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epifanio/PDAL_EM1000.ipynb

Created February 26, 2019 19:16
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PDAL_EM1000.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import datetime as dt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"st_time = dt.datetime.now()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**to speed up the loading and minimize the size of the data I used the `feather format`**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"em_data = pd.read_feather('em_df.ft')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>X</th>\n",
" <th>Y</th>\n",
" <th>Z</th>\n",
" <th>GpsTime</th>\n",
" <th>Amplitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1.226761e+07</td>\n",
" <td>1.226761e+07</td>\n",
" <td>1.226761e+07</td>\n",
" <td>1.226761e+07</td>\n",
" <td>1.226761e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-6.890380e+01</td>\n",
" <td>4.100966e+01</td>\n",
" <td>-7.718564e+01</td>\n",
" <td>5.955593e+10</td>\n",
" <td>-2.601706e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>8.014719e-01</td>\n",
" <td>4.787278e-01</td>\n",
" <td>9.581520e+00</td>\n",
" <td>3.322059e+05</td>\n",
" <td>7.345970e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-6.909418e+01</td>\n",
" <td>-1.320920e-03</td>\n",
" <td>-1.473200e+02</td>\n",
" <td>5.955550e+10</td>\n",
" <td>-6.400000e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-6.899785e+01</td>\n",
" <td>4.096217e+01</td>\n",
" <td>-8.258000e+01</td>\n",
" <td>5.955559e+10</td>\n",
" <td>-3.100000e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-6.891094e+01</td>\n",
" <td>4.101923e+01</td>\n",
" <td>-7.606000e+01</td>\n",
" <td>5.955588e+10</td>\n",
" <td>-2.475000e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>-6.883640e+01</td>\n",
" <td>4.107753e+01</td>\n",
" <td>-7.054000e+01</td>\n",
" <td>5.955639e+10</td>\n",
" <td>-2.075000e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2.546906e-03</td>\n",
" <td>4.114448e+01</td>\n",
" <td>-3.808000e+01</td>\n",
" <td>5.955648e+10</td>\n",
" <td>3.750000e+00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Y Z GpsTime Amplitude\n",
"count 1.226761e+07 1.226761e+07 1.226761e+07 1.226761e+07 1.226761e+07\n",
"mean -6.890380e+01 4.100966e+01 -7.718564e+01 5.955593e+10 -2.601706e+01\n",
"std 8.014719e-01 4.787278e-01 9.581520e+00 3.322059e+05 7.345970e+00\n",
"min -6.909418e+01 -1.320920e-03 -1.473200e+02 5.955550e+10 -6.400000e+01\n",
"25% -6.899785e+01 4.096217e+01 -8.258000e+01 5.955559e+10 -3.100000e+01\n",
"50% -6.891094e+01 4.101923e+01 -7.606000e+01 5.955588e+10 -2.475000e+01\n",
"75% -6.883640e+01 4.107753e+01 -7.054000e+01 5.955639e+10 -2.075000e+01\n",
"max 2.546906e-03 4.114448e+01 -3.808000e+01 5.955648e+10 3.750000e+00"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"em_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>X</th>\n",
" <th>Y</th>\n",
" <th>Z</th>\n",
" <th>GpsTime</th>\n",
" <th>Amplitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-34.527571</td>\n",
" <td>20.512344</td>\n",
" <td>-80.39</td>\n",
" <td>5.955646e+10</td>\n",
" <td>-40.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-34.527742</td>\n",
" <td>20.512322</td>\n",
" <td>-80.92</td>\n",
" <td>5.955646e+10</td>\n",
" <td>-43.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-34.527914</td>\n",
" <td>20.512300</td>\n",
" <td>-79.79</td>\n",
" <td>5.955646e+10</td>\n",
" <td>-43.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-34.528039</td>\n",
" <td>20.512284</td>\n",
" <td>-80.02</td>\n",
" <td>5.955646e+10</td>\n",
" <td>-39.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-34.528134</td>\n",
" <td>20.512273</td>\n",
" <td>-81.11</td>\n",
" <td>5.955646e+10</td>\n",
" <td>-37.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Y Z GpsTime Amplitude\n",
"0 -34.527571 20.512344 -80.39 5.955646e+10 -40.75\n",
"1 -34.527742 20.512322 -80.92 5.955646e+10 -43.75\n",
"2 -34.527914 20.512300 -79.79 5.955646e+10 -43.75\n",
"3 -34.528039 20.512284 -80.02 5.955646e+10 -39.50\n",
"4 -34.528134 20.512273 -81.11 5.955646e+10 -37.00"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"em_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"**Group by `GpsTime`**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"gtimer = em_data.groupby(['GpsTime'], sort=False)['X'].count().reset_index()\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GpsTime</th>\n",
" <th>X</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.955646e+10</td>\n",
" <td>54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.955646e+10</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.955646e+10</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.955646e+10</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.955646e+10</td>\n",
