conda install -c conda-forge vaex
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import vaex
import numpy as np
np.warnings.filterwarnings('ignore')
dstaxi = vaex.open('src/nyc_taxi2015.hdf5') # mmapped, doesn't cost extra memory
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dstaxi.plot_widget("pickup_longitude", "pickup_latitude", f="log", backend="ipyleaflet", shape=600)
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dstaxi.plot_widget("dropoff_longitude", "dropoff_latitude", f="log", backend="ipyleaflet",
z="dropoff_hour", type="slice", z_shape=24, shape=400, z_relative=True,
limits=[None, None, (-0.5, 23.5)])
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ds = vaex.datasets.helmi_de_zeeuw.fetch()
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ds.plot_widget("x", "y", f="log", limits=[-20, 20])
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ds.plot_widget("Lz", "E", f="log")
$ conda install -c conda-forge ipyvolume$ pip install ipyvolume
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import ipyvolume as ipv
import numpy as np
np.warnings.filterwarnings('ignore')
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ipv.example_ylm();
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N = 1000
x, y, z = np.random.random((3, N))
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fig = ipv.figure()
scatter = ipv.scatter(x, y, z, marker='box')
ipv.show()
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scatter.x = scatter.x + 0.1
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scatter.color = "green"
scatter.size = 5
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scatter.color = np.random.random((N,3))
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scatter.size = 2
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ipv.figure()
ipv.style.use('dark')
quiver = ipv.quiver(*ipv.datasets.animated_stream.fetch().data[:,::,::4], size=5)
ipv.animation_control(quiver, interval=200)
ipv.show()
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ipv.style.use('light')
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quiver.size = np.random.random(quiver.x.shape) * 10
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quiver.color = np.random.random(quiver.x.shape + (3,))
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quiver.geo = "cat"
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# stereo
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quiver.geo = "arrow"
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N = 1000*1000
x, y, z = np.random.random((3, N)).astype('f4')
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ipv.figure()
s = ipv.scatter(x, y, z, size=0.2)
ipv.show()
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s.size = 0.1
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#ipv.screenshot(width=2048, height=2048)
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plot3d = ds.plot_widget("x", "y", "z", vx="vx", vy="vy", vz="vz",
backend="ipyvolume", f="log1p", shape=100, smooth_pre=0.5)
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plot3d.vcount_limits = [50, 100000]
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plot3d.backend.quiver.color = "red"
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import ipywidgets as widgets
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widgets.ColorPicker()
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widgets.jslink((plot3d.backend.quiver, 'color'), (_, 'value'))
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ipv.save("kapteyn-lunch-talk-2018.html")
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!open kapteyn-lunch-talk-2018.html
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# webrtc demo if time permits
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import vaex
#gaia = vaex.open("ws://gaia:9000/gaia-dr1")
gaia = vaex.open('/Users/maartenbreddels/datasets/gaia/gaia-dr1-minimal_f4.hdf5')
%matplotlib inline
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f"{len(gaia):,}"
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ra_dec_limits = [[0, 360], [-90, 90]]
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gaia.set_active_fraction(0.01)
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gaia.plot_widget("ra", "dec", limits=ra_dec_limits)
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gaia.mean("phot_g_mean_mag", selection=True)
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gaia.plot1d("phot_g_mean_mag", selection=False, n=True, limits=[10, 22])
gaia.plot1d("phot_g_mean_mag", selection=True, show=True, n=True, limits=[10, 22])
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