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emiliom/MODIS_EVI_SubsetToolPackage.ipynb

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MODIS_EVI_SubsetToolPackage.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Access EVI from MODIS Subset Tool online package\n",
"- Sample Subset Tool package: http://daacmodis.ornl.gov/glb_viz_3/30Mar2016_12:10:50_607958470L40.11384L-75.19914S41L41_MOD13Q1/csv/statistics_250m_16_days_EVI.asc\n",
"- Includes some code from Nanxi Lu \n",
"4/4,1, 3/31/2016"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/mayorga/miniconda/envs/bigcz/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
}
],
"source": [
"#%matplotlib inline # or nbagg | notebook\n",
"%matplotlib inline\n",
"import requests\n",
"import urlparse\n",
"import datetime\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"matplotlib.style.use('ggplot')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# IML\n",
"# 01Apr2016_10:56:57_210106385L41.7296L-91.7373S9L9_MOD13Q1\n",
"# My original \"CRB\" test subset\n",
"# 30Mar2016_12:10:50_607958470L40.11384L-75.19914S41L41_MOD13Q1\n",
"\n",
"url_tmplt = \"http://daacmodis.ornl.gov/glb_viz_3/%s/csv/%s.asc\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"datapack = \"01Apr2016_10:56:57_210106385L41.7296L-91.7373S9L9_MOD13Q1\"\n",
"statsasc = \"statistics_250m_16_days_EVI\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# url = urlparse.urljoin(url_base, datapack, statsasc)\n",
"url = url_tmplt % (datapack, statsasc)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#r = requests.get(url)\n",
"#r.content"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Can read url directly; but using requests would be safer,\n",
"# allow for better error testing\n",
"df = pd.read_csv(url, parse_dates=['date'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date[YYYYDDD]</th>\n",
" <th>date</th>\n",
" <th>product</th>\n",
" <th>band</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>sum</th>\n",
" <th>range</th>\n",
" <th>mean</th>\n",
" <th>variance</th>\n",
" <th>standard_deviation</th>\n",
" <th>tot_pixels</th>\n",
" <th>pixels_pass_qa</th>\n",
" <th>per_cent_pixels_pass_qa</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A2000049</td>\n",
" <td>2000-02-18</td>\n",
" <td>MOD13Q1</td>\n",
" <td>250m_16_days_EVI</td>\n",
" <td>0.1169</td>\n",
" <td>0.2235</td>\n",
" <td>13.6649</td>\n",
" <td>0.1066</td>\n",
" <td>0.1687</td>\n",
" <td>0.0004</td>\n",
" <td>0.0198</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A2000065</td>\n",
" <td>2000-03-05</td>\n",
" <td>MOD13Q1</td>\n",
" <td>250m_16_days_EVI</td>\n",
" <td>0.1448</td>\n",
" <td>0.3125</td>\n",
" <td>16.0559</td>\n",
" <td>0.1677</td>\n",
" <td>0.1982</td>\n",
" <td>0.0011</td>\n",
