def test_sima(self):
"""
(BlockMethod) with SIMA strategy
"""
# NOTE: this test was brittle and failed non-deterministically with any
# more than one source
import sima.segment
# construct the SIMA strategy
simaStrategy = sima.segment.STICA(components=1)
simaStrategy.append(sima.segment.SparseROIsFromMasks(min_size=20))
simaStrategy.append(sima.segment.SmoothROIBoundaries())
simaStrategy.append(sima.segment.MergeOverlapping(threshold=0.5))
tsc = ThunderContext(self.sc)
data = tsc.makeExample('sources',
dims=(60, 60),
centers=[[20, 15]],
noise=0.5,
seed=42)
# create and fit the thunder extraction strategy
strategy = SourceExtraction('sima', simaStrategy=simaStrategy)
model = strategy.fit(data, size=(30, 30))
assert (model.count == 1)
# check that the one center is recovered
ep = 1.5
assert (model[0].distance([20, 15]) < ep)
def test_sima(self):
"""
(BlockMethod) with SIMA strategy
"""
# NOTE: this test was brittle and failed non-deterministically with any
# more than one source
import sima.segment
# construct the SIMA strategy
simaStrategy = sima.segment.STICA(components=1)
simaStrategy.append(sima.segment.SparseROIsFromMasks(min_size=20))
simaStrategy.append(sima.segment.SmoothROIBoundaries())
simaStrategy.append(sima.segment.MergeOverlapping(threshold=0.5))
tsc = ThunderContext(self.sc)
data = tsc.makeExample('sources', dims=(60, 60), centers=[[20, 15]], noise=0.5, seed=42)
# create and fit the thunder extraction strategy
strategy = SourceExtraction('sima', simaStrategy=simaStrategy)
model = strategy.fit(data, size=(30, 30))
assert(model.count == 1)
# check that the one center is recovered
ep = 1.5
assert(model[0].distance([20, 15]) < ep)
def test_local_max(self):
"""
(FeatureMethod) localmax with defaults
"""
tsc = ThunderContext(self.sc)
data = tsc.makeExample('sources', dims=[60, 60], centers=[[10, 10], [40, 40]], noise=0.0, seed=42)
model = SourceExtraction('localmax').fit(data)
# order is irrelevant, but one of these must be true
cond1 = (model[0].distance([10, 10]) == 0) and (model[1].distance([40, 40]) == 0)
cond2 = (model[0].distance([40, 40]) == 0) and (model[1].distance([10, 10]) == 0)
assert(cond1 or cond2)
def test_nmf(self):
"""
(BlockMethod) nmf with defaults
"""
tsc = ThunderContext(self.sc)
data = tsc.makeExample('sources', dims=(60, 60), centers=[[20, 20], [40, 40]], noise=0.1, seed=42)
model = SourceExtraction('nmf', componentsPerBlock=1).fit(data, size=(30, 30))
# order is irrelevant, but one of these must be true
ep = 0.50
cond1 = (model[0].distance([20, 20]) < ep) and (model[1].distance([40, 40]) < ep)
cond2 = (model[0].distance([40, 40]) < ep) and (model[1].distance([20, 20]) < ep)
assert(cond1 or cond2)
def test_sima(self):
"""
(BlockMethod) with SIMA strategy
"""
import sima.segment
# construct the SIMA strategy
simaStrategy = sima.segment.STICA(components=2)
simaStrategy.append(sima.segment.SparseROIsFromMasks(min_size=20))
simaStrategy.append(sima.segment.SmoothROIBoundaries())
simaStrategy.append(sima.segment.MergeOverlapping(threshold=0.5))
tsc = ThunderContext(self.sc)
data = tsc.makeExample('sources', dims=(60, 60), centers=[[20, 15], [40, 45]], noise=0.1, seed=42)
# create and fit the thunder extraction strategy
strategy = SourceExtraction('sima', simaStrategy=simaStrategy)
model = strategy.fit(data, size=(30, 30))
# order is irrelevant, but one of these must be true
ep = 1.5
cond1 = (model[0].distance([20, 15]) < ep) and (model[1].distance([40, 45]) < ep)
cond2 = (model[1].distance([20, 15]) < ep) and (model[0].distance([40, 45]) < ep)
assert(cond1 or cond2)
if use_existing_parameters == 1:
with open(Exp_Folder+filename_save_prefix_forICA+'_save_ICA_variables') as f:
ICA_components_ind, num_ICA_colors_ind, color_map_ind,\
ICA_components_eachexp, num_ICA_colors_eachexp, color_map_eachexp,\
ICA_components_allexp, num_ICA_colors_allexp, color_map_allexp,colors_ica = pickle.load(f)
# Go into the main function that does ICA for indiviudal trials
from ica_thunder_analysis import run_analysis_individualexps
from ica_thunder_analysis import run_analysis_eachexp
from ica_thunder_analysis import run_analysis_allexp
from thunder import ThunderContext
print 'Starting Thunder Now. Check console for details'
tsc = ThunderContext.start(appName="thunderICA")
if files_to_do_ICA[0]== 1:
run_analysis_individualexps(Exp_Folder, filename_save_prefix_forICA, filename_save_prefix_for_textfile, ICA_components_ind, PCA_components_ind, num_ICA_colors_ind, color_map_ind,\
tsc,redo_ICA, num_fish_used, stimulus_pulse, stimulus_on_time, stimulus_off_time,color_mat, time_baseline,colors_ica )
if files_to_do_ICA[1]== 1:
run_analysis_eachexp(Exp_Folder, filename_save_prefix_forICA, filename_save_prefix_for_textfile, ICA_components_eachexp, PCA_components_eachexp, num_ICA_colors_eachexp, color_map_eachexp,\
tsc,redo_ICA, num_fish_used, stimulus_pulse, stimulus_on_time, stimulus_off_time,color_mat, time_baseline,colors_ica )
if files_to_do_ICA[2]== 1:
run_analysis_allexp(Exp_Folder, filename_save_prefix_forICA, filename_save_prefix_for_textfile, ICA_components_allexp, PCA_components_allexp, num_ICA_colors_allexp, color_map_allexp,\
tsc,redo_ICA, num_fish_used, stimulus_pulse, stimulus_on_time, stimulus_off_time,color_mat, time_baseline,colors_ica )
############# Save all imput parameters
with open(Exp_Folder+filename_save_prefix_forICA+'_save_ICA_variables', 'w') as f:
if __name__ == "__main__":
parser = optparse.OptionParser(
description="fit a regression model",
usage="%prog datafile modelfile outputdir [options]")
parser.add_option("--regressmode",
choices=("mean", "linear", "bilinear"),
help="form of regression")
parser.add_option("--k", type=int, default=2)
opts, args = parser.parse_args()
try:
datafile = args[0]
modelfile = args[1]
outputdir = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="regresswithpca")
data = tsc.loadSeries(datafile)
model = RegressionModel.load(modelfile, opts.regressmode) # do regression
betas, stats, resid = model.fit(data)
pca = PCA(opts.k).fit(betas) # do PCA
traj = model.fit(data, pca.comps) # get trajectories
outputdir += "-regress"
export(pca.comps, outputdir, "comps", "matlab")
export(pca.latent, outputdir, "latent", "matlab")
export(pca.scores, outputdir, "scores", "matlab")
export(traj, outputdir, "traj", "matlab")
color_mat = ['#00FFFF','#0000A0','#800080','#FF00FF', '#800000','#A52A2A']
if use_existing_parameters == 1:
with open(Exp_Folder+filename_save_prefix+'_save_kmeans_variables') as f:
kmeans_clusters_ind, kmeans_clusters_eachodor, kmeans_clusters_allodor, time_baseline,ignore_clusters = pickle.load(f)
# Go into the main function that does kmeans for indiviudal trials
from kmeans_thunder_analysis import run_analysis_individualodors
from kmeans_thunder_analysis import run_analysis_eachodor
from kmeans_thunder_analysis import run_analysis_allodor
from thunder import ThunderContext
print 'Starting Thunder Now. Check console for details'
tsc = ThunderContext.start(appName="thunderkmeans")
if files_to_do_kmeans[0]== 1:
run_analysis_individualodors(Exp_Folder, filename_save_prefix, filename_save_prefix_forkmeanswithPCA, kmeans_clusters_ind,\
stimulus_on_time, stimulus_off_time, tsc,redo_kmeans,time_baseline,redo_kmeans_colormap,ignore_clusters)
if files_to_do_kmeans[1]== 1:
run_analysis_eachodor(Exp_Folder, filename_save_prefix, filename_save_prefix_forkmeanswithPCA, kmeans_clusters_eachodor, \
stimulus_on_time, stimulus_off_time, tsc,redo_kmeans,time_baseline,redo_kmeans_colormap,ignore_clusters)
if files_to_do_kmeans[2]== 1:
run_analysis_allodor(Exp_Folder, filename_save_prefix, filename_save_prefix_forkmeanswithPCA, kmeans_clusters_allodor, \
stimulus_on_time, stimulus_off_time, tsc,redo_kmeans, time_baseline,redo_kmeans_colormap,ignore_clusters)
############# Save all imput parameters
with open(Exp_Folder+filename_save_prefix+'_save_kmeans_variables', 'w') as f:
if use_existing_parameters == 1:
with open(Exp_Folder + filename_save_prefix_forICA +
'_save_ICA_variables') as f:
ICA_components_ind, num_ICA_colors_ind, color_map_ind,\
ICA_components_eachexp, num_ICA_colors_eachexp, color_map_eachexp,\
ICA_components_allexp, num_ICA_colors_allexp, color_map_allexp,colors_ica = pickle.load(f)
# Go into the main function that does ICA for indiviudal trials
from ica_thunder_analysis import run_analysis_individualexps
from ica_thunder_analysis import run_analysis_eachexp
from ica_thunder_analysis import run_analysis_allexp
from thunder import ThunderContext
print 'Starting Thunder Now. Check console for details'
tsc = ThunderContext.start(appName="thunderICA")
if files_to_do_ICA[0] == 1:
run_analysis_individualexps(Exp_Folder, filename_save_prefix_forICA, filename_save_prefix_for_textfile, ICA_components_ind, PCA_components_ind, num_ICA_colors_ind, color_map_ind,\
tsc,redo_ICA, num_fish_used, stimulus_pulse, stimulus_on_time, stimulus_off_time,color_mat, time_baseline,colors_ica )
if files_to_do_ICA[1] == 1:
run_analysis_eachexp(Exp_Folder, filename_save_prefix_forICA, filename_save_prefix_for_textfile, ICA_components_eachexp, PCA_components_eachexp, num_ICA_colors_eachexp, color_map_eachexp,\
tsc,redo_ICA, num_fish_used, stimulus_pulse, stimulus_on_time, stimulus_off_time,color_mat, time_baseline,colors_ica )
if files_to_do_ICA[2] == 1:
run_analysis_allexp(Exp_Folder, filename_save_prefix_forICA, filename_save_prefix_for_textfile, ICA_components_allexp, PCA_components_allexp, num_ICA_colors_allexp, color_map_allexp,\
tsc,redo_ICA, num_fish_used, stimulus_pulse, stimulus_on_time, stimulus_off_time,color_mat, time_baseline,colors_ica )
############# Save all imput parameters
with open(Exp_Folder + filename_save_prefix_forICA + '_save_ICA_variables',
"""
import optparse
from thunder import ThunderContext, RegressionModel
if __name__ == "__main__":
parser = optparse.OptionParser(
description="fit a regression model",
