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Read binary data from Lunar Prospector Reduced Spectrometer Data Special Products (how to read binary data from [PDS Geosciences Node Data and Services)
data/
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Bin Size Units Std. Dev. References ASCII_data_url Binary_data_url ASCII_data Binary_data Data Name Pixels Size scaling factor
60km x 60km Counts per 32 sec Yes Feldman et al., 1999, 2000a, 2001a; Elphic et al., 2000; Maurice et al., 2001a https://pds-geosciences.wustl.edu/missions/lunarp/reduced/therms.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/therms.dat therms.txt therms.dat Thermal Neutrons 2 degree 10.0
60km x 60km Counts per 32 sec Yes Feldman et al., 1999, 2000b, 2001a, 2001c; Maurice et al., 2000, 2001a https://pds-geosciences.wustl.edu/missions/lunarp/reduced/epis.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/epis.dat epis.txt epis.dat Epithermal Neutrons 2 degree 10.0
60km x 60km Counts per 32 sec Yes Feldman et al., 1999, 2001b; Maurice et al., 2000, 2001a https://pds-geosciences.wustl.edu/missions/lunarp/reduced/fast.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/fast.dat fast.txt fast.dat Fast Neutrons 2 degree 10.0
60km x 60km Counts per 32 sec Yes Maurice et al., 2000, 2001b https://pds-geosciences.wustl.edu/missions/lunarp/reduced/fastspectra.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/fastspectra.dat fastspectra.txt fastspectra.dat Fast Neutron Spectra 2 degree 10.0
150km x 150km Al wt. % No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/aluminum5d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/aluminum5d.dat aluminum5d.txt aluminum5d.dat Aluminum 5 degree 10.0
150km x 150km Ca wt. % No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/calcium5d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/calcium5d.dat calcium5d.txt calcium5d.dat Calcium 5 degree 10.0
60km x 60km ppm No Feldman et al., 2001c https://pds-geosciences.wustl.edu/missions/lunarp/reduced/hydrogenlow.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/hydrogenlow.dat hydrogenlow.txt hydrogenlow.dat Hydrogen 2 degree 10.0
0.5° x 0.5° ppm No Feldman et al., 2001c https://pds-geosciences.wustl.edu/missions/lunarp/reduced/hydrogenhd.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/hydrogenhd.dat hydrogenhd.txt hydrogenhd.dat Hydrogen half degree 10.0
150km x 150km Fe wt. % No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/iron5d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/iron5d.dat iron5d.txt iron5d.dat Iron 5 degree 10.0
0.5° x 0.5° FeO wt. % No Lawrence et al., 2001c https://pds-geosciences.wustl.edu/missions/lunarp/reduced/ironhd.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/ironhd.dat ironhd.txt ironhd.dat Iron half degree 10.0
150km x 150km Mg wt. % No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/magnesium5d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/magnesium5d.dat magnesium5d.txt magnesium5d.dat Magnesium 5 degree 10.0
150km x 150km O wt. % No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/oxygen5d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/oxygen5d.dat oxygen5d.txt oxygen5d.dat Oxygen 5 degree 10.0
300km lon. x 450km lat. Counts per sec No Lawson et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/polonium210.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/polonium210.dat polonium210.txt polonium210.dat Polonium-210 equal-area pixels 1000.0
150km x 150km ppm No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/potassium5d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/potassium5d.dat potassium5d.txt potassium5d.dat Potassium 5 degree 1.0
60km x 60km ppm No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/potassium2d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/potassium2d.dat potassium2d.txt potassium2d.dat Potassium 2 degree 1.0
300km lon. x 450km lat. Counts per sec No Lawson et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/radon222.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/radon222.dat radon222.txt radon222.dat Radon-222 equal-area pixels 1000.0
60km x 60km μg/g No Elphic et al., 2000 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/samarium2d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/samarium2d.dat samarium2d.txt samarium2d.dat Samarium 2 degree 10.0
150km x 150km Si wt. % No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/silicon5d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/silicon5d.dat silicon5d.txt silicon5d.dat Silicon 5 degree 10.0
60km x 60km μg/g Yes Lawrence et al., 2000 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/thoriumhigh.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/thoriumhigh.dat thoriumhigh.txt thoriumhigh.dat Thorium high-altitude 10.0
60km x 60km μg/g Yes Lawrence et al., 2000 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/thoriumlow.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/thoriumlow.dat thoriumlow.txt thoriumlow.dat Thorium low-altitude 10.0
0.5° x 0.5° ppm No Lawrence et al., 2002a, 2002b https://pds-geosciences.wustl.edu/missions/lunarp/reduced/thoriumhd.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/thoriumhd.dat thoriumhd.txt thoriumhd.dat Thorium half degree 10.0
150km x 150km ppm No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/thorium5d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/thorium5d.dat thorium5d.txt thorium5d.dat Thorium 5 degree 10.0
150km x 150km Ti wt. % No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/titanium5d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/titanium5d.dat titanium5d.txt titanium5d.dat Titanium 5 degree 10.0
60km x 60km Ti wt. % No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/titanium2d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/titanium2d.dat titanium2d.txt titanium2d.dat Titanium 2 degree 10.0
150km x 150km ppm No Prettyman et al., 2002 https://pds-geosciences.wustl.edu/missions/lunarp/reduced/uranium5d.txt https://pds-geosciences.wustl.edu/missions/lunarp/reduced/uranium5d.dat uranium5d.txt uranium5d.dat Uranium 5 degree 10.0
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"source": [
"## Read Lunar Psospector Data\n",
"\n",
"Alissa Madera asked on [OpenPlanetary](https://www.openplanetary.org/) ([Forum](https://forum.openplanetary.org/)) how to read binary data from [PDS Geosciences Node Data and Services: Lunar Prospector Reduced Spectrometer Data Special Products](https://pds-geosciences.wustl.edu/missions/lunarp/reduced_special.html).\n",
"\n",
"This generated several answer, some of them are summarised by June Wang here : [Convert LP NS data from ASCII format to Geotif with a ArcGIS Pro Tool - For data users - PDS Geosciences Node Community](https://geoweb.rsl.wustl.edu/community/index.php?/topic/3426-convert-lp-ns-data-from-ascii-format-to-geotif-with-a-arcgis-pro-tool/&_fromLogin=1).\n",
"\n",
"The gist of it is to read carefulli the instruction :\n",
"> Each binary file is formatted as an image covering the entire planet with an array of 720 samples and 360 lines. Data values are scaled as integers in the image array with two bytes per pixel in IEEE order. For all binary files except the potassium, radon, and polonium, image values are ten times the values given in the ASCII files. The binary files for potassium abundances have integer values that are the same as values given in the corresponding ASCII file. The binary files for the radon and polonium data have integer values that are 1000 times the values given in the corresponding ASCII file. The binary image is a simple cylindrical map projection with 0.5° pixel size. The first image line corresponds to the south pole and the last image line corresponds to the north pole of the moon. The first sample corresponds to -180° East longitude and the last sample corresponds to 180° East longitude. Binary file names have the form <name>.dat and are about 0.5 MB in size.\n",
" \n",
"One caveat raised from June Wang \n",
"> But one thing need to notice is that all the binary data values are scaled to integers. After reading the data and scaling back to its original value, you will find there is slightly difference in the the accuracy of decimal points between ASCII and binary for all the data products if they have non-integer values in the ASCII files initially.\n",
" \n",
"## Instruction\n",
"\n",
"- The first part of this notebook shows how to quickly read the data in python.\n",
"- The second create a list of all datafiles and download them locally.\n"
]
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"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline"
]
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"## Read using numpy\n",
"\n",
"```data = np.fromfile('therms.dat',dtype=np.dtype('>h'),)/10```\n",
"\n",
"10 scaling is from the documentation, changes for each file."
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"# read using numpy\n",
"data = np.fromfile('therms.dat',dtype=np.dtype('>h')).reshape([360,720])/10\n",
"# 10 scaling is from the documentation, changes for each file."
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\n",
"text/plain": [
"<Figure size 576x252 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# 360,720 is from the documentation, changes for each file.\n",
"plt.figure(figsize=[8,3.5])\n",
"plt.imshow(data,origin='lower',cmap=plt.cm.inferno_r)\n",
"plt.colorbar(fraction=0.025)\n",
"plt.title('therms with numpy')"
]
},
{
"cell_type": "markdown",
"id": "420b76fb-1eef-453a-a997-98e984d86656",
"metadata": {
"tags": []
},
"source": [
"## Convert to geo-image with rasterio\n",
"\n",
"Just an initial try following [Writing Datasets — rasterio documentation](https://rasterio.readthedocs.io/en/latest/topics/writing.html)."
]
},
{
"cell_type": "code",
"execution_count": 161,
"id": "e0b220c5-771c-436f-a52d-b36296de0691",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-09T09:54:29.677473Z",
"iopub.status.busy": "2021-11-09T09:54:29.677238Z",
"iopub.status.idle": "2021-11-09T09:54:29.688066Z",
"shell.execute_reply": "2021-11-09T09:54:29.687362Z",
"shell.execute_reply.started": "2021-11-09T09:54:29.677412Z"
},
"tags": []
},
"outputs": [],
"source": [
"import rasterio\n",
"\n",
"defaults = {\n",
" 'driver': 'GTiff',\n",
" 'interleave': 'band',\n",
" 'tiled': False,\n",
" 'height': int(data.shape[1]),\n",
" 'width': int(data.shape[0]),\n",
" 'compress': 'JPEG',\n",
" 'nodata': 0,\n",
" 'dtype': data.dtype.__str__(),\n",
" 'count':1\n",
"}\n",
"profile = rasterio.profiles.DefaultGTiffProfile(defaults)\n",
"profile\n",
"with rasterio.open('therms.tif','w',**profile) as dst_dataset:\n",
" dst_dataset.write(data, 1)"
]
},
{
"cell_type": "markdown",
"id": "a17e26c0-c317-4c60-9513-8ad480b02ce0",
"metadata": {},
"source": [
"## Read using pandas\n"
]
},
{
"cell_type": "code",
"execution_count": 110,
"id": "3ccffa6e-9238-4aa2-859e-a0fb184b4719",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-09T09:33:36.295090Z",
"iopub.status.busy": "2021-11-09T09:33:36.294907Z",
"iopub.status.idle": "2021-11-09T09:33:36.563305Z",
"shell.execute_reply": "2021-11-09T09:33:36.562711Z",
"shell.execute_reply.started": "2021-11-09T09:33:36.295076Z"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'therms with pandas'}, xlabel='Lon_min', ylabel='Lat_min'>"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# read using pandas\n",
"df = pd.read_csv('therms.txt',skiprows=12,sep=',',\n",
" names=['Lat_min','Lat_max',\n",
" 'Lon_min','Lon_max',\n",
" 'Counts' ,'Std' ]\n",
" )\n",
"df.plot.scatter(x='Lon_min',y='Lat_min',c='Counts',\n",
" cmap=plt.cm.inferno_r,figsize=[8,4],\n",
" title='therms with pandas')"
]
},
{
"cell_type": "markdown",
"id": "c08da172-9ec3-4e82-b90d-a64537a0c5b9",
"metadata": {
"tags": []
},
"source": [
"## Generate data table\n",
"\n",
"Read the cleaned up version of `reduced_special.html` and exctract all datafile information.\n",
"\n",
"Save the table to `data_list.csv`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0b9a803b-5644-40e4-97fc-a41566a3fad4",
