INSTRUCTION
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| RESPONSE
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Generic parsing method for all other modes
|
def parse_innotop_mode_b(self):
""" Generic parsing method for all other modes """
with open(self.infile, 'r') as infh:
# Pre processing to figure out different headers
max_row_quot = 0
valrow = -1
thisrowcolumns = {}
data = {}
while True:
line1 = infh.readline()
words = line1.split()
# special case for -I (iostat) option
# skipping all the 'thread' lines
if words[1] == "thread" and self.metric_type == "INNOTOP-I":
while True:
line1 = infh.readline()
words = line1.split()
if naarad.utils.is_number(words[1]):
line1 = infh.readline()
else:
break
if words[1] == "thread" and self.metric_type == "INNOTOP-R":
break
# Skip next line
infh.readline()
last_ts = words[0].strip().replace('T', ' ')
if not naarad.utils.is_number(words[1]):
thisrowcolumns[max_row_quot] = words[1:]
for column in words[1:]:
if self.options and column not in self.options:
continue
data[column] = []
if self.metric_type == "INNOTOP-I":
data["check_pt_age"] = []
max_row_quot += 1
else:
break
# infh.seek(0)
# Real Processing
for line in infh:
l = line.strip().split(' ', 1)
if len(l) <= 1:
continue
ts = l[0].strip().replace('T', ' ')
if not ts == last_ts:
last_ts = ts
valrow = -1
try:
words = l[1].strip().split('\t')
except IndexError:
logger.warn("Bad line: %s", line)
continue
# special case for -I (iostat) option
# skipping all the 'thread' lines
if words[0] == "thread" or (naarad.utils.is_number(words[0]) and "thread" in words[1]):
continue
if naarad.utils.is_number(words[0]):
valrow += 1
quot = valrow % max_row_quot
# Special case for -R, skipping all 'thread' value lines
if quot >= len(thisrowcolumns):
continue
columns = thisrowcolumns[quot]
if len(words) > len(columns):
continue
for i in range(len(words)):
if self.options and columns[i] not in self.options:
continue
column = columns[i]
# Converting -- to 0, seen this for buf_pool_hit_rate
if words[i] == "--":
words[i] = "0"
ts = naarad.utils.reconcile_timezones(ts, self.timezone, self.graph_timezone)
# Calculating check point age
if self.metric_type == "INNOTOP-I":
if column == "log_seq_no":
log_seq_no = int(words[i])
elif column == "log_flushed_to":
check_pt_age = log_seq_no - int(words[i])
tup = [ts, str(check_pt_age)]
data["check_pt_age"].append(tup)
tup = [ts, words[i]]
data[column].append(tup)
# Post Proc, writing the different out files
for column in data:
csvfile = self.get_csv(column)
self.csv_files.append(csvfile)
with open(csvfile, 'w') as outfh:
for tup in data[column]:
outfh.write(','.join(tup))
outfh.write('\n')
return True
|
Special parsing method for Innotop Replication Status results ( innotop -- mode M )
|
def parse_innotop_mode_m(self):
""" Special parsing method for Innotop "Replication Status" results (innotop --mode M)"""
with open(self.infile, 'r') as infh:
# Pre processing to figure out different headers
max_row_quot = 0
valrow = -1
thisrowcolumns = {}
data = {}
last_ts = None
while True:
# 2012-05-11T00:00:02 master_host slave_sql_running time_behind_master slave_catchup_rate slave_open_temp_tables relay_log_pos last_error
line1 = infh.readline()
words = line1.split()
# Skip next line
infh.readline()
is_header = True
for word in words:
if naarad.utils.is_number(word):
last_ts = words[0].strip().replace('T', ' ')
is_header = False
break # from this loop
if len(words) > 2 and is_header:
thisrowcolumns[max_row_quot] = words[2:]
for column in thisrowcolumns[max_row_quot]:
data[column] = []
max_row_quot += 1
else:
break
# from pre-processing. All headers accounted for
# Real Processing
if not last_ts:
logger.warn("last_ts not set, looks like there is no data in file %s", self.infile)
return True
infh.seek(0)
is_bad_line = False
outfilehandlers = {}
for line in infh:
l = line.strip().split(' ', 1)
# Blank line
if len(l) <= 1:
continue
ts = l[0].strip().replace('T', ' ')
if ts != last_ts:
last_ts = ts
valrow = -1
nameval = l[1].strip().split('\t', 1)
try:
words = nameval[1].split('\t')
except IndexError:
logger.warn("Bad line: %s", line)
continue
valrow += 1
command = nameval[0]
if command not in outfilehandlers:
outfilehandlers[command] = {}
quot = valrow % max_row_quot
columns = thisrowcolumns[quot]
for i in range(len(words)):
if len(words) > len(columns):
logger.warn("Mismatched number of columns: %s", line)
logger.warn("%d %d", len(words), len(columns))
break
if words[i] in columns:
logger.warn("Skipping line: %s", line)
valrow -= 1
break
if self.options and columns[i] not in self.options:
continue
if columns[i] not in outfilehandlers[command]:
outfilehandlers[command][columns[i]] = open(self.get_csv_C(command, columns[i]), 'w')
self.csv_files.append(self.get_csv_C(command, columns[i]))
ts = naarad.utils.reconcile_timezones(ts, self.timezone, self.graph_timezone)
outfilehandlers[command][columns[i]].write(ts + ',')
outfilehandlers[command][columns[i]].write(words[i])
outfilehandlers[command][columns[i]].write('\n')
for command in outfilehandlers:
for column in outfilehandlers[command]:
outfilehandlers[command][column].close()
return True
|
Highlight a region on the chart between the specified start and end x - co - ordinates. param pyplot plt: matplotlibk pyplot which contains the charts to be highlighted param string start_x: epoch time millis param string end_x: epoch time millis
|
def highlight_region(plt, start_x, end_x):
"""
Highlight a region on the chart between the specified start and end x-co-ordinates.
param pyplot plt: matplotlibk pyplot which contains the charts to be highlighted
param string start_x : epoch time millis
param string end_x : epoch time millis
"""
start_x = convert_to_mdate(start_x)
end_x = convert_to_mdate(end_x)
plt.axvspan(start_x, end_x, color=CONSTANTS.HIGHLIGHT_COLOR, alpha=CONSTANTS.HIGHLIGHT_ALPHA)
|
graph_data_on_the_same_graph: put a list of plots on the same graph: currently it supports CDF
|
def graph_data_on_the_same_graph(list_of_plots, output_directory, resource_path, output_filename):
"""
graph_data_on_the_same_graph: put a list of plots on the same graph: currently it supports CDF
"""
maximum_yvalue = -float('inf')
minimum_yvalue = float('inf')
plots = curate_plot_list(list_of_plots)
plot_count = len(plots)
if plot_count == 0:
return False, None
graph_height, graph_width, graph_title = get_graph_metadata(plots)
current_plot_count = 0
fig, axis = plt.subplots()
fig.set_size_inches(graph_width, graph_height)
if plot_count < 2:
fig.subplots_adjust(left=CONSTANTS.SUBPLOT_LEFT_OFFSET, bottom=CONSTANTS.SUBPLOT_BOTTOM_OFFSET, right=CONSTANTS.SUBPLOT_RIGHT_OFFSET)
else:
fig.subplots_adjust(left=CONSTANTS.SUBPLOT_LEFT_OFFSET, bottom=CONSTANTS.SUBPLOT_BOTTOM_OFFSET,
right=CONSTANTS.SUBPLOT_RIGHT_OFFSET - CONSTANTS.Y_AXIS_OFFSET * (plot_count - 2))
# Generate each plot on the graph
for plot in plots:
current_plot_count += 1
logger.info('Processing: ' + plot.input_csv + ' [ ' + output_filename + ' ]')
xval, yval = numpy.loadtxt(plot.input_csv, unpack=True, delimiter=',')
axis.plot(xval, yval, linestyle='-', marker=None, color=get_current_color(current_plot_count), label=plot.plot_label)
axis.legend()
maximum_yvalue = max(maximum_yvalue, numpy.amax(yval) * (1.0 + CONSTANTS.ZOOM_FACTOR * current_plot_count))
minimum_yvalue = min(minimum_yvalue, numpy.amin(yval) * (1.0 - CONSTANTS.ZOOM_FACTOR * current_plot_count))
# Set properties of the plots
axis.yaxis.set_ticks_position('left')
axis.set_xlabel(plots[0].x_label)
axis.set_ylabel(plots[0].y_label, fontsize=CONSTANTS.Y_LABEL_FONTSIZE)
axis.set_ylim([minimum_yvalue, maximum_yvalue])
axis.yaxis.grid(True)
axis.xaxis.grid(True)
axis.set_title(graph_title)
plot_file_name = os.path.join(output_directory, output_filename + ".png")
fig.savefig(plot_file_name)
plt.close()
# Create html fragment to be used for creation of the report
with open(os.path.join(output_directory, output_filename + '.div'), 'w') as div_file:
div_file.write('<a name="' + os.path.basename(plot_file_name).replace(".png", "").replace(".diff", "") + '"></a><div class="col-md-12"><img src="' +
resource_path + '/' + os.path.basename(plot_file_name) + '" id="' + os.path.basename(plot_file_name) +
'" width="100%" height="auto"/></div><div class="col-md-12"><p align=center>' + os.path.basename(plot_file_name) + '<br/></p></div>')
return True, os.path.join(output_directory, output_filename + '.div')
|
Compute anomaly scores for the time series.
|
def _set_scores(self):
"""
Compute anomaly scores for the time series.
"""
anom_scores = {}
self._compute_derivatives()
derivatives_ema = utils.compute_ema(self.smoothing_factor, self.derivatives)
for i, (timestamp, value) in enumerate(self.time_series_items):
anom_scores[timestamp] = abs(self.derivatives[i] - derivatives_ema[i])
stdev = numpy.std(anom_scores.values())
if stdev:
for timestamp in anom_scores.keys():
anom_scores[timestamp] /= stdev
self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
|
Method to extract SAR metric names from the section given in the config. The SARMetric class assumes that the section name will contain the SAR types listed in self. supported_sar_types tuple
|
def extract_metric_name(self, metric_name):
"""
Method to extract SAR metric names from the section given in the config. The SARMetric class assumes that
the section name will contain the SAR types listed in self.supported_sar_types tuple
:param str metric_name: Section name from the config
:return: str which identifies what kind of SAR metric the section represents
"""
for metric_type in self.supported_sar_types:
if metric_type in metric_name:
return metric_type
logger.error('Section [%s] does not contain a valid metric type, using type: "SAR-generic". Naarad works better '
'if it knows the metric type. Valid SAR metric names are: %s', metric_name, self.supported_sar_types)
return 'SAR-generic'
|
Find the maximum allowed shift steps based on max_shift_milliseconds. param list timestamps: timestamps of a time series.
|
def _find_allowed_shift(self, timestamps):
"""
Find the maximum allowed shift steps based on max_shift_milliseconds.
param list timestamps: timestamps of a time series.
"""
init_ts = timestamps[0]
residual_timestamps = map(lambda ts: ts - init_ts, timestamps)
n = len(residual_timestamps)
return self._find_first_bigger(residual_timestamps, self.max_shift_milliseconds, 0, n)
|
Find the first element in timestamps whose value is bigger than target. param list values: list of timestamps ( epoch number ). param target: target value. param lower_bound: lower bound for binary search. param upper_bound: upper bound for binary search.
|
def _find_first_bigger(self, timestamps, target, lower_bound, upper_bound):
"""
Find the first element in timestamps whose value is bigger than target.
param list values: list of timestamps(epoch number).
param target: target value.
param lower_bound: lower bound for binary search.
param upper_bound: upper bound for binary search.
