I am quite the novice at matplotlib, so bear with me. I have the code below that plots a cylindrical equidistant grid of precipitation. I set the 'extents' limits that finally aligned my basemap with the data. Now, it appears to have "broken" my plt.text capability as I can no longer see the text 'Precipitation Rate (mm/hour)'. Thanks for any help.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from pylab import *
import pickle
from mpl_toolkits.basemap import Basemap
fp = open('uneven_rgb.pkl', 'rb')
uneven_rgb = pickle.load(fp)
fp.close()
num_lon = 1440
num_lat = 400
precipfile = "/Users/bolvin/3B43.20111001.7.HDF_precip.bin"
fileobj = open(precipfile, mode='rb') # Open file as read only binary
data = np.fromfile (fileobj, dtype ='f')
datat = np.reshape(data, (num_lon, num_lat), order = 'FORTRAN')
datam = datat * 24.0
my_cmap = matplotlib.colors.LinearSegmentedColormap('my_colormap',uneven_rgb)
plt.figure(figsize = (20,10))
mapproj = Basemap(projection = 'cyl', llcrnrlat=-50.0, llcrnrlon=0.0, urcrnrlat=50.0,urcrnrlon=360.0)
mapproj.drawcoastlines()
mapproj.drawcountries()
mapproj.drawparallels(np.array([-30.0, 0.0, 30.0]), labels=[0,0,0,0])
mapproj.drawmeridians(np.array([90.0, 180.0, 270.0]), labels=[0,0,0,0])
myplot = plt.imshow(datam.T, interpolation = 'nearest', cmap = my_cmap, vmin = 0.0, vmax = 20.0, extent = (0.0, 360.0, -50.0, 50.0))
plt.title('October 2011 3B43 Precipitation', fontsize = 36, y = 1.03)
plt.text(1.0, 435.0, 'Precipitation Rate (mm/hour)', size = 20)
cbar = plt.colorbar(myplot, orientation='horizontal', shrink = 0.5, pad = 0.03)
cbar.ax.tick_params(labelsize=20)
plt.gca().axes.get_xaxis().set_visible(False)
plt.gca().axes.get_yaxis().set_visible(False)
plt.show()
fileobj.close()
plt.text gets as first argument the x and y coordinates on which your text will be put.
As you transformed your imshow plot into the bordes 0-360 for x and -50 to 50 for y, y=435 is not in the plot anymore.
You can check your limits with plt.gca().get_xlim().
You have to move it somewhere in your limits.
Your defining the units you are plotting with this text, right? So the natural place for this would be the label of the colorbar:
cbar = plt.colorbar(myplot, orientation='horizontal', shrink = 0.5,
pad = 0.03, label='Precipitation Rate (mm/hour)')
Related
I am trying to fit a gilbrat PDF to a dataset (that I have in form of a list). I want to show the data in a histogram with a logarithmic x-scale and add the fitted curve. However, the curve seems too flat compared to the histogram, like in this picture: I tried to scale the pdf according to Fitting a Gaussian to a histogram with MatPlotLib and Numpy - wrong Y-scaling? , but the problem remains.
Here is a code example with randomly created data:
import scipy.stats as st
import numpy as np
import matplotlib.pyplot as plt
#create random dataset
data = st.gilbrat.rvs(scale = 10, size = 100).tolist()
param = st.gilbrat.fit(data)
x = np.linspace(min(data),max(data),len(data))
pdf = st.gilbrat.pdf(x, param[0], param[1])
plt.figure()
logbins = np.logspace(np.log10(np.min(data)),np.log10(np.max(data)),20)
result = plt.hist(data, bins=logbins, edgecolor="black", alpha = 0.5, label="data")
dx = result[1][1] - result[1][0]
plt.plot(x,pdf * (len(data)*dx), label="fit")
plt.xscale('log')
plt.xlabel("x")
plt.ylabel("Number of occurence")
plt.legend()
Am I missing something?
As your bins aren't equally spaced, the histogram isn't similar to a scaled version of the pdf. The bins at the right represent a much wider x-range than the ones at the left.
To predict the heights of the rectangles given the pdf, each bin region needs a different scaling factor, depending on the width of that bin.
