#!/usr/bin/env python
"""
Utility functions for ncvue.
The utility functions do not depend on the ncvue class.
Functions depending on the class are in ncvmethods.
This module was written by Matthias Cuntz while at Institut National de
Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), Nancy,
France.
:copyright: Copyright 2020- Matthias Cuntz - mc (at) macu (dot) de
:license: MIT License, see LICENSE for details.
.. moduleauthor:: Matthias Cuntz
The following functions are provided:
.. autosummary::
DIMMETHODS
add_cyclic
clone_ncvmain
format_coord_contour
format_coord_map
format_coord_scatter
get_slice
get_standard_name
get_units
list_intersection
parse_entry
selvar
set_axis_label
set_miss
spinbox_values
vardim2var
xzip_dim_name_length
zip_dim_name_length
History
* Written Nov-Dec 2020 by Matthias Cuntz (mc (at) macu (dot) de)
* General get_slice function from individual methods for x, y, y2, z,
Dec 2020, Matthias Cuntz
* Added arithmetics to apply on axis/dimensions such as mean, std, etc.,
Dec 2020, Matthias Cuntz
* Added clone_ncvmain, removing its own module, Dec 2020, Matthias Cuntz
* Added SEPCHAR and DIMMETHODS, Jan 2021, Matthias Cuntz
* Pass only ncvMain widget to clone_ncvmain, Jan 2021, Matthias Cuntz
* Pass only root widget to clone_ncvmain, Jan 2021, Matthias Cuntz
* Set correct missing value for date variable in numpy's datetime64[ms]
format May 2021, Matthias Cuntz
* Added format_coord functions for scatter, contour, and map,
May 2021, Matthias Cuntz
* Replaced add_cyclic_point with add_cyclic as submitted to cartopy,
Jun 2021, Matthias Cuntz
* Removed SEPCHAR, Jun 2021, Matthias Cuntz
* Final add_cyclic, has_cyclic from cartopy v0.20.1,
Nov 2021, Matthias Cuntz
* Address fi.variables[name] directly by fi[name], Jan 2024, Matthias Cuntz
* Add selvar to allow multiple netcdf files, Jan 2024, Matthias Cuntz
* Remove [ms] from check for datetime in format_coord on axes,
Oct 2024, Matthias Cuntz
* Use CustomTkinter in clone_ncvmain if installed, Nov 2024, Matthias Cuntz
* Increased digits in format_coord_scatter, Jan 2025, Matthias Cuntz
* Increased digits in format_coord_contour and format_coord_map,
Jan 2025, Matthias Cuntz
* Use f-string in zip_dim_name_length, Feb 2025, Matthias Cuntz
* Added xzip_dim_name_length, get_standard_name, get_units,
Feb 2025, Matthias Cuntz
* Allow xarray in selvar, Feb 2025, Matthias Cuntz
* Add parse_entry from dfvutils, Jun 2025, Matthias Cuntz
* Use ncvScreen for window sizes, Nov 2025, Matthias Cuntz
"""
import tkinter as tk
try:
from customtkinter import CTkToplevel as Toplevel
except ModuleNotFoundError:
from tkinter import Toplevel
from math import isfinite
import numpy as np
import matplotlib.dates as mpld
import cartopy.crs as ccrs
try:
import xarray as xr
ihavex = True
except ModuleNotFoundError:
ihavex = False
from .ncvscreen import ncvScreen
import ncvue
__all__ = ['DIMMETHODS',
'add_cyclic', 'has_cyclic', 'clone_ncvmain',
'format_coord_contour', 'format_coord_map', 'format_coord_scatter',
'get_slice', 'get_standard_name', 'get_units',
'list_intersection', 'parse_entry',
'selvar', 'set_axis_label', 'set_miss',
'spinbox_values', 'vardim2var',
'xzip_dim_name_length', 'zip_dim_name_length']
DIMMETHODS = ('mean', 'std', 'min', 'max', 'ptp', 'sum', 'median', 'var')
"""
Arithmetic methods implemented on dimensions.
mean - average
std - standard deviation
min - minimum
max - maximum
ptp - point-to-point amplitude = max - min
sum - sum
median - 50-percentile
var - variance
"""
def _add_cyclic_data(data, axis=-1):
"""
Add a cyclic point to a data array.
Parameters
----------
data : ndarray
An n-dimensional array of data to add a cyclic point to.
axis : int, optional
Specifies the axis of the data array to add the cyclic point to.
