From 1cd0a9e91f26e48fb3a96781f9e4981cc634778d Mon Sep 17 00:00:00 2001 From: sparkvilla Date: Thu, 15 Feb 2018 08:36:34 +0000 Subject: [PATCH 1/8] start popcon feature --- tmlib/workflow/dependencies.py | 4 +- tmlib/workflow/popcon/__init__.py | 47 + tmlib/workflow/popcon/api.py | 158 + tmlib/workflow/popcon/args.py | 45 + tmlib/workflow/popcon/cli.py | 41 + tmlib/workflow/popcon/lcc.py | 100 + .../popcon/test/.popcon_test_append.py.swp | Bin 0 -> 16384 bytes tmlib/workflow/popcon/test/DiegoLCC.csv | 86285 ++++++++++++++++ tmlib/workflow/popcon/test/get_centroids.py | 45 + tmlib/workflow/popcon/test/lcc.py | 121 + tmlib/workflow/popcon/test/lcc1.py | 121 + tmlib/workflow/popcon/test/lcc_fromfile.py | 79 + tmlib/workflow/popcon/test/popcon_test.py | 40 + tmlib/workflow/popcon/test/popcon_test1.py | 125 + .../popcon/test/popcon_test_addtodb.py | 103 + .../popcon/test/popcon_test_append.py | 113 + .../popcon/test/sql_append_tofeature_values | 1 + 17 files changed, 87426 insertions(+), 2 deletions(-) create mode 100644 tmlib/workflow/popcon/__init__.py create mode 100644 tmlib/workflow/popcon/api.py create mode 100644 tmlib/workflow/popcon/args.py create mode 100644 tmlib/workflow/popcon/cli.py create mode 100644 tmlib/workflow/popcon/lcc.py create mode 100644 tmlib/workflow/popcon/test/.popcon_test_append.py.swp create mode 100644 tmlib/workflow/popcon/test/DiegoLCC.csv create mode 100644 tmlib/workflow/popcon/test/get_centroids.py create mode 100644 tmlib/workflow/popcon/test/lcc.py create mode 100644 tmlib/workflow/popcon/test/lcc1.py create mode 100644 tmlib/workflow/popcon/test/lcc_fromfile.py create mode 100644 tmlib/workflow/popcon/test/popcon_test.py create mode 100644 tmlib/workflow/popcon/test/popcon_test1.py create mode 100644 tmlib/workflow/popcon/test/popcon_test_addtodb.py create mode 100644 tmlib/workflow/popcon/test/popcon_test_append.py create mode 100644 tmlib/workflow/popcon/test/sql_append_tofeature_values diff --git a/tmlib/workflow/dependencies.py b/tmlib/workflow/dependencies.py index 2df50d23..c0b8a677 100644 --- a/tmlib/workflow/dependencies.py +++ b/tmlib/workflow/dependencies.py @@ -111,7 +111,7 @@ class CanonicalWorkflowDependencies(WorkflowDependencies): 'pyramid_creation': ['illuminati'], 'image_analysis': - ['jterator'] + ['jterator','popcon'] } #: collections.OrderedDict[str, Set[str]]: dependencies between workflow stages @@ -170,7 +170,7 @@ class MultiplexingWorkflowDependencies(CanonicalWorkflowDependencies): 'pyramid_creation': ['illuminati'], 'image_analysis': - ['jterator'] + ['jterator','popcon'] } diff --git a/tmlib/workflow/popcon/__init__.py b/tmlib/workflow/popcon/__init__.py new file mode 100644 index 00000000..a669963a --- /dev/null +++ b/tmlib/workflow/popcon/__init__.py @@ -0,0 +1,47 @@ +# TmLibrary - TissueMAPS library for distibuted image analysis routines. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published +# by the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. +# +# You should have received a copy of the GNU Affero General Public License +# along with this program. If not, see . +'''Workflow step for building and running image analysis pipelines. + +The objective of image analysis is to identify meaningful objects (e.g. "cells") +in the images and extract features for the identified objects. +The `jterator` step provides users an interface to combine individual modules +available via the `JtModules repository `_ +into custom image analysis *pipelines* in a +`CellProfiler `_-like manner. +Outlines of segmented objects and extracted feature values can be stored for +further analyis or interactive visualization in the viewer. + +''' +from tmlib import __version__ + +__dependencies__ = {'jterator'} + +__fullname__ = 'Image analysis pipeline engine' + +__description__ = ( + 'Application of a sequence of algorithms to a set of wells ' + 'to calculate population context for the identified objects.' +) + +__logo__ = ''' + + __ __ __ __ __ + |__)/ \|__)/ / \|\ | + | \__/| \__\__/| \| + {name} ({version}) + {fullname} + https://github.com/TissueMAPS/TmLibrary +'''.format(name=__name__, version=__version__, fullname=__fullname__) + diff --git a/tmlib/workflow/popcon/api.py b/tmlib/workflow/popcon/api.py new file mode 100644 index 00000000..6989171e --- /dev/null +++ b/tmlib/workflow/popcon/api.py @@ -0,0 +1,158 @@ +# TmLibrary - TissueMAPS library for distibuted image analysis routines. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published +# by the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. +# +# You should have received a copy of the GNU Affero General Public License +# along with this program. If not, see . +import os +import re +import sys +import shutil +import logging +import subprocess +import numpy as np +import pandas as pd +import collections +import shapely.geometry +import shapely.ops +from cached_property import cached_property +from sqlalchemy.orm.exc import NoResultFound +from sqlalchemy.sql import func +from sqlalchemy.dialects.postgresql import FLOAT +from psycopg2 import ProgrammingError +from psycopg2.extras import Json +from gc3libs.quantity import Duration, Memory + +import tmlib.models as tm +from tmlib.utils import autocreate_directory_property +from tmlib.utils import flatten +from tmlib.readers import TextReader +from tmlib.readers import ImageReader +from tmlib.writers import TextWriter +from tmlib.models.types import ST_GeomFromText +from tmlib.workflow.api import WorkflowStepAPI +from tmlib.errors import PipelineDescriptionError +from tmlib.errors import JobDescriptionError +from tmlib.workflow.jobs import SingleRunPhase +from tmlib.workflow.jterator.jobs import DebugRunJob +from tmlib.workflow import register_step_api +from tmlib import cfg + +from tmlib.workflow.popcon.lcc import LocalCC + +logger = logging.getLogger(__name__) + + +@register_step_api('popcon') +class CrowdingEstimator(WorkflowStepAPI): + + '''Class for running image analysis pipelines.''' + + def __init__(self, experiment_id): + ''' + Parameters + ---------- + experiment_id: int + ID of the processed experiment + ''' + super(CrowdingEstimator, self).__init__(experiment_id) + + def create_run_batches(self, args): + '''Creates job descriptions for parallel computing. + + Parameters + ---------- + args: tmlib.workflow.jterator.args.BatchArguments + step-specific arguments + + Returns + ------- + generator + job descriptions + ''' + with tm.utils.ExperimentSession(self.experiment_id) as session: + # Distribute wells randomly. Thereby we achieve a certain level + # of load balancing in case wells have different number of cells, + # for example. + wells = session.query(tm.Well.id).order_by(func.random()).all() + well_ids = [w.id for w in wells] + batches = self._create_batches(well_ids, args.batch_size) + for j, batch in enumerate(batches): + yield { + 'id': j + 1, # job IDs are one-based! + 'well_ids': batch, + 'extract_object': args.extract_object, + 'assign_object': args.assign_object + } + + + def run_job(self, batch, assume_clean_state): + '''Runs the pipeline, i.e. executes modules sequentially. After + Parameters + ---------- + batch: dict + job description + assume_clean_state: bool, optional + assume that output of previous runs has already been cleaned up + ''' + + for well_id in batch['well_ids']: + logger.info('process well %d', well_id) + with tm.utils.ExperimentSession(self.experiment_id) as session: +# sites = session.query(tm.Site.id, tm.Site.height, tm.Site.width, tm.Site.y, tm.Site.x).\ +# filter_by(well_id=well_id).all() +# wellY = sites_per_well[0][1]*len(set([i[3] for i in sites_per_well])) +# wellX = sites_per_well[0][2]*len(set([i[4]for i in sites_per_well])) + + extract_mapobject_type_id = session.query(tm.MapobjectType.id).\ + filter_by(name=batch['extract_object']).one()[0] + extract_seg_layer_id = session.query(tm.SegmentationLayer.id).\ + filter_by(mapobject_type_id=mapobject_type_id).one()[0] + extract_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ + filter_by(segmentation_layer_id=extract_seg_layer_id).all() + assign_mapobject_type_id = session.query(tm.MapobjectType.id).\ + filter_by(name=batch['assign_object']).one()[0] + assign_seg_layer_id = session.query(tm.SegmentationLayer.id).\ + filter_by(mapobject_type_id=assign_mapobject_type_id).one()[0] + assign_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_kye).