diff options
Diffstat (limited to 'moonv4/moon_interface/tests/apitests/plot_json.py')
| -rw-r--r-- | moonv4/moon_interface/tests/apitests/plot_json.py | 837 |
1 files changed, 763 insertions, 74 deletions
diff --git a/moonv4/moon_interface/tests/apitests/plot_json.py b/moonv4/moon_interface/tests/apitests/plot_json.py index 61ebb145..f67f1d27 100644 --- a/moonv4/moon_interface/tests/apitests/plot_json.py +++ b/moonv4/moon_interface/tests/apitests/plot_json.py @@ -1,8 +1,13 @@ +import os import argparse import logging import json +import glob +import time +import datetime +import itertools import plotly -from plotly.graph_objs import Scatter, Layout +from plotly.graph_objs import Scatter, Layout, Bar import plotly.figure_factory as ff @@ -10,83 +15,267 @@ logger = None def init(): - global logger, HOST, PORT + global logger + commands = { + "graph": write_graph, + "digraph": write_distgraph, + "average": write_average_graph, + "latency": write_latency, + "request_average": write_request_average, + "throughput": write_throughput, + "global_throughput": write_global_throughput, + "parallel_throughput": write_parallel_throughput, + } parser = argparse.ArgumentParser() + parser.add_argument("command", + help="Command to throw ({})".format(", ".join(commands.keys()))) + parser.add_argument("input", nargs="+", help="files to use with the form \"file1.json,file2.json,...\"") parser.add_argument("--verbose", "-v", action='store_true', help="verbose mode") - parser.add_argument("--host", - help="Set the name of the host to test (default: 172.18.0.11).", - default="172.18.0.11") - parser.add_argument("--port", "-p", - help="Set the port of the host to test (default: 38001).", - default="38001") - parser.add_argument("--test-only", "-t", action='store_true', dest='testonly', help="Do not generate graphs") + parser.add_argument("--debug", "-d", action='store_true', help="debug mode") parser.add_argument("--write", "-w", help="Write test data to a JSON file", default="/tmp/data.json") - parser.add_argument("--input", "-i", help="Get data from a JSON input file") - parser.add_argument("--legend", "-l", help="Set the legend (default: 'rbac,rbac+session')", - default='rbac,rbac+session') - parser.add_argument("--distgraph", "-d", - help="Show a distribution graph instead of a linear graph", - action='store_true') - parser.add_argument("--request-per-second", help="Number of requests per seconds", - type=int, dest="request_second", default=1) - parser.add_argument("--limit", help="Limit request to LIMIT", type=int) + parser.add_argument("--legend", "-l", help="Set the legend (by default get from the file names)") + parser.add_argument("--titles", "-t", + help="Set the general title, x title and y title (ex: Title 1,Title X,Title Y)") + # parser.add_argument("--request-per-second", help="Number of requests per seconds", + # type=int, dest="request_second", default=1) + # parser.add_argument("--limit", help="Limit request to LIMIT", type=int) parser.add_argument("--write-image", help="Write the graph to file IMAGE", dest="write_image") parser.add_argument("--write-html", help="Write the graph to HTML file HTML", dest="write_html", default="data.html") + parser.add_argument("--plot-result", + help="Use specific data like Grant or Deny responses " + "('*' for all or 'Grant,Deny' to separate granted and denied responses)", + dest="plot_result", + default="*") args = parser.parse_args() - FORMAT = '%(asctime)-15s %(levelname)s %(message)s' - logging.basicConfig( - format=FORMAT, - level=logging.INFO) + FORMAT = '%(levelname)s %(message)s' + + if args.verbose: + logging.basicConfig( + format=FORMAT, + level=logging.INFO) + elif args.debug: + logging.basicConfig( + format=FORMAT, + level=logging.DEBUG) + else: + logging.basicConfig( + format=FORMAT, + level=logging.WARNING) logger = logging.getLogger(__name__) - HOST = args.host - PORT = args.port - return args + # args.input = args.input[0] + result_input = [] + for _input in args.input: + if "*" in _input: + filenames = glob.glob(_input) + filenames.sort() + result_input.append(",".join(filenames)) + else: + result_input.append(_input) + args.input = result_input + + if not args.legend: + _legends = [] + for data in args.input: + for filename in data.split(","): + _legends.append(os.path.basename(filename).replace(".json", "")) + args.legend = ",".join(_legends) + return args, commands + + +def __get_legends(legend_str, default_length=10): + if "|" in legend_str: + secondary_legend = legend_str.split("|")[1].split(",") + else: + secondary_legend = ["average"] * default_length + _legends = legend_str.split("|")[0].split(",") + return _legends, secondary_legend -def get_delta(time_data): + +def get_delta_v1(time_data, result=None): time_delta = list() + x_data = list() time_delta_sum1 = 0 + cpt = 0 for key in time_data: - time_delta.append(time_data[key]['delta']) - time_delta_sum1 += time_data[key]['delta'] + if not result or 'result' not in time_data[key] or time_data[key]['result'].lower() == result.lower() or result == "*": + time_delta.append(time_data[key]['delta']) + time_delta_sum1 += time_data[key]['delta'] + if 'index' in time_data[key]: + print("in index {}".format(time_data[key]['index'])) + x_data.append(time_data[key]['index']) + else: + x_data.append(cpt) + cpt += 1 time_delta_average1 = time_delta_sum1 / len(time_data.keys()) - return time_delta, time_delta_average1 + return time_delta, time_delta_average1, x_data + +def get_delta_v2(time_data, result=None): + time_delta = list() + x_data = list() + time_delta_sum1 = 0 + cpt = 0 + for item in time_data: + if not result or 'result' not in item or item['result'].lower() == result.lower() or result == "*": + time_delta.append(item['delta']) + time_delta_sum1 += item['delta'] + x_data.append(cpt) + cpt += 1 + time_delta_average1 = time_delta_sum1 / len(time_data) + return time_delta, time_delta_average1, x_data -def write_graph(legend=None, input=None, image_file=None, html_file=None): + +def get_delta(time_data, result=None): + if type(time_data) is dict: + return get_delta_v1(time_data, result=result) + if type(time_data) is list: + return get_delta_v2(time_data, result=result) + raise Exception("Time data has not a correct profile") + + +def get_latency_v1(time_data, result=None): + time_delta = list() + time_delta_sum1 = 0 + for key in time_data: + if not result or 'result' not in time_data[key] or time_data[key]['result'].lower() == result.lower() or result == "*": + time_delta.append(1/time_data[key]['delta']) + time_delta_sum1 += time_data[key]['delta'] + logger.debug("Adding {} {}".format(1/time_data[key]['delta'], time_data[key]['delta'])) + time_delta_average1 = time_delta_sum1 / len(time_data.keys()) + return time_delta, 1/time_delta_average1 + + +def get_latency_v2(time_data, result=None): + time_delta = list() + time_delta_sum1 = 0 + time_list = list() + for item in time_data: + if not result or 'result' not in item or item['result'].lower() == result.lower() or result == "*": + time_delta.append(1/item['delta']) + time_delta_sum1 += item['delta'] + time_list.append(item['end']) + time_delta_average1 = time_delta_sum1 / len(time_data) + return time_delta, 1/time_delta_average1, time_list + + +def get_latency(time_data, result=None): + if type(time_data) is dict: + return get_latency_v1(time_data, result=result) + if type(time_data) is list: + return get_latency_v2(time_data, result=result) + raise Exception("Time data has not a correct profile") + + +def get_request_per_second_v1(time_data): + result = {} + _min = None + _max = 0 + for key in time_data: + start = str(time_data[key]['start']).split(".")[0] + end = str(time_data[key]['end']).split(".")[0] + middle = str(int((int(end) + int(start)) / 2)) + middle = end + if not _min: + _min = int(middle) + if int(middle) < _min: + _min = int(middle) + if int(middle) > _max: + _max = int(middle) + if middle not in result: + result[middle] = 1 + else: + result[middle] += 1 + for cpt in range(_min+1, _max): + if str(cpt) not in result: + result[str(cpt)] = 0 + # result[str(cpt)] = (result[str(cpt - 1)] + result[str(cpt)]) / 2 + # result[str(cpt - 1)] = result[str(cpt)] + return result + + +def get_request_per_second_v2(time_data): + result = {} + _min = None + _max = 0 + for item in time_data: + start = str(item['start']).split(".")[0] + end = str(item['end']).split(".")