{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# OUTPUT TRACKING ALGORITHM\n",
"\n",
"\n",
"---\n",
"Author: **Helvecio B. Leal Neto** & **Alan J. P. Calheiros**\\\n",
"**National Institute for Space Research - Brazil - (2021)**\n",
"\n",
"\n",
"\n",
"## About\n",
"\n",
"This notebook is designed for viewing the tracking results of the storm/precipitation tracking algorithm beta version. The results presented here refer to the tracking of clusters via radar data provided by the GoAmazon project, for the following periods:\n",
"\n",
"**Start**: 2014-09-07 00:00:00\n",
"\n",
"**End**: 2014-09-09 00:00:00\n",
"\n",
"The tracking threshold is:\n",
"\n",
"* **20** dBZ\n",
"* inner 1 - ***35*** dBZ\n",
"* inner 2 - ***40*** dBZ\n",
"\n",
"Minimum size threshold per cluster:\n",
"\n",
"* **30** pixels\n",
"* inner 1 - ***15*** pixels\n",
"* inner 2 - ***10*** pixels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dependencies libraries"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import sys\n",
"sys.path.append(\"../\")\n",
"import stanalyzer as sta"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import geopandas as gpd\n",
"import numpy as np\n",
"from shapely import wkt\n",
"import matplotlib.pyplot as plt # standard graphics library\n",
"from matplotlib.colors import LinearSegmentedColormap\n",
"import gzip\n",
"import netCDF4\n",
"import cartopy.io.img_tiles as cimgt\n",
"import cartopy.crs as ccrs # cartographic coordinate reference system\n",
"import io\n",
"from urllib.request import urlopen, Request\n",
"from PIL import Image\n",
"from math import floor\n",
"from matplotlib import patheffects"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"VAR = 'DBZc'\n",
"LEVEL = 5\n",
"\n",
"CPT = sta.loadCPT('../stanalyzer/cpt/humidity.cpt')\n",
"CMAP = LinearSegmentedColormap('cpt', CPT)\n",
"VMIN = 0\n",
"VMAX = 65"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<function pandas.io.parsers.readers.read_csv(filepath_or_buffer: 'FilePathOrBuffer', sep=<no_default>, delimiter=None, header='infer', names=<no_default>, index_col=None, usecols=None, squeeze=False, prefix=<no_default>, mangle_dupe_cols=True, dtype: 'DtypeArg | None' = None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal: 'str' = '.', lineterminator=None, quotechar='\"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, encoding_errors: 'str | None' = 'strict', dialect=None, error_bad_lines=None, warn_bad_lines=None, on_bad_lines=None, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options: 'StorageOptions' = None)>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<function pandas.io.pickle.read_pickle(filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~AnyStr], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], compression: Union[str, Dict[str, Any], NoneType] = 'infer', storage_options: Union[Dict[str, Any], NoneType] = None)>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_pickle"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"File not found or path parameter is incorrect.\n",
"File not found or path parameter is incorrect.\n"
]
}
],
"source": [
"track_frame = sta.read_file('../output/tracking_compressed.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Fam_0 0 ../output/data/sbmn_cappi_20140907_0000.nc.gz\n",
" 10 ../output/data/sbmn_cappi_20140907_0012.nc.gz\n",
"Fam_1 1 ../output/data/sbmn_cappi_20140907_0000.nc.gz\n",
" 11 ../output/data/sbmn_cappi_20140907_0012.nc.gz\n",
"Fam_10 20 ../output/data/sbmn_cappi_20140907_0024.nc.gz\n",
" ... \n",
"Fam_97 533 ../output/data/sbmn_cappi_20140907_1900.nc.gz\n",
" 548 ../output/data/sbmn_cappi_20140907_1912.nc.gz\n",
" 566 ../output/data/sbmn_cappi_20140907_1924.nc.gz\n",
"Fam_98 538 ../output/data/sbmn_cappi_20140907_1900.nc.gz\n",
" 554 ../output/data/sbmn_cappi_20140907_1912.nc.gz\n",
"Name: nc_file, Length: 1815, dtype: object"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"track_frame.nc_file"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uid</th>\n",
" <th>times</th>\n",
" <th>begin</th>\n",
" <th>end</th>\n",
" <th>duration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>182</td>\n",
" <td>51</td>\n",
" <td>2014-09-08 14:24:00</td>\n",
" <td>2014-09-09 00:24:00</td>\n",
" <td>0 days 10:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>288</th>\n",
" <td>68</td>\n",
" <td>31</td>\n",
" <td>2014-09-07 17:24:00</td>\n",
" <td>2014-09-07 23:24:00</td>\n",
" <td>0 days 06:00:00</td>\n",
" </tr>\n",
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" <th>23</th>\n",
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" <td>30</td>\n",
" <td>2014-09-07 01:12:00</td>\n",
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" <td>0 days 05:48:00</td>\n",
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" <td>2014-09-08 06:36:00</td>\n",
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" <td>0 days 05:24:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>183</td>\n",
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" <td>2014-09-08 14:36:00</td>\n",
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" <td>0 days 05:12:00</td>\n",
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" <td>0 days 00:12:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>368</td>\n",
" <td>2</td>\n",
" <td>2014-09-09 10:24:00</td>\n",
" <td>2014-09-09 10:36:00</td>\n",
" <td>0 days 00:12:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>369</td>\n",
" <td>2</td>\n",
" <td>2014-09-09 10:36:00</td>\n",
" <td>2014-09-09 10:48:00</td>\n",
" <td>0 days 00:12:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201</th>\n",
" <td>370</td>\n",
" <td>2</td>\n",
" <td>2014-09-09 10:48:00</td>\n",
" <td>2014-09-09 11:00:00</td>\n",
" <td>0 days 00:12:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>309</th>\n",
" <td>98</td>\n",
" <td>2</td>\n",
" <td>2014-09-07 19:00:00</td>\n",
" <td>2014-09-07 19:12:00</td>\n",
" <td>0 days 00:12:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>310 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" uid times begin end duration\n",
"56 182 51 2014-09-08 14:24:00 2014-09-09 00:24:00 0 days 10:00:00\n",
"288 68 31 2014-09-07 17:24:00 2014-09-07 23:24:00 0 days 06:00:00\n",
"23 13 30 2014-09-07 01:12:00 2014-09-07 07:00:00 0 days 05:48:00\n",
"244 414 28 2014-09-08 06:36:00 2014-09-08 12:00:00 0 days 05:24:00\n",
"57 183 27 2014-09-08 14:36:00 2014-09-08 19:48:00 0 days 05:12:00\n",
".. ... ... ... ... ...\n",
"197 367 2 2014-09-09 10:12:00 2014-09-09 10:24:00 0 days 00:12:00\n",
"198 368 2 2014-09-09 10:24:00 2014-09-09 10:36:00 0 days 00:12:00\n",
"199 369 2 2014-09-09 10:36:00 2014-09-09 10:48:00 0 days 00:12:00\n",
"201 370 2 2014-09-09 10:48:00 2014-09-09 11:00:00 0 days 00:12:00\n",
"309 98 2 2014-09-07 19:00:00 2014-09-07 19:12:00 0 days 00:12:00\n",
"\n",
"[310 rows x 5 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## This function returns the duration of events\n",
"lifes = sta.life_cicle(track_frame,sort=True)\n",
"lifes"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"### Filter by time\n",
"TIME_MIN = 0\n",
"TIME_MAX = 15\n",
"UNIT = 'h'"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"## Apply filter by time\n",
"df_filter1 = sta.time_filter(track_frame,TIME_MIN,TIME_MAX,UNIT)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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" <td>0 days 00:12:00</td>\n",
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" <tr>\n",
" <th>197</th>\n",
" <td>368</td>\n",
" <td>2</td>\n",
" <td>2014-09-09 10:24:00</td>\n",
" <td>2014-09-09 10:36:00</td>\n",
" <td>0 days 00:12:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>369</td>\n",
" <td>2</td>\n",
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" <td>2014-09-07 19:00:00</td>\n",
" <td>2014-09-07 19:12:00</td>\n",
" <td>0 days 00:12:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>309 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" uid times begin end duration\n",
"56 182 51 2014-09-08 14:24:00 2014-09-09 00:24:00 0 days 10:00:00\n",
"287 68 31 2014-09-07 17:24:00 2014-09-07 23:24:00 0 days 06:00:00\n",
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"72 205 27 2014-09-08 16:00:00 2014-09-08 21:12:00 0 days 05:12:00\n",
".. ... ... ... ... ...\n",
"196 367 2 2014-09-09 10:12:00 2014-09-09 10:24:00 0 days 00:12:00\n",
"197 368 2 2014-09-09 10:24:00 2014-09-09 10:36:00 0 days 00:12:00\n",
"198 369 2 2014-09-09 10:36:00 2014-09-09 10:48:00 0 days 00:12:00\n",
"200 370 2 2014-09-09 10:48:00 2014-09-09 11:00:00 0 days 00:12:00\n",
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"\n",
"[309 rows x 5 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sta.life_cicle(df_filter1,sort=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"fam_test = df_filter1.query('uid == 118')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>785</th>\n",
" <td>2014-09-07 23:24:00</td>\n",
" <td>117</td>\n",
" <td>118</td>\n",
" <td>5</td>\n",
" <td>-3.705177</td>\n",
" <td>-61.143642</td>\n",
" <td>56.0</td>\n",
" <td>89.0</td>\n",
" <td>233.0</td>\n",
" <td>23.955956</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-61.08965301513672 -3.723203659057...</td>\n",
" <td>LINESTRING (-61.08965301513672 -3.723203659057...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>153.0</td>\n",
" <td>1.811121</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>781</th>\n",
" <td>2014-09-07 23:12:00</td>\n",
" <td>116</td>\n",
" <td>118</td>\n",
" <td>6</td>\n",
" <td>-3.723204</td>\n",
" <td>-61.089653</td>\n",
" <td>59.0</td>\n",
" <td>88.0</td>\n",
" <td>377.0</td>\n",
" <td>28.503227</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-61.05364608764648 -3.723242998123...</td>\n",
" <td>LINESTRING (-61.05364608764648 -3.723242998123...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>144.0</td>\n",
" <td>4.547272</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp time uid id_t lat lon p0 \\\n",
"Fam_118 601 2014-09-07 19:48:00 99 118 23 -3.795664 -60.171463 110.0 \n",
" 743 2014-09-07 22:00:00 110 118 13 -3.813311 -60.801670 75.0 \n",
" 790 2014-09-07 23:36:00 118 118 5 -3.740904 -61.305729 47.0 \n",
" 785 2014-09-07 23:24:00 117 118 5 -3.705177 -61.143642 56.0 \n",
" 781 2014-09-07 23:12:00 116 118 6 -3.723204 -61.089653 59.0 \n",
"\n",
" p1 size_20 mean_ref_20 ... \\\n",
"Fam_118 601 84.0 115.0 42.540183 ... \n",
" 743 83.0 64.0 22.195404 ... \n",
" 790 87.0 80.0 22.144835 ... \n",
" 785 89.0 233.0 23.955956 ... \n",
" 781 88.0 377.0 28.503227 ... \n",
"\n",
" trajectory \\\n",
"Fam_118 601 LINESTRING (-59.7392578125 -4.065142154693604,... \n",
" 743 LINESTRING (-60.74765014648438 -3.813354253768... \n",
" 790 LINESTRING (-61.14364242553711 -3.705176830291... \n",
" 785 LINESTRING (-61.08965301513672 -3.723203659057... \n",
" 781 LINESTRING (-61.05364608764648 -3.723242998123... \n",
"\n",
" vector_20 \\\n",
"Fam_118 601 LINESTRING (-60.13545227050781 -3.795670270919... \n",
" 743 LINESTRING (-60.74765014648438 -3.813354253768... \n",
" 790 LINESTRING (-61.14364242553711 -3.705176830291... \n",
" 785 LINESTRING (-61.08965301513672 -3.723203659057... \n",
" 781 LINESTRING (-61.05364608764648 -3.723242998123... \n",
"\n",
" vector_35 vector_40 dsize_20 \\\n",
"Fam_118 601 GEOMETRYCOLLECTION EMPTY GEOMETRYCOLLECTION EMPTY NaN \n",
" 743 GEOMETRYCOLLECTION EMPTY GEOMETRYCOLLECTION EMPTY -51.0 \n",
" 790 GEOMETRYCOLLECTION EMPTY GEOMETRYCOLLECTION EMPTY 16.0 \n",
" 785 GEOMETRYCOLLECTION EMPTY GEOMETRYCOLLECTION EMPTY 153.0 \n",
" 781 GEOMETRYCOLLECTION EMPTY GEOMETRYCOLLECTION EMPTY 144.0 \n",
"\n",
" dmean_ref_20 dtotal_size_35 dmean_total_ref_35 dtotal_size_40 \\\n",
"Fam_118 601 NaN NaN NaN NaN \n",
" 743 -20.344779 NaN NaN NaN \n",
" 790 -0.050569 NaN NaN NaN \n",
" 785 1.811121 NaN NaN NaN \n",
" 781 4.547272 NaN NaN NaN \n",
"\n",
" dmean_total_ref_40 \n",
"Fam_118 601 NaN \n",
" 743 NaN \n",
" 790 NaN \n",
" 785 NaN \n",
" 781 NaN \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fam_test.head()"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"def get_cmap(n, name='hsv'):\n",
" '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct \n",
" RGB color; the keyword argument name must be a standard mpl colormap name.'''\n",
" return plt.cm.get_cmap(name, n)\n",
"\n",
"def open_ncdf(nc_file):\n",
" ## OPEN RADAR DATA COMPRESS\n",
" with gzip.open(nc_file) as gz:\n",
" with netCDF4.Dataset('dummy', mode='r', memory=gz.read()) as nc:\n",
" data = nc.variables[VAR][0][LEVEL][:].filled()\n",
" extent = [nc.variables['lon0'][:].min(),nc.variables['lon0'][:].max(),nc.variables['lat0'][:].min(),nc.variables['lat0'][:].max()]\n",
" return data,extent\n",
"\n",
"## Image\n",
"def image_spoof(self, tile): # this function pretends not to be a Python script\n",
" url = self._image_url(tile) # get the url of the street map API\n",
" req = Request(url) # start request\n",
" req.add_header('User-agent','Anaconda 3') # add user agent to request\n",
" fh = urlopen(req) \n",
" im_data = io.BytesIO(fh.read()) # get image\n",
" fh.close() # close url\n",
" img = Image.open(im_data) # open image with PIL\n",
" img = img.convert(self.desired_tile_form) # set image format\n",
" return img, self.tileextent(tile), 'lower' # reformat for cartopy\n",
"\n",
"## Scale bar\n",
"def scale_bar(ax, proj, length, location=(0.5, 0.05), linewidth=3,\n",
" units='km', m_per_unit=1000):\n",
" # find lat/lon center to find best UTM zone\n",
" x0, x1, y0, y1 = ax.get_extent(proj.as_geodetic())\n",
" # Projection in metres\n",
" utm = ccrs.UTM(utm_from_lon((x0+x1)/2))\n",
" # Get the extent of the plotted area in coordinates in metres\n",
" x0, x1, y0, y1 = ax.get_extent(utm)\n",
" # Turn the specified scalebar location into coordinates in metres\n",
" sbcx, sbcy = x0 + (x1 - x0) * location[0], y0 + (y1 - y0) * location[1]\n",
" # Generate the x coordinate for the ends of the scalebar\n",
" bar_xs = [sbcx - length * m_per_unit/2, sbcx + length * m_per_unit/2]\n",
" # buffer for scalebar\n",
" buffer = [patheffects.withStroke(linewidth=5, foreground=\"w\")]\n",
" # Plot the scalebar with buffer\n",
" ax.plot(bar_xs, [sbcy, sbcy], transform=utm, color='k',\n",
" linewidth=linewidth, path_effects=buffer)\n",
" # buffer for text\n",
" buffer = [patheffects.withStroke(linewidth=3, foreground=\"w\")]\n",
" # Plot the scalebar label\n",
" t0 = ax.text(sbcx, sbcy, str(length) + ' ' + units, transform=utm,\n",
" horizontalalignment='center', verticalalignment='bottom',\n",
" path_effects=buffer, fontsize=14, zorder=4)\n",
" left = x0+(x1-x0)*(0.9)\n",
" # Plot the N arrow\n",
" t1 = ax.text(left, sbcy, u'\\u25B2\\nN', transform=utm,\n",
" horizontalalignment='center', verticalalignment='bottom',\n",
" path_effects=buffer, fontsize=14, zorder=4)\n",
