matplotlib · rhiever · Oct 22, 2014 · Oct 22, 2014 · Oct 22, 2014 · Oct 22, 2014
diff --git a/examples/showcase/bachelors_degrees_by_gender.py b/examples/showcase/bachelors_degrees_by_gender.py
@@ -0,0 +1,78 @@
+import matplotlib.pyplot as plt
+from matplotlib.mlab import csv2rec
+from matplotlib.cbook import get_sample_data
+
+# Activate the Tableau 20 and clean styling.
+# This call does most of the styling for you.
+plt.style.use(["clean", "tableau_20colors"])
+
+fname = get_sample_data("percent_bachelors_degrees_women_usa.csv")
+gender_degree_data = csv2rec(fname)
+
+# You typically want your plot to be ~1.33x wider than tall. This plot
+# is a rare exception because of the number of lines being plotted on it.
+# Common sizes: (10, 7.5) and (12, 9)
+plt.figure(figsize=(12, 14))
+
+# Limit the range of the plot to only where the data is.
+# Avoid unnecessary whitespace.
+plt.xlim(1970, 2010.1)
+plt.ylim(-0.25, 90)
+
+# Make sure your axis ticks are large enough to be easily read.
+# You don't want your viewers squinting to read your plot.
+plt.xticks(range(1970, 2011, 10))
+plt.yticks(range(0, 91, 10), [str(x) + "%" for x in range(0, 91, 10)])
+
+# Now that the plot is prepared, it's time to actually plot the data!
+# Note that I plotted the majors in order of the highest % in the final year.
+majors = ["Health Professions", "Public Administration", "Education",
+          "Psychology", "Foreign Languages", "English",
+          "Communications\nand Journalism", "Art and Performance", "Biology",
+          "Agriculture", "Social Sciences and History", "Business",
+          "Math and Statistics", "Architecture", "Physical Sciences",
+          "Computer Science", "Engineering"]
+
+y_offsets = {"Foreign Languages": 0.5, "English": -0.5,
+             "Communications\nand Journalism": 0.75,
+             "Art and Performance": -0.25, "Agriculture": 1.25,
+             "Social Sciences and History": 0.25, "Business": -0.75,
+             "Math and Statistics": 0.75, "Architecture": -0.75,
+             "Computer Science": 0.75, "Engineering": -0.25}
+
+for rank, column in enumerate(majors):
+    # Plot each line separately with its own color, using the Tableau 20
+    # color set in order.
+    column_rec_name = column.replace("\n", "_").replace(" ", "_").lower()
+
+    line = plt.plot(gender_degree_data.year,
+                    gender_degree_data[column_rec_name])
+
+    line_color = line[0].get_color()
+
+    # Add a text label to the right end of every line. Most of the code below
+    # is adding specific offsets y position because some labels overlapped.
+    y_pos = gender_degree_data[column_rec_name][-1] - 0.5
+
+    if column in y_offsets:
+        y_pos += y_offsets[column]
+
+    # Again, make sure that all labels are large enough to be easily read
+    # by the viewer.
+    plt.text(2010.5, y_pos, column, color=line_color)
+
+# matplotlib's title() call centers the title on the plot, but not the graph,
+# so I used the text() call to customize where the title goes.
+
+# Make the title big enough so it spans the entire plot, but don"t make it
+# so big that it requires two lines to show.
+
+# Note that if the title is descriptive enough, it is unnecessary to include
+# axis labels; they are self-evident, in this plot's case.
+plt.text(1995, 92, "Percentage of Bachelor's degrees conferred to women in "
+         "the U.S.A. by major (1970-2010)", fontsize=17, ha="center")
+
+# Finally, save the figure as a PNG.
+# You can also save it as a PDF, JPEG, etc.
+# Just change the file extension in this call.
+plt.savefig("percent-bachelors-degrees-women-usa.png")
diff --git a/examples/style_sheets/plot_clean.py b/examples/style_sheets/plot_clean.py
@@ -0,0 +1,19 @@
+"""
+This shows an example of the "clean" styling.
+"""
+
+
+from matplotlib import pyplot as plt
+import numpy as np
+
+x = np.linspace(0, 10)
+
+with plt.style.context('clean'):
+    plt.plot(x, np.sin(x) + x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 2 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 3 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 4 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 5 * x + np.random.randn(50))
+
+plt.show()
diff --git a/examples/style_sheets/plot_colorblind_10colors.py b/examples/style_sheets/plot_colorblind_10colors.py
@@ -0,0 +1,20 @@
+"""
+This shows an example of the "colorblind_10colors" styling,
+which  uses Tableau's "Color Blind 10" color scheme.