[0] + middle = str(int((int(end) + int(start)) / 2)) + middle = end + if not _min: + _min = int(middle) + if int(middle) < _min: + _min = int(middle) + if int(middle) > _max: + _max = int(middle) + if middle not in result: + result[middle] = 1 + else: + result[middle] += 1 + for cpt in range(_min+1, _max): + if str(cpt) not in result: + result[str(cpt)] = 0 + # result[str(cpt)] = (result[str(cpt - 1)] + result[str(cpt)]) / 2 + # result[str(cpt - 1)] = result[str(cpt)] + return result + + +def get_request_per_second(time_data): + if type(time_data) is dict: + return get_request_per_second_v1(time_data) + if type(time_data) is list: + return get_request_per_second_v2(time_data) + raise Exception("Time data has not a correct profile") + + +def write_graph(legend=None, input=None, image_file=None, html_file=None, plot_result="", title=None): logger.info("Writing graph") - legends = legend.split(",") + cpt_max = 0 + logger.debug("legend={}".format(legend)) + for data in input: + cpt_max += len(data.split(",")) + legends, secondary_legend = __get_legends(legend, cpt_max) + logger.debug("legends={}".format(legends)) result_data = [] - for _input in input.split(","): - current_legend = legends.pop(0) - time_data2 = json.load(open(_input)) - time_delta2, time_delta_average2 = get_delta(time_data2) - for key in time_data2.keys(): - if key in time_data2: - time_delta2.append(time_data2[key]['delta']) - else: - time_delta2.append(None) - data2 = Scatter( - x=list(range(len(time_data2.keys()))), - y=time_delta2, - name=current_legend, - line=dict( - color='rgb(255, 192, 118)', - shape='spline') - ) - result_data.append(data2) - data2_a = Scatter( - x=list(range(len(time_data2.keys()))), - y=[time_delta_average2 for x in range(len(time_data2.keys()))], - name=current_legend + " average", - line=dict( - color='rgb(255, 152, 33)', - shape='spline') - ) - result_data.append(data2_a) + cpt_input = 0 + for data in input: + for _input in data.split(","): + try: + current_legend = legends.pop(0) + except IndexError: + current_legend = "" + time_data = json.load(open(_input)) + time_delta, time_delta_average2, x_data = get_delta(time_data) + for item in time_data: + if type(time_data) is dict: + time_delta.append(time_data[item]['delta']) + else: + time_delta.append(item['delta']) + data = Scatter( + x=x_data, + y=time_delta, + name=current_legend, + line=dict( + color="rgb({},{},{})".format(0, cpt_input * 255 / cpt_max, cpt_input * 255 / cpt_max), + # shape='spline' + ) + ) + result_data.append(data) + data_a = Scatter( + x=list(range(len(time_data))), + y=[time_delta_average2 for x in range(len(time_data))], + name=current_legend + " average", + line=dict( + color="rgb({},{},{})".format(255, cpt_input * 255 / cpt_max, cpt_input * 255 / cpt_max), + # shape='spline' + ) + ) + result_data.append(data_a) + cpt_input += 1 if image_file: plotly.offline.plot( @@ -119,44 +308,544 @@ def write_graph(legend=None, input=None, image_file=None, html_file=None): return 0 -def write_distgraph(legend=None, input=None, image_file=None, html_file=None): +def write_distgraph(legend=None, input=None, image_file=None, html_file=None, plot_result="", title=None): logger.info("Writing graph") - legends = legend.split(",") + _legends, secondary_legend = __get_legends(legend, len(input)) result_data = [] + legends = [] + # FIXME: deals with multiple input + input = input[0] for _input in input.split(","): logger.info("Analysing input {}".format(_input)) - time_data2 = json.load(open(_input)) - time_delta2, time_delta_average2 = get_delta(time_data2) - result_data.append(time_delta2) + current_legend = _legends.pop(0) + for result in plot_result.split(","): + time_data2 = json.load(open(_input)) + time_delta2, time_delta_average2, x_data = get_delta(time_data2, result=result) + result_data.append(time_delta2) + if result == "*": + legends.append(current_legend) + else: + legends.append("{} ({})".format(current_legend, result)) # Create distplot with custom bin_size if len(legends) < len(result_data): for _cpt in range(len(result_data)-len(legends)): legends.append("NC") - fig = ff.create_distplot(result_data, legends, bin_size=.2) + fig = ff.create_distplot(result_data, legends, show_hist=False) # Plot! plotly.offline.plot( fig, - image="svg", - image_filename=image_file, - image_height=1000, - image_width=1200, + # image="svg", + # image_filename=image_file, + # image_height=1000, + # image_width=1200, filename=html_file ) return 0 +def