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"""Measurements.py - storage for image and object measurements
CellProfiler is distributed under the GNU General Public License.
See the accompanying file LICENSE for details.
Copyright (c) 2003-2009 Massachusetts Institute of Technology
Copyright (c) 2009-2012 Broad Institute
All rights reserved.
Please see the AUTHORS file for credits.
Website: http://www.cellprofiler.org
"""
from __future__ import with_statement
__version__ = "$Revision$"
import logging
logger = logging.getLogger(__name__)
import numpy as np
import re
from scipy.io.matlab import loadmat
from itertools import repeat
import cellprofiler.preferences as cpprefs
from cellprofiler.utilities.hdf5_dict import HDF5Dict, get_top_level_group
from cellprofiler.utilities.hdf5_dict import VERSION
import tempfile
import numpy as np
import warnings
import os
import os.path
AGG_MEAN = "Mean"
AGG_STD_DEV = "StDev"
AGG_MEDIAN = "Median"
AGG_NAMES = [AGG_MEAN, AGG_MEDIAN, AGG_STD_DEV]
"""The per-image measurement category"""
IMAGE = "Image"
"""The per-experiment measurement category"""
EXPERIMENT = "Experiment"
"""The relationship measurement category"""
RELATIONSHIP = "Relationship"
"""The neighbor association measurement category"""
NEIGHBORS = "Neighbors"
"""The per-object "category" (if anyone needs the word, "Object")"""
OBJECT = "Object"
disallowed_object_names = [IMAGE, EXPERIMENT, RELATIONSHIP]
COLTYPE_INTEGER = "integer"
COLTYPE_FLOAT = "float"
'''16bit Binary Large Object. This object can fit 64K of raw data.
Currently used for storing image thumbnails as 200 x 200px (max) 8-bit pngs.
Should NOT be used for storing larger than 256 x 256px 8-bit pngs.'''
COLTYPE_BLOB = "blob"
'''24bit Binary Large Object. This object can fit 16M of raw data.
Not currently used'''
COLTYPE_MEDIUMBLOB = "mediumblob"
'''32bit Binary Large Object. This object can fit 4GB of raw data.
Not currently used'''
COLTYPE_LONGBLOB = "longblob"
'''SQL format for a varchar column
To get a varchar column of width X: COLTYPE_VARCHAR_FORMAT % X
'''
COLTYPE_VARCHAR_FORMAT = "varchar(%d)"
COLTYPE_VARCHAR = "varchar"
'''# of characters reserved for path name in the database'''
PATH_NAME_LENGTH = 256
'''# of characters reserved for file name in the database'''
FILE_NAME_LENGTH = 128
COLTYPE_VARCHAR_FILE_NAME = COLTYPE_VARCHAR_FORMAT % FILE_NAME_LENGTH
COLTYPE_VARCHAR_PATH_NAME = COLTYPE_VARCHAR_FORMAT % PATH_NAME_LENGTH
'''Column attribute: only available after post_group is run (True / False)'''
MCA_AVAILABLE_POST_GROUP = "AvailablePostGroup"
'''The name of the metadata category'''
C_METADATA = "Metadata"
'''The name of the site metadata feature'''
FTR_SITE = "Site"
'''The name of the well metadata feature'''
FTR_WELL = "Well"
'''The name of the row metadata feature'''
FTR_ROW = "Row"
'''The name of the column metadata feature'''
FTR_COLUMN = "Column"
'''The name of the plate metadata feature'''
FTR_PLATE = "Plate"
M_SITE, M_WELL, M_ROW, M_COLUMN, M_PLATE = \
['_'.join((C_METADATA, x))
for x in (FTR_SITE, FTR_WELL, FTR_ROW, FTR_COLUMN, FTR_PLATE)]
MEASUREMENTS_GROUP_NAME = "Measurements"
IMAGE_NUMBER = "ImageNumber"
OBJECT_NUMBER = "ObjectNumber"
GROUP_NUMBER = "Group_Number"
GROUP_INDEX = "Group_Index"
def get_length_from_varchar(x):
'''Retrieve the length of a varchar column from its coltype def'''
m = re.match(r'^varchar\(([0-9]+)\)$', x)
if m is None:
return None
return int(m.groups()[0])
class Measurements(object):
"""Represents measurements made on images and objects
"""
def __init__(self,
can_overwrite=False,
image_set_start=None,
filename = None,
copy = None):
"""Create a new measurements collection
can_overwrite - DEPRECATED and has no effect
image_set_start - the index of the first image set in the image set list
or None to start at the beginning
filename - store the measurement in an HDF5 file with this name
copy - initialize by copying measurements from here, either an HDF5Dict
or an H5py group or file.
