mirror of
https://github.com/cdtools-developers/cdtools.git
synced 2026-09-11 22:12:38 +02:00
Finish fleshing out the examples
This commit is contained in:
Abe Levitan
+16
-34
@@ -1,71 +1,53 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import CDTools
|
||||
from CDTools.tools import cmath
|
||||
from CDTools import tools
|
||||
from CDTools.tools.plotting import *
|
||||
from matplotlib import pyplot as plt
|
||||
import pickle
|
||||
from time import time
|
||||
import datetime
|
||||
|
||||
import torch as t
|
||||
import numpy as np
|
||||
|
||||
|
||||
# This file is too large to be distributed via Github.
|
||||
# Please contact Abe Levitan (alevitan@mit) if you would like access
|
||||
filename = '/media/Data Bank/CSX_6_17/Processed_CXIs/79511_p.cxi'
|
||||
|
||||
dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(filename)
|
||||
|
||||
# In this dataset, the edges of the patterns are too noisy and are
|
||||
# masked off anyway. We can easily just remove this data instead of
|
||||
# leaving it to float.
|
||||
# In this dataset, the edges of the patterns are masked off anyway
|
||||
# We can easily just remove this data instead of leaving it to float.
|
||||
dataset.patterns = dataset.patterns[:,70:-70,70:-70]
|
||||
dataset.mask = dataset.mask[70:-70,70:-70]
|
||||
|
||||
# This model definition includes lots of parameters.
|
||||
# In this case:
|
||||
# This model definition includes lots of tweaks, described below.
|
||||
#
|
||||
# randomize_ang defines the initial random phase noise's extent
|
||||
# translations_scale defines how aggressive the position reconstruction is
|
||||
# n_modes is the number of incoherent modes
|
||||
# propagation_distance is the distance to propagate from the SHARP-style guess of the probe's focal spot (in this case, the value comes from knowledge of the experimental geometry).
|
||||
model = CDTools.models.FancyPtycho.from_dataset(dataset,
|
||||
randomize_ang = np.pi/4,
|
||||
translation_scale = 4,
|
||||
n_modes=2,
|
||||
propagation_distance=-73e-6)
|
||||
|
||||
|
||||
# Uncomment these to use on the CPU
|
||||
# default is CPU with 32-bit floats
|
||||
# Move to the GPU
|
||||
model.to(device='cuda')
|
||||
dataset.get_as(device='cuda')
|
||||
|
||||
|
||||
# We can run the first phase of phase retrieval while leaving
|
||||
# the probe positions fixed (whether this is good is debatable)
|
||||
# model.translation_offsets.requires_grad = False
|
||||
|
||||
for i, loss in enumerate(model.Adam_optimize(15, dataset, batch_size=15)):
|
||||
print(i,loss)
|
||||
# We turn off position reconstruction for the first phase
|
||||
model.translation_offsets.requires_grad = False
|
||||
for i, loss in enumerate(model.Adam_optimize(10, dataset, batch_size=15)):
|
||||
model.inspect(dataset)
|
||||
|
||||
# And we turn it on for the second phase, as we also lower the learning rate
|
||||
# model.translation_offsets.requires_grad = True
|
||||
|
||||
for i, loss in enumerate(model.Adam_optimize(15, dataset, batch_size=15, lr=0.0005)):
|
||||
print(i,loss)
|
||||
|
||||
# And we turn it on for the second phase
|
||||
model.translation_offsets.requires_grad = True
|
||||
for i, loss in enumerate(model.Adam_optimize(20, dataset, batch_size=15)):
|
||||
model.inspect(dataset)
|
||||
print(i,loss)
|
||||
|
||||
|
||||
# The third phase lowers the rate further
|
||||
for i, loss in enumerate(model.Adam_optimize(10, dataset, batch_size=15, lr=0.00005)):
|
||||
print(i,loss)
|
||||
for i, loss in enumerate(model.Adam_optimize(10, dataset, batch_size=15, lr=0.0005)):
|
||||
model.inspect(dataset)
|
||||
print(i,loss)
|
||||
|
||||
|
||||
model.inspect(dataset)
|
||||
model.compare(dataset)
|
||||
|
||||
plt.show()
|
||||
|
||||
@@ -1,23 +1,12 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from matplotlib import pyplot as plt
|
||||
import pickle
|
||||
|
||||
from CDTools.tools import cmath, plotting
|
||||
from CDTools.tools.analysis import *
|
||||
from CDTools.tools import plotting
|
||||
from CDTools.tools import analysis
|
||||
|
||||
|
||||
#
|
||||
# Note that much of this functionality is duplicated by the convenience
|