Defaults to the right-most axis.
Returns
-------
The data array with a cyclic point added.
"""
slicer = [slice(None)] * data.ndim
try:
slicer[axis] = slice(0, 1)
except IndexError:
raise ValueError(
'The specified axis does not correspond to an array dimension.')
npc = np.ma if np.ma.is_masked(data) else np
return npc.concatenate((data, data[tuple(slicer)]), axis=axis)
def _add_cyclic_x(x, axis=-1, cyclic=360):
"""
Add a cyclic point to a x/longitude coordinate array.
Parameters
----------
x : ndarray
An array which specifies the x-coordinate values for
the dimension the cyclic point is to be added to.
axis : int, optional
Specifies the axis of the x-coordinate array to add the cyclic point
to. Defaults to the right-most axis.
cyclic : float, optional
Width of periodic domain (default: 360)
Returns
-------
The coordinate array ``x`` with a cyclic point added.
"""
npc = np.ma if np.ma.is_masked(x) else np
# get cyclic x-coordinates
# cx is the code from basemap (addcyclic)
# https://github.com/matplotlib/basemap/blob/master/lib/mpl_toolkits/basemap/__init__.py
cx = (np.take(x, [0], axis=axis) +
cyclic * np.sign(np.diff(np.take(x, [0, -1], axis=axis),
axis=axis)))
# basemap ensures that the values do not exceed cyclic
# (next code line). We do not do this to deal with rotated grids that
# might have values not exactly 0.
# cx = npc.where(cx <= cyclic, cx, np.mod(cx, cyclic))
return npc.concatenate((x, cx), axis=axis)
[docs]
def has_cyclic(x, axis=-1, cyclic=360, precision=1e-4):
"""
Check if x/longitude coordinates already have a cyclic point.
Checks all differences between the first and last
x-coordinates along ``axis`` to be less than ``precision``.
Parameters
----------
x : ndarray
An array with the x-coordinate values to be checked for cyclic points.
axis : int, optional
Specifies the axis of the ``x`` array to be checked.
Defaults to the right-most axis.
cyclic : float, optional
Width of periodic domain (default: 360).
precision : float, optional
Maximal difference between first and last x-coordinate to detect
cyclic point (default: 1e-4).
Returns
-------
True if a cyclic point was detected along the given axis,
False otherwise.
Examples
--------
Check for cyclic x-coordinate in one dimension.
>>> import numpy as np
>>> lons = np.arange(0, 360, 60)
>>> clons = np.arange(0, 361, 60)
>>> print(has_cyclic(lons))
False
>>> print(has_cyclic(clons))
True
Check for cyclic x-coordinate in two dimensions.
>>> lats = np.arange(-90, 90, 30)
>>> lon2d, lat2d = np.meshgrid(lons, lats)
>>> clon2d, clat2d = np.meshgrid(clons, lats)
>>> print(has_cyclic(lon2d))
False
>>> print(has_cyclic(clon2d))
True
"""
npc = np.ma if np.ma.is_masked(x) else np
# transform to 0-cyclic, assuming e.g. -180 to 180 if any < 0
x1 = np.mod(npc.where(x < 0, x + cyclic, x), cyclic)
dd = np.diff(np.take(x1, [0, -1], axis=axis), axis=axis)
if npc.all(np.abs(dd) < precision):
return True
else:
return False
[docs]
def add_cyclic(data, x=None, y=None, axis=-1,
cyclic=360, precision=1e-4):
"""
Add a cyclic point to an array and optionally corresponding
x/longitude and y/latitude coordinates.
Checks all differences between the first and last
x-coordinates along ``axis`` to be less than ``precision``.
Parameters
----------
data : ndarray
An n-dimensional array of data to add a cyclic point to.
x : ndarray, optional
An n-dimensional array which specifies the x-coordinate values
for the dimension the cyclic point is to be added to, i.e. normally the
longitudes. Defaults to None.
If ``x`` is given then *add_cyclic* checks if a cyclic point is
already present by checking all differences between the first and last
coordinates to be less than ``precision``.
No point is added if a cyclic point was detected.
If ``x`` is 1- or 2-dimensional, ``x.shape[-1]`` must equal
``data.shape[axis]``, otherwise ``x.shape[axis]`` must equal
``data.shape[axis]``.
y : ndarray, optional
An n-dimensional array with the values of the y-coordinate, i.e.
normally the latitudes.
