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Updated documentation.

lon0
Bill Ladwig 6 years ago
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@ -9,9 +9,9 @@ @@ -9,9 +9,9 @@
| | | | |
| | | degF | |
+--------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| cape_2d | 2D cape (mcape/mcin/lcl/lfc) | J kg-1 ; J kg-1 ; m ; m | **missing** (float): Fill value for output only |
| cape_2d | 2D CAPE (MCAPE/MCIN/LCL/LFC) | J kg-1 ; J kg-1 ; m ; m | **missing** (float): Fill value for output only |
+--------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| cape_3d | 3D cape and cin | J kg-1 | **missing** (float): Fill value for output only |
| cape_3d | 3D CAPE and CIN | J kg-1 | **missing** (float): Fill value for output only |
+--------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| ctt | Cloud Top Temperature | degC | **fill_nocloud** (boolean): Set to True to use fill values for cloud free regions rather than surface temperature. Default is *False*. |
| | | | |

119
doc/source/_templates/subproducts.txt
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@ -0,0 +1,119 @@ @@ -0,0 +1,119 @@
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| Variable Name | Calculated From | Description | Available Units | Additional Keyword Arguments |
+====================+======================+===============================================================+=============================+=========================================================================================================================================================+
| mcape | cape_2d | 2D Max CAPE | J kg-1 | **missing** (float): Fill value for output only |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| mcin | cape_2d | 2D Max CIN | J kg-1 | **missing** (float): Fill value for output only |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| lcl | cape_2d | 2D Lifted Condensation Level | J kg-1 | **missing** (float): Fill value for output only |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| lfc | cape_2d | 2D Level of Free Convection | J kg-1 | **missing** (float): Fill value for output only |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| cape3d_only | cape_3d | 3D CAPE | J kg-1 | **missing** (float): Fill value for output only |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| cin3d_only | cape_3d | 3D CIN | J kg-1 | **missing** (float): Fill value for output only |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| low_cloudfrac | cloudfrac | Cloud Fraction for Low Clouds | % | **vert_type** (str): The vertical coordinate type for the cloud thresholds. Must be 'height_agl', 'height_msl', or 'pres'. Default is 'height_agl'. |
| | | | | |
| | | | | **low_thresh** (float): The low cloud threshold (meters for 'height_agl' and 'height_msl', pascals for 'pres'). Default is 300 m (97000 Pa) |
| | | | | |
| | | | | **mid_thresh** (float): The mid cloud threshold (meters for 'height_agl' and 'height_msl', pascals for 'pres'). Default is 2000 m (80000 Pa) |
| | | | | |
| | | | | **high_thresh** (float): The high cloud threshold (meters for 'height_agl' and 'height_msl', pascals for 'pres'). Default is 6000 m (45000 Pa) |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| mid_cloudfrac | cloudfrac | Cloud Fraction for Mid Clouds | % | **vert_type** (str): The vertical coordinate type for the cloud thresholds. Must be 'height_agl', 'height_msl', or 'pres'. Default is 'height_agl'. |
| | | | | |
| | | | | **low_thresh** (float): The low cloud threshold (meters for 'height_agl' and 'height_msl', pascals for 'pres'). Default is 300 m (97000 Pa) |
| | | | | |
| | | | | **mid_thresh** (float): The mid cloud threshold (meters for 'height_agl' and 'height_msl', pascals for 'pres'). Default is 2000 m (80000 Pa) |
| | | | | |
| | | | | **high_thresh** (float): The high cloud threshold (meters for 'height_agl' and 'height_msl', pascals for 'pres'). Default is 6000 m (45000 Pa) |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| high_cloudfrac | cloudfrac | Cloud Fraction for High Clouds | % | **vert_type** (str): The vertical coordinate type for the cloud thresholds. Must be 'height_agl', 'height_msl', or 'pres'. Default is 'height_agl'. |
| | | | | |
| | | | | **low_thresh** (float): The low cloud threshold (meters for 'height_agl' and 'height_msl', pascals for 'pres'). Default is 300 m (97000 Pa) |
| | | | | |
| | | | | **mid_thresh** (float): The mid cloud threshold (meters for 'height_agl' and 'height_msl', pascals for 'pres'). Default is 2000 m (80000 Pa) |
| | | | | |
| | | | | **high_thresh** (float): The high cloud threshold (meters for 'height_agl' and 'height_msl', pascals for 'pres'). Default is 6000 m (45000 Pa) |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| uvmet_wspd | uvmet_wspd_wdir | Wind Speed | m s-1 | **units** (str) : Set to desired units. Default is *'m s-1'*. |
| | | | | |
| | | Rotated to Earth Coordinates | km h-1 | |
| | | | | |
| | | | mi h-1 | |
| | | | | |
| | | | kt | |
| | | | | |
| | | | ft s-1 | |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| uvmet_wdir | uvmet_wspd_wdir | Wind Direction (wind_from_direction) | m s-1 | **units** (str) : Set to desired units. Default is *'m s-1'*. |
| | | | | |
| | | Rotated to Earth Coordinates | km h-1 | |
| | | | | |
| | | | mi h-1 | |
| | | | | |
| | | | kt | |
| | | | | |
| | | | ft s-1 | |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| uvmet10_wspd | uvmet10_wspd_wdir | 10m Wind Speed | m s-1 | **units** (str) : Set to desired units. Default is *'m s-1'*. |
| | | | | |
| | | Rotated to Earth Coordinates | km h-1 | |
| | | | | |
| | | | mi h-1 | |
| | | | | |
| | | | kt | |
| | | | | |
| | | | ft s-1 | |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| uvmet10_wdir | uvmet10_wspd_wdir | 10m Wind Direction (wind_from_direction) | m s-1 | **units** (str) : Set to desired units. Default is *'m s-1'*. |
| | | | | |
| | | Rotated to Earth Coordinates | km h-1 | |
| | | | | |
| | | | mi h-1 | |
| | | | | |
| | | | kt | |
| | | | | |
| | | | ft s-1 | |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| wspd | wspd_wdir | Wind Speed | m s-1 | **units** (str) : Set to desired units. Default is *'m s-1'*. |
| | | | | |
| | | in Grid Coordinates | km h-1 | |
| | | | | |
| | | | mi h-1 | |
| | | | | |
| | | | kt | |
| | | | | |
| | | | ft s-1 | |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| wdir | wspd_wdir | Wind Direction (wind_from_direction) | m s-1 | **units** (str) : Set to desired units. Default is *'m s-1'*. |
| | | | | |
| | | in Grid Coordinates | km h-1 | |
| | | | | |
| | | | mi h-1 | |
| | | | | |
| | | | kt | |
| | | | | |
| | | | ft s-1 | |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| wspd10 | wspd_wdir10 | 10m Wind Speed | m s-1 | **units** (str) : Set to desired units. Default is *'m s-1'*. |
| | | | | |
| | | in Grid Coordinates | km h-1 | |
| | | | | |
| | | | mi h-1 | |
| | | | | |
| | | | kt | |
| | | | | |
| | | | ft s-1 | |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
| wdir10 | wspd_wdir10 | 10m Wind Direction (wind_from_direction) | m s-1 | **units** (str) : Set to desired units. Default is *'m s-1'*. |
| | | | | |
| | | in Grid Coordinates | km h-1 | |
| | | | | |
| | | | mi h-1 | |
| | | | | |
| | | | kt | |
| | | | | |
| | | | ft s-1 | |
+--------------------+----------------------+---------------------------------------------------------------+-----------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+

14
doc/source/diagnostics.rst
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@ -5,3 +5,17 @@ Table of Available Diagnostics @@ -5,3 +5,17 @@ Table of Available Diagnostics
.. include:: _templates/product_table.txt
.. _subdiagnostic-table:
Table of Subproduct Diagnostics
----------------------------------
Some diagnostics (e.g. cape_2d) include multiple products in its
output. These products have been broken out in to individual diagnostics
to help those utilities that are unable to work with multiple outputs.
These individual diagnostics can be requested like any other diagnostic
using :meth:`wrf.getvar`. These are summarized in the table below.
.. include:: _templates/subproducts.txt

17
doc/source/internal_api/index.rst
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@ -53,6 +53,23 @@ The routines below are called internally by :meth:`wrf.getvar`. @@ -53,6 +53,23 @@ The routines below are called internally by :meth:`wrf.getvar`.
wrf.g_wind.get_w_destag
wrf.g_wind.get_destag_wspd_wdir
wrf.g_wind.get_destag_wspd_wdir10
wrf.g_wind.get_destag_wspd
wrf.g_wind.get_destag_wdir
wrf.g_wind.get_destag_wspd10
wrf.g_wind.get_destag_wdir10
wrf.g_uvmet.get_uvmet_wspd
wrf.g_uvmet.get_uvmet_wdir
wrf.g_uvmet.get_uvmet10_wspd
wrf.g_uvmet.get_uvmet10_wdir
wrf.g_cloudfrac.get_low_cloudfrac
wrf.g_cloudfrac.get_mid_cloudfrac
wrf.g_cloudfrac.get_high_cloudfrac
wrf.g_cape.get_cape2d_only
wrf.g_cape.get_cin2d_only
wrf.g_cape.get_lcl
wrf.g_cape.get_lfc
wrf.g_cape.get_3dcape_only
wrf.g_cape.get_3dcin_only
-------------------------