+"""
+
+
+from matplotlib import pyplot as plt
+import numpy as np
+
+x = np.linspace(0, 10)
+
+with plt.style.context('colorblind_10colors'):
+    plt.plot(x, np.sin(x) + x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 2 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 3 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 4 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 5 * x + np.random.randn(50))
+
+plt.show()
diff --git a/examples/style_sheets/plot_tableau_10colors.py b/examples/style_sheets/plot_tableau_10colors.py
@@ -0,0 +1,20 @@
+"""
+This shows an example of the "tableau_10colors" styling,
+which uses Tableau's "Tableau 10" color scheme.
+"""
+
+
+from matplotlib import pyplot as plt
+import numpy as np
+
+x = np.linspace(0, 10)
+
+with plt.style.context('tableau_10colors'):
+    plt.plot(x, np.sin(x) + x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 2 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 3 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 4 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 5 * x + np.random.randn(50))
+
+plt.show()
diff --git a/examples/style_sheets/plot_tableau_20colors.py b/examples/style_sheets/plot_tableau_20colors.py
@@ -0,0 +1,20 @@
+"""
+This shows an example of the "tableau_20colors" styling,
+which uses Tableau's "Tableau 20" color scheme.
+"""
+
+
+from matplotlib import pyplot as plt
+import numpy as np
+
+x = np.linspace(0, 10)
+
+with plt.style.context('tableau_20colors'):
+    plt.plot(x, np.sin(x) + x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 2 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 3 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 4 * x + np.random.randn(50))
+    plt.plot(x, np.sin(x) + 5 * x + np.random.randn(50))
+
+plt.show()
diff --git a/lib/matplotlib/mpl-data/sample_data/percent_bachelors_degrees_women_usa.csv b/lib/matplotlib/mpl-data/sample_data/percent_bachelors_degrees_women_usa.csv
@@ -0,0 +1,43 @@
+Year,Agriculture,Architecture,Art and Performance,Biology,Business,Communications and Journalism,Computer Science,Education,Engineering,English,Foreign Languages,Health Professions,Math and Statistics,Physical Sciences,Psychology,Public Administration,Social Sciences and History
+1970,4.22979798,11.92100539,59.7,29.08836297,9.064438975,35.3,13.6,74.53532758,0.8,65.57092343,73.8,77.1,38,13.8,44.4,68.4,36.8
+1971,5.452796685,12.00310559,59.9,29.39440285,9.503186594,35.5,13.6,74.14920369,1,64.55648516,73.9,75.5,39,14.9,46.2,65.5,36.2
+1972,7.42071022,13.21459351,60.4,29.81022105,10.5589621,36.6,14.9,73.55451996,1.2,63.6642632,74.6,76.9,40.2,14.8,47.6,62.6,36.1
+1973,9.653602412,14.7916134,60.2,31.14791477,12.80460152,38.4,16.4,73.50181443,1.6,62.94150212,74.9,77.4,40.9,16.5,50.4,64.3,36.4
+1974,14.07462346,17.44468758,61.9,32.99618284,16.20485038,40.5,18.9,73.33681143,2.2,62.41341209,75.3,77.9,41.8,18.2,52.6,66.1,37.3
+1975,18.33316153,19.13404767,60.9,34.44990213,19.68624931,41.5,19.8,72.80185448,3.2,61.64720641,75,78.9,40.7,19.1,54.5,63,37.7
+1976,22.25276005,21.39449143,61.3,36.07287146,23.4300375,44.3,23.9,72.16652471,4.5,62.14819377,74.4,79.2,41.5,20,56.9,65.6,39.2
