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.

hdf5_lib.py

@@ -10,13 +10,13 @@ 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
     Datasets while categorical and numerical variables in the table are represented as attributes of each group.
 
-    Note:DataFrame is constructed columnwise to ensure homogenous data columns.
+    Note: DataFrame is constructed columnwise to ensure homogenous data columns.
 
     Parameters:
 
@@ -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
@@ -117,9 +116,8 @@ def create_group_hierarchy(obj, df, columns):
 
     """
         Input:
-        obj (h5py.File or h5py.Group)
+            obj (h5py.File or h5py.Group)
-        columns (list of strs): denote categorical columns in df to be used to define hdf5 file group hierarchy
+            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)
@@ -136,15 +137,15 @@ def create_group_hierarchy(obj, df, columns):
         sub_df = df[df[columns[0]]==group_name] # same as df.loc[df[columns[0]]==group_name,:]
         group.attrs.create('count',sub_df.shape[0])
 
-        #if group_name == 'MgO powder,H2O,HCl':
+        # if group_name == 'MgO powder,H2O,HCl':
-        #    print('Here:',sub_df.shape)
+        #   print('Here:',sub_df.shape)
         create_group_hierarchy(group, sub_df, columns[1::])
 
 def get_parent_child_relationships(file: h5py.File):
 
-    nodes = []
+    nodes = ['/']
-    parent = []
+    parent = ['']
-    values = []
+    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():
+def split_sample_col_into_sample_and_data_quality_cols(input_data: pd.DataFrame):
-
-    # 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']!=''     , : ]
     
     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
 
-    group_by_sample = lambda x : group_by_df_column(x,'sample')
+    return input_data
-    group_by_type = lambda x : group_by_df_column(x,'filetype')
-    group_by_filenumber = lambda x : group_by_df_column(x,'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'])
+def main():
 
-    #fig = px.treemap(input_data,path=[px.Constant("BeamtimeMetadata.h5"),'sample','filenumber'])
+    # Read BeamTimeMetaData.h5, containing Thorsten's Matlab Table
-    #fig.update_traces(root_color = "lightgrey")
+    input_data_df = read_mtable_as_dataframe('input_files\\BeamTimeMetaData.h5')
-    #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])
 
-    display_group_hierarchy_on_treemap('test.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'
+
+    create_hdf5_file('test.h5',input_data_df, 'top-down', group_by_funcs = [group_by_sample, group_by_type, 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_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()