||||
# script. Try running:
|
||||
#
|
||||
# python -m CDTools.scripts.synthesize example_reconstructions/gold_balls_ensemble.pickle
|
||||
#
|
||||
# Which will perform much of the same analysis on any saved reconstruction
|
||||
# ensemble
|
||||
#
|
||||
|
||||
|
||||
with open('example_reconstructions/gold_balls_ensemble.pickle', 'rb') as f:
|
||||
dataset = pickle.load(f)
|
||||
@@ -29,12 +18,13 @@ if type(dataset) == type([]):
|
||||
for key in dataset[0]}
|
||||
|
||||
|
||||
# Now we synthesize the object using the tool from CDTools
|
||||
synth_probe, synth_obj, aligned_objs = synthesize_reconstructions(
|
||||
# Now we synthesize an average reconstruction
|
||||
synth_probe, synth_obj, aligned_objs = analysis.synthesize_reconstructions(
|
||||
dataset['probe'], dataset['obj'])
|
||||
|
||||
# And then we calculate the consistency PRTF from this
|
||||
freqs, prtf = calc_consistency_prtf(synth_obj, aligned_objs, dataset['basis'][0])
|
||||
freqs, prtf = analysis.calc_consistency_prtf(synth_obj, aligned_objs, dataset['basis'][0])
|
||||
|
||||
|
||||
# Plot the first mode in detail
|
||||
plotting.plot_phase(synth_probe[0],basis=dataset['basis'][0])
|
||||
@@ -44,12 +34,13 @@ plotting.plot_colorized(synth_probe[0],basis=dataset['basis'][0])
|
||||
# Just plot the colorized version of the subdominant modes
|
||||
plotting.plot_colorized(synth_probe[1],basis=dataset['basis'][0])
|
||||
plotting.plot_colorized(synth_probe[2],basis=dataset['basis'][0])
|
||||
|
||||
|
||||
# And now we plot the object
|
||||
plotting.plot_amplitude(synth_obj,basis=dataset['basis'][0])
|
||||
plotting.plot_colorized(synth_obj,basis=dataset['basis'][0])
|
||||
plotting.plot_phase(synth_obj,basis=dataset['basis'][0])
|
||||
|
||||
# Now plot the PRTF
|
||||
# Finally, plot the consistency PRTF
|
||||
plt.figure()
|
||||
plt.plot(freqs*1e-6, prtf)
|
||||
plt.xlabel('Spatial Frequency (cycles/um)')
|
||||
|
||||
@@ -1,12 +1,10 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import CDTools
|
||||
import numpy as np
|
||||
import pickle
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
# Load the data
|
||||
filename = 'example_data/AuBalls_700ms_30nmStep_3_6SS_filter.cxi'
|
||||
|
||||
dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(filename)
|
||||
|
||||
results = []
|
||||
@@ -14,23 +12,23 @@ results = []
|
||||
for idx in range(25):
|
||||
print('Starting Reconstruction', idx)
|
||||
|
||||
model = CDTools.models.FancyPtycho.from_dataset(dataset,n_modes=3,randomize_ang=0.1*np.pi)
|
||||
# Create a new model each time
|
||||
model = CDTools.models.FancyPtycho.from_dataset(dataset,n_modes=3,
|
||||
randomize_ang=0.1*np.pi)
|
||||
|
||||
# default is CPU with 32-bit floats
|
||||
# Work on the GPU
|
||||
model.to(device='cuda')
|
||||
dataset.get_as(device='cuda')
|
||||
|
||||
|
||||
# Run the reconstruction
|
||||
for i, loss in enumerate(model.Adam_optimize(30, dataset, batch_size=100)):
|
||||
print(i,loss)
|
||||
# Here we see how to liveplot the results - this call will create
|
||||
# or update a readout of the various parameters being reconstructed
|
||||
|
||||
# And add the results to the ensemble
|
||||
results.append(model.save_results(dataset))
|
||||
|
||||
|
||||
|
||||
# Save out the ensemble
|
||||
with open('example_reconstructions/gold_balls_ensemble.pickle', 'wb') as f:
|
||||
pickle.dump(results,f)
|
||||
|
||||
model.inspect(dataset)
|
||||
model.compare(dataset)
|
||||
plt.show()
|
||||
@@ -1,36 +1,31 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import CDTools
|
||||
import numpy as np
|
||||
import pickle
|
||||
from matplotlib import pyplot as plt
|
||||
import pickle
|
||||
|
||||
# First, we load an example dataset from a .cxi file
|
||||
filename = 'example_data/AuBalls_700ms_30nmStep_3_6SS_filter.cxi'
|
||||
|
||||
dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(filename)
|
||||
|
||||
|
||||
# Next, we create a ptychography model from the dataset
|
||||
# Note that we explicitly as for two incoherent probe modes
|
||||
model = CDTools.models.FancyPtycho.from_dataset(dataset, n_modes=2)
