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bbrelje/bootstrap3-load-spinner.css

Last active December 20, 2019 14:12
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Script for rendering Tecplot structured CFD surface data (in .dat point format) using Plotly
Raw
bootstrap3-load-spinner.css
.glyphicon-refresh-animate {
-animation: spin .7s infinite linear;
-webkit-animation: spin2 .7s infinite linear;
}
@-webkit-keyframes spin2 {
from { -webkit-transform: rotate(0deg);}
to { -webkit-transform: rotate(360deg);}
}
@keyframes spin {
from { transform: scale(1) rotate(0deg);}
to { transform: scale(1) rotate(360deg);}
}
Raw
button-example.html
<button class="btn btn-lg btn-hidden-viz" data-plotly-src="/assets/viz/cfdexample.js" data-plotly-uid="d58f9b77-df41-4618-b6d2-baa7fb02b0a6" data-height="100%" data-width="100%">
Click to load
</button>
Raw
cfdexample.js
if (document.getElementById("d58f9b77-df41-4618-b6d2-baa7fb02b0a6")) {
Plotly.newPlot(
'd58f9b77-df41-4618-b6d2-baa7fb02b0a6',
[{"colorbar": {"title": {"text": "Cp"}}, "colorscale": [[0, "gold"], [0.5, "mediumturquoise"], [1, "magenta"]], "i": [0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, 27, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, 33, 34, 35, 36, 36, 37, 37, 38, 38, 39, 39, 40, 40, 41, 49, 50, 50, 51, 51, 52, 52, 53, 53, 54, 54, 55, 55, 56, 56, 57, 57, 58, 58, 59, 59, 60, 60, 61, 61, 62, 62, 63, 63, 64, 64, 65, 65, 66, 66, 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 72, 72, 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79, 80, 80, 81, 81, 82, 82, 83, 83, 84, 84, 85, 85, 86, 86, 87, 87, 88, 88, 89, 89, 90, 90, 91, 91, 92, 92, 93, 93, 94, 94, 95, 95, 96, 96, 97, 97, 98, 98, 99, 99, 100, 100, 101, 101, 102, 102, 103, 103, 104, 104, 105, 105, 106, 106, 107, 107, 108, 108, 109, 109, 110, 110, 111, 111, 112, 112, 113, 113, 114, 114, 115, 115, 116, 116, 117, 117, 118, 118, 119, 119, 120, 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{"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "histogram"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}},
{"responsive": true}
)
};
Raw
load-plotly-on-demand.js
//Function to load plotly.js on demand and show a visualization
//Requires jQuery to be loaded first
$(function(){
$('.btn-hidden-viz').on('click', function() {
var thebutton = $(this)
var plotlyuid = $(this).data('plotlyUid');
var plotlysrc = $(this).data('plotlySrc');
var spinnerid = plotlyuid.concat('-spinner');
var height = $(this).data('height');
var width = $(this).data('width');
$('<div id="'.concat(plotlyuid,'" class="plotly-graph-div"></div>')).insertBefore(thebutton);
window.PlotlyConfig = {MathJaxConfig: 'local'};
$.ajax({
timeout: 10000,
url: 'https://cdn.plot.ly/plotly-latest.min.js',
dataType: 'script',
cache: true, // or get new, fresh copy on every page load
beforeSend: function () {
thebutton.html('<span id=\"'.concat(spinnerid,'\"></span> Loading'));
$('#'.concat(spinnerid)).attr("class","glyphicon glyphicon-refresh glyphicon-refresh-animate");
},
success: function() {
// Callback
window.PLOTLYENV=window.PLOTLYENV || {};
$.ajax({
url: plotlysrc,
dataType: 'script',
timeout: 5000,
cache: true, // or get new, fresh copy on every page load
success: function() {
// Callback
$("#".concat(plotlyuid)).attr("style","height:".concat(height,"; width:",width,";"));
thebutton.remove();
},
error: function() {
thebutton.html("error loading visualization");
}
});
},
error: function() {
thebutton.html("error loading visualization");
}
});
});})
Raw
parse-tecplot-structured-to-plotly.py
import sys
import numpy as np
import re
import plotly.graph_objects as go
import plotly.io as pio
def parse_tecplot_structured(filename):
f=open(filename,'r')
lines=f.readlines()
f.close()
x = []
y = []
z = []
p = []
vert1 = []
vert2 = []
vert3 = []
n_prev_tris = 0
delimiters = " ", "=", ","
regexPattern = '|'.join(map(re.escape, delimiters))
first_zone = True
for line in lines:
# skip until "zone_t"
try:
vals = line.split()
x.append(float(vals[0]))
y.append(float(vals[1]))
z.append(float(vals[2]))
p.append(float(vals[3]))
tri_count += 1
if new_zone:
raise ValueError('No new ijk indices parsed for this zone')
except:
if "ZONE T" in line:
