I am working with the matplotlib library to generate colored graphs which need to have specific points overlayed on top of them. After messing around with matplotlib, I came up with a method to properly color my grid, however I am unable to plot points manually.
def generate_grid(x, y, data):
fig, ax = plt.subplots(1, 1, tight_layout=True)
my_cmap = matplotlib.colors.ListedColormap(['grey'])
my_cmap.set_bad(color='w', alpha=0)
for x in range(x + 1):
ax.axhline(x, lw=2, color='k', zorder=5)
for y in range(y+1):
ax.axvline(y, lw=2, color='k', zorder=5)
ax.imshow(data, interpolation='none', cmap=my_cmap,
extent=[0, y, 0, x], zorder=0)
plt.locator_params(axis="x", nbins=x+1)
plt.locator_params(axis="y", nbins=y+1)
locs, labels = plt.xticks()
labels = [int(item)+1 for item in locs]
plt.xticks(locs, labels)
locs, labels = plt.yticks()
z = len(locs)
labels = [z-int(item) for item in locs]
plt.yticks(locs, labels)
ax.xaxis.tick_top()
plt.show()
How would I go about plotting a point at any given location ie at (4,2) or (2,1)?
You may simply use the scatter method from within your generate_grid function, for instance, immediately before plt.show().
However, note that if you simply use ax.scatter(2,1, s=50) the symbol will end up under your grid.
You need to play with the zorder parameter to ensure that it appears over the grid. For instance ax.scatter(2,1, s=50, zorder=50) did the trick for me:
Related
I'm trying to make a matplotlib plot with a second y-axis. This works so far, but I was wondering, wether it was possible to shorten the second y-axis?
Furthermore, I struggle on some other formatting issues.
a) I want to draw an arrow on the second y-axis, just as drawn on the first y-axis.
b) I want to align the second y-axis at -1, so that the intersection of x- and 2nd y-axis is at(...; -1)
c) The x-axis crosses the x- and y-ticks at the origin, which I want to avoid.
d) How can I get a common legend for both y-axis?
Here is my code snippet so far.
fig, ax = plt.subplots()
bx = ax.twinx() # 2nd y-axis
ax.spines['bottom'].set_position(('data',0))
ax.spines['left'].set_position(('data',0))
ax.xaxis.set_ticks_position('bottom')
bx.spines['left'].set_position(('data',-1))
bx.spines['bottom'].set_position(('data',-1))
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
bx.spines["top"].set_visible(False)
bx.spines["bottom"].set_visible(False)
bx.spines["left"].set_visible(False)
## Graphs
x_val = np.arange(0,10)
y_val = 0.1*x_val
ax.plot(x_val, y_val, 'k--')
bx.plot(x_val, -y_val+1, color = 'purple')
## Arrows
ms=2
#ax.plot(1, 0, ">k", ms=ms, transform=ax.get_yaxis_transform(), clip_on=False)
ax.plot(0, 1, "^k", ms=ms, transform=ax.get_xaxis_transform(), clip_on=False)
bx.plot(1, 1, "^k", ms=ms, transform=bx.get_xaxis_transform(), clip_on=False)
plt.ylim((-1, 1.2))
bx.set_yticks([-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5])
## Legend
ax.legend([r'$A_{hull}$'], frameon=False,
loc='upper left', bbox_to_anchor=(0.2, .75))
plt.show()
I've uploaded a screenshot of my plot so far, annotating the questioned points.
EDIT: I've changed the plotted values in the code snippet so that the example is easier to reproduce. However, the question is more or less only related to formatting issues so that the acutual values are not too important. Image is not changed, so don't be surprised when plotting the edited values, the graphs will look differently.
To avoid the strange overlap at x=0 and y=0, you could leave out the calls to ax.spines[...].set_position(('data',0)). You can change the transforms that place the arrows. Explicitly setting the x and y limits to start at 0 will also have the spines at those positions.
ax2.set_bounds(...) shortens the right y-axis.
To put items in the legend, each plotted item needs a label. get_legend_handles_labels can fetch the handles and labels of both axes, which can be combined in a new legend.