" # Plot the scalebar without buffer, in case covered by text buffer\n",
" ax.plot(bar_xs, [sbcy, sbcy], transform=utm, color='k',\n",
" linewidth=linewidth, zorder=6)\n",
"\n",
"def utm_from_lon(lon):\n",
" return floor( ( lon + 180 ) / 6) + 1 "
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"def plot(dframe,t = 0):\n",
" \n",
" ## GET TIMESTAMP LIST\n",
" time_list = [np.unique(sorted(dframe.time))[t]]\n",
" locked_by_time = dframe.query('time <= @time_list')\n",
"\n",
" geo_cols = []\n",
" list_cols = locked_by_time.columns.to_list()\n",
" tresholds = []\n",
" for c in list_cols:\n",
" if 'geom_' in c or 'line' in c or 'traj' in c or 'vect' in c:\n",
" geo_cols.append(c)\n",
" if 'geom_' in c and 'geom_i' not in c:\n",
" tresholds.append(int(c[-2:]))\n",
" \n",
" geo_cols = sorted(geo_cols)\n",
" \n",
" no_geo_cols = [item for item in list_cols if item not in geo_cols]\n",
" ### CREATE GEODATAFRAME\n",
" geo_frame = gpd.GeoDataFrame(locked_by_time[no_geo_cols])\n",
"\n",
" ### OPEN MATRIX\n",
" nc_file = locked_by_time.loc[locked_by_time['time'] == time_list[-1]]['nc_file'].unique()[0]\n",
" data,extent = open_ncdf(nc_file)\n",
" ## FILTER\n",
" data[data <= 0 ] = np.nan\n",
" \n",
" ## FIGURE\n",
" cimgt.Stamen.get_image = image_spoof # reformat web request for street map spoofing\n",
" osm_img = cimgt.Stamen('terrain-background'); # spoofed, downloaded street map\n",
" \n",
" plt.figure(figsize=(10,10),dpi=100)\n",
" ax = plt.axes(projection = osm_img.crs)\n",
" ax.set_extent(extent)\n",
" \n",
" ### BACKGROUND\n",
" zoom = 1\n",
" scale = np.ceil(-np.sqrt(2)*np.log(np.divide(zoom,350.0))) # empirical solve for scale based on zoom\n",
" scale = (scale<20) and scale or 19 # scale cannot be larger than 19\n",
" ax.add_image(osm_img, int(scale)) # add OSM with zoom specification\n",
" \n",
" color_range = ['red','C1','C2','C3','C4','C5']\n",
" \n",
" ## Geometries\n",
" for g in range(len(geo_cols)):\n",
" ## Plot Boundary\n",
" if 'geom_' in geo_cols[g] and 'geom_i' not in geo_cols[g]:\n",
" last_time = time_list[-1]\n",
" last_series = locked_by_time.query('time == @last_time')[geo_cols[g]].apply(wkt.loads)\n",
" geo_frame['geometry'] = last_series\n",
" geo_frame.exterior.plot(ax=ax,color=color_range[g],linewidth=1,label=geo_cols[g],transform = ccrs.PlateCarree())\n",
" \n",
" ## Plot Trajectory\n",
" if 'traj' in geo_cols[g]:\n",
" lock_uids = locked_by_time.query('time == @last_time').uid.values.tolist() \n",
" traject = locked_by_time.query('uid == @lock_uids')[geo_cols[g]].apply(wkt.loads)\n",
" geo_frame['geometry'] = traject\n",
" geo_frame.plot(ax=ax,color='black',label=geo_cols[g],transform = ccrs.PlateCarree())\n",
" \n",
"# # Plot vectores\n",
"# if 'vect' in geo_cols[g]:\n",
"# lock_uids = locked_by_time.query('time == @last_time').uid.values.tolist() \n",
"# traject = locked_by_time.query('uid == @lock_uids')[geo_cols[g]].apply(wkt.loads)\n",
"# geo_frame['geometry'] = traject\n",
"# geo_frame.plot(ax=ax,color='gray',label=geo_cols[g],transform = ccrs.PlateCarree())\n",
"\n",
" ### Plot INFO\n",
" buffer = [patheffects.withStroke(linewidth=3, foreground=\"w\")]\n",
" transform = ccrs.PlateCarree()._as_mpl_transform(ax)\n",
" \n",
" for i,row in locked_by_time.query('time == @last_time').iterrows():\n",
" print(row.p0,row.p1,row.uid)\n",
" ax.annotate(s=' UID \\n'+str(row.uid),\n",
" xy=(float(row.lon),float(row.lat)),\n",
" xycoords=transform, weight='bold', size=10, path_effects=buffer)\n",
" \n",
" \n",
" #### PLOT DATA\n",
" cs = ax.contourf(data, cmap = CMAP, vmin = VMIN, vmax = VMAX, extent = extent,alpha=0.9, transform = ccrs.PlateCarree())\n",
" \n",
" ### LINES LABELS\n",
" gl = ax.gridlines( draw_labels=True,\n",
" linewidth=1, color='gray', alpha=0.5, linestyle='--') # label all axes\n",
" gl.top_labels = False # turn off top label\n",
" gl.right_labels = False # turn off right label\n",
" \n",
" ## PLOT SCALE BAR\n",
" scale_bar(ax, ccrs.Mercator(), 50)\n",
" ## COLOR BAR\n",
" sm = plt.cm.ScalarMappable(cmap=CMAP,norm=plt.Normalize(VMIN,VMAX))\n",
" sm._A = []\n",
" cbar = plt.colorbar(sm,ax=ax,extend='both',pad=0.05,shrink=0.6,orientation='horizontal')\n",
" cbar.set_label('Refletividade (dBZ)')\n",
" \n",
" ## LEGEND\n",
" ax.legend()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['timestamp', 'time', 'uid', 'id_t', 'lat', 'lon', 'p0', 'p1', 'size_20',\n",
" 'mean_ref_20', 'max_ref_20', 'angle_20_orig', 'angle_20_cor',\n",
" 'vel_20_orig', 'vel_20_cor', 'mean_total_ref_35', 'total_size_35',\n",
" 'n_cluster_35', 'avg_angle_35', 'avg_vel_35', 'mean_total_ref_40',\n",
" 'total_size_40', 'n_cluster_40', 'avg_angle_40', 'avg_vel_40', 'status',\n",
" 'delta_t', 'nc_file', 'cluster_file', 'geom_intersect', 'geom_20',\n",
" 'geom_35', 'geom_40', 'trajectory', 'vector_20', 'vector_35',\n",
" 'vector_40', 'dsize_20', 'dmean_ref_20', 'dtotal_size_35',\n",
" 'dmean_total_ref_35', 'dtotal_size_40', 'dmean_total_ref_40',\n",
" 'geometry'],\n",
" dtype='object')"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_filter1.columns"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>time</th>\n",
" <th>uid</th>\n",
" <th>id_t</th>\n",
" <th>lat</th>\n",
" <th>lon</th>\n",
" <th>p0</th>\n",
" <th>p1</th>\n",
" <th>size_20</th>\n",
" <th>mean_ref_20</th>\n",
" <th>...</th>\n",
" <th>vector_20</th>\n",
" <th>vector_35</th>\n",
" <th>vector_40</th>\n",
" <th>dsize_20</th>\n",
" <th>dmean_ref_20</th>\n",
" <th>dtotal_size_35</th>\n",
" <th>dmean_total_ref_35</th>\n",
" <th>dtotal_size_40</th>\n",
" <th>dmean_total_ref_40</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Fam_10</th>\n",
" <th>41</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
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" <td>193.0</td>\n",
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" <td>30.746202</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-58.62321090698242 -3.651007175445...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-16.0</td>\n",
" <td>-8.705933</td>\n",
" <td>-12.0</td>\n",
" <td>-7.147202</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.65909 -3.74088, -58.65909 -3.740...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fam_2</th>\n",
" <th>43</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>13</td>\n",
" <td>-3.346188</td>\n",
" <td>-59.163548</td>\n",
" <td>166.0</td>\n",
" <td>109.0</td>\n",
" <td>599.0</td>\n",
" <td>27.761354</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.07354736328125 -3.364077091217...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>80.0</td>\n",
" <td>-0.120604</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.98330 -3.61551, -58.98330 -3.615...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fam_3</th>\n",
" <th>42</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" <td>-3.562072</td>\n",
" <td>-59.685394</td>\n",
" <td>137.0</td>\n",
" <td>97.0</td>\n",
" <td>125.0</td>\n",
" <td>41.594100</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.63138961791992 -3.562054157257...</td>\n",
" <td>LINESTRING (-59.64939880371094 -3.544094324111...</td>\n",
" <td>LINESTRING (-59.64939117431641 -3.562060356140...</td>\n",
" <td>-3.0</td>\n",
" <td>-0.742426</td>\n",
" <td>-17.0</td>\n",
" <td>0.312405</td>\n",
" <td>-16.0</td>\n",
" <td>0.732436</td>\n",
" <td>POLYGON ((-59.72136 -3.66988, -59.72136 -3.669...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fam_4</th>\n",
" <th>44</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>24</td>\n",
" <td>-2.752866</td>\n",
" <td>-61.286469</td>\n",
" <td>48.0</td>\n",
" <td>142.0</td>\n",
" <td>47.0</td>\n",
" <td>24.270070</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-61.14260864257812 -2.788957357406...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-73.0</td>\n",
" <td>-5.133102</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-61.25061 -2.86070, -61.25061 -2.860...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fam_6</th>\n",
" <th>46</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>28</td>\n",
" <td>-2.520052</td>\n",
" <td>-59.775608</td>\n",
" <td>132.0</td>\n",
" <td>155.0</td>\n",
" <td>101.0</td>\n",
" <td>38.155227</td>\n",
" <td>...</td>\n",
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" <td>LINESTRING (-59.72165298461914 -2.555973052978...</td>\n",
" <td>LINESTRING (-59.72165298461914 -2.555973052978...</td>\n",
" <td>-12.0</td>\n",
" <td>-3.200698</td>\n",
" <td>-20.0</td>\n",
" <td>-2.084470</td>\n",
" <td>-22.0</td>\n",
" <td>-1.507583</td>\n",
" <td>POLYGON ((-59.81156 -2.62786, -59.81156 -2.627...</td>\n",
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" <tr>\n",
" <th>Fam_7</th>\n",
" <th>47</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>30</td>\n",
" <td>-2.376302</td>\n",
" <td>-60.297089</td>\n",
" <td>103.0</td>\n",
" <td>163.0</td>\n",
" <td>152.0</td>\n",
" <td>36.072795</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-60.22516632080078 -2.376318693161...</td>\n",
" <td>LINESTRING (-60.24314117431641 -2.358348608016...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>21.0</td>\n",
" <td>-3.083527</td>\n",
" <td>-24.0</td>\n",
" <td>-0.832133</td>\n",
" <td>-13.0</td>\n",
" <td>-0.991783</td>\n",
" <td>POLYGON ((-60.31511 -2.53799, -60.31511 -2.537...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6 rows × 44 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp time uid id_t lat lon p0 \\\n",
"Fam_10 41 2014-09-07 01:00:00 5 10 10 -3.651080 -58.677219 193.0 \n",
"Fam_2 43 2014-09-07 01:00:00 5 2 13 -3.346188 -59.163548 166.0 \n",
"Fam_3 42 2014-09-07 01:00:00 5 3 12 -3.562072 -59.685394 137.0 \n",
"Fam_4 44 2014-09-07 01:00:00 5 4 24 -2.752866 -61.286469 48.0 \n",
"Fam_6 46 2014-09-07 01:00:00 5 6 28 -2.520052 -59.775608 132.0 \n",
"Fam_7 47 2014-09-07 01:00:00 5 7 30 -2.376302 -60.297089 103.0 \n",
"\n",
" p1 size_20 mean_ref_20 ... \\\n",
"Fam_10 41 92.0 56.0 30.746202 ... \n",
"Fam_2 43 109.0 599.0 27.761354 ... \n",
"Fam_3 42 97.0 125.0 41.594100 ... \n",
"Fam_4 44 142.0 47.0 24.270070 ... \n",
"Fam_6 46 155.0 101.0 38.155227 ... \n",
"Fam_7 47 163.0 152.0 36.072795 ... \n",
"\n",
" vector_20 \\\n",
"Fam_10 41 LINESTRING (-58.62321090698242 -3.651007175445... \n",
"Fam_2 43 LINESTRING (-59.07354736328125 -3.364077091217... \n",
"Fam_3 42 LINESTRING (-59.63138961791992 -3.562054157257... \n",
"Fam_4 44 LINESTRING (-61.14260864257812 -2.788957357406... \n",
"Fam_6 46 LINESTRING (-59.75762557983398 -2.520048618316... \n",
"Fam_7 47 LINESTRING (-60.22516632080078 -2.376318693161... \n",
"\n",
" vector_35 \\\n",
"Fam_10 41 GEOMETRYCOLLECTION EMPTY \n",
"Fam_2 43 GEOMETRYCOLLECTION EMPTY \n",
"Fam_3 42 LINESTRING (-59.64939880371094 -3.544094324111... \n",
"Fam_4 44 GEOMETRYCOLLECTION EMPTY \n",
"Fam_6 46 LINESTRING (-59.72165298461914 -2.555973052978... \n",
"Fam_7 47 LINESTRING (-60.24314117431641 -2.358348608016... \n",
"\n",
" vector_40 dsize_20 \\\n",
"Fam_10 41 GEOMETRYCOLLECTION EMPTY -16.0 \n",
"Fam_2 43 GEOMETRYCOLLECTION EMPTY 80.0 \n",
"Fam_3 42 LINESTRING (-59.64939117431641 -3.562060356140... -3.0 \n",
"Fam_4 44 GEOMETRYCOLLECTION EMPTY -73.0 \n",
"Fam_6 46 LINESTRING (-59.72165298461914 -2.555973052978... -12.0 \n",
"Fam_7 47 GEOMETRYCOLLECTION EMPTY 21.0 \n",
"\n",
" dmean_ref_20 dtotal_size_35 dmean_total_ref_35 dtotal_size_40 \\\n",
"Fam_10 41 -8.705933 -12.0 -7.147202 NaN \n",
"Fam_2 43 -0.120604 NaN NaN NaN \n",
"Fam_3 42 -0.742426 -17.0 0.312405 -16.0 \n",
"Fam_4 44 -5.133102 NaN NaN NaN \n",
"Fam_6 46 -3.200698 -20.0 -2.084470 -22.0 \n",
"Fam_7 47 -3.083527 -24.0 -0.832133 -13.0 \n",
"\n",
" dmean_total_ref_40 \\\n",
"Fam_10 41 NaN \n",
"Fam_2 43 NaN \n",
"Fam_3 42 0.732436 \n",
"Fam_4 44 NaN \n",
"Fam_6 46 -1.507583 \n",
"Fam_7 47 -0.991783 \n",
"\n",
" geometry \n",
"Fam_10 41 POLYGON ((-58.65909 -3.74088, -58.65909 -3.740... \n",
"Fam_2 43 POLYGON ((-58.98330 -3.61551, -58.98330 -3.615... \n",
"Fam_3 42 POLYGON ((-59.72136 -3.66988, -59.72136 -3.669... \n",
"Fam_4 44 POLYGON ((-61.25061 -2.86070, -61.25061 -2.860... \n",
"Fam_6 46 POLYGON ((-59.81156 -2.62786, -59.81156 -2.627... \n",
"Fam_7 47 POLYGON ((-60.31511 -2.53799, -60.31511 -2.537... \n",
"\n",
"[6 rows x 44 columns]"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_filter1.query('time == 5')"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"193.0 92.0 10\n",
"166.0 109.0 2\n",
"137.0 97.0 3\n",
"48.0 142.0 4\n",
"132.0 155.0 6\n",
"103.0 163.0 7\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/helvecioneto/miniconda3/envs/stanalyzer/lib/python3.7/site-packages/ipykernel_launcher.py:75: MatplotlibDeprecationWarning:\n",
"\n",
"The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
"\n",
"/home/helvecioneto/miniconda3/envs/stanalyzer/lib/python3.7/site-packages/cartopy/mpl/geoaxes.py:762: UserWarning:\n",
"\n",
"Approximating coordinate system <cartopy._crs.Geodetic object at 0x7f12194360b0> with the PlateCarree projection.\n",
"\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1000x1000 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot(df_filter1,5)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [],
"source": [
"import plotly.express as px\n",
"import plotly.graph_objects as go"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [],
"source": [
"geo_cols = []\n",
"for c in df_filter1.columns:\n",
" if 'geom_' in c or 'line' in c or 'traj' in c or 'vect' in c:\n",
" geo_cols.append(c)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [],
"source": [
"no_geo_cols = [item for item in df_filter1.columns if item not in geo_cols]"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [],
"source": [
"geo_df = gpd.GeoDataFrame(df_filter1)\n",
"geo_df['geometry'] = df_filter1['geom_20'].apply(wkt.loads)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>time</th>\n",
" <th>uid</th>\n",
" <th>id_t</th>\n",
" <th>lat</th>\n",
" <th>lon</th>\n",
" <th>p0</th>\n",
" <th>p1</th>\n",
" <th>size_20</th>\n",
" <th>mean_ref_20</th>\n",
" <th>...</th>\n",
" <th>vector_20</th>\n",
" <th>vector_35</th>\n",
" <th>vector_40</th>\n",
" <th>dsize_20</th>\n",
" <th>dmean_ref_20</th>\n",
" <th>dtotal_size_35</th>\n",
" <th>dmean_total_ref_35</th>\n",
" <th>dtotal_size_40</th>\n",
" <th>dmean_total_ref_40</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Fam_0</th>\n",
" <th>0</th>\n",
" <td>2014-09-07 00:00:00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>-3.866008</td>\n",
" <td>-58.262753</td>\n",
" <td>216.0</td>\n",
" <td>80.0</td>\n",
" <td>66.0</td>\n",
" <td>35.540233</td>\n",
" <td>...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.37060 -3.97398, -58.37060 -3.973...</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>2014-09-07 00:12:00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>-3.884098</td>\n",
