Matplotlib, Datetimes, And Typeerror: Ufunc 'isfinite' Not Supported For The Input Types…

Here is a tiny piece of code that produces a filled in region between two lines of a graph: import matplotlib.pyplot as plt import numpy as np x = np.arange(0.0, 2, 0.01) y1 = np.

Solution 1:

The problem is, that the numpy ufunc isfinite is not defined for the numpy.datetime64 dtype. There is an effort to change this, though. This issue on numpy's github is being worked on in this pull-request, but as long as this is not finished up and merged, you will not be able to use isfinite on that dtype. This is a problem as matplotlib.pyplot.fill_between is using this function implicitly, when calling numpy.ma.masked_invalid to mask all invalid entries of your input array.

There is a work-around though. As pointed out in this answer to a similar question concerning fill_between plotting of a pandas Series of datetime64 type, pandas registers a custom converter for (among others) numpy arrays of datetime64 dtype with matplotlib. To make use of that, you simply have to import pandas:

import numpy as np
import matplotlib.pyplot as plt
import datetime
# import pandas for its converter that is then used in pyplot!import pandas

x1 = np.arange(0.0, 2, 0.01)
now = np.datetime64(datetime.datetime.now())
x = np.array([now - np.timedelta64(datetime.timedelta(seconds=i))
              for i inrange(200)])
y1 = np.sin(2 * np.pi * x1)
y2 = 1.2 * np.sin(4 * np.pi * x1)

fig, ax1 = plt.subplots(1, 1, sharex=True)

# Test support for masked arrays.
ax1.fill_between(x, 0, y1)
ax1.set_ylabel('between y1 and 0')
y2 = np.ma.masked_greater(y2, 1.0)
ax1.plot(x, y1, x, y2, color='black')
ax1.fill_between(
    x, y1, y2, where=y2 >= y1,
    facecolor='green',
    interpolate=True)
ax1.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red', interpolate=True)
ax1.set_title('Now regions with y2>1 are masked')

# Show the plot.
plt.show()

will work and give you your desired output:

Solution 2:

I had a similar issue with ax.setxlims, because I forgot to cast some datetime to np.datetime.

>>> import datetime
>>> import numpy
>>> a = datetime.datetime.now()
>>> numpy.isfinite(a)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: ufunc 'isfinite'not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''>>> a = numpy.datetime64(a)
>>> numpy.isfinite(a)
True

It looks like it is no longer required to import pandas