Other doc tweaks

mwaskom · mwaskom · commit 204e0cb15de1 · 2013-12-23T11:33:35.000-08:00
diff --git a/doc/.gitignore b/doc/.gitignore
@@ -1,6 +1,7 @@
 *_files/
 _build/
 generated/
+_static/examples/*.png
 aesthetics.rst
 linear_models.rst
 plotting_distributions.rst
diff --git a/doc/api.rst b/doc/api.rst
@@ -1,3 +1,5 @@
+.. _api_ref:
+
 .. currentmodule:: seaborn
 
 API reference
diff --git a/doc/installing.rst b/doc/installing.rst
@@ -1,6 +1,7 @@
+.. _installing:
 
-Installing seaborn
-------------------
+Installing and getting started
+------------------------------
 
 To install the released version of seaborn, you can use ``pip`` or
 ``easy_install``, (i.e. ``pip install seaborn``). Alternatively, you can use
diff --git a/doc/tutorials.rst b/doc/tutorials.rst
@@ -1,3 +1,4 @@
+.. _tutorials:
 
 Tutorial notebooks
 ==================
diff --git a/doc/v0.2.0.txt b/doc/v0.2.0.txt
@@ -137,15 +137,15 @@ Bug fixes
 
 - Fixed a bug in the ``desaturate()`` function.
 
-- Fixed a bug in the ``coefplot`` figure size calculation.
+- Fixed a bug in the ``coefplot()`` figure size calculation.
 
 - Fixed a bug where ``regplot()`` choked on list input.
 
 - Fixed buggy behavior when drawing horizontal boxplots.
 
-- Specifying bins for the ``distplot`` histogram now works.
+- Specifying bins for the ``distplot()`` histogram now works.
 
-- Fixed a bug where ``kdeplot`` would reset the axis height and cut off
+- Fixed a bug where ``kdeplot()`` would reset the axis height and cut off
   existing data.
 
 - All axis styling has been moved out of the top-level ``seaborn.set()``
diff --git a/doc/whatsnew.rst b/doc/whatsnew.rst
@@ -1,6 +1,7 @@
+.. _whatsnew:
 
-What's new in seaborn
-=====================
+What's new in the package
+=========================
 
 A catalog of new features, improvements, and bug-fixes in each release.
 
diff --git a/examples/example_plot.png b/examples/example_plot.png
diff --git a/examples/example_plot.py b/examples/example_plot.py
@@ -5,135 +5,173 @@
 from scipy import stats
 import moss
 
-f = plt.figure(figsize=(10, 12))
-gs = plt.GridSpec(6, 2)
-np.random.seed(0)
+def lmplot(ax):
 
-# Linear regression
-# -----------------
+    n = 80
+    c = "#222222"
+    rs = np.random.RandomState(5)
+    x = rs.normal(4, 1, n)
+    y = 2 + 1.5 * x + rs.normal(0, 3, n)
+    ax.plot(x, y, "o", c=c, alpha=.8)
 
-ax = plt.subplot(gs[:2, 0])
-plt.title("lmplot()")
+    xx = np.linspace(1 + 1e-9, 7 - 1e-9, 100)
+    lmpred = lambda x, y: np.polyval(np.polyfit(x, y, 1), xx)
+    yy = lmpred(x, y)
+    ax.plot(xx, yy, c=c)
+    boots = moss.bootstrap(x, y, func=lmpred, n_boot=100)
+    ci = moss.percentiles(boots, [2.5, 97.5], 0)
+    ax.fill_between(xx, *ci, alpha=.15, color=c)
+    ax.set_title("lmplot()")
 
-n = 80
-c = "#222222"
-rs = np.random.RandomState(5)
-x = rs.normal(4, 1, n)
-y = 2 + 1.5 * x + rs.normal(0, 3, n)
-ax.plot(x, y, "o", c=c, alpha=.8)
 
-xx = np.linspace(1, 7, 100)
-lmpred = lambda x, y: np.polyval(np.polyfit(x, y, 1), xx)
-yy = lmpred(x, y)
-ax.plot(xx, yy, c=c)
-boots = moss.bootstrap(x, y, func=lmpred, n_boot=100)
-ci = moss.percentiles(boots, [2.5, 97.5], 0)
-ax.fill_between(xx, *ci, alpha=.15, color=c)
+def tsplot(ax):
 
