def makeEllipticalFlatTop(self,mx,my,rx,ry) : print "Intensity:Intensity2D:makeEllipticalFlatTop",mx,my,rx,ry mx = pl.double(mx) my = pl.double(my) rx = pl.double(rx) ry = pl.double(ry) self.i = pl.complex64( ((self.xgrid-mx)/rx)**2 + ((self.ygrid-my)/ry)**2 <= 1.0)
def makeRectangularFlatTop(self, mx, my, rx, ry): print "Intensity:Intensity2D:makeRectangularFlatTop", mx, my, rx, ry rx = pl.double(rx) ry = pl.double(ry) a = rx * ry self.i = pl.complex128( (self.xgrid >= -rx / 2 + mx) & (self.xgrid <= rx / 2 + mx) & (self.ygrid >= -ry / 2 + my) & (self.ygrid <= ry / 2 + my)) / a
def makeEllipticalFlatTop(self, mx, my, rx, ry): print "Intensity:Intensity2D:makeEllipticalFlatTop", mx, my, rx, ry mx = pl.double(mx) my = pl.double(my) rx = pl.double(rx) ry = pl.double(ry) self.i = pl.complex64(((self.xgrid - mx) / rx)**2 + ((self.ygrid - my) / ry)**2 <= 1.0)
def ExtraFromString(s): s = s.strip(" \n") lines = [l.strip(" \n") for l in s.split('\n')] lines.pop(0) tvect = [] torques = [] for i in range(len(lines) / 2): tvect.append(double(lines[2 * i])) torques.append(array([double(x) for x in lines[2 * i + 1].split(' ')])) return array(tvect), array(torques)
def makeRectangularFlatTop(self,mx,my,rx,ry) : print "Intensity:Intensity2D:makeRectangularFlatTop",mx,my,rx,ry rx = pl.double(rx) ry = pl.double(ry) a = rx*ry self.i = pl.complex128( (self.xgrid>=-rx/2+mx) & (self.xgrid<=rx/2+mx) & (self.ygrid>=-ry/2+my) & (self.ygrid<=ry/2+my) )/a
def ProfileFromLines(lines): l = lines[0] [duration, dt] = [double(x) for x in l.split(' ')] if duration<= 0 : return None l = lines[1] sarray = array([double(x) for x in l.split(' ')]) l = lines[2] sdarray = array([double(x) for x in l.split(' ')]) return [duration, dt, sarray, sdarray]
def ProfileFromLines(lines): l = lines[0] [duration, dt] = [double(x) for x in l.split(' ')] if duration <= 0: return None l = lines[1] sarray = array([double(x) for x in l.split(' ')]) l = lines[2] sdarray = array([double(x) for x in l.split(' ')]) return [duration, dt, sarray, sdarray]
def loadMNISTImages(filename): f = open(filename, 'rb') # Verify Magic Number s = f.read(4) magic = int(s.encode('hex'),16) assert(magic == 2051) # Get Number of Images s = f.read(4) numImages = int(s.encode('hex'),16) s = f.read(4) numRows = int(s.encode('hex'),16) s = f.read(4) numCols = int(s.encode('hex'),16) # Get Data s = f.read() a = frombuffer(s, uint8) # Use 'F' to ensure that we read by column a = reshape(a, (numCols , numRows, numImages), order='F'); images = transpose(a, (1, 0, 2)) f.close() # Reshape to #pixels * #examples images = reshape(a, (shape(images)[0] * shape(images)[1], numImages), order='F'); images = double(images)/255 return images
def __init__(self, nx = 5012, startx = -1e-3, endx = 1e-3, ny = 5012, starty = -1e-3, endy = 1e-3, wl=532e-9) : self.nx = nx self.startx = pl.double(startx) self.endx = pl.double(endx) self.ny = ny self.starty = pl.double(starty) self.endy = pl.double(endy) self.wl = wl print "Intensity:Intensity2D:__init__",self.nx,self.startx,self.endx,self.ny,self.starty,self.endy,self.wl # make grid self.grid()