164
doc/source/new.rst
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@ -4,8 +4,52 @@ What's New @@ -4,8 +4,52 @@ What's New
Releases
-------------
v1.2.0
^^^^^^^^^^^^^^
v1.3.0 (December 2018)
^^^^^^^^^^^^^^^^^^^^^^^^^
- Release 1.3.0
- Fixed FutureWarning issue with destag routine (thank you honnorat!)
- Fixed computational problems with updraft_helicity, and values are no longer
scaled by 1000.
- Removed version constraints for wrapt and setuptools.
- Fixed xarray being a hard dependency.
- Fixed unit issues with vinterp when pressure values are extracted below
ground. Also added support for height fields in km and pressure fields in
hPa. The documentation has been improved.
- Fixed the smooth2d routine so that it actually works. It never worked
correctly before (nor did it work in NCL). Users can now specify the
center weight of the kernel and the documentation has been updated to
describe how it works.
- Fixed the storm relative helicity algorithm so that it works in the southern
hemisphere. The raw algorithm now requires latitude input if used
in the southern hemisphere, otherwise the northern hemisphere is assumed.
- Fixed an issue with the latest version of cartopy 0.17 (thanks honnorat!)
- Fixed an issue where invalid keyword arguments weren't throwing errors when
extracting standard WRF variables.
- Fixed minor issues related to moving nests when using line interpolation and
vertical cross sections. It is still an error to request all times when
using lat/lon coordinates with a moving nest, but otherwise knows how to
run when all times are requested. This never really worked quite right.
- Removed the pyf file from setup.py since it is generated via the build
system.
- Added an autolevels parameter for the vertical cross section so that users
can specify the number of vertical levels to use if they don't want to
specify them manually.
- The interplevel routine has been improved. Users can now specify a single
level, multiple levels, or a 2D array (e.g. PBLH) to interpolate to.
Performance has been improved when interpolating a multiple product
field like wspd_wdir.
- Products that produce multiple outputs can now have the outputs requested
individually. See :ref:`subdiagnostic-table` for a list of what is available.
- Much of this version of wrf-python has been back ported to NCL in the
upcoming 6.6.0 release. The diagnostics should produce the same results
in both packages.
- Now released under the Apache 2.0 license.
v1.2.0 (May 2018)
^^^^^^^^^^^^^^^^^^^^^^^^^
- Release 1.2.0
- Previous versions of wrf-python promoted the strings used in xarray (e.g.
@ -17,8 +61,8 @@ v1.2.0 @@ -17,8 +61,8 @@ v1.2.0
errors, so we've decided to bump the major version number.
v1.1.3
^^^^^^^^^^^^^^
v1.1.3 (March 2018)
^^^^^^^^^^^^^^^^^^^^^^^^^
- Release 1.1.3
- Fixed/Enhanced the cloud top temperature diagnostic.
@ -39,15 +83,15 @@ v1.1.3 @@ -39,15 +83,15 @@ v1.1.3
cape_3d.
v1.1.2
^^^^^^^^^^^^^^
v1.1.2 (February 2018)
^^^^^^^^^^^^^^^^^^^^^^^^^^
- Release 1.1.2
- Fix OpenMP directive issue with cloud top temperature.
v1.1.1
^^^^^^^^^^^^^^
v1.1.1 (February 2018)
^^^^^^^^^^^^^^^^^^^^^^^^^^
- Release 1.1.1
- Added script for building on Cheyenne with maxed out Intel settings, which
@ -59,8 +103,8 @@ v1.1.1 @@ -59,8 +103,8 @@ v1.1.1
- Fix cape_2d private variable bug when running with multiple CPUs.
v1.1.0
^^^^^^^^^^^^^^
v1.1.0 (January 2018)
^^^^^^^^^^^^^^^^^^^^^^^^^
- Release 1.1.0
- Computational routines now support multiple cores using OpenMP. See
@ -107,15 +151,15 @@ v1.1.0 @@ -107,15 +151,15 @@ v1.1.0
services like AppVeyor.
v1.0.5
^^^^^^^^^^^^^^
v1.0.5 (September 2017)
^^^^^^^^^^^^^^^^^^^^^^^^^^
- Release 1.0.5
- Reduced the CI test file sizes by half.
v1.0.4
^^^^^^^^^^^^^^
v1.0.4 (September 2017)
^^^^^^^^^^^^^^^^^^^^^^^^
- Release 1.0.4
- Fix warnings with CI tests which were caused by fill values being written
@ -123,23 +167,25 @@ v1.0.4 @@ -123,23 +167,25 @@ v1.0.4
- Added the __eq__ operator to the WrfProj projection base class.
- Fixed array order issue when using the raw CAPE routine with 1D arrays.
v1.0.3
^^^^^^^^^^^^^^
v1.0.3 (June 2017)
^^^^^^^^^^^^^^^^^^^^^
- Relase 1.0.3
- Fixed an issue with the cartopy Mercator subclass where the xlimits were
being calculated to the same value (or very close), causing blank plots.
v1.0.2
^^^^^^^^^^^^^^
v1.0.2 (May 2017)
^^^^^^^^^^^^^^^^^^^^^
- Release 1.0.2
- Fixed issue with the wspd_wdir product types when sequences of files are
used.
v1.0.1
^^^^^^^^^^^^^
v1.0.1 (March 2017)
^^^^^^^^^^^^^^^^^^^^^
- Release 1.0.1
- Fixed issue with initialization of PolarStereographic and LatLon map
@ -149,8 +195,8 @@ v1.0.1 @@ -149,8 +195,8 @@ v1.0.1
so wrf-python should as well.
v1.0.0
^^^^^^^^^^^^^
v1.0.0 (March 2017)
^^^^^^^^^^^^^^^^^^^^^
- Release 1.0.0.
- Fixed issue with not being able to set the thread-local coordinate cache to
@ -163,80 +209,6 @@ v1.0.0 @@ -163,80 +209,6 @@ v1.0.0
column of data.
Beta Releases
--------------
v1.0b3
^^^^^^^^^^^^^
- Beta release 3.
- Improvements made for conda-forge integration testing.
- Fixed an incorrectly initialized variable issue with vinterp. This issue
mainly impacts the unit tests for continuous integration testing with
conda-forge, since the data set used for these tests is heavily cropped.
- Back-ported the inspect.BoundArguments.apply_defaults so that Python 3.4
works. Windows users that want to try out wrf-python with Python 3.4
can use the bladwig conda channel to get it.
v1.0b2
^^^^^^^^^^^^^^
- Beta release 2.
- xarray 0.9 no longer includes default index dimensions in the coordinate
mappings. This was causing a crash in the routines that cause a reduction
in dimension shape, mainly the interpolation routines. This has been
fixed.
- Documentation updated to show the new output from xarray.
v1.0b1
^^^^^^^^^^^^^
- Beta release 1.
- Added more packaging boilerplate.
- Note: Currently unable to build with Python 3.5 on Windows, due to
issues with distutils, numpy distutils, and mingw compiler. Will attempt
to find a workaround before the next release. Windows users should use
Python 2.7 or Python 3.4 for now.
----------------
Alpha Releases
----------------
v1.0a3
^^^^^^^^^^^^
- Alpha release 3.
- Added docstrings.
- The mapping API has changed.
- The projection attributes are no longer arrays for moving domains.
- Utility functions have been added for extracting geobounds. It is now
easier to get map projection objects from sliced variables.
- Utility functions have been added for getting cartopy, basemap, and pyngl
objects.
- Users should no longer need to use xarray attributes directly
- Now uses CoordPair for cross sections so that lat/lon can be used instead of
raw x,y grid coordinates.
- Renamed npvalues to to_np which is more intuitive.
- Fixed issue with generator expressions.
- Renamed some functions and arguments.
-------------
Known Issues
--------------
v1.0.0
^^^^^^^^
- Currently unable to build on Windows with Python 3.5+ using open source
mingw compiler. The mingwpy project is working on resolving the
incompatibilities between mingw and Visual Studio 2015 that was used to
build Python 3.5+. Numpy 1.13 also has improved f2py support for
Python 3.5+ on Windows, so this will be revisited when it is released.

