Hello,
Is there any easy way to specify a time-axis using imshow to plot 2D data?
Thanks.
···
–
Gökhan
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
···
On Wed, Nov 9, 2011 at 11:45 PM, Gökhan Sever <gokhansever@…287…> wrote:
Hello,
Is there any easy way to specify a time-axis using imshow to plot 2D data?
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 <jkington@…150…> wrote:
On Wed, Nov 9, 2011 at 11:45 PM, Gökhan Sever <gokhansever@…287…> 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 npGenerate data…
ny = 100
xmin, xmax = mdates.datestr2num([‘01/01/2011’, ‘11/10/2011’])
data = np.random.random((ny, int(xmax-xmin)+1)) - 0.5data = 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
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https://lists.sourceforge.net/lists/listinfo/matplotlib-users
–
Gökhan
Although, this doesn’t give me millisecond precision. Is there any way to get ms precision via datetime module?
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 <gokhansever@…287…> 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 <jkington@…150…> wrote:
On Wed, Nov 9, 2011 at 11:45 PM, Gökhan Sever <gokhansever@…287…> 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 npGenerate data…
ny = 100
xmin, xmax = mdates.datestr2num([‘01/01/2011’, ‘11/10/2011’])
data = np.random.random((ny, int(xmax-xmin)+1)) - 0.5data = 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
Although, this doesn’t give me millisecond precision. Is there any way to get ms precision via datetime module?
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 mdatesfrom 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)) - 0.5data = 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 11:14 AM, Joe Kington <jkington@…150…> wrote:
On Thu, Nov 10, 2011 at 10:06 AM, Gökhan Sever <gokhansever@…287…> 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 <jkington@…150…> wrote:
On Wed, Nov 9, 2011 at 11:45 PM, Gökhan Sever <gokhansever@…287…> 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 npGenerate data…
ny = 100
xmin, xmax = mdates.datestr2num([‘01/01/2011’, ‘11/10/2011’])
data = np.random.random((ny, int(xmax-xmin)+1)) - 0.5data = 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
–
Gökhan