epygram.fields.D3Field — 3-D Field class

Contains the class that handle a Horizontal 2D field.

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class epygram.fields.D3Field._D3CommonField(*args, **kwargs)

Bases: epygram.base.Field

3-Dimensions common field class.

Note

This class is managed by footprint.

• info: Not documented

• priority: PriorityLevel::DEFAULT (rank=1)

Automatic parameters from the footprint:

• comment (builtins.str) - rwd - Not documented, sorry.

  • Optional. Default is None.

• fid (footprints.stdtypes.FPDict) - rwx - Not documented, sorry.

• misc_metadata (footprints.stdtypes.FPDict) - rwd - Not documented, sorry.

  • Optional. Default is FPDict::<<as_dict:: dict()>>.

• structure (builtins.str) - rxx - Type of Field geometry.

  • Values: set([‘3D’])

• units (builtins.str) - rwd - Not documented, sorry.

  • Optional. Default is ‘’.

Constructor. See its footprint for arguments.

as_dicts(subzone=None)

Export values as a list of dicts.

Parameters

subzone – defines the LAM subzone to be included, in LAM case, among: ‘C’, ‘CI’.

as_lists(order='C', subzone=None)

Export values as a dict of lists (in fact numpy arrays).

Parameters

• order – whether to flatten arrays in ‘C’ (row-major) or ‘F’ (Fortran, column-major) order.

• subzone – defines the LAM subzone to be included, in LAM case, among: ‘C’, ‘CI’.

as_points(subzone=None)

Export values as a fieldset of points.

Parameters

subzone – defines the LAM subzone to be included, in LAM case, among: ‘C’, ‘CI’.

as_profiles(subzone=None)

Export values as a fieldset of profiles.

Parameters

subzone – defines the LAM subzone to be included, in LAM case, among: ‘C’, ‘CI’.

as_vcoordinate(force_kind=None)

Returns a VGeometry build from the values of field. Field must have a ‘generic’ fid allowing to recognize the vertical coordinate type (except if force_kind is set)

Parameters

force_kind – if not None, is used as the vertical coordinate kind instead of trying to guess it from the fid

as_vtkGrid(rendering, grid_type, subzone=None, filename=None, name='scalar', grid=None, version='XML', binary=True, compression='ZLib', compression_level=5)

Note

Requires plugin: with_vtk (config.activate_plugins)

Returns a vtkStructuredGrid filled with the field :param rendering: a usevtk.Usevtk instance :param grid_type: can be:

• sgrid_point: structured grid filled with points

• sgrid_cell: structured grid filled with hexahedron

  If the field is 2D, a zero thickness is used. If the field is 3D, thickness are approximately computed

• ugrid_point: unstructured grid filled with points

• ugrid_cell: unstructured grid build filled with cells

  If the field is 2D, a zero thickness is used. If the field is 3D, thickness are approximately computed

Parameters

• subzone –

  optional, among (‘C’, ‘CI’), for LAM grids only, returns the grid resp. for the C or C+I zone off the C+I+E zone.

  Default is no subzone, i.e. the whole field.

• filename – if not None, resulting grid will be written into filename

• name – name to give to the scalar array (useful with the grid option)

• grid – if grid is not None, the method will add the data to it.

• version – must be ‘legacy’ or ‘XML’, used with filename

• binary – True (default) for a binary file, used with filename

• compression – must be None, ‘LZ4’ or ‘ZLib’ only used for binary XML

• compression_level – between 1 and 9, only used for binary XML Zlib-compressed

If grid_type is ‘sgrid_point’, the result is the grid; otherwise the result is the function is the last filter used.

comment

(see the documentation above for more details).

dctspectrum(subzone=None, level_index=None, validity_index=None)

Returns the DCT spectrum of the field, as a epygram.spectra.Spectrum instance.

Parameters

• subzone – LAM zone on which to compute the DCT, among (None, ‘C’, ‘CI’, ‘CIE’). Defaults to ‘C’.