308
doc/source/plot.rst
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@ -10,11 +10,11 @@ for a future release. @@ -10,11 +10,11 @@ for a future release.
Matplotlib With Cartopy
-------------------------
Cartopy is becoming the main tool for base mapping with matplotlib, but you should
be aware of a few shortcomings when working with WRF data.
Cartopy is becoming the main tool for base mapping with matplotlib, but you
should be aware of a few shortcomings when working with WRF data.
- The builtin transformations of coordinates when calling the contouring functions
do not work correctly with the rotated pole projection. The
- The builtin transformations of coordinates when calling the contouring
functions do not work correctly with the rotated pole projection. The
transform_points method needs to be called manually on the latitude and
longitude arrays.
@ -40,7 +40,8 @@ Plotting a Two-dimensional Field @@ -40,7 +40,8 @@ Plotting a Two-dimensional Field
import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature
from wrf import to_np, getvar, smooth2d, get_cartopy, cartopy_xlim, cartopy_ylim, latlon_coords
from wrf import (to_np, getvar, smooth2d, get_cartopy, cartopy_xlim,
cartopy_ylim, latlon_coords)
# Open the NetCDF file
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
@ -48,8 +49,9 @@ Plotting a Two-dimensional Field @@ -48,8 +49,9 @@ Plotting a Two-dimensional Field
# Get the sea level pressure
slp = getvar(ncfile, "slp")
# Smooth the sea level pressure since it tends to be noisy near the mountains
smooth_slp = smooth2d(slp, 3)
# Smooth the sea level pressure since it tends to be noisy near the
# mountains
smooth_slp = smooth2d(slp, 3, cenweight=4)
# Get the latitude and longitude points
lats, lons = latlon_coords(slp)
@ -58,26 +60,29 @@ Plotting a Two-dimensional Field @@ -58,26 +60,29 @@ Plotting a Two-dimensional Field
cart_proj = get_cartopy(slp)
# Create a figure
fig = plt.figure(figsize=(12,9))
fig = plt.figure(figsize=(12,6))
# Set the GeoAxes to the projection used by WRF
ax = plt.axes(projection=cart_proj)
# Download and add the states and coastlines
states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
name='admin_1_states_provinces_shp')
ax.add_feature(states, linewidth=.5)
states = NaturalEarthFeature(category="cultural", scale="50m",
facecolor="none",
name="admin_1_states_provinces_shp")
ax.add_feature(states, linewidth=.5, edgecolor="black")
ax.coastlines('50m', linewidth=0.8)
# Make the contour outlines and filled contours for the smoothed sea level pressure.
# Make the contour outlines and filled contours for the smoothed sea level
# pressure.
plt.contour(to_np(lons), to_np(lats), to_np(smooth_slp), 10, colors="black",
transform=crs.PlateCarree())
plt.contourf(to_np(lons), to_np(lats), to_np(smooth_slp), 10, transform=crs.PlateCarree(),
plt.contourf(to_np(lons), to_np(lats), to_np(smooth_slp), 10,
transform=crs.PlateCarree(),
cmap=get_cmap("jet"))
# Add a color bar
plt.colorbar(ax=ax, shrink=.62)
plt.colorbar(ax=ax, shrink=.98)
# Set the map limits. Not really necessary, but used for demonstration.
# Set the map bounds
ax.set_xlim(cartopy_xlim(smooth_slp))
ax.set_ylim(cartopy_ylim(smooth_slp))
@ -105,7 +110,8 @@ Horizontal Interpolation to a Pressure Level @@ -105,7 +110,8 @@ Horizontal Interpolation to a Pressure Level
import cartopy.crs as crs
from cartopy.feature import NaturalEarthFeature
from wrf import getvar, interplevel, to_np, latlon_coords, get_cartopy, cartopy_xlim, cartopy_ylim
from wrf import (getvar, interplevel, to_np, latlon_coords, get_cartopy,
cartopy_xlim, cartopy_ylim)
# Open the NetCDF file
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
@ -134,27 +140,31 @@ Horizontal Interpolation to a Pressure Level @@ -134,27 +140,31 @@ Horizontal Interpolation to a Pressure Level
ax = plt.axes(projection=cart_proj)
# Download and add the states and coastlines
states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
name='admin_1_states_provinces_shp')
ax.add_feature(states, linewidth=0.5)
states = NaturalEarthFeature(category="cultural", scale="50m",
facecolor="none",
name="admin_1_states_provinces_shp")
ax.add_feature(states, linewidth=0.5, edgecolor="black")
ax.coastlines('50m', linewidth=0.8)
# Add the 500 hPa geopotential height contours
levels = np.arange(520., 580., 6.)
contours = plt.contour(to_np(lons), to_np(lats), to_np(ht_500), levels=levels, colors="black",
contours = plt.contour(to_np(lons), to_np(lats), to_np(ht_500),
levels=levels, colors="black",
transform=crs.PlateCarree())
plt.clabel(contours, inline=1, fontsize=10, fmt="%i")
# Add the wind speed contours
levels = [25, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110, 120]
wspd_contours = plt.contourf(to_np(lons), to_np(lats), to_np(wspd_500), levels=levels,
wspd_contours = plt.contourf(to_np(lons), to_np(lats), to_np(wspd_500),
levels=levels,
cmap=get_cmap("rainbow"),
transform=crs.PlateCarree())
plt.colorbar(wspd_contours, ax=ax, orientation="horizontal", pad=.05)
# Add the 500 hPa wind barbs, only plotting every 125th data point.
plt.barbs(to_np(lons[::125,::125]), to_np(lats[::125,::125]), to_np(u_500[::125, ::125]),
to_np(v_500[::125, ::125]), transform=crs.PlateCarree(), length=6)
plt.barbs(to_np(lons[::125,::125]), to_np(lats[::125,::125]),
to_np(u_500[::125, ::125]), to_np(v_500[::125, ::125]),
transform=crs.PlateCarree(), length=6)
# Set the map bounds
ax.set_xlim(cartopy_xlim(ht_500))
@ -187,8 +197,8 @@ plot, see :ref:`cross_example`. @@ -187,8 +197,8 @@ plot, see :ref:`cross_example`.
import cartopy.feature as cfeature
from netCDF4 import Dataset
from wrf import (getvar, to_np, vertcross, smooth2d, CoordPair, GeoBounds, get_cartopy,
latlon_coords, cartopy_xlim, cartopy_ylim)
from wrf import (getvar, to_np, vertcross, smooth2d, CoordPair, GeoBounds,
get_cartopy, latlon_coords, cartopy_xlim, cartopy_ylim)
# Open the NetCDF file
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
@ -206,12 +216,13 @@ plot, see :ref:`cross_example`. @@ -206,12 +216,13 @@ plot, see :ref:`cross_example`.
start_point = CoordPair(lat=26.76, lon=-80.0)
end_point = CoordPair(lat=26.76, lon=-77.8)
# Compute the vertical cross-section interpolation. Also, include the lat/lon
# points along the cross-section in the metadata by setting latlon to True.
z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point, end_point=end_point,
latlon=True, meta=True)
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point,
latlon=True, meta=True)
# Compute the vertical cross-section interpolation. Also, include the
# lat/lon points along the cross-section in the metadata by setting latlon
# to True.
z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point,
end_point=end_point, latlon=True, meta=True)
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point,
end_point=end_point, latlon=True, meta=True)
dbz_cross = 10.0 * np.log10(z_cross)
# Get the lat/lon points
@ -221,31 +232,37 @@ plot, see :ref:`cross_example`. @@ -221,31 +232,37 @@ plot, see :ref:`cross_example`.
cart_proj = get_cartopy(slp)
# Create a figure that will have 3 subplots
fig = plt.figure(figsize=(10,7))
fig = plt.figure(figsize=(12,9))
ax_ctt = fig.add_subplot(1,2,1,projection=cart_proj)
ax_wspd = fig.add_subplot(2,2,2)
ax_dbz = fig.add_subplot(2,2,4)
# Download and create the states, land, and oceans using cartopy features
states = cfeature.NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',
states = cfeature.NaturalEarthFeature(category='cultural', scale='50m',
facecolor='none',
name='admin_1_states_provinces_shp')
land = cfeature.NaturalEarthFeature(category='physical', name='land', scale='50m',
land = cfeature.NaturalEarthFeature(category='physical', name='land',
scale='50m',
facecolor=cfeature.COLORS['land'])
ocean = cfeature.NaturalEarthFeature(category='physical', name='ocean', scale='50m',
ocean = cfeature.NaturalEarthFeature(category='physical', name='ocean',
scale='50m',
facecolor=cfeature.COLORS['water'])
# Make the pressure contours
contour_levels = [960, 965, 970, 975, 980, 990]
c1 = ax_ctt.contour(lons, lats, to_np(smooth_slp), levels=contour_levels, colors="white",
transform=crs.PlateCarree(), zorder=3, linewidths=1.0)
c1 = ax_ctt.contour(lons, lats, to_np(smooth_slp), levels=contour_levels,
colors="white", transform=crs.PlateCarree(), zorder=3,
linewidths=1.0)
# Create the filled cloud top temperature contours
contour_levels = [-80.0, -70.0, -60, -50, -40, -30, -20, -10, 0, 10]
ctt_contours = ax_ctt.contourf(to_np(lons), to_np(lats), to_np(ctt), contour_levels,
cmap=get_cmap("Greys"), transform=crs.PlateCarree(), zorder=2)
ctt_contours = ax_ctt.contourf(to_np(lons), to_np(lats), to_np(ctt),
contour_levels, cmap=get_cmap("Greys"),
transform=crs.PlateCarree(), zorder=2)
ax_ctt.plot([start_point.lon, end_point.lon], [start_point.lat, end_point.lat],
color="yellow", marker="o", transform=crs.PlateCarree(), zorder=3)
ax_ctt.plot([start_point.lon, end_point.lon],
[start_point.lat, end_point.lat], color="yellow", marker="o",
transform=crs.PlateCarree(), zorder=3)
# Create the color bar for cloud top temperature
cb_ctt = fig.colorbar(ctt_contours, ax=ax_ctt, shrink=.60)
@ -271,7 +288,8 @@ plot, see :ref:`cross_example`. @@ -271,7 +288,8 @@ plot, see :ref:`cross_example`.
# Make the contour plot for dbz
levels = [5 + 5*n for n in range(15)]
dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels, cmap=get_cmap("jet"))
dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels,
cmap=get_cmap("jet"))
cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz)
cb_dbz.ax.tick_params(labelsize=5)
@ -308,9 +326,9 @@ plot, see :ref:`cross_example`. @@ -308,9 +326,9 @@ plot, see :ref:`cross_example`.
Matplotlib with Basemap
-----------------------
Although basemap is in maintenance mode only and becoming deprecated, it is still
widely used by many programmers. Cartopy is becoming the preferred package for
mapping, however it suffers from growing pains in some areas
Although basemap is in maintenance mode only and becoming deprecated, it is
still widely used by many programmers. Cartopy is becoming the preferred
package for mapping, however it suffers from growing pains in some areas
(can't use latitude/longitude labels for many map projections). If you
run in to these issues, basemap is likely to accomplish what you need.
@ -337,8 +355,9 @@ Plotting a Two-Dimensional Field @@ -337,8 +355,9 @@ Plotting a Two-Dimensional Field
# Get the sea level pressure
slp = getvar(ncfile, "slp")
# Smooth the sea level pressure since it tends to be noisy near the mountains
smooth_slp = smooth2d(slp, 3)