write_average_graph(legend=None, input=None, image_file=None, html_file=None, plot_result="", title=None): + + logger.info("Writing average graph") + _legends, secondary_legend = __get_legends(legend, len(input)) + all_data = [] + legends = [] + legend_done = False + html_file = "latency_" + html_file + + cpt_input = 0 + cpt_max = len(input) + for data in input: + result_data = [] + for _input in data.split(","): + logger.info("Analysing input {}".format(_input)) + if not legend_done: + current_legend = _legends.pop(0) + for result in plot_result.split(","): + time_data2 = json.load(open(_input)) + time_delta2, time_delta_average2 = get_delta(time_data2, result=result) + result_data.append(time_delta_average2) + if not legend_done and result == "*": + legends.append(current_legend) + elif not legend_done: + legends.append("{} ({})".format(current_legend, result)) + + if not legend_done: + if len(legends) < len(result_data): + for _cpt in range(len(result_data)-len(legends)): + legends.append("NC") + + data = Scatter( + x=legends, + y=result_data, + name=secondary_legend.pop(0), + line=dict( + color="rgb({},{},{})".format(158, cpt_input * 255 / cpt_max, cpt_input * 255 / cpt_max) + ) + ) + all_data.append(data) + legend_done = True + cpt_input += 1 + if image_file: + plotly.offline.plot( + { + "data": all_data, + "layout": Layout( + title="Latency", + xaxis=dict(title='Request per second'), + yaxis=dict(title='Request latency'), + ) + }, + filename=html_file, + image="svg", + image_filename=image_file, + image_height=1000, + image_width=1200 + ) + else: + plotly.offline.plot( + { + "data": all_data, + "layout": Layout( + title="Latency", + xaxis=dict(title='Requests'), + yaxis=dict(title='Request latency'), + ) + }, + filename=html_file, + ) + return 0 + + +def __get_titles(title): + if title: + titles = title.split(",") + try: + title_generic = titles[0] + except IndexError: + title_generic = "" + try: + title_x = titles[1] + except IndexError: + title_x = "" + try: + title_y = titles[2] + except IndexError: + title_y = "" + else: + title_generic = "" + title_x = "" + title_y = "" + return title_generic, title_x, title_y + + +def __get_time_axis(data): + result_data = [] + start_time = None + for item in data: + if not start_time: + start_time = item + item = item - start_time + millis = int(str(item).split('.')[-1][:6]) + t = time.gmtime(int(item)) + result_data.append( + datetime.datetime(t.tm_year, t.tm_mon, t.tm_mday, t.tm_hour, t.tm_min, t.tm_sec, millis) + ) + return result_data + + +def write_latency(legend=None, input=None, image_file=None, html_file=None, plot_result="", title=None): + + logger.info("Writing latency graph") + _legends, secondary_legend = __get_legends(legend, len(input)) + all_data = [] + legends = [] + legend_done = False + html_file = "latency_" + html_file + title_generic, title_x, title_y = __get_titles(title) + cpt_input = 0 + cpt_max = len(input) + for data in input: + result_data = [] + for _input in data.split(","): + logger.info("Analysing input {}".format(_input)) + if not legend_done: + current_legend = _legends.pop(0) + for result in plot_result.split(","): + time_data2 = json.load(open(_input)) + time_delta2, time_delta_average2, x_data = get_latency(time_data2, result=result) + result_data.append(time_delta_average2) + if not legend_done and result == "*": + legends.append(current_legend) + elif not legend_done: + legends.append("{} ({})".format(current_legend, result)) + + if not legend_done: + if len(legends) < len(result_data): + for _cpt in range(len(result_data)-len(legends)): + legends.append("NC") + + data = Scatter( + x=legends, + y=result_data, + name=secondary_legend.pop(0), + line=dict( + color="rgb({},{},{})".format(158, cpt_input * 255 / cpt_max, cpt_input * 255 / cpt_max) + ) + ) + all_data.append(data) + legend_done = True + cpt_input += 1 + if image_file: + plotly.offline.plot( + { + "data": all_data, + "layout": Layout( + title=title_generic, + xaxis=dict(title=title_x), + yaxis=dict(title=title_y), + ) + }, + filename=html_file, + image="svg", + image_filename=image_file, + image_height=1000, + image_width=1200 + ) + else: + plotly.offline.plot( + { + "data": all_data, + "layout": Layout( + title=title_generic, + xaxis=dict(title=title_x), + yaxis=dict(title=title_y), + font=dict( + size=25 + ) + ) + }, + filename=html_file, + ) + return 