\ + filter_by(segmentation_layer_id=assign_seg_layer_id).all() + + logger.info('Calculating LCC for well_id %s', well_id) + lcc_extract = LocalCC(extract_centroids) + real_lcc = lcc_extract.real_distances() + random_lcc = lcc_extract.random_distances() + lcc = lcc_extract.get_lcc(real_lcc,random_lcc) + + lcc_assign = Local(assign_centroids) + + # remap y,x coordinates of extract object with mapobject_id + # of assign object + lcc_extract.df['mapobject_id'] = lcc_assign.df['mapobject_id'] + + + feature_name = batch['extract_object']+'_{}_lcc'.format(well_id) + feature = session.get_or_create( + tm.Feature, name=feature_name, + mapobject_type_id=assign_mapobject_type_id, + is_aggregate=False) + + + for index, row in lcc_extract.df.iterrows(): + feature_value = session.query(tm.FeatureValues).filter_by(mapobject_id= int(row['mapobject_id']) ).one() + session.append_value(feature_value,str(feature.id),row['lcc'].astype(str)) + session.commit() + + + + + def collect_job_output(self, batch): + pass + diff --git a/tmlib/workflow/popcon/args.py b/tmlib/workflow/popcon/args.py new file mode 100644 index 00000000..16a2901e --- /dev/null +++ b/tmlib/workflow/popcon/args.py @@ -0,0 +1,45 @@ +# TmLibrary - TissueMAPS library for distibuted image analysis routines. +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published +# by the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. +# +# You should have received a copy of the GNU Affero General Public License +# along with this program. If not, see . +from tmlib.workflow.args import Argument +from tmlib.workflow.args import BatchArguments +from tmlib.workflow.args import SubmissionArguments +from tmlib.workflow import register_step_batch_args +from tmlib.workflow import register_step_submission_args + + +@register_step_batch_args('popcon') +class PopConBatchArguments(BatchArguments): + + extract_object = Argument( + type=str, short_flag='e', + help='Object that should be processed, e.g. Nuclei' + ) + + assign_object = Argument( + type=str, short_flag='a', + help='Object that the extract_object get assigned to, e.g. Cells' + ) + + + batch_size = Argument( + type=int, help='number of wells that should be processed per job', + default=100, flag='batch-size', short_flag='b' + ) + + +@register_step_submission_args('popcon') +class PopConSubmissionArguments(SubmissionArguments): + + pass diff --git a/tmlib/workflow/popcon/cli.py b/tmlib/workflow/popcon/cli.py new file mode 100644 index 00000000..07303f04 --- /dev/null +++ b/tmlib/workflow/popcon/cli.py @@ -0,0 +1,41 @@ +# TmLibrary - TissueMAPS library for distibuted image analysis routines. +# Copyright (C) 2016 Markus D. Herrmann, University of Zurich and Robin Hafen +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published +# by the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. +# +# You should have received a copy of the GNU Affero General Public License +# along with this program. If not, see . +import logging + +from tmlib.utils import assert_type +from tmlib.log import map_logging_verbosity +from tmlib.workflow.cli import WorkflowStepCLI +from tmlib.workflow.cli import climethod +from tmlib.workflow.args import Argument + +logger = logging.getLogger(__name__) + + +class PopCon(WorkflowStepCLI): + + @assert_type(api_instance='tmlib.workflow.popcon.api.CrowdingEstimator') + def __init__(self, api_instance, verbosity): + ''' + Parameters + ---------- + api_instance: tmlib.workflow.jterator.api.ImageAnalysisPipelineEngine + instance of API class to which processing is delegated + verbosity: int + logging verbosity + ''' + super(PopCon, self).__init__(api_instance, verbosity) + + diff --git a/tmlib/workflow/popcon/lcc.py b/tmlib/workflow/popcon/lcc.py new file mode 100644 index 00000000..3e283b80 --- /dev/null +++ b/tmlib/workflow/popcon/lcc.py @@ -0,0 +1,100 @@ +import tmlib.models as tm # from within TmLibrary/tmlib directory +from shapely import wkb +import pandas as pd +import numpy as np +import math +from scipy.spatial import distance + + +class LocalCC(object): + '''Class for calculatiing local cell crowding per single well. + ''' + @staticmethod + def _get_yx(element): + ''' Helper. Takes in a WKBElement. + Return a shapely.geometry.point.Point object + ''' + return wkb.loads(bytes(element.data)) + + @staticmethod + def _get_df(np_centroids): + '''Helper. Takes numpy centroids array + Return a sorted dataframe by site and label + ''' + data = np.zeros( (len(np_centroids),5) ) + headers = ['y','x','mapobject_id','label','site'] + data[:] = np_centroids + data_df = pd.DataFrame(data, columns=headers) + df = data_df.sort_values(['site', 'label']) + return df + + def __init__(self, centroids): + ''' + Parameters + ---------- + centroids: list of tuples (centroid WKB element, mapobject_id, label,site); + i.e. query objects from MapobjectSegmentation table + ''' + self.centroids = centroids + + self.centroids_coordinates = [(self._get_yx(element[0]).y,self._get_yx(element[0]).x, int(element[1]), int(element[2]), int(element[3])) for element in self.centroids] + + self.df = self._get_df(self.centroids_coordinates) + self.yx_coordinates = np.asarray((self.df['y'],self.df['x'])).transpose() + self.wellY = np.floor(self.df['y'].min()) + self.wellX = np.ceil(self.df['x'].max()) + self.well_diagonal = np.round(math.sqrt(self.wellX**2+self.wellY**2)) + + + + def real_distances(self): + ''' + Returns + ------- + numpy array of distances from real positions + ''' + real_dists = distance.cdist(self.yx_coordinates, self.yx_coordinates, 'euclidean') + real_masked = np.ma.masked_where(real_dists==0,real_dists) # mask 0 values + real_masked_divide = np.divide(self.well_diagonal, real_masked) + return real_masked_divide.filled(fill_value=0) + + def random_distances(self): + ''' + Returns + ------- + numpy array of distances from random positions + ''' + rand_dists= list() + + for yx_real in self.yx_coordinates: + y_rand= np.random.uniform(self.wellY,0,len(self.yx_coordinates)-1) + x_rand= np.random.uniform(0,self.wellX,len(self.yx_coordinates)-1) + yx_coordinates_random = np.concatenate( (y_rand[:,np.newaxis],x_rand[:,np.newaxis]), axis=1) + + rand_dist = np.divide(self.well_diagonal, distance.cdist(np.transpose(yx_real[:,np.newaxis]), yx_coordinates_random, 'euclidean') ) + rand_dists.append(np.squeeze(rand_dist)) + return np.asarray(rand_dists) + + + def get_lcc(self,real_dists,random_dists): + ''' + Parameters + ---------- + real_dists: numpy arrays of real distances + random_dists: numpy array of random distances + + Returns + ------- + a numpy array i.e. a LCC value and mapobject_id for centroid + ''' + sum_real = np.sum(real_dists, axis=1) + sum_random = np.sum(random_dists, axis=1) + lcc = sum_real-sum_random + + self.df['lcc'] = pd.Series(lcc) + return self.df + # lcc[lcc[:,1].argsort()] sort array based on mapobject_id + # lcc[lcc[:,0].argsort()] sort array based on lcc value + + + diff --git a/tmlib/workflow/popcon/test/.popcon_test_append.py.swp b/tmlib/workflow/popcon/test/.popcon_test_append.py.swp new file mode 100644 index 0000000000000000000000000000000000000000..cc886487ade4d6eabb3ce3183e17c9bc0405577b GIT binary patch literal 16384 zcmeHOQEcQ!8J=ESAV8r4@j?+duswE{Zk*g*#g#yiLhjPrQ<96ao2J(gtXX^3>pR=y 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query_mapobjectsegmentation(object_name): + object_name_id = + + +def db_interact(): + with tm.utils.ExperimentSession(77) as session: + wells = session.query(tm.Well.id,tm.Well.name).all() + for well_id, well_name in wells: + sites = session.query(tm.Site.id, tm.Site.height, tm.Site.width, tm.Site.y, tm.Site.x).filter_by(well_id=well_id).all() + wellY = sites[0][1]*len(set([i[3] for i in sites])) + wellX = sites[0][2]*len(set([i[4]for i in sites])) + well_diagonal = math.sqrt(wellX**2+wellY**2) + object_id = session.query(tm.MapobjectType.id).filter_by(name="Nuclei").one()[0] + + seg_layer_id = session.query(tm.SegmentationLayer.id).filter_by(mapobject_type_id=object_id).one()[0] + centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label, tm.MapobjectSegmentation.partition_key).filter_by(segmentation_layer_id=seg_layer_id).all() + session.expunge_all() + return wellY, wellX, centroids + # check whether features for object already exist +# features = session.query(tm.Feature).filter_by(mapobject_type_id=object_id).all() +# if not features: +# print 'No features found for object_id={}'.format(object_id) +# last_feature_id = session.query(tm.Feature)./ +# order_by(tm.Feature.id.desc()).first() +# feature_name = 'lcc_'+well +# print 'add feature "%s"', feature_name +# feature = session.get_or_create( +# tm.Feature, name=feature_name, +# mapobject_type_id=mapobject_type_ids[obj_name], +# is_aggregate=False +# ) + +if __name__ == '__main__': + + wellY,wellX,centroids = db_interact() + diff --git a/tmlib/workflow/popcon/test/lcc.py b/tmlib/workflow/popcon/test/lcc.py new file mode 100644 index 00000000..d081c9e6 --- /dev/null +++ b/tmlib/workflow/popcon/test/lcc.py @@ -0,0 +1,121 @@ +import tmlib.models as tm # from within TmLibrary/tmlib directory +from shapely import wkb +import pandas as pd +import numpy as np +import math +from scipy.spatial import distance + + +class LocalCC(object): + '''Class for calculatiing local cell crowding per single well. + ''' + @staticmethod + def _get_yx(element): + ''' Helper. Takes in a WKBElement. + Return a shapely.geometry.point.Point object + ''' + return wkb.loads(bytes(element.data)) + +# @staticmethod +# def _get_df(np_centroids): +# '''Helper. Takes numpy centroids array +# Return a sorted dataframe by site and label +# ''' +# data = np.zeros( (len(np_centroids),5) ) +# data[:] = np_centroids +# headers = ['y','x','mapobject_id','label','site'] +# data_df = pd.DataFrame(data, columns=headers) +# df = data_df.sort_values(['site', 'label']) +# return df + + def __init__(self, centroids): + ''' + Parameters + ---------- + centroids: list of tuples (centroid WKB element, mapobject_id, label,site); + i.e. query objects from MapobjectSegmentation table + ''' + self.centroids = centroids + + @property + def centroids_coordinates(self): + return [(self._get_yx(element[0]).y,self._get_yx(element[0]).x, element[1], element[2], element[3]) for element in self.centroids] + + @property + def df(self): + data = np.zeros((len(self.centroids_coordinates),5)) + data[:] = self.coordinates_coordinates + headers = ['y','x','mapobject_id','label','site'] + data_df = pd.DataFrame(data, columns=headers) + df = data_df.sort_values(['site', 'label']) + return df + + #self.df = self._get_df(self.centroids_coordinates) + + @property + def yx_coordinates(self): + return np.asarray((self.df['y'],self.df['x'])).transpose() + + @property + def wellY(self): + return np.floor(self.df['y'].min()) + @property + def wellX(self): + return np.ceil(self.df['x'].max()) + + @property + def well_diagonal(self): + return np.round(math.sqrt(self.wellX**2+self.wellY**2)) + + + + def real_distances(self): + ''' + Returns + ------- + numpy array of distances from real positions + ''' + real_dists = distance.cdist(self.yx_coordinates, self.yx_coordinates, 