Docs: Replace all http links with secure https URLs

ema_workbench/analysis/b_and_w_plotting.py

@@ -70,7 +70,7 @@ def set_ax_lines_bw(ax, colormap, line_style="continuous"):
     suitable for black and white viewing.
 
     Derived from and expanded for use in the EMA workbench from:
-    http://stackoverflow.com/questions/7358118/matplotlib-black-white-colormap-with-dashes-dots-etc
+    https://stackoverflow.com/questions/7358118/matplotlib-black-white-colormap-with-dashes-dots-etc
 
     Parameters
     ----------
@@ -320,7 +320,7 @@ def set_fig_to_bw(fig, style=HATCHING, line_style="continuous"):
 
 
     derived from and expanded for my use from:
-    http://stackoverflow.com/questions/7358118/matplotlib-black-white-colormap-with-dashes-dots-etc
+    https://stackoverflow.com/questions/7358118/matplotlib-black-white-colormap-with-dashes-dots-etc
 
     Parameters
     ----------

ema_workbench/analysis/feature_scoring.py

@@ -180,19 +180,19 @@ def get_rf_feature_scores(
     nr_trees : int, optional
                nr. of trees in forest (default=250)
     max_features : int, optional
-                   see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
+                   see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
     max_depth : int, optional
-                see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
+                see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
     min_samples : int, optional
-                  see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
+                  see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
     min_samples_leaf : int, optional
-                       see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
+                       see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
     bootstrap : bool, optional
-                see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
+                see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
     oob_score : bool, optional
-                see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
+                see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
     random_state : int, optional
-                   see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
+                   see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
 
     Returns
     -------
@@ -267,26 +267,26 @@ def get_ex_feature_scores(
     max_features : int, float, string or None, optional
                    by default, it will use number of features/3, following
                    Jaxa-Rozen & Kwakkel (2018) doi: 10.1016/j.envsoft.2018.06.011
-                   see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
+                   see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
     max_depth : int, optional
-                see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
+                see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
     min_samples_split : int, optional
-                  see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
+                  see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
     min_samples_leaf : int, optional
                        defaults to 1 for N=1000 or lower, from there on
                        proportional to sqrt of N
                        (see discussion in Jaxa-Rozen & Kwakkel (2018) doi: 10.1016/j.envsoft.2018.06.011)
-                       see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
+                       see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
     min_weight_fraction_leaf : float, optional
-                               see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
+                               see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
     max_leaf_nodes: int or None, optional
-                    see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
+                    see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
     bootstrap : bool, optional
-                see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
+                see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
     oob_score : bool, optional
-                see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
+                see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
     random_state : int, optional
-                   see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
+                   see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
 
     Returns
     -------

ema_workbench/analysis/pairs_plotting.py

@@ -30,7 +30,7 @@ def pairs_lines(
 ):
     """
 
-    Generate a `R style pairs <http://www.stat.psu.edu/~dhunter/R/html/graphics/html/pairs.html>`_
+    Generate a `R style pairs <https://www.stat.psu.edu/~dhunter/R/html/graphics/html/pairs.html>`_
     lines multiplot. It shows the behavior of two outcomes over time against
     each other. The origin is denoted with a circle and the end is denoted
     with a '+'.
@@ -168,14 +168,14 @@ def pairs_density(
 ):
     """
 
-    Generate a `R style pairs <http://www.stat.psu.edu/~dhunter/R/html/graphics/html/pairs.html>`_
+    Generate a `R style pairs <https://www.stat.psu.edu/~dhunter/R/html/graphics/html/pairs.html>`_
     hexbin density multiplot. In case of time-series data, the end
     states are used.
 
     hexbin makes hexagonal binning plot of x versus y, where x, y are 1-D
     sequences of the same length, N. If C is None (the default), this is a
     histogram of the number of occurences of the observations at (x[i],y[i]).
-    For further detail see `matplotlib on hexbin <http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.hexbin>`_
+    For further detail see `matplotlib on hexbin <https://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.hexbin>`_
 
     Parameters
     ----------
@@ -207,7 +207,7 @@ def pairs_density(
                controls the gridsize for the hexagonal bining. (default = 50)
     cmap : str
            color map that is to be used in generating the hexbin. For details
-           on the available maps, see `pylab <http://matplotlib.sourceforge.net/examples/pylab_examples/show_colormaps.html#pylab-examples-show-colormaps>`_.
+           on the available maps, see `pylab <https://matplotlib.sourceforge.net/examples/pylab_examples/show_colormaps.html#pylab-examples-show-colormaps>`_.
            (Defaults = coolwarm)
     filter_scalar: bool, optional
                    remove the non-time-series outcomes. Defaults to True.
@@ -418,7 +418,7 @@ def pairs_scatter(
 ):
     """
 
-    Generate a `R style pairs <http://www.stat.psu.edu/~dhunter/R/html/graphics/html/pairs.html>`_
+    Generate a `R style pairs <https://www.stat.psu.edu/~dhunter/R/html/graphics/html/pairs.html>`_
     scatter multiplot. In case of time-series data, the end states are used.
 
