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A collection of diagnostic and interpolation routines for use with output from the Weather Research and Forecasting (WRF-ARW) Model.
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wrf-python/doc/source/basic_usage.rst

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How To Use
============
Introduction
---------------
The API for wrf-python can be summarized as a variable computation/extraction
routine, several interpolation routines, and a few plotting helper utilities.
The API is kept as simple as possible to help minimize the
learning curve for new programmers, students, and scientists. In the future,
we plan to extend xarray for programmers desiring a more object oriented API,
but this remains a work in progress.
The five most commonly used routines can be summarized as:
- :meth:`wrf.getvar` - Extracts WRF-ARW NetCDF variables and
computes diagnostic variables that WRF does not compute (e.g. storm
relative helicity). This is the routine that you will use most often.
- :meth:`wrf.interplevel` - Interpolates a three-dimensional field to a
horizontal plane at a specified level using simple (fast) linear
interpolation (e.g. 850 hPa temperature).
- :meth:`wrf.vertcross` - Interpolates a three-dimensional field to a vertical
plane through a user-specified horizontal line (i.e. a cross section).
- :meth:`wrf.interpline` - Interpolates a two-dimensional field to a
user-specified line.
- :meth:`wrf.vinterp` - Interpolates a three-dimensional field to
user-specified 'surface' levels (e.g. theta-e levels). This is a smarter,
albeit slower, version of :meth:`wrf.interplevel`.
Basic Usage
----------------
.. _diagnostic-usage:
Computing Diagnostic Variables
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The primary use for the :meth:`wrf.getvar` function is to return diagnostic
variables that require a calculation, since WRF does not produce these
variables natively. These diagnostics include CAPE, storm relative helicity,
omega, sea level pressure, etc. A table of all available diagnostics can be
found here: :ref:`diagnostic-table`.
In the example below, sea level pressure is calculated and printed.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the Sea Level Pressure
slp = getvar(ncfile, "slp")
print(slp)
Result:
.. code-block:: none
<xarray.DataArray u'slp' (south_north: 1059, west_east: 1799)>
array([[ 1012.220337, 1012.298157, 1012.247864, ..., 1010.132019,
1009.932312, 1010.067078],
[ 1012.432861, 1012.444763, 1012.33667 , ..., 1010.1073 ,
1010.108459, 1010.047607],
[ 1012.395447, 1012.380859, 1012.417053, ..., 1010.22937 ,
1010.055969, 1010.026794],
...,
[ 1009.042358, 1009.069214, 1008.987793, ..., 1019.19281 ,
1019.144348, 1019.110596],
[ 1009.224854, 1009.075134, 1008.986389, ..., 1019.071899,
1019.042664, 1019.061279],
[ 1009.188965, 1009.107117, 1008.979797, ..., 1018.917786,
1018.956848, 1019.047485]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
Time datetime64[ns] 2016-10-07
Dimensions without coordinates: south_north, west_east
Attributes:
FieldType: 104
MemoryOrder: XY
description: sea level pressure
units: hPa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
.. _extract_ncvars:
Extracting WRF NetCDF Variables
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In addition to computing diagnostic variables (see :ref:`diagnostic-usage`),
the :meth:`wrf.getvar` function can be used to extract regular WRF-ARW output
NetCDF variables.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
p = getvar(ncfile, "P")
print(p)
Result:
.. code-block:: none
<xarray.DataArray u'P' (bottom_top: 50, south_north: 1059, west_east: 1799)>
array([[[ 1.217539e+03, 1.225320e+03, ..., 9.876406e+02, 1.001117e+03],
[ 1.238773e+03, 1.240047e+03, ..., 1.005297e+03, 9.991719e+02],
...,
[ 9.208594e+02, 9.059141e+02, ..., 1.902922e+03, 1.904805e+03],
[ 9.172734e+02, 9.091094e+02, ..., 1.894375e+03, 1.903422e+03]],
[[ 1.219562e+03, 1.210273e+03, ..., 9.973984e+02, 9.907891e+02],
[ 1.224578e+03, 1.223508e+03, ..., 9.985547e+02, 9.921172e+02],
...,
[ 9.012734e+02, 9.052031e+02, ..., 1.897766e+03, 1.894500e+03],
[ 9.137500e+02, 9.071719e+02, ..., 1.893273e+03, 1.893664e+03]],
...,
[[ 7.233154e+00, 7.224121e+00, ..., 3.627930e+00, 3.613770e+00],
[ 7.226318e+00, 7.358154e+00, ..., 3.725098e+00, 3.634033e+00],
...,
[ 5.354248e+00, 5.406006e+00, ..., 1.282715e+01, 1.264844e+01],
[ 5.295410e+00, 5.177490e+00, ..., 1.256274e+01, 1.257642e+01]],
[[ 2.362061e+00, 2.376221e+00, ..., 1.151367e+00, 1.156982e+00],
[ 2.342529e+00, 2.403809e+00, ..., 1.198486e+00, 1.155273e+00],
...,
[ 1.732910e+00, 1.768799e+00, ..., 4.247070e+00, 4.135498e+00],
[ 1.715332e+00, 1.657227e+00, ..., 4.036377e+00, 4.047852e+00]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
Time datetime64[ns] 2016-10-07
Dimensions without coordinates: bottom_top, south_north, west_east
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: perturbation pressure
units: Pa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
Disabling xarray and metadata
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Sometimes you just want a regular numpy array and don't care about metadata.
This is often the case when you are working with compiled extensions. Metadata
can be disabled in one of two ways.
#. disable xarray completely
#. set the *meta* function parameter to False.
The example below illustrates both.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, disable_xarray
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Disable xarray completely
disable_xarray()
p_no_meta = getvar(ncfile, "P")
print (type(p_no_meta))
enable_xarray()
# Disable by using the meta parameter
p_no_meta = getvar(ncfile, "P", meta=False)
print (type(p_no_meta))
Result:
.. code-block:: none
<type 'numpy.ndarray'>
<type 'numpy.ndarray'>
Extracting a Numpy Array from a DataArray
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
If you need to convert an :class:`xarray.DataArray` to a :class:`numpy.ndarray`,
wrf-python provides the :meth:`wrf.to_np` function for this purpose. Although
an :class:`xarray.DataArary` object already contains the
:attr:`xarray.DataArray.values` attribute to extract the Numpy array, there is a
problem when working with compiled extensions. The behavior for xarray (and pandas)
is to convert missing/fill values to NaN, which may cause crashes when working
with compiled extensions. Also, some existing code may be designed to work with
:class:`numpy.ma.MaskedArray`, and numpy arrays with NaN may not work with it.
The :meth:`wrf.to_np` function does the following:
#. If no missing/fill values are used, :meth:`wrf.to_np` simply returns the
:attr:`xarray.DataArray.values` attribute.
#. If missing/fill values are used, then :meth:`wrf.to_np` replaces the NaN
values with the _FillValue found in the :attr:`xarray.DataArray.attrs`
attribute (required) and a :class:`numpy.ma.MaskedArray` is returned.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the 3D CAPE, which contains missing values
cape_3d = getvar(ncfile, "cape_3d")
# Since there are missing values, this should return a MaskedArray
cape_3d_ndarray = to_np(cape_3d)
print(type(cape_3d_ndarray))
Result:
.. code-block:: none
<class 'numpy.ma.core.MaskedArray'>
Sequences of Files
----------------------
Combining Multiple Files Using the 'cat' Method
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The 'cat' (concatenate) method aggregates all files in the sequence along the
'Time' dimension, which will be the leftmost dimension for the output array.
To include all of the times, in all of the files, in the output array, set the
*timeidx* parameter to :data:`wrf.ALL_TIMES` (an alias for None). If a single
value is specified for *timeidx*, then the time index is assumed to be taken from
the concatenation of all times for all files.
