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New variables can be defined as the result of an array-‐based operation. A
mathematical formula can be applied to existing variables to define new variables.
For example, suppose U and V are 3D variables in a Vapor Data Collection (VDC). U
and V are represented in VAPOR as collections of 3D data arrays, one array for each
time step. You can define a new 3D variable “NewVar” with the one-‐line Python
script
Thereafter the variable “NewVar” can be used as a 3D variable in any 3D VAPOR
visualization. Its value at a point (x,y,z) in the VAPOR scene, and at a time step t, will
be determined by applying the above formula to the corresponding point of the U
and V arrays for the same time step t. You can also calculate a variable by operating
on input data arrays element-‐by-‐element; this is discussed below under “Advanced
Usage
Vapor’s data caching and region selection mechanisms are applicable, so that
NewVar will only be re-‐calculated as needed
To define a new variable that is derived from other variables, do the following
From the vaporgui Edit menu, select “Edit Python program defining a new
variable”. This will launch the VAPOR Python editor
From the VAPOR Python editor, check the existing variables that will be used
as input to the script, using the checkboxes at the top of the editor window.
In the above example, you would check “U” and “V” as Input 3D Variables
Specify the name(s) of output variables calculated by your script, using the
“Add 3D Variable” or “Add 2D Variable” pushbuttons in the editor window.
In the above example, “NewVar” would be specified as an Output 3D variable
Type the formula (or Python program) that calculates your output
variable(s) into the center Python editor window. You can also copy and
paste it from another text editor
Optionally, click on the “Test” button at the bottom of the editor window.
This will check that your program is valid by applying it at the current region
and time step, at lowest refinement level, and will print any output text or
error messages in a VAPOR popup
Click on the “Apply” button to finalize the program, defining the new
variable. Henceforth the newly defined variable(s) will appear in the various
variable lists for rendering
It is a good idea to save your session after you click “Apply”. That way the derived
variables and the Python scripts you have defined will be available the next time you
load the session.
Pre-‐defined Python functions
VAPOR provides several Python functions that are available from the VAPOR
environment. General functions are in the module “vapor_utils”. WRF-‐specific
modules are in the module “vapor_wrf”. You can access these functions by issuing
“import vapor_utils” and “import vapor_wrf” in your python shell.
Help is available in the vapor python editor. To obtain help on method named
“method_name” in module “module_name”, type the following into the python
editor
Then click the “Test” button.
On Macintosh the python methods are installed in
Vapor.app/Contents/SharedSupport/python. On Windows or Linux these are
installed in $(VAPOR_HOME)/share/python . You may also find these scripts useful
examples of how to write Python scripts operating on VAPOR data
The vapor_utils module includes:
vapor_utils.mag3d(a1,a2,a3) : return the 3D magnitude of (a1,a2,a3)
vapor_utils.mag2d(a1,a2) : return the 2D magnitude of (a1,a2)
vapor_utils.curl_findiff(inx, iny, inz, order=6): returns (a tuple of) 3 NumPy
arrays corresponding with the three components of the curl of the vector field
(inx,iny,inz). The curl is performed in VAPOR user coordinates, so that the results
may be used in the current VAPOR visualization. Order can be 6, 4, or 2
vapor_utils.grad_findiff(var,order=6): returns (a tuple of) 3 NumPy arrays
corresponding with the three components of the gradient of the variable var. The
gradient is performed in VAPOR user coordinates, so that the results may be used in
the current VAPOR visualization. Order can be 6, 4, or 2
vapor_utils.div_findiff(inx,iny,inz,order=6): returns a NumPy array which is the
divergence of the vector field defined by [inx, iny, inz]. The divergence is performed
in VAPOR user coordinates, so that the results may be used in the current VAPOR
visualization. The order can be 2,4,or 6(default).
vapor_utils.deriv_findiff(a,dir,dx,order=6): return the finite-‐difference (sixth
order) derivative of the NumPy float32 array a, in the direction dir, with a spatial
step of dx. The value of dir is 1,2, or 3, which are associated with the 1 st , 2 nd , and 3 rd
dimensions of the NumPy array, and which correspond to the Z, Y, and X dimensions
in the VAPOR VDC. The floating point value dx is the step-‐size in user coordinates
associated with grid size in the dimension associated with dir. The order can be
either 6, 4, or 2.
vapor_utils.deriv_var_findiff(a,var,dir,order=6): return the finite-‐difference
(sixth order) derivative of the NumPy float32 array a, with respect to the variable
“var”. The value of dir is 1,2, or 3, which are associated with the 1 st , 2 nd , and 3 rd
dimensions of the NumPy array, and which correspond to the Z, Y, and X dimensions
in the VAPOR VDC. The order can be either 6, 4, or 2.
vapor_utils.interp3d(A,PR,val): returns a 2D horizontal variable, obtained by
interpolating the 3D variable A to the level determined by PR=val, where PR
(typically pressure) is a 3D variable that decreases with increased elevation (z) and
val is a scalar.
vapor_utils.vector_rotate(angleRad, latDeg, u, v): returns a 2-‐tuple of variables
obtained by rotating the u and v variables to a vector field on a latitude/longitude
grid. The variable angleRad specifies the angle rotation in radians to convert from
the u,v grid to the lat/lon grid. The latDeg variable indicates the latitude in degrees.
