Merge remote-tracking branch 'origin/Development' into feature/CI

# Conflicts:
#	setup.py

README.rst

@@ -9,7 +9,7 @@ Uncertainty Quantification with python (UQpy)
 
 :Authors: Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Aakash Bangalore Satish, Mohit Singh Chauhan, Lohit Vandanapu, Ketson RM dos Santos, Katiana Kontolati
 :Contact: [email protected]
-:Version: 3.0.3
+:Version: 3.0.4
 
 
 Description
@@ -85,7 +85,7 @@ For assistance with the UQpy software package, please raise an issue on the Gith
 
 .. image:: https://img.shields.io/pypi/dm/UQpy?style=plastic   :alt: PyPI - Downloads
 .. image:: https://img.shields.io/conda/dn/conda-forge/UQpy?style=plastic   :alt: Conda
-.. image:: https://img.shields.io/github/downloads/SURGroup/UQpy/v3.0.1/total?style=plastic   :alt: GitHub Releases (by Release)
+.. image:: https://img.shields.io/github/downloads/SURGroup/UQpy/V3.0.4/total?style=plastic   :alt: GitHub Releases (by Release)
 
 .. image:: https://img.shields.io/pypi/v/UQpy?style=plastic   :alt: PyPI
 .. image:: https://img.shields.io/conda/v/conda-forge/UQpy?style=plastic   :alt: Conda

docs/source/runmodel_doc.rst

@@ -87,7 +87,7 @@ To execute ``RunModel``, the working directory must contain the necessary files
 .. image:: _static/Runmodel_directory_1.png
    :width: 300
 
-To avoid cluttering the working directory with outputs, ``RunModel`` creates a directory for each execution of the model and saves the output generated during the  model execution within the corresponding directory. ``RunModel`` generates the directory name for the sample as `run_n_timestamp`, where `n` is the python index of the run and `timestamp` corresponds to the time at the beginning of the first simulation of the parallel job. This is illustrated in the figure below.
+To avoid cluttering the working directory with outputs, ``RunModel`` creates a directory for each execution of the model and saves the output generated during the  model execution within the corresponding directory. ``RunModel`` generates the directory name for the sample as `run_n`, where `n` is the python index of the run and `timestamp` corresponds to the time at the beginning of the first simulation of the parallel job. This is illustrated in the figure below.
 
 .. image:: _static/Runmodel_directory_2.png
    :width: 300

example/RunModel/Abaqus_Example/abaqus_example.py

@@ -18,7 +18,7 @@
 print('Example: Created the model object.')
 
 # Towards defining the sampling scheme
-# The fire load density is assumed to be uniformly distributed between 50 MJ/number_of_variables^2 and 450 MJ/number_of_variables^2.
+# The fire load density is assumed to be uniformly distributed between 50 MJ/m^2 and 450 MJ/m^2.
 # The yield strength is assumed to be normally distributed, with the parameters
 # being: mean = 250 MPa and coefficient of variation of 7%.
 

example/RunModel/Abaqus_Example/extract_abaqus_output.py

@@ -24,8 +24,8 @@ def extract_disp_temp_output(index):
                     os.remove(os.path.join(dir_name, item))
 
             # Compute the maximum allowable displacement
-            length_of_beam = 1  # in number_of_variables
-            depth_of_beam = 0.035  # in number_of_variables
+            length_of_beam = 1  # in m
+            depth_of_beam = 0.035  # in m
             max_allowable_disp = length_of_beam ** 2 / (400 * depth_of_beam)
 