'euclidean') + real_masked = np.ma.masked_where(real_dists==0,real_dists) # mask 0 values + real_masked_divide = np.divide(self.well_diagonal, real_masked) + return real_masked_divide.filled(fill_value=0) + + def random_distances(self): + ''' + Returns + ------- + numpy array of distances from random positions + ''' + rand_dists= list() + + for yx_real in self.yx_coordinates: + y_rand= np.random.uniform(self.wellY,0,len(self.yx_coordinates)-1) + x_rand= np.random.uniform(0,self.wellX,len(self.yx_coordinates)-1) + yx_coordinates_random = np.concatenate( (y_rand[:,np.newaxis],x_rand[:,np.newaxis]), axis=1) + + rand_dist = np.divide(self.well_diagonal, distance.cdist(np.transpose(yx_real[:,np.newaxis]), yx_coordinates_random, 'euclidean') ) + rand_dists.append(np.squeeze(rand_dist)) + return np.asarray(rand_dists) + + + def get_lcc(self,real_dists,random_dists): + ''' + Parameters + ---------- + real_dists: numpy arrays of real distances + random_dists: numpy array of random distances + + Returns + ------- + a numpy array i.e. a LCC value and mapobject_id for centroid + ''' + sum_real = np.sum(real_dists, axis=1) + sum_random = np.sum(random_dists, axis=1) + lcc = sum_real-sum_random + + #lcc_final = np.concatenate( (lcc[:,np.newaxis],self.mapobject_ids[:,np.newaxis] ,self.label[:,np.newaxis],self.sites[:,np.newaxis]) ,axis = 1) + # lcc[lcc[:,1].argsort()] sort array based on mapobject_id + # lcc[lcc[:,0].argsort()] sort array based on lcc value + return lcc + + diff --git a/tmlib/workflow/popcon/test/lcc1.py b/tmlib/workflow/popcon/test/lcc1.py new file mode 100644 index 00000000..01669631 --- /dev/null +++ b/tmlib/workflow/popcon/test/lcc1.py @@ -0,0 +1,121 @@ +import tmlib.models as tm # from within TmLibrary/tmlib directory +from shapely import wkb +import pandas as pd +import numpy as np +import math +from scipy.spatial import distance + + +class LocalCC(object): + '''Class for calculatiing local cell crowding per single well. + ''' + @staticmethod + def _get_yx(element): + ''' Helper. Takes in a WKBElement. + Return a shapely.geometry.point.Point object + ''' + return wkb.loads(bytes(element.data)) + +# @staticmethod +# def _get_df(np_centroids): +# '''Helper. Takes numpy centroids array +# Return a sorted dataframe by site and label +# ''' +# data = np.zeros( (len(np_centroids),5) ) +# data[:] = np_centroids +# headers = ['y','x','mapobject_id','label','site'] +# data_df = pd.DataFrame(data, columns=headers) +# df = data_df.sort_values(['site', 'label']) +# return df + + def __init__(self, centroids): + ''' + Parameters + ---------- + centroids: list of tuples (centroid WKB element, mapobject_id, label,site); + i.e. query objects from MapobjectSegmentation table + ''' + self.centroids = centroids + + @property + def centroids_coordinates(self): + return [(self._get_yx(element[0]).y,self._get_yx(element[0]).x, element[1], element[2], element[3]) for element in self.centroids] + + @property + def df(self): + data = np.zeros(len(self.centroids_coordinates),5)) + data[:] = self.coordinates_coordinates + headers = ['y','x','mapobject_id','label','site'] + data_df = pd.DataFrame(data, columns=headers) + df = data_df.sort_values(['site', 'label']) + return df + + #self.df = self._get_df(self.centroids_coordinates) + +# @property +# def yx_coordinates(self): +# return np.asarray((self.df['y'],self.df['x'])).transpose() +# +# @property +# def wellY(self): +# return np.floor(self.df['y'].min()) +# @property +# def wellX(self): +# return np.ceil(self.df['x'].max()) +# +# @property +# def well_diagonal(self) +# return np.round(math.sqrt(self.wellX**2+self.wellY**2)) +# +# +# +# def real_distances(self): +# ''' +# Returns +# ------- +# numpy array of distances from real positions +# ''' +# real_dists = distance.cdist(self.yx_coordinates, self.yx_coordinates, 'euclidean') +# real_masked = np.ma.masked_where(real_dists==0,real_dists) # mask 0 values +# real_masked_divide = np.divide(self.well_diagonal, real_masked) +# return real_masked_divide.filled(fill_value=0) +# +# def random_distances(self): +# ''' +# Returns +# ------- +# numpy array of distances from random positions +# ''' +# rand_dists= list() +# +# for yx_real in self.yx_coordinates: +# y_rand= np.random.uniform(self.wellY,0,len(self.yx_coordinates)-1) +# x_rand= np.random.uniform(0,self.wellX,len(self.yx_coordinates)-1) +# yx_coordinates_random = np.concatenate( (y_rand[:,np.newaxis],x_rand[:,np.newaxis]), axis=1) +# +# rand_dist = np.divide(self.well_diagonal, distance.cdist(np.transpose(yx_real[:,np.newaxis]), yx_coordinates_random, 'euclidean') ) +# rand_dists.append(np.squeeze(rand_dist)) +# return np.asarray(rand_dists) +# +# +# def get_lcc(self,real_dists,random_dists): +# ''' +# Parameters +# ---------- +# real_dists: numpy arrays of real distances +# random_dists: numpy array of random distances +# +# Returns +# ------- +# a numpy array i.e. a LCC value and mapobject_id for centroid +# ''' +# sum_real = np.sum(real_dists, axis=1) +# sum_random = np.sum(random_dists, axis=1) +# lcc = sum_real-sum_random +# +# #lcc_final = np.concatenate( (lcc[:,np.newaxis],self.mapobject_ids[:,np.newaxis] ,self.label[:,np.newaxis],self.sites[:,np.newaxis]) ,axis = 1) +# # lcc[lcc[:,1].argsort()] sort array based on mapobject_id +# # lcc[lcc[:,0].argsort()] sort array based on lcc value +# return lcc +# + diff --git a/tmlib/workflow/popcon/test/lcc_fromfile.py b/tmlib/workflow/popcon/test/lcc_fromfile.py new file mode 100644 index 00000000..eb9983f5 --- /dev/null +++ b/tmlib/workflow/popcon/test/lcc_fromfile.py @@ -0,0 +1,79 @@ +import tmlib.models as tm # from within TmLibrary/tmlib directory +from shapely import wkb +import pandas as pd +import numpy as np +import math +from scipy.spatial import distance + + +class LocalCC(object): + '''Class for calculatiing local cell crowding per single well. + ''' + def __init__(self, yx_coordinates): + ''' + Parameters + ---------- + yx_coordinates: y,x numpy array + ''' + + self.yx_coordinates = yx_coordinates + + self.wellY = np.ceil(self.yx_coordinates[:,0].max()) + + self.wellX = np.ceil(self.yx_coordinates[:,0].max()) + + + self.well_diagonal = np.round(math.sqrt(self.wellX**2+self.wellY**2)) + + + + def real_distances(self): + ''' + Returns + ------- + numpy array of distances from real positions + ''' + real_dists = distance.cdist(self.yx_coordinates, self.yx_coordinates, 'euclidean') + real_masked = np.ma.masked_where(real_dists==0,real_dists) # mask 0 values + real_masked_divide = np.divide(self.well_diagonal, real_masked) + return real_masked_divide.filled(fill_value=0) + + def random_distances(self): + ''' + Returns + ------- + numpy array of distances from random positions + ''' + rand_dists= list() + + for yx_real in self.yx_coordinates: + y_rand= np.random.uniform(0,self.wellY,len(self.yx_coordinates)-1) + x_rand= np.random.uniform(0,self.wellX,len(self.yx_coordinates)-1) + yx_coordinates_random = np.concatenate( (y_rand[:,np.newaxis],x_rand[:,np.newaxis]), axis=1) + + rand_dist = np.divide(self.well_diagonal, distance.cdist(np.transpose(yx_real[:,np.newaxis]), yx_coordinates_random, 'euclidean') ) + rand_dists.append(np.squeeze(rand_dist)) + return np.asarray(rand_dists) + + + def get_lcc(self,real_dists,random_dists): + ''' + Parameters + ---------- + real_dists: numpy arrays of real distances + random_dists: numpy array of random distances + + Returns + ------- + a numpy array i.e. a LCC value and mapobject_id for centroid + ''' + sum_real = np.sum(real_dists, axis=1) + sum_random = np.sum(random_dists, axis=1) + lcc = sum_real-sum_random + + #lcc_final = np.concatenate( (lcc[:,np.newaxis],self.mapobject_ids[:,np.newaxis] ,self.label[:,np.newaxis],self.sites[:,np.newaxis]) ,axis = 1) + # lcc[lcc[:,1].argsort()] sort array based on mapobject_id + # lcc[lcc[:,0].argsort()] sort array based on lcc value + return lcc + + diff --git a/tmlib/workflow/popcon/test/popcon_test.py b/tmlib/workflow/popcon/test/popcon_test.py new file mode 100644 index 00000000..31488d2e --- /dev/null +++ b/tmlib/workflow/popcon/test/popcon_test.py @@ -0,0 +1,40 @@ +# Code tried in ipython to get the centroid coordinate of Cells object (id=5) for experiment 77 + +import tmlib.models as tm # from within TmLibrary/tmlib directory +from shapely import wkb +import pandas as pd +import numpy as np +import math +from scipy.spatial import distance +from sqlalchemy.sql import func + + + +with tm.utils.ExperimentSession(77) as session: + wells = session.query(tm.Well.id,tm.Well.name).all() + # well_ids = [w.id for w in wells] + segmentation_layers = session.query(tm.SegmentationLayer).all() + feature_name = 'Nuclei_{}_lcc'.format(str(3)) + feature = session.get_or_create( + tm.Feature, name=feature_name, + mapobject_type_id=6, + is_aggregate=False) + feature_value = session.query(tm.FeatureValues.values).filter_by(mapobject_id=943).one() + + +# for well_id in wells_id: +# sites_per_well = session.query(tm.Site.id, tm.Site.height, tm.Site.width, tm.Site.y, tm.Site.x).filter_by(well_id=well_id).all() +# well_to_sites_map[well_id[0]] = sites_per_well + +# wellY = sites_per_well[0][1]*len(set([i[3] for i in sites_per_well])) +# wellX = sites_per_well[0][2]*len(set([i[4]for i in sites_per_well])) + +# nuclei_id = session.query(tm.MapobjectType.id).filter_by(name="Nuclei").one()[0] +# seg_layer_id = session.query(tm.SegmentationLayer.id).filter_by(mapobject_type_id=nuclei_id).one()[0] +# centroids = session.query(tm.MapobjectSegmentation.partition_key, tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label).filter_by(segmentation_layer_id=seg_layer_id).all() + session.expunge_all() # use this only if you want objects produced by querying a session to be usable outside the scope of the session + + + +#if __name__ == '__main__': + diff --git a/tmlib/workflow/popcon/test/popcon_test1.py b/tmlib/workflow/popcon/test/popcon_test1.py new file mode 100644 index 00000000..1e912269 --- /dev/null +++ b/tmlib/workflow/popcon/test/popcon_test1.py @@ -0,0 +1,125 @@ +# TmLibrary - TissueMAPS library for distibuted image analysis routines. +# Copyright (C) 2016 Markus D. Herrmann, University of Zurich and Robin Hafen +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published +# by the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. +# +# You should have received a copy of the GNU Affero General Public License +# along with this program. If not, see . +import os +import re +import sys +import shutil +import logging +import subprocess +import numpy as np +import pandas as pd +import collections +import shapely.geometry +import shapely.ops +from cached_property import cached_property +from sqlalchemy.orm.exc import NoResultFound +from sqlalchemy.sql import func +from sqlalchemy.dialects.postgresql import FLOAT +from psycopg2 import ProgrammingError +from psycopg2.extras import Json +from gc3libs.quantity import Duration, Memory + +import tmlib.models as tm +from tmlib.utils import autocreate_directory_property +from tmlib.utils import flatten +from tmlib.readers import TextReader +from tmlib.readers import ImageReader +from tmlib.writers import TextWriter +from tmlib.models.types import ST_GeomFromText +from tmlib.workflow.api import WorkflowStepAPI +from tmlib.errors import PipelineDescriptionError +from tmlib.errors import JobDescriptionError +from tmlib.workflow.jobs import SingleRunPhase +from tmlib.workflow.jterator.jobs import DebugRunJob +from tmlib.workflow import register_step_api +from tmlib import cfg + +#from tmlib.workflow.popcon.lcc import LocalCC + +logger = logging.getLogger(__name__) + +batch = {'well_ids': [3], + 'extract_object': 'Nuclei', + 'assign_object': 'Cells'} + +def _append_value(session, mapobject_id, key, val): +# if not isinstance(instance, FeatureValues): +# raise TypeError( +# 'Object must have type tmlib.models.feature.FeatureValues' +# ) + connection = session.get_bind() + connection.execute('''SELECT master_modify_multiple_shards('UPDATE feature_values SET values = values || hstore(''key'',''val'') WHERE mapobject_id = mapobject_id');''', {'mapobject_id':mapobject_id, + 'key': key, + 'val': val} +) + + +for well_id in batch['well_ids']: + logger.info('process well %d', well_id) + with tm.utils.ExperimentSession(77) as session: + #sites = session.query(tm.Site.id, tm.Site.height, tm.Site.width, tm.Site.y, tm.Site.x).\ + #filter_by(well_id=well_id).all() + #wellY = sites[0][1]*len(set([i[3] for i in sites])) + #wellX = sites[0][2]*len(set([i[4]for i in sites])) + +# extract_mapobject_type_id = session.query(tm.MapobjectType.id).\ +# filter_by(name=batch['extract_object']).one()[0] +# extract_seg_layer_id = session.query(tm.SegmentationLayer.id).\ +# filter_by(mapobject_type_id=extract_mapobject_type_id).one()[0] +# extract_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ +# filter_by(segmentation_layer_id=extract_seg_layer_id).all() + + _append_value(session, mapobject_id=45189,key='test',val='value_test') +# assign_mapobject_type_id = session.query(tm.MapobjectType.id).\ +# filter_by(name=batch['assign_object']).one()[0] +# assign_seg_layer_id = session.query(tm.SegmentationLayer.id).\ +# filter_by(mapobject_type_id=assign_mapobject_type_id).one()[0] +# assign_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ +# filter_by(segmentation_layer_id=assign_seg_layer_id).all() +# +# logger.info('Calculating LCC for well_id %s', well_id) +# lcc_extract = LocalCC(extract_centroids) +# real_lcc = lcc_extract.real_distances() +# random_lcc = lcc_extract.random_distances() +# lcc = lcc_extract.get_lcc(real_lcc,random_lcc) +# +# lcc_assign = LocalCC(assign_centroids) +# +# # remap y,x coordinates of extract object with mapobject_id +# # of assign object +# lcc_extract.df['mapobject_id'] = lcc_assign.df['mapobject_id'] +# + #features = session.query(tm.Feature).filter_by(mapobject_type_id=mapobject_type_id) + + #logger.info('No exsisting features found in the database for %s', batch['object_name'])) +# feature_name = batch['extract_object']+'_{}_lcc'.format(well_id) +# logger.debug('add feature "%s"', feature_name) +# feature = session.get_or_create( +# tm.Feature, name=feature_name, +# mapobject_type_id=assign_mapobject_type_id, +# is_aggregate=False) +# feature_values = list() +# for idx, value in enumerate(lcc): + session.commit() +# feature_values.append( +# tm.FeatureValues( +# partition_key=lcc_extract, +# mapobject_id=mapobject_ids[label], +# tpoint=0, values=values) + + +#if __name__ == '__main__': +# run_job(batch) diff --git a/tmlib/workflow/popcon/test/popcon_test_addtodb.py b/tmlib/workflow/popcon/test/popcon_test_addtodb.py new file mode 100644 index 00000000..3e6385ab --- /dev/null +++ b/tmlib/workflow/popcon/test/popcon_test_addtodb.py @@ -0,0 +1,103 @@ +# Create Nuclei feature and new feature-values + + +import os +import re +import sys +import shutil +import logging +import subprocess +import numpy as np +import pandas as pd +import collections +import shapely.geometry +import shapely.ops +from cached_property import cached_property +from sqlalchemy.orm.exc import NoResultFound +from sqlalchemy.sql import func +from sqlalchemy.dialects.postgresql import FLOAT +from psycopg2 import ProgrammingError +from psycopg2.extras import Json +from gc3libs.quantity import Duration, Memory + +import tmlib.models as tm +from tmlib.utils import autocreate_directory_property +from tmlib.utils import flatten +from tmlib.readers import TextReader +from tmlib.readers import ImageReader +from tmlib.writers import TextWriter +from tmlib.models.types import ST_GeomFromText +from tmlib.workflow.api import WorkflowStepAPI +from tmlib.errors import PipelineDescriptionError +from tmlib.errors import JobDescriptionError +from tmlib.workflow.jobs import SingleRunPhase +from tmlib.workflow.jterator.jobs import DebugRunJob +from tmlib.workflow import register_step_api +from tmlib import cfg + +from tmlib.workflow.popcon.lcc_forTM import LocalCC + +logger = logging.getLogger(__name__) + +batch = {'well_ids': [3], + 'extract_object': 'Nuclei', + 'assign_object': 'Cells'} + + + +for well_id in batch['well_ids']: + logger.info('process well %d', well_id) + with tm.utils.ExperimentSession(77) as session: + #sites = session.query(tm.Site.id, tm.Site.height, tm.Site.width, tm.Site.y, tm.Site.x).\ + #filter_by(well_id=well_id).all() + #wellY = sites[0][1]*len(set([i[3] for i in sites])) + #wellX = sites[0][2]*len(set([i[4]for i in sites])) + + extract_mapobject_type_id = session.query(tm.MapobjectType.id).\ + filter_by(name=batch['extract_object']).one()[0] + extract_seg_layer_id = session.query(tm.SegmentationLayer.id).\ + filter_by(mapobject_type_id=extract_mapobject_type_id).one()[0] + extract_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ + filter_by(segmentation_layer_id=extract_seg_layer_id).all() + + logger.info('Calculating LCC for well_id %s', well_id) + lcc_extract = LocalCC(extract_centroids) + real_lcc = lcc_extract.real_distances() + random_lcc = lcc_extract.random_distances() + lcc = lcc_extract.get_lcc(real_lcc,random_lcc) + + + + + #logger.info('No exsisting features found in the database for %s', batch['object_name'])) + feature_name = batch['extract_object']+'_{}_lcc'.format(well_id) +# logger.debug('add feature "%s"', feature_name) + feature = session.get_or_create( + tm.Feature, name=feature_name, + mapobject_type_id=extract_mapobject_type_id, + is_aggregate=False) + feature_values = list() + for index, row in lcc_extract.df.iterrows(): + t=0 + logger.debug( + 'add values for mapobject #%d at time point %d', + row['mapobject_id'], t + ) + values = {str(int(row['mapobject_id'])): row['lcc'].astype(str)} + feature_values.append( + tm.FeatureValues( + partition_key=int(row['site']), + mapobject_id=int(row['mapobject_id']), + tpoint=t, values=values + ) + ) + logger.debug('insert feature values into db table') + + session.add_all(feature_values) + + + session.expunge_all() +# session.commit() + +#if __name__ == '__main__': +# run_job(batch) diff --git a/tmlib/workflow/popcon/test/popcon_test_append.py b/tmlib/workflow/popcon/test/popcon_test_append.py new file mode 100644 index 00000000..ea407af3 --- /dev/null +++ b/tmlib/workflow/popcon/test/popcon_test_append.py @@ -0,0 +1,113 @@ +# TmLibrary - TissueMAPS library for distibuted image analysis routines. +# Copyright (C) 2016 Markus D. Herrmann, University of Zurich and Robin Hafen +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published +# by the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. +# +# You should have received a copy of the GNU Affero General Public License +# along with this program. If not, see . +import os +import re +import sys +import shutil +import logging +import subprocess +import numpy as np +import pandas as pd +import collections +import shapely.geometry +import shapely.ops +from cached_property import cached_property +from sqlalchemy.orm.exc import NoResultFound +from sqlalchemy.sql import func +from sqlalchemy.dialects.postgresql import FLOAT +from psycopg2 import ProgrammingError +from psycopg2.extras import Json +from gc3libs.quantity import Duration, Memory + +import tmlib.models as tm +from tmlib.utils import autocreate_directory_property +from tmlib.utils import flatten +from tmlib.readers import TextReader +from tmlib.readers import ImageReader +from tmlib.writers import TextWriter +from tmlib.models.types import ST_GeomFromText +from tmlib.workflow.api import WorkflowStepAPI +from tmlib.errors import PipelineDescriptionError +from tmlib.errors import JobDescriptionError +from tmlib.workflow.jobs import SingleRunPhase +from tmlib.workflow.jterator.jobs import DebugRunJob +from tmlib.workflow import register_step_api +from tmlib import cfg + +from tmlib.workflow.popcon.lcc_forTM import LocalCC + +logger = logging.getLogger(__name__) + +batch = {'well_ids': [3], + 'extract_object': 'Nuclei', + 'assign_object': 'Cells'} + + + +for well_id in batch['well_ids']: + logger.info('process well %d', well_id) + with tm.utils.ExperimentSession(77) as session: + #sites = session.query(tm.Site.id, tm.Site.height, tm.Site.width, tm.Site.y, tm.Site.x).