This is useful for performing flow integration on the vapor lat-‐lon grid, with ROMS,
MOM, POP, and other models that are reinterpolated to a lat-‐lon grid for
visualization. The horizontal components of the flow must be rotated and scaled for
correct flow integration on the lat-‐lon grid.
VAPOR user preference settings include a “Python directory”, where users can save
the python scripts they are using. By default, on Linux the Python directory is the
current directory. On Mac and Windows it defaults to the user’s home directory
(the value of the HOME environment variable).
Users may also provide a Python startup script for defining methods, variables and
other settings to be used in a particular session. From the Edit menu, select “Edit
Python startup script”. Click “Test” to ensure the script works in the VAPOR
environment. After you click “Apply”, this script will be executed once, prior to any
other scripts. The startup script will be saved in the session file when you save the
session, and will be restored when you restore a session.
When VAPOR is installed, a Python system startup script, named
“pythonSystemStartup.py”, is installed in the directory
$(VAPOR_HOME)/share/python . The system startup script is executed prior to any
other python commands, and is present in all VAPOR sessions. By default, this script
contains only the two lines:
The vapor_wrf module
Note: This module has been extensively updated for vapor 2.2. Previous to VAPOR
2.2, python methods operated in the user coordinate grid, which was reinterpolated
from the WRF terrain-‐following grid. With VAPOR 2.2, the python methods operated
directly on arrays defined on the WRF grid. This results in more accurate
calculation, however modifications whenever using derivative operators to take into
account the grid layering. New methods are provided in this module to support
derivative operators.
Several methods that are commonly used in analysis of WRF-‐ARW output are
installed with vapor, in the vapor_wrf module. To use these you should include the
statement “import vapor_wrf” at the start of your script. These operate on the
standard WRF output variables. They have been adapted from RIP4 and NCL
FORTRAN-‐based utilities. Included are
vapor_wrf.CTT(P,PB,T,QCLOUD,QICE) : (2D) cloud-‐top temperature
vapor_wrf.DBZ(P,PB,QRAIN,QGRAUP,QSNOW,T,QVAPOR,iliqskin,ivarint):
Calculates 3D radar reflectivity. Optional variables iliqskin and ivarint default to 0.
If iliqskin = 1, then frozen particles above freezing are assumed to scatter as a liquid
particle. If ivarint = 1 then intercept parameters are calculated based on the work of
Thompson, Rasmussen and Manning in 2004 Monthly Weather Review.
vapor_wrf.DBZ_MAX(P,PB,QRAIN,QGRAUP,QSNOW,T,QVAPOR,
iliqskin,ivarint): Calculates 2D Radar reflectivity; i.e. the vertical maximum of DBZ,
as described above.
vapor_wrf.ETH(P,PB,T,QVAPOR): Calculates the 3D equivalent potential
temperature.
vapor_wrf.PV(T,P,PB,U,V,ELEVATION,F): Calculates the 3D potential vorticity.
Note that F is a 2D WRF variable. ELEVATION is the VAPOR 3D variable that
specifies the elevation in meters above sea level.
vapor_wrf.RH(P,PB,T,QVAPOR): Calculates the 3D relative humidity.
vapor_wrf.SHEAR(U,V,P,PB,level1=200,level2=850): Calculates horizontal wind
shear, defined as the RMS difference between the horizontal velocity, interpolated to
the specified pressure levels in millibars(level1 and level2)
vapor_wrf.SLP(P,PB,T,QVAPOR,ELEVATION): Calculates the 2D sea-‐level
pressure. Note that ELEVATION is the VAPOR-‐WRF 3D variable indicating vertical
elevation above sea level
vapor_wrf.TD(P,PB,QVAPOR): Calculates 3D dewpoint temperature
vapor_wrf.TK(P,PB,T): Calculates 3D temperature in degrees Kelvin.