"""
while lower_bound < upper_bound:
pos = lower_bound + (upper_bound - lower_bound) / 2
if timestamps[pos] > target:
upper_bound = pos
else:
lower_bound = pos + 1
return pos
|
Create Analysis and save in Naarad from config: param config:: return:
|
def create_analysis(self, config):
"""
Create Analysis and save in Naarad from config
:param config:
:return:
"""
self._default_test_id += 1
self._analyses[self._default_test_id] = _Analysis(ts_start=None, config=config, test_id=self._default_test_id)
|
Initialize an analysis object and set ts_start for the analysis represented by test_id: param test_id: integer that represents the analysis: param config: config can be a ConfigParser. ConfigParser object or a string specifying local or http ( s ) location for config: return: test_id
|
def signal_start(self, config, test_id=None, **kwargs):
"""
Initialize an analysis object and set ts_start for the analysis represented by test_id
:param test_id: integer that represents the analysis
:param config: config can be a ConfigParser.ConfigParser object or a string specifying local or http(s) location
for config
:return: test_id
"""
if not test_id:
self._default_test_id += 1
test_id = self._default_test_id
self._analyses[test_id] = _Analysis(naarad.utils.get_standardized_timestamp('now', None), config,
test_id=test_id)
if kwargs:
if 'description' in kwargs.keys():
self._analyses[test_id].description = kwargs['description']
if 'input_directory' in kwargs.keys():
self._analyses[test_id].input_directory = kwargs['input_directory']
if 'output_directory' in kwargs.keys():
self._analyses[test_id].output_directory = kwargs['output_directory']
return test_id
|
Set ts_end for the analysis represented by test_id: param test_id: integer that represents the analysis: return: test_id
|
def signal_stop(self, test_id=None):
"""
Set ts_end for the analysis represented by test_id
:param test_id: integer that represents the analysis
:return: test_id
"""
if test_id is None:
test_id = self._default_test_id
if self._analyses[test_id].ts_end:
return CONSTANTS.OK
self._analyses[test_id].ts_end = naarad.utils.get_standardized_timestamp('now', None)
return CONSTANTS.OK
|
Returns a list of test_id for which naarad analysis failed: return: list of test_ids
|
def get_failed_analyses(self):
"""
Returns a list of test_id for which naarad analysis failed
:return: list of test_ids
"""
failed_analyses = []
for test_id in self._analyses.keys():
if self._analyses[test_id].status != CONSTANTS.OK:
failed_analyses.append(test_id)
return failed_analyses
|
Get sla data from each metric and set it in the _Analysis object specified by test_id to make it available for retrieval: return: currently always returns CONSTANTS. OK. Maybe enhanced in future to return additional status
|
def _set_sla_data(self, test_id, metrics):
"""
Get sla data from each metric and set it in the _Analysis object specified by test_id to make it available
for retrieval
:return: currently always returns CONSTANTS.OK. Maybe enhanced in future to return additional status
"""
for metric in metrics:
self._analyses[test_id].sla_data[metric.label] = metric.sla_map
return CONSTANTS.OK
|
Get summary stats data from each metric and set it in the _Analysis object specified by test_id to make it available for retrieval: return: currently always returns CONSTANTS. OK. Maybe enhanced in future to return additional status
|
def _set_stats_data(self, test_id, metrics):
"""
Get summary stats data from each metric and set it in the _Analysis object specified by test_id to make it available
for retrieval
:return: currently always returns CONSTANTS.OK. Maybe enhanced in future to return additional status
"""
for metric in metrics:
self._analyses[test_id].stats_data[metric.label] = metric.summary_stats
return CONSTANTS.OK
|
Create the necessary output and resource directories for the specified analysis: param: analysis: analysis associated with a given test_id
|
def _create_output_directories(self, analysis):
"""
Create the necessary output and resource directories for the specified analysis
:param: analysis: analysis associated with a given test_id
"""
try:
os.makedirs(analysis.output_directory)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
try:
resource_directory = os.path.join(analysis.output_directory, analysis.resource_path)
os.makedirs(resource_directory)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
|
If Naarad is run in CLI mode execute any pre run steps specified in the config. ts_start/ ts_end are set based on workload run steps if any.: param: analysis: The analysis object being processed: param: run_steps: list of post run steps
|
def _run_pre(self, analysis, run_steps):
"""
If Naarad is run in CLI mode, execute any pre run steps specified in the config. ts_start/ts_end are set based on
workload run steps if any.
:param: analysis: The analysis object being processed
:param: run_steps: list of post run steps
"""
workload_run_steps = []
for run_step in sorted(run_steps, key=lambda step: step.run_rank):
run_step.run()
if run_step.run_type == CONSTANTS.RUN_TYPE_WORKLOAD:
workload_run_steps.append(run_step)
# Get analysis time period from workload run steps
if len(workload_run_steps) > 0:
analysis.ts_start, analysis.ts_end = naarad.utils.get_run_time_period(workload_run_steps)
return CONSTANTS.OK
|
If Naarad is run in CLI mode execute any post run steps specified in the config: param: run_steps: list of post run steps
|
def _run_post(self, run_steps):
"""
If Naarad is run in CLI mode, execute any post run steps specified in the config
:param: run_steps: list of post run steps
"""
for run_step in sorted(run_steps, key=lambda step: step.run_rank):
run_step.run()
return CONSTANTS.OK
|
When Naarad is run in CLI mode get the CL arguments and update the analysis: param: analysis: The analysis being processed: param: args: Command Line Arguments received by naarad
|
def _process_args(self, analysis, args):
"""
When Naarad is run in CLI mode, get the CL arguments and update the analysis
:param: analysis: The analysis being processed
:param: args: Command Line Arguments received by naarad
"""
if args.exit_code:
self.return_exit_code = args.exit_code
if args.no_plots:
self.skip_plots = args.no_plots
if args.start:
analysis.ts_start = naarad.utils.get_standardized_timestamp(args.start, None)
if args.end:
analysis.ts_end = naarad.utils.get_standardized_timestamp(args.end, None)
if args.variables:
analysis.variables = naarad.utils.get_variables(args)
return CONSTANTS.OK
|
Run all the analysis saved in self. _analyses sorted by test_id. This is useful when Naarad () is used by other programs and multiple analyses are run In naarad CLI mode len ( _analyses ) == 1: param: input_directory: location of log files: param: output_directory: root directory for analysis output: param: ** kwargs: Optional keyword args: return: int: status code.
|
def analyze(self, input_directory, output_directory, **kwargs):
"""
Run all the analysis saved in self._analyses, sorted by test_id.
This is useful when Naarad() is used by other programs and multiple analyses are run
In naarad CLI mode, len(_analyses) == 1
:param: input_directory: location of log files
:param: output_directory: root directory for analysis output
:param: **kwargs: Optional keyword args
:return: int: status code.
"""
is_api_call = True
if len(self._analyses) == 0:
if 'config' not in kwargs.keys():
return CONSTANTS.ERROR
self.create_analysis(kwargs['config'])
if 'args' in kwargs:
self._process_args(self._analyses[0], kwargs['args'])
is_api_call = False
error_count = 0
self._input_directory = input_directory
self._output_directory = output_directory
for test_id in sorted(self._analyses.keys()):
# Setup
if not self._analyses[test_id].input_directory:
self._analyses[test_id].input_directory = input_directory
if not self._analyses[test_id].output_directory:
if len(self._analyses) > 1:
self._analyses[test_id].output_directory = os.path.join(output_directory, str(test_id))
else:
self._analyses[test_id].output_directory = output_directory
if('config' in kwargs.keys()) and (not self._analyses[test_id].config):
self._analyses[test_id].config = kwargs['config']
self._create_output_directories(self._analyses[test_id])
# Actually run analysis
self._analyses[test_id].status = self.run(self._analyses[test_id], is_api_call, **kwargs)
if self._analyses[test_id].status != CONSTANTS.OK:
error_count += 1
if len(self._analyses) == 1:
return self._analyses[0].status
elif error_count > 0:
return CONSTANTS.ERROR
else:
return CONSTANTS.OK
|
: param analysis: Run naarad analysis for the specified analysis object: param ** kwargs: Additional keyword args can be passed in here for future enhancements: return:
|
def run(self, analysis, is_api_call, **kwargs):
"""
:param analysis: Run naarad analysis for the specified analysis object
:param **kwargs: Additional keyword args can be passed in here for future enhancements
:return:
"""
threads = []
crossplots = []
report_args = {}
metrics = defaultdict()
run_steps = defaultdict(list)
discovery_mode = False
graph_timezone = None
graphing_library = None
if isinstance(analysis.config, str):
if not naarad.utils.is_valid_file(analysis.config):
return CONSTANTS.INVALID_CONFIG
config_object = ConfigParser.ConfigParser(analysis.variables)
config_object.optionxform = str
config_object.read(analysis.config)
elif isinstance(analysis.config, ConfigParser.ConfigParser):
config_object = analysis.config
else:
if is_api_call:
return CONSTANTS.INVALID_CONFIG
else:
metrics['metrics'] = naarad.utils.discover_by_name(analysis.input_directory, analysis.output_directory)
if len(metrics['metrics']) == 0:
logger.warning('Unable to auto detect metrics in the specified input directory: %s', analysis.input_directory)
return CONSTANTS.ERROR
else:
discovery_mode = True
metrics['aggregate_metrics'] = []
if not discovery_mode:
metrics, run_steps, crossplots, report_args, graph_timezone, graphing_library = self._process_naarad_config(config_object, analysis)
if graphing_library is None:
graphing_library = CONSTANTS.DEFAULT_GRAPHING_LIBRARY
# If graphing libraries are not installed, skip static images
if graphing_library not in self.available_graphing_modules.keys():
logger.error("Naarad cannot import graphing library %s on your system. Will not generate static charts", graphing_library)
self.skip_plots = True
if not is_api_call:
self._run_pre(analysis, run_steps['pre'])
for metric in metrics['metrics']:
if analysis.ts_start:
metric.ts_start = analysis.ts_start
if analysis.ts_end:
metric.ts_end = analysis.ts_end
thread = threading.Thread(target=naarad.utils.parse_and_plot_single_metrics,
args=(metric, graph_timezone, analysis.output_directory, analysis.input_directory, graphing_library, self.skip_plots))
thread.start()
threads.append(thread)
for t in threads:
t.join()
for metric in metrics['aggregate_metrics']:
thread = threading.Thread(target=naarad.utils.parse_and_plot_single_metrics,
args=(metric, graph_timezone, analysis.output_directory, analysis.input_directory, graphing_library, self.skip_plots))
thread.start()
threads.append(thread)
for t in threads:
t.join()
self._set_sla_data(analysis.test_id, metrics['metrics'] + metrics['aggregate_metrics'])
self._set_stats_data(analysis.test_id, metrics['metrics'] + metrics['aggregate_metrics'])
if len(crossplots) > 0 and not self.skip_plots:
correlated_plots = naarad.utils.nway_plotting(crossplots, metrics['metrics'] + metrics['aggregate_metrics'],
os.path.join(analysis.output_directory, analysis.resource_path),
analysis.resource_path, graphing_library)
else:
correlated_plots = []
rpt = reporting_modules['report'](None, analysis.output_directory, os.path.join(analysis.output_directory, analysis.resource_path), analysis.resource_path,
metrics['metrics'] + metrics['aggregate_metrics'], correlated_plots=correlated_plots, **report_args)
rpt.generate()
if not is_api_call:
self._run_post(run_steps['post'])
if self.return_exit_code:
for metric in metrics['metrics'] + metrics['aggregate_metrics']:
if metric.status == CONSTANTS.SLA_FAILED:
return CONSTANTS.SLA_FAILURE
return CONSTANTS.OK
|
Create a diff report using test_id_1 as a baseline: param: test_id_1: test id to be used as baseline: param: test_id_2: test id to compare against baseline: param: config file for diff ( optional ): param: ** kwargs: keyword arguments
|
def diff(self, test_id_1, test_id_2, config=None, **kwargs):
"""
Create a diff report using test_id_1 as a baseline
:param: test_id_1: test id to be used as baseline
:param: test_id_2: test id to compare against baseline
:param: config file for diff (optional)
:param: **kwargs: keyword arguments
"""
output_directory = os.path.join(self._output_directory, 'diff_' + str(test_id_1) + '_' + str(test_id_2))
if kwargs:
if 'output_directory' in kwargs.keys():
output_directory = kwargs['output_directory']
diff_report = Diff([NaaradReport(self._analyses[test_id_1].output_directory, None),
NaaradReport(self._analyses[test_id_2].output_directory, None)],
'diff', output_directory, os.path.join(output_directory, self._resource_path),
self._resource_path)
if config:
naarad.utils.extract_diff_sla_from_config_file(diff_report, config)
diff_report.generate()
if diff_report.sla_failures > 0:
return CONSTANTS.SLA_FAILURE
if diff_report.status != 'OK':
return CONSTANTS.ERROR
return CONSTANTS.OK
|
Create a diff report using report1 as a baseline: param: report1_location: report to be used as baseline: param: report2_location: report to compare against baseline: param: config file for diff ( optional ): param: ** kwargs: keyword arguments
|
def diff_reports_by_location(self, report1_location, report2_location, output_directory, config=None, **kwargs):
"""
Create a diff report using report1 as a baseline
:param: report1_location: report to be used as baseline
:param: report2_location: report to compare against baseline
:param: config file for diff (optional)
:param: **kwargs: keyword arguments
"""
if kwargs:
if 'output_directory' in kwargs.keys():
output_directory = kwargs['output_directory']
diff_report = Diff([NaaradReport(report1_location, None), NaaradReport(report2_location, None)], 'diff',
output_directory, os.path.join(output_directory, self._resource_path), self._resource_path)
if config:
naarad.utils.extract_diff_sla_from_config_file(diff_report, config)
diff_report.generate()
if diff_report.sla_failures > 0:
return CONSTANTS.SLA_FAILURE
if diff_report.status != 'OK':
return CONSTANTS.ERROR
return CONSTANTS.OK
|
Process the config file associated with a particular analysis and return metrics run_steps and crossplots. Also sets output directory and resource_path for an anlaysis
|
def _process_naarad_config(self, config, analysis):
"""
Process the config file associated with a particular analysis and return metrics, run_steps and crossplots.
