Getting started

Installation

This package has some dependencies (all solved by conda):

• argh
• matplotlib
• palettable

For the moment clone the repo and run pip install -e .

Example usage

All examples are in the example.py file inside the test folder.

Reset matplotlib parameters

Use the settings.reset_rc_params() function to set the fundamental behaviour of your plots:

t=1/10*np.linspace(0,10,100)
x=np.sin(2*np.pi*t)

# Default mpl parameters
f,ax=plt.subplots(1)
ax.plot(t,x)
plt.grid(True)

# Reset parameters
stracolors.settings.reset_rc_params()

f,ax=plt.subplots(1)
ax.plot(t,x)
plt.grid(True)

Look how the appearance changes with the default settings.

Palette choice

There are different types of palettes available: sequential, divergent, qualitative and paired. For all these palettes the number of colors can be specified so that the correct list of colors is returned.

On top of that, binary palettes can be chosen and two colors are returned.

Qualitative colors:

tips = sns.load_dataset("tips")

# Qualitative colors
qual=stracolors.calls.call_palette('qualitative', number_of_colors=4)

f,ax=plt.subplots(1)
sns.boxplot(x="day", y="total_bill",
    data=tips, palette=qual, ax=ax)

Pairs and triplets:

# Paired Colors
paired=stracolors.calls.call_palette_paired(number_of_classes=1)

f,ax=plt.subplots(1)
sns.boxplot(x="day", y="total_bill", hue='smoker',
    data=tips, palette=paired, ax=ax )

# Two classes, three examples each

pal=stracolors.calls.call_palette_triple(number_of_classes=2)

f,ax=plt.subplots(1)
plt.plot(t,y1, color=pal[0])
plt.plot(t,y2, color=pal[1])
plt.plot(t, y3, color=pal[2])

plt.plot(t,ya, color=pal[3])
plt.plot(t,yb, color=pal[4])
plt.plot(t, yc, color=pal[5])

Mutiple classes:

Sequential palettes, Normal red palette with 6 colors:

dots = sns.load_dataset("dots")

# Define a palette to ensure that colors will be
# shared across the facets: sequential red
palette = dict(zip(dots.coherence.unique(),
                stracolors.calls.call_palette('sequential',number_of_colors=6)))

# Plot the lines on two facets
g1=sns.relplot(x="time", y="firing_rate",
            hue="coherence", size="choice", col="align",
            size_order=["T1", "T2"], palette=palette,
            height=5, aspect=.75, facet_kws=dict(sharex=False),
            kind="line", legend="full", data=dots)

Normal blue reversed palette:

# Define a palette to ensure that colors will be
# shared across the facets: sequential blue reversed
palette = dict(zip(dots.coherence.unique(),
                stracolors.calls.call_palette('sequential',number_of_colors=6, shade='blue', reverse=True)))
# Plot the lines on two facets
g1=sns.relplot(x="time", y="firing_rate",
            hue="coherence", size="choice", col="align",
            size_order=["T1", "T2"], palette=palette,
            height=5, aspect=.75, facet_kws=dict(sharex=False),
            kind="line", legend="full", data=dots)

Available palettes

University of Edinburgh colors

Qualitative

Binary

Palette binary colored and black&white

Divergent

Sequential

Sequential red (light, normal, dark):

Sequential blue (light, normal, dark):

Pairs and Triplets

Commands

Use the show-choices command to save the major examples of palettes available.

stracolors show-choices output-folder

Current Version: 1.0.1-dev