datamanager  easy access to and manipulation of data¶
The datamanager classes and functions are useful for locating the correct data file for a particular day and manipulating data and subsets in a generic way.
Authors: Jon Niehof
Institution: University of New Hampshire
Contact: Jonathan.Niehof@unh.edu
Copyright 20152020 contributors
About datamanager¶
Examples¶
Examples go here
Classes

THIS CLASS IS NOT YET COMPLETE, doesn't do much useful. 
Functions

Apply an array of indices to data. 

Create an array containing all elements of both array1 and array2 

Returns array of indices along axis, for all other axes 

Convert multidimensional index into index on flattened array. 

Populate gaps in data with fill. 

Rebin one axis of input data based on values of another array 

From an index, return an index that reverses the action of that index 

Transform values along an axis to their order in a unique sequence. 
 class spacepy.datamanager.DataManager(directories, file_fmt, descend=False, period=None)[source]¶
THIS CLASS IS NOT YET COMPLETE, doesn’t do much useful.
Will have to do something that allows the config file to specify regex and other things, and then just the directory to be changed (since regex, etc.
 Parameters
 directorieslist
A list of directories that might contain the data
 file_fmtstring
Regular expression that matches the files desired. Will also recognize strftime parameters %w %d %m %y %Y %H %M %s %j %U %W, all zeropad. https://docs.python.org/2/library/datetime.html#strftimestrptimebehavior Can have subdirectory reference, but separator should be unixstyle, with no leading slash.
 periodstring
Size of file; can be a number followed by one of d, m, y, H, M, s. Anything else is assumed to be “irregular” and files treated as if there are neither gaps nor overlaps in the sequence. If not specified, will be assumed to match one count of the smallest unit in the format string.
 Examples
 =======
 spacepy.datamanager.apply_index(data, idx)[source]¶
Apply an array of indices to data.
Most useful in dealing with the output from
numpy.argsort()
, and best explained by the example. Parameters
 dataarray
Input data, at least two dimensional. The 0th dimension is treated as a “time” or “record” dimension.
 idxsequence
2D index to apply to the import data. The 0th dimension must be the same size as
data
’s 0th dimension. Dimension 1 must be the same size as one other dimension in data (the first match found is used); this is referred to as the “index dimension.”
 Returns
 datasequence
View of
data
, with index applied. For each index of the 0th dimension, the values along the index dimension are obtained by applying the value ofidx
at the same index in the 0th dimension. This is repeated across any other dimensions indata
.Warning
No guarantee is made whether the returned data is a copy of the input data. Modifying values in the input may change the values of the input. Call
copy()
if a copy is required.
 Raises
 ValueErrorif can’t match the shape of data and indices
Examples
Assume
flux
is a 3D array of fluxes, with a value for each of time, pitch angle, and energy. Assume energy is not necessarily constant in time, nor is ordered in the energy dimension. Ifenergy
is a 2D array of the energies as a function of energy step for each time, then the following will sort the flux at each time and pitch angle in energy order.>>> idx = numpy.argsort(energy, axis=1) >>> flux_sorted = spacepy.datamanager.apply_index(flux, idx)
 spacepy.datamanager.array_interleave(array1, array2, idx)[source]¶
Create an array containing all elements of both array1 and array2
idx
is an index on the output array which indicates which elements will be populated fromarray1
, i.e.,out[idx] == array1
(in order.) The other elements ofout
will be filled, in order, fromarray2
. Parameters
 array1array
Input data.
 array2array
Input data. Must have same number of dimensions as
