Fix spelling mistakes (#195)

* Fix spelling mistakes This commit fixes a bunch of spelling mistakes, found and fixed by codespell v2.2.1. All fixes are checked by hand to be actually a fix for an actually misspelled word. * spelling fix: tarbal --> tarball * ema_ipyparallel: Bug/spelling fix: deamon --> daemon * Fix spelling mistakes in Jupyter notebooks
quaquel · Oct 28, 2022 · f040e03 · f040e03
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -10,7 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Highlights
 With the 2.2 release, the EMAworkbench can now connect to [Vadere](https://www.vadere.org/) models on pedestrian dynamics. When inspecting a Prim Box peeling trajectory, multiple points on the peeling trajectory can be inspected simultaneously by inputting a list of integers into [`PrimBox.inspect()`](https://emaworkbench.readthedocs.io/en/latest/ema_documentation/analysis/prim.html#ema_workbench.analysis.prim.PrimBox.inspect).
 
-When running experiments with multiprocessing using the [`MultiprocessingEvaluator`](https://emaworkbench.readthedocs.io/en/latest/ema_documentation/em_framework/evaluators.html#ema_workbench.em_framework.evaluators.MultiprocessingEvaluator), the number of processes can now be controlled using a negative integer as input for `n_processes` (for example, `-2` on a 12-thread CPU results in 10 threads used). Also, it will now default to max. 61 processes on windows machines due to limitations inherent in Windows in dealing with higher processer counts.  Code quality, CI, and error reporting also have been improved. And finally, generating these release notes is now automated.
+When running experiments with multiprocessing using the [`MultiprocessingEvaluator`](https://emaworkbench.readthedocs.io/en/latest/ema_documentation/em_framework/evaluators.html#ema_workbench.em_framework.evaluators.MultiprocessingEvaluator), the number of processes can now be controlled using a negative integer as input for `n_processes` (for example, `-2` on a 12-thread CPU results in 10 threads used). Also, it will now default to max. 61 processes on windows machines due to limitations inherent in Windows in dealing with higher processor counts.  Code quality, CI, and error reporting also have been improved. And finally, generating these release notes is now automated.
 
 ### What's Changed
 #### 🎉 New features added

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -4,7 +4,7 @@ Contributions to the EMAworkbench are very welcome! In this document you will fi
 ### Issues
 Reporting issues, such as bugs or unclairties in the documentation can be done (as usual) via the [Issues](https://github.com/quaquel/EMAworkbench/issues) section. You can also request new features that you would like to see in the EMAworkbench. Please first search if your bug or request already exists.
 
-In case of a bug, please give us as much useful information possible to work with, including your coding environment, code snippets and if nessesary a model. Important are the exact steps to reproduce and what you already found out yourself.
+In case of a bug, please give us as much useful information possible to work with, including your coding environment, code snippets and if necessary a model. Important are the exact steps to reproduce and what you already found out yourself.
 
 ### PRs
 [Pull requests](https://github.com/quaquel/EMAworkbench/pulls) are very welcome, both to the code itself and the documentation. If you make a code change, make sure to document it well. This can be done inline, which will be picked up by Readthedocs, and in case of a larger feature, also in a tutorial and/or example.
@@ -25,7 +25,7 @@ For a simple single-file code or docs change, you can also just edit any file in
 
 
 ## Maintainers
-_This section is targetted at the maintainers_
+_This section is targeted at the maintainers_
 
 ### Merging Pull Requests
 Pull requests should be merged in a way that only [atomic commits](https://en.wikipedia.org/wiki/Atomic_commit) make it in the main branch. This keeps the git commit history clean and tidy, and therefore useful. This means every commit only pertains one fix or feature, and can't be breaked into multiple commits without breaking things. It also means no merge commits are allowed. For more about atomic commits, see [one](https://www.pauline-vos.nl/atomic-commits/) and [two](https://www.aleksandrhovhannisyan.com/blog/atomic-git-commits/).

diff --git a/docs/source/basic_tutorial.rst b/docs/source/basic_tutorial.rst
@@ -277,7 +277,7 @@ saved results in various analysis scripts. ::
 
 The above code snippet shows how we can use :func:`~util.save_results` for
 saving the results of our experiments. :func:`~util.save_results` stores the as
-csv files in a tarbal.
+csv files in a tarball.
 