usage="%prog datafile modelfile outputdir [options]")
parser.add_option("--regressmode",
choices=("mean", "linear", "bilinear"),
default="linear",
help="form of regression")
opts, args = parser.parse_args()
try:
datafile = args[0]
modelfile = args[1]
outputdir = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="regress")
data = tsc.loadText(datafile)
result = RegressionModel.load(modelfile, opts.regressmode).fit(data)
outputdir += "-regress"
tsc.export(result.select('stats'), outputdir, "stats", "matlab")
tsc.export(result.select('betas'), outputdir, "betas", "matlab")
class TestContextLoading(PySparkTestCaseWithOutputDir):
def setUp(self):
super(TestContextLoading, self).setUp()
self.tsc = ThunderContext(self.sc)
@staticmethod
def _findTestResourcesDir(resourcesdirname="resources"):
testdirpath = os.path.dirname(os.path.realpath(__file__))
testresourcesdirpath = os.path.join(testdirpath, resourcesdirname)
if not os.path.isdir(testresourcesdirpath):
raise IOError("Test resources directory "+testresourcesdirpath+" not found")
return testresourcesdirpath
def __run_loadStacksAsSeries(self, shuffle):
rangeary = np.arange(64*128, dtype=np.dtype('int16'))
filepath = os.path.join(self.outputdir, "rangeary.stack")
rangeary.tofile(filepath)
expectedary = rangeary.reshape((128, 64), order='F')
range_series = self.tsc.loadImagesAsSeries(filepath, dims=(128, 64), shuffle=shuffle)
range_series_ary = range_series.pack()
assert_equals((128, 64), range_series.dims.count)
assert_equals((128, 64), range_series_ary.shape)
assert_true(np.array_equal(expectedary, range_series_ary))
def test_loadStacksAsSeriesNoShuffle(self):
self.__run_loadStacksAsSeries(False)
def test_loadStacksAsSeriesWithShuffle(self):
self.__run_loadStacksAsSeries(True)
def __run_load3dStackAsSeries(self, shuffle):
rangeary = np.arange(32*64*4, dtype=np.dtype('int16'))
filepath = os.path.join(self.outputdir, "rangeary.stack")
rangeary.tofile(filepath)
expectedary = rangeary.reshape((32, 64, 4), order='F')
range_series_noshuffle = self.tsc.loadImagesAsSeries(filepath, dims=(32, 64, 4), shuffle=shuffle)
range_series_noshuffle_ary = range_series_noshuffle.pack()
assert_equals((32, 64, 4), range_series_noshuffle.dims.count)
assert_equals((32, 64, 4), range_series_noshuffle_ary.shape)
assert_true(np.array_equal(expectedary, range_series_noshuffle_ary))
def test_load3dStackAsSeriesNoShuffle(self):
self.__run_load3dStackAsSeries(False)
def test_load3dStackAsSeriesWithShuffle(self):
self.__run_load3dStackAsSeries(True)
def __run_loadMultipleStacksAsSeries(self, shuffle):
rangeary = np.arange(64*128, dtype=np.dtype('int16'))
filepath = os.path.join(self.outputdir, "rangeary01.stack")
rangeary.tofile(filepath)
expectedary = rangeary.reshape((128, 64), order='F')
rangeary2 = np.arange(64*128, 2*64*128, dtype=np.dtype('int16'))
filepath = os.path.join(self.outputdir, "rangeary02.stack")
rangeary2.tofile(filepath)
expectedary2 = rangeary2.reshape((128, 64), order='F')
range_series = self.tsc.loadImagesAsSeries(self.outputdir, dims=(128, 64), shuffle=shuffle)
range_series_ary = range_series.pack()
range_series_ary_xpose = range_series.pack(transpose=True)
assert_equals((128, 64), range_series.dims.count)
assert_equals((2, 128, 64), range_series_ary.shape)
assert_equals((2, 64, 128), range_series_ary_xpose.shape)
assert_true(np.array_equal(expectedary, range_series_ary[0]))
assert_true(np.array_equal(expectedary2, range_series_ary[1]))
assert_true(np.array_equal(expectedary.T, range_series_ary_xpose[0]))
assert_true(np.array_equal(expectedary2.T, range_series_ary_xpose[1]))
def test_loadMultipleStacksAsSeriesNoShuffle(self):
self.__run_loadMultipleStacksAsSeries(False)
def test_loadMultipleStacksAsSeriesWithShuffle(self):
self.__run_loadMultipleStacksAsSeries(True)
def __run_loadTifAsSeries(self, shuffle):
tmpary = np.arange(60*120, dtype=np.dtype('uint16'))
rangeary = np.mod(tmpary, 255).astype('uint8').reshape((60, 120))
pilimg = Image.fromarray(rangeary)
filepath = os.path.join(self.outputdir, "rangetif01.tif")
pilimg.save(filepath)
del pilimg, tmpary
range_series = self.tsc.loadImagesAsSeries(self.outputdir, inputformat="tif-stack", shuffle=shuffle)
range_series_ary = range_series.pack()
assert_equals((60, 120, 1), range_series.dims.count)
assert_equals((60, 120), range_series_ary.shape)
assert_true(np.array_equal(rangeary, range_series_ary))
@unittest.skipIf(not _have_image, "PIL/pillow not installed or not functional")
def test_loadTifAsSeriesNoShuffle(self):
self.__run_loadTifAsSeries(False)
@unittest.skipIf(not _have_image, "PIL/pillow not installed or not functional")
def test_loadTifAsSeriesWithShuffle(self):
self.__run_loadTifAsSeries(True)
def __run_loadTestTifAsSeries(self, shuffle):
testresourcesdir = TestContextLoading._findTestResourcesDir()
imagepath = os.path.join(testresourcesdir, "multilayer_tif", "dotdotdot_lzw.tif")
testimg_pil = Image.open(imagepath)
testimg_arys = list()
testimg_arys.append(pil_to_array(testimg_pil)) # original shape 70, 75
testimg_pil.seek(1)
testimg_arys.append(pil_to_array(testimg_pil))
testimg_pil.seek(2)
testimg_arys.append(pil_to_array(testimg_pil))
range_series_noshuffle = self.tsc.loadImagesAsSeries(imagepath, inputformat="tif-stack", shuffle=shuffle)
range_series_noshuffle_ary = range_series_noshuffle.pack()
range_series_noshuffle_ary_xpose = range_series_noshuffle.pack(transpose=True)
assert_equals((70, 75, 3), range_series_noshuffle.dims.count)
assert_equals((70, 75, 3), range_series_noshuffle_ary.shape)
assert_equals((3, 75, 70), range_series_noshuffle_ary_xpose.shape)
assert_true(np.array_equal(testimg_arys[0], range_series_noshuffle_ary[:, :, 0]))
assert_true(np.array_equal(testimg_arys[1], range_series_noshuffle_ary[:, :, 1]))
assert_true(np.array_equal(testimg_arys[2], range_series_noshuffle_ary[:, :, 2]))
assert_true(np.array_equal(testimg_arys[0].T, range_series_noshuffle_ary_xpose[0]))
assert_true(np.array_equal(testimg_arys[1].T, range_series_noshuffle_ary_xpose[1]))
assert_true(np.array_equal(testimg_arys[2].T, range_series_noshuffle_ary_xpose[2]))
@unittest.skipIf(not _have_image, "PIL/pillow not installed or not functional")
def test_loadTestTifAsSeriesNoShuffle(self):
self.__run_loadTestTifAsSeries(False)
@unittest.skipIf(not _have_image, "PIL/pillow not installed or not functional")
def test_loadTestTifAsSeriesWithShuffle(self):
self.__run_loadTestTifAsSeries(True)
def __run_loadMultipleTifsAsSeries(self, shuffle):
tmpary = np.arange(60*120, dtype=np.dtype('uint16'))
rangeary = np.mod(tmpary, 255).astype('uint8').reshape((60, 120))
pilimg = Image.fromarray(rangeary)
filepath = os.path.join(self.outputdir, "rangetif01.tif")
pilimg.save(filepath)
tmpary = np.arange(60*120, 2*60*120, dtype=np.dtype('uint16'))
rangeary2 = np.mod(tmpary, 255).astype('uint8').reshape((60, 120))
pilimg = Image.fromarray(rangeary2)
filepath = os.path.join(self.outputdir, "rangetif02.tif")
pilimg.save(filepath)
del pilimg, tmpary
range_series = self.tsc.loadImagesAsSeries(self.outputdir, inputformat="tif-stack", shuffle=shuffle)
range_series_ary = range_series.pack()
range_series_ary_xpose = range_series.pack(transpose=True)
assert_equals((60, 120, 1), range_series.dims.count)
assert_equals((2, 60, 120), range_series_ary.shape)
assert_equals((2, 120, 60), range_series_ary_xpose.shape)
assert_true(np.array_equal(rangeary, range_series_ary[0]))
assert_true(np.array_equal(rangeary2, range_series_ary[1]))
assert_true(np.array_equal(rangeary.T, range_series_ary_xpose[0]))
assert_true(np.array_equal(rangeary2.T, range_series_ary_xpose[1]))
@unittest.skipIf(not _have_image, "PIL/pillow not installed or not functional")
def test_loadMultipleTifsAsSeriesNoShuffle(self):
self.__run_loadMultipleTifsAsSeries(False)
@unittest.skipIf(not _have_image, "PIL/pillow not installed or not functional")
def test_loadMultipleTifsAsSeriesWithShuffle(self):
self.__run_loadMultipleTifsAsSeries(True)
# Load thunder
from pyspark import SparkContext, SparkConf
from thunder import Colorize, ThunderContext
image = Colorize.image
import os
#Load Sci-kit image
from skimage.viewer import ImageViewer as skImageViewer
#Load spark context
conf = SparkConf() \
.setAppName("Display face") \
.set("spark.executor.memory", "5g")
sc = SparkContext(conf=conf)
#load thunder bolt context
tsc = ThunderContext(sc)
# Load image using thunder
data = tsc.loadImages(os.path.dirname(os.path.realpath(__file__))+'/mush.png',inputFormat='png')
img = data.first()[1]
# Display image using Sci-kit image
viewer = skImageViewer(img[:,:,0])
viewer.show()
class TestLoadIrregularImages(PySparkTestCaseWithOutputDir):
def setUp(self):
super(TestLoadIrregularImages, self).setUp()
self.tsc = ThunderContext(self.sc)
def _generate_array(self, dtype):
self.ary = arange(256, dtype=dtypeFunc(dtype)).reshape((16, 4, 4)) # 16 pages of 4x4 images
def _write_tiffs(self):
import thunder.rdds.fileio.tifffile as tifffile
writer1 = tifffile.TiffWriter(os.path.join(self.outputdir, "tif01.tif"))
writer1.save(self.ary[:8].transpose((0, 2, 1)), photometric="minisblack") # write out 8 pages
writer1.close()
del writer1
writer2 = tifffile.TiffWriter(os.path.join(self.outputdir, "tif02.tif"))
writer2.save(self.ary.transpose((0, 2, 1)), photometric="minisblack") # write out all 16 pages
writer2.close()
del writer2
def _write_stacks(self):
with open(os.path.join(self.outputdir, "stack01.bin"), "w") as f:
self.ary[:8].tofile(f)
with open(os.path.join(self.outputdir, "stack02.bin"), "w") as f:
self.ary.tofile(f)
def _run_tst(self, imgType, dtype):
self._generate_array(dtype)
if imgType.lower().startswith('tif'):
self._write_tiffs()
inputFormat, ext, dims = "tif", "tif", None
elif imgType.lower().startswith("stack"):
self._write_stacks()
inputFormat, ext, dims = "stack", "bin", (16, 4, 4)
else:
raise ValueError("Unknown imgType: %s" % imgType)