"""
# XXX - allow saving of partial results
if filename is None:
dir = cpprefs.get_default_output_directory()
if not (os.path.exists(dir) and os.access(dir, os.W_OK)):
dir = None
fd, filename = tempfile.mkstemp(prefix='Cpmeasurements', suffix='.hdf5', dir=dir)
is_temporary = True
else:
is_temporary = False
if isinstance(copy, Measurements):
with copy.hdf5_dict.lock:
self.hdf5_dict = HDF5Dict(
filename,
is_temporary = is_temporary,
copy = copy.hdf5_dict.top_group)
elif hasattr(copy, '__getitem__') and hasattr(copy, 'keys'):
self.hdf5_dict = HDF5Dict(
filename,
is_temporary = is_temporary,
copy = copy)
else:
self.hdf5_dict = HDF5Dict(filename, is_temporary = is_temporary)
if is_temporary:
os.close(fd)
self.image_set_number = image_set_start or 1
self.image_set_start = image_set_start
self.__is_first_image = True
self.__initialized_explicitly = False
self.__relationships = set()
self.__relationship_names = set()
def __del__(self):
if hasattr(self, "hdf5_dict"):
self.close()
def close(self):
self.hdf5_dict.close()
del self.hdf5_dict
def __getitem__(self, key):
# we support slicing the last dimension for the limited case of [..., :]
if len(key) == 3 and key[2] == slice(None, None, None):
return self.get_all_measurements(*key[:2])
return self.get_measurement(*key)
def __setitem__(self, key, value):
assert 2 <= len(key) <= 3
if len(key) == 2:
self.add_measurement(key[0], key[1], value)
else:
self.add_measurement(key[0], key[1], value, image_set_number=key[2])
def flush(self):
self.hdf5_dict.flush()
def initialize(self, measurement_columns):
'''Initialize the measurements with a list of objects and features
This explicitly initializes the measurements with a list of
object/feature pairs as would be returned by
get_measurement_columns()
measurement_columns - list of 3-tuples: object name, feature, type
'''
# clear the old data, if any
self.hdf5_dict.clear()
def fix_type(t):
if t == 'integer':
return np.int
if t.startswith('varchar'):
len = t.split('(')[1][:-1]
return np.dtype('a' + len)
return t
for object_name, feature, coltype in measurement_columns:
coltype = fix_type(coltype)
if object_name == EXPERIMENT:
dims = 0
elif object_name == IMAGE:
dims = 1
else:
dims = 2
self.hdf5_dict.add_object(object_name)
self.hdf5_dict.add_feature(object_name, feature)
self.__initialized_explicitly = True
def next_image_set(self, explicit_image_set_number=None):
assert explicit_image_set_number is None or explicit_image_set_number > 0
if explicit_image_set_number is None:
self.image_set_number += 1
else:
self.image_set_number = explicit_image_set_number
self.__is_first_image = False
@property
def image_set_count(self):
'''The number of complete image sets measured'''
# XXX - question for Lee: should this return the minimum number
# of non-null values across columns in the the Image table?
try:
return len(self.hdf5_dict.get_indices('Image', 'ImageNumber'))
except KeyError:
return 0
def get_is_first_image(self):
'''True if this is the first image in the set'''
return self.__is_first_image
def set_is_first_image(self, value):
if not value:
raise ValueError("Can only reset to be first image")
self.__is_first_image = True
self.image_set_number = self.image_set_start_number
is_first_image = property(get_is_first_image, set_is_first_image)
@property
def image_set_start_number(self):
'''The first image set (one-based) processed by the pipeline'''
if self.image_set_start is None:
return 1
return self.image_set_start
@property
def has_image_set_start(self):
'''True if the image set has an explicit start'''
return self.image_set_start is not None
def load(self, measurements_file_name):
'''Load measurements from a matlab file'''
handles = loadmat(measurements_file_name, struct_as_record=True)
self.create_from_handles(handles)
def create_from_handles(self, handles):
'''Load measurements from a handles structure'''
m = handles["handles"][0, 0][MEASUREMENTS_GROUP_NAME][0, 0]
for object_name in m.dtype.fields.keys():
omeas = m[object_name][0, 0]
for feature_name in omeas.dtype.fields.keys():
if object_name == IMAGE:
values = [None if len(x) == 0 else x.flatten()[0]
for x in omeas[feature_name][0]]
elif object_name == EXPERIMENT:
value = omeas[feature_name][0, 0].flatten()[0]
self.add_experiment_measurement(feature_name, value)
continue
else:
values = [x.flatten()
for x in omeas[feature_name][0].tolist()]
self.add_all_measurements(object_name,
feature_name,
values)
#
# Set the image set number to beyond the last in the handles
#
self.image_set_number = self.image_set_count + 1
def add_image_measurement(self, feature_name, data, can_overwrite = False):
"""Add a measurement to the "Image" category
"""
self.add_measurement(IMAGE, feature_name, data)
def add_experiment_measurement(self, feature_name, data):
"""Add an experiment measurement to the measurement
Experiment measurements have one value per experiment
"""
self.add_measurement(EXPERIMENT, feature_name, data)
def get_group_number(self):
'''The number of the group currently being processed'''
return self.get_current_image_measurement(GROUP_NUMBER)
def set_group_number(self, group_number, can_overwrite=False):
self.add_image_measurement(GROUP_NUMBER, group_number)
group_number = property(get_group_number, set_group_number)
def get_group_index(self):
'''The within-group index of the current image set'''
return self.get_current_image_measurement(GROUP_INDEX)
def set_group_index(self, group_index):
self.add_image_measurement(GROUP_INDEX, group_index)
group_index = property(get_group_index, set_group_index)
def get_groupings(self, features):
'''Return groupings of image sets based on feature values
features - a sequence of feature names
returns groupings suitable for return from CPModule.get_groupings.
group_list - a sequence composed of two-tuples.
the first element of the tuple is a dictionary giving
the metadata values for the metadata keys
the second element of the tuple is a sequence of
image numbers comprising the image sets of the group
For instance, an experiment might have key_names of 'Metadata_Row'
and 'Metadata_Column' and a group_list of:
[ ({'Metadata_Row':'A','Metadata_Column':'01'}, [1,97,193]),
({'Metadata_Row':'A','Metadata_Column':'02'), [2,98,194]),... ]
'''
d = {}
image_numbers = self.get_image_numbers()
values = [[unicode(x) for x in self.get_measurement(IMAGE, feature, image_numbers)]
for feature in features]
for i, image_number in enumerate(image_numbers):
key = tuple([(k, v[i]) for k, v in zip(features, values)])
if not d.has_key(key):
d[key] = []
d[key].append(image_number)
return [ (dict(k), d[k]) for k in sorted(d.keys()) ]
def add_relate_measurement(
self, module_number,
relationship,
object_name1, object_name2,
group_indexes1, object_numbers1,
group_indexes2, object_numbers2):
'''Add object relationships to the measurements
module_number - the module that generated the relationship
relationship - the relationship of the two objects, for instance,
"Parent" means object # 1 is the parent of object # 2
object_name1, object_name2 - the name of the segmentation for the first and second objects
group_indexes1, group_indexes2 - for each object, the group index of
that object's image set.
(MUST NOT BE A SCALAR)
object_numbers1, object_numbers2 - for each object, the object number
in the object's object set
This method lets the caller store any sort of arbitrary relationship
between objects as long as they are in the same group. To record
all neighbors within a particular segmentation, call with the same
object name for object_name1 and object_name2 and the same group
index - that of the current image. Relating would have different object
names and TrackObjects would have different group indices.