             # Obtain the maximum midpoint displacement

example/RunModel/Matlab_Example/matlab_model.py

@@ -3,11 +3,11 @@
 
 
 def matlab(index):
-    command1 = "cp ./InputFiles/dummy_model_" + str(index) + ".number_of_variables ."
+    command1 = "cp ./InputFiles/dummy_model_" + str(index) + ".m ."
     command2 = "/Applications/MATLAB_R2018a.app/bin/matlab " \
-               "-nosplash -nojvm -nodisplay -nodesktop -r 'run dummy_model_" + str(index) + ".number_of_variables; exit'"
+               "-nosplash -nojvm -nodisplay -nodesktop -r 'run dummy_model_" + str(index) + ".m; exit'"
     command3 = "mv ./OutputFiles/oupt.out ./OutputFiles/oupt_" + str(index) + ".out"
-    command4 = "rm dummy_model_" + str(index) + ".number_of_variables"
+    command4 = "rm dummy_model_" + str(index) + ".m"
     os.system(command1)
     os.system(command2)
     os.system(command3)

example/RunModel/Matlab_Example/matlab_model_det.py

@@ -3,12 +3,12 @@
 
 
 def matlab(index):
-    command1 = "cp ./InputFiles/prod_determinant_" + str(index) + ".number_of_variables ."
+    command1 = "cp ./InputFiles/prod_determinant_" + str(index) + ".m ."
     # The user will need to modify command2 to point to the Matlab application on their system.
     command2 = "/Applications/MATLAB_R2018a.app/bin/matlab " \
-               "-nosplash -nojvm -nodisplay -nodesktop -r 'run prod_determinant_" + str(index) + ".number_of_variables; exit'"
+               "-nosplash -nojvm -nodisplay -nodesktop -r 'run prod_determinant_" + str(index) + ".m; exit'"
     command3 = "mv ./OutputFiles/oupt.out ./OutputFiles/oupt_" + str(index) + ".out"
-    command4 = "rm prod_determinant_" + str(index) + ".number_of_variables"
+    command4 = "rm prod_determinant_" + str(index) + ".m"
     os.system(command1)
     os.system(command2)
     os.system(command3)

example/RunModel/Matlab_Example/matlab_model_det_index.py

@@ -3,12 +3,12 @@
 
 
 def matlab(index):
-    command1 = "cp ./InputFiles/prod_determinant_index_" + str(index) + ".number_of_variables ."
+    command1 = "cp ./InputFiles/prod_determinant_index_" + str(index) + ".m ."
     # The user will need to modify command2 to point to the Matlab application on their system.
     command2 = "/Applications/MATLAB_R2018a.app/bin/matlab " \
-               "-nosplash -nojvm -nodisplay -nodesktop -r 'run prod_determinant_index_" + str(index) + ".number_of_variables; exit'"
+               "-nosplash -nojvm -nodisplay -nodesktop -r 'run prod_determinant_index_" + str(index) + ".m; exit'"
     command3 = "mv ./OutputFiles/oupt.out ./OutputFiles/oupt_" + str(index) + ".out"
-    command4 = "rm prod_determinant_index_" + str(index) + ".number_of_variables"
+    command4 = "rm prod_determinant_index_" + str(index) + ".m"
     os.system(command1)
     os.system(command2)
     os.system(command3)

example/RunModel/Matlab_Example/matlab_model_det_partial.py

@@ -3,12 +3,12 @@
 
 
 def matlab(index):
-    command1 = "cp ./InputFiles/prod_determinant_partial_" + str(index) + ".number_of_variables ."
+    command1 = "cp ./InputFiles/prod_determinant_partial_" + str(index) + ".m ."
     # The user will need to modify command2 to point to the Matlab application on their system.
     command2 = "/Applications/MATLAB_R2018a.app/bin/matlab " \
-               "-nosplash -nojvm -nodisplay -nodesktop -r 'run prod_determinant_partial_" + str(index) + ".number_of_variables; exit'"
+               "-nosplash -nojvm -nodisplay -nodesktop -r 'run prod_determinant_partial_" + str(index) + ".m; exit'"
     command3 = "mv ./OutputFiles/oupt.out ./OutputFiles/oupt_" + str(index) + ".out"
-    command4 = "rm prod_determinant_partial_" + str(index) + ".number_of_variables"
+    command4 = "rm prod_determinant_partial_" + str(index) + ".m"
     os.system(command1)
     os.system(command2)
     os.system(command3)

