Source code for spacepy.datamanager

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
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 2015


About datamanager
-----------------


Examples
--------
Examples go here

.. currentmodule:: spacepy.datamanager

.. rubric:: Classes

.. autosummary::
    :template: clean_class.rst

    DataManager

.. rubric:: Functions

.. autosummary::

    apply_index
    array_interleave
    axis_index
    flatten_idx
    insert_fill
    rev_index
    values_to_steps
"""

__all__ = ["DataManager", "apply_index", "array_interleave", "axis_index",
           "flatten_idx", "insert_fill", "rev_index", "values_to_steps"]

import datetime
import operator
import os.path
import posixpath
import re

import numpy


[docs]class DataManager(object): """ 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 ========== directories : list A list of directories that might contain the data file_fmt : string Regular expression that matches the files desired. Will also recognize strftime parameters %w %d %m %y %Y %H %M %s %j %U %W, all zero-pad. https://docs.python.org/2/library/datetime.html#strftime-strptime-behavior Can have subdirectory reference, but separator should be unix-style, with no leading slash. period : string 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 ======= """ def __init__(self, directories, file_fmt, descend=False, period=None): """ """ #Duplicate from class docstring. Consider autoclass_content = "both" #Convert to system path format directories = [os.path.expandvars(os.path.expanduser( os.path.normpath(d))) for d in directories] file_fmt = posixpath.normpath(file_fmt) #The period matching might go into RePath if period is None: period = next( ('1{0}'.format(i) for i in 'sMHdmyY' if i in file_fmt), None) elif not re.match(r'^\d+[yYmdHMs]$', period): period = None #irregular, fun! self.directories = directories self.descend = descend self.period = period self.file_fmt = RePath(file_fmt)
[docs] def get_filename(self, dt): """ Returns the filename corresponding to a particular point in time """ if self.period: flist = self.files_matching(dt) else: raise NotImplementedError
#Now figure out the priority..
[docs] def files_matching(self, dt=None): """ Return all the files matching this file format Parameters ========== dt : datetime Optional; if specified, match only files for this date. Returns ======= out : generator Iterates over every file matching the format specified at creation. Note this is specified in native path format! """ #Use os.walk. If descend is False, only continue for matching #the re to this point. If True, compare branch to entire re but #walk everything for d in self.directories: native_d = os.path.normpath(d) #Do the walk in native paths for (dirpath, dirnames, filenames) in \ os.walk(native_d, topdown=True, followlinks=True): #dirpath is FULL DIRECTORY to this point, make relative relpath = dirpath[len(native_d) + 1:] #Convert to POSIX for comparisons if os.path.sep != posixpath.sep and relpath: relpath = posixpath.join(*RePath.path_split( relpath, native=True)) if not self.descend: if relpath and not \ self.file_fmt.match(relpath, dt, 'start'): continue for i in range(-len(dirnames), 0): dirname = dirnames[i] if os.path.sep != posixpath.sep and dirname: dirname = posixpath.join(*RePath.path_split( dirname, native=True)) if not self.file_fmt.match(posixpath.join( relpath, dirname), dt, 'start'): del dirnames[i] for f in filenames: if self.file_fmt.match(posixpath.join(relpath, f), dt, 'end' if self.descend else None): yield os.path.join(dirpath, f)
class RePath(object): """ A path based on a regular expression + time format Parameters ========== expression : string Regular expression that matches the files desired. Will also recognize strftime parameters %w %d %m %y %Y %H %M %s %j %U %W, all zero-pad. https://docs.python.org/2/library/datetime.html#strftime-strptime-behavior Can have subdirectory reference, but separator should be unix-style, with no leading slash. Matching is normally done against entire string, anchors should NOT be included. """ fmt_to_re = { 'd': r'[0-3]\d', 'm': r'[0-2]\d', 'y': r'\d\d', 'Y': r'\d{4}', 'H': r'[0-5]\d', 'M': r'[0-5]\d', 's': r'[0-6]\d', 'j': r'[0-3]\d\d', 'U': r'[0-5]\d', 'W': r'[0-5]\d' } def __init__(self, expression): self.file_fmt = expression #This should have backrefs where the same format occurs twice #(year in both directory and file, for example) self.file_re = re.sub(r'(?!