On Thu, Nov 10, 2011 at 11:14 AM, Joe Kington <jking...@wisc.edu> wrote:
> <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.
>
I260 ncnt = np.array([num2date(1 + time[j]/86400.0) for j in
range(len(time))])
I261 time[0]
O261 32643.785958051682
I262 ncnt[0]
O262 datetime.datetime(1, 1, 1, 9, 4, 3, 785958,
tzinfo=<matplotlib.dates._UTC object at 0x2da2610>)
int conversion for time[j] was eating my milliseconds. Now it is finely
returning the ms part.
>
> 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?)
>
plt.imshow(z.T, interpolation='nearest', aspect='auto', origin='lower',
extent=[xmin, xmax, 0, z.shape[1]])
I need to set both aspect and extent for my case, without the extend I
can't get the time axis placed.
>
>
> 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...)
I am fine seeing the ticks at second resolution. It might be overkill for
my plots to place millisecond ticks. It takes a while to render these
ticks.
>
>
>
> 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
>>>
>>>
>>>
>>>
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>>>
>>
>>
>> --
>> Gökhan
>>
>
>
--
Gökhan
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