It is import to note that no sorting is performed in the :meth:`wrf.getvar`
routine, so all files in the sequence must be sorted prior to calling this
function.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, ALL_TIMES
# Creating a simple test list with three timesteps
wrflist = [Dataset("wrfout_d01_2016-10-07_00_00_00"),
Dataset("wrfout_d01_2016-10-07_01_00_00"),
Dataset("wrfout_d01_2016-10-07_02_00_00")]
# Extract the 'P' variable for all times
p_cat = getvar(wrflist, "P", timeidx=ALL_TIMES, method="cat")
print(p_cat)
Result:
.. code-block:: none
<xarray.DataArray u'P' (Time: 3, bottom_top: 50, south_north: 1059, west_east: 1799)>
array([[[[ 1.21753906e+03, 1.22532031e+03, 1.22030469e+03, ...,
1.00760156e+03, 9.87640625e+02, 1.00111719e+03],
[ 1.23877344e+03, 1.24004688e+03, 1.22926562e+03, ...,
1.00519531e+03, 1.00529688e+03, 9.99171875e+02],
[ 1.23503906e+03, 1.23367188e+03, 1.23731250e+03, ...,
1.01739844e+03, 1.00005469e+03, 9.97093750e+02],
...,
[ 1.77978516e+00, 1.77050781e+00, 1.79003906e+00, ...,
4.22949219e+00, 4.25659180e+00, 4.13647461e+00],
[ 1.73291016e+00, 1.76879883e+00, 1.77978516e+00, ...,
4.24047852e+00, 4.24707031e+00, 4.13549805e+00],
[ 1.71533203e+00, 1.65722656e+00, 1.67480469e+00, ...,
4.06884766e+00, 4.03637695e+00, 4.04785156e+00]]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
* Time (Time) datetime64[ns] 2016-10-07 2016-10-07 2016-10-07
datetime (Time) datetime64[ns] 2016-10-07T00:00:00 ...
Dimensions without coordinates: bottom_top, south_north, west_east
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: perturbation pressure
units: Pa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
Combining Multiple Files Using the 'join' Method
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The 'join' method combines a sequence of files by adding a new leftmost
dimension for the file/sequence index. In situations where there are multiple
files with multiple times, and the last file contains less times than the
previous files, the remaining arrays will be arrays filled with missing values.
There are checks in place within the wrf-python algorithms to look for these missing
arrays, but be careful when calling compiled routines outside of wrf-python.
In most cases, *timeidx* parameter should be set to :data:`wrf.ALL_TIMES`. If
a *timeidx* value is specified, then this time index is used when extracting the
variable from each file. In cases where there are multiple files with multiple
time steps, this is probably nonsensical, since the nth time index for each
file represents a different time.
In general, join is rarely used, so the concatenate method should be used
for most cases.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, ALL_TIMES
# Creating a simple test list with three timesteps
wrflist = [Dataset("wrfout_d01_2016-10-07_00_00_00"),
Dataset("wrfout_d01_2016-10-07_01_00_00"),
Dataset("wrfout_d01_2016-10-07_02_00_00")]
# Extract the 'P' variable for all times
p_join = getvar(wrflist, "P", timeidx=ALL_TIMES, method="join")
print(p_join)
Result:
.. code-block:: none
<xarray.DataArray u'P' (file: 3, bottom_top: 50, south_north: 1059, west_east: 1799)>
array([[[[ 1.217539e+03, ..., 1.001117e+03],
...,
[ 9.172734e+02, ..., 1.903422e+03]],
...,
[[ 2.362061e+00, ..., 1.156982e+00],
...,
[ 1.715332e+00, ..., 4.047852e+00]]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
* file (file) int64 0 1 2
datetime (file) datetime64[ns] 2016-10-07 ...
Dimensions without coordinates: bottom_top, south_north, west_east
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: perturbation pressure
units: Pa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
Note how the 'Time' dimension was replaced with the 'file' dimension, due to
numpy's automatic squeezing of the single element 'Time' dimension. To maintain
the 'Time' dimension, set the *squeeze* parameter to False.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, ALL_TIMES
# Creating a simple test list with three timesteps
wrflist = [Dataset("wrfout_d01_2016-10-07_00_00_00"),
Dataset("wrfout_d01_2016-10-07_01_00_00"),
Dataset("wrfout_d01_2016-10-07_02_00_00")]
# Extract the 'P' variable for all times
p_join = getvar(wrflist, "P", timeidx=ALL_TIMES, method="join", squeeze=False)
print(p_join)
Result
.. code-block:: none
<xarray.DataArray u'P' (file: 3, Time: 1, bottom_top: 50, south_north: 1059, west_east: 1799)>
array([[[[[ 1.217539e+03, ..., 1.001117e+03],
...,
[ 9.172734e+02, ..., 1.903422e+03]],
...,
[[ 2.362061e+00, ..., 1.156982e+00],
...,
[ 1.715332e+00, ..., 4.047852e+00]]]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
* file (file) int64 0 1 2
datetime (file, Time) datetime64[ns] 2016-10-07 2016-10-07 2016-10-07
Dimensions without coordinates: Time, bottom_top, south_north, west_east
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: perturbation pressure
units: Pa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
Dictionaries of WRF File Sequences
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Dictionaries can also be used as input to the :meth:`wrf.getvar` functions.
This can be useful when working with ensembles. However, all WRF files in the
dictionary must have the same dimensions. The result is an array where the
leftmost dimension is the keys from the dictionary. Nested dictionaries
are allowed.
The *method* argument is used to describe how each sequence in the dictionary
will be combined.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, ALL_TIMES
wrf_dict = {"ens1" : [Dataset("ens1/wrfout_d01_2016-10-07_00_00_00"),
Dataset("ens1/wrfout_d01_2016-10-07_01_00_00"),
Dataset("ens1/wrfout_d01_2016-10-07_02_00_00")],
"ens2" : [Dataset("ens2/wrfout_d01_2016-10-07_00_00_00"),
Dataset("ens2/wrfout_d01_2016-10-07_01_00_00"),
Dataset("ens2/wrfout_d01_2016-10-07_02_00_00")]
}
p = getvar(wrf_dict, "P", timeidx=ALL_TIMES)
print(p)
Result:
.. code-block:: none
<xarray.DataArray 'P' (key_0: 2, Time: 2, bottom_top: 50, south_north: 1059, west_east: 1799)>
array([[[[[ 1.217539e+03, ..., 1.001117e+03],
...,
[ 9.172734e+02, ..., 1.903422e+03]],
...,
[[ 2.362061e+00, ..., 1.156982e+00],
...,
[ 1.715332e+00, ..., 4.047852e+00]]]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
* Time (Time) datetime64[ns] 2016-10-07 ...
datetime (Time) datetime64[ns] 2016-10-07 ...
* key_0 (key_0) <U6 u'label1' u'label2'
Dimensions without coordinates: bottom_top, south_north, west_east
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: perturbation pressure
units: Pa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
Interpolation Routines
--------------------------
Interpolating to a Horizontal Level
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The :meth:`wrf.interplevel` function is used to interpolate a 3D field to
a specific horizontal level, usually pressure or height.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, interplevel
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Extract the Geopotential Height and Pressure (hPa) fields
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
# Compute the 500 MB Geopotential Height
ht_500mb = interplevel(z, p, 500.)
print(ht_500mb)
Result:
.. code-block:: none
<xarray.DataArray u'height_500_hPa' (south_north: 1059, west_east: 1799)>
array([[ 5882.16992188, 5881.87939453, 5881.81005859, ...,
5890.14501953, 5890.23583984, 5890.33349609],
[ 5882.71777344, 5882.17529297, 5882.1171875 , ...,
5890.37695312, 5890.38525391, 5890.27978516],
[ 5883.32177734, 5882.47119141, 5882.34130859, ...,
5890.48339844, 5890.42871094, 5890.17724609],
...,
[ 5581.45800781, 5580.46826172, 5579.32617188, ...,
5788.93554688, 5788.70507812, 5788.64453125],
[ 5580.32714844, 5579.51611328, 5578.34863281, ...,
5788.15869141, 5787.87304688, 5787.65527344],
[ 5579.64404297, 5578.30957031, 5576.98632812, ...,
5787.19384766, 5787.10888672, 5787.06933594]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
Time datetime64[ns] 2016-10-07
Dimensions without coordinates: south_north, west_east
Attributes:
FieldType: 104
units: m
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
level: 500 hPa
missing_value: 9.96920996839e+36
_FillValue: 9.96920996839e+36
.. _vert_cross_interp:
Vertical Cross Sections
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The :meth:`wrf.vertcross` function is used to create vertical cross sections.