" <td>-58.334747</td>\n",
" <td>212.0</td>\n",
" <td>79.0</td>\n",
" <td>34.0</td>\n",
" <td>26.850009</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-58.26275253295898 -3.866008281707...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-32.0</td>\n",
" <td>-8.690224</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.28066 -3.91993, -58.28066 -3.919...</td>\n",
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" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Fam_1</th>\n",
" <th>1</th>\n",
" <td>2014-09-07 00:00:00</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>-3.759429</td>\n",
" <td>-59.199203</td>\n",
" <td>164.0</td>\n",
" <td>86.0</td>\n",
" <td>75.0</td>\n",
" <td>39.058735</td>\n",
" <td>...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-59.25313 -3.86727, -59.25313 -3.867...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2014-09-07 00:12:00</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-3.795402</td>\n",
" <td>-59.253189</td>\n",
" <td>161.0</td>\n",
" <td>84.0</td>\n",
" <td>41.0</td>\n",
" <td>37.308171</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.19920349121094 -3.759428739547...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-34.0</td>\n",
" <td>-1.750565</td>\n",
" <td>-9.0</td>\n",
" <td>-2.209392</td>\n",
" <td>-3.0</td>\n",
" <td>-3.066082</td>\n",
" <td>POLYGON ((-59.30714 -3.88527, -59.30714 -3.885...</td>\n",
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" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">Fam_10</th>\n",
" <th>20</th>\n",
" <td>2014-09-07 00:24:00</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>8</td>\n",
" <td>-3.668817</td>\n",
" <td>-58.515167</td>\n",
" <td>202.0</td>\n",
" <td>91.0</td>\n",
" <td>82.0</td>\n",
" <td>38.440909</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-58.40708923339844 -3.704577922821...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.55131 -3.57905, -58.51531 -3.579...</td>\n",
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" <tr>\n",
" <th>28</th>\n",
" <td>2014-09-07 00:36:00</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>-3.668921</td>\n",
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" <td>198.0</td>\n",
" <td>91.0</td>\n",
" <td>93.0</td>\n",
" <td>39.537189</td>\n",
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" <td>LINESTRING (-58.51516723632812 -3.668816804885...</td>\n",
" <td>LINESTRING (-58.47916030883789 -3.668762445449...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>11.0</td>\n",
" <td>1.096280</td>\n",
" <td>-3.0</td>\n",
" <td>2.149453</td>\n",
" <td>5.0</td>\n",
" <td>1.177330</td>\n",
" <td>POLYGON ((-58.56901 -3.77668, -58.56901 -3.776...</td>\n",
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" <tr>\n",
" <th>35</th>\n",
" <td>2014-09-07 00:48:00</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>8</td>\n",
" <td>-3.651007</td>\n",
" <td>-58.623211</td>\n",
" <td>196.0</td>\n",
" <td>92.0</td>\n",
" <td>72.0</td>\n",
" <td>39.452136</td>\n",
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" <td>LINESTRING (-58.58717727661133 -3.668921470642...</td>\n",
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" <td>-21.0</td>\n",
" <td>-0.085053</td>\n",
" <td>-12.0</td>\n",
" <td>1.007357</td>\n",
" <td>-8.0</td>\n",
" <td>0.990272</td>\n",
" <td>POLYGON ((-58.67707 -3.75887, -58.67707 -3.758...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>-3.651080</td>\n",
" <td>-58.677219</td>\n",
" <td>193.0</td>\n",
" <td>92.0</td>\n",
" <td>56.0</td>\n",
" <td>30.746202</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-58.62321090698242 -3.651007175445...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-16.0</td>\n",
" <td>-8.705933</td>\n",
" <td>-12.0</td>\n",
" <td>-7.147202</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.65909 -3.74088, -58.65909 -3.740...</td>\n",
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" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">Fam_2</th>\n",
" <th>2</th>\n",
" <td>2014-09-07 00:00:00</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>-3.489554</td>\n",
" <td>-58.803436</td>\n",
" <td>186.0</td>\n",
" <td>101.0</td>\n",
" <td>422.0</td>\n",
" <td>38.215296</td>\n",
" <td>...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.98303 -3.86702, -58.98303 -3.867...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2014-09-07 00:12:00</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>-3.417735</td>\n",
" <td>-58.839520</td>\n",
" <td>184.0</td>\n",
" <td>105.0</td>\n",
" <td>396.0</td>\n",
" <td>38.074262</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-58.80343627929688 -3.489553928375...</td>\n",
" <td>LINESTRING (-58.73136901855469 -3.543361186981...</td>\n",
" <td>LINESTRING (-58.80339431762695 -3.525484085083...</td>\n",
" <td>-26.0</td>\n",
" <td>-0.141034</td>\n",
" <td>-27.0</td>\n",
" <td>0.521226</td>\n",
" <td>-18.0</td>\n",
" <td>1.282434</td>\n",
" <td>POLYGON ((-58.87525 -3.65132, -58.87525 -3.651...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2014-09-07 00:24:00</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>-3.471631</td>\n",
" <td>-58.839458</td>\n",
" <td>184.0</td>\n",
" <td>102.0</td>\n",
" <td>273.0</td>\n",
" <td>37.208581</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-58.83951950073242 -3.417735338211...</td>\n",
" <td>LINESTRING (-58.76741409301758 -3.507475852966...</td>\n",
" <td>LINESTRING (-58.76739120483398 -3.525440931320...</td>\n",
" <td>-123.0</td>\n",
" <td>-0.865681</td>\n",
" <td>-53.0</td>\n",
" <td>0.716820</td>\n",
" <td>-5.0</td>\n",
" <td>-1.379247</td>\n",
" <td>POLYGON ((-58.73111 -3.74097, -58.73111 -3.740...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2014-09-07 00:36:00</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>-3.435780</td>\n",
" <td>-58.911495</td>\n",
" <td>180.0</td>\n",
" <td>104.0</td>\n",
" <td>262.0</td>\n",
" <td>32.964602</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-58.83945846557617 -3.471630811691...</td>\n",
" <td>LINESTRING (-58.83941650390625 -3.507560968399...</td>\n",
" <td>LINESTRING (-58.82145690917969 -3.471610069274...</td>\n",
" <td>-11.0</td>\n",
" <td>-4.243979</td>\n",
" <td>-17.0</td>\n",
" <td>-3.287830</td>\n",
" <td>-20.0</td>\n",
" <td>-2.113137</td>\n",
" <td>POLYGON ((-59.03736 -3.56166, -59.03736 -3.561...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>2014-09-07 00:48:00</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>-3.364077</td>\n",
" <td>-59.073547</td>\n",
" <td>171.0</td>\n",
" <td>108.0</td>\n",
" <td>519.0</td>\n",
" <td>27.881958</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-58.91149520874023 -3.435779809951...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>257.0</td>\n",
" <td>-5.082643</td>\n",
" <td>-27.0</td>\n",
" <td>-5.080233</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.78516 -3.70511, -58.78516 -3.705...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>13</td>\n",
" <td>-3.346188</td>\n",
" <td>-59.163548</td>\n",
" <td>166.0</td>\n",
" <td>109.0</td>\n",
" <td>599.0</td>\n",
" <td>27.761354</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.07354736328125 -3.364077091217...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>80.0</td>\n",
" <td>-0.120604</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.98330 -3.61551, -58.98330 -3.615...</td>\n",
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" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">Fam_3</th>\n",
" <th>3</th>\n",
" <td>2014-09-07 00:00:00</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>-3.454065</td>\n",
" <td>-59.271454</td>\n",
" <td>160.0</td>\n",
" <td>103.0</td>\n",
" <td>281.0</td>\n",
" <td>38.943956</td>\n",
" <td>...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-59.19935 -3.57977, -59.19935 -3.579...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2014-09-07 00:12:00</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>-3.561943</td>\n",
" <td>-59.397377</td>\n",
" <td>153.0</td>\n",
" <td>97.0</td>\n",
" <td>160.0</td>\n",
" <td>39.939325</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.27145385742188 -3.454065084457...</td>\n",
" <td>LINESTRING (-59.34335327148438 -3.597841024398...</td>\n",
" <td>LINESTRING (-59.34333801269531 -3.615806818008...</td>\n",
" <td>-121.0</td>\n",
" <td>0.995369</td>\n",
" <td>-2.0</td>\n",
" <td>-1.216769</td>\n",
" <td>5.0</td>\n",
" <td>-1.881857</td>\n",
" <td>POLYGON ((-59.39727 -3.74160, -59.39727 -3.741...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2014-09-07 00:24:00</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>-3.579958</td>\n",
" <td>-59.487373</td>\n",
" <td>148.0</td>\n",
" <td>96.0</td>\n",
" <td>145.0</td>\n",
" <td>43.008852</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.39737701416016 -3.561942577362...</td>\n",
" <td>LINESTRING (-59.43336868286133 -3.579929351806...</td>\n",
" <td>LINESTRING (-59.41534805297852 -3.615851163864...</td>\n",
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" <td>3.069527</td>\n",
" <td>1.0</td>\n",
" <td>2.612514</td>\n",
" <td>4.0</td>\n",
" <td>2.505984</td>\n",
" <td>POLYGON ((-59.50529 -3.74166, -59.50529 -3.741...</td>\n",
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" <tr>\n",
" <th>29</th>\n",
" <td>2014-09-07 00:36:00</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>-3.562026</td>\n",
" <td>-59.559387</td>\n",
" <td>144.0</td>\n",
" <td>97.0</td>\n",
" <td>137.0</td>\n",
" <td>42.024722</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.48737335205078 -3.579958200454...</td>\n",
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" <td>-8.0</td>\n",
" <td>-0.984130</td>\n",
" <td>-7.0</td>\n",
" <td>-0.720139</td>\n",
" <td>-7.0</td>\n",
" <td>-0.536718</td>\n",
" <td>POLYGON ((-59.57742 -3.49017, -59.54142 -3.490...</td>\n",
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" <tr>\n",
" <th>36</th>\n",
" <td>2014-09-07 00:48:00</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>-3.562054</td>\n",
" <td>-59.631390</td>\n",
" <td>140.0</td>\n",
" <td>97.0</td>\n",
" <td>128.0</td>\n",
" <td>42.336526</td>\n",
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" <td>0.311804</td>\n",
" <td>8.0</td>\n",
" <td>-0.507579</td>\n",
" <td>10.0</td>\n",
" <td>-0.945654</td>\n",
" <td>POLYGON ((-59.64933 -3.72376, -59.64933 -3.723...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" <td>-3.562072</td>\n",
" <td>-59.685394</td>\n",
" <td>137.0</td>\n",
" <td>97.0</td>\n",
" <td>125.0</td>\n",
" <td>41.594100</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.63138961791992 -3.562054157257...</td>\n",
" <td>LINESTRING (-59.64939880371094 -3.544094324111...</td>\n",
" <td>LINESTRING (-59.64939117431641 -3.562060356140...</td>\n",
" <td>-3.0</td>\n",
" <td>-0.742426</td>\n",
" <td>-17.0</td>\n",
" <td>0.312405</td>\n",
" <td>-16.0</td>\n",
" <td>0.732436</td>\n",
" <td>POLYGON ((-59.72136 -3.66988, -59.72136 -3.669...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">Fam_4</th>\n",
" <th>4</th>\n",
" <td>2014-09-07 00:00:00</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>25</td>\n",
" <td>-2.860910</td>\n",
" <td>-61.052738</td>\n",
" <td>61.0</td>\n",
" <td>136.0</td>\n",
" <td>94.0</td>\n",
" <td>33.522816</td>\n",
" <td>...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-61.12475 -2.91473, -61.12475 -2.914...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2014-09-07 00:12:00</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>-2.752928</td>\n",
" <td>-61.232506</td>\n",
" <td>51.0</td>\n",
" <td>142.0</td>\n",
" <td>87.0</td>\n",
" <td>30.066929</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-61.05273818969727 -2.860909938812...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-7.0</td>\n",
" <td>-3.455887</td>\n",
" <td>-6.0</td>\n",
" <td>-4.177263</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-61.19667 -2.87872, -61.19667 -2.878...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2014-09-07 00:24:00</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>23</td>\n",
" <td>-2.788729</td>\n",
" <td>-61.340473</td>\n",
" <td>45.0</td>\n",
" <td>140.0</td>\n",
" <td>44.0</td>\n",
" <td>22.569323</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-61.23250579833984 -2.752928495407...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-43.0</td>\n",
" <td>-7.497606</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-61.32259 -2.87858, -61.32259 -2.878...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>2014-09-07 00:36:00</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>14</td>\n",
" <td>-2.770973</td>\n",
" <td>-61.160580</td>\n",
" <td>55.0</td>\n",
" <td>141.0</td>\n",
" <td>250.0</td>\n",
" <td>38.682485</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-61.34047317504883 -2.788729429244...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>206.0</td>\n",
" <td>16.113162</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-61.17872 -2.91467, -61.17872 -2.914...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>2014-09-07 00:48:00</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>-2.788957</td>\n",
" <td>-61.142609</td>\n",
" <td>56.0</td>\n",
" <td>140.0</td>\n",
" <td>120.0</td>\n",
" <td>29.403172</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-61.16057968139648 -2.770972967147...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-130.0</td>\n",
" <td>-9.279313</td>\n",
" <td>-72.0</td>\n",
" <td>-5.012903</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-60.98083 -2.91487, -60.98083 -2.914...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>24</td>\n",
" <td>-2.752866</td>\n",
" <td>-61.286469</td>\n",
" <td>48.0</td>\n",
" <td>142.0</td>\n",
" <td>47.0</td>\n",
" <td>24.270070</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-61.14260864257812 -2.788957357406...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-73.0</td>\n",
" <td>-5.133102</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-61.25061 -2.86070, -61.25061 -2.860...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Fam_5</th>\n",
" <th>5</th>\n",
" <td>2014-09-07 00:00:00</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>26</td>\n",
" <td>-2.519303</td>\n",
" <td>-61.304192</td>\n",
" <td>47.0</td>\n",
" <td>155.0</td>\n",
" <td>83.0</td>\n",
" <td>30.758016</td>\n",
" <td>...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-61.26828 -2.57324, -61.28627 -2.573...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2014-09-07 00:12:00</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>23</td>\n",
" <td>-2.519194</td>\n",
" <td>-61.394108</td>\n",
" <td>42.0</td>\n",
" <td>155.0</td>\n",
" <td>89.0</td>\n",
