Plot_util

Objects

r2_lin()
scatter_with_fit()
create_geo_subplots()
create_geo_axes()
ts_diagram()
geo_scatter()
determine_colorbar_extension()
determine_clim_by_standard_deviation()

Python definitions used to help with plotting routines.

Methods Overview -> geo_scatter(): Geographical scatter plot.

r2_lin()

def r2_lin(x, y, fit):

For calculating r-squared of a linear fit. Fit should be a python polyfit object.

scatter_with_fit()

def scatter_with_fit(x, y, s=10, c=k, yex=True, dofit=True):

Does a scatter plot with a linear fit. Will also draw y=x for
comparison.

Parameters
----------
x                : (array) Values for the x-axis
y                : (array) Values for the y-axis
s                : (float or array) Marker size(s)
c                : (float or array) Marker colour(s)
yex : (bool) True to plot y=x
dofit : (bool) True to calculate and plot linear fit

Returns
-------
Figure and axis objects for further customisation

Example Useage
-------
x = np.arange(0,50)
y = np.arange(0,50)/1.5
f,a = scatter_with_fit(x,y)
a.set_title('Example scatter with fit')
a.set_xlabel('Example x axis')
a.set_ylabel('Example y axis')

create_geo_subplots()

def create_geo_subplots(lonbounds, latbounds, n_r=1, n_c=1, figsize=unknown):

A routine for creating an axis for any geographical plot. Within the
specified longitude and latitude bounds, a map will be drawn up using
cartopy. Any type of matplotlib plot can then be added to this figure.
For example:
Example Useage
#############
                f,a = create_geo_axes(lonbounds, latbounds)
                sca = a.scatter(stats.longitude, stats.latitude, c=stats.corr,
                                                                                vmin=.75, vmax=1,
                                                                                edgecolors='k', linewidths=.5, zorder=100)
                f.colorbar(sca)
                a.set_title('SSH correlations
Monthly PSMSL tide gauge vs CO9_AMM15p0',
                                                                fontsize=9)
* Note: For scatter plots, it is useful to set zorder = 100 (or similar
                                positive number)

create_geo_axes()

def create_geo_axes(lonbounds, latbounds):

A routine for creating an axis for any geographical plot. Within the
specified longitude and latitude bounds, a map will be drawn up using
cartopy. Any type of matplotlib plot can then be added to this figure.
For example:

Example Useage
#############

                f,a = create_geo_axes(lonbounds, latbounds)
                sca = a.scatter(stats.longitude, stats.latitude, c=stats.corr,
                                                                                vmin=.75, vmax=1,
                                                                                edgecolors='k', linewidths=.5, zorder=100)
                f.colorbar(sca)
                a.set_title('SSH correlations
Monthly PSMSL tide gauge vs CO9_AMM15p0',
                                                                fontsize=9)

* Note: For scatter plots, it is useful to set zorder = 100 (or similar
                                positive number)

ts_diagram()

def ts_diagram(temperature, salinity, depth):

None

geo_scatter()

def geo_scatter(longitude, latitude, c=None, s=None, scatter_kwargs=None, coastline_kwargs=None, gridline_kwargs=None, figure_kwargs=unknown, title=, figsize=None):

Uses CartoPy to create a geographical scatter plot with land boundaries.

                Parameters
                ----------
                longitude : (array) Array of longitudes of marker locations
                latitude : (array) Array of latitudes of marker locations
                colors                : (array) Array of values to use for colouring markers
                title                : (str) Plot title, to appear at top of figure
                xlim                : (tuple) Tuple of limits to apply to the x-axis (longitude axis)
                ylim                : (tuple) Limits to apply to the y-axis (latitude axis)

                Returns
                -------
                Figure and axis objects for further customisation

determine_colorbar_extension()

def determine_colorbar_extension(color_data, vmin, vmax):

Can be used to automatically determine settings for colorbar
extension arrows. Color_data is the data used for the colormap, vmin
and vmax are the colorbar limits. Will output a string: "both", "max",
"min" or "neither", which can be inserted straight into a call to
matplotlib.pyplot.colorbar().

determine_clim_by_standard_deviation()

def determine_clim_by_standard_deviation(color_data, n_std_dev=2.5):

Automatically determine color limits based on number of standard
deviations from the mean of the color data (color_data). Useful if there
are outliers in the data causing difficulties in distinguishing most of
the data. Outputs vmin and vmax which can be passed to plotting routine
or plt.clim().