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import os
import h5py
import pandas as pd
import numpy as np
def convert_nodes(nodes_file, node_types_file, **params):
is_h5 = False
try:
h5file = h5py.File(nodes_file, 'r')
is_h5 = True
except Exception as e:
pass
if is_h5:
update_h5_nodes(nodes_file, node_types_file, **params)
return
update_csv_nodes(nodes_file, node_types_file, **params)
# columns which need to be renamed, key is original name and value is the updated name
column_renames = {
'id': 'node_id',
'model_id': 'node_type_id',
'electrophysiology': 'dynamics_params',
'level_of_detail': 'model_type',
'morphology': 'morphology',
'params_file': 'dynamics_params',
'x_soma': 'x',
'y_soma': 'y',
'z_soma': 'z'
}
def update_h5_nodes(nodes_file, node_types_file, network_name, output_dir='output',
column_order=('node_type_id', 'model_type', 'model_template', 'model_processing', 'dynamics_params',
'morphology')):
# open nodes and node-types into a single table
input_h5 = h5py.File(nodes_file, 'r')
output_name = '{}_nodes.h5'.format(network_name)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
nodes_output_fn = os.path.join(output_dir, output_name)
# save nodes hdf5
with h5py.File(nodes_output_fn, 'w') as h5:
#h5.copy()
#grp = h5.create_group('/nodes/{}'.format(network_name))
#input_grp = input_h5['/nodes/']
nodes_path = '/nodes/{}'.format(network_name)
h5.copy(input_h5['/nodes/'], nodes_path)
grp = h5[nodes_path]
grp.move('node_gid', 'node_id')
grp.move('node_group', 'node_group_id')
node_types_csv = pd.read_csv(node_types_file, sep=' ')
node_types_csv = node_types_csv.rename(index=str, columns=column_renames)
# Change values for model type
model_type_map = {
'biophysical': 'biophysical',
'point_IntFire1': 'point_process',
'intfire': 'point_process',
'virtual': 'virtual',
'iaf_psc_alpha': 'nest:iaf_psc_alpha',
'filter': 'virtual'
}
node_types_csv['model_type'] = node_types_csv.apply(lambda row: model_type_map[row['model_type']], axis=1)
# Add model_template column
def model_template(row):
model_type = row['model_type']
if model_type == 'biophysical':
return 'ctdb:Biophys1.hoc'
elif model_type == 'point_process':
return 'nrn:IntFire1'
else:
return 'NONE'
node_types_csv['model_template'] = node_types_csv.apply(model_template, axis=1)
# Add model_processing column
def model_processing(row):
model_type = row['model_type']
if model_type == 'biophysical':
return 'aibs_perisomatic'
else:
return 'NONE'
node_types_csv['model_processing'] = node_types_csv.apply(model_processing, axis=1)
# Reorder columns
orig_columns = node_types_csv.columns
col_order = [cn for cn in column_order if cn in orig_columns]
col_order += [cn for cn in node_types_csv.columns if cn not in column_order]
node_types_csv = node_types_csv[col_order]
# Save node-types csv
node_types_output_fn = os.path.join(output_dir, '{}_node_types.csv'.format(network_name))
node_types_csv.to_csv(node_types_output_fn, sep=' ', index=False, na_rep='NONE')
# open nodes and node-types into a single table
'''
print('loading csv files')
nodes_tmp = pd.read_csv(nodes_file, sep=' ')
node_types_tmp = pd.read_csv(node_types_file, sep=' ')
nodes_df = pd.merge(nodes_tmp, node_types_tmp, on='node_type_id')
n_nodes = len(nodes_df.index)
# rename required columns
nodes_df = nodes_df.rename(index=str, columns=column_renames)
# Old versions of node_type_id may be set to strings/floats, convert to integers
dtype_ntid = nodes_df['node_type_id'].dtype
if dtype_ntid == 'object':
# if string, move model_id to pop_name and create an integer node_type_id column
if 'pop_name' in nodes_df.columns:
nodes_df = nodes_df.drop('pop_name', axis=1)
nodes_df = nodes_df.rename(index=str, columns={'node_type_id': 'pop_name'})
ntid_map = {pop_name: indx for indx, pop_name in enumerate(nodes_df['pop_name'].unique())}
nodes_df['node_type_id'] = nodes_df.apply(lambda row: ntid_map[row['pop_name']], axis=1)
elif dtype_ntid == 'float64':
nodes_df['node_type_id'] = nodes_df['node_type_id'].astype('uint64')
# divide columns up into nodes and node-types columns, and for nodes determine which columns are valid for every
# node-type. The rules are
# 1. If all values are the same for a node-type-id, column belongs in node_types csv. If there's any intra
# node-type heterogenity then the column belongs in the nodes h5.
# 2. For nodes h5 columns, a column belongs to a node-type-id if it contains at least one non-null value
print('parsing input')
opt_columns = [n for n in nodes_df.columns if n not in ['node_id', 'node_type_id']]
heterogeneous_cols = {cn: False for cn in opt_columns}
nonnull_cols = {} # for each node-type, a list of columns that contains at least one non-null value
for node_type_id, nt_group in nodes_df.groupby(['node_type_id']):
nonnull_cols[node_type_id] = set(nt_group.columns[nt_group.isnull().any() == False].tolist())
for col_name in opt_columns:
heterogeneous_cols[col_name] |= len(nt_group[col_name].unique()) > 1
nodes_columns = set(cn for cn, val in heterogeneous_cols.items() if val)
nodes_types_columns = [cn for cn, val in heterogeneous_cols.items() if not val]
# Check for nodes columns that has non-numeric values, these will require some special processing to save to hdf5
string_nodes_columns = set()
for col_name in nodes_columns:
if nodes_df[col_name].dtype == 'object':
string_nodes_columns.add(col_name)
if len(string_nodes_columns) > 0:
print('Warning: column(s) {} have non-numeric values that vary within a node-type and will be stored in h5 format'.format(list(string_nodes_columns)))
# Divide the nodes columns into groups and create neccessary lookup tables. If two node-types share the same
# non-null columns then they belong to the same group
grp_idx2cols = {} # group-id --> group-columns
grp_cols2idx = {} # group-columns --> group-id
grp_id2idx = {} # node-type-id --> group-id
group_index = -1
for nt_id, cols in nonnull_cols.items():
group_columns = sorted(list(nodes_columns & cols))
col_key = tuple(group_columns)
if col_key in grp_cols2idx:
grp_id2idx[nt_id] = grp_cols2idx[col_key]
else:
group_index += 1
grp_cols2idx[col_key] = group_index
grp_idx2cols[group_index] = group_columns
grp_id2idx[nt_id] = group_index
# merge x,y,z columns, if they exists, into 'positions' dataset
grp_pos_cols = {}
for grp_idx, cols in grp_idx2cols.items():
pos_list = []
for coord in ['x', 'y', 'z']:
if coord in cols:
pos_list += coord
grp_idx2cols[grp_idx].remove(coord)
if len(pos_list) > 0:
grp_pos_cols[grp_idx] = pos_list
# Create the node_group and node_group_index columns
nodes_df['__bmtk_node_group'] = nodes_df.apply(lambda row: grp_id2idx[row['node_type_id']], axis=1)
nodes_df['__bmtk_node_group_index'] = [0]*n_nodes
for grpid in grp_idx2cols.keys():
group_size = len(nodes_df[nodes_df['__bmtk_node_group'] == grpid])
nodes_df.loc[nodes_df['__bmtk_node_group'] == grpid, '__bmtk_node_group_index'] = range(group_size)
# Save nodes.h5 file
nodes_output_fn = os.path.join(output_dir, '{}_nodes.h5'.format(network_name))
node_types_output_fn = os.path.join(output_dir, '{}_node_types.csv'.format(network_name))
if not os.path.exists(output_dir):
os.mkdir(output_dir)
print('Creating {}'.format(nodes_output_fn))
with h5py.File(nodes_output_fn, 'w') as hf:
hf.create_dataset('nodes/node_gid', data=nodes_df['node_id'], dtype='uint64')
hf['nodes/node_gid'].attrs['network'] = network_name
hf.create_dataset('nodes/node_type_id', data=nodes_df['node_type_id'], dtype='uint64')
hf.create_dataset('nodes/node_group', data=nodes_df['__bmtk_node_group'], dtype='uint32')
hf.create_dataset('nodes/node_group_index', data=nodes_df['__bmtk_node_group_index'], dtype='uint64')
for grpid, cols in grp_idx2cols.items():
group_slice = nodes_df[nodes_df['__bmtk_node_group'] == grpid]
for col_name in cols:
dataset_name = 'nodes/{}/{}'.format(grpid, col_name)
if col_name in string_nodes_columns:
# for columns with non-numeric values
dt = h5py.special_dtype(vlen=bytes)
hf.create_dataset(dataset_name, data=group_slice[col_name], dtype=dt)