" <td>0.0333</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A2000081</td>\n",
" <td>2000-03-21</td>\n",
" <td>MOD13Q1</td>\n",
" <td>250m_16_days_EVI</td>\n",
" <td>0.1603</td>\n",
" <td>0.4715</td>\n",
" <td>21.4342</td>\n",
" <td>0.3112</td>\n",
" <td>0.2646</td>\n",
" <td>0.0047</td>\n",
" <td>0.0689</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A2000097</td>\n",
" <td>2000-04-06</td>\n",
" <td>MOD13Q1</td>\n",
" <td>250m_16_days_EVI</td>\n",
" <td>0.0272</td>\n",
" <td>0.4950</td>\n",
" <td>19.5573</td>\n",
" <td>0.4678</td>\n",
" <td>0.2414</td>\n",
" <td>0.0113</td>\n",
" <td>0.1061</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A2000113</td>\n",
" <td>2000-04-22</td>\n",
" <td>MOD13Q1</td>\n",
" <td>250m_16_days_EVI</td>\n",
" <td>0.1892</td>\n",
" <td>0.6563</td>\n",
" <td>29.2063</td>\n",
" <td>0.4671</td>\n",
" <td>0.3606</td>\n",
" <td>0.0112</td>\n",
" <td>0.1058</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>100</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date[YYYYDDD] date product band min max \\\n",
"0 A2000049 2000-02-18 MOD13Q1 250m_16_days_EVI 0.1169 0.2235 \n",
"1 A2000065 2000-03-05 MOD13Q1 250m_16_days_EVI 0.1448 0.3125 \n",
"2 A2000081 2000-03-21 MOD13Q1 250m_16_days_EVI 0.1603 0.4715 \n",
"3 A2000097 2000-04-06 MOD13Q1 250m_16_days_EVI 0.0272 0.4950 \n",
"4 A2000113 2000-04-22 MOD13Q1 250m_16_days_EVI 0.1892 0.6563 \n",
"\n",
" sum range mean variance standard_deviation tot_pixels \\\n",
"0 13.6649 0.1066 0.1687 0.0004 0.0198 81 \n",
"1 16.0559 0.1677 0.1982 0.0011 0.0333 81 \n",
"2 21.4342 0.3112 0.2646 0.0047 0.0689 81 \n",
"3 19.5573 0.4678 0.2414 0.0113 0.1061 81 \n",
"4 29.2063 0.4671 0.3606 0.0112 0.1058 81 \n",
"\n",
" pixels_pass_qa per_cent_pixels_pass_qa \n",
"0 81 100 \n",
"1 81 100 \n",
"2 81 100 \n",
"3 81 100 \n",
"4 81 100 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Drop date[YYYYDDD], product, band and tot_pixels. The last three are metadata\n",
"# that should be preserved elsewhere, as single values for the whole data frame\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 365 entries, 0 to 364\n",
"Data columns (total 14 columns):\n",
"date[YYYYDDD] 365 non-null object\n",
"date 365 non-null datetime64[ns]\n",
"product 365 non-null object\n",
"band 365 non-null object\n",
"min 363 non-null float64\n",
"max 363 non-null float64\n",
"sum 363 non-null float64\n",
"range 363 non-null float64\n",
"mean 363 non-null float64\n",
"variance 363 non-null float64\n",
"standard_deviation 363 non-null float64\n",
"tot_pixels 365 non-null int64\n",
"pixels_pass_qa 365 non-null int64\n",
"per_cent_pixels_pass_qa 365 non-null float64\n",
"dtypes: datetime64[ns](1), float64(8), int64(2), object(3)\n",
"memory usage: 42.8+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>product</th>\n",
