def wannier_zeeman(self, data, taxis=0, xaxis=1, zaxis=2):
temp = sp.sum(data, axis=zaxis)
temp = fftpack.fft(temp, axis=taxis)
sp.save("freq_x.npy", temp)
temp = fftpack.fft(temp, axis=xaxis)
sp.save("disp.npy", temp)
return 0
def execute(self, nprocesses=1):
params = self.params
params['transfer_redshift(1)'] = params['transfer_redshift']
inifilename= params['output_root'] + 'params.ini'
inifile = open(inifilename, 'w')
#parse_ini.write_params(params, inifile ,prefix='')
try:
for key in params:
print >>inifile, '{0} = {1}'.format(key, params[key])
finally: inifile.close()
cambpath = os.getenv('CAMBPATH') + 'camb'
os.system(cambpath + ' ' + inifilename)
P = []
k = []
fname = params['output_root'] + '_matterpower.dat'
f = open(fname, 'r')
data = f.readlines()
for line in data:
line = line.split()
k.append(line[0])
P.append(line[1])
f.close()
PK = np.ndarray(shape=(2,len(k)))
PK[0] = k
PK[1] = P
sp.save(params['output_root']+'PKcamb', PK)
def create_fft(fn): sample_rate, X = scipy.io.wavfile.read(fn) fft_features = abs(scipy.fft(X)[:1000]) base_fn, ext = os.path.splitext(fn) data_fn = base_fn + ".fft" scipy.save(data_fn, fft_features)
def run_training(self, train_data=None, train_size=None,
val_data=None, val_size=None,
batch_size=1024, nb_epoch=100):
if not train_data:
logger.error('No training data provided')
early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=0, mode='auto')
with K.tf.device(self._keras_device):
self._model.compile(loss='mse', optimizer='Adagrad')
history_object = self._model.fit_generator(train_data, steps_per_epoch=train_size,
validation_data=val_data, validation_steps=val_size,
nb_epoch=nb_epoch)
logger.info("... Training complete, saved as " + 'model_' + self._model_name + '.h5')
self._model.save('model_' + self._model_name + '.h5')
print(history_object.history.keys())
# Plot the training and validation loss for each epoch
history_ = history_object.history
print(history_.keys())
sp.save('history_obj_origin.npy', history_)
plt.figure()
plt.plot(history_object.history['loss'],'-o')
plt.plot(history_object.history['val_loss'],'-o')
plt.title('Mean Squared Error of model ...')
plt.ylabel('mean squared error loss')
plt.xlabel('epoch')
plt.legend(['training set', 'validation set'], loc='upper right')
plt.ylim([0, 0.1])
plt.savefig('training.png')
plt.show()
def wrapper(self, save=None, **kwargs):
state = func(self)
pathdir = os.path.dirname(save)
mkdir(pathdir)
self.logger.info('Saving {} to {}.'.format(self.__class__.__name__,
save))
scipy.save(save, state)
def create_fft(fn):
print(fn)
sample_rate, X = scipy.io.wavfile.read(fn)
fft_features = abs(scipy.fft(X)[:1000])
base_fn, ext = os.path.splitext(fn)
data_fn = base_fn + ".fft"
scipy.save(data_fn, fft_features)
def save_task_data(self, taskdata_path, data, task_id, parent_id):
dlist = [
task_id, parent_id, data['parent_path'], data['branch_num'],
data['t_0'], data['t_1'], data['t_max'], data['state'],
data['coeff'], data['num_children']
]
sp.save(taskdata_path, sp.array(dlist, dtype=object))
def write_file(xs, ys, name):
assert len(xs) == len(ys)
fname = fname_template % (name, )
# with open(fname, 'w') as fh:
# for x, y in zip(xs, ys):
# fh.write('%s\t%s\n' %(repr(x), repr(y)))
scipy.save(fname + '_x', xs)
scipy.save(fname + '_y', ys)
def save_marks(self, event):
"""
Save the current marks to file
"""
extension = os.path.splitext(self.ecgfile)[1]
marks_filename = self.ecgfile.rstrip(extension) + '.ann'
scipy.save(marks_filename, self.model.marks)
print 'saved to ', marks_filename
def ovf_to_npy(self, dst, ovfs=[]):
"""Load the data from multiple .ovf files into memory, then save them into a single .npy file"""
data = []
for ovf in tqdm(ovfs):
data.append(self.read_ovf(ovf, target='data'))
sp.concatenate(data, axis=-1)
sp.save(dst, data)
return 0
def save_marks(self, event):
"""
Save the current marks to file
"""
extension = os.path.splitext(self.ecgfile)[1]
marks_filename = self.ecgfile.rstrip(extension) + '.ann'
scipy.save(marks_filename, self.model.marks)
print 'saved to ', marks_filename
def write_file(xs, ys, name):
assert len(xs) == len(ys)
fname = fname_template %(name,)
# with open(fname, 'w') as fh:
# for x, y in zip(xs, ys):
# fh.write('%s\t%s\n' %(repr(x), repr(y)))
scipy.save(fname+'_x', xs)
scipy.save(fname+'_y', ys)
def RecordProbs(bitstring, density, fname, rpath, outinfo):
"""
Record the final-state probabilities.
