The gallery of matplotlib has a 2D scatter plot with two adjacent histograms for the marginal distribution of x and y values at the top and right, respectively. I want to modify that to show the histogram of y values on the left (instead of the right) but also oriented towards the scatter plot.
All I managed so far was to merely move it from the right to the left (see below), but not re-orientate it towards the scatter plot. How can I achieve that?
Here is my code:
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
# some random data
x = np.random.randn(1000)
y = np.random.randn(1000)
def scatter_hist(x, y, ax, ax_histx, ax_histy):
# no labels
ax_histx.tick_params(axis="x", labelbottom=False)
ax_histy.tick_params(axis="y", labelleft=True,labelright=False)
ax.tick_params(axis="y", left=False,labelleft=False,right=True,labelright=True)
# the scatter plot:
ax.scatter(x, y)
# now determine nice limits by hand:
binwidth = 0.25
xymax = max(np.max(np.abs(x)), np.max(np.abs(y)))
lim = (int(xymax/binwidth) + 1) * binwidth
bins = np.arange(-lim, lim + binwidth, binwidth)
ax_histx.hist(x, bins=bins)
ax_histy.hist(y, bins=bins, orientation='horizontal')
# definitions for the axes
left, width = 0.3, 0.65
bottom, height = 0.1, 0.65
spacing = 0.005
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom + height + spacing, width, 0.2]
rect_histy = [left-spacing-0.2, bottom, 0.2, height]
# start with a square Figure
fig = plt.figure(figsize=(8, 8))
ax = fig.add_axes(rect_scatter)
ax_histx = fig.add_axes(rect_histx, sharex=ax)
ax_histy = fig.add_axes(rect_histy, sharey=ax)
# use the previously defined function
scatter_hist(x, y, ax, ax_histx, ax_histy)
plt.show()
and here the result:
This can be achieved by setting the y-axis limit in the opposite direction.
ax_histy.hist(y, bins=bins, orientation='horizontal')
ax_histy.set_xlim(100,0) # add
Related
i have this several plots and want to correct the title name location. I want to make the Vertical Acceleration (y) on the middle left vertically and the Flare Time (x) on the middle bot horizontally also the Test Title on middle top. Basically I want to be able to move the label location.
Below is the code
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
x = ip.RESULTS
y = Vert
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)
idx = z.argsort()
x, y, z = x[idx], y[idx], z[idx]
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
# start with a rectangular Figure
plt.figure(1, figsize=(8, 8))
axScatter = plt.axes(rect_scatter)
#plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
#plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
axHistx = plt.axes(rect_histx)
axHisty = plt.axes(rect_histy)
# no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
# the scatter plot:
axScatter.scatter(x, y, c=z, s=50, edgecolor='')
# now determine nice limits by hand:
binwidth = 1
xymax = np.max([np.max(np.fabs(x)), np.max(np.fabs(y))])
lim = (int(xymax/binwidth) + 1) * binwidth
bins = np.arange(-lim, lim + binwidth, binwidth)
axHistx.hist(x)
axHisty.hist(y, orientation='horizontal')
plt.title('test title', fontsize=20)
axHisty.set_xlabel("Vertical Acceleration")
axHistx.set_xlabel("Flare Time")
and the results look like this. Any help would be appreciated
You have three Axes objects (plot rectangles to say it sloppy) in your graph: axScatter is your main chart in the bottom left. axHisty is the histogram on the right and axHistx is the histogram on the top. Your axis titles belong on the y- and x-axis of axScatter. So just do:
axScatter.set_ylabel('Vertical Acceleration')
axScatter.set_xlabel('Flare Time')
Based on your vague question I have no idea where you want the "test title", but just figure out which Axes object is best and give it an xlabel, ylabel or title.
Right now I can create a radarchart as follows. Note that I made it a function just so that I can simply insert the function into my larger scatterplot more cleanly.
Radar Chart
def radarChart(PlayerLastName):
playerdf = dg.loc[dg['Player Name'] == name].index.tolist()[0]
#print(playerdf)
labels=np.array(['SOG', 'SH', 'G', 'A'])
stats=dg.loc[playerdf,labels].values
#print(stats)
# Set the angle of polar axis.
# And here we need to use the np.concatenate to draw a closed plot in radar chart.
angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)
# close the plot
stats=np.concatenate((stats,[stats[0]]))
angles=np.concatenate((angles,[angles[0]]))
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
ax.plot(angles, stats, 'o-', linewidth=1)
ax.fill(angles, stats, alpha=0.3)
ax.set_thetagrids(angles * 180/np.pi, labels)
#plt.title(PlayerLastName + ' vs. ' + namegame)
ax.grid(True)
return
I then want to put this figure in the bottom right of my scatter plot I have elsewhere. This other article does not provide me with any way to do this since my plot is circular. Any help would be great!