# Smooth the sea level pressure since it tends to be noisy near the
# mountains
smooth_slp = smooth2d(slp, 3, cenweight=4)
# Get the latitude and longitude points
lats, lons = latlon_coords(slp)
@ -354,8 +373,9 @@ Plotting a Two-Dimensional Field @@ -354,8 +373,9 @@ Plotting a Two-Dimensional Field
bm.drawstates(linewidth=0.25)
bm.drawcountries(linewidth=0.25)
# Convert the lats and lons to x and y. Make sure you convert the lats and lons to
# numpy arrays via to_np, or basemap crashes with an undefined RuntimeError.
# Convert the lats and lons to x and y. Make sure you convert the lats and
# lons to numpy arrays via to_np, or basemap crashes with an undefined
# RuntimeError.
x, y = bm(to_np(lons), to_np(lats))
# Draw the contours and filled contours
@ -458,7 +478,8 @@ plot, see :ref:`cross_example`. @@ -458,7 +478,8 @@ plot, see :ref:`cross_example`.
from matplotlib.cm import get_cmap
from netCDF4 import Dataset
from wrf import getvar, to_np, vertcross, smooth2d, CoordPair, get_basemap, latlon_coords
from wrf import (getvar, to_np, vertcross, smooth2d, CoordPair,
get_basemap, latlon_coords)
# Open the NetCDF file
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
@ -476,19 +497,20 @@ plot, see :ref:`cross_example`. @@ -476,19 +497,20 @@ plot, see :ref:`cross_example`.
start_point = CoordPair(lat=26.76, lon=-80.0)
end_point = CoordPair(lat=26.76, lon=-77.8)
# Compute the vertical cross-section interpolation. Also, include the lat/lon points
# along the cross-section in the metadata by setting latlon to True.
z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point, end_point=end_point,
latlon=True, meta=True)
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point,
latlon=True, meta=True)
# Compute the vertical cross-section interpolation. Also, include the
# lat/lon points along the cross-section in the metadata by setting latlon
# to True.
z_cross = vertcross(Z, z, wrfin=ncfile, start_point=start_point,
end_point=end_point, latlon=True, meta=True)
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point,
end_point=end_point, latlon=True, meta=True)
dbz_cross = 10.0 * np.log10(z_cross)
# Get the latitude and longitude points
lats, lons = latlon_coords(slp)
# Create the figure that will have 3 subplots
fig = plt.figure(figsize=(10,7))
fig = plt.figure(figsize=(12,9))
ax_ctt = fig.add_subplot(1,2,1)
ax_wspd = fig.add_subplot(2,2,2)
ax_dbz = fig.add_subplot(2,2,4)
@ -501,15 +523,16 @@ plot, see :ref:`cross_example`. @@ -501,15 +523,16 @@ plot, see :ref:`cross_example`.
# Make the pressure contours
contour_levels = [960, 965, 970, 975, 980, 990]
c1 = bm.contour(x, y, to_np(smooth_slp), levels=contour_levels, colors="white",
zorder=3, linewidths=1.0, ax=ax_ctt)
c1 = bm.contour(x, y, to_np(smooth_slp), levels=contour_levels,
colors="white", zorder=3, linewidths=1.0, ax=ax_ctt)
# Create the filled cloud top temperature contours
contour_levels = [-80.0, -70.0, -60, -50, -40, -30, -20, -10, 0, 10]
ctt_contours = bm.contourf(x, y, to_np(ctt), contour_levels, cmap=get_cmap("Greys"),
zorder=2, ax=ax_ctt)
ctt_contours = bm.contourf(x, y, to_np(ctt), contour_levels,
cmap=get_cmap("Greys"), zorder=2, ax=ax_ctt)
point_x, point_y = bm([start_point.lon, end_point.lon], [start_point.lat, end_point.lat])
point_x, point_y = bm([start_point.lon, end_point.lon],
[start_point.lat, end_point.lat])
bm.plot([point_x[0], point_x[1]], [point_y[0], point_y[1]], color="yellow",
marker="o", zorder=3, ax=ax_ctt)
@ -522,8 +545,12 @@ plot, see :ref:`cross_example`. @@ -522,8 +545,12 @@ plot, see :ref:`cross_example`.
bm.drawstates(linewidth=0.25, ax=ax_ctt)
bm.drawcountries(linewidth=0.25, ax=ax_ctt)
bm.fillcontinents(color=np.array([ 0.9375 , 0.9375 , 0.859375]),
ax=ax_ctt, lake_color=np.array([ 0.59375 , 0.71484375, 0.8828125 ]))
bm.drawmapboundary(fill_color=np.array([ 0.59375 , 0.71484375, 0.8828125 ]), ax=ax_ctt)
ax=ax_ctt,
lake_color=np.array([0.59375 ,
0.71484375,
0.8828125 ]))
bm.drawmapboundary(fill_color=np.array([ 0.59375 , 0.71484375, 0.8828125 ]),
ax=ax_ctt)
# Draw Parallels
parallels = np.arange(np.amin(lats), 30., 2.5)
@ -547,7 +574,8 @@ plot, see :ref:`cross_example`. @@ -547,7 +574,8 @@ plot, see :ref:`cross_example`.
# Make the contour plot for dbz
levels = [5 + 5*n for n in range(15)]
dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels, cmap=get_cmap("jet"))
dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels,
cmap=get_cmap("jet"))
cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz)
cb_dbz.ax.tick_params(labelsize=5)
@ -583,6 +611,7 @@ plot, see :ref:`cross_example`. @@ -583,6 +611,7 @@ plot, see :ref:`cross_example`.
.. _cross_example:
Vertical Cross Section
-------------------------------
@ -616,10 +645,10 @@ plotted using the standard matplotlib API. @@ -616,10 +645,10 @@ plotted using the standard matplotlib API.
start_point = CoordPair(lat=26.76, lon=-80.0)
end_point = CoordPair(lat=26.76, lon=-77.8)
# Compute the vertical cross-section interpolation. Also, include the lat/lon
# points along the cross-section.
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point, end_point=end_point,
latlon=True, meta=True)
# Compute the vertical cross-section interpolation. Also, include the
# lat/lon points along the cross-section.
wspd_cross = vertcross(wspd, z, wrfin=ncfile, start_point=start_point,
end_point=end_point, latlon=True, meta=True)
# Create the figure
fig = plt.figure(figsize=(12,6))
@ -634,7 +663,8 @@ plotted using the standard matplotlib API. @@ -634,7 +663,8 @@ plotted using the standard matplotlib API.
# Set the x-ticks to use latitude and longitude labels.
coord_pairs = to_np(wspd_cross.coords["xy_loc"])
x_ticks = np.arange(coord_pairs.shape[0])
x_labels = [pair.latlon_str(fmt="{:.2f}, {:.2f}") for pair in to_np(coord_pairs)]
x_labels = [pair.latlon_str(fmt="{:.2f}, {:.2f}")
for pair in to_np(coord_pairs)]
ax.set_xticks(x_ticks[::20])
ax.set_xticklabels(x_labels[::20], rotation=45, fontsize=8)
@ -652,3 +682,141 @@ plotted using the standard matplotlib API. @@ -652,3 +682,141 @@ plotted using the standard matplotlib API.
plt.show()
Cross Section with Mountains
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The example below shows how to make a cross section with the mountainous
terrain filled.
.. image:: _static/images/cross_mtns.png
:scale: 100%
:align: center
.. code-block:: python
import numpy as np
from matplotlib import pyplot
from matplotlib.cm import get_cmap
from matplotlib.colors import from_levels_and_colors
from cartopy import crs
from cartopy.feature import NaturalEarthFeature, COLORS
from netCDF4 import Dataset
from wrf import (getvar, to_np, get_cartopy, latlon_coords, vertcross,
cartopy_xlim, cartopy_ylim, interpline, CoordPair)
wrf_file = Dataset("wrfout_d01_2010-06-04_00:00:00")
# Define the cross section start and end points
cross_start = CoordPair(lat=43.5, lon=-116.5)
cross_end = CoordPair(lat=43.5, lon=-114)
# Get the WRF variables
ht = getvar(wrf_file, "z", timeidx=-1)
ter = getvar(wrf_file, "ter", timeidx=-1)
dbz = getvar(wrf_file, "dbz", timeidx=-1)
max_dbz = getvar(wrf_file, "mdbz", timeidx=-1)
Z = 10**(dbz/10.) # Use linear Z for interpolation
# Compute the vertical cross-section interpolation. Also, include the
# lat/lon points along the cross-section in the metadata by setting latlon
# to True.
z_cross = vertcross(Z, ht, wrfin=wrf_file,
start_point=cross_start,
end_point=cross_end,
latlon=True, meta=True)
# Convert back to dBz after interpolation
dbz_cross = 10.0 * np.log10(z_cross)
# Add back the attributes that xarray dropped from the operations above
dbz_cross.attrs.update(z_cross.attrs)
dbz_cross.attrs["description"] = "radar reflectivity cross section"
dbz_cross.attrs["units"] = "dBZ"
# To remove the slight gap between the dbz contours and terrain due to the
# contouring of gridded data, a new vertical grid spacing, and model grid
# staggering, fill in the lower grid cells with the first non-missing value
# for each column.
# Make a copy of the z cross data. Let's use regular numpy arrays for this.
dbz_cross_filled = np.ma.copy(to_np(dbz_cross))
# For each cross section column, find the first index with non-missing
# values and copy these to the missing elements below.
for i in range(dbz_cross_filled.shape[-1]):
column_vals = dbz_cross_filled[:,i]
# Let's find the lowest index that isn't filled. The nonzero function
# finds all unmasked values greater than 0. Since 0 is a valid value
# for dBZ, let's change that threshold to be -200 dBZ instead.
first_idx = int(np.transpose((column_vals > -200).nonzero())[0])
dbz_cross_filled[0:first_idx, i] = dbz_cross_filled[first_idx, i]
# Get the terrain heights along the cross section line
ter_line = interpline(ter, wrfin=wrf_file, start_point=cross_start,
end_point=cross_end)
# Get the lat/lon points
lats, lons = latlon_coords(dbz)
# Get the cartopy projection object
cart_proj = get_cartopy(dbz)
# Create the figure
fig = pyplot.figure(figsize=(8,6))
ax_cross = pyplot.axes()
dbz_levels = np.arange(5., 80., 5.)
# Create the color table found on NWS pages.
dbz_rgb = np.array([[4,233,231],
[1,159,244], [3,0,244],
[2,253,2], [1,197,1],
[0,142,0], [253,248,2],
[229,188,0], [253,149,0],
[253,0,0], [212,0,0],
[188,0,0],[248,0,253],
[152,84,198]], np.float32) / 255.0
dbz_map, dbz_norm = from_levels_and_colors(dbz_levels, dbz_rgb,
extend="max")
# Make the cross section plot for dbz
dbz_levels = np.arange(5.,75.,5.)
xs = np.arange(0, dbz_cross.shape[-1], 1)
ys = to_np(dbz_cross.coords["vertical"])
dbz_contours = ax_cross.contourf(xs,
ys,
to_np(dbz_cross_filled),
levels=dbz_levels,
cmap=dbz_map,
norm=dbz_norm,
extend="max")
# Add the color bar
cb_dbz = fig.colorbar(dbz_contours, ax=ax_cross)
cb_dbz.ax.tick_params(labelsize=8)
# Fill in the mountain area
ht_fill = ax_cross.fill_between(xs, 0, to_np(ter_line),
facecolor="saddlebrown")
# Set the x-ticks to use latitude and longitude labels
coord_pairs = to_np(dbz_cross.coords["xy_loc"])
x_ticks = np.arange(coord_pairs.shape[0])
x_labels = [pair.latlon_str() for pair in to_np(coord_pairs)]
# Set the desired number of x ticks below
num_ticks = 5
thin = int((len(x_ticks) / num_ticks) + .5)
ax_cross.set_xticks(x_ticks[::thin])
ax_cross.set_xticklabels(x_labels[::thin], rotation=45, fontsize=8)
# Set the x-axis and y-axis labels
ax_cross.set_xlabel("Latitude, Longitude", fontsize=12)
ax_cross.set_ylabel("Height (m)", fontsize=12)
# Add a title
ax_cross.set_title("Cross-Section of Reflectivity (dBZ)", {"fontsize" : 14})
pyplot.show()