"""
path = rpath + outinfo['outdir'] + '/' + fname
if outinfo['binary']:
sp.save(path, density)
else:
sp.savetxt(path, density)
def RecordEigSpec(eigspec, outdir, binary):
"""
Output eigenspectrum to data file.
"""
eigpath = os.path.dirname(os.path.realpath(__file__)) + "/" + outdir + "/eigenspectrum.dat"
if binary:
sp.save(eigpath, eigspec)
else:
sp.savetxt(eigpath, eigspec)
def RecordProbs(bitstring, density, fname, rpath, outinfo):
"""
Record the final-state probabilities.
"""
path = rpath + outinfo["outdir"] + "/" + fname
if outinfo["binary"]:
sp.save(path, density)
else:
sp.savetxt(path, density)
def RecordFidelity(fidelity, outdir, binary):
"""
Output fidelity to data file.
"""
path = os.path.dirname(os.path.realpath(__file__)) + "/" + outdir + "/fidelity.dat"
if binary:
sp.save(path, fidelity)
else:
sp.savetxt(path, fidelity)
def RecordOverlap(overlap, outdir, T, binary):
"""
Output overlap (in time) to data file.
"""
path = os.path.dirname(os.path.realpath(__file__)) + "/" + outdir + "/overlap" + str(T) + ".dat"
if binary:
sp.save(path, overlap)
else:
sp.savetxt(path, overlap)
def make_raw_signal_around_genes(in_files, _, out_pattern, binsize=50, windowsize=20000):
"""Use expression data to sort raw read signal for several datasets sorting by expression data
Regions must be associated with
"""
#from hts_waterworks.visualize import bed_to_bedgraph, bedgraph_to_bigwig
#in_expression, in_genes, in_other_reads = (in_files[0], in_files[1],
# in_files[2:])
in_expression, in_genes, in_bigwigs = (in_files[0], in_files[1],
in_files[2:])
# parse gene expression values
gene_expr = {}
for line in [line for line in open(in_expression) if "N/A" not in line]:
try:
gene_id, expr_val = line.strip().split('\t')
except:
continue
else:
gene_expr[gene_id] = expr_val
gene_expr_sorted = sorted(gene_expr.items(), key=lambda x:float(x[1]))
# gather gene positions
gene_positions = {}
for line in open(in_genes):
chrom, start, stop, name, score, strand = line.strip().split('\t')
gene_positions[name] = (chrom, int(start), int(stop))
sp.save(out_pattern % 'gene_expr', sp.array([e[1] for e in gene_expr_sorted if e[0] in gene_positions]))
for in_wig_name in in_bigwigs:
in_wig = bigwig_file.BigWigFile(open(in_wig_name))
read_density = sp.zeros((windowsize // binsize, len(gene_expr)))
for i, (genename, expr) in enumerate(gene_expr_sorted):
try:
chrom, start, stop = gene_positions[genename]
except KeyError:
print 'skipping', genename
continue
#print genename
start = max(0, start - windowsize // 2)
stop = start + windowsize
#density_by_base = in_wig.get_as_array(chrom, start, stop)
#if density_by_base is None:
# for j in xrange(windowsize // binsize):
# read_density[j,i] = 0
#else:
# density_by_base = sp.ma.masked_invalid(density_by_base)
# for j in xrange(windowsize // binsize):
# read_density[j,i] = sp.ma.compressed(density_by_base[j*binsize:(j+1)*binsize]).sum()
print chrom, start, stop
reads_here = in_wig.get(chrom, start, stop)
if reads_here is None:
continue
for j in xrange(windowsize // binsize):
#reads_here = in_wig.get(chrom, start + j * binsize, start + (j+1) * binsize)
start_bin = start + j*binsize
stop_bin = start + (j+1) * binsize
read_density[j,i] = sum(l[2] for l in reads_here if start_bin <= (l[0] + l[1]) / 2 <= stop_bin)
sp.save(out_pattern % in_wig_name, read_density)
def RecordFidelity(fidelity, outdir, binary):
"""
Output fidelity to data file.
