CMAQ¶

CMAQ uses a reader that inherits many methods from PseudoNetCDFFile, but relies on the ioapi_base class to update meta-data

class PseudoNetCDF.cmaqfiles.ioapi(*args, **kwds)[source]¶

apply(*args, **kwds)¶

Wrapper PseudoNetCDFFile.applyAlongDimensions that corrects ROW, COL, LAY and TIME meta-data according to the ioapi format

Parameters: PseudoNetCDFFile.applyAlongDimensions (see) –

applyAlongDimensions(*args, **kwds)¶

Wrapper PseudoNetCDFFile.applyAlongDimensions that corrects ROW, COL, LAY and TIME meta-data according to the ioapi format

Parameters: PseudoNetCDFFile.applyAlongDimensions (see) –

close()¶: Does nothing. Implemented for continuity with Scientific.IO.NetCDF

copy(props=True, dimensions=True, variables=True, data=True)¶

Function for making copies of the same type

Parameters

props (boolean) – include properties (default: True)
dimensions (boolean) – include dimensions (default: True)
variables (boolean) – include variable structures (default: True)
data (boolean) – include variable data (default: True)

Returns

outf

Return type

PseudoNetCDFFile instance

copyVariable(var, key=None, dtype=None, dimensions=None, fill_value=None, withdata=True)¶: Wrapper on PseudoNetCDF.copyVariable that updates VAR-LIST, NVARS, VAR, and TFLAG

See also

see()

createCompoundType()¶

`createCompoundType(self, datatype, datatype_name)`

Creates a new compound data type named datatype_name from the numpy dtype object datatype.

*Note*: If the new compound data type contains other compound data types (i.e. it is a ‘nested’ compound type, where not all of the elements are homogeneous numeric data types), then the ‘inner’ compound types must be created first.

The return value is the netCDF4.CompoundType class instance describing the new datatype.

createDimension(*args, **kwds)[source]¶

Create a dimension

Parameters

name (string) – name for dimension
length (integer) – maximum length of dimension

Returns

dim – new dimension

Return type

PseudoNetCDFDimensions

createEnumType()¶

`createEnumType(self, datatype, datatype_name, enum_dict)`

Creates a new Enum data type named datatype_name from a numpy integer dtype object datatype, and a python dictionary defining the enum fields and values.

The return value is the netCDF4.EnumType class instance describing the new datatype.

createGroup()¶

`createGroup(self, groupname)`

Creates a new netCDF4.Group with the given groupname.

If groupname is specified as a path, using forward slashes as in unix to separate components, then intermediate groups will be created as necessary (analogous to mkdir -p in unix). For example, createGroup(‘/GroupA/GroupB/GroupC’) will create GroupA, GroupA/GroupB, and GroupA/GroupB/GroupC, if they don’t already exist. If the specified path describes a group that already exists, no error is raised.

The return value is a netCDF4.Group class instance.

createVLType()¶

`createVLType(self, datatype, datatype_name)`

Creates a new VLEN data type named datatype_name from a numpy dtype object datatype.

The return value is the netCDF4.VLType class instance describing the new datatype.

createVariable(*args, **kwds)[source]¶: Wrapper on PseudoNetCDF.createVariable that updates VAR-LIST, NVARS, VAR, and TFLAG

See also

see()

date2num(time, timekey='time')¶

Parameters

time (array-like) – array of datetime.datetime objects
timekey (str) – time variable key which requires units and should have calendar. If calendar is missing, standard is the default. default ‘time’

Returns

num – time in relative time as defined by units of time variable (i.e., timekey) which defaults to ‘time’

Return type

array-like

eval(*args, **kwds)¶

Wrapper PseudoNetCDFFile.eval that corrects VAR-LIST and TFLAG meta-data according to the ioapi format

Parameters: PseudoNetCDFFile.eval (see) –

filepath()¶

`filepath(self,encoding=None)`

Get the file system path (or the opendap URL) which was used to open/create the Dataset. Requires netcdf >= 4.1.2. The path is decoded into a string using sys.getfilesystemencoding() by default, this can be changed using the encoding kwarg.

flush()¶: Does nothing. Implemented for continuity with Scientific.IO.NetCDF

classmethod from_ncf(infile)[source]¶

Parameters: infile (PseudoNetCDF-like file) –
Returns: outf
Return type: PseudoNetcdf-like file

classmethod from_ncvs(*invars, **invarkw)¶

Parameters

invars (list) – NetCDF-like variable must have standard_name, long_name or name
invarkw (kwds) – NetCDF-like variables

Returns

outf

Return type

PseudoNetcdf-like file

getCoords()¶: Return a list of coordkeys

getMap(maptype='basemap_auto', **kwds)¶

Wrapper PseudoNetCDFFile.getMap that uses NCOLS, XCELL NROWS, and YCELL to calculate map bounds if basemap_auto

Parameters: PseudoNetCDFFile.getMap (see) –

getTimes(datetype='datetime', bounds=False)¶

Get an array of datetime objects

Parameters

datetype (string or numpy.dtype) – ‘datetime’ or datetime64 dtype
bounds (boolean) – get time boundaries

Returns

out – datetime objects or array of numpy’s datetype type

Return type

array

Notes

self must have a time or TFLAG variable

getVarlist(update=True)¶

Returns: update – update files attributes to be consistent
Return type: boolean

Notes

If VAR-LIST does not exist, it is added assuming all variables with dimensions (‘TSTEP’, ‘LAY’, …) are variables

get_dest()¶

Returns the path where a new file is created on some action

If None, a file is created in memory. Else, a netcdf file is created on disk.

get_variables_by_attributes()¶

`get_variables_by_attribute(self, **kwargs)`

Returns a list of variables that match specific conditions.

Can pass in key=value parameters and variables are returned that contain all of the matches. For example,

:::python >>> # Get variables with x-axis attribute. >>> vs = nc.get_variables_by_attributes(axis=’X’) >>> # Get variables with matching “standard_name” attribute >>> vs = nc.get_variables_by_attributes(standard_name=’northward_sea_water_velocity’)

Can pass in key=callable parameter and variables are returned if the callable returns True. The callable should accept a single parameter, the attribute value. None is given as the attribute value when the attribute does not exist on the variable. For example,

:::python >>> # Get Axis variables >>> vs = nc.get_variables_by_attributes(axis=lambda v: v in [‘X’, ‘Y’, ‘Z’, ‘T’]) >>> # Get variables that don’t have an “axis” attribute >>> vs = nc.get_variables_by_attributes(axis=lambda v: v is None) >>> # Get variables that have a “grid_mapping” attribute >>> vs = nc.get_variables_by_attributes(grid_mapping=lambda v: v is not None)

get_varopt()¶

Get options

Parameters: None –
Returns: options
Return type: dictionary of optiosn

getncattr()¶

`getncattr(self,name)`

retrieve a netCDF dataset or group attribute. Use if you need to get a netCDF attribute with the same name as one of the reserved python attributes.

option kwarg encoding can be used to specify the character encoding of a string attribute (default is utf-8).

getncatts()¶

Return all ncattrs keys and values as a dictionary

Returns: attdict – key/value pairs of properties
Return type: dictionary

getproj(withgrid=False, projformat='pyproj')¶

Description

Parameters

withgrid (boolean) – use grid units instead of meters
projformat (string) – ‘pyproj’ (default), ‘proj4’ or ‘wkt’ allows function to return a pyproj projection object or a string in the format of proj4 or WKT

Returns

proj – (wkt, proj4) or pyprojProj (pyproj)

Return type

string pyproj.Proj

ij2ll(i, j)¶

Converts i, j to lon, lat (no false easting/northing) using cell centers assuming 0-based i/j

Parameters

i (scalar/iterable) – indicies (0-based) for the west-east dimension
j (scalar/iterable) – indicies (0-based) for the south-north dimension

Returns

lon, lat – longitudes and latitudes in decimal degrees

Return type

scalars or iterables

insertDimension(newonly=True, multionly=False, before=None, after=None, inplace=False, **newdims)¶

Insert dimensions with keys and lengths from newdims

Parameters

**newdims (dictionary) – where key is the new dimension and value is the length
newonly (boolean) – Only add dimension to variables that do not already have it, default True
multionly (boolean) – Only add dimension if there are already more than one (good for ignoring coordinate dimensions)
before (string) – if variable has this dimension, insert the new dimension before it. Otherwise, add to the beginning. (before takes precedence)
after (string) – if variable has this dimension, insert the new dimension after it. Otherwise, add to the beginning.
inplace (boolean) – create the new variable in this netcdf file (default False)

Returns

outf – instance will new dimension in dimensions and variables

Return type

PseudoNetCDFFile

Notes

Adding a non unity dimension will cause the data to be repeated along the new axis.
If order of addition matters, use multiple calls. newdimsuse will be a non-ordered dictionary

interpDimension(dimkey, newdimvals, coordkey=None, **interpkwds)¶

Parameters

dimkey (string) – the new dimension for interpolation
newdimvals (iterable) – the new values to interpolate to
coordkey (string) – the variable to use as the old coordinate values
interptype (string) –

‘linear’ or ‘conserve’. linear uses a linear interpolation
conserve uses a mass conserving interpolation
extrapolate (boolean) – allow extrapolation beyond bounds with linear, default False
fill_value (numeric value) – set fill value (e.g, nan) to prevent extrapolation or edge continuation

Returns

outf – instance with all variables interpolated

Return type

PseudoNetCDFFile

Notes

When extrapolate is false, the edge values are used for points beyond the inputs.

interpSigma(vglvls, vgtop=None, interptype='linear', extrapolate=False, fill_value='extrapolate', verbose=0)¶

Parameters

vglvls (iterable) – the new vglvls (edges)
vgtop (scalar) – Converting to new vgtop
interptype (string) – ‘linear’ uses a linear interpolation ‘conserve’ uses a mass conserving interpolation
extrapolate (boolean) – allow extrapolation beyond bounds with linear, default False
fill_value (boolean) – set fill value (e.g, nan) to prevent extrapolation or edge continuation

Returns

outf – PseudoNetCDFFile with all variables interpolated

Return type

ioapi_base

Notes

When extrapolate is false, the edge values are used for points beyond the inputs.

classmethod isMine(*args, **kwds)[source]¶: True if this file or object can be identified for use by this class. Useful to override for classes that can be initialized from disk.

isopen()¶

`close(self)`

is the Dataset open or closed?

ll2ij(lon, lat, bounds='ignore', clean='none')¶

Converts lon/lat to 0-based indicies (0,M), (0,N)

Parameters

lon (scalar or iterable) – longitudes in decimal degrees
lat (scalar or iterable) – latitudes in decimal degrees
bounds (string) – ignore, error, warn if i,j are out of domain
clean (string) – none - return values regardless of bounds; mask - mask values out of bounds; clip - return min(max(0, v), nx - 1)

Returns

i, j

Return type

indices (0-based) for variables

ll2xy(lon, lat)¶

Converts lon/lat to x distances (no false easting/northing)

Parameters

lon (scalar or iterable) – longitudes in decimal degrees
lat (scalar or iterable) – latitudes in decimal degrees

Returns

x, y – coordinates in map projection (meters or radians)

Return type

tuple of arrays

mask(*args, **kwds)¶: Wrapper on PseudoNetCDFFile.subsetVariables that updates VAR-LIST, NVARS, VAR, and TFLAG

See also

see()

ncattrs()¶

`ncattrs(self)`

return netCDF global attribute names for this netCDF4.Dataset or netCDF4.Group in a list.

plot(varkey, plottype='longitude-latitude', ax_kw=None, plot_kw=None, cbar_kw=None, map_kw=None, dimreduction='mean')¶