Renaming bx to something like ax2 makes the code easier to compare with existing example code. In matplotlib it often also helps to first put the plotting code and only later changes to limits, ticks and embellishments.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
fig, ax = plt.subplots()
ax2 = ax.twinx() # 2nd y-axis
## Graphs
x_val = np.arange(0, 10)
y_val = 0.1 * x_val
ax.plot(x_val, y_val, 'k--', label=r'$A_{hull}$')
ax2.plot(x_val, -y_val + 1, color='purple', label='right axis')
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax2.spines["top"].set_visible(False)
ax2.spines["bottom"].set_visible(False)
ax2.spines["left"].set_visible(False)
ax2_upper_bound = 0.55
ax2.spines["right"].set_bounds(-1, ax2_upper_bound) # shorten the right y-axis
## add arrows to spines
ms = 2
# ax.plot(1, 0, ">k", ms=ms, transform=ax.get_yaxis_transform(), clip_on=False)
ax.plot(0, 1, "^k", ms=ms, transform=ax.transAxes, clip_on=False)
ax2.plot(1, ax2_upper_bound, "^k", ms=ms, transform=ax2.get_yaxis_transform(), clip_on=False)
# set limits to the axes
ax.set_xlim(xmin=0)
ax.set_ylim(ymin=0)
ax2.set_ylim((-1, 1.2))
ax2.set_yticks(np.arange(-1, 0.5001, 0.25))
## Legend
handles1, labels1 = ax.get_legend_handles_labels()
handles2, labels2 = ax2.get_legend_handles_labels()
ax.legend(handles1 + handles2, labels1 + labels2, frameon=False,
loc='upper left', bbox_to_anchor=(0.2, .75))
plt.show()
I have this function:
def scatter_diagdistance(x, y) :
z = abs(y-x)
fig, ax = plt.subplots(dpi=200)
sc = ax.scatter(x, y, c=z, s=50, edgecolor='none')
x_diag = np.arange(min(x*100), max(x*100))/100
ax.plot(x_diag, x_diag, '-', c="red")
cbar = fig.colorbar(sc)
cbar.set_label('Distance from diagonal')
return(fig)
Which gives me this sort of image:
How can I position the "Distance from diagonal" to the left of the colorbar?
(Also, is there a cleaner way to plot the diagonal over a scatter plot like this?)
one way to do it is to use the text as the label for the secondary y-axis. That will keep the text before the colorbar. Also, you can draw a line for the diagonal. The code (without your data) is shown below. If you use transform=ax.transAxes details, the coordinates are interpreted as axes coordinates
fig, ax = plt.subplots(dpi=200)
ax2 = ax.twinx() ##Create secondary axis
ax2.set_yticks([]) ##No ticks for the secondary axis
sc = ax.scatter(0.5, 0.5, c=1, s=50, edgecolor='none')
ax2.set_ylabel('Distance from diagonal') ##Label for secondary axis
ax.plot([0, 1], [0, 1], '-', c="red", transform=ax.transAxes) #Line from 0 to 1
cbar = fig.colorbar(sc)
Plot
I do not understand, how to properly plot my heatmap (pcolormesh) with matplotlib. I want the tick's labels be centered below/beside the corresponding boxes - and only my given data, not some artificially extended ranges.
In the docs I found an example, which works slightly modified to floats just fine for me.
Z = []
for i in range(1, 7):
Z.append([j*i for j in range(1, 11)])
Z = np.asarray(Z)
x = np.linspace(0.1, 1.0, num=10)
y = np.linspace(0.1, 0.6, num=6)
fig, ax = plt.subplots()
ax.pcolormesh(x, y, Z, vmin=np.min(Z), edgecolors='w', linewidths=0.5, vmax=np.max(Z), shading='auto')
plt.show()
The result prints the ticks centered at the boxes, which is exactly what I want.