 ^^^^^^^^^^^^^^^^^^^^^
 Mexican Flu: policies
@@ -330,7 +330,7 @@ comparison of results
 Using the following script, we reproduce figures similar to the 3D figures
 in `Pruyt & Hamarat (2010) <http://www.systemdynamics.org/conferences/2010/proceed/papers/P1389.pdf>`_.
 But using :func:`pairs_scatter`. It shows for the three different policies
-their behavior on the total number of deaths, the height of the heigest peak
+their behavior on the total number of deaths, the height of the highest peak
 of the pandemic, and the point in time at which this peak was reached.
 
 .. literalinclude:: ../../ema_workbench/examples/flu_pairsplot.py

diff --git a/docs/source/indepth_tutorial/directed-search.ipynb b/docs/source/indepth_tutorial/directed-search.ipynb
@@ -73,7 +73,7 @@
     "pip install platypus-opt\n",
     "```\n",
     "\n",
-    "One note of caution: don't install platypus, but platypus-opt. There exists a python package on pip called platypus, but that is quite a different kind of libary. \n",
+    "One note of caution: don't install platypus, but platypus-opt. There exists a python package on pip called platypus, but that is quite a different kind of library. \n",
     "\n",
     "There are three ways in which we can use optimization in the workbench:\n",
     "1. Search over the decision levers, conditional on a reference scenario\n",
@@ -686,7 +686,7 @@
    "source": [
     "### Changing the reference scenario \n",
     "\n",
-    "The workbench offers control over the reference scenario or policy under which you are performing the optimization. This makes it easy to aply multi-scenario MORDM ([Watson & Kasprzyk, 2017](https://www.sciencedirect.com/science/article/pii/S1364815216310593);[Eker & Kwakkel, 2018](https://doi.org/10.1016/j.envsoft.2018.03.029);[Bartholomew & Kwakkel, 2020](https://doi.org/10.1016/j.envsoft.2020.104699)). Alternatively, you can also use it to change the policy for which you are applying worst case scenario discovery (see below). \n",
+    "The workbench offers control over the reference scenario or policy under which you are performing the optimization. This makes it easy to apply multi-scenario MORDM ([Watson & Kasprzyk, 2017](https://www.sciencedirect.com/science/article/pii/S1364815216310593);[Eker & Kwakkel, 2018](https://doi.org/10.1016/j.envsoft.2018.03.029);[Bartholomew & Kwakkel, 2020](https://doi.org/10.1016/j.envsoft.2020.104699)). Alternatively, you can also use it to change the policy for which you are applying worst case scenario discovery (see below). \n",
     "\n",
     "\n",
     "```python\n",

diff --git a/docs/source/indepth_tutorial/dps_lake_model.py b/docs/source/indepth_tutorial/dps_lake_model.py
@@ -10,7 +10,7 @@ def get_antropogenic_release(xt, c1, c2, r1, r2, w1):
     Parameters
     ----------
     xt : float
-         polution in lake at time t
+         pollution in lake at time t
     c1 : float
          center rbf 1
     c2 : float

diff --git a/docs/source/indepth_tutorial/general-introduction.ipynb b/docs/source/indepth_tutorial/general-introduction.ipynb
@@ -35,7 +35,7 @@
     "    Parameters\n",
     "    ----------\n",
     "    xt : float\n",
-    "         polution in lake at time t\n",
+    "         pollution in lake at time t\n",
     "    c1 : float\n",
     "         center rbf 1\n",
     "    c2 : float\n",

diff --git a/docs/source/indepth_tutorial/open-exploration.ipynb b/docs/source/indepth_tutorial/open-exploration.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Open exploration\n",
-    "In this second tuturial, I will showcase how to use the ema_workbench for performing open exploration. This tuturial will continue with the same example as used in the previos tuturial.\n",
+    "In this second tuturial, I will showcase how to use the ema_workbench for performing open exploration. This tuturial will continue with the same example as used in the previous tuturial.\n",
     "\n",
     "## some background\n",
     "In exploratory modeling, we are interested in understanding how regions in the uncertainty space and/or the decision space map to the whole outcome space, or partitions thereof. There are two general approaches for investigating this mapping. The first one is through systematic sampling of the uncertainty or decision space. This is sometimes also known as open exploration. The second one is to search through the space in a directed manner using some type of optimization approach. This is sometimes also known as directed search. \n",

diff --git a/ema_workbench/analysis/b_and_w_plotting.py b/ema_workbench/analysis/b_and_w_plotting.py
@@ -240,7 +240,7 @@ def set_legend_to_bw(leg, style, colormap, line_style="continuous"):
     """
     Takes the figure legend and converts it to black and white. Note that
     it currently only converts lines to black and white, other artist
-    intances are currently not being supported, and might cause errors or
+    instances are currently not being supported, and might cause errors or
     other unexpected behavior.
 
