{ "metadata": { "name": "", "signature": "sha256:ea53907a1fbd9c4a83751c7a4508f5ea83d7e88d76f5ad99f496cf7a7de5e4f0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Task:**\n", "\n", " continue interactive analysis of time series (AO, NAO indexes)\n", "\n", "**Module:**\n", " \n", " pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Notebook file](http://nbviewer.ipython.org/urls/raw.github.com/koldunovn/earthpy.org/master/content/notebooks/time_series_analysis_with_pandas_part_2.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the [previous part](http://earthpy.org/pandas-basics.html) we looked at very basic ways of work with pandas. Here I am going to introduce couple of more advance tricks. We will use very powerful pandas IO capabilities to create time series directly from the text file, try to create seasonal means with *resample* and multi-year monthly means with *groupby*. At the end I will show how new functionality from the upcoming IPython 2.0 can be used to explore your data more efficiently with sort of a simple GUI (*interact* function). There might be easier or better ways to do some of the things discussed here, and I will be happy to hear about them in comments :) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import usual suspects and change some output formatting:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline\n", "pd.set_option('max_rows',15) # this limit maximum numbers of rows" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 76 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also going to download necessary files. their description can be found in the [first part](http://earthpy.org/pandas-basics.html)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "!wget http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii\n", "!wget http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Pandas IO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas is equipped with very rich IO functionality, that allows direct conversion of essentially any text table based data format to Series or DataFrame directly. There is very [good extensive documentation with a lot of examples](http://pandas.pydata.org/pandas-docs/stable/io.html). Here we are going to open AO file in the same way we did in the [first part](http://earthpy.org/pandas-basics.html) and NAO file with pandas io. Then we are going to combine two in one DataFrame." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simple numpy loadtxt, create dates and then Series." ] }, { "cell_type": "code", "collapsed": false, "input": [ "ao = np.loadtxt('monthly.ao.index.b50.current.ascii')\n", "dates = pd.date_range('1950-01', '2014-03', freq='M')\n", "AO = pd.Series(ao[:,2], index=dates)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's open NAO. First remind ourselves how the file looks like:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!tail norm.nao.monthly.b5001.current.ascii" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 2013 5 0.56906E+00\r\n", " 2013 6 0.52076E+00\r\n", " 2013 7 0.67216E+00\r\n", " 2013 8 0.97019E+00\r\n", " 2013 9 0.24060E+00\r\n", " 2013 10 -0.12801E+01\r\n", " 2013 11 0.90082E+00\r\n", " 2013 12 0.94566E+00\r\n", " 2014 1 0.29026E+00\r\n", " 2014 2 0.13352E+01\r\n" ] } ], "prompt_number": 56 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have 3 space separated columns with two first columns containing years and months. Here is the expression that will create time series out of this file:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "NAO = pd.read_table('norm.nao.monthly.b5001.current.ascii', sep='\\s*', \\\n", " parse_dates={'dates':[0, 1]}, header=None, index_col=0, squeeze=True)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "NAO" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 58, "text": [ "dates\n", "1950-01-15 0.92\n", "1950-02-15 0.40\n", "1950-03-15 -0.36\n", "1950-04-15 0.73\n", "1950-05-15 -0.59\n", "...\n", "2013-09-15 0.24060\n", "2013-10-15 -1.28010\n", "2013-11-15 0.90082\n", "2013-12-15 0.94566\n", "2014-01-15 0.29026\n", "2014-02-15 1.33520\n", "Name: 2, Length: 770" ] } ], "prompt_number": 58 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some explanations:\n", "\n", "* first argument is obviously the file name\n", "* '\\s*' - regular expression, that describe separator.\n", "* parse_dates - combine columns 0 and 1, convert resulting column to dates and give it the name *\"dates\"*\n", "* header - don't use 0 row as header\n", "* index_col - make column 0 (this will be already result of the *parse_dates* parsing)\n", "* squeeze - create Series instead of DataFrame." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we would like to combine AO and NAO Series. But there is a little problem - dates in our two Series are different. Pandas date parser returns time stamps, so it uses present day number (15 in my case) and interpret indexes in NAO as points in time. Similar thing happened with AO series. Its index has monthly frequency, but every value is interpreted as point in time associated with last day of the month. As a consequence simple approach will not work:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "aonao = pd.DataFrame({'AO':AO, 'NAO':NAO})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 59 }, { "cell_type": "code", "collapsed": false, "input": [ "aonao.head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AONAO
1950-01-15 NaN 0.92
1950-01-31-0.060310 NaN
1950-02-15 NaN 0.40
1950-02-28 0.626810 NaN
1950-03-15 NaN-0.36
1950-03-31-0.008127 NaN
1950-04-15 NaN 0.73
1950-04-30 0.555100 NaN
1950-05-15 NaN-0.59
1950-05-31 0.071577 NaN
\n", "