\ + #filter_by(well_id=well_id).all() + #wellY = sites[0][1]*len(set([i[3] for i in sites])) + #wellX = sites[0][2]*len(set([i[4]for i in sites])) + + extract_mapobject_type_id = session.query(tm.MapobjectType.id).\ + filter_by(name=batch['extract_object']).one()[0] + extract_seg_layer_id = session.query(tm.SegmentationLayer.id).\ + filter_by(mapobject_type_id=extract_mapobject_type_id).one()[0] + extract_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ + filter_by(segmentation_layer_id=extract_seg_layer_id).all() + + assign_mapobject_type_id = session.query(tm.MapobjectType.id).\ + filter_by(name=batch['assign_object']).one()[0] + assign_seg_layer_id = session.query(tm.SegmentationLayer.id).\ + filter_by(mapobject_type_id=assign_mapobject_type_id).one()[0] + assign_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ + filter_by(segmentation_layer_id=assign_seg_layer_id).all() + + logger.info('Calculating LCC for well_id %s', well_id) + lcc_extract = LocalCC(extract_centroids) + real_lcc = lcc_extract.real_distances() + random_lcc = lcc_extract.random_distances() + lcc = lcc_extract.get_lcc(real_lcc,random_lcc) + + lcc_assign = LocalCC(assign_centroids) + + # remap y,x coordinates of extract object with mapobject_id + # of assign object + lcc_extract.df['mapobject_id'] = lcc_assign.df['mapobject_id'] + + #features = session.query(tm.Feature).filter_by(mapobject_type_id=mapobject_type_id) + + #logger.info('No exsisting features found in the database for %s', batch['object_name'])) + feature_name = batch['extract_object']+'_{}_lcc'.format(well_id) +# logger.debug('add feature "%s"', feature_name) + feature = session.get_or_create( + tm.Feature, name=feature_name, + mapobject_type_id=assign_mapobject_type_id, + is_aggregate=False) +# feature_values = list() + for index, row in lcc_extract.df.iterrows(): + feature_value = session.query(tm.FeatureValues).filter_by(mapobject_id= int(row['mapobject_id']) ).one() + session.append_value(feature_value,str(feature.id),row['lcc'].astype(str)) + + session.commit() + #session.expunge_all() + #session.commit() + +#if __name__ == '__main__': +# run_job(batch) diff --git a/tmlib/workflow/popcon/test/sql_append_tofeature_values b/tmlib/workflow/popcon/test/sql_append_tofeature_values new file mode 100644 index 00000000..cce9b12c --- /dev/null +++ b/tmlib/workflow/popcon/test/sql_append_tofeature_values @@ -0,0 +1 @@ +select master_modify_multiple_shards('update experiment_77.feature_values set values = values || hstore(''diego'',''is cool'') where mapobject_id = 45190'); From 49b69f9de80d01c699e5ac370737b8ac3d61479f Mon Sep 17 00:00:00 2001 From: sparkvilla Date: Thu, 15 Feb 2018 08:38:42 +0000 Subject: [PATCH 2/8] added _append_value method for popcon --- tmlib/models/feature.py | 17 +++++++++++++---- tmlib/models/utils.py | 17 +++++++++++++++++ 2 files changed, 30 insertions(+), 4 deletions(-) diff --git a/tmlib/models/feature.py b/tmlib/models/feature.py index dc9b4696..5084e813 100644 --- a/tmlib/models/feature.py +++ b/tmlib/models/feature.py @@ -142,6 +142,15 @@ def __init__(self, partition_key, mapobject_id, values, tpoint=None): self.tpoint = tpoint self.values = values + @classmethod + def _append_value(cls, connection, instance, key, val): + if not isinstance(instance, FeatureValues): + raise TypeError( + 'Object must have type tmlib.models.feature.FeatureValues' + ) + connection.execute('''SELECT master_modify_multiple_shards('UPDATE feature_values SET values = values || hstore('%(key)s','%(val)s') WHERE mapobject_id = %(mapobject_id)s');''',{'mapobject_id': instance.mapobject_id, 'key': key, 'val':val}) + + @classmethod def _add(cls, connection, instance): if not isinstance(instance, FeatureValues): @@ -150,13 +159,13 @@ def _add(cls, connection, instance): ) connection.execute(''' INSERT INTO feature_values AS v ( - parition_key, values, mapobject_id, tpoint + partition_key, values, mapobject_id, tpoint ) VALUES ( %(partition_key)s, %(values)s, %(mapobject_id)s, %(tpoint)s ) ON CONFLICT - ON CONSTRAINT feature_values_mapobject_id_tpoint_key + -- ON CONSTRAINT feature_values_mapobject_id_fkey DO UPDATE SET values = v.values || %(values)s WHERE v.mapobject_id = %(mapobject_id)s @@ -188,6 +197,6 @@ def _bulk_ingest(cls, connection, instances): def __repr__(self): return ( - '' - % (self.id, self.tpoint, self.mapobject_id) + '' + % (self.partition_key, self.tpoint, self.mapobject_id) ) diff --git a/tmlib/models/utils.py b/tmlib/models/utils.py index 6300ffb7..17d1a582 100644 --- a/tmlib/models/utils.py +++ b/tmlib/models/utils.py @@ -568,6 +568,23 @@ def add(self, instance): else: self._session.add(instance) + def append_value(self, instance, key, val): + '''Append a "key"=>"val" pair to an istance of FeatureValues class. + + Parameters + ---------- + instance: tmlib.models.feature.FeatureValues instance + key: string + value: string + ''' + + cls = instance.__class__ + if isinstance(instance, DistributedExperimentModel): + connection = self._session.get_bind() + with connection.connection.cursor() as c: + cls._append_value(c,instance, key, val) + + def add_all(self, instances): '''Adds multiple instances of a model class. From 692b30a9815961b1029cb335ae614e0968701892 Mon Sep 17 00:00:00 2001 From: sparkvilla Date: Thu, 15 Feb 2018 12:53:47 +0000 Subject: [PATCH 3/8] fix for cli --- tmlib/workflow/popcon/args.py | 4 ++-- tmlib/workflow/popcon/cli.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tmlib/workflow/popcon/args.py b/tmlib/workflow/popcon/args.py index 16a2901e..9ada60bc 100644 --- a/tmlib/workflow/popcon/args.py +++ b/tmlib/workflow/popcon/args.py @@ -20,7 +20,7 @@ @register_step_batch_args('popcon') -class PopConBatchArguments(BatchArguments): +class PopconBatchArguments(BatchArguments): extract_object = Argument( type=str, short_flag='e', @@ -40,6 +40,6 @@ class PopConBatchArguments(BatchArguments): @register_step_submission_args('popcon') -class PopConSubmissionArguments(SubmissionArguments): +class PopconSubmissionArguments(SubmissionArguments): pass diff --git a/tmlib/workflow/popcon/cli.py b/tmlib/workflow/popcon/cli.py index 07303f04..5e0b0179 100644 --- a/tmlib/workflow/popcon/cli.py +++ b/tmlib/workflow/popcon/cli.py @@ -24,7 +24,7 @@ logger = logging.getLogger(__name__) -class PopCon(WorkflowStepCLI): +class Popcon(WorkflowStepCLI): @assert_type(api_instance='tmlib.workflow.popcon.api.CrowdingEstimator') def __init__(self, api_instance, verbosity): @@ -36,6 +36,6 @@ def __init__(self, api_instance, verbosity): verbosity: int logging verbosity ''' - super(PopCon, self).__init__(api_instance, verbosity) + super(Popcon, self).__init__(api_instance, verbosity) From 51bd709493c379fc6208f5b8cbf6185aaca7aa9d Mon Sep 17 00:00:00 2001 From: sparkvilla Date: Thu, 15 Feb 2018 15:36:03 +0000 Subject: [PATCH 4/8] typo fix and add popcon dependecies --- tmlib/workflow/dependencies.py | 4 +- tmlib/workflow/popcon/api.py | 41 ++++++++++++------ .../popcon/test/.popcon_test_append.py.swp | Bin 16384 -> 0 bytes tmlib/workflow/popcon/test/popcon_test1.py | 8 ++-- 4 files changed, 34 insertions(+), 19 deletions(-) delete mode 100644 tmlib/workflow/popcon/test/.popcon_test_append.py.swp diff --git a/tmlib/workflow/dependencies.py b/tmlib/workflow/dependencies.py index c0b8a677..f0a555be 100644 --- a/tmlib/workflow/dependencies.py +++ b/tmlib/workflow/dependencies.py @@ -140,7 +140,9 @@ class CanonicalWorkflowDependencies(WorkflowDependencies): }, 'imextract': { 'metaconfig' - } + }, + 'popcon' : { + 'jterator' } diff --git a/tmlib/workflow/popcon/api.py b/tmlib/workflow/popcon/api.py index 6989171e..3ee73b21 100644 --- a/tmlib/workflow/popcon/api.py +++ b/tmlib/workflow/popcon/api.py @@ -92,7 +92,26 @@ def create_run_batches(self, args): 'extract_object': args.extract_object, 'assign_object': args.assign_object } - + def delete_previous_job_output(self): + '''Deletes all instances of + :class:`MapobjectType ` + that were generated by a prior run of the same pipeline as well as all + children instances for the processed experiment. + ''' + pass + # logger.info('delete existing mapobjects and mapobject types') +# with tm.utils.ExperimentSession(self.experiment_id, False) as session: +# static_types = ['Plates', 'Wells', 'Sites'] +# mapobject_types = session.query(tm.MapobjectType.id).\ +# filter(~tm.MapobjectType.name.in_(static_types)).\ +# all() +# mapobject_type_ids = [t.id for t in mapobject_types] +# session.query(tm.Mapobject).\ +# filter(tm.Mapobject.mapobject_type_id.in_(mapobject_type_ids)).\ +# delete() +# session.query(tm.MapobjectType).\ +# filter(tm.MapobjectType.id.in_(mapobject_type_ids)).\ +#delete() def run_job(self, batch, assume_clean_state): '''Runs the pipeline, i.e. executes modules sequentially. After @@ -112,18 +131,12 @@ def run_job(self, batch, assume_clean_state): # wellY = sites_per_well[0][1]*len(set([i[3] for i in sites_per_well])) # wellX = sites_per_well[0][2]*len(set([i[4]for i in sites_per_well])) - extract_mapobject_type_id = session.query(tm.MapobjectType.id).\ - filter_by(name=batch['extract_object']).one()[0] - extract_seg_layer_id = session.query(tm.SegmentationLayer.id).