     Parameters
@@ -313,7 +313,7 @@ def set_fig_to_bw(fig, style=HATCHING, line_style="continuous"):
     scale, or simple black
 
     Take each axes in the figure and transform its content to black and white.
-    Lines are tranformed based on different line styles. Fills such as can
+    Lines are transformed based on different line styles. Fills such as can
     be used in `meth`:envelopes are gray-scaled. Heathmaps are also gray-scaled.
 
 

diff --git a/ema_workbench/analysis/feature_scoring.py b/ema_workbench/analysis/feature_scoring.py
@@ -303,7 +303,7 @@ def get_ex_feature_scores(
     # min_samples_leaf
     # 1000 - >
     # then proportional based on sqrt of N
-    # dus sqrt(N) / Sqrt(1000) met 1 als minimumd
+    # dus sqrt(N) / Sqrt(1000) met 1 also minimumd
     if max_features is None:
         max_features = int(round(x.shape[1] / 3))
     if min_samples_leaf is None:

diff --git a/ema_workbench/analysis/pairs_plotting.py b/ema_workbench/analysis/pairs_plotting.py
@@ -166,7 +166,7 @@ def pairs_density(
 
     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]).
+    histogram of the number of occurrences of the observations at (x[i],y[i]).
     For further detail see `matplotlib on hexbin <https://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.hexbin>`_
 
     Parameters

diff --git a/ema_workbench/analysis/parcoords.py b/ema_workbench/analysis/parcoords.py
@@ -259,7 +259,7 @@ def legend(self):
         plt.subplots_adjust(wspace=0)
 
     def _plot(self, data, **kwargs):
-        """Plot the data onto the paralel axis
+        """Plot the data onto the parallel axis
 
         Parameters
         ----------

diff --git a/ema_workbench/analysis/plotting.py b/ema_workbench/analysis/plotting.py
@@ -222,7 +222,7 @@ def single_envelope(outcomes, outcome_to_plot, time, density, ax, ax_d, fill, lo
     Parameters
     ----------
     outcomes : OutcomesDict
-               a dictonary containing the various outcomes to plot
+               a dictionary containing the various outcomes to plot
     outcome_to_plot : str
                       the specific outcome to plot
     time : str

diff --git a/ema_workbench/analysis/plotting_util.py b/ema_workbench/analysis/plotting_util.py
@@ -268,7 +268,7 @@ def group_density(ax_d, density, outcomes, outcome_to_plot, group_labels, log=Fa
     Raises
     ------
     EMAError
-        if density is unkown
+        if density is unknown
 
     """
     values = [outcomes[key][outcome_to_plot][:, index] for key in group_labels]
@@ -415,7 +415,7 @@ def make_legend(categories, ax, ncol=3, legend_type=LegendEnum.LINE, alpha=1):
             #                                         edgecolors = 'k',
             #                                         offsets = (0,0)
             #                                         )
-            # TODO work arround, should be a proper proxyartist for scatter
+            # TODO work around, should be a proper proxyartist for scatter
             # legends
             artist = mpl.lines.Line2D([0], [0], linestyle="none", c=color, marker="o")
 
@@ -865,7 +865,7 @@ def make_grid(outcomes_to_show, density=False):
 
 def get_color(index):
     """helper function for cycling over color list if the number of items
-    is higher than the legnth of the color list
+    is higher than the length of the color list
     """
     corrected_index = index % len(COLOR_LIST)
     return COLOR_LIST[corrected_index]
diff --git a/ema_workbench/analysis/prim.py b/ema_workbench/analysis/prim.py
@@ -546,7 +546,7 @@ def inspect_tradeoff(self):
                 items = df[dim].values[0]
                 for j, item in enumerate(items):
                     # we need to have tick labeling to be dynamic?
-                    # adding it to the dict wont work, creates horrible figure
+                    # adding it to the dict won't work, creates horrible figure
                     # unless we can force a selection?
                     name = f"{dim}, {qp.loc[qp.index[0], dim]: .2g}"
                     entry = dict(
@@ -1652,7 +1652,7 @@ def _assert_mode(self, y, mode, update_function):
 