"metadata": {
"collapsed": true,
"execution": {
"iopub.execute_input": "2021-11-24T12:51:57.306336Z",
"iopub.status.busy": "2021-11-24T12:51:57.305627Z",
"iopub.status.idle": "2021-11-24T12:51:57.426519Z",
"shell.execute_reply": "2021-11-24T12:51:57.425856Z",
"shell.execute_reply.started": "2021-11-24T12:51:57.306286Z"
},
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"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>Data Name</th>\n",
" <th>Pixels Size</th>\n",
" <th>Bin Size</th>\n",
" <th>Units</th>\n",
" <th>scaling factor</th>\n",
" <th>Std. Dev.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Epithermal Neutrons</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>Counts per 32 sec</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Fast Neutron Spectra</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>Counts per 32 sec</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Fast Neutrons</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>Counts per 32 sec</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Hydrogen</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>ppm</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Potassium</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>ppm</td>\n",
" <td>1.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Samarium</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>μg/g</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Thermal Neutrons</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>Counts per 32 sec</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Titanium</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>Ti wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Aluminum</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Al wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Calcium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Ca wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Iron</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Fe wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Magnesium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Mg wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Oxygen</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>O wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Potassium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>ppm</td>\n",
" <td>1.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Silicon</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Si wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Thorium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>ppm</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Titanium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Ti wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Uranium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>ppm</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Hydrogen</td>\n",
" <td>half degree</td>\n",
" <td>0.5° x 0.5°</td>\n",
" <td>ppm</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Iron</td>\n",
" <td>half degree</td>\n",
" <td>0.5° x 0.5°</td>\n",
" <td>FeO wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Thorium</td>\n",
" <td>half degree</td>\n",
" <td>0.5° x 0.5°</td>\n",
" <td>ppm</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Thorium</td>\n",
" <td>high-altitude</td>\n",
" <td>60km x 60km</td>\n",
" <td>μg/g</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Thorium</td>\n",
" <td>low-altitude</td>\n",
" <td>60km x 60km</td>\n",
" <td>μg/g</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Polonium-210</td>\n",
" <td>equal-area pixels</td>\n",
" <td>300km lon. x 450km lat.</td>\n",
" <td>Counts per sec</td>\n",
" <td>1000.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Radon-222</td>\n",
" <td>equal-area pixels</td>\n",
" <td>300km lon. x 450km lat.</td>\n",
" <td>Counts per sec</td>\n",
" <td>1000.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Data Name Pixels Size Bin Size \\\n",
"2 Epithermal Neutrons 2 degree 60km x 60km \n",
"4 Fast Neutron Spectra 2 degree 60km x 60km \n",
"3 Fast Neutrons 2 degree 60km x 60km \n",
"7 Hydrogen 2 degree 60km x 60km \n",
"15 Potassium 2 degree 60km x 60km \n",
"17 Samarium 2 degree 60km x 60km \n",
"1 Thermal Neutrons 2 degree 60km x 60km \n",
"24 Titanium 2 degree 60km x 60km \n",
"5 Aluminum 5 degree 150km x 150km \n",
"6 Calcium 5 degree 150km x 150km \n",
"9 Iron 5 degree 150km x 150km \n",
"11 Magnesium 5 degree 150km x 150km \n",
"12 Oxygen 5 degree 150km x 150km \n",
"14 Potassium 5 degree 150km x 150km \n",
"18 Silicon 5 degree 150km x 150km \n",
"22 Thorium 5 degree 150km x 150km \n",
"23 Titanium 5 degree 150km x 150km \n",
"25 Uranium 5 degree 150km x 150km \n",
"8 Hydrogen half degree 0.5° x 0.5° \n",
"10 Iron half degree 0.5° x 0.5° \n",
"21 Thorium half degree 0.5° x 0.5° \n",
"19 Thorium high-altitude 60km x 60km \n",
"20 Thorium low-altitude 60km x 60km \n",
"13 Polonium-210 equal-area pixels 300km lon. x 450km lat. \n",
"16 Radon-222 equal-area pixels 300km lon. x 450km lat. \n",
"\n",
" Units scaling factor Std. Dev. \n",
"2 Counts per 32 sec 10.0 Yes \n",
"4 Counts per 32 sec 10.0 Yes \n",
"3 Counts per 32 sec 10.0 Yes \n",
"7 ppm 10.0 No \n",
"15 ppm 1.0 No \n",
"17 μg/g 10.0 No \n",
"1 Counts per 32 sec 10.0 Yes \n",
"24 Ti wt. % 10.0 No \n",
"5 Al wt. % 10.0 No \n",
"6 Ca wt. % 10.0 No \n",
"9 Fe wt. % 10.0 No \n",
"11 Mg wt. % 10.0 No \n",
"12 O wt. % 10.0 No \n",
"14 ppm 1.0 No \n",
"18 Si wt. % 10.0 No \n",
"22 ppm 10.0 No \n",
"23 Ti wt. % 10.0 No \n",
"25 ppm 10.0 No \n",
"8 ppm 10.0 No \n",
"10 FeO wt. % 10.0 No \n",
"21 ppm 10.0 No \n",
"19 μg/g 10.0 Yes \n",
"20 μg/g 10.0 Yes \n",
"13 Counts per sec 1000.0 No \n",
"16 Counts per sec 1000.0 No "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base_url = 'https://pds-geosciences.wustl.edu/missions/lunarp/reduced/'\n",
"\n",
"html_ls = pd.read_html('reduced_special.html')\n",
"\n",
"data_df = html_ls[-1]\n",
"\n",
"data_df.columns=data_df.loc[0].values\n",
"\n",
"data_df = data_df.drop(index=[0])\n",
"\n",
"data_df['ASCII_data_url'] = data_df['File Name'].map(lambda x: base_url+x.split()[0] )\n",
"data_df['Binary_data_url'] = data_df['File Name'].map(lambda x: base_url+x.split()[1] )\n",
"data_df['ASCII_data'] = data_df['File Name'].map(lambda x: x.split()[0] )\n",
"data_df['Binary_data'] = data_df['File Name'].map(lambda x: x.split()[1] )\n",
"data_df=data_df.drop(columns=['File Name'])\n",
"\n",
"data_df['Data Name']=data_df['Data Type'].map(lambda x: x.split(',')[0])\n",
"data_df['Pixels Size'] = data_df['Data Type'].map(lambda x: x.split(',')[1] if len(x.split(',')) > 1 else 'equal-area pixels' ) \n",
"data_df=data_df.drop(columns=['Data Type'])\n",
"\n",
"data_df['scaling factor'] = 10.\n",
"\n",
"data_df.loc[data_df['Data Name'].str.contains('Potassium'),'scaling factor'] = 1\n",
"data_df.loc[data_df['Data Name'].str.contains('Radon'),'scaling factor'] = 1000\n",
"data_df.loc[data_df['Data Name'].str.contains('Polonium'),'scaling factor'] = 1000\n",
"\n",
"data_df[['Data Name', 'Pixels Size','Bin Size', 'Units','scaling factor', 'Std. Dev.']].sort_values(['Pixels Size','Data Name'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "41193301-b272-403e-98f6-c6af6404cca0",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-24T12:52:11.067296Z",
"iopub.status.busy": "2021-11-24T12:52:11.066383Z",
"iopub.status.idle": "2021-11-24T12:52:11.073131Z",
"shell.execute_reply": "2021-11-24T12:52:11.072431Z",
"shell.execute_reply.started": "2021-11-24T12:52:11.067248Z"
}
},
"outputs": [],
"source": [
"data_df.to_csv('data_list.csv',index=None)"
]
},
{
"cell_type": "markdown",
"id": "16366b27-96b3-4d4b-a3b3-5b9b0e1bebb3",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
"## Download all data "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5f7e4dbd-2bda-45c0-8481-c3327509f080",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-24T12:53:03.046744Z",
"iopub.status.busy": "2021-11-24T12:53:03.046556Z",
"iopub.status.idle": "2021-11-24T12:53:03.084443Z",
"shell.execute_reply": "2021-11-24T12:53:03.083674Z",
"shell.execute_reply.started": "2021-11-24T12:53:03.046704Z"
}
},
"outputs": [],
"source": [
"import requests\n",
"import pathlib\n",
"from urllib.parse import urlparse\n",
"\n",
"datadir = pathlib.Path('data')\n",
"datadir.mkdir(exist_ok=True)"
]
},
{
"cell_type": "markdown",
"id": "6266c0ba-743b-406e-a5cf-9b5d50fd8f4c",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
"### Warning \n",
"\n",
"This uses pure python and it works, but it is __**extremely slow**__. I suggest to skip to the next cell if you are using Linux/Mac and use native tools."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7ea379d-3938-4a3d-8769-44afc43f2712",
"metadata": {},
"outputs": [],
"source": [
"for tup in data_df.itertuples():\n",
"\n",
" url = tup.ASCII_data\n",
" \n",
" url_parsed = urlparse(url)\n",
" print(f'Downloading {url_parsed.path}')\n",
" r = requests.get(url)\n",
" with open('data/'+pathlib.Path(url_parsed.path).name, 'wb') as f:\n",
" f.write(r.content) \n",
"\n",
" url = tup.Binary_data\n",
" \n",
" url_parsed = urlparse(url)\n",
" print(f'Downloading {url_parsed.path}')\n",
" r = requests.get(url)\n",
" with open('data/'+pathlib.Path(url_parsed.path).name, 'wb') as f:\n",
" f.write(r.content) "
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3b69ace5-cad4-470e-8f45-4cef89410a7e",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-24T12:53:27.480847Z",
"iopub.status.busy": "2021-11-24T12:53:27.480654Z",
"iopub.status.idle": "2021-11-24T12:53:27.486786Z",
"shell.execute_reply": "2021-11-24T12:53:27.486112Z",
"shell.execute_reply.started": "2021-11-24T12:53:27.480805Z"
},
"tags": []
},
"outputs": [],
"source": [
"filelist = data_df[['ASCII_data','Binary_data']].values.flatten()\n",
"np.savetxt('urls.txt',filelist, fmt='%s')"
]
},
{
"cell_type": "markdown",
"id": "76962941-df15-4500-9eab-6c46ba6f919d",
"metadata": {},
"source": [
"for this paralle download, you will need:\n",
"- gnu parallel \n",
"- wget\n",
"\n",
"\n",
"Please refer to your OS to install them (mac > homebrew/macports, linux > whatever package manage you use.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9ba0010-6477-4c8f-895d-164e886c285a",
"metadata": {},
"outputs": [],
"source": [
"!parallel -j 15 wget --directory-prefix=data < urls.txt"
]
},
{
"cell_type": "markdown",
"id": "eae14d90-89ae-4dd2-86e7-28a020e90efc",
"metadata": {},
"source": [
"## Read using numpy from local files\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c26fb7fe-a8a3-4aef-a999-2a79c00cf75d",
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-24T12:57:15.250005Z",
"iopub.status.busy": "2021-11-24T12:57:15.249714Z",
"iopub.status.idle": "2021-11-24T12:57:15.265913Z",
"shell.execute_reply": "2021-11-24T12:57:15.265209Z",
"shell.execute_reply.started": "2021-11-24T12:57:15.249956Z"
}
},
"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>Data Name</th>\n",
" <th>Pixels Size</th>\n",
" <th>Bin Size</th>\n",
" <th>Units</th>\n",
" <th>scaling factor</th>\n",
" <th>Std. Dev.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Epithermal Neutrons</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>Counts per 32 sec</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Fast Neutron Spectra</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>Counts per 32 sec</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Fast Neutrons</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>Counts per 32 sec</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Hydrogen</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>ppm</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Potassium</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>ppm</td>\n",
" <td>1.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Samarium</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>μg/g</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Thermal Neutrons</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>Counts per 32 sec</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Titanium</td>\n",
