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IMP Reference Guide  2.5.0
The Integrative Modeling Platform
solutions_io.py
1 """@namespace IMP.EMageFit.solutions_io
2  Utility functions to store and retrieve solution information.
3 """
4 
6 import IMP.EMageFit.database as database
7 
8 import sys
9 import heapq
10 import math
11 import os
12 import csv
13 import time
14 import logging
15 import glob
16 import numpy as np
17 import collections
18 
19 log = logging.getLogger("solutions_io")
20 
21 unit_delim = "/" # separate units within a field (eg, reference frames).
22 field_delim = ","
23 
24 ClusterRecord = collections.namedtuple('ClusterRecord',
25  ['cluster_id', 'n_elements', 'representative',
26  'elements', 'solutions_ids'])
27 
28 #
29 
30 # INPUT/OUTPUT OF SOLUTIONS OBTAINED WITH DominoModel
31 
32 #
33 
34 class HeapRecord(tuple):
35 
36  """
37  The heapq algorithm is a min-heap. I want a max-heap, that pops the
38  larger values out of the heap.
39  For that I have to modify the comparison function and also set the
40  index that is used for the comparison. The index corresponds to
41  the restraint that we desired to order by
42  """
43 
44  def __new__(self, x, i):
45  """
46  Build from a tuple and the index used to compare
47  """
48  self.i = i
49  return tuple.__new__(self, x)
50 
51  def __lt__(self, other):
52  """
53  Compare. To convert the min-heap into a max-heap, the lower than
54  comparison is transformed into a greater-than
55  """
56  i = self.i
57  if(self[i] > other[i]):
58  return True
59  return False
60 
61  # Need __le__ as well for older Pythons
62  def __le__(self, other):
63  i = self.i
64  return self[i] >= other[i]
65 
66 
67 def gather_best_solution_results(fns, fn_output, max_number=50000,
68  raisef=0.1, orderby="em2d"):
69  """
70  Reads a set of database files and merge them into a single file.
71 
72  @param fns List of files with databases
73  @param fn_output The database to create
74  @param max_number Maximum number of records to keep, sorted according
75  to orderby
76  @param raisef Ratio of problematic database files tolerated before
77  raising an error. This option is to tolerate some files
78  of the databases being broken because the cluster fails,
79  fill the disks, etc
80  @param orderby Criterium used to sort the the records
81  NOTE:
82  Makes sure to reorder all column names if neccesary before merging
83  The record for the native solution is only added once (from first file).
84  """
85  tbl = "results"
86  # Get names and types of the columns from first database file
87  db = database.Database2()
88  db.connect(fns[0])
89  names = db.get_table_column_names(tbl)
90  types = db.get_table_types(tbl)
91  indices = get_sorting_indices(names)
92  sorted_names = [names[i] for i in indices]
93  sorted_types = [types[i] for i in indices]
94 
95  names.sort()
96  ind = names.index(orderby)
97  they_are_sorted = field_delim.join(names)
98  # Get the native structure data from the first database
99  sql_command = """SELECT %s FROM %s
100  WHERE assignment="native" LIMIT 1 """ % (they_are_sorted, tbl)
101  native_data = db.retrieve_data(sql_command)
102  db.close()
103  log.info("Gathering results. Saving to %s", fn_output)
104  out_db = database.Database2()
105  out_db.create(fn_output, overwrite=True)
106  out_db.connect(fn_output)
107  out_db.create_table(tbl, sorted_names, sorted_types)
108 
109  best_records = []
110  n_problems = 0
111  for fn in fns:
112  try:
113  log.info("Reading %s", fn)
114  db.connect(fn)
115 # log.debug("Retrieving %s", they_are_sorted)
116  sql_command = """SELECT %s FROM %s
117  WHERE assignment<>"native"
118  ORDER BY %s ASC LIMIT %s """ % (
119  they_are_sorted, tbl, orderby, max_number)
120  data = db.retrieve_data(sql_command)
121  log.info("%s records read from %s", len(data), fn)
122  db.close()
123  # Fill heap
124  for d in data:
125  a = HeapRecord(d, ind)
126  if(len(best_records) < max_number):
127  heapq.heappush(best_records, a)
128  else:
129  # remember that < here compares for greater em2d value,
130  # as a HeapRecord is used
131  if(best_records[0] < a):
132  heapq.heapreplace(best_records, a)
133  except Exception as e:
134  log.error("Error for %s: %s", fn, e)
135  n_problems += 1
136 
137  # If the number of problematic files is too high, report that something
138  # big is going on. Otherwise tolerate some errors from some tasks that
139  # failed (memory errors, locks, writing errors ...)
