diff --git a/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/Rapid_Deploy_Gauges.ipynb b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/Rapid_Deploy_Gauges.ipynb index a8b45f8..5a2d51e 100644 --- a/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/Rapid_Deploy_Gauges.ipynb +++ b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/Rapid_Deploy_Gauges.ipynb @@ -1,49 +1,111 @@ { "metadata": { "name": "", - "signature": "sha256:92302ab3c65488946fc9666dac95027f9dd1b1c34cf249d0bb3f4939a3d40208" + "signature": "sha256:86c7c66fbf61c7cce4fc894b7297f3fe1d95031d205463e74d1aeaddb061b4fa" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "># IOOS System Test: Rapid Deployment gages from USGS\n", + "\n", + "###Can we obtain water level data from the rapid deployment gages, deployment for Hurricane Irene? \n", + "This notebook is based on IOOS System Test: Inundation\n", + "\n", + "#### Methodology:\n", + "\n", + "* USGS gage data (ASCII Text Files) obtained from http://ga.water.usgs.gov/flood/hurricane/irene/sites/datafiles/ and 90% of data zipped up for use in the notebook (large files were removed for efficiency)\n", + "* Station data Automatically gets unzipped\n", + "* Process data files (195 stations), and store data in dictionary for access\n", + "* Plot Water level data for the New Jersey area\n", + "* Plot Barometric pressure data for the New Jersey area\n", + "* Plot Gage Locations on a map, overlaid with the Hurricane Irene track line\n", + "* Plot time series of maximum waterlevel\n", + "* Plot locations of maximum waterlevel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### import required libraries" + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "from bs4 import BeautifulSoup\n", - "import requests\n", "import re\n", "import os\n", - "import pandas as pd\n", - "import json\n", + "import csv\n", + "import uuid\n", + "import zipfile\n", "\n", - "non_decimal = re.compile(r'[^\\d.]+')\n", + "from datetime import datetime\n", + "\n", + "import folium\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib as mpl\n", + "import matplotlib.dates as md\n", + "import matplotlib.pyplot as plt\n", "\n", - "url = 'http://ga.water.usgs.gov/flood/hurricane/irene/sites/datafiles/'\n", - "r = requests.get(url)\n", + "from IPython.display import HTML, Javascript, display\n", "\n", - "soup = BeautifulSoup(r.text)\n" + "from utilities import css_styles, inline_map\n", + "css_styles()" ], "language": "python", "metadata": {}, - "outputs": [], + "outputs": [ + { + "html": [ + "\n", + " \n", + " " + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 1, + "text": [ + "" + ] + } + ], "prompt_number": 1 }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Extract Data - Does the data dir exist, if not extract it
" + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "def processTextFile(url,file_name,data_file_type):\n", - " print url+file_name\n", - " r = requests.get(url+file_name)\n", - " contents = r.text\n", - " file_name = \"./data_files/\"+file_name.split('.text')[0] \n", - " f = open(file_name,'w')\n", - " f.write(contents)\n", - " f.close()\n", - " return file_name" + "def unzip(source_filename, dest_dir):\n", + " with zipfile.ZipFile(source_filename) as zf:\n", + " zf.extractall(dest_dir)" ], "language": "python", "metadata": {}, @@ -54,37 +116,23 @@ "cell_type": "code", "collapsed": false, "input": [ - "wl_count = 0\n", - "file_name_list = []\n", - "for link in soup.findAll('a'):\n", - " html_link = link.get('href')\n", - " if html_link.endswith(\"BP.txt\"):\n", - " #processTextFile(url,html_link,'bp')\n", - " pass\n", - " elif html_link.endswith(\"WL.txt\"):\n", - " #file_name_list.append(processTextFile(url,html_link,'wl'))\n", - " wl_count+=1\n", - "print \"num water level:\",wl_count" + "if os.path.isdir(\"data_files\"):\n", + " pass\n", + "else:\n", + " print(\"Data Dir does not exist... Extracting.\")\n", + " unzip('sample_data_files.zip', os.getcwd())" ], "language": "python", "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "num water level: 203\n" - ] - } - ], + "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ - "files = os.listdir('./data_files/') \n", - "print len(files)" + "files = os.listdir('data_files') \n", + "print(\"Water Level Files: %s\" % len(files))" ], "language": "python", "metadata": {}, @@ -93,1862 +141,2211 @@ "output_type": "stream", "stream": "stdout", "text": [ - "203\n" + "Water Level Files: 195\n" ] } ], "prompt_number": 4 }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Process Data - Read the data files and create a dict of the fields
" + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "count =0\n", - "full_data = {}\n", - "\n", - "for file_name in files:\n", - " print count,file_name\n", - " count+=1\n", - "\n", - " actual_data = {'dates':[]}\n", - " with open('./data_files/'+file_name) as f:\n", + "def parse_metadata(fname):\n", + " meta_data = {}\n", + " non_decimal = re.compile(r'[^\\d.]+')\n", + " fields = {'Sensor location latitude': 'lat',\n", + " 'Sensor location longitude': 'lon',\n", + " 'Site id =': 'name',\n", + " 'Sensor elevation above NAVD 88 =': 'elevation',\n", + " 'Barometric sensor site (source of bp) =': 'bp_source',\n", + " 'Lowest recordable water elevation is': 'lowest_wl'}\n", + " with open(os.path.join('data_files', fname)) as f:\n", " content = f.readlines()\n", - " \n", - " titles_set = True\n", - " titles = ['dates'] \n", - " \n", - " meta_data = {}\n", - " \n", - " \n", - " fields = {\n", - " 'Sensor location latitude':'lat',\n", - " 'Sensor location longitude':'lon',\n", - " 'Site id =':'name',\n", - " 'Sensor elevation above NAVD 88 =':'elevation',\n", - " 'Barometric sensor site (source of bp) =':'bp_source',\n", - " 'Lowest recordable water elevation is':'lowest_wl'\n", - " }\n", - " \n", - " for i,ln in enumerate(content): \n", - " content[i] = ln.strip()\n", - " if content[i].startswith('#'):\n", - " for f in fields:\n", - " #search inside the