lair.utils.plotter#

This module provides utility functions for plotting data.

Functions

`NCL_cmap`(table_name)	Generate matplotlib colormap from NCL color table.
`create_polar_ax`()	Create a polar axis with North at the top.
`diurnalPlot`(data, param[, stats, units, tz, ...])	Plot the diurnal cycle of data.
`format_radial_axis`(ax, x, scale_angle)	Format radial axis of polar plot.
`log10formatter`(x, pos[, deci])	Format ticks to log 10 format with deci number of decimals.
`polarFreq`(data[, x, wd, xbins, scale_angle])	Plot the contoured frequency of the data.
`polarPlot`(data[, param, x, wd, statistic, ...])	Plot polar contour of data.
`seasonalPlot`(data[, param, units, ax])	Plot the seasonal cycle of data by year.
`terrain_cmap`([minval, maxval, n])	Matplotlib terrain cmap.
`truncate_colormap`(cmap[, minval, maxval, n])	Truncate matplotlib colormaps using min and max vals from 0 to 1, and then linearly build a new colormap
`windvectorPlot`(data[, wd, ws, ax, unit_length])	Plot wind vectors.

Classes

HandlerDashedLines([marker_pad, numpoints])

Custom Handler for LineCollection instances.

lair.utils.plotter.log10formatter(x, pos, deci=0) → str[source]#

Format ticks to log 10 format with deci number of decimals.

Parameters:

xfloat: Tick value.
deciint, optional: Number of decimals to display. Defaults to 0.

Returns:

str: Formatted tick label.

Examples

>>> from functools import partial
>>> import numpy as np
>>> data: xr.DataArray  # some data, in this case, 3D (time, lat, lon)
>>> np.log10(data).plot(cbar_kwargs={'format': partial(log10formatter, deci=2)})

lair.utils.plotter.truncate_colormap(cmap: str | Colormap, minval: float = 0.0, maxval: float = 1.0, n: int = 100) → LinearSegmentedColormap[source]#

Truncate matplotlib colormaps using min and max vals from 0 to 1, and then linearly build a new colormap

Parameters:

cmapstr | matplotlib.colors.Colormap: Colormap to be truncated.
minvalfloat, optional: Minimum value to truncate the colormap. Defaults to 0.0.
maxvalfloat, optional: Maximum value to truncate the colormap. Defaults to 1.0.
nint, optional: Number of colors in the new colormap. Defaults to 100.

Returns:

matplotlib.colors.LinearSegmentedColormap: Truncated colormap.

lair.utils.plotter.NCL_cmap(table_name: str) → LinearSegmentedColormap[source]#

Generate matplotlib colormap from NCL color table.

Parameters:

table_namestr

Returns:

matplotlib.colors.LinearSegmentedColormap: matplotlib colormap from NCL color table.

lair.utils.plotter.terrain_cmap(minval: float = 0.42, maxval: float = 1.0, n: int = 256) → LinearSegmentedColormap[source]#

Matplotlib terrain cmap.

Parameters:

minvalfloat, optional: Minimum value to truncate the colormap. Defaults to 0.42.
maxvalfloat, optional: Maximum value to truncate the colormap. Defaults to 1.0.
nint, optional: Number of colors in the new colormap. Defaults to 256.

Returns:

matplotlib.colors.LinearSegmentedColormap: Matplotlib terrain colormap.

lair.utils.plotter.diurnalPlot(data: DataFrame, param: str, stats: str | list[str] = ['std', 'median', 'mean'], units: str | None = None, tz: str = 'UTC', freq: str = '1H', ax: Axes | None = None, colors: str | dict[str, str] = {'mean': 'black', 'median': 'blue', 'std': 'gray'}, min_count: int = 0) → Axes[source]#

Plot the diurnal cycle of data.

Parameters:

datapd.DataFrame: Data to plot.
paramstr: Parameter to plot.
statsstr | list[str], optional: Statistics to plot. Defaults to [‘std’, ‘median’, ‘mean’].
unitsstr | None, optional: Units of the parameter for the ylabel. Defaults to None.
tzstr, optional: Timezone of the data. Defaults to ‘UTC’.

Warning

DOES NOT CONVERT TIMEZONES.
freqstr, optional: Frequency of the data. Defaults to ‘1H’.
axplt.Axes | None, optional: Axis to plot on. Defaults to None.
colorsstr | dict[str, str], optional: Colors of the statistics. Defaults to {‘mean’: ‘black’, ‘median’: ‘blue’, ‘std’: ‘gray’}.
min_countint, optional: Minimum count to plot. Defaults to 0.

