def setUp(self):
data = Data(name='cpg_beta', path=ROOT_DIR, base='fixtures')
experiment = Experiment(type=DataType.cpg,
task='',
method='',
params={})
annotations = Annotations(name='annotations',
exclude=CommonTypes.none.value,
cross_reactive=CrossReactive.exclude.value,
snp=SNP.exclude.value,
chr=Chromosome.non_sex.value,
gene_region=GeneRegion.yes.value,
geo=CommonTypes.any.value,
probe_class=CommonTypes.any.value)
observables = Observables(name='observables', types={})
cells = Cells(name='cells', types=CommonTypes.any.value)
attributes = Attributes(target='age',
observables=observables,
cells=cells)
self.config = Config(
data=data,
experiment=experiment,
annotations=annotations,
attributes=attributes,
)
def betas_horvath_calculator_create_regular(
chip_type,
observables_fn,
data,
data_params,
):
annotations = Annotations(name='annotations',
type=chip_type,
exclude='none',
select_dict={})
cells = Cells(name='cell_counts_part(wo_noIntensity_detP)', types='any')
observables = Observables(name=observables_fn, types={})
attributes = Attributes(target='Age', observables=observables, cells=cells)
config_root = Config(data=copy.deepcopy(data),
experiment=Experiment(
data=DataType.betas_horvath_calculator,
task=Task.create,
method=Method.regular,
data_params=copy.deepcopy(data_params)),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True)
root = Node(name=str(config_root), config=config_root)
build_tree(root)
calc_tree(root)
def setUp(self):
data = Data(name='cpg_beta', path=ROOT_DIR, base='fixtures')
experiment = Experiment(type=None, task=None, method=None, params=None)
annotations = Annotations(name='annotations',
exclude='none',
cross_reactive='ex',
snp='ex',
chr='NS',
gene_region='yes',
geo='any',
probe_class='any')
observables = Observables(name='observables', types={})
cells = Cells(name='cells', types='any')
attributes = Attributes(target='age',
observables=observables,
cells=cells)
self.config = Config(data=data,
experiment=experiment,
annotations=annotations,
attributes=attributes,
is_run=True,
is_root=True)
def setUp(self):
data = Data(path=ROOT_DIR, base='fixtures')
experiment = Experiment(data=None, task=None, method=None)
annotations = Annotations(name='annotations',
type='450k',
exclude='excluded',
select_dict={
'CROSS_R': ['0'],
'Probe_SNPs': ['empty'],
'Probe_SNPs_10': ['empty'],
'CHR': ['-X', '-Y'],
'UCSC_REFGENE_NAME': ['non-empty'],
})
observables = Observables(name='observables', types={})
cells = Cells(name='cells', types='any')
attributes = Attributes(target='age',
observables=observables,
cells=cells)
self.config = Config(data=data,
experiment=experiment,
annotations=annotations,
attributes=attributes,
is_run=True,
is_root=True)
self.config.initialize()
def table_aggregator_approach_4(
data_type,
data,
annotations,
attributes,
observables_list,
data_params,
):
config_root = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=Method.aggregator,
data_params=copy.deepcopy(data_params),
),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True
)
root = Node(name=str(config_root), config=config_root)
for d in observables_list:
observables_lvl_1 = Observables(
name=copy.deepcopy(attributes.observables.name),
types=d
)
cells_lvl_1 = Cells(
name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types)
)
attributes_lvl_1 = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_lvl_1,
cells=cells_lvl_1,
)
config_lvl_1 = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=Method.heteroskedasticity,
data_params=copy.deepcopy(data_params)
),
annotations=copy.deepcopy(annotations),
attributes=attributes_lvl_1,
is_run=True,
is_root=False
)
Node(name=str(config_lvl_1), config=config_lvl_1, parent=root)
build_tree(root)
calc_tree(root)
def cpg_plot_methylation_scatter(
data,
annotations,
attributes,
cpg_list,
observables_list,
child_method=Method.linreg,
):
for cpg in cpg_list:
config_root = Config(
data=data,
experiment=Experiment(
type=DataType.cpg,
task=Task.methylation,
method=Method.scatter,
params={}
),
annotations=annotations,
attributes=attributes
)
config_root.experiment.params['item'] = cpg
root = Node(name=str(config_root), config=config_root)
for d in observables_list:
observables_child = Observables(
name=attributes.observables.name,
types=d
)
cells_child = Cells(
name=attributes.cells.name,
types=attributes.cells.types
)
attributes_child = Attributes(
target=attributes.target,
observables=observables_child,
cells=cells_child,
)
config_child = Config(
data=data,
experiment=Experiment(
type=DataType.cpg,
task=Task.table,
method=child_method,
params={}
),
annotations=annotations,
attributes=attributes_child,
is_run=False
)
Node(name=str(config_child), config=config_child, parent=root)
calc_tree(root)
def betas_plot_variance_histogram_dev(data,
annotations,
attributes,
cpg_list,
observables_list,
child_method=Method.linreg,
method_params=None):
for cpg in cpg_list:
config_root = Config(data=copy.deepcopy(data),
experiment=Experiment(
data=DataType.betas,
task=Task.methylation,
method=Method.variance_histogram,
method_params=copy.deepcopy(method_params)),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True,
is_load_child=False)
if config_root.experiment.method_params is None:
config_root.experiment.method_params = dict()
config_root.experiment.method_params['item'] = cpg
root = Node(name=str(config_root), config=config_root)
for d in observables_list:
observables_child = Observables(name=copy.deepcopy(
attributes.observables.name),
types=d)
cells_child = Cells(name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types))
attributes_child = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_child,
cells=cells_child,
)
config_child = Config(data=copy.deepcopy(data),
experiment=Experiment(
data=DataType.betas,
task=Task.table,
method=copy.deepcopy(child_method)),
annotations=copy.deepcopy(annotations),
attributes=attributes_child,
is_run=False,
is_root=False,
is_load_child=False)
Node(name=str(config_child), config=config_child, parent=root)
build_tree(root)
calc_tree(root)
def plot_scatter(data_type,
data,
annotations,
attributes,
observables_list,
child_method=Method.linreg,
data_params=None,
method_params=None):
task_params = {'type': 'run'}
config_root = Config(data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.plot,
method=Method.scatter,
data_params=copy.deepcopy(data_params),
method_params=copy.deepcopy(method_params),
task_params=task_params),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True,
is_load_child=False)
root = Node(name=str(config_root), config=config_root)
for d in observables_list:
observables_child = Observables(name=copy.deepcopy(
attributes.observables.name),
types=d)
cells_child = Cells(name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types))
attributes_child = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_child,
cells=cells_child,
)
config_child = Config(data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=copy.deepcopy(child_method),
data_params=copy.deepcopy(data_params),
),
annotations=copy.deepcopy(annotations),
attributes=attributes_child,
is_run=False,
is_root=False,
is_load_child=False)
Node(name=str(config_child), config=config_child, parent=root)
build_tree(root)
calc_tree(root)
def cpg_proc_table_polygon(
data,
annotations,
attributes,
observables_list,
child_method = Method.linreg
):
config_root = Config(
data=data,
experiment=Experiment(
type=DataType.cpg,
task=Task.table,
method=Method.polygon,
params={}
),
annotations=annotations,
attributes=attributes
)
root = Node(name=str(config_root), config=config_root)
for d in observables_list:
observables_child = Observables(
name=attributes.observables.name,
types=d
)
cells_child = Cells(
name=attributes.cells.name,
types=attributes.cells.types
)
attributes_child = Attributes(
target=attributes.target,
observables=observables_child,
cells=cells_child,
)
config_child = Config(
data=data,
experiment=Experiment(
type=DataType.cpg,
task=Task.table,
method=child_method,
params={}
),
annotations=annotations,
attributes=attributes_child
)
Node(name=str(config_child), config=config_child, parent=root)
calc_tree(root)
def setUp(self):
data = Data(
path=ROOT_DIR,
base='fixtures'
)
data_params = {'cells': ['B', 'CD4T', 'NK', 'CD8T', 'Gran']}
experiment = Experiment(
data=None,
task=None,
method=None,
data_params=data_params
)
annotations = Annotations(
name='annotations',
exclude='none',
cross_reactive='ex',
snp='ex',
chr='NS',
gene_region='yes',
geo='any',
probe_class='any'
)
observables = Observables(
name='observables',
types={}
)
cells = Cells(
name='cells',
types='any'
)
attributes = Attributes(
target='age',
observables=observables,
cells=cells
)
self.config = Config(
data=data,
experiment=experiment,
annotations=annotations,
attributes=attributes,
is_run=True,
is_root=True
)
self.config.initialize()
def entropy_plot(data,
annotations,
attributes,
observables_list,
method,
data_params=None,
method_params=None):
config_root = Config(data=copy.deepcopy(data),
experiment=Experiment(
data=DataType.entropy,
task=Task.plot,
method=method,
data_params=copy.deepcopy(data_params),
method_params=copy.deepcopy(method_params)),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True)
root = Node(name=str(config_root), config=config_root)
for types in observables_list:
observables_child = Observables(name=copy.deepcopy(
attributes.observables.name),
types=types)
cells_child = Cells(name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types))
attributes_child = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_child,
cells=cells_child,
)
config_child = Config(data=copy.deepcopy(data),
experiment=Experiment(
data=DataType.entropy,
task=Task.table,
method=Method.mock,
data_params=copy.deepcopy(data_params)),
annotations=copy.deepcopy(annotations),
attributes=attributes_child,
is_run=False,
is_root=False)
Node(name=str(config_child), config=config_child, parent=root)
build_tree(root)
calc_tree(root)
def attributes_plot_observables_histogram(
data,
annotations,
attributes,
observables_list,
):
config_root = Config(
data=data,
experiment=Experiment(
type=DataType.attributes,
task=Task.observables,
method=Method.histogram,
params={}
),
annotations=annotations,
attributes=attributes
)
root = Node(name=str(config_root), config=config_root)
for d in observables_list:
observables_child = Observables(
name=attributes.observables.name,
types=d
)
cells_child = Cells(
name=attributes.cells.name,
types=attributes.cells.types
)
attributes_child = Attributes(
target=attributes.target,
observables=observables_child,
cells=cells_child,
)
config_child = Config(
data=data,
experiment=config_root.experiment,
annotations=annotations,
attributes=attributes_child,
is_run=False
)
Node(name=str(config_child), config=config_child, parent=root)
calc_tree(root)
def cpg_proc_table_z_test_linreg_dev(data,
annotations,
attributes,
observables_list,
child_method=Method.linreg,
params=None):
config_root = Config(data=copy.deepcopy(data),
experiment=Experiment(type=DataType.cpg,
task=Task.table,
method=Method.z_test_linreg,
params=copy.deepcopy(params)),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True)
root = Node(name=str(config_root), config=config_root)
for d in observables_list:
observables_child = Observables(name=copy.deepcopy(
attributes.observables.name),
types=d)
cells_child = Cells(name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types))
attributes_child = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_child,
cells=cells_child,
)
config_child = Config(data=copy.deepcopy(data),
experiment=Experiment(
type=DataType.cpg,
task=Task.table,
method=copy.deepcopy(child_method),
params={}),
annotations=copy.deepcopy(annotations),
attributes=attributes_child,
is_run=True,
is_root=False)
Node(name=str(config_child), config=config_child, parent=root)
build_tree(root)
calc_tree(root)
def attributes_plot_observables_histogram_dev(data,
annotations,
attributes,
observables_list,
params=None):
config_root = Config(data=copy.deepcopy(data),
experiment=Experiment(type=DataType.attributes,
task=Task.observables,
method=Method.histogram,
params=copy.deepcopy(params)),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True)
root = Node(name=str(config_root), config=config_root)
for d in observables_list:
observables_child = Observables(name=copy.deepcopy(
attributes.observables.name),
types=d)
cells_child = Cells(name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types))
attributes_child = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_child,
cells=cells_child,
)
config_child = Config(data=copy.deepcopy(data),
experiment=config_root.experiment,
annotations=copy.deepcopy(annotations),
attributes=attributes_child,
is_run=False,
is_root=True)
Node(name=str(config_child), config=config_child, parent=root)
build_tree(root)
calc_tree(root)
def observables_plot_histogram(
data,
annotations,
attributes,
observables_list,
method_params=None
):
"""
Plotting histogram for target observable distribution for provided subjects subsets and provided CpG list.
Possible parameters of experiment:
* ``'bin_size'``: bin size for numeric target. \n
For categorical target is not considered.
* ``'opacity'``: opacity level.
From ``0.0`` to ``1.0``.
* ``'barmode'``: type of barmode. \n
Possible options: \n
``'overlay'`` for overlaid histograms. \n
``'stack'`` for stacked histograms. \n
* ``'x_range'``: can be ``'auto'`` or list with two elements, which are borders of target axis.
:param data: pdm.Data instance, which specifies information about dataset.
:param annotations: pdm.Annotations instance, which specifies subset of CpGs.
:param attributes: pdm.Attributes instance, which specifies information about subjects.
:param cpg_list: List of CpGs for plotting
:param observables_list: list of subjects subsets. Each element in list is dict,
where ``key`` is observable name and ``value`` is possible values for this observable.
:param method_params: parameters of experiment.
"""
config_root = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=DataType.observables,
task=Task.observables,
method=Method.histogram,
method_params=copy.deepcopy(method_params)
),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True,
is_load_child=False
)
root = Node(name=str(config_root), config=config_root)
for d in observables_list:
observables_child = Observables(
name=copy.deepcopy(attributes.observables.name),
types=d
)
cells_child = Cells(
name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types)
)
attributes_child = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_child,
cells=cells_child,
)
config_child = Config(
data=copy.deepcopy(data),
experiment=config_root.experiment,
annotations=copy.deepcopy(annotations),
attributes=attributes_child,
is_run=False,
is_root=True,
is_load_child=False
)
Node(name=str(config_child), config=config_child, parent=root)
build_tree(root)
calc_tree(root)
def betas_plot_curve_clock(data,
annotations,
attributes,
observables_list,
child_method=Method.linreg,
data_params=None,
method_params=None):
data_type = DataType.betas
clock_method_params = {
'type': 'all',
'part': 0.25,
'size': 100,
'runs': 100,
}
config_root = Config(data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.plot,
method=Method.curve,
data_params=copy.deepcopy(data_params),
method_params=copy.deepcopy(method_params)),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True,
is_load_child=True)
root = Node(name=str(config_root), config=config_root)
for d in observables_list:
observables_child = Observables(name=copy.deepcopy(
attributes.observables.name),
types=d)
cells_child = Cells(name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types))
attributes_child = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_child,
cells=cells_child,
)
config_child_lvl_1 = Config(data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.clock,
method=copy.deepcopy(child_method),
data_params=copy.deepcopy(data_params),
method_params=clock_method_params),
annotations=copy.deepcopy(annotations),
attributes=attributes_child,
is_run=False,
is_root=False,
is_load_child=False)
node_lvl_1 = Node(name=str(config_child_lvl_1),
config=config_child_lvl_1,
parent=root)
config_child_lvl_2 = Config(data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=Method.linreg),
annotations=copy.deepcopy(annotations),
attributes=attributes_child,
is_run=False,
is_root=False)
Node(name=str(config_child_lvl_2),
config=config_child_lvl_2,
parent=node_lvl_1)
build_tree(root)
calc_tree(root)
def table_aggregator_linreg(
data_type,
data,
annotations,
attributes,
observables_list,
data_params=None,
task_params=None,
method_params=None
):
child_methods_lvl_1 = [Method.polygon, Method.z_test_linreg]
child_methods_lvl_2 = [Method.linreg]
config_root = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=Method.aggregator,
data_params=copy.deepcopy(data_params),
task_params=copy.deepcopy(task_params),
method_params=copy.deepcopy(method_params)
),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True
)
root = Node(name=str(config_root), config=config_root)
for child_method_lvl_1 in child_methods_lvl_1:
if child_method_lvl_1 == Method.polygon:
method_params = {'method': Method.linreg}
is_load_child = True
elif child_method_lvl_1 == Method.z_test_linreg:
method_params = {}
is_load_child = True
elif child_method_lvl_1 == Method.ancova:
method_params = {}
is_load_child = True
else:
method_params = {}
is_load_child = False
config_lvl_1 = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=child_method_lvl_1,
data_params=copy.deepcopy(data_params),
method_params=method_params
),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=False,
is_load_child=is_load_child
)
node_lvl_1 = Node(name=str(config_lvl_1), config=config_lvl_1, parent=root)
for child_method_lvl_2 in child_methods_lvl_2:
for d in observables_list:
observables_lvl_2 = Observables(
name=copy.deepcopy(attributes.observables.name),
types=d
)
cells_lvl_2 = Cells(
name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types)
)
attributes_lvl_2 = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_lvl_2,
cells=cells_lvl_2,
)
config_lvl_2 = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=copy.deepcopy(child_method_lvl_2),
data_params=copy.deepcopy(data_params),
),
annotations=copy.deepcopy(annotations),
attributes=attributes_lvl_2,
is_run=True,
is_root=False,
is_load_child=is_load_child
)
Node(name=str(config_lvl_2), config=config_lvl_2, parent=node_lvl_1)
build_tree(root)
calc_tree(root)
def cpg_proc_table_aggregator_dev(data,
annotations,
attributes,
observables_list,
params=None):
child_methods_lvl_1 = [Method.polygon, Method.z_test_linreg]
child_methods_lvl_2 = [Method.linreg]
config_root = Config(data=copy.deepcopy(data),
experiment=Experiment(type=DataType.cpg,
task=Task.table,
method=Method.aggregator,
params=copy.deepcopy(params)),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True)
root = Node(name=str(config_root), config=config_root)
for child_method_lvl_1 in child_methods_lvl_1:
config_lvl_1 = Config(data=copy.deepcopy(data),
experiment=Experiment(type=DataType.cpg,
task=Task.table,
method=child_method_lvl_1,
params={}),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=False)
node_lvl_1 = Node(name=str(config_lvl_1),
config=config_lvl_1,
parent=root)
for child_method_lvl_2 in child_methods_lvl_2:
for d in observables_list:
observables_lvl_2 = Observables(name=copy.deepcopy(
attributes.observables.name),
types=d)
cells_lvl_2 = Cells(name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(
attributes.cells.types))
attributes_lvl_2 = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_lvl_2,
cells=cells_lvl_2,
)
config_lvl_2 = Config(
data=copy.deepcopy(data),
experiment=Experiment(
type=DataType.cpg,
task=Task.table,
method=copy.deepcopy(child_method_lvl_2),
params={}),
annotations=copy.deepcopy(annotations),
attributes=attributes_lvl_2,
is_run=True,
is_root=False)
Node(name=str(config_lvl_2),
config=config_lvl_2,
parent=node_lvl_1)
build_tree(root)
calc_tree(root)
def table_aggregator_variance(
data_type,
data,
annotations,
attributes,
observables_list,
data_params,
):
config_root = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=Method.aggregator,
data_params=copy.deepcopy(data_params),
),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True
)
root = Node(name=str(config_root), config=config_root)
config_lvl_1 = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=Method.polygon,
data_params=copy.deepcopy(data_params),
method_params={'method': Method.variance}
),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=False
)
node_lvl_1 = Node(name=str(config_lvl_1), config=config_lvl_1, parent=root)
for d in observables_list:
observables_lvl_2 = Observables(
name=copy.deepcopy(attributes.observables.name),
types=d
)
cells_lvl_2 = Cells(
name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types)
)
attributes_lvl_2 = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_lvl_2,
cells=cells_lvl_2,
)
config_lvl_2 = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=Method.variance,
data_params=copy.deepcopy(data_params),
method_params={
'semi_window': 8,
'box_b': 'Q5',
'box_t': 'Q95',
}
),
annotations=copy.deepcopy(annotations),
attributes=attributes_lvl_2,
is_run=True,
is_root=False
)
Node(name=str(config_lvl_2), config=config_lvl_2, parent=node_lvl_1)
cluster_types = copy.deepcopy(observables_list[0])
cluster_types.pop('gender', None)
cluster_types.pop('sex', None)
observables_cluster = Observables(
name=copy.deepcopy(attributes.observables.name),
types=cluster_types
)
cells_cluster = Cells(
name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types)
)
attributes_cluster = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_cluster,
cells=cells_cluster,
)
config_cluster = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=Method.cluster,
data_params=copy.deepcopy(data_params),
method_params={
'eps': 0.2,
'min_samples_percentage': 1
}
),
annotations=copy.deepcopy(annotations),
attributes=attributes_cluster,
is_run=True,
is_root=False
)
Node(name=str(config_cluster), config=config_cluster, parent=root)
build_tree(root)
calc_tree(root)
def table_ancova(
data_type,
data,
annotations,
attributes,
observables_list,
data_params=None,
task_params=None,
method_params=None
):
config_ancova = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=Method.ancova,
data_params=copy.deepcopy(data_params),
method_params=method_params,
task_params=task_params
),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True,
is_load_child=False,
)
root = Node(name=str(config_ancova), config=config_ancova)
for d in observables_list:
observables_linreg = Observables(
name=copy.deepcopy(attributes.observables.name),
types=d
)
cells_linreg = Cells(
name=copy.deepcopy(attributes.cells.name),
types=copy.deepcopy(attributes.cells.types)
)
attributes_linreg = Attributes(
target=copy.deepcopy(attributes.target),
observables=observables_linreg,
cells=cells_linreg,
)
config_linreg = Config(
data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.table,
method=Method.linreg,
data_params=copy.deepcopy(data_params),
),
annotations=copy.deepcopy(annotations),
attributes=attributes_linreg,
is_run=False,
is_root=False
)
Node(name=str(config_linreg), config=config_linreg, parent=root)
build_tree(root)
calc_tree(root)
def plot_scatter_comparison(
data_type,
data_list,
annotations_list,
attributes_list,
observables_list,
data_params_list,
rows_dict,
cols_dict,
child_method=Method.linreg,
method_params=None,
):
data = copy.deepcopy(data_list[0])
data.base = 'comparison'
annotations = annotations_list[0]
attributes = attributes_list[0]
items = rows_dict['items']
reverses = rows_dict['reverses']
data_bases = cols_dict['data_bases']
task_params = {'type': 'run'}
method_params['items'] = items
method_params['reverses'] = reverses
method_params['data_bases'] = data_bases
if 'aux' in rows_dict:
method_params['aux'] = rows_dict['aux']
method_params['x_ranges'] = []
for data_base_id, data_base in enumerate(data_bases):
method_params['x_ranges'].append([
cols_dict['begins'][data_base_id], cols_dict['ends'][data_base_id]
])
method_params['y_ranges'] = []
for item_id in range(0, len(items)):
method_params['y_ranges'].append(
[rows_dict['begins'][item_id], rows_dict['ends'][item_id]])
config_root = Config(data=copy.deepcopy(data),
experiment=Experiment(
data=data_type,
task=Task.plot,
method=Method.scatter_comparison,
method_params=copy.deepcopy(method_params),
task_params=task_params),
annotations=copy.deepcopy(annotations),
attributes=copy.deepcopy(attributes),
is_run=True,
is_root=True,
is_load_child=False,
is_init=False,
is_init_child=False)
root = Node(name=str(config_root), config=config_root)
for data_base_id, data_base in enumerate(data_bases):
method_params_lvl_1 = copy.deepcopy(method_params)
method_params_lvl_1['items'] = items
method_params_lvl_1['reverses'] = reverses
method_params_lvl_1['x_ranges'] = [
cols_dict['begins'][data_base_id], cols_dict['ends'][data_base_id]
]
method_params_lvl_1['y_ranges'] = []
for item_id in range(0, len(items)):
method_params_lvl_1['y_ranges'].append(
[rows_dict['begins'][item_id], rows_dict['ends'][item_id]])
task_params_lvl_1 = {'type': 'prepare'}
config_lvl_1 = Config(
data=copy.deepcopy(data_list[data_base_id]),
experiment=Experiment(data=data_type,
task=Task.plot,
method=Method.scatter,
data_params=copy.deepcopy(
data_params_list[data_base_id]),
task_params=task_params_lvl_1,
method_params=method_params_lvl_1),
annotations=copy.deepcopy(annotations_list[data_base_id]),
attributes=copy.deepcopy(attributes_list[data_base_id]),
is_run=True,
is_root=False,
is_load_child=False)
node_lvl_1 = Node(name=str(config_lvl_1),
config=config_lvl_1,
parent=root)
for d in observables_list[data_base_id]:
observables_child = Observables(name=copy.deepcopy(
attributes_list[data_base_id].observables.name),
types=d)
cells_child = Cells(
name=copy.deepcopy(attributes_list[data_base_id].cells.name),
types=copy.deepcopy(attributes_list[data_base_id].cells.types))
attributes_child = Attributes(
target=copy.deepcopy(attributes_list[data_base_id].target),
observables=observables_child,
cells=cells_child,
)
config_child = Config(
data=copy.deepcopy(data_list[data_base_id]),
experiment=Experiment(
data=data_type,
task=Task.table,
method=copy.deepcopy(child_method),
data_params=copy.deepcopy(data_params_list[data_base_id]),
),
annotations=copy.deepcopy(annotations_list[data_base_id]),
attributes=attributes_child,
is_run=False,
is_root=False,
is_load_child=False)
Node(name=str(config_child),
config=config_child,
parent=node_lvl_1)
build_tree(root)
calc_tree(root)