{"action":"created","comment":{"url":"https://api.github.com/repos/OpenAstronomy/workshop_sunpy_astropy/pulls/comments/49806553","id":49806553,"diff_hunk":"@@ -0,0 +1,794 @@\n+{\n+ \"cells\": [\n+  {\n+   \"cell_type\": \"code\",\n+   \"execution_count\": null,\n+   \"metadata\": {\n+    \"collapsed\": true\n+   },\n+   \"outputs\": [],\n+   \"source\": [\n+    \"from __future__ import print_function, division\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"# Handling Data over time\\n\",\n+    \"\\n\",\n+    \"There's a widespread trend in solar physics at the moment for correlation over actual science, so being able to handle data over time spans is a skill we all need to have. Python has ample support for this so lets have a look at what we can use.\\n\",\n+    \"\\n\",\n+    \"<section class=\\\"objectives panel panel-warning\\\">\\n\",\n+    \"<div class=\\\"panel-heading\\\">\\n\",\n+    \"<h2><span class=\\\"fa fa-certificate\\\"></span> Learning Objectives </h2>\\n\",\n+    \"</div>\\n\",\n+    \"<br/>\\n\",\n+    \"<ul>\\n\",\n+    \"<li> Understand and use SunPy lightcurve. </li>\\n\",\n+    \"<li> Create a pandas dataframe. </li>\\n\",\n+    \"<li> Utilise the datetime package </li>\\n\",\n+    \"<li> Use the pandas dataframe to plot the data within it. </li>\\n\",\n+    \"</ul>\\n\",\n+    \"<br/>\\n\",\n+    \"</section>\\n\",\n+    \"\\n\",\n+    \"\\n\",\n+    \"\\n\",\n+    \"## SunPy Lightcurve\\n\",\n+    \"\\n\",\n+    \"SunPy provides a lightcurve object to handle this type of time series data. The module has a number of instruments associated with it, including:\\n\",\n+    \"\\n\",\n+    \"* GOES XRS LightCurve\\n\",\n+    \"* SDO EVE LightCurve for level 0CS data\\n\",\n+    \"* Proba-2 LYRA LightCurve\\n\",\n+    \"* NOAA Solar Cycle monthly indices.\\n\",\n+    \"* Nobeyama Radioheliograph Correlation LightCurve.\\n\",\n+    \"* RHESSI X-ray Summary LightCurve.\\n\",\n+    \"\\n\",\n+    \"We're going to examine the lightcurves created by a solar flare on June 7th 2011.\\n\",\n+    \"\\n\",\n+    \"Lets begin with the import statements:\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"code\",\n+   \"execution_count\": null,\n+   \"metadata\": {\n+    \"collapsed\": true,\n+    \"keep\": true\n+   },\n+   \"outputs\": [],\n+   \"source\": [\n+    \"from __future__ import print_function, division\\n\",\n+    \"import numpy as np\\n\",\n+    \"import sunpy\\n\",\n+    \"from sunpy import lightcurve as lc\\n\",\n+    \"import matplotlib.pyplot as plt\\n\",\n+    \"%matplotlib inline\\n\",\n+    \"\\n\",\n+    \"import warnings\\n\",\n+    \"warnings.filterwarnings('ignore')\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"Now lets create some lightcurves\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"code\",\n+   \"execution_count\": null,\n+   \"metadata\": {\n+    \"collapsed\": false\n+   },\n+   \"outputs\": [],\n+   \"source\": [\n+    \"goes_lightcurve = lc.GOESLightCurve.create('2011-06-07 06:00','2011-06-07 08:00')\\n\",\n+    \"\\n\",\n+    \"hsi_lightcurve = lc.RHESSISummaryLightCurve.create('2011-06-07 06:00','2011-06-07 08:00')\\n\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"In terms of LYRA, the server only allows you to download an entire day of data at a time. We can match this to the rest of the data by using the truncate function.\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"code\",\n+   \"execution_count\": null,\n+   \"metadata\": {\n+    \"collapsed\": false\n+   },\n+   \"outputs\": [],\n+   \"source\": [\n+    \"lyra_lightcurve_fullday = lc.LYRALightCurve.create('2011-06-07')\\n\",\n+    \"lyra_lightcurve = lyra_lightcurve_fullday.truncate('2011-06-07 06:00','2011-06-07 08:00')\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"Part of the advantage of using these inbuilt functions we can get a quicklook at our data using short commands:\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"code\",\n+   \"execution_count\": null,\n+   \"metadata\": {\n+    \"collapsed\": false\n+   },\n+   \"outputs\": [],\n+   \"source\": [\n+    \"fig = goes_lightcurve.peek()\\n\",\n+    \"fig = lyra_lightcurve.peek()\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {\n+    \"collapsed\": true\n+   },\n+   \"source\": [\n+    \"### Accessing and using the data\\n\",\n+    \"\\n\",\n+    \"More custom plots can be made easily by accessing the data in the lightcurve functionality. Both the time information and the data are contained within the lightcurve.data code, which is a pandas dataframe. We can see what data is contained in the dataframe by finding which keys it contains and also asking what's in the meta data: \"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"code\",\n+   \"execution_count\": null,\n+   \"metadata\": {\n+    \"collapsed\": false\n+   },\n+   \"outputs\": [],\n+   \"source\": [\n+    \"print(lyra_lightcurve.data.keys())\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"code\",\n+   \"execution_count\": null,\n+   \"metadata\": {\n+    \"collapsed\": false\n+   },\n+   \"outputs\": [],\n+   \"source\": [\n+    \"print(lyra_lightcurve.meta)\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"Notice that the meta data information is stored in something called `OrderedDict`\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"<section class=\\\"objectives panel panel-info\\\">\\n\",\n+    \"<div class=\\\"panel-heading\\\">\\n\",\n+    \"<h2><span class=\\\"fa fa-thumb-tack\\\"></span> On Dictionaries </h2>\\n\",\n+    \"</div>\\n\",\n+    \"<br/>\\n\",\n+    \"We can create keyword-data pairs to form a dictionary (shock horror) of values. In this case we have defined some strings and number to represent temperatures across europe\\n\",\n+    \"\\n\",\n+    \"<pre>\\n\",\n+    \"temps = {'Brussles': 9, 'London': 3, 'Barcelona': 13, 'Rome': 16}\\n\",\n+    \"temps['Rome']\\n\",\n+    \"16\\n\",\n+    \"</pre>\\n\",\n+    \"\\n\",\n+    \"We can also find out what keywords are associated with a given dictionary, In this case:\\n\",\n+    \"\\n\",\n+    \"<pre>\\n\",\n+    \"temps.keys()\\n\",\n+    \"dict_keys(['London', 'Barcelona', 'Rome', 'Brussles'])\\n\",\n+    \"</pre>\\n\",\n+    \"\\n\",\n+    \"</code>\\n\",\n+    \"<br/>\\n\",\n+    \"</section>\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"We can use these keys to plot specific parameters from the lightcurve, Aluminium is 'CHANNEL_3' and Zirconium is 'CHANNEL_4. These measurements are taken on a instuments which detect events at different energy levels.\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"code\",\n+   \"execution_count\": null,\n+   \"metadata\": {\n+    \"collapsed\": false,\n+    \"scrolled\": true\n+   },\n+   \"outputs\": [],\n+   \"source\": [\n+    \"plt.figure(1, figsize=(10,5))\\n\",\n+    \"plt.plot(lyra_lightcurve.data.index, lyra_lightcurve.data['CHANNEL3'], color='blue', label='Al filter')\\n\",\n+    \"plt.plot(lyra_lightcurve.data.index, lyra_lightcurve.data['CHANNEL4'], color='red', label='Zr filter')\\n\",\n+    \"plt.ylabel('Flux (Wm^2)')\\n\",\n+    \"plt.show()\\n\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"### Analysing Lightcurve data\\n\",\n+    \"\\n\",\n+    \"We can asses the degree to which the lyra curves correlate with each other:\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"code\",\n+   \"execution_count\": null,\n+   \"metadata\": {\n+    \"collapsed\": false\n+   },\n+   \"outputs\": [],\n+   \"source\": [\n+    \"cross_correlation = np.correlate(lyra_lightcurve.data['CHANNEL3'],\\n\",\n+    \"                                 lyra_lightcurve.data['CHANNEL4'])\\n\",\n+    \"print(cross_correlation)\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"## Pandas\\n\",\n+    \"\\n\",\n+    \"In its own words Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. Pandas has two forms of structures, 1D series and 2D dataframe. It also has its own functions associated with it.\\n\",\n+    \"\\n\",\n+    \"It is also amazing.