def main():
# STEP 1: Parse args
args = get_arguments(sys.argv[1:])
# STEP 2: Get a list of CSV objects.
csv_list = get_csv_list(args)
# STEP 3: Create a Plotter and generate plot(s)
if args.indiv:
generate_individual_plots(args, csv_list)
else:
plot = Plotter(args, csv_list)
plot.generate_plot()
def protein_length_graphic(paths_dict, height, width, font_scale):
height = int(height.get())
width = int(width.get())
font_scale = float(font_scale.get())
savepath = paths_dict[SAVEPATH]
prot_index = 0
if len(protein_listbox1.curselection()) > 0:
prot_index = protein_listbox1.curselection()[0]
else:
raise Exception("At least one protein must be selected")
prot_name = protein_listbox1.get(prot_index)
facade.protein_length_graphic(prot_name, savepath, height, width, font_scale, sys.stderr)
protein_size_tif = plotter.get_protein_size_tif(savepath, prot_name)
_show_image(protein_size_tif, height, width, 1200, 800)
displayedText.set('Protein plot done!!')
return
def __init__(self):
self.limit = -1
self.fingerprint_loaded = False
self.Decoder = Decoder.Decoder()
self.FingerPrinter = FingerPrinter.FingerPrinter()
self.Recognizer = Recognizer.Recognizer()
self.HashingManager = HashingManager.HashingManager()
self.Plotter = Plotter.Plotter()
self.MicRecorder = MicRecorder.MicRecorder()
def generate_individual_plots(args, csv_list):
args.dir = None
xaxis = args.xaxis if args.xaxis else None
xmin = Plotter.get_x_min(csv_list, xaxis)
xmax = Plotter.get_x_max(csv_list, xaxis)
ymin = Plotter.get_y_min(csv_list, xaxis) + args.offset
ymax = Plotter.get_y_max(csv_list, xaxis) + args.offset
for csv in csv_list:
args.file = csv.fname
plot = Plotter(args, [csv], xmin, xmax, ymin, ymax)
plot.generate_plot()
def stripplot (paths_dict, height, width): height = int(height.get()) width = int(width.get()) savepath = paths_dict[SAVEPATH] facade.stripplot(savepath, height, width, sys.stderr) blast_plot_tif = plotter.get_blast_plot_tif(savepath) _show_image(blast_plot_tif, height, width, 1200, 600) return
def heatmap(paths_dict, height, width, font_scale, right_scale, bottom_scale): height = int(height.get()) width = int(width.get()) font_scale = float(font_scale.get()) bottom_scale = float(bottom_scale.get()) right_scale = float(right_scale.get()) savepath = paths_dict[SAVEPATH] facade.heatmap(savepath, height, width, font_scale, right_scale, bottom_scale, sys.stderr) clustermap = plotter.get_cluster_map_tif(savepath) _show_image(clustermap, height, width, 1200, 800) return
from src import Plotter
# prepare the video input stream
vc = cv2.VideoCapture('./data/Surgery.avi')
# read the first frame of the video stream
_, frameReference = vc.read()
count = 0
plt.ion()
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 5))
plt.axis('off')
segmentation = False
# instantiate the Tracker with the segmentation option
tracker = Tracker.Tracker(segmentation)
plotter = Plotter.Plotter()
start = datetime.datetime.now()
while vc.isOpened():
time1 = datetime.datetime.now()
# grab the next frame
_, frameNew = vc.read()
if frameNew is None:
end = datetime.datetime.now()
print("finishing...")
break
assert frameReference.shape == frameNew.shape
# extract the keypoints and match them according to their descriptors
def test_plotter_arg_filename(self):
args = get_arguments(['-d', 'ex*/my*', '-c', 'hunger', '-I'])
csv_list = get_csv_list(args)
plot = Plotter(args, csv_list)
self.assertEqual(plot.arg['name'], 'my_examples.png')
def test_plotter_default_name_dir(self):
args = get_arguments(['-d', 'ex*/my*', '-c', 'hunger', '-n', 'pet_hunger'])
csv_list = get_csv_list(args)
plot = Plotter(args, csv_list)
self.assertEqual(plot.arg['name'], 'pet_hunger.html')
lr.XY_AVERAGE,
lr.XY_WEIGHTED_AVERAGE,
]
SVM_LIST_COMP1 = [lr.MOVEMENT, lr.XY_MOVEMENT]
SVM_LIST_COMP2 = [
lr.MOVEMENT, lr.ALL_MAJORITY, lr.ALL_AVERAGE, lr.ALL_WEIGHTED_AVERAGE
]
TO_DO_TOGHETER = [(SVM_LIST_NOSHIFT, "setnoshift"),
(SVM_LIST_SHIFT, "setshift"), (SVM_LIST_COMP1, "origshift"),
(SVM_LIST_COMP2, "setall")]
# todo: ottimizza evitando la ripetizione di calcoli
if __name__ == '__main__':
p = plotter.Plotter(Utils.DATASET_NAME_0)
for handwriting in [Utils.ITALIC, Utils.BLOCK_LETTER]:
classifier = lr.WordClassifier(Utils.DATASET_NAME_0, handwriting)
chrono = cr.Chrono("Generating verification outputs...")
ver = VerificationEvaluator(classifier)
for balanced in [True, False]:
names, fprs, tprs, ts, aucs = ver.plots_info_weights(
lr.WEIGHTED_AVERAGE, balanced, np.arange(0, 1.01, 0.2))
p.plotRocs(names, fprs, tprs, aucs, handwriting, balanced,
"weights")
for svm_list, name in TO_DO_TOGHETER:
names, fprs, tprs, ts, aucs = ver.plots_info_names(
svm_list, balanced)
from src.GeneticAlgorythm import *
from src.Plotter import *
import time
import numpy as np
if __name__ == "__main__":
methods = [
"RankSelection", "RankSelectionDependentOnIteration",
"RouletteSelection", "TournamentSelection"
]
iterCount = 1000
plt = Plotter()
meanPlt = Plotter()
crossOverPlt = Plotter()
mutatePlt = Plotter()
bestIndividualsPlt = Plotter()
crossPlot = Plotter()
mutatePlot = Plotter()
for method in methods:
print()
print('Metoda : ', method)
print()
#zbierznosici funkcji celu i najlepsze osobniki
popular = [
1.0, 2.0, 2.14, 3.6, 11.23, 23.51, 17.2, 12.3, 19.2, 7.7, 21.3,
32.7, 29.9, 30.5, 35.4
]
# popular=[3,11,15,23,27,33,41,49,56,59,70,90]
popular.sort()
alg = GeneticAlgorythm(20,