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)
