{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Intro to Python\n", "[NetCDF](#NetCDF) | [Summary stats](#Summary-stats) | [Time slices](#Time-slices) | [Multiple variables](#Multiple-variables) |[Multiple sites](#Multiple-sites) | [Using functions](#Using-functions)\n", "\n", "The first step in using Python is to load in the relevant packages. Assigning abbreviations when importing packages, makes it easier to call the tools later. The abbreviations below are very common." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# load some key packages:\n", "import numpy as np # a numerical package\n", "import pandas as pd # a data analysis package\n", "import matplotlib.pyplot as plt # a scientific plotting package\n", "\n", "# to display the plots in the same document\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the cell throws an error, you haven't probably intalled the required packages correctly. To install them open a terminal/command window and run: `conda install package_name`. \n", "\n", "As a demo of the language, we will plot sin and cos of a range of points on the same axis. The first step is to define the points: " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. 1.1\n", " 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2. 2.1 2.2 2.3\n", " 2.4 2.5 2.6 2.7 2.8 2.9 3. 3.1 3.2 3.3 3.4 3.5\n", " 3.6 3.7 3.8 3.9 4. 4.1 4.2 4.3 4.4 4.5 4.6 4.7\n", " 4.8 4.9 5. 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9\n", " 6. 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7. 7.1\n", " 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8. 8.1 8.2 8.3\n", " 8.4 8.5 8.6 8.7 8.8 8.9 9. 9.1 9.2 9.3 9.4 9.5\n", " 9.6 9.7 9.8 9.9 10. 10.1 10.2 10.3 10.4 10.5 10.6 10.7\n", " 10.8 10.9 11. 11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9\n", " 12. 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9 13. 13.1\n", " 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9 14. 14.1 14.2 14.3\n", " 14.4 14.5 14.6 14.7 14.8 14.9 15. 15.1 15.2 15.3 15.4 15.5\n", " 15.6 15.7 15.8 15.9 16. 16.1 16.2 16.3 16.4 16.5 16.6 16.7\n", " 16.8 16.9 17. 17.1 17.2 17.3 17.4 17.5 17.6 17.7 17.8 17.9\n", " 18. 18.1 18.2 18.3 18.4 18.5 18.6 18.7 18.8]\n" ] } ], "source": [ "# define an array of points (start, end, by)\n", "x = np.arange(0,6*np.pi,0.1)\n", "\n", "# check the array\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Xu3b16IRCpVF9UYHVkzxm/5IZ4t+3L8coBCpH+kJYgjaRaCE0cmZSG3nuB2TS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\nH8TAK/FvauLz3H23+3MFhkWLvBH/mho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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot the sin of these points\n", "plt.plot(x, np.sin(x), 'blue')\n", "\n", "# plot the cos of these points\n", "plt.plot(x, np.cos(x),'red')\n", "\n", "# show that plot below\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this example you can see how tools from different packages are called using the abbreviations assigned at the beginning of the notebook. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NetCDF\n", "[NetCDF](#NetCDF) | [Summary stats](#Summary-stats) | [Time slices](#Time-slices) | [Multiple variables](#Multiple-variables) |[Multiple sites](#Multiple-sites) | [Using functions](#Using-functions)\n", "\n", "There are several different tools for working with netcdf data using python. This notebook focuses on xarray, because of its impressive time capabilities. To open the netcdf file, you need the Data URL which you can find at [THREDDS](http://hydromet-thredds.princeton.edu:9000/thredds). The URL goes in quotes because otherwise Python will think you are trying to call a variable that you already assigned a value to (like x in the example above)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dimensions: (time: 46167)\n", "Coordinates:\n", " lat float64 ...\n", " lon float64 ...\n", " station_name |S64 ...\n", " * time (time) datetime64[ns] 2011-03-01T18:00:00 ...\n", "Data variables:\n", " AirTC_2_Avg (time) float64 ...\n", " AirTC_Avg (time) float64 ...\n", " Albedo_1_Avg (time) float64 ...\n", " Albedo_Avg (time) float64 ...\n", " CmCIR_Avg (time) float64 ...\n", " CmGIR_Avg (time) float64 ...\n", " DnTot_Avg (time) float64 ...\n", " IR01DnCo_Avg (time) float64 ...\n", " IR01Dn_Avg (time) float64 ...\n", " IR01UpCo_Avg (time) float64 ...\n", " IR01Up_Avg (time) float64 ...\n", " LPO2Dn_Avg (time) float64 ...\n", " Max_Run_Tot_mm_24hr_Max (time) float64 ...\n", " Max_Run_Tot_mm_2hr_Max (time) float64 ...\n", " NR01TC_Avg (time) float64 ...\n", " NR01TK_Avg (time) float64 ...\n", " NetRl_Avg (time) float64 ...\n", " NetRs_Avg (time) float64 ...\n", " NetTot_Avg (time) float64 ...\n", " PA_uS (time) float64 ...\n", " PA_uS_2 (time) float64 ...\n", " PA_uS_3 (time) float64 ...\n", " PA_uS_4 (time) float64 ...\n", " RECORD (time) float64 ...\n", " RH (time) float64 ...\n", " RHCroof (time) float64 ...\n", " RH_2 (time) float64 ...\n", " Rain_mm_2_Tot (time) float64 ...\n", " Rain_mm_3600_Tot (time) float64 ...\n", " Rain_mm_3_Tot (time) float64 ...\n", " Rain_mm_Tot (time) float64 ...\n", " SBT_C_2_Avg (time) float64 ...\n", " SBT_C_Avg (time) float64 ...\n", " SR01Dn_Avg (time) float64 ...\n", " SR01Up_Avg (time) float64 ...\n", " Storm_Type_mm_24hr (time) |S64 ...\n", " Storm_Type_mm_2hr (time) |S64 ...\n", " T108_C_Avg_11 (time) float64 ...\n", " T108_C_Avg_12 (time) float64 ...\n", " T108_C_Avg_13 (time) float64 ...\n", " T108_C_Avg_14 (time) float64 ...\n", " T108_C_Avg_15 (time) float64 ...\n", " T108_C_Avg_16 (time) float64 ...\n", " T108_roof10_Avg (time) float64 ...\n", " T108_roof1_Avg (time) float64 ...\n", " T108_roof2_Avg (time) float64 ...\n", " T108_roof3_Avg (time) float64 ...\n", " T108_roof4_Avg (time) float64 ...\n", " T108_roof5_Avg (time) float64 ...\n", " T108_roof6_Avg (time) float64 ...\n", " T108_roof7_Avg (time) float64 ...\n", " T108_roof8_Avg (time) float64 ...\n", " T108_roof9_Avg (time) float64 ...\n", " TCAV_1_Avg (time) float64 ...\n", " TCAV_2_Avg (time) float64 ...\n", " TCAV_3_Avg (time) float64 ...\n", " TCAV_4_Avg (time) float64 ...\n", " TCroof_Avg (time) float64 ...\n", " UpTot_Avg (time) float64 ...\n", " VW (time) float64 ...\n", " VW_2 (time) float64 ...\n", " VW_3 (time) float64 ...\n", " VW_4 (time) float64 ...\n", " WindDir_deg (time) float64 ...\n", " WindDir_std_dev (time) float64 ...\n", " WindSpd_ms (time) float64 ...\n", " hfp01_10_Avg (time) float64 ...\n", " hfp01_11_Avg (time) float64 ...\n", " hfp01_12_Avg (time) float64 ...\n", " hfp01_1_Avg (time) float64 ...\n", " hfp01_2_Avg (time) float64 ...\n", " hfp01_3_Avg (time) float64 ...\n", " hfp01_4_Avg (time) float64 ...\n", " hfp01_5_Avg (time) float64 ...\n", " hfp01_6_Avg (time) float64 ...\n", " hfp01_7_Avg (time) float64 ...\n", " hfp01_8_Avg (time) float64 ...\n", " hfp01_9_Avg (time) float64 ...\n", "Attributes:\n", " featureType: timeSeries\n", " history: Created 2016-06-16 09:37:26.378377\n", " description: Butler Green Roof Station\n", " Conventions: CF-1.6\n", " DODS.strlen: 6\n", " DODS.dimName: string6\n" ] } ], "source": [ "# load the netcdf-handling package:\n", "import xarray as xr\n", "\n", "# from THREDDS server. \n", "data_url = 'http://hydromet-thredds.princeton.edu:9000/thredds/dodsC/MonitoringStations/butler.nc'\n", "\n", "# open the file and assign it the name: ds\n", "ds = xr.open_dataset(data_url)\n", "\n", "# check it out\n", "print(ds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point you haven't pulled over any actual data, you just have a way of referencing the dataset. To get the data we select the variable that we are interested in -in this example we will use air temperature (AirTC_Avg). " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "array([ 6.504, 7.484, 8.48 , ..., 20.61 , 19.6 , 19.02 ])\n", "Coordinates:\n", " lat float64 ...\n", " lon float64 ...\n", " station_name |S64 ...\n", " * time (time) datetime64[ns] 2011-03-01T18:00:00 ...\n", "Attributes:\n", " units: Deg C\n", " method: Avg\n" ] } ], "source": [ "variable = 'AirTC_Avg'\n", "\n", "# check it out\n", "print(ds[variable])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HHprNgUtLOFz37JWWAU88kb/vnzcza5b/jiYiVQzAu5gunitUdWtVvUBVo1yO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\nXgdcwrzbcl3OeBk4MPpWQxy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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot all the data\n", "ds[variable].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary stats\n", "[NetCDF](#NetCDF) | [Summary stats](#Summary-stats) | [Time slices](#Time-slices) | [Multiple variables](#Multiple-variables) |[Multiple sites](#Multiple-sites) | [Using functions](#Using-functions)\n", "\n", "Once you have loaded the dataset and chosen a variable to look at, you can access lots more tools by converting it into a pandas.DataFrame object. With a dataframe you can create boxplots and compute the summary statistics on that variable over the entire span of the data." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AirTC_Avg
