From: Maximilian Friedersdorff <max@friedersdorff.com>
Date: Tue, 29 Jan 2019 21:16:46 +0000 (+0000)
Subject: feat: Stacked bars, pull out time series, saving
X-Git-Url: https://git.friedersdorff.com/?p=max%2Fplotting_with_matplotlib.git;a=commitdiff_plain;h=55daf9eb8772ed32c57ea74be4c95e998551d2f4

feat: Stacked bars, pull out time series, saving
---

diff --git a/slides.rst b/slides.rst
index e49b2bd..97eca22 100644
--- a/slides.rst
+++ b/slides.rst
@@ -104,24 +104,35 @@ Plot data of the form:
 .. math:: y=f(t)
 
 .. code-block:: python
-
-   >>> import matplotlib.pyplot as plt
-   >>>
-   >>> t = range(50)
-   >>> x = (ran.rand(50)*50) + 2000 # I don't have real data
-   >>> plt.plot(t, x)
-   >>> plt.title('Some time series with left title', loc='left')
-   >>> plt.ylabel('Mass of test mass over time')
-   >>> plt.show()
+   :include: time_series.py
 
 Subplots
 --------
 
 .. code-block:: python
    :include: subplot.py
-   :end-at: ax.set_xlabel('XLabel0')
+
+Saving Plots
+------------
+
+So far I've just displayed plots with ``plt.sow()``.  You can actually save 
+the plots from that interface manually, but when scripting, it's convenient
+to do so automatically:
 
 .. code-block:: python
-   :include: subplot.py
-   :start-at: for i in range(3):
+   
+   >>> # Some plotting has previously occured
+   >>> plt.savefig('eggs.pdf', dpi=300, transparent=False)
+
+The output format is interpreted from the file extension.
 
+The keyword arguments are optional here.  Other options exist.
+
+Error Bars
+----------
+
+Stacked Bar Graph
+-----------------
+
+.. code-block:: python
+   :include: stacked_bars.py
diff --git a/stacked_bars.py b/stacked_bars.py
new file mode 100644
index 0000000..afb9f62
--- /dev/null
+++ b/stacked_bars.py
@@ -0,0 +1,30 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+N = 5
+ind = np.arange(N)    # the x locations for the groups
+width = 0.35       # the width of the bars
+
+# Shenanigans because I'm using generated data
+cumulative = np.zeros(N)
+legends = ()
+
+for i in range(0,10):
+    sigma = np.random.rand()*10 + 10
+    mu = np.random.rand()*10 + 50 
+    means = np.abs(sigma*np.random.randn(N) + mu)
+    Stds = np.abs(np.random.rand(N)*2 + 2)
+
+    p = plt.bar(ind, means, width, yerr=Stds, 
+                bottom=cumulative) 
+    cumulative = cumulative + means
+    legends = legends + (p[0],)
+
+plt.ylabel('Scores')
+plt.title('Scores by group and treatment')
+plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
+plt.yticks(np.arange(0, max(cumulative), 30))
+
+plt.legend(legends, range(1, len(legends) + 1))
+
+plt.show()
diff --git a/time_series.py b/time_series.py
new file mode 100644
index 0000000..d763e22
--- /dev/null
+++ b/time_series.py
@@ -0,0 +1,9 @@
+import matplotlib.pyplot as plt
+from numpy import random as ran
+
+t = range(50)
+x = (ran.rand(50)*50) + 2000 # I don't have real data
+plt.plot(t, x)
+plt.title('Some time series with left title', loc='left')
+plt.ylabel('Mass of test mass over time')
+plt.show()