tried to include your suggestions, not sure why it doesn't work:
# face alignment
import face_alignment
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from skimage import io
# Run the 3D face alignment on a test image, without CUDA.
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device='cpu', flip_input=True)
input = io.imread(r'C:/Users/Ihr Name/Pictures/Bewerbungsfotos/neuropic.jpg')
preds = fa.get_landmarks(input)[-1]
#landmarks == preds, input = pixels image
import matplotlib.pyplot as plt
import matplotlib.patches as patches
class DraggablePoints(object):
def __init__(self, artists, tolerance=5):
for artist in artists:
artist.set_picker(tolerance)
self.artists = artists
self.currently_dragging = False
self.current_artist = None
self.offset = (0, 0)
for canvas in set(artist.figure.canvas for artist in self.artists):
canvas.mpl_connect('button_press_event', self.on_press)
canvas.mpl_connect('button_release_event', self.on_release)
canvas.mpl_connect('pick_event', self.on_pick)
canvas.mpl_connect('motion_notify_event', self.on_motion)
def on_press(self, event):
self.currently_dragging = True
def on_release(self, event):
self.currently_dragging = False
self.current_artist = None
def on_pick(self, event):
if self.current_artist is None:
self.current_artist = event.artist
x0, y0 = event.artist.center
x1, y1 = event.mouseevent.xdata, event.mouseevent.ydata
self.offset = (x0 - x1), (y0 - y1)
def on_motion(self, event):
if not self.currently_dragging:
return
if self.current_artist is None:
return
dx, dy = self.offset
self.current_artist.center = event.xdata + dx, event.ydata + dy
self.current_artist.figure.canvas.draw()
if __name__ == '__main__':
fig = plt.figure(figsize=plt.figaspect(.5))
ax = fig.add_subplot(1, 2, 1)
ax.imshow(input)
ax.plot(preds[0:17,0],preds[0:17,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
ax.plot(preds[17:22,0],preds[17:22,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
ax.plot(preds[22:27,0],preds[22:27,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
ax.plot(preds[27:31,0],preds[27:31,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
ax.plot(preds[31:36,0],preds[31:36,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
ax.plot(preds[36:42,0],preds[36:42,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
ax.plot(preds[42:48,0],preds[42:48,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
ax.plot(preds[48:60,0],preds[48:60,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
ax.plot(preds[60:68,0],preds[60:68,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
ax.axis('off')
ax = fig.add_subplot(1, 2, 2, projection='3d')
surf = ax.scatter(preds[:,0]*1.2,preds[:,1],preds[:,2],c="cyan", alpha=0.5, edgecolor='b')
ax.plot3D(preds[:17,0]*1.2,preds[:17,1], preds[:17,2], color='blue' )
ax.plot3D(preds[17:22,0]*1.2,preds[17:22,1],preds[17:22,2], color='blue')
ax.plot3D(preds[22:27,0]*1.2,preds[22:27,1],preds[22:27,2], color='blue')
ax.plot3D(preds[27:31,0]*1.2,preds[27:31,1],preds[27:31,2], color='blue')
ax.plot3D(preds[31:36,0]*1.2,preds[31:36,1],preds[31:36,2], color='blue')
ax.plot3D(preds[36:42,0]*1.2,preds[36:42,1],preds[36:42,2], color='blue')
ax.plot3D(preds[42:48,0]*1.2,preds[42:48,1],preds[42:48,2], color='blue')
ax.plot3D(preds[48:,0]*1.2,preds[48:,1],preds[48:,2], color='blue' )
ax.view_init(elev=90., azim=90.)
