So based on what I spotted in the source code of matplotlib’s _axes.py (https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/axes/_axes.py#L1469-L1493):
def plot(self, *args, scalex=True, scaley=True, data=None, **kwargs):
"""
Plot y versus x as lines and/or markers.
Call signatures::
plot([x], y, [fmt], *, data=None, **kwargs)
plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)
The coordinates of the points or line nodes are given by *x*, *y*.
The optional parameter *fmt* is a convenient way for defining basic
formatting like color, marker and linestyle. It's a shortcut string
notation described in the *Notes* section below.
>>> plot(x, y) # plot x and y using default line style and color
>>> plot(x, y, 'bo') # plot x and y using blue circle markers
>>> plot(y) # plot y using x as index array 0..N-1
>>> plot(y, 'r+') # ditto, but with red plussesI saw that I could use my axe to simply plot the positions of the indicators on in two dimensions. I got this:
Which is pretty much perfectly what I want right now. I did some fairly dirty mucking around with the data to get it to do this, essentially looking for where the row-to-row weight difference crosses a threshold from low-to-high.
# median filter with a rolling window: low pass filter
df['rolling4'] = df['weight'].rolling(4).median()
# normalise by looking for difference over 8 samples
df['diff'] = df['rolling4'].diff(periods=-8)
# Tag with True where the change is over 300g
threshold = 300.0
df['thresholded'] = (df['diff'] > threshold)
# Produce 'highlight' boolean where the threshold is True, AND
# the threshold for the previous row was False. This feels pretty clunky.
df['highlight'] = (df['thresholded'] == True) & (df['thresholded'].shift(1) == False)
# Now create a new dataframe with just the highlights in, and only the interesting columns
highlights = df[df['highlight']][['datetime', 'rolling4']]That’s good isn’t it?
