CSVReadEdit1
Repository source: CSVReadEdit1
Description¶
This example loads a CSV file, edits it and visualises the result.
It demonstrates the use of pandas to read and edit the CSV input file, then create a temporary file containing the desired columns. This temporary file is subsequently read and parsed using vtkDelimitedTextReader.
The key thing about pandas is it can read/write data in various formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format. It is highly optimized for performance and the DataFrame object allows for extensive row/column manipulation. So we can edit the data, creating new columns, and, finally, select only relevant columns for further analysis by VTK.
In this case we create a temporary CSV file of selected columns and read this with vtkDelimitedTextReader.
The process is this:
CSV->pandas(read/edit/select)->CSV->[vtkDelimitedTextReader](https://www.vtk.org/doc/nightly/html/classvtkDelimitedTextReader.html)->[vtkPolyData](https://www.vtk.org/doc/nightly/html/classvtkPolyData.html)
By going down this route we don't overload the delimited text reader with the effort of processing any unneeded columns of data.
The files used to generate the example are:
<DATA>/LakeGininderra.csv
<DATA>/LakeGininderra.kmz
Where:
<DATA>is the path tovtk-examples/src/Testing/DataLakeGininderra.csvis the CSV file used by this program.LakeGininderra.kmzcan be loaded into Google Earth to display the track.
The parameters for typical usage are something like this:
<DATA>/LakeGininderra.csv -e -c -pResults
Further information:
Question
If you have a question about this example, please use the VTK Discourse Forum
Code¶
CSVReadEdit1.py
#!/usr/bin/env python3
import tempfile
from pathlib import Path
import pandas as pd
# noinspection PyUnresolvedReferences
import vtkmodules.vtkInteractionStyle
# noinspection PyUnresolvedReferences
import vtkmodules.vtkRenderingOpenGL2
from vtkmodules.vtkCommonColor import (
vtkNamedColors
)
from vtkmodules.vtkCommonCore import vtkLookupTable
from vtkmodules.vtkCommonDataModel import (
vtkCellArray,
vtkPolyLine
)
from vtkmodules.vtkCommonTransforms import vtkTransform
from vtkmodules.vtkFiltersGeneral import vtkTableToPolyData, vtkTransformPolyDataFilter
from vtkmodules.vtkIOInfovis import vtkDelimitedTextReader
from vtkmodules.vtkIOXML import vtkXMLPolyDataWriter
from vtkmodules.vtkInteractionStyle import vtkInteractorStyleTrackballCamera
from vtkmodules.vtkInteractionWidgets import vtkCameraOrientationWidget, vtkOrientationMarkerWidget
from vtkmodules.vtkRenderingAnnotation import vtkAxesActor, vtkScalarBarActor
from vtkmodules.vtkRenderingCore import (
vtkActor,
vtkColorTransferFunction,
vtkPolyDataMapper,
vtkRenderWindow,
vtkRenderWindowInteractor,
vtkRenderer
)
def get_program_parameters():
import argparse
description = 'Edit data from a CSV file and visualise it.'
epilogue = '''
This program selects ECEF, Geographic or UTM coordinates from the input file and:
1) Visualises the resultant ECEF or UTM points and lines.
2) If ECEF or UTM is selected, optionally creates and saves a VTP file for further analysis.
3) Optionally saves the CSV file.
If Geographic coordinates are selected, only the resultant CSV file can be saved.