When I call radarChart('someones name') I get
I would really like to not have to save it as an image first and then put it in the plot.
I am not sure, what your desired output is. You should always provide a Minimal, Complete, and Verifiable example. Apart from this, I don't know, why a polar plot would be different from any other plot to create an inset:
import matplotlib.pyplot as plt
import numpy as np
#function for the polar plot
def radarChart(Player = "SOG", left = .3, bottom = .6, width = .2, height = .2):
#labels and positions
labels = np.array(['SOG', 'SH', 'G', 'A'])
angles = np.linspace(0, 360, len(labels), endpoint = False)
#inset position
ax = plt.axes([left, bottom, width, height], facecolor = "lightblue", polar = True)
#label polar chart
ax.set_thetagrids(angles, labels)
#polar chart title
plt.title(Player, loc = "left")
return ax
#main figure
x = np.linspace (-3, 1, 1000)
y = 2 * np.exp(3 - x) - 1
plt.plot(x, y)
plt.xlabel("x values")
plt.ylabel("y values")
plt.title("figure with polar insets")
#inset 1
ax = radarChart(Player = "A")
plt.scatter(x[::50], y[::50])
#inset 2
ax = radarChart(left = .6, bottom = .4, width = .2, height = .2)
plt.plot(x, y)
plt.show()
Sample output:
I am trying to plot both a circular histogram and a vector (overlapping) on the same polar plot, but cannot get the vector to show up.
Basically, my data set consists of the times at which unitary events occur during a repeating cycle. This data is in the array "all_phases", which is just a list of degree values for each of these events. I want to show (1) the overall distribution of events w/ a circular histogram (bins corresponding to degree ranges) and (2) a vector sum as a measure of the coherence of all of these values (treating each event as a unit vector).
I can plot either one of these things individually on the subplot titled "histo", but when I try to plot both, only the histogram shows up. I have tried playing with the z-indexes of both objects to no use. The code is:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import math
array = np.array
all_phases = [array([-38.24240218]), array([-120.51570738]), array([-23.70224663]),
array([114.9540152]), array([ 2.94523445]), array([-2.16112692]), array([-18.72274284]),
array([13.2292216]), array([-95.5659992]), array([15.69046269]), array([ 51.12022047]),
array([-89.10567276]), array([ 41.77283949]), array([-9.92584921]), array([-7.59680678]),
array([166.71824996]), array([-178.94642752]), array([-23.75819463]), array([38.69481261]),
array([-52.26651244]), array([-57.40976514]), array([33.68226762]), array([-122.1818295]),
array([ 10.17007425]), array([-38.03726335]),array([44.9504975]), array([ 134.63972923]),
array([ 63.02516932]),array([-106.54049292]), array([-25.6527599])]
number_bins = 60
bin_size = 360/number_bins
cluster_num = 1
counts, theta = np.histogram(all_phases, np.arange(-180, 180 + bin_size, bin_size), density=True)
theta = theta[:-1]+ bin_size/2.
theta = theta * np.pi / 180
a_deg = map(lambda x: np.ndarray.item(x), all_phases)
a_rad = map(lambda x: math.radians(x), a_deg)
a_cos = map(lambda x: math.cos(x), a_rad)
a_sin = map(lambda x: math.sin(x), a_rad)
uv_x = sum(a_cos)/len(a_cos)
uv_y = sum(a_sin)/len(a_sin)
uv_radius = np.sqrt((uv_x*uv_x) + (uv_y*uv_y))
uv_phase = np.angle(complex(uv_x, uv_y))
"""
plot histogram and vector sum
"""
fig = plt.figure()
ax1 = fig.add_axes([0.1, 0.16, 0.05, 0.56])
histo = fig.add_subplot(111, polar=True)
histo.yaxis.set_ticks(())
histo.arrow(0,0,0.11, 1, head_width=.01, zorder=2)
plt.suptitle("Phase distribution for Neuron #" + str(cluster_num), fontsize=15, y=.94)
plt.subplots_adjust(bottom=0.12, right=0.95, top=0.78, wspace=0.4)
width = (2*np.pi) / number_bins
bars = histo.bar(theta, counts, width = width, bottom=0.002)
for r, bar in zip(counts, bars):
bar.set_facecolor(plt.cm.jet(r / max(counts)))
bar.set_alpha(0.7)
bar.set_zorder(1)
norm = matplotlib.colors.Normalize(vmin (counts.min())*len(all_phases)*bin_size, vmax=(counts.max())*len(all_phases)*bin_size)
cb1 = matplotlib.colorbar.ColorbarBase(ax1, cmap=plt.cm.jet,
orientation='vertical', norm=norm, alpha=0.4,
ticks=np.arange(0, (counts.max())*len(all_phases)*bin_size)+1, )
cb1.ax.tick_params(labelsize=9)
cb1.solids.set_rasterized(True)
cb1.set_label("# spikes")
cb1.ax.yaxis.set_label_position('left')
plt.show()
cluster_num = cluster_num + 1
vs_radius and vs_phase are the parameters for the vector sum arrow I want to put on the polar plot, which I end up calling w/ histo.arrow().