     def _assert_dtypes(self, keys, dtypes):
         """
-        helper fucntion that checks whether none of the provided keys
+        helper function that checks whether none of the provided keys
         has a dtype object as value.
         """
 

diff --git a/ema_workbench/analysis/prim_util.py b/ema_workbench/analysis/prim_util.py
@@ -129,7 +129,7 @@ def rotate_subset(experiments, y):
     """
     mean = np.mean(experiments, axis=0)
     std = np.std(experiments, axis=0)
-    std[std == 0] = 1  # in order to avoid a devision by zero
+    std[std == 0] = 1  # in order to avoid a division by zero
     experiments = (experiments - mean) / std
 
     # normalize the data

diff --git a/ema_workbench/analysis/scenario_discovery_util.py b/ema_workbench/analysis/scenario_discovery_util.py
@@ -164,8 +164,8 @@ def _determine_restricted_dims(box_limits, box_init):
 def _determine_nr_restricted_dims(box_lims, box_init):
     """
 
-    determine the number of restriced dimensions of a box given
-    compared to the inital box that contains all the data
+    determine the number of restricted dimensions of a box given
+    compared to the initial box that contains all the data
 
     Parameters
     ----------

diff --git a/ema_workbench/connectors/excel.py b/ema_workbench/connectors/excel.py
@@ -64,7 +64,7 @@ class BaseExcelModel(FileModel):
                model neat with legible names, while not demanding that workbook
                cells be named.  Each (key, value) pair in pointers maps a key that
                gives the (concise) name of an input or output for the model to an
-               identfier for that input or output in the Excel workbook, in
+               identifier for that input or output in the Excel workbook, in
                'sheetName!A1' or 'sheetName!NamedCell' notation (the sheet name
                is optional if the cell or range exists in the default sheet).
 

diff --git a/ema_workbench/connectors/pysd_connector.py b/ema_workbench/connectors/pysd_connector.py
@@ -69,7 +69,7 @@ def run_experiment(self, experiment):
 
     def reset_model(self):
         """
-        Method for reseting the model to its initial state. The default
+        Method for resetting the model to its initial state. The default
         implementation only sets the outputs to an empty dict.
 
         """

diff --git a/ema_workbench/connectors/vensim.py b/ema_workbench/connectors/vensim.py
@@ -169,7 +169,7 @@ def get_data(filename, varname, step=1):
     varname : str
               the name of the variable to retrieve data on
     step : int (optional)
-           steps used in slicing. Defaults to 1, meaning the full recored time
+           steps used in slicing. Defaults to 1, meaning the full recorded time
            series is returned.
 
     Returns

diff --git a/ema_workbench/em_framework/ema_ipyparallel.py b/ema_workbench/em_framework/ema_ipyparallel.py
@@ -79,7 +79,7 @@ def starter():
             pass
 
     logwatcher_thread = threading.Thread(target=starter)
-    logwatcher_thread.deamon = True
+    logwatcher_thread.daemon = True
     logwatcher_thread.start()
 
     return logwatcher, logwatcher_thread

diff --git a/ema_workbench/em_framework/model.py b/ema_workbench/em_framework/model.py
@@ -212,7 +212,7 @@ def retrieve_output(self):
 
     @method_logger(__name__)
     def reset_model(self):
-        """Method for reseting the model to its initial state. The default
+        """Method for resetting the model to its initial state. The default
         implementation only sets the outputs to an empty dict.
 
         """

diff --git a/ema_workbench/em_framework/optimization.py b/ema_workbench/em_framework/optimization.py
@@ -665,8 +665,6 @@ class ArchiveLogger(AbstractConvergenceMetric):
     decision_varnames : list of str
     outcome_varnames : list of str
     base_filename : str, optional
-
-
     """
 
     def __init__(

diff --git a/ema_workbench/em_framework/outcomes.py b/ema_workbench/em_framework/outcomes.py
@@ -91,7 +91,7 @@ class AbstractOutcome(Variable):
     ----------
     name : str
            Name of the outcome.
-    kind : {INFO, MINIMZE, MAXIMIZE}, optional
+    kind : {INFO, MINIMIZE, MAXIMIZE}, optional
     variable_name : str, optional
                     if the name of the outcome in the underlying model
                     is different from the name of the outcome, you can
@@ -258,7 +258,7 @@ class ScalarOutcome(AbstractOutcome):
     ----------
     name : str
            Name of the outcome.
-    kind : {INFO, MINIMZE, MAXIMIZE}, optional
+    kind : {INFO, MINIMIZE, MAXIMIZE}, optional
     variable_name : str, optional
                     if the name of the outcome in the underlying model
                     is different from the name of the outcome, you can
@@ -567,7 +567,7 @@ def create_outcomes(outcomes, **kwargs):
 