(?:%%)+)%([wdmyYHMsjUW])', lambda x: self.fmt_to_re[x.group(1)], expression) self.file_fmt_split = self.path_split(self.file_fmt) self.file_re_split = self.path_split(self.file_re) def match(self, string, dt=None, where=None): """ Matches a string against a path or part thereof, optionally anchored at a particular date/time Other Parameters ================ dt : datetime.datetime The time to specifically match; otherwise matches all files. where : str Where to match: None to match the string to the entire path (default); ``'start'`` to match entire string against the start of the path; ``'end'`` to match entire path against end of string. Note this matches the last elements of the string, not just the last characters, i.e., ``oo/bar`` will not match ``foo/bar``. Similarly, ``'start'`` matches the first elements of the path, i.e., ``foo/ba`` will not match ``foo/bar`` ``start`` matches subset of path, i.e., the string is a directory that may contain full matches further down the tree. ``end`` matches a subset of the string, i.e, the string is a path to a file that would be a complete match except it has additional path elements leading. This is the order that tends to be useful. Returns ======= out : re.MatchObject The result of the match. """ datestr = (datetime.datetime.strftime(dt, self.file_fmt) if dt else self.file_re) if where is None: pat = datestr elif where.lower() == 'end': #Cut down string to match path pattern pat = datestr string = self.path_slice(string, -len(self.file_re_split), None, 1) elif where.lower() == 'start': #Does path pattern start like string? pat = self.path_slice(datestr, 0, len(self.path_split(string))) else: raise(ValueError("where must be 'start', 'stop', or None, not {0}". format(where))) return re.match('^' + pat + '$', string) @staticmethod def path_split(path, native=False): """ Break a path apart into a list for each path element. Parameters ========== path : str Path to split native : bool Is this a native path or UNIX-style? (default False, UNIX) Returns ======= out : list of str One path element (directory or file) per item """ split = os.path.split if native else posixpath.split res = [] while path: base, tail = split(path) if base == path: #No further splitting res.insert(0, path) break res.insert(0, tail) path = base return res @staticmethod def path_slice(path, start, stop=None, step=None, native=False): """ Slice a path by elements, as in getitem or [] Parameters ========== path : str Path to slice start : int First path element to return, or the only element if stop and step are not specified. Other Parameters ================ stop : int First path element to NOT return, i.e., one past the last. If ``stop`` is not specified but ``step`` is, return to end. step : int Increment between each. native : bool Is this a native path or UNIX-style? (default False, UNIX) Returns ======= out : str Selection of the path Examples ======== >>> path = "foo/bar/baz" >>> spacepy.datamanager.DataManager.path_slice(path, 1) "bar" >>> spacepy.datamanager.DataManager.path_slice(path, 0, step=2) "foo/baz" >>> spacepy.datamanager.DataManager.path_slice(path, 1, 3) "bar/baz" """ if stop is None and step is None: return RePath.path_split(path, native=native)[start] else: join = (os.path if native else posixpath).join return join(*RePath.path_split(path, native=native) [start:stop:step])
[docs]def insert_fill(times, data, fillval=numpy.nan, tol=1.5, absolute=None, doTimes=True): """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 ========== times : sequence Values representing when the data were taken. Must be one-dimensional, i.e., each value must be scalar. Not modified data : sequence Input data. Other Parameters ================ fillval : Fill value, same type as ``data``. Default is ``numpy.nan``. If scalar, will be repeated to match the shape of ``data`` (minus the time axis). .. note:: The default value of ``nan`` will not produce good results with integer input. tol : float Tolerance. A single fill value is inserted between adjacent values where the spacing in ``times`` is strictly greater than ``tol`` times the median of the spacing across all ``times``. 