My suspicion is that it might have something to do with trying to put two things on a subplot object?
Any help or thoughts would be very much appreciated!!
The problem is that the FancyArrow that is used by Axes.arrow() does not play well with polar plots.
Instead, you could use the annotate() function to draw a simple arrow that works better in the case of polar plots.
for example:
# Compute pie slices
N = 20
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
radii = 10 * np.random.rand(N)
width = np.pi / 4 * np.random.rand(N)
ax = plt.subplot(111, projection='polar')
bars = ax.bar(theta, radii, width=width, bottom=0.0)
# Use custom colors and opacity
for r, bar in zip(radii, bars):
bar.set_facecolor(plt.cm.viridis(r / 10.))
bar.set_alpha(0.5)
v_angle = 0.275*np.pi
v_length = 4
ax.annotate('',xy=(v_angle, v_length), xytext=(v_angle,0), xycoords='data', arrowprops=dict(width=5, color='red'))
plt.show()
As a general rule, when you deal with polar plot, you have to work just as if you were working with a linear plot. That is to say, you shouldn't try to draw your arrow from (0,0) but rather from (uv_phase, 0)
fig, ax = plt.subplots()
bars = ax.bar(theta, radii, width=width, bottom=0.0)
# Use custom colors and opacity
for r, bar in zip(radii, bars):
bar.set_facecolor(plt.cm.viridis(r / 10.))
bar.set_alpha(0.5)
ax.annotate('',xy=(v_angle, v_length), xytext=(v_angle,0), xycoords='data', arrowprops=dict(width=5, color='red'))
I'm using matplotlib to plot some data that I wish to annotate with arrows (distance markers). These arrows should be offset by several points so as not to overlap with the plotted data:
import matplotlib.pyplot as plt
import matplotlib.transforms as transforms
fig, ax = plt.subplots()
x = [0, 1]
y = [0, 0]
# Plot horizontal line
ax.plot(x, y)
dy = 5/72
offset = transforms.ScaledTranslation(0, dy, ax.get_figure().dpi_scale_trans)
verttrans = ax.transData+offset
# Plot horizontal line 5 points above (works!)
ax.plot(x, y, transform = verttrans)
# Draw arrow 5 points above line (doesn't work--not vertically translated)
ax.annotate("", (0,0), (1,0),
size = 10,
transform=verttrans,
arrowprops = dict(arrowstyle = '<|-|>'))
plt.show()
Is there any way to make lines drawn by ax.annotate() be offset by X points? I wish to use absolute coordinates (e.g., points or inches) instead of data coordinates because the axis limits are prone to changing.
Thanks!
The following code does what I desired. It uses ax.transData and figure.get_dpi():
import matplotlib.pyplot as plt
import matplotlib.transforms as transforms
fig, ax = plt.subplots()
x = [0, 1]
y = [0, 0]
ax.plot(x, y)
dy = 5/72
i = 1 # 0 for dx
tmp = ax.transData.transform([(0,0), (1,1)])
tmp = tmp[1,i] - tmp[0,i] # 1 unit in display coords
tmp = 1/tmp # 1 pixel in display coords
tmp = tmp*dy*ax.get_figure().get_dpi() # shift pixels in display coords
ax.plot(x, y)
ax.annotate("", [0,tmp], [1,tmp],
size = 10,
arrowprops = dict(arrowstyle = '<|-|>'))
plt.show()
What's your expected output? If you're just looking to move the arrow you're drawing vertically, the API for annotate is
annotate(s, xy, xytext=None, ...)
so you can draw something like
ax.annotate("", (0,0.01), (1,0.01),
size = 10,
arrowprops = dict(arrowstyle = '<|-|>'))
which is moved up by 0.01 in data coordinates in the y direction. You can also specify coordinates as a fraction of the total figure size in annotate (see doc). Is that what you wanted?
