@@ -56,32 +56,6 @@ def get_samples_used_from_samplemap_df(samplemap_df, cond1, cond2):
5656 samples_c2 = samplemap_df [[cond2 == x for x in samplemap_df ["condition" ]]]["sample" ]
5757 return list (samples_c1 ), list (samples_c2 )
5858
59- def get_all_samples_from_samplemap_df (samplemap_df ):
60- return list (samplemap_df ["sample" ])
61-
62- # Cell
63- import pandas as pd
64-
65- def get_samplenames_from_input_df (data ):
66- """extracts the names of the samples of the AQ input dataframe"""
67- names = list (data .columns )
68- names .remove ('protein' )
69- names .remove (QUANT_ID )
70- return names
71-
72- # Cell
73- import numpy as np
74- def filter_df_to_min_valid_values (quant_df_wideformat , samples_c1 , samples_c2 , min_valid_values ):
75- """filters dataframe in alphaquant format such that each column has a minimum number of replicates
76- """
77- quant_df_wideformat = quant_df_wideformat .replace (0 , np .nan )
78- df_c1_min_valid_values = quant_df_wideformat [samples_c1 ].dropna (thresh = min_valid_values , axis = 0 )
79- df_c2_min_valid_values = quant_df_wideformat [samples_c2 ].dropna (thresh = min_valid_values , axis = 0 )
80- idxs_both = df_c1_min_valid_values .index .intersection (df_c2_min_valid_values .index )
81- quant_df_reduced = quant_df_wideformat .iloc [idxs_both ].reset_index ()
82- return quant_df_reduced
83-
84-
8559# Cell
8660def get_condpairname (condpair ):
8761 return f"{ condpair [0 ]} _VS_{ condpair [1 ]} "
@@ -102,39 +76,6 @@ def make_dir_w_existcheck(dir):
10276 if not os .path .exists (dir ):
10377 os .makedirs (dir )
10478
105- # Cell
106- import os
107- def get_results_plot_dir_condpair (results_dir , condpair ):
108- results_dir_plots = f"{ results_dir } /{ condpair } _plots"
109- make_dir_w_existcheck (results_dir_plots )
110- return results_dir_plots
111-
112- # Cell
113- def get_middle_elem (sorted_list ):
114- nvals = len (sorted_list )
115- if nvals == 1 :
116- return sorted_list [0 ]
117- middle_idx = nvals // 2
118- if nvals % 2 == 1 :
119- return sorted_list [middle_idx ]
120- return 0.5 * (sorted_list [middle_idx ] + sorted_list [middle_idx - 1 ])
121-
122- # Cell
123- import numpy as np
124- def get_nonna_array (array_w_nas ):
125- res = []
126- isnan_arr = np .isnan (array_w_nas )
127-
128- for idx in range (len (array_w_nas )):
129- sub_res = []
130- sub_array = array_w_nas [idx ]
131- na_array = isnan_arr [idx ]
132- for idx2 in range (len (sub_array )):
133- if not na_array [idx2 ]:
134- sub_res .append (sub_array [idx2 ])
135- res .append (np .array (sub_res ))
136- return np .array (res )
137-
13879# Cell
13980import numpy as np
14081def get_non_nas_from_pd_df (df ):
@@ -152,12 +93,6 @@ def get_ionints_from_pd_df(df):
15293 }
15394
15495# Cell
155- def invert_dictionary (my_map ):
156- inv_map = {}
157- for k , v in my_map .items ():
158- inv_map [v ] = inv_map .get (v , []) + [k ]
159- return inv_map
160-
16196from collections import defaultdict
16297def invert_tuple_list_w_nonunique_values (tuple_list ):
16398 inverted_dict = defaultdict (list )
@@ -373,20 +308,6 @@ def get_path_to_unformatted_file(input_file_name):
373308
374309
375310
376- # Cell
377-
378- # Cell
379- import os
380- def check_for_processed_runs_in_results_folder (results_folder ):
381- contained_condpairs = []
382- folder_files = os .listdir (results_folder )
383- result_files = list (filter (lambda x : "results.tsv" in x ,folder_files ))
384- for result_file in result_files :
385- res_name = result_file .replace (".results.tsv" , "" )
386- if ((f"{ res_name } .normed.tsv" in folder_files ) and (f"{ res_name } .results.ions.tsv" in folder_files )):
387- contained_condpairs .append (res_name )
388- return contained_condpairs
389-
390311# Cell
391312import pandas as pd
392313import os
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