ax.set_xlim(ax.get_xlim()[::-1])
#we want to move preds (landmarks)
for p in preds:
ax.add_patch(p)
dr = DraggablePoints(preds)
plt.show()
Related
I am working with the following image:
from matplotlib import cbook
import matplotlib.patches as mpatches
from matplotlib.axes._base import _TransformedBoundsLocator
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
# a numpy array of 15x15
Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
gs = GridSpec(2, 3)
fig = plt.figure(figsize=(3*3,2*3))
ax1 = fig.add_subplot(gs[:2, :2])
ax2 = fig.add_subplot(gs[1, 2])
Z2 = np.zeros((150, 150))
ny, nx = Z.shape
Z2[30:30+ny, 30:30+nx] = Z
ax1.imshow(Z2)
ax1.set_aspect("equal")
ax2.set_aspect("equal")
plt.tight_layout()
plt.show()
output:
As shown in the image, the x-axis of both plots are aligned. However, when I am adding a patch to the first plot the alignment becomes distorted:
Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
gs = GridSpec(2, 3)
fig = plt.figure(figsize=(3*3,2*3))
ax1 = fig.add_subplot(gs[:2, :2])
ax2 = fig.add_subplot(gs[1, 2])
Z2 = np.zeros((150, 150))
ny, nx = Z.shape
Z2[30:30+ny, 30:30+nx] = Z
ax1.imshow(Z2)
x, y, width, height = 30, 30, 15, 15
ex, ey = (0,1)
xy_data = x + ex * width, y + ey * height
p = mpatches.ConnectionPatch(
xyA=(0,1), coordsA=ax2.transAxes,
xyB=xy_data, coordsB=ax1.transData)
ax1.add_patch(p)
ax1.set_aspect("equal")
ax2.set_aspect("equal")
plt.tight_layout()
plt.show()
output:
Why is this? How can I add a patch whilst retaining the original layout?
I want to add a cross hair that snaps to data points and be updated on mouse move. I found this example that works well:
import numpy as np
import matplotlib.pyplot as plt
class SnappingCursor:
"""
A cross hair cursor that snaps to the data point of a line, which is
closest to the *x* position of the cursor.
For simplicity, this assumes that *x* values of the data are sorted.
"""
def __init__(self, ax, line):
self.ax = ax
self.horizontal_line = ax.axhline(color='k', lw=0.8, ls='--')
self.vertical_line = ax.axvline(color='k', lw=0.8, ls='--')
self.x, self.y = line.get_data()
self._last_index = None
# text location in axes coords
self.text = ax.text(0.72, 0.9, '', transform=ax.transAxes)
def set_cross_hair_visible(self, visible):
need_redraw = self.vertical_line.get_visible() != visible
self.vertical_line.set_visible(visible)
self.horizontal_line.set_visible(visible)
self.text.set_visible(visible)
return need_redraw
def on_mouse_move(self, event):
if not event.inaxes:
self._last_index = None
need_redraw = self.set_cross_hair_visible(False)
if need_redraw:
self.ax.figure.canvas.draw()
else:
self.set_cross_hair_visible(True)
x, y = event.xdata, event.ydata
index = min(np.searchsorted(self.y, y), len(self.y) - 1)
if index == self._last_index:
return # still on the same data point. Nothing to do.
self._last_index = index
x = self.x[index]
y = self.y[index]
# update the line positions
self.horizontal_line.set_ydata(y)
self.vertical_line.set_xdata(x)
self.text.set_text('x=%1.2f, y=%1.2f' % (x, y))
self.ax.figure.canvas.draw()
y = np.arange(0, 1, 0.01)
x = np.sin(2 * 2 * np.pi * y)
fig, ax = plt.subplots()
ax.set_title('Snapping cursor')
line, = ax.plot(x, y, 'o')
snap_cursor = SnappingCursor(ax, line)
fig.canvas.mpl_connect('motion_notify_event', snap_cursor.on_mouse_move)
plt.show()
But I get into trouble when I want to adapt the code with the PyQt5 and show the plot in a GUI. My code is:
from PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QVBoxLayout
import sys
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
import numpy as np
class SnappingCursor:
"""
A cross hair cursor that snaps to the data point of a line, which is
closest to the *x* position of the cursor.
For simplicity, this assumes that *x* values of the data are sorted.