'''
parser = argparse.ArgumentParser(description=description, epilog=epilogue,
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('file_name', help='The CSV file containing the data.')
parser.add_argument('-c', '--csv', action='store_true', help='Save the resultant CSV file.')
parser.add_argument('-v', '--vtp', action='store_true', help='Save the .vtp file.')
parser.add_argument('-p', '--path', default='.',
help='The path to be appended to the .vtp and optional .csv file')
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('-e', '--ecef', action='store_true', help='Use ECEF coordinates.')
group.add_argument('-u', '--utm', action='store_true', help='Use UTM coordinates.')
group.add_argument('-g', '--geo', action='store_true', help='Use geographic coordinates (latitude/longitude).')
args = parser.parse_args()
return args.file_name, args.csv, args.vtp, args.path, args.ecef, args.utm, args.geo
def main():
ifn, csv, vtp, sp, ecef, utm, geo = get_program_parameters()
file_name = Path(ifn)
if not file_name.is_file():
print('Unable to read:', file_name)
return
pth = Path(sp)
if not pth.is_dir():
if pth.is_file():
print(sp, ' must be a path')
return
pth.mkdir(parents=True, exist_ok=True)
# Build the output paths.
csv_fn = Path(pth / Path(ifn).stem).with_suffix('.csv')
vtp_fn = Path(pth / Path(ifn).stem).with_suffix('.vtp')
if ecef:
vtp_fn = vtp_fn.with_stem(vtp_fn.stem + '_ecef')
if utm:
vtp_fn = vtp_fn.with_stem(vtp_fn.stem + '_utm')
# Create a DataFrame from the csv file.
df = pd.read_csv(file_name)
# Use the column called 'Index' as the index.
# This ensures that we can trace back each row to the original data.
df.set_index('Index', inplace=True)
# For ECEF coordinates, we want to look down from the zenith.
# So calculate the mid-point of the latitude.
lat_details = df['Latitude'].describe()
lat_mid_pt = (lat_details['max'] + lat_details['min']) / 2
# Create a temporary csv file with just the needed columns.
tmp_dir = tempfile.gettempdir()
if tmp_dir is None:
print('Unable to find', tmp_dir)
return
tmp_path = Path(tmp_dir, f'tmp_{file_name.name}')
dfv = None
# Copy what we want to a new DataFrame and drop any rows with missing values.
if ecef:
dfv = df[['X(m)', 'Y(m)', 'Z(m)', 'Elevation(m)']].dropna(
subset=['X(m)', 'Y(m)', 'Z(m)'])
if csv:
ecef_csv_fn = csv_fn.with_stem(csv_fn.stem + '_ecef')
dfv.to_csv(ecef_csv_fn, index=True, index_label='Index', header=True)
elif utm:
dfv = df[['Easting(m)', 'Northing(m)', 'Elevation(m)']].dropna(
subset=['Easting(m)', 'Northing(m)', 'Elevation(m)'])
# Duplicate the elevation column, this will become the z-coordinate when UTM is selected.
dfv['Elev'] = dfv.loc[:, 'Elevation(m)']
if csv:
utm_csv_fn = csv_fn.with_stem(csv_fn.stem + '_utm')
dfv.to_csv(utm_csv_fn, index=True, index_label='Index', header=True)
else:
df_geo = df[['Longitude', 'Latitude', 'Elevation(m)']].dropna(
subset=['Longitude', 'Latitude', 'Elevation(m)'])
geo_csv_fn = csv_fn.with_stem(csv_fn.stem + '_geo')
df_geo.to_csv(geo_csv_fn, index=True, index_label='Index', header=True)
if ecef or utm:
# Write out the DataFrame.
dfv.to_csv(tmp_path, index=True, index_label='Index', header=True)
points_reader = vtkDelimitedTextReader()
points_reader.SetFileName(tmp_path)
points_reader.DetectNumericColumnsOn()
points_reader.SetFieldDelimiterCharacters(',')
points_reader.SetHaveHeaders(True)
table_pd = vtkTableToPolyData()
table_pd.SetInputConnection(points_reader.GetOutputPort())
if ecef:
table_pd.SetXColumn('X(m)')
table_pd.SetYColumn('Y(m)')
table_pd.SetZColumn('Z(m)')
elif utm:
table_pd.SetXColumn('Easting(m)')
table_pd.SetYColumn('Northing(m)')
table_pd.SetZColumn('Elev')
else:
# Remove the temporary file, it is not needed any more.
tmp_path.unlink(missing_ok=True)
print('Only ECEF or UTM coordinates can be visualised.')