\ - filter_by(mapobject_type_id=mapobject_type_id).one()[0] - extract_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ - filter_by(segmentation_layer_id=extract_seg_layer_id).all() - assign_mapobject_type_id = session.query(tm.MapobjectType.id).\ - filter_by(name=batch['assign_object']).one()[0] - assign_seg_layer_id = session.query(tm.SegmentationLayer.id).\ - filter_by(mapobject_type_id=assign_mapobject_type_id).one()[0] - assign_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_kye).\ - filter_by(segmentation_layer_id=assign_seg_layer_id).all() + extract_mapobject_type_id = session.query(tm.MapobjectType.id).filter_by(name=batch['extract_object']).one()[0] + extract_seg_layer_id = session.query(tm.SegmentationLayer.id).filter_by(mapobject_type_id=extract_mapobject_type_id).one()[0] + extract_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).filter_by(segmentation_layer_id=extract_seg_layer_id).all() + assign_mapobject_type_id = session.query(tm.MapobjectType.id).filter_by(name=batch['assign_object']).one()[0] + assign_seg_layer_id = session.query(tm.SegmentationLayer.id).filter_by(mapobject_type_id=assign_mapobject_type_id).one()[0] + assign_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).filter_by(segmentation_layer_id=assign_seg_layer_id).all() logger.info('Calculating LCC for well_id %s', well_id) lcc_extract = LocalCC(extract_centroids) @@ -131,7 +144,7 @@ def run_job(self, batch, assume_clean_state): random_lcc = lcc_extract.random_distances() lcc = lcc_extract.get_lcc(real_lcc,random_lcc) - lcc_assign = Local(assign_centroids) + lcc_assign = LocalCC(assign_centroids) # remap y,x coordinates of extract object with mapobject_id # of assign object diff --git a/tmlib/workflow/popcon/test/.popcon_test_append.py.swp b/tmlib/workflow/popcon/test/.popcon_test_append.py.swp deleted file mode 100644 index cc886487ade4d6eabb3ce3183e17c9bc0405577b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 16384 zcmeHOQEcQ!8J=ESAV8r4@j?+duswE{Zk*g*#g#yiLhjPrQ<96ao2J(gtXX^3>pR=y z&e++wRVfdB0R#xC@Ic>+kP1i$A*AvK5)wi}JXETR!~+la0^%LSLsjvO*W-1P&0Wh= zLALZ|?U{f6`TuXmGoF7YgT*Jh8}yR(D8cn0A*0JLJoVO(m&h-^N=V`>mGY3qN@t#a zxjy&AyiB8nt3=Hog!3w4k;?)R@p;AN0hjZOi|5JLrV1DO;krMNOlAv782E#Qv5>ch zffz5uB6dWypj8$3w!XJn9P`Mv;_P%g>YSQ^nt@XoNR#3GV-M_Hyl{R|OEf1A#fIW{X>L&8}I-QG=SR=5b`n*05^fNz@I-z$e)1U0lx-*3cLh-12_N%z;)mf z@G$W24-oQ4;Ag-yKp(gW+z;Fb{Nw$E{0evx5I`4r82INLA-@M+1U%p|;9lU3_Yv}2 z;5WdxfNugEaDi*UIp7R%4?uu7-%H3}fS&`;0B3%hl=*Y79fyTBXw z5%L4z`#>M~B=A=RNPY<1Mj+)!z{|i*;Pb$Pz-{pROF;Adist&AUbMK#T;Mo#nc}a_ z4-?6p#PWDz7VPAJ=fWYPBh(3mwO0b#g_L7<(#q zpvby#Sg^X{ulGCLEHsHbO*)fxepF4GnXKm{% zx!V=Lrh$Y@(QchhR`E$)}TS9zJ`e%0%B&8g+PGi-=h$d?B!aYlR1g4K!R z&HZVm=tD)`3Cm_xyyv)x7FmPi&cFq+uUTD##48!y6XPBywQzgC%Bz90GTsF zRcdAtQ1$z5HoH+hv)xVR3FBr}KC9v9(0Lkq7| z`+BVKsUJ-w_S<;J^FlYOh0Y%-ba6i)VUCxY4lBjjvt0a^9jTF9G0XlQ%tL?eP>c)t z{fPVCD49NweK#3RAG7K0p?K*_ z;549RP6Jw#(}0$qWZ+MKgZg1CWMbx-Y9W*5482*ZOt$16<~z(9_!Hw)p+8I5x(NxE z2x;9E17(U{=7ZGRPNQpLFuPDiwg)!@4&kj4Q@kq9r4+K%30;@Da-<*YvHyzBJ6(9o zk9pun{7xlG>fo9&e787U*@Md~Tit2}p>R36o}N=mn|giwam;*cXpcS`yedAH==D?U zb@pzaR29M}gj*5nhRoyJdD&;Zaq|k$jdW!a?N;$Crx4WG-Z8 z;|;-Em1LyoIg<`Haa+GzNmz6u&$)=Mr|?a{BFw~@sr7P-o`AOL&?O2atQNl|Q)R5~ceG?P+{8Rk)qbeoq?Sy_SP zCQ&HTp!)Q&R}8QtAGJ>Sw;{E}5DWm8B85KjCj5vLKbBC@Bhg=H!IFB%R1avDMKown| zs3Q$1yQ@?_OQDx>>hgr?Ndcucu57PfUD@ofe7U>c?cbo7{?@wv%`3egUAw+bSLoKt zcE7v2v%a!Tw|2I-uJ^9M%^uu?Whg&L6S~{&U%kH5rz@K`=@=FQAY7Xb4hVNJpUTkre?XeH3{Q;Vwk`VPV*jHyn`Gw=ClaCX!tK! 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''' pass - # logger.info('delete existing mapobjects and mapobject types') -# with tm.utils.ExperimentSession(self.experiment_id, False) as session: -# static_types = ['Plates', 'Wells', 'Sites'] -# mapobject_types = session.query(tm.MapobjectType.id).\ -# filter(~tm.MapobjectType.name.in_(static_types)).\ -# all() -# mapobject_type_ids = [t.id for t in mapobject_types] -# session.query(tm.Mapobject).\ -# filter(tm.Mapobject.mapobject_type_id.in_(mapobject_type_ids)).\ -# delete() -# session.query(tm.MapobjectType).\ -# filter(tm.MapobjectType.id.in_(mapobject_type_ids)).\ -#delete() def run_job(self, batch, assume_clean_state): '''Runs the pipeline, i.e. executes modules sequentially. After @@ -126,32 +113,51 @@ def run_job(self, batch, assume_clean_state): for well_id in batch['well_ids']: logger.info('process well %d', well_id) with tm.utils.ExperimentSession(self.experiment_id) as session: -# sites = session.query(tm.Site.id, tm.Site.height, tm.Site.width, tm.Site.y, tm.Site.x).\ -# filter_by(well_id=well_id).all() -# wellY = sites_per_well[0][1]*len(set([i[3] for i in sites_per_well])) -# wellX = sites_per_well[0][2]*len(set([i[4]for i in sites_per_well])) + sites = session.query(tm.Site.id, tm.Site.height, tm.Site.width, tm.Site.y, tm.Site.x).\ + filter_by(well_id=well_id).all() + wellY = sites[0][1]*len(set([i[3] for i in sites])) + wellX = sites[0][2]*len(set([i[4]for i in sites])) + - extract_mapobject_type_id = session.query(tm.MapobjectType.id).filter_by(name=batch['extract_object']).one()[0] - extract_seg_layer_id = session.query(tm.SegmentationLayer.id).filter_by(mapobject_type_id=extract_mapobject_type_id).one()[0] - extract_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).filter_by(segmentation_layer_id=extract_seg_layer_id).all() - assign_mapobject_type_id = session.query(tm.MapobjectType.id).filter_by(name=batch['assign_object']).one()[0] - assign_seg_layer_id = session.query(tm.SegmentationLayer.id).filter_by(mapobject_type_id=assign_mapobject_type_id).one()[0] - assign_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).filter_by(segmentation_layer_id=assign_seg_layer_id).all() + extract_mapobject_type_id = session.query(tm.MapobjectType.id).\ + filter_by(name=batch['extract_object']).one()[0] + extract_seg_layer_id = session.query(tm.SegmentationLayer.id).\ + filter_by(mapobject_type_id=extract_mapobject_type_id).one()[0] + extract_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ + filter_by(segmentation_layer_id=extract_seg_layer_id).all() + assign_mapobject_type_id = session.query(tm.MapobjectType.id).\ + filter_by(name=batch['assign_object']).one()[0] + assign_seg_layer_id = session.query(tm.SegmentationLayer.id).\ + filter_by(mapobject_type_id=assign_mapobject_type_id).one()[0] + assign_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ + filter_by(segmentation_layer_id=assign_seg_layer_id).all() logger.info('Calculating LCC for well_id %s', well_id) - lcc_extract = LocalCC(extract_centroids) + logger.info('Instantiating LCC for extract_object') + lcc_extract = LocalCC(extract_centroids, wellY, wellX) + logger.info('df lcc_extract: %s',lcc_extract.df.head()) + logger.info( + 'wellX: %s, wellY: %s, diagonal: %s' + , lcc_extract.wellX, lcc_extract.wellY, lcc_extract.well_diagonal) + real_lcc = lcc_extract.real_distances() random_lcc = lcc_extract.random_distances() lcc = lcc_extract.get_lcc(real_lcc,random_lcc) - - lcc_assign = LocalCC(assign_centroids) + logger.info('Instantiating LCC for assign_object') + lcc_assign = LocalCC(assign_centroids, wellY, wellX) # remap y,x coordinates of extract object with mapobject_id # of assign object - lcc_extract.df['mapobject_id'] = lcc_assign.df['mapobject_id'] + logger.info( + 'assign mapobject_id of %s to %s' + , batch['assign_object'], batch['extract_object']) + #logger.debug( + # 'assign: %s extract: %s', lcc_assign.df['mapobject_id'], lcc_extract.df['mapobject_id']) + + lcc_extract.df['mapobject_id'] = lcc_assign.df['mapobject_id'] - feature_name = batch['extract_object']+'_{}_lcc'.format(well_id) + feature_name = 'LocalCellCrowding_{}'.format(batch['extract_object']) feature = session.get_or_create( tm.Feature, name=feature_name, mapobject_type_id=assign_mapobject_type_id, diff --git a/tmlib/workflow/popcon/lcc.py b/tmlib/workflow/popcon/lcc.py index 3e283b80..6266d45f 100644 --- a/tmlib/workflow/popcon/lcc.py +++ b/tmlib/workflow/popcon/lcc.py @@ -28,7 +28,7 @@ def _get_df(np_centroids): df = data_df.sort_values(['site', 'label']) return df - def __init__(self, centroids): + def __init__(self, centroids, wellY, wellX): ''' Parameters ---------- @@ -37,12 +37,12 @@ def __init__(self, centroids): ''' self.centroids = centroids - self.centroids_coordinates = [(self._get_yx(element[0]).y,self._get_yx(element[0]).x, int(element[1]), int(element[2]), int(element[3])) for element in self.centroids] + self.centroids_coordinates = [(abs(self._get_yx(element[0]).y),abs(self._get_yx(element[0]).x), int(element[1]), int(element[2]), int(element[3])) for element in self.centroids] self.df = self._get_df(self.centroids_coordinates) self.yx_coordinates = np.asarray((self.df['y'],self.df['x'])).transpose() - self.wellY = np.floor(self.df['y'].min()) - self.wellX = np.ceil(self.df['x'].max()) + self.wellY = wellY + self.wellX = wellX self.well_diagonal = np.round(math.sqrt(self.wellX**2+self.wellY**2)) @@ -67,7 +67,7 @@ def random_distances(self): rand_dists= list() for yx_real in self.yx_coordinates: - y_rand= np.random.uniform(self.wellY,0,len(self.yx_coordinates)-1) + y_rand= np.random.uniform(0,self.wellY,len(self.yx_coordinates)-1) x_rand= np.random.uniform(0,self.wellX,len(self.yx_coordinates)-1) yx_coordinates_random = np.concatenate( (y_rand[:,np.newaxis],x_rand[:,np.newaxis]), axis=1) From 9d8d59a9139cab4edfc098078cc5404354184a0d Mon Sep 17 00:00:00 2001 From: sparkvilla Date: Tue, 20 Mar 2018 13:49:04 +0000 Subject: [PATCH 7/8] optimized lcc for memory efficiency --- tmlib/workflow/dependencies.py | 4 + tmlib/workflow/popcon/api.py | 37 +- tmlib/workflow/popcon/lcc.py | 98 +- .../popcon/test/coordinates_canonical.db | 59816 ++++++++++++++++ tmlib/workflow/popcon/test/lcc_file.py | 103 + tmlib/workflow/popcon/test/popcon_test.py | 108 +- .../workflow/popcon/test/popcon_test_file.py | 24 + 7 files changed, 60093 insertions(+), 97 deletions(-) create mode 100644 tmlib/workflow/popcon/test/coordinates_canonical.db create mode 100644 tmlib/workflow/popcon/test/lcc_file.py create mode 100644 tmlib/workflow/popcon/test/popcon_test_file.py diff --git a/tmlib/workflow/dependencies.py b/tmlib/workflow/dependencies.py index 85ed8759..3be7e68d 100644 --- a/tmlib/workflow/dependencies.py +++ b/tmlib/workflow/dependencies.py @@ -141,6 +141,10 @@ class CanonicalWorkflowDependencies(WorkflowDependencies): 'imextract': { 'metaconfig' }, + + 'jterator':{ + + }, 'popcon' : { 'jterator' } diff --git a/tmlib/workflow/popcon/api.py b/tmlib/workflow/popcon/api.py index f2225216..b67f357c 100644 --- a/tmlib/workflow/popcon/api.py +++ b/tmlib/workflow/popcon/api.py @@ -98,7 +98,7 @@ def delete_previous_job_output(self): that were generated by a prior run of the same pipeline as well as all children instances for the processed experiment. ''' - pass + pass def run_job(self, batch, assume_clean_state): '''Runs the pipeline, i.e. executes modules sequentially. After @@ -117,32 +117,34 @@ def run_job(self, batch, assume_clean_state): filter_by(well_id=well_id).all() wellY = sites[0][1]*len(set([i[3] for i in sites])) wellX = sites[0][2]*len(set([i[4]for i in sites])) - - + + extract_mapobject_type_id = session.query(tm.MapobjectType.id).