(x1,y1,c='b',edgecolors='none',alpha=0.5)
scatter(x2,y2,c='r',edgecolors='none',alpha=0.5)
Which will produce the following:
But what I really want is the following:
I can do it manually by rendering each plot independently to a bitmap:
xlim = plt.xlim()
ylim = plt.ylim()
scatter(x1,y1,c='b',edgecolors='none')
plt.xlim(xlim)
plt.ylim(ylim)
scatter(x2,y2,c='r',edgecolors='none')
plt.xlim(xlim)
plt.ylim(ylim)
plt.savefig(r'scatter_blue.png',transparent=True)
plt.savefig(r'scatter_red.png',transparent=True)
Which gives me the following images:
What you can do then is load them as independent layers in Paint.NET/PhotoShop/gimp and just additive blend them.
Now ideal would be to be able to do this programmatically in Matplotlib, since I'll be processing hundreds of these!
If you only need an image as the result, you can get the canvas buffer as a numpy array, and then do the blending, here is an example:
from matplotlib import pyplot as plt
import numpy as np
fig, ax = plt.subplots()
ax.scatter(x1,y1,c='b',edgecolors='none')
ax.set_xlim(-4, 4)
ax.set_ylim(-4, 4)
ax.patch.set_facecolor("none")
ax.patch.set_edgecolor("none")
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
img = np.frombuffer(fig.canvas.buffer_rgba(), np.uint8).reshape(h, w, -1).copy()
ax.clear()
ax.scatter(x2,y2,c='r',edgecolors='none')
ax.set_xlim(-4, 4)
ax.set_ylim(-4, 4)
ax.patch.set_facecolor("none")
ax.patch.set_edgecolor("none")
fig.canvas.draw()
img2 = np.frombuffer(fig.canvas.buffer_rgba(), np.uint8).reshape(h, w, -1).copy()
img[img[:, :, -1] == 0] = 0
img2[img2[:, :, -1] == 0] = 0
fig.clf()
plt.imshow(np.maximum(img, img2))
plt.subplots_adjust(0, 0, 1, 1)
plt.axis("off")
plt.show()
the result:
This feature is now supported by my matplotlib backend https://github.com/anntzer/mplcairo (master only):
import matplotlib; matplotlib.use("module://mplcairo.qt")
from matplotlib import pyplot as plt
from mplcairo import operator_t
import numpy as np
x1 = np.random.randn(1000)
y1 = np.random.randn(1000)
x2 = np.random.randn(1000) * 5
y2 = np.random.randn(1000)
fig, ax = plt.subplots()
# The figure and axes background must be made transparent.
fig.patch.set(alpha=0)
ax.patch.set(alpha=0)
pc1 = ax.scatter(x1, y1, c='b', edgecolors='none')
pc2 = ax.scatter(x2, y2, c='r', edgecolors='none')
operator_t.ADD.patch_artist(pc2) # Use additive blending.
plt.show()
I'm converting a Shapely MultiPolygon to a PatchCollection, and first colouring each Polygon like so:
# ldn_mp is a MultiPolygon
cm = plt.get_cmap('RdBu')
num_colours = len(ldn_mp)
fig = plt.figure()
ax = fig.add_subplot(111)
minx, miny, maxx, maxy = ldn_mp.bounds
w, h = maxx - minx, maxy - miny
ax.set_xlim(minx - 0.2 * w, maxx + 0.2 * w)
ax.set_ylim(miny - 0.2 * h, maxy + 0.2 * h)
ax.set_aspect(1)
patches = []
for poly in ldn_mp:
colour = cm(1. * len(filter(poly.contains, points)) / num_colours)
patches.append(PolygonPatch(poly, fc=colour, ec='#555555', lw=0.2, alpha=1., zorder=1))
pc = PatchCollection(patches, match_original=True)
ax.add_collection(pc)
ax.set_xticks([])
ax.set_yticks([])
plt.title("Density of NO$^2$ Sensors by Borough")
plt.tight_layout()
plt.show()
But I'd like to add a colorbar to my plot, based upon the PatchCollection colors. I'm not sure how to go about that; do I pass the cmap keyword when creating pc? How do I then call set_array() with the colours I've used?