" <th>band</th>\n",
" <th>tot_pixels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>MOD13Q1</td>\n",
" <td>250m_16_days_EVI</td>\n",
" <td>81</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" product band tot_pixels\n",
"0 MOD13Q1 250m_16_days_EVI 81"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Just to print out the \"metadata\" columns before they're dropped\n",
"df[['product', 'band', 'tot_pixels']].head(1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# axis=1 corresponds to columns; use it to drop columns rather than rows\n",
"df.drop(['date[YYYYDDD]', 'product', 'band', 'tot_pixels'], axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# utc=True, \n",
"#df['dtutc'] = pd.to_datetime(df['date'], infer_datetime_format=True)\n",
"df.set_index('date', inplace=True, drop=False)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['year'] = df.date.dt.year\n",
"df['month'] = df.date.dt.month\n",
"df['day'] = df.date.dt.day\n",
"df['doy'] = df.date.dt.dayofyear"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 365 entries, 2000-02-18 to 2016-02-02\n",
"Data columns (total 14 columns):\n",
"date 365 non-null datetime64[ns]\n",
"min 363 non-null float64\n",
"max 363 non-null float64\n",
"sum 363 non-null float64\n",
"range 363 non-null float64\n",
"mean 363 non-null float64\n",
"variance 363 non-null float64\n",
"standard_deviation 363 non-null float64\n",
"pixels_pass_qa 365 non-null int64\n",
"per_cent_pixels_pass_qa 365 non-null float64\n",
"year 365 non-null int64\n",
"month 365 non-null int64\n",
"day 365 non-null int64\n",
"doy 365 non-null int64\n",
"dtypes: datetime64[ns](1), float64(8), int64(5)\n",
"memory usage: 42.8 KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>sum</th>\n",
" <th>range</th>\n",
" <th>mean</th>\n",
" <th>variance</th>\n",
" <th>standard_deviation</th>\n",
" <th>pixels_pass_qa</th>\n",
" <th>per_cent_pixels_pass_qa</th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>doy</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></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>2000-02-18</th>\n",
" <td>2000-02-18</td>\n",
" <td>0.1169</td>\n",
" <td>0.2235</td>\n",
" <td>13.6649</td>\n",
" <td>0.1066</td>\n",
" <td>0.1687</td>\n",
" <td>0.0004</td>\n",
" <td>0.0198</td>\n",
" <td>81</td>\n",
" <td>100</td>\n",
" <td>2000</td>\n",
" <td>2</td>\n",
" <td>18</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-03-05</th>\n",
" <td>2000-03-05</td>\n",
" <td>0.1448</td>\n",
" <td>0.3125</td>\n",
" <td>16.0559</td>\n",
" <td>0.1677</td>\n",
" <td>0.1982</td>\n",
" <td>0.0011</td>\n",
" <td>0.0333</td>\n",
" <td>81</td>\n",
" <td>100</td>\n",
" <td>2000</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-03-21</th>\n",
" <td>2000-03-21</td>\n",
" <td>0.1603</td>\n",
" <td>0.4715</td>\n",
