I have a group bar chart that I would like to scale.
I am running matplotlib in a Jupyter Notebook and the bar chart is very squashed. I would like to make the axis bigger but can't get it to work in a group bar chart. If I could make it wider it would be much more readable. But if I just increase "width" then the bars start to overlap each other.
The second problem is what to do about the labels. How can the labels be printed to three decimal places?
Note: I recognise that the the values plotted are orders of magnitude different so you cannot really read the small values. Ordinarily you would not combine these onto a single chart - but this is a class exercise to demonstrating why you would not do it so I expect that.
Here is the self-contained code to demonstrate the problem:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
labels = ['0-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80-89', '90-99']
t3=[1.2833333333333332, 1.6970588235294117, 1.7189655172413794, 1.8090163934426229, 1.44140625, 1.5763157894736846, 1.3685185185185187, 1.430120481927711, 1.5352941176470587, 1.9]
tt4= [116.33333333333333, 106.0, 106.93103448275862, 109.47540983606558, 98.734375, 99.84210526315789, 96.72839506172839, 99.40963855421687, 104.94117647058823, 203.0]
tsh= [1.2833333333333332, 1.6970588235294117, 1.7189655172413794, 1.8090163934426229, 1.44140625, 1.5763157894736846, 1.3685185185185187, 1.430120481927711, 1.5352941176470587, 1.9]
hypo_count= [2, 15, 55, 58, 59, 69, 72, 74, 33, 1]
x = np.arange(len(labels)) # the label locations
width = 0.2 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(x, t3, width, label='T3 avg')
rects2 = ax.bar(x+(width), tt4, width, label='TT4 avg')
rects3 = ax.bar(x+(width*2), tsh, width, label='TSH avg')
rects4 = ax.bar(x+(width*3), hypo_count, width, label='# Hypothyroid +ve')
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_title('Age Bracket')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend()
# Print the value on top of each bar
ax.bar_label(rects1, padding=3)
ax.bar_label(rects2, padding=3)
ax.bar_label(rects3, padding=3)
ax.bar_label(rects4, padding=3)
fig.tight_layout()
plt.show()
Related
I am trying create two plots that should have the same width when displayed in a row-wise fashion. I have noticed that adding xticks followed by tight_layout makes the plot (pcolormesh) decrease in width from increasing the x-margins. I would like to move the ticks in such a way that the x-margins are eliminated and both pcolormesh have the same width.
I have the following example:
import numpy as np, matplotlib.pyplot as plt
def plot(ticks=True):
fig, ax = plt.subplots(figsize=(6,1))
np.random.seed(42)
a = np.random.randn(1,6)
ax.pcolormesh(a)
plt.gca().invert_yaxis()
ax.xaxis.tick_top()
ax.set(yticklabels=[])
ax.tick_params(left=False, length=5)
if ticks:
ax.set_xticks([0, 3, 6])
else:
plt.axis('off')
plt.tight_layout()
plt.savefig(f'plot-{ticks}.png', dpi=300, bbox_inches='tight', pad_inches=0.0)
I get the following plots when running with and without the ticks:
The x-margins are not the same, which is more noticeable when increasing the font-size. How do I move the 3 label to right and the 6 label to the left to make both images have the same x-margins (0 margin)?
EDIT
Using the suggestion from Align specific x labels differently to each other? we have
import numpy as np, matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 17})
fig, ax = plt.subplots(figsize=(6,1))
np.random.seed(42)
a = np.random.randn(1,6)
ax.pcolormesh(a)
plt.gca().invert_yaxis()
ax.xaxis.tick_top()
ax.set(yticklabels=[])
ax.tick_params(left=False, length=5)
# get list of x tick objects
xtick_objects = ax.xaxis.get_major_ticks()
xtick_objects[0].label1.set_horizontalalignment('left') # left align first tick
xtick_objects[-1].label1.set_horizontalalignment('right') # right align last tick
ax.set_xticks([0, 3, 6])
plt.tight_layout()
# plt.savefig(f'plot.png', dpi=300, bbox_inches='tight', pad_inches=0.0
plt.show()
which does not seem to change the alignment.
