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wesm/gist:5861474

Created June 25, 2013 19:16
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pandas git stats
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gistfile1.txt
{
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{
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"metadata": {},
"source": [
"GitHub Analytics (For Fun and Profit)\n",
"==="
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import json"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"closed_issues = json.load(open('github/closed.json'))\n",
"open_issues = json.load(open('github/open.json'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"closed_issues[0].keys()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"[u'body',\n",
" u'events_url',\n",
" u'title',\n",
" u'url',\n",
" u'labels_url',\n",
" u'created_at',\n",
" u'labels',\n",
" u'comments_url',\n",
" u'html_url',\n",
" u'comments',\n",
" u'number',\n",
" u'updated_at',\n",
" u'assignee',\n",
" u'state',\n",
" u'user',\n",
" u'milestone',\n",
" u'closed_at',\n",
" u'pull_request',\n",
" u'id']"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"open_issues[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"{u'assignee': None,\n",
" u'body': u'I feel having such a feature would help people get better at Pandas, learn some advanced skills and better ways to do things, from people who have more experience with it. Maybe sort of a community blog or twitter feed. As an end user i feel it would be appreciated.',\n",
" u'closed_at': None,\n",
" u'comments': [],\n",
" u'comments_url': u'https://api.github.com/repos/pydata/pandas/issues/3883/comments',\n",
" u'created_at': u'2013-06-13T12:03:06Z',\n",
" u'events_url': u'https://api.github.com/repos/pydata/pandas/issues/3883/events',\n",
" u'html_url': u'https://github.com/pydata/pandas/issues/3883',\n",
" u'id': 15501451,\n",
" u'labels': [],\n",
" u'labels_url': u'https://api.github.com/repos/pydata/pandas/issues/3883/labels{/name}',\n",
" u'milestone': None,\n",
" u'number': 3883,\n",
" u'pull_request': {u'diff_url': None, u'html_url': None, u'patch_url': None},\n",
" u'state': u'open',\n",
" u'title': u'Feature Request: Pandas Tip of the Day',\n",
" u'updated_at': u'2013-06-13T12:03:06Z',\n",
" u'url': u'https://api.github.com/repos/pydata/pandas/issues/3883',\n",
" u'user': {u'avatar_url': u'https://secure.gravatar.com/avatar/d2f3703551edb49da47d9a3b637cf71f?d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-user-420.png',\n",
" u'events_url': u'https://api.github.com/users/nipunreddevil/events{/privacy}',\n",
" u'followers_url': u'https://api.github.com/users/nipunreddevil/followers',\n",
" u'following_url': u'https://api.github.com/users/nipunreddevil/following{/other_user}',\n",
" u'gists_url': u'https://api.github.com/users/nipunreddevil/gists{/gist_id}',\n",
" u'gravatar_id': u'd2f3703551edb49da47d9a3b637cf71f',\n",
" u'html_url': u'https://github.com/nipunreddevil',\n",
" u'id': 60985,\n",
" u'login': u'nipunreddevil',\n",
" u'organizations_url': u'https://api.github.com/users/nipunreddevil/orgs',\n",
" u'received_events_url': u'https://api.github.com/users/nipunreddevil/received_events',\n",
" u'repos_url': u'https://api.github.com/users/nipunreddevil/repos',\n",
" u'starred_url': u'https://api.github.com/users/nipunreddevil/starred{/owner}{/repo}',\n",
" u'subscriptions_url': u'https://api.github.com/users/nipunreddevil/subscriptions',\n",
" u'type': u'User',\n",
" u'url': u'https://api.github.com/users/nipunreddevil'}}"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"issue = closed_issues[0]\n",
"issue.keys()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"[u'body',\n",
" u'events_url',\n",
" u'title',\n",
" u'url',\n",
" u'labels_url',\n",
" u'created_at',\n",
" u'labels',\n",
" u'comments_url',\n",
" u'html_url',\n",
" u'comments',\n",
" u'number',\n",
" u'updated_at',\n",
" u'assignee',\n",
" u'state',\n",
" u'user',\n",
" u'milestone',\n",
" u'closed_at',\n",
" u'pull_request',\n",
" u'id']"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"issue['created_at']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"u'2013-06-12T18:31:16Z'"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.Timestamp(issue['created_at'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"<Timestamp: 2013-06-12 18:31:16+0000 UTC, tz=UTC>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.Timestamp(closed_issues[0]['created_at'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"<Timestamp: 2013-06-12 18:31:16+0000 UTC, tz=UTC>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.options.display.line_width = 300"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"issue_meta = ['title', 'created_at', 'labels', 'closed_at', 'user', 'id']\n",
"\n",
"iclosed = pd.DataFrame(closed_issues, columns=issue_meta)\n",
"def get_login(x):\n",
" return x['login']\n",
"\n",
"iclosed['user'] = iclosed.user.map(get_login)\n",
"iclosed.user\n",
"#iclosed.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
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"output_type": "pyout",
"prompt_number": 15,
"text": [
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"3230 wesm\n",
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"Name: user, Length: 3232, dtype: object"
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"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"iclosed.groupby('user').size().order(ascending=False)[:10].plot(kind='barh')"
],
"language": "python",
"metadata": {},
"outputs": [
{
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"output_type": "pyout",
"prompt_number": 16,
"text": [
"<matplotlib.axes.AxesSubplot at 0x10d241d50>"
]
},
{
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"output_type": "display_data",
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ENcDuShWxu/Ja2F1JRFWxu5KIiMgDFrkmz651gCr0OC6gx0yAPnMxkzLMpA4W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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x106bb4150>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"iclosed.groupby('user').size().order(ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"user\n",
"wesm 845\n",
"jreback 275\n",
"changhiskhan 214\n",
"y-p 194\n",
"lodagro 97\n",
"cpcloud 78\n",
"adamklein 66\n",
"jseabold 63\n",
"hayd 39\n",
"timmie 35\n",
"gerigk 33\n",
"stephenwlin 26\n",
"cswegger 26\n",
"dieterv77 25\n",
"dalejung 25\n",
"...\n",
"LionelR 1\n",
"Liam3851 1\n",
"JoeGermuska 1\n",
"JasonR055 1\n",
"JWCornV 1\n",
"Honghe 1\n",
"GlenHertz 1\n",
"FedericoV 1\n",
"CerebralMastication 1\n",
"Cd48 1\n",
"BrenBarn 1\n",
"Bklyn 1\n",
"Anaphory 1\n",
"Altons 1\n",
"Aico 1\n",
"Length: 523, dtype: int64"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"iclosed = pd.DataFrame(closed_issues, columns=issue_meta)\n",
"#iclosed.user = iclosed.user.map(lambda x: x['login'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_comments = []\n",
"observed_ids = set()\n",
"for issue in closed_issues:\n",
" if issue['id'] in observed_ids:\n",
" continue\n",
" else:\n",
" observed_ids.add(issue['id'])\n",
" for comment in issue['comments']:\n",
" comment['id'] = issue['id']\n",
" all_comments.append(comment)\n",
"\n",
" \n",
"all_comments = pd.DataFrame(all_comments) \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def convert_timestamp(x):\n",
" return pd.Timestamp(int(x) * 1000000)\n",
"\n",
"all_comments.created = all_comments.created.map(convert_timestamp)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#iclosed = iclosed[-iclosed.id.duplicated()]\n",
"iclosed = iclosed.drop_duplicates(['id'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_comments.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>author</th>\n",
" <th>created</th>\n",
" <th>id</th>\n",
" <th>text</th>\n",
" <th>updated</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> cpcloud</td>\n",
" <td>2013-06-12 17:37:27</td>\n",
" <td> 15453336</td>\n",
" <td> nothing except cython and numpy are getting in...</td>\n",
" <td> 1371058709000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> gliptak</td>\n",
" <td>2013-06-12 17:44:49</td>\n",
" <td> 15453336</td>\n",
" <td> @cpcloud Phillip, would you have an example o...</td>\n",
" <td> 1371059089000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> cpcloud</td>\n",
" <td>2013-06-12 17:49:09</td>\n",
" <td> 15453336</td>\n",
" <td> sure. the &amp;quot;generally accepted&amp;quot; way t...</td>\n",
" <td> 1371059467000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> gliptak</td>\n",
" <td>2013-06-12 21:47:09</td>\n",
" <td> 15453336</td>\n",
" <td> Thanks @cpcloud, I made this change.</td>\n",
" <td> 1371073629000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> cpcloud</td>\n",
" <td>2013-06-12 17:56:16</td>\n",
" <td> 15430880</td>\n",
" <td> that should be removed from travis since there...</td>\n",
" <td> 1371059776000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
" author created id text updated\n",
"0 cpcloud 2013-06-12 17:37:27 15453336 nothing except cython and numpy are getting in... 1371058709000\n",
"1 gliptak 2013-06-12 17:44:49 15453336 @cpcloud Phillip, would you have an example o... 1371059089000\n",
"2 cpcloud 2013-06-12 17:49:09 15453336 sure. the &quot;generally accepted&quot; way t... 1371059467000\n",
"3 gliptak 2013-06-12 21:47:09 15453336 Thanks @cpcloud, I made this change. 1371073629000\n",