'''
# XXX - check overwrite?
# XXX - Should group number be moved out of the measurement name?
group_number = self.group_number
with self.hdf5_dict.lock:
self.hdf5_dict.top_group.require_group(RELATIONSHIP)
relationship_group = self.hdf5_dict.top_group.require_group('%s/%02d_%d_%s_%s_%s' % (RELATIONSHIP, module_number, group_number, relationship, object_name1, object_name2))
features = ["group_number", "group_index1", "group_index2", "object_number1", "object_number2"]
if "group_number" not in relationship_group:
for name in features:
relationship_group.create_dataset(name, (0,), dtype='int32', chunks=(1024,), maxshape=(None,))
current_size = relationship_group['group_number'].shape[0]
for name in features:
relationship_group[name].resize((current_size + len(group_indexes1),))
relationship_group['group_number'][current_size:] = group_number
relationship_group['group_index1'][current_size:] = group_indexes1
relationship_group['group_index2'][current_size:] = group_indexes2
relationship_group['object_number1'][current_size:] = object_numbers1
relationship_group['object_number2'][current_size:] = object_numbers2
self.__relationships.add((module_number, group_number, relationship, object_name1, object_name2))
self.__relationship_names.add(relationship_group.name)
def get_relationship_groups(self):
'''Return the keys of each of the relationship groupings.
The value returned is a list composed of objects with the following
attributes:
module_number - the module number of the module used to generate the relationship
group_number - the group number of the relationship
relationship - the relationship of the two objects
object_name1 - the object name of the first object in the relationship
object_name2 - the object name of the second object in the relationship
'''
return [RelationshipKey(module_number, group_number, relationship, obj1, obj2) for
(module_number, group_number, relationship, obj1, obj2) in self.__relationships]
def get_relationships(self, module_number, relationship, object_name1, object_name2, group_number):
if not (module_number, group_number, relationship, object_name1, object_name2) in self.__relationships:
return np.zeros(0, [("group_index1", int, 1),
("object_number1", int, 1),
("group_index2", int, 1),
("object_number2", int, 1)]).view(np.recarray)
with self.hdf5_dict.lock:
grp = self.hdf5_dict.top_group['%s/%02d_%d_%s_%s_%s' % (RELATIONSHIP, module_number, group_number, relationship, object_name1, object_name2)]
dt = np.dtype([("group_index1", np.int, 1),
("object_number1", np.int, 1),
("group_index2", np.int, 1),
("object_number2", np.int, 1)])
temp = np.zeros(grp['group_index1'].shape, dt)
temp['group_index1'] = grp['group_index1']
temp['object_number1'] = grp['object_number1']
temp['group_index2'] = grp['group_index2']
temp['object_number2'] = grp['object_number2']
return temp.view(np.recarray)
def add_measurement(self, object_name, feature_name, data,
can_overwrite=False, image_set_number=None):
"""Add a measurement or, for objects, an array of measurements to the set
This is the classic interface - like CPaddmeasurements:
ObjectName - either the name of the labeled objects or "Image"
FeatureName - the feature name, encoded with underbars for category/measurement/image/scale
Data - the data item to be stored
"""
if image_set_number is None:
image_set_number = self.image_set_number
# some code adds ImageNumber and ObjectNumber measurements explicitly
if feature_name in (IMAGE_NUMBER, OBJECT_NUMBER):
return
def wrap_string(v):
if isinstance(v, basestring):
return unicode(v).encode('unicode_escape')
return v
if object_name == EXPERIMENT:
if not np.isscalar(data) and data is not None:
data = data[0]
if data is None:
data = []
self.hdf5_dict[EXPERIMENT, feature_name, 0] = wrap_string(data)
elif object_name == IMAGE:
if not np.isscalar(data) and data is not None:
data = data[0]
if data is None:
data = []
self.hdf5_dict[IMAGE, feature_name, image_set_number] = wrap_string(data)
if not self.hdf5_dict.has_data(object_name, 'ImageNumber', image_set_number):
self.hdf5_dict[IMAGE, 'ImageNumber', image_set_number] = image_set_number
else:
self.hdf5_dict[object_name, feature_name, image_set_number] = data
if not self.hdf5_dict.has_data(IMAGE, IMAGE_NUMBER, image_set_number):
self.hdf5_dict[IMAGE, IMAGE_NUMBER, image_set_number] = image_set_number
if not self.hdf5_dict.has_data(object_name, 'ObjectNumber', image_set_number):
self.hdf5_dict[object_name, 'ImageNumber', image_set_number] = [image_set_number] * len(data)
self.hdf5_dict[object_name, 'ObjectNumber', image_set_number] = np.arange(1, len(data) + 1)
def remove_measurement(self, object_name, feature_name, image_number):
'''Remove the measurement for the given image number
object_name - the measurement's object. If other than Image or Experiment,
will remove measurements for all objects
feature_name - name of the measurement feature
image_number - the image set's image number
'''
del self.hdf5_dict[object_name, feature_name, image_number]
def get_object_names(self):
"""The list of object names (including Image) that have measurements
"""
return [x for x in self.hdf5_dict.top_level_names()
if x != RELATIONSHIP]
object_names = property(get_object_names)
def get_feature_names(self, object_name):
"""The list of feature names (measurements) for an object
"""
return [name for name in self.hdf5_dict.second_level_names(object_name) if name not in ('ImageNumber', 'ObjectNumber')]
def get_image_numbers(self):
'''Return the image numbers from the Image table'''
image_numbers = np.array(
self.hdf5_dict.get_indices(IMAGE, IMAGE_NUMBER), int)
image_numbers.sort()
return image_numbers
def has_feature(self, object_name, feature_name):
return self.hdf5_dict.has_feature(object_name, feature_name)
def get_current_image_measurement(self, feature_name):
'''Return the value for the named image measurement
feature_name - the name of the measurement feature to be returned
'''
return self.get_current_measurement(IMAGE, feature_name)
def get_current_measurement(self, object_name, feature_name):
"""Return the value for the named measurement for the current image set
object_name - the name of the objects being measured or "Image"
feature_name - the name of the measurement feature to be returned
"""
return self.get_measurement(object_name, feature_name, self.image_set_number)
def get_measurement(self, object_name, feature_name, image_set_number=None):
"""Return the value for the named measurement and indicated image set
object_name - the name of one of the objects or one of the generic
names such as Image or Experiment
feature_name - the name of the feature to retrieve
image_set_number - the current image set by default, a single
image set number to get measurements for one
image set or a sequence of image numbers to
return measurements for each of the image sets
listed.
"""
def unwrap_string(v):
# hdf5 returns string columns as a wrapped type
if isinstance(v, str):
return unicode(str(v)).decode('unicode_escape')
return v
if object_name == EXPERIMENT:
return unwrap_string(self.hdf5_dict[EXPERIMENT, feature_name, 0][0])
if image_set_number is None:
image_set_number = self.image_set_number
vals = self.hdf5_dict[object_name, feature_name, image_set_number]
if vals is None:
return None
if object_name == IMAGE:
if np.isscalar(image_set_number):
return np.NAN if len(vals) == 0 else unwrap_string(vals[0])
else:
return np.array(
[unwrap_string(v[0]) if v is not None else np.NaN
for v in vals])
if np.isscalar(image_set_number):
return np.array([]) if vals is None else vals.flatten()
return [np.array([]) if v is None else v.flatten() for v in vals]
def has_measurements(self, object_name, feature_name, image_set_number):
if object_name == EXPERIMENT:
return self.hdf5_dict.has_data(EXPERIMENT, feature_name, 0)
return self.hdf5_dict.has_data(object_name, feature_name, image_set_number)
def has_current_measurements(self, object_name, feature_name):
return self.has_measurements(object_name, feature_name, self.image_set_number)
def get_all_measurements(self, object_name, feature_name):
warnings.warn("get_all_measurements is deprecated. Please use "
"get_measurements with an array of image numbers instead",
DeprecationWarning)
return self.get_measurement(object_name, feature_name,
self.get_image_numbers())