\ - filter_by(name=batch['extract_object']).one()[0] + filter_by(name=batch['extract_object']).one()[0] extract_seg_layer_id = session.query(tm.SegmentationLayer.id).\ filter_by(mapobject_type_id=extract_mapobject_type_id).one()[0] extract_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ filter_by(segmentation_layer_id=extract_seg_layer_id).all() assign_mapobject_type_id = session.query(tm.MapobjectType.id).\ - filter_by(name=batch['assign_object']).one()[0] + filter_by(name=batch['assign_object']).one()[0] assign_seg_layer_id = session.query(tm.SegmentationLayer.id).\ filter_by(mapobject_type_id=assign_mapobject_type_id).one()[0] assign_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ filter_by(segmentation_layer_id=assign_seg_layer_id).all() - + logger.info('Calculating LCC for well_id %s', well_id) logger.info('Instantiating LCC for extract_object') lcc_extract = LocalCC(extract_centroids, wellY, wellX) - logger.info('df lcc_extract: %s',lcc_extract.df.head()) + logger.info('df lcc_extract: %s',lcc_extract.df.head()) logger.info( 'wellX: %s, wellY: %s, diagonal: %s' , lcc_extract.wellX, lcc_extract.wellY, lcc_extract.well_diagonal) - - real_lcc = lcc_extract.real_distances() - random_lcc = lcc_extract.random_distances() + + real_lcc = lcc_extract.gen_real_distances() + random_lcc = lcc_extract.gen_random_distances() lcc = lcc_extract.get_lcc(real_lcc,random_lcc) + + lcc_extract.df['lcc'] = lcc_extract.df['lcc'].round() logger.info('Instantiating LCC for assign_object') lcc_assign = LocalCC(assign_centroids, wellY, wellX) @@ -153,18 +155,18 @@ def run_job(self, batch, assume_clean_state): , batch['assign_object'], batch['extract_object']) #logger.debug( - # 'assign: %s extract: %s', lcc_assign.df['mapobject_id'], lcc_extract.df['mapobject_id']) - - lcc_extract.df['mapobject_id'] = lcc_assign.df['mapobject_id'] + # 'assign: %s extract: %s', lcc_assign.df['mapobject_id'], lcc_extract.df['mapobject_id']) + + lcc_extract.df['mapobject_id'] = lcc_assign.df['mapobject_id'] feature_name = 'LocalCellCrowding_{}'.format(batch['extract_object']) feature = session.get_or_create( - tm.Feature, name=feature_name, - mapobject_type_id=assign_mapobject_type_id, - is_aggregate=False) + tm.Feature, name=feature_name, + mapobject_type_id=assign_mapobject_type_id, + is_aggregate=False) - for index, row in lcc_extract.df.iterrows(): + for index, row in lcc_extract.df.iterrows(): feature_value = session.query(tm.FeatureValues).filter_by(mapobject_id= int(row['mapobject_id']) ).one() session.append_value(feature_value,str(feature.id),row['lcc'].astype(str)) session.commit() @@ -174,4 +176,3 @@ def run_job(self, batch, assume_clean_state): def collect_job_output(self, batch): pass - diff --git a/tmlib/workflow/popcon/lcc.py b/tmlib/workflow/popcon/lcc.py index 6266d45f..f38c594b 100644 --- a/tmlib/workflow/popcon/lcc.py +++ b/tmlib/workflow/popcon/lcc.py @@ -12,89 +12,91 @@ class LocalCC(object): @staticmethod def _get_yx(element): ''' Helper. Takes in a WKBElement. - Return a shapely.geometry.point.Point object + Return a shapely.geometry.point.Point object ''' return wkb.loads(bytes(element.data)) @staticmethod - def _get_df(np_centroids): - '''Helper. Takes numpy centroids array - Return a sorted dataframe by site and label + def _get_df(centroids): + '''Helper. Takes a list of centroids coords. + Return a sorted dataframe by site and label ''' - data = np.zeros( (len(np_centroids),5) ) + data = np.zeros( (len(centroids),5) ) headers = ['y','x','mapobject_id','label','site'] - data[:] = np_centroids + data[:] = centroids data_df = pd.DataFrame(data, columns=headers) df = data_df.sort_values(['site', 'label']) return df + + + def __init__(self, centroids, wellY, wellX): ''' Parameters ---------- - centroids: list of tuples (centroid WKB element, mapobject_id, label,site); + centroids: list of tuples (centroid WKB element, mapobject_id, label,site); i.e. query objects from MapobjectSegmentation table ''' - self.centroids = centroids - - self.centroids_coordinates = [(abs(self._get_yx(element[0]).y),abs(self._get_yx(element[0]).x), int(element[1]), int(element[2]), int(element[3])) for element in self.centroids] - + + self.centroids_coordinates = [ + ( round (abs(self._get_yx(element[0]).y),1 ),\ + round (abs(self._get_yx(element[0]).x),1 ),\ + int(element[1]),\ + int(element[2]),\ + int(element[3]) ) for element in centroids] + self.df = self._get_df(self.centroids_coordinates) self.yx_coordinates = np.asarray((self.df['y'],self.df['x'])).transpose() self.wellY = wellY self.wellX = wellX self.well_diagonal = np.round(math.sqrt(self.wellX**2+self.wellY**2)) - - - - def real_distances(self): + + + def gen_real_distances(self): + ''' + Returns + ------- + generator + Sum of distances calculated from real positions + ''' + + for yx_real in self.yx_coordinates: + real_dist = distance.cdist(np.transpose(yx_real[:,np.newaxis]), self.yx_coordinates, 'euclidean') + real_masked = np.ma.masked_where(real_dist==0,real_dist) # mask 0 values + real_masked_divide = np.divide(self.well_diagonal, real_masked) + yield np.sum(real_masked_divide.filled(fill_value=0)) + + + def gen_random_distances(self): ''' Returns ------- - numpy array of distances from real positions - ''' - real_dists = distance.cdist(self.yx_coordinates, self.yx_coordinates, 'euclidean') - real_masked = np.ma.masked_where(real_dists==0,real_dists) # mask 0 values - real_masked_divide = np.divide(self.well_diagonal, real_masked) - return real_masked_divide.filled(fill_value=0) - - def random_distances(self): + generator + Sum of distances calculated from random positions ''' - Returns - ------- - numpy array of distances from random positions - ''' - rand_dists= list() - + for yx_real in self.yx_coordinates: y_rand= np.random.uniform(0,self.wellY,len(self.yx_coordinates)-1) - x_rand= np.random.uniform(0,self.wellX,len(self.yx_coordinates)-1) + x_rand= np.random.uniform(0,self.wellX,len(self.yx_coordinates)-1) yx_coordinates_random = np.concatenate( (y_rand[:,np.newaxis],x_rand[:,np.newaxis]), axis=1) - + rand_dist = np.divide(self.well_diagonal, distance.cdist(np.transpose(yx_real[:,np.newaxis]), yx_coordinates_random, 'euclidean') ) - rand_dists.append(np.squeeze(rand_dist)) - return np.asarray(rand_dists) - + yield np.sum(np.squeeze(rand_dist)) + def get_lcc(self,real_dists,random_dists): ''' Parameters ---------- - real_dists: numpy arrays of real distances - random_dists: numpy array of random distances + real_dists: generator of real distances + random_dists: generator of random distances Returns ------- - a numpy array i.e. a LCC value and mapobject_id for centroid - ''' - sum_real = np.sum(real_dists, axis=1) - sum_random = np.sum(random_dists, axis=1) - lcc = sum_real-sum_random - - self.df['lcc'] = pd.Series(lcc) - return self.df - # lcc[lcc[:,1].argsort()] sort array based on mapobject_id - # lcc[lcc[:,0].argsort()] sort array based on lcc value - + a pandas Dataframe i.e. y,x,mapobject_id,label,site,lcc + ''' + lcc = [re_d-rn_d for re_d,rn_d in zip(real_dists,random_dists)] - + self.df['lcc'] = pd.Series(lcc) + return self.df diff --git a/tmlib/workflow/popcon/test/coordinates_canonical.db b/tmlib/workflow/popcon/test/coordinates_canonical.db new file mode 100644 index 00000000..dd917e6a 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'''Class for calculatiing local cell crowding per single well. + ''' + @staticmethod + def _get_yx(element): + ''' Helper. Takes in a WKBElement. + Return a shapely.geometry.point.Point object + ''' + return wkb.loads(bytes(element.data)) + + @staticmethod + def _get_df(centroids): + '''Helper. Takes a list of centroids coords. + Return a sorted dataframe by site and label + ''' + data = np.zeros( (len(centroids),5) ) + headers = ['y','x','mapobject_id','label','site'] + data[:] = centroids + data_df = pd.DataFrame(data, columns=headers) + df = data_df.sort_values(['site', 'label']) + return df + + + + + def __init__(self, centroids, wellY, wellX): + ''' + Parameters + ---------- + centroids: list of tuples (centroid WKB element, mapobject_id, label,site); + i.e. query objects from MapobjectSegmentation table + ''' + + self.centroids_coordinates = [ + ( round(element[0],1),\ + round(element[1],1 ),\ + int(element[1]),\ + int(element[2]),\ + int(element[3]) ) for element in centroids] + + self.df = self._get_df(self.centroids_coordinates) + self.yx_coordinates = np.asarray((self.df['y'],self.df['x'])).transpose() + self.wellY = wellY + self.wellX = wellX + self.well_diagonal = np.round(math.sqrt(self.wellX**2+self.wellY**2)) + + + def gen_real_distances(self): + ''' + Returns + ------- + generator + Sum of distances calculated from real positions + ''' + + for yx_real in self.yx_coordinates: + real_dist = distance.cdist(np.transpose(yx_real[:,np.newaxis]), self.yx_coordinates, 'euclidean') + real_masked = np.ma.masked_where(real_dist==0,real_dist) # mask 0 values + real_masked_divide = np.divide(self.well_diagonal, real_masked) + yield np.sum(real_masked_divide.filled(fill_value=0)) + + + def gen_random_distances(self): + ''' + Returns + ------- + generator + Sum of distances calculated from random positions + ''' + + for yx_real in self.yx_coordinates: + y_rand= np.random.uniform(0,self.wellY,len(self.yx_coordinates)-1) + x_rand= np.random.uniform(0,self.wellX,len(self.yx_coordinates)-1) + yx_coordinates_random = np.concatenate( (y_rand[:,np.newaxis],x_rand[:,np.newaxis]), axis=1) + + rand_dist = np.divide(self.well_diagonal, distance.cdist(np.transpose(yx_real[:,np.newaxis]), yx_coordinates_random, 'euclidean') ) + yield np.sum(np.squeeze(rand_dist)) + + + def get_lcc(self,real_dists,random_dists): + ''' + Parameters + ---------- + real_dists: generator of real distances + random_dists: generator of random distances + + Returns + ------- + a pandas Dataframe i.e. y,x,mapobject_id,label,site,lcc + ''' + lcc = [re_d-rn_d for re_d,rn_d in zip(real_dists,random_dists)] + + self.df['lcc'] = pd.Series(lcc) + return self.df + + + diff --git a/tmlib/workflow/popcon/test/popcon_test.py b/tmlib/workflow/popcon/test/popcon_test.py index 31488d2e..2feab565 100644 --- a/tmlib/workflow/popcon/test/popcon_test.py +++ b/tmlib/workflow/popcon/test/popcon_test.py @@ -1,40 +1,86 @@ -# Code tried in ipython to get the centroid coordinate of Cells object (id=5) for experiment 77 -import tmlib.models as tm # from within TmLibrary/tmlib directory -from shapely import wkb -import pandas as pd +#import os +#import re +#import sys +#import shutil +import logging +#import subprocess import numpy as np -import math -from scipy.spatial import distance -from sqlalchemy.sql import func - - - -with tm.utils.ExperimentSession(77) as session: - wells = session.query(tm.Well.id,tm.Well.name).all() - # well_ids = [w.id for w in wells] - segmentation_layers = session.query(tm.SegmentationLayer).all() - feature_name = 'Nuclei_{}_lcc'.format(str(3)) - feature = session.get_or_create( - tm.Feature, name=feature_name, - mapobject_type_id=6, - is_aggregate=False) - feature_value = session.query(tm.FeatureValues.values).filter_by(mapobject_id=943).one() - +import pandas as pd +#import collections +import shapely.geometry +#import shapely.ops +#from cached_property import cached_property +#from sqlalchemy.orm.exc import NoResultFound +#from sqlalchemy.sql import func +#from sqlalchemy.dialects.postgresql import FLOAT +from psycopg2 import ProgrammingError +from psycopg2.extras import Json +from gc3libs.quantity import Duration, Memory -# for well_id in wells_id: -# sites_per_well = session.query(tm.Site.id, tm.Site.height, tm.Site.width, tm.Site.y, tm.Site.x).filter_by(well_id=well_id).all() -# well_to_sites_map[well_id[0]] = sites_per_well +import tmlib.models as tm +from tmlib.workflow.popcon.lcc import LocalCC -# wellY = sites_per_well[0][1]*len(set([i[3] for i in sites_per_well])) -# wellX = sites_per_well[0][2]*len(set([i[4]for i in sites_per_well])) +logger = logging.getLogger(__name__) -# nuclei_id = session.query(tm.MapobjectType.id).filter_by(name="Nuclei").one()[0] -# seg_layer_id = session.query(tm.SegmentationLayer.id).filter_by(mapobject_type_id=nuclei_id).one()[0] -# centroids = session.query(tm.MapobjectSegmentation.partition_key, tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label).filter_by(segmentation_layer_id=seg_layer_id).all() - session.expunge_all() # use this only if you want objects produced by querying a session to be usable outside the scope of the session +batch = {'well_ids': [1,2,3,4], + 'extract_object': 'Circles', + 'assign_object': 'Circles'} + +for well_id in batch['well_ids']: + logger.info('process well %d', well_id) + with tm.utils.ExperimentSession(22) as session: + sites = session.query(tm.Site.id, tm.Site.height, tm.Site.width, tm.Site.y, tm.Site.x).\ + filter_by(well_id=well_id).all() + print sites + wellY = sites[0][1]*len(set([i[3] for i in sites])) + wellX = sites[0][2]*len(set([i[4]for i in sites])) + + extract_mapobject_type_id = session.query(tm.MapobjectType.id).\ + filter_by(name=batch['extract_object']).one()[0] + extract_seg_layer_id = session.query(tm.SegmentationLayer.id).\ + filter_by(mapobject_type_id=extract_mapobject_type_id).one()[0] + extract_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ + filter_by(segmentation_layer_id=extract_seg_layer_id).all() -#if __name__ == '__main__': + assign_mapobject_type_id = session.query(tm.MapobjectType.id).\ + filter_by(name=batch['assign_object']).one()[0] + assign_seg_layer_id = session.query(tm.SegmentationLayer.id).\ + filter_by(mapobject_type_id=assign_mapobject_type_id).one()[0] + assign_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ + filter_by(segmentation_layer_id=assign_seg_layer_id).all() + logger.info('Calculating LCC for well_id %s', well_id) + lcc_extract = LocalCC(extract_centroids, wellY, wellX) + real_lcc = lcc_extract.real_distances() + random_lcc = lcc_extract.random_distances() + lcc = lcc_extract.get_lcc(real_lcc,random_lcc) + + lcc_assign = LocalCC(assign_centroids, wellY, wellX) + + # remap y,x coordinates of extract object with mapobject_id + # of assign object + lcc_extract.df['mapobject_id'] = lcc_assign.df['mapobject_id'] + + #features = session.query(tm.Feature).filter_by(mapobject_type_id=mapobject_type_id) + + #logger.info('No exsisting features found in the database for %s', batch['object_name'])) +# feature_name = batch['extract_object']+'_{}_lcc'.format(well_id) +# logger.debug('add feature "%s"', feature_name) +# feature = session.get_or_create( +# tm.Feature, name=feature_name, +# mapobject_type_id=assign_mapobject_type_id, +# is_aggregate=False) +# feature_values = list() +# for index, row in lcc_extract.df.iterrows(): +# feature_value = session.query(tm.FeatureValues).filter_by(mapobject_id= int(row['mapobject_id']) ).one() +# session.append_value(feature_value,str(feature.id),row['lcc'].astype(str)) + +# session.commit() + session.expunge_all() + #session.commit() + +#if __name__ == '__main__': +# run_job(batch) diff --git a/tmlib/workflow/popcon/test/popcon_test_file.py b/tmlib/workflow/popcon/test/popcon_test_file.py new file mode 100644 index 00000000..40edbd89 --- /dev/null +++ b/tmlib/workflow/popcon/test/popcon_test_file.py @@ -0,0 +1,24 @@ +import pickle +import numpy as np +import pandas as pd +import shapely.geometry + +from lcc_file import LocalCC + + +#batch = {'well_ids': [1], +# 'extract_object': 'Circles', +# 'assign_object': 'Circles'} + + + +#for well_id in batch['well_ids']: +with open('coordinates_canonical.db', 'r') as f: + data = pickle.load(f) + +extract_lcc = LocalCC(data,8640,7680) + +#real_lcc = lcc_extract.real_distances() +#random_lcc = lcc_extract.random_distances() +#lcc = lcc_extract.get_lcc(real_lcc,random_lcc) + From 3c18b2b072add4137acd979720ef23486de71737 Mon Sep 17 00:00:00 2001 From: sparkvilla Date: Wed, 21 Mar 2018 14:45:04 +0000 Subject: [PATCH 8/8] filter centroids per well --- tmlib/workflow/popcon/api.py | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/tmlib/workflow/popcon/api.py b/tmlib/workflow/popcon/api.py index b67f357c..6b52688e 100644 --- a/tmlib/workflow/popcon/api.py +++ b/tmlib/workflow/popcon/api.py @@ -115,22 +115,40 @@ def run_job(self, batch, assume_clean_state): with tm.utils.ExperimentSession(self.experiment_id) as session: sites = session.query(tm.Site.id, tm.Site.height, tm.Site.width, tm.Site.y, tm.Site.x).\ filter_by(well_id=well_id).all() + + sites_id = [i[0] for i in sites] #list of site ids for this well + + logger.debug( + 'well_id: %s has site_id: %s' + , well_id, len(sites)) + wellY = sites[0][1]*len(set([i[3] for i in sites])) wellX = sites[0][2]*len(set([i[4]for i in sites])) - - extract_mapobject_type_id = session.query(tm.MapobjectType.id).\ filter_by(name=batch['extract_object']).one()[0] + + logger.debug( + 'id of %s is: %s ' + , batch['extract_object'], extract_mapobject_type_id) + extract_seg_layer_id = session.query(tm.SegmentationLayer.id).\ filter_by(mapobject_type_id=extract_mapobject_type_id).one()[0] + extract_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ - filter_by(segmentation_layer_id=extract_seg_layer_id).all() + filter(tm.MapobjectSegmentation.segmentation_layer_id==extract_seg_layer_id).\ + filter(tm.MapobjectSegmentation.partition_key.in_(sites_id) ).all() + + logger.debug( + 'well_id: %s has %s sites and a total of %s centroids' + , well_id, len(sites), len(extract_centroids)) + assign_mapobject_type_id = session.query(tm.MapobjectType.id).\ filter_by(name=batch['assign_object']).one()[0] assign_seg_layer_id = session.query(tm.SegmentationLayer.id).\ filter_by(mapobject_type_id=assign_mapobject_type_id).one()[0] assign_centroids = session.query(tm.MapobjectSegmentation.geom_centroid,tm.MapobjectSegmentation.mapobject_id,tm.MapobjectSegmentation.label,tm.MapobjectSegmentation.partition_key).\ - filter_by(segmentation_layer_id=assign_seg_layer_id).all() + filter(tm.MapobjectSegmentation.segmentation_layer_id==assign_seg_layer_id).\ + filter(tm.MapobjectSegmentation.partition_key.in_(sites_id) ).all() logger.info('Calculating LCC for well_id %s', well_id) logger.info('Instantiating LCC for extract_object')