" <td>34.124644</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-61.30419158935547 -2.519302606582...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>6.0</td>\n",
" <td>3.366628</td>\n",
" <td>10.0</td>\n",
" <td>2.121668</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-61.44835 -2.75266, -61.44835 -2.752...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2014-09-07 00:24:00</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>25</td>\n",
" <td>-2.555145</td>\n",
" <td>-61.376167</td>\n",
" <td>43.0</td>\n",
" <td>153.0</td>\n",
" <td>59.0</td>\n",
" <td>33.560090</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-61.39410781860352 -2.519193649291...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-30.0</td>\n",
" <td>-0.564554</td>\n",
" <td>-7.0</td>\n",
" <td>0.252037</td>\n",
" <td>-4.0</td>\n",
" <td>1.615381</td>\n",
" <td>POLYGON ((-61.30435 -2.66302, -61.30435 -2.663...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">Fam_6</th>\n",
" <th>6</th>\n",
" <td>2014-09-07 00:00:00</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>27</td>\n",
" <td>-2.573832</td>\n",
" <td>-59.433899</td>\n",
" <td>151.0</td>\n",
" <td>152.0</td>\n",
" <td>44.0</td>\n",
" <td>32.095492</td>\n",
" <td>...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-59.41587 -2.66365, -59.41587 -2.663...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2014-09-07 00:12:00</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>24</td>\n",
" <td>-2.519990</td>\n",
" <td>-59.559811</td>\n",
" <td>144.0</td>\n",
" <td>155.0</td>\n",
" <td>45.0</td>\n",
" <td>30.603859</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.43389892578125 -2.573832273483...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-165.0</td>\n",
" <td>-13.149960</td>\n",
" <td>-82.0</td>\n",
" <td>-10.480783</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-59.54180 -2.59185, -59.54180 -2.591...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>28</td>\n",
" <td>-2.520052</td>\n",
" <td>-59.775608</td>\n",
" <td>132.0</td>\n",
" <td>155.0</td>\n",
" <td>101.0</td>\n",
" <td>38.155227</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.75762557983398 -2.520048618316...</td>\n",
" <td>LINESTRING (-59.72165298461914 -2.555973052978...</td>\n",
" <td>LINESTRING (-59.72165298461914 -2.555973052978...</td>\n",
" <td>-12.0</td>\n",
" <td>-3.200698</td>\n",
" <td>-20.0</td>\n",
" <td>-2.084470</td>\n",
" <td>-22.0</td>\n",
" <td>-1.507583</td>\n",
" <td>POLYGON ((-59.81156 -2.62786, -59.81156 -2.627...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>2014-09-07 00:48:00</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>21</td>\n",
" <td>-2.520049</td>\n",
" <td>-59.757626</td>\n",
" <td>133.0</td>\n",
" <td>155.0</td>\n",
" <td>70.0</td>\n",
" <td>39.969711</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.64971542358398 -2.555953264236...</td>\n",
" <td>LINESTRING (-59.66770553588867 -2.537992477416...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-31.0</td>\n",
" <td>1.814485</td>\n",
" <td>-11.0</td>\n",
" <td>1.658335</td>\n",
" <td>-2.0</td>\n",
" <td>1.418080</td>\n",
" <td>POLYGON ((-59.66768 -2.64579, -59.66768 -2.645...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>2014-09-07 00:36:00</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>-2.555953</td>\n",
" <td>-59.649715</td>\n",
" <td>139.0</td>\n",
" <td>153.0</td>\n",
" <td>48.0</td>\n",
" <td>37.200725</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.57778167724609 -2.555928945541...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-22.0</td>\n",
" <td>-2.768986</td>\n",
" <td>-14.0</td>\n",
" <td>-1.784024</td>\n",
" <td>-9.0</td>\n",
" <td>-1.726475</td>\n",
" <td>POLYGON ((-59.63170 -2.66374, -59.63170 -2.663...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2014-09-07 00:24:00</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>26</td>\n",
" <td>-2.555929</td>\n",
" <td>-59.577782</td>\n",
" <td>143.0</td>\n",
" <td>153.0</td>\n",
" <td>40.0</td>\n",
" <td>36.061215</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.55981063842773 -2.519989967346...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-8.0</td>\n",
" <td>-1.139510</td>\n",
" <td>-5.0</td>\n",
" <td>-0.446076</td>\n",
" <td>-1.0</td>\n",
" <td>-1.474533</td>\n",
" <td>POLYGON ((-59.50583 -2.59183, -59.50583 -2.591...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">Fam_7</th>\n",
" <th>7</th>\n",
" <td>2014-09-07 00:00:00</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>28</td>\n",
" <td>-2.430241</td>\n",
" <td>-59.955444</td>\n",
" <td>122.0</td>\n",
" <td>160.0</td>\n",
" <td>87.0</td>\n",
" <td>43.297086</td>\n",
" <td>...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-59.95544 -2.53804, -59.95544 -2.538...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2014-09-07 00:12:00</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>25</td>\n",
" <td>-2.412275</td>\n",
" <td>-60.009392</td>\n",
" <td>119.0</td>\n",
" <td>161.0</td>\n",
" <td>87.0</td>\n",
" <td>42.292275</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-59.9554443359375 -2.4302411079406...</td>\n",
" <td>LINESTRING (-59.9554443359375 -2.4302411079406...</td>\n",
" <td>LINESTRING (-59.9554443359375 -2.4302411079406...</td>\n",
" <td>0.0</td>\n",
" <td>-1.004810</td>\n",
" <td>-7.0</td>\n",
" <td>-0.350795</td>\n",
" <td>5.0</td>\n",
" <td>-1.587520</td>\n",
" <td>POLYGON ((-60.00939 -2.52007, -60.00939 -2.520...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2014-09-07 00:24:00</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>28</td>\n",
" <td>-2.394305</td>\n",
" <td>-60.081318</td>\n",
" <td>115.0</td>\n",
" <td>162.0</td>\n",
" <td>93.0</td>\n",
" <td>42.694945</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-60.00939178466797 -2.412275075912...</td>\n",
" <td>LINESTRING (-60.02737426757812 -2.412274837493...</td>\n",
" <td>LINESTRING (-60.02737426757812 -2.412274837493...</td>\n",
" <td>6.0</td>\n",
" <td>0.402670</td>\n",
" <td>6.0</td>\n",
" <td>0.127497</td>\n",
" <td>7.0</td>\n",
" <td>-0.087328</td>\n",
" <td>POLYGON ((-60.11729 -2.50210, -60.11729 -2.502...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>2014-09-07 00:36:00</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>21</td>\n",
" <td>-2.394297</td>\n",
" <td>-60.153244</td>\n",
" <td>111.0</td>\n",
" <td>162.0</td>\n",
" <td>118.0</td>\n",
" <td>41.809354</td>\n",
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" <td>LINESTRING (-60.08131790161133 -2.394305467605...</td>\n",
" <td>LINESTRING (-60.08131790161133 -2.394305467605...</td>\n",
" <td>LINESTRING (-60.09929656982422 -2.394303798675...</td>\n",
" <td>25.0</td>\n",
" <td>-0.885592</td>\n",
" <td>6.0</td>\n",
" <td>-0.372081</td>\n",
" <td>-5.0</td>\n",
" <td>0.526667</td>\n",
" <td>POLYGON ((-60.17124 -2.52006, -60.17124 -2.520...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>2014-09-07 00:48:00</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>24</td>\n",
" <td>-2.376319</td>\n",
" <td>-60.225166</td>\n",
" <td>107.0</td>\n",
" <td>163.0</td>\n",
" <td>131.0</td>\n",
" <td>39.156322</td>\n",
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" <td>LINESTRING (-60.17122268676758 -2.376328468322...</td>\n",
" <td>13.0</td>\n",
" <td>-2.653031</td>\n",
" <td>7.0</td>\n",
" <td>-2.897415</td>\n",
" <td>-4.0</td>\n",
" <td>-2.370610</td>\n",
" <td>POLYGON ((-60.20721 -2.52005, -60.20721 -2.520...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>2014-09-07 01:00:00</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>30</td>\n",
" <td>-2.376302</td>\n",
" <td>-60.297089</td>\n",
" <td>103.0</td>\n",
" <td>163.0</td>\n",
" <td>152.0</td>\n",
" <td>36.072795</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-60.22516632080078 -2.376318693161...</td>\n",
" <td>LINESTRING (-60.24314117431641 -2.358348608016...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>21.0</td>\n",
" <td>-3.083527</td>\n",
" <td>-24.0</td>\n",
" <td>-0.832133</td>\n",
" <td>-13.0</td>\n",
" <td>-0.991783</td>\n",
" <td>POLYGON ((-60.31511 -2.53799, -60.31511 -2.537...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Fam_8</th>\n",
" <th>8</th>\n",
" <td>2014-09-07 00:00:00</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>29</td>\n",
" <td>-2.411828</td>\n",
" <td>-58.984432</td>\n",
" <td>176.0</td>\n",
" <td>161.0</td>\n",
" <td>33.0</td>\n",
" <td>31.938102</td>\n",
" <td>...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.98436 -2.50165, -58.98436 -2.501...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2014-09-07 00:12:00</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>26</td>\n",
" <td>-2.411918</td>\n",
" <td>-59.092323</td>\n",
" <td>170.0</td>\n",
" <td>161.0</td>\n",
" <td>62.0</td>\n",
" <td>31.605318</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-58.98443222045898 -2.411827564239...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>29.0</td>\n",
" <td>-0.332785</td>\n",
" <td>3.0</td>\n",
" <td>1.891906</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-59.03829 -2.51967, -59.03829 -2.519...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Fam_9</th>\n",
" <th>9</th>\n",
" <td>2014-09-07 00:00:00</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>33</td>\n",
" <td>-2.214208</td>\n",
" <td>-58.984589</td>\n",
" <td>176.0</td>\n",
" <td>172.0</td>\n",
" <td>152.0</td>\n",
" <td>29.157512</td>\n",
" <td>...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.89465 -2.26802, -58.89465 -2.268...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2014-09-07 00:12:00</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>32</td>\n",
" <td>-2.196195</td>\n",
" <td>-58.930668</td>\n",
" <td>179.0</td>\n",
" <td>173.0</td>\n",
" <td>32.0</td>\n",
" <td>33.655651</td>\n",
" <td>...</td>\n",
" <td>LINESTRING (-58.98458862304688 -2.214208364486...</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>GEOMETRYCOLLECTION EMPTY</td>\n",
" <td>-120.0</td>\n",
" <td>4.498138</td>\n",
" <td>-1.0</td>\n",
" <td>4.502790</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>POLYGON ((-58.89465 -2.26802, -58.89465 -2.268...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>45 rows × 44 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp time uid id_t lat lon p0 \\\n",
"Fam_0 0 2014-09-07 00:00:00 0 0 2 -3.866008 -58.262753 216.0 \n",
" 10 2014-09-07 00:12:00 1 0 3 -3.884098 -58.334747 212.0 \n",
"Fam_1 1 2014-09-07 00:00:00 0 1 5 -3.759429 -59.199203 164.0 \n",
" 11 2014-09-07 00:12:00 1 1 4 -3.795402 -59.253189 161.0 \n",
"Fam_10 20 2014-09-07 00:24:00 2 10 8 -3.668817 -58.515167 202.0 \n",
" 28 2014-09-07 00:36:00 3 10 5 -3.668921 -58.587177 198.0 \n",
" 35 2014-09-07 00:48:00 4 10 8 -3.651007 -58.623211 196.0 \n",
" 41 2014-09-07 01:00:00 5 10 10 -3.651080 -58.677219 193.0 \n",
"Fam_2 2 2014-09-07 00:00:00 0 2 6 -3.489554 -58.803436 186.0 \n",
" 13 2014-09-07 00:12:00 1 2 10 -3.417735 -58.839520 184.0 \n",
" 22 2014-09-07 00:24:00 2 2 10 -3.471631 -58.839458 184.0 \n",
" 30 2014-09-07 00:36:00 3 2 8 -3.435780 -58.911495 180.0 \n",
" 37 2014-09-07 00:48:00 4 2 11 -3.364077 -59.073547 171.0 \n",
" 43 2014-09-07 01:00:00 5 2 13 -3.346188 -59.163548 166.0 \n",
"Fam_3 3 2014-09-07 00:00:00 0 3 8 -3.454065 -59.271454 160.0 \n",
" 12 2014-09-07 00:12:00 1 3 7 -3.561943 -59.397377 153.0 \n",
" 21 2014-09-07 00:24:00 2 3 9 -3.579958 -59.487373 148.0 \n",
" 29 2014-09-07 00:36:00 3 3 6 -3.562026 -59.559387 144.0 \n",
" 36 2014-09-07 00:48:00 4 3 10 -3.562054 -59.631390 140.0 \n",
" 42 2014-09-07 01:00:00 5 3 12 -3.562072 -59.685394 137.0 \n",
"Fam_4 4 2014-09-07 00:00:00 0 4 25 -2.860910 -61.052738 61.0 \n",
" 14 2014-09-07 00:12:00 1 4 20 -2.752928 -61.232506 51.0 \n",
" 24 2014-09-07 00:24:00 2 4 23 -2.788729 -61.340473 45.0 \n",
" 32 2014-09-07 00:36:00 3 4 14 -2.770973 -61.160580 55.0 \n",
" 38 2014-09-07 00:48:00 4 4 17 -2.788957 -61.142609 56.0 \n",
" 44 2014-09-07 01:00:00 5 4 24 -2.752866 -61.286469 48.0 \n",
"Fam_5 5 2014-09-07 00:00:00 0 5 26 -2.519303 -61.304192 47.0 \n",
" 15 2014-09-07 00:12:00 1 5 23 -2.519194 -61.394108 42.0 \n",
" 25 2014-09-07 00:24:00 2 5 25 -2.555145 -61.376167 43.0 \n",
"Fam_6 6 2014-09-07 00:00:00 0 6 27 -2.573832 -59.433899 151.0 \n",
" 16 2014-09-07 00:12:00 1 6 24 -2.519990 -59.559811 144.0 \n",
" 46 2014-09-07 01:00:00 5 6 28 -2.520052 -59.775608 132.0 \n",
" 39 2014-09-07 00:48:00 4 6 21 -2.520049 -59.757626 133.0 \n",
" 33 2014-09-07 00:36:00 3 6 18 -2.555953 -59.649715 139.0 \n",
" 26 2014-09-07 00:24:00 2 6 26 -2.555929 -59.577782 143.0 \n",
"Fam_7 7 2014-09-07 00:00:00 0 7 28 -2.430241 -59.955444 122.0 \n",
" 17 2014-09-07 00:12:00 1 7 25 -2.412275 -60.009392 119.0 \n",
" 27 2014-09-07 00:24:00 2 7 28 -2.394305 -60.081318 115.0 \n",
" 34 2014-09-07 00:36:00 3 7 21 -2.394297 -60.153244 111.0 \n",
" 40 2014-09-07 00:48:00 4 7 24 -2.376319 -60.225166 107.0 \n",
" 47 2014-09-07 01:00:00 5 7 30 -2.376302 -60.297089 103.0 \n",
"Fam_8 8 2014-09-07 00:00:00 0 8 29 -2.411828 -58.984432 176.0 \n",
" 18 2014-09-07 00:12:00 1 8 26 -2.411918 -59.092323 170.0 \n",
"Fam_9 9 2014-09-07 00:00:00 0 9 33 -2.214208 -58.984589 176.0 \n",
" 19 2014-09-07 00:12:00 1 9 32 -2.196195 -58.930668 179.0 \n",
"\n",
" p1 size_20 mean_ref_20 ... \\\n",
"Fam_0 0 80.0 66.0 35.540233 ... \n",
" 10 79.0 34.0 26.850009 ... \n",
"Fam_1 1 86.0 75.0 39.058735 ... \n",
" 11 84.0 41.0 37.308171 ... \n",
"Fam_10 20 91.0 82.0 38.440909 ... \n",
" 28 91.0 93.0 39.537189 ... \n",
" 35 92.0 72.0 39.452136 ... \n",
" 41 92.0 56.0 30.746202 ... \n",
"Fam_2 2 101.0 422.0 38.215296 ... \n",
" 13 105.0 396.0 38.074262 ... \n",
" 22 102.0 273.0 37.208581 ... \n",
" 30 104.0 262.0 32.964602 ... \n",
" 37 108.0 519.0 27.881958 ... \n",
" 43 109.0 599.0 27.761354 ... \n",
"Fam_3 3 103.0 281.0 38.943956 ... \n",
" 12 97.0 160.0 39.939325 ... \n",
" 21 96.0 145.0 43.008852 ... \n",
" 29 97.0 137.0 42.024722 ... \n",
" 36 97.0 128.0 42.336526 ... \n",
" 42 97.0 125.0 41.594100 ... \n",
"Fam_4 4 136.0 94.0 33.522816 ... \n",
" 14 142.0 87.0 30.066929 ... \n",
" 24 140.0 44.0 22.569323 ... \n",
" 32 141.0 250.0 38.682485 ... \n",
" 38 140.0 120.0 29.403172 ... \n",
" 44 142.0 47.0 24.270070 ... \n",
"Fam_5 5 155.0 83.0 30.758016 ... \n",
" 15 155.0 89.0 34.124644 ... \n",
" 25 153.0 59.0 33.560090 ... \n",
"Fam_6 6 152.0 44.0 32.095492 ... \n",
" 16 155.0 45.0 30.603859 ... \n",
" 46 155.0 101.0 38.155227 ... \n",
" 39 155.0 70.0 39.969711 ... \n",
" 33 153.0 48.0 37.200725 ... \n",
" 26 153.0 40.0 36.061215 ... \n",
"Fam_7 7 160.0 87.0 43.297086 ... \n",
" 17 161.0 87.0 42.292275 ... \n",
" 27 162.0 93.0 42.694945 ... \n",
" 34 162.0 118.0 41.809354 ... \n",
" 40 163.0 131.0 39.156322 ... \n",
" 47 163.0 152.0 36.072795 ... \n",