example/RunModel/Matlab_Example/matlab_model_sum_scalar.py

@@ -3,12 +3,12 @@
 
 
 def matlab(index):
-    command1 = "cp ./InputFiles/sum_scalar_" + str(index) + ".number_of_variables ."
+    command1 = "cp ./InputFiles/sum_scalar_" + str(index) + ".m ."
     # The user will need to modify command2 to point to the Matlab application on their system.
     command2 = "/Applications/MATLAB_R2018a.app/bin/matlab " \
-               "-nosplash -nojvm -nodisplay -nodesktop -r 'run sum_scalar_" + str(index) + ".number_of_variables; exit'"
+               "-nosplash -nojvm -nodisplay -nodesktop -r 'run sum_scalar_" + str(index) + ".m; exit'"
     command3 = "mv ./OutputFiles/oupt.out ./OutputFiles/oupt_" + str(index) + ".out"
-    command4 = "rm sum_scalar_" + str(index) + ".number_of_variables"
+    command4 = "rm sum_scalar_" + str(index) + ".m"
     os.system(command1)
     os.system(command2)
     os.system(command3)

example/RunModel/Matlab_Example/matlab_model_sum_vector.py

@@ -3,12 +3,12 @@
 
 
 def matlab(index):
-    command1 = "cp ./InputFiles/sum_vector_" + str(index) + ".number_of_variables ."
+    command1 = "cp ./InputFiles/sum_vector_" + str(index) + ".m ."
     # The user will need to modify command2 to point to the Matlab application on their system.
     command2 = "/Applications/MATLAB_R2018a.app/bin/matlab " \
-               "-nosplash -nojvm -nodisplay -nodesktop -r 'run sum_vector_" + str(index) + ".number_of_variables; exit'"
+               "-nosplash -nojvm -nodisplay -nodesktop -r 'run sum_vector_" + str(index) + ".m; exit'"
     command3 = "mv ./OutputFiles/oupt.out ./OutputFiles/oupt_" + str(index) + ".out"
-    command4 = "rm sum_vector_" + str(index) + ".number_of_variables"
+    command4 = "rm sum_vector_" + str(index) + ".m"
     os.system(command1)
     os.system(command2)
     os.system(command3)

example/RunModel/Matlab_Example/matlab_model_sum_vector_indexed.py

@@ -3,12 +3,12 @@
 
 
 def matlab(index):
-    command1 = "cp ./InputFiles/sum_vector_indexed_" + str(index) + ".number_of_variables ."
+    command1 = "cp ./InputFiles/sum_vector_indexed_" + str(index) + ".m ."
     # The user will need to modify command2 to point to the Matlab application on their system.
     command2 = "/Applications/MATLAB_R2018a.app/bin/matlab " \
-               "-nosplash -nojvm -nodisplay -nodesktop -r 'run sum_vector_indexed_" + str(index) + ".number_of_variables; exit'"
+               "-nosplash -nojvm -nodisplay -nodesktop -r 'run sum_vector_indexed_" + str(index) + ".m; exit'"
     command3 = "mv ./OutputFiles/oupt.out ./OutputFiles/oupt_" + str(index) + ".out"
-    command4 = "rm sum_vector_indexed_" + str(index) + ".number_of_variables"
+    command4 = "rm sum_vector_indexed_" + str(index) + ".m"
     os.system(command1)
     os.system(command2)
     os.system(command3)