else:
hf.create_dataset(dataset_name, data=group_slice[col_name])
# special case for positions
if grpid in grp_pos_cols:
hf.create_dataset('nodes/{}/positions'.format(grpid),
data=group_slice.as_matrix(columns=grp_pos_cols[grpid]))
# Save the node_types.csv file
print('Creating {}'.format(node_types_output_fn))
node_types_table = nodes_df[['node_type_id'] + nodes_types_columns]
node_types_table = node_types_table.drop_duplicates()
if len(sort_order) > 0:
node_types_table = node_types_table.sort_values(by=sort_order)
node_types_table.to_csv(node_types_output_fn, sep=' ', index=False) # , na_rep='NONE')
'''
def update_csv_nodes(nodes_file, node_types_file, network_name, output_dir='network',
column_order=('node_type_id', 'model_type', 'model_template', 'model_processing',
'dynamics_params', 'morphology')):
# open nodes and node-types into a single table
print('loading csv files')
nodes_tmp = pd.read_csv(nodes_file, sep=' ')
node_types_tmp = pd.read_csv(node_types_file, sep=' ')
if 'model_id' in nodes_tmp:
nodes_df = pd.merge(nodes_tmp, node_types_tmp, on='model_id')
elif 'node_type_id' in nodes_tmp:
nodes_df = pd.merge(nodes_tmp, node_types_tmp, on='node_type_id')
else:
raise Exception('Could not find column to merge nodes and node_types')
n_nodes = len(nodes_df.index)
# rename required columns
nodes_df = nodes_df.rename(index=str, columns=column_renames)
# Old versions of node_type_id may be set to strings/floats, convert to integers
dtype_ntid = nodes_df['node_type_id'].dtype
if dtype_ntid == 'object':
# if string, move model_id to pop_name and create an integer node_type_id column
if 'pop_name' in nodes_df:
nodes_df = nodes_df.drop('pop_name', axis=1)
nodes_df = nodes_df.rename(index=str, columns={'node_type_id': 'pop_name'})
ntid_map = {pop_name: indx for indx, pop_name in enumerate(nodes_df['pop_name'].unique())}
nodes_df['node_type_id'] = nodes_df.apply(lambda row: ntid_map[row['pop_name']], axis=1)
elif dtype_ntid == 'float64':
nodes_df['node_type_id'] = nodes_df['node_type_id'].astype('uint64')
# divide columns up into nodes and node-types columns, and for nodes determine which columns are valid for every
# node-type. The rules are
# 1. If all values are the same for a node-type-id, column belongs in node_types csv. If there's any intra
# node-type heterogenity then the column belongs in the nodes h5.
# 2. For nodes h5 columns, a column belongs to a node-type-id if it contains at least one non-null value
print('parsing input')
opt_columns = [n for n in nodes_df.columns if n not in ['node_id', 'node_type_id']]
heterogeneous_cols = {cn: False for cn in opt_columns}
nonnull_cols = {} # for each node-type, a list of columns that contains at least one non-null value
for node_type_id, nt_group in nodes_df.groupby(['node_type_id']):
nonnull_cols[node_type_id] = set(nt_group.columns[nt_group.isnull().any() == False].tolist())
for col_name in opt_columns:
heterogeneous_cols[col_name] |= len(nt_group[col_name].unique()) > 1
nodes_columns = set(cn for cn, val in heterogeneous_cols.items() if val)
nodes_types_columns = [cn for cn, val in heterogeneous_cols.items() if not val]
# Check for nodes columns that has non-numeric values, these will require some special processing to save to hdf5
string_nodes_columns = set()
for col_name in nodes_columns:
if nodes_df[col_name].dtype == 'object':
string_nodes_columns.add(col_name)
if len(string_nodes_columns) > 0:
print('Warning: column(s) {} have non-numeric values that vary within a node-type and will be stored in h5 format'.format(list(string_nodes_columns)))
# Divide the nodes columns into groups and create neccessary lookup tables. If two node-types share the same
# non-null columns then they belong to the same group
grp_idx2cols = {} # group-id --> group-columns
grp_cols2idx = {} # group-columns --> group-id
grp_id2idx = {} # node-type-id --> group-id
group_index = -1
for nt_id, cols in nonnull_cols.items():