# with nplanes=2, this should yield a 12 record Images object, which after converting to
# a series and packing should be a 12 x 4 x 4 x 2 array.
# renumber=True is required in this case in order to ensure sensible results.
series = self.tsc.loadImagesAsSeries(self.outputdir, inputFormat=inputFormat, ext=ext,
blockSize=(2, 1, 1), blockSizeUnits="pixels",
nplanes=2, dims=dims, renumber=True)
packedAry = series.pack()
assert_equals((12, 4, 4, 2), packedAry.shape)
assert_true(array_equal(self.ary[0:2], packedAry[0].T))
assert_true(array_equal(self.ary[2:4], packedAry[1].T))
assert_true(array_equal(self.ary[4:6], packedAry[2].T))
assert_true(array_equal(self.ary[6:8], packedAry[3].T)) # first image was only 4 2-plane records
assert_true(array_equal(self.ary[0:2], packedAry[4].T))
assert_true(array_equal(self.ary[2:4], packedAry[5].T))
assert_true(array_equal(self.ary[4:6], packedAry[6].T))
assert_true(array_equal(self.ary[6:8], packedAry[7].T))
assert_true(array_equal(self.ary[8:10], packedAry[8].T))
assert_true(array_equal(self.ary[10:12], packedAry[9].T))
assert_true(array_equal(self.ary[12:14], packedAry[10].T))
assert_true(array_equal(self.ary[14:16], packedAry[11].T))
def test_loadMultipleSignedIntTifsAsSeries(self):
self._run_tst('tif', 'int16')
def test_loadMultipleUnsignedIntTifsAsSeries(self):
self._run_tst('tif', 'uint16')
# can't currently have binary stack files of different sizes, since we have
# fixed `dims` for all stacks. leaving in place b/c it seems like something
# to support soon.
# def test_loadMultipleBinaryStacksAsSeries(self):
# self._run_tst('stack', 'uint16')
def setUp(self):
super(TestLoadIrregularImages, self).setUp()
self.tsc = ThunderContext(self.sc)
class TestContextLoading(PySparkTestCaseWithOutputDir):
def setUp(self):
super(TestContextLoading, self).setUp()
self.tsc = ThunderContext(self.sc)
@staticmethod
def _findTestResourcesDir(resourcesDirName="resources"):
testDirPath = os.path.dirname(os.path.realpath(__file__))
testResourcesDirPath = os.path.join(testDirPath, resourcesDirName)
if not os.path.isdir(testResourcesDirPath):
raise IOError("Test resources directory "+testResourcesDirPath+" not found")
return testResourcesDirPath
def test_loadStacksAsSeriesWithShuffle(self):
rangeAry = arange(64*128, dtype=dtypeFunc('int16'))
filePath = os.path.join(self.outputdir, "rangeary.stack")
rangeAry.tofile(filePath)
expectedAry = rangeAry.reshape((128, 64), order='F')
rangeSeries = self.tsc.loadImagesAsSeries(filePath, dims=(128, 64))
assert_equals('float32', rangeSeries._dtype) # check before any potential first() calls update this val
rangeSeriesAry = rangeSeries.pack()
assert_equals((128, 64), rangeSeries.dims.count)
assert_equals((128, 64), rangeSeriesAry.shape)
assert_equals('float32', str(rangeSeriesAry.dtype))
assert_true(array_equal(expectedAry, rangeSeriesAry))
def test_load3dStackAsSeriesWithShuffle(self):
rangeAry = arange(32*64*4, dtype=dtypeFunc('int16'))
filePath = os.path.join(self.outputdir, "rangeary.stack")
rangeAry.tofile(filePath)
expectedAry = rangeAry.reshape((32, 64, 4), order='F')
rangeSeries = self.tsc.loadImagesAsSeries(filePath, dims=(32, 64, 4))
assert_equals('float32', rangeSeries._dtype)
rangeSeriesAry = rangeSeries.pack()
assert_equals((32, 64, 4), rangeSeries.dims.count)
assert_equals((32, 64, 4), rangeSeriesAry.shape)
assert_equals('float32', str(rangeSeriesAry.dtype))
assert_true(array_equal(expectedAry, rangeSeriesAry))
def __run_loadMultipleStacksAsSeries(self):
rangeAry = arange(64*128, dtype=dtypeFunc('int16'))
filePath = os.path.join(self.outputdir, "rangeary01.bin")
rangeAry.tofile(filePath)
expectedAry = rangeAry.reshape((128, 64), order='F')
rangeAry2 = arange(64*128, 2*64*128, dtype=dtypeFunc('int16'))
filePath = os.path.join(self.outputdir, "rangeary02.bin")
rangeAry2.tofile(filePath)
expectedAry2 = rangeAry2.reshape((128, 64), order='F')
rangeSeries = self.tsc.loadImagesAsSeries(self.outputdir, dims=(128, 64))
assert_equals('float32', rangeSeries._dtype)
rangeSeriesAry = rangeSeries.pack()
rangeSeriesAry_xpose = rangeSeries.pack(transpose=True)
assert_equals((128, 64), rangeSeries.dims.count)
assert_equals((2, 128, 64), rangeSeriesAry.shape)
assert_equals((2, 64, 128), rangeSeriesAry_xpose.shape)
assert_equals('float32', str(rangeSeriesAry.dtype))
assert_true(array_equal(expectedAry, rangeSeriesAry[0]))
assert_true(array_equal(expectedAry2, rangeSeriesAry[1]))
assert_true(array_equal(expectedAry.T, rangeSeriesAry_xpose[0]))
assert_true(array_equal(expectedAry2.T, rangeSeriesAry_xpose[1]))
def test_loadMultipleMultipointStacksAsSeries(self):
rangeAry = arange(64*128, dtype=dtypeFunc('int16'))
filePath = os.path.join(self.outputdir, "rangeary01.bin")
rangeAry.tofile(filePath)
expectedAry = rangeAry.reshape((32, 32, 8), order='F')
rangeAry2 = arange(64*128, 2*64*128, dtype=dtypeFunc('int16'))
filePath = os.path.join(self.outputdir, "rangeary02.bin")
rangeAry2.tofile(filePath)
expectedAry2 = rangeAry2.reshape((32, 32, 8), order='F')
rangeSeries = self.tsc.loadImagesAsSeries(self.outputdir, dims=(32, 32, 8), nplanes=2)
assert_equals('float32', rangeSeries._dtype)
rangeSeriesAry = rangeSeries.pack()
assert_equals((32, 32, 2), rangeSeries.dims.count)
assert_equals((8, 32, 32, 2), rangeSeriesAry.shape)
assert_equals('float32', str(rangeSeriesAry.dtype))
assert_true(array_equal(expectedAry[:, :, :2], rangeSeriesAry[0]))
assert_true(array_equal(expectedAry[:, :, 2:4], rangeSeriesAry[1]))
assert_true(array_equal(expectedAry[:, :, 4:6], rangeSeriesAry[2]))
assert_true(array_equal(expectedAry[:, :, 6:], rangeSeriesAry[3]))
assert_true(array_equal(expectedAry2[:, :, :2], rangeSeriesAry[4]))
assert_true(array_equal(expectedAry2[:, :, 2:4], rangeSeriesAry[5]))
assert_true(array_equal(expectedAry2[:, :, 4:6], rangeSeriesAry[6]))
assert_true(array_equal(expectedAry2[:, :, 6:], rangeSeriesAry[7]))
@unittest.skipIf(not _have_image, "PIL/pillow not installed or not functional")
def __run_loadTifAsSeries(self):
tmpAry = arange(60*120, dtype=dtypeFunc('uint16'))
rangeAry = mod(tmpAry, 255).astype('uint8').reshape((60, 120))
pilImg = Image.fromarray(rangeAry)
filePath = os.path.join(self.outputdir, "rangetif01.tif")
pilImg.save(filePath)
del pilImg, tmpAry
rangeSeries = self.tsc.loadImagesAsSeries(self.outputdir, inputFormat="tif-stack")
assert_equals('float16', rangeSeries._dtype) # check before any potential first() calls update this val
rangeSeriesAry = rangeSeries.pack()
assert_equals((60, 120), rangeSeries.dims.count) # 2d tif now loaded as 2d image; was 3d with singleton z dim
assert_equals((60, 120), rangeSeriesAry.shape)
assert_equals('float16', str(rangeSeriesAry.dtype))
assert_true(array_equal(rangeAry, rangeSeriesAry))
@unittest.skipIf(not _have_image, "PIL/pillow not installed or not functional")
def test_loadTestTifAsSeriesWithShuffle(self):
testResourcesDir = TestContextLoading._findTestResourcesDir()
imagePath = os.path.join(testResourcesDir, "multilayer_tif", "dotdotdot_lzw.tif")
testimg_pil = Image.open(imagePath)
testimg_arys = list()
testimg_arys.append(array(testimg_pil)) # original shape 70, 75
testimg_pil.seek(1)
testimg_arys.append(array(testimg_pil))
testimg_pil.seek(2)
testimg_arys.append(array(testimg_pil))
rangeSeries = self.tsc.loadImagesAsSeries(imagePath, inputFormat="tif-stack")
assert_true(rangeSeries._dtype.startswith("float"))
rangeSeriesAry = rangeSeries.pack()
rangeSeriesAry_xpose = rangeSeries.pack(transpose=True)
assert_equals((70, 75, 3), rangeSeries.dims.count)
assert_equals((70, 75, 3), rangeSeriesAry.shape)
assert_equals((3, 75, 70), rangeSeriesAry_xpose.shape)
assert_true(rangeSeriesAry.dtype.kind == "f")
assert_true(array_equal(testimg_arys[0], rangeSeriesAry[:, :, 0]))
assert_true(array_equal(testimg_arys[1], rangeSeriesAry[:, :, 1]))
assert_true(array_equal(testimg_arys[2], rangeSeriesAry[:, :, 2]))
assert_true(array_equal(testimg_arys[0].T, rangeSeriesAry_xpose[0]))
assert_true(array_equal(testimg_arys[1].T, rangeSeriesAry_xpose[1]))
assert_true(array_equal(testimg_arys[2].T, rangeSeriesAry_xpose[2]))
@unittest.skipIf(not _have_image, "PIL/pillow not installed or not functional")
def test_loadMultipleTifsAsSeriesWithShuffle(self):
tmpAry = arange(60*120, dtype=dtypeFunc('uint16'))
rangeAry = mod(tmpAry, 255).astype('uint8').reshape((60, 120))
pilImg = Image.fromarray(rangeAry)
filePath = os.path.join(self.outputdir, "rangetif01.tif")
pilImg.save(filePath)
tmpAry = arange(60*120, 2*60*120, dtype=dtypeFunc('uint16'))