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 than ``absolute`` will have fill inserted. doTimes : boolean 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 ====== ValueError : if can't identify the time axis of data Try using :func:`numpy.rollaxis` to put the time axis first in both ``data`` and ``times``. Returns ======= times, data : tuple of sequence Copies of input times and data, fill added in gaps (``doTimes`` True) data : sequence Copy of input data, with fill added in gaps (``doTimes`` False) 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.]) .. plot:: :include-source: 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() """ times = numpy.asanyarray(times) data = numpy.asanyarray(data) assert(len(times.shape) == 1) if len(times) == data.shape[0]: timeaxis = 0 else: matches = numpy.nonzero(numpy.asanyarray(data.shape) == len(times))[0] if len(matches) != 1: raise ValueError( "Unable to uniquely match shape of data to count of times.") timeaxis = matches[0] fillshape = numpy.delete(data.shape, timeaxis) #shape of data w/o time axis if (numpy.shape(fillval) != fillshape).any(): if numpy.shape(fillval) == (): fillval = numpy.tile(fillval, fillshape) else: raise ValueError("Cannot match shape of fill to shape of data") diff = numpy.diff(times) if hasattr(diff[0], 'seconds'): #datetime diff = numpy.vectorize(lambda x: x.days * 86400.0 + x.seconds + x.microseconds / 1.0e6)(diff) if absolute is not None: absolute = absolute.days * 86400.0 + absolute.seconds + \ absolute.microseconds / 1.0e6 if absolute is None: idx = numpy.nonzero(diff > (numpy.median(diff) * tol))[0] + 1 else: idx = numpy.nonzero(diff > absolute)[0] + 1 data = numpy.insert(data, idx, numpy.repeat(fillval, len(idx)), axis=timeaxis) #NOOP if no fill if not doTimes: return data try: filltimes = (times[idx] + times[idx - 1]) / 2.0 except TypeError: filltimes = times[idx - 1] + numpy.vectorize(lambda x: datetime.timedelta(seconds=x / 2.0))(diff[idx - 1]) times = numpy.insert(times, idx, filltimes) return times, data
[docs]def apply_index(data, idx): """Apply an array of indices to data. Most useful in dealing with the output from :func:`numpy.argsort`, and best explained by the example. Parameters ========== data : array Input data, at least two dimensional. The 0th dimension is treated as a "time" or "record" dimension. idx : sequence 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." Raises ====== ValueError : if can't match the shape of data and indices Returns ======= data : sequence 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 of ``idx`` at the same index in the 0th dimension. This is repeated across any other dimensions in ``data``. .. 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 :meth:`~numpy.ndarray.copy` if a copy is required. 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. If ``energy`` 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) """ data = numpy.asanyarray(data) idx = numpy.asanyarray(idx) if len(idx.shape) != 2: raise ValueError("idx must have dimensions 2, not {0}".format( len(idx.shape))) if len(data.shape) < 2: raise ValueError("data must have at least dimensions 2") if idx.shape[0] != data.shape[0]: raise ValueError("data and idx must have same size in " "0th dimension") if not idx.shape[1] in data.shape[1:]: raise ValueError("Size of idx dimension 1 must match a dimension in " "data") idx_dim = data.shape[1:].index(idx.shape[1]) + 1 return numpy.rollaxis( numpy.rollaxis(data, idx_dim, 1) #make time and index dim adjacent #get a 2d array where every element matches index of first axis [numpy.mgrid[0:idx.shape[0], slice(idx.shape[1])][0], idx, #2d array, every element is desired index of second axis ...] #and the other axes come along for the ride , 1, idx_dim + 1) #and put index dim back in place
[docs]def array_interleave(array1, array2, idx): """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 from ``array1``, i.e., ``out[idx] == array1`` (in order.) The other elements of ``out`` will be filled, in order, from ``array2``. Parameters ========== array1 : array Input data. array2 : array Input data. Must have same number of dimensions as ``array1``, and all dimensions except the zeroth must also have the same length. idx : array 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 ======= out : array All elements from ``array1`` and ``array2``, interleaved according to ``idx``. 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]) """ array1 = numpy.asanyarray(array1) array2 = numpy.asanyarray(array2) idx = numpy.asanyarray(idx) assert(len(array1.shape) == len(array2.shape)) assert(array1.shape[1:] == array2.shape[1:]) assert(array1.dtype == array2.dtype) outarray = numpy.empty(dtype=array1.dtype, shape=((array1.shape[0] + array2.shape[0],) + array1.shape[1:])) outarray[idx, ...] = array1 idx_comp = numpy.ones((outarray.shape[0],), dtype=numpy.bool) idx_comp[idx] = False outarray[idx_comp, ...] = array2 return outarray
