find module¶
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find_test_value
(prop_dict, return_type)[source]¶ Find a appropriate value to test the property of the return_type
Parameters: - prop_dict (dict) – Dictionary containing the property informations
- return_type (str) – type of the value for the test (can be different of the property one)
Returns: value – A “return_type” value for the test
Return type: ?
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find_test_ndarray
(prop_dict)[source]¶ Find a correct value to test ndarray
Parameters: prop_dict (dict) – dictionary containing the information of the property to test Returns: value – A value to test the property Return type: numpy.ndarray
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find_num_value
(prop_dict, is_int_return)[source]¶ Find a value to test Double or Integer matching min/max
Parameters: - prop_dict (dict) – dictionary containing the information on the property to test
- is_int_return (bool) – To convert the value to int (if needed)
Returns: value – value for the test
Return type: int/float
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is_type_list
(type_name)[source]¶ Check if the type_name is a list of pyleecan objects “[class_name]”
Parameters: type_name (str) – name of the type to test Returns: is_list – True if the type is a list of pyleecan objects Return type: bool
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is_type_dict
(type_name)[source]¶ Check if the type_name is a dict of pyleecan objects “{class_name}”
Parameters: type_name (str) – name of the type to test Returns: is_list – True if the type is a dict of pyleecan objects Return type: bool
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random_sample
(size=None)¶ Return random floats in the half-open interval [0.0, 1.0).
Results are from the “continuous uniform” distribution over the stated interval. To sample \(Unif[a, b), b > a\) multiply the output of random_sample by (b-a) and add a:
(b - a) * random_sample() + a
Note
New code should use the
random
method of adefault_rng()
instance instead; see random-quick-start.Parameters: size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k)
, thenm * n * k
samples are drawn. Default is None, in which case a single value is returned.Returns: out – Array of random floats of shape size (unless size=None
, in which case a single float is returned).Return type: float or ndarray of floats See also
Generator.random()
- which should be used for new code.
Examples
>>> np.random.random_sample() 0.47108547995356098 # random >>> type(np.random.random_sample()) <class 'float'> >>> np.random.random_sample((5,)) array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428]) # random
Three-by-two array of random numbers from [-5, 0):
>>> 5 * np.random.random_sample((3, 2)) - 5 array([[-3.99149989, -0.52338984], # random [-2.99091858, -0.79479508], [-1.23204345, -1.75224494]])