Develop Reference

Format of legend with equation in matplotlib

How do we show an equation in the legend of a pyplot plot?
Sample code below:
fig = plt.figure(figsize=(5, 3), dpi=my_dpi)
ax = fig.add_subplot(111)
a = 2.0
b = 3.0
xdata = np.arange(6)
ydata = a*np.power(xdata,b)
# I want the b variable in the equation below to be a subscript.
label = '${0:.2f} x^{{0:.2f}}$'.format(a,b)
plt.plot(xdata,ydata, alpha = 0.5, label = label)
plt.legend()
plt.show()

One possible solution: first format your float to strings, than put the strings together:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(5, 3))
ax = fig.add_subplot(111)
a = 2.0
b = 3.0
xdata = np.arange(6)
ydata = a*np.power(xdata,b)
f2str = '{:.2f}'
label = '$%s x^{%s}$' % tuple(f2str.format(t) for t in [a, b])
plt.plot(xdata,ydata, alpha = 0.5, label = label)
plt.legend()
plt.show()

matplotlib: read marker direction from a file

I want to make a plot from a data file with matplotlib.pyplot and I want every marker (triangle) to have an orientation which has been given in the input file.
The input file :
x y angle
1 1 10
1.2 1.2 20
1.3 1.3 30
and this is my code:
import numpy as np
import matplotlib.pyplot as plt
infile = open ('traj_marker.txt')
#for s in xrange(8):
x, y = [], []
m = []
for i in xrange(3):
data = infile.readline()
raw = data.split()
x.append(float(raw[0]))
y.append(float(raw[1]))
m.append(float(raw[2]))
xnp = np.array(x)
ynp = np.array(y)
mnp = np.array(m)
fig, ax = plt.subplots()
ax.scatter(xnp, ynp, 100, marker = (3,0,mnp))
plt.xticks (range(1,3))
plt.yticks (range(1,3))
plt.grid()
fig.savefig ('trj.png')
infile.close()
But the presence of array mnp in marker produces error.
How can I solve this?

Matplotlib doesn't like the marker argument passed as a list, so run it in the following manner ...
N = 20
xnp = np.random.rand(N)
ynp = np.random.rand(N)
mnp = np.random.randint(0, 180, N)
fig, ax = plt.subplots()
for x, y, m in zip(xnp, ynp, mnp):
ax.scatter(x, y, 100, marker = (3,0,m))
plt.show()

Just in case you are not aware, you can use quiver to plot 2D fields:
x = [1, 1.2, 1.3]
y = [1, 1.2, 1.3]
angle = [10, 20, 30]
plt.quiver(x, y, np.cos(np.radians(angle)), np.sin(np.radians(angle)))

Plotting 3d line graph using matplotlib. TypeError: Cannot cast array data from dtype('float64') to dtype('S32') according to the rule 'safe'

This is my code :
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
with open("TestGraph.txt") as f:
data = f.read()
data = data.split('\n')
fig = plt.figure()
ax = fig.add_subplot(111,projection='3d')
x = [row.split(' ')[0] for row in data]
y = [row.split(' ')[1] for row in data]
z = [row.split(' ')[2] for row in data]
ax.plot_wireframe(x, y,z)
plt.show()
TestGraph.txt is
1 2 3
3 4 5
5 6 7
7 8 9
9 1 8
When I print out x , y , z they are all the right values. I don't understand what the error means.

Your x, y and z variables are lists of strings (S32). The plot_wireframe expects float (float64). Here's a simpler solution. You can use pandas too.
import numpy as np
import matplotlib.pyplot as plt
f = r"TestGraph.txt"
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
data = np.loadtxt(f)
x = data[:, 0]
y = data[:, 1]
z = data[:, 2]
ax.plot_wireframe(x, y, z)
plt.show()