Note: The following four methods are useful whenever dealing with layered grids,
such as WRF or ROMS.
vapor_wrf.wrf_deriv_findiff(A,ELEV,dir): Calculates the derivative of a WRF (or
layered) variable in the direction dir (=0,1, or 2), where ELEV is the VAPOR
ELEVATION variable.
vapor_wrf.wrf_grad_findiff(A,ELEV): Returns the gradient of a scalar field A,
where ELEV is the VAPOR ELEVATION variable.
vapor_wrf.wrf_div_findiff(A,B,C,ELEV): Returns the divergence of the vector field
(A,B,C) where ELEV is the VAPOR ELEVATION variable. The result is a 3-‐
dimensional variable representing the divergence of (A,B,C).
vapor_wrf.wrf_curl_findiff(A,B,C,ELEV): Returns the curl of the vector field (A, B,
C) where ELEV is the VAPOR ELEVATION variable. The result is a 3-‐tuple of 3-‐
dimensional arrays.
Python output text:
The Python editor in VAPOR does not have all the normal functionality of a Python
interpreter. Users will not see any results or messages until they press “Test” or
“Apply”. If the script has no output text and no errors, there is a popup indicating
successful execution. To debug these Python scripts it is often useful to print some
results as well as other diagnostic information. To provide such feedback, during
“Test” mode, all Python printed output is presented in a popup window. An
additional popup window presents Python error messages and error traceback.
After “Apply” is pressed, the Python error messages will appear in popup windows;
however, the printed output will not be displayed. Instead it is diverted to the
VAPOR diagnostics. Those printed messages can still be viewed in the VAPOR log
file: From the Edit menu, click “Edit User Preferences” and set the value of “Log File
Max” for Diagnostic Messages to a positive value, to ensure that these messages will
appear in the log file.
Advanced usage:
Python is a powerful programming language and it can be used to do much more
than applying simple formulas to existing variables. Python scripts can use data at
other timesteps or coordinates to determine the value of an output variable at a
specific time and position. For instance, such calculations are useful when
calculating derivative operators or summarizing 3D information in a 2D variable .
VAPOR users can apply existing Python libraries to calculate and visualize derived
variables.
The Python environment used in deriving a VAPOR variable:
When a Python script is used in VAPOR to calculate an output variable, it is required
that the script produce a NumPy (3D or 2D) array of 32-‐bit floating point (float32)
values, with “C” ordering. All the variables in the VAPOR environment are
represented as collections of 2D or 3D arrays, one array for each time step. The 2D
arrays use the horizontal (X and Y) spatial dimensions of the VDC. The 3D arrays
correspond to the three (X,Y,Z) spatial dimensions of the VDC, where Z is the vertical
height. The dimension order of the array in Python is reversed from the dimensions
used in VAPOR, due to the fact that VAPOR data arrays are accessed in FORTRAN
order, while Python defaults to using C coordinate ordering. Thus, the first
coordinate of a 3-‐dimensional array in the Python environment corresponds to the Z
coordinate in the VAPOR visualization. The second Python coordinate is the Y
coordinate in the VAPOR scene, and the third Python coordinate is the X coordinate
in VAPOR. When dealing with two-‐dimensional variables, the first coordinate in
the Python array is the Y coordinate in the VAPOR scene, and the second coordinate
in the Python array is the X coordinate in VAPOR.
The vapor module.
VAPOR provides an internal module, “vapor” that is only visible if you are executing
python scripts in the VAPOR application. This module provides access to the
internal state of VAPOR, which is sometimes needed in deriving variables in the
Python environment.
There are four predefined variables that are defined in the vapor module and may
be needed for calculating a variable:
vapor.TIMESTEP : The integer time step of the output variable(s) being calculated
vapor.REFINEMENT : The integer refinement level of the output variables being
calculated
vapor.LOD : The integer level-‐of-‐detail (compression level) that is being
calculated.
vapor.BOUNDS : This is a 6-‐tuple that specifies the integer extents of the Z, Y, and X
coordinates of the variable being calculated, inside the full 3D domain of the current
VDC. These values may specify a region larger than the region being currently
visualized in VAPOR, because VAPOR always stored variables in blocks (usually 32
or 64 voxels on a side). Using “B” to denote “vapor.BOUNDS”, the output variable
from a script, as a NumPy array, must have shape equal to:
[B[3]-‐B[0] +1, B[4]-‐B[1]+1, B[5]-‐B[2]+1] if it is 3-‐dimensional;
if it is 2-‐dimensional, the output NumPy array shape must be:
[B[4]-‐B[1]+1, B[5]-‐B[2]+1]
Note that the value of vapor.BOUNDS_is based on the extents of the output variable
that is requested at runtime by the VAPOR visualization. When the output variable
is 2D, the Z extents (from BOUNDS[0] to BOUNDS[3] ) are set to the Z bounds of the
full data domain.
