Merge pull request #120 from zmoon/spell

Fix some spelling and add codespell CI job

Author: zmoon

.codespell-exclude

@@ -0,0 +1,4 @@
+        "CNA",
+            #                                     scrip_file=d1.monet.scrip, check_N=20 ) )
+   "SIZ",	"Size distribution"
+   "ALL",	"All of the above retrievals (SIZ to FLX) in one file"

.github/workflows/lint.yml

@@ -18,3 +18,13 @@ jobs:
         with:
           python-version: "3.8"
       - uses: pre-commit/action@v3.0.0
+
+  spell:
+    name: codespell
+    runs-on: ubuntu-latest
+    if: github.repository == 'noaa-oar-arl/monet'
+    steps:
+      - uses: actions/checkout@v3
+      - uses: codespell-project/actions-codespell@v1
+        with:
+          exclude_file: .codespell-exclude

docs/tutorial/CMAQ_hi_volcano.rst

@@ -18,7 +18,7 @@ eruption. First, import MONET and several helper functions for later.
 Now the data can be downloaded from the MONET github page in the
 MONET/data directory. We will assume you already have this downloaded
 and will proceed. Open the simulation. As of right now we still require
-that a seperate grdcro2d (grddot2d) file be loaded for the mass points
+that a separate grdcro2d (grddot2d) file be loaded for the mass points
 (dot points) using the ``grid`` kwarg.
 
 .. code:: python
@@ -138,7 +138,7 @@ aggrigate species in the concentration file.
 
 
 Notice that this looks like the ncdump of the file except that there are
-seperate coordinates including the latitude and longitude and the time
+separate coordinates including the latitude and longitude and the time
 as numpy.datetime64 objects. Also included is the proj4 string, a pyresample area grid
 and default mapping tables to several different observational datasets.
 
@@ -195,7 +195,7 @@ will add a map using the MONETAccessor and use the ``robust=True`` kwarg.
 Better but we can still do much more. There is low concentrations on
 most of this map making it hard to notice the extremely high values and
 the SO2 data is in ppmv and not ppbv as normally viewed as. Also, a
-logscale may be better fo this type of data as it goes from 0-20000 ppbv
+logscale may be better for this type of data as it goes from 0-20000 ppbv
 rather than a linear scale.
 
 .. code:: python
@@ -208,7 +208,7 @@ rather than a linear scale.
 .. image:: CMAQ_hi_volcano_files/CMAQ_hi_volcano_11_3.png
 
 
-Now let’s us view serveral time slices at once. We will average in time
+Now let’s us view several time slices at once. We will average in time
 (every 8 hours) to give us 6 total subplots.
 
 .. code:: python
@@ -246,7 +246,7 @@ It is often useful to be able to pair model data with observational
 data. MONET uses the pyresample library
 (http://pyresample.readthedocs.io/en/latest/) to do a nearest neighbor
 interpolation. First let us get the airnow data for the dates of the
-simulation. We will also rotate it from the raw AirNow long format (stacked variables) to a wide format (each variable is a seperate column)
+simulation. We will also rotate it from the raw AirNow long format (stacked variables) to a wide format (each variable is a separate column)
 
 
 .. code:: python
@@ -290,7 +290,7 @@ new column (model).
 
 
 Let’s look at the distributions to see if the two overlap to get a
-general scence of performance.
+general sense of performance.
 