2
src/wrf/computation.py
Unescape View File

@ -701,7 +701,7 @@ def smooth2d(field, passes, cenweight=2.0, meta=True): @@ -701,7 +701,7 @@ def smooth2d(field, passes, cenweight=2.0, meta=True):
field (:class:`xarray.DataArray` or :class:`numpy.ndarray`): The field
to smooth, which must be at least two dimensions. Missing/fill
values will be ignored as long as the type is either a
:class:`numpy.ma.MaskedArray or a :class:`xarray.DataArray` with
:class:`numpy.ma.MaskedArray` or a :class:`xarray.DataArray` with
a *_FillValue* attribute.
passes (:obj:`int`): The number of smoothing passes.

334
src/wrf/g_cape.py
Unescape View File

@ -12,7 +12,7 @@ from .metadecorators import set_cape_metadata @@ -12,7 +12,7 @@ from .metadecorators import set_cape_metadata
@set_cape_metadata(is2d=True)
def get_2dcape(wrfin, timeidx=0, method="cat", squeeze=True, cache=None,
meta=True, _key=None, missing=default_fill(np.float64)):
"""Return the 2d fields of MCAPE, MCIN, LCL, and LFC.
"""Return the two-dimensional fields of MCAPE, MCIN, LCL, and LFC.
The leftmost dimension of the returned array represents four different
quantities:
@ -227,6 +227,62 @@ def get_3dcape(wrfin, timeidx=0, method="cat", @@ -227,6 +227,62 @@ def get_3dcape(wrfin, timeidx=0, method="cat",
def get_cape2d_only(wrfin, timeidx=0, method="cat", squeeze=True, cache=None,
meta=True, _key=None, missing=default_fill(np.float64)):
"""Return the two-dimensional field of MCAPE (Max Convective Available
Potential Energy).
This functions extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
missing (:obj:`float`): The fill value to use for the output.
Default is :data:`wrf.default_fill(np.float64)`.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The
2D MCAPE field.
If xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_2dcape(wrfin, timeidx, method, squeeze, cache,
meta, _key, missing)[0,:]
@ -239,6 +295,61 @@ def get_cape2d_only(wrfin, timeidx=0, method="cat", squeeze=True, cache=None, @@ -239,6 +295,61 @@ def get_cape2d_only(wrfin, timeidx=0, method="cat", squeeze=True, cache=None,
def get_cin2d_only(wrfin, timeidx=0, method="cat", squeeze=True, cache=None,
meta=True, _key=None, missing=default_fill(np.float64)):
"""Return the two-dimensional field of MCIN (Max Convective Inhibition).
This functions extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
missing (:obj:`float`): The fill value to use for the output.
Default is :data:`wrf.default_fill(np.float64)`.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The
2D MCIN field.
If xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_2dcape(wrfin, timeidx, method, squeeze, cache,
meta, _key, missing)[1,:]
@ -251,6 +362,61 @@ def get_cin2d_only(wrfin, timeidx=0, method="cat", squeeze=True, cache=None, @@ -251,6 +362,61 @@ def get_cin2d_only(wrfin, timeidx=0, method="cat", squeeze=True, cache=None,
def get_lcl(wrfin, timeidx=0, method="cat", squeeze=True, cache=None,
meta=True, _key=None, missing=default_fill(np.float64)):
"""Return the two-dimensional field of LCL (Lifted Condensation Level).
This functions extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
missing (:obj:`float`): The fill value to use for the output.
Default is :data:`wrf.default_fill(np.float64)`.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The
2D LCL field.
If xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_2dcape(wrfin, timeidx, method, squeeze, cache,
meta, _key, missing)[2,:]
@ -263,6 +429,61 @@ def get_lcl(wrfin, timeidx=0, method="cat", squeeze=True, cache=None, @@ -263,6 +429,61 @@ def get_lcl(wrfin, timeidx=0, method="cat", squeeze=True, cache=None,
def get_lfc(wrfin, timeidx=0, method="cat", squeeze=True, cache=None,
meta=True, _key=None, missing=default_fill(np.float64)):
"""Return the two-dimensional field of LFC (Level of Free Convection).
This functions extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
missing (:obj:`float`): The fill value to use for the output.
Default is :data:`wrf.default_fill(np.float64)`.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The
2D LFC field.
If xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_2dcape(wrfin, timeidx, method, squeeze, cache,
meta, _key, missing)[3,:]
@ -276,6 +497,62 @@ def get_lfc(wrfin, timeidx=0, method="cat", squeeze=True, cache=None, @@ -276,6 +497,62 @@ def get_lfc(wrfin, timeidx=0, method="cat", squeeze=True, cache=None,
def get_3dcape_only(wrfin, timeidx=0, method="cat",
squeeze=True, cache=None, meta=True,
_key=None, missing=default_fill(np.float64)):
"""Return the three-dimensional field of CAPE (Convective Available
Potential Energy).
This functions extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
missing (:obj:`float`): The fill value to use for the output.
Default is :data:`wrf.default_fill(np.float64)`.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The
3D CAPE field.
If xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_3dcape(wrfin, timeidx, method, squeeze, cache, meta,
_key, missing)[0,:]
@ -289,6 +566,61 @@ def get_3dcape_only(wrfin, timeidx=0, method="cat", @@ -289,6 +566,61 @@ def get_3dcape_only(wrfin, timeidx=0, method="cat",
def get_3dcin_only(wrfin, timeidx=0, method="cat",
squeeze=True, cache=None, meta=True,
_key=None, missing=default_fill(np.float64)):
"""Return the three-dimensional field of CIN (Convective Inhibition).
This functions extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
missing (:obj:`float`): The fill value to use for the output.
Default is :data:`wrf.default_fill(np.float64)`.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The
3D CIN field.
If xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_3dcape(wrfin, timeidx, method, squeeze, cache, meta,
_key, missing)[1,:]

291
src/wrf/g_cloudfrac.py
Unescape View File

@ -166,6 +166,103 @@ def get_low_cloudfrac(wrfin, timeidx=0, method="cat", squeeze=True, @@ -166,6 +166,103 @@ def get_low_cloudfrac(wrfin, timeidx=0, method="cat", squeeze=True,
cache=None, meta=True, _key=None,
vert_type="height_agl", low_thresh=None, mid_thresh=None,
high_thresh=None, missing=default_fill(np.float64)):
"""Return the cloud fraction for the low level clouds.
If the vertical coordinate type is 'height_agl' or 'height_msl', the
default cloud levels are defined as:
300 m <= low_cloud < 2000 m
2000 m <= mid_cloud < 6000 m
6000 m <= high_cloud
For 'pressure', the default cloud levels are defined as:
97000 Pa <= low_cloud < 80000 Pa
80000 Pa <= mid_cloud < 45000 Pa
45000 Pa <= high_cloud
Note that the default low cloud levels are chosen to
exclude clouds near the surface (fog). If you want fog included, set
*low_thresh* to ~99500 Pa if *vert_type* is set to 'pressure', or 15 m if
using 'height_msl' or 'height_agl'. Keep in mind that the lowest mass grid
points are slightly above the ground, and in order to find clouds, the
*low_thresh* needs to be set to values that are slightly greater than
(less than) the lowest height (pressure) values.
When using 'pressure' or 'height_agl' for *vert_type*, there is a
possibility that the lowest WRF level will be higher than the low_cloud or
mid_cloud threshold, particularly for mountainous regions. When this
happens, a fill value will be used in the output.
This functions extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
vert_type (:obj:`str`, optional): The type of vertical coordinate used
to determine cloud type thresholds. Must be 'height_agl',
'height_msl', or 'pres'. The default is 'height_agl'.
low_thresh (:obj:`float`, optional): The lower bound for what is
considered a low cloud. If *vert_type* is 'pres', the default is
97000 Pa. If *vert_type* is 'height_agl' or 'height_msl', then the
default is 300 m.
mid_thresh (:obj:`float`, optional): The lower bound for what is
considered a mid level cloud. If *vert_type* is 'pres', the
default is 80000 Pa. If *vert_type* is 'height_agl' or
'height_msl', then the default is 2000 m.
high_thresh (:obj:`float`, optional): The lower bound for what is
considered a high level cloud. If *vert_type* is 'pres', the
default is 45000 Pa. If *vert_type* is 'height_agl' or
'height_msl', then the default is 6000 m.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The
cloud fraction array for low level clouds.
If xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_cloudfrac(wrfin, timeidx, method, squeeze,
cache, meta, _key,
vert_type, low_thresh, mid_thresh,
@ -181,6 +278,103 @@ def get_mid_cloudfrac(wrfin, timeidx=0, method="cat", squeeze=True, @@ -181,6 +278,103 @@ def get_mid_cloudfrac(wrfin, timeidx=0, method="cat", squeeze=True,
cache=None, meta=True, _key=None,
vert_type="height_agl", low_thresh=None, mid_thresh=None,
high_thresh=None, missing=default_fill(np.float64)):
"""Return the cloud fraction for the mid level clouds.
If the vertical coordinate type is 'height_agl' or 'height_msl', the
default cloud levels are defined as:
300 m <= low_cloud < 2000 m
2000 m <= mid_cloud < 6000 m
6000 m <= high_cloud
For 'pressure', the default cloud levels are defined as:
97000 Pa <= low_cloud < 80000 Pa
80000 Pa <= mid_cloud < 45000 Pa
45000 Pa <= high_cloud
Note that the default low cloud levels are chosen to
exclude clouds near the surface (fog). If you want fog included, set
*low_thresh* to ~99500 Pa if *vert_type* is set to 'pressure', or 15 m if
using 'height_msl' or 'height_agl'. Keep in mind that the lowest mass grid
points are slightly above the ground, and in order to find clouds, the
*low_thresh* needs to be set to values that are slightly greater than
(less than) the lowest height (pressure) values.
When using 'pressure' or 'height_agl' for *vert_type*, there is a
possibility that the lowest WRF level will be higher than the low_cloud or
mid_cloud threshold, particularly for mountainous regions. When this
happens, a fill value will be used in the output.
This functions extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
vert_type (:obj:`str`, optional): The type of vertical coordinate used
to determine cloud type thresholds. Must be 'height_agl',
'height_msl', or 'pres'. The default is 'height_agl'.
low_thresh (:obj:`float`, optional): The lower bound for what is
considered a low cloud. If *vert_type* is 'pres', the default is
97000 Pa. If *vert_type* is 'height_agl' or 'height_msl', then the
default is 300 m.
mid_thresh (:obj:`float`, optional): The lower bound for what is
considered a mid level cloud. If *vert_type* is 'pres', the
default is 80000 Pa. If *vert_type* is 'height_agl' or
'height_msl', then the default is 2000 m.
high_thresh (:obj:`float`, optional): The lower bound for what is
considered a high level cloud. If *vert_type* is 'pres', the
default is 45000 Pa. If *vert_type* is 'height_agl' or
'height_msl', then the default is 6000 m.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The
cloud fraction array for mid level clouds.
If xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_cloudfrac(wrfin, timeidx, method, squeeze,
cache, meta, _key,
vert_type, low_thresh, mid_thresh,
@ -196,6 +390,103 @@ def get_high_cloudfrac(wrfin, timeidx=0, method="cat", squeeze=True, @@ -196,6 +390,103 @@ def get_high_cloudfrac(wrfin, timeidx=0, method="cat", squeeze=True,
cache=None, meta=True, _key=None,
vert_type="height_agl", low_thresh=None, mid_thresh=None,
high_thresh=None, missing=default_fill(np.float64)):
"""Return the cloud fraction for the high level clouds.
If the vertical coordinate type is 'height_agl' or 'height_msl', the
default cloud levels are defined as:
300 m <= low_cloud < 2000 m
2000 m <= mid_cloud < 6000 m
6000 m <= high_cloud
For 'pressure', the default cloud levels are defined as:
97000 Pa <= low_cloud < 80000 Pa
80000 Pa <= mid_cloud < 45000 Pa
45000 Pa <= high_cloud
Note that the default low cloud levels are chosen to
exclude clouds near the surface (fog). If you want fog included, set
*low_thresh* to ~99500 Pa if *vert_type* is set to 'pressure', or 15 m if
using 'height_msl' or 'height_agl'. Keep in mind that the lowest mass grid
points are slightly above the ground, and in order to find clouds, the
*low_thresh* needs to be set to values that are slightly greater than
(less than) the lowest height (pressure) values.
When using 'pressure' or 'height_agl' for *vert_type*, there is a
possibility that the lowest WRF level will be higher than the low_cloud or
mid_cloud threshold, particularly for mountainous regions. When this
happens, a fill value will be used in the output.
This functions extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
vert_type (:obj:`str`, optional): The type of vertical coordinate used
to determine cloud type thresholds. Must be 'height_agl',
'height_msl', or 'pres'. The default is 'height_agl'.
low_thresh (:obj:`float`, optional): The lower bound for what is
considered a low cloud. If *vert_type* is 'pres', the default is
97000 Pa. If *vert_type* is 'height_agl' or 'height_msl', then the
default is 300 m.
mid_thresh (:obj:`float`, optional): The lower bound for what is
considered a mid level cloud. If *vert_type* is 'pres', the
default is 80000 Pa. If *vert_type* is 'height_agl' or
'height_msl', then the default is 2000 m.
high_thresh (:obj:`float`, optional): The lower bound for what is
considered a high level cloud. If *vert_type* is 'pres', the
default is 45000 Pa. If *vert_type* is 'height_agl' or
'height_msl', then the default is 6000 m.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The
cloud fraction array for high level clouds.
If xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_cloudfrac(wrfin, timeidx, method, squeeze,
cache, meta, _key,
vert_type, low_thresh, mid_thresh,