-# Timeseries plot
-# ---------------
+    n = 20
+    t = 10
+    ax.set_xlim(0, t)
+    x = np.linspace(0, t, 100)
+    s = np.array([stats.gamma.pdf(x, a) for a in [3, 5, 7]])
+    d = s[:, np.newaxis, :]
+    rs = np.random.RandomState(24)
 
-ax = plt.subplot(gs[:2, 1])
-plt.title("tsplot()")
+    d = d * np.array([1, -1])[rs.binomial(1, .3, 3)][:, np.newaxis, np.newaxis]
+    d = d + rs.normal(0, .15, (3, n))[:, :, np.newaxis]
+    d = d + rs.uniform(0, .25, 3)[:, np.newaxis, np.newaxis]
+    d *= 10
+    d = d.transpose((1, 2, 0))
 
-n = 20
-t = 10
-ax.set_xlim(0, t)
-x = np.linspace(0, t, 100)
-s = np.array([stats.gamma.pdf(x, a) for a in [3, 5, 7]])
-d = s[:, np.newaxis, :]
-rs = np.random.RandomState(24)
+    sns.tsplot(d, time=x, ax=ax)
+    ax.set_title("tsplot()")
 
-d = d * np.array([1, -1])[rs.binomial(1, .3, 3)][:, np.newaxis, np.newaxis]
-d = d + rs.normal(0, .15, (3, n))[:, :, np.newaxis]
-d = d + rs.uniform(0, .25, 3)[:, np.newaxis, np.newaxis]
-d *= 10
-d = d.transpose((1, 2, 0))
 
-sns.tsplot(d, time=x, ax=ax)
+def violinplot(ax):
 
-# Violin plots
-# ------------
+    n = 40
+    p = 8
+    rs = np.random.RandomState(8)
+    d = rs.normal(0, 1, (n, p))
+    d += np.log(np.arange(1, p + 1)) * -5 + 10
 
-sns.set(style="whitegrid")
+    sns.violinplot(d, inner="points", ax=ax)
+    ax.set_title("violinplot()")
 
-ax = plt.subplot(gs[2:4, 0])
-plt.title("violinplot()")
 
-n = 40
-p = 8
-rs = np.random.RandomState(8)
-d = rs.normal(0, 1, (n, p))
-d += np.log(np.arange(1, p + 1)) * -5 + 10
+def interactplot(ax):
 
-sns.violinplot(d, inner="points")
+    rs = np.random.RandomState(11)
 
-#sns.despine(ax=ax)
+    n = 80
+    x1 = rs.randn(n)
+    x2 = x1 / 5 + rs.randn(n)
+    b0, b1, b2, b3 = 1.5, 4, -1, 3
+    y = b0  + b1 * x1 + b2 * x2 + b3 * x1 * x2 + rs.randn(n)
 
-# Continuous interaction
-# ----------------------
+    sns.interactplot(x1, x2, y, colorbar=False, ax=ax)
+    ax.set_title("interactplot()")
 
-sns.set(style="darkgrid")
 
-ax = plt.subplot(gs[2:4, 1])
-plt.title("interactplot()")
+def corrplot(ax):
 
-rs = np.random.RandomState(11)
+    rs = np.random.RandomState(0)
+    x0, x1 = rs.randn(2, 60)
+    x2, x3 = rs.multivariate_normal([0, 0], [(1, -.5), (-.5, 1)], 60).T
+    x2 += x0 / 8
+    x4 = x1 + rs.randn(60) * 2
+    data = np.c_[x0, x1, x2, x3, x4]
 
-n = 80
-x1 = rs.randn(n)
-x2 = x1 / 5 + rs.randn(n)
-b0, b1, b2, b3 = 1.5, 4, -1, 3
-y = b0  + b1 * x1 + b2 * x2 + b3 * x1 * x2 + rs.randn(n)
+    sns.corrplot(data, ax=ax)
+    ax.set_title("corrplot()", verticalalignment="top")
 
-sns.interactplot(x1, x2, y, colorbar=False, ax=ax)
 
+def distplot_hist(ax):
 
-# Correlation matrix
-# ------------------
+    ax.set_xlim(0, 1)
+    ax.set_xticklabels([])
 
-ax = plt.subplot(gs[4:, 0])
+    g = sns.color_palette("Set2", desat=.75)[0]
+    n = 1000
+    rs = np.random.RandomState(0)
+    d = rs.beta(8, 13, n)
 
