cmp.plot.taylor();comparison.ComparerPlotter
comparison.ComparerPlotter(self, comparer)Plotter class for Comparer
Examples
>>> cmp.plot.scatter()
>>> cmp.plot.timeseries()
>>> cmp.plot.hist()
>>> cmp.plot.kde()
>>> cmp.plot.qq()
>>> cmp.plot.box()Methods
| Name | Description |
|---|---|
| box | Make a box plot of model data and observations. |
| hist | Plot histogram of model data and observations. |
| kde | Plot kde (kernel density estimates of distributions) of model data and observations. |
| Make quantile-quantile (q-q) plot of model data and observations. | |
| residual_hist | plot histogram of residual values |
| scatter | Scatter plot tailored for model-observation comparison. |
| taylor | Taylor diagram for model skill comparison. |
| timeseries | Timeseries plot showing compared data: observation vs modelled |
box
comparison.ComparerPlotter.box(ax=None, title=None, figsize=None, **kwargs)Make a box plot of model data and observations.
Wraps pandas.DataFrame boxplot() method.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| ax | matplotlib.axes.Axes | axes to plot on, by default None | None |
| title | str | plot title, default: [observation name] | None |
| figsize | tuple | figure size, by default None | None |
| **kwargs | other keyword arguments to df.boxplot() | {} |
Returns
| Name | Type | Description |
|---|---|---|
| matplotlib axes |
Examples
>>> cmp.plot.box()
>>> cmp.plot.box(showmeans=True)
>>> cmp.plot.box(ax=ax, title="Box plot")See also
pandas.DataFrame.boxplot matplotlib.pyplot.boxplot
hist
comparison.ComparerPlotter.hist(
bins=100,
*,
title=None,
ax=None,
figsize=None,
density=True,
alpha=0.5,
**kwargs,
)Plot histogram of model data and observations.
Wraps pandas.DataFrame hist() method.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| bins | int | number of bins, by default 100 | 100 |
| title | str | plot title, default: [model name] vs [observation name] | None |
| ax | matplotlib.axes.Axes | axes to plot on, by default None | None |
| figsize | tuple | figure size, by default None | None |
| density | bool | If True, draw and return a probability density | True |
| alpha | float | alpha transparency fraction, by default 0.5 | 0.5 |
| **kwargs | other keyword arguments to df.plot.hist() | {} |
Returns
| Name | Type | Description |
|---|---|---|
| matplotlib axes |
See also
pandas.Series.plot.hist matplotlib.axes.Axes.hist
kde
comparison.ComparerPlotter.kde(ax=None, title=None, figsize=None, **kwargs)Plot kde (kernel density estimates of distributions) of model data and observations.
Wraps pandas.DataFrame kde() method.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| ax | matplotlib.axes.Axes | axes to plot on, by default None | None |
| title | str | plot title, default: “KDE plot for [observation name]” | None |
| figsize | tuple | figure size, by default None | None |
| **kwargs | other keyword arguments to df.plot.kde() | {} |
Returns
| Name | Type | Description |
|---|---|---|
| matplotlib.axes.Axes |
Examples
>>> cmp.plot.kde()
>>> cmp.plot.kde(bw_method=0.3)
>>> cmp.plot.kde(ax=ax, bw_method='silverman')
>>> cmp.plot.kde(xlim=[0,None], title="Density plot");See also
pandas.Series.plot.kde
comparison.ComparerPlotter.qq(
quantiles=None,
*,
title=None,
ax=None,
figsize=None,
**kwargs,
)Make quantile-quantile (q-q) plot of model data and observations.
Primarily used to compare multiple models.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| quantiles | int | Sequence[float] | None | number of quantiles for QQ-plot, by default None and will depend on the scatter data length (10, 100 or 1000) if int, this is the number of points if sequence (list of floats), represents the desired quantiles (from 0 to 1) | None |
| title | str | plot title, default: “Q-Q plot for [observation name]” | None |
| ax | matplotlib.axes.Axes | axes to plot on, by default None | None |
| figsize | tuple | figure size, by default None | None |
| **kwargs | other keyword arguments to plt.plot() | {} |
Returns
| Name | Type | Description |
|---|---|---|
| matplotlib axes |
Examples
>>> cmp.plot.qq()residual_hist
comparison.ComparerPlotter.residual_hist(
bins=100,
title=None,
color=None,
figsize=None,
ax=None,
**kwargs,
)plot histogram of residual values
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| bins | int | specification of bins, by default 100 | 100 |
| title | str | plot title, default: Residuals, [name] | None |
| color | str | residual color, by default “#8B8D8E” | None |
| figsize | tuple | figure size, by default None | None |
| ax | matplotlib.axes.Axes | list[matplotlib.axes.Axes] | axes to plot on, by default None | None |
| **kwargs | other keyword arguments to plt.hist() | {} |
Returns
| Name | Type | Description |
|---|---|---|
| matplotlib.axes.Axes | list[matplotlib.axes.Axes] |
scatter
comparison.ComparerPlotter.scatter(
model=None,
bins=120,
quantiles=None,
fit_to_quantiles=False,
show_points=None,
show_hist=None,
show_density=None,
norm=None,
backend='matplotlib',
figsize=(8, 8),
xlim=None,
ylim=None,
reg_method='ols',
title=None,
xlabel=None,
ylabel=None,
skill_table=None,
ax=None,
**kwargs,
)Scatter plot tailored for model-observation comparison.