"Fam_8 8 161.0 33.0 31.938102 ... \n",
" 18 161.0 62.0 31.605318 ... \n",
"Fam_9 9 172.0 152.0 29.157512 ... \n",
" 19 173.0 32.0 33.655651 ... \n",
"\n",
" vector_20 \\\n",
"Fam_0 0 GEOMETRYCOLLECTION EMPTY \n",
" 10 LINESTRING (-58.26275253295898 -3.866008281707... \n",
"Fam_1 1 GEOMETRYCOLLECTION EMPTY \n",
" 11 LINESTRING (-59.19920349121094 -3.759428739547... \n",
"Fam_10 20 LINESTRING (-58.40708923339844 -3.704577922821... \n",
" 28 LINESTRING (-58.51516723632812 -3.668816804885... \n",
" 35 LINESTRING (-58.58717727661133 -3.668921470642... \n",
" 41 LINESTRING (-58.62321090698242 -3.651007175445... \n",
"Fam_2 2 GEOMETRYCOLLECTION EMPTY \n",
" 13 LINESTRING (-58.80343627929688 -3.489553928375... \n",
" 22 LINESTRING (-58.83951950073242 -3.417735338211... \n",
" 30 LINESTRING (-58.83945846557617 -3.471630811691... \n",
" 37 LINESTRING (-58.91149520874023 -3.435779809951... \n",
" 43 LINESTRING (-59.07354736328125 -3.364077091217... \n",
"Fam_3 3 GEOMETRYCOLLECTION EMPTY \n",
" 12 LINESTRING (-59.27145385742188 -3.454065084457... \n",
" 21 LINESTRING (-59.39737701416016 -3.561942577362... \n",
" 29 LINESTRING (-59.48737335205078 -3.579958200454... \n",
" 36 LINESTRING (-59.55938720703125 -3.562025547027... \n",
" 42 LINESTRING (-59.63138961791992 -3.562054157257... \n",
"Fam_4 4 GEOMETRYCOLLECTION EMPTY \n",
" 14 LINESTRING (-61.05273818969727 -2.860909938812... \n",
" 24 LINESTRING (-61.23250579833984 -2.752928495407... \n",
" 32 LINESTRING (-61.34047317504883 -2.788729429244... \n",
" 38 LINESTRING (-61.16057968139648 -2.770972967147... \n",
" 44 LINESTRING (-61.14260864257812 -2.788957357406... \n",
"Fam_5 5 GEOMETRYCOLLECTION EMPTY \n",
" 15 LINESTRING (-61.30419158935547 -2.519302606582... \n",
" 25 LINESTRING (-61.39410781860352 -2.519193649291... \n",
"Fam_6 6 GEOMETRYCOLLECTION EMPTY \n",
" 16 LINESTRING (-59.43389892578125 -2.573832273483... \n",
" 46 LINESTRING (-59.75762557983398 -2.520048618316... \n",
" 39 LINESTRING (-59.64971542358398 -2.555953264236... \n",
" 33 LINESTRING (-59.57778167724609 -2.555928945541... \n",
" 26 LINESTRING (-59.55981063842773 -2.519989967346... \n",
"Fam_7 7 GEOMETRYCOLLECTION EMPTY \n",
" 17 LINESTRING (-59.9554443359375 -2.4302411079406... \n",
" 27 LINESTRING (-60.00939178466797 -2.412275075912... \n",
" 34 LINESTRING (-60.08131790161133 -2.394305467605... \n",
" 40 LINESTRING (-60.15324401855469 -2.394297361373... \n",
" 47 LINESTRING (-60.22516632080078 -2.376318693161... \n",
"Fam_8 8 GEOMETRYCOLLECTION EMPTY \n",
" 18 LINESTRING (-58.98443222045898 -2.411827564239... \n",
"Fam_9 9 GEOMETRYCOLLECTION EMPTY \n",
" 19 LINESTRING (-58.98458862304688 -2.214208364486... \n",
"\n",
" vector_35 \\\n",
"Fam_0 0 GEOMETRYCOLLECTION EMPTY \n",
" 10 GEOMETRYCOLLECTION EMPTY \n",
"Fam_1 1 GEOMETRYCOLLECTION EMPTY \n",
" 11 GEOMETRYCOLLECTION EMPTY \n",
"Fam_10 20 GEOMETRYCOLLECTION EMPTY \n",
" 28 LINESTRING (-58.47916030883789 -3.668762445449... \n",
" 35 LINESTRING (-58.5692024230957 -3.6509313583374... \n",
" 41 GEOMETRYCOLLECTION EMPTY \n",
"Fam_2 2 GEOMETRYCOLLECTION EMPTY \n",
" 13 LINESTRING (-58.73136901855469 -3.543361186981... \n",
" 22 LINESTRING (-58.76741409301758 -3.507475852966... \n",
" 30 LINESTRING (-58.83941650390625 -3.507560968399... \n",
" 37 GEOMETRYCOLLECTION EMPTY \n",
" 43 GEOMETRYCOLLECTION EMPTY \n",
"Fam_3 3 GEOMETRYCOLLECTION EMPTY \n",
" 12 LINESTRING (-59.34335327148438 -3.597841024398... \n",
" 21 LINESTRING (-59.43336868286133 -3.579929351806... \n",
" 29 LINESTRING (-59.52337646484375 -3.579975605010... \n",
" 36 LINESTRING (-59.5773811340332 -3.5799994468688... \n",
" 42 LINESTRING (-59.64939880371094 -3.544094324111... \n",
"Fam_4 4 GEOMETRYCOLLECTION EMPTY \n",
" 14 GEOMETRYCOLLECTION EMPTY \n",
" 24 GEOMETRYCOLLECTION EMPTY \n",
" 32 GEOMETRYCOLLECTION EMPTY \n",
" 38 GEOMETRYCOLLECTION EMPTY \n",
" 44 GEOMETRYCOLLECTION EMPTY \n",
"Fam_5 5 GEOMETRYCOLLECTION EMPTY \n",
" 15 GEOMETRYCOLLECTION EMPTY \n",
" 25 GEOMETRYCOLLECTION EMPTY \n",
"Fam_6 6 GEOMETRYCOLLECTION EMPTY \n",
" 16 GEOMETRYCOLLECTION EMPTY \n",
" 46 LINESTRING (-59.72165298461914 -2.555973052978... \n",
" 39 LINESTRING (-59.66770553588867 -2.537992477416... \n",
" 33 GEOMETRYCOLLECTION EMPTY \n",
" 26 GEOMETRYCOLLECTION EMPTY \n",
"Fam_7 7 GEOMETRYCOLLECTION EMPTY \n",
" 17 LINESTRING (-59.9554443359375 -2.4302411079406... \n",
" 27 LINESTRING (-60.02737426757812 -2.412274837493... \n",
" 34 LINESTRING (-60.08131790161133 -2.394305467605... \n",
" 40 LINESTRING (-60.17122268676758 -2.376328468322... \n",
" 47 LINESTRING (-60.24314117431641 -2.358348608016... \n",
"Fam_8 8 GEOMETRYCOLLECTION EMPTY \n",
" 18 GEOMETRYCOLLECTION EMPTY \n",
"Fam_9 9 GEOMETRYCOLLECTION EMPTY \n",
" 19 GEOMETRYCOLLECTION EMPTY \n",
"\n",
" vector_40 dsize_20 \\\n",
"Fam_0 0 GEOMETRYCOLLECTION EMPTY NaN \n",
" 10 GEOMETRYCOLLECTION EMPTY -32.0 \n",
"Fam_1 1 GEOMETRYCOLLECTION EMPTY NaN \n",
" 11 GEOMETRYCOLLECTION EMPTY -34.0 \n",
"Fam_10 20 GEOMETRYCOLLECTION EMPTY NaN \n",
" 28 GEOMETRYCOLLECTION EMPTY 11.0 \n",
" 35 LINESTRING (-58.56917572021484 -3.668895721435... -21.0 \n",
" 41 GEOMETRYCOLLECTION EMPTY -16.0 \n",
"Fam_2 2 GEOMETRYCOLLECTION EMPTY NaN \n",
" 13 LINESTRING (-58.80339431762695 -3.525484085083... -26.0 \n",
" 22 LINESTRING (-58.76739120483398 -3.525440931320... -123.0 \n",
" 30 LINESTRING (-58.82145690917969 -3.471610069274... -11.0 \n",
" 37 GEOMETRYCOLLECTION EMPTY 257.0 \n",
" 43 GEOMETRYCOLLECTION EMPTY 80.0 \n",
"Fam_3 3 GEOMETRYCOLLECTION EMPTY NaN \n",
" 12 LINESTRING (-59.34333801269531 -3.615806818008... -121.0 \n",
" 21 LINESTRING (-59.41534805297852 -3.615851163864... -15.0 \n",
" 29 LINESTRING (-59.52337646484375 -3.579975605010... -8.0 \n",
" 36 LINESTRING (-59.6134033203125 -3.5261151790618... -9.0 \n",
" 42 LINESTRING (-59.64939117431641 -3.562060356140... -3.0 \n",
"Fam_4 4 GEOMETRYCOLLECTION EMPTY NaN \n",
" 14 GEOMETRYCOLLECTION EMPTY -7.0 \n",
" 24 GEOMETRYCOLLECTION EMPTY -43.0 \n",
" 32 GEOMETRYCOLLECTION EMPTY 206.0 \n",
" 38 GEOMETRYCOLLECTION EMPTY -130.0 \n",
" 44 GEOMETRYCOLLECTION EMPTY -73.0 \n",
"Fam_5 5 GEOMETRYCOLLECTION EMPTY NaN \n",
" 15 GEOMETRYCOLLECTION EMPTY 6.0 \n",
" 25 GEOMETRYCOLLECTION EMPTY -30.0 \n",
"Fam_6 6 GEOMETRYCOLLECTION EMPTY NaN \n",
" 16 GEOMETRYCOLLECTION EMPTY -165.0 \n",
" 46 LINESTRING (-59.72165298461914 -2.555973052978... -12.0 \n",
" 39 GEOMETRYCOLLECTION EMPTY -31.0 \n",
" 33 GEOMETRYCOLLECTION EMPTY -22.0 \n",
" 26 GEOMETRYCOLLECTION EMPTY -8.0 \n",
"Fam_7 7 GEOMETRYCOLLECTION EMPTY NaN \n",
" 17 LINESTRING (-59.9554443359375 -2.4302411079406... 0.0 \n",
" 27 LINESTRING (-60.02737426757812 -2.412274837493... 6.0 \n",
" 34 LINESTRING (-60.09929656982422 -2.394303798675... 25.0 \n",
" 40 LINESTRING (-60.17122268676758 -2.376328468322... 13.0 \n",
" 47 GEOMETRYCOLLECTION EMPTY 21.0 \n",
"Fam_8 8 GEOMETRYCOLLECTION EMPTY NaN \n",
" 18 GEOMETRYCOLLECTION EMPTY 29.0 \n",
"Fam_9 9 GEOMETRYCOLLECTION EMPTY NaN \n",
" 19 GEOMETRYCOLLECTION EMPTY -120.0 \n",
"\n",
" dmean_ref_20 dtotal_size_35 dmean_total_ref_35 dtotal_size_40 \\\n",
"Fam_0 0 NaN NaN NaN NaN \n",
" 10 -8.690224 NaN NaN NaN \n",
"Fam_1 1 NaN NaN NaN NaN \n",
" 11 -1.750565 -9.0 -2.209392 -3.0 \n",
"Fam_10 20 NaN NaN NaN NaN \n",
" 28 1.096280 -3.0 2.149453 5.0 \n",
" 35 -0.085053 -12.0 1.007357 -8.0 \n",
" 41 -8.705933 -12.0 -7.147202 NaN \n",
"Fam_2 2 NaN NaN NaN NaN \n",
" 13 -0.141034 -27.0 0.521226 -18.0 \n",
" 22 -0.865681 -53.0 0.716820 -5.0 \n",
" 30 -4.243979 -17.0 -3.287830 -20.0 \n",
" 37 -5.082643 -27.0 -5.080233 NaN \n",
" 43 -0.120604 NaN NaN NaN \n",
"Fam_3 3 NaN NaN NaN NaN \n",
" 12 0.995369 -2.0 -1.216769 5.0 \n",
" 21 3.069527 1.0 2.612514 4.0 \n",
" 29 -0.984130 -7.0 -0.720139 -7.0 \n",
" 36 0.311804 8.0 -0.507579 10.0 \n",
" 42 -0.742426 -17.0 0.312405 -16.0 \n",
"Fam_4 4 NaN NaN NaN NaN \n",
" 14 -3.455887 -6.0 -4.177263 NaN \n",
" 24 -7.497606 NaN NaN NaN \n",
" 32 16.113162 NaN NaN NaN \n",
" 38 -9.279313 -72.0 -5.012903 NaN \n",
" 44 -5.133102 NaN NaN NaN \n",
"Fam_5 5 NaN NaN NaN NaN \n",
" 15 3.366628 10.0 2.121668 NaN \n",
" 25 -0.564554 -7.0 0.252037 -4.0 \n",
"Fam_6 6 NaN NaN NaN NaN \n",
" 16 -13.149960 -82.0 -10.480783 NaN \n",
" 46 -3.200698 -20.0 -2.084470 -22.0 \n",
" 39 1.814485 -11.0 1.658335 -2.0 \n",
" 33 -2.768986 -14.0 -1.784024 -9.0 \n",
" 26 -1.139510 -5.0 -0.446076 -1.0 \n",
"Fam_7 7 NaN NaN NaN NaN \n",
" 17 -1.004810 -7.0 -0.350795 5.0 \n",
" 27 0.402670 6.0 0.127497 7.0 \n",
" 34 -0.885592 6.0 -0.372081 -5.0 \n",
" 40 -2.653031 7.0 -2.897415 -4.0 \n",
" 47 -3.083527 -24.0 -0.832133 -13.0 \n",
"Fam_8 8 NaN NaN NaN NaN \n",
" 18 -0.332785 3.0 1.891906 NaN \n",
"Fam_9 9 NaN NaN NaN NaN \n",
" 19 4.498138 -1.0 4.502790 NaN \n",
"\n",
" dmean_total_ref_40 \\\n",
"Fam_0 0 NaN \n",
" 10 NaN \n",
"Fam_1 1 NaN \n",
" 11 -3.066082 \n",
"Fam_10 20 NaN \n",
" 28 1.177330 \n",
" 35 0.990272 \n",
" 41 NaN \n",
"Fam_2 2 NaN \n",
" 13 1.282434 \n",
" 22 -1.379247 \n",
" 30 -2.113137 \n",
" 37 NaN \n",
" 43 NaN \n",
"Fam_3 3 NaN \n",
" 12 -1.881857 \n",
" 21 2.505984 \n",
" 29 -0.536718 \n",
" 36 -0.945654 \n",
" 42 0.732436 \n",
"Fam_4 4 NaN \n",
" 14 NaN \n",
" 24 NaN \n",
" 32 NaN \n",
" 38 NaN \n",
" 44 NaN \n",
"Fam_5 5 NaN \n",
" 15 NaN \n",
" 25 1.615381 \n",
"Fam_6 6 NaN \n",
" 16 NaN \n",
" 46 -1.507583 \n",
" 39 1.418080 \n",
" 33 -1.726475 \n",
" 26 -1.474533 \n",
"Fam_7 7 NaN \n",
" 17 -1.587520 \n",
" 27 -0.087328 \n",
" 34 0.526667 \n",
" 40 -2.370610 \n",
" 47 -0.991783 \n",
"Fam_8 8 NaN \n",
" 18 NaN \n",
"Fam_9 9 NaN \n",
" 19 NaN \n",
"\n",
" geometry \n",
"Fam_0 0 POLYGON ((-58.37060 -3.97398, -58.37060 -3.973... \n",
" 10 POLYGON ((-58.28066 -3.91993, -58.28066 -3.919... \n",
"Fam_1 1 POLYGON ((-59.25313 -3.86727, -59.25313 -3.867... \n",
" 11 POLYGON ((-59.30714 -3.88527, -59.30714 -3.885... \n",
"Fam_10 20 POLYGON ((-58.55131 -3.57905, -58.51531 -3.579... \n",
" 28 POLYGON ((-58.56901 -3.77668, -58.56901 -3.776... \n",
" 35 POLYGON ((-58.67707 -3.75887, -58.67707 -3.758... \n",
" 41 POLYGON ((-58.65909 -3.74088, -58.65909 -3.740... \n",
"Fam_2 2 POLYGON ((-58.98303 -3.86702, -58.98303 -3.867... \n",
" 13 POLYGON ((-58.87525 -3.65132, -58.87525 -3.651... \n",
" 22 POLYGON ((-58.73111 -3.74097, -58.73111 -3.740... \n",
" 30 POLYGON ((-59.03736 -3.56166, -59.03736 -3.561... \n",
" 37 POLYGON ((-58.78516 -3.70511, -58.78516 -3.705... \n",
" 43 POLYGON ((-58.98330 -3.61551, -58.98330 -3.615... \n",
"Fam_3 3 POLYGON ((-59.19935 -3.57977, -59.19935 -3.579... \n",
" 12 POLYGON ((-59.39727 -3.74160, -59.39727 -3.741... \n",
" 21 POLYGON ((-59.50529 -3.74166, -59.50529 -3.741... \n",
" 29 POLYGON ((-59.57742 -3.49017, -59.54142 -3.490... \n",
" 36 POLYGON ((-59.64933 -3.72376, -59.64933 -3.723... \n",
" 42 POLYGON ((-59.72136 -3.66988, -59.72136 -3.669... \n",
"Fam_4 4 POLYGON ((-61.12475 -2.91473, -61.12475 -2.914... \n",
" 14 POLYGON ((-61.19667 -2.87872, -61.19667 -2.878... \n",
" 24 POLYGON ((-61.32259 -2.87858, -61.32259 -2.878... \n",
" 32 POLYGON ((-61.17872 -2.91467, -61.17872 -2.914... \n",
" 38 POLYGON ((-60.98083 -2.91487, -60.98083 -2.914... \n",
" 44 POLYGON ((-61.25061 -2.86070, -61.25061 -2.860... \n",
"Fam_5 5 POLYGON ((-61.26828 -2.57324, -61.28627 -2.573... \n",
" 15 POLYGON ((-61.44835 -2.75266, -61.44835 -2.752... \n",
" 25 POLYGON ((-61.30435 -2.66302, -61.30435 -2.663... \n",
"Fam_6 6 POLYGON ((-59.41587 -2.66365, -59.41587 -2.663... \n",
" 16 POLYGON ((-59.54180 -2.59185, -59.54180 -2.591... \n",
" 46 POLYGON ((-59.81156 -2.62786, -59.81156 -2.627... \n",
" 39 POLYGON ((-59.66768 -2.64579, -59.66768 -2.645... \n",
" 33 POLYGON ((-59.63170 -2.66374, -59.63170 -2.663... \n",
" 26 POLYGON ((-59.50583 -2.59183, -59.50583 -2.591... \n",
"Fam_7 7 POLYGON ((-59.95544 -2.53804, -59.95544 -2.538... \n",
" 17 POLYGON ((-60.00939 -2.52007, -60.00939 -2.520... \n",
" 27 POLYGON ((-60.11729 -2.50210, -60.11729 -2.502... \n",
" 34 POLYGON ((-60.17124 -2.52006, -60.17124 -2.520... \n",
" 40 POLYGON ((-60.20721 -2.52005, -60.20721 -2.520... \n",
" 47 POLYGON ((-60.31511 -2.53799, -60.31511 -2.537... \n",
"Fam_8 8 POLYGON ((-58.98436 -2.50165, -58.98436 -2.501... \n",
" 18 POLYGON ((-59.03829 -2.51967, -59.03829 -2.519... \n",
"Fam_9 9 POLYGON ((-58.89465 -2.26802, -58.89465 -2.268... \n",
" 19 POLYGON ((-58.89465 -2.26802, -58.89465 -2.268... \n",
"\n",
"[45 rows x 44 columns]"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"geo_df.query('time <= 5')"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"gd_t = geo_df.query('time == 5')"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [],
"source": [
"gd_t = gd_t.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [],
"source": [
"def ncdf(nc_file):\n",
" ## OPEN RADAR DATA COMPRESS\n",
" with gzip.open(nc_file) as gz:\n",
" with netCDF4.Dataset('dummy', mode='r', memory=gz.read()) as nc:\n",
" data = nc.variables[VAR][0][LEVEL][:].filled()\n",
" lon = nc.variables['lon0'][:].filled()\n",
" lat = nc.variables['lat0'][:].filled()\n",
" return data,lon,lat\n",
"\n",
"data_,lon_,lat_ = ncdf(gd_t.nc_file.iloc[0])"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [],
"source": [
"data_[data_ == -9999] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [],
"source": [
"img_pts,lons,lats = [],[],[]\n",
"\n",
"for i in range(len(data_)):\n",
" for j in range(len(data_)):\n",
" if data_[i][j] != np.nan:\n",
" img_pts.append(data_[i][j])\n",
" lons.append(lon_[i][j])\n",
" lats.append(lat_[i][j])\n",
" \n",
"img_df = pd.DataFrame(list(zip(lons,lats,img_pts)),\n",
" columns =['lon','lat','metric'])"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [],
"source": [
"import datashader.transfer_functions as tf\n",
"from colorcet import rainbow\n",
"import datashader as ds\n",
"import json\n",
"\n",
"cvs = ds.Canvas(plot_width=data_.shape[0], plot_height=data_.shape[1])\n",
"agg = cvs.points(img_df, x='lon', y='lat')\n",
"coords_lat, coords_lon = agg.coords['lat'].values, agg.coords['lon'].values\n",
"\n",
"# Corners of the image, which need to be passed to mapbox\n",
"coordinates = [[coords_lon[0], coords_lat[0]],\n",
" [coords_lon[-1], coords_lat[0]],\n",
" [coords_lon[-1], coords_lat[-1]],\n",
" [coords_lon[0], coords_lat[-1]]]"
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-10.434811"
]
},
"execution_count": 306,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.nanmin(agg.values)"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {},
"outputs": [],
"source": [
"# data_[data_ <= 20 ] = np.nan\n",
"\n",
"agg.values = data_\n",
"img = tf.shade(agg, cmap=complet_rbb['HEX'].tolist(),alpha=255)[::-1].to_pil()"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [],
"source": [
"from colormap import rgb2hex"
]
},
{
"cell_type": "code",
"execution_count": 293,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-10.434811"
]
