add images & update chapter03

chapters/03-analytics-01.md

@@ -1,41 +1,109 @@
 #Github项目分析一
 
-#用matplotlib生成图表
+##用matplotlib生成图表
 
 如何分析用户的数据是一个有趣的问题，特别是当我们有大量的数据的时候。
 除了``matlab``，我们还可以用``numpy``+``matplotlib``
 
-##python github用户数据分析##
+###python github用户数据分析##
 
 数据可以在这边寻找到
 
 [https://github.com/gmszone/ml](https://github.com/gmszone/ml)
 
 最后效果图
-<img src="https://raw.githubusercontent.com/gmszone/ml/master/screenshots/2014-01-01.png" width=600>
+
+![2014 01 01](./img/2014-01-01.png)
 
 要解析的json文件位于``data/2014-01-01-0.json``，大小6.6M，显然我们可能需要用每次只读一行的策略，这足以解释为什么诸如sublime打开的时候很慢，而现在我们只需要里面的json数据中的创建时间。。
 
-==
- 这个文件代表什么？
+==这个文件代表什么？
 
 **2014年1月1日零时到一时，用户在github上的操作，这里的用户指的是很多。。一共有4814条数据，从commit、create到issues都有。**
 
-##python json文件解析##
+###python json文件解析##
 
-     import json
-     for line in open(jsonfile):
-          line = f.readline()
+```python
+import json
+for line in open(jsonfile):
+    line = f.readline()
+```
 
 然后再解析json
-<pre><code class="python">
+
+```python
 import dateutil.parser
 
 lin = json.loads(line)
 date = dateutil.parser.parse(lin["created_at"])
-</code></pre>
+```
+
 这里用到了``dateutil``，因为新鲜出炉的数据是string需要转换为``dateutil``，再到数据放到数组里头。最后有就有了``parse_data``
 
+```python
+def parse_data(jsonfile):
+    f = open(jsonfile, "r")
+    dataarray = []
+    datacount = 0
+
+    for line in open(jsonfile):
+        line = f.readline()
+        lin = json.loads(line)
+        date = dateutil.parser.parse(lin["created_at"])
+        datacount += 1
+        dataarray.append(date.minute)
+
+    minuteswithcount = [(x, dataarray.count(x)) for x in set(dataarray)]
+    f.close()
+    return minuteswithcount
+```
+
+下面这句代码就是将上面的解析为
+
+```python
+minuteswithcount = [(x, dataarray.count(x)) for x in set(dataarray)]
+```
+
+这样的数组以便于解析
+
+```python
+[(0, 92), (1, 67), (2, 86), (3, 73), (4, 76), (5, 67), (6, 61), (7, 71), (8, 62), (9, 71), (10, 70), (11, 79), (12, 62), (13, 67), (14, 76), (15, 67), (16, 74), (17, 48), (18, 78), (19, 73), (20, 89), (21, 62), (22, 74), (23, 61), (24, 71), (25, 49), (26, 59), (27, 59), (28, 58), (29, 74), (30, 69), (31, 59), (32, 89), (33, 67), (34, 66), (35, 77), (36, 64), (37, 71), (38, 75), (39, 66), (40, 62), (41, 77), (42, 82), (43, 95), (44, 77), (45, 65), (46, 59), (47, 60), (48, 54), (49, 66), (50, 74), (51, 61), (52, 71), (53, 90), (54, 64), (55, 67), (56, 67), (57, 55), (58, 68), (59, 91)]
+```
+
+##matplotlib
+
+开始之前需要安装``matplotlib
+
+```bash
+sudo pip install matplotlib
+```
+然后引入这个库
+
+      import matplotlib.pyplot as plt
+
+如上面的那个结果，只需要
+
+<pre><code class="python">
+    plt.figure(figsize=(8,4))
+    plt.plot(x, y,label = files)
+    plt.legend()
+    plt.show()
+</code></pre>
+  
+最后代码可见
+
+
+```python
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import json
+import dateutil.parser
+import numpy as np
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+
+
 def parse_data(jsonfile):
     f = open(jsonfile, "r")
     dataarray = []
@@ -53,83 +121,27 @@ def parse_data(jsonfile):
     return minuteswithcount
 
