Add Spell Checker

.github/workflows/core.yml

@@ -44,10 +44,20 @@ defaults:
     shell: bash -l {0}
 
 jobs:
+  spell-check:
+    name: Check Spelling
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout source
+        uses: actions/checkout@v3
+      - name: Run Spell Checker
+        uses: crate-ci/typos@master
+
   pytest:
     # description: Run pytest with dev dependencies
     name: pytest (py${{ matrix.python-version }}/${{ matrix.os }})
     runs-on: ${{ matrix.runner-image }}
+    needs: [spell-check]
     strategy:
       fail-fast: false
       matrix:

idaes_examples/archive/data_reconciliation/boiler_flowsheet_recon_src.ipynb

@@ -614,7 +614,7 @@
    "outputs": [],
    "source": [
     "# Add the model references to the tag metadata based on the strings above.\n",
-    "da.upadate_metadata_model_references(m, df_meta)"
+    "da.update_metadata_model_references(m, df_meta)"
    ]
   },
   {

idaes_examples/archive/data_reconciliation/econ_recon_src.ipynb

@@ -118,7 +118,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It can be useful to visualize the measurement data and estimated uncertainty.  The following creates box and whisker plots for each tag based on the data bins.  A large number of plots may be created, so to manage them more easily, they are saved as PDFs and merged into a single multi-page PDF document.  The deafault file name for the resulting PDF is \"data_plot_book.pdf.\""
+    "It can be useful to visualize the measurement data and estimated uncertainty.  The following creates box and whisker plots for each tag based on the data bins.  A large number of plots may be created, so to manage them more easily, they are saved as PDFs and merged into a single multi-page PDF document.  The default file name for the resulting PDF is \"data_plot_book.pdf.\""
    ]
   },
   {
@@ -384,7 +384,7 @@
    "outputs": [],
    "source": [
     "# Add the model references to the tag metadata based on the strings above.\n",
-    "da.upadate_metadata_model_references(m, df_meta)"
+    "da.update_metadata_model_references(m, df_meta)"
    ]
   },
   {
@@ -413,7 +413,7 @@
     "from idaes.core.util.tags import ModelTagGroup\n",
     "\n",
     "# This function creates a dictionary of streams based of streams based on model arcs.  The function\n",
-    "# also takes an addtional set of stream-like objects for add to the stream dictionary.  In this case,\n",
+    "# also takes an additional set of stream-like objects for add to the stream dictionary.  In this case,\n",
     "# this is a single unit and the flowsheet doesn't contain any arcs, so we add the economized inlet and\n",
     "# outlet ports to the stream dictionary.\n",
     "stream_dict = ta.arcs_to_stream_dict(\n",
@@ -459,7 +459,7 @@
     "        except KeyError:\n",
     "            pass\n",
     "\n",
-    "# Any addtional tags can be added.  This is required for tags that cannot be systematically generated\n",
+    "# Any additional tags can be added.  This is required for tags that cannot be systematically generated\n",
     "# from the model streams.\n",
     "recon_tags.add(expr=m.fs.econ.heat_duty[0], name=\"ECON_Q\", format_string=\"{:.3f}\")"
    ]

idaes_examples/archive/matopt/bifunctional_surface_design_src.ipynb

@@ -43,7 +43,7 @@
    "metadata": {},
    "source": [
     "## Importing Packages\n",
-    "We start by importing several standard Python modules for convienience. "
+    "We start by importing several standard Python modules for convenience. "
    ]
   },
   {

idaes_examples/archive/matopt/bimetallic_nanocluster_design_src.ipynb

@@ -45,7 +45,7 @@
    "source": [
     "## Importing Packages\n",
     "\n",
-    "We start by importing several standard Python modules for convienience."
+    "We start by importing several standard Python modules for convenience."
    ]
   },
   {