0 + + +def write_request_average(legend=None, input=None, image_file=None, html_file=None, plot_result="", title=None): + logger.info("Writing average graph") + _legends, secondary_legend = __get_legends(legend, len(input)) + result_data = [] + html_file = "request_average_" + html_file + + # FIXME: deals with multiple input + input = input[0] + for _input in input.split(","): + logger.info("Analysing input {}".format(_input)) + current_legend = _legends.pop(0) + time_data = json.load(open(_input)) + result = get_request_per_second(time_data) + time_keys = list(result.keys()) + time_keys.sort() + time_value = list(map(lambda x: result[x], time_keys)) + datetime_keys = list() + for _time in time_keys: + t = time.gmtime(int(_time)) + datetime_keys.append(datetime.datetime(t.tm_year, t.tm_mon, t.tm_mday, t.tm_hour, t.tm_min, t.tm_sec)) + data = Bar( + x=datetime_keys, + y=time_value, + name=current_legend, + ) + result_data.append(data) + plotly.offline.plot( + { + "data": result_data, + "layout": Layout( + title="Request per second", + xaxis=dict(title='Time'), + yaxis=dict(title='Request number'), + ) + }, + filename=html_file, + ) + + +def write_throughput(legend=None, input=None, image_file=None, html_file=None, plot_result="", title=None): + logger.info("Writing throughput graph") + _legends, secondary_legend = __get_legends(legend, len(input)) + result_data = [] + html_file = "request_throughput_" + html_file + title_generic, title_x, title_y = __get_titles(title) + + cpt_input = 0 + cpt_request = 0 + cpt_max = 0 + average_data_x = [] + average_data_y = [] + for _i in input: + cpt_max += len(_i.split(",")) + + for data in input: + for _input in data.split(","): + logger.info("Analysing input {}".format(_input)) + current_legend = _legends.pop(0) + time_data = json.load(open(_input)) + result = get_request_per_second(time_data) + time_keys = list(result.keys()) + time_keys.sort() + time_value = list(map(lambda x: result[x], time_keys)) + index_list = list(map(lambda x: cpt_request + x, range(len(time_keys)))) + cpt_request += len(index_list) + import itertools + average_data_y.extend( + [list(itertools.accumulate(result.values()))[-1]/len(result.values())]*len(result.values()) + ) + average_data_x.extend(index_list) + data = Scatter( + x=index_list, + y=time_value, + name=current_legend, + line=dict( + color="rgb({},{},{})".format(0, cpt_input*255/cpt_max, cpt_input*255/cpt_max) + ), + mode="lines+markers" + ) + result_data.append(data) + cpt_input += 1 + data = Scatter( + x=average_data_x, + y=average_data_y, + name="Average", + line=dict( + color="rgb({},{},{})".format(255, 0, 0) + ), + mode="lines" + ) + logger.debug(average_data_x) + logger.debug(average_data_y) + result_data.append(data) + plotly.offline.plot( + { + "data": result_data, + "layout": Layout( + title=title_generic, + xaxis=dict(title=title_x), + yaxis=dict(title=title_y), + font=dict( + size=15 + ) + ) + }, + filename=html_file, + ) + + +def write_global_throughput(legend=None, input=None, image_file=None, html_file=None, plot_result="", title=None): + logger.info("Writing global throughput graph") + _legends, secondary_legend = __get_legends(legend, len(input)) + result_data = [] + # html_file = "request_throughput_" + html_file + title_generic, title_x, title_y = __get_titles(title) + + cpt_input = 0 + cpt_global = 0 + cpt_max = 0 + average_data_x = [] + final_time_data = None + average_data_y = [] + continuous_data_x = [] + continuous_data_y = [] + for _i in input: + cpt_max += len(_i.split(",")) + + for data in input: + for _input in data.split(","): + logger.info("Analysing input {}".format(_input)) + # current_legend = _legends.pop(0) + _time_data = json.load(open(_input)) + result, average, time_data = get_latency(_time_data, plot_result) + if not final_time_data: + final_time_data = time_data + continuous_data_y.extend(result) + cpt_global += len(result) + _cpt = 0 + for item in result: + if len(average_data_y) <= _cpt: + average_data_y.append([item, ]) + average_data_x.append(_cpt) + else: + _list = average_data_y[_cpt] + _list.append(item) + average_data_y[_cpt] = _list + _cpt += 1 + # time_keys = list(map(lambda x: x['url'], result)) + # time_keys.sort() + # time_value = list(map(lambda x: result[x], time_keys)) + # index_list = list(map(lambda