"""
def __init__(self, ax, line):
self.ax = ax
self.horizontal_line = ax.axhline(color='k', lw=0.8, ls='--')
self.vertical_line = ax.axvline(color='k', lw=0.8, ls='--')
self.x, self.y = line.get_data()
self._last_index = None
# text location in axes coords
self.text = ax.text(0.72, 0.9, '', transform=ax.transAxes)
def set_cross_hair_visible(self, visible):
need_redraw = self.vertical_line.get_visible() != visible
self.vertical_line.set_visible(visible)
self.horizontal_line.set_visible(visible)
self.text.set_visible(visible)
return need_redraw
def on_mouse_move(self, event):
if not event.inaxes:
self._last_index = None
need_redraw = self.set_cross_hair_visible(False)
if need_redraw:
self.ax.figure.canvas.draw()
else:
self.set_cross_hair_visible(True)
x, y = event.xdata, event.ydata
index = min(np.searchsorted(self.y, y), len(self.y) - 1)
if index == self._last_index:
return # still on the same data point. Nothing to do.
self._last_index = index
x = self.x[index]
y = self.y[index]
# update the line positions
self.horizontal_line.set_ydata(y)
self.vertical_line.set_xdata(x)
self.text.set_text('x=%1.2f, y=%1.2f' % (x, y))
self.ax.figure.canvas.draw()
class Window(QMainWindow):
def __init__(self):
super().__init__()
widget=QWidget()
vbox=QVBoxLayout()
plot1 = FigureCanvas(Figure(tight_layout=True, linewidth=3))
ax = plot1.figure.subplots()
x = np.arange(0, 1, 0.01)
y = np.sin(2 * 2 * np.pi * x)
line, = ax.plot(x, y, 'o')
snap_cursor = SnappingCursor(ax, line)
plot1.mpl_connect('motion_notify_event', snap_cursor.on_mouse_move)
vbox.addWidget(plot1)
widget.setLayout(vbox)
self.setCentralWidget(widget)
self.setWindowTitle('Example')
self.show()
App = QApplication(sys.argv)
window = Window()
sys.exit(App.exec())
By running the above code, the data is plotted properly, but the cross hair is only shown in its initial position and does not move by mouse movement. Data values are also not displayed.
I have found a similar question here too, but the question is not answered clearly.
There are 2 problems:
snap_cursor is a local variable that will be removed when __init__ finishes executing. You must make him a member of the class.
The initial code of the tutorial is designed so that the point that information is displayed is the horizontal line that passes through the cursor and intersects the curve. In your initial code it differs from the example and also does not work for your new curve so I restored the logic of the tutorial.
import sys
from PyQt5.QtWidgets import QApplication, QMainWindow, QVBoxLayout, QWidget
import numpy as np
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
class SnappingCursor:
"""
A cross hair cursor that snaps to the data point of a line, which is
closest to the *x* position of the cursor.
For simplicity, this assumes that *x* values of the data are sorted.
"""
def __init__(self, ax, line):
self.ax = ax
self.horizontal_line = ax.axhline(color="k", lw=0.8, ls="--")
self.vertical_line = ax.axvline(color="k", lw=0.8, ls="--")
self.x, self.y = line.get_data()
self._last_index = None
# text location in axes coords
self.text = ax.text(0.72, 0.9, "", transform=ax.transAxes)
def set_cross_hair_visible(self, visible):
need_redraw = self.vertical_line.get_visible() != visible
self.vertical_line.set_visible(visible)
self.horizontal_line.set_visible(visible)
self.text.set_visible(visible)
return need_redraw
def on_mouse_move(self, event):
if not event.inaxes:
self._last_index = None
need_redraw = self.set_cross_hair_visible(False)
if need_redraw:
self.ax.figure.canvas.draw()
else:
self.set_cross_hair_visible(True)
x, y = event.xdata, event.ydata
index = min(np.searchsorted(self.x, x), len(self.x) - 1)
if index == self._last_index:
return # still on the same data point. Nothing to do.