I had the same problem a little while ago. For each polygon I saved the corresponding color to a list named mycolors:
mycolors=[]
...
mycolors.append(SSTvalue)
path_patch = patches.PathPatch(mypath, lw=1)
mypatches.append(path_patch)
I looped over a series of multipolygons stored in a Shapefile and stored each patch in a collection. After that I plotted the polygons using the color information I had stored in the list, which was converted to an array eventually, and added a colorbar:
p = PatchCollection(mypatches, cmap=plt.get_cmap('RdYlBu_r'), alpha=1.0)
p.set_array(array(mycolors))
p.set_clim([np.ma.min(mycolors),np.ma.max(mycolors)])
plt.colorbar(p,shrink=0.5)
The full script I used to plot temperature values with colors and a colorbar for large marine ecosystems of the world represented by polygons can be found here. Hope this helps. Cheers, Trond
There is no need to create an additional list. Assuming you work in pandas or numpy arrays.
For example:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib import cm
fig, ax = plt.subplots()
for c_l ,patches in dict_mapindex_mpl_polygon.items():
color = df_map_elements.loc[c_l, 'stress_level']
p = PatchCollection(patches,color=cm.Set2(color),lw=.3,edgecolor='k')
ax.add_collection(p)
ax.autoscale_view()
p.set(array=df_map_elements['stress_level'].values, cmap='Set2')
fig.colorbar(p, label="Stress (index)")
plt.show()
I'm new to Python (was an IDL user before hand) so I hope that I'm asking this in an understandable way. I've been trying to create a polar plot with x number of bins where the data in the bin is averaged and given a colour associated with that value. This seems to work fine while using the plt.fill command where I can define the bin and then the fill colour. The problem comes when I then try to make a colour bar to go with it. I keep getting errors that state AttributeError: 'Figure' object has no attribute 'autoscale_None'
Any advice would be helpful thanks.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.pyplot import figure, show, rc, grid
import pylab
r = np.arange(50)/5.
rstep = r[1] - r[0]
theta = np.arange(50)/50.*2.*np.pi
tstep = theta[1] - theta[0]
colorv = np.arange(50)/50.
# force square figure and square axes looks better for polar, IMO
width, height = mpl.rcParams['figure.figsize']
size = min(width, height)
# make a square figure
fig = figure(figsize=(size, size))
ax = fig.add_axes([0.1, 0.1, .8, .8])#, polar=True)
my_cmap = cm.jet
for j in range(len(r)):
rbox = np.array([r[j], r[j], r[j]+ rstep, r[j] + rstep])
for i in range(len(theta)):
thetabox = np.array([theta[i], theta[i] + tstep, theta[i] + tstep, theta[i]])
x = rbox*np.cos(thetabox)
y = rbox*np.sin(thetabox)
plt.fill(x,y, facecolor = my_cmap(colorv[j]))
# Add colorbar, make sure to specify tick locations to match desired ticklabels
cbar = fig.colorbar(fig, ticks=[np.min(colorv), np.max(colorv)])
cb = plt.colorbar()
plt.show()
* here is a slightly better example of my real data, there are holes missing everywhere, so in this example I've just made a big one in a quarter of the circle. When I've tried meshing, the code seems to try to interpolate over these regions.
r = np.arange(50)/50.*7. + 3.
rstep = r[1] - r[0]
theta = np.arange(50)/50.*1.5*np.pi - np.pi
tstep = theta[1] - theta[0]
colorv = np.sin(r/10.*np.pi)
# force square figure and square axes looks better for polar, IMO
width, height = mpl.rcParams['figure.figsize']
size = min(width, height)
# make a square figure
fig = figure(figsize=(size, size))
ax = fig.add_axes([0.1, 0.1, .8, .8])#, polar=True)
my_cmap = cm.jet
for j in range(len(r)):
rbox = np.array([r[j], r[j], r[j]+ rstep, r[j] + rstep])
for i in range(len(theta)):
thetabox = np.array([theta[i], theta[i] + tstep, theta[i] + tstep, theta[i]])
x = rbox*np.cos(thetabox)
y = rbox*np.sin(thetabox)
plt.fill(x,y, facecolor = my_cmap(colorv[j]))
# Add colorbar, make sure to specify tick locations to match desired ticklabels
#cbar = fig.colorbar(fig, ticks=[np.min(colorv), np.max(colorv)])
#cb = plt.colorbar()
plt.show()
And then with a meshing involved...
from matplotlib.mlab import griddata
r = np.arange(50)/5.