" <td>21.4342</td>\n",
" <td>0.3112</td>\n",
" <td>0.2646</td>\n",
" <td>0.0047</td>\n",
" <td>0.0689</td>\n",
" <td>81</td>\n",
" <td>100</td>\n",
" <td>2000</td>\n",
" <td>3</td>\n",
" <td>21</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-04-06</th>\n",
" <td>2000-04-06</td>\n",
" <td>0.0272</td>\n",
" <td>0.4950</td>\n",
" <td>19.5573</td>\n",
" <td>0.4678</td>\n",
" <td>0.2414</td>\n",
" <td>0.0113</td>\n",
" <td>0.1061</td>\n",
" <td>81</td>\n",
" <td>100</td>\n",
" <td>2000</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-04-22</th>\n",
" <td>2000-04-22</td>\n",
" <td>0.1892</td>\n",
" <td>0.6563</td>\n",
" <td>29.2063</td>\n",
" <td>0.4671</td>\n",
" <td>0.3606</td>\n",
" <td>0.0112</td>\n",
" <td>0.1058</td>\n",
" <td>81</td>\n",
" <td>100</td>\n",
" <td>2000</td>\n",
" <td>4</td>\n",
" <td>22</td>\n",
" <td>113</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date min max sum range mean variance \\\n",
"date \n",
"2000-02-18 2000-02-18 0.1169 0.2235 13.6649 0.1066 0.1687 0.0004 \n",
"2000-03-05 2000-03-05 0.1448 0.3125 16.0559 0.1677 0.1982 0.0011 \n",
"2000-03-21 2000-03-21 0.1603 0.4715 21.4342 0.3112 0.2646 0.0047 \n",
"2000-04-06 2000-04-06 0.0272 0.4950 19.5573 0.4678 0.2414 0.0113 \n",
"2000-04-22 2000-04-22 0.1892 0.6563 29.2063 0.4671 0.3606 0.0112 \n",
"\n",
" standard_deviation pixels_pass_qa per_cent_pixels_pass_qa year \\\n",
"date \n",
"2000-02-18 0.0198 81 100 2000 \n",
"2000-03-05 0.0333 81 100 2000 \n",
"2000-03-21 0.0689 81 100 2000 \n",
"2000-04-06 0.1061 81 100 2000 \n",
"2000-04-22 0.1058 81 100 2000 \n",
"\n",
" month day doy \n",
"date \n",
"2000-02-18 2 18 49 \n",
"2000-03-05 3 5 65 \n",
"2000-03-21 3 21 81 \n",
"2000-04-06 4 6 97 \n",
"2000-04-22 4 22 113 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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1aMduNl0/j1xn8e+1I7GZ+vBygP3SLlIAZ5v56JEHTo/bTfcMsheXyE+JicEG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fX19ejt7UVLS8u01joZdsmR9vZ2/OIXv8CRI0fgcrnQ0tKCffv2Yd68eaipqYGiKOjo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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d71f93f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(x='date', y='mean', figsize=(9, 5));"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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hvCTDEcZUFRS1dYGb3swqdvMNOKTPWxhB0TGg13PbQkQqqeEIYxYLWAz+rzer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\nELqTgEMIIYQQuvv/BZjWf7X7Jx8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d6eb0a790>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the quality (QA) values, \"% pixels that pass QA tests\"\n",
"df.plot(x='date', y='per_cent_pixels_pass_qa', ylim=(0, 105), figsize=(9, 3));"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Use seaborn for nicer plots: panels, histograms, box plots, etc\n",