array element\n", - " if f in content[i]:\n", - " if fields[f] == 'name':\n", - " meta_data[fields[f]] = content[i].split(f)[-1]\n", - " else: \n", - " val = (content[i].split(f)[-1])\n", - " meta_data[fields[f]] = float(non_decimal.sub('', val))\n", - " \n", - " if fields[f] =='lon':\n", - " meta_data[fields[f]] = -meta_data[fields[f]]\n", - "\n", - " else: \n", - " try:\n", - " data_row = content[i].split('\\t')\n", - "\n", - " if len(data_row[0])>1:\n", - " #print the data looks to be ok-ish \n", - " if titles_set: \n", - " titles_set = False\n", - " for t in data_row:\n", - " if not 'date_time' in t:\n", - " titles.append(t) \n", - " actual_data[t]=[]\n", - "\n", + " for k, ln in enumerate(content):\n", + " content[k] = ln.strip()\n", + " if content[k].startswith('#'):\n", + " for fd in fields:\n", + " if fd in content[k]:\n", + " if fields[fd] == 'name':\n", + " meta_data[fields[fd]] = content[k].split(fd)[-1]\n", " else:\n", - " if '.' in data_row:\n", - " data_row[0] = data_row[0].split('.')[0]\n", - " \n", - " dt = datetime.datetime.strptime(data_row[0], '%m-%d-%Y %H:%M:%S')\n", - " actual_data['dates'].append(dt) \n", + " val = (content[k].split(fd)[-1])\n", + " val = float(non_decimal.sub('', val))\n", + " meta_data[fields[fd]] = val\n", + " if fields[fd] == 'lon':\n", + " meta_data[fields[fd]] = -meta_data[fields[fd]]\n", + " return meta_data" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "divid = str(uuid.uuid4())\n", "\n", - " for i in range(1,len(data_row)):\n", - " try:\n", - " val = data_row[i] \n", - " actual_data[titles[i]].append(float(val))\n", - " except Exception, e:\n", - " actual_data[titles[i]].append(numpy.nan) \n", - " except:\n", - " print 'error:',data_row\n", - " break\n", + "pb = HTML(\"\"\"\n", + "
\n", + "
 
\n", + "
\n", + "\"\"\" % divid)\n", "\n", - " full_data[file_name] = {'meta': meta_data,'data': actual_data}\n" + "display(pb)\n", + "full_data = {}\n", + "for count, fname in enumerate(files):\n", + " meta_data = parse_metadata(fname)\n", + " kw = dict(parse_dates=True, sep='\\t', skiprows=29, index_col=0)\n", + " actual_data = pd.read_csv(os.path.join('data_files', fname), **kw)\n", + " full_data[fname] = {'meta': meta_data,\n", + " 'data': actual_data}\n", + " \n", + " percent_complete = ((float(count+1) / float(len(files))) * 100.)\n", + " display(Javascript(\"$('div#%s').width('%i%%')\" %\n", + " (divid, int(percent_complete))))" ], "language": "python", "metadata": {}, "outputs": [ { - "output_type": "stream", - "stream": "stdout", + "html": [ + "\n", + "
\n", + "
 
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usosV4XOtat5+e/jy5b4x/N//bBhWd81v2TJ4/30bdze5hUom4ZxzWrauFMef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\n7lyrIrP66un/63vusWK/EP0aEZYx5qo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"text": [ - "" + "" ] } ], - "prompt_number": 8 + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Show the available fields from the processed data files" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "full_data[full_data.keys()[num]]['meta']" + "print(\"Data Fields: {}, {}, {}\".format(actual_data.index.name,\n", + " *actual_data.columns))" ], "language": "python", "metadata": {}, "outputs": [ { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, + "output_type": "stream", + "stream": "stdout", "text": [ - "{'bp_source': 20.0,\n", - " 'elevation': 2.14,\n", - " 'lat': 40.643161,\n", - " 'lon': -73.1575,\n", - " 'lowest_wl': 2.21,\n", - " 'name': ' SSS-NY-SUF-017WL'}" + "Data Fields: date_time_GMT, elevation, nearest_barometric_sensor_psi\n" ] } ], - "prompt_number": 9 + "prompt_number": 7 }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "from IPython.display import HTML\n", - "import folium" - ], - "language": "python", + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "prompt_number": 10 + "source": [ + "# Remove 'Sensor elevation above NAVD 88 (ft)'" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "def inline_map(map):\n", - " \"\"\"\n", - " Embeds the HTML source of the map directly into the IPython notebook.\n", - " \n", - " This method will not work if the map depends on any files (json data). Also this uses\n", - " the HTML5 srcdoc attribute, which may not be supported in all browsers.\n", - " \"\"\"\n", - " map._build_map()\n", - " return HTML(''.format(srcdoc=map.HTML.replace('\"', '"')))" + "for key, value in full_data.iteritems():\n", + " offset = float(value['meta']['elevation'])\n", + " value['data']['elevation'] -= offset" ], "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 11 + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot all Water Level data in the NJ area" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "map = folium.Map(width=800,height=500,location=[44, -73], zoom_start=3)" + "fig, ax = plt.subplots(figsize=(16, 3))\n", + "\n", + "fig.suptitle('Water Elevation', fontsize=14)\n", + "\n", + "for key, value in full_data.iteritems():\n", + " try:\n", + " if 'SSS-NJ' in key:\n", + " df = value['data'] \n", + " ax.plot(df.index, df['elevation'])\n", + " ax.set_xlabel('Date', fontsize=14)\n", + " ax.set_ylabel('Elevation (ft)', fontsize=14) \n", + " except Exception as e:\n", + " print(e)" ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 12 + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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uiUhLKq4q5s7vg78u/+8f1+4EGcCG2vbycj47VjsU+VdpabB4MUyfDkCNrYaq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\nD95mW5/gBjOZLturq01uH6y0tLfw1bKvCPYO7u+pDFqsGbA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fJEkotm3reN6ekoJSLuej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FAAAg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\nsesvQZyZHUQ3xiIpErhceNLDSKZY9FI84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+ "text": [ + "" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot all Pressure data in the NJ area" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "for st in full_data:\n", - " lat = full_data[st]['meta']['lat']\n", - " lon = full_data[st]['meta']['lon']\n", - " map.simple_marker([lat, lon], popup=st,clustered_marker=True) " + "fig, ax = plt.subplots(figsize=(16, 3))\n", + "\n", + "fig.suptitle('nearest_barometric_sensor_psi', fontsize=14)\n", + "\n", + "for key, value in full_data.iteritems():\n", + " try:\n", + " if 'SSS-NJ' in key:\n", + " df = value['data'] \n", + " ax.plot(df.index, df['nearest_barometric_sensor_psi'])\n", + " ax.set_xlabel('Date', fontsize=14)\n", + " ax.set_ylabel('Nearest barometric sensor (psi)', fontsize=14) \n", + " except Exception as e:\n", + " print(e)" ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 13 + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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dOoMQmXNCCCHEEeoo2HralycR+AY1iVQlUN7iy33fX9GhKRc/R8zkL1n+1lM4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\nibJonE+FALWf2q4m2s/qSsSn9lM3FAKL7V8bMYntakxP/ZluXqNsuyr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+ "text": [ + "" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Map the Gage locations" + ] }, { "cell_type": "code", "collapsed": false, "input": [ + "map = folium.Map(width=800, height=500, location=[30, -73], zoom_start=4)\n", + "\n", + "# Generate the color map for the storms.\n", + "color_list = {\"Tropical Storm\": '#4AD200',\n", + " \"Category 1 Hurricane\": '#CFD900',\n", + " \"Category 2 Hurricane\": '#E16400',\n", + " \"Category 3 Hurricane\": '#ff0000'}\n", + "\n", + "\n", + "# Add the track line.\n", + "with open('track.csv', 'rb') as csvfile:\n", + " spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\n", + " for row in spamreader:\n", + " lon, lat = row[3], row[2]\n", + " popup = \"{} : {}
{}\".format(row[0], row[1], row[6])\n", + " map.circle_marker([lat, lon], popup=popup,\n", + " fill_color=color_list[row[6]],\n", + " radius=10000, line_color='#000000')\n", + "\n", + "# Add the station.\n", + "for st in full_data:\n", + " lat = full_data[st]['meta']['lat']\n", + " lon = full_data[st]['meta']['lon']\n", + " map.simple_marker([lat, lon], popup=st, clustered_marker=True) \n", + "\n", "map.add_layers_to_map()\n", - "inline_map(map) " + "inline_map(map)" ], "language": "python", "metadata": {}, @@ -2000,7 +2397,7 @@ "\n", "\n", "\n", - "
\n", + "
\n", "\n", " \n", "\n", - "\" style=\"width: 100%; height: 510px; border: none\">" + "\" style=\"width: 100%; height: 500px; border: none\">" ], "metadata": {}, "output_type": "pyout", - "prompt_number": 14, + "prompt_number": 11, "text": [ - "" + "" ] } ], - "prompt_number": 14 + "prompt_number": 11 }, { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "prompt_number": 23 + "source": [ + "## Generate Plot Of Maximum Water Levels from each gage" + ] }, { "cell_type": "code", "collapsed": false, - "input": [], + "input": [ + "dt, dv = [], []\n", + "\n", + "fig, ax = plt.subplots(figsize=(16, 3))\n", + "fig.suptitle('Max Water Level (ft), 2011', fontsize=14)\n", + "\n", + "for key, value in full_data.iteritems():\n", + " df = value['data'] \n", + " z = df['elevation'].values\n", + " \n", + " idx = np.argmax(z)\n", + " val = z[idx]\n", + " t = df.index[idx]\n", + " \n", + " dt.append(t)\n", + " dv.append(val)\n", + " \n", + " data_dict = {'elevation': dv,\n", + " 'dates': dt}\n", + " \n", + " df = pd.DataFrame(data=data_dict, index=dt, columns=['elevation', 'dates']) \n", + " \n", + " ax.scatter(df.index, df['elevation'])\n", + " ax.set_xlabel('Date', fontsize=14)\n", + " ax.set_ylabel('Water Level (ft)', fontsize=14) \n", + "\n", + "\n", + "ax.set_ylim(0, 20)\n", + "ax.set_xlim(md.date2num(datetime(2011, 8, 25, 20)),\n", + " md.date2num(datetime(2011, 8, 30)))\n", + "ax.xaxis.set_major_formatter(md.DateFormatter('%B,%d\\n%H:%M'))" + ], "language": "python", "metadata": {}, - "outputs": [], - "prompt_number": 23 + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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7CGZm7cbdg83MzMzMzCyr0pmIqQ+wI/VH/5uZmZmZmZllRKpBaznwADALeB1YO1o+FhiV\n/mKZmZmZmZmZpR60XgX0B7YBliUsfww4NN2FMjMzMzMzM4PkiZgSHYSC0/eon3hpMjA43YUyMzMz\nMzMzg9RbWnuibMINlQNOX2dmZmZmZmYZkWrQ+jaNz8V6EhrjamZmZmZmZpZ2qXYPvgB4GtgMKAZ+\nDQwFdgB2z0zRzMzMzMzMrKNLtaX1dWAXoAT4HPgh8D9gJ+CdzBTNzMzMzMzMOrpGJ2/NQWEYhs2v\nZWZmZmZmZnknCAJoIj5NtaV1EnAO8flZzczMzMzMzDIu1aD1ceA04EvgOeBYoHuGymRmZmZmZmYG\ntKx7cADsCvwUGAF0BR4B7gIeS3/R6nH3YDMzMzMzswKVrHtwa8e0FgPDgcuALYCiVu4nVQ5azczM\nzMzMClSyoDXVKW8SrYtaW38KbAq82uqSmZmZmZmZmSWR6pjWXsDJwMvADOAY4B5gELBHRkpmZmZm\nZmZmHV6q3YOXA3OA+4C7gf9mrESNc/dgMzMzMzOzApWO7sEHAs8Dq9JUJjMzMzMzM7NmtTQR03bA\n+mgKnMVo2pvlwMo0l6sht7SamZmZmZkVqHS0tPYDHgZ2AEJgQxS0/hmoBs5scynNzMzMzMzMGkg1\nEdNfgFlAb2BpwvIHgH1a8Hq3At8BHyQs6wU8C0wFngEqW7A/MzMzMzMzK2CpBq0/BC4E5jVYPg1Y\nrwWvdxua3zXR+Sho3QiNmz2/BfszMzMzMzOzApZq0FpG4+NW+6Duwal6hdUD34OAO6L7dwAHt2B/\nZmZmZmZmVsBSDVpfAY5tsKwzcB5qHW2LfqjLMNHffm3cn5mZmZmZmRWIVBMxnQu8DGwPdAGuAYYC\nPYBd01ieMLqtZtSoUXX3q6qqqKqqSuPLmpmZmZmZWXuZMGECEyZMSGndlkx5sxbwS2DbaLv/AmOA\nb1pYvoHAo8Dm0ePJQBXwbfQaLwIbN9jGU96YmZmZmZkVqHRMeQMKTi9usGwAcD9weKtKJo8AI4Gr\nor8PtWFfZmZmZtZKQdAJTewQAnNxo4GZ5YKWtLQ2ZivU4prq2Nh7gT1QAqfvUBD8MAp81wNmoAB4\nfoPt3NJqZmZmlkEKWPsBNwPLgROA+Q5czaxdpKulNR2OamL5Xu1aCjMzMzNroCcwFtg/ejwX+G32\nimNmFkm1hdTMzMzMCt6SBvdrs1UQM7M6bW1pdX8RMzPLuKjLUB13VzTLhLnAKdHf5WgU19KslsjM\nDJof0/ooCkwbWy9EU978gMy32HpMq5lZB6WAtQzYO1ryHLDUgatZBuh464FaWBf5ODOzdtOWMa3f\n03TQCroUN63VJTMzM2tWBfBrYFT0+BLguqyVxqyQOUg1s1zUXNB6bHsUwszMrGnFwNYJj7em/fMI\nmpmZWba0dcqb9uLuwWZmHVQQlAKbAU9FS/YBPiYMq7NXKLN24LHcZtaR5NKUN2ZmZi20HJgC9I8e\nl1BZWZbF8phlnk7eyoH1gU2AhwmCwIGrmXVIDlrNzCynxU7SDznkEADGjx+fzeKYtaOhwKso3+Vz\nwCHZLY6ZWZa4e7CZmZlZjlFL65nEk47NBdYiDJdnr1BmZhmUrHtwKlPVFAN/Agamr0hmZmZmltyd\nwMfASuAiNPWTmVnHk2pL62LUR2VG5oqSlFtazczMrEMJgs5oJNdKoDuw0GNazaxgtbWlFeAZYM90\nFcjMzMzMkgvDGnbbbXugljBc4IDVzDqsVFtaT0Wzud8HvA0safD8v9NZqEa4pdXMzMwsDTyVjpnl\nomQtrakGrbXNPJ9qi21rOWg1MzOzvDRgwAC+/PJ7oAidMs3PWqAYn0pnbZS2ZDqwxIGrmWVdOroH\nd2rmZmZmZmaNUMC6OfA68ADQY7XWzvbTFTgY+AT4ADgZ6JGlspiZpcYBp5mZmVlGFQE3ApsBewHn\nAiVZKkspsB/xxox9UfnMzHJXqkFrAPwK+AhYBgyOlp8PHJ6BcpmZmZkViE7AzITHXwI1WSrLEmAc\nUI2yEo8FVmSpLGZmqUk1aD0T+B1wU4PlM4HT0loiMzMzs4IyH13jvww4CbiL5tOFZEZxcS0wEagE\neqIJIhZnpSxmZqlKdUDFFOA3wGPAImBLYBqau/VloFdGShfnRExmZjkscXzebrvtxiuvvJLF0pjl\nHh0jxaiFNcxiIqay6F5fYDawzEmYzCwnpCMR03potH5DK4GyRpabmVkHoX8yZWis3sa8+uok+vbt\nm+VSmeWWMAwJwxWEYW1WgsQgCAiCUjSW9ufAVJQUqozTTnOnOTPLbakGrdOBbRtZvi/wcfqKY2Zm\n+acHSjLzLEp9sD2zZ8/ObpHMrM4mm2yCsgZXoM5x96JOc/sCMGbMmKyVzcwsFakGrVcDNwA/jbbZ\nBRgF/DF6zszMOqwaYLfofiegCmcjNcsdkydPRkHrWagd4msgBM4GQvbZZ58sls7MrHktmSTsROAi\nYJ3o8UzgEuCWdBeqER7TamaWo4KgEjgaXdv8DtgR+Mrj5MxyhLrwdwMmEz+NGwX8Aahp0bE6YsQI\nHnzwEaAcJZOa52PdzNIi2ZjW1sxsvQa6lP5dG8rUUg5azcxylP7JVKApNGqBYsJwaXYLZdbBJCZD\nO/roo7n77rv58ssvWX/99ampqUHH6OXA6ehY3RF4v8UBpxI5DQSuBWagPJ1O5mRmbZeOoPUF4FCU\nsz1RD2A8sGdrC5ciB61mZjkuCAIGDBjAjBkzsl0Us4KVGJzGzo2UYKkbsD/wCvA9+++/O48//gpq\nWd0G+He01brAHGA5y5bNprS0tIWvXwG8jiaQAHUxvo4wzM4UPmZWONKRPbgKpZtrqBTYvVWlMjOz\nghKGoQPWDubUU08lCEoIgt4EQc96AZWlnz7frmi6mi4EQddoWQi8C9yJJnso5fHHHwf6A5OAfwCv\nAjUcdthQzjnnOFaunNfigDVuRcL96uj1zcwyp7n/LttE60wE9gG+T3iuCBgO/AIYkJHSxbml1czM\nLMeoq+i6qKvoF8C5uKto5qiV88+ovWAqcBw6NesJzE1YcxfgP8BRwD3RslWo/aEEWN7qltEgKAHW\nRLk4pwFX4jo3s3RI1tLauZlt3064/3Qjzy8DzmhdsczMzCy/FaO5PreMHk8DrstecfJQY919k683\nEbgYWBsFoqCWz78BJwPPo9ZVgEej9bdGuTN3jG63RAHwCs477yyuvPLKlMsbhisIgk7AmcBKHLCa\nWXtornvw4OgGsEPC48FokEQP2id7sJmZmeWcgHjgBApi0hvAnH322QXb7Tze3XcjoIIgqODJJ59s\ndF0FhiXAm8CnwDvAFSiL7xLgfHQR4RBgKWEY0qlTNWqV7YLSkxyC5lN+Obr156qr/kxpaSlBEDBt\n2rSUyh2GtYTh94ThQgesZtYu8mXwibsHm5m1M3UDLENByErmz/+WHj16ZLlUlkv0HVkLdRH9AriM\ndLW89e3bl9mzl6Exk52AFQUXIKm1cyyaMmo+arH+ssn3qSD3XOBP0ZI5wDqEYXXd841tGwTdgceI\nTXEDi4DNgB+g7L8rUcAbssYapcyaNSs9b9DMrAXSkYgJ9Gu2C3Ak8PMGNzMzKyDq/tcPdTV8BViP\nyso1slSWILqVONFPmpxzzjkJn2vrP9MwXAF8BZyGWv3S11VUAeulqOvrW0B3Kioq0rLvXKAW1SVo\ncgaASpQqpDn/BhZE9+9BrajS9Ge/BDgAdRXeHhiDxsFeAAwBZqMxsYcxe/bylrwNM7N2kep/qo3R\nwIhBKNCtQeNha4DlqG9KJrml1SyHTZ8+ncGDN0VTLqxkl12G8tprr2W7WNYGQdAb+Cvw02jJ48BI\nwnBOu5aje/fuLFnSCXVx3BWN25tDGC5r13IUksrKShYsqEX/xstRIp8lOdWKeeGFF3LFFVejgDV2\nqnII8FBOlbOt1NL6V+BYVA9bAv9r8j2ed955/OlPf0fzIfdCgWZqdaeLEwPRuONYxuE+qJX8xGit\nN4F9CcO5je3CzCyj0tHSeh3wXzSGdQmwKbAd8B5wWNuLaGb5bPDgLVBXs5uBM3n99Xf5xS9+keVS\nWdvUAF8mPP6S+mMX28eSJUuADdF10wtQRtQa9ttvv3YvSz5J1oq6YMEC9G98JurSew5q5Wv7vtPl\npJNOQqcosYRC1WhKl0KzCPgVahMYAMxPGoBeddVVhOFCdCr2FS2/2FBN/eRN1ajbcCyT8JMJ9y2X\n6JirJAjKo7/udWIdS6rf+O+BPYAPUZ+UHYAp0bLRwBZpKMsMYCH6NV0ZvUaMW1rNctSsWbPo168/\n8C3QO1p6IDoR6oFOipYXVOtIR9CrVy/mzasGTkKjQ8ZQUrKK5cvbt+ugTswOAh6OlqwEyigqgpqa\nmnYtS77QZ9YNHY/zgZDdd9+al156KXq+G/B74Oxoi8nADlEw1Ny+u6EW2s1QQLms1VOnNP9aAdAd\n2AsFrHOZN+9LKitTD7DzQarZg1M1fvx4Dj30Z0ApCkDnJzxbDuwM/AS4C7VHBKjFtRz4nFxrdTdR\nq/xR6CLTy8DpOHOzFZp0tLQGaHob0MCH/tH9/6FL4OkQAlUoL/sOyVc1s9xWi06YrwRGAt18VTjP\nzJ07l/79e6HrktdSWdml3QPWuGfR2L1PgROAbg5Yk+oBXIRaUWcC6/Hyyy8nPL8U+Ce6oARwH5p6\nPTkdw0UoyH0deANo2TjjoqKiqJWoS9Ri1PS2YRhSUdEJeAj4oiADVtD7jN3aItYSd+ihP0ejusaj\nOV3L0OleETrVeh74LfAaa61VzlVX/Q6YDrzPxhuv6yAoB+k4iU1rtCH6Hdwpq2Uya2+pBq0fEW9N\nfQs4D7WyXgp8lsby+KzWLM/07dsXXdEfjlrDLgZeBJ4BTgHGoQyVlk37778/QRBQXp56CoKvv/6a\nMFxFGK5i3rx5GSxd03QCvQx1odwGeAR1yrGm1aAWGVCL6+FAJ0455ZSEIPFjNM/nAOAa6rfGJbMp\nyhYMsDnQssRItbXd0aiimajLd/ekgeuCBQvqArpCDFjT5cILL0St0iejKXQeROPAj0fjZX+H6nhH\noJQwnEcY1jBz5kx++9vf1n3Gn3zySXbegKVgFer4CLow/E0Wy2LW/lINEoejX8F/A+ujfn9DUK71\nI9AZaltNQ12PV6Gz3JsSnnP3YLMcFk/E1BV131yKfh5iJ5lHA/f6Cn6WxOeC7ExsTFsY5ndLZbq7\nVBaSIKhELa2/QcfizsD7KMDsiYLNF6LnJPVEPt2A11DCoKdQ5tvUuii+++67bLPNdmgcZ9do6fHA\nba7DNlLd9AFmoQsRj6N6BvV22Qr4NTpd+wlh+H1ju7Ecpq75/dCQjRdQT4dFPnasoCTrHty5mW2H\nof9OTyUs+xzYBPX9m0f6Ruzvii4brYH6gk1G8ywAMGrUqLoVq6qqqKqqStPLmllbDRo0qF42V429\nGYHGzb2PuvdZNowfPx4FGn9DM5R9AuzY5HyO+UD/1MqAvsB3BEExYbgyy6XKJQuAS1CdzyWeeKcP\n6jhVirLEVnHyySMZO3ZsSnsNw5AgKEKtdeUoGVDqY+q23nproASlxNgadVV1y176LAUWozGPh6Eu\nwFNQD5idURKzt4Cauos+yerujTfeYKed3AU1V1x22flcfPHF6NheAfiCneW/CRMmMGHChJTWba6l\ntRZdlv8Pujz3AvpPl+kUkpegX94/R4/d0mqWR3r27Mn8+TXEJqtvLiOmZY5OTkuJpyUA2B94Ii/r\nJP5+nkbdH6eibsNOHpMosSX64osv5rLLLkMdo+6Lltai47O2xZ9bW1q5Nf9vD9Rl9V3gv2y33RAm\nTpzYov10ZPr8y1H9rUQjvWqj22DgGJTJ/Tt0CleERnS9hy5oFKEu5DVACePH38XBBx9ct/+ioiJq\na2O/Gd3p27eM7777rn3enJl1aG1JxLQRcCb65TsVeBX94j0JnIumvUnHONSuxOd67Qb8CPggDfs1\nszQYM2ZM3RQXY8aMaXb9efPmEYaLCMO50dgpBxPZoqlhQuDtaMkC4J3sFSgtuqOAFfRvamgWy5Kb\nEpP7XHrppdHSJ9FY1hC4FujWqmOzLYmDlGl4PppJ7yU23XRdB6wtcNddd6Fu3nujcaunotOnX6L2\nhA9QXoFaWUH9AAAgAElEQVSp6FgPgYnAE6gDWxc03nkx6ty2Noccckjd/nfYYQdqa7ug1tmVwBXM\nmrWEjz/+uD3enplZk1oacG6MugxXoct2fdGvYs82lmMQSnMH6rJ8N3BFwvNuaTXLkvhV/dhogho8\njiY/9OjRg4ULQ1R3NSi4+xRNQZSfyYz0feyCRo9sjzLkDgUW+zvZDLVydkb/+kvwZ5Z/4l3jF6CW\nVtAFnP8D7iAxd0D8tzvxWO+NMj8PiR5fDVxcN7xD2+xGwugsoJJttlmfd97J94tdZpbr0jHlTcxk\nlIzp3yiF42LUMtpW01GWgK3Q2ccVyVc3s/YTmydxVnT7Iek57C2TJk2aFAWs+wMPAMeh8cVz8jZg\nhVh31OXouulQNF9ojYOvFIRhLUVFtcCKqCeEP7NcpZ4t5QRBb4KgMhoPHFOLWkFBLamxLsAl9fYx\nbNgw1Pp6R7TkbXTx6oWE/TxLfOojKC0tRRe2lkRLZgBLOeyww9LyvszMWiuVltY+qGV1WHRbH/Ut\nmwC8hLoML2li23RxS6tZlgRBH+A24MBoycPACYThnOwVypqlq5U90BQJsfkZNwamFkSw4uzBVqji\n437XBa4CvgLO4kc/+gFPP/10lOhuCzSl2JNoQocylBtzRb3jIZ7xuQb9BqyI1t0O+Bb4luLialas\nUGKfhQsX0qPHOqhFdpdo/4uc6MzM2kVbWlo/IPZrqV/DM4Fe6JfsQpQJI9MBq5ll1XLUuSKMbuOj\nZZb7VqGTVVDdFc6JZ1vGVXZE3btrPtSioiIWLVqU7eJYUj3RuNV/APuiKU5+xTPPvEwQdEdTBk0E\nTgdeRm0IjxGfYiwuDEPuvvtGYDnDhu2aMO/xK8CnbLfdkLqAFaCiogL9ToTodO84oDjpXLpmZu2h\nuaB1fRSsTkfzqH6Og1SzDmYx8C+UlXIQCmAXZ7VE1rz33nsPXaw8ALgXzZU7hyOOOCKr5bL2FwQl\nLFlSAvyM2toBVFT0d+Ca8wLqXxxcDgwHTga6svXWm0Xr3I+mD9oOJWCqWG1PRx99NGEY8sIL6hac\neMGn8SRYnYDngdFoEodj0vWmrBVOOOGEbBfBLCc0d+msBE3IFku+tDMwG13Wi92mZapwCdw92CyL\nunbtyrJlStRRVlbG0qVLs1wiS8Xw4cN5+ukJqHvgMioqilmwYEGWS2Xt6eabb+bEE09FKSkGo7k8\nN6BLl7lUV2ssY8NWNP+/za54l941gMtQsrG/Am+gOvwxSitSibrx7o6Cy98Df683Z3brXr87asnd\nJFpyLHCHvxftTN+D7uiY7Q4sdB1YwWtL9+AVqA/JZcCeqM/KSBSojkS5879IV0HNLDctXbq07sq8\nA9b88dRTTxGG1YTh94ThUgesHdDdd9+Nsi0PjpZ0BYawfLla8eJjn3+NkqyVp70raGy6rKFDPTVR\nKhSYLEGJ784ErgQeIl6HZShz8LnAP9H53VbAWGJJlfSZ94iSOZVz+eWX13uNXXbZhSAImuh5sQr1\n0LgHuAS15lp7imeJvh99F34MVBAE3euOpyAIKC4uTrofs0LS0v9MxWg8axVqfd05WtbSLMQt5ZZW\nsxzx6aefMnnyZDbYYAM22WST5jcws6yZOXMm/ftvBPwR+BUaA3kA22yzMe+8807UqvY86lQVon/t\nL6WlRWfo0KF89NEXKAgqQdlqnbW4pZR4aShwOTAJuBQYCNwFnIcSKk0lNoWRAp5KFOiuDRwPvEcY\nLor2V4RO3cpRQLSsXp0cccQR3H//v9DFjJW4ztqf6rAKeBFlf74ETWv0NZqm6CjUbvQRffp0Yfbs\n2VkqqVl6taWltTOwK/A79F9tATqCjkWtrb8A1ktTOc0sx40bdzNbbrkrP/vZ39l222FcffV12S6S\nmSWx9tprs846PdG/8RLgADp1qk6Yc3MFsFF0PwA2Tdtrf/TRx9G+ZwFz0bjqHmnbf0fx6KP3AO8B\nh6JTsOtRve2FWkT/jk7VYq3kJagVdg9gQ9QCq9M9tcyVoq7Gs1E34zIOOOCAutf75z//SRjWRD00\n3CU1ez5DY5n/jJJynYguWJyCLka8CqzHnDnO5G8dQ3MtrYtRX6KZ6JdyAprga3pmi7Uat7SaZdmc\nOXNYd90Nqa6eCGwAfE1p6VZMnvwOAwYMqLduvMthbbQklvSlB2p1KQIW+GTIrB299NJL7LHHHvWW\nBUElCnyuReNe9yfWYtdWQdADTbt+arTkXWBPwnBem/fd0eg3tQj9lnZGLa+bomzuoBbTnmjs4wLU\nunpT9NwTwE8Jw3nRfrZFc7bGrAV869/jHHLCCSdw660PouB0Hpp6KDZX78WoBfwKdCFjvOvOCkZb\nWlp/AwwB1kHp426h/QNWM8sBM2fOpLh4bRSwAqxDly4b8NVXX9VbTz845cBFQD90pbgCJRY5CrUY\n/AUo8zQKZu2oYcAqC9B0KeuRzoBVlgCPogtVoODJWkN10hU4DLWSbonmYI5ZiIJa6N69KxqPOhJ1\nKT0SmJ+w7hTgf9H9D4F5bLDBBljuuOWWWxg8uA+6kDQX/e98HrW4Xg/sAzyFZp406xjy5YzRLa1m\nWbZo0SL691+fRYvuRQlb/kPXrgcyY8YnrLHGGnXrBUEn1LJyP3AzGpdzLeriNJ/YiZX28YKvEJsV\nqPgFrF6ol4Vmzcv2MV9VVcVLL71U9zjb5UlVPJtsCZp/uQYYgdKL/BV1iltAGNbSo0cPFi5ciNom\nauu9xyDohn6HN0VBazVhWIO1TZcuXVixohQlw+qMxgrXNrNV88aOHcsvf3kWuvAbuwAUolbYo4Cr\n68Yrm+W7ZC2tDlrNLGUvvvgiBx98JDU1RQTBcu6//x/st99+9dbRD86uKBHIY9HSEJ28voG6tdWi\nE6YpeXPCaJZLhgwZwtSpU+se59px1K9fP2bNmrXa8myXc6uttmLSpGlAfzTe9jlgaZvKdcwxx3DX\nXXfVPW6v96jf2s4okF1BS95HYi+XbNdJoVB3+CrUKfErYG/g+7R+vqq3XwJ/i5YsBcoJw1VNb2SW\nR9rSPdjMrM6wYcOYPfsrPvnkDb7/fuZqAWvc28BH6EQK1BVtBbAfymL6I2AmO++8c8bLbFZogiBg\n6tRv0BQoQ4FuOdHVfo899oim4ujKrFmLUIbbUqCkbsqsbJs0aRKaf/RD4GHgAXRBrWX0PssJghLu\nuusRFARvA3Rtt7oIw5D77rsL9WBpfiqyIAi44YYb6rbNlTopHLWoV1EfNP70dOI9i9LpPuA1dDH4\n7+iihVnhc9BqZi1SUlLCeuutR5cuXRp9XidBy1Fmym3RP+5t0c/NVyj74fMMGNCL119/vX0Kbe1m\n3Lhx9eYRTAftqyKad7JnTgRo2VWCWnE+BT5Ac3kmz8p7zTXXMGTIEC6++OKUXmH33XdvUT0GQcDL\nL7+LAsAiNN5uOgoOSzJeZ7vtthuvvPJKimvvSjyY2An9XqUu3k33UTSH6lD0Xt9B05G0T4bkf/7z\nnxx55EnAusBBQOIcnhXRsVJe9xg6cfrp5/j4yZgiNAY15gPi3XnbrqKiAn3vytAF4EqUlGlh2l7D\nLJflyy+Xuweb5ZkJEyYwbNiwusc+hgvfuHHjOOWUc9FJ+1Yo4XzbE/vohHsHlIDkU+Bn5MLYyGwJ\ngp6oRee4aMlrwIGE4dxG1+/SpSsrVoTAmsC3dOoUsmpVdZP7HzFiBA8++AwwCM3fej/xRD4BEHLF\nFVdw/vnnJ5SpMirPLSjx2tcJe9wZeKPJ+moYRLWkXuPjZpegYL466fZavw/wJmoJPhe4hTCc3+Q2\nje/jBDRm//zo9f8venY6sEW7jDFUOdZFgVJXNIfr9uhzuAh97pcD/4kenwNMRPkEOu7xkymqj27A\nz9GskK+Szs9Zx9hpwO/RuNkfAO+4Hq2guHuwmbU7BaxlKICp9NX9DuCUU05BV/+noFaoV4ASjjrq\nqDbueSVwB7AZcDAKGDqyJShgWopacsbRVIvO9ddfHwWsE1BANZHa2k6MHDmyyb0/+GBsqo23o32/\njoKiocAlwGZccMEfG2y1CvWq6BmVL9aLYgZqbW1cvNVySxRMtbR7bTkKHFcCL9FcV2md4C9AEyN0\nQZ/jgha8XszHqHvm1iionxc9HkdmuoQ2ZWtUNwBboC6qPwTOA3ZHXaCXAWegU74dgWGr7yZF6e5F\nUUj03VqCuuw+TfovDNSiC3YB+t96FPG6Nyt8DlrNLENKUUvGc8CDQC+f6HQIGtcnWwJw3333tXGf\nnYFvEh5/3dSKHcIOO2yNuh72QZl5H6KpLoI33ngjsAYKVkCB5/o89thjja4ftxn63EGnCsUokdol\nqJWyYeBShI71a6PHw1Gytc2A5UlO3jtH676LLnQcBpSm9FtRXl6OArILojLugMbLJxeGKzjyyJ8A\nNYRhavNFxwK1s846K1ryIQr+/oMuBqwN9EYJchQEl5WV1QvyTj/99GZfJ1Xxz+cZNI1YCIxB9bQs\nYc3Y/SnR3+Xou9Oa14x1Te1Pe47dzSeJY4XT3wLaCf0vBdXjv0hlLLNZoXDQamYZUgaMBbZDV/4v\nRd0GrbA9D7xP/CS6hLFjx7Zxn8uAfYGrgGPp6HMTvvnmm4ThQvS5LOThh+9q8gT5uOOOQ+PL34uW\nfApMY++9927mVZ5ELZdLgStQQNYteq4balFPtAD4NQpau0Zl+wRltF1B08pQxtWlwB4oads+xMbB\nPvfcc01uecEFsWD1o2jJCtRFVj09Lr300nrrx8d6duW++55IWJY8+AqCEhQMduP662+Jli5Cn8/1\n0f1qYB477bQZYRiy+eabU11dgn7/LgIGccMNtyV9nVQpeFwL9TjoA+yCLhJegFrhPkAZZu9Ara3F\n0d+j0EWEuYwePTrJ/gOCoJIgKIs+r9hnVAF8gS4ajaY1Cays9bp0qUZjqDdAFw4+4t57781uocza\nUb5cJvOYVrM8EwS9UFe5EdGS/wOuIwyXZK9QlnGap7c4elRMurrI6aS5FAUmtR7H1QLFxWXU1ASo\n58MXBEEttbXLkm4TH59XjYLQSuAsdDzfB1zKWmtVMHPmzAbbxKXaggkbAyNRS+4v0NjYnVDr60Ke\nffbf7LXXXk1s3wkFTweg8ZrfoK7KK9EpTglVVdszYcKEaL1H0FQ3PwXeit7XXCAkDFcf56vy9URd\npQejREuXE4bJuxRru/5obGMJ8H30OFmrc/O0364oeOyDxhoPABYShmHCGN9V0esu4IEH7mfEiBF1\n+xg9ejSnnXZaktfoger5clQHP0EXFWLjeEHHYSnHHjuS225LTzBuzTvxxBO5+WbVwTfffMOaa66Z\n5RKZpZfHtJpZFswDjkcnPr8B/oK7MhW+MKxlu+22AFYwduyf0xZcqrvdMsJwlQPWFlq5chmnnnoc\nfft+j1o+m07CFKPPezFPPfU4OlXYFXgcdcG9ElhSF7DGMhK3pmvkAQccgLKK/x9qBTwBjcN8FLUK\n90naKhyGtahr9D1o/OxKYHPgW9QiuAETJsTG156AWnXXAj4D/oFadqcAZUlaXA9GASsocF/EVltt\nlcK7WwsFjqBu3Okaf9gbBaygoHttIHayV4aySe+Ogu2uHHHEEfXqpamAdfTo0VHSs5UoidBE1MPh\n4GiNZ4mP/30Y6OaAtZ3ddNNNdfXogNU6Gre0mlna6eSpN7ravzhaWuNgwyxLHnjgAQ4/fCQKSIpQ\nMLWoBa2hFajVtRYoJgyXEgTF0b5qUDKl1MaHNv0avVAAupz4NfWjgPua3a+2XwslWDoXTQkCGgN4\nMmpN3Rd4AgVea6OkOTEHAw+v9jra74aoy3sp6v5+ULM9RuIton9FY3bHAtdRWlrDsmXJW7mTWXfd\ndfn667mo6/3RaDzzcXTuvIKamhrUQj0dJWLaDHUZnkIYLm5qlwllrgT2jMo8Jdr/k+jzexu14HZC\nLcbTgWX+TTeztHJLq5m1m3jAOhZlUeyGTmpTGz9mue+yyy4jCIIm5+q13HP44YcDfVHr41LUPTa1\n+UTVsrMAdQmtiQLWWLfZ91CguW/K+2vqNbSfUuC6aOkUFDSl6mAUUL2XsOwdFKiDslnvj3p/hMAL\n0fI5aNqg1Z155pnAdyhw3QvNh9p8jxG9n6Wol8mGaAzo4kYD1pZk5P3qq6+i/Z6BPqsTgKWsXBl7\njzNQXfwaJaV6jPj7b041+t1eB+UhOAp1E54SvaeFKOD/GAesZtbe8uXs0S2tZnkiCHqjYPXwaMmt\nqEtdiMZeTcMnPPkrPkXJStTqVksY1mS3UNYsJRMaBVwYLfkc2KrV84kGQRFKMDQqWjIN2JIwXFQv\n+GrpcR7/fq1CF7tS+35pu8Go5XE4mqN0CfAy6hIdNhIUlqGkNjOi12m8NfKss87i+uuvb/V7Sl7u\nrmjs9/aoO+5KwjC1YRTvvPMO2267bcK+AjSNzwHEs8zOADZOqUt4EJSjLsA7RUuGE0t65t/r9nfm\nmWfy17/ejo6FImLjls0KmVtazaydJWYL/QydlH2Gpol4HChlv/32a2xDy3nlaNqTZajLZPJ5MS1X\nrAReRBcaQK2ObZlPtBZ1Gf0MBYhDgMSEWf2ALlFwm7owDHn22fHAMrbaarOUL4gcc8wxwCyUgbgC\nJVx6iljAGtt3/TG3y1Cm3UVJu89ed911GZnGRJ9ViMb7X0psKppUj6fEgBVigeVy1AX6fDTOdzip\nt08sjtY/HzgQeI2rr77agVKWKGDdE/3ffADoTnFxcdJtzApZvpxpuKXVLE/Ex79dg06Uz0b/eJ9I\nWEtTYvi4zi+q206oXls25tCyK55Vtj8az/kf2nIMxrMLl6Bxk6eh7rYjgLuAQ1Hm2V3rXqdr164s\nW1aLWlJraMsY2OTlklz/TsbHve6OxohuAfwX+Dxp2ZsKamPbxFurS4CFnHTS8YwbNy6lMnXr1o2l\nS5eiCw9F6DhPbeyzpVcQlKIkZWtES04DxrgurKC5pdXM2k18bNp5KCPocuB14MtojccA2HfffbNR\nPGsDzXtZArwZLalG04ZYrgvDkAEDegGTUXDZsoBVYy57EgTdCYIg2nYJCj7PRV1t9we2Jp4xd2s0\nnlOWLeuEppu5Df02NJ6xtyVjPBt7n+luEc2ccpTB+EnUa2E6sd/JkpKSus/gjDPOqNsiHpBugT7b\nbiiHQBeCoAyIfQaLCMPvCcOVKQesQBSwVqAeMe+hbsuejzU7OqPu3TGfZ6kcZrnBQauZpZ1OmuYS\nhvOik8fFwCboZPYoYBlPPPFE0n1Y7tHUJtXA3ihAGQLMjoJZyyYFOL2jW0AQdCEIekU3BX8zZsxo\nVVAXBJ1Rd9/fAYNQl/DK6Nlq1BoE6m47DYiNk/2S+ifamtsTror2N4KGgqAban0cQsOgVu+rmCAo\nKpAu6dWoFwrot3EXYkmTVq4sRtMLDWb06DvYddddo/W