The cyclic point simply copies the last column. Defaults to None.
No cyclic point is added if ``y`` is 1-dimensional.
If ``y`` is 2-dimensional, ``y.shape[-1]`` must equal
``data.shape[axis]``, otherwise ``y.shape[axis]`` must equal
``data.shape[axis]``.
axis : int, optional
Specifies the axis of the arrays to add the cyclic point to,
i.e. axis with changing x-coordinates. Defaults to the right-most axis.
cyclic : int or float, optional
Width of periodic domain (default: 360).
precision : float, optional
Maximal difference between first and last x-coordinate to detect
cyclic point (default: 1e-4).
Returns
-------
cyclic_data
The data array with a cyclic point added.
cyclic_x
The x-coordinate with a cyclic point, only returned if the ``x``
keyword was supplied.
cyclic_y
The y-coordinate with the last column of the cyclic axis duplicated,
only returned if ``x`` was 2- or n-dimensional and the ``y``
keyword was supplied.
Examples
--------
Adding a cyclic point to a data array, where the cyclic dimension is
the right-most dimension.
>>> import numpy as np
>>> data = np.ones([5, 6]) * np.arange(6)
>>> cyclic_data = add_cyclic(data)
>>> print(cyclic_data) # doctest: +NORMALIZE_WHITESPACE
[[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]]
Adding a cyclic point to a data array and an associated x-coordinate.
>>> lons = np.arange(0, 360, 60)
>>> cyclic_data, cyclic_lons = add_cyclic(data, x=lons)
>>> print(cyclic_data) # doctest: +NORMALIZE_WHITESPACE
[[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]]
>>> print(cyclic_lons)
[ 0 60 120 180 240 300 360]
Adding a cyclic point to a data array and an associated 2-dimensional
x-coordinate.
>>> lons = np.arange(0, 360, 60)
>>> lats = np.arange(-90, 90, 180/5)
>>> lon2d, lat2d = np.meshgrid(lons, lats)
>>> cyclic_data, cyclic_lon2d = add_cyclic(data, x=lon2d)
>>> print(cyclic_data) # doctest: +NORMALIZE_WHITESPACE
[[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]]
>>> print(cyclic_lon2d)
[[ 0 60 120 180 240 300 360]
[ 0 60 120 180 240 300 360]
[ 0 60 120 180 240 300 360]
[ 0 60 120 180 240 300 360]
[ 0 60 120 180 240 300 360]]
Adding a cyclic point to a data array and the associated 2-dimensional
x- and y-coordinates.
>>> lons = np.arange(0, 360, 60)
>>> lats = np.arange(-90, 90, 180/5)
>>> lon2d, lat2d = np.meshgrid(lons, lats)
>>> cyclic_data, cyclic_lon2d, cyclic_lat2d = add_cyclic(
... data, x=lon2d, y=lat2d)
>>> print(cyclic_data) # doctest: +NORMALIZE_WHITESPACE
[[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]
[0. 1. 2. 3. 4. 5. 0.]]
>>> print(cyclic_lon2d)
[[ 0 60 120 180 240 300 360]
[ 0 60 120 180 240 300 360]
[ 0 60 120 180 240 300 360]
[ 0 60 120 180 240 300 360]
[ 0 60 120 180 240 300 360]]
>>> print(cyclic_lat2d)
[[-90. -90. -90. -90. -90. -90. -90.]
[-54. -54. -54. -54. -54. -54. -54.]
[-18. -18. -18. -18. -18. -18. -18.]
[ 18. 18. 18. 18. 18. 18. 18.]
[ 54. 54. 54. 54. 54. 54. 54.]]
Not adding a cyclic point if cyclic point detected in x.
>>> lons = np.arange(0, 361, 72)
>>> lats = np.arange(-90, 90, 180/5)
>>> lon2d, lat2d = np.meshgrid(lons, lats)
>>> cyclic_data, cyclic_lon2d, cyclic_lat2d = add_cyclic(
... data, x=lon2d, y=lat2d)
>>> print(cyclic_data) # doctest: +NORMALIZE_WHITESPACE
[[0. 1. 2. 3. 4. 5.]
[0. 1. 2. 3. 4. 5.]
[0. 1. 2. 3. 4. 5.]
[0. 1. 2. 3. 4. 5.]