Also sets output directory and resource_path for an anlaysis
"""
graph_timezone = None
output_directory = analysis.output_directory
resource_path = analysis.resource_path
run_steps = defaultdict(list)
metrics = defaultdict(list)
indir_default = ''
crossplots = []
report_args = {}
graphing_library = None
ts_start, ts_end = None, None
if config.has_section('GLOBAL'):
ts_start, ts_end = naarad.utils.parse_global_section(config, 'GLOBAL')
if config.has_option('GLOBAL', 'user_defined_metrics'):
naarad.utils.parse_user_defined_metric_classes(config, metric_classes)
config.remove_section('GLOBAL')
if config.has_section('REPORT'):
report_args = naarad.utils.parse_report_section(config, 'REPORT')
config.remove_section('REPORT')
for section in config.sections():
# GRAPH section is optional
if section == 'GRAPH':
graphing_library, crossplots, outdir_default, indir_default, graph_timezone = \
naarad.utils.parse_graph_section(config, section, output_directory, indir_default)
elif section.startswith('RUN-STEP'):
run_step = naarad.utils.parse_run_step_section(config, section)
if not run_step:
logger.error('Ignoring section %s, could not parse it correctly', section)
continue
if run_step.run_order == CONSTANTS.PRE_ANALYSIS_RUN:
run_steps['pre'].append(run_step)
# DURING_ANALYSIS_RUN not supported yet
elif run_step.run_order == CONSTANTS.DURING_ANALYSIS_RUN:
run_steps['in'].append(run_step)
elif run_step.run_order == CONSTANTS.POST_ANALYSIS_RUN:
run_steps['post'].append(run_step)
else:
logger.error('Unknown RUN-STEP run_order specified')
else:
# section name is used to create sub-directories, so enforce it.
if not naarad.utils.is_valid_metric_name(section):
logger.critical('Section name %s is invalid! Only letters, digits, dot(.), dash(-), underscore(_) are allowed'
% section)
return CONSTANTS.CRITICAL_FAILURE
if section == 'SAR-*':
hostname, infile, label, ts_start, ts_end, precision, kwargs, rule_strings = \
naarad.utils.parse_basic_metric_options(config, section)
sar_metrics = naarad.utils.get_all_sar_objects(metrics, infile, hostname, output_directory, label, ts_start,
ts_end, None)
for sar_metric in sar_metrics:
if sar_metric.ts_start is None and (sar_metric.ts_end is None or sar_metric.ts_end > ts_start):
sar_metric.ts_start = ts_start
if sar_metric.ts_end is None and (sar_metric.ts_start is None or ts_end > sar_metric.ts_start):
sar_metric.ts_end = ts_end
metrics['metrics'].extend(sar_metrics)
else:
new_metric = naarad.utils.parse_metric_section(config, section, metric_classes, metrics['metrics'],
aggregate_metric_classes, output_directory, resource_path)
if new_metric.ts_start is None and (new_metric.ts_end is None or new_metric.ts_end > ts_start):
new_metric.ts_start = ts_start
if new_metric.ts_end is None and (new_metric.ts_start is None or ts_end > new_metric.ts_start):
new_metric.ts_end = ts_end
metric_type = section.split('-')[0]
if metric_type in aggregate_metric_classes:
metrics['aggregate_metrics'].append(new_metric)
else:
metrics['metrics'].append(new_metric)
return metrics, run_steps, crossplots, report_args, graph_timezone, graphing_library
|
Parse the vmstat file: return: status of the metric parse
|
def parse(self):
"""
Parse the vmstat file
:return: status of the metric parse
"""
file_status = True
for input_file in self.infile_list:
file_status = file_status and naarad.utils.is_valid_file(input_file)
if not file_status:
return False
status = True
cur_zone = None
cur_submetric = None
cur_value = None
data = {} # stores the data of each column
for input_file in self.infile_list:
logger.info('Processing : %s', input_file)
timestamp_format = None
with open(input_file) as fh:
for line in fh:
words = line.replace(',', ' ').split() # [0] is day; [1] is seconds; [2...] is field names:;
if len(words) < 3:
continue
ts = words[0] + " " + words[1]
if not timestamp_format or timestamp_format == 'unknown':
timestamp_format = naarad.utils.detect_timestamp_format(ts)
if timestamp_format == 'unknown':
continue
ts = naarad.utils.get_standardized_timestamp(ts, timestamp_format)
if self.ts_out_of_range(ts):
continue
if words[2] == 'Node': # Node 0 zone DMA
cols = words[2:]
cur_zone = '.'.join(cols)
continue
elif words[2] == 'pages': # pages free 3936
cur_submetric = words[2] + '.' + words[3] # pages.free
cur_value = words[4]
elif words[2] in self.processed_sub_metrics:
cur_submetric = 'pages' + '.' + words[2] # pages.min
cur_value = words[3]
elif words[2] in self.skipped_sub_metrics:
continue
else: # other useful submetrics
cur_submetric = words[2]
cur_value = words[3]
col = cur_zone + '.' + cur_submetric # prefix with 'Node.0.zone.DMA.
# only process zones specified in config
if cur_zone and self.zones and cur_zone not in self.zones:
continue
self.sub_metric_unit[col] = 'pages' # The unit of the sub metric. For /proc/zoneinfo, they are all in pages
# only process sub_metrics specified in config.
if self.sub_metrics and cur_submetric and cur_submetric not in self.sub_metrics:
continue
if col in self.column_csv_map:
out_csv = self.column_csv_map[col]
else:
out_csv = self.get_csv(col) # column_csv_map[] is assigned in get_csv()
data[out_csv] = []
data[out_csv].append(ts + "," + cur_value)
# post processing, putting data in csv files;
for csv in data.keys():
self.csv_files.append(csv)
with open(csv, 'w') as fh:
fh.write('\n'.join(sorted(data[csv])))
return status
|
Take a list of metrics filter all metrics based on hostname and metric_type For each metric merge the corresponding csv files into one update corresponding properties such as csv_column_map. Users can specify functions: raw count ( qps ) sum ( aggregated value ) avg ( averaged value ) The timestamp granularity of aggregated submetrics is in seconds ( sub - second is not supported )
|
def collect(self):
"""
Take a list of metrics, filter all metrics based on hostname, and metric_type
For each metric, merge the corresponding csv files into one,update corresponding properties such as csv_column_map.
Users can specify functions: raw, count (qps), sum (aggregated value), avg (averaged value)
The timestamp granularity of aggregated submetrics is in seconds (sub-second is not supported)
"""
for aggr_metric in self.aggr_metrics: # e.g., SAR-device.sda.await:count,sum,avg
functions_aggr = []
fields = aggr_metric.split(":")
cur_metric_type = fields[0].split(".")[0] # e.g. SAR-device
if len(fields) > 1: # The user has to specify the aggregate functions (i.e., :raw,count,sum,avg)
func_user = ''.join(fields[1].split())
functions_aggr.extend(func_user.split(","))
else: # no user input of aggregate functions
return True
cur_column = '.'.join(fields[0].split('.')[1:]) # e.g. sda.await or all.percent-sys
# Store data points of various aggregation functions
aggr_data = {}
aggr_data['raw'] = [] # Store all the raw values
aggr_data['sum'] = defaultdict(float) # Store the sum values for each timestamp
aggr_data['count'] = defaultdict(int) # Store the count of each timestamp (i.e. qps)
for metric in self.metrics: # Loop the list to find from all metrics to merge
if metric.hostname in self.aggr_hosts and \
cur_column in metric.csv_column_map.values():
file_csv = metric.get_csv(cur_column)
timestamp_format = None
with open(file_csv) as fh:
for line in fh:
aggr_data['raw'].append(line.rstrip())
words = line.split(",")
ts = words[0].split('.')[0] # In case of sub-seconds; we only want the value of seconds;
if not timestamp_format or timestamp_format == 'unknown':
timestamp_format = naarad.utils.detect_timestamp_format(ts)
if timestamp_format == 'unknown':
continue
ts = naarad.utils.get_standardized_timestamp(ts, timestamp_format)
aggr_data['sum'][ts] += float(words[1])
aggr_data['count'][ts] += 1
# "raw" csv file
if 'raw' in functions_aggr:
out_csv = self.get_csv(cur_column, 'raw')
self.csv_files.append(out_csv)
with open(out_csv, 'w') as fh:
fh.write("\n".join(sorted(aggr_data['raw'])))
# "sum" csv file
if 'sum' in functions_aggr:
out_csv = self.get_csv(cur_column, 'sum')
self.csv_files.append(out_csv)
with open(out_csv, 'w') as fh:
for (k, v) in sorted(aggr_data['sum'].items()):
fh.write(k + "," + str(v) + '\n')
# "avg" csv file
if 'avg' in functions_aggr:
out_csv = self.get_csv(cur_column, 'avg')
self.csv_files.append(out_csv)
with open(out_csv, 'w') as fh:
for (k, v) in sorted(aggr_data['sum'].items()):
fh.write(k + "," + str(v / aggr_data['count'][k]) + '\n')
# "count" csv file (qps)
if 'count' in functions_aggr:
out_csv = self.get_csv(cur_column, 'count')
self.csv_files.append(out_csv)
with open(out_csv, 'w') as fh:
for (k, v) in sorted(aggr_data['count'].items()):
fh.write(k + "," + str(v) + '\n')
gc.collect()
return True
|
get start time stamp launch time duration and nus update time duration from JSON object native: param JSON OBJECT native: return: LONG event time stamp LONG launch time and LONG nus update time
|
def get_times(self, native):
"""
get start time stamp, launch time duration, and nus update time duration from JSON object native
:param JSON OBJECT native
:return: LONG event time stamp, LONG launch time, and LONG nus update time
"""
start_time = 0
end_time = 0
launch_time = 0
nus_update_time = 0
for item in native:
if item[CONSTANTS.LIA_TIMING_NAME] == CONSTANTS.LIA_APP_ON_CREATE and item[CONSTANTS.LIA_START] is not None:
start_time = item[CONSTANTS.LIA_START][CONSTANTS.LIA_LONG]
if item[CONSTANTS.LIA_TIMING_NAME] == CONSTANTS.LIA_NUS_UPDATE:
if item[CONSTANTS.LIA_TIMING_VALUE] is not None:
nus_update_time = item[CONSTANTS.LIA_TIMING_VALUE][CONSTANTS.LIA_LONG]
if item[CONSTANTS.LIA_START] is not None:
end_time = item[CONSTANTS.LIA_START][CONSTANTS.LIA_LONG]
if start_time == 0 or end_time == 0:
time_stamp = 0
launch_time = 0
else:
time_stamp = start_time
launch_time = end_time - start_time
return (time_stamp, launch_time, nus_update_time)
|
Perform the Oct2Py speed analysis. Uses timeit to test the raw execution of an Octave command Then tests progressively larger array passing.
|
def run(self):
"""Perform the Oct2Py speed analysis.
