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Description:

ncvue 4.4.3

A minimal GUI for a quick view of netCDF files. Aiming to be a drop-in
replacement for ncview and panoply.

About ncvue
ncvue is a minimal GUI for a quick view of netCDF files. It is
aiming to be a drop-in replacement for ncview and panoply, being
slightly more general than ncview targeting maps but providing
animations, zooming and panning capabilities unlike panoply. If
ncvue is used with maps, it supports mostly structured grids, more
precisely the grids supported by cartopy.
ncvue is a Python script that can be called from within Python or
as a command line tool. It is not supposed to produce
publication-ready plots but rather provide a quick overview of the
netCDF file.
The complete documentation for ncvue is available from:

https://mcuntz.github.io/ncvue/

Quick usage guide
ncvue can be run from the command line:
ncvue netcdf_file.nc
ncvue netcdf_file1.nc netcdf_file2.nc
or from within Python:
from ncvue import ncvue
ncvue(['netcdf_file.nc'])
where the netCDF file is optional. The latter can also be left out and
a netCDF file can be opened with the “Open File” button from within
ncvue. The netCDF has to be given in a list because several netcdf
files can be given as in the second example from the command line.
Note, ncvue uses the TkAgg backend of matplotlib. It must be
called before any other call to matplotlib. This also means that you
cannot launch it from within iPython if it was launched with
–pylab. It can be called from within a standard iPython, though,
or using ipython –gui tk.

General layout
On opening, ncvue presents three panels for different plotting
types: Scatter or Line plots, Contour plots, and Maps. This is the
look in macOS light mode (higher resolution images can be found in the
documentation):

All three panes are organised in this fashion: the plotting canvas,
the Matplotlib navigation toolbar and the pane, where one can choose
the plotting variables and dimensions, as well as plotting
options. You can always choose another panel on top, and open another,
identical window for the same netCDF file with the button “New Window”
on the top right.

Map panel
If ncvue detects latitude and longitude variables with a size
greater than 1, it opens the Map panel by default. This is the Map
panel in macOS dark mode, describing all buttons, sliders, entry
boxes, spinboxes, and menus:

If it happens that the detection of latitudes and longitudes did not
work automatically, you can choose the correct variables manually. Or
you might use the empty entries on top of the dropdown menus of the
latitudes and longitudes, which uses the index and one can hence
display the matrix within the netCDF file. You might want to switch of
the coastlines in this case.
You might want to switch off the automatically detected “global”
option sometimes if your data is on a rotated grid or excludes some
regions such as below minus -60 °S.
All dimensions can be set from 0 to the size of the dimension-1, to
“all”, or to any of the arithmetic operators “mean”, “std” (standard
deviation), “min”, “max”, “ptp” (point-to-point amplitude,
i.e. max-min), “sum”, “median”, “var” (variance).
Be aware that the underlying cartopy/matplotlib may (or may not) need
a long time to plot the data (with the pseudocolor ‘mesh’ option) if
you change the central longitude of the projection from the central
longitude of your data, which is automatically detected if “central
lon” is set to None. Setting “central lon” to the central longitude of
the input data normally eliminates the problem.

Scatter/Line panel
If ncvue does not detect latitude and longitude variables with a
size greater than 1, it opens the Scatter/Line panel by default. This
is the Scatter/Line panel in macOS dark mode, describing all buttons,
sliders, entry boxes, spinboxes, and menus:

The default plot is a line plot with solid lines (line style ‘ls’ is
‘-‘). One can set line style ‘ls’ to None and set a marker symbol,
e.g. ‘o’ for circles, to get a scatter plot. A large variety of line
styles, marker symbols and color notations are supported.
ncvue builds automatically a datetime variable from the time
axis. This is correctly interpreted by the underlying Matplotlib also
when zooming into or panning the axes. But it is also much slower than
using the index. Selecting the empty entry on top of the dropdown menu
for x uses the index for the x-axis and is very fast. Plotting a
line plot with 52608 time points takes about 2.2 s on my Macbook Pro
using the datetime variable and about 0.3 s using the index
(i.e. empty x-variable). This is especially true if one plots multiple
lines with ‘all’ entries from a specific dimension. Plotting all 10
depths of soil water content for the 52608 time points, as in the
example below, takes also about 0.3 s if using the index as x-variable
but more than 11.1 s when using the datetime variable.

Contour panel
The last panel provide by ncvue draws contour plots. This is the
Contour panel in macOS dark mode, describing all buttons, sliders,
entry boxes, spinboxes, and menus:

This produces also either pseudocolor plots (‘mesh’ ticked) or filled
contour plots (‘mesh’ unticked) just as the Map panel but without any
map projection.

Installation
ncvue is an application written in Python. If you have Python
installed, then the best is to install ncvue within the Python
universe. The easiest way to install ncvue is thence via pip if
you have cartopy installed already:
pip install ncvue
Cartopy can, however, be more elaborate to install. The easiest way
to install Cartopy or directly ncvue is by using Conda. After
installing, for example, Miniconda:
conda install -c conda-forge ncvue
See the installation instructions in the documentation for more
information on installing Cartopy and ncvue with pip.

License
ncvue is distributed under the MIT License. See the LICENSE file
for details.
Copyright (c) 2020-2024 Matthias Cuntz
ncvue uses the Azure 2.0 theme by rdbende on Linux and Windows.
The project structure of ncvue was very originally based on a
template provided by Sebastian Müller but has evolved
considerably.
Different netCDF test files were provided by Juliane Mai.