"
+ ]
+ }
+ ],
"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",
+ "\"\"\" % 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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\nREREatT/B2XtzuHjFROcAAAAAElFTkSuQmCC\n",
"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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\n1C9/A2X1Um/qSqeJAAAAAElFTkSuQmCC\n",
+ "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\">"
+ "