def run(self):
results_path = Path(str(directives.path(self.arguments[0])))
if not results_path.is_absolute():
source = str(self.state_machine.input_lines.source(
self.lineno - self.state_machine.input_offset - 1))
results_path = Path(source).resolve().parent().join(results_path)
results = load_results(results_path)
for result in results:
self.state.document.settings.record_dependencies.add(str(
result["path"]))
headers = ["Label", "Angles", "Scales", "Input", "Features", "Metric",\
"Feature Parameters", "Mean Correlation"]
data = [self.extract_result(r) for r in results]
return [self.create_table([headers, ] + data), ]
def __init__(self):
self.db_indivs = load_indivs().to_numpy()
self.digits, self.spread = load_symbol_properties()
self.db_results = []
quotes_time = list(
list(int(time) for time in quotes)
for quotes in load_quotes_time())
quotes_time_string = load_quotes_time_string()
for idx, result in enumerate(load_results()):
quotes = np.array([float(quote) for quote in result[6:]])
self.db_results.append(
Quote(int(result[0]), int(result[1]), int(result[2]),
float(result[3]), float(result[4]), bool(int(result[5])),
quotes[:int(len(quotes) / 2)],
quotes[int(len(quotes) / 2):], quotes_time[idx],
quotes_time_string[idx]))
def __init__(self):
self.digits, self.spread = load_symbol_properties()
self.db_indivs = load_indivs().to_numpy()
self.db_results = []
self.quotes = []
self.params = None
self.quotes_time = [
np.resize(np.array(quotes).astype(int), 400)
for quotes in load_quotes_time()
]
for result in load_results():
self.quotes.append(np.array(result[6:]).astype(float))
self.db_results.append(np.array(result[:6]).astype(float))
self.db_results = np.array(self.db_results)
self.quotes = np.array(self.quotes)
self.quotes_time = np.array(self.quotes_time)
import argparse
import matplotlib.pyplot as plt
from loader import load_results
from metrics import calculate_metrics, plot_metric
if __name__ == '__main__':
# parse args
parser = argparse.ArgumentParser()
parser.add_argument('results_path', action='store', help='path to file containing results in csv format')
parser.add_argument('--scalable', action='store_true', help='switch to scalable metrics')
parser.add_argument('--output', action='store', help='path to output plot file')
args = parser.parse_args()
# load results
results = load_results(args.results_path, grouping_col='npoints')
results = sorted(results.iteritems())
# calculate metrics
results = calculate_metrics(results, scalable=args.scalable)
# plot metrics
plt.figure(figsize=(22, 16), dpi=80)
metrics_type = 'scalable' if args.scalable else 'non-scalable'
plot_title = 'Monte Carlo approximation of PI number - %s metrics of parallel computation' % metrics_type
plt.suptitle(plot_title, size=24)
plot_opts = {'label': lambda npoints: '%s points' % npoints}
legend_opts = {'loc': 'upper center', 'ncol': 3}
import argparse
import matplotlib.pyplot as plt
from loader import load_results
from operator import itemgetter
from metrics import plot_metric
if __name__ == '__main__':
# parse args
parser = argparse.ArgumentParser()
parser.add_argument('results_path', action='store', help='path to file containing results in csv format')
parser.add_argument('--output', action='store', help='path to output plot file')
args = parser.parse_args()
# load results
results = load_results(args.results_path, grouping_col='ncores')
results = sorted(results.iteritems())
# plot performance
plt.figure(figsize=(12, 8), dpi=80)
plot_opts = {
'label': lambda ncores: '%s cores' % ncores,
'title': 'Monte Carlo approximation of PI number - performance of parallel computation'
}
legend_opts = {'loc': 'upper left', 'ncol': 1}
plot_metric('npoints', 'time', results,
metric_arg_name='Number of points',
metric_val_name='Walltime [s]',
plot_opts=plot_opts,
import argparse
import matplotlib.pyplot as plt
from loader import load_results
from metrics import calculate_metrics, plot_metric
if __name__ == '__main__':
# parse args
parser = argparse.ArgumentParser()
parser.add_argument('results_path', action='store', help='path to file containing results in csv format')
parser.add_argument('--scalable', action='store_true', help='switch to scalable metrics')
parser.add_argument('--output', action='store', help='path to output plot file')
args = parser.parse_args()
# load results
results = load_results(args.results_path, grouping_col='size')
results = sorted(results.iteritems())
# calculate metrics
results = calculate_metrics(results, scalable=args.scalable)
# plot metrics
plt.figure(figsize=(22, 16), dpi=80)
metrics_type = 'scalable' if args.scalable else 'non-scalable'
plot_title = 'Matri multiplication - %s metrics of parallel computation' % metrics_type
plt.suptitle(plot_title, size=24)
plot_opts = {'label': lambda size: 'Size: %s' % size}
legend_opts = {'loc': 'upper center', 'ncol': 3}
import collections
#import sys
import matplotlib.pyplot as pyplot
import numpy
from pathlib import Path
from loader import format_label, load_results, naturally_sorted
results = load_results(Path("../results").resolve())
bar_width = 0.8 / len(results)
indices = numpy.arange(len(results[0]["correlations"]))
sketch_correlations = collections.defaultdict(dict)
for result_index, result in enumerate(results):
for sketch_index, sketch in enumerate(result["sketches"]):
sketch_correlations[sketch][result_index] =\
result["correlations"][sketch_index]
sketches = naturally_sorted(sketch_correlations.keys())
bar_plots = []
for result_index, result in enumerate(results):
correlations = [sketch_correlations[s][result_index] for s in sketches]
bar_plots.append(pyplot.bar(
indices + bar_width * result_index,
correlations,
width=bar_width,
color=result["plot_color"],
label=format_label(result),