236
src/wrf/g_uvmet.py
Unescape View File

@ -523,6 +523,65 @@ def get_uvmet10_wspd_wdir(wrfin, timeidx=0, method="cat", squeeze=True, @@ -523,6 +523,65 @@ def get_uvmet10_wspd_wdir(wrfin, timeidx=0, method="cat", squeeze=True,
def get_uvmet_wspd(wrfin, timeidx=0, method="cat", squeeze=True,
cache=None, meta=True, _key=None,
units="m s-1"):
"""Return the wind speed for the wind rotated to earth coordinates.
This function should be used when comparing with observations.
This function extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
units (:obj:`str`): The desired units. Refer to the :meth:`getvar`
product table for a list of available units for 'uvmet_wspd_wdir'.
Default is 'm s-1'.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The wind speed
for the wind rotated to earth coordinates. If
xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_uvmet_wspd_wdir(wrfin, timeidx, method, squeeze,
cache, meta, _key, units)[0,:]
@ -535,6 +594,65 @@ def get_uvmet_wspd(wrfin, timeidx=0, method="cat", squeeze=True, @@ -535,6 +594,65 @@ def get_uvmet_wspd(wrfin, timeidx=0, method="cat", squeeze=True,
def get_uvmet_wdir(wrfin, timeidx=0, method="cat", squeeze=True,
cache=None, meta=True, _key=None,
units="m s-1"):
"""Return the wind direction for the wind rotated to earth coordinates.
This function should be used when comparing with observations.
This function extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
units (:obj:`str`): The desired units. Refer to the :meth:`getvar`
product table for a list of available units for 'uvmet_wspd_wdir'.
Default is 'm s-1'.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The wind direction
for the wind rotated to earth coordinates. If
xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_uvmet_wspd_wdir(wrfin, timeidx, method, squeeze,
cache, meta, _key, units)[1,:]
@ -547,6 +665,65 @@ def get_uvmet_wdir(wrfin, timeidx=0, method="cat", squeeze=True, @@ -547,6 +665,65 @@ def get_uvmet_wdir(wrfin, timeidx=0, method="cat", squeeze=True,
def get_uvmet10_wspd(wrfin, timeidx=0, method="cat", squeeze=True,
cache=None, meta=True, _key=None,
units="m s-1"):
"""Return the wind speed for the 10m wind rotated to earth coordinates.
This function should be used when comparing with observations.
This function extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
units (:obj:`str`): The desired units. Refer to the :meth:`getvar`
product table for a list of available units for 'uvmet_wspd_wdir'.
Default is 'm s-1'.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The wind speed
for the 10m wind rotated to earth coordinates. If
xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_uvmet10_wspd_wdir(wrfin, timeidx, method, squeeze,
cache, meta, _key, units)[0,:]
if meta:
@ -558,6 +735,65 @@ def get_uvmet10_wspd(wrfin, timeidx=0, method="cat", squeeze=True, @@ -558,6 +735,65 @@ def get_uvmet10_wspd(wrfin, timeidx=0, method="cat", squeeze=True,
def get_uvmet10_wdir(wrfin, timeidx=0, method="cat", squeeze=True,
cache=None, meta=True, _key=None,
units="m s-1"):
"""Return the wind direction for the 10m wind rotated to earth coordinates.
This function should be used when comparing with observations.
This function extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
units (:obj:`str`): The desired units. Refer to the :meth:`getvar`
product table for a list of available units for 'uvmet_wspd_wdir'.
Default is 'm s-1'.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The wind direction
for the 10m wind rotated to earth coordinates. If
xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_uvmet10_wspd_wdir(wrfin, timeidx, method, squeeze,
cache, meta, _key, units)[1,:]

235
src/wrf/g_wind.py
Unescape View File

@ -332,7 +332,7 @@ def get_destag_wspd_wdir(wrfin, timeidx=0, method="cat", @@ -332,7 +332,7 @@ def get_destag_wspd_wdir(wrfin, timeidx=0, method="cat",
squeeze=True, cache=None, meta=True, _key=None,
units="m s-1"):
"""Return the wind speed and wind direction for the wind in the projected
coordinate space.
coordinate (i.e. grid) space.
The wind speed and direction from this function will be relative to the
grid. This function should not be used to compare with observations,
@ -423,7 +423,7 @@ def get_destag_wspd_wdir10(wrfin, timeidx=0, method="cat", @@ -423,7 +423,7 @@ def get_destag_wspd_wdir10(wrfin, timeidx=0, method="cat",
squeeze=True, cache=None, meta=True, _key=None,
units="m s-1"):
"""Return the wind speed and wind direction for the 10m wind in
projected coordinate space.
projected coordinate (i.e. grid) space.
The wind speed and direction from this function will be relative to the
grid. This function should not be used to compare with observations,
@ -511,6 +511,63 @@ def get_destag_wspd_wdir10(wrfin, timeidx=0, method="cat", @@ -511,6 +511,63 @@ def get_destag_wspd_wdir10(wrfin, timeidx=0, method="cat",
def get_destag_wspd(wrfin, timeidx=0, method="cat",
squeeze=True, cache=None, meta=True, _key=None,
units="m s-1"):
"""Return the wind speed in the projected coordinate (i.e. grid) space.
This function extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
units (:obj:`str`): The desired units. Refer to the :meth:`getvar`
product table for a list of available units for 'wspd_wdir'.
Default is 'm s-1'.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The wind
speed in projected space. If
xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_destag_wspd_wdir(wrfin, timeidx, method, squeeze, cache,
meta, _key, units)[0,:]
@ -519,9 +576,67 @@ def get_destag_wspd(wrfin, timeidx=0, method="cat", @@ -519,9 +576,67 @@ def get_destag_wspd(wrfin, timeidx=0, method="cat",
return result
def get_destag_wdir(wrfin, timeidx=0, method="cat",
squeeze=True, cache=None, meta=True, _key=None,
units="m s-1"):
"""Return the wind direction in the projected coordinate (i.e. grid) space.
This function extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
units (:obj:`str`): The desired units. Refer to the :meth:`getvar`
product table for a list of available units for 'wspd_wdir'.
Default is 'm s-1'.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The wind
direction in projected space. If
xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_destag_wspd_wdir(wrfin, timeidx, method, squeeze, cache,
meta, _key, units)[1,:]
@ -534,6 +649,64 @@ def get_destag_wdir(wrfin, timeidx=0, method="cat", @@ -534,6 +649,64 @@ def get_destag_wdir(wrfin, timeidx=0, method="cat",
def get_destag_wspd10(wrfin, timeidx=0, method="cat",
squeeze=True, cache=None, meta=True, _key=None,
units="m s-1"):
"""Return the wind speed for the 10m wind in projected coordinate
(i.e. grid) space.
This function extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
units (:obj:`str`): The desired units. Refer to the :meth:`getvar`
product table for a list of available units for
'wspd_wdir10'. Default is 'm s-1'.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The wind speed
for the 10m wind in projected space. If
xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_destag_wspd_wdir10(wrfin, timeidx, method,
squeeze, cache, meta, _key, units)[0,:]
@ -546,6 +719,64 @@ def get_destag_wspd10(wrfin, timeidx=0, method="cat", @@ -546,6 +719,64 @@ def get_destag_wspd10(wrfin, timeidx=0, method="cat",
def get_destag_wdir10(wrfin, timeidx=0, method="cat",
squeeze=True, cache=None, meta=True, _key=None,
units="m s-1"):
"""Return the wind direction for the 10m wind in projected coordinate
(i.e. grid) space.
This function extracts the necessary variables from the NetCDF file
object in order to perform the calculation.
Args:
wrfin (:class:`netCDF4.Dataset`, :class:`Nio.NioFile`, or an \
iterable): WRF-ARW NetCDF
data as a :class:`netCDF4.Dataset`, :class:`Nio.NioFile`
or an iterable sequence of the aforementioned types.
timeidx (:obj:`int` or :data:`wrf.ALL_TIMES`, optional): The
desired time index. This value can be a positive integer,
negative integer, or
:data:`wrf.ALL_TIMES` (an alias for None) to return
all times in the file or sequence. The default is 0.
method (:obj:`str`, optional): The aggregation method to use for
sequences. Must be either 'cat' or 'join'.
'cat' combines the data along the Time dimension.
'join' creates a new dimension for the file index.
The default is 'cat'.
squeeze (:obj:`bool`, optional): Set to False to prevent dimensions
with a size of 1 from being automatically removed from the shape
of the output. Default is True.
cache (:obj:`dict`, optional): A dictionary of (varname, ndarray)
that can be used to supply pre-extracted NetCDF variables to the
computational routines. It is primarily used for internal
purposes, but can also be used to improve performance by
eliminating the need to repeatedly extract the same variables
used in multiple diagnostics calculations, particularly when using
large sequences of files.
Default is None.
meta (:obj:`bool`, optional): Set to False to disable metadata and
return :class:`numpy.ndarray` instead of
:class:`xarray.DataArray`. Default is True.
_key (:obj:`int`, optional): A caching key. This is used for internal
purposes only. Default is None.
units (:obj:`str`): The desired units. Refer to the :meth:`getvar`
product table for a list of available units for
'wspd_wdir10'. Default is 'm s-1'.
Returns:
:class:`xarray.DataArray` or :class:`numpy.ndarray`: The wind direction
for the 10m wind in projected space. If
xarray is enabled and the *meta* parameter is True, then the result
will be a :class:`xarray.DataArray` object. Otherwise, the result will
be a :class:`numpy.ndarray` object with no metadata.
"""
result = get_destag_wspd_wdir10(wrfin, timeidx, method,
squeeze, cache, meta, _key, units)[1,:]