To define a cross section, a start point and an end point needs to be specified.
Alternatively, a pivot point and an angle may be used. The start point,
end point, and pivot point are specified using a :class:`wrf.CoordPair` object,
and coordinates can either be in grid (x,y) coordinates or (latitude,longitude)
coordinates. When using (latitude,longitude) coordinates, a NetCDF file object or
a :class:`wrf.WrfProj` object must be provided.
The vertical levels can also be specified using the *levels* parameter. If
not specified, then approximately 100 levels will be chosen in 1% increments.
Example Using Start Point and End Point
*****************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, vertcross, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the geopotential height (m) and pressure (hPa).
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
# Define a start point and end point in grid coordinates
start_point = CoordPair(x=0, y=(z.shape[-2]-1)//2)
end_point = CoordPair(x=-1, y=(z.shape[-2]-1)//2)
# Calculate the vertical cross section. By setting latlon to True, this
# also calculates the latitude and longitude coordinates along the cross
# section line and adds them to the 'xy_loc' metadata to help with plotting.
p_vert = vertcross(p, z, start_point=start_point, end_point=end_point, latlon=True)
print(p_vert)
Result:
.. code-block:: none
<xarray.DataArray u'pressure_cross' (vertical: 100, idx: 1798)>
array([[ nan, nan, nan, ..., nan,
nan, nan],
[ 989.66168213, 989.66802979, 989.66351318, ..., 988.05737305,
987.99151611, 987.96917725],
[ 959.49450684, 959.50109863, 959.50030518, ..., 958.96948242,
958.92980957, 958.89294434],
...,
[ 24.28092003, 24.27359581, 24.27034378, ..., 24.24800491,
24.2486496 , 24.24947357],
[ 23.2868309 , 23.27933884, 23.27607918, ..., 23.25231361,
23.2530098 , 23.25384521],
[ nan, nan, nan, ..., nan,
nan, nan]], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
* vertical (vertical) float32 0.0 261.828 523.656 785.484 1047.31 1309.14 ...
Dimensions without coordinates: idx
Attributes:
FieldType: 104
description: pressure
units: hPa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
orientation: (0.0, 529.0) to (1797.0, 529.0)
missing_value: 9.96920996839e+36
_FillValue: 9.96920996839e+36
Example Using Pivot Point and Angle
*************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, vertcross, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the geopotential height (m) and pressure (hPa).
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
# Define a pivot point and angle in grid coordinates, with the
# pivot point being the center of the grid.
pivot_point = CoordPair(x=(z.shape[-1]-1)//2, y=(z.shape[-2]-1)//2)
angle = 90.0
# Calculate the vertical cross section. By setting latlon to True, this
# also calculates the latitude and longitude coordinates along the line
# and adds them to the metadata to help with plotting labels.
p_vert = vertcross(p, z, pivot_point=pivot_point, angle=angle, latlon=True)
print (p_vert)
Result:
.. code-block:: none
<xarray.DataArray u'pressure_cross' (vertical: 100, idx: 1798)>
array([[ nan, nan, nan, ..., nan,
nan, nan],
[ 989.66168213, 989.66802979, 989.66351318, ..., 988.05737305,
987.99151611, 987.96917725],
[ 959.49450684, 959.50109863, 959.50030518, ..., 958.96948242,
958.92980957, 958.89294434],
...,
[ 24.28092003, 24.27359581, 24.27034378, ..., 24.24800491,
24.2486496 , 24.24947357],
[ 23.2868309 , 23.27933884, 23.27607918, ..., 23.25231361,
23.2530098 , 23.25384521],
[ nan, nan, nan, ..., nan,
nan, nan]], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
* vertical (vertical) float32 0.0 261.828 523.656 785.484 1047.31 1309.14 ...
Dimensions without coordinates: idx
Attributes:
FieldType: 104
description: pressure
units: hPa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
orientation: (0.0, 529.0) to (1797.0, 529.0) ; center=CoordPair(x=899.0, y=529.0) ; angle=90.0
missing_value: 9.96920996839e+36
_FillValue: 9.96920996839e+36
Example Using Lat/Lon Coordinates
*************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, vertcross, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the geopotential height (m) and pressure (hPa).
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
lats = getvar(ncfile, "lat")
lons = getvar(ncfile, "lon")
# Making the same horizontal line, but with lats/lons
start_lat = lats[(lats.shape[-2]-1)//2, 0]
end_lat = lats[(lats.shape[-2]-1)//2, -1]
start_lon = lons[(lats.shape[-2]-1)//2, 0]
end_lon = lons[(lats.shape[-2]-1)//2, -1]
# Cross section line using start_point and end_point.
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
# When using lat/lon coordinates, you must supply a WRF netcdf file object,
# or a projection object with the lower left latitude and lower left
# longitude points.
p_vert = vertcross(p, z, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True)
print(p_vert)
Result:
.. code-block:: none
<xarray.DataArray u'pressure_cross' (vertical: 100, idx: 1798)>
array([[ nan, nan, nan, ..., nan,
nan, nan],
[ 989.66168213, 989.66802979, 989.66351318, ..., 988.05737305,
987.99151611, 987.96917725],
[ 959.49450684, 959.50109863, 959.50030518, ..., 958.96948242,
958.92980957, 958.89294434],
...,
[ 24.28092003, 24.27359581, 24.27034378, ..., 24.24800491,
24.2486496 , 24.24947357],
[ 23.2868309 , 23.27933884, 23.27607918, ..., 23.25231361,
23.2530098 , 23.25384521],
[ nan, nan, nan, ..., nan,
nan, nan]], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
* vertical (vertical) float32 0.0 261.828 523.656 785.484 1047.31 1309.14 ...
Dimensions without coordinates: idx
Attributes:
FieldType: 104
description: pressure
units: hPa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
orientation: (0.0, 529.0) to (1797.0, 529.0)
missing_value: 9.96920996839e+36
_FillValue: 9.96920996839e+36
Example Using Specified Vertical Levels
*****************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, vertcross, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the geopotential height (m) and pressure (hPa).
z = getvar(ncfile, "z")
p = getvar(ncfile, "pressure")
lats = getvar(ncfile, "lat")
lons = getvar(ncfile, "lon")
# Making the same horizontal line, but with lats/lons
start_lat = lats[(lats.shape[-2]-1)//2, 0]
end_lat = lats[(lats.shape[-2]-1)//2, -1]
start_lon = lons[(lats.shape[-2]-1)//2, 0]
end_lon = lons[(lats.shape[-2]-1)//2, -1]
# Pressure using start_point and end_point. These were obtained using
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
# Specify vertical levels
levels = [1000., 2000., 3000.]
# Calculate the cross section
p_vert = vertcross(p, z, wrfin=ncfile, levels=levels, start_point=start_point, end_point=end_point, latlon=True)
print(p_vert)
Result:
.. code-block:: none
<xarray.DataArray u'pressure_cross' (vertical: 3, idx: 1798)>
array([[ 906.375 , 906.38043213, 906.39367676, ..., 907.6661377 ,
907.63006592, 907.59191895],
[ 804.24737549, 804.26885986, 804.28076172, ..., 806.98632812,
806.95556641, 806.92608643],
[ 713.24578857, 713.2722168 , 713.27886963, ..., 716.09594727,
716.06610107, 716.03503418]], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (idx) object CoordPair(x=0.0, y=529.0, lat=34.5279502869, lon=-127.398925781) ...