"#df.plot(kind='scatter', x='month', y='mean', xlim=(0, 13), figsize=(12, 5));"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#sns.set(style=\"ticks\", palette=\"muted\", color_codes=True)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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YWBhaXrx4sWpraxNdkjENDQ1yu93XXD86H/zotLFXWrFihaqrq+NRGgAgCVmm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\nAMCGCHgAAGyIgAcAwIYIeAAAbOj/AzeUcEngeWXIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d6e9e7610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# couldn't get pandas df.plot.box to work properly ...\n",
"sns.boxplot(x='month', y='mean', data=df, color='g');"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6e4c9f50>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mSVJ9fb2k70Y/JGny5MmaNWuWJXUDAARHgE9g0byEL5B+/fqFV0EAgGUI8Akq\n1osJZ82aRQ8dABIYAT5BsZgQABAIi+wAALAhAjwAADZEgAcAwIaYgwcAwAKxvhKKAA8AQC/rjbTq\nBHgAAHpZb1wJxRw8AAA2RIAHAMCGGKIHAMBinff/kKKzBwgBvpeFu2oylNzxAIDEFo09QAjwIYj0\nF1ZEqyZNrJgEACSmWOz/QYCPQKi/sMgfDwDoLQT4ELDDGgAgUbCKHgAAG6IHDwBAiGKx6j3aCPAA\nAEQoGqveo40ADwBAiBJhTZalAX7VqlXavXu3HA6HZs+ercLCQt+x7du3689//rNSU1M1btw4XXHF\nFRbWFACAxGLZIrvKykrV1NRoyZIlmjNnjlZ2S/7y7LPP6je/+Y3uu+8+bd++Xfv27bOopgAAJB7L\nArzb7VZxcbEkKT8/X01NTWpuz+RWW1urU045RTk5OXI4HBo/frx27NhhVVUBAEg4lgV4j8ejjIwM\n3/2MjAx5PB7fsVNOOcXvMQAAEFzcLLIzDCMq5eTl5QU8npqaaupxVoikbmVlZfrggw989zsu2bj5\n5pt9f7vwwgu73AcA2JdlAT47O7tLr7yxsVFZWVl+jzU0NCg7O9tUudXV1QGPt7a2mnqcFSKpm9fr\n9T1f+u6Sjc5/83q9cfl/AwDCE6hDaFmAHzt2rNavX6+pU6eqqqpKOTk5SmvfZGXgwIH69ttvVVdX\np+zsbG3ZskW//vWvrapqQkiESzYAAL3HsgBfVFSkgoICLVy4UCkpKSopKVFFRYXS09NVXFys66+/\nXn/84x/lcDg0ZcoUDR482KqqAgCQcBxGtCa/40SwIeh42rGtp1SHHdvCWp3mEAAQ3+JyiB4ni8dU\nhwCA4NxutyRp9OjRFtfkOwR4CzFvDgD2sG7dOknxFeDZLhYAgAi43W5VVlaqsrLS15OPBwR4BOV2\nu+PqQwsA8aSj9979ttUYokdQ8Tj0BAAIjB48AorXoScAiBdXX32139tWs30PvqdL0Toul5O4HC2Q\n7kNP9OIBoKvRo0dr5MiRvtvxwvYBvjsuRQMARFs89dw7JF2iG4TG7XartLRUklRaWhpXv04BINmR\n6AZhi9ehJwBAYAR4BBWPQ08AgMAI8AiKnjsAJB4ukwMAwIYI8AAA2BABHgAAGyLAAwBgQwR4AABs\niAAPAIANEeABALAhAjwAADZEgAcAwIYI8AAA2BABHgAAGyLAAwBgQwR4AABsiAAPAIANEeABALAh\nAjwAADYeYuTBAAAScElEQVREgAcAwIacVr1wa2urnnjiCdXV1Sk1NVU33nijBg0a1OUxTU1NWrZs\nmdLS0nT77bdbVFMAABKPZT34jRs3yuVy6b777tPPfvYzrV279qTHrFixQiNGjLCgdgAAJDbLAvyO\nHTt0zjnnSJLOPvts7dq166THzJkzhwAPAEAYLAvwBw8eVEZGhiTJ4XDI4XCotbW1y2PS0tKsqBoA\nAAmvV+bgP/jgA73//vtyOBySJMMw9OWXX3Z5jGEYvVEVAACSQq8E+AsvvFAXXnhhl78tX75cHo9H\nQ4cO9fXcU1NTI36tvLy8iMsAACDRWTZEP2bMGG3atEmS9Mknn2jUqFF+H0fPHgCA0DkMiyJoW1ub\nnnzySdXU1KhPnz6aO3eucnJy9Oqrr2rUqFEqLCzU4sWLdeTIETU0NOj000/XlVde2eMPAQAA8B3L\nAjwAAIgdMtkBAGBDBHgAAGyIAA8AgA1ZloveKnv27NF//Md/6NJLL9VPfvKTiJ+/dOlSHT58WJLk\n9Xr1ve99T7/61a+ClnP06FE98cQTOnjwoI4dO6YrrrhCLpdLzz33nFJTU9W3b1/dfPPNOuWUU0Kq\n39GjR3XHHXfoyiuv1Pe+9z099dRTSklJ0ZAhQ3TdddcpJcXcb7oNGzbo9ddfl9Pp1FVXXaW///3v\nqqqq8tXnsssu0/jx402V1dzcrLKyMjU1Nen48eO66qqrNGbMGL355ptas2aNnn32WfXr189UWYZh\naMWKFfrmm2/kdDp1/fXX689//nPIbeDvc+CvPi+++KK2bdsmSZowYYKmTZsWtKy6ujqVl5ertbVV\nTqdTt9xyizIzM/Vf//Vf+uCDD9SnTx9dcsklOvfcc03XTZK2bdumBx98UP/5n/8pSfr5z3/eJdPj\nokWLfLkmeiqrYw+Impoa9e/fX3fccYcGDBhget8HM5//qVOnas2aNb7n7N27V3feeaeKiooCllVd\nXe3382qmDZ577jl9/vnnamtr0+WXX67CwkKVlZWpra1N2dnZuvnmm+V0Ok23gb/yli9fflKbmmmD\n7mV1ZPDs3p5m2qB7WZ988slJ38vMzExT77+/8jZu3Oj3uxROG5xyyil64YUXlJqaqrS0NN1yyy0a\nMGBA0Dbwd24cNmyY3/Y08/73VJ6/9gzWBmbP2wcOHDDVBv7K6+lca6YNTmIkkebmZqO0tNR46qmn\njLfffjvqz1++fLnx5Zdfmirrf/7nf4zXXnvNMAzDOHDggDFv3jxj6dKlRm1trWEYhrF+/XrjlVde\nCbmOa9euNX73u98ZFRUVxsMPP2xs27bNMAzDeOmll4yNGzeaKuPw4cPGvHnzjObmZqOxsdF46qmn\njCeeeMLYsmVLyPUxDMN46623jLVr1xqGYRgNDQ3Grbfeavz3f/+38cILLxg33XST0dzcbLqszZs3\nG48++qhhGIZRU1NjPPjgg12Om2kDf+3orz61tbXG0qVLDcMwjNbWVmPevHlGY2Nj0LLKysqMTZs2\nGYZhGG+//bbx3HPPGQcPHjTmzZtnHDt2zDh69KixYMEC4+jRo6bqZhiGcfToUaO0tNS44YYbfH8r\nKSkJ+f985513jJUrVxqGYRjvvfee8cknnxiGYRiPPvqo8dJLLxl/+MMfQiqvM3/vfVNTk1FaWmqq\nLH+fVzNt4Ha7fZ+Dw4cPGzfeeKPxxBNP+Npg7dq1xrvvvmu6DYKV19GmhhG8DfyVZRj+2zNYG/RU\nr0Dfy57e/0B169DRnuG2wd133238v//3/wzDMIyXX37ZePXVV021gb9zo7/2NIzg77+Z8jq3Z7A2\nCOe8HagNeqpb9zY10wb+JNUQfZ8+ffT73/9e2dnZUX9+dXW1jhw5osLCQlNlnXfeebrsssskSXV1\ndTr11FN12223aeDAgTIMQw0NDcrJyQmpftXV1dq3b58mTJggwzC0f/9+X33GjBmjzz77zFQ527dv\n15gxY9SvXz9lZWWZGpEIJCMjo0uvICMjQ+ecc47+7d/+LeSyampqdNZZZ0mSTjvtNNXV1flyJZht\nA3/t6K8+AwcO1G233eard0pKivr37x+0rOuvv97XK+n432tra5Wfny+n06k+ffpo2LBh2r17t6m6\nSdIrr7yin/zkJ3I6vxt0M4JcAOOvrC1btuhHP/qRJOmiiy7SD3/4Q0nm9n0I5/P/+uuv66c//amp\nsmpqak76vJppg5EjR/p6WwMGDFBLS4t27typiRMnSpJ++MMfavv27abbwF9511133UltKgVvA39l\nGYbhtz2DtYG/stra2gK+fk/vf6C6SV3bM9w2cLlcOnTokKQToxOnnHKKqTbwd27s3p47duyQZC5P\nir/yemrPYG0Qznk7UBv4K88fM23gT1IN0aekpJgeog71+W+++aYuvvjikMtcuHChGhoadNddd0k6\nMWy3cuVKnX766brgggtCKmv16tUqKSlRRUWFHA6HzjjjDG3dulUXXHCBPvvsMx08eNBUOQcOHFBL\nS4seeeQRNTU16aqrrpIkvfPOO/rrX/+qzMxMlZSUyOVymSrvvPPOU0VFhebNm6empib97ne/C3uf\ngaFDh+qNN97QT3/6U9XU1Ki2tlaHDx9WRkaG6Tbw146B6vPss8/qo48+0qxZs06aSvBXVt++fSWd\nyPXwzjvv6KqrrtLgwYO1Z88eeb1eOZ1OffHFF35zOvgrr7q6Wl9//bWuvvrqLsN+x44d02OPPaa6\nujqdc845uvTSS4OWVVtbq08//VTPPfecsrKydN111yk9Pd1Ue4T6+T969Ki2b9/u94ecv7KGDh3a\n4+c1UBs4HA7fe/7BBx9o/Pjx+uyzz3zBMzMzUx6PR0OGDDHVBv7K89emUvA28FdWTU2N3/YM1gb+\nykpJSdHbb7/t93sZ6P3vqbyO4W1/7RlqG/zsZz9TaWmpXC6X0tPTNWPGDB05csRUG0hdz42LFy/u\n0p6NjY2Sgr//PZXXvT2vvPJKSeb3PzF73g7WBv7K++tf/9rjuTZQG/iTVD34WDl+/Lh27dqlkSNH\nhvzcxYsX684779Tjjz8uSRo3bpyWLVumvLw8vfLKK6bL+dvf/qaioiINHDjQ97dZs2bpo48+0uLF\ni0PKCGgYhrxer+68807ddNNNWr58uS644ALNmDFDixYt0rBhw7Ru3TrT5W3YsEGnnnqqHnvsMS1a\ntEhPP/206ed2N27cOJ111lkqLS3Vm2++qfz8fBmGEVEbBDN79mz98Y9/1GuvvaYDBw6Yek5bW5vK\nyso0evRojRo1Si6XS//+7/+uhx9+WMuXL9cZZ5xhuk1Wr16ta