rangeAry2 = mod(tmpAry, 255).astype('uint8').reshape((60, 120))
pilImg = Image.fromarray(rangeAry2)
filePath = os.path.join(self.outputdir, "rangetif02.tif")
pilImg.save(filePath)
del pilImg, tmpAry
rangeSeries = self.tsc.loadImagesAsSeries(self.outputdir, inputFormat="tif-stack")
assert_equals('float16', rangeSeries._dtype)
rangeSeriesAry = rangeSeries.pack()
rangeSeriesAry_xpose = rangeSeries.pack(transpose=True)
assert_equals((60, 120), rangeSeries.dims.count) # 2d tif now loaded as 2d image; was 3d with singleton z dim
assert_equals((2, 60, 120), rangeSeriesAry.shape)
assert_equals((2, 120, 60), rangeSeriesAry_xpose.shape)
assert_equals('float16', str(rangeSeriesAry.dtype))
assert_true(array_equal(rangeAry, rangeSeriesAry[0]))
assert_true(array_equal(rangeAry2, rangeSeriesAry[1]))
assert_true(array_equal(rangeAry.T, rangeSeriesAry_xpose[0]))
assert_true(array_equal(rangeAry2.T, rangeSeriesAry_xpose[1]))
@unittest.skipIf(not _have_image, "PIL/pillow not installed or not functional")
def test_loadMultipleMultipointTifsAsSeries(self):
testResourcesDir = TestContextLoading._findTestResourcesDir()
imagesPath = os.path.join(testResourcesDir, "multilayer_tif", "dotdotdot_lzw*.tif")
# load only one file, second is a copy of this one
testimg_pil = Image.open(os.path.join(testResourcesDir, "multilayer_tif", "dotdotdot_lzw.tif"))
testimg_arys = [array(testimg_pil)]
for idx in xrange(1, 3):
testimg_pil.seek(idx)
testimg_arys.append(array(testimg_pil))
rangeSeries = self.tsc.loadImagesAsSeries(imagesPath, inputFormat="tif-stack", nplanes=1)
assert_equals((70, 75), rangeSeries.dims.count)
rangeSeriesAry = rangeSeries.pack()
assert_equals((6, 70, 75), rangeSeriesAry.shape)
for idx in xrange(6):
assert_true(array_equal(testimg_arys[idx % 3], rangeSeriesAry[idx]))
@staticmethod
def _tempFileWithPaths(f, blob):
f.write(blob)
f.flush()
return f.name
def test_loadParams(self):
params = json.dumps({"name": "test1", "value": [1, 2, 3]})
f = tempfile.NamedTemporaryFile()
path = TestContextLoading._tempFileWithPaths(f, params)
d = self.tsc.loadParams(path)
assert(d.names() == ["test1"])
assert(array_equal(d.values(), [1, 2, 3]))
params = json.dumps([{"name": "test0", "value": [1, 2, 3]},
{"name": "test1", "value": [4, 5, 6]}])
f = tempfile.NamedTemporaryFile()
path = TestContextLoading._tempFileWithPaths(f, params)
d = self.tsc.loadParams(path)
assert(d.names() == ["test0", "test1"])
assert(array_equal(d.values(), [[1, 2, 3], [4, 5, 6]]))
assert(array_equal(d.values("test0"), [1, 2, 3]))
def test_loadSeriesFromArray(self):
target = array([[0, 1], [0, 2]])
d1 = self.tsc.loadSeriesFromArray([[0, 1], [0, 2]])
d2 = self.tsc.loadSeriesFromArray(array([[0, 1], [0, 2]]))
assert(array_equal(d1.collectValuesAsArray(), target))
assert(d1.keys().collect(), [(0,), (1,)])
assert(array_equal(d2.collectValuesAsArray(), target))
assert(d2.keys().collect(), [(0,), (1,)])
target = array([[0, 1]])
d1 = self.tsc.loadSeriesFromArray([0, 1])
d2 = self.tsc.loadSeriesFromArray(array([0, 1]))
assert(array_equal(d1.collectValuesAsArray(), target))
assert(d1.keys().collect(), [(0,)])
assert(array_equal(d2.collectValuesAsArray(), target))
assert(d2.keys().collect(), [(0,)])
def test_loadImagesFromArray(self):
target = array([[[0, 1], [0, 2]]])
d1 = self.tsc.loadImagesFromArray([[0, 1], [0, 2]])
d2 = self.tsc.loadImagesFromArray(array([[0, 1], [0, 2]]))
assert(array_equal(d1.collectValuesAsArray(), target))
assert(d1.keys().collect() == [0])
assert(array_equal(d2.collectValuesAsArray(), target))
assert(d2.keys().collect() == [0])
target = array([[[0, 1], [0, 2]], [[0, 1], [0, 2]]])
d1 = self.tsc.loadImagesFromArray([[[0, 1], [0, 2]], [[0, 1], [0, 2]]])
d2 = self.tsc.loadImagesFromArray(array([[[0, 1], [0, 2]], [[0, 1], [0, 2]]]))
assert(array_equal(d1.collectValuesAsArray(), target))
assert(d1.keys().collect() == [0, 1])
assert(array_equal(d2.collectValuesAsArray(), target))
assert(d2.keys().collect() == [0, 1])
class TestContextWriting(PySparkTestCaseWithOutputDir):
def setUp(self):
super(TestContextWriting, self).setUp()
self.tsc = ThunderContext(self.sc)
def test_export_npy(self):
from numpy import load
a = array([[1, 2], [2, 3]])
filename = self.outputdir + "/test.npy"
self.tsc.export(a, filename)
aa = load(filename)
assert(array_equal(aa, a))
filename = self.outputdir + "/test"
self.tsc.export(a, filename, outputFormat="npy", overwrite=True)
aa = load(filename + ".npy")
assert(array_equal(aa, a))
def test_export_mat(self):
from scipy.io import loadmat
a = array([[1, 2], [2, 3]])
filename = self.outputdir + "/test.mat"
self.tsc.export(a, filename)
aa = loadmat(filename)
assert(array_equal(aa['test'], a))
filename = self.outputdir + "/test"
self.tsc.export(a, filename, outputFormat="mat", overwrite=True)
aa = loadmat(filename + ".mat")
assert(array_equal(aa['test'], a))
filename = self.outputdir + "/test"
self.tsc.export(a, filename, outputFormat="mat", varname="tmp", overwrite=True)
aa = loadmat(filename + ".mat")
assert(array_equal(aa['tmp'], a))
def test_export_txt(self):
from numpy import loadtxt
a = array([[1, 2], [2, 3]])
filename = self.outputdir + "/test.txt"
self.tsc.export(a, filename)
aa = loadtxt(filename)
assert(array_equal(aa, a))
filename = self.outputdir + "/test"
self.tsc.export(a, filename, outputFormat="txt", overwrite=True)
aa = loadtxt(filename + ".txt")
assert(array_equal(aa, a))
usage="%prog datafile outputdir k [options]")
parser.add_option("--nmfmethod", choices=["als"], default="als")
parser.add_option("--maxiter", type=float, default=20)
parser.add_option("--tol", type=float, default=0.001)
parser.add_option("--w_hist", action="store_true", default=False)
parser.add_option("--recon_hist", action="store_true", default=False)
opts, args = parser.parse_args()
try:
datafile = args[0]
outputdir = args[1]
k = int(args[2])
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="nmf")
data = tsc.loadSeries(datafile).cache()
nmf = NMF(k=k, method=opts.nmfmethod, maxIter=opts.maxiter, tol=opts.tol,
wHist=opts.w_hist, reconHist=opts.recon_hist)
nmf.fit(data)
outputdir += "-nmf"
tsc.export(nmf.w, outputdir, "w", "matlab")
tsc.export(nmf.h, outputdir, "h", "matlab")
if opts.w_hist:
tsc.export(nmf.wConvergence, outputdir, "w_convergence", "matlab")
if opts.recon_hist:
tsc.export(nmf.reconErr, outputdir, "rec_err", "matlab")
if use_existing_parameters == 1:
with open(Exp_Folder + filename_save_prefix + '_save_NMF_variables') as f:
NMF_components_ind, num_NMF_colors_ind, color_map_ind,max_iterations_ind, tolerence_level_ind,\
NMF_components_eachexp, num_NMF_colors_eachexp, color_map_eachexp,max_iterations_eachexp, tolerence_level_eachexp,\
NMF_components_allexp, num_NMF_colors_allexp, color_map_allexp,max_iterations_allexp, tolerence_level_allexp,colors_NMF = pickle.load(f)
# Go into the main function that does NMF for indiviudal trials
from NMF_thunder_analysis import run_analysis_individualodors
from NMF_thunder_analysis import run_analysis_eachodor
from NMF_thunder_analysis import run_analysis_allodor
from thunder import ThunderContext
print 'Starting Thunder Now. Check console for details'
tsc = ThunderContext.start(appName="thunderNMF")
if files_to_do_NMF[0] == 1:
run_analysis_individualodors(Exp_Folder, filename_save_prefix, NMF_components_ind, num_NMF_colors_ind, color_map_ind,\
tsc,redo_NMF, stimulus_on_time, stimulus_off_time, time_baseline,colors_NMF,max_iterations_ind, tolerence_level_ind,remake_colormap)
if files_to_do_NMF[1] == 1:
run_analysis_eachodor(Exp_Folder, filename_save_prefix, NMF_components_eachexp, num_NMF_colors_eachexp, color_map_eachexp,\
tsc,redo_NMF, stimulus_on_time, stimulus_off_time, time_baseline,colors_NMF, max_iterations_eachexp, tolerence_level_eachexp,remake_colormap)
if files_to_do_NMF[2] == 1:
run_analysis_allodor(Exp_Folder, filename_save_prefix, NMF_components_allexp, num_NMF_colors_allexp, color_map_allexp,\
tsc,redo_NMF, stimulus_on_time, stimulus_off_time, time_baseline,colors_NMF, max_iterations_allexp, tolerence_level_allexp,remake_colormap)
############# Save all imput parameters
with open(Exp_Folder + filename_save_prefix + '_save_NMF_variables', 'w') as f:
def CompressImages(inputs, output, confObj):
debugMode = False
st = datetime.now()
imageExt = confObj['ext']
imageHeight = confObj['dims'][0]
imageWidth = confObj['dims'][1]
refImgId = confObj['refImageId']
diffImageFolder = confObj['DiffImageFolder']
if debugMode == True:
print confObj
