def plotLevel0VsLevel1Stats(data, prop0, prop1, label0, label1):
d0 = getLevel0Prop(data, prop0)
m, std, sm = level1Stats(data, prop1)
for (v, p) in [(m, "mean"), (std, "standard deviation"), (sm, "sum")]:
pl.newFigure()
pl.plot(d0, v, style='ko-', xLog=False, yLog=False)
pl.labels(label0, label1 + " " + p)
def wordSimScatterPlot(set1, set2, label1, label2):
pl.newFigure()
sims1, sims2 = intersectionSimilarities(set1, set2)
pl.plot(sims1, sims2, style='ko', markerSize=4)
pl.labels(label1, label2)
print "Spearman correlation between", label1, "and", label2, ":", spearmanCorrelation(
sims1, sims2)
def plotVsMaxInDegree(data, prop, label):
pl.newFigure()
pl.plot(getLevel0Prop(data, "max in-degree"),
getLevel0Prop(data, prop),
style='k-o',
markerSize=4)
pl.labels("In-degree threshold", label)
def on_plot_clicked(self, widget):
self.set_status(Status.PLOTTING)
self.maybe_set_ready()
try:
plotlib.plot(self.plotter_port, self.plotter_baud_rate, self.file)
except Exception as e:
self.set_status(Status.ERROR)
print(e)
else:
self.set_status(Status.DONE)
self.maybe_set_ready()
def similarityDistribution(data):
pl.newFigure()
for d in data["results"]:
hist = d["relative l1 norm"]["histogram"]
bins = [1.0 - b for [b, c] in hist]
counts = [float(c) for [t, c] in hist]
tot = sum(counts)
pl.plot(bins, [c / tot for c in counts],
style='-',
xLog=True,
yLog=True)
pl.labels("$1 - \mathrm{L_{1}}$", "Density")
pl.legend(getLevel0Prop(data, "max in-degree"))
def histograms(data, prop, label):
pl.newFigure()
for d in data["results"]:
hist = d[prop]["histogram"]
bins = [b for [b, c] in hist]
counts = [float(c) for [b, c] in hist]
deltaTot = sum(counts) * (bins[1] - bins[0])
pl.plot(bins, [c / deltaTot for c in counts],
style='-',
xLog=False,
yLog=False)
pl.labels(label, "Density")
pl.legend(getLevel0Prop(data, "max in-degree"))
def correlationsVsLevel0Prop(data, prop0, label0, benchmarks, benchLabels):
pl.newFigure()
l0Prop = getLevel0Prop(data, prop0)
l1Sims = selectedSimilarities(data)
for b in benchmarks:
def spearman(s1, s2):
sims1, sims2 = intersectionSimilarities(s1, s2)
return spearmanCorrelation(sims1, sims2)
correlations = [spearman(b, s1) for s1 in l1Sims]
pl.plot(l0Prop, correlations, style='o-', markerSize=4)
pl.labels(label0, "Spearman rank correlation coefficient")
pl.legend(benchLabels)
import cv2
import dlib
import imutils
from matplotlib import pyplot as plt
from plotlib import fig, plot
detector = dlib.get_frontal_face_detector()
img = load_image_url(
'http://sensestudy-server/api/resource/public/accountstorage-objs/18ff97da-ead9-439d-82b5-deebcc29502a/zy1.jpeg'
)
img = img[:, :, ::-1]
print(img.shape)
plt.imshow(img)
plt.axis('off')
plt.show() # Download as SVG format
img = imutils.resize(img, width=400)
fig() + plot(img) # Download as PNG format
return r[0]
df = pd.read_csv('https://sololearn.com/uploads/files/titanic.csv')
df['male'] = df['Sex'] == 'male'
X = df[[
'Pclass', 'male', 'Age', 'Siblings/Spouses', 'Parents/Children', 'Fare'
]].values
y = df['Survived'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=5)
model = LogisticRegression()
model.fit(X_train, y_train)
# Adjusting the threshhold
# y_pred = model.predict_proba(X_test)[:, 1] > 0.75
y_pred_proba = model.predict_proba(X_test)
fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba[:, 1])
plt.plot(fpr, tpr)
plt.plot([0, 1], [0, 1], linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('1 - specificity')
plt.ylabel('sensitivity')
plt.show()
# print("sensitivity:", sensitivity_score(y_test, y_pred))
# print("specificity:", specificity_score(y_test, y_pred))
return [r[prop] for r in data["results"]]
def plotVsMaxInDegree(data, prop, label):
pl.newFigure()
pl.plot(getLevel0Prop(data, "max in-degree"),
getLevel0Prop(data, prop),
style='k-o',
markerSize=4)