6655qS/z5o1SzfccIPmz5+vjRs3\nqqqqylR5+fn5uueee3TGGWeE9BnrSU/v/ccff6wJEyaYLucXv/hFj59XM23w8ccf68MPP1RJSUmX\nv3eUlZ6eHlIbdC+ve5tK5tugc1mrVq3y255mdS7rggsu0MyZM/1+L82+/93/z57aM9Q2eOaZZ3Tn\nnXfq0Ucf1YgRI/Tuu++G9D1YvHixfvvb3+qxxx7r8vfOjw/lO9D9XNu5PUePHh30fQpUVk/nbbNt\n0PG/Pv744wHPtaGei5KqBx8rlZWVvmFjs6qqqpSZmanc3FydeeaZam1t1aZNmzR58mRJ0rnnnqsX\nX3zRdHlbt25VbW2ttmzZooaGBvXp00e5ubm6++67JUmfffaZPB6PqbKysrJUVFQkh8Oh0047Tf37\n99fQoUN9u/9NnDgxpCC9a9cujRs3TpI0bNgwNTQ0yDAM3y6CoZo+fbrv9rx585SZmant27eH3Ab+\ndK5PfX29Dh48qOHDh2vAgAH6/ve/r6+++qrLj6ieLF++XHl5eb6egSRNmjRJkyZNkiQtW7ZMgwYN\nClpOQ0ODqqur9dhjj8kwDHk8Ht1777265557NHXqVN/jRo8erT179mj48OEBy8vKytIPfvADSdLY\nsWO1fv36oHUIpqfP/5YtW0JayOrv89rQ0CCPxxO0DbZt26ZXX31V8+fPV//+/ZWWlqZjx46pT58+\namho8E0FmG2D7uVJ/tvUTBt0Luvbb7/tsT3N6F6vzoGp+/fSzPvv7//s3p5mvwfdy9qzZ49vUdnZ\nZ5+tjRs3SgreBp3PjcOGDVNbW1uP7Wnm/e9+rm1ra9OhQ4e0evXqk9ozmFDP28HaoPv/2tra6vdc\na/Z70F3S9uBD6dEGe/5XX32lYcOGhfT8nTt36i9/+YskyePxqLm5WS+//LL+7//+T5L05ZdfhrRx\nzq233qoHHnhA999/vy666CJdccUV2rlzp7Zu3SpJ+vDDD33zrcGMGTNG//u//yvDMHT48GE1Nzfr\nT3/6k2prayWdOAGcccYZpus2ePBgffHFF5JODP/379+/y86Cofj6669VXl4u6cQJpaCgQFJ4beDv\n9TvfP3TokFasWKG2tja1tbXpn//8p4YMGRK0zA0bNqhPnz5dThxtbW269957dezYMXk8Hn399ddB\ng7FhGMrJydFjjz2mJUuW6P7771dWVpbuueceX5CQpNbWVu3atctUm4wbN863EreqqqrLZyzUUZ4O\nPb33obbJunXrTvq8Hjx4MGgbHDlyRM8//7zuuusuDRgwQNKJgLJ582ZJ0ubNmzVu3DjTbeCvPH9t\naqYNupfVU3v6e1/N1OsPf/hDj9/LYO+/v/L8Pc/M98BfWVlZWdq3b5+vzCFDhphqA3/nxrPPPlt/\n//vfJX3Xnma/A/7K2759u5xOp9/gHqgNQj1vB2sDf+X5O9ea+R74k1SpaquqqrRmzRodOHBAqamp\nysnJ0W9+8xulp6dH9Px169ZpxIgRvl9xZhw9elRPPvmk6uvrdfToUV111VXKysrSypUru1xu0fFL\nLhTr16/XoEGDVFRU5BtCGjFihGbNmmW6jPfee08ffPCBHA6Hpk2bpv79+2vNmjVKS0tTv379dNNN\nN5muW3Nzs8rLy3Xw4EG1tbVp+vTp2rVrl3bs2KHdu3ersLBQRUVFmjlzZtCyDMNQeXm59u7dq759\n+2revHnKycnRypUrTbeBv3YcM2aMtm/fflJ9Xn31VX388ceSTlyacsUVVwQt69ChQ+rTp4+vR3T6\n6aerpKRE7777rm/b5F/84hd+5x6DfUZvvvlmlZWVSZLWrl2rHTt2KCUlRcXFxbr88suDlvXrX/9a\nzzzzjDwej/r376+5c+fK5XKZ2vch1M//9ddfrxUrVphug5kzZ+qZZ56R1PXzGqwN3nvvPb344otd\nTnhz587Vk08+qWPHjmngwIG66aablJKSYqoN/JVXV1en9PT0k9r0+eefl9vt7rEN/JV18803Kzc3\nt0t7trW1BW0Df2X9y7/8i9566y2/38tA73+gur3++usntWc4bTB9+nQ9//zzcjqdcrlcuvHGGzVg\nwICgbeDv3Dh8+HCVlZWd1J7BvgM9lffKK6/o2LFjXdrz2muvDdoGoZ63g7WBv/LS0tL8nmuDtYE/\nSRXgAQBIFkk7RA8AgJ0R4AEAsCECPAAANkSABwDAhgjwAADYEAEeAAAbIsADCMnnn3+uuXPnWl0N\nAEEQ4AGELJwUwwB6F7noAQT10ksv6b333lNGRoYv5fGxY8e0cuVKVVZWKiUlRePHj9fMmTO1du1a\nHT16VL/85S8lndgqdM6cOSovLze9AyGAyNGDBxDQ3r179cYbb+jhhx/WQw89pD179kiS3njjDTU0\nNOjRRx/VQw89pJ07d+qjjz7SlClTtGnTJt9e5Vu2bNHIkSMJ7kAvI8ADCGjnzp0aOXKkMjIy5HA4\n9KMf/UiGYejTTz/V1KlTfXuBn3/++frss89UUFCg9PR0ud1uSdI//vGPkPZpABAdBHgAAXm93i67\njXVsfHPo0KEuvXKXy6VDhw5JkqZMmaKNGzfq6NGjqqys1DnnnNO7lQZAgAcQWHp6uo4cOeK73xHE\ns7KydPjwYd/fDx8+rMzMTEknAvzHH3+sTz75RCNGjOjyAwFA7yDAAwioqKhIu3bt0uHDh9XW1qYN\nGzbI4XBowoQJev/999XW1qbm5mb97W9/04QJEyRJeXl5Ou200/T888/rvPPOs/g/AJITq+gBBHTm\nmWfqX//1X/Xb3/5WGRkZmjJlir755htdfPHF2r9/v+644w45HA5NnjxZkyZN8j1vypQpWrdunSZO\nnGhh7YHkxX7wAGJi06ZN2rx5s2699VarqwIkJYboAURdS0uLXnvtNV188cVWVwVIWgR4AFG1detW\n3XbbbZo4caK+//3vW10dIGkxRA8AgA3RgwcAwIYI8AAA2BABHgAAGyLAAwBgQwR4AABsiAAPAIAN\n/X/jpt6v3mHUywAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d6e996050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Try