The imshow() function with parameters interpolation=’nearest’ and cmap=’hot’ should do what you want. Please review the interpolation parameter details, and see Interpolations for imshow and Image antialiasing. import matplotlib.pyplot as plt import numpy as np a = np.random.random((16, 16)) plt.imshow(a, cmap=’hot’, interpolation=’nearest’) plt.show()
You can also set figure size by passing dictionary to rc parameter with key ‘figure.figsize’ in seaborn set method: import seaborn as sns sns.set(rc={‘figure.figsize’:(11.7,8.27)}) Other alternative may be to use figure.figsize of rcParams to set figure size as below: from matplotlib import rcParams # figure size in inches rcParams[‘figure.figsize’] = 11.7,8.27 More details can be … Read more
You can use the Axes.set_yscale method. That allows you to change the scale after the Axes object is created. That would also allow you to build a control to let the user pick the scale if you needed to. The relevant line to add is: ax.set_yscale(‘log’) You can use ‘linear’ to switch back to a … Read more
I think this would be best explained by the following picture: To initialize the above, one would type: import matplotlib.pyplot as plt fig = plt.figure() fig.add_subplot(221) #top left fig.add_subplot(222) #top right fig.add_subplot(223) #bottom left fig.add_subplot(224) #bottom right plt.show()
Get current axis via plt.gca(), and then set its limits: ax = plt.gca() ax.set_xlim([xmin, xmax]) ax.set_ylim([ymin, ymax])
Functions dealing with text like label, title, etc. accept parameters same as matplotlib.text.Text. For the font size you can use size/fontsize: from matplotlib import pyplot as plt fig = plt.figure() plt.plot(data) fig.suptitle(‘test title’, fontsize=20) plt.xlabel(‘xlabel’, fontsize=18) plt.ylabel(‘ylabel’, fontsize=16) fig.savefig(‘test.jpg’) For globally setting title and label sizes, mpl.rcParams contains axes.titlesize and axes.labelsize. (From the page): axes.titlesize … Read more
I think @Evert has the right answer: plt.scatter(dates,values) plt.plot(dates, values) plt.show() Which is pretty much the same as plt.plot(dates, values, ‘-o’) plt.show() You can replace -o with another suitable format string as described in the documentation. You can also split the choices of line and marker styles using the linestyle= and marker= keyword arguments.
You may try plt.savefig(‘X:/’ + newName + ‘.png’, bbox_inches=”tight”) Or you may define figure size like fig = plt.figure(figsize=(9, 11)) … plt.savefig(filename, bbox_inches=”tight”)
It sounds like you want axvspan, rather than one of the fill between functions. The differences is that axvspan (and axhspan) will fill up the entire y (or x) extent of the plot regardless of how you zoom. For example, let’s use axvspan to highlight the x-region between 8 and 14: import matplotlib.pyplot as plt … Read more
import matplotlib.pyplot as plt from matplotlib.dates import date2num import datetime x = [ datetime.datetime(2011, 1, 4, 0, 0), datetime.datetime(2011, 1, 5, 0, 0), datetime.datetime(2011, 1, 6, 0, 0) ] x = date2num(x) y = [4, 9, 2] z = [1, 2, 3] k = [11, 12, 13] ax = plt.subplot(111) ax.bar(x-0.2, y, width=0.2, color=”b”, align=’center’) … Read more