PIVpy getting started notebook:¶
This notebook shows an example of how one can use vecpy in order to load manipulate and display analyzed PIV data.
step 1 - import pivpy and dependencies¶
here we import the package code so that we can use it next
In [1]:
import os, sys
# sys.path.append(os.path.abspath('../'))
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
%matplotlib inline
from pivpy import io, pivpy, graphics
# for the sake of this tutorial, ignore warnings
# import warnings
# warnings.filterwarnings('ignore')
step 2 - load the tests data¶
In order to load the data, first we need to set up the path to the data directory. Following that we need to get a list of files names that we would like to view/analyze. Finally we very quickly load the data in to a list of vec instances.
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# pointer to the directory with the data
path_to_data = os.path.abspath('../../tests/data/')
# list the directory
os.listdir(path_to_data)
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# let's read only the files from the Run*
data = io.load_directory(path_to_data,basename='Run*',ext='.vec')
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# let's check if it's read:
data.attrs['files']
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step 3 - plot some arrows¶
first things first - show a quiver plot
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fig, ax = graphics.quiver(data.isel(t=0), nthArr=2, arrScale=20)
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fig, ax = graphics.quiver(data, nthArr=3, arrScale=5)
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# we can read also a single file only into a 1 frame dataset
d = io.load_vec('/Users/alex/Documents/OpenPIV/pivpy/tests/data/Run000001.T000.D000.P000.H001.L.vec')
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graphics.quiver(d,arrScale=10)
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d.isel(t=0).differentiate(coord='x').differentiate(coord='y')['u'].plot.pcolormesh()
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and a vorticity map
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# prepare vorticity
d.piv.vec2scal(property='curl') # it will appear as d['w'] variable, 'w' for all scalar properties
# plot
fig, ax = graphics.contour_plot(d)
Also, velocity histograms in x and y directions
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fig, ax = graphics.histogram(data, normed = True)
We can also plot a whole list of vec's as subplots:
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fig, ax = graphics.quiver(data, nthArr=4, arrScale=10)
fig.set_size_inches(10, 6)
Last but not least - manipulation¶
lets create a linear combinatino of our data and then see how to manipulate the coordinate system
Addition and Scalar multiplication
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v = (data + 3*data - 2 * data.isel(t=0)) / 3.
graphics.quiver(v, arrScale=10)
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Crop
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v = v.piv.crop([5,15,-5,-15]) #(xmin, xmax, ymin, ymax)
graphics.quiver(v, arrScale=10)
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Rotate
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# v.piv.rotate(90) # not implemented
Translation of Coordinate System
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# we can also use some default plot from xarray
data.piv.vorticity()
data.isel(t=0)['w'].plot(robust=True)
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In [17]:
# low level quiver
plt.figure(figsize=(8,6))
plt.quiver(data.x,data.y,data.u[:,:,0], data.v[:,:,0] ,data.u[:,:,0]**2 + data.v[:,:,0]**2,scale=.75)
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In [18]:
test = io.create_sample_field(rows=25,cols=5)
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graphics.quiver(test,arrScale=5)
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