 .. code:: python
 

docs/tutorial/NESDIS_VIIRS_AOD.rst

@@ -100,7 +100,7 @@ Notice that the dimensions changed from 1800x3600 to 720x1440.
 Open Multiple Days
 ~~~~~~~~~~~~~~~~~~
 
-If you want to open multiple days in a sinlge call you could use the
+If you want to open multiple days in a single call you could use the
 open\_mfdataset. Lets grab the first nine days of July 2018.
 
 .. code-block:: python

docs/tutorial/aqs_pams.rst

@@ -28,7 +28,7 @@ Now we have all the imports we could need lets load some data. Most of
 the PAMS data is on daily data so lets add the kwarg daily=True to the
 call. We will also create this for the year 2015 and 2016. Some
 variables that may be valuable are the VOCS, ozone, NO2, NOX,
-temperature. For all of the measurments available please see
+temperature. For all of the measurements available please see
 https://aqs.epa.gov/aqsweb/airdata/download_files.html
 
 .. code-block:: python

docs/tutorial/fv3chem_tutorial.rst

@@ -69,7 +69,7 @@ distinguish aerosols which these readers do not process well.
 
 fv3grib2nc4.py like nemsio2nc4.py tool is a command line tool created to
 convert the grib2 aerosol data to netcdf files. fv3grib2nc4.py will
-create seperate files for each of the three layer types; '1 hybrid
+create separate files for each of the three layer types; '1 hybrid
 layer', 'entire atmosphere', and 'surface'. These are the three layers
 that currently hold aerosol data. The tool is available at
 https://github.com/bbakernoaa/fv3grib2nc4
@@ -116,7 +116,7 @@ Using MONET
 Using MONET with FV3-Chem is much like using MONET with other model
 outputs. It tries to recognize where the files came from (nemsio, grib2,
 etc....) and then processes the data, renaming coordinates (lat lon to
-latitude and longitude) and processing varaibles like geopotential
+latitude and longitude) and processing variables like geopotential
 height and pressure if available. First lets import ``monet`` and
 ``fv3chem`` from MONET
 
@@ -190,8 +190,8 @@ To open a single file
 
 
 Notice this object f has dimensions of (time,z,y,x) with 2d coordinates
-of latitude and longitude. You can get more infomation on single
-varaibles such as pm25 simply by printing the varaible.
+of latitude and longitude. You can get more information on single
+variables such as pm25 simply by printing the variable.
 
 .. code-block:: python
 
@@ -220,7 +220,7 @@ Quick Map Plotting
 ~~~~~~~~~~~~~~~~~~
 
 Now one of the main things that will need to be done is plotting on a
-map. This can be done quickly using the functionallity in MONET. In this
+map. This can be done quickly using the functionality in MONET. In this
 example we will plot the first layer PM2.5 at time 2018-07-01.
 
 .. code-block:: python
@@ -277,7 +277,7 @@ set a minimum of 0 AOD and maximum of 0.5.
 
 
 Now we have all the control that xarray has built into their plotting
-routines. For example, lets have a descrete colorbar with 10 levels,
+routines. For example, lets have a discrete colorbar with 10 levels,
 ``levels=10``, and let it determine the levels by throwing out the top
 and bottom 2% of values using the ``robust=True``
 

docs/tutorial/improve_trends_kmeans.rst

@@ -36,7 +36,7 @@ data.
       ‘,’ by default)
 
 After downloading we can then read the data. Here we included the
-EPACode and State to add additional meta data stored on the EPA auxilary
+EPACode and State to add additional meta data stored on the EPA auxiliary
 files used in the EPA AQS and AirNow datasets in MONET. Let’s make a few
 imports from monet and some other libraries that will aid us later.
 

docs/tutorial/loading.rst

@@ -23,7 +23,7 @@ AirNow is the near realtime dataset for air composition and meteorology measurem
 
     "The U.S. EPA AirNow program is the national repository of real time air quality data and forecasts for the United States. AirNow is the vehicle for providing timely Air Quality Index (AQI) information to the public, media outlets, other federal agencies and their applications, and to the research community. The system is managed by the U.S. EPA’s Office of Air Quality Planning and Standards Outreach and Information Division, Information Transfer Group in Research Triangle Park (RTP), North Carolina. AirNow is currently hosted and operated at a contractor facility, known as the AirNow Data Management Center (DMC), which currently resides outside of RTP." - https://www.airnow.gov/index.cfm?action=ani.airnowUS
 
-AirNow_ data can be dowloaded from the Amazon S3 server and aggregated using the
+AirNow_ data can be downloaded from the Amazon S3 server and aggregated using the
 monet.obs.airnow class.  For example,lets say that we want to look at data from
 2018-05-01 to 2018-05-05.
 
@@ -46,7 +46,7 @@ This provides a structured :py:class:`~pandas.DataFrame`.
 
     df.head()
 
-Some users may want to keep a local copy of the data and not have to retrive the data
+Some users may want to keep a local copy of the data and not have to retrieve the data
 each time they want to access the data.  There is a simple kwarg that can be used to
 download the data, *download=True*.  By default, *download* is set to False.
 