     Parameters

ema_workbench/analysis/prim.py

@@ -73,7 +73,7 @@ def pca_preprocess(experiments, y, subsets=None, exclude=set()):
 
     Pre-process the data by performing a pca based rotation on it.
     This effectively turns the algorithm into PCA-PRIM as described
-    in `Dalal et al (2013) <http://www.sciencedirect.com/science/article/pii/S1364815213001345>`_
+    in `Dalal et al (2013) <https://www.sciencedirect.com/science/article/pii/S1364815213001345>`_
 
     Parameters
     ----------

ema_workbench/connectors/excel.py

@@ -1,7 +1,7 @@
 """
 
 This module provides a base class that can be used to perform EMA on
-Excel models. It relies on `win32com <http://python.net/crew/mhammond/win32/Downloads.html>`_
+Excel models. It relies on `win32com <https://python.net/crew/mhammond/win32/Downloads.html>`_
 
 """
 
@@ -38,7 +38,7 @@ class BaseExcelModel(FileModel):
     Base class for connecting the EMA workbench to models in Excel. To
     automate this connection as much as possible. This implementation relies
     on naming cells in Excel. These names can then be used here as names
-    for the uncertainties and the outcomes. See e.g. `this site <http://spreadsheets.about.com/od/exceltips/qt/named_range.htm>`_
+    for the uncertainties and the outcomes. See e.g. `this site <https://spreadsheets.about.com/od/exceltips/qt/named_range.htm>`_
     for details on naming cells and sets of cells.
 
     The provided implementation here does work with :mod:`parallel_ema`.
@@ -139,7 +139,7 @@ def run_experiment(self, experiment):
         """
         Method for running an instantiated model structures. This
         implementation assumes that the names of the uncertainties correspond
-        to the name of the cells in Excel. See e.g. `this site <http://spreadsheets.about.com/od/exceltips/qt/named_range.htm>`_
+        to the name of the cells in Excel. See e.g. `this site <https://spreadsheets.about.com/od/exceltips/qt/named_range.htm>`_
         for details or use Google and search on 'named range'. One of the
         requirements on the names is that they cannot contains spaces.
 

ema_workbench/em_framework/ema_multiprocessing.py

@@ -176,7 +176,7 @@ class LogQueueReader(threading.Thread):
     This thread reads the records written by subprocesses and writes them to
     the handlers defined in the main process's handlers.
 
-    found `online <http://stackoverflow.com/questions/641420/how-should-i-log-while-using-multiprocessing-in-python>`_
+    found `online <https://stackoverflow.com/questions/641420/how-should-i-log-while-using-multiprocessing-in-python>`_
 
     TODO:: should be generalized with logwatcher used with ipyparallel
 

ema_workbench/examples/excel_example.py

@@ -4,7 +4,7 @@
 This file illustrated the use the EMA classes for a model in Excel.
 
 It used the excel file provided by
-`A. Sharov <http://home.comcast.net/~sharov/PopEcol/lec10/fullmod.html>`_
+`A. Sharov <https://home.comcast.net/~sharov/PopEcol/lec10/fullmod.html>`_
 
 This excel file implements a simple predator prey model.
 

ema_workbench/examples/flu_example.py

@@ -2,7 +2,7 @@
 Created on 20 dec. 2010
 
 This file illustrated the use of the workbench for a model
-specified in Python itself. The example is based on `Pruyt & Hamarat <http://www.systemdynamics.org/conferences/2010/proceed/papers/P1253.pdf>`_.
+specified in Python itself. The example is based on `Pruyt & Hamarat <https://www.systemdynamics.org/conferences/2010/proceed/papers/P1253.pdf>`_.
 For comparison, run both this model and the flu_vensim_no_policy_example.py and
 compare the results.
 

ema_workbench/examples/lookup_example.py

@@ -3,7 +3,7 @@
 
 This is a simple example of the lookup uncertainty provided for
 use in conjuction with vensim models. This example is largely based on
-`Eker et al. (2014) <http://onlinelibrary.wiley.com/doi/10.1002/sdr.1518/suppinfo>`_
+`Eker et al. (2014) <https://onlinelibrary.wiley.com/doi/10.1002/sdr.1518/suppinfo>`_
 
 @author: sibeleker
 @author: jhkwakkel

ema_workbench/examples/prim_wcm_example.py

@@ -4,7 +4,7 @@
 This example demonstrates the use of PRIM. The dataset was generated
 using the world container model
 
-(Tavasszy et al 2011; http://dx.doi.org/10.1016/j.jtrangeo.2011.05.005)
+(Tavasszy et al 2011; https://dx.doi.org/10.1016/j.jtrangeo.2011.05.005)
 
 
 """

ema_workbench/util/ema_exceptions.py

@@ -1,7 +1,7 @@
 """
 
 Exceptions and warning used internally by the EMA workbench. In line with
-advice given in `PEP 8 <http://www.python.org/dev/peps/pep-0008/>`_.
+advice given in `PEP 8 <https://www.python.org/dev/peps/pep-0008/>`_.
 """
 
 # Created on 31 mei 2011