import json import math import os import re import six import actions def table_data_from_rows(table_id, col_names, rows): """ Returns a TableData object built from a table_id, a list of column names, and corresponding row-oriented data. """ column_values = {} for i, col in enumerate(col_names): # Strip leading @ from column headers column_values[col.lstrip('@')] = [row[i] for row in rows] return actions.TableData(table_id, column_values.pop('id'), column_values) def table_data_from_row_dicts(table_id, row_dict_list): """ Returns a TableData object built from table_id and a list of dictionaries, one per row, mapping column names to cell values. """ col_ids = {'id': None} # Collect the set of col_ids. Use a dict for predictable order. for row in row_dict_list: col_ids.update({c: None for c in row}) column_values = {col: [row.get(col) for row in row_dict_list] for col in col_ids} return actions.TableData(table_id, column_values.pop('id'), column_values) def parse_testscript(script_path=None): """ Parses JSON spec for test cases, and returns a tuple of (samples, test_cases). Lines starting with '//' are comments and are skipped. Samples are objects with keys "SCHEMA" and "DATA", each a dictionary mapping table name to actions.TableData object. "SCHEMA" contains "_grist_Tables" and "_grist_Tables_column" tables. Test cases are a list of objects with "TEST_CASE" and "BODY", and the body is a list of steps of the form [line_number, step_name, data], with line_number being an addition by this parser (or None if not available). """ if not script_path: script_path = os.path.join(os.path.dirname(__file__), "testscript.json") comment_re = re.compile(r'^\s*//') add_line_no_re = re.compile(r'"(APPLY|CHECK_OUTPUT|LOAD_SAMPLE)"\s*,') all_lines = [] with open(script_path, "r") as testfile: for i, line in enumerate(testfile): if comment_re.match(line): all_lines.append("\n") else: line = add_line_no_re.sub(r'"\1@%s",' % (i + 1), line) all_lines.append(line) full_text = "".join(all_lines) script = json.loads(full_text) samples = {} test_cases = [] for obj in script: if "TEST_CASE" in obj: body = [] for step, data in obj["BODY"]: step_line = step.split('@', 1) step = step_line[0] line = step_line[1] if len(step_line) > 1 else None body.append([line, step, data]) obj["BODY"] = body test_cases.append(obj) elif "SAMPLE_NAME" in obj: samples[obj["SAMPLE_NAME"]] = parse_test_sample(obj, samples=samples) else: raise ValueError("Unrecognized object in test script: %s" % obj) return (samples, test_cases) def parse_test_sample(obj, samples={}): """ Parses human-readable sample data (with "SCHEMA" or "SCHEMA_FROM", and "DATA" dictionaries; see testscript.json for an example) into a sample containing "SCHEMA" and "DATA" keys, each a dictionary mapping table name to TableData object. """ if "SCHEMA_FROM" in obj: schema = samples[obj["SCHEMA_FROM"]]["SCHEMA"].copy() else: raw_schema = obj["SCHEMA"] # Convert the meta tables to appropriate table representations for loading. schema = { '_grist_Tables': table_data_from_rows( '_grist_Tables', ("id", "tableId"), [(table_row_id, table_id) for (table_row_id, table_id, _) in raw_schema]), '_grist_Tables_column': table_data_from_rows( '_grist_Tables_column', ("parentId", "parentPos", "id", "colId", "type", "isFormula", "formula", "label", "widgetOptions", "recalcWhen", "recalcDeps"), [[table_row_id, i+1] + col_schema_row(*e) for (table_row_id, _, entries) in raw_schema for (i, e) in enumerate(entries)]) } data = {t: table_data_from_rows(t, data[0], data[1:]) for t, data in six.iteritems(obj["DATA"])} return {"SCHEMA": schema, "DATA": data} def col_schema_row(id_, colId, type_, isFormula, formula="", label="", widgetOptions="", recalcWhen=0, recalcDeps=None): """ Helper to specify columns in test SCHEMA descriptions, to allow omitting some column properties. """ return [id_, colId, type_, isFormula, formula, label, widgetOptions, recalcWhen, recalcDeps] def replace_nans(data): """ Convert all NaNs and Infinities in the data to descriptive strings, since they cannot be serialized to JS-compliant JSON. (But we can serialize them using marshalling, so this workaround is just for the testscript-based tests.) """ if isinstance(data, float) and (math.isnan(data) or math.isinf(data)): return "@+Infinity" if data > 0 else "@-Infinity" if data < 0 else "@NaN" return actions.convert_recursive_in_action(replace_nans, data) def repeat_until_passes(count): """ Use as a decorator on test cases to repeat a failing test case up to count times, until it passes. The resulting test cases will fail only if every repetition failed. This is suitable for flaky timing test when unexpected load spikes could cause spurious failures. """ def decorator(f): def wrapped(*args): for i in range(0, count): try: f(*args) return except AssertionError as e: pass # Raises the last caught exception, even outside try/except (see # https://stackoverflow.com/questions/25632147/raise-at-the-end-of-a-python-function-outside-try-or-except-block) raise # pylint: disable=misplaced-bare-raise return wrapped return decorator