6Az9HAWVnlOl3DjCV5NP2pKoITZmzJxrv\nOw51Ybb2txQl0/odcAjwKieeeGJ2i2SWRQ5azSyj4t0SXwZmo+ybrc8yatkVb2F7AviSSy89p26e\nTsuOeJf8v6AgphK1vo0HbgbK2xjMdAf+Hd32RmMv/4xaVwNgG+AkFHRtjLoxbgtsh7ojx8rYA7gM\nuBglcPqskfdRjLLVTkbfsdKEVteuqAfH6aQnQMu22JQ4ITAT+Ge0vBK4AvVomArsyOuvx+aa7YIC\nmRVoHtvjouXroWl+urbxc1mFLjzEfIlPFbNDcxDPB/4IPMQxxxzCjTfemOVSmWVP52wXwMw6ghPR\nSSzoZPffWSyLtVV+dL3sSHoCV6MWOFCQMRYlJQK1dralNbwWtQB+jBI4dUJTrdwRPZ6DuriOBn6M\ngqnN0cn2RBQEVaCpbIZH+7wKzf/Z0HZoyhWAKhTEVkfv8XrgmOi5HsC1bXhPmRMP0Guj25ImjpkF\nqN6uRsMoOkdZjnug6XVALZ/7AK9Gj5cBf4uW9UXzxu6FhmS8BZyCMra3xXg0JdIgVKcL27g/ay3/\n1prF+fKZmbWDScS7mH2ETkTNLH0ST25rUYtZzLwGj1uqBjgVBY/fR8tWodbBxNd4Dk05cyIKMh9D\nLUXFxBIvxS1A08w0PCmfSHz8+3PE5xgNUAKpmHVoeN29urqa6urmp3bJJAWsvdDn8B/Uzblbo+uG\nYcj11/8BWExFRVnCVDcBcAOqx/koCI3N77o02m9P1HPlxyiR02aoy/4aJKvr+Hjh4roW2QMPPDDq\nct0TdVNeijIPX4GnWTGzXJEvfWucPdgsT8W7B2+DTuDuARb7RMgsTeLH2HXExyTWoJbO2ah3Q+sz\nBV944YVcccVfUEDTG7WyPgv8l3322YWnnnoqoQzFqPv4IehYjwVg56PA+mJ0vfxSGmuBVDKhAFgL\n+Kau3MqkOhi4O9r/YcAswjCkurqazTffic8++xCA9dffjA8/fJPS0tJWvd+2CILuqAv02dGSicCP\nCMN5LdhH7LMMUTfgEiZNeo0tttiiwTr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+ "text": [ + "" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Plot of maximum water level and its location" + ] }, { "cell_type": "code", "collapsed": false, - "input": [], + "input": [ + "mpl.rcParams['legend.fontsize'] = 10\n", + "\n", + "x, y, zz, bpz = [], [], [], []\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "\n", + "for key, value in full_data.iteritems():\n", + " df = value['data']\n", + " z = df['elevation'].values\n", + " bp = df['nearest_barometric_sensor_psi'].values\n", + " lon = value['meta']['lon']\n", + " lat = value['meta']['lat']\n", + " \n", + " idx = np.argmax(z)\n", + " bpz.append(bp[idx])\n", + " zz.append(z[idx])\n", + " x.append(lon)\n", + " y.append(lat)\n", + "\n", + "bpz = np.array(bpz) * 10.\n", + "pts = ax.scatter(x, y, c=zz, s=bpz)\n", + "ax.set_xlabel('Lon')\n", + "ax.set_ylabel('Lat')\n", + "title = (\"Plot Showing Locations of Maximum Water Level\\n\"\n", + " \"Color coded by Maximum water level (ft)\\n\"\n", + " \"Sized by barometric pressure (psi)\")\n", + "ax.set_title(title)\n", + "cb = fig.colorbar(pts)" + ], "language": "python", "metadata": {}, - "outputs": [] + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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VEF/TGahGKqERERGROk8lNCIiIg1UfQoCVEIjIiIidV59Cs5ERESkCtSGRkRE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\nuPIPUEREp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+ "text": [ + "" + ] + } + ], + "prompt_number": 13 } ], "metadata": {} diff --git a/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/Rapid_Deploy_Gauges.py b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/Rapid_Deploy_Gauges.py index 0cf427a..7995330 100644 --- a/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/Rapid_Deploy_Gauges.py +++ b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/Rapid_Deploy_Gauges.py @@ -1,189 +1,280 @@ -# -*- coding: utf-8 -*- -# 3.0 -# +# coding: utf-8 + +# ># IOOS System Test: Rapid Deployment gages from USGS +# +# ###Can we obtain water level data from the rapid deployment gages, deployment for Hurricane Irene? +# This notebook is based on IOOS System Test: Inundation +# +# #### Methodology: +# +# * USGS gage data (ASCII Text Files) obtained from http://ga.water.usgs.gov/flood/hurricane/irene/sites/datafiles/ and 90% of data zipped up for use in the notebook (large files were removed for efficiency) +# * Station data Automatically gets unzipped +# * Process data files (195 stations), and store data in dictionary for access +# * Plot Water level data for the New Jersey area +# * Plot Barometric pressure data for the New Jersey area +# * Plot Gage Locations on a map, overlaid with the Hurricane Irene track line +# * Plot time series of maximum waterlevel +# * Plot locations of maximum waterlevel + +# ### import required libraries + +# In[1]: -from bs4 import BeautifulSoup -import requests import re import os +import csv +import uuid +import zipfile + +from datetime import datetime + +import folium +import numpy as np import pandas as pd -import json +import matplotlib as mpl +import matplotlib.dates as md +import matplotlib.pyplot as plt -non_decimal = re.compile(r'[^\d.]+') +from IPython.display import HTML, Javascript, display -url = 'http://ga.water.usgs.gov/flood/hurricane/irene/sites/datafiles/' -r = requests.get(url) +from utilities import css_styles, inline_map +css_styles() -soup = BeautifulSoup(r.text) -# +#
Extract Data - Does the data dir exist, if not extract it
-def processTextFile(url,file_name,data_file_type): - print url+file_name - r = requests.get(url+file_name) - contents = r.text - file_name = "./data_files/"+file_name.split('.text')[0] - f = open(file_name,'w') - f.write(contents) - f.close() - return file_name +# In[2]: -# +def unzip(source_filename, dest_dir): + with zipfile.ZipFile(source_filename) as zf: + zf.extractall(dest_dir) -wl_count = 0 -file_name_list = [] -for link in soup.findAll('a'): - html_link = link.get('href') - if html_link.endswith("BP.txt"): - #processTextFile(url,html_link,'bp') - pass - elif html_link.endswith("WL.txt"): - #file_name_list.append(processTextFile(url,html_link,'wl')) - wl_count+=1 -print "num water level:",wl_count -# +# In[3]: -files = os.listdir('./data_files/') -print len(files) +if os.path.isdir("data_files"): + pass +else: + print("Data Dir does not exist... Extracting.") + unzip('sample_data_files.zip', os.getcwd()) -# -count =0 -full_data = {} +# In[4]: -for file_name in files: - print count,file_name - count+=1 +files = os.listdir('data_files') +print("Water Level Files: %s" % len(files)) - actual_data = {'dates':[]} - with open('./data_files/'+file_name) as f: - content = f.readlines() - - titles_set = True - titles = ['dates'] - - meta_data = {} - - - fields = { - 'Sensor location latitude':'lat', - 'Sensor location longitude':'lon', - 'Site id =':'name', - 'Sensor elevation above NAVD 88 =':'elevation', - 'Barometric sensor site (source of bp) =':'bp_source', - 'Lowest recordable water elevation is':'lowest_wl' - } - - for i,ln in enumerate(content): - content[i] = ln.strip() - if content[i].startswith('#'): - for f in fields: - #search inside the array element - if f in content[i]: - if fields[f] == 'name': - meta_data[fields[f]] = content[i].split(f)[-1] - else: - val = (content[i].split(f)[-1]) - meta_data[fields[f]] = float(non_decimal.sub('', val)) - - if fields[f] =='lon': - meta_data[fields[f]] = -meta_data[fields[f]] - - else: - try: - data_row = content[i].split('\t') - - if len(data_row[0])>1: - #print the data looks to be ok-ish - if titles_set: - titles_set = False - for t in data_row: - if not 'date_time' in t: - titles.append(t) - actual_data[t]=[] +#