\\n\",\n+    \"\\n\",\n+    \"Lightcurve uses these in built Pandas functions, so we can find out things like the maximum of curves:\\n\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"code\",\n+   \"execution_count\": null,\n+   \"metadata\": {\n+    \"collapsed\": false\n+   },\n+   \"outputs\": [],\n+   \"source\": [\n+    \"# max time argument taken from long and short GOES channels\\n\",\n+    \"max_t_goes_long = goes_lightcurve.data['xrsb'].idxmax()\\n\",\n+    \"max_t_goes_short = goes_lightcurve.data['xrsa'].idxmax()\\n\",\n+    \"\\n\",\n+    \"# max_time argument taken from channel 3 & 4 LYRA channe;s\\n\",\n+    \"max_t_lyra_al = lyra_lightcurve.data['CHANNEL3'].idxmax()\\n\",\n+    \"max_t_lyra_zr = lyra_lightcurve.data['CHANNEL4'].idxmax()\\n\",\n+    \"\\n\",\n+    \"print('GOES long: ', max_t_goes_long)\\n\",\n+    \"print('GOES short: ', max_t_goes_short)\\n\",\n+    \"print('LYRA Al: ', max_t_lyra_al)\\n\",\n+    \"print('LYRA Zr: ', max_t_lyra_zr)\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"markdown\",\n+   \"metadata\": {},\n+   \"source\": [\n+    \"So lets plot them on the graph\"\n+   ]\n+  },\n+  {\n+   \"cell_type\": \"code\",\n+   \"execution_count\": null,\n+   \"metadata\": {\n+    \"collapsed\": false\n+   },\n+   \"outputs\": [],\n+   \"source\": [\n+    \"# create figure with raw curves\\n\",\n+    \"plt.figure(1, figsize=(10,5))\\n\",\n+    \"plt.plot(lyra_lightcurve.data.index, lyra_lightcurve.data['CHANNEL3'], color='blue', label='Al filter')\\n\",\n+    \"plt.plot(lyra_lightcurve.data.index, lyra_lightcurve.data['CHANNEL4'], color='red', label='Zr filter')\\n\",\n+    \"plt.ylabel('Flux (Wm^2)')\\n\",","path":"09-Time-Series-Data.ipynb","position":300,"original_position":300,"commit_id":"bb6f0d76731739dcd07bdce276c45926aac2c648","original_commit_id":"bb6f0d76731739dcd07bdce276c45926aac2c648","user":{"login":"dpshelio","id":963242,"avatar_url":"https://avatars.githubusercontent.com/u/963242?v=3","gravatar_id":"","url":"https://api.github.com/users/dpshelio","html_url":"https://github.com/dpshelio","followers_url":"https://api.github.com/users/dpshelio/followers","following_url":"https://api.github.com/users/dpshelio/following{/other_user}","gists_url":"https://api.github.com/users/dpshelio/gists{/gist_id}","starred_url":"https://api.github.com/users/dpshelio/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dpshelio/subscriptions","organizations_url":"https://api.github.com/users/dpshelio/orgs","repos_url":"https://api.github.com/users/dpshelio/repos","events_url":"https://api.github.com/users/dpshelio/events{/privacy}","received_events_url":"https://api.github.com/users/dpshelio/received_events","type":"User","site_admin":false},"body":"If the lightcurve includes the unit, then I would create the label from it (I don't know whether it's somewhere), eg:\r\n\r\n```python\r\n'Flux ({})'.format(lyra_lightcurve.unit)\r\n```","created_at":"2016-01-15T00:21:15Z","updated_at":"2016-01-15T00:21:15Z","html_url":"https://github.com/OpenAstronomy/workshop_sunpy_astropy/pull/18#discussion_r49806553","pull_request_url":"https://api.github.com/repos/OpenAstronomy/workshop_sunpy_astropy/pulls/18","_links":{"self":{"href":"https://api.github.com/repos/OpenAstronomy/workshop_sunpy_astropy/pulls/comments/49806553"},"html":{"href":"https://github.com/OpenAstronomy/workshop_sunpy_astropy/pull/18#discussion_r49806553"},"pull_request":{"href":"https://api.github.com/repos/OpenAstronomy/workshop_sunpy_astropy/pulls/18"}}},"pull_request":{"url":"https://api.github.com/repos/OpenAstronomy/workshop_sunpy_astropy/pulls/18","id":56056315,"html_url":"https://github.com/OpenAstronomy/workshop_sunpy_astropy/pull/18","diff_url":"https://github.com/OpenAstronomy/workshop_sunpy_astropy/pull/18.diff","patch_url":"https://github.com/OpenAstronomy/workshop_sunpy_astropy/pull/18.patch","issue_url":"https://api.github.com/repos/OpenAstronomy/workshop_sunpy_astropy/issues/18","number":18,"state":"open","locked":false,"title":"Time 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