示例#1
0
 def load_log(self, file_name=None, processed_lines=None, update=True):
   from iotbx import data_plots
   if processed_lines :
     self.tables = data_plots.import_ccp4i_logfile(log_lines=processed_lines)
   else :
     self.tables = data_plots.import_ccp4i_logfile(file_name=file_name)
   if update :
     self.update_interface()
示例#2
0
def exercise_logfile():
    log_file = libtbx.env.find_in_repositories(
        relative_path="phenix_regression/tracking/scala.log",
        test=os.path.isfile)
    if log_file is not None:
        tables = data_plots.import_ccp4i_logfile(log_file)
        assert ([t.title for t in tables] == [
            '>>> Scales v rotation range, Unspecified ',
            'Analysis against Batch, Unspecified ',
            'Analysis against resolution , Unspecified ',
            'Analysis against intensity, Unspecified ',
            'Completeness, multiplicity, Rmeas v. resolution, Unspecified ',
            'Correlations within dataset, Unspecified ',
            'Axial reflections, axis h, Unspecified ',
            'Axial reflections, axis k, Unspecified ',
            'Axial reflections, axis l, Unspecified ',
            'Run     1, standard deviation v. Intensity, Unspecified '
        ])
    loggraph3 = """\
$TABLE: Matthews coefficients:
$GRAPHS: Protein crystal computed at resolution of 2.450 :A:1,2,3,4
$$ Nmol/asym Matthews_Coeff sovlent_frac P(2.450) $$
$$
    1         3.13            0.61         0.99
    2         1.56            0.21         0.01
$$
"""
    t3 = data_plots.table_data(None)
    t3.import_loggraph(loggraph3)
    g3 = t3.get_graph("Protein crystal computed at resolution of 2.450")
    p3 = g3.get_plots()
示例#3
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def exercise_logfile () :
  log_file = libtbx.env.find_in_repositories(
    relative_path="phenix_regression/tracking/scala.log",
    test=os.path.isfile)
  if log_file is not None :
    tables = data_plots.import_ccp4i_logfile(log_file)
    assert ([ t.title for t in tables ] ==
      ['>>> Scales v rotation range, Unspecified ',
       'Analysis against Batch, Unspecified ',
       'Analysis against resolution , Unspecified ',
       'Analysis against intensity, Unspecified ',
       'Completeness, multiplicity, Rmeas v. resolution, Unspecified ',
       'Correlations within dataset, Unspecified ',
       'Axial reflections, axis h, Unspecified ',
       'Axial reflections, axis k, Unspecified ',
       'Axial reflections, axis l, Unspecified ',
       'Run     1, standard deviation v. Intensity, Unspecified '])
  loggraph3 = """\
$TABLE: Matthews coefficients:
$GRAPHS: Protein crystal computed at resolution of 2.450 :A:1,2,3,4
$$ Nmol/asym Matthews_Coeff sovlent_frac P(2.450) $$
$$
    1         3.13            0.61         0.99
    2         1.56            0.21         0.01
$$
"""
  t3 = data_plots.table_data(None)
  t3.import_loggraph(loggraph3)
  g3 = t3.get_graph("Protein crystal computed at resolution of 2.450")
  p3 = g3.get_plots()
示例#4
0
def exercise_inline():
    loggraph1 = """\
$TABLE: Resolution shell statistics
$GRAPHS
:R-free vs. resolution:A:1,3:
:FOM vs. resolution:A:1,4:
$$
1/resol^2  Nrefl      R-free     FOM       $$
$$
0.02       2004       0.25       0.89
0.04       2084       0.23       0.88
0.06       *          0.27       nan
0.08       1949       0.28       0.75
0.1        1783       0.38       *
$$
"""
    # Different newline arrangement, otherwise identical
    loggraph_input = """\
$TABLE: Resolution shell statistics: $GRAPHS :R-free vs. resolution:A:1,3: :FOM vs. resolution:A:1,4:
$$
1/resol^2  Nrefl      """ + """
R-free     FOM       $$ $$
0.02       2004       """ + """
0.25       0.89
0.04       2084       0.23
       0.88
0.06       nan        0.27       nan
0.08       1949       0.28       0.75 0.1        1783       0.38       * $$
"""
    t = data_plots.table_data(None)
    t.import_loggraph(loggraph_input)