},
"execution_count": 293,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.nanmin(data_)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"map(int, results)"
]
},
{
"cell_type": "code",
"execution_count": 280,
"metadata": {},
"outputs": [],
"source": [
"tab = pd.read_table('../stanalyzer/cpt/NEXRAD.cpt',sep=';',names=['RGB','DBZ'])\n",
"tab['HEX'] = tab['RGB'].apply(lambda x: rgb2hex(int(x.split(\",\")[0]),int(x.split(\",\")[1]),int(x.split(\",\")[2])))"
]
},
{
"cell_type": "code",
"execution_count": 281,
"metadata": {},
"outputs": [],
"source": [
"tab2 = pd.DataFrame(columns=['RGB','DBZ','HEX'])\n",
"\n",
"tab2['DBZ'] = np.arange(-186,-30)\n",
"tab2['HEX'] = ['#000000'] * len(tab2['DBZ'])\n",
"tab2['RGB'] = ['0, 0, 0'] * len(tab2['DBZ'])"
]
},
{
"cell_type": "code",
"execution_count": 283,
"metadata": {},
"outputs": [],
"source": [
"complet_rbb = pd.concat([tab2,tab])"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RGB</th>\n",
" <th>DBZ</th>\n",
" <th>HEX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0, 0, 0</td>\n",
" <td>-186</td>\n",
" <td>#000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0, 0, 0</td>\n",
" <td>-185</td>\n",
" <td>#000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0, 0, 0</td>\n",
" <td>-184</td>\n",
" <td>#000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0, 0, 0</td>\n",
" <td>-183</td>\n",
" <td>#000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0, 0, 0</td>\n",
" <td>-182</td>\n",
" <td>#000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>141,255,255</td>\n",
" <td>66</td>\n",
" <td>#8DFFFF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>154,255,255</td>\n",
" <td>67</td>\n",
" <td>#9AFFFF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>166,255,255</td>\n",
" <td>68</td>\n",
" <td>#A6FFFF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>179,255,255</td>\n",
" <td>69</td>\n",
" <td>#B3FFFF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>192,255,255</td>\n",
" <td>70</td>\n",
" <td>#C0FFFF</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>256 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" RGB DBZ HEX\n",
"0 0, 0, 0 -186 #000000\n",
"1 0, 0, 0 -185 #000000\n",
"2 0, 0, 0 -184 #000000\n",
"3 0, 0, 0 -183 #000000\n",
"4 0, 0, 0 -182 #000000\n",
".. ... ... ...\n",
"95 141,255,255 66 #8DFFFF\n",
"96 154,255,255 67 #9AFFFF\n",
"97 166,255,255 68 #A6FFFF\n",
"98 179,255,255 69 #B3FFFF\n",
"99 192,255,255 70 #C0FFFF\n",
"\n",
"[256 rows x 3 columns]"
]
},
"execution_count": 308,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complet_rbb"
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colors.LinearSegmentedColormap at 0x7f121a455a90>"
]
},
"execution_count": 307,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CMAP"
]
},
{
"cell_type": "code",
"execution_count": 303,
"metadata": {},
"outputs": [
{
"data": {
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" '#A6FFFF',\n",
" '#B3FFFF',\n",
" '#C0FFFF']"
]
},
"execution_count": 303,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complet_rbb['HEX'].tolist()"
]
},
{
"cell_type": "code",
"execution_count": 302,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['#0034f8',\n",
" '#0037f6',\n",
" '#003af3',\n",
" '#003df0',\n",
" '#003fed',\n",
" '#0041ea',\n",
" '#0044e7',\n",
" '#0046e4',\n",
" '#0048e1',\n",
" '#004ade',\n",
" '#004cdb',\n",
" '#004fd8',\n",
" '#0051d5',\n",
" '#0053d2',\n",
" '#0054d0',\n",
" '#0056cd',\n",
" '#0058ca',\n",
" '#005ac7',\n",
" '#005cc4',\n",
" '#005ec1',\n",
" '#0060be',\n",
" '#0061bb',\n",
" '#0063b8',\n",
" '#0065b6',\n",
" '#0066b3',\n",
" '#0068b0',\n",
" '#006aad',\n",
" '#006baa',\n",
" '#006da7',\n",
" '#006ea5',\n",
" '#006fa2',\n",
" '#00719f',\n",
" '#00729d',\n",
" '#00739a',\n",
" '#007598',\n",
" '#007695',\n",
" '#077793',\n",
" '#0d7890',\n",
" '#13798e',\n",
" '#187a8b',\n",
" '#1c7b89',\n",
" '#1f7c87',\n",
" '#237d84',\n",
" '#267e82',\n",
" '#287f7f',\n",
" '#2b807d',\n",
" '#2d817b',\n",
" '#2f8278',\n",
" '#318376',\n",
" '#328473',\n",
" '#348571',\n",
" '#35866f',\n",
" '#36876c',\n",
" '#37886a',\n",
" '#388967',\n",
" '#398a65',\n",
" '#3a8b62',\n",
" '#3b8c60',\n",
" '#3c8e5d',\n",
" '#3c8f5b',\n",
" '#3d9058',\n",
" '#3d9155',\n",
" '#3e9253',\n",
" '#3e9350',\n",
" '#3e944d',\n",
" '#3e954a',\n",
" '#3e9647',\n",
" '#3f9745',\n",
" '#3f9842',\n",
" '#3e993e',\n",
" '#3e9a3b',\n",
" '#3e9b38',\n",
" '#3e9c35',\n",
" '#3e9d32',\n",
" '#3e9e2e',\n",
" '#3e9f2b',\n",
" '#3fa027',\n",
" '#3fa124',\n",
" '#40a221',\n",
" '#41a31d',\n",
" '#42a41a',\n",
" '#44a517',\n",
" '#45a615',\n",
" '#47a713',\n",
" '#4aa711',\n",
" '#4ca80f',\n",
" '#4fa90e',\n",
" '#51a90d',\n",
" '#54aa0d',\n",
" '#57ab0d',\n",
" '#5aab0d',\n",
" '#5dac0d',\n",
" '#5fad0d',\n",
" '#62ad0e',\n",
" '#65ae0e',\n",
" '#67ae0e',\n",
" '#6aaf0f',\n",
" '#6db00f',\n",
" '#6fb00f',\n",
" '#72b110',\n",
" '#74b110',\n",
" '#77b211',\n",
" '#79b211',\n",
" '#7cb311',\n",
" '#7eb412',\n",
" '#80b412',\n",
" '#83b512',\n",
" '#85b513',\n",
" '#88b613',\n",
" '#8ab613',\n",
" '#8cb714',\n",
" '#8fb814',\n",
" '#91b815',\n",
" '#93b915',\n",
" '#95b915',\n",
" '#98ba16',\n",
" '#9aba16',\n",
" '#9cbb16',\n",
" '#9fbb17',\n",
" '#a1bc17',\n",
" '#a3bc18',\n",
" '#a5bd18',\n",
" '#a7be18',\n",
" '#aabe19',\n",
" '#acbf19',\n",
" '#aebf19',\n",
" '#b0c01a',\n",
" '#b2c01a',\n",
" '#b5c11b',\n",
" '#b7c11b',\n",
" '#b9c21b',\n",
" '#bbc21c',\n",
" '#bdc31c',\n",
" '#c0c31c',\n",
" '#c2c41d',\n",
" '#c4c41d',\n",
" '#c6c51d',\n",
" '#c8c51e',\n",
" '#cac61e',\n",
" '#cdc61f',\n",
" '#cfc71f',\n",
" '#d1c71f',\n",
" '#d3c820',\n",
" '#d5c820',\n",
" '#d7c920',\n",
" '#d9c921',\n",
" '#dcca21',\n",
" '#deca22',\n",
" '#e0ca22',\n",
" '#e2cb22',\n",
" '#e4cb23',\n",
" '#e6cc23',\n",
" '#e8cc23',\n",
" '#eacc24',\n",
" '#eccd24',\n",
" '#eecd24',\n",
" '#f0cd24',\n",
" '#f2cd24',\n",
" '#f3cd24',\n",
" '#f5cc24',\n",
" '#f6cc24',\n",
" '#f8cb24',\n",
" '#f9ca24',\n",
" '#f9c923',\n",
" '#fac823',\n",
" '#fbc722',\n",
" '#fbc622',\n",
" '#fcc521',\n",
" '#fcc421',\n",
" '#fcc220',\n",
" '#fdc120',\n",
" '#fdc01f',\n",
" '#fdbe1f',\n",
" '#fdbd1e',\n",
" '#febb1d',\n",
" '#feba1d',\n",
" '#feb91c',\n",
" '#feb71b',\n",
" '#feb61b',\n",
" '#feb51a',\n",
" '#ffb31a',\n",
" '#ffb219',\n",
" '#ffb018',\n",
" '#ffaf18',\n",
" '#ffae17',\n",
" '#ffac16',\n",
" '#ffab16',\n",
" '#ffa915',\n",
" '#ffa815',\n",
" '#ffa714',\n",
" '#ffa513',\n",
" '#ffa413',\n",
" '#ffa212',\n",
" '#ffa111',\n",
" '#ff9f10',\n",
" '#ff9e10',\n",
" '#ff9c0f',\n",
" '#ff9b0e',\n",
" '#ff9a0e',\n",
" '#ff980d',\n",
" '#ff970c',\n",
" '#ff950b',\n",
" '#ff940b',\n",
" '#ff920a',\n",
" '#ff9109',\n",
" '#ff8f08',\n",
" '#ff8e08',\n",
" '#ff8c07',\n",
" '#ff8b06',\n",
" '#ff8905',\n",
" '#ff8805',\n",
" '#ff8604',\n",
" '#ff8404',\n",
" '#ff8303',\n",
" '#ff8102',\n",
" '#ff8002',\n",
" '#ff7e01',\n",
" '#ff7c01',\n",
" '#ff7b00',\n",
" '#ff7900',\n",
" '#ff7800',\n",
" '#ff7600',\n",
" '#ff7400',\n",
" '#ff7200',\n",
" '#ff7100',\n",
" '#ff6f00',\n",
" '#ff6d00',\n",
" '#ff6c00',\n",
" '#ff6a00',\n",
" '#ff6800',\n",
" '#ff6600',\n",
" '#ff6400',\n",
" '#ff6200',\n",
" '#ff6100',\n",
" '#ff5f00',\n",
" '#ff5d00',\n",
" '#ff5b00',\n",
" '#ff5900',\n",
" '#ff5700',\n",
" '#ff5500',\n",
" '#ff5300',\n",
" '#ff5000',\n",
" '#ff4e00',\n",
" '#ff4c00',\n",
" '#ff4a00',\n",
" '#ff4700',\n",
" '#ff4500',\n",
" '#ff4200',\n",
" '#ff4000',\n",
" '#ff3d00',\n",
" '#ff3a00',\n",
" '#ff3700',\n",
" '#ff3400',\n",
" '#ff3100',\n",
" '#ff2d00',\n",
" '#ff2a00']"
]
},
"execution_count": 302,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rainbow"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"geo1 = json.loads(gpd.GeoSeries(gd_t['geom_20'].apply(wkt.loads)).to_json())\n",
"geo2 = json.loads(gpd.GeoSeries(gd_t['geom_35'].apply(wkt.loads)).to_json())\n",
"geo3 = json.loads(gpd.GeoSeries(gd_t['geom_40'].apply(wkt.loads)).to_json())\n",
"traj = json.loads(gpd.GeoSeries(gd_t['trajectory'].apply(wkt.loads)).to_json())"
]
},
{
"cell_type": "code",
"execution_count": 300,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGBA size=241x241 at 0x7F12181EE310>"
]
},
"execution_count": 300,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGBA size=241x241 at 0x7F121A4538D0>"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGBA size=241x241 at 0x7F121A879B90>"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'geo1' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-a4c572a0e638>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m layers1 = [ \n\u001b[0;32m----> 2\u001b[0;31m {'source': geo1,\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m'type'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"line\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m'color'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"black\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m'line'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'geo1' is not defined"
]
}
],
"source": [
"layers1 = [ \n",
" {'source': geo1,\n",
" 'type': \"line\",\n",
" 'color': \"black\",\n",
" 'line':dict(width=1.5)\n",
" },\n",
" \n",
" {'source': geo2,\n",
" 'type': \"line\",\n",
" 'color': \"royalblue\",\n",
" 'name':'ds',\n",
" 'line':dict(width=0.8)\n",
" },\n",
" \n",
" {'source': geo3,\n",
" 'type': \"line\",\n",
" 'color': \"red\",\n",
" 'name':'ds',\n",
" 'line':dict(width=0.8)\n",
" },\n",
" \n",
" {'source': traj,\n",
" 'type': \"line\",\n",
" 'color': \"red\",\n",
" 'name':'ds',\n",
" 'line':dict(width=1.8)\n",
" },\n",
" \n",
" {\"sourcetype\": \"image\",\n",
" 'below': \"traces\",\n",
" \"source\": img,\n",
" \"coordinates\": coordinates,\n",
" }\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"colorbar=dict(thickness=20,\n",
" ticklen=3, tickcolor='orange',\n",
" tickfont=dict(size=14, color='orange'))\n",
"\n",
"showscale=True,\n",
"colorscale=[[0, 'green'],[1, 'red']],\n",
"cmin=0,\n",
"cmax=10"
]
},
{
"cell_type": "code",
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\",\"sourcetype\":\"image\"}],\"style\":\"light\",\"zoom\":6},\"margin\":{\"b\":0,\"l\":0,\"pad\":0,\"r\":0,\"t\":0},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}}}, {\"responsive\": true} ).then(function(){\n",
" \n",
"var gd = document.getElementById('a02f2035-b331-4a75-af02-77099cb09bdd');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; }); </script> </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mapbox_access_token = 'pk.eyJ1Ijoic3BsaW50ZXJzdG0iLCJhIjoiY2tydWxjMzdlMTRxYTJwcGZlYmc0aWJyYSJ9.3-nO2w18a1fbjmAXrH7kEA'\n",
"\n",
"hover_info = dict(bgcolor=\"white\",\n",
" font_size=14,\n",
" font_family=\"Rockwell\")\n",
"\n",
"hover_temp = \"<b>%{customdata[0]} </b><br><br>\" + \\\n",
" \"longitude: %{lon}<br>\" + \\\n",
" \"latitude: %{lat}<br>\" + \\\n",
" \"UID: %{customdata[1]}<extra></extra>\"\n",
"\n",
"### FIGURE\n",
"data = go.Scattermapbox(customdata=gd_t[['timestamp','uid','time']],\n",
" lat=list(gd_t[\"lat\"]), lon=list(gd_t[\"lon\"]),\n",
" textfont=dict(size=16, color='black'),\n",
" hoverlabel=hover_info,\n",
" mode='markers',\n",
" marker=dict(size=10, color='black',\n",
" showscale=True,colorscale=[[0, 'green'],[1, 'red']],cmin=0,cmax=50,\n",
" colorbar=dict(thickness=20,\n",
" ticklen=3, tickcolor='orange',\n",
" tickfont=dict(size=14, color='orange'))),\n",
" hovertemplate = hover_temp,\n",
" )\n",
"\n",
"layout = dict(margin=dict(l=0, t=0, r=0, b=0, pad=0),\n",
" mapbox=dict(layers=layers1,\n",
" accesstoken=mapbox_access_token,\n",
" center=dict(lat=-3.14, lon=-60),\n",
" style='light',\n",
" zoom=6),\n",
" \n",
" )\n",
"\n",
"fig = go.Figure(data=data, layout=layout)\n",
"\n",
"fig.update_mapboxes()\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = go.Figure()\n",
"\n",
"fig = px.scatter_mapbox(gd_t, lat=\"lat\", lon=\"lon\", hover_name=\"uid\", hover_data=[\"time\", \"uid\",\"max_ref_20\"],\n",
" color_discrete_sequence=[\"fuchsia\"],width=900, height=700)\n",
"\n",
"fig.update_layout(mapbox=dict(layers=layers,\n",
" style=\"stamen-terrain\",\n",
" center = {'lon': -60, 'lat': -3.14},\n",
" zoom = 6))\n",
"\n",
"# buttons for updatemenu\n",
"buttons = [dict(method='restyle',\n",
" label='linear',\n",
" visible=True,\n",
" args=[{'label': 'linear',\n",
" 'visible':[True, False],}]),\n",
" \n",
" dict(method='restyle',\n",
" label='sqrt',\n",
" visible=True,\n",
" args=[{'label': 'linear',\n",
" 'visible':[False, True],}])]\n",
"\n",
"# specify updatemenu \n",
"um = [{'buttons':buttons, 'direction': 'down'}]\n",
"\n",
"fig.update_layout(updatemenus=um)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig.update_layout(\n",
" updatemenus=[\n",
" dict(\n",
" buttons=list([\n",
" dict(label=\"High\",\n",
" method=\"update\", \n",
" args=[{\"visible\": [False]},\n",
" {\"shapes\": layers1[3]}])\n",
" ]),\n",
" \n",
" type = \"buttons\",\n",
" direction=\"right\",\n",
" pad={\"r\": 0, \"t\": 0},\n",
" showactive=True,\n",
" x=0.38,\n",
" xanchor=\"left\",\n",
" y=1,\n",
" yanchor=\"top\"\n",
" )\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"width=800, height=400"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = px.density_mapbox(img_df,lat=\"lat\", lon=\"lon\",z='metric',radius=5)\n",
"\n",
"fig.update_layout(mapbox=dict(layers=layers,\n",
" style=\"stamen-terrain\",\n",
" center = {'lon': -60, 'lat': -3.14},\n",
" zoom = 6\n",
" ),\n",
" )\n",
"\n",
"fig.update_layout(mapbox=dict(layers=layers,\n",
" style=\"stamen-terrain\",\n",
" center = {'lon': -60, 'lat': -3.14},\n",
" zoom = 6\n",
" ),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_states = []\n",
"y_states = []\n",
"\n",
"for i,row in gd_t.iterrows():\n",
" x = row.geometry.exterior.xy[0].tolist()\n",
" y = row.geometry.exterior.xy[0].tolist()\n",
" for segment in range(len(x)):\n",
" x_states = x_states + x\n",
" y_states = y_states + y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"state_trace = [dict(\n",
" type = 'scatter',\n",
" legendgroup = \"States\",\n",
" line = dict(color='white', width=1),\n",
" x = x_states,\n",
" y = y_states,\n",
" hoverinfo='none',\n",
" showlegend=False,\n",
" mode='lines')]"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['__class__',\n",
" '__contains__',\n",
" '__delattr__',\n",
" '__dict__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__getitem__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__init_subclass__',\n",
" '__iter__',\n",
" '__le__',\n",
" '__lt__',\n",
" '__module__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__setitem__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" '__weakref__',\n",
" '_build_repr_for_class',\n",
" '_dispatch_change_callbacks',\n",
" '_dispatch_on_click',\n",
" '_dispatch_on_deselect',\n",
" '_dispatch_on_hover',\n",
" '_dispatch_on_selection',\n",
