def test_list():
f = tempfile.NamedTemporaryFile()
d = {}
d["hello"] = ["a", "b", "c", "d"]
d["blaise"] = [0, 1, 2, 3, 4, 5, 6, -124]
d["mixture"] = ["blaise", "wisconsin", "echo", "wisconsin"]
tidy_headers.write(f.name, d)
assert_dict_equal(d, tidy_headers.read(f.name))
f.close()
def test_array_nans():
f = tempfile.NamedTemporaryFile()
d = {}
d["nan"] = np.array([np.nan])
d["inf"] = np.array([np.inf])
d["-inf"] = np.array([-np.inf])
tidy_headers.write(f.name, d)
assert_dict_equal(d, tidy_headers.read(f.name))
f.close()
def test_array():
f = tempfile.NamedTemporaryFile()
d = {}
d["5"] = np.random.random(5)
d["12, 45"] = np.random.random((12, 45))
d["7, 15, 2"] = np.random.random((7, 15, 2))
tidy_headers.write(f.name, d)
assert_dict_equal(d, tidy_headers.read(f.name))
f.close()
def test_list():
f = tempfile.NamedTemporaryFile()
d = {}
d['hello'] = ['a', 'b', 'c', 'd']
d['blaise'] = [0, 1, 2, 3, 4, 5, 6, -124]
d['mixture'] = ['blaise', 'wisconsin', 'echo', 'wisconsin']
tidy_headers.write(f.name, d)
assert_dict_equal(d, tidy_headers.read(f.name))
f.close()
def test_str():
f = tempfile.NamedTemporaryFile()
d = {
"blaise": "thompson",
"red": "tomato",
"love": "hate",
"madison": "wisconsin"
}
tidy_headers.write(f.name, d)
assert_dict_equal(d, tidy_headers.read(f.name))
f.close()
def test_str():
f = tempfile.NamedTemporaryFile()
d = {
'blaise': 'thompson',
'red': 'tomato',
'love': 'hate',
'madison': 'wisconsin'
}
tidy_headers.write(f.name, d)
assert_dict_equal(d, tidy_headers.read(f.name))
f.close()
def test_float():
f = tempfile.NamedTemporaryFile()
d = {
"zero": 0.0,
"four": 4.0,
"large": 124.0,
"negative": -12.0,
"fraction": 0.5
}
tidy_headers.write(f.name, d)
assert_dict_equal(d, tidy_headers.read(f.name))
f.close()
def test_float():
f = tempfile.NamedTemporaryFile()
d = {
'zero': 0.,
'four': 4.,
'large': 124.,
'negative': -12.,
'fraction': 0.5
}
tidy_headers.write(f.name, d)
assert_dict_equal(d, tidy_headers.read(f.name))
f.close()
def from_PyCMDS(filepath, name=None, parent=None, verbose=True):
"""Create a data object from a single PyCMDS output file.
Parameters
----------
filepath : str
The file to load. Can accept .data, .fit, or .shots files.
name : str or None (optional)
The name to be applied to the new data object. If None, name is read
from file.
parent : WrightTools.Collection (optional)
Collection to place new data object within. Default is None.
verbose : bool (optional)
Toggle talkback. Default is True.
Returns
-------
data
A Data instance.