• level_index – the level index on which to compute the DCT

• validity_index – the validity index on which to compute the DCT

decumulate(center=False)

Decumulate cumulated fields (for a field with temporal dimension !).

Parameters

center –

• if False, values at t are mean values between t-1 and t, except at t=0 where it remains untouched.

• if True, values at t are then (v[t-1, t] + v[t,t+1])/2., except for t=0 where it becomes v[0, 1] and t=last where it remains the same.

dump_to_nc(filename, variablename=None, fidkey=None)

Dumps the field in a netCDF file.

Parameters

• filename – filename to dump in

• variablename – variable name in netCDF

• fidkey – forces variablename to self.fid[fidkey]; defaults to ‘netCDF’

extend(another_field_with_time_dimension)

Extend the field with regard to time dimension with the field given as argument.

Be careful no check is done for consistency between the two fields geometry (except that dimensions match) nor their validities.

extract_physicallevels(getdata=True)

Returns a new field excluding levels with no physical meaning.

Parameters

getdata – if False returns a field without data

extract_subarray(first_i, last_i, first_j, last_j, getdata=True, deepcopy=True)

Extract a rectangular sub-array from the field, given the i,j index limits of the sub-array, and return the extracted field. If deepcopy is False, current field and returned field will share some attributes (like validity).

extract_subdomain(geometry, interpolation='nearest', external_distance=None, getdata=True)

Extracts a subdomain from a field, given a new geometry.

Parameters

• geometry – defines the geometry on which extract data

• interpolation –

  defines the interpolation function used to compute

  the profile at requested lon/lat from the fields grid:

  • if ‘nearest’ (default), extracts profile at the horizontal nearest neighboring gridpoint;

  • if ‘linear’, computes profile with horizontal linear interpolation;

  • if ‘cubic’, computes profile with horizontal cubic interpolation.

• external_distance – can be a dict containing the target point value and an external field on the same grid as self, to which the distance is computed within the 4 horizontally nearest points; e.g. {‘target_value’:4810, ‘external_field’:an_H2DField_with_same_geometry}. If so, the nearest point is selected with distance = abs(target_value - external_field.data)

• getdata – if False returns a field without data

extract_subsample(sample_x, sample_y, sample_z, getdata=True, deepcopy=True)

Extract a subsample from field by decreasing resolution.

Parameters

• sample_x – take one over <sample_x> points in the x direction

• sample_y – same for the y direction

• sample_z – same for the z direction

If deepcopy is False, current field and returned field will share some attributes (like validity).

The extension zone of the original field is kept in the new field. Hence, it’s recommended to call this method on field without extension zone.

Because the resolution change during this operation, field must be localize on the grid center (use center method before if not).

extract_zoom(zoom, extra_10th=False)

Extract an unstructured field with the gridpoints contained in zoom.

Parameters

• zoom – a dict(lonmin=, lonmax=, latmin=, latmax=).

• extra_10th – if True, add 1/10th of the X/Y extension of the zoom (regular_lonlat grid case only).

extractprofile(lon, lat, interpolation='nearest', external_distance=None, exclude_extralevels=True, getdata=True)

Extracts a vertical profile from the field, given the geographic location (lon/lat) of the profile.

Parameters

• lon – the longitude of the desired point.

• lat – the latitude of the desired point. If both None, extract a horizontally-averaged profile.

• interpolation –

  defines the interpolation function used to compute the profile at requested lon/lat from the fields grid:

  • if ‘nearest’ (default), extracts profile at the horizontal nearest neighboring gridpoint;

  • if ‘linear’, computes profile with horizontal linear spline interpolation;

  • if ‘cubic’, computes profile with horizontal cubic spline interpolation.

• external_distance – can be a dict containing the target point value and an external field on the same grid as self, to which the distance is computed within the 4 horizontally nearest points; e.g. {‘target_value’:4810, ‘external_field’:an_H2DField_with_same_geometry}. If so, the nearest point is selected with distance = abs(target_value - external_field.data)

• exclude_extralevels – if True levels with no physical meaning are suppressed.

