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Implemented 1) function to add metadata to root folder in existing hdf5 file, 2) piece of code to display root folder's metadata on treemap's hoover.

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florez_j
2023-11-02 15:46:14 +01:00
parent 86be738216
commit 25c0f07cc3
1 changed files with 77 additions and 57 deletions
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114
hdf5_lib.py
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@@ -10,7 +10,7 @@ import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
def read_hdf5_as_dataframe(filename):
def read_mtable_as_dataframe(filename):
""" Reconstruct a Matlab Table encoded in a .h5 file as a Pandas DataFrame. The input h5. file
contains as many groups as rows in the Matlab Table, and each group stores dataset-like variables in the Table as
@@ -69,11 +69,12 @@ def read_hdf5_as_dataframe(filename):
return output_dataframe
def is_callable_list(x : list):
return all([callable(item) for item in x])
def is_str_list(x : list):
return all([isinstance(item,str) for item in x])
def is_nested_hierarchy(df) -> bool:
"""receives a dataframe with categorical columns and checks whether rows form a nested group hierarchy.
That is, from bottom to top, subsequent hierarchical levels contain nested groups. The lower level groups belong to exactly one group in the higher level group.
@@ -103,8 +104,6 @@ def is_nested_hierarchy(df) -> bool:
return all([are_nested(df_tmp,'level_'+str(i)+'_groups','level_'+str(i+1)+'_groups') for i in range(len(df_tmp.columns)-1)])
def get_attr_names(input_data):
# TODO: extend this to file-system paths
@@ -119,7 +118,6 @@ def create_group_hierarchy(obj, df, columns):
Input:
obj (h5py.File or h5py.Group)
columns (list of strs): denote categorical columns in df to be used to define hdf5 file group hierarchy
"""
if not columns:
@@ -128,6 +126,9 @@ def create_group_hierarchy(obj, df, columns):
# Determine categories associated with first categorical column
unique_values = df[columns[0]].unique()
if obj.name == '/':
obj.attrs.create('count',df.shape[0])
for group_name in unique_values:
group = obj.require_group(group_name)
@@ -142,9 +143,9 @@ def create_group_hierarchy(obj, df, columns):
def get_parent_child_relationships(file: h5py.File):
nodes = []
parent = []
values = []
nodes = ['/']
parent = ['']
values = [file.attrs['count']]
def node_visitor(name,obj):
if isinstance(obj,h5py.Group):
@@ -184,12 +185,21 @@ def format_group_names(names: list):
def display_group_hierarchy_on_treemap(filename: str):
def display_group_hierarchy_on_a_treemap(filename: str):
with h5py.File(filename,'r') as file:
nodes, parents, values = get_parent_child_relationships(file)
#formating_df = format_group_names(nodes + ["/"])
metadata_list = []
metadata_dict={}
for key in file.attrs.keys():
if 'metadata' in key:
metadata_dict[key[key.find('_')+1::]]= file.attrs[key]
metadata_list.append(key[key.find('_')+1::]+':'+file.attrs[key])
metadata = '<br>'.join(['<br>'] + metadata_list)
customdata_series = pd.Series(nodes)
customdata_series[0] = metadata
fig = make_subplots(1, 1, specs=[[{"type": "domain"}]],)
fig.add_trace(go.Treemap(
@@ -197,18 +207,26 @@ def display_group_hierarchy_on_treemap(filename: str):
parents=parents,#formating_df['formated_names'][parents],
values=values,
branchvalues='total',
customdata= pd.Series(nodes),
customdata= customdata_series,
#marker=dict(
# colors=df_all_trees['color'],
# colorscale='RdBu',
# cmid=average_score),
#hovertemplate='<b>%{label} </b> <br> Number of files: %{value}<br> Success rate: %{color:.2f}',
hovertemplate='<b>%{label} </b> <br> Count: %{value} <br> Path: %{customdata}',
name=''
name='',
root_color="lightgrey"
))
fig.update_layout(width = 800, height= 600, margin = dict(t=50, l=25, r=25, b=25))
fig.show()
def annotate_root_dir(filename,annotation_dict: dict):
with h5py.File(filename,'r+') as file:
for key in annotation_dict:
file.attrs.create('metadata_'+key, annotation_dict[key])
def create_hdf5_file(ofilename, input_data, approach : str, group_by_funcs : list, extract_attrs_func = None):
""" Creates an hdf5 file with as many levels as indicated by len(group_by_funcs).
