This surprises me as the AutoDateFormatter automatically kicks in once you
pass through some datetime objects, and the AutoDateFormatter & Locator do
the right thing when zooming (the format changes depending on the temporal
resolution). For example, the following code behaves nicely when I zoom in
to a small segment of the line:
import datetime as dt
import numpy as np
import matplotlib.pyplot as plt
d1 = dt.datetime(1990, 1, 1)
n = 365
x = np.array([d1 + dt.timedelta(days=i) for i in range(n)], dtype=object)
y = np.sin(np.linspace(0, np.pi * 2, n))
plt.plot(x, y)
plt.show()
Are you definately passing through datetime objects, or are you passing
through the datetime "ordinals" / Julian time?
Cheers,
Phil
On 11 July 2013 16:04, Skip Montanaro <s...@pobox.com> wrote:
> > I have a small matplotlib app I wrote to plot columns of a CSV files.
> > The X axis is almost always time. Once displayed, I will often zoom in
> > on a small patch of a plot. I'm currently selecting the strftime
> > format based on the original time range of the input. As I zoom in,
> > however, that doesn't work so well....
>
> > but when I use it in the obvious way, all I get is the current year
> > for all tick labels, despite the fact that the scaled attribute of the
> > formatter has keys which are much smaller than a year.
>
> I kind of got this working. I had to associate the locator with the X
> axis and call it's autoscale() method before calling plot.show(). Now
> I get %H:%M:%S formatting for everything, even when I'm zoomed way out
> on a data set containing two-days worth of time series data. I
> suppose I can fiddle with the AutoDateFormatter's scaled attribute,
> but the default looks like it ought to work. Any thoughts on what I'm
> (still) missing?
>
> The locator/formatter code looks like this:
>
> locator = matplotlib.dates.AutoDateLocator()
> formatter = matplotlib.dates.AutoDateFormatter(locator)
> ...
> left_plot = figure.add_subplot(111)
> left_plot.set_title(title)
> left_plot.set_axisbelow(True)
> left_plot.yaxis.set_major_formatter(pylab.FormatStrFormatter('%g'))
> ...
> locator.set_axis(left_plot.xaxis)
> left_plot.xaxis.set_major_formatter(formatter)
> locator.autoscale()
> pylab.show()
>
> This works fine except for the lack of dynamic scaling and apparently
> incorrect choice of labels on plots over large time scales.
>
> Skip
>
>
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