    #  print t.format_loggraph()
    #  print "---"
    #  print loggraph1
    assert (len(t.data) == 4)
    assert (t.data[0] == [0.02, 0.04, 0.06, 0.08, 0.10])
    assert (t.data[3][4] is None)
    assert (t.format_loggraph() == loggraph1), t.format_loggraph()
    assert (t.export_rows()[-1] == ['0.1', '1783', '0.38', '*'])
    json_t = t.export_json_table()
    json_d = json.loads(json_t)
    assert (json_d['rows'] == [["1/resol^2", "Nrefl", "R-free", "FOM"],
                               ["0.02", "2004", "0.25", "0.89"],
                               ["0.04", "2084", "0.23", "0.88"],
                               ["0.06", "*", "0.27", "nan"],
                               ["0.08", "1949", "0.28", "0.75"],
                               ["0.1", "1783", "0.38", "*"]]), json_d['rows']
    assert (json_d['title'] == "Resolution shell statistics"), json_d['title']

    f = open("_tst_data_plots.log", "w")
    f.write("\nRandom non-loggraph text\n\n")
    f.write(loggraph1)
    f.write("\n\n")
    f.write(loggraph1)
    f.close()
    tables = data_plots.import_ccp4i_logfile("_tst_data_plots.log")
    assert len(tables) == 2
    assert tables[0].format_loggraph() == loggraph1
    assert tables[0].format_loggraph() == tables[1].format_loggraph()
    t2 = data_plots.table_data(
        title="Resolution shell statistics",
        column_labels=["1/resol^2", "Nrefl", "R-free", "FOM"],
        graph_names=["R-free vs. resolution", "FOM vs. resolution"],
        graph_columns=[[0, 2], [0, 3]],
        data=[[0.02, 0.04, 0.06, 0.08, 0.10], [2004, 2084, None, 1949, 1783],
              [0.25, 0.23, 0.27, 0.28, 0.38],
              [0.89, 0.88, float('NaN'), 0.75, None]])
    #print loggraph1
    #print "---"
    #print t2.format_loggraph()
    assert t2.format_loggraph() == loggraph1
    g1 = t2.get_graph("R-free vs. resolution")
    g2 = t2.get_graph("FOM vs. resolution")
    assert len(g1.data) == 2 and len(g2.data) == 2
    p1 = g1.get_plots(fill_in_missing_y=None)
    p2 = g2.get_plots(fill_in_missing_y=None)
    assert len(p1) == 1 and len(p2) == 1
    (plot_x, plot_y) = p1.pop()
    assert len(plot_x) == 5
    (plot_x, plot_y) = p2.pop()
    assert len(plot_x) == 4
    formatted_table = """\
  -------------------------------------------------
  | Resolution shell statistics                   |
  |-----------------------------------------------|
  | 1/resol^2 | Nrefl     | R-free    | FOM       |
  |-----------------------------------------------|
  | 0.02      | 2004      | 0.25      | 0.89      |
  | 0.04      | 2084      | 0.23      | 0.88      |
  | 0.06      | *         | 0.27      | nan       |
  | 0.08      | 1949      | 0.28      | 0.75      |
  | 0.1       | 1783      | 0.38      | *         |
  -------------------------------------------------
"""
    #print t2.format()
    assert t2.format(indent=2) == formatted_table
    simple_table = """\
  Resolution shell statistics
  1/resol^2 Nrefl     R-free    FOM
  0.02      2004      0.25      0.89
  0.04      2084      0.23      0.88
  0.06      *         0.27      nan
  0.08      1949      0.28      0.75
  0.1       1783      0.38      *
"""
    # as above, but without indentation
    simpler_table = """\
Resolution shell statistics
1/resol^2 Nrefl     R-free    FOM
0.02      2004      0.25      0.89
0.04      2084      0.23      0.88
0.06      *         0.27      nan
0.08      1949      0.28      0.75
0.1       1783      0.38      *
"""
    formatted = t2.format_simple(indent=2)
    assert (formatted == simple_table), formatted
    assert str(t2) == simpler_table
    json_str = t2.export_json()
    json_dict = json.loads(json_str)
    expected_dict = {
        "graph_types": ["A", "A"],
        "graph_columns": [[0, 2], [0, 3]],
        "title":
        "Resolution shell statistics",
        "column_labels": ["1/resol^2", "Nrefl", "R-free", "FOM"],
        "data": [[0.02, 0.04, 0.06, 0.08, 0.1], [2004, 2084, None, 1949, 1783],
                 [0.25, 0.23, 0.27, 0.28, 0.38],
                 [0.89, 0.88, float('nan'), 0.75, None]],
        "graph_names": ["R-free vs. resolution", "FOM vs. resolution"],
        "x_is_inverse_d_min":
        False
    }
    for key in expected_dict:
        if key != 'data':
            assert (key in json_dict), key
            assert (json_dict[key] == expected_dict[key]), \
                    (key, json_dict[key], expected_dict[key])
    assert (
        '"data": [[0.02, 0.04, 0.06, 0.08, 0.1], [2004, 2084, null, 1949, 1783], [0.25, 0.23, 0.27, 0.28, 0.38], [0.89, 0.88, NaN, 0.75, null]]'
        in json_str), json_str
示例#5
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def parse_scala (lines) :
  from iotbx import data_plots
  info = scaling_info("SCALA")
  tables = data_plots.import_ccp4i_logfile(log_lines=lines)
  d_max = None
  for i, line in enumerate(lines) :
    if ("Summary data for " in line) :
      if (lines[i+1].startswith("</p>")) or ("<br" in line) :
        continue
      j = i
      n_refl = None
      n_refl_all = None
      while (j < len(lines)) :
        line = lines[j].strip()
        if line.startswith("Low resolution limit") :
          d_max = float(line.split()[3])
        elif line.startswith("Rmerge") and (not "bin" in line) :
          info.add_overall_stat("r_merge", float(line.split()[1]))
        elif line.startswith("Total number of observations") :
          n_refl_all = float(line.split()[4])
        elif line.startswith("Total number unique") :
          n_refl = float(line.split()[3])
          info.set_n_refl(n_refl, n_refl_all)
        elif (line.startswith("Mean((I)/sd(I))") or
              line.startswith("Mean(I)/sd(I)")) :
          info.add_overall_stat("i/sigma", float(line.split()[1]))
        elif line.startswith("Completeness") :
          info.add_overall_stat("completeness", float(line.split()[1]))
        elif line.startswith("Multiplicity") :
          info.add_overall_stat("multiplicity", float(line.split()[1]))
        elif ("Outlier rejection" in line) or ("$$" in line) :
          break
        j += 1
  assert (d_max is not None)
  for table in tables :
    if table.title.startswith("Analysis against resolution") :
      d_min_by_bin = table.get_column_by_label("Dmin(A)")
      bin_d_max = d_max
      bins = []
      for bin_d_min in d_min_by_bin :
        bins.append((bin_d_max, bin_d_min))
        bin_d_max = bin_d_min
      info.set_bins(bins)
      rmerge = table.get_column_by_label("Rmrg")
      for (rmerge_bin, bin) in zip(rmerge, bins) :
        info.add_bin_stat(bin, "r_merge", rmerge_bin)
      try :
        s2n = table.get_column_by_label("Mn(I/sd)")
      except Exception :
        s2n = table.get_column_by_label("Mn(I)/sd")
      for (s2n_bin, bin) in zip(s2n, bins) :
        info.add_bin_stat(bin, "i/sigma", s2n_bin)
    elif table.title.startswith("Completeness, multiplicity, Rmeas") :
      completeness = table.get_column_by_label("%poss")
      for (comp_bin, bin) in zip(completeness, bins) :
        info.add_bin_stat(bin, "completeness", comp_bin)
      multiplicity = table.get_column_by_label("Mlplct")
      for (mult_bin, bin) in zip(multiplicity, bins) :
        info.add_bin_stat(bin, "multiplicity", mult_bin)
      break
  return info
示例#6
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def extract_loggraph_tables(logfile):
    from iotbx import data_plots

    return data_plots.import_ccp4i_logfile(file_name=logfile)
示例#7
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def exercise_inline () :
  loggraph1 = """\
$TABLE: Resolution shell statistics
$GRAPHS
:R-free vs. resolution:A:1,3:
:FOM vs. resolution:A:1,4:
$$
1/resol^2  Nrefl      R-free     FOM       $$
$$
0.02       2004       0.25       0.89
0.04       2084       0.23       0.88
0.06       2037       0.27       0.83
0.08       1949       0.28       0.75
0.1        1783       0.38       *
$$
"""
  t = data_plots.table_data(None)
  t.import_loggraph(loggraph1)
#  print t.format_loggraph()
#  print "---"
#  print loggraph1
  assert (len(t.data) == 4)
  assert (t.data[0] == [0.02, 0.04, 0.06, 0.08, 0.10])
  assert (t.data[3][4] is None)
  assert (t.format_loggraph() == loggraph1), t.format_loggraph()
  assert (t.export_rows()[-1] == ['0.1', '1783', '0.38', '*'])
  assert (t.export_json_table() == """{"rows": [["1/resol^2", "Nrefl", "R-free", "FOM"], ["0.02", "2004", "0.25", "0.89"], ["0.04", "2084", "0.23", "0.88"], ["0.06", "2037", "0.27", "0.83"], ["0.08", "1949", "0.28", "0.75"], ["0.1", "1783", "0.38", "*"]], "title": "Resolution shell statistics"}""")
  f = open("_tst_data_plots.log", "w")
  f.write("\nRandom non-loggraph text\n\n")
  f.write(loggraph1)
  f.write("\n\n")
  f.write(loggraph1)
  f.close()
  tables = data_plots.import_ccp4i_logfile("_tst_data_plots.log")
  assert len(tables) == 2
  assert tables[0].format_loggraph() == loggraph1
  assert tables[0].format_loggraph() == tables[1].format_loggraph()
  t2 = data_plots.table_data(
    title = "Resolution shell statistics",
    column_labels = ["1/resol^2", "Nrefl", "R-free", "FOM"],
    graph_names = ["R-free vs. resolution", "FOM vs. resolution"],
    graph_columns = [[0,2], [0,3]],
    data = [[0.02, 0.04, 0.06, 0.08, 0.10],
            [2004, 2084, 2037, 1949, 1783],
            [0.25, 0.23, 0.27, 0.28, 0.38],
            [0.89, 0.88, 0.83, 0.75, None]]
  )
  #print loggraph1
  #print "---"
  #print t2.format_loggraph()
  assert t2.format_loggraph() == loggraph1
  g1 = t2.get_graph("R-free vs. resolution")
  g2 = t2.get_graph("FOM vs. resolution")
  assert len(g1.data) == 2 and len(g2.data) == 2
  p1 = g1.get_plots(fill_in_missing_y=None)
  p2 = g2.get_plots(fill_in_missing_y=None)
  assert len(p1) == 1 and len(p2) == 1
  (plot_x, plot_y) = p1.pop()
  assert len(plot_x) == 5
  (plot_x, plot_y) = p2.pop()
  assert len(plot_x) == 4
  formatted_table = """\
  -------------------------------------------------
  | Resolution shell statistics                   |
  |-----------------------------------------------|
  | 1/resol^2 | Nrefl     | R-free    | FOM       |
  |-----------------------------------------------|
  | 0.02      | 2004      | 0.25      | 0.89      |
  | 0.04      | 2084      | 0.23      | 0.88      |
  | 0.06      | 2037      | 0.27      | 0.83      |
  | 0.08      | 1949      | 0.28      | 0.75      |
  | 0.1       | 1783      | 0.38      | *         |
  -------------------------------------------------
"""
  #print t2.format()
  assert t2.format(indent=2) == formatted_table
  simple_table = """\
  Resolution shell statistics
  1/resol^2 Nrefl     R-free    FOM
  0.02      2004      0.25      0.89
  0.04      2084      0.23      0.88
  0.06      2037      0.27      0.83
  0.08      1949      0.28      0.75
  0.1       1783      0.38      *
"""
  # as above, but without indentation
  simpler_table = """\
Resolution shell statistics
1/resol^2 Nrefl     R-free    FOM
0.02      2004      0.25      0.89
0.04      2084      0.23      0.88
0.06      2037      0.27      0.83
0.08      1949      0.28      0.75
0.1       1783      0.38      *
"""
  formatted = t2.format_simple(indent=2)
  assert (formatted == simple_table), formatted
  assert str(t2) == simpler_table
  json_str = t2.export_json()
  assert (json_str == '{"graph_types": ["A", "A"], "graph_columns": [[0, 2], [0, 3]], "title": "Resolution shell statistics", "column_labels": ["1/resol^2", "Nrefl", "R-free", "FOM"], "data": [[0.02, 0.04, 0.06, 0.08, 0.1], [2004, 2084, 2037, 1949, 1783], [0.25, 0.23, 0.27, 0.28, 0.38], [0.89, 0.88, 0.83, 0.75, null]], "graph_names": ["R-free vs. resolution", "FOM vs. resolution"], "x_is_inverse_d_min": false}')
示例#8
0
文件: html.py 项目: xia2/xia2
def extract_loggraph_tables(logfile):
  from iotbx import data_plots
  return data_plots.import_ccp4i_logfile(file_name=logfile)