10 rows \u00d7 2 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 60, "text": [ " AO NAO\n", "1950-01-15 NaN 0.92\n", "1950-01-31 -0.060310 NaN\n", "1950-02-15 NaN 0.40\n", "1950-02-28 0.626810 NaN\n", "1950-03-15 NaN -0.36\n", "1950-03-31 -0.008127 NaN\n", "1950-04-15 NaN 0.73\n", "1950-04-30 0.555100 NaN\n", "1950-05-15 NaN -0.59\n", "1950-05-31 0.071577 NaN\n", "\n", "[10 rows x 2 columns]" ] } ], "prompt_number": 60 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But our data are monthly means, so they are related not to some particular point in time, but rather to the time interval, or time span. We can convert time stamps in our Series to time periods, and then combine them. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "aonao = pd.DataFrame({'AO':AO.to_period(freq='M'), 'NAO':NAO.to_period(freq='M')} )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "aonao.head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AONAO
1950-01-0.060310 0.92
1950-02 0.626810 0.40
1950-03-0.008127-0.36
1950-04 0.555100 0.73
1950-05 0.071577-0.59
1950-06 0.538570-0.06
1950-07-0.802480-1.26
1950-08-0.851010-0.05
1950-09 0.357970 0.25
1950-10-0.378900 0.85
\n", "

10 rows \u00d7 2 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ " AO NAO\n", "1950-01 -0.060310 0.92\n", "1950-02 0.626810 0.40\n", "1950-03 -0.008127 -0.36\n", "1950-04 0.555100 0.73\n", "1950-05 0.071577 -0.59\n", "1950-06 0.538570 -0.06\n", "1950-07 -0.802480 -1.26\n", "1950-08 -0.851010 -0.05\n", "1950-09 0.357970 0.25\n", "1950-10 -0.378900 0.85\n", "\n", "[10 rows x 2 columns]" ] } ], "prompt_number": 62 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that now index show only years and months. Below you can see that type of indexes for original time series and for converted one differ:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "type(AO.index)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 63, "text": [ "pandas.tseries.index.DatetimeIndex" ] } ], "prompt_number": 63 }, { "cell_type": "code", "collapsed": false, "input": [ "type(AO.to_period(freq='M').index)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "pandas.tseries.period.PeriodIndex" ] } ], "prompt_number": 64 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Seasonal means with resample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initially pandas was created for analysis of financial information and it thinks not in seasons, but in quarters. So we have to resample our data to quarters. We also need to make a shift from standard quarters, so they correspond with seasons. This is done by using 'Q-NOV' as a time frequency, indicating that year in our case ends in November: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "q_mean = aonao.resample('Q-NOV')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [ "q_mean.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AONAO
1950Q1 0.283250 0.660000
1950Q2 0.206183-0.073333
1950Q3-0.371640-0.456667
1950Q4-0.178680-0.053333
1951Q1-0.804333-0.080000
\n", "

5 rows \u00d7 2 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 66, "text": [ " AO NAO\n", "1950Q1 0.283250 0.660000\n", "1950Q2 0.206183 -0.073333\n", "1950Q3 -0.371640 -0.456667\n", "1950Q4 -0.178680 -0.053333\n", "1951Q1 -0.804333 -0.080000\n", "\n", "[5 rows x 2 columns]" ] } ], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [ "q_mean[q_mean.index.quarter==1].plot(figsize=(8,5))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 67, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Irn5hPxghmu7UqeSloky8xj8acJmWAWFpQzn1ORgfNB7ODvzpZlToWhDq0+XG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"text": [ "" ] } ], "prompt_number": 67 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you don't mind to sacrifice first two data points (that strictly speaking can't represent the whole winter of 1949-1950), there is another way to do similar thing by just resampling to 3M (3 months) interval starting from March (third data point):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "m3_mean = aonao[2:].resample('3M', closed='left' )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "m3_mean.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AONAO
1950-05-31 0.206183-0.073333
1950-08-31-0.371640-0.456667
1950-11-30-0.178680-0.053333
1951-02-28-0.804333-0.080000
1951-05-31-1.191120-0.610000
\n", "

