Merge pull request #562 from e-kwsm/docs

fix typos, math functions, and markdown syntax

CITATIONS.rst

@@ -66,7 +66,7 @@ Software
 Blogs
 ~~~~~
 
-* `Sensitivity Analyis in Python <http://www.perrygeo.com/sensitivity-analysis-in-python.html>`_
+* `Sensitivity Analysis in Python <http://www.perrygeo.com/sensitivity-analysis-in-python.html>`_
 * `Sensitivity Analysis with SALib <http://keyboardscientist.weebly.com/blog/sensitivity-analysis-with-salib>`_
 * `Running Sobol using SALib <https://waterprogramming.wordpress.com/2013/08/05/running-sobol-sensitivity-analysis-using-salib/>`_
 * `Extensions of SALib for more complex sensitivity analyses <https://waterprogramming.wordpress.com/2014/02/11/extensions-of-salib-for-more-complex-sensitivity-analyses/>`_

CONTRIBUTING.md

@@ -26,34 +26,38 @@ To contribute new code, submit a pull request. There are two instances in which
 
 ### Making a development environment
 
-.. note::
-
-    We **strongly** recommend using a virtual environment setup, such as
-    ``venv`` or ``conda``.
+Note: We **strongly** recommend using a virtual environment setup, such as
+`venv` or `conda`.
 
 First, fork a copy of the main SALib repository in GitHub onto your own
-account and then create your local repository via::
+account and then create your local repository via:
 
-    git clone git@github.com:YOURUSERNAME/SALib.git
-    cd SALib
+```sh
+git clone git@github.com:YOURUSERNAME/SALib.git
+cd SALib
+```
 
 Next, set up your development environment.
 
-With ``conda`` installed (through
-`Miniforge or Mambaforge <https://github.com/conda-forge/miniforge>`_,
-`Miniconda <https://docs.conda.io/en/latest/miniconda.html>`_ or
-`Anaconda <https://www.anaconda.com/products/individual>`_), execute the
+With `conda` installed (through
+[Miniforge or Mambaforge](https://github.com/conda-forge/miniforge),
+[Miniconda](https://docs.conda.io/en/latest/miniconda.html) or
+[Anaconda](https://www.anaconda.com/products/individual)), execute the
 following commands at the terminal from the base directory of your
-`SALib <https://github.com/SALib/SALib>`_ clone::
+[SALib](https://github.com/SALib/SALib) clone:
 
-    # Create an environment with all development dependencies
-    conda env create -f environment.yml  # works with `mamba` too
-    # Activate the environment
-    conda activate SALib
+```sh
+# Create an environment with all development dependencies
+conda env create -f environment.yml  # works with `mamba` too
+# Activate the environment
+conda activate SALib
+```
 
-Finally, you can install SALib in editable mode in your environment::
+Finally, you can install SALib in editable mode in your environment:
 
-    pip install -e .
+```sh
+pip install -e .
+```
 
 ### Fixing a Bug
 
@@ -64,7 +68,7 @@ Then, please create a new branch with the name `bug_xxx` where xxx is the number
 If possible, write a test which reproduces the bug. The tests are stored in the `SALib/tests/` folder, and are run using `pytest` from the root folder of the library.
 You can run the tests with the command `pytest` from the root project directory. Individual tests can be run with by specifying a file, or file and test function.
 
-For example::
+For example:
 
 ```bash
 $ pytest  # run all tests
@@ -128,10 +132,10 @@ Thanks again!
 Following is the process that the development team follows in order to make
 a release:
 
-1. Document overview of changes since last release in ``CHANGELOG.MD``
-2. Update the version in the main ``__init__.py``.
-3. Build locally using ``hatch build``, and verify the content of the artifacts
-4. Submit PR, wait for tests to pass, and merge release into ``main``
+1. Document overview of changes since last release in `CHANGELOG.MD`
+2. Update the version in the main `__init__.py`.
+3. Build locally using `hatch build`, and verify the content of the artifacts
+4. Submit PR, wait for tests to pass, and merge release into `main`
 5. Tag release with version number and push to SALib repo
 6. Check that release has been deployed to PyPI
 7. Check documentation is built and deployed to readthedocs (http://salib.readthedocs.org)

README.rst

@@ -220,7 +220,7 @@ Software
 Blogs
 ~~~~~
 
-* `Sensitivity Analyis in Python <http://www.perrygeo.com/sensitivity-analysis-in-python.html>`_
+* `Sensitivity Analysis in Python <http://www.perrygeo.com/sensitivity-analysis-in-python.html>`_
 * `Sensitivity Analysis with SALib <http://keyboardscientist.weebly.com/blog/sensitivity-analysis-with-salib>`_
 * `Running Sobol using SALib <https://waterprogramming.wordpress.com/2013/08/05/running-sobol-sensitivity-analysis-using-salib/>`_
 * `Extensions of SALib for more complex sensitivity analyses <https://waterprogramming.wordpress.com/2014/02/11/extensions-of-salib-for-more-complex-sensitivity-analyses/>`_

docs/user_guide/basics.rst

@@ -60,7 +60,7 @@ analysis methods because it exhibits strong nonlinearity and nonmonotonicity.
 
 .. math::
 
-    f(x) = sin(x_1) + a sin^2(x_2) + b x_3^4 sin(x_1)
+    f(x) = \sin(x_1) + a \sin^2(x_2) + b x_3^4 \sin(x_1)
 
 Importing SALib
 ~~~~~~~~~~~~~~~
@@ -333,7 +333,7 @@ Now we can extract the first-order Sobol indices for each bin of :math:`x` and p
     :width: 800
     :align: center
 