pl.labels("In-degree threshold", label)
def level0Plot(data, (prop1, label1), (prop2, label2)):
fig = pl.newFigure()
pl.plot(getLevel0Prop(data, prop1),
getLevel0Prop(data, prop2),
style='k-o',
markerSize=4)
pl.labels(label1, label2)
return fig
def histograms(data, prop, label):
pl.newFigure()
for d in data["results"]:
hist = d[prop]["histogram"]
bins = [b for [b, c] in hist]
counts = [float(c) for [b, c] in hist]
deltaTot = sum(counts) * (bins[1] - bins[0])
pl.plot(bins, [c / deltaTot for c in counts],
style='-',
xLog=False,
im = Image.open(file_path)
im = im.convert('RGB')
pixel = im.load()
output_path = os.path.join(os.path.dirname(__file__), "output/")
size_x = int(im.size[0])
size_y = int(im.size[1])
totalPx = size_x * size_y
print "Pixel: %spx X %spx " % (size_x, size_y)
print "Size: %.2fmm X %.2fmm " % (size_x * pxScale, size_y * pxScale)
target = open(output_path + filename + '.nc', 'w')
target.seek(0)
plotter = plotlib.plot(int(size_x/renderScale), int(size_y/renderScale)) # just for visualization and debugging
plotter.setBackground(0, 0, 0)
precalc = math.sqrt(math.pow(255, 2) + math.pow(255, 2) + math.pow(255, 2))
# precalculation of the length of an 3 dimensional RGB-color vector (255,255,255) - white
if (threshold >= 255):
threshold = 255
elif (threshold <= 0):
threshold = 0
# Header of Gcode
target.write('/#############################################################/ \n')
target.write('/###########Gcode generated with PathImg.py V0.4##############/ \n')
target.write('/######written by Nick Sidney Lemberger aka Holysocks#########/ \n')
target.write('/#############################################################/ \n\n')
import cv2
import numpy as np
import os
import plotlib
plotter = plotlib.plot()
plotter.setBackground(0,0,0)
dim = plotter.getDim()
video_capture = cv2.VideoCapture(0)
t_minus = cv2.cvtColor(video_capture.read()[1], cv2.COLOR_RGB2GRAY)
t = cv2.cvtColor(video_capture.read()[1], cv2.COLOR_RGB2GRAY)
t_plus = cv2.cvtColor(video_capture.read()[1], cv2.COLOR_RGB2GRAY)
def diffImg(t0, t1, t2):
d1 = cv2.absdiff(t2, t1)
d2 = cv2.absdiff(t1, t0)
return cv2.bitwise_and(d1, d2)
def show(img):
for x in xrange(dim[0]):
for y in xrange(dim[1]):
plotter.setColor(img.item(y,x,3),img.item(y,x,2),img.item(y,x,1))
while True:
img = ("Motion",diffImg(t_minus, t, t_plus))
show(img)
t_minus = t
t = t_plus
from plotlib import fig, plot
img_list = []
label_list = []
feat_list = []
trainset, testset = load_list()
img_list, label_list = trainset
print(len(img_list))
for i in range(5):
img = load_img(img_list[i])
fig() + plot(img)
print(label_list[i])
HF = hogfeature()
def extract_hog(path):
img = load_img(path)
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img_resize = cv2.resize(img_gray, (100, 100))
return HF.getHog(img_resize, 0)
for img in img_list:
feat = extract_hog(img)
feat_list.append(feat)
model = linear_classifier()
model.train(feat_list, label_list)
pred = model.predict(feat_list)
print(accuracy(pred, label_list))
if i > yi + 1: y = float(line[yi + 1:i]) else: continue if xmin < 0 or x < xmin: xmin = x if ymin < 0 or y < ymin: ymin = y if xmax < 0 or x > xmax: xmax = x if ymax < 0 or y > ymax: ymax = y print xmin,xmax,ymin,ymax plotter = plotlib.plot(int((xmax-xmin)*pixelpermm), int((ymax-ymin)*pixelpermm)) # just for visualization and debugging plotter.setBackground(0, 0, 0) #plotter = plotlib.plot(int(size_x/renderScale), int(size_y/renderScale)) # just for visualization and debugging #plotter.setBackground(0, 0, 0) def step(x, y, dirx, diry): global color if x: global pos_x pos_x = pos_x + 1 if dirx else pos_x - 1 if y: global pos_y pos_y = pos_y + 1 if diry else pos_y - 1 plotter.setColor(color[0], color[1], color[2]) plotter.plotdot(pos_x-xmin*pixelpermm, pos_y-ymin*pixelpermm) if not skipAnimation: plotter.show()