" <td>58</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GpsTime X\n",
"0 5.955646e+10 54\n",
"1 5.955646e+10 56\n",
"2 5.955646e+10 59\n",
"3 5.955646e+10 59\n",
"4 5.955646e+10 58"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gtimer.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Compute the index for the nadir beam of each ping**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| Beam Count | Nadir Index | integer resulting from: |\n",
"|------|------|------------------------------------------------|\n",
"| 54 | 27| $\\frac{54}{2}$ |\n",
"| 56 | 82| $54+\\frac{56}{2}$ |\n",
"| 59 | 139| $54+56+\\frac{59}{2}$ |\n",
"| 59 | 198| $54+56+59+\\frac{59}{2}$ |\n",
"| 58 | 257| $54+56+59+59+\\frac{58}{2}$ |"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"beam_list = list(gtimer['X'].values)\n",
"#beam_list[0:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"beam_indexes = [[i,v] for i,v in enumerate(beam_list)]\n",
"#beam_indexes[0:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# consider using np.cumsum(beamlist) \n",
"# and then add the alf part to get the middlepoint\n",
"def getNadirIndex(beam_index, beam_list):\n",
" if beam_index[0] == 0:\n",
" nadir_index = int( beam_index[1]/2)\n",
" if beam_index[0] != 0:\n",
" nadir_index = int(sum(beam_list[0:beam_index[0]])+(beam_index[1]/2))\n",
" return nadir_index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#beam_indexes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Test getNadirIndex**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#for i in range(5):\n",
"# mdp = getNadirIndex(beam_indexes[i], beam_list)\n",
"# print(mdp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Parallelize `getNadirIndex` to loop over `beamlist`**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import parmap\n",
"#nadir_indexs = parmap.map(getNadirIndex, beam_indexes, beam_list, pm_processes=48, pm_pbar=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#nadir_indexs = parmap.map(getNadirIndex, beam_indexes, beam_list, pm_processes=48, pm_pbar=True, pm_chunksize=4000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#import pickle"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#nadirIndex_pickle = open(\"nadirIndex.pickle\",\"wb\")\n",
"#pickle.dump(nadirIndex, nadirIndex_pickle)\n",
"#nadirIndex_pickle.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"**Transform the Longitude and Latitude to UTM**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pyproj import Proj, transform"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from scipy.spatial import distance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"wgs84 = Proj(\"+init=EPSG:4326\")\n",
"UTM19N = Proj(\"+init=EPSG:32619\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xy = em_data[['X','Y']].values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"em_data['XUTM'], em_data['YUTM'] = transform(wgs84, UTM19N,xy[:,0], xy[:,1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\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>X</th>\n",
" <th>Y</th>\n",
" <th>Z</th>\n",
" <th>GpsTime</th>\n",
" <th>Amplitude</th>\n",
" <th>XUTM</th>\n",
" <th>YUTM</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-34.527571</td>\n",
" <td>20.512344</td>\n",
" <td>-80.39</td>\n",
" <td>5.955646e+10</td>\n",
" <td>-40.75</td>\n",
" <td>4.266364e+06</td>\n",
" <td>2.702726e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-34.527742</td>\n",
" <td>20.512322</td>\n",
" <td>-80.92</td>\n",
" <td>5.955646e+10</td>\n",
" <td>-43.75</td>\n",
" <td>4.266344e+06</td>\n",
" <td>2.702718e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-34.527914</td>\n",
" <td>20.512300</td>\n",
" <td>-79.79</td>\n",
" <td>5.955646e+10</td>\n",
" <td>-43.75</td>\n",
" <td>4.266325e+06</td>\n",
" <td>2.702711e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-34.528039</td>\n",
" <td>20.512284</td>\n",
" <td>-80.02</td>\n",
" <td>5.955646e+10</td>\n",
" <td>-39.50</td>\n",
" <td>4.266310e+06</td>\n",
" <td>2.702705e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-34.528134</td>\n",
" <td>20.512273</td>\n",
" <td>-81.11</td>\n",
" <td>5.955646e+10</td>\n",
" <td>-37.00</td>\n",
" <td>4.266299e+06</td>\n",
" <td>2.702701e+06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Y Z GpsTime Amplitude XUTM \\\n",
"0 -34.527571 20.512344 -80.39 5.955646e+10 -40.75 4.266364e+06 \n",
"1 -34.527742 20.512322 -80.92 5.955646e+10 -43.75 4.266344e+06 \n",
"2 -34.527914 20.512300 -79.79 5.955646e+10 -43.75 4.266325e+06 \n",
"3 -34.528039 20.512284 -80.02 5.955646e+10 -39.50 4.266310e+06 \n",
"4 -34.528134 20.512273 -81.11 5.955646e+10 -37.00 4.266299e+06 \n",
"\n",
" YUTM \n",
"0 2.702726e+06 \n",
"1 2.702718e+06 \n",
"2 2.702711e+06 \n",
"3 2.702705e+06 \n",