* vertical (vertical) float32 1000.0 2000.0 3000.0
Dimensions without coordinates: idx
Attributes:
FieldType: 104
description: pressure
units: hPa
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
orientation: (0.0, 529.0) to (1797.0, 529.0)
missing_value: 9.96920996839e+36
_FillValue: 9.96920996839e+36
Interpolating Two-Dimensional Fields to a Line
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Two-dimensional fields can be interpolated along a line, in a manner similar to
the vertical cross section (see :ref:`vert_cross_interp`), using the
:meth:`wrf.interpline` function. To define the line
to interpolate along, a start point and an end point needs to be specified.
Alternatively, a pivot point and an angle may be used. The start point,
end point, and pivot point are specified using a :class:`wrf.CoordPair` object,
and coordinates can either be in grid (x,y) coordinates or (latitude,longitude)
coordinates. When using (latitude,longitude) coordinates, a NetCDF file object or
a :class:`wrf.WrfProj` object must also be provided.
Example Using Start Point and End Point
*****************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, interpline, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the 2m temperature
t2 = getvar(ncfile, "T2")
# Create a south-north line in the center of the domain using
# start point and end point
start_point = CoordPair(x=(t2.shape[-1]-1)//2, y=0)
end_point = CoordPair(x=(t2.shape[-1]-1)//2, y=-1)
# Calculate the vertical cross section. By setting latlon to True, this
# also calculates the latitude and longitude coordinates along the line
# and adds them to the metadata to help with plotting labels.
t2_line = interpline(t2, start_point=start_point, end_point=end_point, latlon=True)
print(t2_line, "\n")
Result:
.. code-block:: none
<xarray.DataArray u'T2_line' (line_idx: 1058)>
array([ 302.07214355, 302.08505249, 302.08688354, ..., 279.18557739,
279.1998291 , 279.23132324], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (line_idx) object CoordPair(x=899.0, y=0.0, lat=24.3645858765, lon=-97.5) ...
Dimensions without coordinates: line_idx
Attributes:
FieldType: 104
description: TEMP at 2 M
units: K
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
orientation: (899.0, 0.0) to (899.0, 1057.0)
Example Using Pivot Point and Angle
*****************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, interpline, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the 2m temperature
t2 = getvar(ncfile, "T2")
# Create a south-north line using pivot point and angle
pivot_point = CoordPair((t2.shape[-1]-1)//2, (t2.shape[-2]-1)//2)
angle = 0.0
# Calculate the vertical cross section. By setting latlon to True, this
# also calculates the latitude and longitude coordinates along the line
# and adds them to the metadata to help with plotting labels.
t2_line = interpline(t2, pivot_point=pivot_point, angle=angle, latlon=True)
print(t2_line, "\n")
Result:
.. code-block:: none
<xarray.DataArray u'T2_line' (line_idx: 1058)>
array([ 302.07214355, 302.08505249, 302.08688354, ..., 279.18557739,
279.1998291 , 279.23132324], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (line_idx) object CoordPair(x=899.0, y=0.0, lat=24.3645858765, lon=-97.5) ...
Dimensions without coordinates: line_idx
Attributes:
FieldType: 104
description: TEMP at 2 M
units: K
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
orientation: (899.0, 0.0) to (899.0, 1057.0) ; center=CoordPair(x=899, y=529) ; angle=0.0
Example Using Lat/Lon Coordinates
*************************************
.. code-block:: python
from __future__ import print_function, division
from netCDF4 import Dataset
from wrf import getvar, interpline, CoordPair
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
t2 = getvar(ncfile, "T2")
lats = getvar(ncfile, "lat")
lons = getvar(ncfile, "lon")
# Select the latitude,longitude points for a vertical line through
# the center of the domain.
start_lat = lats[0, (lats.shape[-1]-1)//2]
end_lat = lats[-1, (lats.shape[-1]-1)//2]
start_lon = lons[0, (lons.shape[-1]-1)//2]
end_lon = lons[-1, (lons.shape[-1]-1)//2]
# Create the CoordPairs
start_point = CoordPair(lat=start_lat, lon=start_lon)
end_point = CoordPair(lat=end_lat, lon=end_lon)
# Calculate the interpolated line. To use latitude and longitude points,
# you must supply a WRF NetCDF file object, or a projection object along
# with the lower left latitude and lower left longitude points.
# Also, by setting latlon to True, this routine will
# also calculate the latitude and longitude coordinates along the line
# and adds them to the metadata to help with plotting labels.
t2_line = interpline(t2, wrfin=ncfile, start_point=start_point, end_point=end_point, latlon=True)
print (t2_line)
Result:
.. code-block:: none
<xarray.DataArray u'T2_line' (line_idx: 1058)>
array([ 302.07214355, 302.08505249, 302.08688354, ..., 279.18557739,
279.1998291 , 279.23132324], dtype=float32)
Coordinates:
Time datetime64[ns] 2016-10-07
xy_loc (line_idx) object CoordPair(x=899.0, y=0.0, lat=24.3645858765, lon=-97.5) ...
Dimensions without coordinates: line_idx
Attributes:
FieldType: 104
description: TEMP at 2 M
units: K
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
orientation: (899.0, 0.0) to (899.0, 1057.0)
Interpolating a 3D Field to a Surface Type
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The :meth:`wrf.vinterp` is used to interpolate a field to a type of surface.
The available surfaces are pressure, geopotential height, theta, and theta-e.
The surface levels to interpolate also need to be specified.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, vinterp
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
   tk = getvar(ncfile, "tk")
   # Interpolate tk to theta-e levels                
   interp_levels = [200, 300, 500, 1000]
interp_field = vinterp(ncfile,
field=tk,
vert_coord="eth",
interp_levels=interp_levels,
extrapolate=True,
field_type="tk",
log_p=True)
print(interp_field)
Result:
.. code-block:: none
<xarray.DataArray u'temp' (interp_level: 4, south_north: 1059, west_east: 1799)>
array([[[ 296.12872314, 296.1166687 , 296.08905029, ..., 301.71026611,
301.67956543, 301.67791748],
[ 296.11352539, 295.95581055, 295.91555786, ..., 301.63052368,
301.62905884, 301.65887451],
[ 296.07556152, 295.91577148, 295.88214111, ..., 301.61499023,
301.60287476, 301.63961792],
...,
[ 219.11134338, 219.08581543, 219.08602905, ..., 218.29879761,
218.30923462, 218.3787384 ],
[ 219.09260559, 219.07765198, 219.08340454, ..., 218.2855072 ,
218.30444336, 218.37931824],
[ 219.07936096, 219.08181763, 219.10089111, ..., 218.31173706,
218.34288025, 218.3687439 ]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 -122.72 -122.693 -122.666 ...
XLAT (south_north, west_east) float32 21.1381 21.1451 21.1521 ...
Time datetime64[ns] 2016-10-07
* interp_level (interp_level) int64 200 300 500 1000
Dimensions without coordinates: south_north, west_east
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: temperature
units: K
stagger:
coordinates: XLONG XLAT
projection: LambertConformal(stand_lon=-97.5, moad_cen_lat=38.5000038147,
truelat1=38.5, truelat2=38.5, pole_lat=90.0,
pole_lon=0.0)
vert_interp_type: eth
Lat/Lon <-> XY Routines
--------------------------
wrf-python includes a set of routines for converting back and forth between
latitude,longitude space and x,y space. The methods are :meth:`wrf.xy_to_ll`,
:meth:`wrf.xy_to_ll_proj`, :meth:`wrf.ll_to_xy`, :meth:`wrf.ll_to_xy_proj`.
The *latitude*, *longitude*, *x*, and *y* parameters to these methods
can contain sequences if multiple points are desired to be converted.