self._last_index = index
x = self.x[index]
y = self.y[index]
# update the line positions
self.horizontal_line.set_ydata(y)
self.vertical_line.set_xdata(x)
self.text.set_text("x=%1.2f, y=%1.2f" % (x, y))
self.ax.figure.canvas.draw()
class Window(QMainWindow):
def __init__(self):
super().__init__()
widget = QWidget()
vbox = QVBoxLayout(widget)
x = np.arange(0, 1, 0.01)
y = np.sin(2 * 2 * np.pi * x)
canvas = FigureCanvas(Figure(tight_layout=True, linewidth=3))
ax = canvas.figure.subplots()
ax.set_title("Snapping cursor")
(line,) = ax.plot(x, y, "o")
self.snap_cursor = SnappingCursor(ax, line)
canvas.mpl_connect("motion_notify_event", self.snap_cursor.on_mouse_move)
vbox.addWidget(canvas)
self.setCentralWidget(widget)
self.setWindowTitle("Example")
app = QApplication(sys.argv)
w = Window()
w.show()
app.exec()
I'm trying to set the title as 'Electric Field of mode (i)', where i goes from 1 to N, but I don't find the way. After I import Numpy, Matplotlib, cmath and Animation, and define the parameters and neccesary funtions (modo) I have:
fig = plt.figure()
subplot = plt.axes(xlim=(0, 60*(10**(-15))), xlabel=("t[s]"), ylim=(-10, 10), ylabel=("Amplitud [u.a.]"))
modo, = subplot.plot([], [], lw=2)
def init1():
modo.set_data([],[])
return modo,
def animacion1(i):
EiR, Ei = Modo(Eo, wo, dw, t, phio, i)
modo.set_data(t,EiR)
return modo,
anim1 = animation.FuncAnimation(fig, animacion1, init_func=init1, frames=N, interval=20, blit=True, save_count=20000)
plt.show()
Here is a minimal example:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
x = np.linspace(0, 7.5, 100)
y1 = np.sin(x)
fig, ax = plt.subplots()
line1, = ax.plot(x, y1, color='orange')
def update(num):
line1.set_data(x[:num], y1[:num])
ax.set_title(f'{num}')
return line1
ani = animation.FuncAnimation(fig, func=update, frames=len(x),
interval = 50)
plt.show()
In your case you need to do subplot.set_title(f'Electric Field of mode ({i})') in your animacion1() method.
I have a plot from matplotlib for which I would like to display labels on the marker points when hover over with the mouse.
I found this very helpful working example on SO and I was trying to integrate the exact same plot into a pyqt5 application.
Unfortunately when having the plot in the application the hovering doesn't work anymore.
Here is a full working example based on the mentioned SO post:
import matplotlib.pyplot as plt
import scipy.spatial as spatial
import numpy as np
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
import sys
pi = np.pi
cos = np.cos
def fmt(x, y):
return 'x: {x:0.2f}\ny: {y:0.2f}'.format(x=x, y=y)
class FollowDotCursor(object):
"""Display the x,y location of the nearest data point.
https://stackoverflow.com/a/4674445/190597 (Joe Kington)
https://stackoverflow.com/a/13306887/190597 (unutbu)
https://stackoverflow.com/a/15454427/190597 (unutbu)
"""
def __init__(self, ax, x, y, tolerance=5, formatter=fmt, offsets=(-20, 20)):
try:
x = np.asarray(x, dtype='float')
except (TypeError, ValueError):
x = np.asarray(mdates.date2num(x), dtype='float')
y = np.asarray(y, dtype='float')
mask = ~(np.isnan(x) | np.isnan(y))
x = x[mask]
y = y[mask]
self._points = np.column_stack((x, y))
self.offsets = offsets
y = y[np.abs(y-y.mean()) <= 3*y.std()]
self.scale = x.ptp()
self.scale = y.ptp() / self.scale if self.scale else 1
self.tree = spatial.cKDTree(self.scaled(self._points))
self.formatter = formatter
self.tolerance = tolerance
self.ax = ax
self.fig = ax.figure
self.ax.xaxis.set_label_position('top')
self.dot = ax.scatter(
[x.min()], [y.min()], s=130, color='green', alpha=0.7)
self.annotation = self.setup_annotation()
plt.connect('motion_notify_event', self)
def scaled(self, points):
points = np.asarray(points)
return points * (self.scale, 1)
def __call__(self, event):
ax = self.ax
# event.inaxes is always the current axis. If you use twinx, ax could be
# a different axis.
if event.inaxes == ax:
x, y = event.xdata, event.ydata
elif event.inaxes is None:
return
else:
inv = ax.transData.inverted()
x, y = inv.transform([(event.x, event.y)]).ravel()
annotation = self.annotation
x, y = self.snap(x, y)
annotation.xy = x, y
annotation.set_text(self.formatter(x, y))
self.dot.set_offsets((x, y))
bbox = ax.viewLim
event.canvas.draw()
def setup_annotation(self):
"""Draw and hide the annotation box."""