+1977,24.6401766,23.74054054,62,38.33138629,27.16342715,46.9,25.7,72.45639481,6.8,62.72306675,74.3,80.5,41.1,21.3,59,69.3,40.5
+1978,27.14619175,25.84923973,62.5,40.11249564,30.52751868,49.9,28.1,73.19282134,8.4,63.61912216,74.3,81.9,41.6,22.5,61.3,71.5,41.8
+1979,29.63336549,27.77047744,63.2,42.06555109,33.62163381,52.3,30.2,73.82114234,9.4,65.08838972,74.2,82.3,42.3,23.7,63.3,73.3,43.6
+1980,30.75938956,28.08038075,63.4,43.99925716,36.76572529,54.7,32.5,74.98103152,10.3,65.28413007,74.1,83.5,42.8,24.6,65.1,74.6,44.2
+1981,31.31865519,29.84169408,63.3,45.24951206,39.26622984,56.4,34.8,75.84512345,11.6,65.83832154,73.9,84.1,43.2,25.7,66.9,74.7,44.6
+1982,32.63666364,34.81624758,63.1,45.96733794,41.94937335,58,36.3,75.84364914,12.4,65.84735212,72.7,84.4,44,27.3,67.5,76.8,44.6
+1983,31.6353471,35.82625735,62.4,46.71313451,43.54206966,58.6,37.1,75.95060123,13.1,65.91837999,71.8,84.6,44.3,27.6,67.9,76.1,44.1
+1984,31.09294748,35.45308311,62.1,47.66908276,45.12403027,59.1,36.8,75.86911601,13.5,65.74986233,72.1,85.1,46.2,28,68.2,75.9,44.1
+1985,31.3796588,36.13334795,61.8,47.9098841,45.747782,59,35.7,75.92343971,13.5,65.79819852,70.8,85.3,46.5,27.5,69,75,43.8
+1986,31.19871923,37.24022346,62.1,48.30067763,46.53291505,60,34.7,76.14301516,13.9,65.98256091,71.2,85.7,46.7,28.4,69,75.7,44
+1987,31.48642948,38.73067535,61.7,50.20987789,46.69046648,60.2,32.4,76.96309168,14,66.70603055,72,85.5,46.5,30.4,70.1,76.4,43.9
+1988,31.08508746,39.3989071,61.7,50.09981147,46.7648277,60.4,30.8,77.62766177,13.9,67.14449816,72.3,85.2,46.2,29.7,70.9,75.6,44.4
+1989,31.6124031,39.09653994,62,50.77471585,46.7815648,60.5,29.9,78.11191872,14.1,67.01707156,72.4,84.6,46.2,31.3,71.6,76,44.2
+1990,32.70344407,40.82404662,62.6,50.81809432,47.20085084,60.8,29.4,78.86685859,14.1,66.92190193,71.2,83.9,47.3,31.6,72.6,77.6,45.1
+1991,34.71183749,33.67988118,62.1,51.46880537,47.22432481,60.8,28.7,78.99124597,14,66.24147465,71.1,83.5,47,32.6,73.2,78.2,45.5
+1992,33.93165961,35.20235628,61,51.34974154,47.21939541,59.7,28.2,78.43518191,14.5,65.62245655,71,83,47.4,32.6,73.2,77.3,45.8
+1993,34.94683208,35.77715877,60.2,51.12484404,47.63933161,58.7,28.5,77.26731199,14.9,65.73095014,70,82.4,46.4,33.6,73.1,78,46.1
+1994,36.03267447,34.43353129,59.4,52.2462176,47.98392441,58.1,28.5,75.81493264,15.7,65.64197772,69.1,81.8,47,34.8,72.9,78.8,46.8
+1995,36.84480747,36.06321839,59.2,52.59940342,48.57318101,58.8,27.5,75.12525621,16.2,65.93694921,69.6,81.5,46.1,35.9,73,78.8,47.9
+1996,38.96977475,35.9264854,58.6,53.78988011,48.6473926,58.7,27.1,75.03519921,16.7,66.43777883,69.7,81.3,46.4,37.3,73.9,79.8,48.7
+1997,40.68568483,35.10193413,58.7,54.99946903,48.56105033,60,26.8,75.1637013,17,66.78635548,70,81.9,47,38.3,74.4,81,49.2
+1998,41.91240333,37.59854457,59.1,56.35124789,49.2585152,60,27,75.48616027,17.8,67.2554484,70.1,82.1,48.3,39.7,75.1,81.3,50.5
+1999,42.88720191,38.63152919,59.2,58.22882288,49.81020815,61.2,28.1,75.83816206,18.6,67.82022113,70.9,83.5,47.8,40.2,76.5,81.1,51.2
+2000,45.05776637,40.02358491,59.2,59.38985737,49.80361649,61.9,27.7,76.69214284,18.4,68.36599498,70.9,83.5,48.2,41,77.5,81.1,51.8
+2001,45.86601517,40.69028156,59.4,60.71233149,50.27514494,63,27.6,77.37522931,19,68.57852029,71.2,85.1,47,42.2,77.5,80.9,51.7