"""
# header
headers = tidy_headers.read(filepath)
# name
if name is None: # name not given in method arguments
data_name = headers['data name']
else:
data_name = name
if data_name == '': # name not given in PyCMDS
data_name = headers['data origin']
# create data object
kwargs = {'name': data_name, 'kind': 'PyCMDS', 'source': filepath,
'created': headers['file created'],
}
if parent is not None:
data = parent.create_data(**kwargs)
else:
data = Data(**kwargs)
# array
arr = np.genfromtxt(filepath).T
# get axes and scanned variables
axes = []
for name, identity, units in zip(headers['axis names'],
headers['axis identities'],
headers['axis units']):
# points and centers
points = np.array(headers[name + ' points'])
if name + ' centers' in headers.keys():
centers = headers[name + ' centers']
else:
centers = None
# create
axis = {'points': points, 'units': units, 'name': name, 'identity': identity,
'centers': centers}
axes.append(axis)
shape = tuple([a['points'].size for a in axes])
for i, ax in enumerate(axes):
sh = [1] * len(shape)
sh[i] = len(ax['points'])
data.create_variable(name=ax['name'] + '_points',
values=np.array(ax['points']).reshape(sh))
if ax['centers'] is not None:
sh = [1] * len(shape)
sh[i - 1] = len(axes[i - 1]['points'])
data.create_variable(name=ax['name'] + '_centers',
values=np.array(ax['centers']).reshape(sh))
# get assorted remaining things
# variables and channels
for index, kind, name in zip(range(len(arr)), headers['kind'], headers['name']):
values = np.full(np.prod(shape), np.nan)
values[:len(arr[index])] = arr[index]
values.shape = shape
if name == 'time':
data.create_variable(name='labtime', values=values)
if kind == 'hardware':
# sadly, recorded tolerances are not reliable
# so a bit of hard-coded hacking is needed
# if this ends up being too fragile, we might have to use the points arrays
# ---Blaise 2018-01-09
units = headers['units'][index]
label = headers['label'][index]
if 'w' in name and name.startswith(tuple(data.variable_names)):
inherited_shape = data[name.split('_')[0]].shape
for i, s in enumerate(inherited_shape):
if s == 1:
values = np.mean(values, axis=i)
values = np.expand_dims(values, i)
else:
tolerance = headers['tolerance'][index]
for i in range(len(shape)):
if tolerance is None:
break
if 'd' in name:
tolerance = 3.
if 'zero' in name:
tolerance = 1e-10
try:
assert i == headers['axis names'].index(name)
tolerance = 0
except (ValueError, AssertionError):
if (name in headers['axis names'] and
'%s_centers' % name not in data.variable_names):
tolerance = np.inf
mean = np.nanmean(values, axis=i)
mean = np.expand_dims(mean, i)
values, meanexp = wt_kit.share_nans(values, mean)
if np.allclose(meanexp, values, atol=tolerance, equal_nan=True):
values = mean
if name in headers['axis names']:
points = np.array(headers[name + ' points'])
pointsshape = [1, ] * len(values.shape)
for i, ax in enumerate(axes):
if ax['name'] == name:
pointsshape[i] = len(points)
break
points.shape = pointsshape
for i in range(points.ndim):
if points.shape[i] == 1:
points = np.repeat(points, values.shape[i], axis=i)
values[np.isnan(values)] = points[np.isnan(values)]
data.create_variable(name, values=values, units=units, label=label)
if kind == 'channel':
data.create_channel(name=name, values=values, shape=values.shape)
# axes
for a in axes:
expression = a['identity']
if expression.startswith('D'):
expression = expression[1:]
expression.replace('=D', '=')
a['expression'] = expression
data.transform(*[a['expression'] for a in axes])
# return
if verbose:
print('data created at {0}'.format(data.fullpath))
print(' axes: {0}'.format(data.axis_names))
print(' shape: {0}'.format(data.shape))
return data
import os import time import collections import numpy as np import tidy_headers # --- define ------------------------------------------------------------------------------------- here = os.path.abspath(os.path.dirname(__file__)) # --- workspace ---------------------------------------------------------------------------------- filepath = os.path.join(here, "minimal.txt") # create metadata meta = collections.OrderedDict() meta["time"] = time.time() meta["name"] = "Blaise Thompson" meta["colors"] = ["red", "blue", "green", "white"] meta["number"] = 42 meta["array"] = np.random.random((5, 3)) # write metadata tidy_headers.write(filepath, meta) # read metadata print(tidy_headers.read(filepath))
def test_int():
f = tempfile.NamedTemporaryFile()
d = {"zero": 0, "four": 4, "large": 1200000, "negative": -1}
tidy_headers.write(f.name, d)
assert_dict_equal(d, tidy_headers.read(f.name))
f.close()
def from_PyCMDS(filepath,
name=None,
parent=None,
verbose=True,
*,
collapse=True) -> Data:
"""Create a data object from a single PyCMDS output file.