array1
, and all dimensions except the zeroth must also have the same length. idxarray
A 1D array of indices on the zeroth dimension of the output array. Must have the same length as the zeroth dimension of
array1
.
 Returns
 outarray
All elements from
array1
andarray2
, interleaved according toidx
.
Examples
>>> import numpy >>> import spacepy.datamanager >>> a = numpy.array([10, 20, 30]) >>> b = numpy.array([1, 2]) >>> idx = numpy.array([1, 2, 4]) >>> spacepy.datamanager.array_interleave(a, b, idx) array([ 1, 10, 20, 2, 30])
 spacepy.datamanager.axis_index(shape, axis= 1)[source]¶
Returns array of indices along axis, for all other axes
 Parameters
 shapetuple
Shape of the output array
 Returns
 idxarray
An array of indices. The value of each element is that element’s index along
axis
.
 Other Parameters
 axisint
Axis along which to return indices, defaults to the last axis.
See also
numpy.mgrid
This function is a special case
Examples
For a shape of
(i, j, k, l)
andaxis
= 1,idx[i, j, k, :] = range(l)
for alli
,j
,k
.Similarly, for the same shape and
axis = 1
,idx[i, :, k, l] = range(j)
for alli
,k
,l
.>>> import numpy >>> import spacepy.datamanager >>> spacepy.datamanager.axis_index((5, 3)) array([[0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2], [0, 1, 2]]) >>> spacepy.datamanager.axis_index((5, 3), 0) array([[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]])
 spacepy.datamanager.flatten_idx(idx, axis= 1)[source]¶
Convert multidimensional index into index on flattened array.
Convert a multidimensional index, that is values along a particular axis, so that it can derefence the flattened array properly. Note this is not the same as
ravel_multi_index()
. Parameters
 idxarray
Input index, i.e. a list of elements along a particular axis, in the style of
argsort()
.
 Returns
 flatarray
A 1D array of indices suitable for indexing the flat version of the array
 Other Parameters
 axisint
Axis along which
idx
operates, defaults to the last axis.
See also
Examples
>>> import numpy >>> import spacepy.datamanager >>> data = numpy.array([[3, 1, 2], [3, 2, 1]]) >>> idx = numpy.argsort(data, 1) >>> idx_flat = spacepy.datamanager.flatten_idx(idx) >>> data.ravel() #flat array array([3, 1, 2, 3, 2, 1]) >>> idx_flat #indices into the flat array array([1, 2, 0, 5, 4, 3]) >>> data.ravel()[idx_flat] #index applied to the flat array array([1, 2, 3, 1, 2, 3])
 spacepy.datamanager.insert_fill(times, data, fillval=nan, tol=1.5, absolute=None, doTimes=True)[source]¶
Populate gaps in data with fill.
Continuous data are often treated differently from discontinuous data, e.g., matplotlib will draw lines connecting data points but break the line at fill. Often data will be irregularly sampled but also contain large gaps that are not explicitly marked as fill. This function adds a single record of explicit fill to each gap, defined as places where the spacing between input times is a certain multiple of the median spacing.
 Parameters
 timessequence
Values representing when the data were taken. Must be onedimensional, i.e., each value must be scalar. Not modified
 datasequence
Input data.
 Returns
 times, datatuple of sequence
Copies of input times and data, fill added in gaps (
doTimes
True) datasequence
Copy of input data, with fill added in gaps (
doTimes
False)
 Other Parameters
 fillval :
Fill value, same type as
data
. Default isnumpy.nan
. If scalar, will be repeated to match the shape ofdata
(minus the time axis).Note
The default value of
nan
will not produce good results with integer input. tolfloat
Tolerance. A single fill value is inserted between adjacent values where the spacing in
times
is strictly greater thantol
times the median of the spacing across alltimes
. The inserted time for fill is halfway between the time on each side. (Default 1.5) absolute :
An absolute value for maximum spacing, of a type that would result from a difference in
times