|
||||
|
||||
|
||||
|
||||
# default is CPU with 32-bit floats
|
||||
# Let's do this reconstruction on the GPU, shall we?
|
||||
model.to(device='cuda')
|
||||
dataset.get_as(device='cuda')
|
||||
|
||||
|
||||
for i, loss in enumerate(model.Adam_optimize(30, dataset, batch_size=100)):
|
||||
print(i,loss)
|
||||
# Here we see how to liveplot the results - this call will create
|
||||
# or update a readout of the various parameters being reconstructed
|
||||
# And we liveplot the updates to the model as they happen
|
||||
model.inspect(dataset)
|
||||
print(i,loss)
|
||||
|
||||
|
||||
# And we save the reconstruction out to a file
|
||||
with open('example_reconstructions/gold_balls.pickle', 'wb') as f:
|
||||
pickle.dump(model.save_results(dataset),f)
|
||||
|
||||
|
||||
# Finally, we plot the results
|
||||
model.inspect(dataset)
|
||||
dataset.inspect()
|
||||
model.compare(dataset)
|
||||
plt.show()
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import CDTools
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
# First, we load an example dataset from a .cxi file
|
||||
filename = 'example_data/AuBalls_700ms_30nmStep_3_6SS_filter.cxi'
|
||||
dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(filename)
|
||||
|
||||
# And we take a look at the data
|
||||
dataset.inspect()
|
||||
plt.show()
|
||||
@@ -12,7 +12,6 @@ model = CDTools.models.SimplePtycho.from_dataset(dataset)
|
||||
|
||||
# Now, we run a short reconstruction from the dataset!
|
||||
for i, loss in enumerate(model.Adam_optimize(10, dataset)):
|
||||
model.inspect(dataset)
|
||||
print(i, loss)
|
||||
|
||||
# Finally, we plot the results
|
||||
|
||||
@@ -1,44 +1,37 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import CDTools
|
||||
from CDTools.tools import cmath
|
||||
from CDTools import tools
|
||||
from CDTools.tools.plotting import *
|
||||
from matplotlib import pyplot as plt
|
||||
import pickle
|
||||
from time import time
|
||||
import datetime
|
||||
|
||||
import h5py
|
||||
import torch as t
|
||||
import numpy as np
|
||||
|
||||
|
||||
# This file is too large to be distributed via Github.
|
||||
# Please contact Abe Levitan (alevitan@mit) if you would like access
|
||||
filename = '/media/Data Bank/CSX_10_18/Processed_CXIs/110531_p.cxi'
|
||||
|
||||
dataset = CDTools.datasets.Ptycho_2D_Dataset.from_cxi(filename)
|
||||
|
||||
|
||||
# This model definition includes lots of tweaks, described below.
|
||||
#
|
||||
# randomize_ang defines the initial random phase noise's extent
|
||||
# translations_scale defines how aggressive the position reconstruction is
|
||||
# scattering_mode overrides any sample normal information stored in the .cxi file
|
||||
model = CDTools.models.FancyPtycho.from_dataset(dataset,
|
||||
randomize_ang = np.pi/4,
|
||||
padding=0,
|
||||
translation_scale=10,
|
||||
scattering_mode='reflection')
|
||||
|
||||
|
||||
# Uncomment these to use on the CPU
|
||||
# default is CPU with 32-bit floats
|
||||
# Move to the GPU
|
||||
model.to(device='cuda')
|
||||
dataset.get_as(device='cuda')
|
||||
|
||||
|
||||
# Run the reconstruction
|
||||
for i, loss in enumerate(model.Adam_optimize(250, dataset,batch_size=5)):
|
||||
print(i,loss)
|
||||
model.inspect(dataset)
|
||||
|
||||
|
||||
model.inspect(dataset)
|
||||
dataset.inspect()
|
||||
model.compare(dataset)
|
||||
plt.show()
|
||||
Reference in New Issue
Block a user