[0. 1. 2. 3. 4. 5.]]
>>> print(cyclic_lon2d)
[[ 0 72 144 216 288 360]
[ 0 72 144 216 288 360]
[ 0 72 144 216 288 360]
[ 0 72 144 216 288 360]
[ 0 72 144 216 288 360]]
>>> print(cyclic_lat2d)
[[-90. -90. -90. -90. -90. -90.]
[-54. -54. -54. -54. -54. -54.]
[-18. -18. -18. -18. -18. -18.]
[ 18. 18. 18. 18. 18. 18.]
[ 54. 54. 54. 54. 54. 54.]]
"""
if x is None:
return _add_cyclic_data(data, axis=axis)
# if x was given
if x.ndim > 2:
xaxis = axis
else:
xaxis = -1
if x.shape[xaxis] != data.shape[axis]:
estr = (f'x.shape[{xaxis}] does not match the size of the'
f' corresponding dimension of the data array:'
f' x.shape[{xaxis}] = {x.shape[xaxis]},'
f' data.shape[{axis}] = {data.shape[axis]}.')
raise ValueError(estr)
if has_cyclic(x, axis=xaxis, cyclic=cyclic, precision=precision):
if y is None:
return data, x
# if y was given
return data, x, y
# if not has_cyclic, add cyclic points to data and x
out_data = _add_cyclic_data(data, axis=axis)
out_x = _add_cyclic_x(x, axis=xaxis, cyclic=cyclic)
if y is None:
return out_data, out_x
# if y was given
if y.ndim == 1:
return out_data, out_x, y
if y.ndim > 2:
yaxis = axis
else:
yaxis = -1
if y.shape[yaxis] != data.shape[axis]:
estr = (f'y.shape[{yaxis}] does not match the size of the'
f' corresponding dimension of the data array:'
f' y.shape[{yaxis}] = {y.shape[yaxis]},'
f' data.shape[{axis}] = {data.shape[axis]}.')
raise ValueError(estr)
out_y = _add_cyclic_data(y, axis=yaxis)
return out_data, out_x, out_y
[docs]
def clone_ncvmain(widget):
"""
Duplicate the main ncvue window.
Parameters
----------
widget : ncvue.ncvMain
widget of ncvMain class.
Returns
-------
Another ncvue window will be created.
Examples
--------
>>> self.newwin = ttk.Button(
... self.rowwin, text="New Window",
... command=partial(clone_ncvmain, self.master))
"""
# parent = widget.nametowidget(widget.winfo_parent())
if widget.name != 'ncvMain':
print('clone_ncvmain failed. Widget should be ncvMain.')
print('widget.name is: ', widget.name)
import sys
sys.exit()
root = Toplevel()
root.name = 'ncvClone'
root.title("Secondary ncvue window")
# root.geometry('1000x800+150+100')
root.top = widget.top
screen = ncvScreen(root.top)
root.geometry(screen.secondwin)
# https://stackoverflow.com/questions/46505982/is-there-a-way-to-clone-a-tkinter-widget
cls = widget.__class__
clone = cls(root)
try:
for key in widget.configure():
if key != 'class':
clone.configure({key: widget.cget(key)})
except TypeError:
# in case of CustomTkinter
cls = ncvue.ncvMain
clone = cls(root)
clone.pack(fill=tk.BOTH, expand=1)
return clone
[docs]
def get_slice(dimspins, y):
"""
Get slice of variable `y` inquiring the spinboxes `dimspins`.
Parameters
----------
dimspins : list
List of tk.Spinbox widgets of dimensions
y : ndarray or netCDF4._netCDF4.Variable
Input array or netcdf variable
Returns
-------
ndarray
Slice of `y` chosen with spinboxes.