Process Data - Read the data files and create a dict of the fields
+ +# In[5]: + +def parse_metadata(fname): + meta_data = {} + non_decimal = re.compile(r'[^\d.]+') + fields = {'Sensor location latitude': 'lat', + 'Sensor location longitude': 'lon', + 'Site id =': 'name', + 'Sensor elevation above NAVD 88 =': 'elevation', + 'Barometric sensor site (source of bp) =': 'bp_source', + 'Lowest recordable water elevation is': 'lowest_wl'} + with open(os.path.join('data_files', fname)) as f: + content = f.readlines() + for k, ln in enumerate(content): + content[k] = ln.strip() + if content[k].startswith('#'): + for fd in fields: + if fd in content[k]: + if fields[fd] == 'name': + meta_data[fields[fd]] = content[k].split(fd)[-1] else: - if '.' in data_row: - data_row[0] = data_row[0].split('.')[0] - - dt = datetime.datetime.strptime(data_row[0], '%m-%d-%Y %H:%M:%S') - actual_data['dates'].append(dt) + val = (content[k].split(fd)[-1]) + val = float(non_decimal.sub('', val)) + meta_data[fields[fd]] = val + if fields[fd] == 'lon': + meta_data[fields[fd]] = -meta_data[fields[fd]] + return meta_data - for i in range(1,len(data_row)): - try: - val = data_row[i] - actual_data[titles[i]].append(float(val)) - except Exception, e: - actual_data[titles[i]].append(numpy.nan) - except: - print 'error:',data_row - break - full_data[file_name] = {'meta': meta_data,'data': actual_data} +# In[6]: -# +divid = str(uuid.uuid4()) -print titles +pb = HTML(""" +
+
 
+
+""" % divid) -# +display(pb) +full_data = {} +for count, fname in enumerate(files): + meta_data = parse_metadata(fname) + kw = dict(parse_dates=True, sep='\t', skiprows=29, index_col=0) + actual_data = pd.read_csv(os.path.join('data_files', fname), **kw) + full_data[fname] = {'meta': meta_data, + 'data': actual_data} + + percent_complete = ((float(count+1) / float(len(files))) * 100.) + display(Javascript("$('div#%s').width('%i%%')" % + (divid, int(percent_complete)))) -num = 0 -df = pd.DataFrame(data=full_data[full_data.keys()[num]]['data'],index=full_data[full_data.keys()[num]]['data']['dates'],columns = titles ) -# +# #### Show the available fields from the processed data files +# In[7]: -fig = plt.figure(figsize=(16, 3)) -plt.plot(df.index, df[titles[1]]) -fig.suptitle(titles[1], fontsize=14) -plt.xlabel('Date', fontsize=14) -plt.ylabel(titles[1]+' m', fontsize=14) +print("Data Fields: {}, {}, {}".format(actual_data.index.name, + *actual_data.columns)) -# -full_data[full_data.keys()[num]]['meta'] +# # Remove 'Sensor elevation above NAVD 88 (ft)' -# +# In[8]: -from IPython.display import HTML -import folium +for key, value in full_data.iteritems(): + offset = float(value['meta']['elevation']) + value['data']['elevation'] -= offset -# -def inline_map(map): - """ - Embeds the HTML source of the map directly into the IPython notebook. - - This method will not work if the map depends on any files (json data). Also this uses - the HTML5 srcdoc attribute, which may not be supported in all browsers. - """ - map._build_map() - return HTML(''.format(srcdoc=map.HTML.replace('"', '"'))) +# ## Plot all Water Level data in the NJ area + +# In[9]: + +fig, ax = plt.subplots(figsize=(16, 3)) + +fig.suptitle('Water Elevation', fontsize=14) + +for key, value in full_data.iteritems(): + try: + if 'SSS-NJ' in key: + df = value['data'] + ax.plot(df.index, df['elevation']) + ax.set_xlabel('Date', fontsize=14) + ax.set_ylabel('Elevation (ft)', fontsize=14) + except Exception as e: + print(e) + + +# ## Plot all Pressure data in the NJ area + +# In[10]: + +fig, ax = plt.subplots(figsize=(16, 3)) -# +fig.suptitle('nearest_barometric_sensor_psi', fontsize=14) -map = folium.Map(width=800,height=500,location=[44, -73], zoom_start=3) +for key, value in full_data.iteritems(): + try: + if 'SSS-NJ' in key: + df = value['data'] + ax.plot(df.index, df['nearest_barometric_sensor_psi']) + ax.set_xlabel('Date', fontsize=14) + ax.set_ylabel('Nearest barometric sensor (psi)', fontsize=14) + except Exception as e: + print(e) -# +# ## Map the Gage locations + +# In[11]: + +map = folium.Map(width=800, height=500, location=[30, -73], zoom_start=4) + +# Generate the color map for the storms. +color_list = {"Tropical Storm": '#4AD200', + "Category 1 Hurricane": '#CFD900', + "Category 2 Hurricane": '#E16400', + "Category 3 Hurricane": '#ff0000'} + + +# Add the track line. +with open('track.csv', 'rb') as csvfile: + spamreader = csv.reader(csvfile, delimiter=',', quotechar='|') + for row in spamreader: + lon, lat = row[3], row[2] + popup = "{} : {}