Example With Single Coordinates
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, interpline, CoordPair, xy_to_ll, ll_to_xy
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
lat_lon = xy_to_ll(ncfile, 400, 200)
print(lat_lon)
x_y = ll_to_xy(ncfile, lat_lon[0], lat_lon[1])
print (x_y)
Result:
.. code-block:: none
<xarray.DataArray u'latlon' (lat_lon: 2)>
array([ 28.55816408, -112.67827617])
Coordinates:
* lat_lon (lat_lon) <U3 u'lat' u'lon'
xy_coord object CoordPair(x=400, y=200)
Dimensions without coordinates: idx
<xarray.DataArray u'xy' (x_y: 2)>
array([400, 200])
Coordinates:
latlon_coord object CoordPair(lat=28.5581640822, lon=-112.678276173)
* x_y (x_y) <U1 u'x' u'y'
Dimensions without coordinates: idx
Example With Multiple Coordinates
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, interpline, CoordPair, xy_to_ll, ll_to_xy
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
lat_lon = xy_to_ll(ncfile, [400,105], [200,205])
print(lat_lon)
x_y = ll_to_xy(ncfile, lat_lon[0,:], lat_lon[1,:])
print (x_y)
Result:
.. code-block:: none
<xarray.DataArray u'latlon' (lat_lon: 2, idx: 2)>
array([[ 28.55816408, 27.03835783],
[-112.67827617, -121.36392174]])
Coordinates:
* lat_lon (lat_lon) <U3 u'lat' u'lon'
xy_coord (idx) object CoordPair(x=400, y=200) CoordPair(x=105, y=205)
Dimensions without coordinates: idx
<xarray.DataArray u'xy' (x_y: 2, idx: 2)>
array([[400, 105],
[200, 205]])
Coordinates:
latlon_coord (idx) object CoordPair(lat=28.5581640822, lon=-112.678276173) ...
* x_y (x_y) <U1 u'x' u'y'
Dimensions without coordinates: idx
Mapping Helper Routines
-------------------------
wrf-python includes several routines to assist with plotting, primarily for
obtaining the mapping object used for cartopy, basemap, and PyNGL. For all
three plotting systems, the mapping object can be determined directly from
a variable when using xarray, or can be obtained from the WRF output file(s)
if xarray is turned off.
Also included are utilities for extracting the geographic boundaries
directly from xarray variables. This can be useful in situations where you
only want to work with subsets (slices) of a large domain, but don't want to
define the map projection over the subset region.
Cartopy Example Using a Variable
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, we're going to extract the cartopy mapping object from a
diagnostic variable (slp), the lat,lon coordinates, and the geographic
boundaries. Next, we're going to take a subset of the diagnostic variable
and extract the geographic boundaries. Some of the variables
will be printed for demonstration.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, get_cartopy, latlon_coords, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Use SLP for the example variable
slp = getvar(ncfile, "slp")
# Get the cartopy mapping object
cart_proj = get_cartopy(slp)
print (cart_proj)
# Get the latitude and longitude coordinate. This is usually needed for plotting.
lats, lons = latlon_coords(slp)
# Get the geobounds for the SLP variable
bounds = geo_bounds(slp)
print (bounds)
# Get the geographic boundaries for a subset of the domain
slp_subset = slp[150:250, 150:250]
slp_subset_bounds = geo_bounds(slp_subset)
print (slp_subset_bounds)
Result:
.. code-block:: none
<cartopy.crs.LambertConformal object at 0x115374290>
GeoBounds(CoordPair(lat=25.9246292114, lon=-119.675048828), CoordPair(lat=29.0761833191, lon=-117.46484375))
GeoBounds(CoordPair(lat=25.9246292114, lon=-119.675048828), CoordPair(lat=29.0761833191, lon=-117.46484375))
Cartopy Example Using WRF Output Files
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, the cartopy mapping object and geographic boundaries
will be extracted directly from the netcdf variable.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import get_cartopy, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the cartopy mapping object from the netcdf file
cart_proj = get_cartopy(wrfin=ncfile)
print (cart_proj)
# Get the geobounds from the netcdf file (by default, uses XLAT, XLONG)
# You can supply a variable name to get the staggered boundaries
bounds = geo_bounds(wrfin=ncfile)
print (bounds)
Result:
.. code-block:: none
<cartopy.crs.LambertConformal object at 0x11d3be650>
GeoBounds(CoordPair(lat=21.1381225586, lon=-122.719528198), CoordPair(lat=47.8436355591, lon=-60.9013671875))
Basemap Example Using a Variable
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, we're going to extract the basemap mapping object from a
diagnostic variable (slp), the lat,lon coordinates, and the geographic
boundaries. Next, we're going to take a subset of the diagnostic variable
and extract the geographic boundaries. Some of the variables will be
printed for demonstration.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, get_basemap, latlon_coords, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
slp = getvar(ncfile, "slp")
# Get the basemap mapping object
bm = get_basemap(slp)
print (bm)
# Get the latitude and longitude coordinate. This is usually needed for plotting.
lats, lons = latlon_coords(slp)
# Get the geobounds for the SLP variable
bounds = geo_bounds(slp)
print(bounds)
# Get the geographic boundaries for a subset of the domain
slp_subset = slp[150:250, 150:250]
slp_subset_bounds = geo_bounds(slp_subset)
print (slp_subset_bounds)
Result:
.. code-block:: none
<mpl_toolkits.basemap.Basemap object at 0x114d65650>
GeoBounds(CoordPair(lat=21.1381225586, lon=-122.719528198), CoordPair(lat=47.8436355591, lon=-60.9013671875))
GeoBounds(CoordPair(lat=25.9246292114, lon=-119.675048828), CoordPair(lat=29.0761833191, lon=-117.46484375)
Basemap Example Using WRF Output Files
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, the basemap mapping object and geographic boundaries
will be extracted directly from the netcdf variable.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import get_basemap, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the basemap object from the netcdf file
bm = get_basemap(wrfin=ncfile)
print (bm)
# Get the geographic boundaries from the netcdf file
bounds = geo_bounds(wrfin=ncfile)
print (bounds)
Result:
.. code-block:: none
<mpl_toolkits.basemap.Basemap object at 0x125bb4750>
GeoBounds(CoordPair(lat=21.1381225586, lon=-122.719528198), CoordPair(lat=47.8436355591, lon=-60.9013671875))
PyNGL Example Using a Variable
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, we're going to extract the basemap mapping object from a
diagnostic variable (slp), the lat,lon coordinates, and the geographic
boundaries. Next, we're going to take a subset of the diagnostic variable
and extract the geographic boundaries. Some of the variables will be
printed for demonstration.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import getvar, get_pyngl, latlon_coords, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Use SLP as the example variable
slp = getvar(ncfile, "slp")
# Get the pyngl resources from the variable
pyngl_resources = get_pyngl(slp)
print (pyngl_resources)
# Get the latitude and longitude coordinate. This is needed for plotting.
lats, lons = latlon_coords(slp)
# Get the geobounds from the SLP variable
bounds = geo_bounds(slp)
print(bounds)
# Get the geographic boundaries for a subset of the domain
slp_subset = slp[150:250, 150:250]
slp_subset_bounds = geo_bounds(slp_subset)
print (slp_subset_bounds)
Result:
.. code-block:: none
<Ngl.Resources instance at 0x114cabbd8>
GeoBounds(CoordPair(lat=21.1381225586, lon=-122.719528198), CoordPair(lat=47.8436355591, lon=-60.9013671875))
GeoBounds(CoordPair(lat=25.9246292114, lon=-119.675048828), CoordPair(lat=29.0761833191, lon=-117.46484375))
PyNGL Example Using WRF Output Files
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this example, the basemap mapping object and geographic boundaries
will be extracted directly from the netcdf variable.
.. code-block:: python
from __future__ import print_function
from netCDF4 import Dataset
from wrf import get_pyngl, geo_bounds
ncfile = Dataset("wrfout_d01_2016-10-07_00_00_00")
# Get the pyngl resources from the netcdf file
pyngl_resources = get_pyngl(wrfin=ncfile)
print (pyngl_resources)
# Get the geographic boundaries from the netcdf file
bounds = geo_bounds(wrfin=ncfile)
print (bounds)
Result:
.. code-block:: none
<Ngl.Resources instance at 0x115391f80>
GeoBounds(CoordPair(lat=21.1381225586, lon=-122.719528198), CoordPair(lat=47.8436355591, lon=-60.9013671875))
Moving Nests
^^^^^^^^^^^^^^^^^^^^
When a domain nest is moving, the domain boundaries become a function of time when
combining the files using the 'cat' method. When using 'join', the domain boundaries
become a function of both file and time. As a result, the methods that
depend on geographic boundaries (:meth:`wrf.geo_bounds`, :meth:`wrf.get_basemap`, etc)
will return arrays of objects rather than a single object when multiple times
and/or files are detected in the underlying coordinate variables.