Uses timeit to test the raw execution of an Octave command,
Then tests progressively larger array passing.
"""
print('Oct2Py speed test')
print('*' * 20)
time.sleep(1)
print('Raw speed: ')
avg = timeit.timeit(self.raw_speed, number=10) / 10
print(' {0:0.01f} usec per loop'.format(avg * 1e6))
sides = [1, 10, 100, 1000]
runs = [10, 10, 10, 5]
for (side, nruns) in zip(sides, runs):
self.array = np.reshape(np.arange(side ** 2), (-1))
print('Put {0}x{1}: '.format(side, side))
avg = timeit.timeit(self.large_array_put, number=nruns) / nruns
print(' {0:0.01f} msec'.format(avg * 1e3))
print('Get {0}x{1}: '.format(side, side))
avg = timeit.timeit(self.large_array_get, number=nruns) / nruns
print(' {0:0.01f} msec'.format(avg * 1e3))
self.octave.exit()
print('*' * 20)
print('Test complete!')
|
Quits this octave session and cleans up.
|
def exit(self):
"""Quits this octave session and cleans up.
"""
if self._engine:
self._engine.repl.terminate()
self._engine = None
|
Put a variable or variables into the Octave session. Parameters ---------- name: str or list Name of the variable ( s ). var: object or list The value ( s ) to pass. timeout: float Time to wait for response from Octave ( per line ). ** kwargs: Deprecated kwargs ignored. Examples -------- >>> from oct2py import octave >>> y = [ 1 2 ] >>> octave. push ( y y ) >>> octave. pull ( y ) array ( [[ 1. 2. ]] ) >>> octave. push ( [ x y ] [ spam [ 1 2 3 4 ]] ) >>> octave. pull ( [ x y ] ) # doctest: + SKIP [ u spam array ( [[ 1 2 3 4 ]] ) ] Notes ----- Integer type arguments will be converted to floating point unless convert_to_float = False.
|
def push(self, name, var, timeout=None, verbose=True):
"""
Put a variable or variables into the Octave session.
Parameters
----------
name : str or list
Name of the variable(s).
var : object or list
The value(s) to pass.
timeout : float
Time to wait for response from Octave (per line).
**kwargs: Deprecated kwargs, ignored.
Examples
--------
>>> from oct2py import octave
>>> y = [1, 2]
>>> octave.push('y', y)
>>> octave.pull('y')
array([[ 1., 2.]])
>>> octave.push(['x', 'y'], ['spam', [1, 2, 3, 4]])
>>> octave.pull(['x', 'y']) # doctest: +SKIP
[u'spam', array([[1, 2, 3, 4]])]
Notes
-----
Integer type arguments will be converted to floating point
unless `convert_to_float=False`.
"""
if isinstance(name, (str, unicode)):
name = [name]
var = [var]
for (n, v) in zip(name, var):
self.feval('assignin', 'base', n, v, nout=0, timeout=timeout,
verbose=verbose)
|
Retrieve a value or values from the Octave session. Parameters ---------- var: str or list Name of the variable ( s ) to retrieve. timeout: float optional. Time to wait for response from Octave ( per line ). ** kwargs: Deprecated kwargs ignored. Returns ------- out: object Object returned by Octave. Raises ------ Oct2PyError If the variable does not exist in the Octave session. Examples -------- >>> from oct2py import octave >>> y = [ 1 2 ] >>> octave. push ( y y ) >>> octave. pull ( y ) array ( [[ 1. 2. ]] ) >>> octave. push ( [ x y ] [ spam [ 1 2 3 4 ]] ) >>> octave. pull ( [ x y ] ) # doctest: + SKIP [ u spam array ( [[ 1 2 3 4 ]] ) ]
|
def pull(self, var, timeout=None, verbose=True):
"""
Retrieve a value or values from the Octave session.
Parameters
----------
var : str or list
Name of the variable(s) to retrieve.
timeout : float, optional.
Time to wait for response from Octave (per line).
**kwargs: Deprecated kwargs, ignored.
Returns
-------
out : object
Object returned by Octave.
Raises
------
Oct2PyError
If the variable does not exist in the Octave session.
Examples
--------
>>> from oct2py import octave
>>> y = [1, 2]
>>> octave.push('y', y)
>>> octave.pull('y')
array([[ 1., 2.]])
>>> octave.push(['x', 'y'], ['spam', [1, 2, 3, 4]])
>>> octave.pull(['x', 'y']) # doctest: +SKIP
[u'spam', array([[1, 2, 3, 4]])]
"""
if isinstance(var, (str, unicode)):
var = [var]
outputs = []
for name in var:
exist = self._exist(name)
if exist == 1:
outputs.append(self.feval('evalin', 'base', name,
timeout=timeout, verbose=verbose))
else:
outputs.append(self.get_pointer(name, timeout=timeout))
if len(outputs) == 1:
return outputs[0]
return outputs
|
Get a pointer to a named object in the Octave workspace. Parameters ---------- name: str The name of the object in the Octave workspace. timemout: float optional. Time to wait for response from Octave ( per line ). Examples -------- >>> from oct2py import octave >>> octave. eval ( foo = [ 1 2 ] ; ) >>> ptr = octave. get_pointer ( foo ) >>> ptr. value array ( [[ 1. 2. ]] ) >>> ptr. address foo >>> # Can be passed as an argument >>> octave. disp ( ptr ) # doctest: + SKIP 1 2 >>> from oct2py import octave >>> sin = octave. get_pointer ( sin ) # equivalent to octave. sin >>> sin. address
|
def get_pointer(self, name, timeout=None):
"""Get a pointer to a named object in the Octave workspace.
Parameters
----------
name: str
The name of the object in the Octave workspace.
timemout: float, optional.
Time to wait for response from Octave (per line).
Examples
--------
>>> from oct2py import octave
>>> octave.eval('foo = [1, 2];')
>>> ptr = octave.get_pointer('foo')
>>> ptr.value
array([[ 1., 2.]])
>>> ptr.address
'foo'
>>> # Can be passed as an argument
>>> octave.disp(ptr) # doctest: +SKIP
1 2
>>> from oct2py import octave
>>> sin = octave.get_pointer('sin') # equivalent to `octave.sin`
>>> sin.address
'@sin'
>>> x = octave.quad(sin, 0, octave.pi())
>>> x
2.0
Notes
-----
Pointers can be passed to `feval` or dynamic functions as function arguments. A pointer passed as a nested value will be passed by value instead.
Raises
------
Oct2PyError
If the variable does not exist in the Octave session or is of
unknown type.
Returns
-------
A variable, object, user class, or function pointer as appropriate.
"""
exist = self._exist(name)
isobject = self._isobject(name, exist)
if exist == 0:
raise Oct2PyError('"%s" is undefined' % name)
elif exist == 1:
return _make_variable_ptr_instance(self, name)
elif isobject:
return self._get_user_class(name)
elif exist in [2, 3, 5]:
return self._get_function_ptr(name)
raise Oct2PyError('Unknown type for object "%s"' % name)
|
Extract the figures in the directory to IPython display objects. Parameters ---------- plot_dir: str The plot dir where the figures were created. remove: bool optional. Whether to remove the plot directory after saving.
|
def extract_figures(self, plot_dir, remove=False):
"""Extract the figures in the directory to IPython display objects.
Parameters
----------
plot_dir: str
The plot dir where the figures were created.
remove: bool, optional.
Whether to remove the plot directory after saving.
"""
figures = self._engine.extract_figures(plot_dir, remove)
return figures
|
Run a function in Octave and return the result. Parameters ---------- func_path: str Name of function to run or a path to an m - file. func_args: object optional Args to send to the function. nout: int optional Desired number of return arguments defaults to 1. store_as: str optional If given saves the result to the given Octave variable name instead of returning it. verbose: bool optional Log Octave output at INFO level. If False log at DEBUG level. stream_handler: callable optional A function that is called for each line of output from the evaluation. timeout: float optional The timeout in seconds for the call. plot_dir: str optional If specificed save the session s plot figures to the plot directory instead of displaying the plot window. plot_name: str optional Saved plots will start with plot_name and end with _%%. xxx where %% is the plot number and xxx is the plot_format. plot_format: str optional The format in which to save the plot. plot_width: int optional The plot with in pixels. plot_height: int optional The plot height in pixels. Notes ----- The function arguments passed follow Octave calling convention not Python. That is all values must be passed as a comma separated list not using x = foo assignment. Examples -------- >>> from oct2py import octave >>> cell = octave. feval ( cell 10 10 10 ) >>> cell. shape ( 10 10 10 ) >>> from oct2py import octave >>> x = octave. feval ( linspace 0 octave. pi ()/ 2 ) >>> x. shape ( 1 100 ) >>> from oct2py import octave >>> x = octave. feval ( svd octave. hilb ( 3 )) >>> x array ( [[ 1. 40831893 ] [ 0. 12232707 ] [ 0. 00268734 ]] ) >>> # specify three return values >>> ( u v d ) = octave. feval ( svd octave. hilb ( 3 ) nout = 3 ) >>> u. shape ( 3 3 ) Returns ------- The Python value ( s ) returned by the Octave function call.
|
def feval(self, func_path, *func_args, **kwargs):
"""Run a function in Octave and return the result.
Parameters
----------
func_path: str
Name of function to run or a path to an m-file.
func_args: object, optional
Args to send to the function.
nout: int, optional
Desired number of return arguments, defaults to 1.
store_as: str, optional
If given, saves the result to the given Octave variable name
instead of returning it.
verbose : bool, optional
Log Octave output at INFO level. If False, log at DEBUG level.
stream_handler: callable, optional
A function that is called for each line of output from the
evaluation.
timeout: float, optional
The timeout in seconds for the call.
plot_dir: str, optional
If specificed, save the session's plot figures to the plot
directory instead of displaying the plot window.
plot_name : str, optional
Saved plots will start with `plot_name` and
end with "_%%.xxx' where %% is the plot number and
xxx is the `plot_format`.
plot_format: str, optional
The format in which to save the plot.
plot_width: int, optional
The plot with in pixels.
plot_height: int, optional
The plot height in pixels.
Notes
-----
The function arguments passed follow Octave calling convention, not
Python. That is, all values must be passed as a comma separated list,
not using `x=foo` assignment.
Examples
--------
>>> from oct2py import octave
>>> cell = octave.feval('cell', 10, 10, 10)
>>> cell.shape
(10, 10, 10)
>>> from oct2py import octave
>>> x = octave.feval('linspace', 0, octave.pi() / 2)
>>> x.shape
(1, 100)
>>> from oct2py import octave
>>> x = octave.feval('svd', octave.hilb(3))
>>> x
array([[ 1.40831893],
[ 0.12232707],
[ 0.00268734]])
>>> # specify three return values
>>> (u, v, d) = octave.feval('svd', octave.hilb(3), nout=3)
>>> u.shape
(3, 3)
Returns
-------
The Python value(s) returned by the Octave function call.