def add_all_measurements(self, object_name, feature_name, values):
'''Add a list of measurements for all image sets
object_name - name of object or Images
feature_name - feature to add
values - list of either values or arrays of values
'''
values = [unicode(value).encode('unicode_escape')
if isinstance(value, (str, unicode)) else value
for value in values]
if ((not self.hdf5_dict.has_feature(IMAGE, IMAGE_NUMBER)) or
(np.max(self.get_image_numbers()) < len(values))):
self.hdf5_dict.add_all(
IMAGE, IMAGE_NUMBER,
[i+1 if value is not None else None
for i, value in enumerate(values)])
self.hdf5_dict.add_all(object_name, feature_name, values)
def get_experiment_measurement(self, feature_name):
"""Retrieve an experiment-wide measurement
"""
return self.get_measurement(EXPERIMENT, feature_name) or 'N/A'
def apply_metadata(self, pattern, image_set_number=None):
"""Apply metadata from the current measurements to a pattern
pattern - a regexp-like pattern that specifies how to insert
metadata into a string. Each token has the form:
"\(?<METADATA_TAG>\)" (matlab-style) or
"\g<METADATA_TAG>" (Python-style)
image_name - name of image associated with the metadata (or None
if metadata is not associated with an image)
image_set_number - # of image set to use to retrieve data.
None for current.
returns a string with the metadata tags replaced by the metadata
"""
if image_set_number == None:
image_set_number = self.image_set_number
result_pieces = []
double_backquote = "\\\\"
single_backquote = "\\"
for piece in pattern.split(double_backquote):
# Replace tags in piece
result = ''
while(True):
# Replace one tag
m = re.search('\\(\\?[<](.+?)[>]\\)', piece)
if not m:
m = re.search('\\\\g[<](.+?)[>]', piece)
if not m:
result += piece
break
result += piece[:m.start()]
measurement = '%s_%s' % (C_METADATA, m.groups()[0])
result += str(self.get_measurement("Image", measurement,
image_set_number))
piece = piece[m.end():]
result_pieces.append(result)
return single_backquote.join(result_pieces)
def group_by_metadata(self, tags):
"""Return groupings of image sets with matching metadata tags
tags - a sequence of tags to match.
Returns a sequence of MetadataGroup objects. Each one represents
a set of values for the metadata tags along with the image numbers of
the image sets that match the values
"""
if len(tags) == 0:
# if there are no tags, all image sets match each other
return [MetadataGroup({}, self.get_image_numbers())]
#
# The flat_dictionary has a row of tag values as a key
#
flat_dictionary = {}
image_numbers = self.get_image_numbers()
values = [self.get_measurement(
IMAGE, "%s_%s" % (C_METADATA, tag), image_numbers)
for tag in tags]
for i, image_number in enumerate(image_numbers):
key = tuple([(k, v[i]) for k, v in zip(tags, values)])
if not flat_dictionary.has_key(key):
flat_dictionary[key] = []
flat_dictionary[key].append(image_number)
result = []
for row in flat_dictionary.keys():
tag_dictionary = dict(row)
result.append(MetadataGroup(tag_dictionary, flat_dictionary[row]))
return result
def match_metadata(self, features, values):
'''Match vectors of metadata values to existing measurements
This method finds the image sets that match each row in a vector
of metadata values. Imagine being given an image set with metadata
values of plate, well and site and annotations for each well
with metadata values of plate and well and annotation. You'd like
to match each annotation with all of the sites for it's well. This
method will return the image numbers that match.
The method can also be used to match images, for instance when
different illumination correction functions need to be matched
against plates or sites.
features - the measurement names for the incoming metadata
values - a sequence of vectors, one per feature, giving the
metadata values to be matched.
returns a sequence of vectors of image numbers of equal length
to the values. An exception is thrown if the metadata for more
than one row in the values matches the same image set unless the number
of values in each vector equals the number of image sets - in that case,
the vectors are assumed to be arranged in the correct order already.