import glob
totImages = len(glob.glob(inputs + "*." + imageExt))
if os.path.exists(output):
shutil.rmtree(output)
conf = SparkConf().setAppName('ImgCompress')
sc = SparkContext(conf=conf)
imageHeight = sc.broadcast(imageHeight)
imageWidth = sc.broadcast(imageWidth)
tsc = ThunderContext(sc)
tscImages = tsc.loadImages(inputs, (imageHeight.value, imageWidth.value),
imageExt, imageExt).cache()
floatingPixelRdd = tscImages.rdd.flatMapValues(lambda r: r).zipWithIndex().map(lambda l: ((l[0][0],(l[1]-(l[0][0]*int(imageHeight.value)))),l[0][1]))\
.flatMapValues(lambda r: r).zipWithIndex().\
map(lambda l: ((l[0][0][1],(l[1]-(l[0][0][0]*int(imageWidth.value)*int(imageHeight.value) + l[0][0][1]*int(imageWidth.value)))),(l[0][0][0],l[0][1])))
if debugMode == True:
floatingPixelRdd.saveAsTextFile(output + "\\Stage-1-FloatingPixel")
temporalVoxelRdd = floatingPixelRdd.groupByKey().map(
lambda x: (x[0], list(x[1]))).cache()
if debugMode == True:
temporalVoxelRdd.saveAsTextFile(output + "\\Stage-2-TemporalVoxel")
iMapImageDict = {}
for imgIndex in range(totImages):
imgIndexBD = sc.broadcast(imgIndex)
#-------------------------------HELPER FUNCTIONS-------------------------------------
def ReAdjustImapList(l):
intList = l[1]
mKey = intList.pop(imgIndexBD.value)[1]
return (mKey, intList)
def calculateMedainForEachImage(l):
intRdd = sc.parallelize(l[1]).flatMap(
lambda l: l).groupByKey().map(lambda x: (x[0], list(x[1])))
intRddSorted = intRdd.map(lambda l: (l[0], sorted(l[1])))
intRddMedian = intRddSorted.map(lambda l:
(l[0], l[1][int(len(l[1]) / 2)]))
return intRddMedian.collect()
#-------------------------------HELPER FUNCTIONS-------------------------------------
imapRdd = temporalVoxelRdd.map(lambda l: ReAdjustImapList(l)).sortBy(lambda l: l[0], False)\
.groupByKey().map(lambda x : (x[0], list(x[1])))
if debugMode == True:
imapRdd.saveAsTextFile(output + "\\Stage-3__IMAP-Stage-1_imgIdx-" +
str(imgIndex))
imapRdd = imapRdd.flatMapValues(lambda l: l).flatMapValues(lambda l: l).map(lambda l: ((l[0],l[1][0]),l[1][1])).\
groupByKey().map(lambda x : (x[0], list(x[1]))).\
map(lambda l: (l[0],sorted(l[1]))).map(lambda l: (l[0], l[1][int(len(l[1])/2)])).\
map(lambda l: (l[0][0],(l[0][1], l[1])))
if debugMode == True:
imapRdd.saveAsTextFile(output + "\\Stage-3__IMAP-Stage-2_imgIdx-" +
str(imgIndex))
imapRdd = imapRdd.groupByKey().map(
lambda x: (x[0], sorted(list(x[1]), key=lambda k: k[0])))
if debugMode == True:
imapRdd.saveAsTextFile(output + "\\Stage-3__IMAP-Stage-3_imgIdx-" +
str(imgIndex))
imapDict = imapRdd.collectAsMap()
iMapImageDict[imgIndex] = imapDict
iMapDictBD = sc.broadcast(iMapImageDict)
refImageIdBD = sc.broadcast(refImgId)
def CalcMapValue(pix, iMapVal):
pImgIdx = pix[0]
residual = 0
for iIdx in iMapVal:
if pImgIdx == iIdx[0]:
residual = int(iIdx[1]) - pix[1]
break
return (pix[0], residual)
def ApplyIMap(l):
voxel = l[1]
refIntensity = l[1][refImageIdBD.value][1]
iMapValues = iMapDictBD.value[refImageIdBD.value][refIntensity]
resdualValues = []
for pixel in voxel:
if pixel[0] != refImageIdBD.value:
resdualValues.append(CalcMapValue(pixel, iMapValues))
else:
resdualValues.append(pixel)
return (l[0], resdualValues)
diffFVRdd = temporalVoxelRdd.map(lambda l: ApplyIMap(l))
if debugMode == True:
diffFVRdd.saveAsTextFile(output + "\\Stage-4__Diff-Stage-1")
residualImages = diffFVRdd.flatMapValues(lambda l: l).map(lambda l: ((l[1][0], l[0][0]),(l[0][1],l[1][1])))\
.groupByKey().map(lambda x : (x[0], sorted(list(x[1]),key = lambda k: (k[0]))))\
.map(lambda l: (l[0][0],(l[0][1],l[1])))\
.groupByKey().map(lambda x : (x[0], sorted(list(x[1]),key = lambda k: (k[0]))))\
.map(lambda l: (l[0], removeRCIndex(l[1])))
residualImages.saveAsTextFile(output + "\\" + diffImageFolder)
for ingIdx in range(totImages):
iMapImageDict[str(ingIdx)] = dict([
(str(k), str(v)) for k, v in iMapImageDict[ingIdx].items()
])
del iMapImageDict[ingIdx]
import json
with open(output + "\\imap.json", 'w') as f:
json.dump(iMapImageDict, f)
with open(output + '\\conf.json', 'w') as f:
json.dump(confObj, f)
# clean up
sc.stop()
en = datetime.now()
td = en - st
print td
from thunder import ThunderContext, RegressionModel, PCA
if __name__ == "__main__":
parser = optparse.OptionParser(description="fit a regression model",
usage="%prog datafile modelfile outputdir [options]")
parser.add_option("--regressmode", choices=("mean", "linear", "bilinear"), help="form of regression")
parser.add_option("--k", type=int, default=2)
opts, args = parser.parse_args()
try:
datafile = args[0]
modelfile = args[1]
outputdir = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="regresswithpca")
data = tsc.loadSeries(datafile)
model = RegressionModel.load(modelfile, opts.regressmode) # do regression
betas, stats, resid = model.fit(data)
pca = PCA(opts.k).fit(betas) # do PCA
traj = model.fit(data, pca.comps) # get trajectories
outputdir += "-regress"
tsc.export(pca.comps, outputdir, "comps", "matlab")
tsc.export(pca.latent, outputdir, "latent", "matlab")
tsc.export(pca.scores, outputdir, "scores", "matlab")
tsc.export(traj, outputdir, "traj", "matlab")
Exp_Folder ='/Users/seetha/Desktop/Ruey_Habenula/Habenula/Short_Stimulus/Fish104_Block2_Blue&UV1c/'
filename_save_prefix = 'Test1'
from thunder import ThunderContext
print 'Starting Thunder Now. Check console for details'
tsc = ThunderContext.start(appName="thunderNMF")
import os
filesep = os.path.sep
import matplotlib.pyplot as plt
import numpy as np
from thunder_NMF import run_NMF
from thunder_NMF import make_NMF_maps
from thunder_NMF_plots import plot_NMF_maps
from thunder import Colorize
image = Colorize.image
Stimulus_Directories = [f for f in os.listdir(Exp_Folder) if os.path.isdir(os.path.join(Exp_Folder, f)) and f.find('Figures')<0]
#Stimulus_Directories
ii = 0
Trial_Directories = [f for f in os.listdir(os.path.join(Exp_Folder, Stimulus_Directories[ii]))\
if os.path.isdir(os.path.join(Exp_Folder, Stimulus_Directories[ii], f)) and f.find('Figures')<0]
Trial_Directories
jj = 0
stim_start = 10 #Stimulus Starting time point
stim_end = 14 #Stimulus Ending time point
def execute(self):
"""
Execute this pull request
"""
printer.status("Executing pull request %s from user %s" % (self.id, self.login))
base, module = self.clone()
f = open(base + 'info.json', 'r')
info = json.loads(f.read())
sys.path.append(module)
run = importlib.import_module('run')
spark = os.getenv('SPARK_HOME')
if spark is None or spark == '':
raise Exception('must assign the environmental variable SPARK_HOME with the location of Spark')
sys.path.append(os.path.join(spark, 'python'))
sys.path.append(os.path.join(spark, 'python/lib/py4j-0.8.2.1-src.zip'))
from thunder import ThunderContext
tsc = ThunderContext.start(master="local", appName="neurofinder")
datasets = ['data-0', 'data-1', 'data-2', 'data-3', 'data-4', 'data-5']
centers = [5, 7, 9, 11, 13, 15]
metrics = {'accuracy': [], 'overlap': [], 'distance': [], 'count': [], 'area': []}
try:
for ii, name in enumerate(datasets):
data, ts, truth = tsc.makeExample('sources', dims=(200, 200),
centers=centers[ii], noise=1.0, returnParams=True)
sources = run.run(data)
accuracy = truth.similarity(sources, metric='distance', thresh=10, minDistance=10)
overlap = truth.overlap(sources, minDistance=10)
distance = truth.distance(sources, minDistance=10)
count = sources.count
area = mean(sources.areas)
metrics['accuracy'].append({"dataset": name, "value": accuracy})
metrics['overlap'].append({"dataset": name, "value": nanmean(overlap)})
metrics['distance'].append({"dataset": name, "value": nanmean(distance)})
metrics['count'].append({"dataset": name, "value": count})
metrics['area'].append({"dataset": name, "value": area})
im = sources.masks(base=data.mean())
self.post_image(im, name)
for k in metrics.keys():
overall = mean([v['value'] for v in metrics[k]])
metrics[k].append({"dataset": "overall", "value": overall})
msg = "Execution successful"
printer.success()
self.update_status("executed")
except Exception:
metrics = None
msg = "Execution failed"
printer.error("failed, returning error")
print(traceback.format_exc())
self.send_message(msg)
return metrics, info
def setUp(self):
super(TestContextLoading, self).setUp()
self.tsc = ThunderContext(self.sc)
def setUp(self):
super(TestContextLoading, self).setUp()
self.tsc = ThunderContext(self.sc)
Exp_Folder = '/Users/seetha/Desktop/KCTD/Fish14_KCTDHUC_5dpf/Tiff/Cropped/Registered/Thresholded_OB/Registered_Stimulus/'
filename_save_prefix = 'ThresholdedOB_T81'