group_columns = sorted(list(nodes_columns & cols))
col_key = tuple(group_columns)
if col_key in grp_cols2idx:
grp_id2idx[nt_id] = grp_cols2idx[col_key]
else:
group_index += 1
grp_cols2idx[col_key] = group_index
grp_idx2cols[group_index] = group_columns
grp_id2idx[nt_id] = group_index
# merge x,y,z columns, if they exists, into 'positions' dataset
grp_pos_cols = {}
for grp_idx, cols in grp_idx2cols.items():
pos_list = []
for coord in ['x', 'y', 'z']:
if coord in cols:
pos_list += coord
grp_idx2cols[grp_idx].remove(coord)
if len(pos_list) > 0:
grp_pos_cols[grp_idx] = pos_list
# Create the node_group and node_group_index columns
nodes_df['__bmtk_node_group'] = nodes_df.apply(lambda row: grp_id2idx[row['node_type_id']], axis=1)
nodes_df['__bmtk_node_group_index'] = [0]*n_nodes
for grpid in grp_idx2cols.keys():
group_size = len(nodes_df[nodes_df['__bmtk_node_group'] == grpid])
nodes_df.loc[nodes_df['__bmtk_node_group'] == grpid, '__bmtk_node_group_index'] = range(group_size)
# Save nodes.h5 file
nodes_output_fn = os.path.join(output_dir, '{}_nodes.h5'.format(network_name))
node_types_output_fn = os.path.join(output_dir, '{}_node_types.csv'.format(network_name))
if not os.path.exists(output_dir):
os.mkdir(output_dir)
print('Creating {}'.format(nodes_output_fn))
with h5py.File(nodes_output_fn, 'w') as hf:
grp = hf.create_group('/nodes/{}'.format(network_name))
grp.create_dataset('node_id', data=nodes_df['node_id'], dtype='uint64')
grp.create_dataset('node_type_id', data=nodes_df['node_type_id'], dtype='uint64')
grp.create_dataset('node_group_id', data=nodes_df['__bmtk_node_group'], dtype='uint32')
grp.create_dataset('node_group_index', data=nodes_df['__bmtk_node_group_index'], dtype='uint64')
for grpid, cols in grp_idx2cols.items():
group_slice = nodes_df[nodes_df['__bmtk_node_group'] == grpid]
for col_name in cols:
dataset_name = '{}/{}'.format(grpid, col_name)
if col_name in string_nodes_columns:
# for columns with non-numeric values
dt = h5py.special_dtype(vlen=bytes)
grp.create_dataset(dataset_name, data=group_slice[col_name], dtype=dt)
else:
grp.create_dataset(dataset_name, data=group_slice[col_name])
# special case for positions
if grpid in grp_pos_cols:
grp.create_dataset('{}/positions'.format(grpid),
data=group_slice.as_matrix(columns=grp_pos_cols[grpid]))
# Create empty dynamics_params
grp.create_group('{}/dynamics_params'.format(grpid))
# Save the node_types.csv file
print('Creating {}'.format(node_types_output_fn))
node_types_table = nodes_df[['node_type_id'] + nodes_types_columns]
node_types_table = node_types_table.drop_duplicates()
# Change values for model type
model_type_map = {
'biophysical': 'biophysical',
'point_IntFire1': 'point_process',
'virtual': 'virtual',
'intfire': 'point_process',
'filter': 'virtual'
}
node_types_table['model_type'] = node_types_table.apply(lambda row: model_type_map[row['model_type']], axis=1)
if 'set_params_function' in node_types_table:
node_types_table = node_types_table.drop('set_params_function', axis=1)
# Add model_template column
def model_template(row):
model_type = row['model_type']
if model_type == 'biophysical':
return 'ctdb:Biophys1.hoc'
elif model_type == 'point_process':
return 'nrn:IntFire1'
else:
return 'NONE'
node_types_table['model_template'] = node_types_table.apply(model_template, axis=1)
# Add model_processing column
def model_processing(row):
model_type = row['model_type']
if model_type == 'biophysical':
return 'aibs_perisomatic'
else:
return 'NONE'
node_types_table['model_processing'] = node_types_table.apply(model_processing, axis=1)
# Reorder columns
orig_columns = node_types_table.columns
col_order = [cn for cn in column_order if cn in orig_columns]
col_order += [cn for cn in node_types_table.columns if cn not in column_order]
node_types_table = node_types_table[col_order]
node_types_table.to_csv(node_types_output_fn, sep=' ', index=False, na_rep='NONE')
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