return
table_pd.Update()
# Remove the temporary file, it is not needed any more.
tmp_path.unlink(missing_ok=True)
poly_data = table_pd.GetOutput()
# poly_data = transform_filter.GetOutput()
# We use the elevation as the active scalars.
poly_data.GetPointData().SetActiveScalars('Elevation(m)')
elev_range = poly_data.GetPointData().GetScalars().GetRange()
num_pts = poly_data.GetNumberOfPoints()
poly_line = vtkPolyLine()
poly_line.GetPointIds().SetNumberOfIds(num_pts)
for i in range(0, num_pts):
poly_line.GetPointIds().SetId(i, i)
# Create a cell array to store the lines in and add the lines to it.
cells = vtkCellArray()
cells.InsertNextCell(poly_line)
# Add the lines to the dataset
poly_data.SetLines(cells)
poly_data.Modified()
transform = vtkTransform()
if utm:
# Scale the elevation.
transform.Scale(1, 1, 1)
if ecef:
# Rotate the ECEF coordinates
# into VTK coordinates so that on the screen:
# Y points North, X points East and Z points up.
transform.RotateX(-(90 - lat_mid_pt))
transform.RotateY(0)
transform.RotateZ(90 - lat_mid_pt)
transform_filter = vtkTransformPolyDataFilter()
transform_filter.SetInputData(table_pd.GetOutput())
transform_filter.SetTransform(transform)
transform_filter.Update()
if vtp:
writer = vtkXMLPolyDataWriter()
writer.SetFileName(vtp_fn)
writer.SetInputConnection(transform_filter.GetOutputPort())
writer.SetDataModeToBinary()
writer.Write()
colors = vtkNamedColors()
colors.SetColor("ParaViewBkg", [82, 87, 110, 255])
lut = get_diverging_lut('cool_warm')
# lut = get_diverging_lut1('DarkRed', 'Gainsboro', 'Green')
mapper = vtkPolyDataMapper()
mapper.SetInputConnection(transform_filter.GetOutputPort())
mapper.SetScalarRange(elev_range)
mapper.SetLookupTable(lut)
mapper.ScalarVisibilityOn()
actor = vtkActor()
actor.SetMapper(mapper)
window_width = 1024
window_height = 1024
# Create a scalar bar
scalar_bar = vtkScalarBarActor()
scalar_bar.SetLookupTable(mapper.GetLookupTable())
scalar_bar.SetTitle('Elevation')
scalar_bar.UnconstrainedFontSizeOff()
scalar_bar.SetNumberOfLabels(6)
scalar_bar.SetVerticalTitleSeparation(50)
scalar_bar.SetMaximumWidthInPixels(window_width // 8)
scalar_bar.SetMaximumHeightInPixels(window_height // 2)
scalar_bar.SetBarRatio(scalar_bar.GetBarRatio() * 0.6)
scalar_bar.SetPosition(0.87, 0.1)
renderer = vtkRenderer()
ren_win = vtkRenderWindow()
ren_win.AddRenderer(renderer)
ren_win.SetSize(window_width, window_height)
if ecef:
ren_win.SetWindowName('ECEF')
elif utm:
ren_win.SetWindowName('UTM')
iren = vtkRenderWindowInteractor()
iren.SetRenderWindow(ren_win)
style = vtkInteractorStyleTrackballCamera()
iren.SetInteractorStyle(style)
renderer.AddActor(actor)
renderer.AddActor(scalar_bar)
renderer.SetBackground(colors.GetColor3d('ParaViewBkg'))
cam_orient_manipulator = vtkCameraOrientationWidget()
cam_orient_manipulator.SetParentRenderer(renderer)
cam_orient_manipulator.On()
axes = vtkAxesActor()
axes.SetXAxisLabelText('East')
axes.SetYAxisLabelText('North')
# Zenith
axes.SetZAxisLabelText('Zenith')