" '_dispatch_on_unhover',\n",
" '_get_child_prop_defaults',\n",
" '_get_child_props',\n",
" '_get_prop_validator',\n",
" '_get_validator',\n",
" '_in_batch_mode',\n",
" '_init_child_props',\n",
" '_init_props',\n",
" '_mapped_properties',\n",
" '_parent_path_str',\n",
" '_path_str',\n",
" '_process_kwargs',\n",
" '_prop_defaults',\n",
" '_prop_descriptions',\n",
" '_prop_set_child',\n",
" '_props',\n",
" '_raise_on_invalid_property_error',\n",
" '_relayout_child',\n",
" '_restyle_child',\n",
" '_send_prop_set',\n",
" '_set_array_prop',\n",
" '_set_compound_prop',\n",
" '_set_prop',\n",
" '_valid_props',\n",
" '_validators',\n",
" '_vals_equal',\n",
" 'autocolorscale',\n",
" 'below',\n",
" 'coloraxis',\n",
" 'colorbar',\n",
" 'colorscale',\n",
" 'customdata',\n",
" 'customdatasrc',\n",
" 'featureidkey',\n",
" 'figure',\n",
" 'geojson',\n",
" 'hoverinfo',\n",
" 'hoverinfosrc',\n",
" 'hoverlabel',\n",
" 'hovertemplate',\n",
" 'hovertemplatesrc',\n",
" 'hovertext',\n",
" 'hovertextsrc',\n",
" 'ids',\n",
" 'idssrc',\n",
" 'legendgroup',\n",
" 'legendgrouptitle',\n",
" 'legendrank',\n",
" 'locations',\n",
" 'locationssrc',\n",
" 'marker',\n",
" 'meta',\n",
" 'metasrc',\n",
" 'name',\n",
" 'on_change',\n",
" 'on_click',\n",
" 'on_deselect',\n",
" 'on_hover',\n",
" 'on_selection',\n",
" 'on_unhover',\n",
" 'parent',\n",
" 'plotly_name',\n",
" 'pop',\n",
" 'reversescale',\n",
" 'selected',\n",
" 'selectedpoints',\n",
" 'showlegend',\n",
" 'showscale',\n",
" 'stream',\n",
" 'subplot',\n",
" 'text',\n",
" 'textsrc',\n",
" 'to_plotly_json',\n",
" 'type',\n",
" 'uid',\n",
" 'uirevision',\n",
" 'unselected',\n",
" 'update',\n",
" 'visible',\n",
" 'z',\n",
" 'zauto',\n",
" 'zmax',\n",
" 'zmid',\n",
" 'zmin',\n",
" 'zsrc']"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(go.Choroplethmapbox)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "Object of type Polygon is not JSON serializable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-39-bec4559ee083>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m# fig.update_layout(margin={'r':0, 't':0, 'l':0, 'b':0})\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# pio.renderers.default = 'browser'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/site-packages/plotly/basedatatypes.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3396\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3398\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3400\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/site-packages/plotly/io/_renderers.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 388\u001b[0m \u001b[0;31m# Mimetype renderers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m \u001b[0mbundle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_mime_bundle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderers_string\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 390\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbundle\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 391\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mipython_display\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/site-packages/plotly/io/_renderers.py\u001b[0m in \u001b[0;36m_build_mime_bundle\u001b[0;34m(self, fig_dict, renderers_string, **kwargs)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m \u001b[0mbundle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_mimebundle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mbundle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/site-packages/plotly/io/_base_renderers.py\u001b[0m in \u001b[0;36mto_mimebundle\u001b[0;34m(self, fig_dict)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m json_compatible_fig_dict = json.loads(\n\u001b[0;32m---> 96\u001b[0;31m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremove_uids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 97\u001b[0m )\n\u001b[1;32m 98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/site-packages/plotly/io/_json.py\u001b[0m in \u001b[0;36mto_json\u001b[0;34m(fig, validate, pretty, remove_uids, engine)\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"uid\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mto_json_plotly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpretty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpretty\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/site-packages/plotly/io/_json.py\u001b[0m in \u001b[0;36mto_json_plotly\u001b[0;34m(plotly_object, pretty, engine)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m_plotly_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPlotlyJSONEncoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplotly_object\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPlotlyJSONEncoder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 125\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"orjson\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0mJsonConfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_orjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/json/__init__.py\u001b[0m in \u001b[0;36mdumps\u001b[0;34m(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0mcheck_circular\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_circular\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_nan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindent\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0mseparators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseparators\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdefault\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort_keys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m **kw).encode(obj)\n\u001b[0m\u001b[1;32m 239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/site-packages/_plotly_utils/utils.py\u001b[0m in \u001b[0;36mencode\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m 57\u001b[0m \"\"\"\n\u001b[1;32m 58\u001b[0m \u001b[0;31m# this will raise errors in a normal-expected way\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m \u001b[0mencoded_o\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPlotlyJSONEncoder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 60\u001b[0m \u001b[0;31m# Brute force guessing whether NaN or Infinity values are in the string\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;31m# We catch false positive cases (e.g. strings such as titles, labels etc.)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36mencode\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0;31m# exceptions aren't as detailed. The list call should be roughly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0;31m# equivalent to the PySequence_Fast that ''.join() would do.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m \u001b[0mchunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_one_shot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 200\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0mchunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36miterencode\u001b[0;34m(self, o, _one_shot)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey_separator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem_separator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_keys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 256\u001b[0m self.skipkeys, _one_shot)\n\u001b[0;32m--> 257\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_iterencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m def _make_iterencode(markers, _default, _encoder, _indent, _floatstr,\n",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/site-packages/_plotly_utils/utils.py\u001b[0m in \u001b[0;36mdefault\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mNotEncodable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_json\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mJSONEncoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/envs/stanalyzer/lib/python3.7/json/encoder.py\u001b[0m in \u001b[0;36mdefault\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 178\u001b[0m \"\"\"\n\u001b[0;32m--> 179\u001b[0;31m raise TypeError(f'Object of type {o.__class__.__name__} '\n\u001b[0m\u001b[1;32m 180\u001b[0m f'is not JSON serializable')\n\u001b[1;32m 181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Object of type Polygon is not JSON serializable"
]
}
],
"source": [
"\n",
"fig = go.Figure(go.Choroplethmapbox(geojson=gd_t.geometry,\n",
" locations=gd_t.index,\n",
" z=gd_t.uid,\n",
" colorscale='Viridis',\n",
" zauto=True,\n",
" marker_opacity=0.5,\n",
" marker_line_width=0.5)\n",
" )\n",
"# fig.update_layout(mapbox_style='white-bg',\n",
"# #mapbox_accesstoken=mapbox_token,\n",
"# mapbox_zoom=12,\n",
"# mapbox_center={'lat': 45.41117, 'lon': -75.69812})\n",
"# fig.update_layout(margin={'r':0, 't':0, 'l':0, 'b':0})\n",
"# pio.renderers.default = 'browser'\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>continent</th>\n",
" <th>year</th>\n",
" <th>lifeExp</th>\n",
" <th>pop</th>\n",
" <th>gdpPercap</th>\n",
" <th>iso_alpha</th>\n",
" <th>iso_num</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>1952</td>\n",
" <td>28.801</td>\n",
" <td>8425333</td>\n",
" <td>779.445314</td>\n",
" <td>AFG</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>1957</td>\n",
" <td>30.332</td>\n",
" <td>9240934</td>\n",
" <td>820.853030</td>\n",
" <td>AFG</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>1962</td>\n",
" <td>31.997</td>\n",
" <td>10267083</td>\n",
" <td>853.100710</td>\n",
" <td>AFG</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>1967</td>\n",
" <td>34.020</td>\n",
" <td>11537966</td>\n",
" <td>836.197138</td>\n",
" <td>AFG</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>1972</td>\n",
" <td>36.088</td>\n",
" <td>13079460</td>\n",
" <td>739.981106</td>\n",
" <td>AFG</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1699</th>\n",
" <td>Zimbabwe</td>\n",
" <td>Africa</td>\n",
" <td>1987</td>\n",
" <td>62.351</td>\n",
" <td>9216418</td>\n",
" <td>706.157306</td>\n",
" <td>ZWE</td>\n",
" <td>716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1700</th>\n",
" <td>Zimbabwe</td>\n",
" <td>Africa</td>\n",
" <td>1992</td>\n",
" <td>60.377</td>\n",
" <td>10704340</td>\n",
" <td>693.420786</td>\n",
" <td>ZWE</td>\n",
" <td>716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1701</th>\n",
" <td>Zimbabwe</td>\n",
" <td>Africa</td>\n",
" <td>1997</td>\n",
" <td>46.809</td>\n",
" <td>11404948</td>\n",
" <td>792.449960</td>\n",
" <td>ZWE</td>\n",
" <td>716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1702</th>\n",
" <td>Zimbabwe</td>\n",
" <td>Africa</td>\n",
" <td>2002</td>\n",
" <td>39.989</td>\n",
" <td>11926563</td>\n",
" <td>672.038623</td>\n",
" <td>ZWE</td>\n",
" <td>716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1703</th>\n",
" <td>Zimbabwe</td>\n",
" <td>Africa</td>\n",
" <td>2007</td>\n",
" <td>43.487</td>\n",
" <td>12311143</td>\n",
" <td>469.709298</td>\n",
" <td>ZWE</td>\n",
" <td>716</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1704 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" country continent year lifeExp pop gdpPercap iso_alpha \\\n",
"0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG \n",
"1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG \n",
"2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG \n",
"3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG \n",
"4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG \n",
"... ... ... ... ... ... ... ... \n",
"1699 Zimbabwe Africa 1987 62.351 9216418 706.157306 ZWE \n",
"1700 Zimbabwe Africa 1992 60.377 10704340 693.420786 ZWE \n",
"1701 Zimbabwe Africa 1997 46.809 11404948 792.449960 ZWE \n",
"1702 Zimbabwe Africa 2002 39.989 11926563 672.038623 ZWE \n",
"1703 Zimbabwe Africa 2007 43.487 12311143 469.709298 ZWE \n",
"\n",
" iso_num \n",
"0 4 \n",
"1 4 \n",
"2 4 \n",
"3 4 \n",
"4 4 \n",
"... ... \n",
"1699 716 \n",
"1700 716 \n",
"1701 716 \n",
"1702 716 \n",
"1703 716 \n",
"\n",
"[1704 rows x 8 columns]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"coloraxis": "coloraxis",
"geo": "geo",
"hovertemplate": "<b>%{hovertext}</b><br><br>year=1952<br>iso_alpha=%{location}<br>lifeExp=%{z}<extra></extra>",
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Rep.\",\"Korea, Rep.\",\"Kuwait\",\"Lebanon\",\"Lesotho\",\"Liberia\",\"Libya\",\"Madagascar\",\"Malawi\",\"Malaysia\",\"Mali\",\"Mauritania\",\"Mauritius\",\"Mexico\",\"Mongolia\",\"Montenegro\",\"Morocco\",\"Mozambique\",\"Myanmar\",\"Namibia\",\"Nepal\",\"Netherlands\",\"New Zealand\",\"Nicaragua\",\"Niger\",\"Nigeria\",\"Norway\",\"Oman\",\"Pakistan\",\"Panama\",\"Paraguay\",\"Peru\",\"Philippines\",\"Poland\",\"Portugal\",\"Puerto Rico\",\"Reunion\",\"Romania\",\"Rwanda\",\"Sao Tome and Principe\",\"Saudi Arabia\",\"Senegal\",\"Serbia\",\"Sierra Leone\",\"Singapore\",\"Slovak Republic\",\"Slovenia\",\"Somalia\",\"South Africa\",\"Spain\",\"Sri Lanka\",\"Sudan\",\"Swaziland\",\"Sweden\",\"Switzerland\",\"Syria\",\"Taiwan\",\"Tanzania\",\"Thailand\",\"Togo\",\"Trinidad and Tobago\",\"Tunisia\",\"Turkey\",\"Uganda\",\"United Kingdom\",\"United States\",\"Uruguay\",\"Venezuela\",\"Vietnam\",\"West Bank and Gaza\",\"Yemen, Rep.\",\"Zambia\",\"Zimbabwe\"],\"locations\":[\"AFG\",\"ALB\",\"DZA\",\"AGO\",\"ARG\",\"AUS\",\"AUT\",\"BHR\",\"BGD\",\"BEL\",\"BEN\",\"BOL\",\"BIH\",\"BWA\",\"BRA\",\"BGR\",\"BFA\",\"BDI\",\"KHM\",\"CMR\",\"CAN\",\"CAF\",\"TCD\",\"CHL\",\"CHN\",\"COL\",\"COM\",\"COD\",\"COG\",\"CRI\",\"CIV\",\"HRV\",\"CUB\",\"CZE\",\"DNK\",\"DJI\",\"DOM\",\"ECU\",\"EGY\",\"SLV\",\"GNQ\",\"ERI\",\"ETH\",\"FIN\",\"FRA\",\"GAB\",\"GMB\",\"DEU\",\"GHA\",\"GRC\",\"GTM\",\"GIN\",\"GNB\",\"HTI\",\"HND\",\"HKG\",\"HUN\",\"ISL\",\"IND\",\"IDN\",\"IRN\",\"IRQ\",\"IRL\",\"ISR\",\"ITA\",\"JAM\",\"JPN\",\"JOR\",\"KEN\",\"KOR\",\"KOR\",\"KWT\",\"LBN\",\"LSO\",\"LBR\",\"LBY\",\"MDG\",\"MWI\",\"MYS\",\"MLI\",\"MRT\",\"MUS\",\"MEX\",\"MNG\",\"MNE\",\"MAR\",\"MOZ\",\"MMR\",\"NAM\",\"NPL\",\"NLD\",\"NZL\",\"NIC\",\"NER\",\"NGA\",\"NOR\",\"OMN\",\"PAK\",\"PAN\",\"PRY\",\"PER\",\"PHL\",\"POL\",\"PRT\",\"PRI\",\"REU\",\"ROU\",\"RWA\",\"STP\",\"SAU\",\"SEN\",\"SRB\",\"SLE\",\"SGP\",\"SVK\",\"SVN\",\"SOM\",\"ZAF\",\"ESP\",\"LKA\",\"SDN\",\"SWZ\",\"SWE\",\"CHE\",\"SYR\",\"TWN\",\"TZA\",\"THA\",\"TGO\",\"TTO\",\"TUN\",\"TUR\",\"UGA\",\"GBR\",\"USA\",\"URY\",\"VEN\",\"VNM\",\"PSE\",\"YEM\",\"ZMB\",\"ZWE\"],\"name\":\"\",\"type\":\"choropleth\",\"z\":[42.129,75.651,70.994,41.003,74.34,80.37,78.98,74.795,62.013,78.32,54.406,63.883,74.09,46.634,71.006,72.14,50.65,47.36,56.752,49.856,79.77,43.308,50.525,77.86,72.028,71.682,62.974,44.966,52.97,78.123,46.832,74.876,77.158,75.51,77.18,53.373,70.847,74.173,69.806,70.734,49.348,55.24,50.725,78.37,79.59,56.761,58.041,78.67,58.453,78.256,68.978,53.676,45.504,58.137,68.565,81.495,72.59,80.5,62.879,68.588,69.451,57.046,77.783,79.696,80.24,72.047,82.0,71.263,50.992,66.662,77.045,76.904,71.028,44.593,43.753,72.737,57.286,45.009,73.044,51.818,62.247,71.954,74.902,65.033,73.981,69.615,44.026,59.908,51.479,61.34,78.53,79.11,70.836,54.496,46.608,79.05,74.193,63.61,74.712,70.755,69.906,70.303,74.67,77.29,77.778,75.744,71.322,43.413,64.337,71.626,61.6,73.213,41.012,78.77,73.8,76.66,45.936,53.365,79.78,70.815,56.369,43.869,80.04,80.62,73.053,76.99,49.651,68.564,57.561,68.976,73.042,70.845,47.813,78.471,77.31,75.307,72.766,73.017,72.37,60.308,39.193,39.989]}],\"name\":\"2002\"},{\"data\":[{\"coloraxis\":\"coloraxis\",\"geo\":\"geo\",\"hovertemplate\":\"<b>%{hovertext}</b><br><br>year=2007<br>iso_alpha=%{location}<br>lifeExp=%{z}<extra></extra>\",\"hovertext\":[\"Afghanistan\",\"Albania\",\"Algeria\",\"Angola\",\"Argentina\",\"Australia\",\"Austria\",\"Bahrain\",\"Bangladesh\",\"Belgium\",\"Benin\",\"Bolivia\",\"Bosnia and Herzegovina\",\"Botswana\",\"Brazil\",\"Bulgaria\",\"Burkina Faso\",\"Burundi\",\"Cambodia\",\"Cameroon\",\"Canada\",\"Central African Republic\",\"Chad\",\"Chile\",\"China\",\"Colombia\",\"Comoros\",\"Congo, Dem. Rep.