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Updated kaguya cropping notebook to handle bands
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@acpaquette
acpaquette committed Dec 9, 2020
1 parent da6126e commit d502889
Showing 1 changed file with 45 additions and 145 deletions.
190 changes: 45 additions & 145 deletions isis/notebooks/crop_kaguya.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -17,19 +17,17 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#kaguya_file = '/scratch/arsanders/kaguyatc/TC2S2B0_01_03043N107E3384.img'\n",
"#kaguya_file = '/home/arsanders/testData/apollo/AS15-M-1450.lbl'\n",
"kaguya_file = '/home/arsanders/testData/marci/MOI_000009_0294_MU_00N044W.IMG'\n",
"kaguya_file = '/Path/to/kaguyami/or/kaguyatc.img'\n",
"image_file = kaguya_file"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -38,104 +36,35 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PVLModule([\n",
" ('PDS_VERSION_ID', 'PDS3')\n",
" ('FILE_NAME', 'MOI_000009_0294_MU_00N044W.IMG')\n",
" ('RECORD_TYPE', 'FIXED_LENGTH')\n",
" ('RECORD_BYTES', 128)\n",
" ('FILE_RECORDS', 2259)\n",
" ('LABEL_RECORDS', 11)\n",
" ('^IMAGE', 12)\n",
" ('SPACECRAFT_NAME', 'MARS_RECONNAISSANCE_ORBITER')\n",
" ('INSTRUMENT_NAME', 'MARS COLOR IMAGER')\n",
" ('INSTRUMENT_HOST_NAME', 'MARS RECONNAISSANCE ORBITER')\n",
" ('MISSION_PHASE_NAME', 'POSTMOI')\n",
" ('TARGET_NAME', 'MARS')\n",
" ('INSTRUMENT_ID', 'MARCI')\n",
" ('PRODUCER_ID', 'MRO_MARCI_TEAM')\n",
" ('DATA_SET_ID', 'MRO-M-MARCI-2-EDR-L0-V1.0')\n",
" ('PRODUCT_CREATION_TIME',\n",
" datetime.datetime(2007, 5, 17, 18, 31, 28, tzinfo=datetime.timezone.utc))\n",
" ('SOFTWARE_NAME', 'makepds05 $Revision: 1.7 $')\n",
" ('UPLOAD_ID', 'UNK')\n",
" ('ORIGINAL_PRODUCT_ID', '4A_05_0001000200')\n",
" ('PRODUCT_ID', 'MOI_000009_0294_MU_00N044W')\n",
" ('START_TIME',\n",
" datetime.datetime(2006, 3, 24, 4, 25, 53, 96000, tzinfo=datetime.timezone.utc))\n",
" ('STOP_TIME',\n",
" datetime.datetime(2006, 3, 24, 4, 55, 48, 296000, tzinfo=datetime.timezone.utc))\n",
" ('SPACECRAFT_CLOCK_START_COUNT', '827641567:30')\n",
" ('SPACECRAFT_CLOCK_STOP_COUNT', 'N/A')\n",
" ('INTERFRAME_DELAY', Quantity(value=3.2, units='SECONDS'))\n",
" ('FOCAL_PLANE_TEMPERATURE', Quantity(value=240.9, units='K'))\n",
" ('SAMPLE_BIT_MODE_ID', 'SQROOT')\n",
" ('LINE_EXPOSURE_DURATION', Quantity(value=3112.237, units='MSEC'))\n",
" ('SAMPLING_FACTOR', 8)\n",
" ('SAMPLE_FIRST_PIXEL', 0)\n",
" ('FILTER_NAME', frozenset({'LONG_UV', 'SHORT_UV'}))\n",
" ('RATIONALE_DESC', 'Post-MOI image of Argyre and Mare Erythraeum region')\n",
" ('DATA_QUALITY_DESC', 'OK')\n",
" ('ORBIT_NUMBER', 9)\n",
" ('IMAGE',\n",
" {'CHECKSUM': 12820826,\n",
" 'LINES': 2248,\n",
" 'LINE_PREFIX_BYTES': 0,\n",
" 'LINE_SAMPLES': 128,\n",
" 'LINE_SUFFIX_BYTES': 0,\n",
" 'SAMPLE_BITS': 8,\n",
" 'SAMPLE_BIT_MASK': 255,\n",
" 'SAMPLE_TYPE': 'UNSIGNED_INTEGER'})\n",
"])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"header"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"header[\"^IMAGE\"]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open(kaguya_file, 'rb') as f:\n",
" try:\n",
" # For marci image \n",
" image_offset = header['^IMAGE'] * header['LABEL_RECORDS'] * header[\"RECORD_BYTES\"] - (header['IMAGE']['SAMPLE_BITS']//8)\n",
" # image_offset = header['^IMAGE'] * header['LABEL_RECORDS'] * header[\"RECORD_BYTES\"] - (header['IMAGE']['SAMPLE_BITS']//8)\n",
" \n",
" # For others\n",
" # image_offset = header[\"^IMAGE\"].value-(header['IMAGE']['SAMPLE_BITS']//8) \n",
" image_offset = header[\"^IMAGE\"].value-(header['IMAGE']['SAMPLE_BITS']//8) \n",
" \n",
" f.seek(image_offset)\n",
" b_image_data = f.read()\n",
Expand All @@ -149,82 +78,53 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"n_lines = 40\n",
"n_lines = 20\n",
"n_bands = 2\n",
"if \"BANDS\" not in header[\"IMAGE\"].keys():\n",
" n_bands = 1\n",
"line_length = header['IMAGE']['LINE_SAMPLES'] * (header['IMAGE']['SAMPLE_BITS']//8)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image_data = []\n",
"for j in range(n_lines):\n",
" image_sample = np.frombuffer(b_image_data[j*line_length:(j+1)*line_length], dtype=np.int16, count=int(line_length/2))\n",
" image_data.append(image_sample)\n",
"for i in range(n_bands):\n",
" image_data.append([])\n",
" for j in range(n_lines):\n",
" image_sample = np.frombuffer(b_image_data[j*line_length:(j+1)*line_length], dtype=np.int16, count=int(line_length/2))\n",
" image_data[i].append(image_sample)\n",
"image_data = np.array(image_data)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(40, 64)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"image_data.shape"
"image_data.min()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x10ec70fd0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"plt.imshow(image_data)"
"plt.imshow(image_data[0])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -241,7 +141,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -256,6 +156,7 @@
"\n",
"# Overwrite the number of lines in the label\n",
"header['IMAGE']['LINES'] = n_lines\n",
"header['IMAGE']['BANDS'] = n_bands\n",
"\n",
"if kaguya_file != image_file:\n",
" # If detached label, point the mini label to the mini image\n",
Expand All @@ -268,30 +169,20 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1820"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"label_fn, label_ext = os.path.splitext(kaguya_file)\n",
"out_label = label_fn + crop + label_ext\n",
"print(out_label)\n",
"\n",
"pvl.dump(header, out_label, encoder=encoder, grammar=grammar)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -301,6 +192,15 @@
" f.write(b_reduced_image_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pvl.load(mini_image_fn)"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -325,7 +225,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.0"
"version": "3.7.1"
}
},
"nbformat": 4,
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