2
src/wrf/version.py
Unescape View File

@ -1,2 +1,2 @@ @@ -1,2 +1,2 @@
__version__ = "1.2.0"
__version__ = "1.3.0"

257
test/ipynb/Doc_Examples.ipynb
Unescape View File

@ -108,8 +108,6 @@ @@ -108,8 +108,6 @@
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import (absolute_import, division, print_function, unicode_literals)\n",
"\n",
"import numpy as np\n",
@ -146,11 +144,11 @@ @@ -146,11 +144,11 @@
"levels = [5 + 5*n for n in range(15)]\n",
"\n",
"# Make the contour plot\n",
"a = axes[0].contourf(to_np(wspd_cross))\n",
"a = axes[0].contourf(to_np(wspd_cross), cmap=get_cmap(\"jet\"))\n",
"# Add the color bar\n",
"fig.colorbar(a, ax=axes[0])\n",
"\n",
"b = axes[1].contourf(to_np(dbz_cross), levels=levels)\n",
"b = axes[1].contourf(to_np(dbz_cross), levels=levels, cmap=get_cmap(\"jet\"))\n",
"fig.colorbar(b, ax=axes[1])\n",
"\n",
"# Set the x-ticks to use latitude and longitude labels.\n",
@ -185,6 +183,13 @@ @@ -185,6 +183,13 @@
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cartopy Panel Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -193,8 +198,6 @@ @@ -193,8 +198,6 @@
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import (absolute_import, division, print_function, unicode_literals)\n",
"\n",
"import numpy as np\n",
@ -236,7 +239,7 @@ @@ -236,7 +239,7 @@
"cart_proj = get_cartopy(slp)\n",
"\n",
"# Create a figure that will have 3 subplots\n",
"fig = plt.figure(figsize=(10,7))\n",
"fig = plt.figure(figsize=(12,9))\n",
"ax_ctt = fig.add_subplot(1,2,1,projection=cart_proj)\n",
"ax_wspd = fig.add_subplot(2,2,2)\n",
"ax_dbz = fig.add_subplot(2,2,4)\n",
@ -282,13 +285,13 @@ @@ -282,13 +285,13 @@
"wspd_contours = ax_wspd.contourf(to_np(wspd_cross), cmap=get_cmap(\"jet\"))\n",
"# Add the color bar\n",
"cb_wspd = fig.colorbar(wspd_contours, ax=ax_wspd)\n",
"cb_wspd.ax.tick_params(labelsize=5)\n",
"cb_wspd.ax.tick_params(labelsize=6)\n",
"\n",
"# Make the contour plot for dbz\n",
"levels = [5 + 5*n for n in range(15)]\n",
"dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels, cmap=get_cmap(\"jet\"))\n",
"cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz)\n",
"cb_dbz.ax.tick_params(labelsize=5)\n",
"cb_dbz.ax.tick_params(labelsize=6)\n",
"\n",
"# Set the x-ticks to use latitude and longitude labels\n",
"coord_pairs = to_np(dbz_cross.coords[\"xy_loc\"])\n",
@ -297,25 +300,25 @@ @@ -297,25 +300,25 @@
"ax_wspd.set_xticks(x_ticks[::20])\n",
"ax_wspd.set_xticklabels([], rotation=45)\n",
"ax_dbz.set_xticks(x_ticks[::20])\n",
"ax_dbz.set_xticklabels(x_labels[::20], rotation=45, fontsize=4) \n",
"ax_dbz.set_xticklabels(x_labels[::20], rotation=45, fontsize=6) \n",
"\n",
"# Set the y-ticks to be height\n",
"vert_vals = to_np(dbz_cross.coords[\"vertical\"])\n",
"v_ticks = np.arange(vert_vals.shape[0])\n",
"ax_wspd.set_yticks(v_ticks[::20])\n",
"ax_wspd.set_yticklabels(vert_vals[::20], fontsize=4) \n",
"ax_wspd.set_yticklabels(vert_vals[::20], fontsize=6) \n",
"ax_dbz.set_yticks(v_ticks[::20])\n",
"ax_dbz.set_yticklabels(vert_vals[::20], fontsize=4) \n",
"ax_dbz.set_yticklabels(vert_vals[::20], fontsize=6) \n",
"\n",
"# Set the x-axis and y-axis labels\n",
"ax_dbz.set_xlabel(\"Latitude, Longitude\", fontsize=5)\n",
"ax_wspd.set_ylabel(\"Height (m)\", fontsize=5)\n",
"ax_dbz.set_ylabel(\"Height (m)\", fontsize=5)\n",
"ax_dbz.set_xlabel(\"Latitude, Longitude\", fontsize=8)\n",
"ax_wspd.set_ylabel(\"Height (m)\", fontsize=8)\n",
"ax_dbz.set_ylabel(\"Height (m)\", fontsize=8)\n",
"\n",
"# Add a title\n",
"ax_ctt.set_title(\"Cloud Top Temperature (degC)\", {\"fontsize\" : 7})\n",
"ax_wspd.set_title(\"Cross-Section of Wind Speed (kt)\", {\"fontsize\" : 7})\n",
"ax_dbz.set_title(\"Cross-Section of Reflectivity (dBZ)\", {\"fontsize\" : 7})\n",
"ax_ctt.set_title(\"Cloud Top Temperature (degC)\", {\"fontsize\" : 10})\n",
"ax_wspd.set_title(\"Cross-Section of Wind Speed (kt)\", {\"fontsize\" : 10})\n",
"ax_dbz.set_title(\"Cross-Section of Reflectivity (dBZ)\", {\"fontsize\" : 10})\n",
"\n",
"plt.savefig(\"/Users/ladwig/Documents/workspace/wrf_python/doc/source/_static/images/matthew.png\",\n",
" transparent=True, bbox_inches=\"tight\")\n",
@ -330,8 +333,6 @@ @@ -330,8 +333,6 @@
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"# SLP\n",
"from __future__ import (absolute_import, division, print_function, unicode_literals)\n",
" \n",
"from netCDF4 import Dataset \n",
@ -349,7 +350,7 @@ @@ -349,7 +350,7 @@
"slp = getvar(ncfile, \"slp\")\n",
"\n",
"# Smooth the sea level pressure since it tends to be noisy near the mountains\n",
"smooth_slp = smooth2d(slp, 3)\n",
"smooth_slp = smooth2d(slp, 3, cenweight=4)\n",
"\n",
"# Get the latitude and longitude points\n",
"lats, lons = latlon_coords(slp)\n",
@ -358,22 +359,22 @@ @@ -358,22 +359,22 @@
"cart_proj = get_cartopy(slp)\n",
"\n",
"# Create a figure\n",
"fig = plt.figure(figsize=(12,9))\n",
"fig = plt.figure(figsize=(12,6.0))\n",
"# Set the GeoAxes to the projection used by WRF\n",
"ax = plt.axes(projection=cart_proj)\n",
"\n",
"# Download and add the states and coastlines\n",
"states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',\n",
" name='admin_1_states_provinces_shp')\n",
"ax.add_feature(states, linewidth=.5)\n",
"ax.coastlines('50m', linewidth=0.8)\n",
"ax.add_feature(states, linewidth=.5, edgecolor=\"black\")\n",
"ax.coastlines('50m', linewidth=0.8, zorder=3)\n",
"\n",
"# Make the contour outlines and filled contours for the smoothed sea level pressure.\n",
"plt.contour(to_np(lons), to_np(lats), to_np(smooth_slp), 10, colors=\"black\", transform=crs.PlateCarree())\n",
"plt.contourf(to_np(lons), to_np(lats), to_np(smooth_slp), 10, transform=crs.PlateCarree(), cmap=get_cmap(\"jet\"))\n",
"\n",
"# Add a color bar\n",
"plt.colorbar(ax=ax, shrink=.62)\n",
"plt.colorbar(ax=ax, shrink=.98)\n",
"\n",
"# Set the map limits. Not really necessary, but used for demonstration.\n",
"ax.set_xlim(cartopy_xlim(smooth_slp))\n",
@ -388,14 +389,19 @@ @@ -388,14 +389,19 @@
" transparent=True, bbox_inches=\"tight\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Vertical Cross Section"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from __future__ import (absolute_import, division, print_function, unicode_literals)\n",
"\n",
"import numpy as np\n",
@ -457,6 +463,151 @@ @@ -457,6 +463,151 @@
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Vertical Cross Section with Mountains"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"from matplotlib import pyplot\n",
"from matplotlib.cm import get_cmap\n",
"from matplotlib.colors import from_levels_and_colors\n",
"from cartopy import crs\n",
"from cartopy.feature import NaturalEarthFeature, COLORS\n",
"from netCDF4 import Dataset\n",
"from wrf import (getvar, to_np, get_cartopy, latlon_coords, vertcross,\n",
" cartopy_xlim, cartopy_ylim, interpline, CoordPair)\n",
"\n",
"wrf_file = Dataset(\"/Users/ladwig/Documents/wrf_files/boise_tutorial/orig/wrfout_d01_2010-06-04_00:00:00\")\n",
"\n",
"# Define the cross section start and end points\n",
"cross_start = CoordPair(lat=43.5, lon=-116.5)\n",
"cross_end = CoordPair(lat=43.5, lon=-114)\n",
"\n",
"# Get the WRF variables\n",
"ht = getvar(wrf_file, \"z\", timeidx=-1)\n",
"ter = getvar(wrf_file, \"ter\", timeidx=-1)\n",
"dbz = getvar(wrf_file, \"dbz\", timeidx=-1)\n",
"max_dbz = getvar(wrf_file, \"mdbz\", timeidx=-1)\n",
"Z = 10**(dbz/10.) # Use linear Z for interpolation\n",
"\n",
"# Compute the vertical cross-section interpolation. Also, include the lat/lon\n",
"# points along the cross-section in the metadata by setting latlon to True.\n",
"z_cross = vertcross(Z, ht, wrfin=wrf_file, \n",
" start_point=cross_start, \n",
" end_point=cross_end,\n",
" latlon=True, meta=True)\n",
"\n",
"# Convert back to dBz after interpolation\n",
"dbz_cross = 10.0 * np.log10(z_cross)\n",
"\n",
"# Add back the attributes that xarray dropped from the operations above\n",
"dbz_cross.attrs.update(z_cross.attrs)\n",
"dbz_cross.attrs[\"description\"] = \"radar reflectivity cross section\"\n",
"dbz_cross.attrs[\"units\"] = \"dBZ\"\n",
"\n",
"# To remove the slight gap between the dbz and terrain due to contouring, the new vertical \n",