{}".format(row[0], row[1], row[6]) + map.circle_marker([lat, lon], popup=popup, + fill_color=color_list[row[6]], + radius=10000, line_color='#000000') + +# Add the station. for st in full_data: - lat = full_data[st]['meta']['lat'] + lat = full_data[st]['meta']['lat'] lon = full_data[st]['meta']['lon'] - map.simple_marker([lat, lon], popup=st,clustered_marker=True) - -# + map.simple_marker([lat, lon], popup=st, clustered_marker=True) map.add_layers_to_map() -inline_map(map) +inline_map(map) + + +# ## Generate Plot Of Maximum Water Levels from each gage + +# In[12]: + +dt, dv = [], [] -# +fig, ax = plt.subplots(figsize=(16, 3)) +fig.suptitle('Max Water Level (ft), 2011', fontsize=14) + +for key, value in full_data.iteritems(): + df = value['data'] + z = df['elevation'].values + + idx = np.argmax(z) + val = z[idx] + t = df.index[idx] + + dt.append(t) + dv.append(val) + + data_dict = {'elevation': dv, + 'dates': dt} + + df = pd.DataFrame(data=data_dict, index=dt, columns=['elevation', 'dates']) + + ax.scatter(df.index, df['elevation']) + ax.set_xlabel('Date', fontsize=14) + ax.set_ylabel('Water Level (ft)', fontsize=14) -# +ax.set_ylim(0, 20) +ax.set_xlim(md.date2num(datetime(2011, 8, 25, 20)), + md.date2num(datetime(2011, 8, 30))) +ax.xaxis.set_major_formatter(md.DateFormatter('%B,%d\n%H:%M')) -# +# ## Generate Plot of maximum water level and its location +# In[13]: + +mpl.rcParams['legend.fontsize'] = 10 + +x, y, zz, bpz = [], [], [], [] + +fig, ax = plt.subplots(figsize=(10, 10)) + +for key, value in full_data.iteritems(): + df = value['data'] + z = df['elevation'].values + bp = df['nearest_barometric_sensor_psi'].values + lon = value['meta']['lon'] + lat = value['meta']['lat'] + + idx = np.argmax(z) + bpz.append(bp[idx]) + zz.append(z[idx]) + x.append(lon) + y.append(lat) + +bpz = np.array(bpz) * 10. +pts = ax.scatter(x, y, c=zz, s=bpz) +ax.set_xlabel('Lon') +ax.set_ylabel('Lat') +title = ("Plot Showing Locations of Maximum Water Level\n" + "Color coded by Maximum water level (ft)\n" + "Sized by barometric pressure (psi)") +ax.set_title(title) +cb = fig.colorbar(pts) diff --git a/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/conda-requirements.txt b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/conda-requirements.txt index 8c89465..9e4a3d0 100644 --- a/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/conda-requirements.txt +++ b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/conda-requirements.txt @@ -2,3 +2,14 @@ beautifulsoup4 requests pandas folium +pandas +matplotlib +OWSLib +netCDF4 +lxml +pyoos +cython +pyshp +Pillow +cartopy +pyke \ No newline at end of file diff --git a/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/pip-requirements.txt b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/pip-requirements.txt index 8c89465..214daed 100644 --- a/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/pip-requirements.txt +++ b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/pip-requirements.txt @@ -2,3 +2,20 @@ beautifulsoup4 requests pandas folium +cython +numpy +scipy +pandas +matplotlib +OWSLib +netCDF4 +lxml +pyoos +pyshp +Pillow +pyke +Shapely +biggus +prettyplotlib +pandas +matplotlib \ No newline at end of file diff --git a/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/sample_data_files.zip b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/sample_data_files.zip new file mode 100644 index 0000000..706a57f Binary files /dev/null and b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/sample_data_files.zip differ diff --git a/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/track.csv b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/track.csv new file mode 100644 index 0000000..40372d5 --- /dev/null +++ b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/track.csv @@ -0,0 +1,35 @@ +08/20/2011,23 GMT,14.90,-58.50,50,1006,Tropical Storm +08/21/2011,3 GMT,15.30,-59.90,50,1006,Tropical Storm +08/21/2011,9 GMT,16.40,-61.30,50,1006,Tropical Storm +08/21/2011,15 GMT,17.00,-63.20,50,1007,Tropical Storm +08/21/2011,21 GMT,17.70,-64.40,50,999,Tropical Storm +08/22/2011,3 GMT,17.90,-65.50,70,993,Tropical Storm +08/22/2011,9 GMT,18.40,-66.40,75,987,Category 1 Hurricane +08/22/2011,15 GMT,19.20,-67.50,80,988,Category 1 Hurricane +08/22/2011,21 GMT,19.50,-68.60,80,988,Category 1 Hurricane +08/23/2011,0 GMT,19.70,-68.70,100,981,Category 2 Hurricane +08/23/2011,3 GMT,19.90,-69.20,100,980,Category 2 Hurricane +08/23/2011,9 GMT,20.30,-70.10,100,978,Category 2 Hurricane +08/23/2011,15 GMT,20.50,-71.00,100,980,Category 2 Hurricane +08/23/2011,21 GMT,20.90,-71.50,90,976,Category 1 Hurricane +08/24/2011,3 GMT,21.30,-72.40,90,969,Category 1 Hurricane +08/24/2011,9 GMT,21.60,-72.90,110,962,Category 2 Hurricane +08/24/2011,15 GMT,22.40,-73.90,115,956,Category 3 Hurricane +08/24/2011,21 GMT,23.10,-74.70,120,954,Category 3 Hurricane +08/25/2011,3 GMT,23.80,-75.40,120,952,Category 3 Hurricane +08/25/2011,9 GMT,24.60,-76.20,115,950,Category 3 Hurricane +08/25/2011,15 GMT,25.90,-76.80,115,951,Category 3 Hurricane +08/25/2011,21 GMT,27.00,-77.30,115,950,Category 3 Hurricane +08/26/2011,3 GMT,28.30,-77.30,115,942,Category 3 Hurricane +08/26/2011,9 GMT,29.30,-77.20,110,942,Category 2 Hurricane +08/26/2011,15 GMT,30.70,-77.30,105,946,Category 2 Hurricane +08/26/2011,21 GMT,31.70,-77.40,100,951,Category 2 Hurricane +08/27/2011,3 GMT,32.60,-76.90,100,951,Category 2 Hurricane +08/27/2011,9 GMT,34.10,-76.50,90,952,Category 1 Hurricane +08/27/2011,15 GMT,35.20,-76.40,85,952,Category 1 Hurricane +08/27/2011,21 GMT,36.20,-76.00,80,950,Category 1 Hurricane +08/28/2011,3 GMT,37.30,-75.40,80,954,Category 1 Hurricane +08/28/2011,9 GMT,39.20,-74.50,75,958,Category 1 Hurricane +08/28/2011,15 GMT,41.40,-73.70,60,966,Tropical Storm +08/28/2011,21 GMT,42.70,-72.80,50,975,Tropical Storm +08/29/2011,3 GMT,45.30,-71.30,50,980,Tropical Storm \ No newline at end of file diff --git a/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/utilities.py b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/utilities.py new file mode 100644 index 0000000..64d3387 --- /dev/null +++ b/Theme_2_Extreme_Events/Scenario_2A/USGS_Gauges/utilities.py @@ -0,0 +1,33 @@ +""" +Utility functions +""" +from IPython.display import HTML + +def inline_map(m): + """From http://nbviewer.ipython.org/gist/rsignell-usgs/ + bea6c0fe00a7d6e3249c.""" + m._build_map() + srcdoc = m.HTML.replace('"', '"') + embed = HTML(''.format(srcdoc=srcdoc)) + return embed + + +def css_styles(): + return HTML(""" + + """) \ No newline at end of file