-rs = np.random.RandomState(0)
-x0, x1 = rs.randn(2, 60)
-x2, x3 = rs.multivariate_normal([0, 0], [(1, -.5), (-.5, 1)], 60).T
-x2 += x0 / 8
-x4 = x1 + rs.randn(60) * 2
-data = np.c_[x0, x1, x2, x3, x4]
+    sns.distplot(d, color=g, ax=ax)
+    sns.despine(ax=ax)
+    ax.set_title("distplot()")
 
-sns.corrplot(data, ax=ax)
-ax.set_title("corrplot()", verticalalignment="top")
 
+def distplot_kde(ax):
 
-# Beta distributions
-# ------------------
+    ax.set_xlim(0, 1)
 
-sns.set(style="nogrid")
+    p = sns.color_palette("Set2", desat=.75)[2]
+    n = 80
+    rs = np.random.RandomState(0)
+    d = rs.beta(50, 25, n)
 
-ax = plt.subplot(gs[4, 1])
-plt.title("distplot()")
-plt.xlim(0, 1)
-ax.set_xticklabels([])
+    sns.distplot(d, hist=False, rug=True, color=p,
+                 kde_kws=dict(shade=True), ax=ax)
+    sns.despine(ax=ax)
 
-g, _, p = sns.color_palette("Set2", 3, desat=.75)
-n = 1000
-rs = np.random.RandomState(0)
-d = rs.beta(8, 13, n)
 
-sns.distplot(d, color=g)
-sns.despine(ax=ax)
+if __name__ == "__main__":
 
-ax = plt.subplot(gs[5, 1], sharey=ax)
-plt.xlim(0, 1)
+    f = plt.figure(figsize=(10, 12))
+    gs = plt.GridSpec(6, 2)
+    np.random.seed(0)
 
-d = rs.beta(50, 25, n / 15)
+    # Linear regression
+    ax = plt.subplot(gs[:2, 0])
+    lmplot(ax)
 
-sns.distplot(d, hist=False, rug=True, color=p, kde_kws=dict(shade=True))
-sns.despine(ax=ax)
+    # Timeseries plot
+    ax = plt.subplot(gs[:2, 1])
+    tsplot(ax)
 
+    # Violin plots
+    sns.set(style="whitegrid")
+    ax = plt.subplot(gs[2:4, 0])
+    violinplot(ax)
 
-# Save the plot
-# -------------
+    # Continuous interaction
+    sns.set(style="darkgrid")
+    ax = plt.subplot(gs[2:4, 1])
+    interactplot(ax)
 
-f.tight_layout()
-f.savefig("%s/example_plot.png" % os.path.dirname(__file__))
+    # Correlation matrix
+    ax = plt.subplot(gs[4:, 0])
+    corrplot(ax)
+
+    # Beta distributions
+    sns.set(style="nogrid")
+    ax = plt.subplot(gs[4, 1])
+    distplot_hist(ax)
+
+    ax = plt.subplot(gs[5, 1], sharey=ax)
+    distplot_kde(ax)
+
+    # Save the plot
+    f.tight_layout()
+    png_template = os.path.dirname(__file__) + "/%s.png"
+    f.savefig(png_template % "example_plot")
+
+    # Carousel images
+    plots = ["lmplot", "tsplot", "violinplot", "interactplot",
+             "corrplot", "distplot"]
+    styles = {p: "darkgrid" for p in plots}
+    styles["violinplot"] = "whitegrid"
+    styles["distplot"] = "nogrid"
+
+    figsize = (6, 4.2)
+    for plot in plots:
+        sns.set_axes_style(styles[plot], "notebook")
+
+        if plot == "distplot":
+            f, (ax1, ax2) = plt.subplots(2, 1, sharey=True, figsize=figsize)
+            distplot_hist(ax1)
+            distplot_kde(ax2)
+        else:
+            plot_func = locals()[plot]
+            f, ax = plt.subplots(figsize=figsize)
+            plot_func(ax)
+
+        f.tight_layout()
+        f.savefig(png_template % plot)

Original file line number	Diff line number	Diff line change
`@@ -1,3 +1,5 @@`
	`1`	`+.. _api_ref:`
	`2`	`+`
`1`	`3`	`.. currentmodule:: seaborn`
`2`	`4`
`3`	`5`	`API reference`
Original file line number	Diff line number	Diff line change
`@@ -1,3 +1,4 @@`
	`1`	`+.. _tutorials:`
`1`	`2`
`2`	`3`	`Tutorial notebooks`
`3`	`4`	`==================`