setup.py

@@ -1,30 +1,30 @@
-#!/usr/bin/env python
-import sys
-version = sys.argv[1]
-del sys.argv[1]
-from setuptools import setup, find_packages
-
-setup(
-    name='UQpy',
-    version=version,
-    url='https://github.com/SURGroup/UQpy',
-    description="UQpy is a general purpose toolbox for Uncertainty Quantification",
-    author="Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Aakash Bangalore-Satish, Mohit Chauhan, "
-           "Lohit Vandanapu, Ketson R.M. dos Santos",
-    author_email="[email protected]",
-    license='MIT',
-    platforms=["OSX", "Windows", "Linux"],
-    packages=find_packages("src"),
-    package_dir={"": "src"},
-    package_data={"": ["*.pdf"]},
-    install_requires=[
-        "numpy", "scipy", "matplotlib", "scikit-learn", 'fire'
-    ],
-    classifiers=[
-        'Programming Language :: Python :: 3',
-        'Intended Audience :: Science/Research',
-        'Topic :: Scientific/Engineering :: Mathematics',
-        'License :: OSI Approved :: MIT License',
-        'Natural Language :: English',
-    ],
-)
+#!/usr/bin/env python
+import sys
+version = sys.argv[1]
+del sys.argv[1]
+from setuptools import setup, find_packages
+
+setup(
+    name='UQpy',
+    version=version,
+    url='https://github.com/SURGroup/UQpy',
+    description="UQpy is a general purpose toolbox for Uncertainty Quantification",
+    author="Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Aakash Bangalore-Satish, Mohit Chauhan, "
+           "Lohit Vandanapu, Ketson R.M. dos Santos",
+    author_email="[email protected]",
+    license='MIT',
+    platforms=["OSX", "Windows", "Linux"],
+    packages=find_packages("src"),
+    package_dir={"": "src"},
+    package_data={"": ["*.pdf"]},
+    install_requires=[
+        "numpy", "scipy", "matplotlib", "scikit-learn", 'fire'
+    ],
+    classifiers=[
+        'Programming Language :: Python :: 3',
+        'Intended Audience :: Science/Research',
+        'Topic :: Scientific/Engineering :: Mathematics',
+        'License :: OSI Approved :: MIT License',
+        'Natural Language :: English',
+    ],
+)

src/UQpy/Distributions.py

@@ -181,7 +181,7 @@ class Distribution:
             Generated iid samples, `ndarray` of shape `(npoints, dimension)`.
 
     **moments** *(moments2return='mvsk')*
-        Computes the mean 'number_of_variables', variance/covariance ('v'), skewness ('s') and/or kurtosis ('k') of the distribution.
+        Computes the mean 'm', variance/covariance ('v'), skewness ('s') and/or kurtosis ('k') of the distribution.
 
         For a univariate distribution, mean, variance, skewness and kurtosis are returned. For a multivariate
         distribution, the mean vector, covariance and vectors of marginal skewness and marginal kurtosis are returned.
@@ -816,16 +816,16 @@ class Binomial(DistributionDiscrete1D):
     """
     Binomial distribution having probability mass function:
 
-    .. math:: f(x) = {number_of_dimensions \choose x} p^x(1-p)^{number_of_dimensions-x}
+    .. math:: f(x) = {n \choose x} p^x(1-p)^{n-x}
 
-    for :math:`x\inumber_of_dimensions\{0, 1, 2, ..., number_of_dimensions\}`.
+    for :math:`x\in\{0, 1, 2, ..., n\}`.
 
     In this standard form `(loc=0)`. Use `loc` to shift the distribution. Specifically, this is equivalent to computing
     :math:`f(y)` where :math:`y=x-loc`.
 
     **Inputs:**
 
-    * **number_of_dimensions** (`int`):
+    * **n** (`int`):
         number of trials, integer >= 0
     * **p** (`float`):
         success probability for each trial, real number in [0, 1]
@@ -837,7 +837,7 @@ class Binomial(DistributionDiscrete1D):
     * ``cdf``, ``pmf``, ``log_pmf``, ``icdf``, ``rvs, moments``.
     """
     def __init__(self, n, p, loc=0.):
-        super().__init__(n=n, p=p, loc=loc, order_params=('number_of_dimensions', 'p', 'loc'))
+        super().__init__(n=n, p=p, loc=loc, order_params=('n', 'p', 'loc'))
         self._construct_from_scipy(scipy_name=stats.binom)
 