. If specified,tol
is ignored and any gap strictly larger thanabsolute
will have fill inserted. doTimesboolean
If True (default), will return a tuple of the times (with new values inserted for the fill records) and the data with new fill values. If False, will only return the data – useful for applying fill to multiple arrays of data on the same timebase.
 Raises
 ValueErrorif can’t identify the time axis of data
Try using
numpy.rollaxis()
to put the time axis first in bothdata
andtimes
.
Examples
This example shows simple hourly data with a gap, populated with fill. Note that only a single fill value is inserted, to break the sequence of valid data rather than trying to match the existing cadence.
>>> import datetime >>> import numpy >>> import spacepy.datamanager >>> t = [datetime.datetime(2012, 1, 1, 0), datetime.datetime(2012, 1, 1, 1), datetime.datetime(2012, 1, 1, 2), datetime.datetime(2012, 1, 1, 5), datetime.datetime(2012, 1, 1, 6)] >>> temp = [30.0, 28, 27, 32, 35] >>> filled_t, filled_temp = spacepy.datamanager.insert_fill(t, temp) >>> filled_t array([datetime.datetime(2012, 1, 1, 0, 0), datetime.datetime(2012, 1, 1, 1, 0), datetime.datetime(2012, 1, 1, 2, 0), datetime.datetime(2012, 1, 1, 3, 30), datetime.datetime(2012, 1, 1, 5, 0), datetime.datetime(2012, 1, 1, 6, 0)], dtype=object) >>> filled_temp array([ 30., 28., 27., nan, 32., 35.])
This example plots “gappy” data with and without explicit fill values.
>>> import matplotlib.pyplot as plt >>> import numpy >>> import spacepy.datamanager >>> x = numpy.append(numpy.arange(0, 6, 0.1), numpy.arange(12, 18, 0.1)) >>> y = numpy.sin(x) >>> xf, yf = spacepy.datamanager.insert_fill(x, y) >>> fig = plt.figure() >>> ax0 = fig.add_subplot(211) >>> ax0.plot(x, y) >>> ax1 = fig.add_subplot(212) >>> ax1.plot(xf, yf) >>> plt.show()
(Source code, png, hires.png, pdf)
 spacepy.datamanager.rebin(data, bindata, bins, axis= 1, bintype='mean', weights=None, clip=False, bindatadelta=None)[source]¶
Rebin one axis of input data based on values of another array
This is clearest with an example. Consider a flux as a function of time, energy, and the look direction of a detector (could be multiple detectors, or spin sectors.) The flux is then 3D, dimensioned Nt x Ne x Nl. Now consider that each look direction has an associated pitch angle that is a function of time and thus stored in an array Nt x Nl. Then this function will redimension the flux into pitch angle bins (rather than tags.)
So consider the PA bins to have dimension Np + 1 (because it represents the edges, the number of bins is one less than the dimension.) Then the output will be dimensioned Nt x Ne x Np.
bindata
must be same or lesser dimensionality thandata
. Any axes which are present must be either of size 1, or the same size asdata
. So fordata
100x5x20,bindata
may be 100x5x20, or 100, or 100x1x20, but not 100x20x5. This function will insert axes of size 1 as needed to match dimensionality. Parameters
 data
ndarray
Ndimensional array of data to be rebinned.
nan
are ignored. bindata
ndarray
Mdimensional (M<=N) array of values to be compared to the bins.
 bins
ndarray
1D array of bin edges. Output dimension will be this size minus 1. Any values in
bindata
that don’t fall in the bins will be omitted from the output. (Seeclip
to change this behavior).
 data
 Returns
ndarray
data
with one axis redimensioned, from its original dimension to the bin dimension.
 Other Parameters
 axisint
Axis of data to rebin. This axis will disappear in the output and be replaced with an axis of the size of
bins
less one. (Default 1, last axis) bintypestr
 Type of rebinning to perform:
 mean
Return the mean of all values in the bin (default)
 unc
Return the quadrature mean of all values in the bin, for propagating uncertainty
 count
Return the count of values that fall in each bin.
 weights
ndarray
Relative weight of each sample in
bindata
. Must be same shape asbindata