count46167.000000
mean12.993200
std10.209512
min-17.900000
10%-0.473400
25%5.023000
50%13.430000
75%21.020000
90%26.210000
max40.770000
\n", "
" ], "text/plain": [ " AirTC_Avg\n", "count 46167.000000\n", "mean 12.993200\n", "std 10.209512\n", "min -17.900000\n", "10% -0.473400\n", "25% 5.023000\n", "50% 13.430000\n", "75% 21.020000\n", "90% 26.210000\n", "max 40.770000" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert to a pandas object and then to a dataframe\n", "df = ds[variable].to_pandas().to_frame(name=variable)\n", "\n", "# get a summary of the data including the percentiles listed\n", "df.describe(percentiles=[.1,.25,.5,.75,.9])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can explore the annual temperature cycle by creating box plots for each month. We can control the whiskers and set them to .1 and .9 percentile (expressed as percentages so 10, 90). We can also eliminate outliers by setting sym='' (no symbol so they won't plot)." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['2012-06-03'].boxplot(column=variable, by=df['2012-06-03'].index.hour)\n", "# set the labels\n", "plt.xlabel(' ')\n", "plt.ylabel('Temperature [C]')\n", "plt.title('Monthly boxplots')\n", "plt.suptitle('')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create a box plot\n", "df.boxplot(column=variable, by=df.index.month, whis= [10, 90], sym='')\n", "\n", "# set the labels\n", "plt.xlabel('month')\n", "plt.ylabel('Temperature [C]')\n", "plt.title('Monthly boxplots')\n", "plt.suptitle('')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Time slices\n", "[NetCDF](#NetCDF) | [Summary stats](#Summary-stats) | [Time slices](#Time-slices) | [Multiple variables](#Multiple-variables) |[Multiple sites](#Multiple-sites) | [Using functions](#Using-functions)\n", "\n", "We often are more interested in looking at one particular time, rather than the whole record. Here is one way to select a particular time range:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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QTYcoa0UVGhrkfKmoyIdwbdqU9KiaM28ecMopTW/LmkMEAGefDXzmM/nf4+5F\ntG2blNz2KnfvRZYE0SuveB+jgqhI3nlHGmu1a5fNRfy3vw38/vfhP+9TTwFXXy07NY88Ire99ZbY\nlV4Qtcw8IqtDRCQT55Ytzsdam1z26SMXKiW71NVJwu+KFUmPJBpUEGWDAwfEffdboCXpEO3YIaHC\n3bp5H5fFHKKsOERG9GzcKOK5XTv5PY15RMxSjOC005rePnasXG+TLA4CyPlsDeXzorwcuPXWpr/H\nWVSplHA5IJleRMUIomOPVYcoMrZvlzekvDx9Fws/6uvl++LF4T7v4cNSrnP8eOCmm0QY7dolORR2\na9uJllh62+oQAd5hc3v35gVR0uErSuns3CkJv++9l0yfhqhRQZQNamtlI8apuqeVJJssBi1TnMaQ\nuZbgEDU0SK+8mprm5c/TmEe0ZImcC6a/n6FzZ5lj16xJZFjvU4hDZMdURIuLUgVREs5yISW3DcOG\n+QtlFURFsmePfPgGDcqeIDK7D2HvQtTUyK5Iu3YSFzt7NnDnncCTTwInnOD/+JaYR2R1iAA5X9wa\n3e3dmz9WBVH2qauTTZPevVtmXykVRNmgpsa/oAKQ7DUnqCBKW8hcY6NsjnotKLt1E/c/zZsimzbJ\n5/mFF7IhiJ5+urk7ZJg+PflCNoXkENnp2jXestthOERZKKpAJC6RFyqIisQIorjVfBhs3ixjDztH\nxa7aL7oI+OY3ZbfmrLP8H98SQ+bsDtHEiRJu6XesCqLsU1cn4TKjR7fMsLkDB1pPUQUiGkRE84jo\nHSJ6m4i+krt9ChG9TESLiOhVIjra77niZvNm//whIBuCKG0O0fbt8hlv29b9mLIyua7H3VumEMym\n7vPPNxdEcee0BOGZZ6SHjxOVlUBVVZyjaU4pDlHXrvkonjjIoiAqJmQOkDwiL1QQFYkRRHGr+TDY\nvBmYMiV8h8iu2k89Ffjyl4G//jUfj+xFSyy9bXeIJk50L/1oPbZXL5lslexiqsyNHt0yCyscPNiq\nHKJDAK5h5gkAZgL4IhGNB3ArgJuYeRqAmwDcluAYm/Dgg8CFF4rYCCKIkiyqUIhDlCZBVFMTrN9M\nv37AqlXRj6dYqquBAQOcBVHaHKJDh4D58yUCxYm0CKKgOUR2unRRh8iPjRuLq/CsDlFEZFkQ7dgh\ni7Tt28Nt/rlmDTB0aP73Dh2An/xEmlMGoTU4RKNGuTfqVIeo5cCcd4iOPRZ47rmkRxQ+rSlkjpk3\nM/MbuZ9XQRf5AAAgAElEQVTrASwDUAGgEUD33GE9AKQmgPo//wFefBH4xjdajkOUtpC5tWubigc3\nrrgCuOWW6MdTLNXVwPnnS2+fxYubC6I0pQW8+aacK25CdNIkWeQnJeIaG/ObYcUQ95qytjZbgohZ\n8nOLeX2POcb7fhVERZJlQbRnjyzUBgxwz2cphnfe8a8m50VLdYisgmjwYPd8Eqsg6tXLvRqdkn72\n7RNXtEMH4JxzgLlzvXMImOW4r3wlvjGWSmsSRFaIaBiAqQBeAfA1AD8monUQt+iG5EbWlNdeAx56\nSBa6U6b4H59kUYWVK2WzyI+0hcytWdM8sd+Jq66STRG/xpBJsWEDMGIEcNRRIqLT7BCtXSsVft1o\n0wY46aTkNqF275bzNEhUjBNxryntjmChxC2IDh6U97iYcG2/KpYeka+KF0YQtW+fPUG0d6+Mffp0\nmTS9Li6AVOY4fNj/BFyyBLj00uLH1VIdIuvuS79+EmZx8snA4483/YBaQ+amTZNy5Tt3inhVsoVx\nhwBJaO/SRSZyt8XT6tXSV+Nf/wJuv907JyEttEZBRERdADwE4GpmrieiL+R+foSIPgrgLgCnOz12\nzpw57/9cWVmJysrKyMa5d6/krU2fDtx7b7DHJOkQLV8uUQt+pM0hWr06mCDq0gW4/nqpvvqPf0Q/\nrkKprpZz5YQTJBzNSRAxF9+rJkyChEuZsLlPfjKOETWllHA5IH5BtHZt08ieQom7iqJZewelqqoK\nVQFjKDMw7aaTPXvEYTl8OHv9YvbsESfiqKNEEF14offxl10G/Pe//g3Pgu6WudFSHSJrDlFZmXyf\nP1+s/xNPzN9ndYi6d5eSzQsWAGecEd94lXCwh0xMmSLvt9vnY8ECcYiqquSxpYQwxEVrKqoAAETU\nFiKG7mHmR3M3X8rMVwMAMz9ERH90e7xVEEXNm2+KW1+IYE1KEJnQ7SC5OGnLIVq92j8Mx/A//wPM\nmVN6zkYUmCT1Xr1EvFmvXV27yry1a1c6NueCCqLf/CaW4TSjlIIKQDIOUSmCyDjLcQnm+vrCBJF9\n8+nmm292PVZD5orEvClZDZnr3DkviPx49FFg2TLvY5jlQh9kUnOjJTpEdkEESLGJESOax2Vby24D\nskuXpthtJTh2QTR5sjh+bqxcKc2MgxbTWL5cFrzMpY+1WFpZUQVA3J8lzPxzy20biOhkACCiUwGk\nonzGf/8r1/dCSKqowooV4g4FWUylLWRu9WrpbxKEI44QByaN+YTV1VIh9uSTgdtua/5epCmPKIgg\nmjRJNqqT6EdUSsltQM6TAwfCze92Y9cuuY6X4mi1by9fcVXG27Mn368xbAIJIiLaTUS7bF/riehh\nIhoRzdDSTZZziIwTcdRRwOuvey+qmKVpG5DvZO3E7t2ye1fKAqk1OESA9FA477zmcdn2Agxpi91W\nglOoINqyRcIpgwqi3/1OHNskKxFmMWSu2LmMiGYBuAjA7FyJ7deJ6EwAnwfwEyJaBOB/AVwRz3/i\nzX//Kw5zIfToISG6cYtsI4iCYEJJzZyUNEFD5gynnAI8+2x04ymGxsa8yOjUSZwsO2mai4IIojZt\ngI98RCrcxk2pDhGRLPjjEBgmXK5UZydOd7lQh6gQgjpEPwPwdQADAQwCcB2A+wA8ANk1a3WYPJws\nCiIj5vr3l0lwyRL3Y+vqJDnw8svlwv/YY87HbdlSehhAS23M2rFj89sHDgQWLpQv67FW8TRwYHp2\n5ZTCsFfBmTwZWLTI/fjaWnFXe/YM7hABsrObFFkURChyLmPmF5m5jJmnMvM0Zp7OzP9h5peY+ejc\nbTOZ2eNdjo/XXitcELVrJ9efuOezQgQRkJ6wuZ07ZXe9kHkvjYJo61ZZx9g37qykSRBt2hSs5PKn\nPgXcd1/047FTag4REN+6stT8IUPQeSsMEneIAJzPzL9n5t3MvIuZ/wDgDGb+K4AStHB2MYuBLAsi\nwP8Cbcqh/vjHwK9+BVxwAXD11c2PCyMuuiWGzO3f7zzRDBoEPPAAMGOGvB+HDsmup3WBqYIou+zc\nKdcGw7hxsngyVabuvrtpY+QtW0QQFRIyl3RIZUYFUYufy+rrxbmYMKHwxyaRR7RiRbAKc4ZOndJR\nWMG4Q4Xsrk+bJsIi7KbopRCkyeWAAd4RInEStAfNiSdK2NzixdGPyUqpDhEQX3PWUivMGfr0iS+X\nvtCiCoUQVBDtJaILiahN7utCAGaPJsEo9twAEhhBQ4PsqGVREFlDsyZO9G4aaQRR9+7A5z4nFYt+\n//vmr3kYgijskLnvfQ+4557wnq8YnELmgKZhFs88ky90YZ1chw9PdzM/xR27EG7TRioe3X+/NET+\n7GelT4zBGjLntyBtbJTY+JNOUoeoCFI9l4XBG2+IGCqm4EVSgiiLDlGh4XKAFCc48cTkG4daMflD\nXvTunY7iUfv3i1Do3dv/WOs1N05KzSEC4mvOGpZDFOf5kYaQuYsAXAygFkBN7udPE1EnAF+KZmgC\nEa0hojdzcduvOh0TZi+doGRZEFkVtp8L8ec/56voEAEXXST/c21t0+NMyE8phO0Qfec7wG9/G97z\nFYNbyJwpdX7++cCHPyw9nOw7NaNHA++9J5UMlWyxf3/z9/3TnwbuvDNf/cjqBBXiEG3bJuXahw4V\ncZUUBw9msspcYnNZXCxZIknlxRB3YQXmwgVRWgorFCOIgPSFzQUVREnmKxo2bZI2BkFduU9+Mv48\norAcoiwJoj59ZFM8DtIQMrebmc9j5j7M3Df380pm3sfML0QztPdpBFCZi9Ge4XSANQ8jLtImiKqq\nxBEJgnEjAH9B9OKLwOc/3/S2oUObV2/ZuNHfdvcjTIfIVGgJ0pAwStxC5oybdt11wPHHi5Nl7wd1\nxBHiGrz4YvTjVMLF6X2fPFly8b73PXFd162T2xsbReT06RNMEG3eLIuCvn2Tbd6bUYcoybksFpYt\nkxDNYoi7OevWrbK4DbLjb0hLL6I1a4JXmLNyyinpcoiChMz16pUOhyhouJxh8mR5n+LcVMxSDlGp\nJbcNrc0hepGI5hLR54ioh//hoULwGedLL4X3x959Vyo4+ZE2QfTjH4sjEgS7Q+SVLFlf3zQXApAP\n0D//mV/QAYVfqJwI0yEy8c5JN7h0C5kjksasM2dKGe7f/a5phTnD5ZcDt9wS/TiVcHFzBj//eeD0\n04Frrsk723V1suPVrl2w5FSrIIprV86JjAqiJOeyWChVEMXpEBVSctuQ5ZA5QMLUq6vjFZ5eZMkh\nKnSd0batuJ5xjj2MkLk4HaIs5hAl6hAx8xgA3wYwAcDrRPRPIvp0NENq/ucBPEVEC4no804H3Hdf\neNVEPvMZ4Atf8M9LMoKoSxd5g5LsBwLkBc7Bg/7HWitgVVTIAquxsflxzPKhtJ98s2YB//u/kiBq\nxGgYgihMh2j9evmedJEGt4UxAJx7rlyw/9//Ax5+WNwiO2efnZ5kViU4TiFzgCyg5s4VEWyqO1rD\nTYM4RDU1UiGyT5/CHKKwr1FZFEQJz2WxsHRp9gRRIWQ9ZK5tW2D6dCmNngaCOERpySEqZp3Rt2/z\nEP8oyUrI3MGDsqFW6roNkPMjzpC5qByiwPvnzPwqgFeJ6AcAbgfwJwD3RjOsJsxi5k1E1BcijJba\nQxs+9KE5+PznJQ/j9NObdqUtFLOAfu+95iFMVowgKiuThU+UqjUIZtFcXS1NP91glp0p03G6fXsR\nR7W1sutsZf9++R/btWt6+5VXyge2XTvg298G5s2TC9WAAaX9D2E6REYQJV3G280hstKmDfDBDzrf\nV16ebJ6IUhxuoZKG8ePlHN29O58/BAQTRLW1EkpZSMjchg2yCxxEFO3fD1x8MXDDDbJwc3/OKjz8\ncJVv0+a0keBcFjl798r1opiFOhC/IFq1qrAKc0A6QuaYJRSr2Nd5xgzg1VeB004LdVhFETRkLosO\nESDXyjhDi7MiiNavlzVbGFE0cTpEQYtqFEPQxqzdiOhSIvo3gJcAbALgmM8TNsy8Kfd9C4CHnf7u\n7343BxMmzME558wpSQwB8oGbNMm/upc1oTgNYXPV1TJR+AmKffvyIs7glkdUX+8s8jp3ltCfiy6S\nXbJLL5WLe6E7fU7PG6ZDNHJk8oLIb2Hsh7mYa2GFbOHlDAIyCU2cCLz5Zr7CHBCsytzevfK5LCRk\nrpCO7WvWAA895J/n0K1bJS65ZA7mzJGvLJDkXBYHK1bIda/YRU7cRRU2by58I82EzD3xRHJVOGtq\nZBzduhX3eCOI0oD1+uOGySFKOhImKw5RFnKIwiq5DbQchyhoDtGbAKYC+C4zj2HmbzDza9EMKQ8R\nHUFEXXI/dwbwAQCOVeUnTBCHyE4hJ9XBg+KejBvnr3aNQwSkQxBt3y6J2n4CYOfOvDtkcBNEu3c3\nzx+y0q4d8PTTEu4FlF52u3v38CbjLVtk9y4NIXOlCKJ27WSRkoZwBSU4biFzVqZPl2atNTVNBZHf\nTqx5blPZJ8gixfQ9CRJSa3IK/c65LIbMIaG5LC5KyR8C5DyMc/Fo3M5C6NRJrutXXSV5mEnw7rvA\n2LHFP37GjGSKQdk5fDjYAr5TJ9lITXo+3bSpcAEd5zl9+DCwa1fzNVahxLGmDKvCHBC/Q5S0IBrB\nzF9j5pcBgIg6EtHHohlSE/oDeIGIFgFYAOBxZp7rdODEidJ/wcqiRbKDE3RXwyxM+vYtTBDFqY6d\nOHRITpIBA/wFUV1dPn/IUKhDZGXkSOC228KxMPv1k8WgqRBXCnV18n9F5RCtWCGL0oYG92PMfaVa\n0ho2lz2CCqLXXpPFlXFXTciS1zXLuE8dO4pLvWuX/3hMCGmQZn/mWtBCBVFSc1kslCqIBgyI91oT\nxJ2w07GjlK1eu7Yw5zNMli8Hxowp/vFDh8r8kHTj7bo6WSMFmaPSkEeU9pA505C7rKy054lDEJkw\n6jCI2yFKuqgCE1EZEZ1NRPcAWAvg49EMqcnfXc3MU3Mltycx84/cjj3jDOCxx5oWBzAXy/feC/b3\nTB5MkA++VRANHy6hY0lhcoK6do1fEAGSUxTGh6FtW3ntw9jN2bEjWkF0772yIHziCfdjSg2XM6gg\nyh5B3nsjiJYulZwiQK4pHTt6T4ZWsRU0bO7tt+V7EEG0caOEUrREQZTUXBYXpQqiuK81xThEHTtK\nb5lp05Kbd0t1iIjSETZXSEP1NOQRpT1kLoz8IUDWckGu1aVQjNvmRvfuslEXJAKhVBJ1iIjoZCL6\nPYA1AD4H4HQAw5n5o9EMqTgmTpQT8QVLuYWVK+X7ggXBnmPTJvmwBam5bxVEI0YEF11RsH27jDlI\nDk4hgsgvZC4KBgzwLgMeFOMQRWXxL1smPSicwjQNpYbLGVQQZQ+/HCJArlmrVsmiyAgiwH/hYRVE\nQSrNHTgAPPigfO6DTLKrV0v/rpYmiLIyl5WCVVwXQ3l5vFUti2no3amTzE033JBdhwjIniBK2iHa\nu1eufYUKjjgdIrMWK5UuXaJ3iMIURETx9apKzCEiomoAPwTwAoAjmfkjAPYxc8KRpM5ceCHwyCP5\n35cvl/K0prytH6U4RFkWRBUVpTlEYVJREc6EHHXIXG0tcNxx3km9QRbFQVBBlD2ChMx16CA7a3V1\nTatVFSKIgjhEmzfL3xk9OpggmjcP+NCHggkiU1gm7WRtLiuGxkaZ80pxLrp2lXDNqHenATmP9+1r\nPh/50bEjcOyxUqFt9epkEv1LdYgA4Jhjks8jypJDtH69zOmF9KwC4nWICnk9vYgjZC5MQQTEJ5iT\nLKrwEIAKSEjBebnCBgnXGXFn2rSm4ueFF4DLLpNdsyAYO7ZQQTRkSD5GPwlMI7AggsitqIKTK7Nt\nWzi7HYUwYEB4gmjQoOguKlu2SFNVP0GkDlHrJGi45K23Su8z6yTvV2nOLoj8dj9NJa8uXfwXuvX1\nUrFy9mz/xc/Bg5lyiDI1lxVDdbXMA6VsYhHF5xKZcvOFLnBPPhm48UYRUkTxVsUDZO5fu9a7LUcQ\npkyRKpNJVm7LkkO0bl1xRQDidIi2bQsnnzqLgsgU+YmaxELmmPmrAIYD+AmASgDvAuhLRBea6m9p\nYtw4vN8TY9s2WeRfeSXw/PPA3/7m/3hzggT54FvLbruFnMWFqWoSpI9PISFzYe12FEJYIXM7dkjV\nvYMHo4lrra2VHBCv9z3MHKIwXhOldF58MXgvnyDu4MUXA3fd1fS2Qh2iIIKovDxYGIa5lgSpGpSl\nkLk0zGVhFIvxYsuW5r3kiiGuDZhi8ocA4MwzgfPPFzGURP7u6tWycVqq+z9ggPwPSTbezpJDVIog\nisshyoogYi6u5L0XcTpEiRVVYOFZZr4CMqF8EsAFkDjsVDFsmEwKO3fKh2fYMLlgPvEE8NWvNi24\n4MS6deIq+L2xjY3yZSqJJC2IjIVYbMhcr16yyLKLKWvDyLgIK2TOOGFRNBo0pUqHD/e+aIUVMjdk\niJybSvKccEKwMJdS3vuePYMLov798yW13di0SRa5QRJ1TZjsEUfINc6rAWaWBBGQ/FwW9aIsrPyF\nuCrNFVNhzk4Sgujdd0vPHwJEDE2Z0rw6bpxkzSEqpm9Or16yHvCqCBsWWQmZq6uTDf0jjgjvOeMq\nvZ2GstsAAGZuYOZ/MvNFAAab24no76GPrAjKyiSvY/78pnbgjBlSWvL1170fv3KlxNn7ffBNuJyx\n+rt3l0VyUr2I9u6VE/uII4oTRETOeURbt8YviMIImWtokB2QDh38F5fFsGWLPK+50LoRVsjc0KES\noqEky4ED8j2IOA3qEDlRiEMUZPfT7AR26yaffy9MIRUi/+tg1gSRlULnMiIaRETziOgdInqbiL5i\nue/LRLQ0d7trJdSoN83CEkRxhcwV6xBZGTYsX1ghrmtkqXlaVkzYXFJkzSEqRhC1aROfmMuKQxR2\nuBwQT+ntw4dl3gljXeVEQYLICjNb9w5HhDCWUDj1VOCZZ/IFEgzHHSd9idw4eFAeM3SoCJw9e9x3\nFKz5Q4AsHpJ0iUp1iADn8W/ZEn/I3KBBwEsvlfbB2rcvv/Phl49RDMuWyYTYsaPsopuFstM4wvjg\nVlTI6xFHSUvFHXNOfuxj/ruNpYRLRiGIysubLh7dsBZSCSKIslJUwYuAc9khANcw8wQAMwFcRUTj\niKgSwHkAJjHzJAA/dvs7UYe9Zs0hCkMQGYdo+3ZxbeLI5Q2joIIhS4Ioqw4REF9hhW3bwlkzdewo\nIbZRuVpRCKI4HCKz+d+maOXiTVhPm5rk1FNPlSpJ9jd84kRg8WL3x61bJ6KgXTt5sb2cBbsgApIV\nROYkCVpUoRBBFLdDNH26TDbz5hX/HOb1AKIJmVuyRErbEsmuu1tjzFJcAitt28pEsHx56c+lFM+W\nLfn300uwM5cWMhe2IDIhc6NGSUNhL6yl9r0WQIcPy1dLEEQ2HOcyZt7MzG/kfq4HsBTAQABfAPAj\nZj6Uu8/1zIhaEIVVBCdOh6jU+cWI/L//XTaM/M7vMAij5LYhDYIoqKORtEO0dm1xOURAfIUVCnk9\nvSCK1iXKqkMUdeXjiHRWchx1lEwMjzzStAvv5MneDtHWrU13q7wWA2kTRIU6RPYqc0C+0twDD+Qr\npyVRVIEImDSptJ0GqyCK4iK+ZAlw5JHyc/fu7oIoLIcIAE46CXjuuXCeKwq2bm1a8r4lsmULcPzx\nIti9RPahQ3IeB+n+7oSfq2kVWwMG+F93TMhcEEFknXC8qgbt2pUPrWttENEwAFMBvAJgDICTiGgB\nET1LREe7PS5LIXNZc4juv1/OcdN7MCqYpfdcKc1vrYwbJwt9r1y9KMmKQ9TYKFUUBw/2P9aJuAor\nhBUyB0TbnDWrDlGUJbcBoMgpuxmpmRbbtgW+/GVp2vab3+RvP/ZYySFyi3u3TyZe1ZucBJFbL584\nKMQhcjuhKiok3OCOO4DLLweuvz4Zhwgo/cK7d29eiARpXFkoS5cC550nP3s5RGEKog98ALjqKhH2\nJ54YznOGya23ArfdlmwJ2agxn4e9e/3LYpfyvhdSVKG8XK5pXhOxCZkrL5fddK9dNqtDNGpUvmqn\nHVONrgXiOZflKtI9BOBqZq4norYAejLzcUR0DIAH4RJ298QTc94XyZWVlaisrAxz3Ni+Xa4PpZKl\nkLlhw0QEbd4s837UgmjxYrnmWzdbS6F9e9lgWbxY+hLFTVZyiGpr5XUv9roaV8hcmJvIUTZn3bRJ\n1nxhEpdDVKggqqqqQlVVVaBj/Rqz9iWiIx1uP5KIrEvlbxQ0woj58Ifl+9Sp+du6dQMmTJAS3E6Y\nXj6G8nL36k3WktuGgQNlByMJrA6RX9ltp7EDMgkuXCg7yK++Ks/T2BitGnej1A+W1SGqqAg/VMXu\nELkVVggrZA4QAbZtmzhFhw+H85xhYiaboBPmPfeEf0GOmoUL5RpSSJ+gYigkZI5IzkW3XmumvGr/\n/vKYKVPk8+2GVSxNmQK89ZbzcU79zNJMGHNZTvw8BOAeZn40d/N6AP8AAGZeCKCRiBylab9+czBn\njnyFLYaA7BVVCKPKXNeuMrd/6EMSFh+1IHrmGQnLD5OkKs01NMiCO2hjXHNdSmLTq5T8ISC+kLmw\nHaIshczF5RAVGjJXWVn5/nV3zpw5nsf6hcz9EoCT3u0N4OfmF2aeW9gQo2XMGFmg2Z2gj34UeOgh\n58fs2NF0MvEqZ+vkECXZnLUQh8jNIauokB4rRx8NVFXJB6aYpnlhEIZDZARR2KGM+/bJuWJ2CHv1\ncl88hOkQmTLIQcosJ8HKlTLGuQGvBAsWyOvmVpAijcydC5x9tr+DU2q5db8NAfumxujR7gtB8/qa\n83DiRO9cNKtDNH26iECnBVDWBBHCmcvuArCEmX9uue0RALMBgIjGAGjHzI5Xr6wUVejXT66/UW+8\nhOEQAeKKXXqpOJpRC6KnnwZOOy3c55w8GXj77XCfMwhmE9i0D/GjfXu5riVRTbfYHkSGOByivXvl\nWhlWKeusCaI4HKKoQ+b8BNEoZp5vv5GZnwcQgjkfHU6hXsccI7v7Ttgnk/793cMGnFyWJEsjm5Mk\nSNltN0E0cKB8v/RSWWBdd10y4XJAugWRea2NUDzjDODqq50bIoYpiAD5m6YseW2tiNdvfzsdYWp1\ndcAXvxisATKQL1397rvRjSlstm2TjQO/Qh2lOkTl5d5VBe0bMj16uLuUVoED+Atqa0jC6NGyKDY5\nhVYyKIhKmsuIaBaAiwDMJqJFRPQ6EZ0J4G4AI4jobQD3AbjE7TmyIojatpXniXJHnTmcogqAuDYn\nnQSMHCnnalTXw4YGiTA55ZRwn3f8ePfQ1CgpJrwrqTyitWvT7xCZ1zOsTeSsCaIePWS8UfZ7Srqo\nQleP+9p53JdKBg92d3HsIXNeCwenhW6SgqgQh8gtZG7kSODll4EvfEESVB95JLiVHjZhC6I1a4BX\nXgllaE3ykwDgIx+RC21NTfMFbNiCCMg3rv33v2VB+uijwJNPhvs3iqGuDpg5M3gD2RUr5PPm1xcn\nTRgxHEQQlfK+t20rk5VbCK5dEHmFbdbXNxdEXvkhe/fmBRERcO65UmjFzs6dEqqUIUqay5j5RWYu\nY+apzDyNmacz839y/YwuZuZJzHw0M7uWPtmzJ9rk+bAEERB92Fx9vVRzDWO31yxAu3WTxVJU+U8L\nFwIjRoS/UTh+vHvIa5QUI4iSyiMKI2Qu6siKMMPlgOwJorIyKXISpUubtEO0kojOtt9IRGcBeC+a\nIUWHqaTW2Nj8PieHqBBB1KuXVJfyatQZFYVUmXNziIikV1NZmSSqVlYC11wTxWj9CVMQjRghIvi4\n48IZm7XHESATytVXy8/28qnFxLv6MWCAnMN//zvw6U/L/5VUqKaVHTtkYg+6iNqwQfJxkvi8FANz\ncEFUasgc4L7BYsKYrGEuXoJo9+6m56BXbiTQfMK59FLnMOMMOkSJz2VhNJ12g7n5pl4pRF1pLqxw\nOTtRhs1FES4HyEJ/27b4Q9Gy5BCFIYiiDpkLqweRoUeP8FuGAPkem1FcvydNijb8s5iiCoXgV2Xu\nawD+SUQXAngtd9vRkMZ050Y3rGjo2FFOgtra5iFOdkHktXBwEkREsiBcvBiYNSvccftRqEMUpLv8\ns8+GM7ZiCFMQBY2PDorTe/+zn8k5tWxZ00pBUXx4Bw8G/u//JA/k/vtlZzHJ3hCAnFMNDSI+a2tl\nw8GrcdquXXLM0KHZcYj27RNntaxMrhNeJfzDKKbhJoic8he7d3cPuSk0ZM4uiCZOFDfP/p5msMpc\n4nOZqUQ6IoI25vX1cn4GubYHIUrxBkQviKKoxPncc8C114b/vG3aSN7z8uXSNiQusuYQlZJDFEfu\nbVg9iAxmAz9sjDsURX74pElSiOfCC8N/biCaTWYrng4RMy8HMAnAcwCG5b6eAzA5d1/mcLP0nIoq\nuO2QWRfcVqZNS6ZajFnEmFA4t9wDZrnPvqBKGyYW9dCh4h5vD2t7553wGki6hcGZCc1KFB/e//kf\nWZTffru8534Vz+Kgrk7esw4dJGzFT8x27y7vkZezkTasQiHqkDnAWxDZ+xsVEjLn1x7Afm3r2lWe\n3z4xZ80hSsNcFtUCBwg3XA7IO9FREUaFOSeidIjeekvm+CgYNy7+PKJiHI0oGp0HodQcom7d5Nrp\nV4W3FMIOmYuqcnEU4XKGrDtEfmW35zLzAWa+m5mvzX3dxcz7oxtStEyYIAtkO045RLW1zgmabovi\nqVOT6TptXcR06eLezMuIIa/d+zRQViaLLfuFd+/eYBcI+w798OHh7YZ4CSJ7gYAoPrz9+0ti7yW5\n1G2/imdxYAQRUFg/rh49suMQ2QVR0LLYxeImiA4dcnaInn3WuUCFPWRuyBCZEN02TZxitEePbt7Q\nNWuCKA1zWZS96rZvD3cxNmhQtH31Nm2Sa1nYRCWIamslXNWpeE4YJJFHlBWHaNs2ue6VkrtFFL1L\nFHtssokAACAASURBVHYj+0GDohFEYV8rrERdMTFRhwhAQnXGomPCBOdKc/Ydtk6dxFVw2nl1WxSP\nHetd0jYqrIsYr+RBt4IKacQpbO7WWyVkzK+KiT0ssGNHuc0pd6xQ7O6TYcYMKVtuFdBRf3iBdDhE\n27blNxOGDpUiFl4MGiQL7O7dsyuI/HKISg1dKiRkzrhy48Y1bxJsD5lr104W5m7FL5wE0ZAhzY/P\nmiBCCuayLDlEUS3GDGvXlhYC5UZUguidd2TtEFUbiiQcomIW8Ek4RMuWyetT6msftSCKwiGKYlPC\nPieEyciR8hpHlQ+XdFGF7kT0Ybev6IYlENGZRLSMiJYTUSjNX488srlDxCwfcntCakWF86RQSNhU\n1DQ2Ng3R8foQuRVUSCN9+jRPgjTlf/0uEgcONBV+RPJ/7w9hL9heVMEwcqQ4W9bJOGp7F0iHQ7R6\ntbhwgHxfvdr7+O3bZafVq1x02rCKW79FgT1MrRgKEUTmunXGGcBTTzW9z2nyGzHCuZQ24BwO7FSd\nM4NV5hKdy4BomkQbVBAJxs0MY/PLihFEUZGEINq8ufCwxSQcoqVLxUErlawKorDLyEcpiMrK8rn0\nUZBoyByA7pCE0/McviJNRCWiNgB+BeAMABMAfJKIxpX6vE4hc/X1smC2uydO4glwzyEaOFBOtjir\nsJiKViYMzmvSDVpQIQ1MnQr8979NbzO71H7lzZ2csCOOCKfkrZsYJpLzxSqIW4tDtGqVCELAXxDt\n3y+LepP/lETFomIoxCHatat0seA2GToJonHjZBNg0qTmoW2vvpoXq4Yjj3Tvx+a0A+ckiDJYVCGx\nucwQZcjctm3hC6Ioq1dGJYh69JAvP5e6UKIWRGPGyHW02LzZYrBet4OShEO0dKlc40olayFzplBW\n2HNklIIIiDaPKOmQubXM/Flm/ozD12ejGxYAYAaAFcy8lpkbADwA4IJSn3TwYHlRt28HXnpJdlS7\ndXPeKXF7Y90WxW3aSLPM004TkXXrraWO1h9r3xDA3yHKSshcZaVU9bGydq2UmfbrdePkhHXqFE5C\npVdvoREjgPcsBXxbi0O0cqWEqgCyaHj9dfdjTfESonia5YWFVSiY999NYIchiLp0kY0O+3vrJIgA\n+Vzbc32WLgXmzgUuv7zpsZMnS4K4E24hc3aRm8GQuSTnMgDZCpnr21cWTlH1TYpKEAGymRZ2caOo\nBVGnTpLo7ueuh0VDgziAw4YV9rgkNuCWLWudDhEQTWGFMCIYvIgyjyhphyiiiNlADARg3aOqzt1W\nEmYnf+FCKY/9gQ/I7U4nyKhRzhcor0XxddfJxfj664FvhBLk582ePU3dqpYSMjdiRNMLwaFDspgI\n0nfHySHq1Clahwho7o60FoeopiafbHziiVJYxC03yFq8JI5meWFhdYWJvHdKwxBEgPNk6CaIgOaC\n6MkngY99rPlYJk50dr6B5hssgHzmXnml6YZCBgVRknMZgLxDFHYIDBC+IGrTJrochoMH5XM/sOTZ\n3JmwBRFz9IIIiDdsbu1aEWCFbpAmsQGXFYco7D5EQDShq/ZCO2HTkh2ii4mojIgS7EoTPhMmAJ/8\nZNMdqoqK5sd17epcsc1rUXzOOcAnPgHcfbf8HrUFbt/R9QrLyFJRhb59m7oHGzfKbQMH+lvITqGB\nYQoip3BJQBak1gkt6gRAQM7RPXviDbWwY71IdeokLunLLzsfW1eXF0SmkmOSHDwInH++VO7z4sCB\nppXjvBYGYQkip8nQTxBZQzZ373ZeJFdUOPeYYXYuGtK7t+z6vfBC/rYMCqLE57IuXeS9iyJvLmxB\nBESXR1RdLYvxqNo/hC2IamryjnaUTJwYX9uOVavyrn4hxB0yt2+fXKvC6N0VR8hcFA5R2JsScYTM\nvfVWNBs/UYs5z8aszLwYAIiokYi6M3OcKdAbAFgrzw/K3daMOXPmvP9zZWUlKisrPZ940iTgrruA\nv/1Nwtv695cml3a6dnWuluGWQ2Q47jjggQfk5127ZKI6fBi45RbgyivD/dDYx+IVlpElh8gIImaZ\njExjtt69/XcfnEIDwxJEblXmAKk0d/nlMuZ9+yTBMGoB2qZNvnx12LtTQamvb3qRmjlTBNFZZzU/\n1ioWevSQ1zPJ8/Khh4DHH5cxeTVztJ9TcThEvXsHD5kDROjs3p3/+7t3O19rrC0FrJWbTHU8p2bG\nM2YAr70mjvquXTKON96owvz5VUX/f3GS8Fz2PmbDypSpD4soSulGJYiiDJcDgClTwm1/EXWFOcMJ\nJwC//nW0f8NQTP4QEH9RheXLRQzZe68VQ5SC6OBByY8Nu9BMVA5RlIKof395vzZuDN8FjmLjx0rQ\n06wewNtE9BSAPeZGZv5KJKMSFgIYRURDAWwC8AkAn3Q60CqIgvDZzwLz5olwAURQOE1QXoLIq/Hi\nzJnyvXNn2Q3s1Qv44x+Bb30LmD4dOPPMgobrid2F8NpRyFJRhSOOkMW+cR9MY7Y+fZJ3iNwuehUV\n4iKsXSuTZyl9EwrBuBVJCiLrOTh9OvCXvzgfu2tX/mJsekNs2lR4LHtYLFwInHuufxK23V2NQxA5\nXX+8BFGbNrLru3QpcOyx8lin17VjR/myunWAc7icYcYM4EtfksXh5ZdLCMvs2ZWYPbvy/WNuvvnm\nwP9bgiQxl72P2bAKO/wqKocoisIKUQui4cPlsxnWaxJHuBwgIfwXXyxufxgCwIuVK4sTRJ07yzUo\nrk2ssCrMAdEKIuOYhy2aBw6UXPcwiVoQAfk8orAFURR5WlaCtuj8B4D/B2A+gNcsX5HBzIcBfAnA\nXADvAHiAmUNpXdatG/DYY/LhvuYa4Mtfdj7Orcmp34Jn6lTgi1+UnQ3TF+S+++TiXEiI0P79/sfb\nm0CWl8tjnEKoslRUAWgaNlddLQUxnPoT2XH6P6OuMmcYNEgW+GFXnPFi507gZz+L5285YY/rdWrk\nadi9u+lnZ+LEZJoZG958E7jgAv9kZrsg8srdSkoQAZIz9Ic/yM9eE5/T4sCej2jlox8FnnlGXq9T\nToku/yMGYp/LrERVejsKQTR4cDYdojZtwnWJ3nlH8o6jpndvmT/iuB4WGzLnlz8ZNqYHURhEKYjC\nuubbicIhskd0REEUeUT79kmkVZJFFQAAzPwnp6/ohvX+3/0PM49l5tHM/KMo/sZPfgJ87WvO97k5\nRH7x8+3bi/XdvXs+XnzjRnGOCvlATpkiX17YF2qm2pTTRTVLIXNAU0FUWysXtD59RGx44eQQde0a\nzkXcK4cIkDjzBx6QccflEAHAb38bfu+NoNgvsKNGyYTrNB77Iv2ooyQUKymWLZOw2a1b5fPhhv2c\n8loUhLUDV4wgOuecfLl6r4mvvLx5HpFXzltZmez6mYXhT3/qP/40ktRcZoiq9HbYZbeB7IbMAeHm\nES1ZEo9DBEjYrl8+YxgUGzIHxCuIwnSIevYUFzyMfoR2ohJEUVSZi8MhMnlEYWI2faIMXfUURET0\nYO7720T0lu0rwX3deChWEBm6dcsLos2bRdwU4hAtX968WawdpwXSSScBVVXNj7UnhqcduyDq1y+Y\nIHJyiI46SipllYqfQ9S7N/CLXwDz58fnENXUiAiOqhmaF8zNF9KdO8uC26lJsf1iPH68u5sUNQcO\nyLk0eLD/eWXfeOjXTz7TTvjlGAalGEE0cqRMRJde6j3xOTXP8wqZM9xxhyymwkhyjpO0zGVRlN5m\nblq9MSyiEkRr1kQfIhuWIIqrwpwhDkHU2CjtIYr9DMcpiJYvB8aODee5TGGMKAr5RCUyBg3KXlEF\nIBqHKIo8STt+DtHVue/WhnbnA7gKLgUOWhImZM5eLSNoU0LjEO3ZIwuZ0aODO0RmF8PvxHVaIH3w\ng8Bf/+r8nFkXRD17yv/sVanJqZrezJnhCCK//DETIvnWW/EJIiLg5JOb922Kg/375bW2J+LPmgW8\n+GLz4+07aVGFEAVhwwb5+2Vl/k1i7efUkCHu+RV+ojkoxQgi89r++c/eE98xx0j+lJUgVRG7dcue\nGMqRirksCodo5065rodxzlnJskM0ZUo4gmjzZrk+RF1hzmAEURQVugybNsnnuNhFcZyFFcIWz1GF\nzUXlEPXoIdd8p435YolDEE2YIGK2oSG854zCBbfjKYiYeVPu+1oAvSA5PVUAvgvgX9EOLXnatpVF\nkD33JKhD1KOH7KSYPi2FfBjNYssph8mK0wLp1FOBRYuahwDt25d9QUQkCzKvnA+nkLmBA8PZGfJb\n7Jpz5fXX4w2ZO/lk6Xv15JPx/U3AvVHa7NnAf/7T/Hb7xThJQbRunQgbwD83zS6Ihg6VhZ0TSQoi\nQMqemypzXoLIhNYZvHKIsk6pcxkRDSKieUT0Ts5l+ort/mtzFew8p+woHKKNG6WMddj06yfzl1co\naaE0NorIGjLE/9hSMAuyUscepzsEyOvSqZOzux4WpYTLAfE5RLt2yXU3TFcga4KIKHyXKOrGrIDM\nI4MGhRv9EXVBBcA/ZG4MEd1ERMsA/BLAOgDEzKcw86+iHVo6aN9eur0bmIOf/KYiWk2NTC6F9F1Z\nu7ZpUQY3nBZIbdvKYtz+t1qCQwTI6/Lee+6PcwqZC2tXyy+H6I47JI9j06Z4q75deCHwve8Bt90W\n398E3PNUPvhB4KmnZIfPuttpL6pgBFGUO6JubN6cbyhbqCAaMkQElRNJC6KHHpING68coiOPlPFb\nnz9IyFxWCWEuOwTgGmaeAGAmgKuIaFzuuQcBOB2Ai0TOE8UGwKZN0QiisrLwHa1Nm2RBHfU81KmT\nLPqXlliGKW5BBAAf+pDMI1FRbIU5Q1wOkXESw8wZyZogAsKt9nj4sKwD49j4CjuPKA0hc8sAzAZw\nLjOfwMy/BHA42iGli5/+FJgzRy6wDQ2yi9qhQ7CmcmZBb5LBCvkwbtsmF61iBBEgCz3738qqIGJu\nWqTATxA5OUTduuVDF0vBb7E7cKBMaEC8gqh9eykf7eZaRIVb5+hevcSxGjcO+OUv87fbXYuuXaUq\nVF1d9GO1YxUAfpO8XRANHCiCyn4+NTbKsWF8zooVRL17Sz6Ul0PUrp0USbAWtIijkXCClDSXMfNm\nZn4j93M9gKUATK29nwL4epDnGTBArsuHQ5xFN250biweBmGHza1YIWWx4yCMPKIkBNH110tjd6fm\nyWFQbIU5Q1wOURShlVEJoijD0Lw23wrFRHRE3VMLCD+PKPGQOQAfhvQAepaI7iCiUwHE8FKmh0sv\nlQ/Q/v2i0uvqgu8EmETtHTvkImIW+EGqgdXXi6ipr/c+3m2B5PTB378//DjzKBk8WC7edXWyo2FE\nTjEOkbWBaSkE2f03F/E4Q+YAeb3Wr4+32pxbyBwA3HCDuBWPPZa/bceO5j2/xo0rfSe3GKzvZaEO\nUbt28hmz7/bv3y/naRgTTp8+zQs3NDT49yjp3FnOAWvPJyeOPrpp2FxLDplDiHMZEQ0DMBXAK0R0\nPoD1zBxo6m/XTuYC43yHQVQOEQCMGRNusZZnngF8+qaHRlYFUUWFrDt+FEld3XBC5uJ0iMLEaaM4\nDKJ0iMIURHHkDxkmTw7XIUo8ZI6ZH2HmTwAYB+BZAF8F0I+IfktEH4h2aOmgrAz4ZK4d7HvvSQnO\noHXx+/ZtKojatw9e/nnPHgl76dRJfnbDSxBlPWTu+OMlF2fVqqZJrcU4REA4Vr9fUQUgfxGPu1Hq\nEUfIRTmqXgtO+PU0mDULWLAgvyNuPgtWwr5wBsUqiAp1iACZqOyOXFjhcoAsRmtrm47r0CF/h4hI\nJo4OHbzF04wZwAsv5H9vySFzYc1lRNQFwEOQIg2HAdwI4CbrIX7PEUUYWlSCaPZs4Omnw3u+f/87\n3MbkXpRaWCHuCnNWvvEN4N57o2mMG0bIXFYdogEDoil7H6Ug8spXLZQ4BdFxx0lT2bA2aKPotWYn\naB+iPcx8HzOfB2AQgEUAvhHpyFLE978PfPzjUqXsjDOkmWQQ+vSRncAdO/JvZL9+wRasZqFpLd3t\nhJsgciovmTVB1Lmz2K5PPtlcEK1a5f44twa0Xove+nrgiiv8xxRkwTt4sHyPWxABkhsSdrlLL9xC\n5gw9e8rrYASskyA65phkKuRZ30trzzAnnATR0KHNd+7CFERlZcC0aVIgxTqOIOG6vXv7N987/3yZ\nsEzia5STelooZS4jorYQMXQPMz8KYCSAYQDeJKLVued7jYgca5LNmTMHc+bMwZ49c/Dkk1Wl/zM5\nogyZO+004NlnnRt9F0pNjSzGjz++9OcKghFExeYnbtwomwpJXMf79we+8AXgO98J/7nDCJnLqkM0\nYUI082NWBFEcTVkNFRWybgur0XCxDlFVVdX71945c+Z4HhtIEFlh5h3M/AdmPrXwoWWTTp3EJfr2\nt+X3z30u2ONM80PrItBUf/LDxPNbCws44bRQA5wXeFkTRIBcwJ59tqkgGj5c4trdKgi5LUq9BFFV\nlSSy+jVt8yuqAMhrfNdd8YfMAVJe/OWXw3u+//zH+3z1CpkzTJ6cn4ScQuYuvBB44glvJzQKrG5f\nMYIoaocIEPFv/RtBe4n17u2/E9itG3DJJXKuAhJOGqR6ZkuhiLnsLgBLmPnnuccvZuZyZh7BzMMB\nVAOYxsyOpXPMhFxZOQe9elWG8j8A0TpE5eWywWOvSFgMc+dKBdQggj4M+vWTa1Oxi8mk3CHD9deL\noxZ2YnpjY2mhR1l2iMaOlU2svXvDfV57saAwyWrIHCAO8zPPhPNcxRZVqKysjE4QtVYuuEBCf959\nV2KTg9C/v3zw1q7NC6LOnYMt/IyS90sCdHOIWpIgeuaZpjugHTpIbwKn0qTM7otGrwu5CQvxC1EI\nuuD9zGckbyluZs6UXf8wYAbOOgt48EH3Y4LsOI0fL5+b/fsldM4uKHv1kokqyE5SmAne1veyWzfv\nAiZJOESAFG+whniEKYgAOU///Gd5X3bubC5WFYGIZgG4CMBsIlpERK8TkT34ixEgZM7+npbKpk3R\nOUSAuERhhM3FGS5nKCWPaPHiZAVRt27At74lwigsTP5QKTmOWS6q0K6dpDyE3cTcL1+zFIYMkXkv\njNCzJATRvHnhPFcaiiooFo49VuL6g0IkbsaLL+Zt90IEUefOskNnT6y2UoggylofIkD66wDACSc0\nvf3II2UHz45pFOokRrwcoqeflvu9dhMbGkQkxLXDWQzHHSehnbW1wJe+VNpF1IRSeblmQSqTmR2u\nujqZTJ0m46OOalrxzE5trYSuDh4spbzDoNSQOaedu7ALl9gXz6Zogx99+gSb+CZMkIXX4sXy/qgg\ncoaZX2TmMmaeyszTmHk6M//HdswIZvYNJpo0KZwm0fI3o+tDZDj9dCmhXwqHD4tDdNZZ4YwpKFOn\nFh+yM29e83knbq68UgrO2JsoF8vKlaWFywHxhMzt3y9/I4rzesqU8MK4DFGGzHXsKK95GFUH4xZE\nlZWSpxpGg9bEiyoopTNkiCzmTNx0ly7BBJHJzfCritLSHaJp04APfEBCLayMHOncnNVrh95NEO3Y\nIQvbs8/2tqbNexJHycpi6ddP3vuvfhX49a9Lm7hMqIaXSAziEBknxSl/yDBxonulOWbgxhvFyRg0\nyH/Hd+NGCcHzK21cqCCyC5GhQ2Vhaw3dDNsh6t8f+P3vxZ0GCnOIgsaKjxmTr+bYmkLmkuL00yWs\nNYzu87t3y/UoykXOSSfJZkUpFTpvvFE2PQYNCm9cQZg5E3j88cI3hg4cAObPF3csSdq3B66+Grj9\n9nCer9QKc0DeIYqyd9z69bIZVFYW/nNHUcQn6vzLsPKI4hZEvXuLAC9V0DOnqKiCUjzf/rYkRxpl\n27mzLCL9sIbMheUQZVEQEUlRBXs+jltcrdeC1G1ny4RGVFR4N84Nki+TBoYPBx5+WH4upbzvypWy\ngHESnga/ogpAPtdmzZp8wQk7XpUD779fFjXz5knOi5cgWrRIJtJzz/UPiyjVIRo1SpwY62IlbEFk\nHFKTwxHUIQoaMgfI/2EEkTpE0dO1qzi5YYShRR0uB8g174orpEz7I49IOf1Zs6RHn9P19J13pNT+\n88/Lz3feKeX377sv2nE6cfbZEi1g/9v79nk/7oUXJNQ36h3pIHzuc+KuhZFHEoYg6thRqldGmfMZ\nRbicIQqHKMocIiC8PKI4iyoYwsgjqq+XdW7U61cVRBFz/PHAb36T/z1oyJwJRSo2ZM4pJyKLgsgN\ntx0Tr6IHbg7R4sXiUJiqgG4kcTEphqFD5b0eOLB0QXT22ZL/40YQkTh8uIiqZcuA0aOdj3GrHPjS\nS8BFFwE/+5n8P2PH5kP5nHjsMeC664BzzmnqONXVNV+AWs8V83n54Q/FibLjVLmwY0dZFD75ZP62\nsJub9ukD/Pzn+fDQoA7R9OnBq3mNHCnvtTpE8XHuueJilkrU4XKG228HfvIT4Oab5Ry84QZxjSZM\naFohcsECCZO54w455mMfA378Y+DRR5MRF23ayGf0hhtkw+UHPxAx2qWLbLS48eST8ec7udG9O3DZ\nZcAvflH6c4URMgdEX1ghSkFkHKIwHa4oc4iA7DpEgDjic+eW9hzFFlQoFBVEMVNoUYVSQ+Y2bBCX\nZcmSbOYQuTF0qHMuiVefIDdBtHatLMhN3yg3siKIzP9w9NHN/x+/MDIr770ngmjFCveyu0Fek65d\nRcz885/uOXjDhsn7YA9tWb9eFuwf+5j8bsSVGwsWyO71tGl5QbRnjyyCTj+9aT6U1c3p2lV+v/FG\n4JvfbP68btUcZ82SBeEtt8jv27e7hwUWi1UEBnWIKiuBr30t2POPHKkhc3FjBFGpidJRVpizc8EF\n4sDefruM/957gXvukSqRX/mKLNg/+EHgT38SR/eFF2TeWbYseKuKKJg1S74mT5Y54/vfl+qZN97o\nXqn0ySelxUZauOoqeV1LPV/CcIgAcfqj7B0XpSDq21c2wsKq3NbYKPOgCiJnTj5ZHLlSBHQcBRUA\nFUSxU0yVuWIcoh49ZIFjHvvqq8HCm7LCmDEyudlDH7xClvr2dRaXJtm/pThE114rjoq9ZPtzz0mo\nQ9BJdfNmmTwrKpwr+gHBL7BHHSUhb24VGjt2lOexC7i6OrHcTYNRL0HELOf5scdKuMuyZXL7T38q\nAunoo5sms1vPlTZtJCwBcBaNboLIhJgZEeWVJ1UsffrIhAAEd4gKYdQoea327NGQubgYOVLEp7XH\nVDHEETLnxWmnySZEebkskH/3O9lESRt33y2v1R/+IPmop58uxS1+/evmx27cKIvlGTPiH6cbI0bI\n+eJUSCgoe/bI9WngwNLH8+Uvi9sWVR5RlIIICDdsbs8emUeiyHcyhBUyl4Qg6thRRJE1kqJQ4iio\nAKggip2gOUTWkLliHKKePWXxZB67fXv0uxhx0rGj7Jzbm6x5CaLBg53LapvciSAOURZyiGbPlkRc\nJ0EE5BP0/di8WQT56ae7h/cEFQAf/7h8nzXL/ZgBA5pX0rGXgq6okHPZqfLdj38sn4cBA0QQLV0q\nE/bf/y47rFOn5kUS0PxcGTtWvhciiKwcOhRN4mfv3nlBFEXY65Ah8l737p1MqfjWyplnll69La6Q\nOS+GDxe35c47xSFKI506/f/2zjzeqrL6/+91ucwzKCiiIAgKyCA4ViqOpV8HNHLATE3NIX+WfrMs\nc2rQ0rKcmzTLSkXz61AOZYGmWaKkYs4iiqKYiAqiKJf1+2Pt3d333HPOPefcc85+Nme9X6/9uufs\n6XzuHp/1rPWs1f7d973v2ZRM5b9mjXVu7LdfaydMKOyyi9Xkq5SFC+1cVeMeP/hge1d2Rk8xFi0y\nrbVi8uTOd0bE1KOgdbU8RGl16u69N9xxR+Xb1yOhArhBVHfK9RANGmRWfSHXfiGDSMRuorhHafly\n208WPBylMnFi+4HzxcYQDR1qDezcxnRsEK23XsdJFbJ0/GIvYcy8efaS76jWEtj1tmKFNZJnzrRB\n0fkodSD+jBlmnBdLWb7hhu29oblhXE1NpinXcF240MYJnHeefY+N5WnTrJGz/fbtDcRCBlFcLylJ\nMYMoNsq7drUip9X2EA0e3BrquXp1aSFz5dC1qz0r0igk3MjssEPn02/XM2RuXWP8eKvxs+ee1uHw\n5pswa5Y9g5LjfkOhswZRtcLlwLwhZ5wB3/pWdfaXS2y81YqddrJi7NWg1gkVoNUg6qxHLg0PEVi6\n/bvuqjzk0z1E6yh9+pSXZa6pyV54S5bkX69YQ23kyFaDYV3zEIE9MHN7TYp5iJqaLFwgt7hn3Kgf\nPtwaGIVy5mfNIMrNnPbyyzbYvpRU3G+8YQ3kpiZzdz//fP5rsJzMZEOGFF9eiocIzHDNNYj+8Q84\n8ECrvQRmFJ99tmWle+wxMwRzQyJzr5XTToMzz7Tew1tuabv/YvdZcnzEf/5T/Z6sPn3s91evrl1i\nlNGj3SCqN9ttZwZRZxo5S5akGzKXdb7yFfMGbbmlhY4OGGDjn0KMBNhlF/PyV9qofOqp8uoodsSs\nWeZxL5QdtFJWr