"4 2.702701e+06 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"em_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#gtimer['X'].iloc[0], nadir_indexs[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#midpoint = em_data[['XUTM','YUTM']].iloc[nadir_indexs[0]].values\n",
"#midpoint"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#coords = em_data[['XUTM','YUTM']].values\n",
"#coords"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#mid_points = []\n",
"#for i in range(len(nadir_indexs)):\n",
"# mid_points.append(coords[nadir_indexs[i]])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Z = em_data['Z'].values\n",
"#Z"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xyz = em_data[['XUTM','YUTM', 'Z']].values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ping_indexes = np.cumsum(beam_list)\n",
"#ping_indexes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def getAngle(ping_index, xyz, ping_indexes, beam_indexes):\n",
" if ping_index == 0:\n",
" start=0\n",
" stop=ping_indexes[ping_index]\n",
" shape=ping_indexes[ping_index]\n",
" nadir_index = int( beam_indexes[ping_index][1]/2)\n",
" else:\n",
" start=ping_indexes[ping_index-1]\n",
" stop=ping_indexes[ping_index]\n",
" shape=ping_indexes[ping_index] - ping_indexes[ping_index-1]\n",
" nadir_index = int(sum(beam_list[0: beam_indexes[ping_index][0]])+( beam_indexes[ping_index][1]/2))\n",
" #coords = xyz[:, 0:2]\n",
" Z = xyz[:, 2]\n",
" #midpoint = coords[nadir_index]\n",
" midpoint = xyz[:, 0:2][nadir_index] \n",
" \n",
" pings = coords[start:stop]\n",
" Zi = Z[start:stop] \n",
" XY = [tuple(midpoint)]\n",
" coord = [tuple(j) for j in tuple(pings)]\n",
" d = distance.cdist(XY, coord, 'euclidean')\n",
" angle = np.rad2deg(np.arctan2(np.abs(Zi),d)).reshape(shape,)\n",
" return angle"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#ping_indexes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#beam_indexes"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# consider using np.cumsum(beamlist) \n",
"# and then add the alf part to get the middlepoint\n",
"def getNadirIndex(beam_index, beam_list):\n",
" if beam_index[0] == 0:\n",
" nadir_index = int( beam_index[1]/2)\n",
" if beam_index[0] != 0:\n",
" nadir_index = int(sum(beam_list[0:beam_index[0]])+(beam_index[1]/2))\n",
" #print(beam_list[0:beam_index[0]], beam_index[1]/2, '\\n')\n",
" #print(beam_index[1])\n",
" return nadir_index\n",
"\n",
"for i in range(5):\n",
" mdp = getNadirIndex(beam_indexes[i], beam_list)\n",
" print(mdp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#getAngle(2, xyz, ping_indexes, beam_indexes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"input_indexes = list(range(len(ping_indexes)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 76%|███████▌ | 157905/209062 [03:44<01:13, 699.78it/s]"
]
}
],
"source": [
"angles = parmap.map(getAngle, input_indexes, xyz, ping_indexes, beam_indexes, pm_processes=48, pm_pbar=True)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"em_data['angle']=\"\"\n",
"angles=[]\n",
"\n",
"for i in range(len(ping_indexes)):\n",
" if i == 0:\n",
" start=0\n",
" stop=ping_indexes[i]\n",
" shape=ping_indexes[i]\n",
" else:\n",
" start=ping_indexes[i-1]\n",
" stop=ping_indexes[i]\n",
" shape=ping_indexes[i] - ping_indexes[i-1]\n",
" \n",
" midpoint = coords[nadir_indexs[i]]\n",
" pings = coords[start:stop]\n",
" Zi = Z[start:stop] \n",
" XY = [tuple(midpoint)]\n",
" coord = [tuple(j) for j in tuple(pings)]\n",
" d = distance.cdist(XY, coord, 'euclidean')\n",
" angle = np.rad2deg(np.arctan2(np.abs(Zi),d)).reshape(shape,)\n",
" angles.append(angle)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#ping_indexes\n",
"#nadir_indexs\n",
"#coord\n",
"#Z"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"angle_values = [beam for ping in angles for beam in ping]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"em_data['angle'] = angle_values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"end_time = dt.datetime.now()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"end_time-st_time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"em_data.head(60)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(em_data['angle'].iloc[0:54].values, em_data['Amplitude'].iloc[0:54].values, '-');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#!gist PDAL_EM1000.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#em_data.to_csv('em_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"em_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"em_data.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# need to start to discard some bad data ...\n",
"em_data['Amplitude'][em_data['Amplitude']>=0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gist PDAL_EM1000.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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