Update Notebooks with JurseSF research
This commit is contained in:
parent
506eb11946
commit
9d1360fbe6
@ -199,7 +199,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"sys.path.append('..')\n",
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"import ld2dap"
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]
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},
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@ -231,7 +230,7 @@
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"outputs": [],
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"source": [
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"tif = ld2dap.LoadTIFF('../Data/phase1_rasters/DSM_C12/UH17c_GEF051_TR.tif')\n",
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"trh = ld2dap.Treshold(1e4)\n",
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"#trh = ld2dap.Treshold(1e4)\n",
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"aps = ld2dap.SelfDualAttributeProfiles(area=[100, 1e3, 1e4])\n",
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"dsp = ld2dap.ShowFig(symb=True)\n",
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"dif = ld2dap.Differential()\n",
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@ -240,8 +239,8 @@
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"ddsp.input = dif\n",
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"dif.input = aps\n",
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"dsp.input = aps\n",
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"aps.input = trh\n",
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"trh.input = tif\n",
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"aps.input = tif\n",
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"#trh.input = tif\n",
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"\n",
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"tif.run()"
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]
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@ -6,16 +6,11 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"from pathlib import Path\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import collections\n",
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"\n",
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"ld2dap_path = Path('../')\n",
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"sys.path.append(str(ld2dap_path.resolve()))\n",
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"import ld2dap\n",
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"from ld2dap.core import Filter"
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"import ld2dap"
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]
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},
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{
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293
Notebooks/Split First.ipynb
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293
Notebooks/Split First.ipynb
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@ -0,0 +1,293 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import triskele\n",
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"import ld2dap\n",
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"\n",
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"figsize = np.array((16, 9)) * 2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Split First for JURSE\n",
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"\n",
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"Load raster, $n$ split along dimension $d$, describe and merge in description stack."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"n = 3; d = 0"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"layers_files = [\n",
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" '../Data/phase1_rasters/DEM+B_C123/UH17_GEM051_TR.tif',\n",
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" '../Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif',\n",
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" '../Data/phase1_rasters/DEM_C123_TLI/UH17_GEG05_TR.tif',\n",
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" '../Data/phase1_rasters/DSM_C12/UH17c_GEF051_TR.tif',\n",
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" '../Data/phase1_rasters/Intensity_C1/UH17_GI1F051_TR.tif',\n",
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" '../Data/phase1_rasters/Intensity_C2/UH17_GI2F051_TR.tif',\n",
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" '../Data/phase1_rasters/Intensity_C3/UH17_GI3F051_TR.tif'\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"load = ld2dap.LoadTIFF(layers_files)\n",
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"#trsh = ld2dap.Treshold(1e4)\n",
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"rout = ld2dap.RawOutput()\n",
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"\n",
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"rout.input = load\n",
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"trsh.input = load\n",
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"\n",
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"rout.run()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Split"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"step = int(rout.data.shape[d] / n)\n",
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"\n",
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"fig, axs = plt.subplots(n, figsize=figsize * n)\n",
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"\n",
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"view = np.moveaxis(rout.data, d, 0)\n",
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"\n",
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"cuts = list()\n",
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"for i in range(n):\n",
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" cut = np.moveaxis(view[i*step:(i+1)*step+1], 0, d)\n",
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" cuts.append(cut)\n",
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" \n",
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" # Disp\n",
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" axs[i].imshow(cut[:,:,0], cmap=plt.get_cmap('GnBu'))\n",
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" axs[i].set_title('Cut {}'.format(i))\n",
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" \n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Describe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dcuts = list()\n",
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"\n",
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"for cut in cuts:\n",
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" rinp = ld2dap.RawInput(cut, rout.metadata)\n",
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" aps = ld2dap.AttributeProfiles(area=[100,1e3,1e4])\n",
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" vout = ld2dap.RawOutput()\n",
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" \n",
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" vout.input = aps\n",
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" aps.input = rinp\n",
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" \n",
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" vout.run()\n",
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" \n",
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" dcuts.append(vout.data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Merge"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"descriptors = np.zeros(rout.data.shape[:2] + (dcuts[0].shape[-1],))\n",
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"\n",
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"for i, cut in enumerate(dcuts):\n",
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" descriptors[i*step:(i+1)*step+1] = cut"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Visual test"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"t_inp = ld2dap.RawInput(descriptors, vout.metadata)\n",
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"t_dsp = ld2dap.ShowFig(stack_id=3, symb=True, fname='../Res/bands.png')\n",
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"\n",
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"t_dsp.input = t_inp\n",
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"\n",
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"t_dsp.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"descriptors.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dim = 5\n",
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"A = np.arange(64).reshape((4, 8, 2))\n",
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"A = A[:,:,np.newaxis] if A.ndim == 2 else A\n",
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"B = np.zeros(A.shape[:2] + (A.shape[2] * dim,))\n",
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"A.shape, B.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"step = int(A.shape[0] / n)\n",
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"cuts = list()\n",
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"\n",
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"for i in range(n):\n",
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" cut = A[i*step:(i+1)*step+1]\n",
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" cut = np.repeat(cut, dim, axis=2)\n",
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" cuts.append(cut)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for i, cut in enumerate(cuts):\n",
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" B[i*step:(i+1)*step+1] = cut\n",
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"B"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"cuts[0].shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.tile(A[:,:,np.newaxis], (1,1,10))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"A.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.repeat(A.reshape(, 2, axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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73
Notebooks/dtypes are evil.ipynb
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73
Notebooks/dtypes are evil.ipynb
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@ -0,0 +1,73 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import ld2dap\n",
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"import triskele"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"load = ld2dap.LoadTIFF('../Data/phase1_rasters/Intensity_C1/UH17_GI1F051_TR.tif')\n",
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"trsh = ld2dap.Treshold(1e4)\n",
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"disp = ld2dap.ShowFig()\n",
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"rout = ld2dap.RawOutput()\n",
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"\n",
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"rout.input = trsh\n",
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"disp.input = trsh\n",
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"trsh.input = load\n",
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"\n",
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"disp.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"rinp = ld2dap.RawInput(rout.data.astype(np.uint16), rout.metadata)\n",
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"\n",
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"disp.input = rinp\n",
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"\n",
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"disp.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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