rstep = r[1] - r[0]
theta = np.arange(50)/50.*1.5*np.pi - np.pi
tstep = theta[1] - theta[0]
colorv = np.sin(r/10.*np.pi)
# force square figure and square axes looks better for polar, IMO
width, height = mpl.rcParams['figure.figsize']
size = min(width, height)
# make a square figure
fig = figure(figsize=(size, size))
ax = fig.add_axes([0.1, 0.1, .8, .8])#, polar=True)
my_cmap = cm.jet
x = r*np.cos(theta)
y = r*np.sin(theta)
X,Y = np.meshgrid(x,y)
data = griddata(x,y,colorv,X,Y)
cax = plt.contourf(X,Y, data)
plt.colorbar()
# Add colorbar, make sure to specify tick locations to match desired ticklabels
#cbar = fig.colorbar(fig, ticks=[np.min(colorv), np.max(colorv)])
#cb = plt.colorbar()
plt.show()
colorbar needs things to be an instance of ScalarMappable in order to make a colorbar from them.
Because you're manually setting each tile, there's nothing that essentially has a colorbar.
There are a number of ways to fake it from your colormap, but in this case there's a much simpler solution.
pcolormesh does exactly what you want, and will be much faster.
As an example:
import numpy as np
import matplotlib.pyplot as plt
# Linspace makes what you're doing _much_ easier (and includes endpoints)
r = np.linspace(0, 10, 50)
theta = np.linspace(0, 2*np.pi, 50)
fig = plt.figure()
ax = fig.add_subplot(111, projection='polar')
# "Grid" r and theta into 2D arrays (see the docs for meshgrid)
r, theta = np.meshgrid(r, theta)
cax = ax.pcolormesh(theta, r, r, edgecolors='black', antialiased=True)
# We could just call `plt.colorbar`, but I prefer to be more explicit
# and pass in the artist that I want it to extract colors from.
fig.colorbar(cax)
plt.show()
Or, if you'd prefer non-polar axes, as in your example code:
import numpy as np
import matplotlib.pyplot as plt
r = np.linspace(0, 10, 50)
theta = np.linspace(0, 2*np.pi, 50)
# "Grid" r and theta and convert them to cartesian coords...
r, theta = np.meshgrid(r, theta)
x, y = r * np.cos(theta), r * np.sin(theta)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.axis('equal')
cax = ax.pcolormesh(x, y, r, edgecolors='black', antialiased=True)
fig.colorbar(cax)
plt.show()
Note: If you'd prefer the boundary lines a bit less dark, just specify linewidth=0.5 or something similar to pcolormesh.
Finally, if you did want to directly make the colorbar from the colormap in your original code, you'd create an instance of ScalarMappable from it and pass this to colorbar. It's easier than it sounds, but it's a bit verbose.
As an example, in your original code, if you do something like the following:
cax = cm.ScalarMappable(cmap=my_cmap)
cax.set_array(colorv)
fig.colorbar(cax)
It should do what you want.
So I've found a workaround. Since I know of a region where I definitely won't have data, I've plotted some there. I've made sure that the data covers the entire range of what I'm potting. I then cover it up (this region was going to be covered anyway, it shows where the "earth" is located). Now I can go ahead and use plt.fill as I had originally and use the colour bar from the randomly potted data. I know this isn't probably the correct way, but it works and doesn't try to interpolate my data.
Thanks so much for helping get this sorted. and if you know of a better way, I'd be happy to hear it!
hid = plt.pcolormesh(X,Y, data, antialiased=True)
#here we cover up the region that we just plotted in
r3 = [1 for i in range(360)]
theta3 = np.arange(360)*np.pi/180.
plt.fill(theta3, r3, 'w')
#now we can go through and fill in all the regions
for j in range(len(r)):
rbox = np.array([r[j], r[j], r[j]+ rstep, r[j] + rstep])
for i in range(len(theta)):
thetabox = np.array([theta[i], theta[i] + tstep, theta[i] + tstep, theta[i]])
x = rbox*np.cos(thetabox)
y = rbox*np.sin(thetabox)
colorv = np.sin(r[j]/10.*np.pi)
plt.fill(thetabox,rbox, facecolor = my_cmap(colorv))
#And now we can plot the color bar that fits the data Tada :)
plt.colorbar()
plt.show()