from __future__ import absolute_import, division, print_function from iotbx import data_plots import libtbx.load_env import json import os 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 def exercise_column_formats(): t = data_plots.table_data( title = "Resolution shell statistics", column_labels = ["1/resol^2", "Nrefl", "R-free", "FOM"], column_formats = ["%.2g", "%i", "%.2f", "%.2f"], 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]] ) assert t.format() == """\ ------------------------------------------------- | 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 | * | ------------------------------------------------- """ 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() if __name__ == "__main__" : exercise_column_formats() exercise_inline() exercise_logfile() print("OK")