+2002,47.13465821,41.13295053,60.9,61.8951284,50.5523346,63.7,27,78.64424394,18.7,68.82995959,70.5,85.8,45.7,41.1,77.7,81.3,51.5
+2003,47.93518721,42.75854266,61.1,62.1694558,50.34559774,64.6,25.1,78.54494815,18.8,68.89448726,70.6,86.5,46,41.7,77.8,81.5,50.9
+2004,47.88714025,43.46649345,61.3,61.91458697,49.95089449,64.2,22.2,78.65074774,18.2,68.45473436,70.8,86.5,44.7,42.1,77.8,80.7,50.5
+2005,47.67275409,43.10036784,61.4,61.50098432,49.79185139,63.4,20.6,79.06712173,17.9,68.57122114,69.9,86,45.1,41.6,77.5,81.2,50
+2006,46.79029957,44.49933107,61.6,60.17284465,49.21091439,63,18.6,78.68630551,16.8,68.29759443,69.6,85.9,44.1,40.8,77.4,81.2,49.8
+2007,47.60502633,43.10045895,61.4,59.41199314,49.00045935,62.5,17.6,78.72141311,16.8,67.87492278,70.2,85.4,44.1,40.7,77.1,82.1,49.3
+2008,47.570834,42.71173041,60.7,59.30576517,48.88802678,62.4,17.8,79.19632674,16.5,67.59402834,70.2,85.2,43.3,40.7,77.2,81.7,49.4
+2009,48.66722357,43.34892051,61,58.48958333,48.84047414,62.8,18.1,79.5329087,16.8,67.96979204,69.3,85.1,43.3,40.7,77.1,82,49.4
+2010,48.73004227,42.06672091,61.3,59.01025521,48.75798769,62.5,17.6,79.61862451,17.2,67.92810557,69,85,43.1,40.2,77,81.7,49.3
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diff --git a/lib/matplotlib/mpl-data/stylelib/clean.mplstyle b/lib/matplotlib/mpl-data/stylelib/clean.mplstyle
@@ -0,0 +1,35 @@
+# Author: Randal S. Olson (randalolson.com / @randal_olson)
+# Produces a clean plotting template with default matplotlib colors.
+
+figure.edgecolor: white
+figure.facecolor: white
+
+lines.linewidth: 2.5
+lines.markeredgewidth: 0
+lines.markersize: 10
+lines.dash_capstyle: butt
+
+legend.fancybox: True
+
+font.size: 14
+
+axes.linewidth: 0
+axes.titlesize: 22
+axes.labelsize: 16
+
+xtick.labelsize: 14
+ytick.labelsize: 14
+xtick.major.size: 0
+xtick.minor.size: 0
+ytick.major.size: 0
+ytick.minor.size: 0
+
+axes.grid: True
+grid.alpha: 0.3
+grid.linewidth: 0.5
+grid.linestyle: --
+grid.color: black
+
+savefig.transparent: False
+savefig.bbox: tight
+savefig.format: png
diff --git a/lib/matplotlib/mpl-data/stylelib/colorblind_10colors.mplstyle b/lib/matplotlib/mpl-data/stylelib/colorblind_10colors.mplstyle
@@ -0,0 +1,4 @@
+# Author: Randal S. Olson (randalolson.com / @randal_olson)
+# Uses Tableau's Color Blind 10 color scheme
+
+axes.color_cycle: 006ba4, ff800e, ababab, 595959, 5f9ed1, c85200, 898989, a2c8ec, ffbc79, cfcfcf
diff --git a/lib/matplotlib/mpl-data/stylelib/tableau_10colors.mplstyle b/lib/matplotlib/mpl-data/stylelib/tableau_10colors.mplstyle
@@ -0,0 +1,4 @@
+# Author: Randal S. Olson (randalolson.com / @randal_olson)
+# Uses Tableau's Tableau 10 color scheme
+
+axes.color_cycle: 1f77b4, ff7f0e, 2ca02c, d62728, 9467bd, 8c564b, e377c2, 7f7f7f, bcbd22, 17becf
diff --git a/lib/matplotlib/mpl-data/stylelib/tableau_20colors.mplstyle b/lib/matplotlib/mpl-data/stylelib/tableau_20colors.mplstyle
@@ -0,0 +1,4 @@
+# Author: Randal S. Olson (randalolson.com / @randal_olson)
+# Uses Tableau's Tableau 20 color scheme
+
+axes.color_cycle: 1f77b4, aec7e8, ff7f0e, ffbb78, 2ca02c, 98df8a, d62728, ff9896, 9467bd, c5b0d5, 8c564b, c49c94, e377c2, f7b6d2, 7f7f7f, c7c7c7, bcbd22, dbdb8d, 17becf, 9edae5