• getdata – if False returns a field without data

extractsection(end1, end2, points_number=None, resolution=None, interpolation='linear', exclude_extralevels=True, getdata=True)

Extracts a vertical section from the field, given the geographic (lon/lat) coordinates of its ends. The section is returned as a V2DField.

Parameters

• end1 – must be a tuple (lon, lat).

• end2 – must be a tuple (lon, lat).

• points_number – defines the total number of horizontal points of the section (including ends). If None, defaults to a number computed from the ends and the resolution.

• resolution – defines the horizontal resolution to be given to the field. If None, defaults to the horizontal resolution of the field.

• interpolation –

  defines the interpolation function used to compute the profile points locations from the fields grid:

  • if ‘nearest’, each horizontal point of the section is taken as the horizontal nearest neighboring gridpoint;

  • if ‘linear’ (default), each horizontal point of the section is computed with linear spline interpolation;

  • if ‘cubic’, each horizontal point of the section is computed with linear spline interpolation.

• exclude_extralevels – if True levels with no physical meaning are suppressed.

• getdata – if False returns a field without data

fid

(see the documentation above for more details).

getvalue_ll(lon=None, lat=None, level=None, k=None, validity=None, interpolation='nearest', neighborinfo=False, one=True, external_distance=None)

Returns the value of the field on point of coordinates (lon, lat, level). lon and lat may be list/numpy.array.

Parameters

• interpolation –

  if:

  • ’nearest’ (default), returns the value of the nearest neighboring gridpoint;

  • ’linear’, computes and returns the field value with bilinear or linear spline interpolation;

  • ’cubic’, computes and returns the field value with cubic spline interpolation;

• level – is the True level not the index of the level. Depending on the vertical coordinate, it could be expressed in Pa, m.

• k – is the index of the level.

• validity – is a FieldValidity or a FieldValidityList instance

• neighborinfo – if set to True, returns a tuple (value, (lon, lat)), with (lon, lat) being the actual coordinates of the neighboring gridpoint (only for interpolation == ‘nearest’).

• one – if False and len(lon) is 1, returns [value] instead of value.

• external_distance – can be a dict containing the target point value and an external field on the same grid as self, to which the distance is computed within the 4 horizontally nearest points; e.g. {‘target_value’:4810, ‘external_field’:an_H2DField_with_same_geometry}. If so, the nearest point is selected with distance = abs(target_value - external_field.data)

When linear interpolation method is choosen, if the grid is rectangular, the interpolation is a bilinear one on the model grid, otherwise it is a spline interpolation on the lat/lon domain.

Warning: for interpolation on Gauss geometries, requires the pyproj module.

global_shift_center(longitude_shift)

Shifts the center of the geometry (and the data accordingly) by longitude_shift (in degrees).

Parameters

longitude_shift – has to be a multiple of the grid’s resolution in longitude.

For global RegLLGeometry grids only.

histogram(subzone=None, over=(None, None), title=None, get_n_bins_patches=False, together_with=[], mask_threshold=None, center_hist_on_0=False, minmax_in_title=True, figsize=None, **hist_kwargs)

Build an histogram of the field data.

Parameters

• subzone – LAM zone on which to compute the DCT, among (None, ‘C’, ‘CI’, ‘CIE’).

• over – any existing figure and/or ax to be used for the plot, given as a tuple (fig, ax), with None for missing objects. fig is the frame of the matplotlib figure, containing eventually several subplots (axes); ax is the matplotlib axes on which the drawing is done. When given (is not None), these objects must be coherent, i.e. ax being one of the fig axes.

• title – title for the plot

• get_n_bins_patches – if True, returns n, bins, patches (cf. matplotlib hist()) in addition

• together_with – another field or list of fields to plot on the same histogram

• mask_threshold – dict with min and/or max value(s) to mask outside.

• center_hist_on_0 – to center the histogram on 0.

• minmax_in_title – if True and range is not None, adds min and max values in title.