Returns:

plt.Axes: Axis with the plot

lair.utils.plotter.seasonalPlot(data: DataFrame, param: str = 'CH4', units: str = 'ppm', ax: Axes | None = None) → Axes[source]#

Plot the seasonal cycle of data by year.

Parameters:

datapd.DataFrame: Data to plot.
paramstr, optional: Parameter to plot. Defaults to ‘CH4’.
unitsstr, optional: Units of the parameter for the ylabel. Defaults to ‘ppm’.
axplt.Axes | None, optional: Axis to plot on. Defaults to None.

Returns:

plt.Axes: Axis with the plot

lair.utils.plotter.create_polar_ax() → Axes[source]#

Create a polar axis with North at the top.

Returns:

plt.Axes: Polar axis.

lair.utils.plotter.format_radial_axis(ax: Axes, x: str, scale_angle: float) → None[source]#

Format radial axis of polar plot.

Parameters:

axplt.Axes: Axis to format.
xstr: Label of the radial axis.
scale_anglefloat: Angle to position the label.

Returns:

None

lair.utils.plotter.polarPlot(data: DataFrame, param: str = 'CH4', x: str = 'ws', wd: str = 'wd', statistic: str = 'mean', units: str = 'ppm', min_bin: int = 1, xbins: int = 30, scale_angle: float | None = None) → Axes[source]#

Plot polar contour of data.

Parameters:

datapd.DataFrame: Data to plot.
paramstr, optional: Parameter to plot. Defaults to ‘CH4’.
xstr, optional: Variable to bin in the radial axis. Defaults to ‘ws’.
wdstr, optional: Variable to bin in the angular axis. Defaults to ‘wd’.
statisticstr, optional: Statistic to plot. Defaults to ‘mean’.
unitsstr, optional: Units of the parameter for the colorbar label. Defaults to ‘ppm’.
min_binint, optional: Minimum count in each bin to plot. Defaults to 1.
xbinsint, optional: Number of bins in the radial axis. Defaults to 30.
scale_anglefloat | None, optional: Angle to position the radial axis label. Defaults to None.

Returns:

plt.Axes: Axis with the plot

lair.utils.plotter.polarFreq(data: DataFrame, x: str = 'ws', wd: str = 'wd', xbins: int = 30, scale_angle: float | None = None) → Axes[source]#

Plot the contoured frequency of the data.

Parameters:

datapd.DataFrame: Data to plot.
xstr, optional: Variable to bin in the radial axis. Defaults to ‘ws’.
wdstr, optional: Variable to bin in the angular axis. Defaults to ‘wd’.
xbinsint, optional: Number of bins in the radial axis. Defaults to 30.
scale_anglefloat | None, optional: Angle to position the radial axis label. Defaults to None.

Returns:

plt.Axes: Axis with the plot

lair.utils.plotter.windvectorPlot(data: DataFrame, wd: str = 'WD', ws: str = 'WS', ax: Axes | None = None, unit_length: bool = False, **kwargs) → Axes[source]#

Plot wind vectors.

Parameters:

datapd.DataFrame: Data to plot.
wdstr, optional: Wind direction variable. Defaults to ‘WD’.
wsstr, optional: Wind speed variable. Defaults to ‘WS’.
axplt.Axes | None, optional: Axis to plot on. Defaults to None.
unit_lengthbool, optional: Normalize vectors to unit length. Defaults to False.
**kwargs: Additional arguments to pass to plt.quiver.

Returns:

plt.Axes: Axis with the plot

class lair.utils.plotter.HandlerDashedLines(marker_pad=0.3, numpoints=None, **kwargs)[source]#

Custom Handler for LineCollection instances.

https://stackoverflow.com/questions/31544489/two-line-styles-in-legend

There’s a potentially better version in the above link

This needs a new names and better documentation.

create_artists(legend, orig_handle, xdescent, ydescent, width, height, fontsize, trans)[source]#

Return the legend artists generated.

Parameters:

legend~matplotlib.legend.Legend: The legend for which these legend artists are being created.
orig_handle~matplotlib.artist.Artist or similar: The object for which these legend artists are being created.
xdescent, ydescent, width, heightint: The rectangle (xdescent, ydescent, width, height) that the legend artists being created should fit within.
fontsizeint: The fontsize in pixels. The legend artists being created should be scaled according to the given fontsize.
trans~matplotlib.transforms.Transform: The transform that is applied to the legend artists being created. Typically from unit coordinates in the handler box to screen coordinates.