from thunder import ThunderContext
print 'Starting Thunder Now. Check console for details'
tsc = ThunderContext.start(appName="thunderpca")
import os
filesep = os.path.sep
import time
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import seaborn as sns
from thunder import KMeans
from thunder import Colorize
from thunder_kmeans_plots import plot_kmeans_maps
from thunder_kmeans import make_kmeans_maps
from kmeans_thunder_analysis import run_kmeans_thunder
#Stimulus_Directories = [f for f in os.listdir(Exp_Folder) if os.path.isdir(os.path.join(Exp_Folder, f)) and f.find('Figures')<0]
##Stimulus_Directories
#ii = 1
#Trial_Directories = [f for f in os.listdir(os.path.join(Exp_Folder, Stimulus_Directories[ii]))\
#if os.path.isdir(os.path.join(Exp_Folder, Stimulus_Directories[ii], f)) and f.find('Figures')<0]
#Trial_Directories
#jj = 0
def execute(self, lock, pipe):
"""
Execute this pull request
"""
lock.acquire()
base, module = self.clone()
f = open(base + 'info.json', 'r')
info = json.loads(f.read())
printer.status("Executing pull request %s from user %s"
% (self.id, self.login))
printer.status("Branch name: %s" % self.branch)
printer.status("Algorithm name: %s" % info['algorithm'])
sys.path.append(module)
run = importlib.import_module('run', module)
spark_home = os.getenv('SPARK_HOME')
if spark_home is None or spark_home == '':
raise Exception('must assign the environmental variable SPARK_HOME with the location of Spark')
sys.path.append(os.path.join(spark_home, 'python'))
sys.path.append(os.path.join(spark_home, 'python/lib/py4j-0.8.2.1-src.zip'))
with quiet():
from thunder import ThunderContext
from thunder.utils.launch import findThunderEgg
tsc = ThunderContext.start(master=self.get_master(), appName="neurofinder")
tsc.addPyFile(findThunderEgg())
log4j = tsc._sc._jvm.org.apache.log4j
log4j.LogManager.getRootLogger().setLevel(log4j.Level.ERROR)
time.sleep(5)
base_path = 'neuro.datasets.private/challenges/neurofinder.test'
datasets = ['00.00.test', '00.01.test', '01.00.test', '01.01.test',
'02.00.test', '02.01.test', '03.00.test']
metrics = {'score': [], 'recall': [], 'precision': [], 'overlap': [], 'exactness': []}
try:
for ii, name in enumerate(datasets):
printer.status("Proccessing data set %s" % name)
data_path = 's3n://' + base_path + '/' + name
data_info = self.load_info(base_path, name)
data = tsc.loadImages(data_path + '/images/', recursive=True,
npartitions=600)
truth = tsc.loadSources(data_path + '/sources/sources.json')
sources = run.run(data, info=data_info)
threshold = 6.0 / data_info['pixels-per-micron']
recall, precision, score = truth.similarity(sources, metric='distance', minDistance=threshold)
stats = truth.overlap(sources, method='rates', minDistance=threshold)
if sum(~isnan(stats)) > 0:
overlap, exactness = tuple(nanmean(stats, axis=0))
else:
overlap, exactness = 0.0, 1.0
contributors = str(", ".join(data_info["contributors"]))
animal = data_info["animal"]
region = data_info["region"]
lab = data_info["lab"]
base = {"dataset": name, "contributors": contributors,
"lab": lab, "region": region, "animal": animal}
m = {"value": score}
m.update(base)
metrics['score'].append(m)
m = {"value": recall}
m.update(base)
metrics['recall'].append(m)
m = {"value": precision}
m.update(base)
metrics['precision'].append(m)
m = {"value": overlap}
m.update(base)
metrics['overlap'].append(m)
m = {"value": exactness}
m.update(base)
metrics['exactness'].append(m)
base = data.mean()
im = sources.masks(outline=True, base=base.clip(0, percentile(base, 99.9)))
self.post_image(im, name)
for k in metrics.keys():
overall = mean([v['value'] for v in metrics[k]])
metrics[k].append({"dataset": "overall", "value": overall,
"contributors": "", "region": "", "animal": ""})
msg = "Execution successful"
printer.success()
self.update_status("executed")
except Exception:
metrics = None
msg = "Execution failed"
printer.error("failed, returning error")
print(traceback.format_exc())
self.send_message(msg)
tsc.stop()
sys.path.remove(module)
pipe.send((metrics, info))
lock.release()
if use_existing_parameters == 1:
with open(Exp_Folder + filename_save_prefix +
'_save_kmeans_variables') as f:
kmeans_clusters_ind, kmeans_clusters_eachodor, kmeans_clusters_allodor, time_baseline, ignore_clusters = pickle.load(
f)
# Go into the main function that does kmeans for indiviudal trials
from kmeans_thunder_analysis import run_analysis_individualodors
from kmeans_thunder_analysis import run_analysis_eachodor
from kmeans_thunder_analysis import run_analysis_allodor
from thunder import ThunderContext
print 'Starting Thunder Now. Check console for details'
tsc = ThunderContext.start(appName="thunderkmeans")
if files_to_do_kmeans[0] == 1:
run_analysis_individualodors(Exp_Folder, filename_save_prefix, filename_save_prefix_forkmeanswithPCA, kmeans_clusters_ind,\
stimulus_on_time, stimulus_off_time, tsc,redo_kmeans,time_baseline,redo_kmeans_colormap,ignore_clusters)
if files_to_do_kmeans[1] == 1:
run_analysis_eachodor(Exp_Folder, filename_save_prefix, filename_save_prefix_forkmeanswithPCA, kmeans_clusters_eachodor, \
stimulus_on_time, stimulus_off_time, tsc,redo_kmeans,time_baseline,redo_kmeans_colormap,ignore_clusters)
if files_to_do_kmeans[2] == 1:
run_analysis_allodor(Exp_Folder, filename_save_prefix, filename_save_prefix_forkmeanswithPCA, kmeans_clusters_allodor, \
stimulus_on_time, stimulus_off_time, tsc,redo_kmeans, time_baseline,redo_kmeans_colormap,ignore_clusters)
############# Save all imput parameters
with open(Exp_Folder + filename_save_prefix + '_save_kmeans_variables',
"""
Example standalone app for calculating series statistics
"""
import optparse
from thunder import ThunderContext
if __name__ == "__main__":
parser = optparse.OptionParser(description="compute summary statistics on time series data",
usage="%prog datafile outputdir mode [options]")
parser.add_option("--preprocess", action="store_true", default=False)
opts, args = parser.parse_args()
try:
datafile = args[0]
outputdir = args[1]
mode = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="stats")
data = tsc.loadSeries(datafile).cache()
vals = data.seriesStat(mode)
outputdir += "-stats"
tsc.export(vals, outputdir, "stats_" + mode, "matlab")
)
print "Found {0} faces!".format(len(faces))
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), -1)
return img
# Load images using thundear and pass it to OpenCV haar cascase one by one
if __name__ == "__main__":
# Define Spark and Thunder context
conf = SparkConf().setAppName("Collaborative Filter").set("spark.executor.memory", "5g")
sc = SparkContext(conf=conf)
tsc = ThunderContext(sc)
# Load all images in data directory
data = tsc.loadImages("/home/vj/Desktop/CS-Project/data", inputFormat="png")
# Loop through each image and convert them to gray
grayImages = data.apply(lambda (k, v): (k, convertToGray(v)))
# Loop through all the gray images and find faces
FaceImages = grayImages.apply(lambda (k, v): (k, detectFaces(v)))
print (data.dims)
print (data.nrecords)
cv2.imshow("image1", grayImages[0])
cv2.imshow("Face detected1", FaceImages[0])
cv2.imshow("image2", grayImages[1])
cv2.imshow("Face detected2", FaceImages[1])
class TestContextLoading(PySparkTestCaseWithOutputDir):
def setUp(self):
super(TestContextLoading, self).setUp()
self.tsc = ThunderContext(self.sc)
@staticmethod
def _findTestResourcesDir(resourcesdirname="resources"):
testdirpath = os.path.dirname(os.path.realpath(__file__))
testresourcesdirpath = os.path.join(testdirpath, resourcesdirname)
if not os.path.isdir(testresourcesdirpath):
raise IOError("Test resources directory " + testresourcesdirpath +
" not found")
return testresourcesdirpath
def __run_loadStacksAsSeries(self, shuffle):
rangeary = np.arange(64 * 128, dtype=np.dtype('int16'))
filepath = os.path.join(self.outputdir, "rangeary.stack")
rangeary.tofile(filepath)
expectedary = rangeary.reshape((128, 64), order='F')
range_series = self.tsc.loadImagesAsSeries(filepath,
dims=(128, 64),
shuffle=shuffle)
assert_equals(
'float32', range_series._dtype
) # check before any potential first() calls update this val
range_series_ary = range_series.pack()
assert_equals((128, 64), range_series.dims.count)
assert_equals((128, 64), range_series_ary.shape)
assert_equals('float32', str(range_series_ary.dtype))
assert_true(np.array_equal(expectedary, range_series_ary))
def test_loadStacksAsSeriesNoShuffle(self):
self.__run_loadStacksAsSeries(False)
def test_loadStacksAsSeriesWithShuffle(self):