" <td>2 degree</td>\n",
" <td>60km x 60km</td>\n",
" <td>Ti wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Aluminum</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Al wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Calcium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Ca wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Iron</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Fe wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Magnesium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Mg wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Oxygen</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>O wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Potassium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>ppm</td>\n",
" <td>1.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Silicon</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Si wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Thorium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>ppm</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Titanium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>Ti wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Uranium</td>\n",
" <td>5 degree</td>\n",
" <td>150km x 150km</td>\n",
" <td>ppm</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Hydrogen</td>\n",
" <td>half degree</td>\n",
" <td>0.5° x 0.5°</td>\n",
" <td>ppm</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Iron</td>\n",
" <td>half degree</td>\n",
" <td>0.5° x 0.5°</td>\n",
" <td>FeO wt. %</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Thorium</td>\n",
" <td>half degree</td>\n",
" <td>0.5° x 0.5°</td>\n",
" <td>ppm</td>\n",
" <td>10.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Thorium</td>\n",
" <td>high-altitude</td>\n",
" <td>60km x 60km</td>\n",
" <td>μg/g</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Thorium</td>\n",
" <td>low-altitude</td>\n",
" <td>60km x 60km</td>\n",
" <td>μg/g</td>\n",
" <td>10.0</td>\n",
" <td>Yes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Polonium-210</td>\n",
" <td>equal-area pixels</td>\n",
" <td>300km lon. x 450km lat.</td>\n",
" <td>Counts per sec</td>\n",
" <td>1000.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Radon-222</td>\n",
" <td>equal-area pixels</td>\n",
" <td>300km lon. x 450km lat.</td>\n",
" <td>Counts per sec</td>\n",
" <td>1000.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Data Name Pixels Size Bin Size \\\n",
"1 Epithermal Neutrons 2 degree 60km x 60km \n",
"3 Fast Neutron Spectra 2 degree 60km x 60km \n",
"2 Fast Neutrons 2 degree 60km x 60km \n",
"6 Hydrogen 2 degree 60km x 60km \n",
"14 Potassium 2 degree 60km x 60km \n",
"16 Samarium 2 degree 60km x 60km \n",
"0 Thermal Neutrons 2 degree 60km x 60km \n",
"23 Titanium 2 degree 60km x 60km \n",
"4 Aluminum 5 degree 150km x 150km \n",
"5 Calcium 5 degree 150km x 150km \n",
"8 Iron 5 degree 150km x 150km \n",
"10 Magnesium 5 degree 150km x 150km \n",
"11 Oxygen 5 degree 150km x 150km \n",
"13 Potassium 5 degree 150km x 150km \n",
"17 Silicon 5 degree 150km x 150km \n",
"21 Thorium 5 degree 150km x 150km \n",
"22 Titanium 5 degree 150km x 150km \n",
"24 Uranium 5 degree 150km x 150km \n",
"7 Hydrogen half degree 0.5° x 0.5° \n",
"9 Iron half degree 0.5° x 0.5° \n",
"20 Thorium half degree 0.5° x 0.5° \n",
"18 Thorium high-altitude 60km x 60km \n",
"19 Thorium low-altitude 60km x 60km \n",
"12 Polonium-210 equal-area pixels 300km lon. x 450km lat. \n",
"15 Radon-222 equal-area pixels 300km lon. x 450km lat. \n",
"\n",
" Units scaling factor Std. Dev. \n",
"1 Counts per 32 sec 10.0 Yes \n",
"3 Counts per 32 sec 10.0 Yes \n",
"2 Counts per 32 sec 10.0 Yes \n",
"6 ppm 10.0 No \n",
"14 ppm 1.0 No \n",
"16 μg/g 10.0 No \n",
"0 Counts per 32 sec 10.0 Yes \n",
"23 Ti wt. % 10.0 No \n",
"4 Al wt. % 10.0 No \n",
"5 Ca wt. % 10.0 No \n",
"8 Fe wt. % 10.0 No \n",
"10 Mg wt. % 10.0 No \n",
"11 O wt. % 10.0 No \n",
"13 ppm 1.0 No \n",
"17 Si wt. % 10.0 No \n",
"21 ppm 10.0 No \n",
"22 Ti wt. % 10.0 No \n",
"24 ppm 10.0 No \n",
"7 ppm 10.0 No \n",
"9 FeO wt. % 10.0 No \n",
"20 ppm 10.0 No \n",
"18 μg/g 10.0 Yes \n",
"19 μg/g 10.0 Yes \n",
"12 Counts per sec 1000.0 No \n",
"15 Counts per sec 1000.0 No "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_table = pd.read_csv('data_list.csv')\n",
"data_table[['Data Name', 'Pixels Size','Bin Size', 'Units','scaling factor', 'Std. Dev.']].sort_values(['Pixels Size','Data Name'])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "da09ab94-76aa-48cc-b10d-7cf091cba609",
"metadata": {
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{
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" <tbody>\n",
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" <th>4</th>\n",
" <td>aluminum5d.txt</td>\n",
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" ascii binary scale\n",
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}
],
"source": [
"def get_data_file(data=None,\n",
" search=None,\n",
" column='Data Name'):\n",
" return data.loc[data[column].str.contains(search, case=False),['ASCII_data', 'Binary_data','scaling factor']].rename(\n",
" columns=dict(zip(['ASCII_data', 'Binary_data','scaling factor'],['ascii','binary','scale'])))\n",
"\n",
"aluminum_datafiles = get_data_file(data_table,'aluminum')\n",
"aluminum_datafiles"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "3879e564-6d51-4f20-9400-4e13fd10f37d",
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"tags": []
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"outputs": [
{
"data": {
"text/plain": [
"array([[15.1, 15.1, 15.1, ..., 15.1, 15.1, 15.1],\n",
" [15.1, 15.1, 15.1, ..., 15.1, 15.1, 15.1],\n",
" [15.1, 15.1, 15.1, ..., 15.1, 15.1, 15.1],\n",
" ...,\n",
" [15.1, 15.1, 15.1, ..., 15.1, 15.1, 15.1],\n",
" [15.1, 15.1, 15.1, ..., 15.1, 15.1, 15.1],\n",
" [15.1, 15.1, 15.1, ..., 15.1, 15.1, 15.1]])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = np.fromfile('data/'+aluminum_datafiles['binary'].values[0],dtype=np.dtype('>h')).reshape([360,720])/aluminum_datafiles['scale'].values[0]\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "ce628dc0-056a-4583-b4da-ceb4fb1c9140",
"metadata": {
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"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Lat_min</th>\n",
" <th>Lat_max</th>\n",
" <th>Lon_min</th>\n",
" <th>Lon_max</th>\n",
" <th>Counts</th>\n",
" <th>Std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-90.00</td>\n",
" <td>-87.5</td>\n",
" <td>-180.0</td>\n",
" <td>180.0</td>\n",
" <td>15.151</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-87.50</td>\n",
" <td>-82.5</td>\n",
" <td>-180.0</td>\n",
" <td>-135.0</td>\n",
" <td>13.394</td>\n",
" <td>NaN</td>\n",
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" <td>-87.50</td>\n",
" <td>-82.5</td>\n",
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" <td>-90.0</td>\n",
" <td>13.634</td>\n",
" <td>NaN</td>\n",
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" <th>4</th>\n",
" <td>-87.50</td>\n",
" <td>-82.5</td>\n",
" <td>-90.0</td>\n",
" <td>-45.0</td>\n",
" <td>14.642</td>\n",
" <td>NaN</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",
" </tr>\n",
" <tr>\n",
" <th>1786</th>\n",
" <td>82.50</td>\n",
" <td>87.5</td>\n",
" <td>0.0</td>\n",
" <td>45.0</td>\n",
" <td>14.908</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1787</th>\n",
" <td>82.50</td>\n",
" <td>87.5</td>\n",
" <td>45.0</td>\n",
" <td>90.0</td>\n",
" <td>14.201</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1788</th>\n",
" <td>82.50</td>\n",
" <td>87.5</td>\n",
" <td>90.0</td>\n",
" <td>135.0</td>\n",
" <td>14.718</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1789</th>\n",
" <td>82.50</td>\n",
" <td>87.5</td>\n",
" <td>135.0</td>\n",
" <td>180.0</td>\n",
" <td>15.137</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1790</th>\n",
" <td>87.50</td>\n",
" <td>90.0</td>\n",
" <td>-180.0</td>\n",
" <td>180.0</td>\n",
" <td>15.185</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1791 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Lat_min Lat_max Lon_min Lon_max Counts Std\n",
"0 # NaN NaN NaN NaN NaN\n",
"1 -90.00 -87.5 -180.0 180.0 15.151 NaN\n",
"2 -87.50 -82.5 -180.0 -135.0 13.394 NaN\n",
"3 -87.50 -82.5 -135.0 -90.0 13.634 NaN\n",
"4 -87.50 -82.5 -90.0 -45.0 14.642 NaN\n",
"... ... ... ... ... ... ...\n",
"1786 82.50 87.5 0.0 45.0 14.908 NaN\n",
"1787 82.50 87.5 45.0 90.0 14.201 NaN\n",
"1788 82.50 87.5 90.0 135.0 14.718 NaN\n",
"1789 82.50 87.5 135.0 180.0 15.137 NaN\n",
"1790 87.50 90.0 -180.0 180.0 15.185 NaN\n",
"\n",
"[1791 rows x 6 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" pd.read_csv('data/'+aluminum_datafiles['ascii'].values[0],skiprows=12,sep=',',\n",
" names=['Lat_min','Lat_max',\n",
" 'Lon_min','Lon_max',\n",
" 'Counts' ,'Std' ]\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3489e357-5d39-4652-be13-ec389905dcab",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"jupytext": {
"formats": "ipynb,auto:percent"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.12"
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# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.13.1
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# name: python3
# ---
# %% [markdown] tags=[]
# ## Read Lunar Psospector Data
#
# Alissa Madera asked on [OpenPlanetary](https://www.openplanetary.org/) ([Forum](https://forum.openplanetary.org/)) how to read binary data from [PDS Geosciences Node Data and Services: Lunar Prospector Reduced Spectrometer Data Special Products](https://pds-geosciences.wustl.edu/missions/lunarp/reduced_special.html).
#
# This generated several answer, some of them are summarised by June Wang here : [Convert LP NS data from ASCII format to Geotif with a ArcGIS Pro Tool - For data users - PDS Geosciences Node Community](https://geoweb.rsl.wustl.edu/community/index.php?/topic/3426-convert-lp-ns-data-from-ascii-format-to-geotif-with-a-arcgis-pro-tool/&_fromLogin=1).
#
# The gist of it is to read carefulli the instruction :
# > Each binary file is formatted as an image covering the entire planet with an array of 720 samples and 360 lines. Data values are scaled as integers in the image array with two bytes per pixel in IEEE order. For all binary files except the potassium, radon, and polonium, image values are ten times the values given in the ASCII files. The binary files for potassium abundances have integer values that are the same as values given in the corresponding ASCII file. The binary files for the radon and polonium data have integer values that are 1000 times the values given in the corresponding ASCII file. The binary image is a simple cylindrical map projection with 0.5° pixel size. The first image line corresponds to the south pole and the last image line corresponds to the north pole of the moon. The first sample corresponds to -180° East longitude and the last sample corresponds to 180° East longitude. Binary file names have the form <name>.dat and are about 0.5 MB in size.
#
# One caveat raised from June Wang
# > But one thing need to notice is that all the binary data values are scaled to integers. After reading the data and scaling back to its original value, you will find there is slightly difference in the the accuracy of decimal points between ASCII and binary for all the data products if they have non-integer values in the ASCII files initially.
#
# ## Instruction
#
# - The first part of this notebook shows how to quickly read the data in python.
# - The second create a list of all datafiles and download them locally.
#
# %% tags=[]
import numpy as np
import pandas as pd
import matplotlib
from matplotlib import pyplot as plt
# %matplotlib inline
# %% [markdown] tags=[]
# ## Read using numpy
#
# ```data = np.fromfile('therms.dat',dtype=np.dtype('>h'),)/10```
#
# 10 scaling is from the documentation, changes for each file.
# %% tags=[]
# read using numpy
data = np.fromfile('therms.dat',dtype=np.dtype('>h')).reshape([360,720])/10