'''
#
# Get image features populated by previous modules. If there are any,
# then we launch the desperate heuristics that attempt to match
# to them, either by order or by common metadata
#
image_set_count = len(self.get_image_numbers())
by_order = [[i+1] for i in range(len(values[0]))]
if image_set_count == 0:
return by_order
image_features = self.get_feature_names(IMAGE)
metadata_features = [x for x in image_features
if x.startswith(C_METADATA + "_")]
common_features = [x for x in metadata_features
if x in features]
if len(common_features) == 0:
if image_set_count > len(values[0]):
raise ValueError(
"The measurements and data have no metadata in common")
return by_order
#
# This reduces numberlike things to integers so that they can be
# more loosely matched.
#
def cast(x):
if isinstance(x,basestring) and x.isdigit():
return int(x)
return x
common_tags = [f[(len(C_METADATA)+1):] for f in common_features]
groupings = self.group_by_metadata(common_tags)
groupings = dict([(tuple([cast(d[f]) for f in common_tags]),
d.image_numbers)
for d in groupings])
if image_set_count == len(values[0]):
#
# Test whether the common features uniquely identify
# all image sets. If so, then we can match by metadata
# and that will be correct, even when the user wants to
# match by order (assuming the user really did match
# the metadata)
#
if any([len(v) != 1 for v in groupings.values()]):
return by_order
#
# Create a list of values that matches the common_features
#
result = []
vv = [values[features.index(c)] for c in common_features]
for i in range(len(values[0])):
key = tuple([cast(vvv[i]) for vvv in vv])
if not groupings.has_key(key):
raise ValueError(
"There was no image set whose metadata matched row %d.\n"
"Metadata values: " +
", ".join(["%s = %s" % (k, v)
for k,v in zip(common_features, key)]))
result.append(groupings[key])
return result
def agg_ignore_object(self, object_name):
"""Ignore objects (other than 'Image') if this returns true"""
if object_name in (EXPERIMENT, NEIGHBORS):
return True
def agg_ignore_feature(self, object_name, feature_name):
"""Return true if we should ignore a feature during aggregation"""
if self.agg_ignore_object(object_name):
return True
if self.hdf5_dict.has_feature(object_name, "SubObjectFlag"):
return True
return agg_ignore_feature(feature_name)
def compute_aggregate_measurements(self, image_set_number,
aggs=AGG_NAMES):
"""Compute aggregate measurements for a given image set
returns a dictionary whose key is the aggregate measurement name and
whose value is the aggregate measurement value
"""
d = {}
if len(aggs) == 0:
return d
for object_name in self.get_object_names():
if object_name == 'Image':
continue
for feature in self.get_feature_names(object_name):
if self.agg_ignore_feature(object_name, feature):
continue
feature_name = "%s_%s" % (object_name, feature)
values = self.get_measurement(object_name, feature,
image_set_number)
if values is not None:
values = values[np.isfinite(values)]
#
# Compute the mean and standard deviation
#
if AGG_MEAN in aggs:
mean_feature_name = get_agg_measurement_name(
AGG_MEAN, object_name, feature)
mean = values.mean() if values is not None else np.NaN
d[mean_feature_name] = mean
if AGG_MEDIAN in aggs:
median_feature_name = get_agg_measurement_name(
AGG_MEDIAN, object_name, feature)
median = np.median(values) if values is not None else np.NaN
d[median_feature_name] = median
if AGG_STD_DEV in aggs:
stdev_feature_name = get_agg_measurement_name(
AGG_STD_DEV, object_name, feature)
stdev = values.std() if values is not None else np.NaN
d[stdev_feature_name] = stdev
return d
def load_measurements(filename, dest_file = None, can_overwrite = False,
run_name = None):
'''Load measurements from an HDF5 file
filename - path to file containing the measurements or file-like object
if .mat
dest_file - path to file to be created. This file is used as the backing
store for the measurements.
can_overwrite - True to allow overwriting of existing measurements (not
supported any longer)
run_name - name of the run (an HDF file can contain measurements
from multiple runs). By default, takes the last.