The following code rescales each region independently, resulting in a discontinuously scaled pdf.
import scipy.stats as st
import numpy as np
import matplotlib.pyplot as plt
# create random dataset
np.random.seed(1)
data = st.gilbrat.rvs(scale=10, size=100)
param = st.gilbrat.fit(data)
x = np.logspace(np.log10(data.min()), np.log10(data.max()), 500)
pdf = st.gilbrat.pdf(x, param[0], param[1])
plt.figure()
logbins = np.logspace(np.log10(data.min()), np.log10(data.max()), 20)
heights, bins, rectangles = plt.hist(data, bins=logbins, edgecolor="black", alpha=0.5, label="data")
for b0, b1 in zip(bins[:-1], bins[1:]):
dx = b1 - b0
x_bin = np.logspace(np.log10(b0), np.log10(b1), 100)
pdf_bin = st.gilbrat.pdf(x_bin, param[0], param[1])
plt.plot(x_bin, pdf_bin * (len(data) * dx), color='crimson',
label="expected bin height" if b0 == bins[0] else None)
plt.xscale('log')
plt.xlabel("x")
plt.ylabel("Number of occurence")
plt.legend()
plt.tight_layout()
plt.show()
Here is another take, smoothing out the scaling of the pdf to any log-scale histogram. The dx is different at each x-position, in contrast to the histogram with linearly spaced bins.
import scipy.stats as st
import numpy as np
import matplotlib.pyplot as plt
# create random dataset
np.random.seed(1)
data = st.gilbrat.rvs(scale=10, size=100)
param = st.gilbrat.fit(data)
x = np.logspace(np.log10(data.min()), np.log10(data.max()), 500)
pdf = st.gilbrat.pdf(x, param[0], param[1])
plt.figure()
logbins = np.logspace(np.log10(data.min()), np.log10(data.max()), 20)
heights, bins, rectangles = plt.hist(data, bins=logbins, edgecolor="black", alpha=0.5, label="data")
dx_array = np.logspace(np.log10(bins[1] - bins[0]), np.log10(bins[-1] - bins[-2]), len(x))
plt.plot(x, pdf * len(data) * dx_array, color='crimson', label="pdf rescaled like histogram")
plt.xscale('log')
plt.xlabel("x")
plt.ylabel("Number of occurence")
plt.legend()
plt.tight_layout()
plt.show()
I'd like to invert the bars in this diagram when they are below 1, not when they are negative. Additionally I'd like to have even spacing between the ticks/steps on the y-axis
Here is my current code
import matplotlib.pyplot as plt
import numpy as np
labels = ['A','B','C']
Vals1 = [28.3232, 12.232, 9.6132]
Vals2 = [0.00456, 17.868, 13.453]
Vals3 = [0.0032, 1.234, 0.08214]
x = np.arange(len(labels))
width = 0.2
fig, ax = plt.subplots()
rects1 = ax.bar(x - width, Vals1, width, label='V1')
rects2 = ax.bar(x, Vals2, width, label='V2')
rects3 = ax.bar(x + width, Vals3, width, label='V3')
ax.set_xticks(x)
ax.set_xticklabels(labels)
plt.xticks(rotation=90)
ax.legend()
yScale = [0.0019531,0.0039063,0.0078125,0.015625,0.03125,0.0625,0.125,0.25,0.5,1,2,4,8,16,32]
ax.set_yticks(yScale)
plt.show()
I believe I've stumbled upon the answer, here it is for anyone else looking for the solution. Add the argument bottom='1' to ax.bar instantiation, and then flip the values in the array.
for i in range(len(Vals1)):
Vals1[i] = (1 - Vals1[i]) * -1
As you mentioned, the key is the bottom param of Axes.bar:
bottom (default: 0): The y coordinate(s) of the bars bases.
But beyond that, you can simplify your plotting code using pandas:
Put your data into a DataFrame:
import pandas as pd
df = pd.DataFrame({'V1': Vals1, 'V2': Vals2, 'V3': Vals3}, index=labels)
# V1 V2 V3
# A 28.3232 0.00456 0.00320
# B 12.2320 17.86800 1.23400
# C 9.6132 13.45300 0.08214
Then use DataFrame.sub to subtract the offset and DataFrame.plot.bar with the bottom param:
bottom = 1
ax = df.sub(bottom).plot.bar(bottom=bottom)
Does anyone know how to show the labels of the minor ticks on a logarithmic scale with Python/Matplotlib?