• hist_kwargs – any keyword argument to be passed to matplotlib’s hist()

• figsize – figure sizes in inches, e.g. (5, 8.5). Default figsize is config.plotsizes.

max(subzone=None)

Returns the maximum value of data.

mean(subzone=None)

Returns the mean value of data.

min(subzone=None)

Returns the minimum value of data.

misc_metadata

(see the documentation above for more details).

nonzero(subzone=None)

Returns the number of non-zero values (whose absolute value > config.epsilon).

plot3DColor(rendering, color, opacity=1.0, colorbar=True, subzone=None)

Note

Requires plugin: with_vtk (config.activate_plugins)

This method color the field. :param rendering: a usevtk.Usevtk instance :param color: look up table or color transfer function to color the contour lines :param opacity: opacity value :param colorbar: True to plot a colorbar :param subzone: optional, among (‘C’, ‘CI’), for LAM grids only, returns

the grid resp. for the C or C+I zone off the C+I+E zone.

Default is no subzone, i.e. the whole field.

Returns

actor, mapper, colorbaractor

plot3DContour(rendering, levels, color='Black', opacity=1.0, smoothing=None, colorbar=True, subzone=None)

Note

Requires plugin: with_vtk (config.activate_plugins)

This method adds contour lines. If the field is 3D, contours appear as isosurface. :param rendering: a usevtk.Usevtk instance :param levels: list of values to use to compute the contour lines :param color: color name or lookup table or color transfer function

for coloring the contour lines

Parameters

• opacity – opacity value for the contour lines

• smoothing –

  None to prevent smoothing otherwise a dictionary with optional keys (cf. vtkWindowedSincPolyDataFilter doc on internet):

  • nonManifoldSmoothing (boolean)

  • numberOfIterations (integer)

  • featureEdgeSmoothing (boolean)

  • boundarySmoothing (boolean)

  • featureAngle (float)

  • edgeAngle (float)

  • passBand (float)

• colorbar – True to plot a colorbar

• subzone –

  optional, among (‘C’, ‘CI’), for LAM grids only, returns the grid resp. for the C or C+I zone off the C+I+E zone.

  Default is no subzone, i.e. the whole field.

Returns

actor, mapper, colorbaractor

plot3DOutline(rendering, color='Black', subzone=None)

Note

Requires plugin: with_vtk (config.activate_plugins)

Plots the outline of the data :param rendering: a usevtk.Usevtk instance :param color: color name to use for the outline :param subzone: optional, among (‘C’, ‘CI’), for LAM grids only, returns

the grid resp. for the C or C+I zone off the C+I+E zone.

Default is no subzone, i.e. the whole field.

Returns

actor, mapper, None

plot3DVolume(rendering, threshold, color, opacity, algo='OpenGLProjectedTetrahedra', colorbar=True, subzone=None)

Note

Requires plugin: with_vtk (config.activate_plugins)

Adds a volume to the vtk rendering system :param rendering: a usevtk.Usevtk instance :param threshold: a tuple (minval, maxval) used to discard values outside of the interval

minval and/or maxval can be replace by None value

Parameters

• color – a vtk.vtkColorTransferFunction object or None to describe the color

• opacity – a vtk.vtkPiecewiseFunction object or None to describe the alpha channel

• algo – among ‘RayCast’, ‘ZSweep’, ‘ProjectedTetrahedra’, ‘OpenGLProjectedTetrahedra’

• colorbar – True to plot a colorbar

• subzone –

  optional, among (‘C’, ‘CI’), for LAM grids only, returns the grid resp. for the C or C+I zone off the C+I+E zone.

  Default is no subzone, i.e. the whole field.

Returns

actor, mapper, colorbaractor

quadmean(subzone=None)

Returns the quadratic mean of data.

remove_level(k)

Remove one level from the current field :param k: index of the level to remove

resample(target_geometry, weighting='nearest', subzone=None, neighbour_info=None, radius_of_influence=None, neighbours=8, epsilon=0, reduce_data=True, nprocs=1, segments=None, fill_value=None, sigma=None, with_uncert=False)

Resample data (interpolate) on a target geometry, using pyresample.

Parameters

• weighting –

  among [‘nearest’, ‘gauss’ (in which case sigma can be specified,

  cf. sigma option below),

  lambda r:1/r**2 (or other function, r in m)]

• subzone – restrain source field to LAMzone in LAM geometry case

• neighbour_info –

  • if True: skips the resampling, only compute

    neighbours and return this information as a tuple;

  • if given as a tuple, skips the computation of neighbours, taking those information given in argument (from a former call with neighbour_info=True)

Options from pyresample, neighbouring:

Parameters

• radius_of_influence – Cut off distance in meters, default value computed from geometry

• neighbours – The number of neigbours to consider for each grid point

• epsilon – Allowed uncertainty in meters. Increasing uncertainty reduces execution time

• reduce_data – Perform initial coarse reduction of source dataset in order to reduce execution time

• nprocs – Number of processor cores to be used

• segments – Number of segments to use when resampling. If set to None an estimate will be calculated

Options from pyresample, resampling:

Parameters

• fill_value – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked

• sigma – sigma (in m) to use for the gauss weighting w = exp(-dist^2/sigma^2) Default is twice the resolution.

• with_uncert – Calculate uncertainty estimates (returned as side fields)

resample_on_regularll(borders, resolution_in_degrees, **kwargs)

Resample data (interpolate) on a target geometry defined as a regular lonlat grid of given resolution. Wrapper to resample() method, building target geometry.

Parameters

• borders (dict) – dict(lonmin=, latmin=, lonmax=, latmax=)

• resolution_in_degrees – resolution of the lonlat grid in degrees. Supposed to be a divisor of lonmax-lonmin and latmax-latmin.

Other kwargs to be passed to resample() method.

scatter_with(other, fig=None, ax=None, figsize=None, mask_outside=1e+19, subzone=None, fidkey=None, xlabel='__auto__', ylabel='__auto__', bisect=True, **scatter_kwargs)

Make a scatter plot of self data against other data.

Parameters

• fig – a preexisting figure on which to plot

• ax – a preexisting axis on which to plot

• figsize – figure sizes in inches, e.g. (5, 8.5). Default figsize is config.plotsizes.

• mask_outside – threshold to mask values outside (-mask_outside, +mask_outside)

• subzone – LAM subzone

• fidkey – fid key to be written on axes labels

• xlabel – custom x label

• ylabel – custom y label

• bisect – plot x=y line

Other kwargs are passed to scatter() method.

sgmean()

Returns an average of the field, weighted by the map factor at each point.

shave(minval=None, maxval=None)

Shave data: cut values greater (resp. lower) than maxval to maxval (resp. minval).

Parameters

• maxval – upper threshold

• minval – lower threshold

property spectral

Returns True if the field is spectral.

stats(subzone=None)

Computes some basic statistics on the field, as a dict containing: {‘min’, ‘max’, ‘mean’, ‘std’, ‘quadmean’, ‘nonzero’}. See each of these methods for details.

Parameters

subzone – optional, among (‘C’, ‘CI’), for LAM fields only, plots the data resp. on the C or C+I zone. Default is no subzone, i.e. the whole field.

std(subzone=None)

Returns the standard deviation of data.

structure

Type of Field geometry (see the documentation above for more details).

time_reduce(reduce_function='mean')

Make a time reduction of field, i.e. apply the requested reduce_function on the time dimension of the field. Replace data (and validity) in place.

Parameters

reduce_function – must be a numpy.ndarray method, e.g. ‘mean’, ‘sum’, ‘min’, ‘max’, …

time_smooth(length, window='center')

Smooth data in time.

Parameters

• length – time span to average on

• window –

  replace data[t] by:

  • data[t-length/2:t+length/2].mean() if ‘center’;

  • data[t-length:t].mean() if ‘left’;

  • data[t:t+length/2].mean() if ‘right’.

units

(see the documentation above for more details).

use_field_as_vcoord(field, force_kind=None, validity_check=True)

Use values from the field as vertical coordinates. One example of use: field1 is a temperature field extracted from a resource on hybrid levels whereas field2 is the pressure field expressed on the same grid. field1.use_field_as_vcoord(field2) transforms the vertical coordinate of field1 to obtain a field on pressure levels.

Parameters

• field – must be a field on the same geometry and validity as self that we will use to replace the vertical coordinate. ‘generic’ fid must allow to recognize the vertical coordinate type (except if force_kind is set)

• force_kind – if not None, is used as the vertical coordinate kind instead of trying to guess it from the fid

• validity_check – if True (default) checks if validity of current object and of field parameter are the same

vtk_guess_param_from_field(reverseZ=None, z_factor=None, hCoord=None, offset=None, geoid=None)

Note

Requires plugin: with_vtk (config.activate_plugins)

Guess suitable values for setting up a usevtk.Usevtk instance:

• hCoord

• z_factor

• offset

• reverseZ

• geoid

Parameters

geoid (reverseZ, z_factor, hCoord, offset,) – if set, this value are not guessed

Returns

dictionary holding all the values

hCoord can be:

• ‘geoid’

• ‘ll’

• None to use the field projection

• a basemap/pyproj-like object

• a cratopy.crs object

what(out=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, validity=True, vertical_geometry=True, cumulativeduration=True, arpifs_var_names=False, fid=True)

Writes in file a summary of the field.

Parameters

• out – the output open file-like object.

• vertical_geometry – if True, writes the validity of the field.

• vertical_geometry – if True, writes the vertical geometry of the field.

• cumulativeduration – if False, not written.

• arpifs_var_names – if True, prints the equivalent ‘arpifs’ variable names.

• fid – if True, prints the fid.

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class epygram.fields.D3Field.D3Field(*args, **kwargs)

Bases: epygram.fields.D3Field._D3CommonField

3-Dimensions field class. A field is defined by its identifier ‘fid’, its data, its geometry (gridpoint and optionally spectral), and its validity.

The natural being of a field is gridpoint, so that: a field always has a gridpoint geometry, but it has a spectral geometry only in case it is spectral.

Note

This class is managed by footprint.

• info: Not documented

• priority: PriorityLevel::DEFAULT (rank=1)

Automatic parameters from the footprint:

• comment (builtins.str) - rwd - Not documented, sorry.

  • Optional. Default is None.

• fid (footprints.stdtypes.FPDict) - rwx - Not documented, sorry.

• geometry (epygram.geometries.AbstractGeometry.Geometry) - rxx - Geometry defining the position of the field gridpoints.

• misc_metadata (footprints.stdtypes.FPDict) - rwd - Not documented, sorry.

  • Optional. Default is FPDict::<<as_dict:: dict()>>.

• processtype (builtins.str) - rwx - Generating process.

  • Optional. Default is None.

• spectral_geometry (epygram.geometries.SpectralGeometry.SpectralGeometry) - rxx - For a spectral field, its spectral geometry handles spectral transforms and dimensions.

  • Optional. Default is None.

• structure (builtins.str) - rxx - Type of Field geometry.

  • Values: set([‘3D’])

• units (builtins.str) - rwd - Not documented, sorry.

  • Optional. Default is ‘’.

• validity (epygram.base.FieldValidityList) - rwx - Validity of the field.

  • Optional. Default is FieldValidityList::<<as_list:: [FieldValidity::<epygram.base.FieldValidity object at 0x74bef5524c40>]>>.

Constructor. See its footprint for arguments.

center()

Performs an averaging on data values to center the field on the grid (aka shuman) and modify geometry.

comment

(see the documentation above for more details).

compute_xy_spderivatives()

Compute the derivatives of field in spectral space, then come back in gridpoint space. Returns the two derivative fields.

The spectral transform and derivatives subroutines are actually included in the spectral geometry’s compute_xy_spderivatives() method.

property data

Accessor to the field data.

fid

(see the documentation above for more details).

geometry

Geometry defining the position of the field gridpoints (see the documentation above for more details).

getdata(subzone=None, d4=False)

Returns the field data, with 3D shape if the field is not spectral, 2D if spectral.