An exception is :meth:`wrf.get_cartopy`, which contains no geographic
boundary information in the mapping object. Instead, the
:meth:`wrf.cartopy_xlim` and :meth:`wrf.cartopy_ylim` methods can be used to
get the array of matplotlib axes boundaries (returned in the axes projection
coordinates).
Geographic Boundaries with Moving Nest Example
***************************************************
In this example, the geographic boundaries are extracted from a sequence
of files that use a moving nest. The result will be an array of
:class:`wrf.GeoBounds` objects.
.. code-block:: python
from __future__ import print_function
from glob import glob
from netCDF4 import Dataset as nc
from wrf import getvar, ALL_TIMES, geo_bounds
# Get all the domain 02 files
wrf_filenames = glob("wrf_files/wrf_vortex_multi/wrfout_d02_*")
ncfiles = [nc(x) for x in wrf_filenames]
# SLP is the example variable and includes all times
slp = getvar(ncfiles, "slp", timeidx=ALL_TIMES)
# Get the geographic boundaries
bounds = geo_bounds(slp)
print (bounds)
Result:
.. code-block:: none
[ GeoBounds(CoordPair(lat=21.3020038605, lon=-90.5740585327), CoordPair(lat=29.0274410248, lon=-82.0291671753))
GeoBounds(CoordPair(lat=21.3020038605, lon=-90.3042221069), CoordPair(lat=29.0274410248, lon=-81.7593231201))
GeoBounds(CoordPair(lat=21.3020038605, lon=-90.8438949585), CoordPair(lat=29.0274410248, lon=-82.2990036011))
GeoBounds(CoordPair(lat=21.3020038605, lon=-91.1137390137), CoordPair(lat=29.0274410248, lon=-82.5688400269))
GeoBounds(CoordPair(lat=21.8039493561, lon=-91.6534042358), CoordPair(lat=29.4982528687, lon=-83.1085205078))
GeoBounds(CoordPair(lat=22.0542640686, lon=-92.193107605), CoordPair(lat=29.7328338623, lon=-83.6481933594))
GeoBounds(CoordPair(lat=22.5535621643, lon=-92.7327728271), CoordPair(lat=30.2003688812, lon=-84.1878738403))
GeoBounds(CoordPair(lat=22.8025398254, lon=-93.0026092529), CoordPair(lat=30.4333114624, lon=-84.4577102661))
GeoBounds(CoordPair(lat=23.0510597229, lon=-93.2724456787), CoordPair(lat=30.665681839, lon=-84.7275543213))]
Cartopy Mapping with Moving Nest Example
********************************************
In this example, a cartopy mapping object is extracted from a variable
that uses a moving nest. Since cartopy objects do not include geographic
boundary information, only a single cartopy object is returned. However,
if the axes xlimits and ylimits are desired, the :meth:`wrf.cartopy_xlim` and
:meth:`wrf.cartopy_ylim` functions can be used to obtain the array of
moving boundaries in the axes projected coordinate space.
.. code-block:: python
from __future__ import print_function
from glob import glob
from netCDF4 import Dataset as nc
from wrf import getvar, ALL_TIMES, get_cartopy, cartopy_xlim, cartopy_ylim
# Get all of the domain 02 WRF output files
wrf_filenames = glob("wrf_files/wrf_vortex_multi/wrfout_d02_*")
ncfiles = [nc(x) for x in wrf_filenames]
# Use SLP as the example variable and include all times
slp = getvar(ncfiles, "slp", timeidx=ALL_TIMES)
# Get the cartopy mapping object
cart_proj = get_cartopy(slp)
print (cart_proj)
print ("\n")
# Get the array of axes x-limits
xlims = cartopy_xlim(slp)
print (xlims)
print ("\n")
# Get the array of axes y-limits
ylims = cartopy_ylim(slp)
print (ylims)
Result:
.. code-block:: none
<wrf.projection.MercatorWithLatTS object at 0x13893c9b0>
[[-174999.8505754546, 774999.5806103835]
[-145000.11853874932, 805000.1608638937]
[-204999.58261215844, 744999.8485736783]
[-235000.16286567, 715000.1165369744]
[-294998.77872227144, 654999.804246759]
[-355001.6356629085, 595000.34017335]
[-415000.25151950994, 535000.0278831345]
[-444999.98355621524, 505000.29584642925]
[-474999.7155929191, 474999.7155929177]]
[[2424828.507236154, 3374828.14098255]
[2424828.507236154, 3374828.14098255]
[2424828.507236154, 3374828.14098255]
[2424828.507236154, 3374828.14098255]
[2484829.1182174017, 3434828.972518358]
[2514829.1041871575, 3464828.196283651]
[2574829.0041584675, 3524828.8880928173]
[2604829.1786526926, 3554829.5610342724]
[2634828.9016262344, 3584828.016406863]]
Basemap Mapping with Moving Nest Example
*******************************************
In this example, basemap objects are extracted from a variable that uses a moving
nest. An array of basemap objects is returned because the
basemap object includes geographic boundary information.
.. code-block:: python
from __future__ import print_function
from glob import glob
from netCDF4 import Dataset as nc
from wrf import getvar, ALL_TIMES, get_basemap
# Get all of the domain 02 WRF output files
wrf_filenames = glob("wrf_files/wrf_vortex_multi/wrfout_d02_*")
ncfiles = [nc(x) for x in wrf_filenames]
# Use SLP as the reference variable and include all times
slp = getvar(ncfiles, "slp", timeidx=ALL_TIMES)
# Get the array of basemap objects
bm = get_basemap(slp)
print (bm)
print ("\n")
Result:
.. code-block:: none
[<mpl_toolkits.basemap.Basemap object at 0x1327bc510>
<mpl_toolkits.basemap.Basemap object at 0x115a9a790>
<mpl_toolkits.basemap.Basemap object at 0x115a9a750>
<mpl_toolkits.basemap.Basemap object at 0x115a9a7d0>
<mpl_toolkits.basemap.Basemap object at 0x115a9a850>
<mpl_toolkits.basemap.Basemap object at 0x115a9a8d0>
<mpl_toolkits.basemap.Basemap object at 0x115a9a950>
<mpl_toolkits.basemap.Basemap object at 0x115a9a9d0>
<mpl_toolkits.basemap.Basemap object at 0x115a9aa50>]
PyNGL Mapping with Moving Nest Example
*****************************************
In this example, pyngl resource objects are extracted from a variable that uses
a moving nest. An array of pyngl resource objects is returned because the
pyngl object includes geographic boundary information.
.. code-block:: python
from __future__ import print_function
from glob import glob
from netCDF4 import Dataset as nc
from wrf import getvar, ALL_TIMES, get_pyngl
# Get the domain 02 WRF output files
wrf_filenames = glob("wrf_files/wrf_vortex_multi/wrfout_d02_*")
ncfiles = [nc(x) for x in wrf_filenames]
# Use SLP as the example variable and include all times
slp = getvar(ncfiles, "slp", timeidx=ALL_TIMES)
# Get the array of pyngl resource objects
bm = get_pyngl(slp)
print (bm)
print ("\n")
Result:
.. code-block:: none
[<Ngl.Resources instance at 0x140cd30e0>
<Ngl.Resources instance at 0x11d3187a0>
<Ngl.Resources instance at 0x11d3185a8>
<Ngl.Resources instance at 0x11d3188c0>
<Ngl.Resources instance at 0x11d318878>
<Ngl.Resources instance at 0x11d3183f8>
<Ngl.Resources instance at 0x11d318950>
<Ngl.Resources instance at 0x11d318a70>
<Ngl.Resources instance at 0x11d318710>]
.. _using_omp:
Using OpenMP
-------------------------
Beginning in version 1.1, the Fortran computational routines in wrf-python make
use of OpenMP directives. OpenMP enables the calculations to use multiple CPU
cores, which can improve performance. In order to use OpenMP features,
wrf-python has to be compiled with OpenMP enabled (most pre-built binary
installations will have this enabled).