self.__run_loadStacksAsSeries(True)
def __run_load3dStackAsSeries(self, shuffle):
rangeary = np.arange(32 * 64 * 4, dtype=np.dtype('int16'))
filepath = os.path.join(self.outputdir, "rangeary.stack")
rangeary.tofile(filepath)
expectedary = rangeary.reshape((32, 64, 4), order='F')
range_series = self.tsc.loadImagesAsSeries(filepath,
dims=(32, 64, 4),
shuffle=shuffle)
assert_equals('float32', range_series._dtype)
range_series_ary = range_series.pack()
assert_equals((32, 64, 4), range_series.dims.count)
assert_equals((32, 64, 4), range_series_ary.shape)
assert_equals('float32', str(range_series_ary.dtype))
assert_true(np.array_equal(expectedary, range_series_ary))
def test_load3dStackAsSeriesNoShuffle(self):
self.__run_load3dStackAsSeries(False)
def test_load3dStackAsSeriesWithShuffle(self):
self.__run_load3dStackAsSeries(True)
def __run_loadMultipleStacksAsSeries(self, shuffle):
rangeary = np.arange(64 * 128, dtype=np.dtype('int16'))
filepath = os.path.join(self.outputdir, "rangeary01.stack")
rangeary.tofile(filepath)
expectedary = rangeary.reshape((128, 64), order='F')
rangeary2 = np.arange(64 * 128, 2 * 64 * 128, dtype=np.dtype('int16'))
filepath = os.path.join(self.outputdir, "rangeary02.stack")
rangeary2.tofile(filepath)
expectedary2 = rangeary2.reshape((128, 64), order='F')
range_series = self.tsc.loadImagesAsSeries(self.outputdir,
dims=(128, 64),
shuffle=shuffle)
assert_equals('float32', range_series._dtype)
range_series_ary = range_series.pack()
range_series_ary_xpose = range_series.pack(transpose=True)
assert_equals((128, 64), range_series.dims.count)
assert_equals((2, 128, 64), range_series_ary.shape)
assert_equals((2, 64, 128), range_series_ary_xpose.shape)
assert_equals('float32', str(range_series_ary.dtype))
assert_true(np.array_equal(expectedary, range_series_ary[0]))
assert_true(np.array_equal(expectedary2, range_series_ary[1]))
assert_true(np.array_equal(expectedary.T, range_series_ary_xpose[0]))
assert_true(np.array_equal(expectedary2.T, range_series_ary_xpose[1]))
def test_loadMultipleStacksAsSeriesNoShuffle(self):
self.__run_loadMultipleStacksAsSeries(False)
def test_loadMultipleStacksAsSeriesWithShuffle(self):
self.__run_loadMultipleStacksAsSeries(True)
def __run_loadTifAsSeries(self, shuffle):
tmpary = np.arange(60 * 120, dtype=np.dtype('uint16'))
rangeary = np.mod(tmpary, 255).astype('uint8').reshape((60, 120))
pilimg = Image.fromarray(rangeary)
filepath = os.path.join(self.outputdir, "rangetif01.tif")
pilimg.save(filepath)
del pilimg, tmpary
range_series = self.tsc.loadImagesAsSeries(self.outputdir,
inputformat="tif-stack",
shuffle=shuffle)
assert_equals(
'float16', range_series._dtype
) # check before any potential first() calls update this val
range_series_ary = range_series.pack()
assert_equals((60, 120, 1), range_series.dims.count)
assert_equals((60, 120), range_series_ary.shape)
assert_equals('float16', str(range_series_ary.dtype))
assert_true(np.array_equal(rangeary, range_series_ary))
@unittest.skipIf(not _have_image,
"PIL/pillow not installed or not functional")
def test_loadTifAsSeriesNoShuffle(self):
self.__run_loadTifAsSeries(False)
@unittest.skipIf(not _have_image,
"PIL/pillow not installed or not functional")
def test_loadTifAsSeriesWithShuffle(self):
self.__run_loadTifAsSeries(True)
def __run_loadTestTifAsSeries(self, shuffle):
testresourcesdir = TestContextLoading._findTestResourcesDir()
imagepath = os.path.join(testresourcesdir, "multilayer_tif",
"dotdotdot_lzw.tif")
testimg_pil = Image.open(imagepath)
testimg_arys = list()
testimg_arys.append(pil_to_array(testimg_pil)) # original shape 70, 75
testimg_pil.seek(1)
testimg_arys.append(pil_to_array(testimg_pil))
testimg_pil.seek(2)
testimg_arys.append(pil_to_array(testimg_pil))
range_series = self.tsc.loadImagesAsSeries(imagepath,
inputformat="tif-stack",
shuffle=shuffle)
assert_true(range_series._dtype.startswith("float"))
range_series_ary = range_series.pack()
range_series_ary_xpose = range_series.pack(transpose=True)
assert_equals((70, 75, 3), range_series.dims.count)
assert_equals((70, 75, 3), range_series_ary.shape)
assert_equals((3, 75, 70), range_series_ary_xpose.shape)
assert_true(range_series_ary.dtype.kind == "f")
assert_true(np.array_equal(testimg_arys[0], range_series_ary[:, :, 0]))
assert_true(np.array_equal(testimg_arys[1], range_series_ary[:, :, 1]))
assert_true(np.array_equal(testimg_arys[2], range_series_ary[:, :, 2]))
assert_true(
np.array_equal(testimg_arys[0].T, range_series_ary_xpose[0]))
assert_true(
np.array_equal(testimg_arys[1].T, range_series_ary_xpose[1]))
assert_true(
np.array_equal(testimg_arys[2].T, range_series_ary_xpose[2]))
@unittest.skipIf(not _have_image,
"PIL/pillow not installed or not functional")
def test_loadTestTifAsSeriesNoShuffle(self):
self.__run_loadTestTifAsSeries(False)
@unittest.skipIf(not _have_image,
"PIL/pillow not installed or not functional")
def test_loadTestTifAsSeriesWithShuffle(self):
self.__run_loadTestTifAsSeries(True)
def __run_loadMultipleTifsAsSeries(self, shuffle):
tmpary = np.arange(60 * 120, dtype=np.dtype('uint16'))
rangeary = np.mod(tmpary, 255).astype('uint8').reshape((60, 120))
pilimg = Image.fromarray(rangeary)
filepath = os.path.join(self.outputdir, "rangetif01.tif")
pilimg.save(filepath)
tmpary = np.arange(60 * 120, 2 * 60 * 120, dtype=np.dtype('uint16'))
rangeary2 = np.mod(tmpary, 255).astype('uint8').reshape((60, 120))
pilimg = Image.fromarray(rangeary2)
filepath = os.path.join(self.outputdir, "rangetif02.tif")
pilimg.save(filepath)
del pilimg, tmpary
range_series = self.tsc.loadImagesAsSeries(self.outputdir,
inputformat="tif-stack",
shuffle=shuffle)
assert_equals('float16', range_series._dtype)
range_series_ary = range_series.pack()
range_series_ary_xpose = range_series.pack(transpose=True)
assert_equals((60, 120, 1), range_series.dims.count)
assert_equals((2, 60, 120), range_series_ary.shape)
assert_equals((2, 120, 60), range_series_ary_xpose.shape)
assert_equals('float16', str(range_series_ary.dtype))
assert_true(np.array_equal(rangeary, range_series_ary[0]))
assert_true(np.array_equal(rangeary2, range_series_ary[1]))
assert_true(np.array_equal(rangeary.T, range_series_ary_xpose[0]))
assert_true(np.array_equal(rangeary2.T, range_series_ary_xpose[1]))
@unittest.skipIf(not _have_image,
"PIL/pillow not installed or not functional")
def test_loadMultipleTifsAsSeriesNoShuffle(self):
self.__run_loadMultipleTifsAsSeries(False)
@unittest.skipIf(not _have_image,
"PIL/pillow not installed or not functional")
def test_loadMultipleTifsAsSeriesWithShuffle(self):
self.__run_loadMultipleTifsAsSeries(True)
parser.add_option("--nmfmethod", choices=["als"], default="als")
parser.add_option("--maxiter", type=float, default=20)
parser.add_option("--tol", type=float, default=0.001)
parser.add_option("--w_hist", action="store_true", default=False)
parser.add_option("--recon_hist", action="store_true", default=False)
opts, args = parser.parse_args()
try:
datafile = args[0]
outputdir = args[1]
k = int(args[2])
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="nmf")
data = tsc.loadSeries(datafile).cache()
nmf = NMF(k=k,
method=opts.nmfmethod,
maxIter=opts.maxiter,
tol=opts.tol,
wHist=opts.w_hist,
reconHist=opts.recon_hist)
nmf.fit(data)
outputdir += "-nmf"
tsc.export(nmf.w, outputdir, "w", "matlab")
tsc.export(nmf.h, outputdir, "h", "matlab")
if opts.w_hist:
tsc.export(nmf.wConvergence, outputdir, "w_convergence", "matlab")
parser = argparse.ArgumentParser(
description="do independent components analysis")
parser.add_argument("datafile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("k", type=int)
parser.add_argument("c", type=int)
parser.add_argument("--svdmethod",
choices=("direct", "em"),
default="direct",
required=False)
parser.add_argument("--maxiter", type=float, default=100, required=False)
parser.add_argument("--tol", type=float, default=0.000001, required=False)
parser.add_argument("--seed", type=int, default=0, required=False)
args = parser.parse_args()
tsc = ThunderContext.start(appName="ica")
data = tsc.loadSeries(args.datafile).cache()
model = ICA(k=args.k,
c=args.c,
svdmethod=args.svdmethod,
maxiter=args.maxiter,
tol=args.tol,
seed=args.seed)
result = model.fit(data)
outputdir = args.outputdir + "-ica"
export(result.a, outputdir, "a", "matlab")
export(result.sigs, outputdir, "sigs", "matlab")
"""
Example standalone app for calculating series statistics
"""
import optparse
from thunder import ThunderContext