# 10 scaling is from the documentation, changes for each file.
# %% tags=[]
# 360,720 is from the documentation, changes for each file.
plt.figure(figsize=[8,3.5])
plt.imshow(data,origin='lower',cmap=plt.cm.inferno_r)
plt.colorbar(fraction=0.025)
plt.title('therms with numpy')
# %% [markdown] tags=[]
# ## Convert to geo-image with rasterio
#
# Just an initial try following [Writing Datasets — rasterio documentation](https://rasterio.readthedocs.io/en/latest/topics/writing.html).
# %% tags=[]
import rasterio
defaults = {
'driver': 'GTiff',
'interleave': 'band',
'tiled': False,
'height': int(data.shape[1]),
'width': int(data.shape[0]),
'compress': 'JPEG',
'nodata': 0,
'dtype': data.dtype.__str__(),
'count':1
}
profile = rasterio.profiles.DefaultGTiffProfile(defaults)
profile
with rasterio.open('therms.tif','w',**profile) as dst_dataset:
dst_dataset.write(data, 1)
# %% [markdown]
# ## Read using pandas
#
# %% tags=[]
# read using pandas
df = pd.read_csv('therms.txt',skiprows=12,sep=',',
names=['Lat_min','Lat_max',
'Lon_min','Lon_max',
'Counts' ,'Std' ]
)
df.plot.scatter(x='Lon_min',y='Lat_min',c='Counts',
cmap=plt.cm.inferno_r,figsize=[8,4],
title='therms with pandas')
# %% [markdown] tags=[]
# ## Generate data table
#
# Read the cleaned up version of `reduced_special.html` and exctract all datafile information.
#
# Save the table to `data_list.csv`.
# %% jupyter={"outputs_hidden": true} tags=[]
base_url = 'https://pds-geosciences.wustl.edu/missions/lunarp/reduced/'
html_ls = pd.read_html('reduced_special.html')
data_df = html_ls[-1]
data_df.columns=data_df.loc[0].values
data_df = data_df.drop(index=[0])
data_df['ASCII_data_url'] = data_df['File Name'].map(lambda x: base_url+x.split()[0] )
data_df['Binary_data_url'] = data_df['File Name'].map(lambda x: base_url+x.split()[1] )
data_df['ASCII_data'] = data_df['File Name'].map(lambda x: x.split()[0] )
data_df['Binary_data'] = data_df['File Name'].map(lambda x: x.split()[1] )
data_df=data_df.drop(columns=['File Name'])
data_df['Data Name']=data_df['Data Type'].map(lambda x: x.split(',')[0])
data_df['Pixels Size'] = data_df['Data Type'].map(lambda x: x.split(',')[1] if len(x.split(',')) > 1 else 'equal-area pixels' )
data_df=data_df.drop(columns=['Data Type'])
data_df['scaling factor'] = 10.
data_df.loc[data_df['Data Name'].str.contains('Potassium'),'scaling factor'] = 1
data_df.loc[data_df['Data Name'].str.contains('Radon'),'scaling factor'] = 1000
data_df.loc[data_df['Data Name'].str.contains('Polonium'),'scaling factor'] = 1000
data_df[['Data Name', 'Pixels Size','Bin Size', 'Units','scaling factor', 'Std. Dev.']].sort_values(['Pixels Size','Data Name'])
# %%
data_df.to_csv('data_list.csv',index=None)
# %% [markdown] jp-MarkdownHeadingCollapsed=true tags=[]
# ## Download all data
# %%
import requests
import pathlib
from urllib.parse import urlparse
datadir = pathlib.Path('data')
datadir.mkdir(exist_ok=True)
# %% [markdown] jp-MarkdownHeadingCollapsed=true tags=[]
# ### Warning
#
# This uses pure python and it works, but it is __**extremely slow**__. I suggest to skip to the next cell if you are using Linux/Mac and use native tools.
# %%
for tup in data_df.itertuples():
url = tup.ASCII_data
url_parsed = urlparse(url)
print(f'Downloading {url_parsed.path}')
r = requests.get(url)
with open('data/'+pathlib.Path(url_parsed.path).name, 'wb') as f:
f.write(r.content)
url = tup.Binary_data
url_parsed = urlparse(url)
print(f'Downloading {url_parsed.path}')
r = requests.get(url)
with open('data/'+pathlib.Path(url_parsed.path).name, 'wb') as f:
f.write(r.content)
# %% tags=[]
filelist = data_df[['ASCII_data','Binary_data']].values.flatten()
np.savetxt('urls.txt',filelist, fmt='%s')
# %% [markdown]
# for this paralle download, you will need:
# - gnu parallel
# - wget
#
#
# Please refer to your OS to install them (mac > homebrew/macports, linux > whatever package manage you use.)
# %%
# !parallel -j 15 wget --directory-prefix=data < urls.txt
# %% [markdown]
# ## Read using numpy from local files
#
# %%
data_table = pd.read_csv('data_list.csv')
data_table[['Data Name', 'Pixels Size','Bin Size', 'Units','scaling factor', 'Std. Dev.']].sort_values(['Pixels Size','Data Name'])
# %% tags=[]
def get_data_file(data=None,
search=None,
column='Data Name'):
return data.loc[data[column].str.contains(search, case=False),['ASCII_data', 'Binary_data','scaling factor']].rename(
columns=dict(zip(['ASCII_data', 'Binary_data','scaling factor'],['ascii','binary','scale'])))
aluminum_datafiles = get_data_file(data_table,'aluminum')
aluminum_datafiles
# %% tags=[]
data = np.fromfile('data/'+aluminum_datafiles['binary'].values[0],dtype=np.dtype('>h')).reshape([360,720])/aluminum_datafiles['scale'].values[0]
data
# %% tags=[]
pd.read_csv('data/'+aluminum_datafiles['ascii'].values[0],skiprows=12,sep=',',
names=['Lat_min','Lat_max',
'Lon_min','Lon_max',
'Counts' ,'Std' ]
)
# %%
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta name="keywords" content="Planetary Data System Geosciences Node">
<meta name="GENERATOR" content="Microsoft FrontPage 12.0">
<meta name="ProgId" content="FrontPage.Editor.Document">
<title>PDS Geosciences Node Data and Services: Lunar Prospector Reduced
Spectrometer Data Special Products</title>
<meta name="description" content="This site provides planetary science data, tools, and documentation from the PDS Geosciences Node.">
<meta http-equiv="Content-Type" content="text/html; charset=windows-1252">
<style>
* {
color: black;
background-color:white ;
}
</style> </head>
<body>
<p>Lunar
Prospector Reduced Spectrometer Data - Special Products</p>
<p>These Lunar Prospector (LP) gamma ray and neutron
spectrometer special products and associated documentation have been prepared by the LP Spectrometer
Team as part of a NASA Lunar Data
Analysis Program. These spectrometer data products integrate data collected between
January 16, 1998 and July 31, 1999. The data file descriptions for these
products were provided by: </p>
<center>
<p>William C. Feldman, Tom H. Prettyman, Richard D. Belian, Richard C. Elphic, <br>
Olivier Gasnault, David J. Lawrence, Stefanie L. Lawson, and Kurt R. Moore
<br>Los Alamos National Laboratory, Los Alamos, NM </p>
<p>Alan B. Binder
<br>Lunar Research Institute, Tuscon, AZ
<p>Sylvestre Maurice
<br>Observatoire Midi-Pyrenees, Toulouse, France
</center>
<p><strong>IMPORTANT: The data on this page
are special products that may be of interest to the geoscience community. They do not represent a PDS peer-reviewed
archive. Please contact <a href="mailto:wfeldman@lanl.gov">William Feldman</a>
or <a href="mailto:djlawrence@lanl.gov">David Lawrence</a> of LANL for more
information.&nbsp; Please report any
other anomalies in the data or documentation to
<a href="mailto:geosci@wunder.wustl.edu">geosci@wunder.wustl.edu</a>.</strong></p>
<p><strong style="font-weight: 400"><i><a href="reduced.html">Back to Lunar
Prospector Reduced Spectrometer Data</a></i></strong></p>
<h2>Introduction</h2>
<p>These Lunar Prospector (LP) spectrometer special products contain data from
the neutron and gamma ray spectrometers. All data
products are provided in two formats: an ASCII table and a binary image.</p>
<p>Each ASCII file contains header information followed by column formatted data separated
by commas. Header records begin with a '#' character in the first column. The first four
columns of the ASCII version contain the minimum and maximum latitude and longitude values
for each pixels. East longitudes are
positive. The remaining columns give the parameter value, and, if available, standard
deviation values. The ASCII format files are named &lt;<i>name</i>&gt;.txt. File
sizes of the ASCII versions vary depending of the bin size.</p>
<p>Each binary file is formatted as an image covering the entire planet with an
array of 720 samples and 360 lines. Data values are scaled as integers in the image array
with two bytes per pixel in IEEE order. For all binary files except the potassium, radon, and polonium, image values are ten times the values given in the ASCII
files. The binary files for potassium abundances have integer values that are the same
as values given in the corresponding ASCII file. The binary files for the radon
and polonium data have integer values that are 1000 times the values given in
the corresponding ASCII file. The binary image is a simple cylindrical map
projection with 0.5&deg; pixel size. The first image line corresponds to the south pole and
the last image line corresponds to the north pole of the moon.
The first sample corresponds to -180&deg; East longitude and the last sample
corresponds to 180° East longitude. Binary file names have the form &lt;<i>name</i>&gt;.dat and are about
0.5 MB in size.</p>
<h2>Neutron Counting Rate Data Products</h2>
<h3>Thermal Neutron Counting Rate</h3>
<p>The thermal neutron counting rate data product contains data from the LP neutron spectrometer
Sn and Cd covered <sup>3</sup>He detectors [Feldman
<i>et al.</i>, 1999, 2001a]. The thermal neutron counting rate is defined as the counting rate
difference per 32 seconds between the two detectors and corresponds to neutrons having energies
ranging from 0 to 0.4 eV. This data
product has been described by Feldman <i>et al.</i> [2000a] and Elphic <i>et al.</i> [2000]. A
detailed description of the data reduction for this data
product is given by Maurice <i>et al.</i> [2001a]. The map bin size is 60 km by 60 km.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td width=140>ASCII File:</td>
<td width=140><a href="reduced/therms.txt">therms.txt</a></td>
<td width=140>(0.74 MB file size)</td>
<td width=140>Version: Nov. 3, 2000</td>
</tr><tr>
<td width=140>Binary Image File:</td>
<td width=140><a href="reduced/therms.dat">therms.dat</a></td>
<td width=140>(0.51 MB file size)</td>
<td width=140>Version: Nov. 3, 2000</td>
</tr>
</table>
<h3>Epithermal Neutron Counting Rate</h3>
<p>The epithermal neutron counting rate data product contains data from the LP neutron
spectrometer Cd covered <sup>3</sup>He detector [Feldman
<i>et al.</i>, 1999, 2001a]. The epithermal neutron counting rate is defined as the
counting rate per 32 seconds from the Cd covered detector and corresponds to neutrons
having energies ranging from 0.4 to about 100 eV. The data
product has been described by Feldman <i>et al.</i> [2000b, 2001c] and Maurice
<i>et al.</i> [2001b]. A detailed description of the data reduction for this data
product is given by Maurice <i>et al.</i> [2001a]. The map bin size is 60 km by 60 km.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td width=140>ASCII File:</td>
<td width=140><a href="reduced/epis.txt">epis.txt</a></td>
<td width=140>(0.74 MB file size)</td>
<td width=140>Version: Nov. 3, 2000</td>
</tr><tr>
<td width=140>Binary Image File:</td>
<td width=140><a href="reduced/epis.dat">epis.dat</a></td>
<td width=140>(0.51 MB file size)</td>
<td width=140>Version: Nov. 3, 2000</td>
</tr>
</table>
<h3>Fast Neutron Counting Rate</h3>
<p>The fast neutron counting rate data product contains data from the LP gamma ray spectrometer [Feldman
<i>et al.</i>, 1999, 2001b]. The fast neutron counting rate is defined as the counting
rate per 32 seconds of fast neutrons having energies from 0.6 to about 9 MeV. The data
product has been described by Maurice <i>et al.</i> [2000]. A detailed description of the
data reduction for this data
product is given by Maurice <i>et al.</i> [2001a]. The map bin size is 60 km by 60 km.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td width=140>ASCII File:</td>
<td width=140><a href="reduced/fast.txt">fast.txt</a></td>
<td width=140>(0.74 MB file size)</td>
<td width=140>Version: Nov. 3, 2000</td>
</tr><tr>
<td width=140>Binary Image File:</td>
<td width=140><a href="reduced/fast.dat">fast.dat</a></td>
<td width=140>(0.51 MB file size)</td>
<td width=140>Version: Nov. 3, 2000</td>
</tr>
</table>
<h3>Fast Neutron Spectra Counting Rate</h3>
<p>The fast neutron spectra counting rate data product contains data from the LP gamma ray
spectrometer [Feldman
<i>et al.</i>, 1999, 2001b]. The fast neutron spectra counting rate is defined as the counting