Examples
--------
>>> gy, vy = vardim2var(y, self.groups)
>>> yy = selvar(self, vy)
>>> miss = get_miss(self, yy)
>>> yy = get_slice(self.yd, yy).squeeze()
>>> yy = set_miss(miss, yy)
"""
isxarray = False
if ihavex:
if isinstance(y, xr.DataArray):
isxarray = True
methods = ['all']
methods.extend(DIMMETHODS)
dd = []
ss = []
for i in range(y.ndim):
dim = dimspins[i].get()
if dim in methods:
s = slice(0, y.shape[i])
else:
idim = int(dim)
s = slice(idim, idim + 1)
dd.append(dim)
ss.append(s)
if len(ss) > 0:
imeth = list_intersection(dd, DIMMETHODS)
if len(imeth) > 0:
if isxarray:
sargs = dict(zip(y.dims, ss))
yout = y.isel(**sargs).to_numpy()
else:
yout = y[tuple(ss)]
ii = [ i for i, d in enumerate(dd) if d in imeth ]
ii.reverse() # last axis first
for i in ii:
if dd[i] == 'mean':
yout = np.ma.mean(yout, axis=i)
elif dd[i] == 'std':
yout = np.ma.std(yout, axis=i)
elif dd[i] == 'min':
yout = np.ma.min(yout, axis=i)
elif dd[i] == 'max':
yout = np.ma.max(yout, axis=i)
elif dd[i] == 'ptp':
yout = np.ma.ptp(yout, axis=i)
elif dd[i] == 'sum':
yout = np.ma.sum(yout, axis=i)
elif dd[i] == 'median':
yout = np.ma.median(yout, axis=i)
elif dd[i] == 'var':
yout = np.ma.var(yout, axis=i)
return yout
else:
if isxarray:
sargs = dict(zip(y.dims, ss))
yout = y.isel(**sargs).to_numpy()
else:
yout = y[tuple(ss)]
return yout
else:
return np.array([], dtype=y.dtype)
[docs]
def get_standard_name(ivar):
"""
Get standard_name, long_name, or name attribute of netcdf variable
Parameters
----------
ivar : netCDF4._netCDF4.Variable
netcdf variable
Returns
-------
string
standard_name, long_name, or name of variable `ivar`
Examples
--------
>>> name = get_standard_name(self.fi['Tair])
"""
if hasattr(ivar, 'standard_name'):
return ivar.standard_name
elif hasattr(ivar, 'long_name'):
return ivar.long_name
else:
return ivar.name
[docs]
def get_units(ivar):
"""
Get units attribute of netcdf variable
Parameters
----------
ivar : netCDF4._netCDF4.Variable
netcdf variable
Returns
-------
string
units or empty string
Examples
--------
>>> units = get_units(self.fi['Tair])
"""
if hasattr(ivar, 'units'):
return ivar.units
else:
return ''
[docs]
def list_intersection(lst1, lst2):
"""
Intersection of two lists.
From:
https://stackoverflow.com/questions/3697432/how-to-find-list-intersection
Using list comprehension for small lists and set() method with builtin
intersection for longer lists.
Parameters
----------
lst1, lst2 : list
Python lists
Returns
-------
list
List with common elements in both input lists.
Examples
--------
>>> lst1 = [ 4, 9, 1, 17, 11, 26, 28, 28, 26, 66, 91]
>>> lst2 = [9, 9, 74, 21, 45, 11, 63]
>>> print(Intersection(lst1, lst2))
[9, 11]
"""
if (len(lst1) > 10) or (len(lst2) > 10):
return list(set(lst1).intersection(lst2))
else:
return [ ll for ll in lst1 if ll in lst2 ]
[docs]
def parse_entry(text):
"""
Convert text string to correct data type
Parse an entry field to None, bool, int, float, list, dict
Parameters
----------
text : str
String from entry field
Returns
-------
None, bool, int, float, list, dict
Examples
--------
>>> parse_entry('7')
7
>>> parse_entry('7,3')
[7, 3]
"""
if text == 'None':
# None
tt = None
elif text == 'True':
# bool True
tt = True
elif text == 'False':
# bool False
tt = False
elif ':' in text:
# dict or str
try:
tt = eval(f'{{{text}}}')
except SyntaxError:
tt = text
elif ',' in text:
# list or str
try:
tt = eval(f'[{text}]')
except SyntaxError:
tt = text
else:
try:
# int
tt = int(text)
except ValueError:
try:
# float
tt = float(text)
except ValueError:
# str
tt = text
try:
if not isfinite(tt):
# keep NaN and Inf string
tt = text
except TypeError:
pass
return tt
[docs]
def selvar(self, var):
"""
Extract variable from correct file.