Parameters
----------
filepath : path-like
Path to the .data file
Can be either a local or remote file (http/ftp).
Can be compressed with gz/bz2, decompression based on file name.
name : str or None (optional)
The name to be applied to the new data object. If None, name is read
from file.
parent : WrightTools.Collection (optional)
Collection to place new data object within. Default is None.
verbose : bool (optional)
Toggle talkback. Default is True.
Returns
-------
data
A Data instance.
"""
filestr = os.fspath(filepath)
# header
ds = np.DataSource(None)
file_ = ds.open(filestr, "rt")
headers = tidy_headers.read(file_)
file_.seek(0)
# name
if name is None: # name not given in method arguments
data_name = headers["data name"]
else:
data_name = name
if data_name == "": # name not given in PyCMDS
data_name = headers["data origin"]
# create data object
kwargs = {
"name": data_name,
"kind": "PyCMDS",
"source": filestr,
"created": headers["file created"],
}
if parent is not None:
data = parent.create_data(**kwargs)
else:
data = Data(**kwargs)
if collapse:
# array
arr = np.genfromtxt(file_).T
# get axes and scanned variables
axes = []
for name, identity, units in zip(headers["axis names"],
headers["axis identities"],
headers["axis units"]):
# points and centers
points = np.array(headers[name + " points"])
if name + " centers" in headers.keys():
centers = headers[name + " centers"]
else:
centers = None
# create
axis = {
"points": points,
"units": units,
"name": name,
"identity": identity,
"centers": centers,
}
axes.append(axis)
shape = tuple([a["points"].size for a in axes])
for i, ax in enumerate(axes):
sh = [1] * len(shape)
sh[i] = len(ax["points"])
data.create_variable(name=ax["name"] + "_points",
values=np.array(ax["points"]).reshape(sh))
if ax["centers"] is not None:
centers = np.array(ax["centers"])
sh = list(shape)
sh[i] = 1
for j, s in enumerate(sh):
if centers.size % s:
sh[j] = 1
data.create_variable(name=ax["name"] + "_centers",
values=np.array(centers.reshape(sh)))
# get assorted remaining things
# variables and channels
try:
signed = iter(headers["channel signed"])
except KeyError:
signed = itertools.repeat(False)
for index, (kind, name) in enumerate(zip(headers["kind"],
headers["name"])):
if collapse:
_collapse_read_in(data, headers, axes, arr, signed, index, kind,
name, shape)
else:
_no_collapse_create(data, headers, signed, index, kind, name,
shape)
if not collapse:
_no_collapse_fill(data, headers, file_, shape, verbose)
file_.close()
# axes
for a in axes:
expression = a["identity"]
if expression.startswith("D"):
expression = expression[1:]
expression.replace("=D", "=")
a["expression"] = expression
data.transform(*[a["expression"] for a in axes])
for a, u in zip(data.axes, headers["axis units"]):
if u is not None:
a.convert(u)
if (headers["system name"] == "fs"
and int(headers["PyCMDS version"].split(".")[0]) == 0
and int(headers["PyCMDS version"].split(".")[1]) < 10):
# in versions of PyCMDS up to (and including) 0.9.0
# there was an incorrect hard-coded conversion factor between mm and fs
# this ONLY applied to Newport MFA stages
# we apply this correction knowing that Newport MFAs were only used on the "fs" system
# and knowing that the Newport MFAs were always assigned as "d1", "d2" and "d3"
# ---Blaise 2019-04-09
for delay in ("d1", "d2", "d3", "d1_points", "d2_points", "d3_points"):
if delay not in data.variable_names:
continue
data[delay][:] *= 6000.671281903963041 / 6671.281903963041
if verbose:
print(f"Correction factor applied to {delay}")
# return
if verbose:
print("data created at {0}".format(data.fullpath))
print(" axes: {0}".format(data.axis_names))
print(" shape: {0}".format(data.shape))
return data
def test_int():
f = tempfile.NamedTemporaryFile()
d = {'zero': 0, 'four': 4, 'large': 1200000, 'negative': -1}
tidy_headers.write(f.name, d)
assert_dict_equal(d, tidy_headers.read(f.name))
f.close()