Note also that, for efficient data access, VAPOR always stores variables on block
boundaries, so the values of BOUNDS will often describe a volume of data that
properly contains the current region in the VAPOR scene. Each element of BOUNDS
will typically be a multiple of 32, except that the largest value in BOUNDS will be no
greater than the largest value of the corresponding coordinate in the VDC.
There are also a few Python methods that are provided for use in accessing data in
the VDC. These are also in the “vapor” module, which is already imported prior to
script execution. They include:
userCoord = vapor.MapVoxToUser(voxcoord, refinementLevel) : This returns a
floating-‐point 3-‐tuple, “userCoord” that is obtained by converting the specified
integer 3-‐tuple voxcoord, at the specified integer refinement level.
voxCoord = vapor.MapUserToVox(usercoord, refinementLevel) : This returns
an integer 3-‐tuple, “voxCoord” that is obtained by converting the specified floating-‐
point 3-‐tuple usercoord, at the specified integer refinement level.
maxCoord = vapor.GetValidRegionMax(timestep,variableName,
refinementLevel) : This returns an integer 3-‐tuple, “maxCoord” that is the
maximum of the valid voxel coordinates of the variable at the specified timestep and
refinement level.
minCoord = vapor.GetValidRegionMin(timestep,variableName,
refinementLevel) : This returns an integer 3-‐tuple, “minCoord” that is the
minimum valid voxel coordinates of the variable at the specified timestep and
refinement level.
var3 = vapor.Get3DVariable(timestep, variablename, refinement, lod, bounds)
returns a 3-‐dimensional array of variable data from the VDC, based on:
timestep (integer time step)
variablename (the variable name, a string)
refinement (the refinement level, an integer)
lod (the level of detail or compression level, an integer)
bounds (a 6-‐tuple specify the location of the data in the full domain, as with
vapor.BOUNDS described above.
var2 = vapor.Get2DVariable(timestep, variablename, refinement, lod, bounds)
returns a two-‐dimensional array of data from a 2-‐dimensional variable in the VDC,
with arguments as above. The “bounds” argument must be an integer 6-‐tuple,
however the first and fourth elements are ignored.
val = vapor.VariableExists(timestep, varname [refinement,[lod]]): returns 0 or
1 (False or True) depending on whether the specified variable is present at the
specified timestep, refinement, and compression lod. If the refinement level or lod
is not specified, the default value is 0.
msk = vapor.Get3DMask(timestep, varname ,refinement,lod, bounds) returns
the boolean 3D mask variable indicating the location where the 3d variable
“varname” is not set to its missing value.
msk = vapor.Get2DMask(timestep, varname ,refinement, lod, bounds) returns
the boolean 2D mask variable indicating the location where the 2d variable
“varname” is not set to its missing value.
Note that when you write your own Python script, the script will have access to all
variables in the VDC (at the current refinement, bounds, timestep, and lod) that are
specified in the “Input Variables” checkboxes. It is only necessary to use the above
Get2Dvariable() and Get3Dvariable() methods when the VDC variables are needed
at coordinates, refinement levels, levels-‐of-‐detail, or timesteps different from those
of the output variables.
Missing Values. When the VDC contains variables with missing values (e.g. ROMS,
POP, or MOM model outputs), the vapor python environment utilizes the numpy.ma
(masked array) module, so that array operations are only applied to valid data.
This should be transparent to users unless user python routines access individual
elements in the model variables. In that case, user routines may need to utilize the
Get2DMask() and Get3DMask() in order to ensure that operations are only applied
to valid data in the arrays.
Using VAPOR with other Python modules
The installation of VAPOR contains an installation of Python 2.7 with NumPy 1.6 and
SciPy 0.10 in the python2.7 subdirectory of the VAPOR lib directory. The
corresponding Python interpreter executable is installed in the VAPOR bin
directory. VAPOR ensures that this installation of Python is used by setting the
environment variable PYTHONHOME prior to running the Python scripts. On
Windows, the PYTHONHOME variable is set by the installer. On Linux and
Macintosh, the PYTHONHOME variable is set at runtime. Users can modify the
default Python installation in (at least) two ways:
• Extend the VAPOR Python environment: Other useful Python modules, such
as PyNIO, etc. can be installed inside the VAPOR python environment. Apply
the VAPOR python executable to the setup.py script associated with the
module. The module must be compatible with Python 2.7.
• Specify that VAPOR use a different Python 2.7 environment: VAPOR can be
redirected to use another Python 2.7 environment by setting the
environment variable PYTHONHOME to the root directory of the new Python
environment prior to starting vaporgui. On startup (on Linux and
Macintosh), VAPOR will display a warning message indicating that the value
of PYTHONHOME has been changed. It is required that NumPy be installed
in the site-‐packages directory in your Python environment, and it is also
necessary that the new version of Python be the same version of Python (2.7)
with which VAPOR is linked.
mathematical formula can be applied to existing variables to define new variables.