I am pretty new to python and want to plot a dataset using a histogram and a heatmap below. However, I am a bit confused about
How to put a title above both plots and
How to insert some text into bots plots
How to reference the upper and the lower plot
For my first task I used the title instruction, which inserted a caption in between both plots instead of putting it above both plots
For my second task I used the figtext instruction. However, I could not see the text anywhere in the plot. I played a bit with the x, y and fontsize parameters without any success.
Here is my code:
def drawHeatmap(xDim, yDim, plot, threshold, verbose):
global heatmapList
stableCells = 0
print("\n[I] - Plotting Heatmaps ...")
for currentHeatmap in heatmapList:
if -1 in heatmapList[currentHeatmap]:
continue
print("[I] - Plotting heatmap for PUF instance", currentHeatmap,"(",len(heatmapList[currentHeatmap])," values)")
# Convert data to ndarray
#floatMap = list(map(float, currentHeatmap[1]))
myArray = np.array(heatmapList[currentHeatmap]).reshape(xDim,yDim)
# Setup two plots per page
fig, ax = plt.subplots(2)
# Histogram
weights = np.ones_like(heatmapList[currentHeatmap]) / len(heatmapList[currentHeatmap])
hist, bins = np.histogram(heatmapList[currentHeatmap], bins=50, weights=weights)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
ax[0].bar(center, hist, align='center', width=width)
stableCells = calcPercentageStable(threshold, verbose)
plt.figtext(100,100,"!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!", fontsize=40)
heatmap = ax[1].pcolor(myArray, cmap=plt.cm.Blues, alpha=0.8, vmin=0, vmax=1)
cbar = fig.colorbar(heatmap, shrink=0.8, aspect=10, fraction=.1,pad=.01)
#cbar.ax.tick_params(labelsize=40)
for y in range(myArray.shape[0]):
for x in range(myArray.shape[1]):
plt.text(x + 0.5, y + 0.5, '%.2f' % myArray[y, x],
horizontalalignment='center',
verticalalignment='center',
fontsize=(xDim/yDim)*5
)
#fig = plt.figure()
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(60.5,55.5)
plt.savefig(dataDirectory+"/"+currentHeatmap+".pdf", dpi=800, papertype="a3", format="pdf")
#plt.title("Heatmap for PUF instance "+str(currentHeatmap[0][0])+" ("+str(numberOfMeasurements)+" measurements; "+str(sizeOfMeasurements)+" bytes)")
if plot:
plt.show()
print("\t[I] - Done ...")
And here is my current output:
Perhaps this example will make things easier to understand. Things to note are:
Use fig.suptitle to add a title to the top of a figure.
Use ax[i].text(x, y, str) to add text to an Axes object
Each Axes object, ax[i] in your case, holds all the information about a single plot. Use them instead of calling plt, which only really works well with one subplot per figure or to modify all subplots at once. For example, instead of calling plt.figtext, call ax[0].text to add text to the top plot.
Try following the example code below, or at least read through it to get a better idea how to use your ax list.
import numpy as np
import matplotlib.pyplot as plt
histogram_data = np.random.rand(1000)
heatmap_data = np.random.rand(10, 100)
# Set up figure and axes
fig = plt.figure()
fig.suptitle("These are my two plots")
top_ax = fig.add_subplot(211) #2 rows, 1 col, 1st plot
bot_ax = fig.add_subplot(212) #2 rows, 1 col, 2nd plot
# This is the same as doing 'fig, (top_ax, bot_ax) = plt.subplots(2)'
# Histogram
weights = np.ones_like(histogram_data) / histogram_data.shape[0]
hist, bins = np.histogram(histogram_data, bins=50, weights=weights)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
# Use top_ax to modify anything with the histogram plot
top_ax.bar(center, hist, align='center', width=width)
# ax.text(x, y, str). Make sure x,y are within your plot bounds ((0, 1), (0, .5))
top_ax.text(0.5, 0.5, "Here is text on the top plot", color='r')
# Heatmap
heatmap_params = {'cmap':plt.cm.Blues, 'alpha':0.8, 'vmin':0, 'vmax':1}
# Use bot_ax to modify anything with the heatmap plot
heatmap = bot_ax.pcolor(heatmap_data, **heatmap_params)
cbar = fig.colorbar(heatmap, shrink=0.8, aspect=10, fraction=.1,pad=.01)
# See how it looks
plt.show()