\",\"Congo, Rep.\",\"Costa Rica\",\"Cote d'Ivoire\",\"Croatia\",\"Cuba\",\"Czech Republic\",\"Denmark\",\"Djibouti\",\"Dominican Republic\",\"Ecuador\",\"Egypt\",\"El Salvador\",\"Equatorial Guinea\",\"Eritrea\",\"Ethiopia\",\"Finland\",\"France\",\"Gabon\",\"Gambia\",\"Germany\",\"Ghana\",\"Greece\",\"Guatemala\",\"Guinea\",\"Guinea-Bissau\",\"Haiti\",\"Honduras\",\"Hong Kong, China\",\"Hungary\",\"Iceland\",\"India\",\"Indonesia\",\"Iran\",\"Iraq\",\"Ireland\",\"Israel\",\"Italy\",\"Jamaica\",\"Japan\",\"Jordan\",\"Kenya\",\"Korea, Dem. Rep.\",\"Korea, Rep.\",\"Kuwait\",\"Lebanon\",\"Lesotho\",\"Liberia\",\"Libya\",\"Madagascar\",\"Malawi\",\"Malaysia\",\"Mali\",\"Mauritania\",\"Mauritius\",\"Mexico\",\"Mongolia\",\"Montenegro\",\"Morocco\",\"Mozambique\",\"Myanmar\",\"Namibia\",\"Nepal\",\"Netherlands\",\"New Zealand\",\"Nicaragua\",\"Niger\",\"Nigeria\",\"Norway\",\"Oman\",\"Pakistan\",\"Panama\",\"Paraguay\",\"Peru\",\"Philippines\",\"Poland\",\"Portugal\",\"Puerto Rico\",\"Reunion\",\"Romania\",\"Rwanda\",\"Sao Tome and Principe\",\"Saudi Arabia\",\"Senegal\",\"Serbia\",\"Sierra Leone\",\"Singapore\",\"Slovak Republic\",\"Slovenia\",\"Somalia\",\"South Africa\",\"Spain\",\"Sri Lanka\",\"Sudan\",\"Swaziland\",\"Sweden\",\"Switzerland\",\"Syria\",\"Taiwan\",\"Tanzania\",\"Thailand\",\"Togo\",\"Trinidad and Tobago\",\"Tunisia\",\"Turkey\",\"Uganda\",\"United Kingdom\",\"United States\",\"Uruguay\",\"Venezuela\",\"Vietnam\",\"West Bank and Gaza\",\"Yemen, Rep.\",\"Zambia\",\"Zimbabwe\"],\"locations\":[\"AFG\",\"ALB\",\"DZA\",\"AGO\",\"ARG\",\"AUS\",\"AUT\",\"BHR\",\"BGD\",\"BEL\",\"BEN\",\"BOL\",\"BIH\",\"BWA\",\"BRA\",\"BGR\",\"BFA\",\"BDI\",\"KHM\",\"CMR\",\"CAN\",\"CAF\",\"TCD\",\"CHL\",\"CHN\",\"COL\",\"COM\",\"COD\",\"COG\",\"CRI\",\"CIV\",\"HRV\",\"CUB\",\"CZE\",\"DNK\",\"DJI\",\"DOM\",\"ECU\",\"EGY\",\"SLV\",\"GNQ\",\"ERI\",\"ETH\",\"FIN\",\"FRA\",\"GAB\",\"GMB\",\"DEU\",\"GHA\",\"GRC\",\"GTM\",\"GIN\",\"GNB\",\"HTI\",\"HND\",\"HKG\",\"HUN\",\"ISL\",\"IND\",\"IDN\",\"IRN\",\"IRQ\",\"IRL\",\"ISR\",\"ITA\",\"JAM\",\"JPN\",\"JOR\",\"KEN\",\"KOR\",\"KOR\",\"KWT\",\"LBN\",\"LSO\",\"LBR\",\"LBY\",\"MDG\",\"MWI\",\"MYS\",\"MLI\",\"MRT\",\"MUS\",\"MEX\",\"MNG\",\"MNE\",\"MAR\",\"MOZ\",\"MMR\",\"NAM\",\"NPL\",\"NLD\",\"NZL\",\"NIC\",\"NER\",\"NGA\",\"NOR\",\"OMN\",\"PAK\",\"PAN\",\"PRY\",\"PER\",\"PHL\",\"POL\",\"PRT\",\"PRI\",\"REU\",\"ROU\",\"RWA\",\"STP\",\"SAU\",\"SEN\",\"SRB\",\"SLE\",\"SGP\",\"SVK\",\"SVN\",\"SOM\",\"ZAF\",\"ESP\",\"LKA\",\"SDN\",\"SWZ\",\"SWE\",\"CHE\",\"SYR\",\"TWN\",\"TZA\",\"THA\",\"TGO\",\"TTO\",\"TUN\",\"TUR\",\"UGA\",\"GBR\",\"USA\",\"URY\",\"VEN\",\"VNM\",\"PSE\",\"YEM\",\"ZMB\",\"ZWE\"],\"name\":\"\",\"type\":\"choropleth\",\"z\":[43.828,76.423,72.301,42.731,75.32,81.235,79.829,75.635,64.062,79.441,56.728,65.554,74.852,50.728,72.39,73.005,52.295,49.58,59.723,50.43,80.653,44.741,50.651,78.553,72.961,72.889,65.152,46.462,55.322,78.782,48.328,75.748,78.273,76.486,78.332,54.791,72.235,74.994,71.338,71.878,51.579,58.04,52.947,79.313,80.657,56.735,59.448,79.406,60.022,79.483,70.259,56.007,46.388,60.916,70.198,82.208,73.338,81.757,64.698,70.65,70.964,59.545,78.885,80.745,80.546,72.567,82.603,72.535,54.11,67.297,78.623,77.588,71.993,42.592,45.678,73.952,59.443,48.303,74.241,54.467,64.164,72.801,76.195,66.803,74.543,71.164,42.082,62.069,52.906,63.785,79.762,80.204,72.899,56.867,46.859,80.196,75.64,65.483,75.537,71.752,71.421,71.688,75.563,78.098,78.746,76.442,72.476,46.242,65.528,72.777,63.062,74.002,42.568,79.972,74.663,77.926,48.159,49.339,80.941,72.396,58.556,39.613,80.884,81.701,74.143,78.4,52.517,70.616,58.42,69.819,73.923,71.777,51.542,79.425,78.242,76.384,73.747,74.249,73.422,62.698,42.384,43.487]}],\"name\":\"2007\"}]);\n",
" }).then(function(){\n",
" \n",
"var gd = document.getElementById('f8e60f99-e49e-46f5-85ac-54ad31350c13');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; }); </script> </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"\n",
"df = px.data.gapminder()\n",
"fig = px.choropleth(df, locations=\"iso_alpha\",\n",
" color=\"lifeExp\", # lifeExp is a column of gapminder\n",
" hover_name=\"country\", # column to add to hover information\n",
" color_continuous_scale=px.colors.sequential.Plasma,\n",
" animation_frame='year')\n",
"\n",
"# fig2 = px.scatter_geo(df, locations=\"iso_alpha\",\n",
"# size=\"lifeExp\", # lifeExp is a column of gapminder\n",
"# hover_name=\"country\", # column to add to hover information\n",
"# color_continuous_scale=px.colors.sequential.Plasma,\n",
"# animation_frame='year')\n",
"fig.add_trace(fig2.data[0])\n",
"\n",
"# for i, frame in enumerate(fig.frames):\n",
"# fig.frames[i].data += (fig2.frames[i].data[0],)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!conda install -c plotly plotly_express -y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"open_ncdf(df_filter1['nc_file'].iloc[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from shapely import wkt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fam_test = df_filter1.query('uid == 118')\n",
"fam_test['geom_35'].fillna(value='GEOMETRYCOLLECTION EMPTY', inplace=True)\n",
"\n",
"\n",
"geo_frame = gpd.GeoDataFrame()\n",
"geo_frame['geometry'] = fam_test['geom_35'].apply(wkt.loads)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"geo_frame.tail(2).plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fam_test['geom_35'].apply(wkt.loads)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tt = gpd.GeoDataFrame()\n",
"tt['geometry'] = fam_test['geom_intersect'].apply(wkt.loads)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tt.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import geopandas as gpd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"### PATH\n",
"PATH_FILE = '../tracks/S201409070000_E201409100000_VDBZc_T20_L5.pkl'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"### Read tracking file\n",
"df = sta.read_file(PATH_FILE)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sta.life_cicle()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"life_cicle(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Dataframe library\n",
"import pandas as pd\n",
"# Numerical Python library\n",
"import numpy as np\n",
"# netCDF4 library\n",
"import netCDF4\n",
"# Import gzip to open netCDF\n",
"import gzip\n",
"# Visualization library\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variables\n",
"\n",
"**Fam_Nº**-> Refers to the number of the Tracked Family.\n",
"<br>\n",
"\n",
"**timestamp** ->A digital record of the time of occurrence of a particular event.\n",
"<br>\n",
"**time** -> Refers to the tracking time in the algorithm.\n",
"<br>\n",
"**uid** -> Unique IDentifier, it is used to generate the families.\n",
"<br>\n",
"**id_t** -> Referring cluster identifier at the time of tracking occurrence. From the DBSCAN clustering algorithm.\n",
"<br>\n",
"**lat** -> Refers latitude centroid, taken from the reference matrix of the original nc files.\n",
"<br>\n",
"**lon** -> Refers longitude centroid, taken from the reference matrix of the original nc files.\n",
"<br>\n",
"**p0** -> The first coordinate point of centroid in matrix (clusters or nc_file): (p0,p1)=(x,y)=(lon,lat).\n",
"<br>\n",
"**p1** -> The second coordinate point of centroid in matrix (clusters or nc_file): (p0,p1)=(x,y)=(lon,lat).\n",
"<br>\n",
"**size_%THRESHOLD** -> Total number of Pixels in the main cluster. Each point depends on the sensor's spatial resolution (pixel size): RADAR 2x2km.\n",
"<br>\n",
"**mean_ref_%THRESHOLD** -> Averaged reflectivity of the cluster. Value in dBZ.\n",
"<br>\n",
"**max_ref_%THRESHOLD** -> Max reflectivity of the cluster. Value in dBZ.\n",
"<br>\n",
"**angle_%THRESHOLD_orig** -> Original displacement angle of the cluster at the current time.\n",
"<br>\n",
"**angle_%THRESHOLD_cor** -> Corrected displacement angle of the cluster at the current time. \n",
"<br>\n",
"**vel_%THRESHOLD_orig** -> Original displacement velocity of the cluster at the current time in kilometers per hour (km/h).\n",
"<br>\n",
"**vel_%THRESHOLD_cor** -> Corrected displacement velocity of the cluster at the current time in kilometers per hour (km/h).\n",
"<br>\n",
"**mean_total_ref_%THRESHOLD** -> Average reflectivity of the inner clusters by threshold (Value in dBZ).\n",
"<br>\n",
"**total_size_%THRESHOLD** -> Total size of inner clusters by threshold (number of pixels).\n",
"<br>\n",
"**n_cluster_%THRESHOLD** -> Total number of inner clusters by Threshold.\n",
"<br>\n",
"**avg_angle_%THRESHOLD** -> Averaged angle for the inner cluster by threshold (Value in degree).\n",
"<br>\n",
"**avg_vel_%THRESHOLD** -> Averaged velocity for inner clusters by threshold (Value in km/h).\n",
"<br>\n",
"**status** -> Status of occurrence, type: NEW-> New cluster; CONT-> Continous cluster; SPLT -> Splitted cluster; MERG -> Merged Cluster.\n",
"<br>\n",
"**delta_t** -> Time interval for cluster life cycle.\n",
"<br>\n",
"**nc_file** -> Path of netCDF file.\n",
"<br>\n",
"**cluster_file** -> Path of cluster file (From DBSCAN).\n",
"<br>\n",
"**dsize_%THRESHOLD** -> Difference between the sizes of two consecutive clusters (in Pixel).\n",
"<br>\n",
"**dmean_ref_%THRESHOLD** -> Difference between the mean reflectivities of two consecutive clusters for main threshold (in dBZ).\n",
"<br>\n",
"**dmean_total_ref_%THRESHOLD** -> Difference between the mean reflectivities of all clusters between two consecutive times for an inner threshold (in dBZ).\n",
"<br>\n",
"**dtotal_size_%THRESHOLD** -> Difference between the total size (in pixel) of all clusters between two consecutive times for an inner threshold (values in pixel)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read tracking file\n",
"\n",
"Tracking DataFrame."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fam_tracking = pd.read_pickle(\"./S201409070000_E201409100000_VDBZc_T20_L5.pkl\")\n",
"fam_tracking"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example how to select a FAM by uid"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"uid = 97\n",
"selected_fam = fam_tracking.query('uid == @uid')\n",
"selected_fam"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example how to select a cluster in the family"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"line = 0 #first line \n",
"\n",
"selected_line = selected_fam.iloc[[line]]\n",
"selected_line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example how to open the cluster file and the original data to extract reflectivity values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## OPEN CLUSTERS\n",
"def open_cluster(path):\n",
" try:\n",
" cluster = np.load(path['cluster_file'].values[0])['arr_0']\n",
" cluster[cluster == 0] = np.NAN\n",
" return cluster\n",
" except:\n",
" print('File not found!')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"selected_line"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cluster_matrix_all = open_cluster(selected_line)\n",
"print('Original dimensions of cluster->',cluster_matrix_all.shape)\n",
"\n",
"THRESHOLD_LEVEL = 0 #to select the main threshold (ex: 0-20dBZ,1-35dBZ,2-40dBZ)\n",
"cluster_matrix = cluster_matrix_all[:,:,THRESHOLD_LEVEL]\n",
"print('Selected dimensions of cluster->',cluster_matrix.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"### OPEN NETCDF\n",
"def open_file(file_path):\n",
" VAR_NAME = 'DBZc'\n",
" LEVEL = 5 #2.5km height\n",
" THRESHOLDS = [20,35,40] #dBZ\n",
" with gzip.open(file_path['nc_file'].values[0]) as gz:\n",
" with netCDF4.Dataset('dummy', mode='r', memory=gz.read()) as nc:\n",
" data = nc.variables[VAR_NAME][0][LEVEL][:].filled()\n",
" data[data == -9999.] = np.NAN\n",
" data[data < THRESHOLDS[0]] = np.nan\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nc_matrix = open_file(selected_line)\n",
"print('NetCDF Max/Min values (thresholded):\\n',np.nanmax(nc_matrix),np.nanmin(nc_matrix))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, (ax,ax1) = plt.subplots(1,2, figsize=(15,6))\n",
"\n",
"ax.imshow(nc_matrix)\n",
"ax1.imshow(cluster_matrix);\n",
"ax.set_title('Original file')\n",
"ax1.set_title('Cluster file');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extracting reflectivities from the selected cluster\n",
"\n",
"To extract the reflectivity values of an individual cluster, you will need to choose the tracking 'id_t', this should be done as follows:\n",
"\n",
"Visualization of individual line."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"selected_line"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"### This line shows that id_t is equal to 20. \n",
"selected_id_t = selected_line.id_t.values[0]\n",
"\n",
"### Get XY coordinates from cluster matrix\n",
"x,y = np.where(cluster_matrix == selected_id_t)\n",
"\n",
"### Get reflectivities values from nc_file cluster\n",
"dbz_list = nc_matrix[x,y]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('List with reflectivity values of an individual cluster.\\n',dbz_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster location view"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, (ax,ax1) = plt.subplots(1,2, figsize=(15,6))\n",
"ax.imshow(nc_matrix)\n",
"ax1.imshow(cluster_matrix);\n",
"ax.set_title('Original file')\n",
"ax1.set_title('Cluster file');\n",
"\n",
"ax.scatter(selected_line.p0,selected_line.p1,marker='x',color='r',s=100)\n",
"ax1.scatter(selected_line.p0,selected_line.p1,marker='x',color='r',s=100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"file_extension": ".py",
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