# test_Import_test module¶

@date Created on Fri Feb 22 13:36:22 2019 @author pierre_b

uniform(low=0.0, high=1.0, size=None)

Draw samples from a uniform distribution.

Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform.

Note

New code should use the uniform method of a default_rng() instance instead; see random-quick-start.

Parameters: low (float or array_like of floats, optional) – Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0. high (float or array_like of floats) – Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0. size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if low and high are both scalars. Otherwise, np.broadcast(low, high).size samples are drawn. out – Drawn samples from the parameterized uniform distribution. ndarray or scalar

randint()
Discrete uniform distribution, yielding integers.
random_integers()
Discrete uniform distribution over the closed interval [low, high].
random_sample()
Floats uniformly distributed over [0, 1).
random()
Alias for random_sample.
rand()
Convenience function that accepts dimensions as input, e.g., rand(2,2) would generate a 2-by-2 array of floats, uniformly distributed over [0, 1).
Generator.uniform()
which should be used for new code.

Notes

The probability density function of the uniform distribution is

$p(x) = \frac{1}{b - a}$

anywhere within the interval [a, b), and zero elsewhere.

When high == low, values of low will be returned. If high < low, the results are officially undefined and may eventually raise an error, i.e. do not rely on this function to behave when passed arguments satisfying that inequality condition.

Examples

Draw samples from the distribution:

>>> s = np.random.uniform(-1,0,1000)


All values are within the given interval:

>>> np.all(s >= -1)
True
>>> np.all(s < 0)
True


Display the histogram of the samples, along with the probability density function:

>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 15, density=True)
>>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
>>> plt.show()

class unittest_Import_meth(methodName='runTest')[source]

Bases: unittest.case.TestCase

unittest for Import object methods

test_ImportMatrix_01(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_02(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_03(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_04(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_05(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_06(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_07(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_08(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_09(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_10(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_11(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_12(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_13(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_14(test_dict)

Check that the import of a Matrix is correct

test_ImportMatrix_15(test_dict)

Check that the import of a Matrix is correct

test_ImportGenMatrixSin_init()[source]

Check that the ImportGenMatrixSin can be set by list

test_ImportGenMatrixSin_Error()[source]

Check that the ImportGenMatrixSin can detect wrong input