def get_projection_energy(exp_id, res=50):
'''
Wraps allensdk to fetch projection energy given experiment ID
Parameters
__________
exp_id : int
Allen experiment ID
res : int
Sets voxel size for Allen data. Must be 100, 50, 25, or 10. Default is
50.
Returns
_______
aff : ndarray
Affine matrix
energy : ndarray
Projection energy image for given experiment, in Allen CCF
'''
if res not in [100, 50, 25, 10]:
raise ValueError('Res must be 100, 50, 25, or 10')
fn = manifest_file.split('manifest.json')[
0] + f'/experiment_{exp_id}/projection_energy_{res}.nrrd'
gda = GridDataApi(res)
gda.download_projection_grid_data(exp_id,
image=['projection_energy'],
resolution=res,
save_file_path=fn)
energy, _ = nrrd.read(fn)
energy = reorient_ara_data(energy)
aff = make_aff(res / 1000)
return aff, energy
def download_data_mask(self, path, experiment_id, resolution):
return GridDataApi().download_projection_grid_data(
experiment_id, [GridDataApi.DATA_MASK], resolution, path)
def download_injection_fraction(self, path, experiment_id, resolution):
return GridDataApi().download_projection_grid_data(
experiment_id, [GridDataApi.INJECTION_FRACTION], resolution, path)
def download_projection_density(self, path, experiment_id, resolution):
return GridDataApi().download_projection_grid_data(
experiment_id, [GridDataApi.PROJECTION_DENSITY], resolution, path)
def grid_data():
gda = GridDataApi()
gda.retrieve_file_over_http = \
MagicMock(name='retrieve_file_over_http')
return gda
def grid_data():
gda = GridDataApi()
gda.retrieve_file_over_http = \
MagicMock(name='retrieve_file_over_http')
return gda
def __init__(self) -> None:
self.rma = RmaApi()
self.gda = GridDataApi()
self.res = None # type: List
class ISHFetcher:
''' A downloader object for Section Data Sets
Methods
-------
find_id_ish:
Returns the ids of Section Data Sets (a single gene experiment) sorted by qc time
download_grid_all:
Dowloads all the expression energy 3d density file (200um grid) that satisfy the query
download_grid_recent:
Dowloads the most recently qc-ed expression energy 3d density file (200um grid) that satisfy the query
Attributes
----------
rma:
Rma Api instance
gda
GridData Api instance
res
results of the find_id_ish query
'''
def __init__(self) -> None:
self.rma = RmaApi()
self.gda = GridDataApi()
self.res = None # type: List
def find_id_ish(self,
gene: str,
sag_or_cor: str = "sagittal",
adu_or_dev: str = "adult",
time_point: str = "P56") -> List:
"""Returns the ids of Section Data Sets (a single gene experiment)
Args
----
gene: str
the gene to search for
sag_or_cor: str (accepts * wild cards)
`coronal` or `sagittal` or `*`
adu_or_dev: str (accepts * wild cards)
`adult`, `development`, `both`
time_point: str (it will be autmatically wildcarded)
e.g. "P56", "E", "E13", "P"
Returns
-------
list of ids:
sorted by most_recent to mose ancient
"""
if adu_or_dev == "adult" and "E" in time_point:
raise ValueError("there is not adult with age %s" % time_point)
if adu_or_dev == "adult":
adu_or_dev = "Mouse"
elif adu_or_dev == "development":
adu_or_dev = "DevMouse"
elif adu_or_dev == "both":
adu_or_dev = "*Mouse"
else:
raise ValueError("adu_or_dev='%s' is not valid" % adu_or_dev)
criteria = [
"[failed$eq'false']",
"reference_space[name$li'*%s*']" % time_point,
"products[abbreviation$li'%s']" % adu_or_dev,
"plane_of_section[name$li'%s']" % sag_or_cor,
"genes[acronym$eq'%s']" % gene
]
# include='reference_space',
self.res = self.rma.model_query("SectionDataSet",
criteria=','.join(criteria),
only=["id", "qc_date"],
num_rows='all')
if isinstance(self.res, str):
raise ValueError("Bad query! Server returned :\n%s" % self.res)
if self.res == []:
return []
qc_date = []
for i in self.res:
if i["qc_date"] is None:
qc_date.append('')
else:
qc_date.append(i["qc_date"])
ix = np.argsort(qc_date)
ix = ix[::-1]
results = []
for i in ix:
results.append(int(self.res[i]["id"]))
return results
def download_grid_all(self,
gene: str,
folder: str = '../data',
sag_or_cor: str = "sagittal",
adu_or_dev: str = "adult",
time_point: str = "P56") -> None:
"""Dowloads all the files
Args
----
gene: str
the gene to search for
sag_or_cor: str (accepts * wild cards)
`coronal` or `sagittal` or `*`
adu_or_dev: str (accepts * wild cards)
`adult`, `development`, `both`
time_point: str (it will be autmatically wildcarded)
e.g. "P56", "E", "E13", "P"
"""
ids = self.find_id_ish(gene,
sag_or_cor=sag_or_cor,
adu_or_dev=adu_or_dev,
time_point=time_point)
for idd in ids:
self.gda.download_expression_grid_data(
idd,
path=os.path.join(
folder,
"%s_%s_%s_%s.zip" % (gene, sag_or_cor, time_point, idd)))
def download_grid_recent(self,
gene: str,
folder: str = '../data',
sag_or_cor: str = "sagittal",
adu_or_dev: str = "adult",
time_point: str = "P56") -> Union[str, bool]:
"""Dowloads the most recently qc-ed file among the ones available
Args
----
gene: str
the gene to search for
sag_or_cor: str (accepts * wild cards)
`coronal` or `sagittal` or `*`
adu_or_dev: str (accepts * wild cards)
`adult`, `development`, `both`
time_point: str (it will be autmatically wildcarded)
e.g. "P56", "E", "E13", "P"
Returns
-------
output_path: output_path or bool
if the download was successfull returns the path to the file otherwise False
"""
ids = self.find_id_ish(gene,
sag_or_cor=sag_or_cor,
adu_or_dev=adu_or_dev,
time_point=time_point)
try:
idd = ids[0]
output_path = os.path.join(
folder,
"%s_%s_%s_%s.zip" % (gene, sag_or_cor, time_point, idd))
self.gda.download_expression_grid_data(idd, path=output_path)
return output_path
except IndexError:
logging.warn("Experiment %s was never performed" % gene)
return False
def getAsync(self, from_cache, aggregations):
# load data once with from_cache = False, then change it to True to read it from disk instead of fetching it from the api
if not from_cache:
# we use the RmaApi to query specific information, such as the section data sets of a specific gene
# for docs, see: https://alleninstitute.github.io/AllenSDK/allensdk.api.queries.rma_api.html
rma = RmaApi()
# there might be a way to retrieve data in higher resolution, as stated here (default is 25, 10 is also available - but resolution is ignored for download_gene_expression_grid_data)
# https://alleninstitute.github.io/AllenSDK/_modules/allensdk/api/queries/grid_data_api.html
# See `Downloading 3-D Projection Grid Data <http://help.brain-map.org/display/api/Downloading+3-D+Expression+Grid+Data#name="Downloading3-DExpressionGridData-DOWNLOADING3DPROJECTIONGRIDDATA">`_
gdApi = GridDataApi()
# http://api.brain-map.org/examples/rma_builder/index.html
# http://api.brain-map.org/examples/rma_builder/rma_builder.html
# https://allensdk.readthedocs.io/en/latest/data_api_client.html
sectionDataSets = pd.DataFrame(
rma.model_query(
model='SectionDataSet',
#! criteria="plane_of_section[name$eqcoronal]", note that saggital only spans the left hemisphere, so this is tough to compare with human data.
filters={'failed': 'false'},
include=
f"genes[acronym$il{self.geneAcronym}],products[id$eq1]", # $il = case-insensitive like | yes, weird notation... id = 1 = mouse brain atlas (not developing!)
num_rows='all'))
# model's documentation: http://api.brain-map.org/doc/SectionDataSet.html
# https://community.brain-map.org/t/attempting-to-download-substructures-for-coronal-p56-mouse-atlas/174/2
experiments = {}
# http://help.brain-map.org/display/mousebrain/Documentation
annotations = np.fromfile(Utils.getRelativeFilepath(
"annotations\\P56_Mouse_gridAnnotation\\gridAnnotation.raw"),
dtype="uint32")
# https://community.brain-map.org/t/how-to-acquire-the-structure-label-for-the-expression-grid-data/150/4
# for Mouse P56, structure_graph_id = 1 according to http://help.brain-map.org/display/api/Atlas+Drawings+and+Ontologies
structure_map = StructureMap.StructureMap(
reference_space_key='annotation/ccf_2017',
resolution=25).get(structure_graph_id=1) # , annotation, meta
# from http://alleninstitute.github.io/AllenSDK/_static/examples/nb/reference_space.html#Downloading-an-annotation-volume
for index, row in sectionDataSets.iterrows(
): # https://stackoverflow.com/questions/16476924/how-to-iterate-over-rows-in-a-dataframe-in-pandas
exp_id = row['id']
exp_path = f"cache\\mouse_ish-expr\\{exp_id}\\"
try:
# https://community.brain-map.org/t/whole-mouse-brain-gene-expression-data/447/4
# explanation of what "energy" means here:
# expression density = sum of expressing pixels / sum of all pixels in division
# expression intensity = sum of expressing pixel intensity / sum of expressing pixels
# expression energy = expression intensity * expression density
gdApi.download_gene_expression_grid_data(
exp_id, GridDataApi.ENERGY, exp_path)
expression_levels = np.fromfile(exp_path + "energy.raw",
dtype=np.float32)
# According to the doc @ http://help.brain-map.org/display/api/Downloading+3-D+Expression+Grid+Data
# we have "A raw uncompressed float (32-bit) little-endian volume representing average expression energy per voxel.
# A value of "-1" represents no data. This file is returned by default if the volumes parameter is null."
data = pd.DataFrame({
Constants.EXPR_LVL: expression_levels,
"structure_id": annotations
})
# some expression_levels are assigned to a structure of id 0. same is true for Jure's approach.
# according to the Allen institue, this is just due to background-noise:
# https://community.brain-map.org/t/how-to-acquire-the-structure-label-for-the-expression-grid-data/150/4
# values of -1 mean "no value obtained", hence we filter them out:
data = data[(data[Constants.EXPR_LVL] != -1)
& (data.structure_id != 0)]
data[Constants.Z_SCORE] = Utils.z_score(
data[Constants.EXPR_LVL])
# https://stackoverflow.com/questions/31528819/using-merge-on-a-column-and-index-in-pandas
# https://stackoverflow.com/questions/45147100/pandas-drop-columns-with-all-nans
name = f'mouse_{exp_id}_{Constants.PlaneOfSections[row["plane_of_section_id"]]}'
data = Utils.merge_with_structure(
data, structure_map, MouseISHData.VALUE_COLUMNS,
aggregations)
Utils.save(data, self.cache_path, name + '.pkl')
experiments['mouse - ' + Constants.PlaneOfSections[
row["plane_of_section_id"]]] = data
except Exception as e:
print(
f"Error retrieving mouse-ish experiment {exp_id}: {str(e)}"
)
raise e
return experiments
else:
if not glob.glob(self.cache_path):
Utils.log.warning(
f"No cached dataframe found. Check whether you have access to file '{self.cache_path}' and whether it exists. Obtaining data without caching now..."
)
return self.get(False, aggregations)
return {
'mouse - ' + Utils.getFilename(file).split('_')[2]:
Utils.load(file)
for file in glob.glob(f'{self.cache_path}/*.pkl')
}