def generate_timing_stats(file_list, var_list):
timing_result = dict()<EOL>timing_summary = dict()<EOL>for file in file_list:<EOL><INDENT>timing_result[file] = functions.parse_gptl(file, var_list)<EOL><DEDENT>for var in var_list:<EOL><INDENT>var_time = []<EOL>for f, data in timing_result.items():<EOL><INDENT>try:<EOL><INDENT>var_time.append(data[var])<EOL><DEDENT>except:<EOL><INDENT>continue<EOL><DEDENT><DEDENT>if len(var_time):<EOL><INDENT>timing_summary[var] = {'<STR_LIT>': np.mean(var_time),<EOL>'<STR_LIT>': np.max(var_time),<EOL>'<STR_LIT>': np.min(var_time),<EOL>'<STR_LIT>': np.std(var_time)}<EOL><DEDENT><DEDENT>return timing_summary<EOL>
Parse all of the timing files, and generate some statistics
about the run.

Args:
    file_list: A list of timing files to parse
    var_list: A list of variables to look for in the timing file

Returns:
    A dict containing values that have the form:
        [mean, min, max, mean, standard deviation]