-With the help of the plots, we interprete the Sobol indices. At
+With the help of the plots, we interpret the Sobol indices. At
 :math:`x=0`, the variation in :math:`y` can be explained to 100 % by
 parameter :math:`a` as the contribution to :math:`y` from :math:`b
 x^2` vanishes. With larger :math:`|x|`, the contribution to the

docs/user_guide/basics_with_interface.rst

@@ -18,7 +18,7 @@ function, shown below.
 
 .. math::
 
-    f(x) = sin(x_1) + a sin^2(x_2) + b x_3^4 sin(x_1)
+    f(x) = \sin(x_1) + a \sin^2(x_2) + b x_3^4 \sin(x_1)
 
 
 The ProblemSpec Interface
@@ -551,7 +551,7 @@ Now we can extract the first-order Sobol indices for each bin of :math:`x` and p
     :align: center
 
 
-With the help of the plots, we interprete the Sobol indices. At
+With the help of the plots, we interpret the Sobol indices. At
 :math:`x=0`, the variation in :math:`y` can be explained to 100 % by
 parameter :math:`a` as the contribution to :math:`y` from :math:`b
 x^2` vanishes. With larger :math:`|x|`, the contribution to the

src/SALib/analyze/hdmr.py

@@ -177,7 +177,7 @@ def analyze(
     settings = _check_settings(X, Y, maxorder, maxiter, m, K, R, alpha, lambdax)
     init_vars = _init(X, Y, settings)
 
-    # Sensitivity Analysis Computation with/without bootstraping
+    # Sensitivity Analysis Computation with/without bootstrapping
     SA, Em, RT, Y_em, idx = _compute(X, Y, settings, init_vars)
 
     # Finalize results
@@ -228,7 +228,7 @@ def _check_settings(X, Y, maxorder, maxiter, m, K, R, alpha, lambdax):
     # Important next check for maxorder - as maxorder relates to d
     if (d == 2) and (maxorder > 2):
         raise RuntimeError(
-            'SALib-HDMR ERRROR: Field "maxorder" of options has to be 2 as'
+            'SALib-HDMR ERROR: Field "maxorder" of options has to be 2 as'
             " d = 2 (X has two columns)"
         )
 
@@ -373,7 +373,7 @@ def _init(X, Y, settings):
         # No Bootstrap
         idx = np.arange(0, N).reshape(N, 1)
     else:
-        # Now setup the boostrap matrix with selection matrix, idx, for samples
+        # Now setup the bootstrap matrix with selection matrix, idx, for samples
         idx = np.argsort(np.random.rand(N, K), axis=0)[:R]
 
     # Compute normalized X-values
@@ -397,7 +397,7 @@ def _init(X, Y, settings):
         c3 = np.asarray(list(itertools.combinations(np.arange(0, d), 3)))
         n3 = c3.shape[0]
 
-    # calulate total number of coefficients
+    # calculate total number of coefficients
     n = n1 + n2 + n3
 
     # Initialize m1, m2 and m3 - number of coefficients first, second, third

src/SALib/analyze/rbd_fast.py

@@ -131,7 +131,7 @@ def compute_first_order(permuted_outputs, M):
 
 def unskew_S1(S1, M, N):
     """
-    Unskew the sensivity indice
+    Unskew the sensitivity indices
     (Jean-Yves Tissot, Clémentine Prieur (2012) "Bias correction for the
     estimation of sensitivity indices based on random balance designs.",
     Reliability Engineering and System Safety, Elsevier, 107, 205-213.

src/SALib/plotting/hdmr.py

@@ -3,7 +3,7 @@
 
 @author: @sahin-abdullah
 
-This submodule produces two diffent figures: (1) emulator vs simulator,
+This submodule produces two different figures: (1) emulator vs simulator,
 (2) regression lines of first order component functions
 """
 import matplotlib.pyplot as plt

src/SALib/sample/morris/morris.py

@@ -224,7 +224,7 @@ def _generate_trajectory(
     # Matrix J - a (g+1)-by-num_params matrix of ones
     J = np.ones((num_groups + 1, num_params))
 
-    # Matrix D* - num_params-by-num_params matrix which decribes whether
+    # Matrix D* - num_params-by-num_params matrix which describes whether
     # factors move up or down
     D_star = np.diag(rd.choice([-1, 1], num_params))
 

src/SALib/util/__init__.py

@@ -232,7 +232,7 @@ def _nonuniform_scale_samples(params, bounds, dists):
                 )
 
         # lognormal distribution (ln-space, not base-10)
-        # paramters are ln-space mean and standard deviation
+        # parameters are ln-space mean and standard deviation
         elif dists[i] == "lognorm":
             # checking for valid parameters
             if b2 <= 0: