Lydia Ng lydiang

## download_adult_human_atlases.py
import urllib.request
import json
import os

def fetch_atlas_metadata( atlas_id ) :

    # RMA query to find images for atlas
    query_url = "http://api.brain-map.org/api/v2/data/query.json?criteria=model::Atlas"
    query_url += ",rma::criteria,[id$eq%d]" % (atlas_id)
    query_url += ",rma::include,structure_graph(ontology),graphic_group_labels"

## value_count.csv

          
            count
            value

            
              44658375
              0

            
              6931
              1

            
              7339
              2

            
              134366
              6

            
              70327
              7

            
              14877
              9

            
              130357
              10

            
              5559
              12

            
              14706
              15

## unique_value_counts.py
import SimpleITK as sitk
import numpy as np
import pandas as pd

# downloaded from http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/annotation/ccf_2017/
filename = 'annotation_25.nrrd'
itkimage = sitk.ReadImage(filename)
print(itkimage)

numpyImage = sitk.GetArrayFromImage(itkimage)

## bucket_structure_and_metadata.py
#
# Bucket Organization
#
'''
allen_mouse_brain_atlas
+-- manifest.json               # for later integration with AllenSDK
+-- section_data_sets.json      # high level metadata for each section data set
+-- section_data_set_<section_data_set_id>
¦   +-- section_data_set.json   # detailed metadata for each section data set
¦   +-- downsample_<downsample_factor>

## developing_mouse_atlases_sections.txt
http://api.brain-map.org/api/v2/data/query.xml?criteria=
model::AtlasImage
,
rma::criteria,
[annotated$eqtrue],atlas_data_set(atlases[id$in181276165,181276164,181276162,181276160,181276151,181276130,181275741])
,
rma::include,
atlas_data_set(atlases)
,rma::options
[only$eq'atlases.name,atlases.id,data_sets.section_thickness,sub_images.section_number,sub_images.id']

## hba_position_example.py
import os
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#
# Example python code to plot microarray sample on MRI image
# and replicate an instance of the planar view
#

## ccf_unique_ids.py
import urllib
import os
import pandas as pd
import numpy as np
import nrrd

#
# Example code to get all unique ID in CCF annotation volume
# and match them up to structure acronyms
#

## structure_api_query.txt
http://api.brain-map.org/api/v2/data/query.csv?criteria=model::Structure,
rma::criteria,[ontology_id$eq1],
rma::options[order$eq%27structures.graph_order$asc%27][num_rows$eqall]

## break_up_atlas_svg.py
import urllib, json
import os
import xml.etree.ElementTree as ET

#
# Example python code to break up an atlas svg by structure
#
# Read the Allen Brain Atlas API documentation:
# http://help.brain-map.org/display/api/Atlas+Drawings+and+Ontologies
#

## download_image_series_8bit.py
import urllib, json
import os

#
# Example python code to download images from an image series
#
# Find the image series you want from the Allen Brain Atlas portal
# Take a note of the dataset id
#
	import urllib.request
	import json
	import os

	def fetch_atlas_metadata( atlas_id ) :

	# RMA query to find images for atlas
	query_url = "http://api.brain-map.org/api/v2/data/query.json?criteria=model::Atlas"
	query_url += ",rma::criteria,[id$eq%d]" % (atlas_id)
	query_url += ",rma::include,structure_graph(ontology),graphic_group_labels"
	count	value
	44658375	0
	6931	1
	7339	2
	134366	6
	70327	7
	14877	9
	130357	10
	5559	12
	14706	15
	import SimpleITK as sitk
	import numpy as np
	import pandas as pd

	# downloaded from http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/annotation/ccf_2017/
	filename = 'annotation_25.nrrd'
	itkimage = sitk.ReadImage(filename)
	print(itkimage)

	numpyImage = sitk.GetArrayFromImage(itkimage)
	#
	# Bucket Organization
	#
	'''
	allen_mouse_brain_atlas
	+-- manifest.json # for later integration with AllenSDK
	+-- section_data_sets.json # high level metadata for each section data set
	+-- section_data_set_<section_data_set_id>
	¦ +-- section_data_set.json # detailed metadata for each section data set
	¦ +-- downsample_<downsample_factor>
	http://api.brain-map.org/api/v2/data/query.xml?criteria=
	model::AtlasImage
	,
	rma::criteria,
	[annotated$eqtrue],atlas_data_set(atlases[id$in181276165,181276164,181276162,181276160,181276151,181276130,181275741])
	,
	rma::include,
	atlas_data_set(atlases)
	,rma::options
	[only$eq'atlases.name,atlases.id,data_sets.section_thickness,sub_images.section_number,sub_images.id']
	import os
	import nibabel as nib
	import numpy as np
	import matplotlib.pyplot as plt
	import pandas as pd

	#
	# Example python code to plot microarray sample on MRI image
	# and replicate an instance of the planar view
	#
	import urllib
	import os
	import pandas as pd
	import numpy as np
	import nrrd

	#
	# Example code to get all unique ID in CCF annotation volume
	# and match them up to structure acronyms
	#
	http://api.brain-map.org/api/v2/data/query.csv?criteria=model::Structure,
	rma::criteria,[ontology_id$eq1],
	rma::options[order$eq%27structures.graph_order$asc%27][num_rows$eqall]
	import urllib, json
	import os
	import xml.etree.ElementTree as ET

	#
	# Example python code to break up an atlas svg by structure
	#
	# Read the Allen Brain Atlas API documentation:
	# http://help.brain-map.org/display/api/Atlas+Drawings+and+Ontologies
	#
	import urllib, json
	import os

	#
	# Example python code to download images from an image series
	#
	# Find the image series you want from the Allen Brain Atlas portal
	# Take a note of the dataset id
	#