According to lsprofcalltree, the slowness appears to be entirely in the
units code by a wide margin -- which is unfortunately code I understand
very little about. The difference in timing before and after adding the
line to the axes appears to be because the unit conversion is not
invalidated until the line has been added to an axes.
In units.get_converter(), it iterates through every *value* in the data
to see if any of them require unit conversion, and returns the first one
it finds. It seems like if we're passing in a numpy array of numbers
(i.e. not array of objects), then we're pretty much guaranteed from the
get-go not to find a single value that requires unit conversion so we
might as well not look. Am I making the wrong assumption?
However, for lists, it also seems that, since the code returns the first
converter it finds, maybe it could just look at the first element of the
sequence, rather than the entire sequence. It the first is not in the
same unit as everything else, then the result will be broken anyway.
For example, if I hack evans_test.py to contain a single int amongst the
list of "Foo" objects in the data, I get an exception anyway, even as
the code stands now.
I have attached a patch against unit.py to speed up the first case
(passing Numpy arrays). I think I need more feedback from the units
experts whether my suggestion for lists (to only look at the first
element) is reasonable.
Feel free to commit the patch if it seems reasonable to those who know
more about units than I do.
Mike
Eric Firing wrote:
I am getting very inconsistent timings when looking into plotting a line
with a very large number of points. Axes.add_line() is very slow, and
the time is taken by Axes._update_line_limits(). But when I simply run
the latter, on a Line2D of the same dimensions, it can be fast.
import matplotlib
matplotlib.use('template')
import numpy as np
import matplotlib.lines as mlines
import matplotlib.pyplot as plt
ax = plt.gca()
LL = mlines.Line2D(np.arange(1.5e6), np.sin(np.arange(1.5e6)))
from time import time
t = time(); ax.add_line(LL); time()-t
###16.621543884277344
LL = mlines.Line2D(np.arange(1.5e6), np.sin(np.arange(1.5e6)))
t = time(); ax.add_line(LL); time()-t
###16.579419136047363
## We added two identical lines, each took 16 seconds.
LL = mlines.Line2D(np.arange(1.5e6), np.sin(np.arange(1.5e6)))
t = time(); ax._update_line_limits(LL); time()-t
###0.1733548641204834
## But when we made another identical line, updating the limits was
## fast.
# Below are similar experiments:
LL = mlines.Line2D(np.arange(1.5e6), 2*np.sin(np.arange(1.5e6)))
t = time(); ax._update_line_limits(LL); time()-t
###0.18362092971801758
## with a fresh axes:
plt.clf()
ax = plt.gca()
LL = mlines.Line2D(np.arange(1.5e6), 2*np.sin(np.arange(1.5e6)))
t = time(); ax._update_line_limits(LL); time()-t
###0.22244811058044434
t = time(); ax.add_line(LL); time()-t
###16.724560976028442
What is going on? I used print statements inside add_line() to verify
that all the time is in _update_line_limits(), which runs one or two
orders of magnitude slower when run inside of add_line than when run
outside--even if I run the preceding parts of add_line first.
Eric
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Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA
Index: lib/matplotlib/units.py
===================================================================
--- lib/matplotlib/units.py (revision 6142)
+++ lib/matplotlib/units.py (working copy)
@@ -44,6 +44,7 @@
"""
from matplotlib.cbook import iterable, is_numlike
+import numpy as np
class AxisInfo:
'information to support default axis labeling and tick labeling'
@@ -127,6 +128,9 @@
converter = self.get(classx)
if converter is None and iterable(x):
+ if isinstance(x, np.ndarray) and x.dtype != np.object:
+ return None
+
for thisx in x:
converter = self.get_converter( thisx )
if converter: break
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