# new zone.
if not first_zone:
n_prev_tris += ni*nj
if tri_count != ni*nj:
raise ValueError('Not enough vertices parsed')
new_zone = True
first_zone = False
tri_count = 0
elif "I=" in line:
nijk = [int(s) for s in re.split(regexPattern, line) if s.isdigit()]
ni = nijk[0]
nj = nijk[1]
print('Zone: ni='+str(ni)+' nj='+str(nj))
new_zone = False
for j in range(nj-1):
joffset = j*ni+n_prev_tris
for i in range(ni-1):
vert1.append(i+joffset)
vert3.append(i+1+joffset)
vert2.append(i+ni+joffset)
vert1.append(i+1+joffset)
vert2.append(i+ni+joffset)
vert3.append(i+ni+1+joffset)
x = np.array(x)
y = np.array(y)
z = np.array(z)
p = np.array(p)
vert1 = np.array(vert1)
vert2 = np.array(vert2)
vert3 = np.array(vert3)
return x, y, z, p, vert1, vert2, vert3
def generate_plotly_cfd(filename):
x, y, z, p, vert1, vert2, vert3 = parse_tecplot_structured(filename)
fig = go.Figure(data=[
go.Mesh3d(
x=x,
y=y,
z=z,
colorbar_title='Cp',
colorscale='viridis',
# Intensity of each vertex, which will be interpolated and color-coded
intensity=p,
# i, j and k give the vertices of triangles
# here we represent the 4 triangles of the tetrahedron surface
i=vert1,
j=vert2,
k=vert3,
#flatshading=True,
#lighting=dict(diffuse=0.9),
name='y',
showscale=True
)
])
fig.update_layout(scene_aspectmode='data')
fig.show()
pio.write_html(fig, 'cfdoutput.html', include_plotlyjs='cdn', include_mathjax=False, auto_open=True)
if __name__ == "__main__":
filename = sys.argv[1]
print(filename)
generate_plotly_cfd(filename)
Raw
tecplot-structured-data.dat
TITLE = "Translation of CGNS file fc0_153_surf.cgns"
VARIABLES = "CoordinateX"
"CoordinateY"
"CoordinateZ"
"p3D"
DATASETAUXDATA Common.Gamma="1.4"
DATASETAUXDATA Common.Incompressible="FALSE"
DATASETAUXDATA Common.ReferenceMachNumber="0.3"
DATASETAUXDATA Common.SteadyState="TRUE"
DATASETAUXDATA Common.UVar="4"
DATASETAUXDATA Common.VectorVarsAreVelocity="TRUE"
DATASETAUXDATA Common.VVar="5"
DATASETAUXDATA Common.WVar="6"
ZONE T="NSWallAdiabaticBCZone1"
STRANDID=0, SOLUTIONTIME=153
I=7, J=7, K=1, ZONETYPE=Ordered
DATAPACKING=POINT
AUXDATA CGNS.CGNSBase_t="BaseSurfaceSol"
AUXDATA CGNS.Zone_t="NSWallAdiabaticBCZone1"
DT=(SINGLE SINGLE SINGLE SINGLE )
1.482255459E-01 -6.317630410E-03 -1.031955797E-02 7.962425351E-01
1.504750550E-01 -7.337676361E-03 -5.579479970E-03 6.982120275E-01
1.523738205E-01 -7.679532282E-03 -2.414225368E-03 5.478359461E-01
1.541845798E-01 -7.748003583E-03 2.114466333E-04 4.767358303E-01
1.561999321E-01 -7.615496870E-03 2.831594087E-03 4.587537944E-01
1.589078754E-01 -7.212655619E-03 5.981925875E-03 4.177888036E-01
1.635468900E-01 -6.099810824E-03 1.066546980E-02 3.463115096E-01
1.468069851E-01 -3.478754777E-03 -1.202554256E-02 9.238828421E-01
1.485810280E-01 -4.248100333E-03 -6.982204970E-03 8.656069040E-01
1.505511850E-01 -4.472642206E-03 -3.172763158E-03 7.197331190E-01
1.527354717E-01 -4.505565856E-03 1.213515570E-04 6.082710624E-01
1.553585529E-01 -4.401813261E-03 3.409739817E-03 5.439983606E-01
1.589499414E-01 -4.107575864E-03 7.209978998E-03 4.534604549E-01
1.645169854E-01 -3.240859834E-03 1.220952999E-02 3.224727809E-01
1.463378072E-01 -1.635770779E-03 -1.262633596E-02 9.206809402E-01
1.478607208E-01 -1.993926242E-03 -7.445031311E-03 9.256316423E-01
1.498770863E-01 -2.076334087E-03 -3.417586442E-03 7.916103601E-01