     Parameters
     ----------
-    outcomes : DataFrame, or something convertable to a DataFrame
+    outcomes : DataFrame, or something convertible to a DataFrame
                in case of string, the string will be passed
 
     Returns

diff --git a/ema_workbench/em_framework/parameters.py b/ema_workbench/em_framework/parameters.py
@@ -333,7 +333,7 @@ class CategoricalParameter(IntegerParameter):
     multivalue : boolean
                  if categories have a set of values, for each variable_name
                  a different one.
-    # TODO: should multivalue not be a seperate class?
+    # TODO: should multivalue not be a separate class?
     # TODO: multivalue as label is also horrible
 
     """

diff --git a/ema_workbench/em_framework/points.py b/ema_workbench/em_framework/points.py
@@ -247,7 +247,7 @@ def experiment_generator(scenarios, model_structures, policies, combine="factori
 
     # TODO combine_ functions can be made more generic
     # basically combine any collection
-    # wrap around to yield specifc type of class (e.g. point
+    # wrap around to yield specific type of class (e.g. point
 
     if combine == "sample":
         jobs = zip_cycle(model_structures, policies, scenarios)

diff --git a/ema_workbench/examples/eijgenraam_example.py b/ema_workbench/examples/eijgenraam_example.py
@@ -212,7 +212,7 @@ def find_height(t):
     ]  # @UndefinedVariable
 
     # having a list like parameter were values are automagically wrappen
-    # into a list can be quite usefull.....
+    # into a list can be quite useful.....
     model.levers = [RealParameter(f"X{i}", 0, 500) for i in range(1, 7)] + [
         RealParameter(f"T{i}", 0, 300) for i in range(1, 7)
     ]

diff --git a/ema_workbench/examples/flu_timeseries_clustering.py b/ema_workbench/examples/flu_timeseries_clustering.py
@@ -12,7 +12,7 @@
 experiments, outcomes = load_results("./data/1000 flu cases no policy.tar.gz")
 data = outcomes["infected fraction R1"]
 
-# calcuate distances
+# calculate distances
 distances = clusterer.calculate_cid(data)
 
 # plot dedrog

diff --git a/ema_workbench/examples/lake_model_dps.py b/ema_workbench/examples/lake_model_dps.py
@@ -40,7 +40,7 @@ def get_antropogenic_release(xt, c1, c2, r1, r2, w1):
     Parameters
     ----------
     xt : float
-         polution in lake at time t
+         pollution in lake at time t
     c1 : float
          center rbf 1
     c2 : float

diff --git a/ema_workbench/examples/lake_model_dps_robust.py b/ema_workbench/examples/lake_model_dps_robust.py
@@ -37,7 +37,7 @@ def get_antropogenic_release(xt, c1, c2, r1, r2, w1):
     Parameters
     ----------
     xt : float
-         polution in lake at time t
+         pollution in lake at time t
     c1 : float
          center rbf 1
     c2 : float

diff --git a/ema_workbench/examples/lake_model_sample_jointly.py b/ema_workbench/examples/lake_model_sample_jointly.py
@@ -33,7 +33,7 @@ def get_antropogenic_release(xt, c1, c2, r1, r2, w1):
     Parameters
     ----------
     xt : float
-         polution in lake at time t
+         pollution in lake at time t
     c1 : float
          center rbf 1
     c2 : float

diff --git a/ema_workbench/examples/lookup_example.py b/ema_workbench/examples/lookup_example.py
@@ -2,7 +2,7 @@
 Created on Oct 1, 2012
 
 This is a simple example of the lookup uncertainty provided for
-use in conjuction with vensim models. This example is largely based on
+use in conjunction with vensim models. This example is largely based on
 `Eker et al. (2014) <https://onlinelibrary.wiley.com/doi/10.1002/sdr.1518/suppinfo>`_
 
 @author: sibeleker