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# -*- coding: utf-8 -*-
"""
Utilities that assist in writing scientific data to HDF5 files
Created on Thu Sep 7 21:14:25 2017
@author: Suhas Somnath, Chris Smith
"""
from __future__ import division, print_function, unicode_literals, absolute_import
import sys
from itertools import groupby
import numpy as np
if sys.version_info.major == 3:
from collections.abc import Iterable
unicode = str
xrange = range
else:
from collections import Iterable
__all__ = ['get_slope', 'to_ranges', 'contains_integers', 'integers_to_slices',
'get_exponent', 'build_ind_val_matrices']
def get_slope(values, tol=1E-3):
"""
Attempts to get the slope of the provided values. This function will be handy
for checking if a dimension has been varied linearly or not.
If the values vary non-linearly, a ValueError will be raised
Parameters
----------
values : array-like
List of numbers
tol : float, optional. Default = 1E-3
Tolerance in the variation of the slopes.
Returns
-------
float
Slope of the line
"""
if not isinstance(tol, float):
raise TypeError('tol should be a float << 1')
if len(values)==1:
step_size=[0]
else:
step_size = np.unique(np.diff(values))
if len(step_size) > 1:
# often we end up here. In most cases,
step_avg = step_size.max()
step_size -= step_avg
var = np.mean(np.abs(step_size))
if var / step_avg < tol:
step_size = [step_avg]
else:
# Non-linear dimension! - see notes above
raise ValueError('Provided values cannot be expressed as a linear trend')
return step_size[0]
def to_ranges(iterable):
"""
Converts a sequence of iterables to range tuples
From https://stackoverflow.com/questions/4628333/converting-a-list-of-integers-into-range-in-python
Credits: @juanchopanza and @luca
Parameters
----------
iterable : collections.Iterable object
iterable object like a list
Returns
-------
iterable : generator object
Cast to list or similar to use
"""
iterable = sorted(set(iterable))
for key, group in groupby(enumerate(iterable), lambda t: t[1] - t[0]):
group = list(group)
if sys.version_info.major == 3:
yield range(group[0][1], group[-1][1]+1)
else:
yield xrange(group[0][1], group[-1][1]+1)
def contains_integers(iter_int, min_val=None):
"""
Checks if the provided object is iterable (list, tuple etc.) and contains integers optionally greater than equal to
the provided min_val
Parameters
----------
iter_int : :class:`collections.Iterable`
Iterable (e.g. list, tuple, etc.) of integers
min_val : int, optional, default = None
The value above which each element of iterable must possess. By default, this is ignored.
Returns
-------
bool
Whether or not the provided object is an iterable of integers
Examples
--------
>>> item = [1, 2, -3, 4]
>>> print('{} : contains integers? : {}'.format(item, sidpy.base.num_utils.contains_integers(item)))
[1, 2, -3, 4] : contains integers? : True
>>> item = [1, 4.5, 2.2, -1]
>>> print('{} : contains integers? : {}'.format(item, sidpy.base.num_utils.contains_integers(item)))
[1, 4.5, 2.2, -1] : contains integers? : False
>>> item = [1, 5, 8, 3]
>>> min_val = 2
>>> print('{} : contains integers >= {} ? : {}'.format(item, min_val, sidpy.base.num_utils.contains_integers(item, min_val=min_val)))
[1, 5, 8, 3] : contains integers >= 2 ? : False
"""
if not isinstance(iter_int, Iterable):
raise TypeError('iter_int should be an Iterable')
if len(iter_int) == 0:
return False
if min_val is not None:
if not isinstance(min_val, (int, float)):
raise TypeError('min_val should be an integer. Provided object was of type: {}'.format(type(min_val)))
if min_val % 1 != 0:
raise ValueError('min_val should be an integer')
try:
if min_val is not None:
return np.all([x % 1 == 0 and x >= min_val for x in iter_int])
else:
return np.all([x % 1 == 0 for x in iter_int])
except TypeError:
return False
def integers_to_slices(int_array):
"""
Converts a sequence of iterables to a list of slice objects denoting sequences of consecutive numbers
Parameters
----------
int_array : :class:`collections.Iterable`
iterable object like a :class:`list` or :class:`numpy.ndarray`
Returns
-------
sequences : list
List of :class:`slice` objects each denoting sequences of consecutive numbers
"""
if not contains_integers(int_array):
raise ValueError('Expected a list, tuple, or numpy array of integers')
def integers_to_consecutive_sections(integer_array):
"""
Converts a sequence of iterables to tuples with start and stop bounds
@author: @juanchopanza and @luca from stackoverflow
Parameters
----------
integer_array : :class:`collections.Iterable`
iterable object like a :class:`list`
Returns
-------
iterable : :class:`generator`
Cast to list or similar to use
Note
----
From https://stackoverflow.com/questions/4628333/converting-a-list-of-integers-into-range-in-python
"""
integer_array = sorted(set(integer_array))
for key, group in groupby(enumerate(integer_array),
lambda t: t[1] - t[0]):
group = list(group)
yield group[0][1], group[-1][1]
sequences = [slice(item[0], item[1] + 1) for item in integers_to_consecutive_sections(int_array)]
return sequences
def get_exponent(vector):
"""
Gets the scale / exponent for a sequence of numbers. This is particularly useful when wanting to scale a vector
for the purposes of plotting
Parameters
----------
vector : array-like
Array of numbers
Returns
-------
exponent : int
Scale / exponent for the given vector
"""
if not isinstance(vector, np.ndarray):
raise TypeError('vector should be of type numpy.ndarray. Provided object of type: {}'.format(type(vector)))
if np.isnan(vector).any():
raise TypeError('vector should not contain NaN values')
if np.max(np.abs(vector)) == np.max(vector):
exponent = np.log10(np.max(vector))
else:
# negative values
exponent = np.log10(np.max(np.abs(vector)))
return int(np.floor(exponent))
def build_ind_val_matrices(unit_values):
"""
Builds indices and values matrices using given unit values for each dimension.
This function is originally from pyUSID.io
Unit values must be arranged from fastest varying to slowest varying
Parameters
----------
unit_values : list / tuple
Sequence of values vectors for each dimension
Returns
-------
ind_mat : 2D numpy array
Indices matrix
val_mat : 2D numpy array
Values matrix
"""
if not isinstance(unit_values, (list, tuple)):
raise TypeError('unit_values should be a list or tuple')
if not np.all([np.array(x).ndim == 1 for x in unit_values]):
raise ValueError('unit_values should only contain 1D array')
lengths = [len(x) for x in unit_values]
tile_size = [np.prod(lengths[x:]) for x in range(1, len(lengths))] + [1]
rep_size = [1] + [np.prod(lengths[:x]) for x in range(1, len(lengths))]
val_mat = np.zeros(shape=(len(lengths), np.prod(lengths)))
ind_mat = np.zeros(shape=val_mat.shape, dtype=np.uint32)
for ind, ts, rs, vec in zip(range(len(lengths)), tile_size, rep_size, unit_values):
val_mat[ind] = np.tile(np.repeat(vec, rs), ts)
ind_mat[ind] = np.tile(np.repeat(np.arange(len(vec)), rs), ts)
val_mat = val_mat.T
ind_mat = ind_mat.T
return ind_mat, val_mat
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