7b3Ty1TtO+0k2XurEamvFonVAC7LlWLZ0IthbQMopEj7f0SZ0stF0+qsI4yYEDH\nF/WHH9pDL26AFcrABYU9RNDqIWpuNgt71aowH/SVki+xQkdpj/OFzcVeiO7drXFRyDWdRYMomWlw\n8WIbWFyKQbR0qXnUwK6vqVPzp7uuZqrmfAZRvoH++UIbn3uufQa7c86xekxxiEhHHqJhw6zHc8YM\nSxmcpJSQuZhqe4hEzMhatqw2HiKwxqAbRPVlxAjzXuZ20JTD669bWLVTOeefDzffbAmIfvaz2txf\n1WDDDe2ZXOnYl4cequ64qOZmC8+udtjcyy+bMVTLkMU+feyddt99nd9XPULmRIq3A0slLYMILBHL\nzTdXtm3DJ1UQkbNF5BURmR9NgSTB7BwDBnScbSO3AOjo0YV7YYoZRCNHmkEUZ4Tp3n3dGiMwQ0UV\nyQAAIABJREFUcmT1DKK4Ub/ZZoXTOr/1VraycPXr12p8v/eeHZuxY0sziJYvb/sAmjCh/YDetWvt\nZVCtY5IvxXypHqJ8BlEuuYZUoWtlq61g/vy288oxiGoRwhSPI6qVh2jChNakEk59EDEv0UMPVb6P\n//zHDdnOImLhi1noLNxlF0tOUy6q5o3cbrvq6tltt8r0FKPW4XIxe+zR+TF8UB+DCOzdXSi5Uamk\nOWziyCMtU2KhCJxivPmme4gALlLVqdF0V9piqkFc5bkYubVMKvUQjRxpDaiNNzZ3eUfF6LJGnKo5\nSUc3/MYbt8/WkjSIdtwRZs/Ov+1LL9lvZoVkyNwrr1ivW3I8SjFykyVst53VFnnggdaQjfhYVyu7\nTj4P0fLl+Q2i3GyApRpExTxEMVtsYSGCScoxiLbYorT1yiE+l7XyEB1/vA0wd+pLHDZXCS0tdk1U\n2yPphMuee5oXq1C2uULv+FdfNW9ktTO37bqrGUTVzDb34ovW5qk1u+9eneLI9RhDBDbGtVhNwFJI\nc9jEuHH2jr799vK2e/ddG39ULCFTtQjdIJK0BVSbgQPbDnTPR25oVlwnJB8dhcyBPbRqmcIyLeIx\nP8kaOfk8Ckk22aSth+iDD+xhHve6n3yy3bD5emIWLcquQbR4sR2vQrWYcolTkccccgicdRYccIDF\n3YMZIdXstclnEOXrAd9gg/ZevnINoriXqlDK+mRY69q1do0VC+G47z648kr7XItQj7597blQKw9R\nly7hZdVqBLbdtnKDaPlya4j5eWsc9t/fyipMnw7HHAMXXtgaPbJggT0bcztzoNU7JFVuUW26qT2P\nkkWwO0u9PERbb23vkWJlTUqhXh6izTevjocozXHkX/iCGfTlcPnlVhOso/d7NQjdIDpJRB4VkV+I\nSIaClQoTh8wV61EpxyAq1mMcX0A9e8L//E9lekOmWzeLqU7G4HdUXHKTTdp6lWLvUPyiGDDABuZf\ncEH7bbNuEG28cf4MbfnI9cyIwGc/a6Fk11xj4VuHHWaZ3apFPoMoTu6Q5BOfaM0QtGKFaWlp6Th0\nKPYsqRbPRti7t91XH35o3z/6yK61Yo2JHXc0L0tnCycWom/f1myToY5xcMpn223tnipU+LgYy5bZ\nNe00DiLWqHz4YQvtXbTIwv2uv96Mpebm/OH1tQiXi6l22Fy9PETNzRaC+Kc/dW4/9UiqABYy1xkP\nUUuLdailGRr66U/btVusuHqSlSutjuAZZ9RWV0yqBpGI/FlEHk9MC6K/+wJXAKNUdQrwOnBRmlqr\nRY8e1htbLHwtX8jcwoX5jahiDaT4Zfnii2YQFWoAZpmRI9sai++8U9wgGj++bbhBPo/S5z9vXqLk\n8V6yxBqkWRpnkUyqEIfMDRtWOGNhklwPUczw4WaQfOMbdo0ee2z19MbaYqPiww/zFwv92McsBOTw\nw61n7v77LUyto97PXr3sJbhiRfGxZiJtvUQffli6EVLtHtiY2CDKWmIPpzgDBljmyyefLH/besXV\nO+ExYoTVVrv8crjxRotsOOgg2Hff/Ek6/vnP2hWajcPm8tHSAkccAX//e+n7e/HF+niIwIzI3Iyi\n5VLvMUSVhifGdRRr9Y4qhZ49bSzRZZeVtv4vf2ne0HHjaqmqlVSd7aq6R4mr/hwoGHl4zjnn/Pfz\n9OnTmT59eqd01Zp4HFEhA2XVqrbLBg2yZAj5egQ7aqzNm2cv3A03rE6KydDYZRe47TbrpYKOQ+ZG\njLB14jEy+bKkbbyxHe+XX24NNbzuOnPbZik8pU8fu5ZaWsxDtNVWbY2OYgk2li+345CPffe1Xsqj\nj66+3v79Td/w4a0Nvlyd3bvDtdfCZz5jA2MPOsj0lEKcWKGpqXjyjdggWn/98sYP1YrYIEo75CHJ\n3LlzmVutYh4NzNZbm5do0qTytnMPkQOWQvqll6yz9cwz2xtEa9bY9VUrg2i33SyqIl9nzemn27tz\n1CjryCqFhQvr4yECe5edfHL7TuhyWLGiPkMSBg60d9Zrr1WWaj+UOpRf/rI96775zY7HPz71lF3f\n9SLY5p2IbKCqcXTngcAThdZNGkRZIA6b22ij/Mvz9V7HYXO5L8DVq4s31rbeunNaQ2fvva2nLKaj\nkLmmJsuotWCB3Wj5DCIRG8A3Z471Zqhaatastf2amqzx/O67Nm5ovfXsuurTx4yCYnWBcpMqJIlf\nrKW+4MphzBgbDzR8uIW3FdK4666WGvzVV81LWOpDMx5H1K9fcYOof//WsX6hGERvvmne5WLFbetJ\nbufTueeem56YDDNlSv6U9h3hHiInJn6WDR/ePkPmk09aW6Na5RFyGTrUOiZ/8xsLG4654QZLs3zx\nxaVnc3v7bQtRrtd1PWiQvc/uvtvq2FVCvTxE0DqOqBKDKJQ6lMOHW+2vK6+0SJNiFGsD1IKQxxBd\nEIXPPQrsDJyStqBq0VFihXwDpwtlmmv0MQVDh7YdE9NRyBzAxIlmEEHhOjqf/rS5a1Wtx6pXr7YF\nOLNCnHr7nXdaH9obbWSGRDGK1ReaMAHOO8+KnlabsWOt2B/kHz+UpLm5tZbLzJml7X/99W2/HaVn\nHzAgLIOoTx87ZyG80BoRERkuIn8VkX9Hod3/L5p/gYg8FY11/b2IlN00mjy5MoPIPUROLsOHt/cQ\n/etfVnOnlpx0koVBxeFca9eat+rqq81Yevzx0vYTjx+qZ1jXgQdWXh8H6jeGCDo3jiik6IKvfAUu\nucTausXoqA1QbYI1iFT1c6o6SVWnqOoMVV2atqZq0VHq7Q8+yO8hyjdYMoTGWprkpmDuyEMEZhDF\nD+hCDf+ZM+0BcsMNVh182rTqaa4ncWKFZL2gUgyiYh6i5mZLplCL8MFp0yzME0qvsdKlS+kv0DjL\n4KpVHRtE8T0awj3Wt6+FEobyQmtA1gCnquoEYAcs4c8WwJ+ACdFY1+eAstOMxAZRuWMDli1zD5HT\nlnwG0YIF5Ydjlsv06Xb93nuvff/jH+19s9NOVtvvtdfsfdoR9Rw/FDNjhumNk+iUSxoeokoIySDa\ncksb99tR2nP3EDUAHRVnff/99h6iIUPa114B9xD17m1jZFatsgfT0qWFx77ETJ1qmU7AQsnyGURd\nu8K3vw3f/77FusapprNGnFgh+dAu1UOURn2T7bdvTUNci6KTcYKSjjxEI0ZYYo0DDgjHIHrttXBe\naI2Gqr6uqo9Gn1cCTwEbqeo9qhrnFvwHMLzcfQ8ZYs+x3JpqHfHmm+4hctpSyCCqdXSDiHmJzjzT\nDPUf/hBOPdXmNzdbMqM4KqMY9Uq5nWTDDa0j7rzzKtu+XnWIoHO1iEIZQxSz557wl78UX8c9RA1A\nKSFzuY21Ql6lcjJgrYuI2A2zbJllstl6647rtEybZmFZ771nve6F4nH32MN6bsePr09RsFqQ9BCV\nYxDlK4haDyZOtIbh22/XziB64YWOPYkTJ1rihltusR65tA2iQYOsseAGUfqIyEhgCpBbQejzwJ2V\n7LOScUTuIXJyGTzYOgeTCZQWLLDnWa05+mjYZht7Xy5c2DaMefLk0sLmnniifhnFkvz2tzZdfnn5\n29bTQ7SuhMyBJeMoZhCtWWNtl3o+49wgSoFSQuZyG/X5CmqqhtF7nTZx2Nwzz5T24O/Rw9Z7+GEz\nDAolt+jWzVI6/9//VVdvPanEIFItPoaoljQ3mwdv3jzrHaq2u3zMGDOGX3yxeE2pZDKSAw9M/x6b\nMCGcQbGNjIj0AW4CvhR5iuL5ZwAfqervKtnvlCnw2GPlbeMeIicXEfMSxc/3ZcvMOKpHuYhu3eCi\ni+Cmm2zsUDL5y6RJpV3fDz9sRlW9GTrUEj9ceCEcemjhuo/5WLasftEUo0ebB7BY2ZZChPb+mDbN\nwteXFhgMs2yZtUG6dKmfpmCzzK3LDBhgKZ0LkS9kLjaiWlpaL5CPPrLPxdInNwLDhtmNVcy4yWWH\nHeDBB81DVGybrHqGYvr3t+tm1apWd/nw4fbSKsR779nLLC3P43bbWdhcLTxEEybYdTJ/vhVTLcTE\niWZgb765fU/bIIqNt48+SlVGQyMizZgxdK2q3pqYfySwN7Brse2LlYeYMgV+V6Yp5R4iJx9x2NzY\nsa3hcvVMUpDvuTp5so3HLcbKlVZoNq3kRSNHWo3Ciy6yd9CiRR2HmMXFsut1H3brZu+w+fPLb5uE\n5iFqboadd7YaVoce2n55tcYPlVMewg2iFBg4sLj7+IMP2vfODxxoPdubbNLa+9Po4XIxW2xhjdcl\nS0p/mE6fbg++V1+tLIVlVlh/fXj+eXuwx4bzqFHFK0UXS6hQD7bf3noYFy8u7sWphOZme9ndcEPH\nWfLGjm39nLZB1NQEP/+5N4BT5mrgSVW9OJ4hIp8CTgN2UtXVxTYuVh5iyhT46lfLE+Npt518JMcR\n1StcriMmTTItxerfzZ9vWtMsK9C7t42DevBBq284a1bx9V96ycab1tPg3G47eOih7BtEALvvbmFz\n+Qyiao0fKqc8RIP7FtKho5C5Qh6it96yRv/KKFCjoxpEjcKmm8LXvmYPsFI9RJ/6lNVnWLGi9G2y\nyJgxNrYqaeCMHGkeypaW/NuklVAhZued7eW4YIEZu9Vmr73sbzl1lEK4z445xpI8OPVHRD4OHAbs\nKiL/EpH5IrIXcCnQB/hzNO+KSvY/erQZOLlh0YVYu9beIW4QObmEaBANHGidvMU64ubNC6du4qxZ\npXlsFy2qfqddR8QRFOUSWlIFsHFE99yTP8NmvTPMgRtEqdBRlrl8SRUGDbK/PXu2xlw2eoa5mH33\ntdDBFSssxWcpdO8OBx9snqJapI8OhbFj7UWTTLvao4c9aBYvzr9NWgkVYtZbD047zT5XWj28GF/8\nojUUyvkfQ+tZc+qLqj6gql2iMhBbqepUVb1TVceo6ojo+1RVPbGS/Tc1WRKXG28sbf133rHaaKEU\n6XXCIUSDCDpOrDBvXjrjh/Kx//7wt7+1rXGYj9hDVE+23bYygyi0MURgHZ4rVuQfR1TvDHPgBlEq\nVFKYtWdPc+NOmtR68XhCBWPECOtZffjh8h5O558PV1TUn5sdYg/L+PFt58fpp/ORtocI4OSTraBg\nLejevfTQyiOOsL9xh4Tj1IoTT7QsV6XUI/KirE4hYoNo7VobExOKQdRRYoWQDKK+fS2SoNhYW0jH\nQzR2rL2j33ijvO1CDJkTsf/nuefaL3MPUYNQScgc2NiKoUPdQ5SPfv3KL57at++6PX4ILKnCs8+2\nH59QzCBK20ME5rWbMiVdDQDXXGN/i9UscpxqsNtu1sn1wAMdr+vjh5xCxAbRbbdZR07anVsxkycX\nNoiWLbNrOk5iEwKzZsF11xVfJw0PUVOTGY4PPVTediEaRGBh/fkMIvcQNQiVhMzFDBnS1kPkBpHT\nEWPGtPdwjB4dtocoNNwgcmqNCJxwQmlea/cQOYUYPtzGxx53XMeZ3epJsZC5Rx6xcgshZczdc08z\n4Ip5YtLwEEFl44hCHEMEhQ0i9xA1CH37mtFTKIVuIQ8RtA2386QKTqWMGmXZ5/KRdpa5EOmo2K/j\nVIMjjoA77yxcmyPGPUROIYYMgV13tbo6222XtppWNtsMXn/dauLlElK4XEyPHmYU3X574XXS8BBB\nZeOI3EPUMW4QpYCIeYkKjSMq5iGKC22Ch8w5lTNuHDz1VP5laRVlDZmQQjmcdZcBA+Azn4Ff/KL4\neu4hcgrR1AR33NE2kU4IdOliNXSeeKL9shANIrCsnoUKs69aZcbd0KH11QTmTSu3kHOISRWguIfI\nDaIGoVjYXL6kCjH9+7f2sLz3XpguUCd8Nt/cPERr1rRf5h6itqxYYY1Ux6kHJ54IP/lJ/nszxj1E\nThYplFghpJTbSfbeG+67z94Bubz8Mmy8cTphfsOGmUFWLDlXLiF7iJ5/vn0ymTfe8JC5hqFYprn3\n3y/sIerXr9VDFGpMqBM+vXpZ/aUXXmi/LISkCiHRp099C+85jc2UKdbQ+sMfCq/jHiIni+RLrLBk\niUW7pDEWpyP694cddoC7726/LK3xQ9Cane2ZZ0rfJtT2Yr9+ZqgtWdI676OPrOO/3tld3SBKiWKZ\n5kr1EK1cGeYF7mSD8eNt8G0unlTBcdLli1+0FNyFcA+Rk0XyJVaIw+VC7XQ64AC4/vr289MaPxSz\n+ealG0QtLdbRXou6ftUgN2wufr7V2/vmBlFKdDSGqJBBlPQQuUHkdIbx461ORS7uIXKcdJk50+7N\nfOMtwBoPIfaoO04xJk60YrEtLa3zQh0/FHPYYRY29/TTbeen6SGC8gyilSvNGAopi1+SXIMojfFD\n4AZRahTzEBULmXMPkVMtJk+2Yra5uIfIcdKle3fzEl10Uftlq1ZZzH1og+YdpyMGDrSEPn/8Y+u8\nhx8O2yDq2xe+9CU477y287PkIQo1oUJMrkGUxvghcIMoNToTMvfOO9ZoffNNN4icytlzT5gzxwzw\nJJ5UwXHS5/jjLcPV66+3nf/YY9ao9AyjThY55ZRWQ1813IQKSU46ydLhx6UqWlqsMOoWW6SnqRyD\nKNSECjG5BtFrr7mHqKEolGWupcUGlBWqLzR4MLz4ojVYf/CDsC9yJ2wGD7b0nffc0zrvo4/MIHdD\n23HSZfBgOPRQuOyytvMffjj8BqTjFGLmTCsK/sgjFj7XsydsuGHaqorTvz+cfDKcdpoZcbNnwwYb\nwLRp6WkaO9YMtGT4YSFCTagQM2aMhSSq2v9z8cWw33711+EGUUoMGWJxkrmsXm3eoUIDDHPHdhQK\nrXOcUpgxA265pfV7XIMo1AGujtNInHaapeB++eXWeY884gaRk126djXj4qijrIDs17+etqLSOO00\nePZZ+N3v4NvfhrPOSvc92bu3ZZpMPhsKEbqHKPZ4f/vbcPXV1gaeNav+OlI1iERkpog8ISItIjI1\nZ9nXReQ5EXlKRPZMS2OtGDYMXn21/fxi4XJJtt/e/r71VnV1OY3F/vtbJe645snSpem4qh3Hac+m\nm9r4hZNPbp3nHiIn6xx7LOy0Ezz4oI2VywI9esA115j2fv1gjz3SVlR62FzoY4i6dYO77oJrr4VT\nT4VLL03H2EzbQ7QAOAC4NzlTRMYBBwHjgL2AK0TWrT7rYcPa5l2PKZZQIUl8NHr1qq4up7EYOdJ6\nZ264wb4vWmSNMMdxwuCrX4WnnoJbb7Vi3AsXwoQJaatynMrp399CQceMSVtJeWyzjYVz/fjHYURR\nlGoQhe4hAgtB/POf4YorYKut0tHQnM7PGqr6DEAeY2d/4HpVXQMsEpHngG2Bf9ZZYs3YaKP8BlEp\nHqJbb4VRo2wf/frVRp/TOHzzm9YLfeih6acSdRynLd27w1VXwac/bSElW25ZeIyp4zi15dhj01bQ\nSjkGUchjiGJGjky3/ZG2h6gQGwGLE99fjeatMwwaZOlTczN8leIh2m8/eykOHAhdutROo9MY7L67\nGdbXXw8vvOAGkeOExic+YUl0jj/ew+UcxzHGjctfXD2XLHiIQqDmHiIR+TMwNDkLUOAMVb29Gr9x\nzjnn/Pfz9OnTmT59ejV2W1NELLPKkiUwenTr/FLHEDlOtRCBH/3IBri2tMD8+WkrcrLA3LlzmTt3\nbtoy6oKIDAd+jb3L1gI/V9VLRGQgcAMwAlgEHKSq79RCw+GH21iALbesxd4dx8kakyZZGn7V4iF8\nbhCVRs0NIlWtZOjZq8DGie/Do3l5SRpEWWKjjSyxQtIgiisKO0492WEH+OUv7bM3uJxSyO18Ovfc\nc9MTU3vWAKeq6qMi0gd4RET+BBwF3KOqF4jI14CvA6fXSsQJJ9Rqz47jZI2hQ60DffFi2GSTwuut\nXGntTac4IYXMJe3b24BDRKSbiGwKbAY8lI6s2pEvsYIXxXTS4pBDbHIcpy2q+rqqPhp9Xgk8hXXU\n7Q/8KlrtV8CMdBQ6jtOITJ4Mjz5afB33EJVG2mm3Z4jIYmB74A8icieAqj4JzAaeBO4ATlRVTU9p\nbciXWMENIsdxnHARkZHAFOAfwFBVXQpmNAFD0lPmOE6jMWWKhc0VIytJFdIm7SxztwC3FFh2PnB+\nfRXVl403hpdeajvPDSLHcZwwicLlbgK+pKorRSS3o65gx10Wx7o6jhM2U6bA7NnF12lkD1E5Y11T\nNYganbFjLe96EjeIHMdxwkNEmjFj6FpVvTWavVREhqrqUhHZAHij0PZZHevqOE64TJkC3/hG8XUa\n2SAqZ6xrSGOIGo6xY+HZZ9vOc4PIcRwnSK4GnlTVixPzbgOOjD4fAdyau5HjOE6t2GwzWLoU3imS\n23LlysY1iMrBDaIU2XRTu5Dffrt1nhtEjuM4YSEiHwcOA3YVkX+JyHwR+RTwfWAPEXkG2A34Xpo6\nHcdpLLp0scywjz9eeB0fQ1QaHjKXIl27ws47w113tWb3WrbMirY6juM4YaCqDwCFymDvXk8tjuM4\nSeLECjvumH95I4fMlYN7iFLm8MPhkktavy9dChtskJ4ex3Ecx3EcJxtsuy387W+Fl7tBVBpuEKXM\nQQdZUa0nn7TvS5dasS3HcRzHcRzHKcY++8Ddd8MHH7Rf9sEH8NFH0Lt3/XVlDTeIUqapCWbMgNtv\nh5YWePNNWH/9tFU5juM4juM4oTNkiBVoveee9sueew5Gj7a2plMcP0QBsOOO8Pe/2/ihAQNsbJHj\nOI7jOI7jdMSBB8LNN7ef//TTMG5c/fVkETeIAuDjH4cHHoBHH7VirY7jOI7jOI5TCgccALfdBmvW\ntJ3/9NOwxRbpaMoabhAFwEYb2XTwwXDSSWmrcRzHcRzHcbLCJpvAqFEwZ07b+W4QlY4bRIFw8MFW\nj+jAA9NW4jiO4ziO42SJU0+FY4+FV15pnecGUel4HaJAOOUUGD/exhA5juM4juM4TqkccohlLd5z\nTxuG0b8/PPMMbL552sqygahq2ho6hYho1v8Hx3GcrCMiqKqkrSNE/D3lOE69mDULttrKIo+23x6W\nLElbUTgUe095yJzjOI7jOI7jrAN84Qvwm994hrlycYPIcRzHcRzHcdYBdtoJli+H2bN9/FA5uEHk\nOI7jOI7jOOsATU1w2GFwzTVuEJWDG0SO4ziO4ziOs47w2c9CS4sbROXgBpHjOI7jOI7jrCNMmAAn\nnABTp6atJDt4ljnHcRyn03iWucL4e8pxHCd9PMuc4ziO4ziO4zhOHlI1iERkpog8ISItIjI1MX+E\niKwSkfnRdEUa+ubOnZvGz+YlFC2uoz2haAlFB4SjxXW0JyQtWUJErhKRpSLyeGLeZBF5UET+JSIP\nicjWaWqsJlm8TrKmOWt6IXuas6YXXHNapO0hWgAcANybZ9nzqjo1mk6ssy4grBMcihbX0Z5QtISi\nA8LR4jraE5KWjPFL4JM58y4AzlbVrYCzgQvrrqpGZPE6yZrmrOmF7GnOml5wzWnRnOaPq+ozACKS\nL57PY9Edx3GcIFDV+0VkRM7stUD/6PMA4NX6qnIcx3GqQaoGUQeMFJH5wDvAmap6f9qCHMdxHCfB\nKcDdIvJDrBPvYynrcRzHcSqg5lnmROTPwNDkLECBM1T19midOcD/qur86HtXoI+qLo/GFt0CjFfV\nlXn276l7HMdxAmBdzzIXeYhuV9VJ0feLgTmqeouIzASOU9U98mzn7ynHcZwAKPSeqrmHKN/LoYRt\nPgKWR5/ni8gLwFhgfp511+kXsOM4jhMsR6jqlwBU9SYRuSrfSv6echzHCZu0kyok+e8LQ0TWE5Gm\n6PMoYDNgYVrCHMdxHAd7TyWNm1dFZGcAEdkNeDYVVY7jOE6nSHUMkYjMAC4F1gP+ICKPqupewE7A\nt0TkQ2zQ6nGq+naKUh3HcZwGRkR+B0wHBovIy1hWuWOBS0SkC/AB8IX0FDqO4ziVUvMxRFlBAikl\nHooOCEeL62hPKFpC0QHhaAlFB4SlxXEcx3FCJaSQubojIp8Xke8BpNloCEVHSFpcR7haQtERkpZQ\ndISmpZEQkd5payiHrOkF11wPsqYXXHM9yJreSmhIg0hE+onIncAhwF0F6iA1jI6QtLiOcLWEoiMk\nLaHoCE1LIyEiw0VkNnBCFDoXNFnTC665HmRNL7jmepA1vZ2hIQ0iYAzwrqruqapzSa8IbCg6QtLi\nOsLVEoqOkLSEoiM0LQ2BiJwM/BV4TFV/oKotaWsqRtb0gmuuB1nTC665HmRNb2cJuTBr1RGRrlFK\n7zXAhyLSDzgd6CoiC1X1ykbSEZIW1xGullB0hKQlFB2haWkkRKQH8AngV6r63WjeUFVdmq6y/GRN\nL7jmepA1veCa60HW9FaDdd5DJCLHiMi8RKMBrFDsCuBrwACs8OvJIvK5dV1HSFpcR7haQtERkpZQ\ndISmpZEQkc0kiqVX1Q+Aa4ENROQkEfkDcJmIfE1EtozWT9VLlzW9kQbXXGOypjfS4JprTNb0Vh1V\nXWcn4DDgbuBJ4OeJ+d2AG4D7gc2ieQcAj6zLOkLS4jrC1RKKjpC0hKIjNC2NMgF9gN8CbwDn5iy7\nBPgHcBCwA/Ad4Deu1zWHpjlrel2z663rcUhbQA1ObFda04mPBYYDPYF3gXGJ9T4J/BHYP/o+FLge\n6Lsu6QhJi+sIV0soOkLSEoqO0LQ04hQd898CM4CbgcmJZcOAYYnv22M9q0Ncr2sOSXPW9Lpm11vP\naZ0KmROR87Ewke+KiKjqs8ASVX0fs3J/Fq+rqncDs4H/EZGrgH8C96vqinVFR0haXEe4WkLREZKW\nUHSEpqWREJGPiWXu6xId81OAOcC/gBPj9VR1iaouSWy6DbBaVd9wvcVxza43H67Z9aZC2hZZtSas\nYvitwKbA74GLgRE567wCzMyZNww4MnfdrOsISYvrCFdLKDpC0hKKjtC0NMoEbAjchoUk/pT2ISST\ngJuA/aLvseduerTdfcB2rtc1+3XhmkPTnDW9dT2XaQuo4kn+HvDt6PN6wC+xxkTfxDogvuz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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# choose a date period (such as a month)\n", "a_month = ds[variable].sel(time='2016-01')\n", "\n", "# or grab the range between two specific days\n", "a_week = ds[variable].sel(time=slice('2015-07-06', '2015-07-13'))\n", "\n", "# Create a figure with two subplots \n", "fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(14,4))\n", "\n", "# plot the month of data in the first subplot\n", "a_month.plot(ax=axes[0])\n", "axes[0].set_title('A month')\n", "\n", "# plot the week of data in the first subplot\n", "a_week.plot(ax=axes[1])\n", "axes[1].set_title('A week')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple variables\n", "[NetCDF](#NetCDF) | [Summary stats](#Summary-stats) | [Time slices](#Time-slices) | [Multiple variables](#Multiple-variables) |[Multiple sites](#Multiple-sites) | [Using functions](#Using-functions)\n", "\n", "To plot several variables over a defined period of time, we can use a list of variables, and a slice of time to generate a pandas.DataFrame. Converting to a pandas.DataFrame gives us lots of options for analysis, but it also means that we are going to load the data into local memory, so be careful how much data you try to load. This can get slow if you try to load all the variables for years of data being collected every minute. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Dimensions: (time: 192)\n", "Coordinates:\n", " lat float64 ...\n", " station_name |S64 ...\n", " lon float64 ...\n", " * time (time) datetime64[ns] 2015-07-06 2015-07-06T01:00:00 ...\n", "Data variables:\n", " Rain_mm_3_Tot (time) float64 ...\n", " VW (time) float64 ...\n", "Attributes:\n", " featureType: timeSeries\n", " history: Created 2016-06-09 09:36:49.261552\n", " description: Butler Green Roof Station\n", " Conventions: CF-1.6\n", " DODS.strlen: 6\n", " DODS.dimName: string6\n" ] } ], "source": [ "# slice the dataset by time and grab variables of interest\n", "vars_for_a_week = ds[['Rain_mm_3_Tot', 'VW']].sel(time=slice('2015-07-06', '2015-07-13'))\n", "print(vars_for_a_week)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Rain_mm_3_TotVW
time
2015-07-06 00:00:000.00.181
2015-07-06 01:00:000.00.180
2015-07-06 02:00:000.00.179
2015-07-06 03:00:000.00.178
2015-07-06 04:00:000.00.178
\n", "
" ], "text/plain": [ " Rain_mm_3_Tot VW\n", "time \n", "2015-07-06 00:00:00 0.0 0.181\n", "2015-07-06 01:00:00 0.0 0.180\n", "2015-07-06 02:00:00 0.0 0.179\n", "2015-07-06 03:00:00 0.0 0.178\n", "2015-07-06 04:00:00 0.0 0.178" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert to pandas.dataframe\n", "df = vars_for_a_week.to_dataframe()[['Rain_mm_3_Tot', 'VW']]\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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WtvdSSh1Ifv2slKqXPF5JKfWTUuqIUuqQUurF/P4DCJET0hYi7JWTk/nz/deN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\nzof/UkacAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot on left and right axes\n", "df.plot(secondary_y='Rain_mm_3_Tot', figsize=(12,4))\n", "\n", "# by setting the limits as (max, min) we flip the axis so that rain comes down from the top\n", "plt.ylim(12,0)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple sites\n", "[NetCDF](#NetCDF) | [Summary stats](#Summary-stats) | [Time slices](#Time-slices) | [Multiple variables](#Multiple-variables) |[Multiple sites](#Multiple-sites) | [Using functions](#Using-functions)\n", "\n", "There are numerous situations in which you can imagine wanting to combine data from multiple monitoring stations. This can be a bit tricky because the data are recorded at different frequencies. For instance: Butler records hourly data, and Uppper Washington Stream records 1minute data. In this example we combine water levels from upstream, downstream, and the lake, and compare these to rainfall from Broadmead." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_url = 'http://hydromet-thredds.princeton.edu:9000/thredds/dodsC/MonitoringStations/broadmead.nc'\n", "ds = xr.open_dataset(data_url)\n", "broadmead_rain_ds = ds[['Rain_1_mm_Tot', 'Rain_2_mm_Tot']].sel(time=slice('2016-02-23', '2016-02-26'))\n", "broadmead_rain = broadmead_rain_ds.to_dataframe().drop(['lat','lon','station_name'], axis=1)\n", "ds.close()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_url = 'http://hydromet-thredds.princeton.edu:9000/thredds/dodsC/MonitoringStations/washington_lake.nc'\n", "ds = xr.open_dataset(data_url)\n", "washington_lake_level_ds = ds['Lvl_cm_Avg'].sel(time=slice('2016-02-23', '2016-02-26'))\n", "washington_lake_level = washington_lake_level_ds.to_dataframe().drop(['lat','lon','station_name'], axis=1)\n", "ds.close()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_url = 'http://hydromet-thredds.princeton.edu:9000/thredds/dodsC/MonitoringStations/washington_up.nc'\n", "ds = xr.open_dataset(data_url)\n", "washington_up_level_ds = ds['Corrected_cm_Avg'].sel(time=slice('2016-02-23', '2016-02-26'))\n", "washington_up_level = washington_up_level_ds.to_dataframe().drop(['lat','lon','station_name'], axis=1)\n", "ds.close()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_url = 'http://hydromet-thredds.princeton.edu:9000/thredds/dodsC/MonitoringStations/washington_down.nc'\n", "ds = xr.open_dataset(data_url)\n", "washington_down_level_ds = ds['Corrected_cm_Avg'].sel(time=slice('2016-02-23', '2016-02-26'))\n", "washington_down_level = washington_down_level_ds.to_dataframe().drop(['lat','lon','station_name'], axis=1)\n", "ds.