def mk_zip_code_map(): ''' Parse zip code data Returns: map from zipcode to (latitude, longitude) ''' rv = {} for z in open("zipcodes/zipcode.csv").readlines()[1:]: z = z.strip(string.whitespace) if z == "": continue zip = z.strip().split(",") zipcode = zip[0][1:-1] lat = pylab.double(zip[3][1:-1]) lon = pylab.double(zip[4][1:-1]) rv[zipcode] = (lat,lon) return rv
def FromString(trajectorystring): buff = StringIO.StringIO(trajectorystring) chunkslist = [] while buff.pos < buff.len: duration = double(buff.readline()) dimension = int(buff.readline()) poly_vector = [] for i in range(dimension): poly_vector.append(Polynomial.FromString(buff.readline())) chunkslist.append(Chunk(duration, poly_vector)) return PiecewisePolynomialTrajectory(chunkslist)
def __init__(self, nx=5012, startx=-1e-3, endx=1e-3, ny=5012, starty=-1e-3, endy=1e-3, wl=532e-9): self.nx = nx self.startx = pl.double(startx) self.endx = pl.double(endx) self.ny = ny self.starty = pl.double(starty) self.endy = pl.double(endy) self.wl = wl print "Intensity:Intensity2D:__init__", self.nx, self.startx, self.endx, self.ny, self.starty, self.endy, self.wl # make grid self.grid()
def FromString(trajectorystring): buff = io.StringIO(text_type(trajectorystring)) chunkslist = [] while buff.tell() < len(trajectorystring): duration = double(buff.readline()) dimension = int(buff.readline()) poly_vector = [] for i in range(dimension): poly_vector.append(Polynomial.FromString(buff.readline())) chunkslist.append(Chunk(duration, poly_vector)) return PiecewisePolynomialTrajectory(chunkslist)
def FromString(trajectorystring): buff = io.StringIO(trajectorystring) chunkslist = [] while True: read_line = buff.readline() if read_line == '': break duration = double(read_line) dimension = int(buff.readline()) poly_vector = [] for i in range(dimension): poly_vector.append(Polynomial.FromString(buff.readline())) chunkslist.append(Chunk(duration, poly_vector)) return PiecewisePolynomialTrajectory(chunkslist)
def fromString(self, s): buff = StringIO(s) self.vertices = [] while (True): l = buff.readline() l = l.strip(" \n") if len(l) < 2: break x, y = [double(x) for x in l.split(' ')] vnew = Vertex([x, y]) self.vertices.append(vnew) for i in range(len(self.vertices) - 1): self.vertices[i].next = self.vertices[i + 1] self.vertices[-1].next = self.vertices[0]
def fromString(self, s): buff = StringIO(s) self.vertices = [] while(True): l = buff.readline() l = l.strip(" \n") if len(l) < 2: break x, y = [double(x) for x in l.split(' ')] vnew = Vertex([x, y]) self.vertices.append(vnew) for i in range(len(self.vertices)-1): self.vertices[i].next = self.vertices[i+1] self.vertices[-1].next = self.vertices[0]
def computePerformance(self,idx=None,round_prec=4): if(idx==None): trials = self.trials else: trials = [trial for trial in self.trials if (trial.target_index==idx)]; trial_types = sorted(pl.unique([round(trial.target_contrast,round_prec) for trial in trials])); scores = [[] for i in trial_types]; for trial in trials: for i,trial_type in enumerate(trial_types): if(round(trial.target_contrast,round_prec)==trial_type): scores[i].append(trial.score); ks = pl.array([sum(el) for el in scores]); ns = pl.array([len(el) for el in scores]); xs = trial_types; ps = ks/pl.double(ns); return pl.array([xs,ks,ns]);