Parameters
----------
self : class
ncvue Tk class having fi and groups
var : string
Variable name including group or file identifier,
such as 'Qle', 'group1/Qle', or 'file0001/Qle'
Returns
-------
netcdf variable
Pointer to variable in file
Examples
--------
selvar(self, var)
"""
if len(self.fi) == 0:
return
elif self.usex:
return self.fi[var]
elif len(self.fi) == 1:
fil = self.fi[0]
return fil[var]
else:
if '/' in var:
f = var[0:var.find('/')].rstrip()
idxf = list(self.groups).index(f)
vv = var[var.find('/') + 1:].rstrip()
fil = self.fi[idxf]
return fil[vv]
else:
fil = self.fi[0]
return fil[var]
[docs]
def set_axis_label(ncvar):
"""
Set label plotting axis from name and unit of given variable
`ncvar`.
Parameters
----------
ncvar : netCDF4._netCDF4.Variable
netcdf variables class
Returns
-------
str
Label string: name (unit)
Examples
--------
>>> ylab = set_axis_label(fi['w_soil'])
"""
try:
lab = ncvar.long_name
except AttributeError:
try:
lab = ncvar.standard_name
except AttributeError:
lab = ncvar.name
try:
lab += ' (' + ncvar.units + ')'
except AttributeError:
pass
return lab
[docs]
def set_miss(miss, x):
"""
Set `x` to NaN or NaT for all values in miss.
Parameters
----------
miss : iterable
values which shall be set to np.nan or np.datetime64('NaT') in `x`
x : ndarray
numpy array
Returns
-------
ndarray
`x` with all values set np.nan or np.datetime64('NaT')
that are equal to any value in miss.
Examples
--------
>>> x = fi['time']
>>> miss = get_miss(self, x)
>>> x = set_miss(miss, x)
"""
if np.issubdtype(x.dtype, np.datetime64):
default = np.datetime64('NaT')
else:
default = np.nan
for mm in miss:
x = np.where(x == mm, default, x)
return x
[docs]
def spinbox_values(ndim):
"""
Tuple for Spinbox values with 'all' before range(`ndim`) and
'mean', 'std', etc. after range(`ndim`) if `ndim`>1,
otherwise single entry (0,).
Parameters
----------
ndim : int
Size of dimension.
Returns
-------
tuple
(('all',) + tuple(range(ndim)) + ('mean','std',...)) if ndim > 1
(0,) else
Examples
--------
>>> self.xd0.config(values=spinbox_values(xx.shape[0]))
"""
if ndim > 1:
return (('all',) + tuple(range(ndim)) + DIMMETHODS)
else:
return (0,)
[docs]
def vardim2var(vardim, groups=[]):
"""
Extract group index and variable name from 'group/variable (dim1=ndim1,)'.
Parameters
----------
vardim : string
Variable name with dimensions, such as 'latitude (lat=32,lon=64)'
or with group 'file1/latitude (lat=32,lon=64)'
Returns
-------
int, string
group index, variable name
Examples
--------
>>> vardim2var('latitude (lat=32,lon=64)')
0 latitude
"""
var = vardim[0:vardim.rfind('(')].rstrip()
if '/' in var:
g = var[0:var.find('/')].rstrip()
ig = list(groups).index(g)
else:
ig = 0
return ig, var
[docs]
def xzip_dim_name_length(ncvar):
"""
Combines dimension names and length of xarray variable in list of strings.
Parameters
----------
ncvar : xarray.DataArray
xarray variable
Returns
-------
list
List of dimension name and length in the form 'dim=len'.
Examples
--------
>>> import xarray as xr
>>> ifile = 'test.nc'
>>> fi = xr.open_dataset(ifile)
>>> w_soil = fi['w_soil']
>>> print(zip_dim_name_length(w_soil))
('ntime=17520', 'nsoil=30')
"""
out = []
for i in range(ncvar.ndim):
out.append(f'{ncvar.dims[i]}={ncvar.shape[i]}')
return out
[docs]
def zip_dim_name_length(ncvar):
"""
Combines dimension names and length of netcdf variable in list of strings.
Parameters
----------
ncvar : netCDF4._netCDF4.Variable
netcdf variables class
Returns
-------
list
List of dimension name and length in the form 'dim=len'.
Examples
--------
>>> import netCDF4 as nc
>>> ifile = 'test.nc'
>>> fi = nc.Dataset(ifile, 'r')
>>> w_soil = fi['w_soil']
>>> print(zip_dim_name_length(w_soil))
('ntime=17520', 'nsoil=30')
"""
out = []
for i in range(ncvar.ndim):
out.append(f'{ncvar.dimensions[i]}={ncvar.shape[i]}')
return out