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute storm depth by subtracting base depth (taken as the first value in the data for the selected time)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "washington_up_storm = washington_up_level-washington_up_level.iloc[0,0]\n", "washington_down_storm = washington_down_level-washington_down_level.iloc[0,0]\n", "washington_lake_storm = washington_lake_level-washington_lake_level.iloc[0,0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate a presentation-quality plot and save it" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+TKgvbmv+jsajbn7e3V8lZMKfIZSJfBZ4stB+5rgP2NrMNmqnfUd/d7S+5r/N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\ndRUAQ4cOBSAjI4O0tDQ+++wzJk+ezEEHHcQVV1xBly5dGD9+PACTJk1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DiIhILNQVqFtk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SH+6eC8VsXrvbaPSGdL2tVtR0ZBBot3L67y8QsKwFFvDf/+EVp+t0oWliOoK8JfUB9LHmxFOe2x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\ntG1NubJYn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9tJvjOfvKI88px5xeOsgizS89PJKMiQcX5G8XyBqwCn20mRq0jG7qLi+SJ3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\nM58zEP4YBcvHwKGOXHMN/N+/D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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create a figure with 2 subplots \n", "fig, axes = plt.subplots(ncols=1, nrows=2, figsize=(12,10), sharex=True)\n", "\n", "broadmead_rain.plot(ax=axes[0], linewidth=2)\n", "washington_up_storm.plot(ax=axes[1], linewidth=2)\n", "washington_down_storm.plot(ax=axes[1], linewidth=2)\n", "washington_lake_storm.plot(ax=axes[1], linewidth=2)\n", "\n", "# set titles and legends\n", "plt.suptitle('Timing of Rainfall and Stream Depth peak during February Storm', fontsize=18)\n", "axes[0].set_title('Rainfall (mm)')\n", "axes[0].set_ylabel('5 min rain (mm)')\n", "\n", "axes[1].set_title('Stream level minus base level (cm)')\n", "axes[1].legend(['upstream','downstream', 'lake'])\n", "axes[1].set_ylabel('Storm depth (cm)')\n", "axes[1].set_xlabel('Time in UTC')\n", "\n", "# save fig to current folder\n", "plt.savefig('Rain and discharge.png')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using functions\n", "[NetCDF](#NetCDF) | [Summary stats](#Summary-stats) | [Time slices](#Time-slices) | [Multiple variables](#Multiple-variables) |[Multiple sites](#Multiple-sites) | [Using functions](#Using-functions)\n", "\n", "You might have noticed that the way we pulled data from multiple datasets was redundant. To simplify this process, we can write a quick function that can be used to pull from any site, any variable and setting your own start and end times. To show how this works, we will use a function to compare tipping bucket rain gage data to disdrometer data." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def select(site, var, start, end):\n", " \"\"\"\n", " Select data from netcdf file hosted on the Princeton Hydrometeorology thredds server\n", "\n", " Parameters\n", " -----------\n", " site: one of the monitoring stations in quotes ('broadmead')\n", " var: one of the variables from this site in quotes ('Rain_1_mm_Tot'), \n", " or a list of variables(['Hc', 'Hs'])\n", " start: starting time for data.frame ('YYYY-MM-DD hh:mm:ss')\n", " end: ending time for data.frame ('YYYY-MM-DD hh:mm:ss')\n", "\n", " Returns\n", " -------\n", " df: pandas.DataFrame object with time index and the variable(s) as the column(s)\n", " \"\"\"\n", "\n", " import xarray as xr\n", "\n", " data_url = 'http://hydromet-thredds.princeton.edu:9000/thredds/dodsC/MonitoringStations/'+ site+'.nc'\n", " ds = xr.open_dataset(data_url)\n", " _ds = ds[var].sel(time=slice(start, end))\n", " df = _ds.to_dataframe().drop(['lat','lon','station_name'], axis=1)\n", " ds.close()\n", " return df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions can be defined within a notebook as we did above, but it is often more helpful to save them in files that we can use again and again from anywhere. The above function is saved in a file called [select-time-slice.py](http://nbviewer.jupyter.org/github/jsignell/intro-to-netcdf/blob/master/select-time-slice.py). To load the function from there first make sure that you have downloaded the file and placed it in the current folder (right click and save to same folder as notebook), then you can use the function by importing the function from the package:\n", "\n", " from select_time_slice import select\n", "\n", "If you forget how the function works, run: `select?` to see the documentation." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "broadmead_rain = select('broadmead', ['Rain_1_mm_Tot','Rain_2_mm_Tot'], '2016-02-23', '2016-02-26 12:00')\n", "washington_lake_level = select('washington_lake', 'Lvl_cm_Avg', '2016-02-23', '2016-02-26 12:00')\n", "washington_down_level = select('washington_down', 'Corrected_cm_Avg', '2016-02-23', '2016-02-26 12:00:00')\n", "washington_up_level = select('washington_up', 'Corrected_cm_Avg', '2016-02-23', '2016-02-26 12:00')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the storm depth and plot exactly as we did above:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "washington_up_storm = washington_up_level-washington_up_level.iloc[0,0]\n", "washington_down_storm = washington_down_level-washington_down_level.iloc[0,0]\n", "washington_lake_storm = washington_lake_level-washington_lake_level.iloc[0,0]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3Rrzlm6cqZI/J+TkFua82Myj8WO1MtivHbED+QcIPrLMIx0WSE65EJb2SZ1j2uN2F/J9z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\nxhtvMHbsWAoKCpg6dSrZ2dnExsbSvn17YoMB8zt37szatWvJzs6uct9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DD2FHtEVyc3NJ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jdNc4hpmhk1cT1FURTl5Ke0EHdHxAtCxZoSve82AP637ZsavV4Zj/js2QCsb3QJBQRVOu5Mhbii\nVEJoKNx7L2zeDD/9BIMGSTGAt94ST/nw4fDbb6dk+sPqCPF5hmGMNQyjcU2nL1QURVGUo6VcIR6Y\nhd12gEBrIB2OTATg9+TZ5DqOv7JPQUHxQkA2ybnJBFoDpWx9sS1lWyfJllKZEHenMNRc4opSCRYL\nXHaZeMjXrZOBnP7+Ur3zoouge3cR55mZdd3SGqM6Qnw0xT5xajF9oaIoiqJUh3KFeIxEwzvEdKB5\nSBs40Ae7K4852+cc9/XcQjzdbwsgthRLRqZE6KxWdna8vMR+5aERcUU5Srp1E+/4vn0waRLExsKG\nDWJXadwYbr8d/vrrpI+SVyrEDcOwADf6pC2slfSFiqIoilJdyhXiDUWId2zYkcaNgYTrAfhm4/Hb\nU9wC+4D5NwC9mvSSbA9FRXDBBZiRUSX2Kw/NJa4ox0hcHDz9tAjyr76CCy+E/Hz46CPo2xd69Dip\no+SVCnHTNF3A1BPUFkVRFEWpknKL8jWUgZqdYjqJEN84EkyDOdvmkGXPOq7ruQX2nsK/AOjTtI/H\nlsKIEdhsJfcrj+YN1JqiKMdFYCBcfz0sWgRbt8Kjj0JMDKxf742S33KLPCQ7HHXd2mpTHWvKr4Zh\nXGNo4lNFURSlHlBYXtFMtxBv2InoaCC7KbH287A77czeOvu4rucW2NvzVwDQN7oHzJsnK6+80iPE\n8/MrPodaUxSlBjnzTJg8WTIXff21N0r+6afiMY+LE+vKzz9X8IVRf6iOEB8HzAAchmFkGYaRbRjG\n8YUXFEVRFOUYKTciHuO1pkRHy6q4w2JP+Trh6+O6Xn4+EJRGon07Nj8b3RJSJKPDWWdBixYEBcl+\nlUXEm4U3A+BA1gGcLudxtUdRlGICA2HUKImSb9sGzz4LXbpARoZYV4YOhUaNJCf5ggUVfHnULVUK\ncdM0w0zTtJim6W+aZnjx6/AT0ThFURRFKU2Z31L/PIjcjcX0o21UW6KK83oF7roGi2Hhl52/kJ6f\nfszXKygAmoo//OzGZ+P340+yYYRkS6mONcXmZyMuJA6n6eRQzqFjbouiKBXQrh08+aQM6Ny4UQZ4\nduwog0g++EAKCDVtChMmwKZNVZ7uRFGdiDiGYQw3DGNK8XR5bTdKURRFUSrCLcQbNCheEb0VDJMI\nVzsCrAHExMjqrMQ4Lmp1EYWuQmZumXnM1xMhLv7wvo16e/KHH40QB/WJK8oJo1MnGeC5aRMkJMBT\nT0k+8sOHYcoU6NwZ+vWD99+HrLo1eVQpxA3DeBF4ANhUPD1gGMYLtd0wRVEUpfrY7XDOOTJ+6VTH\nLcTdkW93xpRYS0d52VBWp6TAqM6jgOOzpxQUAM1EiA89Eik/5q1bSxc4eKwpP/5Y+XnUJ64odUDn\nzvDMM1IsaMUKGDsWwsK8y40awc03w+LF4HKd8OZVJyI+DBhsmuZ00zSnA5cCl9VusxRFUZSjYfFi\nWLkSXnml8sIypwLuhAheIS7dzGcEdwIgMhIMA9LTYXi7q/Gz+LFo9yIO5x4+puvlF5gea0qvvw/I\nyhEj5CJ4I+J5eXDkSMXnaRGuQlxR6gzDgD594N13ISlJBnYOHCiDQD77TAZ8Nm0Ko0fLPlu3npAc\n5dWypgARPssNKtxLURRFqROsVu9yQkLdteNE4BbiAQEyuYV4uwgR4lYrngGbZl4UQ9oMwWk6+X7T\n98d0vRz/nRB8hIa2WMLnLZKVV13l2e773ufkVHwejzUlS60pilKnBAfDTTdJUa6dO8Vb3ry5CPSv\nv4a774YOHaBJk5LCvBaojhB/AYg3DONjwzA+QSprPl8rrVEURVGOCV+bY/qxj0s8KfAV4lu24MmY\nck6rjp59fO0p13c+vuI+eVF/0fEwLPjEhbFzp5y8Xz/Pdt/3PjW14vOoNUVR6iGtW0u2lb17xVP+\n9ttw3XVSybO0MK8F/KrawTTNrwzDWAz0Ll410TTNpFppjaIoinJM+BaVy8iou3acCHyFeNMWDiwN\nt+PC4JoL2nv2cQ/YTEmBK/tfSaA1kN/3/k5idiJNwppU+1quzGyeOfwKD74D/q5UCbV/+GGJMHjb\ntt7977gD1q4t/1xnRp8JwKLdiziQdcCT0lBRlHqAYUiWlY4d4Z57xJayZYv4/tzT4WOzt1VGdQZr\n/mqa5iHTNGcXT0mGYfxa4y1RFEVRjpnTVYhvP7IdF07aRLYmOCDIs487Ip6aCuGB4QxtNxQTkxkb\nZ1TvIqYpkbCOHZiwOx6rCw6MuVy6p6+4osSu3bp5l9etq/iUXWO7cmX7K8l2ZHPfvPuq1w5FUeoG\ntzC/5x745huJjtcCFQpxwzBshmFEATGGYUQahhFVPLUEmtZKaxRFUZRjwrd43KkuxO12mQcGwuZU\nsaV0atipxD6+1hQ4SnvKpk0waBCMHo3lUCJ/NTE45y4In/6F13x+DBiGwdRhUwkNCGXWllnM3Hzs\nKRUVRTnB1FKB+coi4uMQP3iH4rl7+gGYWiutURRFUY4Jp0+xxlPdI+4eEBkaCptSZKBmx5iOJfaJ\ni5O5O4h1+ZmXE+wfzPIDy9mbsbf8E+fnw2OPQffuMogrOpr1Tz1BvztN1gV1Ijyw4lp27tSFVen0\nZuHNeGGQZAAeP288mQWZlR+gKMopTYVC3DTNN0zTbAU8appma9M0WxVP3U3