def plotPerformance(self,idx=None,fixed_slope=None): x,k,n = self.computePerformance(idx,round_prec=4); thresh,slope = weibFit(x,k,n,fixed_slope); # Now estimate lapse rate by computing 99% threshold and computing the proportion # of errors made at contrasts above that threshold t99 = invPsyWeib(.99,thresh,slope); if t99<max(x): lapse_rate = 0.0 else: lapse_rate = 1.0-float(sum((x>t99)*k))/sum((x>t99)*n); # For plotting purposes, recompute performance parameters after rounding contrasts # to nearest percent. x,k,n = self.computePerformance(idx,round_prec=2); p = pl.double(k)/n; fig = plt.figure(); if(idx!=None): fig.suptitle('Theta = %2.0f deg.'%((idx-1)*45)); ax1 = fig.add_subplot(2,1,1); ax2 = fig.add_subplot(2,1,2); ax1.plot(x,p,'bo',x,psyWeib(x,thresh,slope),'b-',lw=2.0); ax1.set_xlim(0,0.5); ax1.xaxis.set_ticklabels([]) ax1.set_yticks(pl.linspace(0.4,1.0,4)); ax1.set_ylim(0.4,1.0); ax1.set_ylabel('p(correct)'); ax1.text(0.38,0.65,r'$\hat{\alpha}$'+' = %2.3f'%thresh); ax1.text(0.38,0.58,r'$\hat{\beta}$' +' = %2.2f'%slope); ax1.text(0.38,0.51,r'$\hat{\lambda}$' +' = %2.3f'%lapse_rate); ax1.text(0.38,0.44,r'$n$' +' = %2.0f'%sum(n)); ylim = pl.array(ax1.get_ylim()); ax1.vlines([thresh,t99],ylim.min(),ylim.max(),colors=['k','0.5'],linestyles='dashed'); ax1.text(thresh+0.01,0.41,r'$c_{\ 0.82}$' +' = %2.2f'%thresh); ax1.text(t99+0.01,0.50,r'$c_{\ 0.99}$' +' = %2.2f'%t99,color='0.5'); ax2.bar(x-0.01,n,0.01); ylim = pl.array(ax2.get_ylim()); ax2.vlines(thresh,ylim.min(),ylim.max(),colors = 'k',linestyles='dashed'); ax2.set_xlim(0,0.5); ax2.set_ylabel('Contrast freq.'); ax2.set_xlabel('Target contrast'); plt.show();
ra = t.array['RA'] dec = t.array['Dec'] name = t.array['Name'] d = t.array['D'] num = len(ra) # Make some output f = open(outfile, 'w') tout = '#Index\tName\tRA\tDec\tD\t' tout += 'X\tY\tZ\n' f.write(tout) # Calculate for all for i in range(len(ra)): print ra[i], dec[i], d[i] x, y, z = xyz(double(ra[i]), double(dec[i]), double(d[i])) # print x,y,z na = name[i] na = na.replace(' ', '_') tout = '%2d\t"%s"\t%3.10f\t%3.10f\t%3.2f\t' % ((i + 1), na, ra[i], dec[i], double(d[i])) tout += '%3.2f\t%3.2f\t%3.2f\n' % (x, y, z) print tout f.write(tout) f.close()
def FromString(polynomial_string): s = polynomial_string.strip(" \n") coeff_list = [double(x) for x in s.split(' ')] return Polynomial(coeff_list)
def VectorFromString(s): # left for compatibility TODO: remove? s = s.strip(" \n") return array([double(x) for x in s.split(' ')])
reader = csv.reader(csvfile, delimiter = ' ') for row in reader: testfilenames.append(row[0]) num_test=len(testfilenames) # prepare net net = None net = caffe.Net(model_def,model_weights,caffe.TEST) batch_size,numchannel,crop_h,crop_w = net.blobs['data'].data.shape caffe.set_mode_gpu() # calculate batches fi = 0 num_batches = np.floor(num_test/batch_size).astype(np.int)+1 #prob = np.zeros((batch_size*num_batches,crop_h,crop_w),np.float) cvimg = np.zeros((crop_h,crop_w,3),np.uint8) for ibatch in range(num_batches): print [pylab.double(ibatch)/num_batches, batch_size] output = net.forward() # prob[ibatch*batch_size:(ibatch+1)*batch_size,:,:] = output['prob'][:,1,:,:] for i in range(batch_size): if fi < num_test: img = (output['prob'][i,1,:,:] * 255).astype(np.uint8) cvimg[:,:,0] = img cv2.imwrite(resultfolder+'mask_'+testfilenames[fi], cvimg) fi += 1 #prob=prob[0:num_test,:,:] #np.save(resultfolder+'segprob.npy',prob)