 
-下面这句代码就是将上面的解析为
+def draw_date(files):
+    x = []
+    y = []
+    mwcs = parse_data(files)
+    for mwc in mwcs:
+        x.append(mwc[0])
+        y.append(mwc[1])
 
-      minuteswithcount = [(x, dataarray.count(x)) for x in set(dataarray)]
-
-这样的数组以便于解析
-
-      [(0, 92), (1, 67), (2, 86), (3, 73), (4, 76), (5, 67), (6, 61), (7, 71), (8, 62), (9, 71), (10, 70), (11, 79), (12, 62), (13, 67), (14, 76), (15, 67), (16, 74), (17, 48), (18, 78), (19, 73), (20, 89), (21, 62), (22, 74), (23, 61), (24, 71), (25, 49), (26, 59), (27, 59), (28, 58), (29, 74), (30, 69), (31, 59), (32, 89), (33, 67), (34, 66), (35, 77), (36, 64), (37, 71), (38, 75), (39, 66), (40, 62), (41, 77), (42, 82), (43, 95), (44, 77), (45, 65), (46, 59), (47, 60), (48, 54), (49, 66), (50, 74), (51, 61), (52, 71), (53, 90), (54, 64), (55, 67), (56, 67), (57, 55), (58, 68), (59, 91)]
-
-##matplotlib##
-开始之前需要安装``matplotlib
-
-      sudo pip install matplotlib
-
-然后引入这个库
-
-      import matplotlib.pyplot as plt
-
-如上面的那个结果，只需要
-
-<pre><code class="python">
     plt.figure(figsize=(8,4))
     plt.plot(x, y,label = files)
     plt.legend()
     plt.show()
-</code></pre>
-  
-最后代码可见
 
-    #!/usr/bin/env python
-    # -*- coding: utf-8 -*-
-    
-    import json
-    import dateutil.parser
-    import numpy as np
-    import matplotlib.mlab as mlab
-    import matplotlib.pyplot as plt
-    
-    
-    def parse_data(jsonfile):
-        f = open(jsonfile, "r")
-        dataarray = []
-        datacount = 0
-    
-        for line in open(jsonfile):
-            line = f.readline()
-            lin = json.loads(line)
-            date = dateutil.parser.parse(lin["created_at"])
-            datacount += 1
-            dataarray.append(date.minute)
-    
-        minuteswithcount = [(x, dataarray.count(x)) for x in set(dataarray)]
-        f.close()
-        return minuteswithcount
-    
-    
-    def draw_date(files):
-        x = []
-        y = []
-        mwcs = parse_data(files)
-        for mwc in mwcs:
-            x.append(mwc[0])
-            y.append(mwc[1])
-    
-        plt.figure(figsize=(8,4))
-        plt.plot(x, y,label = files)
-        plt.legend()
-        plt.show()
-    
-    draw_date("data/2014-01-01-0.json")
+draw_date("data/2014-01-01-0.json")
+```
 
-
-#每周分析
+##每周分析
 
 继上篇之后，我们就可以分析用户的每周提交情况，以得出用户的真正的工具效率，每个程序员的工作时间可能是不一样的，如
-![Phodal Huang's Report][1]
 
-  [1]: https://www.phodal.com/static/media/uploads/screen_shot_2014-04-12_at_9.58.52_am.png
+![Phodal Huang's Report](./img/phodal-results)
 
 这是我的每周情况，显然如果把星期六移到前面的话，随着工作时间的增长，在github上的使用在下降，作为一个
 
@@ -137,11 +149,11 @@ def parse_data(jsonfile):
 
 不过这个是osrc的分析结果。
 
-##python github 每周情况分析##
+###python github 每周情况分析
 
 看一张分析后的结果
 
-<img src="https://raw.githubusercontent.com/gmszone/ml/master/screenshots/feb-results.png" width=600>
+![Feb Results](./img/feb-results.png)
 