rate per 32 seconds of fast neutrons having energies from 0.6 to about 9 MeV. These energies
are then divided into 16 channels having midpoint energies of 0.743, 1.40, 2.09, 2.67, 3.24,
3.75, 4.23, 4.70, 5.18, 5.65, 6.11, 6.55, 6.98, 7.41, 7.82, and 8.24 MeV. This data
product has been described by Maurice <i>et al</i>. [2000]. A detailed description of the data
reduction for this data
product is given by Maurice <i>et al.</i> [2001a]. Furthermore, a description of how the counting
rates can be converted into fluxes is given by Maurice
<i>et al.</i> [2000] and Byrd and Urban
[1994]. The map bin size is 60 km by 60 km. For the binary image file, the
channels are stored in band sequential order.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td width=140>ASCII File:</td>
<td width=140><a href="reduced/fastspectra.txt">fastspectra.txt</a></td>
<td width=140>(4.8 MB file size)</td>
<td width=140>Version: July 6, 2001</td>
</tr><tr>
<td width=140>Binary Image File:</td>
<td width=140><a href="reduced/fastspectra.dat">fastspectra.dat</a></td>
<td width=140>(8.1 MB file size)</td>
<td width=140>Version: July 6, 2001</td>
</tr>
</table>
<h2>5 Degree Elemental Abundance Data Products</h2>
<p>Elemental abundance values for O, Si, Ti, Al, Fe, Mg, Ca, U, and K were
derived from LP gamma ray spectrometer [Feldman <i>et al.</i>, 1999]
observations acquired during the high-altitude portion of the LP mission. For
the elements O, Si, Ti, Al, Fe, Mg, and Ca, the data are given in units of
elemental weight percent. For the elements U and K, the data are given in units
of ppm. A description of the reduction of these data products is given by
Prettyman <i>et al.</i> [2002]. The Thorium data has been calculated using full
modeling and response function analysis [Prettyman <i>et al.,</i> 2002]. The map bin size is 150 km by 150 km.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td>Oxygen</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/oxygen5d.txt">
oxygen5d.txt</a><br>
<a href="reduced/oxygen5d.dat">oxygen5d.dat</a></td>
<td>(0.1 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr><tr>
<td>Silicon</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/silicon5d.txt">
silicon5d.txt</a><br>
<a href="reduced/silicon5d.dat">silicon5d.dat</a></td>
<td>(0.1 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr><tr>
<td>Titanium</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/titanium5d.txt">
titanium5d.txt</a><br>
<a href="reduced/titanium5d.dat">titanium5d.dat</a></td>
<td>(0.1 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr><tr>
<td>Aluminum</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/aluminum5d.txt">
aluminum5d.txt</a><br>
<a href="reduced/aluminum5d.dat">aluminum5d.dat</a></td>
<td>(0.1 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr><tr>
<td>Iron</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/iron5d.txt">iron5d.txt</a><br>
<a href="reduced/iron5d.dat">iron5d.dat</a></td>
<td>(0.1 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr><tr>
<td>Magnesium</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/magnesium5d.txt">
magnesium5d.txt</a><br>
<a href="reduced/magnesium5d.dat">magnesium5d.dat</a></td>
<td>(0.1 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr><tr>
<td>Calcium</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/calcium5d.txt">
calcium5d.txt</a><br>
<a href="reduced/calcium5d.dat">calcium5d.dat</a></td>
<td>(0.1 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr><tr>
<td>Uranium</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/uranium5d.txt">
uranium5d.txt</a><br>
<a href="reduced/uranium5d.dat">urnaium5d.dat</a></td>
<td>(0.1 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr><tr>
<td>Potassium</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/potassium5d.txt">
potassium5d.txt</a><br>
<a href="reduced/potassium5d.dat">potassium5d.dat</a></td>
<td>(0.1 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr>
<tr>
<td>Thorium</td>
<td>ASCII File:<br>
Binary Image File:</td>
<td><a href="reduced/thorium5d.txt">
thorium5d.txt</a><br>
<a href="reduced/thorium5d.dat">thorium5d.dat</a></td>
<td>(0.1 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr>
</table>
<h2>2 Degree Elemental Abundance Data Products</h2>
<h3>Samarium Abundance Data Product</h3>
<p>The samarium abundance data product contains data from the LP neutron and gamma ray
spectrometers [Feldman
<i>et al.</i> 1999, 2001a] taken during the low-altitude portion of the LP mission. This data
product is given in units of microgram/gram. A detailed description of the data reduction of
this data type is given by Elphic
<i>et al.</i> [2000]. The map bin size is 60 km x 60 km.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td width=140>ASCII File:</td>
<td width=140><a href="reduced/samarium2d.txt">samarium2d.txt</a></td>
<td width=140>(0.61 MB file size)</td>
<td width=140>Version: July 6, 2001</td>
</tr><tr>
<td width=140>Binary Image File:</td>
<td width=140><a href="reduced/samarium2d.dat">samarium2d.dat</a></td>
<td width=140>(0.51 MB file size)</td>
<td width=140>Version: July 6, 2001</td>
</tr>
</table>
<h3>Titanium and Potassium Abundance Data Products</h3>
<p>These data products contain elemental abundances values for Ti and K derived
from LP gamma ray spectrometer [Feldman <i>et al.</i>, 1999] observations
acquired during the high- and low-altitude portions of the LP mission. For Ti,
the data are given in units of elemental weight percent. For K, the data are
given in units of ppm. A description of the reduction of these data products is
given by Prettyman <i>et al.</i> [2002]. The map bin size is 60 km by 60 km.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td>Titanium</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/titanium2d.txt">
titanium2d.txt</a><br>
<a href="reduced/titanium2d.dat">titanium2d.dat</a></td>
<td>(0.61 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr><tr>
<td>Potassium</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/potassium2d.txt">
potassium2d.txt</a><br>
<a href="reduced/potassium2d.dat">potassium2d.dat</a></td>
<td>(0.61 MB file size)<br>(0.51 MB file size)</td>
<td>Version: June 15, 2002</td>
</tr>
</table>
<h3>Hydrogen Abundance Data</h3>
<p>The hydrogen abundance data product contains data from the LP neutron spectrometer [Feldman
<i>et al.</i>, 1999,
2001a] acquired during the low-altitude portion of the LP mission. Hydrogen abundances are derived from epithermal neutron data that has been corrected by
thermal neutron data [Feldman <i>et al.</i>, 2001c]. Equations 3 and 4 of Feldman
<i>et al.</i> [2001c] show how
the corrected epithermal data is converted into hydrogen abundances as parts per million (ppm).
Note, however, that these abundances are not necessarily reliable in regions of high thorium and
rare-earth element abundances [Maurice <i>et al.</i>, 2001a]. The map bin size is 60 km
x 60 km.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td width=140>ASCII File:</td>
<td width=140><a href="reduced/hydrogenlow.txt">hydrogenlow.txt</a></td>
<td width=140>(0.61 MB file size)</td>
<td width=140>Version: July 6, 2001</td>
</tr><tr>
<td width=140>Binary Image File:</td>
<td width=140><a href="reduced/hydrogenlow.dat">hydrogenlow.dat</a></td>
<td width=140>(0.51 MB file size)</td>
<td width=140>Version: July 6, 2001</td>
</tr>
</table>
<h3>Thorium Abundance Data</h3>
<p>The 2 degree thorium abundance data products contains data from the LP gamma ray spectrometer [Feldman
<i>et al.</i>,
1999, 2001b]. These absolute abundances are given in units of microgram/gram. There are two
versions containing data taken from the high- and low-altitude portions of the LP mission
[Lawrence <i>et al.</i>, 2000]. These data have been described by Lawrence <i>et al.</i> [2000]. A
detailed
description of the data reduction for these data is given by Lawrence <i>et al.</i> [2001b]. The map
bin size is 60 km x 60 km.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td width=200>High Altitude Version</td>
<td width=140>ASCII File:</td>
<td width=140><a href="reduced/thoriumhigh.txt">thoriumhigh.txt</a></td>
<td width=140>(0.74 MB file size)</td>
<td width=140>Version: July 6, 2001</td>
</tr><tr>
<td width=200>&nbsp;</td>
<td width=140>Binary Image File:</td>
<td width=140><a href="reduced/thoriumhigh.dat">thoriumhigh.dat</a></td>
<td width=140>(0.51 MB file size)</td>
<td width=140>Version: July 6, 2001</td>
</tr><tr>
<td width=200>Low Altitude Version</td>
<td width=140>ASCII File:</td>
<td width=140><a href="reduced/thoriumlow.txt">thoriumlow.txt</a></td>
<td width=140>(0.74 MB file size)</td>
<td width=140>Version: July 6, 2001</td>
</tr><tr>
<td width=200>&nbsp;</td>
<td width=140>Binary Image File:</td>
<td width=140><a href="reduced/thoriumlow.dat">thoriumlow.dat</a></td>
<td width=140>(0.51 MB file size)</td>
<td width=140>Version: July 6, 2001</td>
</tr>
</table>
<h2>Half Degree Abundance Data Products</h2>
<h3>Thorium Abundance Data</h3>
<p>The half degree thorium abundance data product contains data from the LP
gamma ray spectrometer [Feldman <i>et al.</i>, 1999]. The absolute abundances
are given in units of ppm. These data are taken from the low-altitude portion of
the LP mission. A description of the reduction of these data products
is given by Lawrence <i>et al.</i> [2002a, 2002b]. The map bin size is 0.5&deg; by
0.5&deg;.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td width=140>ASCII File:</td>
<td width=140><a href="reduced/thoriumhd.txt">thoriumhd.txt</a></td>
<td width=140>(13.9 MB file size)</td>
<td width=140>Version: Jan. 3, 2002</td>
</tr><tr>
<td width=140>Binary Image File:</td>
<td width=140><a href="reduced/thoriumhd.dat">thoriumhd.dat</a></td>
<td width=140>(0.51 MB file size)</td>
<td width=140>Version: Jan. 3, 2002</td>
</tr>
</table>
<h3>Iron Abundance Data</h3>
<p>The half degree iron abundance data product contains data from the LP
gamma ray spectrometer [Feldman <i>et al.</i>, 1999] acquired during the
low-altitude portion of the LP mission. The absolute abundances are given in
units of FeO weight percent. A description of the reduction of these data products
is given by Lawrence <i>et al.</i> [2002a, 2002b]. The map bin size is 0.5&deg; by
0.5&deg;.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td width=140>ASCII File:</td>
<td width=140><a href="reduced/ironhd.txt">ironhd.txt</a></td>
<td width=140>(13.9 MB file size)</td>
<td width=140>Version: Jan. 3, 2002</td>
</tr><tr>
<td width=140>Binary Image File:</td>
<td width=140><a href="reduced/ironhd.dat">ironhd.dat</a></td>
<td width=140>(0.51 MB file size)</td>
<td width=140>Version: Jan. 3, 2002</td>
</tr>
</table>
<h3>Hydrogen Abundance Data</h3>
<p>The half degree hydrogen abundance data product contains data from the LP
neutron spectrometer [Feldman <i>et al.</i>, 1999]. Hydrogen abundances are
derived from epithermal neutron data that has been corrected by thermal neutron
data [Feldman <i>et al.</i>, 2001c]. Equations 3 and 4 of Feldman
<i>et al.</i> [2001c] show how
the corrected epithermal data is converted into hydrogen abundances as parts per million (ppm).
Note, however, that these abundances are not necessarily reliable in regions of high thorium and
rare-earth element abundances [Maurice <i>et al.</i>, 2001a]. The map bin size is 0.5&deg; by
0.5&deg;.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td width=140>ASCII File:</td>
<td width=140><a href="reduced/hydrogenhd.txt">hydrogenhd.txt</a></td>
<td width=140>(13.9 MB file size)</td>
<td width=140>Version: Jan. 3, 2002</td>
</tr><tr>
<td width=140>Binary Image File:</td>
<td width=140><a href="reduced/hydrogenhd.dat">hydrogenhd.dat</a></td>
<td width=140>(0.51 MB file size)</td>
<td width=140>Version: Jan. 3, 2002</td>
</tr>
</table>
<h2>Radon-222 and Polonium-210 Relative Count Rate Data Products</h2>
<p>Relative count rate data products for radon-222 and polonium-210 contain data
from the LP alpha particle spectrometer acquired during the high- and