For example, suppose U and V are 3D variables in a Vapor Data Collection (VDC). U
and V are represented in VAPOR as collections of 3D data arrays, one array for each
time step. You can define a new 3D variable “NewVar” with the one-‐line Python
script
Thereafter the variable “NewVar” can be used as a 3D variable in any 3D VAPOR
visualization. Its value at a point (x,y,z) in the VAPOR scene, and at a time step t, will
be determined by applying the above formula to the corresponding point of the U
and V arrays for the same time step t. You can also calculate a variable by operating
on input data arrays element-‐by-‐element; this is discussed below under “Advanced
Usage
Vapor’s data caching and region selection mechanisms are applicable, so that
NewVar will only be re-‐calculated as needed
To define a new variable that is derived from other variables, do the following
From the vaporgui Edit menu, select “Edit Python program defining a new
variable”. This will launch the VAPOR Python editor
From the VAPOR Python editor, check the existing variables that will be used
as input to the script, using the checkboxes at the top of the editor window.
In the above example, you would check “U” and “V” as Input 3D Variables
Specify the name(s) of output variables calculated by your script, using the
“Add 3D Variable” or “Add 2D Variable” pushbuttons in the editor window.
In the above example, “NewVar” would be specified as an Output 3D variable
Type the formula (or Python program) that calculates your output
variable(s) into the center Python editor window. You can also copy and
paste it from another text editor
Optionally, click on the “Test” button at the bottom of the editor window.
This will check that your program is valid by applying it at the current region
and time step, at lowest refinement level, and will print any output text or
error messages in a VAPOR popup
Click on the “Apply” button to finalize the program, defining the new
variable. Henceforth the newly defined variable(s) will appear in the various
variable lists for rendering
It is a good idea to save your session after you click “Apply”. That way the derived
variables and the Python scripts you have defined will be available the next time you
load the session.
Pre-‐defined Python functions
VAPOR provides several Python functions that are available from the VAPOR
environment. General functions are in the module “vapor_utils”. WRF-‐specific
modules are in the module “vapor_wrf”. You can access these functions by issuing
“import vapor_utils” and “import vapor_wrf” in your python shell.
Help is available in the vapor python editor. To obtain help on method named
“method_name” in module “module_name”, type the following into the python
editor
Then click the “Test” button.
On Macintosh the python methods are installed in
Vapor.app/Contents/SharedSupport/python. On Windows or Linux these are
installed in $(VAPOR_HOME)/share/python . You may also find these scripts useful
examples of how to write Python scripts operating on VAPOR data
The vapor_utils module includes:
vapor_utils.mag3d(a1,a2,a3) : return the 3D magnitude of (a1,a2,a3)
vapor_utils.mag2d(a1,a2) : return the 2D magnitude of (a1,a2)
vapor_utils.curl_findiff(inx, iny, inz, order=6): returns (a tuple of) 3 NumPy
arrays corresponding with the three components of the curl of the vector field
(inx,iny,inz). The curl is performed in VAPOR user coordinates, so that the results
may be used in the current VAPOR visualization. Order can be 6, 4, or 2
vapor_utils.grad_findiff(var,order=6): returns (a tuple of) 3 NumPy arrays
corresponding with the three components of the gradient of the variable var. The
gradient is performed in VAPOR user coordinates, so that the results may be used in
the current VAPOR visualization. Order can be 6, 4, or 2
vapor_utils.div_findiff(inx,iny,inz,order=6): returns a NumPy array which is the
divergence of the vector field defined by [inx, iny, inz]. The divergence is performed
in VAPOR user coordinates, so that the results may be used in the current VAPOR
visualization. The order can be 2,4,or 6(default).
vapor_utils.deriv_findiff(a,dir,dx,order=6): return the finite-‐difference (sixth
order) derivative of the NumPy float32 array a, in the direction dir, with a spatial
step of dx. The value of dir is 1,2, or 3, which are associated with the 1 st , 2 nd , and 3 rd
dimensions of the NumPy array, and which correspond to the Z, Y, and X dimensions
in the VAPOR VDC. The floating point value dx is the step-‐size in user coordinates
associated with grid size in the dimension associated with dir. The order can be
either 6, 4, or 2.
vapor_utils.deriv_var_findiff(a,var,dir,order=6): return the finite-‐difference
(sixth order) derivative of the NumPy float32 array a, with respect to the variable
“var”. The value of dir is 1,2, or 3, which are associated with the 1 st , 2 nd , and 3 rd
dimensions of the NumPy array, and which correspond to the Z, Y, and X dimensions
in the VAPOR VDC. The order can be either 6, 4, or 2.
vapor_utils.interp3d(A,PR,val): returns a 2D horizontal variable, obtained by
interpolating the 3D variable A to the level determined by PR=val, where PR
(typically pressure) is a 3D variable that decreases with increased elevation (z) and
val is a scalar.
vapor_utils.vector_rotate(angleRad, latDeg, u, v): returns a 2-‐tuple of variables
obtained by rotating the u and v variables to a vector field on a latitude/longitude
grid. The variable angleRad specifies the angle rotation in radians to convert from
the u,v grid to the lat/lon grid. The latDeg variable indicates the latitude in degrees.
