<snip>
> Although, this doesn't give me millisecond precision. Is there any way to
> get ms precision via datetime module?
>
<snip>
Well, datetime objects, matplotlib's internal float dates, and numpy
datetime64 objects all support microsecond resolution.
However matplotlib's locator rules can't handle microsecond or millisecond
resolution. There aren't any locators for less than second resolution.
Also, imshow sets the aspect of the plot to 1 by default, which is probably
why you're having to set the extents manually. If you specify
"aspect='auto'" in the imshow call you can avoid that step. (However,
strange things happen when the span of the extents drops below 100
microseconds... I'm guessing something is being cast to float32's
somewhere?)
As a quick example to demonstrate using sub-second resolution (without a
proper tick locator):
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
from datetime import datetime
# Generate data...
ny = 100
nx = 100
xmin, xmax = mdates.date2num([datetime(2011, 01, 01, microsecond=1),
datetime(2011, 01, 01, microsecond=nx)])
data = np.random.random((ny, nx)) - 0.5
data = data.cumsum(axis=1)
# Plot...
fig, ax = plt.subplots()
ax.imshow(data, aspect='auto', extent=[xmin, xmax, 0, ny])
ax.xaxis_date()
plt.show()
At any rate, you can write a quick-and-dirty millisecond locator... Give
me a bit and I'll cobble one together. (It's turning out to be slightly
more complex than I thought...)
On Thu, Nov 10, 2011 at 10:06 AM, Gökhan Sever <gokhanse...@gmail.com>wrote:
> Thanks Joe,
>
> I forgot to convert my numeric time array into a form that mpl can
> understand.
>
> I198 time
> O198
> array([ 32643.78595805, 32643.82032609, 32643.85445309, ...,
> 32871.46535802, 32871.49946594, 32871.53384495])
>
> I199 ncnt
> O199
> array([0001-01-01 09:04:03+00:00, 0001-01-01 09:04:03+00:00,
> 0001-01-01 09:04:03+00:00, ..., 0001-01-01 09:07:51+00:00,
> 0001-01-01 09:07:51+00:00, 0001-01-01 09:07:51+00:00], dtype=object)
>
> Although, this doesn't give me millisecond precision. Is there any way to
> get ms precision via datetime module?
> This is not a matter for plotting, since second precision is good enough
> for eyes.
>
> Then setting extent properly and either calling ax.xaxis_date or calling
> setters manually
>
> I196 xmin = mdates.date2num(ncnt[0])
>
> I197 xmax = mdates.date2num(ncnt[-1])
>
> plt.imshow(z.T, interpolation='nearest', aspect='auto', origin='lower',
> extent=[xmin, xmax, 0, z.shape[1]])
>
> ax = plt.gca()
> ax.xaxis.set_major_formatter(DateFormatter('%H:%M:%S'))
> ax.xaxis.set_major_locator(SecondLocator(interval=30))
> ax.xaxis.set_minor_locator(SecondLocator(interval=5))
>
> gives me better control over the major/minor ticks.
>
>
> On Thu, Nov 10, 2011 at 8:15 AM, Joe Kington <jking...@wisc.edu> wrote:
>
>> On Wed, Nov 9, 2011 at 11:45 PM, Gökhan Sever <gokhanse...@gmail.com>wrote:
>>
>>> Hello,
>>>
>>> Is there any easy way to specify a time-axis using imshow to plot 2D
>>> data?
>>>
>>>
>> Sure, just call "ax.xaxis_date()" (or "yaxis_date", depending on which
>> axis you want to represent a date).
>>
>> As a quick example:
>>
>> import matplotlib.pyplot as plt
>> import matplotlib.dates as mdates
>> import numpy as np
>>
>> # Generate data...
>> ny = 100
>> xmin, xmax = mdates.datestr2num(['01/01/2011', '11/10/2011'])
>> data = np.random.random((ny, int(xmax-xmin)+1)) - 0.5
>> data = data.cumsum(axis=1)
>>
>> # Plot...
>> fig, ax = plt.subplots()
>> ax.imshow(data, extent=[xmin, xmax, 0, ny])
>> ax.xaxis_date()
>> fig.autofmt_xdate()
>>
>> plt.show()
>>
>> Cheers,
>> -Joe
>>
>>
>>
>>
>> ------------------------------------------------------------------------------
>> RSA(R) Conference 2012
>> Save $700 by Nov 18
>> Register now
>> http://p.sf.net/sfu/rsa-sfdev2dev1
>> _______________________________________________
>> Matplotlib-users mailing list
>> Matplotlib-users@lists.sourceforge.net
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>>
>
>
> --
> Gökhan
>
------------------------------------------------------------------------------
RSA(R) Conference 2012
Save $700 by Nov 18
Register now
http://p.sf.net/sfu/rsa-sfdev2dev1
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users