TVCGuKIpSj/CtNnmq\nC/HcXJn7CvHSEfEmxTbwxESZhwSEcMWZYin5duO3ZU+6Zg2cfTZMnixPNXffDdu2Mat/I0wL+Kf0\nqbRNgwbJPDu76vbf0+se+jbrS2J2Iv/89Z9VH6AoyilLZdaU3oZhNDJN883i1zcbhvGDYRj/Lbas\nKIqiKPUEXyGellZ37TgRuK03YWEVC/HGjWXuFuLgLe7zVcJX3pVFRfDcc5JfePNmyYywYgVMmwZR\nUcQflkI+gVUIcZtNxm86HF4Pe0VYLVbeu/w9/Cx+TFs1jWX7l1Vxx4qinKpUZk15F3AAGIYxAHgR\n+BTIBN6r/aYpiqIo1cXXmlJZUZlTAbfvO7phEduObAOgQ0zJ1GLuiPihQ951Q9sNJcIWQXxSPOuS\n1sG2bXDeeZJDuKgI7r9fIuPnnOM5Jj5FhLgttXIhbhjev8HBg1XfQ9e4rjzW/zFMTMb+OBaHswr1\nrijKKUllQtxqmqY7rjIKeM80ze9N03wSaFvJcYqiKMoJ5nSKiHsqh0buxu600zy8OWGBYSX2cUfE\nfYW4zc/GjV1vBBO2Pvcg9OwJf/0FzZrBwoXwxhveevVAal4qe7N3giOYwMwu1W7flCnV2+//Bvwf\nbaPasjFlI5OXTq72+RVFOXWoVIgbhuHOMz4IWOSzrcr844qiKErN8913cOmlJQUmnF4RcXcq39yg\n8m0pIIM1rVbZ151lBWBckyuY9zlc99ZiqUl/442wYYPX5O3D3welrD2HzsZqVP2z5x4gmpkJc+fC\nt+VY0X0J8g/i3cvfBeDfv//bE91XFOX0oTIh/hWwxDCMH4B84A8AwzDaIvYURVEU5QQzciTMnw8v\nvFByvW9EPCOjpDA/1XAL8SMWSV1YOmMKgJ8ftGgh6cD3upOk7N9Pl0tu4tKdcCQI/pzyAHz2GURE\nlHudvw6ILYUDfbBUow71l1/K/Isv4LLLYNSoyittAlzU6iJu7XErdqedcT+NwzTNqi+kKMopQ2VZ\nU/4DPAJ8DJxner8dLIBWIlAURalDNm0q+dpXeJvmqZtL3DSlpg5AChVHxEEqVwPs2oW8QTfdBIcP\nk9ijLV3uhUkNEyq91l8H3UK8b7WEeO/eZdftq0Y1+ymDpxATHMPiPYv5eO3HVR+gKMopQ6VfLaZp\nrjBNc6Zpmrk+67aZprmm9pumKIqi+OIbLPX4pIvxjYjDqWtPycwUq0lYGOzIrFyIt2kj8+3bgZdf\nhiVLIC6OkB/mkhFp49fdv7IrfVe5x7pMl9eacrB6EfGwsLLrLr646uOig6N5/ZLXAXjkl0c4nFvz\nZbQVRamfVOOrRVEURakPZGV5l/fsKbmttBXlVBXibqtHdIyLLalbAOjYsKw1BSQTIUD+kr/hqafk\nxSef0KBFO0Z2GgnAR/EflXvs9iPbSS9IJ9bWBLKaVUuIl0d1c7qP6TqGIW2GkF6Qzpjvx2ihH0U5\nTVAhriiKcpKQm+tdzsqSsYZuPBHxrl/CkEdITD410+G5hXhYs/3kFuYSFxJHVFD5pS06dIBQsrlh\nzhh5gx58EC65BIA7et4BwEdrP8LpKmuod9tSukZJ2sJjFeLVxTAM3rnsHWKCY/h196/0/bAv249s\nr92LKopS56gQVxRFOUkoXSjG157idAJBaTD8Tuj/Km9vf+yEtu1E4Y70BzSt3JYCcNZZ8Cb30bRg\nJ2b37vDii55tA84YQNuothzMPsj8nfPLHOseqNkl4viF+IED1duvVWQr/r7zb7rEdmFL6hb6fNCH\nhbsWHvuFFUWp99S5EDcM40PDMJINw1jvsy7SMIxfDMPYahjGfMMwGtRlGxVFUeoDvmn4oKQQLyoC\nek4H/3wAFuW9wXebvjtxjTtBuCPiZkzFGVPcxC3+hlv5hHxsJL/6JQQGerYZhuGJin8Y/2GZY90R\n8c5HKcSTkqBrV3jrLe+6lSurdyyIGF92+zKGtx9OekE6l35+KVP/nqrZVBTlFKXOhTjwEXBJqXWP\nAwtN02yP5C9/4oS3SlEUpZ5RWUS80OmE3sXqb/MIAG7/4XZ2pO04Qa07Mbgj4vawKiLie/fCuHEA\nPMRrJLjK7ndL91uwGlZmb51Nck6yZ31+YT7rktdhMSx0CO8FVF+Ix8XB+vVw771wyy2ybtq06h3r\nJiwwjJmjZvLP8/6J03Ry37z7uPunu7X6pqKcgtS5EDdN80+g9HCWK4FPipc/AUac0EYpiqLUQ0oL\n8WSvdmR/0E8QuYewwjbw7XfEHbmGbEc2I2eMJL8w/8Q2tBZxR8SzAisR4kVFUqgnM5O1Z1zJu4xj\ny5ayuzUOa8ywdsMochXx2frPPOvjk+IpchXRuWFnAo1Q4NisKeuL+3kXLDj6Yy2Ghf8M+g9fXP0F\nNj8b7615j8GfDSY1r4rE5IqinFTUuRCvgFjTNJMBTNNMAmLruD2Koih1Tmlryu7d3uXtkf8FoJ91\nPJhWbPM/pE1kG9YmreXBnx886mvt2AFnnw0//HA8La55JCJukmoUW1PKy5jywgvw55/QuDHLbv8A\nMMoV4gB3nnUnAB+s+cBj/3D7w/s07YPLJfsdixCPjvYurznGpL9juo7h91t/p3FoY37f+zu93+/N\nwl0LcZmuYzuhoij1ivoqxEuj5jhFUU57SkfEdxS7ThIOJ5AStggcIdzQ+TYaNIC92xrw9oXfEWgN\n5L017/H5+s+P6lp33SXicUQ9649MTQVCk8g3M4i0RRIXEldyh+XL4ZlnZPnTT2nTJwYQz3Z51UaH\ntRtGo9BGbD2ylWX7lwFef3ifZn082Wj8/Y++rePHe5cvvBCPqD9aejftzaqxq+jdpDd7MvYw+LPB\ntHitBY/+8ijxh+LVP64oJzH1VYgnG4YRB2AYRiOgwuoGkyZN8kyLFy8+Ue1TFEU54bgj4sHBMncL\n8al/T5WFtbcQGdSAtm3lZXhuD94c+iYA434ax6aUUuU4KyHTJ411fdJ5qalAQ68txTAM78bdu2H0\naFHcEybAxRdz/vnezQnlFNL0s/hxa/dbAe+gTY8Qb+oV4n5+R9/W4cOhU7FzJisLZs06+nO4aRLW\nhCW3LuHpC56mVUQrDmYf5JXlr3DWe2fR+e3O/Of3/7A7fXfVJ1IUpdosXry4hM6sDYz68CRtGEZL\n4EfTNLsWv34JSDNN8yXDMCYCkaZpPl7OcWZ9aL+iKMqJ4McfRdz16gWrVkFUFOw4kE6z15qRV5gH\nUzex8OuOvPUWzJwJ33wDI0ea3DzrZj5f/zmdGnbi7zv/JiQgpMprDRoEixbJ8oED0LRpLd9cNene\nHdbbpsKw+7jrrLt474r3ZMPatTB0qKQtOecc+OMPCAgAoF8/WLECPvtMrOOl2WTTFzYAACAASURB\nVH5kO2dOPZNg/2DW3b2Odm+2IzQglIyJGfy2yMrgwfJ+LDyGTILjx3szqERGQlraMd64D6ZpsvzA\ncr7c8CXfbPymhG+8X7N+DG07lH7N+3FO03MIDww//gsqigJItiXTNI2q96w+dR4RNwzjS2AZcKZh\nGPsMw7gNeBEYbBjGVmBQ8WtFUZTTGrc1pUULCAkRUTd16XTyCvMITxkMqR0JC5PtAPv3yw/HtMum\n0TGmI5tSNnHPnHuqZWXIzvYub9tWCzdzjGRl4YmIe1IXLloEAwaICL/oIhkdWSzCAS69VObr11Mu\n7aLbMeCMAeQV5vHw/IcB6NWkF1aLlcJC2edYrCkAd97pXU5Ph337ju08vhiGQf/m/Zk6bCqJDycy\nZ8wcxnQdQ7B/MMsPLOepxU8x+LPBRLwYQddpXRn741g+iv+IzSmb1VuuKPWMY+hsq1lM0xxTwaaL\nT2hDFEVR6jlua0pgILRtC+vWO5m2RmwpwRvuJwto0MArxPfulXloQCjfXfcdvd/vzWfrP2PAGQM8\ngxQrwteasmOHeJwBjuQdIb8on2bhzWrwzqqPrxDv1LATfPst3HSTPKWMGgWffFIiXzhAt24yr0iI\nA9zZ805+3/s7P277ERBbCnBc1hSAHj1gxgwYOVJeDxgAe/Yc27nKw9/qz7B2wxjWbhg5jhzmbZ/H\n0v1LWX5gOfGH4kk4nEDC4QTeX/M+ABG2CLrHdadzw850ju3smccEx9RcoxRFqTZ1LsQVRVGU6uGO\niAcEFAvxgjkcyt9D68jWHPh7KADNmkH79rLfhg3eYzs17MQ7l73DzbNu5h9z/0GANYCbu99c4bWy\nsrzLbi/6T9t+Ysz3YzAxWXf3OlpHtq7J26sS0yx+QCgu5nPO//6CxyfJhgcegFdfLTe9iVuIr1wp\nwro8UX1Np2sYP288WXa58ZoS4gBXXOFd3rsXbrsNpk8Ho0Y7uOWBa2TnkYzsLKq/oKiANYfWsHz/\ncpYfkCkxO5Ele5ewZO+SEsfGhcR5hPlZjc+iV5NedIzpiNVirdlGKopSAhXiiqIoJwm+QrxdOyBE\nUhbe3GE8kwqsREeLZeXss2W/1aslU4dbm97U/SbWJ69nyvIp3DLrFtYlreOlwS/hZyn7U+AbEV+7\nzmTy0ilMXDgRsziJ1SO/PMLMUTNr61bLJT8fnIGpEHKYKb/5E7nkadnw4ovw2GMVKtvWrcVPn5YG\nX34JN5fz/BHsH8wNXW9g2iqpvtOnWUkhfqzWFJAAvcPhdct8/LG4Z8Q6dOznrQqbn43+zfvTv3l/\nQLzlB7MPknA4gY2HN7IxRaZNKZtIzk0meXcyi3Yv8hwf4h/iEeW9m/SmV5NetI1qW3KArKIox4UK\ncUVRlJMEX2tKaKuNYPsVHMFMuvI2AM44Q7Y3aiSR8QMHYPt2b4QcYPKQybSNasv4eeN5dcWrbEzZ\nyFfXfEVkUKRnn8JCEb0Ahr+dBcHj+GWh1Fib0H8C01ZNY9aWWfyy8xeGtBlS6/ftJisL/KI38P4s\nuHVdIVit8OGH3hKWFWAYMGQIfP01vPtu+UIcJKf4tFXTaB3ZmiZhTQDve348EXEQIf/eezB2rLw+\neBCuu04i40FBx3/+6mAYBs3Cm9EsvBmXtr3Us95lutiXuY+Nhzey4fAGVh9azcqDK9mbuZc/9v3B\nH/v+8OwbYYvgsnaXcUPXGxjcZnC5D3GKolQf/Q9SFEU5SfCNiK8PKk5ZuO4WKIgAJBru5uyzRYh/\n8gk8/3zJ84zrNY6ODTtyzbfXMH/nfPp80IfZo2fTIaYDIGMeAWJaJpN7+VXkxyzHZg3mi2s+4+qO\nVxMTHMPEhRN54OcHWHf3OgKsAdQ6LheFc39lQfp4Bu4He6AfgTNnS6aUavDQQyLEV62q2J5yVuOz\nmDtmLk3DvSli3FlOoqKO/xbuugsKCuD+++X1d9/JBNImax25QCyGhZYRLWkZ0ZLLzrzMsz4lN4VV\niatYlbiKlYkrWZm4kqScJL7Y8AVfbPiChsENGdV5FGO6jqFvs74aKVeUY6DOs6YoiqIo1cMtxF2B\n6fy0/1N58be3asxDD3n3bV1s3543r/xzDThjACvvWkm3uG5sT9tOnw/6MHf7XKC4YmejtWSN6k1+\nzHLIbM7zbZZydcerAXigzwO0i2rHltQt3hzmtUV6Orz2GnToQPM7hjAwZxspwfDNf8dVW4QD9O4N\nZ54p7+GLleThGtpuKN3iunlep6TIvGHDY72Bktx3H0yZUnb9kCFeG0x9oWFIQ4a2G8qTFzzJ7NGz\nSXw4ke33beffF/6bDjEdSMlLYerKqfSf3p+2b7blyUVPsiW1ghKmiqKUiwpxRVGUkwS3TWJTwEfk\nFebRIPViSOlE8+Yi4q66yruvO1/2jh0VF+RpGdGSZbcv45qO15Blz+LyLy/n5aUv893G/8Ht5+II\n2k+jwn7w3krMQz08xwX6BfLGpW8AMGnxJJJykmr+ZlevhjvukATmDz8M27eTF9Ocf/VoQ+d7IfKC\nS47qdIYhSVUAnnyypAe+MmpaiAM88kjZdYsWHZ8P/URgGAZto9ryfwP+j033bmLN2DU80u8RmoQ1\nYVf6Lp774zk6vtWR/h/257N1n1FQVFDXTVaUeo8KcUVRlHrMnDnwr3+Jb9vhAAwnKw2JQv/7ivu5\n5hopNFPa1tCjB8TGQk4O7NxZ8flDAkL4duS3PDPwGUxMJi6cyJuHr4GAPLo4b2Zio98gN65EBhaQ\nyPHlZ15OtiObJ3594vhv1DRhyxaYNg369JGqRdOni1n9kktg1iymPf0Xzw/KIyW0OHXhUXLvvd7l\niAjvg01luIV4TA1n91uxovz1119fs9epLQzDoGfjnkwZMoV9D+5j0c2LuKPnHYQHhrP8wHJunnUz\nTV9tyqO/PMr2I9vrurmKUm+pF5U1jxWtrKkoyqmMy+UV2B9/LHmwX/3pRxgznFYRrdh+3/ZK08sN\nGybWlLg4GRxYlQd55uaZ3DTzJnIdebDgZd65+RG6dzfo10+ytGzdWjLLx460HXR+uzMOp4MVd6zw\nZBqp9s1t3AhLlsj0++9w+LB3e2QkrttuZf2IfswsSmDejnmsSlyFiYm/GUL+U5nHlFrv5Zdh4kRZ\njo2V96WygZIDBkiRzkWLvLnUa5rGjb2+fIDXX5dsjCcjuY5cvk74mmmrprH60GrP+otbX8zdZ9/N\n8PbD8bfW89C/olRAbVTWVCGuKIpST1m3TiLbAP/4B5hGEW/nnw/NV/DKkFd4uN/DlR4/ezZceaUs\nT58u+aur4kDWAZq3zYSUzvz8MwwcKII1K0uqQjZvXnL/JxY+wYtLX6R3k96suHMFFqOCjtbsbFiz\nRpJ5L10qwrt0vfdGjSg4ty+rujfkvTbpzDmwiLR87z4WMwDXrgu4o9u9fPDoiKpvphxMs2Sq8f79\npSkVPaR06gSbN0tO9i5djumSVZKeLolffvzRuy42Vrz6wcG1c80TwcqDK3ln1Tt8lfAV+UWShicu\nJI6ucV2JC4mjUWgj7zxU5rEhsYQGhBLkF6Q5zJV6hwrxUqgQVxTlVObtt0WAg7g17Of9i7VhzxNu\nacTeCZuJsEVUeY6wMLGndOokAejycLlEpIeHwxNPiC0bIDFRorXuyPqTT8Kzz5Y8NseRQ/up7UnM\nTuTD4R9ye8/bIS8P1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fO/dy/cdhtMn172WqYpQX21RZw4MjLgq69k0GxubuWRc9+UmIpy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\nfraqHgGOBt4O/GuSB4CT2X0GpGuav18Afm7EuyLNS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create a figure with 2 subplots \n", "fig, axes = plt.subplots(ncols=1, nrows=2, figsize=(12,10), sharex=True)\n", "\n", "broadmead_rain.plot(ax=axes[0], linewidth=2)\n", "washington_up_storm.plot(ax=axes[1], linewidth=2)\n", "washington_down_storm.plot(ax=axes[1], linewidth=2)\n", "washington_lake_storm.plot(ax=axes[1], linewidth=2)\n", "\n", "# set titles and legends\n", "plt.suptitle('Timing of Rainfall and Stream Depth peak during February Storm', fontsize=18)\n", "axes[0].set_title('Rainfall (mm)')\n", "axes[0].set_ylabel('5 min rain (mm)')\n", "\n", "axes[1].set_title('Stream level minus base level (cm)')\n", "axes[1].legend(['upstream','downstream', 'lake'])\n", "axes[1].set_ylabel('Storm depth (cm)')\n", "axes[1].set_xlabel('Time in UTC')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you are comfortable with the material in this notebook, you can move on to [Advanced Methods in Python](http://nbviewer.jupyter.org/github/jsignell/intro-to-netcdf/blob/master/Advanced%20methods%20in%20Python.ipynb)" ] } ], "metadata": { "celltoolbar": "Raw Cell Format", "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 }