5 rows \u00d7 2 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 69, "text": [ " AO NAO\n", "1950-05-31 0.206183 -0.073333\n", "1950-08-31 -0.371640 -0.456667\n", "1950-11-30 -0.178680 -0.053333\n", "1951-02-28 -0.804333 -0.080000\n", "1951-05-31 -1.191120 -0.610000\n", "\n", "[5 rows x 2 columns]" ] } ], "prompt_number": 69 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now in order to select all winter months we have to choose Februaries (last month of the season):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "m3_mean[m3_mean.index.month==2].plot(figsize=(8,5))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 70, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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B1FQyQEEdYxOpvrvyHfLr81HbXmvyWsTm8JHDtl6C3WLKPVPbQf5vS5tLRV0LVboUCsWm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"text": [ "" ] } ], "prompt_number": 70 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Result is the same except for the first point. This method allows to use any possible time frequency, but one will have to deal with time stamps again, since periods for arbitrary frequencies are not yet implemented." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Multi-year monthly means with *groupby*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First step will be to add another column to our DataFrame with month numbers:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "aonao['mon'] = aonao.index.month\n", "aonao" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AONAOmon
1950-01-0.060310 0.92 1
1950-02 0.626810 0.40 2
1950-03-0.008127-0.36 3
1950-04 0.555100 0.73 4
1950-05 0.071577-0.59 5
1950-06 0.538570-0.06 6
1950-07-0.802480-1.26 7
1950-08-0.851010-0.05 8
1950-09 0.357970 0.25 9
1950-10-0.378900 0.85 10
1950-11-0.515110-1.26 11
1950-12-1.928100-1.02 12
1951-01-0.084969 0.08 1
1951-02-0.399930 0.70 2
1951-03-1.934100-1.02 3
.........
\n", "

770 rows \u00d7 3 columns

\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 71, "text": [ " AO NAO mon\n", "1950-01 -0.060310 0.92 1\n", "1950-02 0.626810 0.40 2\n", "1950-03 -0.008127 -0.36 3\n", "1950-04 0.555100 0.73 4\n", "1950-05 0.071577 -0.59 5\n", "1950-06 0.538570 -0.06 6\n", "1950-07 -0.802480 -1.26 7\n", "1950-08 -0.851010 -0.05 8\n", "1950-09 0.357970 0.25 9\n", "1950-10 -0.378900 0.85 10\n", "1950-11 -0.515110 -1.26 11\n", "1950-12 -1.928100 -1.02 12\n", "1951-01 -0.084969 0.08 1\n", "1951-02 -0.399930 0.70 2\n", "1951-03 -1.934100 -1.02 3\n", " ... ... ...\n", "\n", "[770 rows x 3 columns]" ] } ], "prompt_number": 71 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can use [*groupby*](http://pandas.pydata.org/pandas-docs/stable/groupby.html) to group our values by months and calculate mean for each of the groups (month in our case):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "monmean = aonao['1950':'2013'].groupby('mon').aggregate(mean)\n", "monmean.plot(kind='bar')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 72, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 72 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are very large negative values for winter months of AO. In order to see what is going on there it is useful to look at the [box plots](http://en.wikipedia.org/wiki/Box_plot) for every month:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ax = aonao.boxplot(column=['AO'], by='mon')\n", "ax = aonao.boxplot(column=['NAO'], by='mon')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 73 }, { "cell_type": "markdown", "metadata": {}, "source": [ "While NAO show more or less uniform spread, AO have pronounced seasonal variations, with largest spread during winter months." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Interactive exploration of data (only for IPython 2.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Say we would like to look at variability of our indexes by individual months, and also (if necessary) do a bit of smoothing in order to filter out high frequencies. Something like this will work: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.rolling_mean(aonao[['AO','NAO']][aonao.mon==1], window=1).plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 74, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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eESU0fDVTmJEzAwsKFqBGXeN37IGCntOJE8CkSWRbZia3z63VAhkZxJfPzg6s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\n6Df0o7O3E9/WlyK0037DJGAt3OvmrMN/3fFfAKQLtxSPWw3CI1ZZYjA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"text": [ "" ] } ], "prompt_number": 74 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is data for January and there is no smoothing (window=1)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "pd.rolling_mean(aonao[['AO','NAO']][aonao.mon==2], window=10).plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 75, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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lIrU0XbpUbd1dp+CemJgIOzu7kmNbW1skJiZW2iYhIUGXYUWF2LRCIZCaT1XV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\nKznXqVMnOnPmDBERbd68mb777jsiIoqNjS3Trph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"text": [ "" ] } ], "prompt_number": 75 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This one is February and rolling mean with 10 year window is applied. Would it be nice to be able to change our two parameters (month and window) somehow interactively? IPython developers include very simple way for such interactive interaction in the upcoming 2.0 version. The following code will only work on local machine, and only with IPython > 2.0." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import *interact*:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.html.widgets import interact" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define function that will use our parameters as input:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def kp(mm=1, wind=1):\n", " pd.rolling_mean(aonao[['AO','NAO']][aonao.mon==mm], window=wind).plot(ylim=(-4,4))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 46 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now you call *interact* with our previously defined function as first argument. Other arguments are our parameters with value limits and step size: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "cc = interact(kp, mm=(1, 12, 1), wind=(1,10,1))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As simple as that you get two controls for months and window size, that you can operate with mouse or arrow keys. More about this feature you can find in [this talk of Brian Granger](https://github.com/ellisonbg/talk-strata-sc2014)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Those who does not have IPython 2.0 yet can enjoy video of the process below :)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('Ba9GAq5PR_8')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " \n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 50, "text": [ "" ] } ], "prompt_number": 50 } ], "metadata": {} } ] }