. Purely relative, i.e. the output is only affected based on the total ofweights
ifbintype
iscount
. Note ifweights
is specified,count
returns the sum of the weights, not the count of individual samples. clipboolean
Clip data to the bins. If true, all input data will be assigned a bin and data outside the range of the bin edges will be assigned to the extreme bins. If false (default), input data outside the bin ranges will be ignored.
 bindatadelta
ndarray
By default, the
bindata
are treated as point values. Ifbindatadelta
is specified, it is treated as the halfwidth of thebindata
, allowing a single input value to be split between output bins. Must be scalar, or same shape as bindata. Note that input values are not weighted by the bin width, but by number of input values or byweights
. (Combiningweights
withbindatadelta
is not comprehensively tested.)
Examples
Consider a particle flux distribution that’s a function of energy and pitch angle. For simplicity, assume that the energy dependence is a simple power law and the pitch angle dependence is Gaussian, with a peak whose position oscillates in time over a period of about one hour. This is fairly nonphysical but illustrative.
First making the relevant imports:
>>> import matplotlib.pyplot >>> import numpy >>> import spacepy.datamanager >>> import spacepy.plot
The functional form of the flux is then:
>>> def j(e, t, a): ... return e ** 2 * (1 / (90 * numpy.sqrt(2 * numpy.pi))) \ ... * numpy.exp( ... 0.5 * ((a  90 + 90 * numpy.sin(t / 573.)) / 90.) ** 2)
Illustrating the flux at one energy as a function of pitch angle:
>>> times = numpy.arange(0., 7200, 5) >>> alpha = numpy.arange(0, 181., 2) # Add a dimension so the flux is a 2D array >>> flux = j(1., numpy.expand_dims(times, 1), ... numpy.expand_dims(alpha, 0)) >>> spacepy.plot.simpleSpectrogram(times, alpha, flux, cb=False, ... ylog=False) >>> matplotlib.pyplot.ylabel('Pitch angle (deg)') >>> matplotlib.pyplot.xlabel('Time (sec)') >>> matplotlib.pyplot.title('Flux at 1 MeV')
(Source code, png, hires.png, pdf)
Or the flux at one pitch angle as a function of energy:
>>> energies = numpy.logspace(0, 3, 50) >>> flux = j(numpy.expand_dims(energies, 0), ... numpy.expand_dims(times, 1), 90.) >>> spacepy.plot.simpleSpectrogram(times, energies, flux, cb=False) >>> matplotlib.pyplot.ylabel('Energy (MeV)') >>> matplotlib.pyplot.xlabel('Time (sec)') >>> matplotlib.pyplot.title('Flux at 90 degrees')
(Source code, png, hires.png, pdf)
The measurement is usually not aligned with a pitch angle grid, and the detector pointing in pitch angle space usually varies with time. Taking a very simple case of eight detectors that sweep through pitch angle space in an organized fashion at ten degrees per minute, the measured pitch angle as a function of detector and time is:
>>> def pa(d, t): ... return (d * 22.5 + t * (2 * (d % 2)  1)) % 180 >>> lines = matplotlib.pyplot.plot( ... times, pa(numpy.arange(8).reshape(1, 1), times.reshape(1, 1)), ... marker='o', ms=1, linestyle='') >>> matplotlib.pyplot.legend(lines, ... ['Detector {}'.format(i) for i in range(4)], loc='best') >>> matplotlib.pyplot.xlabel('Time (sec)') >>> matplotlib.pyplot.ylabel('Pitch angle (deg)') >>> matplotlib.pyplot.title('Measured pitch angle by detector')
(Source code, png, hires.png, pdf)
Assuming a coarser measurement in time and energy than used to illustrate the distribution above, the measured flux as a function of time, detector, and energy is constructed:
>>> times = numpy.arange(0., 7200, 300) #5 min cadence >>> alpha = pa(numpy.arange(8).reshape(1, 1), times.reshape(1, 1)) >>> energies = numpy.logspace(0, 3, 10) #10 energy channels (3/decade) # Every dimension (t, detector, e) gets its own numpy axis >>> flux = j(numpy.reshape(energies, (1, 1, 1)), numpy.reshape(times, (1, 1, 1)), numpy.expand_dims(alpha, 1)) >>> flux.shape (24, 8, 10)
The flux at an energy as a function of detector is not very useful:
>>> spacepy.plot.simpleSpectrogram(times, numpy.arange(8), ... flux[..., 0], cb=False, ylog=False) >>> matplotlib.pyplot.ylabel('Detector') >>> matplotlib.pyplot.xlabel('Time (sec)') >>> matplotlib.pyplot.title('Flux at 1 MeV')
(Source code, png, hires.png, pdf)
As a function of energy for one detector, the energy dependence is apparent but time and pitch angle effects are confounded:
>>> spacepy.plot.simpleSpectrogram(times, energies, flux[:, 0, :], ... cb=False) >>> matplotlib.pyplot.ylabel('Energy (MeV)') >>> matplotlib.pyplot.xlabel('Time (sec)') >>> matplotlib.pyplot.title('Flux in detector 0')
(Source code, png, hires.png, pdf)
What is needed is to recover the array of flux dimensioned by time, pitch angle, and energy, with appropriate pitch angle bins. The assumption is that the pitch angle as a function of time and detector is measured and thus the
alpha
array is available. Using that array,rebin
can change flux from time, detector, energy bins to time, pitch angle, energy bins. The axis 1 changes from a detector dimension to pitch angle:>>> pa_bins = numpy.arange(0, 181, 36) >>> flux_by_pa = spacepy.datamanager.rebin( ... flux, alpha, pa_bins, axis=1) >>> flux_by_pa.shape (24, 6, 10)
This can then be visualized. The pitch angle coverage is not perfect, but the original shape of the distribution is apparent, and further analysis can be performed on the regular pitch angle grid:
>>> spacepy.plot.simpleSpectrogram(times, pa_bins, flux_by_pa[..., 0], ... cb=False, ylog=False) >>> matplotlib.pyplot.ylabel('Pitch angle (deg)') >>> matplotlib.pyplot.xlabel('Time (sec)') >>> matplotlib.pyplot.title('Flux at 1MeV')
(Source code, png, hires.png, pdf)
Or by energy:
>>> spacepy.plot.simpleSpectrogram(times, energies, flux_by_pa[:, 2, :], ... cb=False) >>> matplotlib.pyplot.ylabel('Energy (MeV)') >>> matplotlib.pyplot.xlabel('Time (sec)') >>> matplotlib.pyplot.title('Flux at 90 degrees')
(Source code, png, hires.png, pdf)
rebin
can be used for higher dimension data, if the pitch angle itself depends on energy (e.g. if an energy sweep takes substantial time), and to propagate uncertainties through the rebinning. It can also be used to rebin on the time axis, e.g. for transforming time base.
 spacepy.datamanager.rev_index(idx, axis= 1)[source]¶
From an index, return an index that reverses the action of that index
Essentially,
a[idx][rev_index(idx)] == a
Note
This becomes more complicated in multiple dimensions, due to the vagaries of applying a multidimensional index.
 Parameters
 idxarray
Indices onto an array, often the output of
argsort()
.
 Returns
 rev_idxarray
Indices that, when applied to an array after
idx
, will return the original array (before the application ofidx
).
 Other Parameters
 axisint
Axis along which to return indices, defaults to the last axis.
See also
Examples
>>> import numpy >>> import spacepy.datamanager >>> data = numpy.array([7, 2, 4, 6, 3]) >>> idx = numpy.argsort(data) >>> data[idx] #sorted array([2, 3, 4, 6, 7]) >>> data[idx][spacepy.datamanager.rev_index(idx)] #original array([7, 2, 4, 6, 3])
 spacepy.datamanager.values_to_steps(array, axis= 1)[source]¶
Transform values along an axis to their order in a unique sequence.
Useful in, e.g., converting a list of energies to their steps.
 Parameters
 arrayarray
Input data.
 Returns
 stepsarray
An array, the same size as
array
, with values alongaxis
corresponding to the position of the value inarray
in a unique, sorted, set of the values inarray
along that axis. Differs fromargsort()
in that identical values will have identical step numbers in the output.
 Other Parameters
 axisint
Axis along which to find the steps.
Examples
>>> import numpy >>> import spacepy.datamanager >>> data = [[10., 12., 11., 9., 10., 12., 11., 9.], [10., 12., 11., 9., 14., 16., 15., 13.]] >>> spacepy.datamanager.values_to_steps(data) array([[1, 3, 2, 0, 1, 3, 2, 0], [1, 3, 2, 0, 5, 7, 6, 4]])