You can use plt.tick_params(axis='y', which='minor') to set the minor ticks on and format them with the matplotlib.ticker FormatStrFormatter. For example,
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
x = np.linspace(0,4,1000)
y = np.exp(x)
plt.plot(x, y)
ax = plt.gca()
ax.set_yscale('log')
plt.tick_params(axis='y', which='minor')
ax.yaxis.set_minor_formatter(FormatStrFormatter("%.1f"))
plt.show()
One option is to use matplotlib.ticker.LogLocator
import numpy
import pylab
import matplotlib.pyplot
import matplotlib.ticker
## setup styles
from matplotlib import rc
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Times-Roman']})
rc('text', usetex = True)
matplotlib.rcParams['text.latex.preamble'] = [r"\usepackage{amsmath}"]
## make figure
figure, ax = matplotlib.pyplot.subplots(1, sharex = True, squeeze = True)
x = numpy.linspace(0.0, 20.0, 1000)
y = numpy.exp(x)
ax.plot(x, y)
ax.set_yscale('log')
## set y ticks
y_major = matplotlib.ticker.LogLocator(base = 10.0, numticks = 5)
ax.yaxis.set_major_locator(y_major)
y_minor = matplotlib.ticker.LogLocator(base = 10.0, subs = numpy.arange(1.0, 10.0) * 0.1, numticks = 10)
ax.yaxis.set_minor_locator(y_minor)
ax.yaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())
## save figure
pylab.tight_layout()
pylab.savefig('./test.png', dpi = 200)
you would get
the only thing you need to manually adjust is the numticks input for both major and minor ticks, they both have to be a fraction of total possible number of major ticks.
If you want to insert a small plot inside a bigger one you can use Axes, like here.
The problem is that I don't know how to do the same inside a subplot.
I have several subplots and I would like to plot a small plot inside each subplot.
The example code would be something like this:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
for i in range(4):
ax = fig.add_subplot(2,2,i)
ax.plot(np.arange(11),np.arange(11),'b')
#b = ax.axes([0.7,0.7,0.2,0.2])
#it gives an error, AxesSubplot is not callable
#b = plt.axes([0.7,0.7,0.2,0.2])
#plt.plot(np.arange(3),np.arange(3)+11,'g')
#it plots the small plot in the selected position of the whole figure, not inside the subplot
Any ideas?
I wrote a function very similar to plt.axes. You could use it for plotting yours sub-subplots. There is an example...
import matplotlib.pyplot as plt
import numpy as np
#def add_subplot_axes(ax,rect,facecolor='w'): # matplotlib 2.0+
def add_subplot_axes(ax,rect,axisbg='w'):
fig = plt.gcf()
box = ax.get_position()
width = box.width
height = box.height
inax_position = ax.transAxes.transform(rect[0:2])
transFigure = fig.transFigure.inverted()
infig_position = transFigure.transform(inax_position)
x = infig_position[0]
y = infig_position[1]
width *= rect[2]
height *= rect[3] # <= Typo was here
#subax = fig.add_axes([x,y,width,height],facecolor=facecolor) # matplotlib 2.0+
subax = fig.add_axes([x,y,width,height],axisbg=axisbg)
x_labelsize = subax.get_xticklabels()[0].get_size()
y_labelsize = subax.get_yticklabels()[0].get_size()
x_labelsize *= rect[2]**0.5
y_labelsize *= rect[3]**0.5
subax.xaxis.set_tick_params(labelsize=x_labelsize)
subax.yaxis.set_tick_params(labelsize=y_labelsize)
return subax
def example1():
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111)
rect = [0.2,0.2,0.7,0.7]
ax1 = add_subplot_axes(ax,rect)
ax2 = add_subplot_axes(ax1,rect)
ax3 = add_subplot_axes(ax2,rect)
plt.show()
def example2():
fig = plt.figure(figsize=(10,10))
axes = []
subpos = [0.2,0.6,0.3,0.3]
x = np.linspace(-np.pi,np.pi)
for i in range(4):
axes.append(fig.add_subplot(2,2,i))
for axis in axes:
axis.set_xlim(-np.pi,np.pi)
axis.set_ylim(-1,3)
axis.plot(x,np.sin(x))
subax1 = add_subplot_axes(axis,subpos)
subax2 = add_subplot_axes(subax1,subpos)
subax1.plot(x,np.sin(x))
subax2.plot(x,np.sin(x))
if __name__ == '__main__':
example2()
plt.show()
You can now do this with matplotlibs inset_axes method (see docs):
from mpl_toolkits.axes_grid.inset_locator import inset_axes
inset_axes = inset_axes(parent_axes,
width="30%", # width = 30% of parent_bbox
height=1., # height : 1 inch
loc=3)
Update: As Kuti pointed out, for matplotlib version 2.1 or above, you should change the import statement to:
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
There is now also a full example showing all different options available.
