Working with large data using datashader#

import datashader as ds
import numpy as np
import holoviews as hv
import pandas as pd
import numpy as np

from holoviews import opts
from holoviews.operation.datashader import datashade, shade, dynspread, spread
from holoviews.operation.datashader import rasterize, ResamplingOperation
from holoviews.operation import decimate

hv.extension('bokeh','matplotlib', width=100)

# Default values suitable for this notebook
decimate.max_samples=1000
dynspread.max_px=20
dynspread.threshold=0.5
ResamplingOperation.width=500
ResamplingOperation.height=500

def random_walk(n, f=5000):
    """Random walk in a 2D space, smoothed with a filter of length f"""
    xs = np.convolve(np.random.normal(0, 0.1, size=n), np.ones(f)/f).cumsum()
    ys = np.convolve(np.random.normal(0, 0.1, size=n), np.ones(f)/f).cumsum()
    xs += 0.1*np.sin(0.1*np.array(range(n-1+f))) # add wobble on x axis
    xs += np.random.normal(0, 0.005, size=n-1+f) # add measurement noise
    ys += np.random.normal(0, 0.005, size=n-1+f)
    return np.column_stack([xs, ys])

def random_cov():
    """Random covariance for use in generating 2D Gaussian distributions"""
    A = np.random.randn(2,2)
    return np.dot(A, A.T)

def time_series(T = 1, N = 100, mu = 0.1, sigma = 0.1, S0 = 20):  
    """Parameterized noisy time series"""
    dt = float(T)/N
    t = np.linspace(0, T, N)
    W = np.random.standard_normal(size = N) 
    W = np.cumsum(W)*np.sqrt(dt) # standard brownian motion
    X = (mu-0.5*sigma**2)*t + sigma*W 
    S = S0*np.exp(X) # geometric brownian motion
    return S