140  ratio = float(n_problems) / float(len(fns))
141  if ratio > raisef:
142  raise IOError("There are %8.1f %s of the database "
143  "files to merge with problems! " % (ratio * 100, "%"))
144  # append the native data to the best_records
145  heapq.heappush(best_records, native_data[0])
146  out_db.store_data(tbl, best_records)
147  out_db.close()
148 
149 
150 def gather_solution_results(fns, fn_output, raisef=0.1):
151  """
152  Reads a set of database files and puts them in a single file
153  Makes sure to reorder all column names if neccesary before merging
154  @param fns List of database files
155  @param fn_output Name of the output database
156  @param raisef See help for gather_best_solution_results()
157  """
158  tbl = "results"
159  # Get names and types of the columns from first database file
160  db = database.Database2()
161  db.connect(fns[0])
162  names = db.get_table_column_names(tbl)
163  types = db.get_table_types(tbl)
164  indices = get_sorting_indices(names)
165  sorted_names = [names[i] for i in indices]
166  sorted_types = [types[i] for i in indices]
167  log.info("Gathering results. Saving to %s", fn_output)
168  out_db = database.Database2()
169  out_db.create(fn_output, overwrite=True)
170  out_db.connect(fn_output)
171  out_db.create_table(tbl, sorted_names, sorted_types)
172 
173  n_problems = 0
174  for fn in fns:
175  try:
176  log.info("Reading %s", fn)
177  db.connect(fn)
178  names = sorted(db.get_table_column_names(tbl))
179  they_are_sorted = field_delim.join(names)
180  log.debug("Retrieving %s", they_are_sorted)
181  sql_command = "SELECT %s FROM %s" % (they_are_sorted, tbl)
182  data = db.retrieve_data(sql_command)
183  out_db.store_data(tbl, data)
184  db.close()
185  except Exception as e:
186  log.error("Error for file %s: %s", fn, e)
187  n_problems += 1
188  ratio = float(n_problems) / float(len(fns))
189  if ratio > raisef:
190  raise IOError("There are %8.1f %s of the database "
191  "files to merge with problems! " % (ratio * 100, "%"))
192  out_db.close()
193 
194 
196  """ Return indices that sort the list l """
197  pairs = sorted([(element, i) for i, element in enumerate(l)])
198  indices = [p[1] for p in pairs]
199  return indices
200 
201 
202 def get_best_solution(fn_database, Nth, fields=False, orderby=False,
203  tbl="results"):
204  """
