Thanks again for all your help!

You bet!

What's the soon-to-be-released function going to be called out of interest, so I can look out for it.

It is called stumpy.stimp and actually computes what is referred to as a "pan matrix profile" (see this paper for more details). A work-in-progress tutorial can be found here

Here is the code that I used for your reference (please be warned that this is still in development and subject to change):

min_m, max_m = 10, 100
co2_pan = stumpy.stimp(c, min_m=min_m, max_m=max_m, percentage=1.0) # This percentage controls the extent of `stumpy.scrump` completion
percent_m = 1.0 # The percentage of windows to compute
n = np.ceil((max_m - min_m) * percent_m).astype(int)
for _ in range(n):
 co2_pan.update()

And you can plot the results with:

import matplotlib.pyplot as plt
from matplotlib import cm
fig = plt.figure()
fig.canvas.toolbar_visible = False
fig.canvas.header_visible = False
fig.canvas.footer_visible = False
color_map = cm.get_cmap("Greys_r", 256)
threshold = 0.2 # 0.2 is usually an excellent default but this is something that you'll need to play around with
im = plt.imshow(co2_pan.pan(threshold=threshold), cmap=color_map, origin="lower", interpolation="none", aspect="auto")
plt.xlabel("Time", fontsize="20")
plt.ylabel("m", fontsize="20")
plt.clim(0.0, 1.0)
plt.colorbar()
plt.tight_layout()
plt.show()

download-5

As you adjust the threshold, what you are looking for is the location of the peak of the right angle triangles that are formed. Of course, this is definitely more art than science but at least it is a "better" way to explore the data.

Sorry for the delay in replying. I'll have a look at the tutorial and paper you referenced - they look very handy!

No apologies necessary. Just an FYI that the new version was released today!

Thanks a lot for your help, it's much appreciated!

I adapted your changes, to use the 'stumpy.match' function to find all matches for each motif.

I decided to use just 2 motifs as I'm mainly curious about the entering / leaving of a room.

Figure_1

I also added the 'max_distance' parameter to stumpy.match to get a couple more matches.

import stumpy
import csv
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = [20, 6] # width, height
plt.rcParams['xtick.direction'] = 'out'
co2 = []
with open('MUC2020/csv/RoomA_CO2.csv') as csvfile:
 reader = csv.DictReader(csvfile, delimiter=';')
 for row in reader:
 if len(row['CO2-Concentration (in ppm)'].strip()) > 0:
 co2.append(float(row['CO2-Concentration (in ppm)']))
m = 24
c = np.array(co2)
p = stumpy.stump(c, m)
dists, inde = stumpy.motifs(c, p[:, 0], max_motifs=2)
fig, axs = plt.subplots(2, sharex=True, gridspec_kw={'hspace': 0})
plt.suptitle('Motif (Pattern) Discovery', fontsize='30')
axs[0].plot(co2)
axs[0].set_ylabel('CO2 ppm', fontsize='20')
cols = ['red' , 'green', 'blue' ]
for z in range(0, inde.shape[0]):
 col = cols[z]
 start = inde[z, 0]
 stop = inde[z, 0] + m
 matches = stumpy.match(c[start:stop],c, max_distance=2.0) 
 for mt in range(matches.shape[0]):
 s = matches[mt, 1]
 st = s + m
 axs[0].plot(np.arange(s, st), c[s : st], c=col)
axs[1].plot(p[:, 0])
axs[1].set_ylabel('Matrix profile', fontsize='20')
plt.show()

This example doesn't seem to be working for Stumpy 1.11.1. The inde returned from the call to stumpy.motifs is of shape (1, 10) @chrisruk.

This example doesn't seem to be working for Stumpy 1.11.1. The inde returned from the call to stumpy.motifs is of shape (1, 10) @chrisruk.

@amurthy-sunysb Please see this discussion