So based on what I spotted in the source code of matplotlib’s
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 plusses
I 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?