annotation = self.ax.annotate(
'', xy=(0, 0), ha = 'right',
xytext = self.offsets, textcoords = 'offset points', va = 'bottom',
bbox = dict(
boxstyle='round,pad=0.5', fc='yellow', alpha=0.75),
arrowprops = dict(
arrowstyle='->', connectionstyle='arc3,rad=0'))
return annotation
def snap(self, x, y):
"""Return the value in self.tree closest to x, y."""
dist, idx = self.tree.query(self.scaled((x, y)), k=1, p=1)
try:
return self._points[idx]
except IndexError:
# IndexError: index out of bounds
return self._points[0]
class MainWindow(QMainWindow):
def __init__(self):
super().__init__()
self.width = 1000
self.height = 800
self.setGeometry(0, 0, self.width, self.height)
canvas = self.get_canvas()
w = QWidget()
w.layout = QHBoxLayout()
w.layout.addWidget(canvas)
w.setLayout(w.layout)
self.setCentralWidget(w)
self.show()
def get_canvas(self):
fig, ax = plt.subplots()
x = np.linspace(0.1, 2*pi, 10)
y = cos(x)
markerline, stemlines, baseline = ax.stem(x, y, '-.')
plt.setp(markerline, 'markerfacecolor', 'b')
plt.setp(baseline, 'color','r', 'linewidth', 2)
cursor = FollowDotCursor(ax, x, y, tolerance=20)
canvas = FigureCanvas(fig)
return canvas
app = QApplication(sys.argv)
win = MainWindow()
sys.exit(app.exec_())
What would I have to do to make the labels also show when hovering over in the pyqt application?
The first problem may be that you don't keep a reference to the FollowDotCursor.
So to make sure the FollowDotCursor stays alive, you can make it a class variable
self.cursor = FollowDotCursor(ax, x, y, tolerance=20)
instead of cursor = ....
Next make sure you instatiate the Cursor class after giving the figure a canvas.
canvas = FigureCanvas(fig)
self.cursor = FollowDotCursor(ax, x, y, tolerance=20)
Finally, keep a reference to the callback inside the FollowDotCursor and don't use plt.connect but the canvas itself:
self.cid = self.fig.canvas.mpl_connect('motion_notify_event', self)
I use matplotlib.pyplot.pcolor() to plot a heatmap with matplotlib:
import numpy as np
import matplotlib.pyplot as plt
def heatmap(data, title, xlabel, ylabel):
plt.figure()
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
c = plt.pcolor(data, edgecolors='k', linewidths=4, cmap='RdBu', vmin=0.0, vmax=1.0)
plt.colorbar(c)
def main():
title = "ROC's AUC"
xlabel= "Timeshift"
ylabel="Scales"
data = np.random.rand(8,12)
heatmap(data, title, xlabel, ylabel)
plt.show()
if __name__ == "__main__":
main()
Is any way to add the corresponding value in each cell, e.g.:
(from Matlab's Customizable Heat Maps)
(I don't need the additional % for my current application, though I'd be curious to know for the future)
You need to add all the text by calling axes.text(), here is an example:
import numpy as np
import matplotlib.pyplot as plt
title = "ROC's AUC"
xlabel= "Timeshift"
ylabel="Scales"
data = np.random.rand(8,12)
plt.figure(figsize=(12, 6))
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
c = plt.pcolor(data, edgecolors='k', linewidths=4, cmap='RdBu', vmin=0.0, vmax=1.0)
def show_values(pc, fmt="%.2f", **kw):
from itertools import izip
pc.update_scalarmappable()
ax = pc.get_axes()
for p, color, value in izip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):
x, y = p.vertices[:-2, :].mean(0)
if np.all(color[:3] > 0.5):
color = (0.0, 0.0, 0.0)
else:
color = (1.0, 1.0, 1.0)
ax.text(x, y, fmt % value, ha="center", va="center", color=color, **kw)
show_values(c)
plt.colorbar(c)
the output:
You could use Seaborn, which is a Python visualization library based on matplotlib that provides a high-level interface for drawing attractive statistical graphics.