"""
if not self._engine:
raise Oct2PyError('Session is not open')
nout = kwargs.get('nout', None)
if nout is None:
nout = 1
plot_dir = kwargs.get('plot_dir')
settings = dict(backend='inline' if plot_dir else self.backend,
format=kwargs.get('plot_format'),
name=kwargs.get('plot_name'),
width=kwargs.get('plot_width'),
height=kwargs.get('plot_height'),
resolution=kwargs.get('plot_res'))
self._engine.plot_settings = settings
dname = osp.dirname(func_path)
fname = osp.basename(func_path)
func_name, ext = osp.splitext(fname)
if ext and not ext == '.m':
raise TypeError('Need to give path to .m file')
if func_name == 'clear':
raise Oct2PyError('Cannot use `clear` command directly, use' +
' eval("clear(var1, var2)")')
stream_handler = kwargs.get('stream_handler')
verbose = kwargs.get('verbose', True)
store_as = kwargs.get('store_as', '')
timeout = kwargs.get('timeout', self.timeout)
if not stream_handler:
stream_handler = self.logger.info if verbose else self.logger.debug
return self._feval(func_name, func_args, dname=dname, nout=nout,
timeout=timeout, stream_handler=stream_handler,
store_as=store_as, plot_dir=plot_dir)
|
Evaluate an Octave command or commands. Parameters ---------- cmds: str or list Commands ( s ) to pass to Octave. verbose: bool optional Log Octave output at INFO level. If False log at DEBUG level. stream_handler: callable optional A function that is called for each line of output from the evaluation. timeout: float optional Time to wait for response from Octave ( per line ). If not given the instance timeout is used. nout: int optional. The desired number of returned values defaults to 0. If nout is 0 the ans will be returned as the return value. temp_dir: str optional If specified the session s MAT files will be created in the directory otherwise a the instance temp_dir is used. a shared memory ( tmpfs ) path. plot_dir: str optional If specificed save the session s plot figures to the plot directory instead of displaying the plot window. plot_name: str optional Saved plots will start with plot_name and end with _%%. xxx where %% is the plot number and xxx is the plot_format. plot_format: str optional The format in which to save the plot ( PNG by default ). plot_width: int optional The plot with in pixels. plot_height: int optional The plot height in pixels. plot_res: int optional The plot resolution in pixels per inch. ** kwargs Deprectated kwargs. Examples -------- >>> from oct2py import octave >>> octave. eval ( disp ( hello ) ) # doctest: + SKIP hello >>> x = octave. eval ( round ( quad (
|
def eval(self, cmds, verbose=True, timeout=None, stream_handler=None,
temp_dir=None, plot_dir=None, plot_name='plot', plot_format='svg',
plot_width=None, plot_height=None, plot_res=None,
nout=0, **kwargs):
"""
Evaluate an Octave command or commands.
Parameters
----------
cmds : str or list
Commands(s) to pass to Octave.
verbose : bool, optional
Log Octave output at INFO level. If False, log at DEBUG level.
stream_handler: callable, optional
A function that is called for each line of output from the
evaluation.
timeout : float, optional
Time to wait for response from Octave (per line). If not given,
the instance `timeout` is used.
nout : int, optional.
The desired number of returned values, defaults to 0. If nout
is 0, the `ans` will be returned as the return value.
temp_dir: str, optional
If specified, the session's MAT files will be created in the
directory, otherwise a the instance `temp_dir` is used.
a shared memory (tmpfs) path.
plot_dir: str, optional
If specificed, save the session's plot figures to the plot
directory instead of displaying the plot window.
plot_name : str, optional
Saved plots will start with `plot_name` and
end with "_%%.xxx' where %% is the plot number and
xxx is the `plot_format`.
plot_format: str, optional
The format in which to save the plot (PNG by default).
plot_width: int, optional
The plot with in pixels.
plot_height: int, optional
The plot height in pixels.
plot_res: int, optional
The plot resolution in pixels per inch.
**kwargs Deprectated kwargs.
Examples
--------
>>> from oct2py import octave
>>> octave.eval('disp("hello")') # doctest: +SKIP
hello
>>> x = octave.eval('round(quad(@sin, 0, pi/2));')
>>> x
1.0
>>> a = octave.eval('disp("hello");1;') # doctest: +SKIP
hello
>>> a = octave.eval('disp("hello");1;', verbose=False)
>>> a
1.0
>>> from oct2py import octave
>>> lines = []
>>> octave.eval('for i = 1:3; disp(i);end', \
stream_handler=lines.append)
>>> lines # doctest: +SKIP
[' 1', ' 2', ' 3']
Returns
-------
out : object
Octave "ans" variable, or None.
Notes
-----
The deprecated `log` kwarg will temporarily set the `logger` level to
`WARN`. Using the `logger` settings directly is preferred.
The deprecated `return_both` kwarg will still work, but the preferred
method is to use the `stream_handler`. If `stream_handler` is given,
the `return_both` kwarg will be honored but will give an empty string
as the reponse.
Raises
------
Oct2PyError
If the command(s) fail.
"""
if isinstance(cmds, (str, unicode)):
cmds = [cmds]
prev_temp_dir = self.temp_dir
self.temp_dir = temp_dir or self.temp_dir
prev_log_level = self.logger.level
if kwargs.get('log') is False:
self.logger.setLevel(logging.WARN)
for name in ['log', 'return_both']:
if name not in kwargs:
continue
msg = 'Using deprecated `%s` kwarg, see docs on `Oct2Py.eval()`'
warnings.warn(msg % name, stacklevel=2)
return_both = kwargs.pop('return_both', False)
lines = []
if return_both and not stream_handler:
stream_handler = lines.append
ans = None
for cmd in cmds:
resp = self.feval('evalin', 'base', cmd,
nout=nout, timeout=timeout,
stream_handler=stream_handler,
verbose=verbose, plot_dir=plot_dir,
plot_name=plot_name, plot_format=plot_format,
plot_width=plot_width, plot_height=plot_height,
plot_res=plot_res)
if resp is not None:
ans = resp
self.temp_dir = prev_temp_dir
self.logger.setLevel(prev_log_level)
if return_both:
return '\n'.join(lines), ans
return ans
|
Restart an Octave session in a clean state
|
def restart(self):
"""Restart an Octave session in a clean state
"""
if self._engine:
self._engine.repl.terminate()
executable = self._executable
if executable:
os.environ['OCTAVE_EXECUTABLE'] = executable
if 'OCTAVE_EXECUTABLE' not in os.environ and 'OCTAVE' in os.environ:
os.environ['OCTAVE_EXECUTABLE'] = os.environ['OCTAVE']
self._engine = OctaveEngine(stdin_handler=self._handle_stdin,
logger=self.logger)
# Add local Octave scripts.
self._engine.eval('addpath("%s");' % HERE.replace(osp.sep, '/'))
|
Run the given function with the given args.
|
def _feval(self, func_name, func_args=(), dname='', nout=0,
timeout=None, stream_handler=None, store_as='', plot_dir=None):
"""Run the given function with the given args.
"""
engine = self._engine
if engine is None:
raise Oct2PyError('Session is closed')
# Set up our mat file paths.
out_file = osp.join(self.temp_dir, 'writer.mat')
out_file = out_file.replace(osp.sep, '/')
in_file = osp.join(self.temp_dir, 'reader.mat')
in_file = in_file.replace(osp.sep, '/')
func_args = list(func_args)
ref_indices = []
for (i, value) in enumerate(func_args):
if isinstance(value, OctavePtr):
ref_indices.append(i + 1)
func_args[i] = value.address
ref_indices = np.array(ref_indices)
# Save the request data to the output file.
req = dict(func_name=func_name, func_args=tuple(func_args),
dname=dname or '', nout=nout,
store_as=store_as or '',
ref_indices=ref_indices)
write_file(req, out_file, oned_as=self._oned_as,
convert_to_float=self.convert_to_float)
# Set up the engine and evaluate the `_pyeval()` function.
engine.stream_handler = stream_handler or self.logger.info
if timeout is None:
timeout = self.timeout
try:
engine.eval('_pyeval("%s", "%s");' % (out_file, in_file),
timeout=timeout)
except KeyboardInterrupt as e:
stream_handler(engine.repl.interrupt())
raise
except TIMEOUT:
stream_handler(engine.repl.interrupt())
raise Oct2PyError('Timed out, interrupting')
except EOF:
stream_handler(engine.repl.child.before)
self.restart()
raise Oct2PyError('Session died, restarting')
# Read in the output.
resp = read_file(in_file, self)
if resp['err']:
msg = self._parse_error(resp['err'])
raise Oct2PyError(msg)
result = resp['result'].ravel().tolist()
if isinstance(result, list) and len(result) == 1:
result = result[0]
# Check for sentinel value.
if (isinstance(result, Cell) and
result.size == 1 and
isinstance(result[0], string_types) and
result[0] == '__no_value__'):
result = None
if plot_dir:
self._engine.make_figures(plot_dir)
return result
|
Create a traceback for an Octave evaluation error.
|
def _parse_error(self, err):
"""Create a traceback for an Octave evaluation error.
"""
self.logger.debug(err)
stack = err.get('stack', [])
if not err['message'].startswith('parse error:'):
err['message'] = 'error: ' + err['message']
errmsg = 'Octave evaluation error:\n%s' % err['message']
if not isinstance(stack, StructArray):
return errmsg
errmsg += '\nerror: called from:'
for item in stack[:-1]:
errmsg += '\n %(name)s at line %(line)d' % item
try:
errmsg += ', column %(column)d' % item
except Exception:
pass
return errmsg
|
Get the documentation of an Octave procedure or object. Parameters ---------- name: str Function name to search for. Returns ------- out: str Documentation string. Raises ------ Oct2PyError If the procedure or object function has a syntax error.
|
def _get_doc(self, name):
"""
Get the documentation of an Octave procedure or object.
Parameters
----------
name : str
Function name to search for.
Returns
-------
out : str
Documentation string.
Raises
------
Oct2PyError
If the procedure or object function has a syntax error.
"""
doc = 'No documentation for %s' % name
engine = self._engine
doc = engine.eval('help("%s")' % name, silent=True)
if 'syntax error:' in doc.lower():
raise Oct2PyError(doc)
if 'error:' in doc.lower():
doc = engine.eval('type("%s")' % name, silent=True)
doc = '\n'.join(doc.splitlines()[:3])
default = self.feval.__doc__
default = ' ' + default[default.find('func_args:'):]
default = '\n'.join([line[8:] for line in default.splitlines()])
doc = '\n'.join(doc.splitlines())
doc = '\n' + doc + '\n\nParameters\n----------\n' + default
doc += '\n**kwargs - Deprecated keyword arguments\n\n'
doc += 'Notes\n-----\n'
doc += 'Keyword arguments to dynamic functions are deprecated.\n'
doc += 'The `plot_*` kwargs will be ignored, but the rest will\n'
doc += 'used as key - value pairs as in version 3.x.\n'
doc += 'Use `set_plot_settings()` for plot settings, and use\n'
doc += '`func_args` directly for key - value pairs.'
return doc
|
Test whether a name exists and return the name code. Raises an error when the name does not exist.
|
def _exist(self, name):
"""Test whether a name exists and return the name code.
Raises an error when the name does not exist.
"""
cmd = 'exist("%s")' % name
resp = self._engine.eval(cmd, silent=True).strip()
exist = int(resp.split()[-1])
if exist == 0:
msg = 'Value "%s" does not exist in Octave workspace'
raise Oct2PyError(msg % name)
return exist
|
Test whether the name is an object.
|
def _isobject(self, name, exist):
"""Test whether the name is an object."""
if exist in [2, 5]:
return False
cmd = 'isobject(%s)' % name
resp = self._engine.eval(cmd, silent=True).strip()
return resp == 'ans = 1'
|
Get or create a function pointer of the given name.
|
def _get_function_ptr(self, name):
"""Get or create a function pointer of the given name."""
func = _make_function_ptr_instance
self._function_ptrs.setdefault(name, func(self, name))
return self._function_ptrs[name]
|
Get or create a user class of the given type.
|
def _get_user_class(self, name):
"""Get or create a user class of the given type."""
self._user_classes.setdefault(name, _make_user_class(self, name))
return self._user_classes[name]
|
Clean up resources used by the session.
|
def _cleanup(self):
"""Clean up resources used by the session.
"""
self.exit()
workspace = osp.join(os.getcwd(), 'octave-workspace')
if osp.exists(workspace):
os.remove(workspace)
|
Play a demo script showing most of the oct2py api features. Parameters ========== delay: float Time between each command in seconds.
|
def demo(delay=1, interactive=True):
"""
Play a demo script showing most of the oct2py api features.
Parameters
==========
delay : float
Time between each command in seconds.
"""
script = """
#########################
# Oct2Py demo
#########################
import numpy as np
from oct2py import Oct2Py
oc = Oct2Py()
# basic commands
print(oc.abs(-1))
print(oc.upper('xyz'))
# plotting
oc.plot([1,2,3],'-o', 'linewidth', 2)
raw_input('Press Enter to continue...')
oc.close()
xx = np.arange(-2*np.pi, 2*np.pi, 0.2)
oc.surf(np.subtract.outer(np.sin(xx), np.cos(xx)))
raw_input('Press Enter to continue...')
oc.close()
# getting help
help(oc.svd)
# single vs. multiple return values
print(oc.svd(np.array([[1,2], [1,3]])))
U, S, V = oc.svd([[1,2], [1,3]], nout=3)
print(U, S, V)
# low level constructs
oc.eval("y=ones(3,3)")
print(oc.pull("y"))
oc.eval("x=zeros(3,3)", verbose=True)
t = oc.eval('rand(1, 2)', verbose=True)
y = np.zeros((3,3))
oc.push('y', y)
print(oc.pull('y'))
from oct2py import Struct
y = Struct()
y.b = 'spam'
y.c.d = 'eggs'
print(y.c['d'])
print(y)
#########################
# Demo Complete!
#########################
"""
if not PY2:
script = script.replace('raw_input', 'input')
for line in script.strip().split('\n'):
line = line.strip()
if not 'input(' in line:
time.sleep(delay)
print(">>> {0}".format(line))
time.sleep(delay)
if not interactive:
if 'plot' in line or 'surf' in line or 'input(' in line:
line = 'print()'
exec(line)
|
Kill all octave instances ( cross - platform ). This will restart the octave instance. If you have instantiated Any other Oct2Py objects you must restart them.
|
def kill_octave():
"""Kill all octave instances (cross-platform).
This will restart the "octave" instance. If you have instantiated
Any other Oct2Py objects, you must restart them.
"""
import os
if os.name == 'nt':
os.system('taskkill /im octave /f')
else:
os.system('killall -9 octave')
os.system('killall -9 octave-cli')
octave.restart()
|
Start a number of threads and verify each has a unique Octave session. Parameters ========== nthreads: int Number of threads to use. Raises ====== Oct2PyError If the thread does not sucessfully demonstrate independence.
|
def thread_check(nthreads=3):
"""
Start a number of threads and verify each has a unique Octave session.
Parameters
==========
nthreads : int
Number of threads to use.
Raises
======
Oct2PyError
If the thread does not sucessfully demonstrate independence.
"""
print("Starting {0} threads at {1}".format(nthreads,
datetime.datetime.now()))
threads = []
for i in range(nthreads):
thread = ThreadClass()
thread.setDaemon(True)
thread.start()
threads.append(thread)
for thread in threads:
thread.join()
print('All threads closed at {0}'.format(datetime.datetime.now()))
|
Create a unique instance of Octave and verify namespace uniqueness. Raises ====== Oct2PyError If the thread does not sucessfully demonstrate independence
|
def run(self):
"""
Create a unique instance of Octave and verify namespace uniqueness.
Raises
======
Oct2PyError
If the thread does not sucessfully demonstrate independence
"""
octave = Oct2Py()
# write the same variable name in each thread and read it back
octave.push('name', self.getName())
name = octave.pull('name')
now = datetime.datetime.now()
print("{0} got '{1}' at {2}".format(self.getName(), name, now))
octave.exit()
try:
assert self.getName() == name
except AssertionError: # pragma: no cover
raise Oct2PyError('Thread collision detected')
return
|
Read the data from the given file path.
|
def read_file(path, session=None):
"""Read the data from the given file path.
"""
try:
data = loadmat(path, struct_as_record=True)
except UnicodeDecodeError as e:
raise Oct2PyError(str(e))
out = dict()
for (key, value) in data.items():
out[key] = _extract(value, session)
return out
|
Save a Python object to an Octave file on the given path.
|
def write_file(obj, path, oned_as='row', convert_to_float=True):
"""Save a Python object to an Octave file on the given path.
"""
data = _encode(obj, convert_to_float)
try:
# scipy.io.savemat is not thread-save.
# See https://github.com/scipy/scipy/issues/7260
with _WRITE_LOCK:
savemat(path, data, appendmat=False, oned_as=oned_as,
long_field_names=True)
except KeyError: # pragma: no cover
raise Exception('could not save mat file')
|
Convert the Octave values to values suitable for Python.
|
def _extract(data, session=None):
"""Convert the Octave values to values suitable for Python.
"""
# Extract each item of a list.
if isinstance(data, list):
return [_extract(d, session) for d in data]
# Ignore leaf objects.
if not isinstance(data, np.ndarray):
return data
# Extract user defined classes.
if isinstance(data, MatlabObject):
cls = session._get_user_class(data.classname)
return cls.from_value(data)
# Extract struct data.
if data.dtype.names:
# Singular struct
if data.size == 1:
return _create_struct(data, session)
# Struct array
return StructArray(data, session)
# Extract cells.
if data.dtype.kind == 'O':
return Cell(data, session)
# Compress singleton values.
if data.size == 1:
return data.item()
# Compress empty values.
if data.size == 0:
if data.dtype.kind in 'US':
return ''
return []
# Return standard array.
return data
|
Create a struct from session data.
|
def _create_struct(data, session):
"""Create a struct from session data.
"""
out = Struct()
for name in data.dtype.names:
item = data[name]
# Extract values that are cells (they are doubly wrapped).
if isinstance(item, np.ndarray) and item.dtype.kind == 'O':
item = item.squeeze().tolist()
out[name] = _extract(item, session)
return out
|
Convert the Python values to values suitable to send to Octave.
|
def _encode(data, convert_to_float):
"""Convert the Python values to values suitable to send to Octave.
"""
ctf = convert_to_float
# Handle variable pointer.
if isinstance(data, (OctaveVariablePtr)):
return _encode(data.value, ctf)
# Handle a user defined object.
if isinstance(data, OctaveUserClass):
return _encode(OctaveUserClass.to_value(data), ctf)
# Handle a function pointer.
if isinstance(data, (OctaveFunctionPtr, MatlabFunction)):
raise Oct2PyError('Cannot write Octave functions')
# Handle matlab objects.
if isinstance(data, MatlabObject):
view = data.view(np.ndarray)
out = MatlabObject(data, data.classname)
for name in out.dtype.names:
out[name] = _encode(view[name], ctf)
return out
# Handle pandas series and dataframes
if isinstance(data, (DataFrame, Series)):
return _encode(data.values, ctf)
# Extract and encode values from dict-like objects.
if isinstance(data, dict):
out = dict()
for (key, value) in data.items():
out[key] = _encode(value, ctf)
return out
# Send None as nan.
if data is None:
return np.NaN
# Sets are treated like lists.
if isinstance(data, set):
return _encode(list(data), ctf)
# Lists can be interpreted as numeric arrays or cell arrays.
if isinstance(data, list):
if _is_simple_numeric(data):
return _encode(np.array(data), ctf)
return _encode(tuple(data), ctf)
# Tuples are handled as cells.
if isinstance(data, tuple):
obj = np.empty(len(data), dtype=object)
for (i, item) in enumerate(data):
obj[i] = _encode(item, ctf)
return obj
# Sparse data must be floating type.
if isinstance(data, spmatrix):
return data.astype(np.float64)
# Return other data types unchanged.
if not isinstance(data, np.ndarray):
return data
# Extract and encode data from object-like arrays.
if data.dtype.kind in 'OV':
out = np.empty(data.size, dtype=data.dtype)
for (i, item) in enumerate(data.ravel()):
if data.dtype.names:
for name in data.dtype.names:
out[i][name] = _encode(item[name], ctf)
else:
out[i] = _encode(item, ctf)
return out.reshape(data.shape)
# Complex 128 is the highest supported by savemat.
if data.dtype.name == 'complex256':
return data.astype(np.complex128)
# Convert to float if applicable.
if ctf and data.dtype.kind in 'ui':
return data.astype(np.float64)
# Return standard array.
return data
|
Test if a list contains simple numeric data.
|
def _is_simple_numeric(data):
"""Test if a list contains simple numeric data."""
for item in data:
if isinstance(item, set):
item = list(item)
if isinstance(item, list):
if not _is_simple_numeric(item):
return False
elif not isinstance(item, (int, float, complex)):
return False
return True
|
Return a console logger. Output may be sent to the logger using the debug info warning error and critical methods. Parameters ---------- name: str Name of the log. References ----------.. [ 1 ] Logging facility for Python http:// docs. python. org/ library/ logging. html
|
def get_log(name=None):
"""Return a console logger.
Output may be sent to the logger using the `debug`, `info`, `warning`,
`error` and `critical` methods.
Parameters
----------
name : str
Name of the log.
References
----------
.. [1] Logging facility for Python,
http://docs.python.org/library/logging.html
"""
if name is None:
name = 'oct2py'
else:
name = 'oct2py.' + name
log = logging.getLogger(name)
log.setLevel(logging.INFO)
return log
|
Configure root logger.
|
def _setup_log():
"""Configure root logger.
"""
try:
handler = logging.StreamHandler(stream=sys.stdout)
except TypeError: # pragma: no cover
handler = logging.StreamHandler(strm=sys.stdout)
log = get_log()
log.addHandler(handler)
log.setLevel(logging.INFO)
log.propagate = False
|
Make an Octave class for a given class name
|
def _make_user_class(session, name):
"""Make an Octave class for a given class name"""
attrs = session.eval('fieldnames(%s);' % name, nout=1).ravel().tolist()
methods = session.eval('methods(%s);' % name, nout=1).ravel().tolist()
ref = weakref.ref(session)
doc = _DocDescriptor(ref, name)
values = dict(__doc__=doc, _name=name, _ref=ref, _attrs=attrs,
__module__='oct2py.dynamic')
for method in methods:
doc = _MethodDocDescriptor(ref, name, method)
cls_name = '%s_%s' % (name, method)
method_values = dict(__doc__=doc)
method_cls = type(str(cls_name),
(OctaveUserClassMethod,), method_values)
values[method] = method_cls(ref, method, name)
for attr in attrs:
values[attr] = OctaveUserClassAttr(ref, attr, attr)
return type(str(name), (OctaveUserClass,), values)
|
This is how an instance is created when we read a MatlabObject from a MAT file.
|
def from_value(cls, value):
"""This is how an instance is created when we read a
MatlabObject from a MAT file.
"""
instance = OctaveUserClass.__new__(cls)
instance._address = '%s_%s' % (instance._name, id(instance))
instance._ref().push(instance._address, value)
return instance
|
Convert to a value to send to Octave.
|
def to_value(cls, instance):
"""Convert to a value to send to Octave."""
if not isinstance(instance, OctaveUserClass) or not instance._attrs:
return dict()
# Bootstrap a MatlabObject from scipy.io
# From https://github.com/scipy/scipy/blob/93a0ea9e5d4aba1f661b6bb0e18f9c2d1fce436a/scipy/io/matlab/mio5.py#L435-L443
# and https://github.com/scipy/scipy/blob/93a0ea9e5d4aba1f661b6bb0e18f9c2d1fce436a/scipy/io/matlab/mio5_params.py#L224
dtype = []
values = []
for attr in instance._attrs:
dtype.append((str(attr), object))
values.append(getattr(instance, attr))
struct = np.array([tuple(values)], dtype)
return MatlabObject(struct, instance._name)
|
Get a pointer to the private object.
|
def to_pointer(cls, instance):
"""Get a pointer to the private object.
"""
return OctavePtr(instance._ref, instance._name, instance._address)
|
Decorator to make functional view documentable via drf - autodocs
|
def document_func_view(serializer_class=None,
response_serializer_class=None,
filter_backends=None,
permission_classes=None,
authentication_classes=None,
doc_format_args=list(),
doc_format_kwargs=dict()):
"""
Decorator to make functional view documentable via drf-autodocs
"""
def decorator(func):
if serializer_class:
func.cls.serializer_class = func.view_class.serializer_class = serializer_class
if response_serializer_class:
func.cls.response_serializer_class = func.view_class.response_serializer_class = response_serializer_class
if filter_backends:
func.cls.filter_backends = func.view_class.filter_backends = filter_backends
if permission_classes:
func.cls.permission_classes = func.view_class.permission_classes = permission_classes
if authentication_classes:
func.cls.authentication_classes = func.view_class.authentication_classes = authentication_classes
if doc_format_args or doc_format_kwargs:
func.cls.__doc__ = func.view_class.__doc__ = getdoc(func).format(*doc_format_args, **doc_format_kwargs)
return func
return decorator
|
Decorator for clean docstring formatting
|
def format_docstring(*args, **kwargs):
"""
Decorator for clean docstring formatting
"""
def decorator(func):
func.__doc__ = getdoc(func).format(*args, **kwargs)
return func
return decorator
|
Return true if file is a valid RAR file.
|
def is_rarfile(filename):
"""Return true if file is a valid RAR file."""
mode = constants.RAR_OM_LIST_INCSPLIT
archive = unrarlib.RAROpenArchiveDataEx(filename, mode=mode)
try:
handle = unrarlib.RAROpenArchiveEx(ctypes.byref(archive))
except unrarlib.UnrarException:
return False
unrarlib.RARCloseArchive(handle)
return (archive.OpenResult == constants.SUCCESS)
|
Read current member header into a RarInfo object.
|
def _read_header(self, handle):
"""Read current member header into a RarInfo object."""
header_data = unrarlib.RARHeaderDataEx()
try:
res = unrarlib.RARReadHeaderEx(handle, ctypes.byref(header_data))
rarinfo = RarInfo(header=header_data)
except unrarlib.ArchiveEnd:
return None
except unrarlib.MissingPassword:
raise RuntimeError("Archive is encrypted, password required")
except unrarlib.BadPassword:
raise RuntimeError("Bad password for Archive")
except unrarlib.UnrarException as e:
raise BadRarFile(str(e))
return rarinfo
|
Process current member with op operation.
|
def _process_current(self, handle, op, dest_path=None, dest_name=None):
"""Process current member with 'op' operation."""
unrarlib.RARProcessFileW(handle, op, dest_path, dest_name)
|
Load archive members metadata.
|
def _load_metadata(self, handle):
"""Load archive members metadata."""
rarinfo = self._read_header(handle)
while rarinfo:
self.filelist.append(rarinfo)
self.NameToInfo[rarinfo.filename] = rarinfo
self._process_current(handle, constants.RAR_SKIP)
rarinfo = self._read_header(handle)
|
Open RAR archive file.
|
def _open(self, archive):
"""Open RAR archive file."""
try:
handle = unrarlib.RAROpenArchiveEx(ctypes.byref(archive))
except unrarlib.UnrarException:
raise BadRarFile("Invalid RAR file.")
return handle
|
Return file - like object for member.
|
def open(self, member, pwd=None):
"""Return file-like object for 'member'.
'member' may be a filename or a RarInfo object.
"""
if isinstance(member, RarInfo):
member = member.filename
archive = unrarlib.RAROpenArchiveDataEx(
self.filename, mode=constants.RAR_OM_EXTRACT)
handle = self._open(archive)
password = pwd or self.pwd
if password is not None:
unrarlib.RARSetPassword(handle, b(password))
# based on BrutuZ (https://github.com/matiasb/python-unrar/pull/4)
# and Cubixmeister work
data = _ReadIntoMemory()
c_callback = unrarlib.UNRARCALLBACK(data._callback)
unrarlib.RARSetCallback(handle, c_callback, 0)
try:
rarinfo = self._read_header(handle)
while rarinfo is not None:
if rarinfo.filename == member:
self._process_current(handle, constants.RAR_TEST)
break
else:
self._process_current(handle, constants.RAR_SKIP)
rarinfo = self._read_header(handle)
if rarinfo is None:
data = None
except unrarlib.MissingPassword:
raise RuntimeError("File is encrypted, password required")
except unrarlib.BadPassword:
raise RuntimeError("Bad password for File")
except unrarlib.BadDataError:
if password is not None:
raise RuntimeError("File CRC error or incorrect password")
else:
raise RuntimeError("File CRC error")
except unrarlib.UnrarException as e:
raise BadRarFile("Bad RAR archive data: %s" % str(e))
finally:
self._close(handle)
if data is None:
raise KeyError('There is no item named %r in the archive' % member)
# return file-like object
return data.get_bytes()
|
Return a list of file names in the archive.
|
def namelist(self):
"""Return a list of file names in the archive."""
names = []
for member in self.filelist:
names.append(member.filename)
return names
|
Return the instance of RarInfo given name.
|
def getinfo(self, name):
"""Return the instance of RarInfo given 'name'."""
rarinfo = self.NameToInfo.get(name)
if rarinfo is None:
raise KeyError('There is no item named %r in the archive' % name)
return rarinfo
|
Print a table of contents for the RAR file.
|
def printdir(self):
"""Print a table of contents for the RAR file."""
print("%-46s %19s %12s" % ("File Name", "Modified ", "Size"))
for rarinfo in self.filelist:
date = "%d-%02d-%02d %02d:%02d:%02d" % rarinfo.date_time[:6]
print("%-46s %s %12d" % (
rarinfo.filename, date, rarinfo.file_size))
|
Extract a member from the archive to the current working directory using its full name. Its file information is extracted as accurately as possible. member may be a filename or a RarInfo object. You can specify a different directory using path.
|
def extract(self, member, path=None, pwd=None):
"""Extract a member from the archive to the current working directory,
using its full name. Its file information is extracted as accurately
as possible. `member' may be a filename or a RarInfo object. You can
specify a different directory using `path'.
"""
if isinstance(member, RarInfo):
member = member.filename
if path is None:
path = os.getcwd()
self._extract_members([member], path, pwd)
return os.path.join(path, member)
|
Extract all members from the archive to the current working directory. path specifies a different directory to extract to. members is optional and must be a subset of the list returned by namelist ().
|
def extractall(self, path=None, members=None, pwd=None):
"""Extract all members from the archive to the current working
directory. `path' specifies a different directory to extract to.
`members' is optional and must be a subset of the list returned
by namelist().
"""
if members is None:
members = self.namelist()
self._extract_members(members, path, pwd)
|
Extract the RarInfo objects members to a physical file on the path targetpath.
|
def _extract_members(self, members, targetpath, pwd):
"""Extract the RarInfo objects 'members' to a physical
file on the path targetpath.
"""
archive = unrarlib.RAROpenArchiveDataEx(
self.filename, mode=constants.RAR_OM_EXTRACT)
handle = self._open(archive)
password = pwd or self.pwd
if password is not None:
unrarlib.RARSetPassword(handle, b(password))
try:
rarinfo = self._read_header(handle)
while rarinfo is not None:
if rarinfo.filename in members:
self._process_current(
handle, constants.RAR_EXTRACT, targetpath)
else:
self._process_current(handle, constants.RAR_SKIP)
rarinfo = self._read_header(handle)
except unrarlib.MissingPassword:
raise RuntimeError("File is encrypted, password required")
except unrarlib.BadPassword:
raise RuntimeError("Bad password for File")
except unrarlib.BadDataError:
raise RuntimeError("File CRC Error")
except unrarlib.UnrarException as e:
raise BadRarFile("Bad RAR archive data: %s" % str(e))
finally:
self._close(handle)
|
Convert a RAR archive member DOS time to a Python time tuple.
|
def dostime_to_timetuple(dostime):
"""Convert a RAR archive member DOS time to a Python time tuple."""
dostime = dostime >> 16
dostime = dostime & 0xffff
day = dostime & 0x1f
month = (dostime >> 5) & 0xf
year = 1980 + (dostime >> 9)
second = 2 * (dostime & 0x1f)
minute = (dostime >> 5) & 0x3f
hour = dostime >> 11
return (year, month, day, hour, minute, second)
|
Wrap c function setting prototype.
|
def _c_func(func, restype, argtypes, errcheck=None):
"""Wrap c function setting prototype."""
func.restype = restype
func.argtypes = argtypes
if errcheck is not None:
func.errcheck = errcheck
return func
|
Load and validate the header of a pcap file.
|
def _load_savefile_header(file_h):
"""
Load and validate the header of a pcap file.
"""
try:
raw_savefile_header = file_h.read(24)
except UnicodeDecodeError:
print("\nMake sure the input file is opened in read binary, 'rb'\n")
raise InvalidEncoding("Could not read file; it might not be opened in binary mode.")
# in case the capture file is not the same endianness as ours, we have to
# use the correct byte order for the file header
if raw_savefile_header[:4] in [struct.pack(">I", _MAGIC_NUMBER),
struct.pack(">I", _MAGIC_NUMBER_NS)]:
byte_order = b'big'
unpacked = struct.unpack('>IhhIIII', raw_savefile_header)
elif raw_savefile_header[:4] in [struct.pack("<I", _MAGIC_NUMBER),
struct.pack("<I", _MAGIC_NUMBER_NS)]:
byte_order = b'little'
unpacked = struct.unpack('<IhhIIII', raw_savefile_header)
else:
raise UnknownMagicNumber("No supported Magic Number found")
(magic, major, minor, tz_off, ts_acc, snaplen, ll_type) = unpacked
header = __pcap_header__(magic, major, minor, tz_off, ts_acc, snaplen,
ll_type, ctypes.c_char_p(byte_order),
magic == _MAGIC_NUMBER_NS)
if not __validate_header__(header):
raise InvalidHeader("Invalid Header")
else:
return header
|
Parse a savefile as a pcap_savefile instance. Returns the savefile on success and None on failure. Verbose mode prints additional information about the file s processing. layers defines how many layers to descend and decode the packet. input_file should be a Python file object.
|
def load_savefile(input_file, layers=0, verbose=False, lazy=False):
"""
Parse a savefile as a pcap_savefile instance. Returns the savefile
on success and None on failure. Verbose mode prints additional information
about the file's processing. layers defines how many layers to descend and
decode the packet. input_file should be a Python file object.
"""
global VERBOSE
old_verbose = VERBOSE
VERBOSE = verbose
__TRACE__('[+] attempting to load {:s}', (input_file.name,))
header = _load_savefile_header(input_file)
if __validate_header__(header):
__TRACE__('[+] found valid header')
if lazy:
packets = _generate_packets(input_file, header, layers)
__TRACE__('[+] created packet generator')
else:
packets = _load_packets(input_file, header, layers)
__TRACE__('[+] loaded {:d} packets', (len(packets),))
sfile = pcap_savefile(header, packets)
__TRACE__('[+] finished loading savefile.')
else:
__TRACE__('[!] invalid savefile')
sfile = None
VERBOSE = old_verbose
return sfile
|
Read packets from the capture file. Expects the file handle to point to the location immediately after the header ( 24 bytes ).
|
def _load_packets(file_h, header, layers=0):
"""
Read packets from the capture file. Expects the file handle to point to
the location immediately after the header (24 bytes).
"""
pkts = []
hdrp = ctypes.pointer(header)
while True:
pkt = _read_a_packet(file_h, hdrp, layers)
if pkt:
pkts.append(pkt)
else:
break
return pkts
|
Read packets one by one from the capture file. Expects the file handle to point to the location immediately after the header ( 24 bytes ).
|
def _generate_packets(file_h, header, layers=0):
"""
Read packets one by one from the capture file. Expects the file
handle to point to the location immediately after the header (24
bytes).
"""
hdrp = ctypes.pointer(header)
while True:
pkt = _read_a_packet(file_h, hdrp, layers)
if pkt:
yield pkt
else:
break
|
Reads the next individual packet from the capture file. Expects the file handle to be somewhere after the header on the next per - packet header.
|
def _read_a_packet(file_h, hdrp, layers=0):
"""
Reads the next individual packet from the capture file. Expects
the file handle to be somewhere after the header, on the next
per-packet header.
"""
raw_packet_header = file_h.read(16)
if not raw_packet_header or len(raw_packet_header) != 16:
return None
# in case the capture file is not the same endianness as ours, we have to
# use the correct byte order for the packet header
if hdrp[0].byteorder == 'big':
packet_header = struct.unpack('>IIII', raw_packet_header)
else:
packet_header = struct.unpack('<IIII', raw_packet_header)
(timestamp, timestamp_us, capture_len, packet_len) = packet_header
raw_packet_data = file_h.read(capture_len)
if not raw_packet_data or len(raw_packet_data) != capture_len:
return None
if layers > 0:
layers -= 1
raw_packet = linklayer.clookup(hdrp[0].ll_type)(raw_packet_data,
layers=layers)
else:
raw_packet = raw_packet_data
packet = pcap_packet(hdrp, timestamp, timestamp_us, capture_len,
packet_len, raw_packet)
return packet
|
Given a raw IPv4 address ( i. e. as an unsigned integer ) return it in dotted quad notation.
|
def parse_ipv4(address):
"""
Given a raw IPv4 address (i.e. as an unsigned integer), return it in
dotted quad notation.
"""
raw = struct.pack('I', address)
octets = struct.unpack('BBBB', raw)[::-1]
ipv4 = b'.'.join([('%d' % o).encode('ascii') for o in bytearray(octets)])
return ipv4
|
Remove the IP packet layer yielding the transport layer.
|
def strip_ip(packet):
"""
Remove the IP packet layer, yielding the transport layer.