Parameters

• subzone – optional, among (‘C’, ‘CI’), for LAM fields only, returns the data resp. on the C or C+I zone. Default is no subzone, i.e. the whole field.

• d4 –

  • if True, returned values are shaped in a 4 dimensions array

  • if False, shape of returned values is determined with respect to geometry.

Shape of 4D data:

• Rectangular grids:

  grid[t,k,0,0] is SW, grid[t,k,-1,-1] is NE

  grid[t,k,0,-1] is SE, grid[t,k,-1,0] is NW

  with k the level, t the temporal dimension

• Gauss grids:

  grid[t,k,0,:Nj] is first (Northern) band of latitude, masked after Nj = number of longitudes for latitude j

  grid[t,k,-1,:Nj] is last (Southern) band of latitude (idem).

  with k the level, t the temporal dimension

getlevel(level=None, k=None)

Returns a level of the field as a new field.

Parameters

• level – the requested level expressed in coordinate value (Pa, m…)

• k – the index of the requested level

getvalidity(index_or_validity)

Returns the field restrained to one of its temporal validity as a new field.

Parameters

index_or_validity – can be either a epygram.base.FieldValidity instance or the index of the requested validity in the field’s FieldValidityList.

getvalue_ij(i=None, j=None, k=None, t=None, one=True)

Returns the value of the field on point of indices (i, j, k, t). Take care (i, j, k, t) is python-indexing, ranging from 0 to dimension-1.

Parameters

• i – the index in X direction

• j – the index in Y direction

• k – the index of the level (not a value in Pa or m…)

• t – the index of the temporal dimension (not a validity object)

k and t can be scalar even if i and j are arrays.

If one is False, returns [value] instead of value.

gp2sp(spectral_geometry)

Transforms the gridpoint field into spectral space, according to the spectral_geometry mandatorily passed as argument. Replaces data in place.

Parameters

spectral_geometry – instance of SpectralGeometry, actually containing spectral transform subroutine (in in its own gp2sp() method).

misc_metadata

(see the documentation above for more details).

processtype

Generating process (see the documentation above for more details).

select_subzone(subzone)

If a LAMzone defines the field, select only the subzone from it.

Parameters

subzone – among (‘C’, ‘CI’).

Warning: modifies the field and its geometry in place !

setdata(data)

Sets field data, checking data to have the good shape according to geometry.

Parameters

data – dimensions should in any case be ordered in a subset of

(t,z,y,x), or (t,z,n) if spectral (2D spectral coefficients must be 1D with ad hoc ordering, n being the total number of spectral coefficients).

data may be 4D (3D if spectral) even if the field is not, as long as the above dimensions ordering is respected.

sp2gp()

Transforms the spectral field into gridpoint, according to its spectral geometry. Replaces data in place.

The spectral transform subroutine is actually included in the spectral geometry’s sp2gp() method.

spectral_geometry

For a spectral field, its spectral geometry handles spectral transforms and dimensions (see the documentation above for more details).

structure

Type of Field geometry (see the documentation above for more details).

units

(see the documentation above for more details).

validity

Validity of the field (see the documentation above for more details).

---

class epygram.fields.D3Field.D3VirtualField(*args, **kwargs)

Bases: epygram.fields.D3Field._D3CommonField

3-Dimensions Virtual field class.

Data is taken from other fields, either: - a given fieldset - a resource in which are stored fields defined by resource_fids.

Note

This class is managed by footprint.

• info: Not documented

• priority: PriorityLevel::DEFAULT (rank=1)

Automatic parameters from the footprint:

• comment (builtins.str) - rwd - Not documented, sorry.

  • Optional. Default is None.

• fid (footprints.stdtypes.FPDict) - rwx - Not documented, sorry.

• fieldset (epygram.base.FieldSet) - rxx - Set of real fields that can compose the Virtual Field.

  • Optional. Default is FieldSet::<<as_list:: []>>.

• misc_metadata (footprints.stdtypes.FPDict) - rwd - Not documented, sorry.

