jade.basic.plotting package

jade.basic.plotting.MakeFigure module

class jade.basic.plotting.MakeFigure.MakeFigure(rows=1, columns=1, share_x=True, share_y=True)

Deprecated. NOW - GO Checkout SEABORN instead of this class! Essentially, this is an interface to a facet grid. Seaborn does this awesomely.

My take on a plotting interface. Because I think matplotlib’s interface sucks.

I wrote this before I knew of pandas.

You need to know the number of plots ahead of time by passing the grid.

1x1 will make one plot. 2x2 will make a grid of 4 plots. 1x3 is 3 columns of grids horizontally 3x1 is a list of figures.

share_x and share_y tell the full sublplot to share the axis.

add_data(x, y, label)

add_grid(x_grid=True, y_grid=True)

fill_subplot(title, labels, x_axis_label=None, y_axis_label=None, index=None, grid=None, add_legend=False, linestyle='--', marker='^', colors=None)

This will add data to a particular subplot/plot.

: title: : labels: : x_axis_label: : y_axis_label: : specify_index: : add_legend: : linestyle: : marker: : colors: :return:

get_data(label)

get_labels()

get_plot(n=0)

Parameters:n – int Returns:mpl.axes.SubplotBase

get_x_data(label)

get_y_as_list(labels)

get_y_data(label)

merge_data(data_dict, replace_current_labels=False)

reset(rows=1, columns=1, share_x=True, share_y=True)

save_plot(outpath, tight=True)

set_data(data_dict)

set_x_limits(min, max, plot_num=None)

set_x_scale(scale='log', plot_num=None)

set_y_limits(min, max, plot_num=None)

set_y_scale(scale='log', plot_num=None)

jade.basic.plotting.MakeFigure.pad_single_title(ax, x=0.5, y=1.05)

Move the Title up in reference to the plot, essentially adding padding. SINGLE AXES :param ax:Axes :param x: :param y: :return:

jade.basic.plotting.MakeFigure.plot_general_pandas(df, title, outpath, plot_type, x, y=None, z=None, top_p=0.95, reverse=True)

Plot anything in pandas. Make it look descent. Save the figure.

If you are doing this multiple times in a Notebook:

Don’t forget to call (matplotlib.pyplot)

plot.show() plot.close()

Parameters:

• df – pandas.DataFrame
• title – str
• outpath – str
• plot_type – str
• x – str
• y – str
• z – str
• top_p – float
• reverse – bool

Return type:

matplotlib.Axes

jade.basic.plotting.MakeFigure.plot_x_vs_y_sea_with_regression(df, title, outpath, x, y, top_p=0.95, reverse=True)

Plot X vs Y using a Pandas Dataframe and Seaborn, with regression line., save the figure, and return the Axes.

If you are doing this multiple times in a Notebook:

Don’t forget to call (matplotlib.pyplot)

plot.show() plot.close()

Parameters:

• df – pandas.DataFrame
• title – str
• outpath – str
• x – str
• y – str
• top_p – float
• reverse – bool

Return type:

matplotlib.Axes

jade.basic.plotting.MakeFigure.set_common_title(fig, title, size=16, x=0, y=1.05)

for FACETED plots, add a common title.

Parameters:

• fig – Figure
• title – str
• x – int
• y – int

Returns:

jade.basic.plotting.MakeFigure.set_common_x_y_label(fig, x_text, y_text)

For FACETED plots, add a common X or Y.

Parameters:

• fig – Figure
• x_text – str
• y_text – str

Returns:

jade.basic.plotting.correlations module

jade.basic.plotting.correlations.annotate_r_value(data, x, y, ax, func=<function pearsonr>, template=None, stat=None, loc='best')

Forked from seaborn JointPlot for use with regplot, scatter, etc. Woot. Needs to actually go into Seaborn Now!

Annotate the plot with a statistic about the relationship.

data: pandas.DataFrame x: str y: str ax: matplotlib.Axes

func : callable

Statistical function that maps the x, y vectors either to (val, p) or to val.

template : string format template, optional

The template must have the format keys “stat” and “val”; if func returns a p value, it should also have the key “p”.

stat : string, optional

Name to use for the statistic in the annotation, by default it uses the name of func.

loc : string or int, optional

Matplotlib legend location code; used to place the annotation.

jade.basic.plotting.error_bars module

jade.basic.plotting.error_bars.calculate_set_errorbars_hist(ax, data, x, y, binomial_distro=True, total_column='total_entries', y_freq_column=None, x_order=None, hue_order=None, hue=None, caps=True, color='k', linewidth=0.75, base_columnwidth=0.8, full=True)

Calculates the standard deviation of the data, sets erorr bars for a bar chart. Default base_columnwidth for seaborn plots is .8

Optionally give x_order and/or hue_order for the ordering of the columns. Make sure to pass this while plotting. Note:

If Hue is enabled, this base is divided by the number of hue_names for the final width used for plotting.

Parameters:

• ax – mpl.Axes
• data – pandas.DataFrame
• x – str
• y – str
• binomial_distro – bool
• total_column – str
• y_freq_column – str
• x_order – list
• hue_order – list
• hue – str
• caps – bool
• color – str
• linewidth – float
• base_columnwidth – float
• full – bool

Return type:

None

jade.basic.plotting.error_bars.calculate_set_errorbars_scatter(ax, data, x, y, binomial_distro=False, total_column='total_entries', caps=False, color='k', lw=1.5)

(Untested) - Calculates the standard deviation of the data, sets error bars for a typical scatter plot