Row Titles within a matplotlib GridSpec

I have an GridSpec defined layout with to subgrids, one is supposed to include a colorbar
import pylab as plt
import numpy as np
gs_outer = plt.GridSpec(1, 2, width_ratios=(10, 1))
gs_inner = plt.matplotlib.gridspec.GridSpecFromSubplotSpec(2, 3, gs_outer[0])
ax = []
for i in xrange(6):
ax.append(plt.subplot(gs_inner[i]))
plt.setp(ax[i].get_xticklabels(), visible=False)
plt.setp(ax[i].get_yticklabels(), visible=False)
ax.append(plt.subplot(gs_outer[1]))
plt.show()
I'd now like to get for the left part a row-wise labeling like this:
I tried to add another GridSpec into the GridSpec, but that did not work out:
import pylab as plt
import numpy as np
fig = plt.figure()
gs_outer = plt.GridSpec(1, 2, width_ratios=(10, 1))
gs_medium = plt.matplotlib.gridspec.GridSpecFromSubplotSpec(3, 1, gs_outer[0])
ax_title0 = plt.subplot(gs_medium[0])
ax_title0.set_title('Test!')
gs_row1 = plt.matplotlib.gridspec.GridSpecFromSubplotSpec(1, 3, gs_medium[0])
ax00 = plt.subplot(gs_row1[0]) # toggle this line to see the effect
plt.show()
Adding the ax00 = plt.subplot... line seems to erase the previously created axis

Following CT Zhu comment I came up with the following answer (I don't really like it, but it seems to work)
import pylab as plt
import numpy as np
fig = plt.figure()
rows = 2
cols = 3
row_fraction = 9
row_size = row_fraction / float(rows)
gs_outer = plt.GridSpec(1,2, width_ratios=(9,1))
gs_plots= plt.matplotlib.gridspec.GridSpecFromSubplotSpec(rows * 2, cols, subplot_spec=gs_outer[0], height_ratios = rows * [1, row_size])
# Create title_axes
title_ax = []
for ta in xrange(rows):
row_index = (ta) * 2
title_ax.append(plt.subplot(gs_plots[row_index, :]))
# Create Data axes
ax = []
for row in xrange(rows):
row_index = (row + 1) * 2 -1
for col in xrange(cols):
try:
ax.append(plt.subplot(gs_plots[row_index, col], sharex=ax[0], sharey=ax[0]))
except IndexError:
if row == 0 and col == 0:
ax.append(plt.subplot(gs_plots[row_index, col]))
else:
raise IndexError
# Delete Boxes and Markers from title axes
for ta in title_ax:
ta._frameon = False
ta.xaxis.set_visible(False)
ta.yaxis.set_visible(False)
# Add labels to title axes:
for ta, label in zip(title_ax, ['Row 1', 'Row 2']):
plt.sca(ta)
plt.text(
0.5, 0.5, label, horizontalalignment='center', verticalalignment='center')
# Add common colorbar
gs_cb = plt.matplotlib.gridspec.GridSpecFromSubplotSpec(
1, 1, subplot_spec=gs_outer[1])
ax.append(plt.subplot(gs_cb[:, :]))
Of course labeling and ticklabels could be improved. But how to achive that is likely already explained on SO.

Let's define an example grid pltgrid:
pltgrid = gridspec.GridSpec(ncols=3, nrows=2,
width_ratios=[1]*3, wspace=0.3,
hspace=0.6, height_ratios=[1]*2)
Before your for loop, you can define a list ax using map:
num=list(range(7))
ax=list(map(lambda x : 'ax'+str(x), num))
You may have a list plotnames containing the names. As an example, I'll plot a normal distribution Q-Q plot for each i in the for loop:
for i in xrange(6):
ax[i]=fig.add.subplot(pltgrid[i])
res = stats.probplot(x, dist="norm", plot=ax[i])
# set title for subplot using existing 'plotnames' list
ax[i].set_title(plotnames[i])
# display subplot
ax[i]

Matplotlib imshow/matshow display values on plot

I am trying to create a 10x10 grid using either imshow or matshow in Matplotlib. The function below takes a numpy array as input, and plots the grid. However, I'd like to have values from the array also displayed inside the cells defined by the grid. So far I could not find a proper way to do it. I can use plt.text to place things over the grid, but this requires coordinates of each cell, totally inconvenient. Is there a better way to do what I am trying to accomplish?
Thanks!
NOTE: The code below does not take the values from the array yet, I was just playing with plt.text.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
board = np.zeros((10, 10))
def visBoard(board):
cmap = colors.ListedColormap(['white', 'red'])
bounds=[0,0.5,1]
norm = colors.BoundaryNorm(bounds, cmap.N)
plt.figure(figsize=(4,4))
plt.matshow(board, cmap=cmap, norm=norm, interpolation='none', vmin=0, vmax=1)
plt.xticks(np.arange(0.5,10.5), [])
plt.yticks(np.arange(0.5,10.5), [])
plt.text(-0.1, 0.2, 'x')
plt.text(0.9, 0.2, 'o')
plt.text(1.9, 0.2, 'x')
plt.grid()
visBoard(board)
Output:

Can you do something like:
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
min_val, max_val = 0, 10
ind_array = np.arange(min_val + 0.5, max_val + 0.5, 1.0)
x, y = np.meshgrid(ind_array, ind_array)
for i, (x_val, y_val) in enumerate(zip(x.flatten(), y.flatten())):
c = 'x' if i%2 else 'o'
ax.text(x_val, y_val, c, va='center', ha='center')
#alternatively, you could do something like
#for x_val, y_val in zip(x.flatten(), y.flatten()):
# c = 'x' if (x_val + y_val)%2 else 'o'
ax.set_xlim(min_val, max_val)
ax.set_ylim(min_val, max_val)
ax.set_xticks(np.arange(max_val))
ax.set_yticks(np.arange(max_val))
ax.grid()
Edit:
Here is an updated example with an imshow background.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
min_val, max_val, diff = 0., 10., 1.
#imshow portion
N_points = (max_val - min_val) / diff
imshow_data = np.random.rand(N_points, N_points)
ax.imshow(imshow_data, interpolation='nearest')
#text portion
ind_array = np.arange(min_val, max_val, diff)
x, y = np.meshgrid(ind_array, ind_array)
for x_val, y_val in zip(x.flatten(), y.flatten()):
c = 'x' if (x_val + y_val)%2 else 'o'
ax.text(x_val, y_val, c, va='center', ha='center')
#set tick marks for grid
ax.set_xticks(np.arange(min_val-diff/2, max_val-diff/2))
ax.set_yticks(np.arange(min_val-diff/2, max_val-diff/2))
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_xlim(min_val-diff/2, max_val-diff/2)
ax.set_ylim(min_val-diff/2, max_val-diff/2)
ax.grid()
plt.show()

For your graph you should should try with pyplot.table:
import matplotlib.pyplot as plt
import numpy as np
board = np.zeros((10, 10))
board[0,0] = 1
board[0,1] = -1
board[0,2] = 1
def visBoard(board):
data = np.empty(board.shape,dtype=np.str)
data[:,:] = ' '
data[board==1.0] = 'X'
data[board==-1.0] = 'O'
plt.axis('off')
size = np.ones(board.shape[0])/board.shape[0]
plt.table(cellText=data,loc='center',colWidths=size,cellLoc='center',bbox=[0,0,1,1])
plt.show()
visBoard(board)

Some elaboration on the code of #wflynny making it into a function that takes any matrix no matter what size and plots its values.
import numpy as np
import matplotlib.pyplot as plt
cols = np.random.randint(low=1,high=30)
rows = np.random.randint(low=1,high=30)
X = np.random.rand(rows,cols)
def plotMat(X):
fig, ax = plt.subplots()
#imshow portion
ax.imshow(X, interpolation='nearest')
#text portion
diff = 1.
min_val = 0.
rows = X.shape[0]
cols = X.shape[1]
col_array = np.arange(min_val, cols, diff)
row_array = np.arange(min_val, rows, diff)
x, y = np.meshgrid(col_array, row_array)
for col_val, row_val in zip(x.flatten(), y.flatten()):
c = '+' if X[row_val.astype(int),col_val.astype(int)] < 0.5 else '-'
ax.text(col_val, row_val, c, va='center', ha='center')
#set tick marks for grid
ax.set_xticks(np.arange(min_val-diff/2, cols-diff/2))
ax.set_yticks(np.arange(min_val-diff/2, rows-diff/2))
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_xlim(min_val-diff/2, cols-diff/2)
ax.set_ylim(min_val-diff/2, rows-diff/2)
ax.grid()
plt.show()
plotMat(X)