The Fortran computational routines have all been built using runtime
scheduling, instead of compile time scheduling, so that the user can choose the
scheduler type within their Python application. By default, the scheduling
type is set to :data:`wrf.OMP_SCHED_STATIC` using only 1 CPU core, so
wrf-python will behave similarly to the non-OpenMP built versions. For the most
part, the difference between the scheduling types is minimal, with the exception
being the :data:`wrf.OMP_SCHED_DYNAMIC` scheduler that is much slower due to
the additional overhead associated with it. For new users, using the default
scheduler should be sufficient.
Verifying that OpenMP is Enabled
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
To take advantage of the performance improvements offered by OpenMP, wrf-python
needs to have been compiled with OpenMP features enabled. The example below
shows how you can determine if OpenMP is enabled in your build of wrf-python.
.. code-block:: python
from __future__ import print_function
from wrf import omp_enabled
print(omp_enabled())
Result:
.. code-block:: none
True
Determining the Number of Available Processors
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The example below shows how you can get the maximum number of processors
that are available on your system.
.. code-block:: python
from __future__ import print_function
from wrf import omp_get_num_procs
print(omp_get_num_procs())
Result:
.. code-block:: none
8
Specifying the Number of Threads
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
To enable multicore support via OpenMP, specifying the maximum number
of OpenMP threads (i.e. CPU cores) is the only step that you need to take.
In the example below, :meth:`wrf.omp_set_num_threads` is used to set the
maximum number of threads to use, and :meth:`wrf.omp_get_max_threads` is used
to retrieve (and print) the maximum number of threads used.
.. note::
Although there is an OpenMP routine named :meth:`wrf.omp_get_num_threads`,
this routine will always return 1 when called from the sequential part of
the program. Use :meth:`wrf.omp_get_max_threads` to return the value set by
:meth:`wrf.omp_set_num_threads`.
.. code-block:: python
from __future__ import print_function
from wrf import omp_set_num_threads, omp_get_max_threads
omp_set_num_threads(4)
print (omp_get_max_threads())
Result:
.. code-block:: none
4
Setting a Different Scheduler Type
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
When an OpenMP directive is encountered in the Fortran code, a scheduler is
used to determine how the work is divided among the threads. All of the
Fortran routines are compiled to use a 'runtime' scheduler, which indicates
that the scheduler type (from the four listed below) is to be chosen at
runtime (i.e. inside a Python script)
By default, the scheduler chosen is the :data:`wrf.OMP_SCHED_STATIC` scheduler,
which should be sufficient for most users. However, OpenMP and wrf-python
include the following options for the scheduler type:
- :data:`wrf.OMP_SCHED_STATIC`
- :data:`wrf.OMP_SCHED_DYNAMIC`
- :data:`wrf.OMP_SCHED_GUIDED`
- :data:`wrf.OMP_SCHED_AUTO`
Refer to the
`OpenMP Specification <http://www.openmp.org/wp-content/uploads/openmp-4.5.pdf>`_.
for more information about these scheduler types. In local testing,
:data:`wrf.OMP_SCHED_GUIDED` produced the best results, but
differences between :data:`wrf.OMP_SCHED_STATIC`,
:data:`wrf.OMP_SCHED_GUIDED`, and
:data:`wrf.OMP_SCHED_AUTO` were minor. However,
:data:`wrf.OMP_SCHED_DYNAMIC` produced noticeably slower results
due to the overhead of using a dynamic scheduler.
When setting a scheduler type, the :meth:`wrf.omp_set_schedule` takes two
arguments. The first is the scheduler type (one from the list above), and the
second optional argument is a modifier, which is usually referred as the chunk
size. If the modifier/chunk_size is set to 0, then the OpenMP default
implementation is used. For :data:`wrf.OMP_SCHED_AUTO`, the
modifier is ignored.
If you are new to OpenMP and all this sounds confusing, don't worry about
setting a scheduler type. The default static scheduler will be good enough.
In the example below, the scheduler type is set to
:data:`wrf.OMP_SCHED_GUIDED` and uses the default chunk size of 0. The
scheduler type is then read back using :meth:`wrf.omp_get_schedule`
and printed.
.. code-block:: python
from __future__ import print_function
from wrf import omp_set_schedule, omp_get_schedule, OMP_SCHED_GUIDED
omp_set_schedule(OMP_SCHED_GUIDED, 0)
sched, modifier = omp_get_schedule()
print(sched, modifier)
Result:
.. code-block:: none
3 1
Notice that the printed scheduler type (*sched* variable) is set to a
value of 3, which is the actual integer constant value for the
:data:`wrf.OMP_SCHED_GUIDED` scheduler type. The *modifier* is returned as a
value of 1, which is different than the 0 that was supplied to the
:meth:`wrf.omp_set_schedule` routine. This is because the 0 tells OpenMP to use
its own default value for the scheduler, which is 1 for this type of scheduler.
.. _performance:
Performance Tips
--------------------
Memory Issues with :data:`wrf.ALL_TIMES`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The use of :data:`wrf.ALL_TIMES` for the *timeidx* parameter to
:meth:`wrf.getvar` is convenient for computing diagnostic variables across
multiple files/times, but there is something that users should be aware of.
When :data:`wrf.ALL_TIMES` is set as the *timeidx* argument, all arrays used
in the computation are extracted for all times before the computation
is started. This can cause serious memory issues on smaller hardware systems
like laptops.
In this example, the user wants to use a data set that is 289 x 39 x 300 x 300
and compute z for the entire data set. The user is using a laptop with
8 GB of memory.
.. code-block:: python
from netCDF4 import Dataset
from wrf import getvar, ALL_TIMES
file_list = [Dataset("/path/to/file1"), Dataset("/path/to/file2"),...]
z = getvar(file_list, "z", ALL_TIMES)
Five hours later, the computation finished. What happened?
In wrf-python, all of the computational routines use 8-byte REAL variables so
that both the 4-byte and 8-byte version of WRF output can be used. The
calculation for z extracts three variables (P, PHB, and HGT) and returns a
fourth array (RESULT). The RESULT will get cut in half to 4-byte REALs
after the computation, but needs an 8-byte REAL when the result is computed.
Let's look at the approximate amount memory needed:
**P**: 289 x 39 x 300 x 300 x 8 = 8,115,120,000 bytes (~8 GB!)
**PHB**: 289 x 39 x 300 x 300 x 8 = 8,115,120,000 bytes (~8 GB!)
**HGT**: 289 x 300 x 300 x 8 = 208,080,000 (~208 MB)
**RESULT**: 289 x 39 x 300 x 300 x 8 = 8,115,120,000 bytes (~8 GB!)
Yikes! So, in order to do this calculation using :data:`wrf.ALL_TIMES` as
the *timeidx*, over 24.2 GB are needed for this one calculation. When the
laptop runs out of memory, it begins using the hard drive for swap memory,
which runs hundreds of times slower than real memory.
To fix this situation, it is better to allocate the output array yourself and
run the calculation for each time step in a loop
("loop-and-fill"). The required memory requirements change to:
(Note: only need to store the result in a 4-byte REAL)
**FINAL_RESULT**: 289 x 39 x 300 x 300 x 4 = 4,057560,000 bytes (~4 GB)
(Note: the numbers below are for each loop iteration)
**P**: 39 x 300 x 300 x 8 = 28,080,000 bytes (~28 MB)
**PHB**: 39 x 300 x 300 x 8 = 28,080,000 bytes (~28 MB)
**HGT**: 300 x 300 x 8 = 720,000 bytes (720 KB)
**RESULT**: 39 x 300 x 300 x 8 = 28,080,000 bytes (~28 MB)
Since the memory for the computation is deleted after each
loop iteration, the total memory usage drops to approximately 4.1 GB.
