TrendVis

TrendVis is a plotting package that uses matplotlib to create information-dense, sparkline-like, quantitative visualizations of multiple disparate data sets in a common plot area against a common variable. This plot type is particularly well-suited for time-series data. The results speak for themselves:

Installation

TrendVis is pure Python, natively supports Python 2 and 3, and depends only on matplotlib version 1.2 or greater.

Setup and installation is simple:

pip install -U trendvis

or, if you would like to develop the package, fork and clone the repo then run:

python setup.py develop

at package root.

Quick Examples

Below are several examples showing various features in TrendVis and a typical workflow. Version >= 0.2.1 is required.

Single column XGrid

import numpy as np
import matplotlib.pyplot as plt
import trendvis

# Pseudorandom data and plot attributes
random_generator = np.random.RandomState(seed=123)
yvals = random_generator.rand(10)

# Plot attributes
nums = 10
lw = 1.5

# convenience function trendvis.gridwrapper() is available
# to initialize XGrid and do most of the formatting shown here
ex0 = trendvis.XGrid([1,2,1], figsize=(5,5))

# Convenience function for plotting line data
# Automatically colors y axis spines to
# match line colors (auto_spinecolor=True)
trendvis.plot_data(ex0,
    [[(np.linspace(0, 9.5, num=nums), yvals, 'blue')],
     [(np.linspace(1, 9, num=nums), yvals*5, 'red')],
     [(np.linspace(0.5, 10, num=nums), yvals*10, 'green')]],
    lw=lw, markeredgecolor='none', marker='s')

# Get rid of extra spines
ex0.cleanup_grid()
ex0.set_spinewidth(lw)

ex0.set_all_ticknums([(2, 1)], [(0.2, 0.1), (1, 0.5), (2, 1)])
ex0.set_ticks(major_dim=(7, 3), minor_dim=(4, 2))

ex0.set_ylabels(['stack axis 0', 'stack axis 1', 'stack axis 2'])

# In XGrid.fig.axes, axes live in a 1 level list
# In XGrid.axes, axes live in a nested list of [row][column]
ex0.axes[2][0].set_xlabel('Main Axis', fontsize=14)

# Compact the plot
ex0.fig.subplots_adjust(hspace=-0.3)

Two-row YGrid with frame

import numpy as np
import matplotlib.pyplot as plt
import trendvis

# Pseudorandom data
random_generator = np.random.RandomState(seed=1234)
xvals = random_generator.rand(20)

# Plot attributes
numpts = 20
lw = 1.5

# Initialize a YGrid
ex1 = trendvis.YGrid([1, 2, 1], yratios=[1, 2], figsize=(5,5))

# Convenience function
trendvis.plot_data(ex1,
    [[(xvals, np.linspace(2, 18.5, num=numpts), 'blue')],
     [(xvals*5, np.linspace(1, 17, num=numpts),  'red')],
     [(xvals*10, np.linspace(0.5, 20, num=numpts), 'green')]],
    lw=lw, auto_spinecolor=True, markeredgecolor='none', marker='s')

# Remove extra spines, color stack (y) ticks
ex1.cleanup_grid()
ex1.set_spinewidth(lw)

# Tick, tick label formatting
ex1.set_all_ticknums([(0.2, 0.1), (1, 0.5), (2, 1)], [(2, 1), (2, 1)])
ex1.set_ticks(major_dim=(7, 3), minor_dim=(4, 2))
ex1.set_ylim([(0, 15, 20), (1, 0, 11)])

# Axes labels
ex1.set_xlabels(['stack axis 0', 'stack axis 1', 'stack axis 2'])
ex1.axes[0][0].set_ylabel('Main Axis 0', fontsize=14)
ex1.axes[2][1].set_ylabel('Main Axis 1', fontsize=14,
              rotation=270, labelpad=14)

# Draw boxes around each row
ex1.draw_frame()

# Broken axis cutout marks also available, try this instead of the frame:
# ex0.draw_cutout(di=0.05)

# Compact the plot
ex1.fig.subplots_adjust(wspace=-0.3)

Three-column XGrid with advanced features

import numpy as np
import matplotlib.pyplot as plt
import trendvis

# Make some pseudorandom data
random_generator = np.random.RandomState(seed=123)
yvals = random_generator.rand(40)
yvals1 = np.copy(yvals)
yvals1[20:] = np.array([0.2, 0.3, 0.2, 0.5, 0.34, 0.24,
            0.15, 0.23, 0.26, 0.21] * 2)
numpts = 40
lw = 1.5
x0 = np.linspace(2, 49.5, num=numpts)
x1 = np.linspace(1, 49, num=numpts)
x11 = np.linspace(1.5, 47.5, num=numpts)
twin0 = np.linspace(2, 50, num=numpts)
twin1 = np.linspace(0.5, 48, num=numpts)

# Initialize XGrid and twin axes
ex2 = trendvis.XGrid([3, 4], xratios=[1, 3, 2], figsize=(5, 5),
                     startside='right')
ex2.make_twins([0, 1])

# Convenience function
trendvis.plot_data(ex2,
    [[(x0, yvals, 'blue')],
     [(x1, yvals1*5, 'red'), (x11, yvals1*5.2, 'orchid')],
     [],
     [(twin1, yvals*2, '0.5')]],
    lw=lw, marker=None)

# Adjust twinned y-axis positions for readability
ex2.move_spines(twin_shift=0.6)

# For any other kind of plot (fill_between, scatter, errorbar, etc),
# get axis and plot directly
# Note: ex2.axes[2][2] == ex2.get_axis(0, xpos=2, is_twin=True)
for ax in ex2.axes[2]:
    ax.fill_between(twin0, yvals+0.075, yvals-0.1,
                    edgecolor='none', color='darkorange')

# Handle axis ticks
ex2.cleanup_grid()
ex2.set_spinewidth(lw)
ex2.autocolor_spines()
ex2.set_all_ticknums([(2, 1), (2, 1), (2, 1)],
                     [(0.2, 0.1), (1, 0.5), (1, 0.25), (0.5, 0.25)])
ex2.set_ticks(major_dim=(6, 1.5), minor_dim=(3, 1))

ex2.set_ylabels(['row 0', 'row 1', 'twin row 0', 'twin row 1'])

# Rotate x-axis tick labels
for ax in ex2.fig.axes:
    plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)

# Draw a vertical bar behind the data - horizontal bars available too
ex2.draw_bar(ex2.axes[1][2], ex2.axes[0][2], (45, 47), color='lightblue')

# Ok to set axis limits after drawing on figure using TrendVis methods,
# TrendVis will reset the bar to the right place!
ex2.set_xlim([(0, 0, 3), (1, 13, 24), (2, 43, 50)])
ex2.set_ylim([(2, 0, 2)])

# matplotlib annotations supported
ex2.get_axis(0).text(0, 0.75, 'Text')

# Cutouts instead of frames
ex2.draw_cutout(lw=lw)

# Set the suptitle and compact the plot
ex2.fig.suptitle('Title', fontsize=16, y=1.05);
ex2.fig.subplots_adjust(hspace=-0.1)