clipt.plot

functions for plotting with matplotlib

clipt.plot.tick_from_arg(ax, xs, ys, a4, kwargs)

sets ticks based on standard argument parsing

Parameters

• ax (matplotlib subplot) –

• a4 (kwargs for ticks) –

• kwargs (input arguments) –

Returns

ax

Return type

matplotlib subplot

clipt.plot.get_user_labels(labels, mlab)

returns data based on standard argument parsing

Parameters

• labels (list) – labels returned by read_all_data

• mlab (list) – manual labels for data

Returns

labels – list of labels for each y_val

Return type

list

clipt.plot.get_labels(dataf, labs, a1, ycnt, xheader=False, xlabels=None, labels=None, rows=False, drange=None, y_vals=[- 1], **kwargs)

clipt.plot.ax_limits(x)

clipt.plot.get_axes(arg, xs, ys, polar=False, polarauto=True, stacked=False, pery=False, **kwargs)

parse axes string argument xname,xmin,xmax,yname,ymin,ymax

clipt.plot.plot_setup(fg='black', bg='white', polar=False, areaonly=False, params={}, **kwargs)

setup plot with uniform styling and create axes for plot

Parameters

• fg (color) – foreground color

• bg (color) – background color

• polar (bool) – if true make polar plot

• params (dict) – rcParams to update

Returns

• ax (matplotlib sublot)

• fig (matplotlib figure)

clipt.plot.series_ticks(ax, locs, xlabels, xrotate='a')

clipt.plot.ticks(ax, xdata=[0, 1], ydata=[0, 1], tcol='black', labels=['X', 'Y'], xgrid=True, ygrid=True, xscale='linear', yscale='linear', annualx=False, dayy=False, pery=False, ticklines=False, pph=1, bottom=None, bg='white', xlabels=None, dpy=365, hpd=24, sh=0, polar=False, xticks=None, yticks=None, labelweight='ultralight', matchxy=False, xrotate='a', **kwargs)

setup ticks/axes for plot

Parameters

• ax (matplotlib sublot) –

• xdata (list) – x data (or min and max)

• ydata (list) – y data (or min and max)

• tcol (color) – tick color

• labels (list) – x and y axis labels

• xgrid (bool) – show x grid

• ygrid (bool) – show y grid

• xscale (str) – linear or log

• yscale (str) – linear or log

• annualx (bool) – plot xaxis as full year with month labels

• dayy (bool) – plot yaxis as day with hour labels

• pery (bool/int) – if > 1 label y axis according to percentile of data with pery bins

• ticklines (bool) – if not grid, show ticklines

clipt.plot.plot_legend(ax, handles, bbox_to_anchor=1.05, 1, loc=2, fg='black', bg='white', title=None)

add legend to figure

clipt.plot.add_colorbar(fig, pc, axes=[0.3, 0.0, 0.4, 0.02], ticks=None, ticklabels=None, orientation='horizontal')

add colorbar scale to figure below plot

clipt.plot.plot_graph(fig, saveimage, width=5, height=5, bg='white', fg='black', handles=[], handles2=[], dpi=200, bbox_to_anchor=1.05, 1, loc=2, legend=False, background=None, front=False, alpha=0.5, areaonly=False, polar=False, **kwargs)

add legend and save image of plot

clipt.plot.get_colors(cmap, step=None, positions=None, funcs=[], **kwargs)

get colormap from cmap name, color list or CliptColors spec

Parameters

• cmap (str or list of color tuples) – cmap selector

• positions (list) – if cmap is a list positions are the mapping points else positions are the sample points used to remap

• step (int or None) – # of steps to map colors using step function

• funcs (list of custom color map functions to try before standard) – matplotlib cmap name check and cmap_from_list func should accept a cmap argument, and step and position (call cmap_tune within to utilize) and return a matplotlib.cm.ScalarMappable

Returns

colormap

Return type

matplotlib.colors.LinearSegmentedColormap

clipt.plot.plot_cmaps(ru=False)

plot predefined colormaps showing impact of step and position

Returns

fig – can be saved using plt.savefig(…) or plotutil.plot_graph(…)

Return type

matplotlib graph

clipt.plot.plot_criteria(ax, x, y, criteria, flipxy=False, kwargs={'linewidth': 0, 'marker': 'o', 'markersize': 4, 'mew': 0})

add dot on line matching criteria to scatter plot

clipt.plot.color_inc_i(fcol, i, n, step)

class clipt.plot.WedgeMark(wedge, d)

Bases: object

clipt.plot.plot_scatter(fig, ax, xs, ys, labels, colormap, criteria=None, lw=2, ms=0, mrk='o', step=None, fcol=0.0, mew=0.0, emap=None, estep=None, flipxy=False, cs=None, cmin=None, cmax=None, y2=None, msd=None, mmin=None, mmax=None, legend=True, polar=False, areas=[], falpha=0.5, **kwargs)

adds scatterplots/lines to ax and returns ax and handles for legend

clipt.plot.plot_heatmap(fig, ax, data, colormap, vmin=None, vmax=None, dst=False, ticks=None, labels=None, legend=True, hpd=24, dpy=365, sh=0, **kwargs)

adds heatmap and colorbar to ax returns ax

clipt.plot.plot_bar(ax, xs, ys, labels, colormap, stacked=False, rwidth=0.8, step=None, estep=None, bwidth=0.9, fcol=0.0, brng=[0, 1], bottom=0, polar=False, polar0=False, emap=None, ew=[0], **kwargs)

adds bar plots to ax and returns ax and handles for legend

clipt.plot.plot_box(ax, data, labels, colormap, ylim, rwidth=0.8, step=None, mark='x', whis=1.5, mew=0.5, ms=3.0, lw=1.0, fcol=0.0, clw=1.0, clbg=True, fillalpha=1.0, notch=False, series=1, sgap=0.5, bg='white', inline=False, mean=False, fliers=True, xlabels=None, **kwargs)

adds box plots to ax and returns ax and handles for legend

clipt.plot.weighted_quantile(d, q, w=None, t=0.0)

clipt.plot.weighted_median(d, w=None, t=0.0)

clipt.plot.kernel(d, w=None, mi=None, mx=None, n=1000, t=0.0001, bws=0.5)

prepare a gaussian kernel

bws is a scale factor to the bw_method

gaussian kernel selection by Scott’s rule, see: https://docs.scipy.org/doc/scipy/reference/generated/ scipy.stats.gaussian_kde.html

clipt.plot.box_stats(d, wg=None, ci=0.75, ciw=0.95, t=0.0)

clipt.plot.bootsamps(samples, d, w=None)

clipt.plot.quant_box(x, w=None, ci=0.75, ciw=0.95)

create box and whiskers for weighted data

clipt.plot.conf_box(x, w=None, ci=0.75, ciw=0.95, nsamp=100, t=0.0)

bootstrap a confidence interval for the mean of a weighted sample

clipt.plot.plot_violin(ax, data, labels, colormap, ylim, rwidth=0.8, step=None, lw=1.0, kernelwidth=0.5, clw=1.0, clbg=True, fcol=0.0, fillalpha=1.0, median=True, conf=None, confm=None, series=1, bg='white', inline=False, mean=False, weights=None, weightlimit=0.0, fliers=False, **kwargs)

adds violin plots to ax and returns ax and handles for legend

clipt.plot.plot_histo(ax, data, labels, colormap, ylim, stacked=False, rwidth=0.8, step=None, fcol=0.0, bwidth=0.9, bins='auto', brange=None, tails=False, ylog=False, density=False, weights=None, **kwargs)

adds histo plots to ax and returns ax and handles for legend

clipt.plot.dayhour(mo, day, ho=0, hpd=24, dpy=365, sh=0)

returns hour of year (no DST)

clipt.plot.shift_data(data, pph=1, sa=3, 10, fb=11, 2, hpd=24, dpy=365, sh=0)

shift annual data for DST for correct hour axis on heatmap

Parameters

• data (list) – length 8760*pph

• pph (int) – data points per hour in data.

• sa (tuple of ints) – (month, day) for spring ahead

• fb (tuple of ints) – (month, day) for fall back

Returns

data2 – data with 2AM on “spring ahead” duplicated and 1AM on “fall back” deleted

Return type

list

clipt.plot.get_tick_distribution(data, divisions)

returns values demarcating equal size bins on data

Parameters

• data (list) – list of float values

• divisions (int) – number of divisions to make (cannot exceed len(data))

Returns

ticks – values from data at each division point

Return type

list

clipt.plot.flat(l)

flattens any depth iterable (except strings)

clipt.plot.pad_scale(data, n=8760, mult=1, missing=0.0)

scale and pad data returns numpy.array

clipt.plot.ticks_x_annual(ax, fg='black', dpy=365)

set xaxis to days in year with month labels

clipt.plot.ticks_y_day(ax, tcol='black', pph=1, hpd=24, sh=0)

set yaxis to hours in day

clipt.plot.ticks_y_per(ax, ydata, tickdiv, tcol='black')

label y axis according to percentile of data with pery bins

class clipt.plot.CliptColors

Bases: object

class containing custom clipt colors

clipt.plot.cmap_from_list(cmap, step=None, positions=None, name='custom')

return colormap from list of colors

clipt.plot.cmap_tune(colormap, step=None, positions=None, name='custom')

clipt.plot.cmap_from_mpl(cmap, step=None, positions=None)

return colormap from matplotlib colormaps

clipt.plot.cmap_from_clipt(cmap, step=None, positions=None)

return colormap from CliptColors key

clipt.plot.get_nth(li, i)

if i < len(l) returns l[i] else l[-1]

clipt.plot.get_nth_loop(li, i)

returns l[i % len(l)]

clipt.plot.quick_scatter(xs, ys, ax=None, fig=None, outf=None, colors='viridis', tkwargs=None, ckwargs=None, polar=False, labels=None, legend=False, width=5, height=5, areaonly=False, **kwargs)