But as soon as I use my own data it ends up with some weird behaviour
data = pd.DataFrame(index=[0, 0.25, 0.5], data={0: [31.40455938, 101.43291831, 101.67128077], 0.25: [31.40455938, 89.81448724, 99.65066293], 0.5: [31.40455938, 57.01406046, 101.47536496]})
x = data.columns.astype(np.float64).to_numpy()
y = data.index.astype(np.float64).to_numpy()
z = data.to_numpy()
cmap = LinearSegmentedColormap.from_list('G2R', ["green", "red"])
fig, ax = plt.subplots()
ax.pcolormesh(x, y, z, shading='auto', cmap=cmap, edgecolors='w', linewidths=0.5, vmin=0, vmax=100) # shading='gouraud'
ax.set_title('not what i want')
ax.set_xlabel('X')
ax.set_ylabel('Y')
plt.show()
How do I get my heatmap to simply plot the given floats as centered tick labels without those unwanted floats? I literally want to specify my tick labels (floats or strings) to be shown centered to the boxes. I would assume there must be a way, to specify a list or array as tick labels. How do I accomplish that?
After plotting the pcolormesh you can set x and y tick with matplotlib.axes.Axes.set_xticks and matplotlib.axes.Axes.set_yticks respectively:
ax.pcolormesh(x, y, z, shading='auto', cmap=cmap, edgecolors='w', linewidths=0.5, vmin=0, vmax=100) # shading='gouraud'
ax.set_xticks(data.columns)
ax.set_yticks(data.index)
In pyplot, you can change the order of different graphs using the zorder option or by changing the order of the plot() commands. However, when you add an alternative axis via ax2 = twinx(), the new axis will always overlay the old axis (as described in the documentation).
Is it possible to change the order of the axis to move the alternative (twinned) y-axis to background?
In the example below, I would like to display the blue line on top of the histogram:
import numpy as np
import matplotlib.pyplot as plt
import random
# Data
x = np.arange(-3.0, 3.01, 0.1)
y = np.power(x,2)
y2 = 1/np.sqrt(2*np.pi) * np.exp(-y/2)
data = [random.gauss(0.0, 1.0) for i in range(1000)]
# Plot figure
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax2.hist(data, bins=40, normed=True, color='g',zorder=0)
ax2.plot(x, y2, color='r', linewidth=2, zorder=2)
ax1.plot(x, y, color='b', linewidth=2, zorder=5)
ax1.set_ylabel("Parabola")
ax2.set_ylabel("Normal distribution")
ax1.yaxis.label.set_color('b')
ax2.yaxis.label.set_color('r')
plt.show()
Edit: For some reason, I am unable to upload the image generated by this code. I will try again later.
You can set the zorder of an axes, ax.set_zorder(). One would then need to remove the background of that axes, such that the axes below is still visible.
ax2 = ax1.twinx()
ax1.set_zorder(10)
ax1.patch.set_visible(False)
I have a color coded plot. Here is a part of the code:
fig = plt.figure(figsize=(10,10))
color_scheme = plt.get_cmap('cool')
gs = gridspec.GridSpec(1, 1)
ax1 = plt.subplot(gs[0])
gs.update(left=0.15,bottom=0.15,right=0.80,top=0.95)
cax = fig.add_axes([0.80, 0.15, 0.03, 0.80])
im = ax1.scatter(x, y, c=z, edgecolors='black', marker='.', s=40, lw=1, cmap=color_scheme, vmin=0, vmax=10)
cb = fig.colorbar(im, cax=cax)
for t in cb.ax.get_yticklabels(): t.set_fontsize(12)
The problem is that I want to connect the dots with a line, and it doesn't work to use marker='-' and it also doesn't work if I use ax1.plt. How can I do this?
What I actually need is to fit a line to some points and color it the same color as the points (the points I fit to will all have same color)
Instead of using
ax1.scatter(x, y, ...)
use
ax1.plot(x, y, 'o-', ...) # three dots meaning you can configure markers, linestyle, etc.
This works bacause of 'o-' argument, which is a line plot with markers at every data point.
Plot the same x and y-data separately with a standard ax.plot behind your scatter plot.
ax1.plot(x, y, '-')
im = ax1.scatter(x, y, c=z, edgecolors='black', marker='.', s=40, lw=1, cmap=color_scheme, vmin=0, vmax=10)
This should give you your cmapped scatter plot with the lines behind the scatter-points.