idaes_examples/archive/matopt/monometallic_nanocluster_design_src.ipynb

@@ -34,18 +34,18 @@
     "\n",
     "In this module, we introduce the **MatOpt** interface for representing material properties and specifying optimization problems. \n",
     "\n",
-    "We have designed the interface with severl goals in mind:\n",
+    "We have designed the interface with several goals in mind:\n",
     "\n",
     "1. To **simplify the representation of nanostructured materials,** streamlining the creation of materials optimization problems. \n",
-    "2. To provide a simple interface so that users **do not need to understand the details of building mathematical optmization models** or the syntax of the Pyomo package. \n",
+    "2. To provide a simple interface so that users **do not need to understand the details of building mathematical optimization models** or the syntax of the Pyomo package. \n",
     "3. To **automate many of the common steps of materials optimization,** speeding up the development of new models. \n",
     "\n",
     "As an example system, we will consider the minimization of cohesive energy in nanoclusters, recently demonstrated in:\n",
     "\n",
     "Isenberg, N. M., et al., \"Identification of Optimally Stable Nanocluster Geometries via Mathematical Optimization and Density Functional Theory,\" *Molecular Systems Design & Engineering* 5 (2020): 232-244. DOI: [10.1039/C9ME00108E](https://pubs.rsc.org/en/content/articlelanding/2020/me/c9me00108e#!divAbstract).\n",
     "\n",
     "We seek to identify the geometry that minimizes the cohesive energy of a nanocluster on the face-centered cubic (FCC) lattice. \n",
-    "As a model for cohesive energy, we use model based on the square-root of coordination number, refered to as the Tomanek model [[1]](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.28.665).\n",
+    "As a model for cohesive energy, we use model based on the square-root of coordination number, referred to as the Tomanek model [[1]](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.28.665).\n",
     "In the equation below, we define the normalized cohesive energy, as the normalized contribution of the square root of the coordination number. \n",
     "\n",
     "$$\\hat{E}^{\\text{surf}} = \\frac{1}{N \\sqrt{12}} \\displaystyle \\sum_i \\sqrt{CN_i} $$\n",
@@ -178,7 +178,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The MatOptModel additionally hold lists of  ***MaterialDescriptor*** objects that define the relevant material desriptors. \n",
+    "The MatOptModel additionally hold lists of  ***MaterialDescriptor*** objects that define the relevant material descriptors. \n",
     "By default, several universal site descriptors are pre-defined in the model. \n",
     "From these, all other material descriptors can be defined.\n",
     "\n",
@@ -353,7 +353,7 @@
       "            Default: True\n",
       "        tilim (float): Optional, solver time limit (in seconds).\n",
       "            Default: 3600\n",
-      "        trelim (float): Optional, solver tree memeory limit (in MB).\n",
+      "        trelim (float): Optional, solver tree memory limit (in MB).\n",
       "            Default: None (i.e., Pyomo/CPLEX default)\n",
       "        solver (str): Solver choice. Currently only cplex or neos-cplex are supported\n",
       "            Default: cplex\n",

idaes_examples/archive/matopt/nanowire_design_src.ipynb

@@ -40,7 +40,7 @@
    "metadata": {},
    "source": [
     "## Importing Packages\n",
-    "We start by importing several standard Python modules for convienience. "
+    "We start by importing several standard Python modules for convenience. "
    ]
   },
   {
@@ -161,7 +161,7 @@
    "source": [
     "## Building a Model\n",
     "\n",
-    "In this example, we will build a model that maximizes the cohesive energy of the nanowire as an indicator of it's thermal stability. To begin, we start by creating a **MatOptModel** object to hold information about the model. Notice that we use a list of empty atoms as our set of building blocks because we will use a cohesive energy function for semiconductor materials that is indepedent of atom types. Thus, we do not need to specify the types of building blocks in our model."
+    "In this example, we will build a model that maximizes the cohesive energy of the nanowire as an indicator of it's thermal stability. To begin, we start by creating a **MatOptModel** object to hold information about the model. Notice that we use a list of empty atoms as our set of building blocks because we will use a cohesive energy function for semiconductor materials that is independent of atom types. Thus, we do not need to specify the types of building blocks in our model."
    ]
   },
   {

idaes_examples/archive/matopt/surface_design_src.ipynb

@@ -42,7 +42,7 @@
    "metadata": {},
    "source": [
     "## Importing Packages\n",
-    "We start by importing several standard Python modules for convienience. "
+    "We start by importing several standard Python modules for convenience. "
    ]
   },
   {

idaes_examples/archive/power_gen/rsofc/cpu.py

@@ -640,7 +640,7 @@ def initialize(blk, outlvl=idaeslog.NOTSET, solver=None, optarg=None):
             outlvl : sets output level of initialisation routine
 
             optarg : solver options dictionary object (default={'tol': 1e-6})
-            solver : str indicating whcih solver to use during
+            solver : str indicating which solver to use during
                      initialization (default = 'ipopt')
 
         Returns: