def generate_schema():
"""
This is the main, global defintion for the data type 'hello_tablelike'.
From this definition we can generate Eve configurations for server
endpoints that read/write the data, a python client for working with
batches of tablelike_entries that conform to this schema and talk to the
server, and auto generated smoke tests that exercise the endpoints with
randomly generated data.
"""
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_numeric_attribute('flt_val_0', min_val=0.0, max_val=None)
schema.add_numeric_attribute('flt_val_1', min_val=-10.0, max_val=10.0)
schema.add_categoric_attribute('string_val', valid_values=['x', 'y', 'z'])
schema.add_categoric_list_attribute('cat_list_val',
valid_values=['a', 'b', 'c'])
schema.add_numeric_list_attribute('num_list_val')
schema.add_foreignid_attribute('foreignid_val')
schema.add_foreignid_list_attribute('foreignid_list_val')
schema.add_json_attribute('json_val')
schema.add_int_attribute('int_val')
schema.add_int_list_attribute('int_list_val')
schema.add_index(['string_val'])
schema.add_index(['string_val', 'int_val'])
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_categoric_attribute('date_of_run', valid_values=None)
schema.add_categoric_attribute('display_name_short', valid_values=None)
schema.add_numeric_attribute('num_samples', min_val=0, max_val=None)
schema.add_numeric_attribute('num_otus', min_val=0, max_val=None)
return schema
def generate_schema():
"""
A schema that will be given non-standard behavior in the
api endpoints.
"""
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_numeric_attribute('flt_val_0_sub', min_val=-10.0, max_val=10.0)
schema.add_categoric_attribute('string_val_sub',
valid_values=['x', 'y', 'z'])
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_foreignid_attribute('project_id')
schema.add_categoric_attribute('filename')
# TODO change filesize to an int; values are too large to store as current
# numeric attribute (TOOL-398)
schema.add_categoric_attribute('filesize')
schema.add_categoric_attribute('filetype')
schema.add_categoric_attribute('uploadername')
schema.add_categoric_attribute('_created')
schema.add_categoric_attribute('notes')
schema.add_boolean_attribute('favorite')
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_foreignid_attribute('comparison_id')
schema.add_categoric_attribute('node_level', valid_values=None)
schema.add_categoric_attribute('node_name', valid_values=None)
schema.add_numeric_attribute('node_idx', min_val=0)
schema.add_numeric_attribute('parent_node_idx', min_val=-1)
schema.add_numeric_list_attribute('top_fst_otu_rankings_in_node')
schema.add_index(['comparison_id', 'node_level'])
schema.add_index(['comparison_id', 'parent_node_idx'])
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_categoric_attribute('species_number', valid_values=None)
schema.add_categoric_attribute('short_latin_name', valid_values=None)
schema.add_categoric_attribute('name', valid_values=None)
schema.add_int_attribute('display_order', min_val=None, max_val=None)
schema.add_categoric_attribute('group_name', valid_values=None)
schema.add_boolean_attribute('selected_by_default')
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_categoric_attribute('species_number', valid_values=None)
schema.add_categoric_attribute('analysis_network_name', valid_values=None)
schema.add_categoric_attribute('edge_type_name', valid_values=None)
schema.add_boolean_attribute('selected_by_default')
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_categoric_attribute('super_collection', valid_values=None)
schema.add_categoric_attribute('species_number', valid_values=None)
schema.add_categoric_attribute('edge_type_name', valid_values=None)
schema.add_numeric_attribute('super_collection_display_index')
schema.add_categoric_attribute('collection', valid_values=None)
schema.add_boolean_attribute('collection_selected_by_default')
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_categoric_attribute('study_key', valid_values=None)
schema.add_foreignid_attribute('tornado_run_id')
schema.add_categoric_attribute('tornado_sample_key', valid_values=None)
schema.add_categoric_attribute('study_sample_key', valid_values=None)
#the ordering of these values is the same as index_within_tornado_run in the otus endpoint
#FIXME this probably needs to be a new kind of attribute: sparse_numeric_list_attribute
#schema.add_numeric_list_attribute('otu_counts', min_val=None, max_val=None, min_num_vals=0, max_num_vals=None)
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_categoric_attribute('display_name_short', valid_values=None)
schema.add_categoric_attribute('display_name_long', valid_values=None)
schema.add_foreignid_attribute('tornado_run_id')
schema.add_categoric_attribute('query', valid_values=None)
schema.add_numeric_attribute('num_samples', min_val=0, max_val=None)
schema.add_categoric_list_attribute('sample_ids',
valid_values=None,
min_num_vals=0,
max_num_vals=None)
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_foreignid_attribute('tornado_run_id')
schema.add_numeric_attribute('index_within_tornado_run',
min_val=0,
max_val=None)
#the 'id' of the otu as an observation in the biom_table associated with the tornado
#run
schema.add_categoric_attribute('tornado_observation_key',
valid_values=None)
schema.add_categoric_attribute('otu_name', valid_values=None)
for taxa_level in TAXONOMY_LEVELS:
schema.add_categoric_attribute(taxa_level, valid_values=None)
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_categoric_attribute('username')
schema.add_categoric_attribute('given_name')
schema.add_categoric_attribute('family_name')
schema.add_categoric_attribute('thumb_url')
schema.add_categoric_attribute('passlib_hash')
schema.add_boolean_attribute('is_superuser')
#origin is simple name for how the user was created, e.g.
#users defined in the core nest_config have origin 'config:core'
schema.add_categoric_attribute('origin')
#optional: if 'origin' is an external system, external_id
#allows you to save an identifier (as a string) to
#cross-reference across the two systems
schema.add_categoric_attribute('external_id')
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_categoric_attribute('name')
return schema
def generate_schema():
num_quantile_levels = NUM_QUANTILES + 1
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_foreignid_attribute('cohort_id')
schema.add_categoric_attribute('node_level', valid_values=None)
schema.add_categoric_attribute('node_name', valid_values=None)
schema.add_numeric_attribute('node_idx', min_val=0)
schema.add_numeric_attribute('parent_node_idx', min_val=-1)
schema.add_index(['cohort_id', 'node_level'])
schema.add_index(['cohort_id', 'parent_node_idx'])
#schema.add_numeric_attribute('num_unique_otus_median',
# min_val=0, max_val=None)
schema.add_numeric_attribute('num_unique_otus_mean',
min_val=0, max_val=None)
#schema.add_numeric_list_attribute('num_unique_otus_quantiles',
# min_val=None, max_val=None,
# min_num_vals=num_quantile_levels, max_num_vals=num_quantile_levels)
schema.add_numeric_list_attribute('num_unique_otus_density_plot_x',
min_val=0.0, max_val=None,
min_num_vals=None, max_num_vals=None)
schema.add_numeric_list_attribute('num_unique_otus_density_plot_y',
min_val=0.0, max_val=None,
min_num_vals=None, max_num_vals=None)
schema.add_numeric_list_attribute('num_unique_otus_histo_bin_start_x',
min_val=0.0, max_val=None,
min_num_vals=None, max_num_vals=None)
schema.add_numeric_list_attribute('num_unique_otus_histo_bin_end_x',
min_val=0.0, max_val=None,
min_num_vals=None, max_num_vals=None)
schema.add_numeric_list_attribute('num_unique_otus_histo_bin_height_y',
min_val=0.0, max_val=None,
min_num_vals=None, max_num_vals=None)
schema.add_numeric_attribute('num_unique_otus_histo_num_zeros',
min_val=0.0, max_val=None)
schema.add_numeric_attribute('normalized_entropy_mean',
min_val=0, max_val=None)
schema.add_numeric_list_attribute('normalized_entropy_histo_bin_start_x',
min_val=0.0, max_val=None,
min_num_vals=None, max_num_vals=None)
schema.add_numeric_list_attribute('normalized_entropy_histo_bin_end_x',
min_val=0.0, max_val=None,
min_num_vals=None, max_num_vals=None)
schema.add_numeric_list_attribute('normalized_entropy_histo_bin_height_y',
min_val=0.0, max_val=None,
min_num_vals=None, max_num_vals=None)
schema.add_numeric_attribute('normalized_entropy_histo_num_zeros',
min_val=0.0, max_val=None)
#schema.add_numeric_attribute('relative_abundance_median',
# min_val=0, max_val=None)
schema.add_numeric_attribute('relative_abundance_mean',
min_val=0, max_val=None)
schema.add_numeric_list_attribute('relative_abundance_quantiles',
min_val=None, max_val=None,
min_num_vals=num_quantile_levels, max_num_vals=num_quantile_levels)
#schema.add_numeric_list_attribute('relative_abundance_density_plot_x',
# min_val=0.0, max_val=None,
# min_num_vals=num_quantile_levels, max_num_vals=num_quantile_levels)
#schema.add_numeric_list_attribute('relative_abundance_density_plot_y',
# min_val=0.0, max_val=None,
# min_num_vals=num_quantile_levels, max_num_vals=num_quantile_levels)
schema.add_numeric_list_attribute('relative_abundance_histo_bin_start_x',
min_val=0.0, max_val=None,
min_num_vals=None, max_num_vals=None)
schema.add_numeric_list_attribute('relative_abundance_histo_bin_end_x',
min_val=0.0, max_val=None,
min_num_vals=None, max_num_vals=None)
schema.add_numeric_list_attribute('relative_abundance_histo_bin_height_y',
min_val=0.0, max_val=None,
min_num_vals=None, max_num_vals=None)
schema.add_numeric_attribute('relative_abundance_histo_num_zeros',
min_val=0.0, max_val=None)
return schema
def generate_schema():
"""
"""
pipelines = [
'sample_clustering', 'gene_prioritization',
'gene_set_characterization', 'phenotype_prediction'
]
status_states = ['running', 'completed', 'failed']
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_categoric_attribute('name')
schema.add_categoric_attribute('notes')
schema.add_foreignid_attribute('project_id')
schema.add_categoric_attribute('pipeline', valid_values=pipelines)
schema.add_categoric_attribute('status', valid_values=status_states)
schema.add_categoric_attribute('error')
schema.add_categoric_attribute('_created')
schema.add_categoric_attribute('_updated')
schema.add_json_attribute('parameters')
schema.add_boolean_attribute('favorite')
return schema
def generate_schema():
schema = TablelikeSchema(COLLECTION_NAME)
schema.add_categoric_attribute('set_id', valid_values=None)
schema.add_categoric_attribute('set_name', valid_values=None)
schema.add_numeric_attribute('species_id')
schema.add_numeric_attribute('gene_count')
schema.add_categoric_attribute('collection', valid_values=None)
schema.add_categoric_attribute('edge_type_name', valid_values=None)
schema.add_categoric_attribute('supercollection', valid_values=None)
schema.add_categoric_attribute('url', valid_values=None)
schema.add_index(['set_id'])
return schema