The moral of the story is that you need to make sure that your system has
enough memory to extract everything it needs up front if you want to use
:data:`wrf.ALL_TIMES`, otherwise it is better to "loop-and-fill" yourself.
Here is an example of the "loop-and-fill" technique:
.. code-block:: python
from __future__ import print_function, division
import numpy as np
from netCDF4 import Dataset
from wrf import getvar, ALL_TIMES
filename_list = ["/path/to/file1", "/path/to/file2",...]
# Result shape (hard coded for this example)
result_shape = (289, 39, 300, 300)
# Only need 4-byte floats
z_final = np.empty(result_shape, np.float32)
# Modify this number if using more than 1 time per file
times_per_file = 1
for timeidx in range(result_shape[0]):
# Compute the file index and the time index inside the file
fileidx = timeidx // times_per_file
file_timeidx = timeidx % times_per_file
f = Dataset(filename_list[fileidx])
z = getvar(f, "z", file_timeidx)
z_final[timeidx,:] = z[:]
f.close()
The *cache* Argument for :meth:`wrf.getvar`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
If you have read through the documentation, you may have noticed that the
:meth:`wrf.getvar` routine contains a *cache* argument. What is this for?
Internally, if metadata is turned on, a variable is extracted from the NetCDF
file and its metadata is copied to form the result's metadata. Often this
variable is one of the computation's function arguments, so rather than
spend time extracting the variable again for the computation, it is placed
in a cache (dictionary) and passed on to the computational function.
What isn't widely known is that this cache argument can also be supplied by
end users wishing to speed up their application. This can be useful in
situations where numerous calculations are being performed on the same
data set. For many algorithms, the time needed to extract the arrays from the
NetCDF file is on par with the time needed to perform the calculation. If you
are computing numerous diagnostics, extracting the variables up front allows
you to only pay this extraction penalty once, rather than inside of each call
to :meth:`wrf.getvar`.
The cache is nothing more than a dictionary where each key is the variable
name (e.g. "P") and the value is the :class:`xarray.DataArray` or
:class:`numpy.ndarray` variable. Creating the cache dictionary is easy,
since the :meth:`wrf.extract_vars` routine returns a dictionary for a
sequence of variables.
.. note::
The *timeidx* parameter supplied to :meth:`extract_vars`
must be the same *timeidx* parameter that you plan to use for
:meth:`wrf.getvar`. Otherwise, it will crash with dimension mismatch errors.
Some common variables that you can use to create an effective cache are: P, PB,
PH, PHB, T, QVAPOR, HGT, PSFC, U, V, W.
Below is an example showing the same computation done with and without the
cache. The execution time is printed. The hardware used is a 2.8 GHz Intel Core
i7, which contains 4 CPU cores with 2 hyper threads (8 total threads). This
will be interpreted as 8 CPUs for OpenMP.
.. code-block:: python
from __future__ import print_function
import time
from netCDF4 import Dataset
from wrf import getvar, ALL_TIMES, extract_vars
# The first two files contain four times, the last file contains only one.
wrf_filenames = ["/path/to/wrfout_d02_2005-08-28_00:00:00",
"/path/to/wrfout_d02_2005-08-28_12:00:00",
"/path/to/wrfout_d02_2005-08-29_00:00:00"]
wrfin = [Dataset(x) for x in wrf_filenames]
start = time.time()
my_cache = extract_vars(wrfin, ALL_TIMES, ("P", "PSFC", "PB", "PH", "PHB",
"T", "QVAPOR", "HGT", "U", "V",
"W"))
end = time.time()
print ("Time taken to build cache: ", (end-start), "s")
vars = ("avo", "eth", "cape_2d", "cape_3d", "ctt", "dbz", "mdbz",
"geopt", "helicity", "lat", "lon", "omg", "p", "pressure",
"pvo", "pw", "rh2", "rh", "slp", "ter", "td2", "td", "tc",
"theta", "tk", "tv", "twb", "updraft_helicity", "ua", "va",
"wa", "uvmet10", "uvmet", "z", "cfrac", "zstag", "geopt_stag")
# No cache
start = time.time()
for var in vars:
v = getvar(wrfin, var, ALL_TIMES)
end = time.time()
no_cache_time = (end-start)
print ("Time taken without variable cache: ", no_cache_time, "s")
# With a cache
start = time.time()
for var in vars:
v = getvar(wrfin, var, ALL_TIMES, cache=my_cache)
end = time.time()
cache_time = (end-start)
print ("Time taken with variable cache: ", cache_time, "s")
improvement = ((no_cache_time-cache_time)/no_cache_time) * 100
print ("The cache decreased computation time by: ", improvement, "%")
Result:
.. code-block:: none
Time taken to build cache: 0.28154706955 s
Time taken without variable cache: 11.0905270576 s
Time taken with variable cache: 8.25931215286 s
The cache decreased computation time by: 25.5282268378 %
By removing the repeated extraction of common variables in the
:meth:`wrf.getvar` routine, for the single threaded case, the computation
time has been reduced by 25.5% in this particular example.
Things get more interesting when OpenMP is turned on and set to use the
maximum number of processors (in this case 8 threads are used).
.. code-block:: python
from __future__ import print_function
import time
from netCDF4 import Dataset
from wrf import (getvar, ALL_TIMES, extract_vars,
omp_set_num_threads, omp_get_num_procs)
# The first two files contain four times, the last file contains only one.
wrf_filenames = ["/path/to/wrfout_d02_2005-08-28_00:00:00",
"/path/to/wrfout_d02_2005-08-28_12:00:00",
"/path/to/wrfout_d02_2005-08-29_00:00:00"]
wrfin = [Dataset(x) for x in wrf_filenames]
start = time.time()
my_cache = extract_vars(wrfin, ALL_TIMES, ("P", "PSFC", "PB", "PH", "PHB",
"T", "QVAPOR", "HGT", "U", "V",
"W"))
end = time.time()
print ("Time taken to build cache: ", (end-start), "s")
omp_set_num_threads(omp_get_num_procs())
vars = ("avo", "eth", "cape_2d", "cape_3d", "ctt", "dbz", "mdbz",
"geopt", "helicity", "lat", "lon", "omg", "p", "pressure",
"pvo", "pw", "rh2", "rh", "slp", "ter", "td2", "td", "tc",
"theta", "tk", "tv", "twb", "updraft_helicity", "ua", "va",
"wa", "uvmet10", "uvmet", "z", "cfrac", "zstag", "geopt_stag")
# No cache
start = time.time()
for var in vars:
v = getvar(wrfin, var, ALL_TIMES)
end = time.time()
no_cache_time = (end-start)
print ("Time taken without variable cache: ", no_cache_time, "s")
# With a cache
start = time.time()
for var in vars:
v = getvar(wrfin, var, ALL_TIMES, cache=my_cache)
end = time.time()
cache_time = (end-start)
print ("Time taken with variable cache: ", cache_time, "s")
improvement = ((no_cache_time-cache_time)/no_cache_time) * 100
print ("The cache decreased computation time by: ", improvement, "%")
Result:
.. code-block:: none
Time taken to build cache: 0.2700548172 s
Time taken without variable cache: 6.02652812004 s
Time taken with variable cache: 3.27777099609 s
The cache decreased computation time by: 45.6109565772 %
In this example, 4 CPU cores (8 total threads) are used. When the cache is
used, the computation time drops by 45%, so almost half the time was spent
simply extracting variables from the NetCDF file. When compared to the
11.09 s needed to compute the single threaded case with no variable cache, the
computation time drops by roughly 70% (compared to 45% with 8 threads but
no cache).
In summary, if you are computing a lot of diagnostic variables, consider using
the *cache* argument to improve performance, particularly if you want to
maximize your multithreaded performance with OpenMP.