I am working some meteorological data to plot contour lines on a basemap. The full working example code I have done earlier is here How to remove/omit smaller contour lines using matplotlib. All works fine and I don’t complain with the contour plot. However there is a special case that I have to hide all contour lines over a specific region (irregular lat & lon) on a Basemap.
The only possible solution I can think of is to draw a ploygon lines over a desired region and fill with the color of same as Basemap. After lot of search I found this link How to draw rectangles on a Basemap (code below)
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
def draw_screen_poly( lats, lons, m):
x, y = m( lons, lats )
xy = zip(x,y)
poly = Polygon( xy, facecolor='red', alpha=0.4 )
plt.gca().add_patch(poly)
lats = [ -30, 30, 30, -30 ]
lons = [ -50, -50, 50, 50 ]
m = Basemap(projection='sinu',lon_0=0)
m.drawcoastlines()
m.drawmapboundary()
draw_screen_poly( lats, lons, m )
plt.show()
It seems to work partially. However, I want to draw a region which is irregular.
Any solution is appreciated.
Edit: 1
I have understood where the problem is. It seems that any colour (facecolor) filled within the polygon region does not make it hide anything below. Always it is transparent only, irrespective of alpha value used or not. To illustrate the problem, I have cropped the image which has all three regions ie. contour, basemap region and polygon region. Polygon region is filled with red colour but as you can see, the contour lines are always visible. The particular line I have used in the above code is :-
poly = Polygon(xy, facecolor='red', edgecolor='b')
Therefore the problem is not with the code above. It seem the problem with the polygon fill. But still no solution for this issue. The resulting image (cropped image) is below (See my 2nd edit below the attached image):-
Edit 2:
Taking clue from this http://matplotlib.1069221.n5.nabble.com/Clipping-a-plot-inside-a-polygon-td41950.html which has the similar requirement of mine, I am able to remove some the data. However, the removed data is only from outside of polygon region instead of within. Here is the code I have taken clue from:-
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import RegularPolygon
data = np.arange(100).reshape(10, 10)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.contourf(data)
poly = RegularPolygon([ 0.5, 0.5], 6, 0.4, fc='none',
ec='k', transform=ax.transAxes)
for artist in ax.get_children():
artist.set_clip_path(poly)
Now my question is that what command is used for removing the data within the polygon region?
Didn't noticed there was a claim on this so I might just give the solution already proposed here. You can tinker with the zorder to hide stuff behind your polygon:
import matplotlib
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
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)
# Create a simple contour plot with labels using default colors. The
# inline argument to clabel will control whether the labels are draw
# over the line segments of the contour, removing the lines beneath
# the label
fig = plt.figure()
ax = fig.add_subplot(111)
CS = plt.contour(X, Y, Z,zorder=3)
plt.clabel(CS, inline=1, fontsize=10)
plt.title('Simplest default with labels')
rect1 = matplotlib.patches.Rectangle((0,0), 2, 1, color='white',zorder=5)
ax.add_patch(rect1)
plt.show()
, the result is:
I'm trying to make a plot with one panel up top (colspan = 2) and two plots below, with a controlled amount of space between them. I'd like the bounds of the plots to be in alignment. Here's what I'm starting with:
import cartopy
from matplotlib import pyplot
from matplotlib.gridspec import GridSpec
gs = GridSpec(2, 2, height_ratios=[2, 1], hspace=0, wspace=0)
ax0 = pyplot.subplot(gs[0, :], projection=cartopy.crs.LambertConformal())
ax0.add_feature(cartopy.feature.COASTLINE)
ax0.set_extent([-120, -75, 20, 52], cartopy.crs.Geodetic())
ax1 = pyplot.subplot(gs[1, 0], projection=cartopy.crs.LambertConformal())
ax1.add_feature(cartopy.feature.COASTLINE)
ax1.set_extent([-90, -75, 20, 30], cartopy.crs.Geodetic())
ax2 = pyplot.subplot(gs[1, 1], projection=cartopy.crs.LambertConformal())
ax2.add_feature(cartopy.feature.COASTLINE)
ax2.set_extent([-90, -75, 20, 30], cartopy.crs.Geodetic())
pyplot.show()
First problem is that the wspace=0 parameter doesn't take.
Second problem is (at least this is my guess on how to proceed) calculating a height ratio that will make the width of the upper subplot equal the combined width of the lower subplots (plus any wspace).
