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()