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jonaslindemannjonaslindemann
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Updated visualisation examples.
1 parent 5208713 commit b8f5559

16 files changed

+899218
-31
lines changed

‎data/data_handling.ipynb

+16-14
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@@ -386,18 +386,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import scipy as sp\n",
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"import scipy.misc as spm"
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"import imageio"
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]
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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": 21,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
@@ -421,7 +415,7 @@
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}
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],
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"source": [
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"mos_image = spm.imread('MOS6581.png')\n",
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"mos_image = imageio.imread('MOS6581.png')\n",
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"print(mos_image.shape, mos_image.dtype)"
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]
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},
@@ -434,9 +428,17 @@
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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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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MOS6581.raw\n"
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]
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}
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],
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"source": [
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"mos_image.tofile('MOS6581.raw')\n",
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"%ls MOS6581.raw"
@@ -586,7 +588,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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"version": "3.7.4"
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}
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},
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"nbformat": 4,

‎dataviz/PyVTK.ipynb

+153-17
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@@ -41,10 +41,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pyvtk import *\n",
@@ -84,7 +82,10 @@
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"vtk.tofile('example3')"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(299520, 6)\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"uvw_arr = np.loadtxt('uvw.dat', skiprows=2)\n",
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"print(uvw_arr.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": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"variables=\"x\",\"y\",\"z\",\"u\",\"v\",\"w\"\n",
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"zone i=96,j=65,k=48,f=point\n",
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"0 -1 0 -7.00895e-05 2.28715e-05 7.11905e-05\n",
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"0.0327249 -1 0 -8.01278e-05 1.89072e-05 4.83821e-05\n",
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"0.0654498 -1 0 -6.8715e-05 8.60358e-06 -1.31806e-06\n",
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"0.0981748 -1 0 -3.79768e-05 -4.03025e-06 -5.09628e-05\n",
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"0.1309 -1 0 -8.62436e-06 -1.93002e-05 -9.18307e-05\n",
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"0.163625 -1 0 5.18301e-06 -2.96726e-05 -8.77444e-05\n",
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"0.19635 -1 0 3.97951e-06 -2.64684e-05 -4.38229e-05\n",
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"0.229074 -1 0 -8.44003e-06 -1.01639e-05 -7.55249e-06\n"
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]
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}
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],
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"source": [
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"!head uvw.dat"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"points = uvw_arr[:,0:3]"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(299520, 3)\n"
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]
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}
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],
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"source": [
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"print(points.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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"point_data = uvw_arr[:,3:6]"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(299520, 3)\n"
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]
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}
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],
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"source": [
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"print(point_data.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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"vtk = VtkData(UnstructuredGrid(point_data), PointData(Vectors(point_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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"vtk.tofile(\"uvw.vtk\")"
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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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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": []
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}
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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"version": "3.7.4"
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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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"nbformat_minor": 4
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}

‎dataviz/ex1.py

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# -*- coding: utf-8 -*-
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"""
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Created on Wed Jun 7 14:58:44 2017
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@author: Jonas Lindemann
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"""
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import numpy as np
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import pyvtk as vtk
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print("Reading from uvw.dat...")
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xyzuvw = np.loadtxt('uvw.dat', skiprows=2)
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print("Converting to points and vectors")
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points = xyzuvw[:, 0:3].tolist()
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vectors = xyzuvw[:, 3:].tolist()
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pointdata = vtk.PointData(vtk.Vectors(vectors, name="vec1"), vtk.Vectors(vectors, name="vec2"))
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data = vtk.VtkData(vtk.StructuredGrid([96, 65, 48], points), pointdata)
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data.tofile('uvw','ascii')
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‎dataviz/ex2.py

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# -*- coding: utf-8 -*-
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"""
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Created on Thu Jun 8 12:03:31 2017
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@author: Jonas Lindemann
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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import pyvtk as vtk
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import os
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f = open("colorado_elev.vit", "rb") # reopen the file
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f.seek(268, os.SEEK_SET) # seek
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x = np.fromfile(f, dtype=np.ubyte) # read the data into numpy
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elevation = np.reshape(x, (400,400))
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plt.imshow(elevation)
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plt.show()
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pointdata = vtk.PointData(vtk.Scalars(x))
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data = vtk.VtkData(vtk.StructuredPoints([400,400]), pointdata)
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data.tofile('elevation','ascii')
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