widget = vtkOrientationMarkerWidget()
rgba = [0] * 4
colors.GetColor('Carrot', rgba)
widget.SetOutlineColor(rgba[0], rgba[1], rgba[2])
widget.SetOrientationMarker(axes)
widget.SetInteractor(iren)
widget.SetViewport(0.0, 0.0, 0.2, 0.2)
widget.SetEnabled(1)
widget.InteractiveOn()
renderer.ResetCamera()
renderer.GetActiveCamera().Elevation(0)
iren.Initialize()
ren_win.Render()
iren.Start()
def get_diverging_lut(color_map: str, table_size: int = 256):
"""
See: [Diverging Color Maps for Scientific Visualization](https://www.kennethmoreland.com/color-maps/)
start-point mid-point end-point\n
cool to warm: 0.230, 0.299, 0.754 0.865, 0.865, 0.865 0.706, 0.016, 0.150\n
purple to orange: 0.436, 0.308, 0.631 0.865, 0.865, 0.865 0.759, 0.334, 0.046\n
green to purple: 0.085, 0.532, 0.201 0.865, 0.865, 0.865 0.436, 0.308, 0.631\n
blue to brown: 0.217, 0.525, 0.910 0.865, 0.865, 0.865 0.677, 0.492, 0.093\n
green to red: 0.085, 0.532, 0.201 0.865, 0.865, 0.865 0.758, 0.214, 0.233\n
:param color_map: The color map to use e.g. cool_warm.
:param table_size: The table size.
:return:
"""
color_maps = dict()
color_maps['cool_warm'] = {'start': (0.230, 0.299, 0.754), 'mid': (0.865, 0.865, 0.865),
'end': (0.706, 0.016, 0.150)}
color_maps['purple_orange'] = {'start': (0.436, 0.308, 0.631), 'mid': (0.865, 0.865, 0.865),
'end': (0.759, 0.334, 0.046)}
color_maps['green_purple'] = {'start': (0.085, 0.532, 0.201), 'mid': (0.865, 0.865, 0.865),
'end': (0.436, 0.308, 0.631)}
color_maps['blue_brown'] = {'start': (0.217, 0.525, 0.910), 'mid': (0.865, 0.865, 0.865),
'end': (0.677, 0.492, 0.093)}
color_maps['green_red'] = {'start': (0.085, 0.532, 0.201), 'mid': (0.865, 0.865, 0.865),
'end': (0.758, 0.214, 0.233)}
ctf = vtkColorTransferFunction()
ctf.SetColorSpaceToDiverging()
cm = color_maps[color_map]
ctf.AddRGBPoint(0.0, *cm['start'])
ctf.AddRGBPoint(0.5, *cm['mid'])
ctf.AddRGBPoint(1.0, *cm['end'])
lut = vtkLookupTable()
lut.SetNumberOfTableValues(table_size)
lut.Build()
for i in range(0, table_size):
rgba = list(ctf.GetColor(float(i) / table_size))
rgba.append(1)
lut.SetTableValue(i, rgba)
return lut
def get_diverging_lut1(start: str, mid: str, end: str, table_size: int = 256):
"""
Create a diverging lookup table from three named colors.
:param start: The start-point point color.
:param mid: The mid-point color.
:param end: The end-point color.
:param table_size: The table size.
:return:
"""
colors = vtkNamedColors()
# Colour transfer function.
ctf = vtkColorTransferFunction()
ctf.SetColorSpaceToDiverging()
p1 = [0.0] + list(colors.GetColor3d(start))
p2 = [0.5] + list(colors.GetColor3d(mid))
p3 = [1.0] + list(colors.GetColor3d(end))
ctf.AddRGBPoint(*p1)
ctf.AddRGBPoint(*p2)
ctf.AddRGBPoint(*p3)
lut = vtkLookupTable()
lut.SetNumberOfTableValues(table_size)
lut.Build()
for i in range(0, table_size):
rgba = list(ctf.GetColor(float(i) / table_size))
rgba.append(1)
lut.SetTableValue(i, rgba)
return lut
if __name__ == '__main__':
main()