"4 cpcloud 2013-06-12 17:56:16 15430880 that should be removed from travis since there... 1371059776000"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.to_datetime('11/10/2013', format='%d/%m/%Y')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"datetime.datetime(2013, 11, 10, 0, 0)"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"merged = pd.merge(all_comments, iclosed, on='id')\n",
"merged['created_at'] = pd.to_datetime(merged.created_at)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def delta_to_minutes(x):\n",
" return x.view(np.int64) / (1e9 * 60)\n",
"\n",
"time_since_issue_opened = merged.created - merged.created_at\n",
"minutes_elapsed = time_since_issue_opened.map(delta_to_minutes)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"min_response_time = minutes_elapsed.groupby(merged.id).min()\n",
"ts = pd.Series(merged.id.values, merged.created_at)\n",
"ts.map(min_response_time).sort_index().resample('M', how='mean')['2011-07':].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"<matplotlib.axes.AxesSubplot at 0x10d2e1090>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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v7wTsqWOifo6su75Ggn8d9qZ+1q3Tf9QdO9b/+aRJ+kvg7bdbVz5vsHcvGI1n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\nBB2V4gAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10d31c990>"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_comments = []\n",
"for issue in closed_issues:\n",
" for c in issue['comments']:\n",
" c['id'] = issue['id']\n",
" all_comments.append(c)\n",
" \n",
"ccomments = pd.DataFrame(all_comments, columns=['author', 'text', 'id'])\n",
"ccomments"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<pre>\n",
"&ltclass 'pandas.core.frame.DataFrame'&gt\n",
"Int64Index: 8879 entries, 0 to 8878\n",
"Data columns (total 3 columns):\n",
"author 8879 non-null values\n",
"text 8879 non-null values\n",
"id 8879 non-null values\n",
"dtypes: int64(1), object(2)\n",
"</pre>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 8879 entries, 0 to 8878\n",
"Data columns (total 3 columns):\n",
"author 8879 non-null values\n",
"text 8879 non-null values\n",
"id 8879 non-null values\n",
"dtypes: int64(1), object(2)"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"iclosed['closed'] = True"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"iclosed.id.duplicated()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
"5 False\n",
"6 False\n",
"7 False\n",
"8 False\n",
"9 False\n",
"10 False\n",
"11 False\n",
"12 False\n",
"13 False\n",
"14 False\n",
"...\n",
"3217 False\n",
"3218 False\n",
"3219 False\n",
"3220 False\n",
"3221 False\n",
"3222 False\n",
"3223 False\n",
"3224 False\n",
"3225 False\n",
"3226 False\n",
"3227 False\n",
"3228 False\n",
"3229 False\n",
"3230 False\n",
"3231 False\n",
"Name: id, Length: 3232, dtype: bool"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"iclosed[-iclosed.id.duplicated()]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<pre>\n",
"&ltclass 'pandas.core.frame.DataFrame'&gt\n",
"Int64Index: 3232 entries, 0 to 3231\n",
"Data columns (total 7 columns):\n",
"title 3232 non-null values\n",
"created_at 3232 non-null values\n",
"labels 3232 non-null values\n",
"closed_at 3232 non-null values\n",
"user 3232 non-null values\n",
"id 3232 non-null values\n",
"closed 3232 non-null values\n",
"dtypes: bool(1), int64(1), object(5)\n",
"</pre>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
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"\n",
" issue_meta = pd.DataFrame(blob, columns=issue_meta)\n",
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],
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"12 2013-06-10 17:04:32\n",
"13 2013-06-10 16:59:14\n",
"14 2013-06-10 16:21:32\n",
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"3866 2011-11-24 02:08:03\n",
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"3868 2011-11-22 16:20:05\n",
"3869 2011-11-18 17:34:38\n",
"3870 2011-10-21 04:55:54\n",
"3871 2011-10-19 14:39:56\n",
"3872 2011-10-17 16:13:36\n",
"3873 2011-10-14 12:40:38\n",
"3874 2011-10-02 03:33:45\n",
"3875 2011-09-22 15:09:54\n",
"3876 2011-09-21 16:05:33\n",
"3877 2011-09-21 16:03:43\n",
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"issues['closed_at'] = pd.to_datetime(issues.closed_at)"
],
"language": "python",
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"Feb 9 2012|v0.7.0\n",
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"Mar 16 2012|v0.7.2\n",
"Apr 12 2012|0.7.3\n",
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"2012-02-29 v0.7.1\n",
"2012-03-16 v0.7.2\n",
"2012-04-12 0.7.3\n",
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"\n",
"- Chattiness over time\n",
"- Mean time til first comment over time\n",
"- Time til issues closed (by month)\n",
"- Issue churn / days to release\n",
"- Number of issues of each label type over time\n",
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" </tr>\n",
" <tr>\n",
" <th>2013-06-12 14:22:18</th>\n",
" <td> CLN: avoid Unboundlocal error in tools/merge/_...</td>\n",
" <td>2013-06-12 14:22:18</td>\n",
" <td> []</td>\n",
" <td>2013-06-12 14:47:58</td>\n",
" <td> jreback</td>\n",
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" <tr>\n",
" <th>2013-06-12 02:26:18</th>\n",
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" <td>2013-06-12 02:26:18</td>\n",
" <td> []</td>\n",
" <td>2013-06-12 03:20:07</td>\n",
" <td> hayd</td>\n",
" <td> 15431944</td>\n",
" <td> True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-06-12 01:16:19</th>\n",
" <td> ci/after_script.sh missing?</td>\n",
" <td>2013-06-12 01:16:19</td>\n",
" <td> []</td>\n",
" <td>2013-06-12 19:37:29</td>\n",
" <td> gliptak</td>\n",
" <td> 15430880</td>\n",
" <td> True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 49,
"text": [
" title created_at labels closed_at user id closed\n",
"created_at \n",
"2013-06-12 18:31:16 BLD: remove after_script.sh from travis since ... 2013-06-12 18:31:16 [] 2013-06-12 19:37:29 cpcloud 15466125 True\n",
"2013-06-12 14:24:44 ImportError: No module named lxml.html with 2.... 2013-06-12 14:24:44 [] 2013-06-12 21:47:09 gliptak 15453336 True\n",
"2013-06-12 14:22:18 CLN: avoid Unboundlocal error in tools/merge/_... 2013-06-12 14:22:18 [] 2013-06-12 14:47:58 jreback 15453214 True\n",
"2013-06-12 02:26:18 TST slicing regression test 2013-06-12 02:26:18 [] 2013-06-12 03:20:07 hayd 15431944 True\n",
"2013-06-12 01:16:19 ci/after_script.sh missing? 2013-06-12 01:16:19 [] 2013-06-12 19:37:29 gliptak 15430880 True"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"issues.created_at"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"created_at\n",
"2013-06-12 18:31:16 2013-06-12 18:31:16\n",
"2013-06-12 14:24:44 2013-06-12 14:24:44\n",
"2013-06-12 14:22:18 2013-06-12 14:22:18\n",
"2013-06-12 02:26:18 2013-06-12 02:26:18\n",
"2013-06-12 01:16:19 2013-06-12 01:16:19\n",
"2013-06-11 22:55:48 2013-06-11 22:55:48\n",
"2013-06-11 15:44:15 2013-06-11 15:44:15\n",
"2013-06-11 06:56:40 2013-06-11 06:56:40\n",
"2013-06-11 03:47:13 2013-06-11 03:47:13\n",
"2013-06-10 21:24:08 2013-06-10 21:24:08\n",
"2013-06-10 20:40:22 2013-06-10 20:40:22\n",
"2013-06-10 19:13:04 2013-06-10 19:13:04\n",
"2013-06-10 17:04:32 2013-06-10 17:04:32\n",
"2013-06-10 16:59:14 2013-06-10 16:59:14\n",
"2013-06-10 16:21:32 2013-06-10 16:21:32\n",
"...\n",
"2011-11-24 02:08:03 2011-11-24 02:08:03\n",
"2011-11-22 17:37:17 2011-11-22 17:37:17\n",
"2011-11-22 16:20:05 2011-11-22 16:20:05\n",
"2011-11-18 17:34:38 2011-11-18 17:34:38\n",
"2011-10-21 04:55:54 2011-10-21 04:55:54\n",
"2011-10-19 14:39:56 2011-10-19 14:39:56\n",
"2011-10-17 16:13:36 2011-10-17 16:13:36\n",
"2011-10-14 12:40:38 2011-10-14 12:40:38\n",
"2011-10-02 03:33:45 2011-10-02 03:33:45\n",
"2011-09-22 15:09:54 2011-09-22 15:09:54\n",
"2011-09-21 16:05:33 2011-09-21 16:05:33\n",
"2011-09-21 16:03:43 2011-09-21 16:03:43\n",
"2011-09-04 02:07:02 2011-09-04 02:07:02\n",
"2011-07-15 23:11:41 2011-07-15 23:11:41\n",
"2011-04-06 00:15:26 2011-04-06 00:15:26\n",
"Name: created_at, Length: 3881, dtype: datetime64[ns]"
]
}
],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"issues.user.resample('M', how='count')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 51,
"text": [
"created_at\n",
"2010-09-30 11\n",
"2010-10-31 8\n",
"2010-11-30 2\n",
"2010-12-31 4\n",
"2011-01-31 9\n",
"2011-02-28 2\n",
"2011-03-31 2\n",
"2011-04-30 1\n",
"2011-05-31 7\n",
"2011-06-30 9\n",
"2011-07-31 31\n",
"2011-08-31 31\n",
"2011-09-30 70\n",
"2011-10-31 123\n",
"2011-11-30 116\n",
"2011-12-31 136\n",
"2012-01-31 163\n",
"2012-02-29 116\n",
"2012-03-31 143\n",
"2012-04-30 190\n",
"2012-05-31 194\n",
"2012-06-30 184\n",
"2012-07-31 163\n",
"2012-08-31 116\n",
"2012-09-30 168\n",
"2012-10-31 148\n",
"2012-11-30 253\n",
"2012-12-31 221\n",
"2013-01-31 165\n",
"2013-02-28 165\n",
"2013-03-31 275\n",
"2013-04-30 272\n",
"2013-05-31 234\n",
"2013-06-30 149\n",
"dtype: int64"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"issues.user.resample('M', how='count').plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": [
"<matplotlib.axes.AxesSubplot at 0x10d3580d0>"
]
},
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x8XC73Wjfvj3i4+OxYMECn+VbrdSpqElveujoLXz7oLM3M7pgKJvI09PT4Xa7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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10d359290>"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Total number of issues opened over time\n",
"issues.index = issues.created_at\n",
"\n",
"issues_by_month = issues.user.resample('M', 'count')\n",
"\n",
"fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8, 8))\n",
"plt.subplots_adjust(hspace=0.05)\n",
"issues_by_month.cumsum().plot(ax=axes[1])\n",