Optionally, with density histogram.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| bins | int | float | bins for the 2D histogram on the background. By default 120 bins. if int, represents the number of bins of 2D if float, represents the bin size if sequence (list of int or float), represents the bin edges | 120 |
| quantiles | int | Sequence[float] | None | number of quantiles for QQ-plot, by default None and will depend on the scatter data length (10, 100 or 1000); if int, this is the number of points; if sequence (list of floats), represents the desired quantiles (from 0 to 1) | None |
| fit_to_quantiles | bool | by default the regression line is fitted to all data, if True, it is fitted to the quantiles which can be useful to represent the extremes of the distribution, by default False | False |
| show_points | (bool, int, float) | Should the scatter points be displayed? None means: show all points if fewer than 1e4, otherwise show 1e4 sample points, by default None. float: fraction of points to show on plot from 0 to 1. e.g. 0.5 shows 50% of the points. int: if ‘n’ (int) given, then ‘n’ points will be displayed, randomly selected | None |
| show_hist | bool | show the data density as a a 2d histogram, by default None | None |
| show_density | Optional[bool] | show the data density as a colormap of the scatter, by default None. If both show_density and show_hist are None, then show_density is used by default. If number of points is less than 200, then show_density is False as default. For binning the data, the kword bins=Float is used. |
None |
| norm | matplotlib.colors norm | colormap normalization. If None, defaults to matplotlib.colors.PowerNorm(vmin=1, gamma=0.5) | None |
| backend | str | use “plotly” (interactive) or “matplotlib” backend, by default “matplotlib” | 'matplotlib' |
| figsize | tuple | width and height of the figure, by default (8, 8) | (8, 8) |
| xlim | tuple | plot range for the observation (xmin, xmax), by default None | None |
| ylim | tuple | plot range for the model (ymin, ymax), by default None | None |
| reg_method | str or bool | method for determining the regression line “ols” : ordinary least squares regression “odr” : orthogonal distance regression, False : no regression line by default “ols” | 'ols' |
| title | str | plot title, by default None | None |
| xlabel | str | x-label text on plot, by default None | None |
| ylabel | str | y-label text on plot, by default None | None |
| skill_table | (str, List[str], bool) | list of modelskill.metrics or boolean, if True then by default modelskill.options.metrics.list. This kword adds a box at the right of the scatter plot, by default False | None |
| ax | matplotlib.axes.Axes | axes to plot on, by default None | None |
| **kwargs | other keyword arguments to plt.scatter() | {} |
Examples
>>> cmp.plot.scatter()
>>> cmp.plot.scatter(bins=0.2, backend='plotly')
>>> cmp.plot.scatter(show_points=False, title='no points')
>>> cmp.plot.scatter(xlabel='all observations', ylabel='my model')
>>> cmp.sel(model='HKZN_v2').plot.scatter(figsize=(10, 10))taylor
comparison.ComparerPlotter.taylor(
normalize_std=False,
figsize=(7, 7),
marker='o',
marker_size=6.0,
title='Taylor diagram',
)Taylor diagram for model skill comparison.
Taylor diagram showing model std and correlation to observation in a single-quadrant polar plot, with r=std and theta=arccos(cc).
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| normalize_std | bool | plot model std normalized with observation std, default False | False |
| figsize | tuple | width and height of the figure (should be square), by default (7, 7) | (7, 7) |
| marker | str | marker type e.g. “x”, “*“, by default”o” | 'o' |
| marker_size | float | size of the marker, by default 6 | 6.0 |
| title | str | title of the plot, by default “Taylor diagram” | 'Taylor diagram' |
Returns
| Name | Type | Description |
|---|---|---|
| matplotlib.figure.Figure |
Examples
Notes
Copin, Y. (2018). https://gist.github.com/ycopin/3342888, Yannick Copin yannick.copin@laposte.net
timeseries
comparison.ComparerPlotter.timeseries(
title=None,
ylim=None,
ax=None,
figsize=None,
backend='matplotlib',
**kwargs,
)Timeseries plot showing compared data: observation vs modelled
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| title | str | plot title, by default None | None |
| ylim | (float, float) | plot range for the model (ymin, ymax), by default None | None |
| ax | matplotlib.axes.Axes | axes to plot on, by default None | None |
| figsize | (float, float) | figure size, by default None | None |
| backend | str | use “plotly” (interactive) or “matplotlib” backend, by default “matplotlib” | 'matplotlib' |
| **kwargs | other keyword arguments to fig.update_layout (plotly backend) | {} |
Returns
| Name | Type | Description |
|---|---|---|
| matplotlib.axes.Axes or plotly.graph_objects.Figure |