@@ -93,8 +93,8 @@ MONET is able to use the EPA AQS data that is collected and reported on an hourl
     prepare reports for Congress as mandated by the Clean Air Act." - https://www.epa.gov/aqs
 
 We will begin by loading hourly ozone concentrations from 2018.  The EPA AQS data
-is seperated into yearly files and seperate files for hourly and daily data.  The
-files are also seperated by which variable is measured.  For instance, hourly ozone files
+is separated into yearly files and separate files for hourly and daily data.  The
+files are also separated by which variable is measured.  For instance, hourly ozone files
 for the entire year of 2018 are found in https://aqs.epa.gov/aqsweb/airdata/hourly_44201_2018.zip.
 We will first load a single variable and then add multiple later on.
 
@@ -146,12 +146,12 @@ Let's load variables PM10 and OZONE using hourly data to get an idea of how to g
 
   df = aqs.add_data(dates, param=['OZONE','PM10'])
 
-Loading Specfic Network
-^^^^^^^^^^^^^^^^^^^^^^^
+Loading Specific Network
+^^^^^^^^^^^^^^^^^^^^^^^^
 
 Sometimes you may want to load a specific network that is available in the AQS data
 files.  For instance, lets load data from the Chemical Speciation Network (CSN; https://www3.epa.gov/ttnamti1/speciepg.html).
-As of writting this tutorial we will load the 2017 data as it is complete.
+As of writing this tutorial we will load the 2017 data as it is complete.
 
 .. code::   python
 
@@ -175,7 +175,7 @@ AERONET
 
     "The AERONET (AErosol RObotic NETwork) project is a federation of ground-based
     remote sensing aerosol networks established by NASA and PHOTONS (PHOtométrie pour le Traitement Opérationnel de Normalisation Satellitaire; Univ. of Lille 1, CNES, and CNRS-INSU)
-    and is greatly expanded by networks (e.g., RIMA, AeroSpan, AEROCAN, and CARSNET) and collaborators from national agencies, institutes, universities, individual scientists, and partners. Fo more than 25 years, the project has provided long-term, continuous and readily accessible public domain database of aerosol optical, microphysical and radiative properties for aerosol research and characterization, validation of satellite retrievals, and synergism with other databases. The network imposes standardization of instruments, calibration, processing and distribution.
+    and is greatly expanded by networks (e.g., RIMA, AeroSpan, AEROCAN, and CARSNET) and collaborators from national agencies, institutes, universities, individual scientists, and partners. For more than 25 years, the project has provided long-term, continuous and readily accessible public domain database of aerosol optical, microphysical and radiative properties for aerosol research and characterization, validation of satellite retrievals, and synergism with other databases. The network imposes standardization of instruments, calibration, processing and distribution.
 
     AERONET collaboration provides globally distributed observations of spectral aerosol optical depth (AOD), inversion products, and precipitable water in diverse aerosol regimes. Version 3 AOD data are computed for three data quality levels: Level 1.0 (unscreened), Level 1.5 (cloud-screened and quality controlled), and Level 2.0 (quality-assured). Inversions, precipitable water, and other AOD-dependent products are derived from these levels and may implement additional quality checks. " -https://aeronet.gsfc.nasa.gov
 
@@ -206,7 +206,7 @@ Available Measurements
    :widths: 20, 20
 
    "SIZ",	"Size distribution"
-   "RIN",	"Refractive indicies (real and imaginary)"
+   "RIN",	"Refractive indices (real and imaginary)"
    "CAD",	"Coincident AOT data with almucantar retrieval"
    "VOL",	"Volume concentration, volume mean radius, effective radius and standard deviation"
    "TAB",	"AOT absorption"
@@ -216,7 +216,7 @@ Available Measurements
    "FRC",	"Radiative Forcing"
    "LID",	"Lidar and Depolarization Ratios"
    "FLX",	"Spectral flux"
-   "ALL",	"All of the above retrievals (SIZ to FLUX) in one file"
+   "ALL",	"All of the above retrievals (SIZ to FLX) in one file"
    "PFN*",	"Phase function (available for only all points data format: AVG=10)"
 
 Loading AOD and SDA

monet/met_funcs.py

@@ -467,7 +467,7 @@ def calc_Psi_H(zoL):
     Returns
     -------
     Psi_H : float
-        adiabatic corrector factor fof heat transport (unitless).
+        adiabatic corrector factor for heat transport (unitless).
 