From matplotlib 3.0 on, you can use matplotlib.axes.Axes.inset_axes:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(2,2)
for ax in axes.flat:
ax.plot(np.arange(11),np.arange(11))
ins = ax.inset_axes([0.7,0.7,0.2,0.2])
plt.show()
The difference to mpl_toolkits.axes_grid.inset_locator.inset_axes mentionned in #jrieke's answer is that this is a lot easier to use (no extra imports etc.), but has the drawback of being slightly less flexible (no argument for padding or corner locations).
source: https://matplotlib.org/examples/pylab_examples/axes_demo.html
from mpl_toolkits.axes_grid.inset_locator import inset_axes
import matplotlib.pyplot as plt
import numpy as np
# create some data to use for the plot
dt = 0.001
t = np.arange(0.0, 10.0, dt)
r = np.exp(-t[:1000]/0.05) # impulse response
x = np.random.randn(len(t))
s = np.convolve(x, r)[:len(x)]*dt # colored noise
fig = plt.figure(figsize=(9, 4),facecolor='white')
ax = fig.add_subplot(121)
# the main axes is subplot(111) by default
plt.plot(t, s)
plt.axis([0, 1, 1.1*np.amin(s), 2*np.amax(s)])
plt.xlabel('time (s)')
plt.ylabel('current (nA)')
plt.title('Subplot 1: \n Gaussian colored noise')
# this is an inset axes over the main axes
inset_axes = inset_axes(ax,
width="50%", # width = 30% of parent_bbox
height=1.0, # height : 1 inch
loc=1)
n, bins, patches = plt.hist(s, 400, normed=1)
#plt.title('Probability')
plt.xticks([])
plt.yticks([])
ax = fig.add_subplot(122)
# the main axes is subplot(111) by default
plt.plot(t, s)
plt.axis([0, 1, 1.1*np.amin(s), 2*np.amax(s)])
plt.xlabel('time (s)')
plt.ylabel('current (nA)')
plt.title('Subplot 2: \n Gaussian colored noise')
plt.tight_layout()
plt.show()
I am trying to plot some histogrammed data on a polar axis but it wont seem to work properly. An example is below, I use the custom projection found How to make the angles in a matplotlib polar plot go clockwise with 0° at the top? it works for a scatter plot so I think my problem is with the histogram function. This has been driving me nuts all day, does anyone know what I am doing wrong...........
import random
import numpy as np
import matplotlib.pyplot as plt
baz = np.zeros((20))
freq = np.zeros((20))
pwr = np.zeros((20))
for x in range(20):
baz[x] = random.randint(20,25)*10
freq[x] = random.randint(1,10)*10
pwr[x] = random.randint(-10,-1)*10
baz = baz*np.pi/180.
abins = np.linspace(0,2*np.pi,360) # 0 to 360 in steps of 360/N.
sbins = np.linspace(1, 100)
H, xedges, yedges = np.histogram2d(baz, freq, bins=(abins,sbins), weights=pwr)
plt.figure(figsize=(14,14))
plt.subplot(1, 1, 1, projection='northpolar')
#plt.scatter(baz, freq)
plt.pcolormesh(H)
plt.show()
Your code works if you explicitly pass a mgrid (with similar characteristics than your a bins and sbins) to the pcolormesh command.
Below is an example inspired by your code:
import matplotlib.pyplot as plt
import numpy as np
#Generate the data
size = 200
baz = 10*np.random.randint(20, 25, size)*np.pi/180.
freq = 10*np.random.randint(1, 10, size)
pwr = 10*np.random.randint(-10, -1, size)
abins = np.linspace(0, 2*np.pi, 360) # 0 to 360 in steps of 360/N.
sbins = np.linspace(1, 100, 50)
H, xedges, yedges = np.histogram2d(baz, freq, bins=(abins,sbins), weights=pwr)
#Grid to plot your data on using pcolormesh
theta, r = np.mgrid[0:2*np.pi:360j, 1:100:50j]
fig, ax = plt.subplots(figsize=(14,14), subplot_kw=dict(projection='northpolar'))
ax.pcolormesh(theta, r, H)
ax.set_yticklabels([]) #remove yticklabels
plt.show()