client = local.SimClient(cfg=cfg) iterator = set_iterator(cfg) # pc = PlatformCoordinates(theta=0, phi=0, height=cfg.sample_height, cfg=cfg) image_dict = dict() fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(25, 15)) fig.show() for index, theta, phi in iterator: # pupil = generate_pupil(theta, phi, power, cfg.video_size, # cfg.wavelength, cfg.pixel_size, cfg.objective_na) pc.set_coordinates(theta=theta, phi=phi, units='degrees') t_corr, p_corr = pc.source_coordinates(mode='angular') power = 100 im_array = client.acquire(t_corr, p_corr, power) image_dict[(theta, phi)] = im_array ax1.cla(), ax2.cla() img = ax1.imshow(im_array, cmap=plt.get_cmap('hot'), vmin=0, vmax=255) if index == 0: fig.colorbar(img) # plt.xlim([0,450]) # plt.ylim([0,2.5]) ax1.annotate('Mean value: %.4f \nPHI: %.1f THETA: %.1f' % (np.mean(im_array), phi, theta), xy=(0,0), xytext=(80,10), fontsize=12, color='white') fig.canvas.draw() plt.show() save_yaml_metadata(dt.generate_out_file(cfg.output_sim), cfg) np.save(dt.generate_out_file(cfg.output_sim), image_dict)
import itertools as it import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt import matplotlib.cm as cm from scipy import misc # import h5py import pyfpm.fpmmath as fpm from pyfpm import web import pyfpm.coordtrans as ct import pyfpm.data as dt # Simulation parameters cfg = dt.load_config() out_file = dt.generate_out_file(cfg.output_sample) # Connect to a web client running serve_microscope.py client = web.Client(cfg.server_ip) # xoff=1250, yoff=950 def acquire_image_pattern(ss, pattern, Nmean=1): image_mean = np.zeros(cfg.patch_size) for i in range(Nmean): image_response = client.acquire_ledmatrix_pattern(pattern=pattern, power=255, color='R', shutter_speed=ss, iso=400, xoff=0, yoff=0) image_i = np.array(image_response).reshape(cfg.patch_size) image_mean += image_i return image_mean/(Nmean) def mencoded(angle): matrix = fpm.create_source_pattern(shape='semicircle', angle=angle, int_radius=2, radius=5,) pattern = fpm.hex_encode(matrix)
import itertools as it import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt import matplotlib.cm as cm from scipy import misc # import h5py import pyfpm.fpmmath as fpm from pyfpm import web import pyfpm.coordtrans as ct import pyfpm.data as dt # Simulation parameters cfg = dt.load_config() out_file = dt.generate_out_file(cfg.output_sample) # Connect to a web client running serve_microscope.py client = web.Client(cfg.server_ip) def acquire_image(client, nx, ny, shutter_speed, iso, power): img = client.acquire_ledmatrix(nx, ny, power, shutter_speed=shutter_speed, iso=iso) return misc.imread(img, 'F') out_file = dt.generate_out_file(fname='outest.npy') image_dict = dict()
import time import os import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt import numpy as np import datetime import pyfpm.local as local import pyfpm.coordtrans as ct import pyfpm.fpmmath as fpmm import pyfpm.data as dt # Simulation parameters cfg = dt.load_config() out_file = dt.generate_out_file(fname='simtest.npy') iterator = ct.set_iterator(cfg) simclient = local.SimClient(cfg=cfg) fig, ax1 = plt.subplots(1, 1, figsize=(5, 5)) fig.show() image_dict = dict() # First take DPC images im_up = simclient.acquire_pattern(angle=0, acqpars=None, pupil_radius=fpmm.get_pupil_radius(cfg)) image_dict[(0, -1)] = im_up im_down = simclient.acquire_pattern(angle=180, acqpars=None, pupil_radius=fpmm.get_pupil_radius(cfg)) image_dict[(-1, 0)] = im_down
import matplotlib.pyplot as plt import matplotlib.cm as cm from scipy import misc # import h5py import pyfpm.fpmmath as fpm from pyfpm import web import pyfpm.coordtrans as ct import pyfpm.data as dt # Simulation parameters cfg = dt.load_config() # Connect to a web client running serve_microscope.py client = web.Client(cfg.server_ip) out_file = dt.generate_out_file(out_folder=cfg.output_sample, fname=None) image_dict = dict() def acquire_image(ss, nx, ny, Nmean=1): image_mean = np.zeros(cfg.patch_size) for i in range(Nmean): print(i) image_response = client.acquire_ledmatrix(nx=nx, ny=ny, power=255, shutter_speed=ss, iso=400, xoff=1296, yoff=950) image_i = np.array(image_response).reshape(cfg.patch_size)