     References
     ----------
@@ -479,7 +479,7 @@ def calc_Psi_H(zoL):
     zoL = np.asarray(zoL)
     Psi_H = np.zeros(zoL.shape)
 
-    # for stable and netural (zoL = 0 -> Psi_H = 0) conditions
+    # for stable and neutral (zoL = 0 -> Psi_H = 0) conditions
     i = zoL >= 0.0
     a = 6.1
     b = 2.5
@@ -507,7 +507,7 @@ def calc_Psi_M(zoL):
     Returns
     -------
     Psi_M : float
-        adiabatic corrector factor fof momentum transport (unitless).
+        adiabatic corrector factor for momentum transport (unitless).
 
     References
     ----------
@@ -519,7 +519,7 @@ def calc_Psi_M(zoL):
     zoL = np.asarray(zoL)
 
     Psi_M = np.zeros(zoL.shape)
-    # for stable and netural (zoL = 0 -> Psi_M = 0) conditions
+    # for stable and neutral (zoL = 0 -> Psi_M = 0) conditions
     i = zoL >= 0.0
     a = 6.1
     b = 2.5
@@ -608,7 +608,7 @@ def calc_u_star(u, z_u, L, d_0, z_0M):
         Cambridge: Cambridge University Press.
     """
 
-    # Covert input scalars to numpy arrays
+    # Convert input scalars to numpy arrays
     u, z_u, L, d_0, z_0M = map(np.asarray, (u, z_u, L, d_0, z_0M))
 
     # calculate correction factors in other conditions

monet/monet_accessor.py

@@ -835,7 +835,7 @@ def interp_constant_lon(self, lon=None, **kwargs):
                 return _rename_latlon(out)
 
     def nearest_ij(self, lat=None, lon=None, **kwargs):
-        """Uses pyresample to intepolate to find the i, j index of grid with respect to the given lat lon.
+        """Uses pyresample to interpolate to find the i, j index of grid with respect to the given lat lon.
 
         Parameters
         ----------
@@ -879,7 +879,7 @@ def nearest_ij(self, lat=None, lon=None, **kwargs):
             return x, y
 
     def nearest_latlon(self, lat=None, lon=None, cleanup=True, esmf=False, **kwargs):
-        """Uses xesmf to intepolate to a given latitude and longitude.  Note
+        """Uses xesmf to interpolate to a given latitude and longitude.  Note
         that the conservative method is not available.
 
         Parameters
@@ -1136,20 +1136,20 @@ def _tight_layout(self):
 
         subplots_adjust(0, 0, 1, 1)
 
-    def _check_swath_def(self, defin):
+    def _check_swath_def(self, defn):
         """checks if it is a pyresample SwathDefinition or AreaDefinition.
 
         Parameters
         ----------
-        defin : type
+        defn : pyresample.geometry.SwathDefinition or pyresample.geometry.AreaDefinition
 
         Returns
         -------
-        type
+        bool
         """
         from pyresample.geometry import SwathDefinition
 
-        if isinstance(defin, SwathDefinition):
+        if isinstance(defn, SwathDefinition):
             return True
         else:
             return False
@@ -1159,8 +1159,7 @@ def _get_CoordinateDefinition(self, data=None):
 
         Returns
         -------
-        pyreseample.geometry.CoordinateDefinition
-
+        pyresample.geometry.CoordinateDefinition
         """
         from pyresample import geometry as geo
 
@@ -1473,7 +1472,7 @@ def _get_CoordinateDefinition(self, data=None):
 
         Returns
         -------
-        pyreseample.geometry.CoordinateDefinition
+        pyresample.geometry.CoordinateDefinition
         """
         from pyresample import geometry as geo
 
@@ -1622,7 +1621,7 @@ def remap_nearest_unstructured(self, data):
         return result
 
     def nearest_ij(self, lat=None, lon=None, **kwargs):
-        """Uses pyresample to intepolate to find the i, j index of grid with respect to the given lat lon.
+        """Uses pyresample to interpolate to find the i, j index of grid with respect to the given lat lon.
 
         Parameters
         ----------
@@ -1664,7 +1663,7 @@ def nearest_ij(self, lat=None, lon=None, **kwargs):
             return x, y
 
     def nearest_latlon(self, lat=None, lon=None, cleanup=True, esmf=False, **kwargs):
-        """Uses xesmf to intepolate to a given latitude and longitude.  Note
+        """Uses xesmf to interpolate to a given latitude and longitude.  Note
         that the conservative method is not available.
 
         Parameters