205  Recover the reference frame of the n-th best solution from a database.
206  The index Nth stars at 0
207  """
208  f = get_fields_string(fields)
209  sql_command = """ SELECT %s FROM %s
210  ORDER BY %s
211  ASC LIMIT 1 OFFSET %d """ % (f, tbl, orderby, Nth)
212  data = database.read_data(fn_database, sql_command)
213  if len(data) == 0:
214  raise ValueError("The requested %s-th best solution does not exist. "
215  "Only %s solutions found" % (Nth, len(data)))
216  # the only field last record is the solution requested
217  return data[0][0]
218 
219 
220 def get_pca(string, delimiter="/"):
221  pca = string.split(delimiter)
222  pca = [float(p) for p in pca]
223  return pca
224 
225 
226 def get_fields_string(fields):
227  """
228  Get a list of fields and return a string with them. If there are no
229  fields, return an *, indicating SQL that all the fields are requested
230  @param fields A list of strings
231  @return a string
232  """
233 
234  if fields:
235  return field_delim.join(fields)
236  return "*"
237 
238 
240 
241  """
242  Class for managing the results of the experiments
243  """
244 
245  def __init__(self, ):
246  self.records = []
247  self.native_table_name = "native"
248  self.results_table = "results"
249  self.placements_table = "placements"
250  self.ccc_table_name = "ccc"
251  self.cluster_records = []
252 
253  # columns describing a solution in the results
254  self.results_description_columns = ["solution_id", "assignment",
255  "reference_frames"]
256  self.results_description_types = [int, str, str]
257  # columns describing measures for a result
258  self.results_measures_columns = ["drms", "cdrms", "crmsd"]
259  self.results_measures_types = [float, float, float]
260 
261  def add_results_table(self, restraints_names, add_measures=False):
262  """
263  Build the table of results
264  @param restraints_names The names given to the columns of the table
265  @param add_measures If True, add fields for comparing models
266  and native conformation
267  """
268  table_fields = self.results_description_columns + \
269  ["total_score"] + restraints_names
270  table_types = self.results_description_types + \
271  [float] + [float for r in restraints_names]
272  if add_measures:
273  # Add columns for measures
274  table_fields += self.results_measures_columns
275  table_types += self.results_measures_types
276  log.debug("Creating table %s\n%s", table_fields, table_types)
277  self.create_table(self.results_table, table_fields, table_types)
278  # create a table for the native assembly if we are benchmarking
279  if add_measures:
280  self.create_table(
281  self.native_table_name,
282  table_fields,
283  table_types)
284 
285  def get_solutions_results_table(self, fields=False,
286  max_number=None, orderby=False):
287  """
288  Recovers solutions
289  @param fields Fields to recover from the table
290  @param max_number Maximum number of solutions to recover
291  @param orderby Name of the restraint used for sorting the states
292  """
293  self.check_if_is_connected()
294  log.info("Getting %s from solutions", fields)
295  f = self.get_fields_string(fields)
296  sql_command = "SELECT %s FROM %s " % (f, self.results_table)
297  if orderby:
298  sql_command += " ORDER BY %s ASC" % orderby
299  if max_number not in (None, False):
300  sql_command += " LIMIT %d" % (max_number)
301  log.debug("Using %s", sql_command)
302  data = self.retrieve_data(sql_command)
303  return data
304 
305  def get_solutions(self, fields=False, max_number=None, orderby=False):
306  """
307  Get solutions from the database.
308  @param fields Fields requested. If the fields are in different
309  tables, a left join is done. Otherwise get_solutions_results_table()
310  is called. See get_solutions_results_table() for the meaning
311  of the parameters.