low-altitude portions of the LP mission. These data are given in units of counts
per second. A description of the reduction of these data products is given by
Lawson <i>et al.</i> [2002]. The data are mapped onto equal-area pixels having
an approximate size at the equator of 300 km in the longitude direction and 450
km in the latitude direction.</p>
<table border=1 cellpadding=4 cellspacing=1>
<tr>
<td>Radon-222</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/radon222.txt">
radon222.txt</a><br>
<a href="reduced/radon222.dat">radon222.dat</a></td>
<td>(19 KB file size)<br>(0.51 MB file size)</td>
<td>Version: Jan. 3, 2002</td>
</tr><tr>
<td>Polonium-210</td>
<td>ASCII File:<br>Binary Image File:</td>
<td><a href="reduced/polonium210.txt">
polonium210.txt</a><br>
<a href="reduced/polonium210.dat">polonium210.dat</a></td>
<td>(19 KB file size)<br>(0.51 MB file size)</td>
<td>Version: Jan. 3, 2002</td>
</tr>
</table>
<h2>Data Product Summary</h2>
<table border=2 cellpadding=2>
<tr>
<td><b>Data Type</b></td>
<td><b>File Name</b></td>
<td><b>Bin Size</b></td>
<td><b>Units</b></td>
<td><b>Std. Dev.</b></td>
<td width=150><b>References</b></td>
</tr><tr>
<td><b>Thermal Neutrons, 2 degree</b></td>
<td><a href="reduced/therms.txt">therms.txt</a><br>
<a href="reduced/therms.dat">therms.dat</a></td>
<td>60km x 60km</td>
<td>Counts per 32 sec</td>
<td>Yes</td>
<td width=150>Feldman <i>et al.</i>, 1999, 2000a, 2001a; Elphic
<i>et al.</i>, 2000; Maurice <i>et al.</i>, 2001a</td>
</tr><tr>
<td><b>Epithermal Neutrons, 2 degree</b></td>
<td><a href="reduced/epis.txt">epis.txt</a><br>
<a href="reduced/epis.dat">epis.dat</a></td>
<td>60km x 60km</td>
<td>Counts per 32 sec</td>
<td>Yes</td>
<td width=150>Feldman <i>et al.</i>, 1999, 2000b, 2001a, 2001c; Maurice
<i>et al.</i>,
2000, 2001a</td>
</tr><tr>
<td><b>Fast Neutrons, 2 degree</b></td>
<td><a href="reduced/fast.txt">fast.txt</a><br>
<a href="reduced/fast.dat">fast.dat</a></td>
<td>60km x 60km</td>
<td>Counts per 32 sec</td>
<td>Yes</td>
<td width=150>Feldman <i>et al.</i>, 1999, 2001b; Maurice <i>et al.</i>, 2000, 2001a</td>
</tr><tr>
<td><b>Fast Neutron Spectra, 2 degree</b></td>
<td><a href="reduced/fastspectra.txt">fastspectra.txt</a><br>
<a href="reduced/fastspectra.dat">fastspectra.dat</a></td>
<td>60km x 60km</td>
<td>Counts per 32 sec</td>
<td>Yes</td>
<td width=150>Maurice <i>et al.</i>, 2000, 2001b</td>
</tr><tr>
<td><b>Aluminum, 5 degree</b></td>
<td><a href="reduced/aluminum5d.txt">aluminum5d.txt</a><br>
<a href="reduced/aluminum5d.dat">aluminum5d.dat</a></td>
<td>150km x 150km</td>
<td>Al wt. %</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Calcium, 5 degree</b></td>
<td><a href="reduced/calcium5d.txt">calcium5d.txt</a><br>
<a href="reduced/calcium5d.dat">calcium5d.dat</a></td>
<td>150km x 150km</td>
<td>Ca wt. %</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Hydrogen, 2 degree</b></td>
<td><a href="reduced/hydrogenlow.txt">hydrogenlow.txt</a><br>
<a href="reduced/hydrogenlow.dat">hydrogenlow.dat</a></td>
<td>60km x 60km</td>
<td>ppm</td>
<td>No</td>
<td width=150>Feldman <i>et al.</i>, 2001c</td>
</tr><tr>
<td><b>Hydrogen, half degree</b></td>
<td><a href="reduced/hydrogenhd.txt">hydrogenhd.txt</a><br>
<a href="reduced/hydrogenhd.dat">hydrogenhd.dat</a></td>
<td>0.5&deg; x 0.5&deg;</td>
<td>ppm</td>
<td>No</td>
<td width=150>Feldman <i>et al.</i>, 2001c</td>
</tr><tr>
<td><b>Iron, 5 degree</b></td>
<td><a href="reduced/iron5d.txt">iron5d.txt</a><br>
<a href="reduced/iron5d.dat">iron5d.dat</a></td>
<td>150km x 150km</td>
<td>Fe wt. %</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Iron, half degree</b></td>
<td><a href="reduced/ironhd.txt">ironhd.txt</a><br>
<a href="reduced/ironhd.dat">ironhd.dat</a></td>
<td>0.5&deg; x 0.5&deg;</td>
<td>FeO wt. %</td>
<td>No</td>
<td width=150>Lawrence <i>et al.</i>, 2001c</td>
</tr><tr>
<td><b>Magnesium, 5 degree</b></td>
<td><a href="reduced/magnesium5d.txt">magnesium5d.txt</a><br>
<a href="reduced/magnesium5d.dat">magnesium5d.dat</a></td>
<td>150km x 150km</td>
<td>Mg wt. %</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Oxygen, 5 degree</b></td>
<td><a href="reduced/oxygen5d.txt">oxygen5d.txt</a><br>
<a href="reduced/oxygen5d.dat">oxygen5d.dat</a></td>
<td>150km x 150km</td>
<td>O wt. %</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Polonium-210</b></td>
<td><a href="reduced/polonium210.txt">polonium210.txt</a><br>
<a href="reduced/polonium210.dat">polonium210.dat</a></td>
<td>300km lon. x 450km lat.</td>
<td>Counts per sec</td>
<td>No</td>
<td width=150>Lawson <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Potassium, 5 degree</b></td>
<td><a href="reduced/potassium5d.txt">potassium5d.txt</a><br>
<a href="reduced/potassium5d.dat">potassium5d.dat</a></td>
<td>150km x 150km</td>
<td>ppm</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Potassium, 2 degree</b></td>
<td><a href="reduced/potassium2d.txt">potassium2d.txt</a><br>
<a href="reduced/potassium2d.dat">potassium2d.dat</a></td>
<td>60km x 60km</td>
<td>ppm</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Radon-222</b></td>
<td><a href="reduced/radon222.txt">radon222.txt</a><br>
<a href="reduced/radon222.dat">radon222.dat</a></td>
<td>300km lon. x 450km lat.</td>
<td>Counts per sec</td>
<td>No</td>
<td width=150>Lawson <i>et al., 2002</i></td>
</tr><tr>
<td><b>Samarium, 2 degree</b></td>
<td><a href="reduced/samarium2d.txt">samarium2d.txt</a><br>
<a href="reduced/samarium2d.dat">samarium2d.dat</a></td>
<td>60km x 60km</td>
<td>&mu;g/g</td>
<td>No</td>
<td width=150>Elphic <i>et al.</i>, 2000</td>
</tr><tr>
<td><b>Silicon, 5 degree</b></td>
<td><a href="reduced/silicon5d.txt">silicon5d.txt</a><br>
<a href="reduced/silicon5d.dat">silicon5d.dat</a></td>
<td>150km x 150km</td>
<td>Si wt. %</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Thorium, high-altitude</b></td>
<td><a href="reduced/thoriumhigh.txt">thoriumhigh.txt<br>
</a><a href="reduced/thoriumhigh.dat">thoriumhigh.dat</a></td>
<td>60km x 60km</td>
<td>&mu;g/g</td>
<td>Yes</td>
<td width=150>Lawrence <i>et al.</i>, 2000</td>
</tr><tr>
<td><b>Thorium, low-altitude</b></td>
<td><a href="reduced/thoriumlow.txt">thoriumlow.txt<br>
</a><a href="reduced/thoriumlow.dat">thoriumlow.dat</a></td>
<td>60km x 60km</td>
<td>&mu;g/g</td>
<td>Yes</td>
<td width=150>Lawrence <i>et al.</i>, 2000</td>
</tr><tr>
<td><b>Thorium, half degree</b></td>
<td><a href="reduced/thoriumhd.txt">thoriumhd.txt</a><br>
<a href="reduced/thoriumhd.dat">thoriumhd.dat</a></td>
<td>0.5&deg; x 0.5&deg;</td>
<td>ppm</td>
<td>No</td>
<td width=150>Lawrence <i>et al.</i>, 2002a, 2002b</td>
</tr><tr>
<td><b>Thorium, 5 degree</b></td>
<td><a href="reduced/thorium5d.txt">thorium5d.txt</a><br>
<a href="reduced/thorium5d.dat">thorium5d.dat</a></td>
<td>150km x 150km</td>
<td>ppm</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Titanium, 5 degree</b></td>
<td><a href="reduced/titanium5d.txt">titanium5d.txt</a><br>
<a href="reduced/uranium5d.dat">titanium5d.dat</a></td>
<td>150km x 150km</td>
<td>Ti wt. %</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Titanium, 2 degree</b></td>
<td><a href="reduced/titanium2d.txt">titanium2d.txt</a><br>
<a href="reduced/titanium2d.dat">titanium2d.dat</a></td>
<td>60km x 60km</td>
<td>Ti wt. %</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr><tr>
<td><b>Uranium, 5 degree</b></td>
<td><a href="reduced/uranium5d.txt">uranium5d.txt</a><br>
<a href="reduced/uranium5d.dat">uranium5d.dat</a></td>
<td>150km x 150km</td>
<td>ppm</td>
<td>No</td>
<td width=150>Prettyman <i>et al.</i>, 2002</td>
</tr>
</table>
<h2>References</h2>
<p>(<i>Contact <a href="mailto:djlawrence@lanl.gov">David Lawrence</a> of Los
Alamos National Laboratory for information on references that are in preparation
or in press</i>).</p>
<p>Byrd, R. C., and W. T. Urban, Calculation of the neutron response of
Boron-loaded scintillators, LAUR-12833-MS, Los Alamos, National Laboratory, Los
Alamos, NM, 1994.</p>
<p>Elphic, R. C., D. J. Lawrence, W. C. Feldman, B. L. Barraclough, S. Maurice,
A. B. Binder, and P. G. Lucey, Determination of lunar global rare earth element
abundances using Lunar Prospector neutron spectrometer observations, <i>J. Geophys.
Res., 105</i>, 20,333-20,346, 2000.</p>
<p>Feldman W. C., B. L. Barraclough, K. R. Fuller, D. J. Lawrence, S. Maurice,
M. C. Miller, T. H. Prettyman, and A. B. Binder, the Lunar Prospector Gamma-Ray
and Neutron Spectrometers, <i>Nuclear Instruments and Methods in Physics Research
A, 422</i>, 562-566, 1999.</p>
<p>Feldman, W. C., D. J. Lawrence, R. C. Elphic, D. T. Vaniman, D. R. Thomsen,
B. L. Barraclough, S. Maurice, and A. B. Binder, The chemical information
content of lunar thermal and epithermal neutrons, <i>J. Geophys. Res., 105</i>,
20,347-20,363, 2000a.</p>
<p>Feldman, W. C., D. J. Lawrence, R. C. Elphic, B. L. Barraclough, S. Maurice,
I. Genetay, and A. B. Binder, Polar hydrogen deposits on the Moon, <i>J. Geophys.
Res., 105</i>, 4175-4195, 2000b.</p>
<p>Feldman, W. C., et al., Instrument description of the Lunar Prospector
Neutron Spectrometer, in preparation, 2001a.</p>
<p>Feldman, W. C., et al., Instrument description of the Lunar Prospector
Gamma-ray Spectrometer, in preparation, 2001b.</p>
<p>Feldman, W. C., S. Maurice, D. J. Lawrence, R. C. Little, S. L. Lawson, O.
Gasnault, R. C. Wiens, B. L. Barraclough, R. C. Elphic, T. H. Prettyman, J. T.
Steinberg, and A. B. Binder, Evidence for water ice near lunar poles, <i>J. Geophys.
Res.</i>, in press, 2001c.</p>
<p>Lawrence, D. J., W. C. Feldman, B. L. Barraclough, R. C. Elphic, T. H.
Prettyman, S. Maurice, A. B. Binder, and M. C. Miller, Thorium abundances on the
lunar surface, <i>J. Geophys. Res., 105</i>, 20,307-20,331, 2000.</p>
<p>Lawrence, D. J., W. C. Feldman, R. C. Elphic, S. Maurice, T. H. Prettyman,
and A.&nbsp; B. Binder, Iron abundances on the lunar surface as measured by the
Lunar Prospector Gamma-Ray Spectrometer, <i>32nd Lunar and Planetary Science
Conference, Abstract #1830</i>, 2001a.</p>
<p>Lawrence D. J., et al., Data reduction procedures for the Lunar Prospector
Gamma-ray Spectrometer, in preparation, 2001b.</p>
<p>Lawrence, D. J., W. C. Feldman, R. C. Elphic, R. C. Little, T. H. Prettyman,
S. Maurice, P. G. Lucey, and A. B. Binder, Iron abundances on the lunar surface
as measured by the Lunar Prospector Gamma-Ray and Neutron Spectrometers, <i>J.
Geophys. Res.</i>, submitted, 2001c.</p>
<p>Lawrence, D. J., R. C. Elphic, W. C. Feldman, O. Gasnault, I. Genetay, S.
Maurice, and T. H. Prettyman, Optimizing the spatial resolution for gamma-ray
measurements of thorium abundances on the lunar surface, <i>New Views of the
Moon, Europe</i>, 12-14 Jan., 2002a.</p>
<p>Lawrence, D. J., R. C. Elphic, W. C. Feldman, O. Gasnault, I. Genetay, S.
Maurice, and T. H. Prettyman, Small-area thorium enhancements on the lunar
surface, <i>33rd Lunar and Planetary Science Conference, Abstract #1970</i>,
2002b</p>
<p>Lawson, S. L., W. C. Feldman, D. J. Lawrence, K. R. Moore, S. Maurice, R. D.
Belian, and A. B. Binder, Maps of lunar radon-222 and polonium-210, <i>33rd
Lunar and Planetary Science Conference, Abstract #1835</i>, 2002.</p>
<p>Maurice, S., W. C. Feldman, D. J. Lawrence, O. Gasnault, C. d'Uston, and P.
G. Lucey, High-energy neutrons from the Moon, <i>J. Geophys. Res., 105</i>,
20,365-20,375, 2000.</p>
<p>Maurice, S., et al., Data reduction procedures for the Lunar Prospector
Neutron Spectrometer, in preparation, 2001a.</p>
<p>Maurice, S., R. C. Elphic, W. C. Feldman, R. Little, D. J. Lawrence, I.
Genetay, C. d'Uston, O. Gasnault, S. Chevrel, and A. B. Binder, Rare-earth
elements on the Moon from epithermal neutrons, <i>J. Geophys. Res.</i>, submitted,
2001b.</p>
<p>Prettyman, T. H., W. C. Feldman, D. J. Lawrence, G. W. McKinney, A. B.
Binder, R. C. Elphic, O. M. Gasnault, S. Maurice, and K. R. Moore, Library least
squares analysis of Lunar Prospector gamma-ray spectra, <i>33rd Lunar and
Planetary Science Conference, Abstract #2012</i>, 2002.</p>
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Lunar Prospector Reduced Spectrometer Data - Special Products