"issues_by_month.plot(kind='bar', ax=axes[0])\n",
"axes[0].xaxis.set_visible(False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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k9uUvBzsaDXeLiIgA8PbbkJEBTz5pjgTdHSXpADHLHIwMbGqHYgbBaoe1tTBj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\n",
"text": [
"<matplotlib.figure.Figure at 0x10d5e1550>"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"issues.closed"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 54,
"text": [
"created_at\n",
"2013-06-12 18:31:16 True\n",
"2013-06-12 14:24:44 True\n",
"2013-06-12 14:22:18 True\n",
"2013-06-12 02:26:18 True\n",
"2013-06-12 01:16:19 True\n",
"2013-06-11 22:55:48 True\n",
"2013-06-11 15:44:15 True\n",
"2013-06-11 06:56:40 True\n",
"2013-06-11 03:47:13 True\n",
"2013-06-10 21:24:08 True\n",
"2013-06-10 20:40:22 True\n",
"2013-06-10 19:13:04 True\n",
"2013-06-10 17:04:32 True\n",
"2013-06-10 16:59:14 True\n",
"2013-06-10 16:21:32 True\n",
"...\n",
"2011-11-24 02:08:03 False\n",
"2011-11-22 17:37:17 False\n",
"2011-11-22 16:20:05 False\n",
"2011-11-18 17:34:38 False\n",
"2011-10-21 04:55:54 False\n",
"2011-10-19 14:39:56 False\n",
"2011-10-17 16:13:36 False\n",
"2011-10-14 12:40:38 False\n",
"2011-10-02 03:33:45 False\n",
"2011-09-22 15:09:54 False\n",
"2011-09-21 16:05:33 False\n",
"2011-09-21 16:03:43 False\n",
"2011-09-04 02:07:02 False\n",
"2011-07-15 23:11:41 False\n",
"2011-04-06 00:15:26 False\n",
"Name: closed, Length: 3881, dtype: bool"
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Time to closed\n",
"stuff = issues[issues.closed]\n",
"\n",
"time_to_close = []\n",
"for x, y in zip(stuff.closed_at, stuff.created_at):\n",
" days = (x - y).total_seconds() / (3600. * 24)\n",
" time_to_close.append(days)\n",
"\n",
"time_to_close_id = pd.Series(time_to_close, index=stuff.id)\n",
"time_to_close = pd.Series(time_to_close, index=stuff.created_at)\n",
"time_to_close_id\n",
"\n",
"time_to_close = time_to_close.sort_index()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Time to closed\n",
"stuff = issues[issues.closed]\n",
"\n",
"time_to_closed = []\n",
"for created_at, closed_at in zip(stuff.created_at, stuff.closed_at):\n",
" days = (closed_at - created_at).total_seconds() / (60. * 60. * 24)\n",
" time_to_closed.append(days)\n",
"\n",
"time_to_closed_id = pd.Series(time_to_closed, index=stuff.id)\n",
"time_to_closed = pd.Series(time_to_closed, index=stuff.created_at)\n",
"\n",
"time_to_closed = time_to_closed.sort_index()\n",
"#time_to_closed.resample('M', how='mean').plot()\n",
"time_to_closed['2011-08':].resample('M', how='mean').plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"<matplotlib.axes.AxesSubplot at 0x10d5e3810>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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huMxMSEjQyfuHH/QSm7nJu3Vr/diTBg/Ww/jeeMOz1zXTzz/r/Tc90SN96CH9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\nJGELIYRN/H9CjAzF77GT7gAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10d6b7a10>"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a_series = pd.Series(comments.author.values, index=comments.created)\n",
"a_series = a_series.sort_index()\n",
"a_series"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 57,
"text": [
"created\n",
"2010-09-30 02:11:47 wesm\n",
"2010-09-30 02:22:45 wesm\n",
"2010-09-30 22:30:39 wesm\n",
"2010-10-03 17:25:35 hector13\n",
"2010-10-03 19:18:08 wesm\n",
"2010-11-19 18:31:27 wesm\n",
"2010-11-19 18:42:08 wesm\n",
"2010-12-11 06:05:26 wesm\n",
"2010-12-11 06:11:59 wesm\n",
"2010-12-11 06:14:32 wesm\n",
"2010-12-11 06:19:58 wesm\n",
"2010-12-11 19:50:13 wesm\n",
"2010-12-11 22:53:14 wesm\n",
"2010-12-14 19:53:34 wesm\n",
"2010-12-14 20:07:42 tgefell\n",
"...\n",
"2013-06-13 03:54:25 cpcloud\n",
"2013-06-13 04:06:26 cpcloud\n",
"2013-06-13 04:08:57 selasley\n",
"2013-06-13 04:19:22 cpcloud\n",
"2013-06-13 04:57:48 cpcloud\n",
"2013-06-13 05:23:50 cpcloud\n",
"2013-06-13 07:33:33 jreback\n",
"2013-06-13 08:58:43 hayd\n",
"2013-06-13 09:07:11 hayd\n",
"2013-06-13 10:11:11 hayd\n",
"2013-06-13 10:23:44 jreback\n",
"2013-06-13 10:34:23 hayd\n",
"2013-06-13 11:48:30 hayd\n",
"2013-06-13 11:53:26 nipunreddevil\n",
"2013-06-13 12:20:09 cpcloud\n",
"Length: 10962, dtype: object"
]
}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a_series.value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 58,
"text": [
"wesm 2553\n",
"jreback 2172\n",
"y-p 1251\n",
"cpcloud 825\n",
"chang 548\n",
"hayd 221\n",
"lodagro 212\n",
"stephenwlin 199\n",
"adamklein 147\n",
"jseabold 127\n",
"timmie 118\n",
"takluyver 90\n",
"jtratner 90\n",
"michaelaye 65\n",
"dalejung 60\n",
"...\n",
"jtornero 1\n",
"xyang2013 1\n",
"moconnell 1\n",
"bgbg 1\n",
"tlmaloney 1\n",
"Anaphory 1\n",
"maj888 1\n",
"drbunsen 1\n",
"e9t 1\n",
"cournape 1\n",
"Lamarth 1\n",
"dosequis 1\n",
"mtkni 1\n",
"languitar 1\n",
"danxshap 1\n",
"Length: 513, dtype: int64"
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# https://github.com/blog/1204-notifications-stars\n",
"# github started notifying participants when a comment\n",
"# is posted.\n",
"# 8/6/2012\n",
"\n",
"a_series = pd.Series(comments.author.values, index=comments.created)\n",
"a_series = a_series.sort_index()\n",
"\n",
"def most_common(x):\n",
" x = x.dropna()\n",
" if len(x) > 0:\n",
" counts = x.value_counts()\n",
" return counts.index[0], float(counts[0]) / counts.sum()\n",
" #return counts.index[0] \n",
" else:\n",
" return None\n",
"\n",
"def nparticipants(x):\n",
" return len(x.unique())\n",
" \n",
"results = a_series.resample('M', how=['count', most_common, nparticipants])\n",
"results"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>most_common</th>\n",
" <th>nparticipants</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-09-30</th>\n",
" <td> 3</td>\n",
" <td> (wesm, 1.0)</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-10-31</th>\n",
" <td> 2</td>\n",
" <td> (hector13, 0.5)</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-11-30</th>\n",
" <td> 2</td>\n",
" <td> (wesm, 1.0)</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-31</th>\n",
" <td> 30</td>\n",
" <td> (wesm, 0.733333333333)</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-31</th>\n",
" <td> 13</td>\n",
" <td> (wesm, 0.846153846154)</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-02-28</th>\n",
" <td> 11</td>\n",
" <td> (wesm, 0.909090909091)</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-03-31</th>\n",
" <td> 10</td>\n",
" <td> (wesm, 0.5)</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-04-30</th>\n",
" <td> 0</td>\n",
" <td> None</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-05-31</th>\n",
" <td> 6</td>\n",
" <td> (wesm, 0.5)</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-06-30</th>\n",
" <td> 27</td>\n",
" <td> (wesm, 0.925925925926)</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-31</th>\n",
" <td> 58</td>\n",
" <td> (wesm, 0.603448275862)</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-08-31</th>\n",
" <td> 20</td>\n",
" <td> (wesm, 0.75)</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-09-30</th>\n",
" <td> 76</td>\n",
" <td> (wesm, 0.763157894737)</td>\n",
" <td> 11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-10-31</th>\n",
" <td> 176</td>\n",
" <td> (wesm, 0.636363636364)</td>\n",
" <td> 18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-11-30</th>\n",
" <td> 185</td>\n",
" <td> (wesm, 0.681081081081)</td>\n",
" <td> 26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-31</th>\n",
" <td> 219</td>\n",
" <td> (wesm, 0.62100456621)</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-31</th>\n",
" <td> 305</td>\n",
" <td> (wesm, 0.481967213115)</td>\n",
" <td> 31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-29</th>\n",
" <td> 216</td>\n",
" <td> (wesm, 0.444444444444)</td>\n",
" <td> 25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-03-31</th>\n",
" <td> 227</td>\n",
" <td> (wesm, 0.220264317181)</td>\n",
" <td> 36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-04-30</th>\n",
" <td> 241</td>\n",
" <td> (wesm, 0.477178423237)</td>\n",
" <td> 37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-31</th>\n",
" <td> 325</td>\n",
" <td> (wesm, 0.510769230769)</td>\n",
" <td> 30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-06-30</th>\n",
" <td> 319</td>\n",
" <td> (wesm, 0.485893416928)</td>\n",
" <td> 45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-07-31</th>\n",
" <td> 224</td>\n",
" <td> (wesm, 0.334821428571)</td>\n",
" <td> 43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-08-31</th>\n",
" <td> 188</td>\n",
" <td> (wesm, 0.356382978723)</td>\n",
" <td> 46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-09-30</th>\n",
" <td> 333</td>\n",
" <td> (wesm, 0.525525525526)</td>\n",
" <td> 47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-10-31</th>\n",
" <td> 261</td>\n",
" <td> (wesm, 0.329501915709)</td>\n",
" <td> 48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-30</th>\n",
" <td> 699</td>\n",
" <td> (wesm, 0.367668097282)</td>\n",
" <td> 73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31</th>\n",
" <td> 782</td>\n",
" <td> (wesm, 0.313299232737)</td>\n",
" <td> 70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-31</th>\n",
" <td> 453</td>\n",
" <td> (wesm, 0.251655629139)</td>\n",
" <td> 57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-02-28</th>\n",
" <td> 641</td>\n",
" <td> (jreback, 0.307332293292)</td>\n",
" <td> 55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-03-31</th>\n",
" <td> 1287</td>\n",
" <td> (jreback, 0.334887334887)</td>\n",