@@ -279,9 +297,9 @@ def create_hdf5_file(ofilename, input_data, approach : str, group_by_funcs : lis
# f.create_group(join_path(group_name,subgroup_name))
# Get groups at the bottom of the hierarchy
bottom_level_groups = get_groups_at_a_level(file, file.attrs['depth'])
#bottom_level_groups = get_groups_at_a_level(file, file.attrs['depth'])
nodes, parents, values = get_parent_child_relationships(file)
#nodes, parents, values = get_parent_child_relationships(file)
print(':)')
#fig = px.treemap(values=values,names=nodes, parents= parents)
#fig.update_traces(root_color="lightgrey")
@@ -299,7 +317,7 @@ def create_hdf5_file(ofilename, input_data, approach : str, group_by_funcs : lis
#
# Add datasets to groups and the groups and the group's attributes
return 0
#return 0
def augment_with_filetype(df):
@@ -323,27 +341,13 @@ def group_by_df_column(df, column_name: str):
return df[column_name]
def main():
# input data frame
input_data = read_hdf5_as_dataframe('input_files\\BeamTimeMetaData.h5')
# Rename column 'name' with 'filename'. get_filetype finds filetypes based on extension of filenames assumed to be located at the column 'filename'.
input_data = input_data.rename(columns = {'name':'filename'})
# Add column with filetypes to input_data
input_data = augment_with_filenumber(input_data)
input_data = augment_with_filetype(input_data)
#input_data['filetype'] = get_filetype(input_data)
print(input_data['filetype'].unique())
# Reduce input_data to files of ibw type
input_data = input_data.loc[input_data['filetype']=='ibw', : ]
#input_data = input_data.loc[input_data['sample']!='' , : ]
def split_sample_col_into_sample_and_data_quality_cols(input_data: pd.DataFrame):
sample_name = []
sample_quality = []
for item in input_data['sample']:
if item.find('(')!=-1:
print(item)
#print(item)
sample_name.append(item[0:item.find('(')])
sample_quality.append(item[item.find('(')+1:len(item)-1])
else:
@@ -356,32 +360,48 @@ def main():
input_data['sample'] = sample_name
input_data['data_quality'] = sample_quality
return input_data
def main():
# Read BeamTimeMetaData.h5, containing Thorsten's Matlab Table
input_data_df = read_mtable_as_dataframe('input_files\\BeamTimeMetaData.h5')
# Preprocess Thorsten's input_data dataframe so that i can be used to create a newer .h5 file
# under certain grouping specificiations.
input_data_df = input_data_df.rename(columns = {'name':'filename'})
input_data_df = augment_with_filenumber(input_data_df)
input_data_df = augment_with_filetype(input_data_df)
input_data_df = split_sample_col_into_sample_and_data_quality_cols(input_data_df)
input_data_df['lastModifiedDatestr'] = input_data_df['lastModifiedDatestr'].astype('datetime64[s]')
# Define grouping functions to be passed into create_hdf5_file function. These can also be set
# as strings refering to categorical columns in input_data_df.
test_grouping_funcs = True
if test_grouping_funcs:
group_by_sample = lambda x : group_by_df_column(x,'sample')
group_by_type = lambda x : group_by_df_column(x,'filetype')
group_by_filenumber = lambda x : group_by_df_column(x,'filenumber')
else:
group_by_sample = 'sample'
group_by_type = 'filetype'
group_by_filenumber = 'filenumber'
#fig = px.treemap(values=[10,4,3,3,2],names=[1,2,3,4,5], parents=[None,1,1,1,2],hover_name=['si senhor',':)',':)',':)','bottom'])
create_hdf5_file('test.h5',input_data_df, 'top-down', group_by_funcs = [group_by_sample, group_by_type, group_by_filenumber])
#fig = px.treemap(input_data,path=[px.Constant("BeamtimeMetadata.h5"),'sample','filenumber'])
#fig.update_traces(root_color = "lightgrey")
#fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
#fig.show()
success = create_hdf5_file('test.h5',input_data, 'top-down', group_by_funcs = [group_by_sample, group_by_filenumber])
annotation_dict = {'Campaign name': 'SLS-Campaign-2023',
'Users':'Thorsten, Luca, Zoe',
'Startdate': str(input_data_df['lastModifiedDatestr'].min()),
'Enddate': str(input_data_df['lastModifiedDatestr'].max())
}
annotate_root_dir('test.h5',annotation_dict)
display_group_hierarchy_on_treemap('test.h5')
display_group_hierarchy_on_a_treemap('test.h5')
print(':)')
#success = create_hdf5_file('test_v2.h5',input_data, 'top-down', group_by_funcs = ['sample','filenumber','filetype'])
#df['file_group']
#print(df.head())
if __name__ == '__main__':
main()