irfpy.cena.plot.axis_timeseries

Module to return an axis that has energy-time diagram for CENA.

irfpy.cena.plot.axis_timeseries.axis_time_spectra(xaxis, yaxis, data, rect=None, vmin=None, vmax=None, vscale='linear', voffset=0, colorbar=False, kwds_axes=None, **kwds)[source]

Draw time-something image to the current figure.

Parameters

  • xaxis – Array-like for the time. Time should be matplotlib time. datetime.datetime should be processed prior by date2num() function.

  • yaxis – Array-like for y-axis.

  • data (numpy.array or numpy.ma.masked_array) – The data to be plotted. len(xaxis) x len(yaxis). Masked array is also supported.

  • rect – A rectangle to be given to axes. [xmin, ymin, width, height]. If you want to make several axes in one figure, this values look to be used for identification. See pylab.axes for details.

  • kwds_axes – A dictionaly of the keyword to be given to plt.axes(). For example, kwds_axes = {“axisbg”:”k”} will change the background color.

  • dateformat – A format for the x_axis.

  • vscale – A scale of the colorbar. ‘linear’ or ‘log’.

Returns

The axis produced.

irfpy.cena.plot.axis_timeseries.insert_datagap(xlist, datalist, gap=1)[source]

Insert a datagap into the datalist.

Parameters

  • xlist – List of float. It should be monotonically increasing.

  • datalist – List of the data. Should be ndarray or masked_array. datalist[:, …] should be the same size as datetime_list. Use transpose() to make the target axis to the first.

Returns

Gap enbedded data as tuple of array and masked_array.

Return type

tuple of (array, masked_array)

>>> from datetime import datetime
>>> from datetime import timedelta

xlist has 11 elements. 5 is missing.

>>> x = [0., 1, 2, 3, 4, 6, 7, 8, 9, 10, 11]

Data is defined here. I assume 11 x 3 x 8 x 7 array (4-D). Note the time list has length of 11.

>>> dat = np.arange(11*3*8*7).reshape(11, 3, 8, 7)
>>> dat.shape
(11, 3, 8, 7)

Now, the data gap will be inserted

Before:

Index:  0  1  2  3  4  5  6  7  8  9 10
X:      0  1  2  3  4  6  7  8  9 10 11
Data:  D0 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10

After:

Index:  0  1  2  3  4  5  6  7  8  9 10 11
X:      0  1  2  3  4  5  6  7  8  9 10 11
Data:  D0 D1 D2 D3 D4  M D5 D6 D7 D8 D9 D10

>>> x2, dat2 = insert_datagap(x, dat)
>>> print(len(x2))
12
>>> print(dat2.shape)
(12, 3, 8, 7)
>>> print(x2[5])
5
>>> print(dat2.mask[0, 2, 1, 4])   # mask[0, :, :, :] is False
False
>>> print(dat2.mask[5, 1, 7, 2])   # mask[5, :, :, :] is True
True
>>> print(dat2[3, 2, 5, 1] == dat[3, 2, 5, 1])
True
>>> print(dat2[9, 1, 3, 6] == dat[8, 1, 3, 6])
True