returns a Measurements object
'''
HDF5_HEADER = (chr(137) + chr(72) + chr(68) + chr(70) + chr(13) + chr(10) +
chr (26) + chr(10))
if hasattr(filename, "seek"):
filename.seek(0)
header = filename.read(len(HDF5_HEADER))
filename.seek(0)
else:
fd = open(filename, "rb")
header = fd.read(len(HDF5_HEADER))
fd.close()
if header == HDF5_HEADER:
f, top_level = get_top_level_group(filename)
try:
if VERSION in f.keys():
if run_name is not None:
top_level = top_level[run_name]
else:
# Assume that the user wants the last one
last_key = sorted(top_level.keys())[-1]
top_level = top_level[last_key]
m = Measurements(filename=dest_file, copy = top_level)
return m
except:
logger.error("Error loading HDF5 %s", filename, exc_info=True)
finally:
f.close()
else:
m = Measurements(filename = dest_file)
m.load(filename)
return m
class MetadataGroup(dict):
"""A set of metadata tag values and the image set indexes that match
The MetadataGroup object represents a group of image sets that
have the same values for a given set of tags. For instance, if an
experiment has metadata tags of "Plate", "Well" and "Site" and
we form a metadata group of "Plate" and "Well", then each metadata
group will have image set indexes of the images taken of a particular
well
"""
def __init__(self, tag_dictionary, image_numbers):
super(MetadataGroup, self).__init__(tag_dictionary)
self.__image_numbers = image_numbers
@property
def image_numbers(self):
return self.__image_numbers
def __setitem__(self, tag, value):
raise NotImplementedError("The dictionary is read-only")
def find_metadata_tokens(pattern):
"""Return a list of strings which are the metadata token names in a pattern
pattern - a regexp-like pattern that specifies how to find
metadata in a string. Each token has the form:
"(?<METADATA_TAG>...match-exp...)" (matlab-style) or
"\g<METADATA_TAG>" (Python-style replace)
"(?P<METADATA_TAG>...match-exp..)" (Python-style search)
"""
result = []
while True:
m = re.search('\\(\\?[<](.+?)[>]', pattern)
if not m:
m = re.search('\\\\g[<](.+?)[>]', pattern)
if not m:
m = re.search('\\(\\?P[<](.+?)[>]', pattern)
if not m:
break
result.append(m.groups()[0])
pattern = pattern[m.end():]
return result
def extract_metadata(pattern, text):
"""Return a dictionary of metadata extracted from the text
pattern - a regexp that specifies how to find
metadata in a string. Each token has the form:
"\(?<METADATA_TAG>...match-exp...\)" (matlab-style) or
"\(?P<METADATA_TAG>...match-exp...\)" (Python-style)
text - text to be searched
We do a little fixup in here to change Matlab searches to Python ones
before executing.
"""
# Convert Matlab to Python
orig_pattern = pattern
pattern = re.sub('(\\(\\?)([<].+?[>])', '\\1P\\2', pattern)
match = re.search(pattern, text)
if match:
return match.groupdict()
else:
raise ValueError("Metadata extraction failed: regexp '%s' does not match '%s'" % (orig_pattern, text))
def is_well_row_token(x):
'''True if the string represents a well row metadata tag'''
return x.lower() in ("wellrow", "well_row", "row")
def is_well_column_token(x):
'''true if the string represents a well column metadata tag'''
return x.lower() in ("wellcol", "well_col", "wellcolumn", "well_column",
"column", "col")
def get_agg_measurement_name(agg, object_name, feature):
'''Return the name of an aggregate measurement
agg - one of the names in AGG_NAMES, like AGG_MEAN
object_name - the name of the object that we're aggregating
feature - the name of the object's measurement
'''
return "%s_%s_%s" % (agg, object_name, feature)
def agg_ignore_feature(feature_name):
'''Return True if the feature is one to be ignored when aggregating'''
if feature_name.startswith('Description_'):
return True
if feature_name.startswith('ModuleError_'):
return True
if feature_name.startswith('TimeElapsed_'):
return True
if feature_name == "Number_Object_Number":
return True
return False
class RelationshipKey:
def __init__(self, module_number, group_number, relationship,
object_name1, object_name2):
self.module_number = module_number
self.group_number = group_number
self.relationship = relationship
self.object_name1 = object_name1
self.object_name2 = object_name2
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