I want to place a line at one level (e.g., 0) in the colorbar of a contourf plot with matplotlib.
With the following code, I can do it but not all the properties of the contour lines are conserved (i.e., the color and width of the line are correct, but I can't have it dotted in the colorbar).
Any idea of how to have a dotted line corresponding to a desired level in the colorbar?
import matplotlib.pyplot as plt
import numpy
x=y=range(10)
z=numpy.random.normal(0,2,size=(10,10))
surfplot=plt.contourf(x,y,z, cmap=plt.cm.binary_r)
cont=plt.contour(surfplot, levels=[0], colors='r', linewidths=5, linestyles=':')
cbar=plt.colorbar(surfplot)
cbar.add_lines(cont)
plt.show()
You could plot a horizontal line on your color bar directly.
cax = cbar.ax
cax.hlines(0.5, 0, 1, colors = 'r', linewidth = 10, linestyles = ':')
You'll have to calculate the y-coordinate of the line based on the data and the coloramp.
Colorbar.add_lines() currently only retains the colors and line widths.
However, you can update the line style of the new LineCollection after adding it:
import matplotlib.pyplot as plt
import numpy
plt.style.use('classic') # to match the look in the question
x = y = range(10)
z = numpy.random.normal(0, 2, size=(10, 10))
surfplot = plt.contourf(x, y, z, cmap=plt.cm.binary_r)
cont = plt.contour(surfplot, levels=[0], colors='r', linewidths=5, linestyles=':')
cbar = plt.colorbar(surfplot)
cbar.add_lines(cont)
cbar.lines[-1].set_linestyles(cont.linestyles) # adopt the contour's line styles
plt.show()
I am plotting a histogram for some data points with bar heights being the percentage of that bin from the whole data:
x = normal(size=1000)
hist, bins = np.histogram(x, bins=20)
plt.bar(bins[:-1], hist.astype(np.float32) / hist.sum(), width=(bins[1]-bins[0]), alpha=0.6)
The result is:
I would like all bars that sum up to be 50% of the data to be in a different color, for example:
(I selected the colored bars without actually checking whether their sum adds to 50%)
Any suggestions how to accomplish this?
Here is how you can plot the first half of the bins with a different color, this looks like your mock, but I am not sure it complies to %50 of the data (it is not clear to me what do you mean by that).
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
fig = plt.figure()
ax = fig.add_subplot(111)
# the histogram of the data
n, bins, patches = ax.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
# now that we found the index we color all the beans smaller than middle index
for p in patches[:len(bins)/2]:
p.set_facecolor('red')
# hist uses np.histogram under the hood to create 'n' and 'bins'.
# np.histogram returns the bin edges, so there will be 50 probability
# density values in n, 51 bin edges in bins and 50 patches. To get
# everything lined up, we'll compute the bin centers
bincenters = 0.5*(bins[1:]+bins[:-1])
# add a 'best fit' line for the normal PDF
y = mlab.normpdf( bincenters, mu, sigma)
l = ax.plot(bincenters, y, 'r--', linewidth=1)
ax.set_xlabel('Smarts')
ax.set_ylabel('Probability')
ax.set_xlim(40, 160)
ax.set_ylim(0, 0.03)
ax.grid(True)
plt.show()
And the output is:
update
The key method you want to look at is patch.set_set_facecolor. You have to understand that almost everything you plot inside the axes object is a Patch, and as such it has this method, here is another example, I arbitrary choose the first 3 bars to have another color, you can choose based on what ever you decide:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
## the data
N = 5
menMeans = [18, 35, 30, 35, 27]
## necessary variables
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars
## the bars
rects1 = ax.bar(ind, menMeans, width,
color='black',
error_kw=dict(elinewidth=2,ecolor='red'))
for patch in rects1.patches[:3]:
patch.set_facecolor('red')
ax.set_xlim(-width,len(ind)+width)
ax.set_ylim(0,45)
ax.set_ylabel('Scores')
xTickMarks = ['Group'+str(i) for i in range(1,6)]
ax.set_xticks(ind)
xtickNames = ax.set_xticklabels(xTickMarks)
plt.setp(xtickNames, rotation=45, fontsize=10)
plt.show()