to change the x-axis tick labels to calendar labels \n",
"# (eg, month, or at least 1-12), and use vertical grids for months\n",
"# Also add 8 days, so the boxes are centered on the middle of the 16d period\n",
"# Except for the last 16d period, which is actually more like 12d??!\n",
"# Could be nice to color (hue?) the boxes by month\n",
"\n",
"sns.boxplot(x='doy', y='mean', data=df, color='c');\n",
"# Alternatively, can use x=df.date.dt.dayofyear"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"--------------------------------------------------------------\n",
"\n",
"### From Nanxi\n",
"With some minor cleanups from Emilio"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"peak_greenness = 0.596340\n"
]
},
{
"data": {
"image/png": 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OVTk+JycHwP/dceiv+vXrh+Dg4Hqtd8+ePVWO1+l0dV7mXymVlX9eDAgIqFdm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\nEckES52IiEgmWOpEREQy8f8B3FfvRu93lJgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d6e5ae7d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# look at the distribution of peak greenness days over years\n",
"# Definition of peak greenness value: \n",
"# http://www.neonscience.org/updates-events/update/neons-2016-remote-sensing-flight-schedule-now-available\n",
"\n",
"site_max = max(df['mean'])\n",
"# definition of peak greenness from NEON: 90% of the site maximum, I used maximum of mean here\n",
"peak_greenness = 0.9 * site_max\n",
"print(\"peak_greenness = %f\" % peak_greenness)\n",
"peak_greenness_days = df[df['mean'] >= peak_greenness]\n",
"#peak_greenness_data = pd.Series(peak_greenness_days.doy)\n",
"\n",
"sns.boxplot(x=peak_greenness_days['year'], y=peak_greenness_days['doy'], \n",
" data=peak_greenness_days, color='c');\n",
"plt.title(\"Peak greenness days over year\")\n",
"sns.plt.ylim(150,);"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"197.740740741\n",
"2016-07-15\n"
]
}
],
"source": [
"# Translate \"date of year\" to year/month/day\n",
"# Use the median of all the peak greenness days over year\n",
"doy = peak_greenness_days['doy'].mean() # or .median()\n",
"print(doy)\n",
"peak_greenness_date = datetime.date(2016,1,1) + datetime.timedelta(days=(doy-1))\n",
"print(peak_greenness_date.strftime(\"%Y-%m-%d\"))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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