Parameters

varkey (string) – the variable to plot
plottype (string) – longitude-latitude, latitude-pressure, longitude-pressure, vertical-profile, time-longitude, time-latitude, time-pressure, default, longitude-latitude
ax_kw (dictionary) – keywords for the axes to be created
plot_kw (dictionary) – keywords for the plot (plot, scatter, or pcolormesh) to be created
cbar_kw (dictionary) – keywords for the colorbar
map_kw (dictionary) – keywords for the getMap routine, which is only used with plottype=’longitude-latitude’
dimreduction (string or function) – dimensions not being used in the plot are removed using applyAlongDimensions(dimkey=dimreduction) where each dimenions

removeSingleton(dimkey=None)¶

Return a netcdflike object with dimensions sliced

Parameters: dimkey (string) – key of dimension to be evaluated for removal; if None, evaluate all. only singleton dimensions will be removed.
Returns: outf – instance with dimensions removed
Return type: PseudoNetCDFFile

renameAttribute()¶

`renameAttribute(self, oldname, newname)`

rename a netCDF4.Dataset or netCDF4.Group attribute named oldname to newname.

renameDimension(oldkey, newkey, inplace=False)¶

Rename dimension (oldkey) in dimensions and in all variables

Parameters

oldkey (string) – dimension to be renamed
newkey (string) – new dame for dimension
inplace (boolean) – create the new variable in this netcdf file (default False)

Returns

outf – instance with renamed variable (this file if inplace = True)

Return type

PseudoNetCDFFile

renameDimensions(inplace=False, **newkeys)¶

Rename dimension (oldkey) in dimensions and in all variables

Parameters

**newkeys (dictionary) – where key is the oldkey and value is the newkey
inplace (boolean) – create the new variable in this netcdf file (default False)

Returns

outf – instance with renamed variable (this file if inplace = True)

Return type

PseudoNetCDFFile

renameGroup()¶

`renameGroup(self, oldname, newname)`

rename a netCDF4.Group named oldname to newname (requires netcdf >= 4.3.1).

renameVariable(oldkey, newkey, inplace=False, copyall=True)¶

Rename variable (oldkey)

Parameters

oldkey (string) – variable to be renamed
newkey (string) – new dame for variable
inplace (boolean) – create the new variable in this netcdf file (default False)
copyall (boolean) – if not inplace, should all variables be copied to new file

Returns

outf – instance with renamed variable (this file if inplace = True)

Return type

PseudoNetCDFFile

renameVariables(inplace=False, copyall=True, **newkeys)¶

Rename variables for each oldkey: newkey dictionary item

Parameters

**newkeys (dictionary) – where key is the oldkey and value is the newkey
inplace (boolean) – create the new variable in this netcdf file (default False)
copyall (boolean) – if not inplace, should all variables be copied to new file

Returns

outf – instance with renamed variable (this file if inplace = True)

Return type

PseudoNetCDFFile

reorderDimensions(oldorder, neworder, inplace=False)¶

Evaluate expr and return a PseudoNetCDFFile object with resutl

Parameters

oldorder (iterable of strings) – dimension names in existing order
neworder (iterable of strings) – dimension names in new order

Returns

outf – instance with dimensions reordered in variables

Return type

PseudoNetCDFFile

save(*args, **kwds)¶

Provides access to pncwrite for self

Parameters: Help pncwrite (see) –
Returns
Return type: see Help pncwrite

setCoords(keys, missing='ignore')¶

Set a variable as a coordinate variable

Parameters

keys (iterable of strings) – keys for coord variables
missing (string) – action if missing ‘ignore’, ‘skip’ or ‘error’ ignore - add in case used later skip - do not add error - raise an error

Returns

Return type

None

Notes

Coordinate variables are excluded from math

set_always_mask()¶

`set_always_mask(self, True_or_False)`

Call netCDF4.Variable.set_always_mask for all variables contained in this netCDF4.Dataset or netCDF4.Group, as well as for all variables in all its subgroups.

`True_or_False`: Boolean determining if automatic conversion of masked arrays with no missing values to regular numpy arrays shall be applied for all variables. Default True. Set to False to restore the default behaviour in versions prior to 1.4.1 (numpy array returned unless missing values are present, otherwise masked array returned).