These Lunar Prospector (LP) gamma ray and neutron spectrometer special products and associated documentation have been prepared by the LP Spectrometer Team as part of a NASA Lunar Data Analysis Program. These spectrometer data products integrate data collected between January 16, 1998 and July 31, 1999. The data file descriptions for these products were provided by:

William C. Feldman, Tom H. Prettyman, Richard D. Belian, Richard C. Elphic,
Olivier Gasnault, David J. Lawrence, Stefanie L. Lawson, and Kurt R. Moore
Los Alamos National Laboratory, Los Alamos, NM

Alan B. Binder
Lunar Research Institute, Tuscon, AZ

Sylvestre Maurice
Observatoire Midi-Pyrenees, Toulouse, France

IMPORTANT: The data on this page are special products that may be of interest to the geoscience community. They do not represent a PDS peer-reviewed archive. Please contact William Feldman or David Lawrence of LANL for more information.  Please report any other anomalies in the data or documentation to geosci@wunder.wustl.edu.

Back to Lunar Prospector Reduced Spectrometer Data


Introduction

These Lunar Prospector (LP) spectrometer special products contain data from the neutron and gamma ray spectrometers. All data products are provided in two formats: an ASCII table and a binary image.

Each ASCII file contains header information followed by column formatted data separated by commas. Header records begin with a '#' character in the first column. The first four columns of the ASCII version contain the minimum and maximum latitude and longitude values for each pixels. East longitudes are positive. The remaining columns give the parameter value, and, if available, standard deviation values. The ASCII format files are named <name>.txt. File sizes of the ASCII versions vary depending of the bin size.

Each binary file is formatted as an image covering the entire planet with an array of 720 samples and 360 lines. Data values are scaled as integers in the image array with two bytes per pixel in IEEE (MSB first) order. For all binary files except the potassium, radon, and polonium, image values are ten times the values given in the ASCII files. The binary files for potassium abundances have integer values that are the same as values given in the corresponding ASCII file. The binary files for the radon and polonium data have integer values that are 1000 times the values given in the corresponding ASCII file. The binary image is a simple cylindrical map projection with 0.5° pixel size. The first image line corresponds to the south pole and the last image line corresponds to the north pole of the moon. The first sample corresponds to -180° East longitude and the last sample corresponds to 180° East longitude. Binary file names have the form <name>.dat and are about 0.5 MB in size.


Neutron Counting Rate Data Products

Thermal Neutron Counting Rate

The thermal neutron counting rate data product contains data from the LP neutron spectrometer Sn and Cd covered ³He detectors [Feldman et al., 1999, 2001a]. The thermal neutron counting rate is defined as the counting rate difference per 32 seconds between the two detectors and corresponds to neutrons having energies ranging from 0 to 0.4 eV. This data product has been described by Feldman et al. [2000a] and Elphic et al. [2000]. A detailed description of the data reduction for this data product is given by Maurice et al. [2001a]. The map bin size is 60 km by 60 km.

[TABLE]

Epithermal Neutron Counting Rate

The epithermal neutron counting rate data product contains data from the LP neutron spectrometer Cd covered ³He detector [Feldman et al., 1999, 2001a]. The epithermal neutron counting rate is defined as the counting rate per 32 seconds from the Cd covered detector and corresponds to neutrons having energies ranging from 0.4 to about 100 eV. The data product has been described by Feldman et al. [2000b, 2001c] and Maurice et al. [2001b]. A detailed description of the data reduction for this data product is given by Maurice et al. [2001a]. The map bin size is 60 km by 60 km.

[TABLE]

Fast Neutron Counting Rate

The fast neutron counting rate data product contains data from the LP gamma ray spectrometer [Feldman et al., 1999, 2001b]. The fast neutron counting rate is defined as the counting rate per 32 seconds of fast neutrons having energies from 0.6 to about 9 MeV. The data product has been described by Maurice et al. [2000]. A detailed description of the data reduction for this data product is given by Maurice et al. [2001a]. The map bin size is 60 km by 60 km.