312  @param max_number
313  @param orderby
314  """
315  tables = self.get_tables_names()
316  log.debug("tables %s", tables)
317  required_tables = set()
318  pairs_table_field = []
319 # fields_string = self.get_fields_string(fields)
320  if not fields:
321  fields = ["*", ]
322  for f, t in [(f, t) for f in fields for t in tables]:
323  if t == "native" or f == "solution_id":
324  continue
325  columns = self.get_table_column_names(t)
326  if f in columns:
327  required_tables.add(t)
328  pairs_table_field.append((t, f))
329  required_tables = list(required_tables)
330  log.debug("required_tables %s", required_tables)
331  log.debug("pairs_table_field %s", pairs_table_field)
332  if len(required_tables) == 0:
333  data = self.get_solutions_results_table(fields,
334  max_number, orderby)
335  return data
336  elif len(required_tables) == 1 and required_tables[0] == "results":
337  data = self.get_solutions_results_table(fields,
338  max_number, orderby)
339  return data
340  elif len(required_tables) > 1:
341  sql_command = self.get_left_join_command(pairs_table_field,
342  required_tables)
343  if orderby:
344  sql_command += " ORDER BY %s ASC" % orderby
345  log.debug("Using %s", sql_command)
346  data = self.retrieve_data(sql_command)
347  return data
348  else:
349  raise ValueError("Fields not found in the database")
350 
351  def get_native_solution(self, fields=False):
352  """
353  Recover data for the native solution
354  @param fields Fields to recover
355  """
356 
357  f = self.get_fields_string(fields)
358  sql_command = "SELECT %s FROM %s " % (f, self.native_table_name)
359  data = self.retrieve_data(sql_command)
360  return data
361 
362  def add_record(self, solution_id, assignment, RFs, total_score,
363  restraints_scores, measures):
364  """
365  Add a recorde to the database
366  @param solution_id The key for the solution
367  @param assignment The assigment for the solution provided by
368  domino
369  @param RFs Reference frames of the rigid bodies of the components
370  of the assembly in the solution
371  @param total_score Total value of the scoring function
372  @param restraints_scores A list with all the values for the
373  restraints
374  @param measures A list with the values of all the measures for
375  benchmark
376  """
377  words = [io.ReferenceFrameToText(ref).get_text() for ref in RFs]
378  RFs_txt = unit_delim.join(words)
379  record = [solution_id, assignment, RFs_txt, total_score] + \
380  restraints_scores
381  if measures is not None:
382  record = record + measures
383  self.records.append(record)
384 
385  def add_native_record(self, assignment, RFs, total_score,
386  restraints_scores):
387  """
388  Add a record for the native structure to the database
389  see add_record() for the meaning of the parameters
390  """
391  words = [io.ReferenceFrameToText(ref).get_text() for ref in RFs]
392  RFs_txt = unit_delim.join(words)
393  solution_id = 0
394  record = [solution_id, assignment, RFs_txt, total_score] + \
395  restraints_scores
396  measures = [0, 0, 0] # ["drms", "cdrms", "crmsd"]
397  record = record + measures
398  self.store_data(self.native_table_name, [record])
399 
400  def save_records(self, table="results"):
401  self.store_data(table, self.records)
402 
403  def format_placement_record(self, solution_id, distances, angles):
404  """ both distances and angles are expected to be a list of floats """
405  return [solution_id] + distances + angles
406 
407  def add_placement_scores_table(self, names):
408  """
409  Creates a table to store the values of the placement scores for the
410  models.
411  @param names Names of the components of the assembly
412  """
413  self.check_if_is_connected()
414  self.placement_table_name = self.placements_table
415  table_fields = ["solution_id"]
416  table_fields += ["distance_%s" % name for name in names]
417  table_fields += ["angle_%s" % name for name in names]
418  table_types = [int] + [float for f in table_fields]
419  self.drop_table(self.placement_table_name)
420  self.create_table(self.placement_table_name, table_fields, table_types)
421  self.add_columns(self.native_table_name,
422  table_fields, table_types, check=True)
423  # update all placements scores to 0 for the native assembly
424  native_values = [0 for t in table_fields]
425  log.debug("%s", self.native_table_name)
426  log.debug("table fields %s", table_fields)
427  self.update_data(self.native_table_name,