 
@@ -947,27 +947,27 @@ def fit(self, x):
         return {'mean': mle_mu, 'cov': mle_cov}
 
     def moments(self, moments2return='mv'):
-        if moments2return == 'number_of_variables':
+        if moments2return == 'm':
             return self.get_params()['mean']
         elif moments2return == 'v':
             return self.get_params()['cov']
         elif moments2return == 'mv':
             return self.get_params()['mean'], self.get_params()['cov']
         else:
-            raise ValueError('UQpy: moments2return must be "number_of_variables", "v" or "mv".')
+            raise ValueError('UQpy: moments2return must be "m", "v" or "mv".')
 
 
 class Multinomial(DistributionND):
     """
     Multinomial distribution having probability mass function
 
-    .. math:: f(x) = \dfrac{number_of_dimensions!}{x_1!\dots x_k!}p_1^{x_1}\dots p_k^{x_k}
+    .. math:: f(x) = \dfrac{n!}{x_1!\dots x_k!}p_1^{x_1}\dots p_k^{x_k}
 
-    for :math:`x=\{x_1,\dots,x_k\}` where each :math:`x_i` is a non-negative integer and :math:`\sum_i x_i = number_of_dimensions`.
+    for :math:`x=\{x_1,\dots,x_k\}` where each :math:`x_i` is a non-negative integer and :math:`\sum_i x_i = n`.
 
     **Inputs:**
 
-    * **number_of_dimensions** (`int`):
+    * **n** (`int`):
         number of trials
     * **p** (`array_like`):
         probability of a trial falling into each category; should sum to 1
@@ -994,24 +994,24 @@ def rvs(self, nsamples=1, random_state=None):
             size=nsamples, random_state=random_state, **self.params).reshape((nsamples, -1))
 
     def moments(self, moments2return='mv'):
-        if moments2return == 'number_of_variables':
-            mean = self.get_params()['number_of_dimensions'] * np.array(self.get_params()['p'])
+        if moments2return == 'm':
+            mean = self.get_params()['n'] * np.array(self.get_params()['p'])
             return mean
         elif moments2return == 'v':
-            n, p = self.get_params()['number_of_dimensions'], np.array(self.get_params()['p'])
+            n, p = self.get_params()['n'], np.array(self.get_params()['p'])
             d = len(p)
             cov = - n * np.tile(p[np.newaxis, :], [d, 1]) * np.tile(p[:, np.newaxis], [1, d])
             np.fill_diagonal(cov, n * p * (1. - p))
             return cov
         elif moments2return == 'mv':
-            n, p = self.get_params()['number_of_dimensions'], np.array(self.get_params()['p'])
+            n, p = self.get_params()['n'], np.array(self.get_params()['p'])
             d = len(p)
             cov = - n * np.tile(p[np.newaxis, :], [d, 1]) * np.tile(p[:, np.newaxis], [1, d])
             np.fill_diagonal(cov, n * p * (1. - p))
             mean = n * p
             return mean, cov
         else:
-            raise ValueError('UQpy: moments2return must be "number_of_variables", "v" or "mv".')
+            raise ValueError('UQpy: moments2return must be "m", "v" or "mv".')
 
 
 class JointInd(DistributionND):
@@ -1514,8 +1514,7 @@ class Frank(Copula):
 
     **Input:**
 
-    * **theta** (`float`):
-        Parameter of the copula, real number in correlation_function\{0}.
+    * **theta** (`float`):R\{0}.
 
     This copula possesses the following methods:
 
@@ -1526,7 +1525,7 @@ class Frank(Copula):
     def __init__(self, theta):
         # Check the input copula_params
         if theta is not None and ((not isinstance(theta, (float, int))) or (theta == 0.)):
-            raise ValueError('Input theta should be a float in correlation_function\{0}.')
+            raise ValueError('Input theta should be a float in R\{0}.')
         super().__init__(theta=theta)
 
     def evaluate_cdf(self, unif):