1.521360576E-01 -2.075610217E-03 5.747483374E-05 6.478260756E-01
1.549536437E-01 -2.005182207E-03 3.532360774E-03 5.506414771E-01
1.588234901E-01 -1.852010493E-03 7.557834964E-03 4.235869944E-01
1.648519188E-01 -1.393880113E-03 1.271717064E-02 2.737793922E-01
1.461991817E-01 -1.216586679E-04 -1.281702239E-02 8.949918747E-01
1.476474702E-01 -7.410733815E-05 -7.604122162E-03 9.209603071E-01
1.496368498E-01 -3.895624468E-05 -3.531674622E-03 7.889319062E-01
1.519336998E-01 -3.044171081E-06 -4.826016720E-06 6.376057863E-01
1.547878683E-01 3.333039058E-05 3.527861787E-03 5.392538309E-01
1.587494463E-01 7.043226651E-05 7.605661638E-03 4.096668959E-01
1.649112403E-01 1.219206461E-04 1.281708851E-02 2.568794787E-01
1.462912112E-01 1.394285820E-03 -1.270895265E-02 9.244255424E-01
1.477973163E-01 1.851896173E-03 -7.551189978E-03 9.303097129E-01
1.497998983E-01 2.004114212E-03 -3.533098381E-03 7.967101336E-01
1.520455629E-01 2.072961070E-03 -6.595038576E-05 6.529642344E-01
1.548571885E-01 2.072391100E-03 3.411983838E-03 5.554315448E-01
1.587200761E-01 1.990739023E-03 7.441620808E-03 4.266707897E-01
1.647562087E-01 1.636050176E-03 1.261891797E-02 2.747329175E-01
1.467148066E-01 3.241328523E-03 -1.220079418E-02 9.324542880E-01
1.484477222E-01 4.110984970E-03 -7.203508168E-03 8.725665212E-01
1.503931880E-01 4.405266140E-03 -3.408952383E-03 7.268926501E-01
1.525551528E-01 4.506184254E-03 -1.265661704E-04 6.165378690E-01
1.551679671E-01 4.469979554E-03 3.168445081E-03 5.518541336E-01
1.587483287E-01 4.244991578E-03 6.978083402E-03 4.590664208E-01
1.643302441E-01 3.479236271E-03 1.201782282E-02 3.258338571E-01
1.480384171E-01 6.099810824E-03 -1.066546980E-02 8.049359322E-01
1.502252519E-01 7.217536215E-03 -5.981234834E-03 7.029955387E-01
1.520937979E-01 7.620239165E-03 -2.831171965E-03 5.535423756E-01
1.538781077E-01 7.747960743E-03 -2.109922643E-04 4.869794250E-01
1.558745801E-01 7.674274035E-03 2.414868446E-03 4.690557718E-01
1.585725546E-01 7.332142908E-03 5.580137018E-03 4.264635444E-01
1.632268876E-01 6.317630410E-03 1.031955797E-02 3.544164300E-01
ZONE T="NSWallAdiabaticBCZone11"
STRANDID=0, SOLUTIONTIME=153
I=73, J=7, K=1, ZONETYPE=Ordered
DATAPACKING=POINT
AUXDATA CGNS.CGNSBase_t="BaseSurfaceSol"
AUXDATA CGNS.Zone_t="NSWallAdiabaticBCZone11"
DT=(SINGLE SINGLE SINGLE SINGLE )
1.635468900E-01 -6.099810824E-03 1.066546980E-02 3.463115096E-01
1.670151800E-01 -7.337546442E-03 1.295834221E-02 2.741341293E-01
1.734558791E-01 -9.357417002E-03 1.667178422E-02 1.886113286E-01
1.808472574E-01 -1.143714506E-02 2.039701492E-02 9.152434021E-02
1.874639243E-01 -1.322092582E-02 2.333542146E-02 2.059139498E-02
1.935490221E-01 -1.487475447E-02 2.582748979E-02 -2.637963928E-02
1.991442293E-01 -1.640496217E-02 2.793399990E-02 -8.044466376E-02
2.043336332E-01 -1.781491190E-02 2.973892540E-02 -1.390817761E-01
2.091794610E-01 -1.909659058E-02 3.129515052E-02 -1.992859095E-01
2.137287110E-01 -2.024143748E-02 3.263696283E-02 -2.763379812E-01
2.179800570E-01 -2.121977694E-02 3.374479711E-02 -3.363068104E-01
2.220637500E-01 -2.210144326E-02 3.474657610E-02 -3.802790046E-01
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2.332788408E-01 -2.409469523E-02 3.695002943E-02 -5.452167988E-01
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