428  table_fields, native_values,
429  ["assignment"], ["\"native\""])
430 
432  """
433  Return the names of the placement score fields in the database
434  """
435  columns = self.get_table_column_names(self.placements_table)
436  fields = [
437  col for col in columns if "distance" in col or "angle" in col]
438  return fields
439 
440  def add_ccc_table(self):
441  """
442  Add a table to the database for store the values of the cross
443  correlation coefficient between a model and the native configuration
444  """
445 
446  self.check_if_is_connected()
447  table_fields = ["solution_id", "ccc"]
448  table_types = [int, float]
449  self.drop_table(self.ccc_table_name)
450  self.create_table(self.ccc_table_name, table_fields, table_types)
451  # update values for the native assembly
452  self.add_columns(self.native_table_name,
453  table_fields, table_types, check=True)
454  self.update_data(self.native_table_name,
455  table_fields, [0, 1.00], ["assignment"], ["\"native\""])
456 
457  def format_ccc_record(self, solution_id, ccc):
458  """ Format for the record to store in the ccc table """
459  return [solution_id, ccc]
460 
461  def get_ccc(self, solution_id):
462  """
463  Recover the cross-correlation coefficient for a solution
464  @param solution_id
465  """
466  sql_command = """ SELECT ccc FROM %s
467  WHERE solution_id=%d """ % (self.ccc_table_name,
468  solution_id)
469  data = self.retrieve_data(sql_command)
470  return data[0][0]
471 
472  def store_ccc_data(self, ccc_data):
473  self.store_data(self.ccc_table_name, ccc_data)
474 
475  def store_placement_data(self, data):
476  log.debug("store placement table %s", data)
477  self.store_data(self.placement_table_name, data)
478 
479  def get_left_join_command(self, pairs_table_field, tables_names):
480  """
481  Format a left join SQL command that recovers all fileds from the
482  tables given
483  @param pairs_table_field Pairs of (table,field)
484  @param tables_names Names of the tables
485 
486  E.g. If pairs_table_filed = ((table1,a), (table2,b), (table3,c),
487  (table2,d)) and tables_names = (table1, table2, table3)
488 
489  The SQL command is:
490  SELECT table1.a, table2.b, table3.c, table2.d FROM table1
491  LEFT JOIN table2 ON table1.solution_id = table2.solution_id
492  LEFT JOIN table3 ON table1.solution_id = table3.solution_id
493  WHERE table1.solution_id IS NOT NULL AND
494  table2.solution_id IS NOT NULL AND
495  table3.solution_id IS NOT NULL
496  """
497 
498  txt = ["%s.%s" % (p[0], p[1]) for p in pairs_table_field]
499  fields_requested = field_delim.join(txt)
500  sql_command = " SELECT %s FROM %s " % (
501  fields_requested, tables_names[0])
502  n_tables = len(tables_names)
503  for i in range(1, n_tables):
504  a = tables_names[i - 1]
505  b = tables_names[i]
506  sql_command += " LEFT JOIN %s " \
507  "ON %s.solution_id = %s.solution_id " % (b, a, b)
508  # add the condition of solution_id being not null, so there are not
509  # problems if some solutions are missing in one table
510  for i in range(n_tables - 1):
511  sql_command += "WHERE %s.solution_id " \
512  "IS NOT NULL AND " % tables_names[
513  i]
514  sql_command += " %s.solution_id IS NOT NULL " % tables_names[
515  n_tables - 1]
516  log.debug("%s" % sql_command)
517  return sql_command
518 
519  def add_clusters_table(self, name):
520  """
521  Add a table to store information about the clusters of structures
522  @param name Name of the table
523  """
524  self.cluster_table_name = name
525  self.check_if_is_connected()
526  table_fields = ("cluster_id", "n_elements",
527  "representative", "elements", "solutions_ids")
528  table_types = (int, int, int, str, str)
529  self.drop_table(name)
530  self.create_table(name, table_fields, table_types)
531 
532  def add_cluster_record(self, cluster_id, n_elements, representative,
533  elements, solutions_ids):
534  """
535  Add a record to the cluster database. Actually, only stores it
536  in a list (that will be added later)
537  @param cluster_id Number with the id of the cluster
538  @param n_elements Number of elements in the cluster
539  @param representative Number with the id of the representative
540  element
541  @param elements List with the number of the elements of the cluster
542  @param solutions_ids The numbers above are provided by the
543  clustering algorithm. The solutions_ids are the ids of the models
544  in "elements".
545  """
546 
547  record = (cluster_id, n_elements, representative, elements,
548  solutions_ids)
549  log.debug("Adding cluster record: %s", record)
550  self.cluster_records.append(record)
551 
553  """
554  Store the data for the clusters
555  """
556  log.info("Storing data of clusters. Number of records %s",
557  len(self.cluster_records))
558  self.store_data(self.cluster_table_name, self.cluster_records)
559 
560  def get_solutions_from_list(self, fields=False, solutions_ids=[]):
561  """
562  Recover solutions for a specific list of results
563  @param fields Fields to recover fro the database
564  @param solutions_ids A list with the desired solutions. E.g. [0,3,6]
565  """
566  sql_command = """ SELECT %s FROM %s WHERE solution_id IN (%s) """
567  f = self.get_fields_string(fields)
568  str_ids = ",".join(map(str, solutions_ids))
569  data = self.retrieve_data(
570  sql_command %
571  (f, self.results_table, str_ids))
572  return data
573 
574  def get_native_rank(self, orderby):
575  """
576  Get the position of the native configuration
577  @param orderby Criterium used to sort the solutions
578  """
579  import numpy as np
580 
581  data = self.get_native_solution([orderby, ])
582  native_value = data[0][0]
583  data = self.get_solutions_results_table(fields=[orderby, ],
584  orderby=orderby)
585  values = [row[0] for row in data]
586  rank = np.searchsorted(values, native_value)
587  return rank
588 
589  def get_nth_largest_cluster(self, position, table_name="clusters"):
590  """
591  Recover the the information about the n-th largest cluster
592  @param position Cluster position (by size) requested
593  (1 is the largest cluster)
594  @param table_name Table where the information about the
595  clusters is stored
596  """
597  s = """ SELECT * FROM %s ORDER BY n_elements DESC """ % table_name
598  data = self.retrieve_data(s)
599  record = ClusterRecord(*data[position - 1])
600  return record
601 
602  def get_individual_placement_statistics(self, solutions_ids):
603  """
604  Recovers from the database the placement scores for a set of
605  solutions, and returns the mean and standard deviation of the
606  placement score for each of the components of the complex being
607  scored. This function will be typical used to compute the variation
608  of the placement of each component within a cluster of solutions
609  @param solutions_ids The ids of the solutions used to compute
610  the statistics
611  @return The output are 4 numpy vectors:
612  placement_distances_mean - The mean placement distance for each
613  component
614  placement_distances_stddev - The standardd deviation of the
615  placement distance for each component
616  placement_angles_mean - The mean placement angle for each
617  component
618  placement_angles_stddev - The standard deviation of the placement
619  angle for each component,
620  """
621 
622  self.check_if_is_connected()
623  table = self.placements_table
624  fields = self.get_table_column_names(table)
625  distance_fields = [x for x in fields if 'distance' in x]
626  angle_fields = [x for x in fields if 'angle' in x]
627  sql_command = """ SELECT %s FROM %s WHERE solution_id IN (%s) """
628  # string with the solution ids to pass to the sql_command
629  str_ids = ",".join(map(str, solutions_ids))
630  log.debug("Solutions considered %s", solutions_ids)
631  s = sql_command % (",".join(distance_fields), table, str_ids)
632  data_distances = self.retrieve_data(s)
633  s = sql_command % (",".join(angle_fields), table, str_ids)
634  data_angles = self.retrieve_data(s)
635  D = np.array(data_distances)
636  placement_distances_mean = D.mean(axis=0)
637  placement_distances_stddev = D.std(axis=0)
638  A = np.array(data_angles)
639  placement_angles_mean = A.mean(axis=0)
640  placement_angles_stddev = A.std(axis=0)
641  return [placement_distances_mean, placement_distances_stddev,
642  placement_angles_mean, placement_angles_stddev]
643 
644  def get_placement_statistics(self, solutions_ids):
645  """
646  Calculate the placement score and its standard deviation for
647  the complexes in a set of solutions. The values returned are
648  averages, as the placement score for a complex is the average
649  of the placement scores of the components. This function is used
650  to obtain global placement for a cluster of solutions.
651  @param solutions_ids The ids of the solutions used to compute
652  the statistics
653  @return The output are 4 values:
654  plcd_mean - Average of the placement distance for the entire
655  complex over all the solutions.
656  plcd_std - Standard deviation of the placement distance for
657  the entire complex over all the solutions.
658  plca_mean - Average of the placement angle for the entire
659  complex over all the solutions.
660  plca_std - Standard deviation of the placement angle for
661  the entire complex over all the solutions.
662  """
663  [placement_distances_mean, placement_distances_stddev,
664  placement_angles_mean, placement_angles_stddev] = \
665  self.get_individual_placement_statistics(solutions_ids)
666  plcd_mean = placement_distances_mean.mean(axis=0)
667  plcd_std = placement_distances_stddev.mean(axis=0)
668  plca_mean = placement_angles_mean.mean(axis=0)
669  plca_std = placement_angles_stddev.mean(axis=0)
670  return [plcd_mean, plcd_std, plca_mean, plca_std]
def get_left_join_command
Format a left join SQL command that recovers all fileds from the tables given.
def get_table_column_names
Get the names of the columns for a given table.
Definition: database.py:229
def __new__
Build from a tuple and the index used to compare.
Definition: solutions_io.py:44
Class to manage a SQL database built with sqlite3.
Definition: database.py:13
def gather_solution_results
Reads a set of database files and puts them in a single file Makes sure to reorder all column names i...
def add_clusters_table
Add a table to store information about the clusters of structures.
def get_placement_fields
Return the names of the placement score fields in the database.
def store_data
Inserts information in a given table of the database.
Definition: database.py:94
def add_ccc_table
Add a table to the database for store the values of the cross correlation coefficient between a model...
def format_placement_record
both distances and angles are expected to be a list of floats
The heapq algorithm is a min-heap.
Definition: solutions_io.py:34
def create_table
Creates a table.
Definition: database.py:45
def get_solutions
Get solutions from the database.
def retrieve_data
Retrieves data from the database using the sql_command returns the records as a list of tuples...
Definition: database.py:116
def add_record
Add a recorde to the database.
def gather_best_solution_results
Reads a set of database files and merge them into a single file.
Definition: solutions_io.py:69
Utility functions to handle IO.
Definition: io.py:1
Utility functions to manage SQL databases with sqlite3.
Definition: database.py:1
def add_placement_scores_table
Creates a table to store the values of the placement scores for the models.
def get_best_solution
Recover the reference frame of the n-th best solution from a database.
def drop_table
Delete a table if it exists.
Definition: database.py:61
def get_sorting_indices
Return indices that sort the list l.
def get_individual_placement_statistics
Recovers from the database the placement scores for a set of solutions, and returns the mean and stan...
def get_native_solution
Recover data for the native solution.
def add_native_record
Add a record for the native structure to the database see add_record() for the meaning of the paramet...
Class for managing the results of the experiments.
def add_cluster_record
Add a record to the cluster database.
def get_placement_statistics
Calculate the placement score and its standard deviation for the complexes in a set of solutions...
def get_nth_largest_cluster
Recover the the information about the n-th largest cluster.
def store_cluster_data
Store the data for the clusters.
def add_results_table
Build the table of results.
def get_fields_string
Get a list of fields and return a string with them.
def get_ccc
Recover the cross-correlation coefficient for a solution.
def format_ccc_record
Format for the record to store in the ccc table.
def add_columns
Add columns to the database.
Definition: database.py:255
def get_native_rank
Get the position of the native configuration.
def get_solutions_from_list
Recover solutions for a specific list of results.
def get_solutions_results_table
Recovers solutions.
def update_data
updates the register in the table identified by the condition values for the condition fields ...
Definition: database.py:124
def check_if_is_connected
Checks if the class is connected to the database filename.
Definition: database.py:39