[TABLE]

Fast Neutron Spectra Counting Rate

The fast neutron spectra counting rate data product contains data from the LP gamma ray spectrometer [Feldman et al., 1999, 2001b]. The fast neutron spectra counting rate is defined as the counting rate per 32 seconds of fast neutrons having energies from 0.6 to about 9 MeV. These energies are then divided into 16 channels having midpoint energies of 0.743, 1.40, 2.09, 2.67, 3.24, 3.75, 4.23, 4.70, 5.18, 5.65, 6.11, 6.55, 6.98, 7.41, 7.82, and 8.24 MeV. This data product has been described by Maurice et al. [2000]. A detailed description of the data reduction for this data product is given by Maurice et al. [2001a]. Furthermore, a description of how the counting rates can be converted into fluxes is given by Maurice et al. [2000] and Byrd and Urban [1994]. The map bin size is 60 km by 60 km. For the binary image file, the channels are stored in band sequential order.

[TABLE]


5 Degree Elemental Abundance Data Products

Elemental abundance values for O, Si, Ti, Al, Fe, Mg, Ca, U, and K were derived from LP gamma ray spectrometer [Feldman et al., 1999] observations acquired during the high-altitude portion of the LP mission. For the elements O, Si, Ti, Al, Fe, Mg, and Ca, the data are given in units of elemental weight percent. For the elements U and K, the data are given in units of ppm. A description of the reduction of these data products is given by Prettyman et al. [2002]. The Thorium data has been calculated using full modeling and response function analysis [Prettyman et al., 2002]. The map bin size is 150 km by 150 km.

[TABLE]


2 Degree Elemental Abundance Data Products

Samarium Abundance Data Product

The samarium abundance data product contains data from the LP neutron and gamma ray spectrometers [Feldman et al. 1999, 2001a] taken during the low-altitude portion of the LP mission. This data product is given in units of microgram/gram. A detailed description of the data reduction of this data type is given by Elphic et al. [2000]. The map bin size is 60 km x 60 km.

[TABLE]

Titanium and Potassium Abundance Data Products

These data products contain elemental abundances values for Ti and K derived from LP gamma ray spectrometer [Feldman et al., 1999] observations acquired during the high- and low-altitude portions of the LP mission. For Ti, the data are given in units of elemental weight percent. For K, the data are given in units of ppm. A description of the reduction of these data products is given by Prettyman et al. [2002]. The map bin size is 60 km by 60 km.

[TABLE]

Hydrogen Abundance Data

The hydrogen abundance data product contains data from the LP neutron spectrometer [Feldman et al., 1999, 2001a] acquired during the low-altitude portion of the LP mission. Hydrogen abundances are derived from epithermal neutron data that has been corrected by thermal neutron data [Feldman et al., 2001c]. Equations 3 and 4 of Feldman et al. [2001c] show how the corrected epithermal data is converted into hydrogen abundances as parts per million (ppm). Note, however, that these abundances are not necessarily reliable in regions of high thorium and rare-earth element abundances [Maurice et al., 2001a]. The map bin size is 60 km x 60 km.

[TABLE]

Thorium Abundance Data

The 2 degree thorium abundance data products contains data from the LP gamma ray spectrometer [Feldman et al., 1999, 2001b]. These absolute abundances are given in units of microgram/gram. There are two versions containing data taken from the high- and low-altitude portions of the LP mission [Lawrence et al., 2000]. These data have been described by Lawrence et al. [2000]. A detailed description of the data reduction for these data is given by Lawrence et al. [2001b]. The map bin size is 60 km x 60 km.

[TABLE]


Half Degree Abundance Data Products

Thorium Abundance Data

The half degree thorium abundance data product contains data from the LP gamma ray spectrometer [Feldman et al., 1999]. The absolute abundances are given in units of ppm. These data are taken from the low-altitude portion of the LP mission. A description of the reduction of these data products is given by Lawrence et al. [2002a, 2002b]. The map bin size is 0.5° by 0.5°.

[TABLE]

Iron Abundance Data

The half degree iron abundance data product contains data from the LP gamma ray spectrometer [Feldman et al., 1999] acquired during the low-altitude portion of the LP mission. The absolute abundances are given in units of FeO weight percent. A description of the reduction of these data products is given by Lawrence et al. [2002a, 2002b]. The map bin size is 0.5° by 0.5°.

[TABLE]

Hydrogen Abundance Data

The half degree hydrogen abundance data product contains data from the LP neutron spectrometer [Feldman et al., 1999]. Hydrogen abundances are derived from epithermal neutron data that has been corrected by thermal neutron data [Feldman et al., 2001c]. Equations 3 and 4 of Feldman et al. [2001c] show how the corrected epithermal data is converted into hydrogen abundances as parts per million (ppm). Note, however, that these abundances are not necessarily reliable in regions of high thorium and rare-earth element abundances [Maurice et al., 2001a]. The map bin size is 0.5° by 0.5°.

[TABLE]


Radon-222 and Polonium-210 Relative Count Rate Data Products

Relative count rate data products for radon-222 and polonium-210 contain data from the LP alpha particle spectrometer acquired during the high- and low-altitude portions of the LP mission. These data are given in units of counts per second. A description of the reduction of these data products is given by Lawson et al. [2002]. The data are mapped onto equal-area pixels having an approximate size at the equator of 300 km in the longitude direction and 450 km in the latitude direction.

[TABLE]


Data Product Summary

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References

(Contact David Lawrence of Los Alamos National Laboratory for information on references that are in preparation or in press).

Byrd, R. C., and W. T. Urban, Calculation of the neutron response of Boron-loaded scintillators, LAUR-12833-MS, Los Alamos, National Laboratory, Los Alamos, NM, 1994.

Elphic, R. C., D. J. Lawrence, W. C. Feldman, B. L. Barraclough, S. Maurice, A. B. Binder, and P. G. Lucey, Determination of lunar global rare earth element abundances using Lunar Prospector neutron spectrometer observations, J. Geophys. Res., 105, 20,333-20,346, 2000.

Feldman W. C., B. L. Barraclough, K. R. Fuller, D. J. Lawrence, S. Maurice, M. C. Miller, T. H. Prettyman, and A. B. Binder, the Lunar Prospector Gamma-Ray and Neutron Spectrometers, Nuclear Instruments and Methods in Physics Research A, 422, 562-566, 1999.

Feldman, W. C., D. J. Lawrence, R. C. Elphic, D. T. Vaniman, D. R. Thomsen, B. L. Barraclough, S. Maurice, and A. B. Binder, The chemical information content of lunar thermal and epithermal neutrons, J. Geophys. Res., 105, 20,347-20,363, 2000a.

Feldman, W. C., D. J. Lawrence, R. C. Elphic, B. L. Barraclough, S. Maurice, I. Genetay, and A. B. Binder, Polar hydrogen deposits on the Moon, J. Geophys. Res., 105, 4175-4195, 2000b.

Feldman, W. C., et al., Instrument description of the Lunar Prospector Neutron Spectrometer, in preparation, 2001a.

Feldman, W. C., et al., Instrument description of the Lunar Prospector Gamma-ray Spectrometer, in preparation, 2001b.

Feldman, W. C., S. Maurice, D. J. Lawrence, R. C. Little, S. L. Lawson, O. Gasnault, R. C. Wiens, B. L. Barraclough, R. C. Elphic, T. H. Prettyman, J. T. Steinberg, and A. B. Binder, Evidence for water ice near lunar poles, J. Geophys. Res., in press, 2001c.

Lawrence, D. J., W. C. Feldman, B. L. Barraclough, R. C. Elphic, T. H. Prettyman, S. Maurice, A. B. Binder, and M. C. Miller, Thorium abundances on the lunar surface, J. Geophys. Res., 105, 20,307-20,331, 2000.

Lawrence, D. J., W. C. Feldman, R. C. Elphic, S. Maurice, T. H. Prettyman, and A.  B. Binder, Iron abundances on the lunar surface as measured by the Lunar Prospector Gamma-Ray Spectrometer, 32nd Lunar and Planetary Science Conference, Abstract #1830, 2001a.

Lawrence D. J., et al., Data reduction procedures for the Lunar Prospector Gamma-ray Spectrometer, in preparation, 2001b.

Lawrence, D. J., W. C. Feldman, R. C. Elphic, R. C. Little, T. H. Prettyman, S. Maurice, P. G. Lucey, and A. B. Binder, Iron abundances on the lunar surface as measured by the Lunar Prospector Gamma-Ray and Neutron Spectrometers, J. Geophys. Res., submitted, 2001c.

Lawrence, D. J., R. C. Elphic, W. C. Feldman, O. Gasnault, I. Genetay, S. Maurice, and T. H. Prettyman, Optimizing the spatial resolution for gamma-ray measurements of thorium abundances on the lunar surface, New Views of the Moon, Europe, 12-14 Jan., 2002a.

Lawrence, D. J., R. C. Elphic, W. C. Feldman, O. Gasnault, I. Genetay, S. Maurice, and T. H. Prettyman, Small-area thorium enhancements on the lunar surface, 33rd Lunar and Planetary Science Conference, Abstract #1970, 2002b

Lawson, S. L., W. C. Feldman, D. J. Lawrence, K. R. Moore, S. Maurice, R. D. Belian, and A. B. Binder, Maps of lunar radon-222 and polonium-210, 33rd Lunar and Planetary Science Conference, Abstract #1835, 2002.

Maurice, S., W. C. Feldman, D. J. Lawrence, O. Gasnault, C. d'Uston, and P. G. Lucey, High-energy neutrons from the Moon, J. Geophys. Res., 105, 20,365-20,375, 2000.

Maurice, S., et al., Data reduction procedures for the Lunar Prospector Neutron Spectrometer, in preparation, 2001a.

Maurice, S., R. C. Elphic, W. C. Feldman, R. Little, D. J. Lawrence, I. Genetay, C. d'Uston, O. Gasnault, S. Chevrel, and A. B. Binder, Rare-earth elements on the Moon from epithermal neutrons, J. Geophys. Res., submitted, 2001b.

Prettyman, T. H., W. C. Feldman, D. J. Lawrence, G. W. McKinney, A. B. Binder, R. C. Elphic, O. M. Gasnault, S. Maurice, and K. R. Moore, Library least squares analysis of Lunar Prospector gamma-ray spectra, 33rd Lunar and Planetary Science Conference, Abstract #2012, 2002.

 

[TABLE]

[TABLE]

therms.txt
therms.dat
epis.txt
epis.dat
fast.txt
fast.dat
fastspectra.txt
fastspectra.dat
aluminum5d.txt
aluminum5d.dat
calcium5d.txt
calcium5d.dat
hydrogenlow.txt
hydrogenlow.dat
hydrogenhd.txt
hydrogenhd.dat
iron5d.txt
iron5d.dat
ironhd.txt
ironhd.dat
magnesium5d.txt
magnesium5d.dat
oxygen5d.txt
oxygen5d.dat
polonium210.txt
polonium210.dat
potassium5d.txt
potassium5d.dat
potassium2d.txt
potassium2d.dat
radon222.txt
radon222.dat
samarium2d.txt
samarium2d.dat
silicon5d.txt
silicon5d.dat
thoriumhigh.txt
thoriumhigh.dat
thoriumlow.txt
thoriumlow.dat
thoriumhd.txt
thoriumhd.dat
thorium5d.txt
thorium5d.dat
titanium5d.txt
titanium5d.dat
titanium2d.txt
titanium2d.dat
uranium5d.txt
uranium5d.dat
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