Jupyter-Time-Series-Visualization

Scalable Time Series Visualization in Jupyter Notebook

Installation

1. Follow carefully the installation guide to install vispy with its Jupyter extension (refer to the notebook or JupyterLab sections).
2. Check whether you can run vispy successfully by running the following code in Jupyter (you should be able to see a simple line chart):

import numpy as np

from vispy import plot as vp
from vispy.io import load_data_file

data = np.load(load_data_file('electrophys/iv_curve.npz'))['arr_0']
time = np.arange(0, data.shape[1], 1e-4)

fig = vp.Fig(size=(800, 800), show=False)

x = np.linspace(0, 10, 20)
y = np.cos(x)
line = fig[0, 0].plot((x, y), symbol='o', width=3, title='I/V Curve',
                     xlabel='Current (pA)', ylabel='Membrane Potential (mV)')
grid = vp.visuals.GridLines(color=(0, 0, 0, 0.5))
grid.set_gl_state('translucent')
fig[0, 0].view.add(grid)

fig.show(run=True)

3. Install the remaining dependencies: pip install sect shapely tqdm
4. To use the library, simply copy the TSVis folder to your working directory.

Usage

• Basic Usage

from TSVis.widget import TimeSeriesVis
import numpy as np
data = [
   #four time series in the first chart
   np.array([[1,2,3,4,5],
             [1,0,1,0,1],
             [2,2,2,2,2],
             [5,1,5,1,5]]),
   #three time series starting from t = 3 in the second chart
   np.array([[np.nan,np.nan,3,4,5],
             [np.nan,np.nan,5,4,3],
             [np.nan,np.nan,2,2,1]]), 
]
vis = TimeSeriesVis()
vis.fit(data) #prepare the buffers

vis.visualize() #launch the widget in another cell

• More detailed parameters

from TSVis.synthetic_data import generateSyntheticData
from TSVis.widget import TimeSeriesVis
import numpy as np

#some synthetic data with different trends
data = generateSyntheticData(50,40) 

#center line for each chart
cl = [random.choice(_d) for _d in data]

#labels for each chart
labels = ['blue','blue','blue','orange','red']

vis = TimeSeriesVis(
    #x domain labels (useful for specifying date)
    x_domain = np.arange(40),
    #number of columns
    n_cols=3,
    #chart width (px)
    chart_width=300,
    #chart height (px)
    chart_height=150,
    #padding between charts (px)
    padding=50,
    #number of x,y ticks
    n_ticks=5,
    #number of bins in density map (~resolution)
    interpolate=200
)

vis.fit(
    data,
    central_line=cl,
    labels=labels
)

Contact

For more questions or feature requests, feel free to reach me (Gromit Chan) through my e-mail.