"# grid spacing, and grid staggering, let's fill in the lower grid cells with the first non-missing \n",
"# value for each column.\n",
"\n",
"# Make a copy of the z cross data. Let's use regular numpy arrays for this.\n",
"dbz_cross_filled = np.ma.copy(to_np(dbz_cross))\n",
"\n",
"# For each cross section column, find the first index with non-missing values and copy\n",
"# these to the missing elements below.\n",
"for i in range(dbz_cross_filled.shape[-1]):\n",
" column_vals = dbz_cross_filled[:,i]\n",
" # Let's find the lowest index that isn't filled. The nonzero function \n",
" # finds all unmasked values greater than 0. Since 0 is a valid value\n",
" # for dBZ, let's change that threshold to be -200 dBZ instead. \n",
" first_idx = int(np.transpose((column_vals > -200).nonzero())[0])\n",
" dbz_cross_filled[0:first_idx, i] = dbz_cross_filled[first_idx, i]\n",
" \n",
"# Get the terrain heights along the cross section line\n",
"ter_line = interpline(ter, wrfin=wrf_file, start_point=cross_start, end_point=cross_end)\n",
"\n",
"# Get the lat/lon points\n",
"lats, lons = latlon_coords(dbz)\n",
"\n",
"# Get the cartopy projection object\n",
"cart_proj = get_cartopy(dbz)\n",
"\n",
"# Create a figure that will have 2 subplots (1 row, 2 columns)\n",
"fig = pyplot.figure(figsize=(12,9))\n",
"ax_cross = pyplot.axes()\n",
"\n",
"dbz_levels = np.arange(5., 75., 5.)\n",
"\n",
"# This is the NWS color table.\n",
"dbz_rgb = np.array([[4,233,231],\n",
" [1,159,244], [3,0,244],\n",
" [2,253,2], [1,197,1],\n",
" [0,142,0], [253,248,2],\n",
" [229,188,0], [253,149,0],\n",
" [253,0,0], [212,0,0],\n",
" [188,0,0],[248,0,253],\n",
" [152,84,198]], np.float32) / 255.0\n",
" \n",
"dbz_map, dbz_norm = from_levels_and_colors(dbz_levels, dbz_rgb, extend=\"max\")\n",
"\n",
"# Make the cross section plot for dbz\n",
"dbz_levels = np.arange(5.,75.,5.)\n",
"xs = np.arange(0, dbz_cross.shape[-1], 1)\n",
"ys = to_np(dbz_cross.coords[\"vertical\"])\n",
"dbz_contours = ax_cross.contourf(xs, \n",
" ys, \n",
" to_np(dbz_cross_filled), \n",
" levels=dbz_levels,\n",
" cmap=dbz_map, \n",
" norm=dbz_norm, \n",
" extend=\"max\")\n",
"cb_dbz = fig.colorbar(dbz_contours, ax=ax_cross)\n",
"cb_dbz.ax.tick_params(labelsize=8)\n",
"\n",
"# Fill in the mountain area\n",
"ht_fill = ax_cross.fill_between(xs, 0, to_np(ter_line), facecolor=\"saddlebrown\")\n",
"\n",
"# Set the x-ticks to use latitude and longitude labels\n",
"coord_pairs = to_np(dbz_cross.coords[\"xy_loc\"])\n",
"x_ticks = np.arange(coord_pairs.shape[0])\n",
"x_labels = [pair.latlon_str() for pair in to_np(coord_pairs)]\n",
"\n",
"# Set the desired number of x ticks below\n",
"num_ticks = 5\n",
"thin = int((len(x_ticks) / num_ticks) + .5)\n",
"ax_cross.set_xticks(x_ticks[::thin])\n",
"ax_cross.set_xticklabels(x_labels[::thin], rotation=45, fontsize=8)\n",
"\n",
"# Set the x-axis and y-axis labels\n",
"ax_cross.set_xlabel(\"Latitude, Longitude\", fontsize=12)\n",
"ax_cross.set_ylabel(\"Height (m)\", fontsize=12)\n",
"\n",
"# Add a title\n",
"ax_cross.set_title(\"Cross-Section of Reflectivity (dBZ)\", {\"fontsize\" : 14})\n",
"\n",
"plt.savefig(\"/Users/ladwig/Documents/workspace/wrf_python/doc/source/_static/images/cross_mtns.png\",\n",
" transparent=True, bbox_inches=\"tight\")\n",
"\n",
"pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Wind Barbs"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -503,7 +654,7 @@ @@ -503,7 +654,7 @@
"# Download and add the states and coastlines\n",
"states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',\n",
" name='admin_1_states_provinces_shp')\n",
"ax.add_feature(states, linewidth=0.5)\n",
"ax.add_feature(states, linewidth=0.5, edgecolor=\"black\")\n",
"ax.coastlines('50m', linewidth=0.8)\n",
"\n",
"# Add the 500 hPa geopotential height contours\n",
@ -623,7 +774,7 @@ @@ -623,7 +774,7 @@
"slp = getvar(ncfile, \"slp\")\n",
"\n",
"# Smooth the sea level pressure since it tends to be noisy near the mountains\n",
"smooth_slp = smooth2d(slp, 3)\n",
"smooth_slp = smooth2d(slp, 3, cenweight=4)\n",
"\n",
"# Get the latitude and longitude points\n",
"lats, lons = latlon_coords(slp)\n",
@ -632,7 +783,7 @@ @@ -632,7 +783,7 @@
"bm = get_basemap(slp)\n",
"\n",
"# Create a figure\n",
"fig = plt.figure(figsize=(12,9))\n",
"fig = plt.figure(figsize=(12,6))\n",
"\n",
"# Add geographic outlines\n",
"bm.drawcoastlines(linewidth=0.25)\n",
@ -648,7 +799,7 @@ @@ -648,7 +799,7 @@
"bm.contourf(x, y, to_np(smooth_slp), 10, cmap=get_cmap(\"jet\"))\n",
"\n",
"# Add a color bar\n",
"plt.colorbar(shrink=.62)\n",
"plt.colorbar(shrink=.98)\n",
"\n",
"plt.title(\"Sea Level Pressure (hPa)\")\n",
"\n",
@ -664,7 +815,6 @@ @@ -664,7 +815,6 @@
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from __future__ import (absolute_import, division, print_function, unicode_literals)\n",
"\n",
"from netCDF4 import Dataset \n",
@ -731,14 +881,19 @@ @@ -731,14 +881,19 @@
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basemap Panel Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.cm import get_cmap\n",
@ -772,7 +927,7 @@ @@ -772,7 +927,7 @@
"lats, lons = latlon_coords(slp)\n",
"\n",
"# Create the figure that will have 3 subplots\n",
"fig = plt.figure(figsize=(10,7))\n",
"fig = plt.figure(figsize=(12,9))\n",
"ax_ctt = fig.add_subplot(1,2,1)\n",
"ax_wspd = fig.add_subplot(2,2,2)\n",
"ax_dbz = fig.add_subplot(2,2,4)\n",
@ -827,13 +982,13 @@ @@ -827,13 +982,13 @@
"wspd_contours = ax_wspd.contourf(to_np(wspd_cross), cmap=get_cmap(\"jet\"))\n",
"# Add the color bar\n",
"cb_wspd = fig.colorbar(wspd_contours, ax=ax_wspd)\n",
"cb_wspd.ax.tick_params(labelsize=5)\n",
"cb_wspd.ax.tick_params(labelsize=6)\n",
"\n",
"# Make the contour plot for dbz\n",
"levels = [5 + 5*n for n in range(15)]\n",
"dbz_contours = ax_dbz.contourf(to_np(dbz_cross), levels=levels, cmap=get_cmap(\"jet\"))\n",
"cb_dbz = fig.colorbar(dbz_contours, ax=ax_dbz)\n",
"cb_dbz.ax.tick_params(labelsize=5)\n",
"cb_dbz.ax.tick_params(labelsize=6)\n",
"\n",
"# Set the x-ticks to use latitude and longitude labels.\n",
"coord_pairs = to_np(dbz_cross.coords[\"xy_loc\"])\n",
@ -842,25 +997,25 @@ @@ -842,25 +997,25 @@
"ax_wspd.set_xticks(x_ticks[::20])\n",
"ax_wspd.set_xticklabels([], rotation=45)\n",
"ax_dbz.set_xticks(x_ticks[::20])\n",
"ax_dbz.set_xticklabels(x_labels[::20], rotation=45, fontsize=4) \n",
"ax_dbz.set_xticklabels(x_labels[::20], rotation=45, fontsize=6) \n",
"\n",
"# Set the y-ticks to be height.\n",
"vert_vals = to_np(dbz_cross.coords[\"vertical\"])\n",
"v_ticks = np.arange(vert_vals.shape[0])\n",
"ax_wspd.set_yticks(v_ticks[::20])\n",
"ax_wspd.set_yticklabels(vert_vals[::20], fontsize=4) \n",
"ax_wspd.set_yticklabels(vert_vals[::20], fontsize=6) \n",
"ax_dbz.set_yticks(v_ticks[::20])\n",
"ax_dbz.set_yticklabels(vert_vals[::20], fontsize=4) \n",
"ax_dbz.set_yticklabels(vert_vals[::20], fontsize=6) \n",
"\n",
"# Set the x-axis and y-axis labels\n",
"ax_dbz.set_xlabel(\"Latitude, Longitude\", fontsize=5)\n",
"ax_wspd.set_ylabel(\"Height (m)\", fontsize=5)\n",
"ax_dbz.set_ylabel(\"Height (m)\", fontsize=5)\n",
"ax_dbz.set_xlabel(\"Latitude, Longitude\", fontsize=8)\n",
"ax_wspd.set_ylabel(\"Height (m)\", fontsize=8)\n",
"ax_dbz.set_ylabel(\"Height (m)\", fontsize=8)\n",
"\n",
"# Add titles\n",
"ax_ctt.set_title(\"Cloud Top Temperature (degC)\", {\"fontsize\" : 7})\n",
"ax_wspd.set_title(\"Cross-Section of Wind Speed (kt)\", {\"fontsize\" : 7})\n",
"ax_dbz.set_title(\"Cross-Section of Reflectivity (dBZ)\", {\"fontsize\" : 7})\n",
"ax_ctt.set_title(\"Cloud Top Temperature (degC)\", {\"fontsize\" : 10})\n",
"ax_wspd.set_title(\"Cross-Section of Wind Speed (kt)\", {\"fontsize\" : 10})\n",
"ax_dbz.set_title(\"Cross-Section of Reflectivity (dBZ)\", {\"fontsize\" : 10})\n",
"\n",
"plt.savefig(\"/Users/ladwig/Documents/workspace/wrf_python/doc/source/_static/images/basemap_front.png\",\n",
" transparent=False, bbox_inches=\"tight\")\n",
@ -1261,21 +1416,21 @@ @@ -1261,21 +1416,21 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"display_name": "Python 3",
"language": "python",
"name": "python2"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.14"
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,

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