  • Optional. Default is FPDict::<<as_dict:: dict()>>.

• resource (epygram.base.Resource) - rxx - Resource in which is stored the fields defined by resource_fids.

  • Optional. Default is None.

• resource_fids (footprints.stdtypes.FPList) - rxx - Definition of the fields in resource that compose the virtual field.

  • Optional. Default is FPList::<<as_list:: []>>.

• structure (builtins.str) - rxx - Type of Field geometry.

  • Values: set([‘3D’])

• units (builtins.str) - rwd - Not documented, sorry.

  • Optional. Default is ‘’.

Constructor. See its footprint for arguments.

comment

(see the documentation above for more details).

property data

Returns the field data, with 3D shape if the field is not spectral, 2D if spectral.

Parameters

• subzone – optional, among (‘C’, ‘CI’), for LAM fields only, returns the data resp. on the C or C+I zone. Default is no subzone, i.e. the whole field.

• d4 –

  • if True, returned values are shaped in a 4 dimensions array

  • if False, shape of returned values is determined with respect to geometry

Shape of 3D data:

• Rectangular grids:

  grid[k,0,0] is SW, grid[k,-1,-1] is NE

  grid[k,0,-1] is SE, grid[k,-1,0] is NW

  with k the level

• Gauss grids:

  grid[k,0,:Nj] is first (Northern) band of latitude, masked after Nj = number of longitudes for latitude j

  grid[k,-1,:Nj] is last (Southern) band of latitude (idem).

  with k the level

deldata()

deldata() not implemented on virtual fields.

fid

(see the documentation above for more details).

fieldset

Set of real fields that can compose the Virtual Field (see the documentation above for more details).

getdata(subzone=None, d4=False)

Returns the field data, with 3D shape if the field is not spectral, 2D if spectral.

Parameters

• subzone – optional, among (‘C’, ‘CI’), for LAM fields only, returns the data resp. on the C or C+I zone. Default is no subzone, i.e. the whole field.

• d4 –

  • if True, returned values are shaped in a 4 dimensions array

  • if False, shape of returned values is determined with respect to geometry

Shape of 3D data:

• Rectangular grids:

  grid[k,0,0] is SW, grid[k,-1,-1] is NE

  grid[k,0,-1] is SE, grid[k,-1,0] is NW

  with k the level

• Gauss grids:

  grid[k,0,:Nj] is first (Northern) band of latitude, masked after Nj = number of longitudes for latitude j

  grid[k,-1,:Nj] is last (Southern) band of latitude (idem).

  with k the level

getlevel(level=None, k=None)

Returns a level of the field as a new field.

Parameters

• level – the requested level expressed in coordinate value (Pa, m…)

• k – the index of the requested level

getvalue_ij(i=None, j=None, k=None, t=None, one=True)

Returns the value of the field on point of indices (i, j, k, t). Take care (i, j, k, t) is python-indexing, ranging from 0 to dimension-1.

Parameters

• i – the index in X direction

• j – the index in Y direction

• k – the index of the level (not a value in Pa or m…)

• t – the index of the temporal dimension (not a validity object)

k and t can be scalar even if i and j are arrays.

If one is False, returns [value] instead of value.

gp2sp(spectral_geometry)

Transforms the gridpoint field into spectral space, according to the spectral_geometry mandatorily passed as argument. Replaces data in place.

Parameters

spectral_geometry – instance of SpectralGeometry, actually containing spectral transform subroutine (in in its own gp2sp() method).

misc_metadata

(see the documentation above for more details).

resource

Resource in which is stored the fields defined by resource_fids (see the documentation above for more details).

resource_fids

Definition of the fields in resource that compose the virtual field (see the documentation above for more details).

setdata(data)

setdata() not implemented on virtual fields.

sp2gp()

Transforms the spectral field into gridpoint, according to its spectral geometry. Replaces data in place.

The spectral transform subroutine is actually included in the spectral geometry’s sp2gp() method.

structure

Type of Field geometry (see the documentation above for more details).

units

(see the documentation above for more details).