" <td> 79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-04-30</th>\n",
" <td> 1132</td>\n",
" <td> (y-p, 0.320671378092)</td>\n",
" <td> 84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-05-31</th>\n",
" <td> 1278</td>\n",
" <td> (jreback, 0.412363067293)</td>\n",
" <td> 82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-06-30</th>\n",
" <td> 1213</td>\n",
" <td> (jreback, 0.382522671063)</td>\n",
" <td> 54</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 59,
"text": [
" count most_common nparticipants\n",
"created \n",
"2010-09-30 3 (wesm, 1.0) 1\n",
"2010-10-31 2 (hector13, 0.5) 2\n",
"2010-11-30 2 (wesm, 1.0) 1\n",
"2010-12-31 30 (wesm, 0.733333333333) 4\n",
"2011-01-31 13 (wesm, 0.846153846154) 2\n",
"2011-02-28 11 (wesm, 0.909090909091) 2\n",
"2011-03-31 10 (wesm, 0.5) 3\n",
"2011-04-30 0 None 0\n",
"2011-05-31 6 (wesm, 0.5) 2\n",
"2011-06-30 27 (wesm, 0.925925925926) 2\n",
"2011-07-31 58 (wesm, 0.603448275862) 9\n",
"2011-08-31 20 (wesm, 0.75) 5\n",
"2011-09-30 76 (wesm, 0.763157894737) 11\n",
"2011-10-31 176 (wesm, 0.636363636364) 18\n",
"2011-11-30 185 (wesm, 0.681081081081) 26\n",
"2011-12-31 219 (wesm, 0.62100456621) 22\n",
"2012-01-31 305 (wesm, 0.481967213115) 31\n",
"2012-02-29 216 (wesm, 0.444444444444) 25\n",
"2012-03-31 227 (wesm, 0.220264317181) 36\n",
"2012-04-30 241 (wesm, 0.477178423237) 37\n",
"2012-05-31 325 (wesm, 0.510769230769) 30\n",
"2012-06-30 319 (wesm, 0.485893416928) 45\n",
"2012-07-31 224 (wesm, 0.334821428571) 43\n",
"2012-08-31 188 (wesm, 0.356382978723) 46\n",
"2012-09-30 333 (wesm, 0.525525525526) 47\n",
"2012-10-31 261 (wesm, 0.329501915709) 48\n",
"2012-11-30 699 (wesm, 0.367668097282) 73\n",
"2012-12-31 782 (wesm, 0.313299232737) 70\n",
"2013-01-31 453 (wesm, 0.251655629139) 57\n",
"2013-02-28 641 (jreback, 0.307332293292) 55\n",
"2013-03-31 1287 (jreback, 0.334887334887) 79\n",
"2013-04-30 1132 (y-p, 0.320671378092) 84\n",
"2013-05-31 1278 (jreback, 0.412363067293) 82\n",
"2013-06-30 1213 (jreback, 0.382522671063) 54"
]
}
],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.rolling_mean(a_series.resample('D', how='count'), 30).plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 60,
"text": [
"<matplotlib.axes.AxesSubplot at 0x10d6af350>"
]
},
{
"metadata": {},
"output_type": "display_data",
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TTcJu27YtVqxYge3bt2P9+vWYPn16zWNHjhzBlClTsHfvXoSEhOCr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\nrxJ/DCEAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10d691f10>"
]
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"results['count'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 61,
"text": [
"<matplotlib.axes.AxesSubplot at 0x10e0a7250>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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wB4/04D1HavBCBIjvv4eEBPj3v/WY8XOdOAHl5bB/v/7ZsgWysmD7dmjRwjtt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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10e0bb1d0>"
]
}
],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def nice(x):\n",
" return x.rename(lambda x: x.strftime('%b-%Y'))\n",
"\n",
"plt.figure(figsize=(4, 8))\n",
"nice(results.nparticipants).plot(kind='barh')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 62,
"text": [
"<matplotlib.axes.AxesSubplot at 0x10e320c90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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JaAlN/h23JtpcJ+/r0KKjoxEbG4sHDx482ZCqHRsSEoKDBw+iqKgI6enp8Pf3\nR1JSEidQT09PBwBcv34doaGhSEhIQN++fQHU9MiuX7/O2bp+/Tp69+6t5uPJubzS0lIEBwfjP//5\nDzdc1T/JAvDBt33mw5B8tLk5tFosLS25iOa1jYmPjw++/vprAMD//d//YejQoQBqNm0cOHAg5s+f\nj5CQEIhEIowfP54TqHt6eqK4uBhjxozBBx98gMGDB3N+rK2t0aVLF5w6dQpEhISEBI0C9boNaUVF\nBcaPH4+pU6ciNDSU76+CwWDwjY57iRwWFhbc59u3b1OnTp1o1apVRER09epV8vf3J4lEQiNGjKD8\n/Hzu2B07dpCJiQkdO3ZMo93Vq1dT586dOUG5TCaju3fvEhHRmTNnyMPDgxwcHCgqKoo7JzU1lWxs\nbKhz587UvXt38vDwICKihIQEMjMzU7F19uxZFX8AG3KyJLQE3fzIeUab62QLaxuBqQQYQsNYlAIG\ntbBWSBARr0mhUBi9DyHcQ1vxoYvGrM3OoTEYDIa+YEPORmBDTuFiLEOvtoo2Q07WoDUCE6cLGeMQ\nabdVDHIO7fr163jhhRfg7OwMR0dHREdHo7Kyst7j161bx8UVeBLhCtOTBeCDb/v68SGEvcr04aNN\nzqEREUJDQxEaGors7GxkZ2fj/v37WLFiRb3nrF+/Hg8fPtRYJ0xhOoPB0BnEIz/99BMNHTpUpay0\ntJS6d+9ODx48oEWLFpGHhwdJJBLauHEjbdiwgdq3b09isZj8/f0btF1dXU3dunWjiooKKigooP79\n+3N1iYmJNGfOHJXj65M91SKVSuny5ctq5QBbhybcxOvjz2gh2vx9eBWnZ2VlYcCAASplFhYWsLOz\nw9atW3H16lWcPXsWJiYmKCoqgqWlJT766CMkJyejW7duDdrWnzAd0I84neVbI29I4uy2njdocToR\n0YYNG2jhwoVq5TKZjMaPH69RfG5vb0+FhYUN2tWXMJ1IXz00hQB8GOM9qD/++gjPJgQfhhrGjtc5\nNDc3N6SlpamUlZaWIj8/v0lvMHbt2tUGhOkMBkNn6L5dVcXLy4u++OILIiJSKpU0a9YsWrx4MW3e\nvJkmTJjAbSN07949IiISi8WUm5ur0VZRURFJJBJKSkpSqxs0aBCdPHmSqquradSoUVwA4lqmTZum\n0kN7/Pgx+fv707p16xq8fuilh8ZS6yTeH39GC9Dm78P7XzQ/P59CQkLIycmJHBwcaP78+VRRUUFK\npZLeeOMmTp0bAAAgAElEQVQNcnNzI6lUSh9//DEREW3cuJFcXFw0vhTQtzCdiDVowk6sQTNktPn7\nsIW1jcCUAsJFk1KgdsdkPhGCD33cg0EurBUCRMIWKxuDfT58MNmT8GA9tEbQ5n8JBoPRcrT57bHI\n6U2ADTuFBROlCxdeh5zvvvsup5WUy+VITU1tsc2XX34Z/fv3h1gsxsyZM6FUKrm6+fPnw8nJCVKp\nFBkZGVx5REQEevXqBbFYrGLrn//8J1xdXSGVShEaGoqSkpJ6vBLPSSEAH8ZzD2VlRagPIegs9eGj\nzWk5T5w4gX379iEjIwNnz57Fzz//DFtb2xbbnTJlCi5duoTz58+jvLwcW7duBQDs378fly9fRk5O\nDrZs2YLIyEjunBkzZnDazroEBgYiKysLZ8+ehbOzM957770WXx+DwWg9eGvQbt26hR49esDMzAwA\n0K1bN1hbWyMtLQ1+fn7w8vJCUFAQbt26BaBG+hAdHQ25XA6xWIzTp09rtDtq1Cju88CBAzmJ0+7d\nuzFt2jQAgLe3N4qLiznbzz//PCwtLdVsBQQEcMGHvb29VRbn6hc/Afjg275+fPD95k4oPvRxD9rA\nW4MWGBiI/Px8uLi44LXXXsOxY8dQWVmJqKgo7Ny5E2fOnMGMGTO4nTdEIhHKy8uRkZGBTz75BBER\nEQ3ar6ysxJdffomgoCAAQEFBgUoP0MbGpll6zm3btmH06NFa3CmDwTAUeHsp0LlzZ6SlpSElJQUK\nhQKTJk1CTEwMsrKyMGLECABAVVUVnnnmGe6c8PBwADU9qtLSUpSWlqJLly4a7c+bNw++vr4YMmQI\nV/bkG5GmTua/++67aN++vdoWQ38zHfyK0zMBROvQnqZ8bZmx2q9rWxf2/so9IZZet24dZDIZr2Ls\nzMxMREdH82a/llrBt7HYN3hxel127NhBw4YNo8GDB2us9/PzUxG82tnZUUlJCQUGBpJMJqNXX32V\nq1u5ciWNHz9e5fw5c+ZQYmIil3dxcaFbt25x+dzcXE4lUJe4uDjy8fGh8vJyjdcFMHG6YdjXpY/6\nH3shCMf14cNQxem8NWi///47ZWdnc/kVK1bQvHnzyMnJiU6cOEFERBUVFZSVlUVENQ3a3LlziYgo\nJSWFJBKJRruff/65xgZo3759NGrUKCIiOnHiBHl7e6vUa2rQDhw4QG5ubpx8ShP6adBY0m/i7bFn\n6BCDatDS0tLIx8eH3NzcSCKRUFhYGBUWFlJmZiYNHTqUpFIpubu709atW4mopkGLjo4muVxOYrGY\nTp8+rdFuu3btyNHRkdNfrl69mqt77bXXyMHBgSQSCaWlpXHlkydPJmtra2rfvj3Z2NjQtm3biIjI\n0dGR7OzsOFuRkZFq/liDJsTEGjRjwKAatObi5+en0ggZCmzIaSj2demj/sdeCMNBffgw1CEnUwo0\nCaYUEBIWFupLeBjCgGk5G4FpORmM1oHttsFgMNo0rEFrAiKRiCUjT126NBx0pxYh6Cz14aPNaTl3\n7doFExMT/P777zqz2TrCdABGIrpuXR+GfQ8NCdIZAkLHLyY4Jk6cSCEhIfTWW2/pzOb+/fu5z+Hh\n4fTpp58SkeoatJMnT6qsQTt27Bilp6errUE7dOgQVVVVERHR0qVLaenSpRp9AmzZhjASb486gye0\n+Zvx0kO7f/8+Tp06hU2bNuGbb74BUNNFDQkJ4Y55/fXXER8fD6BmpwxXV1d4eXlh/vz5KsfVRZjC\ndAaDoSt4adB2796NoKAg2NnZwcrKCunp6Wq6ytq5jUePHmHu3Lk4ePAgzpw5gz///LNRDabwhOnJ\nAvDBt339+BDC/JY+fBjqHBov69ASExOxcOFCAMCLL76IxMREBAcHqx1HRLh06RL69euHPn36AKgR\nqG/ZsqVB+/oVpgP6Eafr0p6mPBqpN3T7urm+xsTSmZmZDdbrSpzOdyTypt6vIdk3SHF6YWEhderU\nifr06UP29vZka2tLdnZ2lJKSQqNHj+aOmzVrFm3fvp0yMzPJ19eXK9+9ezcX9by1helEbA5NOInN\noRkb2vzNdD7k3LFjB6ZOnYq8vDzk5ubi2rVr6Nu3L6qrq/Hbb7+hoqICxcXF+PnnnyESieDi4oIr\nV67g6tWrAIBvvvmG6139+OOPyMjI4HpsW7duxaFDh/DVV1+p+Bw7diy++OILAMDJkyfRtWtX9OrV\nq8HrPHjwIP73v/9h9+7d6NChg66/BgaD0QrovEH7+uuvMX78eJWysLAwfP3115g4cSI8PDwwadIk\neHp6AgA6dOiATz75BEFBQfDy8kKXLl3q3QMtMjISd+7cweDBgyGXy/HOO+8AAEaPHo1+/frB0dER\nc+bMwSeffMKdEx4eDh8fH2RnZ8PW1hZxcXEAgKioKNy/fx8BAQGQy+WYN2+err+KZpAsAB9829eP\nDyHMb+nDR5uZQzty5IhaWVRUFPf5gw8+UKsfNmwYLl68CAB47bXXMHDgQI22Kysr6/W7adMmjeWJ\niYkay3Nycuq1xWAwjBOD0HKuW7cO8fHxqKiogKenJz7//HODGQayEHbCgIWuMz600XIaRINmyDBx\nOoPROjBxupHC5lSYD2Pz0Wbm0IQIG3YaB2xYyWiVHpq5uXmD9X5+fkhLS1MrP3z4MLy8vCCRSODl\n5QWFQsHVpaWlQSwWw8nJCQsWLODKjx07Bk9PT5iZmWHnzp1ceWZmJnx8fLjI7t9++20DV0QsGUHS\nhQBdCDEz9eHDUONytspqQ3Nz8wbr69uOOyMjg27evElERBcuXKDevXtzdQMHDqRTp04REdGoUaPo\nwIEDRESUl5dH586do6lTp9KOHTu447Ozs+ny5ctERFRQUEDW1tZUUlKi5hNgC2uNJ7HFs0JCm79n\nq82hHT16tF6xen3IZDI8/fTTAAA3NzeUl5ejsrISN2/eRFlZGQYNGgQAmDp1Knbt2gUA6NOnD8Ri\nMSdEr8XJyQkODg4AAGtra/Ts2RN3797V2f01j2QB+ODbvn58CGF+Sx8+DHUOzWBeCtSK1ZvKzp07\nMWDAAJiZmeHGjRuwsbHh6nr37t0scXpqaioqKyu5Bo7BYBgnRvlSICsrC8uWLcPhw4dbbOvmzZuY\nOnUqJ53SzHTwK06vi67sCTHv1+jxLRVL15YZu3jcGPMGKU5vCubm5vTLL7+oiNVnzpxJ8fHxRFQz\nh3bmzBlKSkriYmbWzqnl5+eTs7Mz/frrr9y5BQUF1L9/fy7/1Vdf0Zw5c1R8Tp8+nXbu3KlSVlJS\nQp6enmrldQGbQzOixObQhIQ2f89WG3L26dNHRaz+pGRKJBJh3LhxyMjIQEZGBjw9PVFcXIwxY8bg\ngw8+wODBg7ljra2t0aVLF5w6dQpEhISEBIwbN07FHhGh5juqoaKiAuPHj8fUqVMRGhrK7802SrIA\nfPBtXz8+hDC/pQ8fbA7tL5RKJZ566inY2NhoFKs3xKZNm/DHH39g1apVkMvlkMvl+PPPPwEAn3zy\nCWbNmgUnJyc4Ojpymz+ePn0atra22LFjB+bMmcPFFvj222+RkpKC7du3c7bOnTvH340zGAze0bv0\n6ezZs5gzZw5OnjypT7daU/OiQq9fEUNrmExNSGgjfdLrS4HNmzdj48aNWL9+vT7d6gCmFDAGWER0\nhl6HnHPnzkVWVhZGjBihT7ctpnb+ja+kUCiM3och3IMuZE9CmN/Shw82h8ZgMBg8w7YPagQmTDdu\nmGDdeDHo7YMaE6Q3ldaJnk4sGWliEdPbFnpr0HTV05kyZQouXbqE8+fPo7y8HFu3bgVQE6z48uXL\nyMnJwZYtWxAZGcmdM2PGDBw8eFDNVmBgILKysnD27Fk4Ozvjvffe08k1Np9kAfjg275+fAhhfksf\nPtgcGhoWpNvb22PlypUYMGAAJBIJfv/9d402WPR0BoNRH636UqCuIF0kEsHKygppaWmIjIzEmjVr\nGjxXWNHT/QTgg2/7+vEhhL3K9OHDUPdDMyhxeq0EydPTE99//32Dx+o3evp08C9OZ3l+8voRm7N8\ny/NGJU5vTJBub29PhYWFRER0+vRp8vPzI6LWj54O6EOcrhCAD0O9h+Y94gqFolnHa4MQfOjjHrRp\nnvTaQ6srSH/48CGOHDmCoUOHNnjOjz/+qJKvjZ7+888/q5SPHTsWmzZtwuTJk5sdPf3o0aMGEzaP\nwWC0AB4aVjUqKyupe/fuRES0ZMkScnJyosDAQAoLC9PYQztz5gwNGzZMcwvcrh05Ojpy2wqtXr2a\nq3vttdfIwcGBJBKJyhbekydPJmtra2rfvj3Z2NjQtm3biIjI0dGR7OzsOFuRkZFq/qCXHhpL/CW9\nPOIMHtDmb6eXhbXGJkivCxOnGztMsG6sGOTC2s2bN+Oll17CO++8w7crHhGxZKSpuYJ1IawR04eP\nNrsOzVgF6XUhMm7huD58GOo9MNlT24JpORtBm24vg8FoOQa/H5qxwgTqjcNE4AxDQOdDThMTE7zy\nyitcXqlUwsrKSkXypC3l5eUYM2YMXF1d4eHhgeXLl3N1jx8/xqRJk+Dk5IRnn30WV69e5eqCgoJg\naWmpdg0NCd1VIZ6Twuh96EMELoS5J6H4aDNzaJ07d0ZWVhYePXoEADh8+DBsbGx01stZsmQJLl68\niIyMDBw/fpwTncfGxqJ79+7IycnBwoULsXTpUpVzEhIS1GzVJ3RnMBjGCS8vBUaPHo19+/YBABIT\nExEeHs6NhVNTU+Hj4wNPT08MGTIE2dnZAABfX1+cPXuWs/Hcc8/h/PnzKnY7duwIX19fAICZmRk8\nPT05veaePXs4cXpYWJjKwlt/f3+N2xc9KXRvPXG6n0B88IsQNJBC8WGoWk5eGrRJkybh66+/xuPH\nj3H+/Hl4e3tzda6urkhJSUF6ejpWrVqFN998EwAwc+ZMTseVnZ2Nx48fq+1fVpfi4mL88MMPGD58\nOADgxo0bnDi9Xbt2+Mc//oF795o2p1MrdK/bwDEYDOODl5cCYrEYeXl5SExMxJgxY1TqiouLMXXq\nVFy+fBkikQiVlZUAgAkTJmD16tX43//+h23btmHGjBn12lcqlQgPD8eCBQu0F7HWQZPQXZXp4Fec\nngkgWof2NOVry/i0z794mbsTnsTS69atg0wm41WMnZmZiejoaN7s11L3OzMG+wYpTjc3Nyciorff\nfpu6d+9OFy5cIIVCQcHBwURENG3aNNq4cSMREeXl5ZG9vT13bmRkJH333XfUr18/Ki4uJqVSSVKp\nlGQyGb311lvccTNmzKAFCxao+B05ciSdOHGCiGqkVj169FCpT05O5q6hLpqE7nUBmDi9aUnnj5Ia\nQhB1C8VHmxOnR0REwNLSEu7u7iqtemlpKZ555hkAQFxcnMo5s2bNQnBwMHx9ffGPf/wDAJCZmaly\nTExMDEpLSxEbG6tSPnbsWMTHx+PZZ5/Fjh07uKFoLaRhPUt9Qnf94ycQH/wihLknofgw1Dk0nf+3\namFhoVaWnJxMISEhRER04sQJcnZ2JrlcTjExMdS3b1+VY/v3708//vijRtv5+fkkEonIzc2NE5TH\nxsYSEdGjR4/oxRdfJEdHR/L29qbc3FzuvOeee46srKyoY8eOZGNjQ4cOHSKihoXutUAvPTQhJP57\naIy2hTbPlEE9hTdu3CBnZ+fWvgwV9NOg8T0c1IcP/h8lIQzVhOKjzQ05m8sXX3yBmJgYrF27trUv\nRQNMKdAYHTvqJqoXg9ESmJazEZiWk8FoHQxy+yAGg8HQF6xBawK10alY4j916dKt3r+DEDSQQvHR\nZrScmti1axdMTEzqjbXZVFonajoAIxeO68eHbuyzSOeMFqHjFxMamThxIoWEhKgsjm0KVVVVKvn9\n+/dzn8PDw+nTTz8lIqJ9+/bRqFGjiIjo5MmT5O3tzR137NgxSk9PV4v4dOjQIc7+0qVLaenSpRqv\nAWDLNvSb9PJIMowAbZ4F3nto9+/fx6lTp7Bp0yZ88803AGq6q0OHDkVwcDD69++PyMhIbvLP3Nwc\nixcvhkwmU4tBwKKmMxiMhuC9Qdu9ezeCgoJgZ2cHKysrpKenAwBOnz6NTZs24bfffsMff/zBBRZ+\n+PAhnn32WWRmZsLHx0ejTWFFTQee1EMapw++7Qtj7kkoPgx1Do33dWiJiYlYuHAhAODFF19EYmIi\ngoODMWjQIE6AGh4ejl9++QVhYWEwNTVFWFhYgzb1GzUd0I84XZf2NOXRSL2h2G/dSOe1Uju+xel8\n308txmTfIMXpdSksLKROnTpRnz59yN7enmxtbcnOzo4UCgX5+vpyx8XGxtIbb7xBRH+L24lqBOet\nGTWdiM2hsTk0RmuhzbPA69Pz2Wef0dy5c1XKfH19adWqVdSxY0fKzc2lqqoqCgwMpO+//56IVBu0\nJ/n88881NkB1XwqcOHFC5aUAkeYG7cCBA+Tm5kZ3795t8B5Yg8YaNEbrYHAN2rBhw9SE5hs2bCBX\nV1caOnQojRkzhlxcXFQilmsSt9ei76jpRPpq0BQC8KEr+/U/kkLQQArFR5vUch45ckStLCoqChKJ\nBGvWrMEPP/ygVl9aWlqvvdrNIDWxadMmjeWJiYkay3Nycuq1xWAwjJNW0XIePXoUH374Ifbs2aNv\n182GhbDTLywcHqMWbbScTJzeCEyczmC0DkycbqSwdUnMh7H5aLPr0IQAG3byCxtmMnQFG3I2Qk1j\nxr4ifmHDeoY6BjfkNDExwSuvvMLllUolrKysEBISorXNWkmUh4cHpFIpvv32W64uNzcX3t7ecHJy\nwuTJk7m3opcuXcLgwYPRoUMHfPjhh9zx+fn5GDZsGNzd3eHh4YENGzZofV0MBqP14bVB69y5M7Ky\nsvDo0SMAwOHDh2FjY9OsIVzdLYJqbSYkJODChQs4ePAgoqOjuaUeS5cuxaJFi5CTkwNLS0suMlT3\n7t2xceNGLF68WMWWmZkZ1q5di6ysLJw8eRIff/wxLl682JJb1pJkAfjg274w5p6E4sNQ59B4fykw\nevRo7Nu3D0DNmrDw8HCuG5mamgofHx94enpiyJAhyM7OBgBs374dY8eOxfDhwxEQEKBiz8nJCQ4O\nDgAAa2tr9OzZE3fv3gURQaFQYMKECQCAadOmYdeuXQAAKysreHl5wczMTMXW008/DZlMBqBmlw9X\nV1cUFBTw9E0wGAy+4f2lwKRJk/D2228jODgY58+fx8yZM5GSkgIAcHV1RUpKCkxNTfHTTz/hzTff\nxI4dOwAAGRkZOH/+PLp27Vqv7dTUVFRUVMDBwQF//vknunbtym0J1Lt372btuJGXl4eMjAx4e3tr\nqJ0OfsXpddGVPWPL/5VrQLzMZyTwunEm9SGOb8r9trW8wYvTa3WZXl5eFBcXRytWrFCJYH7t2jUa\nN24ceXh4kFgsJldXVyKqEY1HREQ0aLugoIBcXFzo1KlTRER09+5dcnR05OqvXbumpt9cuXIlrVmz\nRs1WWVkZDRgwgJKSktTqoBfpU1tPvD6GDCNFm+dCL+vQxo4di8WLF6sMNwHgX//6F4YPH47z58/j\nhx9+QHl5OVfXqVMnAMCpU6cgl8shl8uxd+9eADXyqODgYPznP//BoEGDANTMkxUXF6O6uhoAcP36\ndfTu3bvRa6usrERYWBimTJmCcePG6eyem0eyAHzwbV8Yc09C8WGoc2j1Djnv3Wt4XVC3bvUHs3iS\niIgIWFpawt3dXeWLKC0txTPPPAMAiIuL03iut7e3SoyAiooKjB8/HlOnTkVoaChXLhKJMGzYMHz3\n3XeYNGkS4uPj1Rqouo1pbX7mzJlwc3NDdHR0k++HwWAYKPV13Wr3MOvTpw+JRCLq1q0bdevWjUQi\nEdnb2zep+6dp54zk5GQKCQkhopqtfpydnUkul1NMTAz17duXiIi2b99OUVFRGm0mJCSQmZkZt0uG\nTCajs2fPEhHRlStXaNCgQeTo6EgTJ06kiooKIiK6efMm2djYUJcuXahr165ka2tLZWVllJKSQiKR\niKRSKWfrwIEDKv7AhpxsyMloFbR5LhpdWPvqq69i/Pjx3BbVBw4cQFJSErZs2cJ/a2sAMJUA/zCl\nAEMTvCysPXHihMp++6NGjcKvv/7a/KszYoiI16RQKIzeR0vsN7UxE8Lck1B8GN0cWi3PPPMM3nnn\nHUyZMgVEhK+++qpJk+0MBoOhbxodchYWFmLVqlXc2rGhQ4firbfeatZLAWOmrQ452TCQ0drwuh/a\ngwcP0LlzZ60uzJhpu+J0JhhntC68zKH9+uuvcHNzQ//+/QEAZ8+exbx585pk3NTUlFtDJpfLce3a\ntXqP9fPzQ1paWqM2X375ZfTv3x9isRgzZ85U0XrOnz8fTk5OkEqlKks9IiIi0KtXL4jFYhVb//zn\nP+Hq6gqpVIrQ0FCUlJQ06b50T7LR+xDCvBDzYTj2taXRBi06OhoHDx5Ejx49AABSqRRHjx5tkvFO\nnTohIyODS3Z2dvUe29Sh3ZQpU3Dp0iWcP38e5eXl2Lp1KwBg//79uHz5MnJycrBlyxZERkZy58yY\nMQMHDx5UsxUYGIisrCycPXsWzs7OeO+995p0DQwGwzBpklLgyYaoXTvtJaBpaWnw8/ODl5cXgoKC\ncOvWLa4uISEBcrkcYrEYp0+f1nj+qFGjuM8DBw7k9Jq7d+/GtGnTANQsxi0uLuZsP//887C0tFSz\nFRAQwGk/vb29cf36da3vq2X4Gb2PulpI5kP4PvRxD9rQaMtkZ2eH48ePA6hZpb9hwwa4uro2yXh5\neTnkcjkAoF+/fvjmm28QFRWFH374Ad27d8c333yDFStWIDY2FkSE8vJyZGRkICUlBRERETh//ny9\ntisrK/Hll19ye5gVFBTA1taWq7exscGNGzfw9NNPN+lat23bhvDw8Hpqp4N/cbqh5f/KGZB4meWF\nndeLOP3OnTsUHh5OVlZW1KNHD3rppZfozz//bOw0IlIPGnz+/Hnq0qULtypfLBbTyJEjiYjIz89P\nJdafnZ0dlZSU1Gt71qxZtHDhQi4fHBxMv/zyC5cfPny4SnzO+qKnExG98847FBoaqrEOelEKKAzQ\nR6OPhgpCiDXJfBiOfaLmP4NETYjLmZ2dja+++kql7Pjx4xgyZIg2jSfc3d2btTB35MiRuHPnDgYO\nHMipE1atWoXCwkJ8/vnn3HG9e/dGfn4+l2+qOH379u3Yv38/fv7552bcCYPBMEgaa/FkMlmTyjTx\nZA/t8ePH5OjoSCdOnCAiooqKCsrKyiIiIl9fX5o7dy4REaWkpJBEItFo8/PPPycfHx8qLy9XKd+3\nbx+NGjWKiGo0ot7e3ir1mnpoBw4cIDc3N7p792699wC99NAMMTX/f0cGQ5do8wzW20M7ceIEfv31\nV9y9excfffQRauwDZWVl3BY9jfHkm8v27dtjx44dmD9/PkpKSqBUKrFw4UK4ublBJBKhQ4cO8PT0\nhFKpxLZt2zTajIyMhL29PQYPHgwACAsLQ0xMDEaPHo39+/fD0dERnTt3Vtm9Izw8HEePHkVhYSFs\nbW3x9ttvY8aMGYiKikJFRQW3K+7gwYPxySefNOneGAyG4VHvwtqjR49CoVDgs88+w9y5c7lyCwsL\nhISEwMnJSW8X2ZowpUDTSK6zyytfMB+G40Mf96DNwtp6e2i+vr7w9fXF9OnTtX/jIBCa+6U2FyE8\n4AyGIdCo9OnOnTv473//i99++43bUVYkEuHIkSN6ucDWRpv/JRgMRsvhRfpUKzW6cuUKVq5cCXt7\ne3h5eWl9kcaISCRiqRmpS5e2sXEBw/BotEErLCzErFmz0L59e/j6+iIuLk6r3pm5ublWF1iXhgID\n37t3DwEBAXB2dkZgYCCKi4u58mHDhsHCwgJRUVHc8eXl5RgzZgxcXV3h4eGB5cuXN+CZeE4KAfj4\n235ZWVED36X2CEEDKRQfRqvlbN++PYCaGJZ79+5Feno6ioqa/8DqYnJdU2DgS5cuAQDef/99BAQE\nIDs7G8OHD8f7778PAOjQoQPeeecdrFmzRs3ekiVLcPHiRWRkZOD48eMa9Z4MBsOIaGxdx549e6io\nqIjOnTtHvr6+JJfLaffu3c1eH2Jubk7379+n4cOHk6enJ4nFYs5Obm4u9e/fn1599VVyd3enwMBA\ntXVmmnjhhRfop59+IiIiFxcXunXrFhHVxBBwcXFROTYuLo5ef/31em0tWLCAtm7dqlYOtNV1aGwN\nG6N10eY50tuTZ25uTlVVVVRaWkpEqnE0c3NzqV27dlywk4kTJ9KXX37ZoL3c3Fyys7OjsrIyIiLq\n2rUrV1ddXa2SJ6oJvFJfg1ZUVET9+vWj3NxctTrWoLEGjdE6aPMcNSp9+v333zFv3jzcunULWVlZ\nOHfuHPbs2YOYmJhm9warq6uxfPlypKSkwMTEBAUFBbhz5w4AoG/fvpBIJACAAQMGIC8vr1479+/f\nx4QJE7B+/XqNc3O1k9NNQalUIjw8HAsWLGhgecp08CtOzwQQ3YzjtcnXlunL/l+1OhYvc554Ekuv\nW7cOMpmMVzF2ZmYmFzaRL7F3bZkx2deLOP3555+nkydPcnKn6upqcnNza3bLaW5uTtu3b6dJkyaR\nUqkkIiJ7e3u6evWqmixpzZo1tHLlSsrPz+dCzH322WdEVCOXCgwMpLVr16rYd3FxoZs3bxLR31HV\n61JfD23GjBm0YMGCeq8beumhKQTgo659fnpoQhB1C8WH0YrTHz58CG9vby4vEolgZmamVeNZUlKC\nnj17wtTUFAqFAlevXm3weBsbG2RmZtZtfOsNDDx27FjEx8dj6dKlTQoyDAAxMTEoLS1FbGysVvej\nO/wE4INv+8LYR0woPgx1kXajDZqVlRUuX77M5Xfs2AFra+tmOVEqlXjqqafw8ssvIyQkBBKJBF5e\nXir7qj05RNQ0ZDx+/Di+/PJLSCQSbp+19957D0FBQVi2bBkmTpyI2NhY2Nvb49tvv+XOs7e3R1lZ\nGe94YaYAACAASURBVCoqKrBr1y4cPnwY5ubm+M9//gNXV1d4enoCAKKiohAREdGse2MwGAZEY124\ny5cvk7+/P3Xs2JGsra3Jx8dH4+R5Q2RmZqrtfmEsgA052ZCT+dC7fSIehpxVVVX49NNP8fPPP+P+\n/fuorq5Gly5dmtVgbt68GRs3bsT69eu1b3VbnbYpUNcWCwv17c4ZDH3QqJbz2WefxYkTJ9rsrhNM\ny8lgtA463W2jFplMhhdeeAEvvvgiOnXqxDkKDQ3V7ioZDAaDJxqVPj1+/Bjdu3fHkSNHsHfvXi61\nJVpb7G0MInIh6BOZD8Oxry2N9tCqqqqwfv16LgzcvXv3sGjRIt4vzLDge8iZDP6XPaj7KCtrm9MI\nDAHT2FsDqVTapLK6mJiYkEwmI3d3d5JKpfThhx9SdXV1s99YPMm1a9fIz8+P3NzcyN3dndavX8/V\nFRYW0ogRI8jJyYkCAgKoqKiIK/fz8yNzc3O1hbVvvvkm2draqsU+qAv08paztRI/byMZDF2gzfPZ\n6JCTiHDv3t9bMd+7dw9VVVUNnlMbMf3ChQs4fPgwDhw4gFWrVrWw6dX9bhsvvPACUlNTW3xdDAbD\nQGisxYuPjydnZ2eKiYmhFStWkLOzM8XHxzd4zpM9nitXrlD37t2JiEipVNLixYtp4MCBJJFIOEkT\nEdH7779PYrGYpFIpLVu2rNHWWFe7bbR+D43vNWL1+dBdD00Ia6uYD8OxT8TDOjQAmDp1KgYMGIAj\nR45AJBIhKSkJbm5uzWo0+/bti6qqKty5cwe7du1C165dkZqaisePH+O5555DYGAgLl68iD179iA1\nNRUdOnRodM+1vLw8ZGRkcLKs27dvo1evXgCAXr164fbt2yrHt2zZyXTwL07XpT1NeWis16VYWZf2\nWitfK7XjW5zO9/3UYkz2dSFOb3QdmjZYWFigrKxMpczS0pLbueP8+fPcEpDS0lJs3rwZP/74I1xd\nXTFz5sxG7d+/fx9+fn6IiYnhNJuWlpYqjWC3bt1Uhsrx8fE4c+YMNm7c2KTrraWmIRTqOjS2xo5h\nuGizDq3ROTRdcOXKFZiamqJnz54AgE2bNiEjIwMZGRn4448/uLiYT158fn4+5HI55HI5FzW9srIS\nYWFhmDJliooAvVevXrh16xYA4ObNm5wvBoPRduC9Qbt79y7mzp3L7ec/cuRIfPLJJ1AqlQCA7Oxs\nPHz4EAEBAYiLi+MiSxUVFcHW1pZr+GbPng2ixnfbANDk3TYMh2Sj9yGEtVXMh+HY1xZeGrTy8nLI\n5XJ4eHggICAAQUFB+Pe//w0AmDVrFtzc3ODp6QmxWIzIyEhUVVVh5MiRGDt2LLy8vCCXy/Hhhx+q\n2a3dbUOhUHA9t9o4AMuWLcPhw4fh7OyMI0eOYNmyZdx59vb2WLRoEbZv3w5bW1vuzeiSJUtga2uL\n8vJyLqI6g8EwXniZQxMSQtawNjc6OoOhT3jRcjIMfbjKYDBq0ctLAUbDsDkV5sPYfBjqHBrroTUB\nIQ87G4MNSxnGBJtDawRhr0NrCmytGqN1MKh1aCYmJli8eDGXX7NmjU70nIcPH4aXlxcXl0ChUHB1\naWlpEIvFcHJywoIFC7jyY8eOwdPTE2ZmZti5c6eKvaCgIFhaWiIkJKTF18ZgMFoX3hq09u3bIykp\nCYWFhQB0N2yzsrLC3r17ce7cOcTHx+OVV17h6iIjIxEbG4ucnBzk5ORwSzr69OmD+Ph4vPTSS2r2\nlixZgoSEBJ1cm/YkG70PIcwLMR+GY19beGvQzMzMMHv2bKxdu1atLi8vD/7+/pBKpRgxYgTy8/NR\nUlKiot968OAB7Ozs1Hb2kMlkePrppwEAbm5uKC8vR2VlJW7evImysjIMGjQIQI0GddeuXQBqGjSx\nWAwTE/Xb9ff31xismMFgGB+8vhSYN28eJBIJlixZolIeFRWFGTNm4JVXXkFcXBzmz5+PpKQkyGQy\nJCcnw8/PD3v37kVQUBBMTU3rtb9z504MGDAAZmZmuHHjBmxsbLi63r1748aNGzq6k+ngV5xeF13Z\n01Ue3N+k9jPQOuJxXUfq1pTX1/3W9cXn/RhTXhfidN52+Kvdkuff//43rV69mouGTkTUo0cPLnp6\nRUUF9ejRg4iIvvrqK5o7dy4REY0bN47bGkgTFy5cIAcHB7py5QoREZ0+fZpGjBjB1R87doyCg4NV\nzpk+fTrt2LFDzZZCoVA7thYIeoPHpiTeHhEGo0G0efZ4X4cWHR2N2NhYPHjw4MmGVO3YkJAQHDx4\nEEVFRUhPT4e/vz+SkpI4mVN6ejoA4Pr16wgNDUVCQgL69u0LoKZHdv36dc7W9evX0bt3bzUfmuby\nWn9ZRrLR+xDCvBDzYTj2tYX3Bs3S0pKLaF7bcPj4+ODrr78GAPzf//0fhg4dCgAwNzfHwIEDMX/+\nfISEhEAkEmH8+PGcQN3T0xPFxcUYM2YMPvjgAwwePJjzY21tjS5duuDUqVMgIiQkJGgUqGtqSDWV\nMRgMI0S3ncS/sbCw4D7fvn2bOnXqRKtWrSIioqtXr5K/vz9JJBIaMWIE5efnc8fu2LGDTExM6Nix\nYxrtrl69mjp37kwymYxLd+/eJSKiM2fOkIeHBzk4OFBUVBR3TmpqKtnY2FDnzp2pe/fu5OHhwdU9\n99xzZGVlRR07diQbGxs6dOiQij+wIWfLHwYGQwu0efbYwtpGaP3haOvClAKM1sKgFtYKCfprqMpX\nUigUBuujqY2ZEOaFmA/Dsa8trEFjMBiCgQ05G8EYh5xsmMgQAtoMOVmD1gjGKU5ngnKG8WOQc2jX\nr1/HCy+8AGdnZzg6OiI6OhqVlZX1Hr9u3ToursCTCFeYnsy/BwHMqTAfhuOjTc6hERFCQ0MRGhqK\n7OxsZGdn4/79+1ixYkW956xfvx4PHz7UWCdMYTqDwdAZzV7o0Qx++uknGjp0qEpZaWkpde/enR48\neECLFi0iDw8PkkgktHHjRtqwYQO1b9+exGIx+fv7N2i7urqaunXrRhUVFVRQUED9+/fn6hITE2nO\nnDkqx2sjeyIiI12HxtaOMYwfbZ5jXsXpWVlZGDBggEqZhYUF7OzssHXrVly9ehVnz56FiYkJioqK\nYGlpiY8++gjJycno1q1bg7b1J0wH9CNO13X+r5wBiY9ZnuWNVpxORLRhwwZauHChWrlMJqPx48dr\nFJ/b29tTYWFhg3b1JUwn0lcPTcF7D02hUDT4nbYUvu0zH4blQx/3oE3zxOscmpubG9LS0lTKSktL\nkZ+f36Q3GLt27WoDwnQGg6EzdN+uquLl5UVffPEFEREplUqaNWsWLV68mDZv3kwTJkzgthG6d+8e\nERGJxWLKzc3VaKuoqIgkEgklJSWp1Q0aNIhOnjxJ1dXVNGrUKDpw4IBK/bRp0wy4h8bm0BiMJ9Hm\nOeb9yc/Pz6eQkBBycnIiBwcHmj9/PlVUVJBSqaQ33niD3NzcSCqV0scff0xERBs3biQXFxeNLwX0\nLUwnYg0ag9FaGGSDZuzUNGjGlSwsLNXuQwhzKsyH4fhok3NoQoHIMIXj9SUme2K0VZj0qRG0kV8w\nGIyWo81vj0VObwL6ehPKROUMRsvgdcj57rvvwsPDA1KpFHK5HKmpqS22+fLLL6N///4Qi8WYOXMm\nlEolVzd//nw4OTlBKpUiIyODK4+IiECvXr0gFotVbH333f+3d+ZRTZ1bG39AsVZFoRRxCAjKDIEE\nUSpaQZHBClbFqlhFUVtF63S12l79VrW2Vb+rt3Wotx+OqBW9RdE6iwOVVhDBxCIOYAsV54pSHKKA\n7O8PLucSE0TjCSS4f2tlLd4z7OecxPN63uF59w/w8PBAo0aNhGkh2tF3t9cxAIR79+7q+K3UTkPw\n9rGG4Wi8cl7OtLQ07N27FwqFAmfOnMGRI0dga2v70nFHjBiBCxcuIDs7GyqVCmvWrAEA7Nu3D5cu\nXUJeXh7i4uIQGxsrnBMTEyN4O6sjlUqRlJQk5DRgGMa40VuFduPGDbz55pswMzMDALzxxhto27Yt\nsrKyEBgYCF9fX4SFheHGjRsAKq0P06ZNg1wuh1QqxalTp7TG7du3r/B3ly5dBIvTrl27MGrUKACA\nn58fiouLhdhvv/02LC0tNWK5urrC2dlZvJvWmUD9K1TLO2mM8VnDsDTq4h50QW8VWkhICAoLC+Hi\n4oJJkybh+PHjKCsrw+TJk7F9+3ZkZmYiJiZGWHnDxMQEKpUKCoUCq1atwpgxY54Zv6ysDJs3b0ZY\nWBgA4Nq1a2pvgBKJRGQ/J8Mwho7eBgWaN2+OrKwspKam4tixYxg6dCjmzp2LnJwc9OnTBwDw5MkT\ntGvXTjgnKioKQOUbVUlJCUpKStCyZUut8SdOnIiAgAB0795d2Pb0iIh4nfmjoV9zuhLAtMqSHjN1\nVxmAjTF+9dj6ig9Urscnk8n0asZWKpWYNo1/76fLBm9Or05iYiL16tWLunXrpnV/YGCg2mQ9Ozs7\n+uuvvygkJIRkMhl98MEHwr558+bRwIED1c4fP348JSQkCGUXFxe6ceOGUM7Pz1dzCTytnZWVpXUf\n6sQpcEzvM/wbwkRL1jAcjVduYm1ubi7y8vKEskKhgJubG27fvo309HQAlc3Gc+fOCcds27YNAPDz\nzz/DwsICLVu2xMGDB6FQKBAXFwcAWLNmDQ4dOoQtW7ao6fXv3x8bN24EAKSnp8PCwgI2NjbPfb1U\nr3PNAvWv0AD6VFjDcDQMtQ9Nb68EWVlZ5O/vT+7u7uTl5UWRkZFUVFRESqWSevbsSd7e3uTh4UFr\n1qwhosq3pGnTppFcLiepVEqnTp3SGrdx48bk6OgoeDkXLFgg7Js0aRJ16tSJvLy81N64hg0bRm3b\ntqUmTZqQRCKhdevWERHRjh07SCKRUNOmTcnGxobCwsI09FAnb2jswWSYp9HleTCYJ+hZzb76pG4q\nNG5ysoZxaRhqk5OdAs9F3TkFGIbRHfZy1gJ7ORmmfjDINHYMwzB1BVdoz4GJiclzf1q2fHZyF22w\nt481jE3jlfNy7ty5E6amprh48aJoMevHmA68iNFcnwZzhmFqQeSBCYEhQ4ZQREQEffbZZ6LF3Ldv\nn/B3VFQU/etf/yIior1791Lfvn2JiCg9PZ38/PyE444fP06nT5/WmFR7/vx5unjxYq2jq3jhUU6D\nGThmGKNGl2dJL29o9+/fx8mTJ7Fy5UphsmxKSgoiIiKEYz766CPEx8cDqFwpw83NDb6+vpgyZYra\ncdVpmMZ0hmHEQi8V2q5duxAWFgY7OztYW1vj9OnTGr7Kqj6nR48eYcKECThw4AAyMzNx+/btWj2Y\nDc2Yzn0qrGFsGobah6aXeWgJCQmYPn06AOC9995DQkICwsPDNY4jIly4cAEdO3ZEhw4dAFQa1Kts\nTjVRt8Z04MXM6ZU/9oualfVphq6Oscavq7JSqdS7Hv/eRmROLyoqombNmlGHDh3I3t6ebG1tyc7O\njlJTU+mdd94Rjhs3bhxt2LCBlEolBQQECNt37dol5Mmsb2M6EfehMUx9ocuzJHqTMzExEdHR0Sgo\nKEB+fj4uX74MBwcHVFRU4Ny5cygtLUVxcTGOHDkCExMTuLi44Pfff8cff/wBoNKgXvV29WoY0xmG\nEQvRK7StW7di4MCBatsiIyOxdetWDBkyBJ6enhg6dCh8fHwAAE2bNsWqVasQFhYGX19ftGzZssY1\n0GJjY3Hr1i1069YNcrkcX3zxBQDgnXfeQceOHeHo6Ijx48dj1apVwjlRUVHw9/dHbm4ubG1tsX79\negBAUlISbG1tkZ6ejn79+qkNONQ13KfCGsam8cr0oR09elRj2+TJk4W/Fy9erLG/V69eOH/+PABg\n0qRJ6NKli9bYZWVlNequXLlS6/aEhASt2wcOHKhR8TIMY9wYhJfzm2++QXx8PEpLS+Hj44PVq1ej\nadOm9X1ZAF58cIFT0TGMOOji5TSICs2QYXM6w9QPbE43UrhPhTWMTeOV6UNriOgjczo3TRlGD4g4\nbeS5ad68+TP3BwQEUGZmpsb2Q4cOUefOnUkqlVLnzp3p6NGjwr7MzEzy9PQkR0dHmjJlirD9p59+\nIrlcTo0bN6bExES1eKGhoWRhYSHMe9MG9LZiLc9XY5hnocszUi9NztreeKpsUU9jbW2NPXv24Ndf\nf0V8fDxGjhwp7IuNjcXatWuRl5eHvLw8IVN6hw4dEB8fj+HDh2vEmzVrFjZt2vSSd8MwjKFQb31o\nP/30U41m9ZqQyWRo06YNAMDd3R0qlQplZWW4fv067t27h65duwIAoqOjsXPnTgCVFZpUKoWpqeat\n9u7dGy1atBDrlnSG+1RYw9g0DLUPzWAGBWp6K6uJ7du3o3PnzjAzM8PVq1chkUiEfe3btzcqczrD\nMOJglIMCOTk5+OSTT5CcnFxHiqMhfub0/5QamLlbn+UqA7M+9aq2Gbt53BjLKYZoTn8eWrRoQT//\n/LOaWX3s2LEUHx9PRJWG8czMTEpKShLyb1YZyAsLC8nZ2ZlOnDghnHvt2jVydXUVylu2bKHx48er\naY4ePZq2b9+ucS0pKSk8KMAwBoguz0i9NTk7dOigZlZ/2jJlYmKCAQMGQKFQQKFQwMfHB8XFxejX\nrx8WL16Mbt26Cce2bdsWLVu2xMmTJ0FE2LRpEwYMGKAWjypzkGpch7ZtdQ33qbCGsWlwH9p/KC8v\nx2uvvQaJRKLVrP4sVq5cid9++w3z58+HXC6HXC7H7du3AQCrVq3CuHHj4OTkBEdHR2Hxx1OnTsHW\n1haJiYkYP368Wm6Bt99+G0OGDMGRI0dga2tbh01YhmH0QZ1bn86cOYPx48cjPT29LmV1pnKgQh9f\nEVuqGOZZ6GJ9qtNBge+++w4rVqzAsmXL6lJWBPTjFGAYRlzqtMk5YcIE5OTkoE+fPnUp+9JU9b+J\n+alue+I+FdYwNg3uQ2MYhtEzvHxQLWib7MvGcobRPwa9fJBYFqP6yZ7O2dEZxhioswpNrCV4RowY\ngQsXLiA7OxsqlQpr1qwBUJms+NKlS8jLy0NcXBxiY2OFc2JiYgSzenWkUimSkpLQs2dPUa5NV7hP\nhTWMTYP70PBsQ7q9vT3mzZuHzp07w8vLCxcvXtQag7OnMwxTE/U6KFDdkG5iYgJra2tkZWUhNjYW\nS5Yseea5DSl7enUfobFqNIR7YA3Dia8rBmVOHzRoEADAx8cHO3bseOaxdZs9fTTUzen/xZDMvVzm\nsjGXU4zJnF6bId3e3p6KioqIiOjUqVMUGBhIRPWfPR1azenifm3Hjh0TNV59aDSEe2ANw4lPpNtz\nVqdvaNUN6Q8fPsTRo0dr7ZA/ePCgWrkqe/qRI0fUtvfv3x8rV67EsGHDOHs6w7yqiF+valJWVkZW\nVlZERDRr1ixycnKikJAQioyM1PqGlpmZSb169dJeAzduTI6OjsKyQgsWLBD2TZo0iTp16kReXl5q\nb1zDhg2jtm3bUpMmTUgikdC6deuIiGjHjh0kkUioadOmZGNjQ2FhYRp6qIM3NIZhNNHlOauTibXG\nZkivjnZzOhvLGUbfGOTE2u+++w7Dhw/HF198oW8pPWKi9hHbWM7zkljD2DQMdR6a3vvQJkyYgAkT\nJuhbRq/w2xjDGAfs5awFXV57GYZ5eQyyydkQqJoAXPVp2fKN+r4khmG0IHqFZmpqqpYAuLy8HNbW\n1mqWJ11RqVTo168f3Nzc4OnpiU8//VTY9/jxYwwdOhROTk5466238Mcffwj7wsLCYGlpqXENK1eu\nhKOjI0xNTXHnzrNWz9CvOZ37VFjD2DQMtQ9N9AqtefPmyMnJwaNHjwAAycnJkEgkos3anzVrFs6f\nPw+FQoFffvlFMJ2vXbsWVlZWyMvLw/Tp0zF79my1c7RlSO/RoweOHDmCDh06iHJtDMPUM+LNGqmk\nRYsWNGfOHEpMTCQiopEjR9LixYuFVHEnT56kbt26kVwuJ39/f7p48SIREfXs2ZOUSqUQp3v37vTr\nr78+U2vq1Km0Zs0aIiIKDQ2l9PR0Iqqc9/bmm2+qHXvs2LEa09VVnwP3NOB5aAxTL+jynOmlD23o\n0KHYunUrHj9+jOzsbPj5+Qn73NzckJqaitOnT2P+/Pn4+9//DgAYO3as4OPKzc3F48ePNdYvq05x\ncTF2796NoKAgAMDVq1cFc3rjxo3RqlWrWpqRDMM0NPQybUMqlaKgoAAJCQno16+f2r7i4mJER0fj\n0qVLMDExQVlZGQBg8ODBWLBgAf7xj39g3bp1iImJqTF+eXk5oqKiMHXqVN1NrC/EaOjTnK5UKjFt\n2jTR4mkrV20z1vjVY+srPgB88803kMlkejVj8+9tROb0Fi1aEBHR559/TlZWVnT27Fm15t6oUaNo\nxYoVRERUUFBA9vb2wrmxsbH0ww8/UMeOHam4uJjKy8vJ29ubZDIZffbZZ8JxMTExNHXqVDXd0NBQ\nSktLIyLtTc6UZ2RIr+8mJ5uVWcPYNF45c/qYMWNgaWkJDw8PtVq9pKQE7dq1AwCsX79e7Zxx48Yh\nPDwcAQEBaNWqFQBAqVSqHTN37lyUlJRg7dq1atv79++P+Ph4vPXWW0hMTBSaolVQLfNZatuvT6r+\ntzJmjYZwD6xhOPF1Ruxa1dzcXGNbSkoKRUREEBFRWloaOTs7k1wup7lz55KDg4Pasa6urnTw4EGt\nsQsLC8nExITc3d0Fc/ratWuJiOjRo0f03nvvkaOjI/n5+VF+fr5wXo8ePcja2ppef/11kkgkdOjQ\nISIiWrZsGUkkEjIzM6N27dqpLVFUBXhQgGHqBV2eM4N6Mq9evUrOzs71fRlq1EWFxk0Q1jA2DUNt\nchqMU2Djxo1466238NVXX9X3pWhBv+Z0hmHEgb2ctcBeToapH9jLyTDMKw1XaAYAe/tYw9g0Xhkv\npzZ27twJU1PTGnNtPi/1kzWdYRijQeSBCa0MGTKEIiIi1CbHPg9PnjxRK+/bt0/4Oyoqiv71r38R\nEdHevXupb9++RESUnp5Ofn5+wnHHjx+n06dPa2R8On/+PF28ePGZGZ+IeIoGw9QXujx7en9Du3//\nPk6ePImVK1di27ZtACpfV3v27Inw8HC4uroiNjZW6Pxr0aIFZs6cCZlMppGDgLOmMwzzLPReoe3a\ntQthYWGws7ODtbW10Lw7deoUVq5ciXPnzuG3334TEgs/fPgQb731FpRKJfz9/bXGbEhZ0wHuU2EN\n49Mw1D40vecUSEhIwPTp0wEA7733HhISEhAeHo6uXbsKBtSoqCj8/PPPiIyMRKNGjRAZGfnMmHWb\nNR0YPXq0cK0WFhaim5eVSqVezdDVMdb4dVWustrp25zOv7dmOcUQzenVKSoqombNmlGHDh3I3t6e\nbG1tyc7Ojo4dO0YBAQHCcWvXrqW//e1vRPRfcztRpeG8PrOmE3EfGsPUF7o8e3ptciYmJiI6OhoF\nBQXIz8/H5cuX4eDggOPHjyMjIwMFBQWoqKjAtm3b0KNHD43zDxw4AIVCgbi4OAD/zZq+ZcsWteP6\n9++PjRs3AgBnTWeYVxnx69X/0qtXLw2j+fLly8nNzY169uxJ/fr1IxcXF4qNjRX2azO3V1HXWdOJ\n6uYNjb19rGFsGobq5dRrH9rRo0c1tk2ePBleXl5YsmQJdu/erbG/pKSkxnhVi0FqY+XKlVq3JyQk\naN0+cOBADBw4sMZ4DMMYH/Xi5fzpp5+wdOlS/Pjjj3Ut/cKwl5Nh6gddnj02p9cCV2gMUz+wOd1I\n4XlJrGFsGoY6D40rNIZhGgzc5KyFZ03QNTe3REkJp8pjGH1gcE1OU1NTjBw5UiiXl5fD2toaERER\nOsesskR5enrC29sb//73v4V9+fn58PPzg5OTE4YNGyaMil64cAHdunVD06ZNsXTpUrV4Na3GoQ5p\n/dy7d1fn+2AYRnz0WqE1b94cOTk5ePToEQAgOTkZEonkhWxJ1ZcIqoq5adMmnD17FgcOHMC0adOE\nqR6zZ8/GjBkzkJeXB0tLSyEzlJWVFVasWIGZM2dqxI+JicGBAwd0vUVR4D4V1jA2jVe2D+2dd97B\n3r17AVTOCYuKihJeIzMyMuDv7w8fHx90794dubm5AIANGzagf//+CAoKQnBwsFo8JycndOrUCQDQ\ntm1btG7dGn/++SeICMeOHcPgwYMBAKNGjcLOnTsBANbW1vD19YWZmZnG9dW0GgfDMMaH3s3pQ4cO\nxeeff47w8HBkZ2dj7NixSE1NBQC4ubkhNTUVjRo1wuHDh/H3v/8diYmJAACFQoHs7GxYWFjUGDsj\nIwOlpaXo1KkTbt++DQsLC5iaVtbR7du3F3HFjdFQz5wuAxAIwLDNvg2tXGVg1qde1TZjN48bYznF\n0M3pVUZzX19fWr9+Pc2ZM0ctg/nly5dpwIAB5OnpSVKplNzc3IiIaP369TRmzJhnxr527Rq5uLjQ\nyZMniYjozz//JEdHR2H/5cuXNQzp8+bNoyVLlmjEepZ5HVrT2HF+TobRN7o8X3UybaN///6YOXOm\nWnMTAP7nf/4HQUFByM7Oxu7du6FSqYR9zZo1AwCcPHkScrkccrkce/bsAVBpjwoPD8dXX32Frl27\nAqjsJysuLkZFRQUA4MqVK2jfvn1d3N5Lw30qrGFsGobah6b3JidQOZJoaWkJDw8PtS+ipKQE7dq1\nAwCsX79e67l+fn5qOQJKS0sxcOBAREdHY9CgQcJ2ExMT9OrVCz/88AOGDh2K+Ph4DBgwQC0W8QwV\nhmnYiP6eWA1tK2ekpKRQREQEERGlpaWRs7MzyeVymjt3Ljk4OBAR0YYNG2jy5MlaY27atInMzMyE\nFTdkMhmdOXOGiIh+//136tq1Kzk6OtKQIUOotLSUiIiuX79OEomEWrZsSRYWFmRra0v37t0joppX\n46gC3ORkmHpBl+eLJ9bWAk+sZZj6weAm1jYUiEjrR6zKjPtUWMPYNAy1D40rNIZhGgzc5KwFtn2r\nhAAAGVJJREFUXj6IYeoHbnLqCRMTE7VPy5Zv1PclMQyjBb1WaI0aNRLmkMnlcly+fLnGYwMDA5GV\nlVVrzPfffx+urq6QSqUYO3asmtdzypQpcHJygre3t9pUj5oM6D/88AM8PDzQqFEjIV+odvRrSuc+\nFdYwNo1Xsg+tWbNmUCgUwsfOzq7GY5/XsD5ixAhcuHAB2dnZUKlUWLNmDQBg3759uHTpEvLy8hAX\nF4fY2FjhnJoM6FKpFElJSejZs+cL3hnDMIZInTc5s7KyEBgYCF9fX4SFheHGjRvCvk2bNkEul0Mq\nleLUqVNaz+/bt6/wd5cuXQS/5q5duzBq1CgAlZNxi4uLhdg1GdBdXV3h7Ows2r3pSnUfobFqNIR7\nYA3Dia8renUKqFQqyOVyAEDHjh2xbds2TJ48Gbt374aVlRW2bduGOXPmYO3atSAiqFQqKBQKpKam\nYsyYMcjOzq4xdllZGTZv3ozly5cDAK5duwZbW1thv0QiwdWrV9GmTRsR7mQ01M3p/8WQzL1c5rIx\nl1OMxZxeRXZ2NrVs2VKY4S+VSik0NJSIKjOYV8/1Z2dnR3/99VeNsceNG0fTp08XyuHh4fTzzz8L\n5aCgILX8nLpmT4dWp4C4XxvnaWQNY9N4JfNyaqk84eHhgRMnTjz3OaGhobh16xa6dOkiZFCfP38+\nioqKsHr1auG49u3bo7CwUCgbkzmdYRiREL9e/S9Pv6E9fvyYHB0dKS0tjYiISktLKScnh4iIAgIC\naMKECURElJqaSl5eXlpjrl69mvz9/UmlUqlt37t3L/Xt25eIKj2ifn5+avtre0PLzMzUug918IbG\nMIwmujxneh0UeHrkskmTJkhMTMTs2bMhk8kgl8uRlpYmHNu0aVP4+Phg4sSJwvLZTxMbG4tbt26h\nW7dukMvl+OKLLwBUrozbsWNHODo6Yvz48Vi1apVwTlRUFPz9/ZGbmwtbW1thZY+kpCTY2toiPT0d\n/fr1UxtwYBjGCNFDxdqggJbsKObmlqJqcJ8KaxibBvehGTHE1ieGMQrYy1kL7OVkmPqBvZwMw7zS\n1FmF1qJFi5eOUVhYiF69esHDwwOenp7CpFoAuHPnDoKDg+Hs7IyQkBAUFxcL23v16gVzc3NMnjxZ\nLd6cOXNgZ2cHc3PzZ+rq25zO3j7WMDaNV9LLWZ0XSS5cE2ZmZvj666+Rk5OD9PR0fPvtt7hw4QIA\nYNGiRQgODkZubi6CgoKwaNEiAEDTpk3xxRdfYMmSJRrx3n33XWRkZDyHMmdMZxijQNxxiZpp0aIF\n3b9/n4KCgsjHx4ekUint2rWLiCrniLm6utIHH3xAHh4eFBISojHPTBvvvvsuHT58mIiIXFxc6MaN\nG0RUmUPAxcVF7dj169fTRx99VOO11QR4HhrD1Au6PGd12of2+uuvIykpCVlZWTh69ChmzJgh7Lt0\n6RI++ugjnD17FhYWFti+ffszYxUUFEChUMDPzw8AcPPmTdjY2AAAbGxscPPmTbXjxXhDZBjGsKnT\nCq2iogKffvopvL29ERwcjGvXruHWrVsAAAcHB3h5eQEAOnfujIKCghrj3L9/H4MHD8ayZcu09s1V\n9XUZC9ynwhrGpmGofWh1Og/t+++/x+3bt3H69Gk0atQIDg4OePToEQDgtddeE45r1KgRVCoVrly5\ngvDwcJiYmCA2NhYffvghysrKEBkZiREjRqjl3bSxscGNGzfQpk0bXL9+Ha1btxbxykdDn6ttKJVK\nva9mIOb11kf8uiorlUq96/Hvrb/VNupsHpq5uTm+/PJLXLp0CcuXL8exY8cQFBSEgoICVFRUICIi\nQlguaOnSpbh//z4+++wztRhEhFGjRsHKygpff/212r5Zs2bBysoKs2fPxqJFi1BcXCwMDADAhg0b\nkJWVhRUrVmi9tnv37mm97so3vae/Ip6bxjD6xmDnoZWXl+O1117D+++/j8zMTHh5eWHTpk1wc3MT\njnm6iaityfjLL79g8+bNOHbsmLCsd9VKtJ988gmSk5Ph7OyMo0eP4pNPPhHOs7e3x4wZM7BhwwbY\n2toKI6OzZs2Cra0tVCoVbG1t8fnnn+vj9hmGqSvEHZfQjlKp1Fj9wlgAr4dmEPFZw7A0DNXLqfc3\ntO+++w7Dhw8XVsUwTkzUPubmmst5MwxT/7CXsxbYy8kw9YPB9qExDMPUBVyhGQA8L4k1jE3DUOeh\ncYX2HDxtTtf24WzqDGMAiDwwUTnSYGpKMpmMPDw8yNvbm5YuXUoVFRUvHffy5csUGBhI7u7u5OHh\nQcuWLRP2FRUVUZ8+fcjJyYmCg4Pp7t27wvbAwEBq0aKFhpczMzOTPD09ydHRkaZMmaJVE1pHObV9\n2N/JMGKiyzOllze0qozpZ8+eRXJyMvbv34/58+e/dFyxV9uIjY3F2rVrkZeXh7y8PK3Z1RmGMR70\n3uS0trZGXFwcVq5cCQB48uQJPv74Y3Tt2hXe3t5CajoAWLx4Mby8vCCTyfDpp59qxGrTpg1kMhmA\nyvXV3NzchMzpP/74o5A5fdSoUdi5cyeAysq1e/fuatYqALh+/Tru3buHrl27AgCio6OFc+oa7lNh\nDWPTMNQ+tDrxcjo4OODJkye4desWdu7cCQsLC2RkZODx48fo0aMHQkJCcP78efz444/IyMhA06ZN\ncffus9cce9nVNq5evQqJRCKU27dvL1SODMMYJ3WeJOXQoUPIzs5GYmIiAKCkpAR5eXk4cuQIxowZ\ng6ZNmwIALC1rnrxa96ttjIa6OV0GIPA/5RS1Iw3J7NvQylUGZn3qVW0zdvO4MZZTRDCn66Un++kF\nE3/77TeysrIiIqLIyEg6dOiQxjkzZsyg1atXq227fPkyyWQykslk9H//939EVJmcOCQkhL7++mu1\nY11cXOj69etERHTt2jWNBR43bNigNihw7do1cnV1Fcpbtmyh8ePHa1wXeFCAYeoFXZ4pvfeh/fnn\nn5gwYYKwnn9oaChWrVqF8vJyAEBubi4ePnyI4OBgrF+/HiqVCgBw9+5d2NraQqFQQKFQ4MMPPwQR\nYezYsXB3d8e0adPUdPr374/4+HgAQHx8vNrSQoBmKrq2bduiZcuWOHnyJIgImzZt0jinruA+FdYw\nNo1Xqg9NpVJBLpejrKwMjRs3RnR0NKZPnw4AGDduHAoKCuDj4wMiQuvWrbFz506EhoZCqVTC19cX\nTZo0Qb9+/TT8n1WrbXh5eUEulwMAFi5ciLCwMHzyyScYMmQI1q5dC3t7e/z73/8WzrO3t8e9e/dQ\nWlqKnTt3Ijk5Ga6urli1ahVGjx4NlUqFd955B2FhYfr4OhiGqSPYy1kLz9sXZ25uiZKSO3q+GoZ5\nddDFy8mZ058DrvMZxjhg65MBwH0qrGFsGobah8YVGsMwDQbuQ6uFqj407iNjmLrFYNZDMzU1xcyZ\nM4XykiVLRPFyJicnw9fXF15eXvD19cWxY8eEfVlZWZBKpXBycsLUqVOF7cePH4ePjw/MzMw0cn3G\nx8fD2dkZzs7O2Lhx4zOUOVs6wxgDeqnQmjRpgqSkJBQVFQEQL8mvtbU19uzZg19//RXx8fEYOXKk\nsK8mo3mHDh0QHx+P4cOHq8W6c+cOPv/8c2RkZCAjIwPz589HcXGxKNf5onCfCmsYm8Yr1YdmZmaG\nDz/8UCPVHFDpwezduze8vb3Rp08fFBYW4q+//lKzOjx48AB2dnZ48uSJ2rkymQxt2rQBALi7u0Ol\nUqGsrOyZRvMOHTpAKpXC1FT9Vg8ePIiQkBBYWFjAwsICwcHBvNoGwxg5ehsUmDhxIr7//nuUlJSo\nbZ88eTJiYmJw5swZvP/++5gyZQpatWoFmUwm1Pp79uxBWFgYGjVqVGP87du3o3PnzjAzM9PJaH7t\n2jW1cyQSSb2Z06v7CI1VoyHcA2sYTnxd0ds8NHNzc0RHR2P58uV4/fXXhe3p6enC29OIESMwa9Ys\nAMDQoUOxbds2BAYGYuvWrfjoo49qjJ2TkyPk4awbRgMA5s2bBwsLC8hkMoMw83KZyw2pnGLo5vQ7\nd+6Qvb09zZ8/n+bNm0dERG+++SaVlZURUaXR/M033yQionv37pG9vT3duXOH7OzsqKKignbs2CGY\n07OysoiIqLCwkJydnenEiROC3vMYzUePHk3bt28XygkJCWrHfPjhh7R161aNe4FgTtef+ZzzNLKG\nsWm8knk5LS0tBX9l1cCAv78/tm7dCgD4/vvv0bNnTwCVCzZ26dIFU6ZMQUREBExMTDBw4EDBnO7j\n44Pi4mL069cPixcvRrdu3QSd5zGaE5HaEHBoaCgOHTqE4uJi3L17F8nJyQgNDdXn18EwjL4Ru1Yl\nIjI3Nxf+vnnzJjVr1ozmz59PRER//PEH9e7dm7y8vKhPnz5UWFgoHJuYmEimpqZ0/PhxrXEXLFhA\nzZs3F97aZDIZ/fnnn0T03/wAnTp1osmTJwvnZGRkkEQioebNm5OVlRV5enoK+9atW0eOjo7k6OhI\nGzZs0KqJOnhDYxhGE12eOZ5YWws8sZZh6geDmVjb0CAivVZmPC+JNYxN45Wah8YwDFMfcJOzFnR5\n7WUY5uXhJqee4CzpDGMc6L1Cu3LlCt599104OzvD0dER06ZNQ1lZWY3Hf/PNN0JegaepT3N69Y/Y\nRnXuU2ENY9N4JfvQiAiDBg3CoEGDkJubi9zcXNy/fx9z5syp8Zxly5bh4cOHWvc1VHM6wzAiId6s\nEU0OHz5MPXv2VNtWUlJCVlZW9ODBA5oxYwZ5enqSl5cXrVixgpYvX05NmjQhqVRKvXv3fmbsiooK\neuONN6i0tFTDKfC0C4Co0imQmJgolLds2UITJkwQyuPHj6eEhAQNHWhNY8dz0hhG3+jynOk1p0BO\nTg46d+6sts3c3Bx2dnZYs2YN/vjjD5w5cwampqa4e/cuLC0t8c9//hMpKSl4441n91M1JHM6wzDi\noNcKraZ10IgIKSkpmDRpkrCsz7MypT9N/ZjT7f/zt4XaHjHMuUqlUsgzqi/zb9U2Y41fPba+4gOV\nfbj6XnyAf28jM6dXoa3J+ddff5GVlRUNGjSIkpOTNc6xt7enoqIiIiJKSkoyIHO6/pqcbFZmDWPT\nMFRzut47g3x9fWnjxo1ERFReXk7jxo2jmTNn0nfffUeDBw+m8vJyIqpcmYOISCqVUn5+vtZYd+/e\nJS8vL0pKStLY17VrV0pPT6eKigrq27cv7d+/X23/qFGj1PrQ7ty5Qw4ODnT37l21v5+mLio0hmE0\nMcgKrbCwkCIiIsjJyYk6depEU6ZModLSUiovL6e//e1v5O7uTt7e3vTtt98SEdGKFSvIxcVF66BA\n/ZrTuUJjmLrEICs0Y4ebnIYRnzUMS8NQm5ycOf25UB/cMDd//gEMhmHqDvZy1gJ7ORmmfmAvJ8Mw\nrzR6rdC+/PJLeHp6wtvbG3K5HBkZGS8d8/3334erqyukUinGjh2L8vJyYd+UKVPg5OQEb29vKBQK\nYfuYMWNgY2MDqVSqFuvOnTsIDg6Gs7MzQkJCarQ+6duQzt4+1jA2jVfOy5mWloa9e/dCoVDgzJkz\nOHLkCGxtbV867ogRI3DhwgVkZ2dDpVJhzZo1AIB9+/bh0qVLyMvLQ1xcHGJjY4VzYmJitObcXLRo\nEYKDg5Gbm4ugoCAsWrSoBlXOnM4wRoHIAxMCO3bsoIiICI3tmZmZFBAQQJ07d6bQ0FC6fv06EREF\nBATQ1KlTSSaTkaenJ2VkZNSq8c9//pPmzp1LRJoTY11cXITYRET5+flqUzaqjrlx4wYREV2/fp1c\nXFw0NMA5BRimXtDlmdPbG1pISAgKCwvh4uKCSZMm4fjx4ygrK8PkyZOxfft2ZGZmIiYmRlh5w8TE\nBCqVCgqFAqtWrcKYMWOeGb+srAybN29GWFgYgEpvZvU3wOfxZt68eRM2NjYAABsbG9y8efNlbplh\nmHpGbxVa8+bNkZWVhbi4OFhbW2Po0KGIi4tDTk4O+vTpA7lcji+//FKt0omKigIAvP322ygpKdHI\nul6diRMnIiAgAN27dxe20VMjIjV5SbVRtXhjfcB9KqxhbBqG2oem13lopqamCAgIQEBAAKRSKb79\n9lt4eHjgxIkTzx0jNDQUt27dQpcuXRAXFwcAmD9/PoqKirB69WrhuPbt26OwsFAoX7lyBe3bt39m\nbBsbG9y4cQNt2rTB9evX0bp16xqOHA1Af5nTlUqlXs3Q1THW+HVVViqVetfj39sIzekXL16k3Nxc\noTxnzhyaOHEiOTk5UVpaGhFVZk7PyckhIqLAwEBhfbLU1FTy8vLSGnf16tXk7+9PKpVKbfvevXup\nb9++RESUlpZGfn5+avu19aF9/PHHtGjRIiIiWrhwIc2ePVtDD9yHxjD1gi7PnN6e0qysLPL39yd3\nd3fy8vKiyMhIKioqIqVSST179iRvb2/y8PCgNWvWEFFlhTZt2jSSy+UklUrp1KlTWuM2btyYHB0d\nBS/nggULhH2TJk2iTp06kZeXl7A6BxHRsGHDqG3bttSkSROSSCS0bt06IiIqKiqioKAgcnJyouDg\n4FrM6VyhMUxdYlAV2osSGBioVgkZCnVRobG3jzWMTcNQvZzsFDAAqvptjFmjIdwDaxhOfF0xGHN6\n9QxOhoeJXg3pdZGcRd8aDeEeWMNw4uuKwVRohgyxOZ1hjAJuchoABQUFRq/REO6BNQwnvq7w8kG1\nUF+TbRmGefHWETc5a4Hre4YxHrjJyTBMg4ErNIZhGgxcoTEM02DgCq0GDhw4AFdXVzg5OWHx4sWi\nxNS2cu7zrpr7vBQWFqJXr17w8PCAp6cnli9fLrrOo0eP4OfnB5lMBnd3d3z66ad6uZcnT55ALpcj\nIiJCL/Ht7e3h5eUFuVyOrl276kWjuLgYgwcPhpubG9zd3XHy5ElRNS5evAi5XC58WrVqheXLl4t+\nHwsXLoSHhwekUimGDx+Ox48fi6qxbNkySKVSeHp6YtmyZQB0/C3ENSs0DMrLy6lTp06Un59PpaWl\n5O3tTefOnXvpuMePH6fTp0+rmeQ//vhjWrx4MRERLVq0SKtB/kW4fv06KRQKIiK6d+8eOTs707lz\n50TXefDgARERlZWVkZ+fH6WmpoqusXTpUho+fLiwUKjY8e3t7amoqEhtm9ga0dHRtHbtWiKq/K6K\ni4tF16jiyZMn1KZNG7p8+bKoGvn5+eTg4ECPHj0iIqIhQ4bQhg0bRNPIzs4mT09PUqlUVF5eTn36\n9KFLly7pFJ8rNC2cOHGCQkNDhfLChQtp4cKFosR+etWP51k192V49913KTk5WW86Dx48IF9fXzp7\n9qyoGoWFhRQUFERHjx6l8PBwIhL/u7K3t6fbt2+rbRNTo7i4mBwcHDS26+u3OHjwIPXo0UN0jaKi\nInJ2dqY7d+5QWVkZhYeH06FDh0TT+OGHH2js2LFCecGCBbR48WKd4nOTUwtXr1594dVvdUWfq+YW\nFBRAoVDAz89PdJ2KigrIZDLY2NgITVwxNaZPn45//OMfMDX97z9Rse/BxMQEffr0ga+vr7C2npga\n+fn5sLa2RkxMDHx8fPDBBx/gwYMHevvNt27dKiySKqbGG2+8gRkzZsDOzg7t2rWDhYUFgoODRdPw\n9PREamoq7ty5g4cPH2Lfvn24cuWKTvG5QtNCfU2mFXPV3Pv37yMyMhLLli2Dubm56DqmpqZQKpW4\ncuUKjh8/ruHFfRmNPXv2oHXr1pDL5TXOAxTjHn755RcoFArs378f3377LVJTU0XVKC8vx+nTpzFx\n4kScPn0azZs310jEI9ZvXlpait27d+O9997T2PeyGr/99hu++eYbFBQU4Nq1a7h//z42b94smoar\nqytmz56NkJAQ9O3bFzKZDI0aNdIpPldoWnh69dvCwkJIJBK9aFWtmgugllVzn5+ysjJERkZi5MiR\nGDBggN50AKBVq1bo168fsrKyRNM4ceIEfvzxRzg4OCAqKgpHjx7FyJEjRb+Htm3bAgCsra0xcOBA\nZGRkiKohkUggkUjQpUsXAMDgwYNx+vRptGnTRvTfYv/+/ejcuTOsra0BiPt7Z2Zmwt/fH1ZWVmjc\nuDEGDRqEtLQ0Ue9jzJgxyMzMxE8//QRLS0s4OzvrdA9coWnB19cXeXl5KCgoQGlpKbZt24b+/fvr\nRat///6Ij48HAMTHxwsVkK4QEcaOHQt3d3dMmzZNLzq3b98WRpxUKhWSk5Mhl8tF0/jqq69QWFiI\n/Px8bN26Fb1798amTZtEvYeHDx/i3r17AIAHDx7g0KFDkEqlomq0adMGtra2yM3NBQAcPnwYHh4e\niIiIEPU3B4CEhAShuQmI+3u7uroiPT0dKpUKRITDhw/D3d1d1Pu4desWAODy5cvYsWMHhg8frts9\n6NSL9wqwb98+cnZ2pk6dOtFXX30lSsyqlXPNzMyElXOfZ9XcFyE1NZVMTEzI29tbWNV3//79our8\n+uuvJJfLydvbm6RSKf3v//4vET3fCsAvSkpKijDKKWb833//nby9vYWVk6t+Y7HvQalUkq+vL3l5\nedHAgQOpuLhYdI379++TlZUVlZSUCNvE1li8eDG5u7uTp6cnRUdHU2lpqagab7/9Nrm7u5O3tzcd\nPXpU53tgczrDMA0GbnIyDNNg4AqNYZgGA1doDMM0GLhCYximwcAVGsMwDYb/B9nEuD6zOUqIAAAA\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10e0ae350>"
]
}
],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# \"Chattiness\"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Time til first collaborator comment\n",
"issues"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<pre>\n",
"&ltclass 'pandas.core.frame.DataFrame'&gt\n",
"DatetimeIndex: 3881 entries, 2013-06-12 18:31:16 to 2011-04-06 00:15:26\n",
"Data columns (total 7 columns):\n",
"title 3881 non-null values\n",
"created_at 3881 non-null values\n",
"labels 3881 non-null values\n",
"closed_at 3232 non-null values\n",
"user 3881 non-null values\n",
"id 3881 non-null values\n",
"closed 3881 non-null values\n",
"dtypes: bool(1), datetime64[ns](2), int64(1), object(3)\n",
"</pre>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 64,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 3881 entries, 2013-06-12 18:31:16 to 2011-04-06 00:15:26\n",
"Data columns (total 7 columns):\n",
"title 3881 non-null values\n",
"created_at 3881 non-null values\n",
"labels 3881 non-null values\n",
"closed_at 3232 non-null values\n",
"user 3881 non-null values\n",
"id 3881 non-null values\n",
"closed 3881 non-null values\n",
"dtypes: bool(1), datetime64[ns](2), int64(1), object(3)"
]
}
],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"closed_issues[1000]['labels']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 65,
"text": [
"[]"
]
}
],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_ids = []\n",
"all_labels = []\n",
"\n",
"for iid, labels in zip(issues.id, issues.labels):\n",
" for lab in labels:\n",
" all_ids.append(iid)\n",
" all_labels.append(lab['name'])\n",
" \n",
" if len(labels) == 0:\n",
" all_ids.append(iid)\n",
" all_labels.append(None)\n",
" \n",
"labels = pd.DataFrame({'id': all_ids, 'label': all_labels})\n",
"labels.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 15466125</td>\n",
" <td> None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 15453336</td>\n",
" <td> None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 15453214</td>\n",
" <td> None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 15431944</td>\n",
" <td> None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 15430880</td>\n",
" <td> None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 66,