if __name__ == "__main__":
parser = optparse.OptionParser(
description="compute summary statistics on time series data",
usage="%prog datafile outputdir mode [options]")
parser.add_option("--preprocess", action="store_true", default=False)
opts, args = parser.parse_args()
try:
datafile = args[0]
outputdir = args[1]
mode = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="stats")
data = tsc.loadSeries(datafile).cache()
vals = data.seriesStat(mode)
outputdir += "-stats"
tsc.export(vals, outputdir, "stats_" + mode, "matlab")
stimulus_pulse = 1
if stimulus_pulse == 1:
stimulus_on_time = [10,28,47,65,83,101]
stimulus_off_time = [14,32,51,69,87,105]
color_mat = ['#00FFFF','#0000A0','#800080','#FF00FF', '#800000','#A52A2A']
# Go into the main function that does pca for indiviudal trials
from pca_thunder_analysis import run_analysis_individualodors
from pca_thunder_analysis import run_analysis_eachodor
from pca_thunder_analysis import run_analysis_allodor
from thunder import ThunderContext
print 'Starting Thunder Now. Check console for details'
tsc = ThunderContext.start(appName="thunderpca")
if files_to_do_PCA[0]== 1:
run_analysis_individualodors(Exp_Folder, filename_save_prefix_forPCA, filename_save_prefix_for_textfile, pca_components_ind, num_pca_colors_ind, num_samples_ind, thresh_pca_ind, color_map_ind,\
tsc,redo_pca,reconstruct_pca, stimulus_on_time, stimulus_off_time,color_mat,required_pcs,time_baseline )
if files_to_do_PCA[1]== 1:
run_analysis_eachodor(Exp_Folder, filename_save_prefix_forPCA, filename_save_prefix_for_textfile, pca_components_eachodor, num_pca_colors_eachodor, num_samples_eachodor, thresh_pca_eachodor, color_map_eachodor,\
tsc,redo_pca,reconstruct_pca, stimulus_on_time, stimulus_off_time,color_mat,required_pcs,time_baseline )
if files_to_do_PCA[2]== 1:
run_analysis_allodor(Exp_Folder, filename_save_prefix_forPCA, filename_save_prefix_for_textfile, pca_components_allodor, num_pca_colors_allodor, num_samples_allodor, thresh_pca_allodor, color_map_allodor,\
tsc,redo_pca,reconstruct_pca, stimulus_on_time, stimulus_off_time,color_mat,required_pcs,time_baseline )
############# Save all imput parameters
with open(Exp_Folder+filename_save_prefix_forPCA+'_save_pca_variables', 'w') as f:
pickle.dump([pca_components_ind, num_pca_colors_ind, num_samples_ind, thresh_pca_ind, color_map_ind,\
from thunder import ThunderContext, ICA, export
if __name__ == "__main__":
parser = optparse.OptionParser(description="do independent components analysis",
usage="%prog datafile outputdir k c [options]")
parser.add_option("--svdmethod", choices=("direct", "em"), default="direct")
parser.add_option("--maxiter", type=float, default=100)
parser.add_option("--tol", type=float, default=0.000001)
parser.add_option("--seed", type=int, default=0)
opts, args = parser.parse_args()
try:
datafile = args[0]
outputdir = args[1]
k = int(args[2])
c = int(args[3])
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="ica")
data = tsc.loadSeries(datafile).cache()
model = ICA(k=k, c=c, svdmethod=opts.svdmethod, maxiter=opts.maxiter, tol=opts.tol, seed=opts.seed)
result = model.fit(data)
outputdir += "-ica"
export(result.a, outputdir, "a", "matlab")
export(result.sigs, outputdir, "sigs", "matlab")
"""
Example standalone app for mass-univariate regression
"""
import optparse
from thunder import ThunderContext, RegressionModel
if __name__ == "__main__":
parser = optparse.OptionParser(description="fit a regression model",
usage="%prog datafile modelfile outputdir [options]")
parser.add_option("--regressmode", choices=("mean", "linear", "bilinear"),
default="linear", help="form of regression")
opts, args = parser.parse_args()
try:
datafile = args[0]
modelfile = args[1]
outputdir = args[2]
except IndexError:
parser.print_usage()
raise Exception("too few arguments")
tsc = ThunderContext.start(appName="regress")
data = tsc.loadText(datafile)
result = RegressionModel.load(modelfile, opts.regressmode).fit(data)
outputdir += "-regress"
tsc.export(result.select('stats'), outputdir, "stats", "matlab")
tsc.export(result.select('betas'), outputdir, "betas", "matlab")
def CompressImages(inputs, output, confObj):
debugMode=False
st=datetime.now()
imageExt = confObj['ext']
imageHeight = confObj['dims'][0]
imageWidth = confObj['dims'][1]
refImgId = confObj['refImageId']
diffImageFolder = confObj['DiffImageFolder']
if debugMode == True:
print confObj
import glob
totImages = len(glob.glob(inputs+"*."+imageExt))
if os.path.exists(output):
shutil.rmtree(output)
conf = SparkConf().setAppName('ImgCompress')
sc = SparkContext(conf=conf)
imageHeight = sc.broadcast(imageHeight)
imageWidth = sc.broadcast(imageWidth)
tsc = ThunderContext(sc)
tscImages = tsc.loadImages(inputs, (imageHeight.value,imageWidth.value), imageExt, imageExt).cache()
floatingPixelRdd = tscImages.rdd.flatMapValues(lambda r: r).zipWithIndex().map(lambda l: ((l[0][0],(l[1]-(l[0][0]*int(imageHeight.value)))),l[0][1]))\
.flatMapValues(lambda r: r).zipWithIndex().\
map(lambda l: ((l[0][0][1],(l[1]-(l[0][0][0]*int(imageWidth.value)*int(imageHeight.value) + l[0][0][1]*int(imageWidth.value)))),(l[0][0][0],l[0][1])))
if debugMode == True:
floatingPixelRdd.saveAsTextFile(output+"\\Stage-1-FloatingPixel")
temporalVoxelRdd = floatingPixelRdd.groupByKey().map(lambda x : (x[0], list(x[1]))).cache()
if debugMode == True:
temporalVoxelRdd.saveAsTextFile(output+"\\Stage-2-TemporalVoxel")
iMapImageDict = {}
for imgIndex in range(totImages):
imgIndexBD = sc.broadcast(imgIndex)
#-------------------------------HELPER FUNCTIONS-------------------------------------
def ReAdjustImapList(l):
intList = l[1]
mKey = intList.pop(imgIndexBD.value)[1]
return (mKey, intList)
def calculateMedainForEachImage(l):
intRdd = sc.parallelize(l[1]).flatMap(lambda l: l).groupByKey().map(lambda x : (x[0], list(x[1])))
intRddSorted = intRdd.map(lambda l: (l[0],sorted(l[1])))
intRddMedian = intRddSorted.map(lambda l: (l[0], l[1][int(len(l[1])/2)]))
return intRddMedian.collect()
#-------------------------------HELPER FUNCTIONS-------------------------------------
imapRdd = temporalVoxelRdd.map(lambda l: ReAdjustImapList(l)).sortBy(lambda l: l[0], False)\
.groupByKey().map(lambda x : (x[0], list(x[1])))
if debugMode == True:
imapRdd.saveAsTextFile(output+"\\Stage-3__IMAP-Stage-1_imgIdx-" + str(imgIndex))
imapRdd = imapRdd.flatMapValues(lambda l: l).flatMapValues(lambda l: l).map(lambda l: ((l[0],l[1][0]),l[1][1])).\
groupByKey().map(lambda x : (x[0], list(x[1]))).\
map(lambda l: (l[0],sorted(l[1]))).map(lambda l: (l[0], l[1][int(len(l[1])/2)])).\
map(lambda l: (l[0][0],(l[0][1], l[1])))
if debugMode == True:
imapRdd.saveAsTextFile(output+"\\Stage-3__IMAP-Stage-2_imgIdx-" + str(imgIndex))
imapRdd = imapRdd.groupByKey().map(lambda x : (x[0], sorted(list(x[1]),key = lambda k: k[0])))
if debugMode == True:
imapRdd.saveAsTextFile(output+"\\Stage-3__IMAP-Stage-3_imgIdx-" + str(imgIndex))
imapDict = imapRdd.collectAsMap()
iMapImageDict[imgIndex] = imapDict
iMapDictBD = sc.broadcast(iMapImageDict)
refImageIdBD=sc.broadcast(refImgId)
def CalcMapValue(pix, iMapVal):
pImgIdx = pix[0]
residual=0
for iIdx in iMapVal:
if pImgIdx == iIdx[0]:
residual = int(iIdx[1]) - pix[1]
break
return (pix[0], residual)
def ApplyIMap(l):
voxel = l[1]
refIntensity = l[1][refImageIdBD.value][1]
iMapValues= iMapDictBD.value[refImageIdBD.value][refIntensity]
resdualValues=[]
for pixel in voxel:
if pixel[0] != refImageIdBD.value:
resdualValues.append( CalcMapValue(pixel, iMapValues) )
else:
resdualValues.append( pixel )
return (l[0], resdualValues)
diffFVRdd = temporalVoxelRdd.map(lambda l: ApplyIMap(l))
if debugMode == True:
diffFVRdd.saveAsTextFile(output+"\\Stage-4__Diff-Stage-1")
residualImages = diffFVRdd.flatMapValues(lambda l: l).map(lambda l: ((l[1][0], l[0][0]),(l[0][1],l[1][1])))\
.groupByKey().map(lambda x : (x[0], sorted(list(x[1]),key = lambda k: (k[0]))))\
.map(lambda l: (l[0][0],(l[0][1],l[1])))\
.groupByKey().map(lambda x : (x[0], sorted(list(x[1]),key = lambda k: (k[0]))))\
.map(lambda l: (l[0], removeRCIndex(l[1])))
residualImages.saveAsTextFile(output+"\\"+diffImageFolder)
for ingIdx in range(totImages):
iMapImageDict[str(ingIdx)] = dict([(str(k), str(v)) for k, v in iMapImageDict[ingIdx].items()])
del iMapImageDict[ingIdx]
import json
with open(output + "\\imap.json",'w') as f:
json.dump(iMapImageDict,f)
with open(output + '\\conf.json', 'w') as f:
json.dump(confObj, f)
# clean up
sc.stop()
en=datetime.now()
td = en-st
print td