x: cpt_request + x, range(len(time_keys)))) + # cpt_request += len(index_list) + # average_data_y.extend( + # [list(itertools.accumulate(result.values()))[-1]/len(result.values())]*len(result.values()) + # ) + # average_data_x.extend(index_list) + cpt_input += 1 + data_continuous = Scatter( + x=list(range(len(continuous_data_y))), + y=continuous_data_y, + name="continuous_data_y", + line=dict( + color="rgb({},{},{})".format(0, 0, 255) + ), + mode="lines" + ) + for index, item in enumerate(average_data_y): + av = list(itertools.accumulate(item))[-1]/len(item) + average_data_y[index] = av + + average_data = [] + for cpt in range(len(time_data)): + average_data.append([average_data_y[cpt], time_data[cpt]]) + + sorted(average_data, key=lambda x: x[1]) + + average_data_x = [] + start_time = None + for item in map(lambda x: x[1], average_data): + if not start_time: + start_time = item + item = item - start_time + millis = int(str(item).split('.')[-1][:6]) + t = time.gmtime(int(item)) + average_data_x.append( + datetime.datetime(t.tm_year, t.tm_mon, t.tm_mday, t.tm_hour, t.tm_min, t.tm_sec, millis) + ) + + data_average = Scatter( + x=average_data_x, + y=list(map(lambda x: x[0], average_data)), + name="Average", + line=dict( + color="rgb({},{},{})".format(0, 0, 255) + ), + mode="lines" + ) + plotly.offline.plot( + { + "data": [data_average, ], + "layout": Layout( + title=title_generic, + xaxis=dict(title=title_x), + yaxis=dict(title=title_y), + font=dict( + size=15 + ) + ) + }, + filename="average_throughput_" + html_file, + ) + plotly.offline.plot( + { + "data": [data_continuous, ], + "layout": Layout( + title=title_generic, + xaxis=dict(title=title_x), + yaxis=dict(title=title_y), + font=dict( + size=15 + ) + ) + }, + filename="continuous_throughput_" + html_file, + ) + + +def write_parallel_throughput(legend=None, input=None, image_file=None, html_file=None, plot_result="", title=None): + logger.info("Writing global throughput graph") + _legends, secondary_legend = __get_legends(legend, len(input)) + result_data = [] + title_generic, title_x, title_y = __get_titles(title) + + cpt_input = 0 + cpt_global = 0 + cpt_max = 0 + overhead_data = [] + MAX = 60 + for _i in input: + cpt_max += len(_i.split(",")) + for data in input: + for _input in data.split(","): + logger.info("Analysing input {}".format(_input)) + current_legend = _legends.pop(0) + _time_data = json.load(open(_input)) + result, average, time_data = get_latency(_time_data, plot_result) + result = result[:MAX] + cpt_global += len(result) + if not overhead_data: + for _data in result: + overhead_data.append(list()) + for _index, _data in enumerate(result): + _item = overhead_data[_index] + _item.append(_data) + overhead_data[_index] = _item + + data_continuous = Scatter( + x=__get_time_axis(time_data), + # x=list(range(len(result))), + y=result, + name=current_legend, + line=dict( + color="rgb({},{},{})".format(0, cpt_input * 255 / cpt_max, cpt_input * 255 / cpt_max) + ), + mode="lines" + ) + cpt_input += 1 + result_data.append(data_continuous) + + for _index, _data in enumerate(overhead_data): + if len(_data) == 2: + _item = overhead_data[_index] + overhead_data[_index] = 1-_item[1]/_item[0] + data_overhead = Scatter( + x=__get_time_axis(time_data), + # x=list(range(len(result))), + y=overhead_data, + name="Overhead", + line=dict( + color="rgb({},{},{})".format(255, 0, 0) + ), + mode="lines" + ) + # result_data.append(data_overhead) + plotly.offline.plot( + { + "data": result_data, + "layout": Layout( + title=title_generic, + xaxis=dict(title=title_x), + yaxis=dict(title=title_y), + font=dict( + size=20 + ) + ) + }, + filename="parallel_throughput_" + html_file, + ) + + def main(): - args = init() - if args.distgraph: - write_distgraph(legend=args.legend, input=args.input, image_file=args.write_image, - html_file=args.write_html) + args, commands = init() + if args.command in commands: + commands[args.command]( + legend=args.legend, + input=args.input, + image_file=args.write_image, + html_file=args.write_html, + plot_result=args.plot_result, + title=args.titles + ) else: - write_graph(legend=args.legend, input=args.input, image_file=args.write_image, - html_file=args.write_html) + logger.error("Unkwnon command: {}".format(args.command)) if __name__ == "__main__": |