"text": [
" id label\n",
"0 15466125 None\n",
"1 15453336 None\n",
"2 15453214 None\n",
"3 15431944 None\n",
"4 15430880 None"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"merged = pd.merge(issues, labels, on='id')\n",
"merged.groupby('label').size().order()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 67,
"text": [
"label\n",
"Error Reporting 3\n",
"Dupe 4\n",
"Multithreading 5\n",
"Usage 6\n",
"Stats 10\n",
"Can't Repro 11\n",
"Missing-data 11\n",
"Note To Selves 11\n",
"Prio-low 14\n",
"Regression 14\n",
"Reshaping 14\n",
"Unicode 15\n",
"Good as first PR 16\n",
"API 25\n",
"Refactor 31\n",
"Prio-medium 41\n",
"Performance 44\n",
"Ideas 52\n",
"Community 54\n",
"Dtypes 56\n",
"Output-Formatting 64\n",
"Build problem 70\n",
"Testing 80\n",
"Groupby 83\n",
"Indexing 93\n",
"Visualization 117\n",
"Prio-high 128\n",
"Docs 138\n",
"Data IO 198\n",
"Timeseries 363\n",
"Enhancement 858\n",
"Bug 1128\n",
"dtype: int64"
]
}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with_labels = pd.merge(issues, labels, on='id')\n",
"with_labels.groupby('label').size().order(ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 68,
"text": [
"label\n",
"Bug 1128\n",
"Enhancement 858\n",
"Timeseries 363\n",
"Data IO 198\n",
"Docs 138\n",
"Prio-high 128\n",
"Visualization 117\n",
"Indexing 93\n",
"Groupby 83\n",
"Testing 80\n",
"Build problem 70\n",
"Output-Formatting 64\n",
"Dtypes 56\n",
"Community 54\n",
"Ideas 52\n",
"Performance 44\n",
"Prio-medium 41\n",
"Refactor 31\n",
"API 25\n",
"Good as first PR 16\n",
"Unicode 15\n",
"Reshaping 14\n",
"Regression 14\n",
"Prio-low 14\n",
"Note To Selves 11\n",
"Missing-data 11\n",
"Can't Repro 11\n",
"Stats 10\n",
"Usage 6\n",
"Multithreading 5\n",
"Dupe 4\n",
"Error Reporting 3\n",
"dtype: int64"
]
}
],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"time_to_close_id"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 69,
"text": [
"id\n",
"15466125 0.045984\n",
"15453336 0.307234\n",
"15453214 0.017824\n",
"15431944 0.037373\n",
"15430880 0.764699\n",
"15426986 0.082477\n",
"15405714 0.064699\n",
"15385558 0.091250\n",
"15380909 0.436632\n",
"15369588 1.928391\n",
"15367526 0.877095\n",
"15363520 0.000150\n",
"15358003 0.020150\n",
"15357764 1.885648\n",
"15356080 0.119410\n",
"...\n",
"358943 59.589699\n",
"356064 131.537049\n",
"352369 796.912072\n",
"344725 69.230937\n",
"341583 142.049699\n",
"341581 265.878900\n",
"341577 229.176354\n",
"339355 266.377986\n",
"338909 72.604225\n",
"337994 73.023958\n",
"337736 79.076088\n",
"337730 73.224363\n",
"337728 94.957870\n",
"337726 79.088426\n",
"337721 143.936308\n",
"Length: 3232, dtype: float64"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"labels['time_to_close'] = labels.id.map(time_to_close_id)\n",
"\n",
"everything = pd.merge(issues, labels)\n",
"\n",
"grouped = everything.groupby('label').time_to_close\n",
"statistics = grouped.agg(['count', 'mean', 'median', 'max', 'min'])\n",
"\n",
"statistics['min_minutes'] = statistics['min'] * 24 * 60\n",
"\n",
"statistics[statistics['count'] > 100]\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>median</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" <th>min_minutes</th>\n",
" </tr>\n",
" <tr>\n",
" <th>label</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Bug</th>\n",
" <td> 1014</td>\n",
" <td> 13.275700</td>\n",
" <td> 2.625590</td>\n",
" <td> 394.091736</td>\n",
" <td> 0.000845</td>\n",
" <td> 1.216667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Data IO</th>\n",
" <td> 126</td>\n",
" <td> 32.557035</td>\n",
" <td> 7.970220</td>\n",
" <td> 329.296389</td>\n",
" <td> 0.001204</td>\n",
" <td> 1.733333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Docs</th>\n",
" <td> 102</td>\n",
" <td> 23.702124</td>\n",
" <td> 5.713495</td>\n",
" <td> 287.376782</td>\n",
" <td> 0.000255</td>\n",
" <td> 0.366667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Enhancement</th>\n",
" <td> 588</td>\n",
" <td> 48.035074</td>\n",
" <td> 8.611881</td>\n",
" <td> 796.912072</td>\n",
" <td> 0.000058</td>\n",
" <td> 0.083333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Prio-high</th>\n",
" <td> 123</td>\n",
" <td> 14.590127</td>\n",
" <td> 1.830845</td>\n",
" <td> 252.790949</td>\n",
" <td> 0.000845</td>\n",
" <td> 1.216667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Timeseries</th>\n",
" <td> 288</td>\n",
" <td> 21.710290</td>\n",
" <td> 4.882153</td>\n",
" <td> 261.369537</td>\n",
" <td> 0.002431</td>\n",
" <td> 3.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 70,
"text": [
" count mean median max min min_minutes\n",
"label \n",
"Bug 1014 13.275700 2.625590 394.091736 0.000845 1.216667\n",
"Data IO 126 32.557035 7.970220 329.296389 0.001204 1.733333\n",
"Docs 102 23.702124 5.713495 287.376782 0.000255 0.366667\n",
"Enhancement 588 48.035074 8.611881 796.912072 0.000058 0.083333\n",
"Prio-high 123 14.590127 1.830845 252.790949 0.000845 1.216667\n",
"Timeseries 288 21.710290 4.882153 261.369537 0.002431 3.500000"
]
}
],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"labels['time_to_close'] = labels.id.map(time_to_closed_id)\n",
"labels.groupby('label').time_to_close.agg(['mean', 'count']).sort('count')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>label</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Note To Selves</th>\n",
" <td> 36.273669</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Error Reporting</th>\n",
" <td> 2.001192</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Multithreading</th>\n",
" <td> 42.334792</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dupe</th>\n",
" <td> 7.953403</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Usage</th>\n",
" <td> 7.595692</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Good as first PR</th>\n",
" <td> 58.303630</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Missing-data</th>\n",
" <td> 44.201061</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Stats</th>\n",
" <td> 64.405637</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Reshaping</th>\n",
" <td> 8.085771</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Regression</th>\n",
" <td> 2.025528</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Prio-low</th>\n",
" <td> 18.580193</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Can't Repro</th>\n",
" <td> 53.689413</td>\n",
" <td> 10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>API</th>\n",
" <td> 9.047280</td>\n",
" <td> 11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unicode</th>\n",
" <td> 34.888754</td>\n",
" <td> 13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ideas</th>\n",
" <td> 29.522814</td>\n",
" <td> 14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Refactor</th>\n",
" <td> 16.248433</td>\n",
" <td> 17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Performance</th>\n",
" <td> 10.628944</td>\n",
" <td> 20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Community</th>\n",
" <td> 65.968383</td>\n",
" <td> 25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dtypes</th>\n",
" <td> 9.780838</td>\n",
" <td> 27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Prio-medium</th>\n",
" <td> 66.635368</td>\n",
" <td> 35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Output-Formatting</th>\n",
" <td> 26.399247</td>\n",
" <td> 39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Groupby</th>\n",
" <td> 35.504929</td>\n",
" <td> 54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indexing</th>\n",
" <td> 17.853236</td>\n",
" <td> 57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Build problem</th>\n",
" <td> 14.625669</td>\n",
" <td> 64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Testing</th>\n",
" <td> 15.196128</td>\n",
" <td> 65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Visualization</th>\n",
" <td> 17.075993</td>\n",
" <td> 76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Docs</th>\n",
" <td> 23.702124</td>\n",
" <td> 102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Prio-high</th>\n",
" <td> 14.590127</td>\n",
" <td> 123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Data IO</th>\n",
" <td> 32.557035</td>\n",
" <td> 126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Timeseries</th>\n",
" <td> 21.710290</td>\n",
" <td> 288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Enhancement</th>\n",
" <td> 48.035074</td>\n",
" <td> 588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bug</th>\n",
" <td> 13.275700</td>\n",
" <td> 1014</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 71,
"text": [
" mean count\n",
"label \n",
"Note To Selves 36.273669 1\n",
"Error Reporting 2.001192 1\n",
"Multithreading 42.334792 3\n",
"Dupe 7.953403 4\n",
"Usage 7.595692 5\n",
"Good as first PR 58.303630 5\n",
"Missing-data 44.201061 6\n",
"Stats 64.405637 6\n",
"Reshaping 8.085771 8\n",
"Regression 2.025528 8\n",
"Prio-low 18.580193 9\n",
"Can't Repro 53.689413 10\n",
"API 9.047280 11\n",
"Unicode 34.888754 13\n",
"Ideas 29.522814 14\n",
"Refactor 16.248433 17\n",
"Performance 10.628944 20\n",
"Community 65.968383 25\n",
"Dtypes 9.780838 27\n",
"Prio-medium 66.635368 35\n",
"Output-Formatting 26.399247 39\n",
"Groupby 35.504929 54\n",
"Indexing 17.853236 57\n",
"Build problem 14.625669 64\n",
"Testing 15.196128 65\n",
"Visualization 17.075993 76\n",
"Docs 23.702124 102\n",
"Prio-high 14.590127 123\n",
"Data IO 32.557035 126\n",
"Timeseries 21.710290 288\n",
"Enhancement 48.035074 588\n",
"Bug 13.275700 1014"
]
}
],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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