This is useful for performing flow integration on the vapor lat-‐lon grid, with ROMS,
MOM, POP, and other models that are reinterpolated to a lat-‐lon grid for
visualization. The horizontal components of the flow must be rotated and scaled for
correct flow integration on the lat-‐lon grid.
VAPOR user preference settings include a “Python directory”, where users can save
the python scripts they are using. By default, on Linux the Python directory is the
current directory. On Mac and Windows it defaults to the user’s home directory
(the value of the HOME environment variable).
Users may also provide a Python startup script for defining methods, variables and
other settings to be used in a particular session. From the Edit menu, select “Edit
Python startup script”. Click “Test” to ensure the script works in the VAPOR
environment. After you click “Apply”, this script will be executed once, prior to any
other scripts. The startup script will be saved in the session file when you save the
session, and will be restored when you restore a session.
When VAPOR is installed, a Python system startup script, named
“pythonSystemStartup.py”, is installed in the directory
$(VAPOR_HOME)/share/python . The system startup script is executed prior to any
other python commands, and is present in all VAPOR sessions. By default, this script
contains only the two lines:
The vapor_wrf module
Note: This module has been extensively updated for vapor 2.2. Previous to VAPOR
2.2, python methods operated in the user coordinate grid, which was reinterpolated
from the WRF terrain-‐following grid. With VAPOR 2.2, the python methods operated
directly on arrays defined on the WRF grid. This results in more accurate
calculation, however modifications whenever using derivative operators to take into
account the grid layering. New methods are provided in this module to support
derivative operators.
Several methods that are commonly used in analysis of WRF-‐ARW output are
installed with vapor, in the vapor_wrf module. To use these you should include the
statement “import vapor_wrf” at the start of your script. These operate on the
standard WRF output variables. They have been adapted from RIP4 and NCL
FORTRAN-‐based utilities. Included are
vapor_wrf.CTT(P,PB,T,QCLOUD,QICE) : (2D) cloud-‐top temperature
vapor_wrf.DBZ(P,PB,QRAIN,QGRAUP,QSNOW,T,QVAPOR,iliqskin,ivarint):
Calculates 3D radar reflectivity. Optional variables iliqskin and ivarint default to 0.
If iliqskin = 1, then frozen particles above freezing are assumed to scatter as a liquid
particle. If ivarint = 1 then intercept parameters are calculated based on the work of
Thompson, Rasmussen and Manning in 2004 Monthly Weather Review.
vapor_wrf.DBZ_MAX(P,PB,QRAIN,QGRAUP,QSNOW,T,QVAPOR,
iliqskin,ivarint): Calculates 2D Radar reflectivity; i.e. the vertical maximum of DBZ,
as described above.
vapor_wrf.ETH(P,PB,T,QVAPOR): Calculates the 3D equivalent potential
temperature.
vapor_wrf.PV(T,P,PB,U,V,ELEVATION,F): Calculates the 3D potential vorticity.
Note that F is a 2D WRF variable. ELEVATION is the VAPOR 3D variable that
specifies the elevation in meters above sea level.
vapor_wrf.RH(P,PB,T,QVAPOR): Calculates the 3D relative humidity.
vapor_wrf.SHEAR(U,V,P,PB,level1=200,level2=850): Calculates horizontal wind
shear, defined as the RMS difference between the horizontal velocity, interpolated to
the specified pressure levels in millibars(level1 and level2)
vapor_wrf.SLP(P,PB,T,QVAPOR,ELEVATION): Calculates the 2D sea-‐level
pressure. Note that ELEVATION is the VAPOR-‐WRF 3D variable indicating vertical
elevation above sea level
vapor_wrf.TD(P,PB,QVAPOR): Calculates 3D dewpoint temperature
vapor_wrf.TK(P,PB,T): Calculates 3D temperature in degrees Kelvin.