"""
if not isinstance(packet, IP):
packet = IP(packet)
payload = packet.payload
return payload
|
Strip the Ethernet frame from a packet.
|
def strip_ethernet(packet):
"""
Strip the Ethernet frame from a packet.
"""
if not isinstance(packet, Ethernet):
packet = Ethernet(packet)
payload = packet.payload
return payload
|
Given an Ethernet frame determine the appropriate sub - protocol ; If layers is greater than zerol determine the type of the payload and load the appropriate type of network packet. It is expected that the payload be a hexified string. The layers argument determines how many layers to descend while parsing the packet.
|
def load_network(self, layers=1):
"""
Given an Ethernet frame, determine the appropriate sub-protocol;
If layers is greater than zerol determine the type of the payload
and load the appropriate type of network packet. It is expected
that the payload be a hexified string. The layers argument determines
how many layers to descend while parsing the packet.
"""
if layers:
ctor = payload_type(self.type)[0]
if ctor:
ctor = ctor
payload = self.payload
self.payload = ctor(payload, layers - 1)
else:
# if no type is found, do not touch the packet.
pass
|
calls wifi packet discriminator and constructor.: frame: ctypes. Structure: no_rtap: Bool: return: packet object in success: return: int - 1 on known error: return: int - 2 on unknown error
|
def WIFI(frame, no_rtap=False):
"""calls wifi packet discriminator and constructor.
:frame: ctypes.Structure
:no_rtap: Bool
:return: packet object in success
:return: int
-1 on known error
:return: int
-2 on unknown error
"""
pack = None
try:
pack = WiHelper.get_wifi_packet(frame, no_rtap)
except Exception as e:
logging.exception(e)
return pack
|
Discriminates Wi - Fi packet and creates packet object.: frame: ctypes. Structure: no_rtap: Bool: return: obj Wi - Fi packet
|
def get_wifi_packet(frame, no_rtap=False):
"""Discriminates Wi-Fi packet and creates
packet object.
:frame: ctypes.Structure
:no_rtap: Bool
:return: obj
Wi-Fi packet
"""
_, packet = WiHelper._strip_rtap(frame)
frame_control = struct.unpack('BB', packet[:2])
cat = (frame_control[0] >> 2) & 0b0011
s_type = frame_control[0] >> 4
if cat not in _CATEGORIES_.keys():
logging.warning("unknown category: %d" % (cat))
return Unknown(frame, no_rtap)
if s_type not in _SUBTYPES_[cat].keys():
logging.warning("unknown subtype %d in %s category" % (s_type, _CATEGORIES_[cat]))
return Unknown(frame, no_rtap)
if cat == 0:
if s_type == 4:
return ProbeReq(frame, no_rtap)
elif s_type == 5:
return ProbeResp(frame, no_rtap)
elif s_type == 8:
return Beacon(frame, no_rtap)
else:
return Management(frame, no_rtap)
elif cat == 1:
if s_type == 11:
return RTS(frame, no_rtap)
elif s_type == 12:
return CTS(frame, no_rtap)
elif s_type == 9:
return BACK(frame, no_rtap)
else:
return Control(frame, no_rtap)
elif cat == 2:
if s_type == 8:
return QosData(frame, no_rtap, parse_amsdu=True)
else:
return Data(frame, no_rtap)
|
strip injected radiotap header.: return: ctypes. Structure radiotap header: return: ctypes. Structure actual layer 2 Wi - Fi payload
|
def _strip_rtap(frame):
"""strip injected radiotap header.
:return: ctypes.Structure
radiotap header
:return: ctypes.Structure
actual layer 2 Wi-Fi payload
"""
rtap_len = WiHelper.__get_rtap_len(frame)
rtap = frame[:rtap_len]
packet = frame[rtap_len:]
return rtap, packet
|
strip ( 4 byte ) radiotap. present. Those are flags that identify existence of incoming radiotap meta - data.: idx: int: return: str: return: namedtuple
|
def strip_present(payload):
"""strip(4 byte) radiotap.present. Those are flags that
identify existence of incoming radiotap meta-data.
:idx: int
:return: str
:return: namedtuple
"""
present = collections.namedtuple(
'present', ['tsft', 'flags', 'rate', 'channel', 'fhss',
'dbm_antsignal', 'dbm_antnoise', 'lock_quality',
'tx_attenuation', 'db_tx_attenuation', 'dbm_tx_power',
'antenna', 'db_antsignal', 'db_antnoise', 'rxflags',
'txflags', 'rts_retries', 'data_retries', 'xchannel',
'mcs', 'ampdu', 'vht', 'rtap_ns', 'ven_ns', 'ext'])
val = struct.unpack('<L', payload)[0]
bits = format(val, '032b')[::-1]
present.tsft = int(bits[0]) # timer synchronization function
present.flags = int(bits[1]) # flags
present.rate = int(bits[2]) # rate
present.channel = int(bits[3]) # channel
present.fhss = int(bits[4]) # frequency hoping spread spectrum
present.dbm_antsignal = int(bits[5]) # dbm antenna signal
present.dbm_antnoise = int(bits[6]) # dbm antenna noinse
present.lock_quality = int(bits[7]) # quality of barker code lock
present.tx_attenuation = int(bits[8]) # transmitter attenuation
present.db_tx_attenuation = int(bits[9]) # decibel transmit attenuation
present.dbm_tx_power = int(bits[10]) # dbm transmit power
present.antenna = int(bits[11]) # antenna
present.db_antsignal = int(bits[12]) # db antenna signal
present.db_antnoise = int(bits[13]) # db antenna noise
present.rxflags = int(bits[14]) # receiver flags
present.txflags = int(bits[15]) # transmitter flags
present.rts_retries = int(bits[16]) # rts(request to send) retries
present.data_retries = int(bits[17]) # data retries
present.xchannel = int(bits[18]) # xchannel
present.mcs = int(bits[19]) # modulation and coding scheme
present.ampdu = int(bits[20]) # aggregated mac protocol data unit
present.vht = int(bits[21]) # very high throughput
present.rtap_ns = int(bits[29]) # radiotap namespace
present.ven_ns = int(bits[30]) # vendor namespace
present.ext = int(bits[31]) # extension
return present, bits
|
strip ( 8 byte ) radiotap. mactime: idx: int: return: int idx: return: int mactime
|
def strip_tsft(self, idx):
"""strip(8 byte) radiotap.mactime
:idx: int
:return: int
idx
:return: int
mactime
"""
idx = Radiotap.align(idx, 8)
mactime, = struct.unpack_from('<Q', self._rtap, idx)
return idx + 8, mactime
|
strip ( 1 byte ) radiotap. flags: idx: int: return: int idx: return: collections. namedtuple
|
def strip_flags(self, idx):
"""strip(1 byte) radiotap.flags
:idx: int
:return: int
idx
:return: collections.namedtuple
"""
flags = collections.namedtuple(
'flags', ['cfp', 'preamble', 'wep', 'fragmentation', 'fcs',
'datapad', 'badfcs', 'shortgi'])
val, = struct.unpack_from('<B', self._rtap, idx)
bits = format(val, '08b')[::-1]
flags.cfp = int(bits[0])
flags.preamble = int(bits[1])
flags.wep = int(bits[2])
flags.fragmentation = int(bits[3])
flags.fcs = int(bits[4])
flags.datapad = int(bits[5])
flags.badfcs = int(bits[6])
flags.shortgi = int(bits[7])
return idx + 1, flags
|
strip ( 1 byte ) radiotap. datarate note that unit of this field is originally 0. 5 Mbps: idx: int: return: int idx: return: double rate in terms of Mbps
|
def strip_rate(self, idx):
"""strip(1 byte) radiotap.datarate
note that, unit of this field is originally 0.5 Mbps
:idx: int
:return: int
idx
:return: double
rate in terms of Mbps
"""
val, = struct.unpack_from('<B', self._rtap, idx)
rate_unit = float(1) / 2 # Mbps
return idx + 1, rate_unit * val
|
strip ( 2 byte ) radiotap. channel. flags: idx: int: return: int idx: return: collections. namedtuple
|
def strip_chan(self, idx):
"""strip(2 byte) radiotap.channel.flags
:idx: int
:return: int
idx
:return: collections.namedtuple
"""
chan = collections.namedtuple(
'chan', ['freq', 'turbo', 'cck', 'ofdm', 'two_g', 'five_g',
'passive', 'dynamic', 'gfsk', 'gsm', 'static_turbo',
'half_rate', 'quarter_rate'])
idx = Radiotap.align(idx, 2)
freq, flags, = struct.unpack_from('<HH', self._rtap, idx)
chan.freq = freq
bits = format(flags, '016b')[::-1]
chan.turbo = int(bits[4])
chan.cck = int(bits[5])
chan.ofdm = int(bits[6])
chan.two_g = int(bits[7])
chan.five_g = int(bits[8])
chan.passive = int(bits[9])
chan.dynamic = int(bits[10])
chan.gfsk = int(bits[11])
chan.gsm = int(bits[12])
chan.static_turbo = int(bits[13])
chan.half_rate = int(bits[14])
chan.quarter_rate = int(bits[15])
return idx + 4, chan
|
strip ( 2 byte ) radiotap. fhss. hopset ( 1 byte ) and radiotap. fhss. pattern ( 1 byte ): idx: int: return: int idx: return: collections. namedtuple
|
def strip_fhss(self, idx):
"""strip (2 byte) radiotap.fhss.hopset(1 byte) and
radiotap.fhss.pattern(1 byte)
:idx: int
:return: int
idx
:return: collections.namedtuple
"""
fhss = collections.namedtuple('fhss', ['hopset', 'pattern'])
fhss.hopset, fhss.pattern, = struct.unpack_from('<bb', self._rtap, idx)
return idx + 2, fhss
|
strip ( 1 byte ) radiotap. dbm. ant_signal: idx: int: return: int idx: return: int
|
def strip_dbm_antsignal(self, idx):
"""strip(1 byte) radiotap.dbm.ant_signal
:idx: int
:return: int
idx
:return: int
"""
dbm_antsignal, = struct.unpack_from('<b', self._rtap, idx)
return idx + 1, dbm_antsignal
|
strip ( 1 byte ) radiotap. dbm_antnoise: idx: int: return: int idx: return: int
|
def strip_dbm_antnoise(self, idx):
"""strip(1 byte) radiotap.dbm_antnoise
:idx: int
:return: int
idx
:return: int
"""
dbm_antnoise, = struct.unpack_from('<b', self._rtap, idx)
return idx + 1, dbm_antnoise
|
strip ( 2 byte ) lock quality: idx: int: return: int idx: return: int
|
def strip_lock_quality(self, idx):
"""strip(2 byte) lock quality
:idx: int
:return: int
idx
:return: int
"""
idx = Radiotap.align(idx, 2)
lock_quality, = struct.unpack_from('<H', self._rtap, idx)
return idx + 2, lock_quality
|
strip ( 1 byte ) tx_attenuation: idx: int: return: int idx: return: int
|
def strip_tx_attenuation(self, idx):
"""strip(1 byte) tx_attenuation
:idx: int
:return: int
idx
:return: int
"""
idx = Radiotap.align(idx, 2)
tx_attenuation, = struct.unpack_from('<H', self._rtap, idx)
return idx + 2, tx_attenuation
|
strip ( 1 byte ) db_tx_attenuation: return: int idx: return: int
|
def strip_db_tx_attenuation(self, idx):
"""strip(1 byte) db_tx_attenuation
:return: int
idx
:return: int
"""
idx = Radiotap.align(idx, 2)
db_tx_attenuation, = struct.unpack_from('<H', self._rtap, idx)
return idx + 2, db_tx_attenuation
|
strip ( 1 byte ) dbm_tx_power: return: int idx: return: int
|
def strip_dbm_tx_power(self, idx):
"""strip(1 byte) dbm_tx_power
:return: int
idx
:return: int
"""
idx = Radiotap.align(idx, 1)
dbm_tx_power, = struct.unpack_from('<b', self._rtap, idx)
return idx + 1, dbm_tx_power
|
strip ( 1 byte ) radiotap. antenna: return: int idx: return: int
|
def strip_antenna(self, idx):
"""strip(1 byte) radiotap.antenna
:return: int
idx
:return: int
"""
antenna, = struct.unpack_from('<B', self._rtap, idx)
return idx + 1, antenna
|
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