diff --git a/doc/pub/week45/html/._week45-bs005.html b/doc/pub/week45/html/._week45-bs005.html index d25c825f9..fce8bb477 100644 --- a/doc/pub/week45/html/._week45-bs005.html +++ b/doc/pub/week45/html/._week45-bs005.html @@ -197,7 +197,16 @@ MathJax.Hub.Config({

-

heads_proba = 0.51
+
# Common imports
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+plt.rcParams['axes.labelsize'] = 14
+plt.rcParams['xtick.labelsize'] = 12
+plt.rcParams['ytick.labelsize'] = 12
+
+heads_proba = 0.51
 coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)
 cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)
 plt.figure(figsize=(8,3.5))
diff --git a/doc/pub/week45/html/week45-reveal.html b/doc/pub/week45/html/week45-reveal.html
index 2c774648a..098fb5483 100644
--- a/doc/pub/week45/html/week45-reveal.html
+++ b/doc/pub/week45/html/week45-reveal.html
@@ -230,7 +230,16 @@ The example here shows how we can implement the coin tossing case, clealry demos
 

 
 
-
heads_proba = 0.51
+
# Common imports
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+plt.rcParams['axes.labelsize'] = 14
+plt.rcParams['xtick.labelsize'] = 12
+plt.rcParams['ytick.labelsize'] = 12
+
+heads_proba = 0.51
 coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)
 cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)
 plt.figure(figsize=(8,3.5))
diff --git a/doc/pub/week45/html/week45-solarized.html b/doc/pub/week45/html/week45-solarized.html
index 57e21a0d0..8195e8ed0 100644
--- a/doc/pub/week45/html/week45-solarized.html
+++ b/doc/pub/week45/html/week45-solarized.html
@@ -219,7 +219,16 @@ The example here shows how we can implement the coin tossing case, clealry demos
 

 
 
-
heads_proba = 0.51
+
# Common imports
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+plt.rcParams['axes.labelsize'] = 14
+plt.rcParams['xtick.labelsize'] = 12
+plt.rcParams['ytick.labelsize'] = 12
+
+heads_proba = 0.51
 coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)
 cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)
 plt.figure(figsize=(8,3.5))
diff --git a/doc/pub/week45/html/week45.html b/doc/pub/week45/html/week45.html
index f6214a740..3cb890ee6 100644
--- a/doc/pub/week45/html/week45.html
+++ b/doc/pub/week45/html/week45.html
@@ -224,7 +224,16 @@ The example here shows how we can implement the coin tossing case, clealry demos
 

 
 
-
heads_proba = 0.51
+
# Common imports
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+plt.rcParams['axes.labelsize'] = 14
+plt.rcParams['xtick.labelsize'] = 12
+plt.rcParams['ytick.labelsize'] = 12
+
+heads_proba = 0.51
 coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)
 cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)
 plt.figure(figsize=(8,3.5))
diff --git a/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz b/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz
index 2e7cca3f4..9163b35cb 100644
Binary files a/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz and b/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz differ
diff --git a/doc/pub/week45/ipynb/week45.ipynb b/doc/pub/week45/ipynb/week45.ipynb
index d8e9702c6..def1c0bf6 100644
--- a/doc/pub/week45/ipynb/week45.ipynb
+++ b/doc/pub/week45/ipynb/week45.ipynb
@@ -73,6 +73,17 @@
    },
    "outputs": [],
    "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "# Common imports\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "plt.rcParams['axes.labelsize'] = 14\n",
+    "plt.rcParams['xtick.labelsize'] = 12\n",
+    "plt.rcParams['ytick.labelsize'] = 12\n",
+    "\n",
     "heads_proba = 0.51\n",
     "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
     "cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)\n",
@@ -316,8 +327,6 @@
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from sklearn.model_selection import  train_test_split \n",
diff --git a/doc/src/week45/week45.do.txt b/doc/src/week45/week45.do.txt
index e65e72e5d..923bbb800 100644
--- a/doc/src/week45/week45.do.txt
+++ b/doc/src/week45/week45.do.txt
@@ -55,6 +55,15 @@ The example here shows how we can implement the coin tossing case, clealry demos
 !split
 ===== Simple Voting Example, head or tail =====
 !bc pycod
+# Common imports
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+plt.rcParams['axes.labelsize'] = 14
+plt.rcParams['xtick.labelsize'] = 12
+plt.rcParams['ytick.labelsize'] = 12
+
 heads_proba = 0.51
 coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)
 cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)
@@ -995,3 +1004,5 @@ plt.show()
 
 
 
+
+