Note: The following four methods are useful whenever dealing with layered grids,
such as WRF or ROMS.
vapor_wrf.wrf_deriv_findiff(A,ELEV,dir): Calculates the derivative of a WRF (or
layered) variable in the direction dir (=0,1, or 2), where ELEV is the VAPOR
ELEVATION variable.
vapor_wrf.wrf_grad_findiff(A,ELEV): Returns the gradient of a scalar field A,
where ELEV is the VAPOR ELEVATION variable.
vapor_wrf.wrf_div_findiff(A,B,C,ELEV): Returns the divergence of the vector field
(A,B,C) where ELEV is the VAPOR ELEVATION variable. The result is a 3-‐
dimensional variable representing the divergence of (A,B,C).
vapor_wrf.wrf_curl_findiff(A,B,C,ELEV): Returns the curl of the vector field (A, B,
C) where ELEV is the VAPOR ELEVATION variable. The result is a 3-‐tuple of 3-‐
dimensional arrays.
Python output text:
The Python editor in VAPOR does not have all the normal functionality of a Python
interpreter. Users will not see any results or messages until they press “Test” or
“Apply”. If the script has no output text and no errors, there is a popup indicating
successful execution. To debug these Python scripts it is often useful to print some
results as well as other diagnostic information. To provide such feedback, during
“Test” mode, all Python printed output is presented in a popup window. An
additional popup window presents Python error messages and error traceback.
After “Apply” is pressed, the Python error messages will appear in popup windows;
however, the printed output will not be displayed. Instead it is diverted to the
VAPOR diagnostics. Those printed messages can still be viewed in the VAPOR log
file: From the Edit menu, click “Edit User Preferences” and set the value of “Log File
Max” for Diagnostic Messages to a positive value, to ensure that these messages will
appear in the log file.
Advanced usage:
Python is a powerful programming language and it can be used to do much more
than applying simple formulas to existing variables. Python scripts can use data at
other timesteps or coordinates to determine the value of an output variable at a
specific time and position. For instance, such calculations are useful when
calculating derivative operators or summarizing 3D information in a 2D variable .
VAPOR users can apply existing Python libraries to calculate and visualize derived
variables.
The Python environment used in deriving a VAPOR variable:
When a Python script is used in VAPOR to calculate an output variable, it is required
that the script produce a NumPy (3D or 2D) array of 32-‐bit floating point (float32)
values, with “C” ordering. All the variables in the VAPOR environment are
represented as collections of 2D or 3D arrays, one array for each time step. The 2D
arrays use the horizontal (X and Y) spatial dimensions of the VDC. The 3D arrays
correspond to the three (X,Y,Z) spatial dimensions of the VDC, where Z is the vertical
height. The dimension order of the array in Python is reversed from the dimensions
used in VAPOR, due to the fact that VAPOR data arrays are accessed in FORTRAN
order, while Python defaults to using C coordinate ordering. Thus, the first
coordinate of a 3-‐dimensional array in the Python environment corresponds to the Z
coordinate in the VAPOR visualization. The second Python coordinate is the Y
coordinate in the VAPOR scene, and the third Python coordinate is the X coordinate
in VAPOR. When dealing with two-‐dimensional variables, the first coordinate in
the Python array is the Y coordinate in the VAPOR scene, and the second coordinate
in the Python array is the X coordinate in VAPOR.
The vapor module.
VAPOR provides an internal module, “vapor” that is only visible if you are executing
python scripts in the VAPOR application. This module provides access to the
internal state of VAPOR, which is sometimes needed in deriving variables in the
Python environment.
There are four predefined variables that are defined in the vapor module and may
be needed for calculating a variable:
vapor.TIMESTEP : The integer time step of the output variable(s) being calculated
vapor.REFINEMENT : The integer refinement level of the output variables being
calculated
vapor.LOD : The integer level-‐of-‐detail (compression level) that is being
calculated.
vapor.BOUNDS : This is a 6-‐tuple that specifies the integer extents of the Z, Y, and X
coordinates of the variable being calculated, inside the full 3D domain of the current
VDC. These values may specify a region larger than the region being currently
visualized in VAPOR, because VAPOR always stored variables in blocks (usually 32
or 64 voxels on a side). Using “B” to denote “vapor.BOUNDS”, the output variable
from a script, as a NumPy array, must have shape equal to:
[B[3]-‐B[0] +1, B[4]-‐B[1]+1, B[5]-‐B[2]+1] if it is 3-‐dimensional;
if it is 2-‐dimensional, the output NumPy array shape must be:
[B[4]-‐B[1]+1, B[5]-‐B[2]+1]
Note that the value of vapor.BOUNDS_is based on the extents of the output variable
that is requested at runtime by the VAPOR visualization. When the output variable
is 2D, the Z extents (from BOUNDS[0] to BOUNDS[3] ) are set to the Z bounds of the
full data domain.