Heatmap example:
import seaborn as sns
sns.set()
flights_long = sns.load_dataset("flights")
flights = flights_long.pivot("month", "year", "passengers")
sns.heatmap(flights, annot=True, fmt="d")
# To display the heatmap
import matplotlib.pyplot as plt
plt.show()
# To save the heatmap as a file:
fig = heatmap.get_figure()
fig.savefig('heatmap.pdf')
Documentation: https://seaborn.pydata.org/generated/seaborn.heatmap.html
If that's of interest to anyone, here is below the code I use to imitate the picture from Matlab's Customizable Heat Maps I had included in the question).
import numpy as np
import matplotlib.pyplot as plt
def show_values(pc, fmt="%.2f", **kw):
'''
Heatmap with text in each cell with matplotlib's pyplot
Source: http://stackoverflow.com/a/25074150/395857
By HYRY
'''
from itertools import izip
pc.update_scalarmappable()
ax = pc.get_axes()
for p, color, value in izip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):
x, y = p.vertices[:-2, :].mean(0)
if np.all(color[:3] > 0.5):
color = (0.0, 0.0, 0.0)
else:
color = (1.0, 1.0, 1.0)
ax.text(x, y, fmt % value, ha="center", va="center", color=color, **kw)
def cm2inch(*tupl):
'''
Specify figure size in centimeter in matplotlib
Source: http://stackoverflow.com/a/22787457/395857
By gns-ank
'''
inch = 2.54
if type(tupl[0]) == tuple:
return tuple(i/inch for i in tupl[0])
else:
return tuple(i/inch for i in tupl)
def heatmap(AUC, title, xlabel, ylabel, xticklabels, yticklabels):
'''
Inspired by:
- http://stackoverflow.com/a/16124677/395857
- http://stackoverflow.com/a/25074150/395857
'''
# Plot it out
fig, ax = plt.subplots()
c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap='RdBu', vmin=0.0, vmax=1.0)
# put the major ticks at the middle of each cell
ax.set_yticks(np.arange(AUC.shape[0]) + 0.5, minor=False)
ax.set_xticks(np.arange(AUC.shape[1]) + 0.5, minor=False)
# set tick labels
#ax.set_xticklabels(np.arange(1,AUC.shape[1]+1), minor=False)
ax.set_xticklabels(xticklabels, minor=False)
ax.set_yticklabels(yticklabels, minor=False)
# set title and x/y labels
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# Remove last blank column
plt.xlim( (0, AUC.shape[1]) )
# Turn off all the ticks
ax = plt.gca()
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
# Add color bar
plt.colorbar(c)
# Add text in each cell
show_values(c)
# resize
fig = plt.gcf()
fig.set_size_inches(cm2inch(40, 20))
def main():
x_axis_size = 19
y_axis_size = 10
title = "ROC's AUC"
xlabel= "Timeshift"
ylabel="Scales"
data = np.random.rand(y_axis_size,x_axis_size)
xticklabels = range(1, x_axis_size+1) # could be text
yticklabels = range(1, y_axis_size+1) # could be text
heatmap(data, title, xlabel, ylabel, xticklabels, yticklabels)
plt.savefig('image_output.png', dpi=300, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures
plt.show()
if __name__ == "__main__":
main()
#cProfile.run('main()') # if you want to do some profiling
Output:
It looks nicer when there are some patterns:
Same as #HYRY aswer, but python3 compatible version:
import numpy as np
import matplotlib.pyplot as plt
title = "ROC's AUC"
xlabel= "Timeshift"
ylabel="Scales"
data = np.random.rand(8,12)
plt.figure(figsize=(12, 6))
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
c = plt.pcolor(data, edgecolors='k', linewidths=4, cmap='RdBu', vmin=0.0, vmax=1.0)
def show_values(pc, fmt="%.2f", **kw):
pc.update_scalarmappable()
ax = pc.axes
for p, color, value in zip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):
x, y = p.vertices[:-2, :].mean(0)
if np.all(color[:3] > 0.5):
color = (0.0, 0.0, 0.0)
else:
color = (1.0, 1.0, 1.0)
ax.text(x, y, fmt % value, ha="center", va="center", color=color, **kw)
show_values(c)
plt.colorbar(c)