[docs]def values_to_steps(array, axis=-1): """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 ========== array : array Input data. Other Parameters ================ axis : int Axis along which to find the steps. Returns ======= steps : array An array, the same size as ``array``, with values along ``axis`` corresponding to the position of the value in ``array`` in a unique, sorted, set of the values in ``array`` along that axis. Differs from :func:`~numpy.argsort` in that identical values will have identical step numbers in the output. 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]]) """ array = numpy.asanyarray(array) sortidx = array.argsort(axis=axis) steps = rev_index(sortidx, axis=axis) d = numpy.diff(apply_index(array, sortidx), axis=axis) #Everywhere in SORTED array that the VALUE is same as one before same = numpy.insert(d==0, 0, False, axis=axis) #Number of duplicate indices BEFORE current index, in SORTED array delta = numpy.cumsum(same, axis=-1) #Get delta into the unsorted frame, and correct for uniqueness return steps - delta.ravel()[ flatten_idx(rev_index(sortidx, axis=axis), axis=axis)]\ .reshape(steps.shape)
[docs]def flatten_idx(idx, axis=-1): """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 :func:`~numpy.ravel_multi_index`. Parameters ========== idx : array Input index, i.e. a list of elements along a particular axis, in the style of :func:`~numpy.argsort`. Other Parameters ================ axis : int Axis along which ``idx`` operates, defaults to the last axis. Returns ======= flat : array A 1D array of indices suitable for indexing the flat version of the array See Also ======== apply_index 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]) """ idx = numpy.asanyarray(idx) if not idx.dtype.kind in ('i', 'u'): idx = idx.astype(int) preshape = idx.shape[:axis] postshape = idx.shape[axis:] stride = int(numpy.product(postshape[1:])) #1 if applied to empty #The index on this axis moves stride elements in flat outidx = idx.flatten() * stride #makes a copy #First add the offsets to get us to [..., idx @ axis = 0, 0...) outidx += numpy.repeat( numpy.arange(0, len(outidx), int(numpy.product(postshape)), dtype=idx.dtype), numpy.product(postshape)) #Now offsets for non-zero on the trailing axes [0, 0, ... 0@axis, ...] outidx += numpy.tile(numpy.arange(0, stride, dtype=idx.dtype), int(numpy.product(preshape)) * idx.shape[axis]) return outidx
[docs]def axis_index(shape, axis=-1): """Returns array of indices along axis, for all other axes Parameters ========== shape : tuple Shape of the output array Other Parameters ================ axis : int Axis along which to return indices, defaults to the last axis. Returns ======= idx : array An array of indices. The value of each element is that element's index along ``axis``. See Also ======== numpy.mgrid : This function is a special case Examples ======== For a shape of ``(i, j, k, l)`` and ``axis`` = -1, ``idx[i, j, k, :] = range(l)`` for all ``i``, ``j``, ``k``. Similarly, for the same shape and ``axis = 1``, ``idx[i, :, k, l] = range(j)`` for all ``i``, ``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]]) """ return operator.getitem(numpy.mgrid, [slice(i) for i in shape])[axis]
[docs]def rev_index(idx, axis=-1): """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 ========== idx : array Indices onto an array, often the output of :func:`~numpy.argsort`. Other Parameters ================ axis : int Axis along which to return indices, defaults to the last axis. Returns ======= rev_idx : array Indices that, when applied to an array after ``idx``, will return the original array (before the application of ``idx``). See Also ======== apply_index 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]) """ #Want an idx2 such that x[idx][idx2] == x #idx is position to value map #Populate every POSITION in idx2 with the POSITION in idx that #has the VALUE of the idx2 position #searchsorted on range? idx_out = numpy.empty_like(idx).ravel() idx_out[flatten_idx(idx, axis)] = axis_index(idx.shape, axis).ravel() return idx_out.reshape(idx.shape)