 结果正好与我的情况相反？似乎图上是这么说的，但是数据上是这样的情况。
 
@@ -174,67 +186,71 @@ def parse_data(jsonfile):
       8474, 7984, 12933, 13504, 13763, 13544, 12940,
       7119, 7346, 13412, 14008, 12555
 
-##python 数据分析##
+###python 数据分析
 
 重写了一个新的方法用于计算提交数，直至后面才意识到其实我们可以算行数就够了，但是方法上有点hack
 
-<pre><code class="python">
-    def get_minutes_counts_with_id(jsonfile):
-        datacount, dataarray = handle_json(jsonfile)
-        minuteswithcount = [(x, dataarray.count(x)) for x in set(dataarray)]
-        return minuteswithcount
-    
-    
-    def handle_json(jsonfile):
-        f = open(jsonfile, "r")
-        dataarray = []
-        datacount = 0
-    
-        for line in open(jsonfile):
-            line = f.readline()
-            lin = json.loads(line)
-            date = dateutil.parser.parse(lin["created_at"])
-            datacount += 1
-            dataarray.append(date.minute)
-    
-        f.close()
-        return datacount, dataarray
-    
-    
-    def get_minutes_count_num(jsonfile):
-        datacount, dataarray = handle_json(jsonfile)
-        return datacount
-    
-    
-    def get_month_total():
-        """
-    
-        :rtype : object
-        """
-        monthdaycount = []
-        for i in range(1, 20):
-            if i < 10:
-                filename = 'data/2014-02-0' + i.__str__() + '-0.json'
-            else:
-                filename = 'data/2014-02-' + i.__str__() + '-0.json'
-            monthdaycount.append(get_minutes_count_num(filename))
-        return monthdaycount
-</code></pre>
+```python
+def get_minutes_counts_with_id(jsonfile):
+    datacount, dataarray = handle_json(jsonfile)
+    minuteswithcount = [(x, dataarray.count(x)) for x in set(dataarray)]
+    return minuteswithcount
+
+
+def handle_json(jsonfile):
+    f = open(jsonfile, "r")
+    dataarray = []
+    datacount = 0
+
+    for line in open(jsonfile):
+        line = f.readline()
+        lin = json.loads(line)
+        date = dateutil.parser.parse(lin["created_at"])
+        datacount += 1
+        dataarray.append(date.minute)
+
+    f.close()
+    return datacount, dataarray
+
+
+def get_minutes_count_num(jsonfile):
+    datacount, dataarray = handle_json(jsonfile)
+    return datacount
+
+
+def get_month_total():
+    """
+
+    :rtype : object
+    """
+    monthdaycount = []
+    for i in range(1, 20):
+        if i < 10:
+            filename = 'data/2014-02-0' + i.__str__() + '-0.json'
+        else:
+            filename = 'data/2014-02-' + i.__str__() + '-0.json'
+        monthdaycount.append(get_minutes_count_num(filename))
+    return monthdaycount
+```
+
 接着我们需要去遍历每个结果，后面的后面会发现这个效率真的是太低了，为什么木有多线程？
 
-##python matplotlib图表##
+###python matplotlib图表
+
 让我们的matplotlib来做这些图表的工作
 
-    if __name__ == '__main__':
-        results = pd.get_month_total()
-        print results
-    
-        plt.figure(figsize=(8, 4))
-        plt.plot(results.__getslice__(0, 7), label="first week")
-        plt.plot(results.__getslice__(7, 14), label="second week")
-        plt.plot(results.__getslice__(14, 21), label="third week")
-        plt.legend()
-        plt.show()
+```python
+if __name__ == '__main__':
+    results = pd.get_month_total()
+    print results
+
+    plt.figure(figsize=(8, 4))
+    plt.plot(results.__getslice__(0, 7), label="first week")
+    plt.plot(results.__getslice__(7, 14), label="second week")
+    plt.plot(results.__getslice__(14, 21), label="third week")
+    plt.legend()
+    plt.show()
+```
 
 蓝色的是第一周，绿色的是第二周，蓝色的是第三周就有了上面的结果。