Note also that, for efficient data access, VAPOR always stores variables on block
boundaries, so the values of BOUNDS will often describe a volume of data that
properly contains the current region in the VAPOR scene. Each element of BOUNDS
will typically be a multiple of 32, except that the largest value in BOUNDS will be no
greater than the largest value of the corresponding coordinate in the VDC.
There are also a few Python methods that are provided for use in accessing data in
the VDC. These are also in the “vapor” module, which is already imported prior to
script execution. They include:
userCoord = vapor.MapVoxToUser(voxcoord, refinementLevel) : This returns a
floating-‐point 3-‐tuple, “userCoord” that is obtained by converting the specified
integer 3-‐tuple voxcoord, at the specified integer refinement level.
voxCoord = vapor.MapUserToVox(usercoord, refinementLevel) : This returns
an integer 3-‐tuple, “voxCoord” that is obtained by converting the specified floating-‐
point 3-‐tuple usercoord, at the specified integer refinement level.
maxCoord = vapor.GetValidRegionMax(timestep,variableName,
refinementLevel) : This returns an integer 3-‐tuple, “maxCoord” that is the
maximum of the valid voxel coordinates of the variable at the specified timestep and
refinement level.
minCoord = vapor.GetValidRegionMin(timestep,variableName,
refinementLevel) : This returns an integer 3-‐tuple, “minCoord” that is the
minimum valid voxel coordinates of the variable at the specified timestep and
refinement level.
var3 = vapor.Get3DVariable(timestep, variablename, refinement, lod, bounds)
returns a 3-‐dimensional array of variable data from the VDC, based on:
timestep (integer time step)
variablename (the variable name, a string)
refinement (the refinement level, an integer)
lod (the level of detail or compression level, an integer)
bounds (a 6-‐tuple specify the location of the data in the full domain, as with
vapor.BOUNDS described above.
var2 = vapor.Get2DVariable(timestep, variablename, refinement, lod, bounds)
returns a two-‐dimensional array of data from a 2-‐dimensional variable in the VDC,
with arguments as above. The “bounds” argument must be an integer 6-‐tuple,
however the first and fourth elements are ignored.
val = vapor.VariableExists(timestep, varname [refinement,[lod]]): returns 0 or
1 (False or True) depending on whether the specified variable is present at the
specified timestep, refinement, and compression lod. If the refinement level or lod
is not specified, the default value is 0.
msk = vapor.Get3DMask(timestep, varname ,refinement,lod, bounds) returns
the boolean 3D mask variable indicating the location where the 3d variable
“varname” is not set to its missing value.
msk = vapor.Get2DMask(timestep, varname ,refinement, lod, bounds) returns
the boolean 2D mask variable indicating the location where the 2d variable
“varname” is not set to its missing value.
Note that when you write your own Python script, the script will have access to all
variables in the VDC (at the current refinement, bounds, timestep, and lod) that are
specified in the “Input Variables” checkboxes. It is only necessary to use the above
Get2Dvariable() and Get3Dvariable() methods when the VDC variables are needed
at coordinates, refinement levels, levels-‐of-‐detail, or timesteps different from those
of the output variables.
Missing Values. When the VDC contains variables with missing values (e.g. ROMS,
POP, or MOM model outputs), the vapor python environment utilizes the numpy.ma
(masked array) module, so that array operations are only applied to valid data.
This should be transparent to users unless user python routines access individual
elements in the model variables. In that case, user routines may need to utilize the
Get2DMask() and Get3DMask() in order to ensure that operations are only applied
to valid data in the arrays.
Using VAPOR with other Python modules
The installation of VAPOR contains an installation of Python 2.7 with NumPy 1.6 and
SciPy 0.10 in the python2.7 subdirectory of the VAPOR lib directory. The
corresponding Python interpreter executable is installed in the VAPOR bin
directory. VAPOR ensures that this installation of Python is used by setting the
environment variable PYTHONHOME prior to running the Python scripts. On
Windows, the PYTHONHOME variable is set by the installer. On Linux and
Macintosh, the PYTHONHOME variable is set at runtime. Users can modify the
default Python installation in (at least) two ways:
• Extend the VAPOR Python environment: Other useful Python modules, such
as PyNIO, etc. can be installed inside the VAPOR python environment. Apply
the VAPOR python executable to the setup.py script associated with the
module. The module must be compatible with Python 2.7.
• Specify that VAPOR use a different Python 2.7 environment: VAPOR can be
redirected to use another Python 2.7 environment by setting the
environment variable PYTHONHOME to the root directory of the new Python
environment prior to starting vaporgui. On startup (on Linux and
Macintosh), VAPOR will display a warning message indicating that the value
of PYTHONHOME has been changed. It is required that NumPy be installed
in the site-‐packages directory in your Python environment, and it is also
necessary that the new version of Python be the same version of Python (2.7)
with which VAPOR is linked.
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