"""
path = os.path.dirname(os.path.realpath(__file__)) + "/" + \
outdir + "/fidelity.dat"
if binary:
sp.save(path, fidelity)
else:
sp.savetxt(path, fidelity)
def calc_dispersion_npy(self, src, dst, axis=1):
data = sp.load(src)
background = data[0, :, :, :, :]
data = data - background[None, :, :, :, :]
disp = sp.sum(sp.absolute(
fftpack.fftshift(fftpack.fft2(data, axes=(0, axis)),
axes=(0, axis))),
axis=tuple([a for a in range(5) if a not in (axis, 0)]))
sp.save(dst, disp)
return 0
def full_image(i=None):
global _full
if _full is None:
path = Path('cache/full.npy')
if not path.exists():
ims = [_full_image(i) for i in range(1, COUNTS['full'] + 1)]
sp.save(path, sp.stack(ims))
_full = sp.load(path)
ims = _full[i - 1] if i is not None else _full
return ims
def saveNpy(filename):
print("============saveNpy============:"+filename)
if filename.find('.txt')!=-1: filename = filename.split('.txt')[0]
f = open(filename+'.txt', 'r')
data = np.loadtxt(f)
x = data[:,:-1]
y = data[:,-1]
sp.save(filename+'_data.npy', x.data)
sp.save(filename+'_target.npy', y.data)
def RecordOverlap(overlap, outdir, T, binary):
"""
Output overlap (in time) to data file.
"""
path = os.path.dirname(os.path.realpath(__file__)) + "/" + \
outdir + "/overlap" + str(T) + ".dat"
if binary:
sp.save(path, overlap)
else:
sp.savetxt(path, overlap)
def RecordEigSpec(eigspec, outdir, binary):
"""
Output eigenspectrum to data file.
"""
eigpath = os.path.dirname(os.path.realpath(__file__)) + "/" + \
outdir + "/eigenspectrum.dat"
if binary:
sp.save(eigpath, eigspec)
else:
sp.savetxt(eigpath, eigspec)
def save_state(self, file):
"""Saves the parameter tensors self.A to a file.
Uses numpy binary format.
Parameters
----------
file ; path or file
The file to save the state into.
"""
sp.save(file, self.A)
def save_state(self, file):
"""Saves the parameter tensors self.A to a file.
Uses numpy binary format.
Parameters
----------
file ; path or file
The file to save the state into.
"""
sp.save(file, self.A)
def regjac(NRadj):
NRadjHalf = NRadj / 2
mpr = sp.load('mprior.npy')
Npr = len(mpr)
Nregsmooth = 2 * 15820 # 2 times the number of connections between adjustable rock-types
Nreglocalxz = 8000 # Number of adjustable rock-types
Nreg = Nregsmooth + Nreglocalxz + Npr
sp.save('Nreg.npy', Nreg)
rJac = lil_matrix((Nreg, NRadj))
x = 0
# Create horizontal smoothing of log10kx (perm index 1):
for i in range(0, 80):
for j in range(0, 99):
rJac[x, j + i * 100] = 1
rJac[x, j + i * 100 + 1] = -1
x += 1
# Create vertical smoothing of log10kx (perm index 1):
for i in range(0, 79):
for j in range(0, 100):
rJac[x, j + i * 100] = 1
rJac[x, j + (i + 1) * 100] = -1
x += 1
# Create horizontal smoothing of log10kz (perm index 3):
for i in range(0, 80):
for j in range(0, 99):
rJac[x, j + i * 100 + NRadjHalf] = 1
rJac[x, j + i * 100 + 1 + NRadjHalf] = -1
x += 1
## Create vertical smoothing of log10kz (perm index 3):
for i in range(0, 79):
for j in range(0, 100):
rJac[x, j + i * 100 + NRadjHalf] = 1
rJac[x, j + (i + 1) * 100 + NRadjHalf] = -1
x += 1
# Add regularization to make log10kx similar to log10kz:
for i in range(0, Nreglocalxz):
rJac[x, i] = 1
rJac[x, i + NRadjHalf] = -1
x += 1
# Add prior paramater regularisation:
for i in range(0, Npr):
rJac[x, i] = 1 * 0.001
x += 1
return csr_matrix(rJac)
def quadrant_image(i=None):
global _quadrant
if _quadrant is None:
path = Path('cache/quadrant.npy')
if not path.exists():
ims = [
_quadrant_image(i) for i in range(1, COUNTS['quadrant'] + 1)
]
sp.save(path, sp.stack(ims))
_quadrant = sp.load(path)
ims = _quadrant[i - 1] if i is not None else _quadrant
return ims
def get_train_wider_calib_data(n=None, k=12):
'''
for calibration net
return X - features
y - labels
cnt - count of examples
'''
X, y = [], []
sn = (0.83, 0.91, 1.0, 1.10, 1.21)
xn = (-0.17, 0.0, 0.17)
yn = (-0.17, 0.0, 0.17)
prod = [e for e in itertools.product(sn, xn, yn)]
inv_calib = lambda i, j, h, w, n: [
round(i - (-prod[n][1] * w / (prod[n][0]**-1))),
round(j - (-prod[n][2] * h / (prod[n][0]**-1))),
round(h / prod[n][0]**-1),
round(w / prod[n][0]**-1)
]
suff = str(k)
X_name = 'train_data_icalib_' + suff + '.npy'
y_name = 'labels_icalib_' + suff + '.npy'
root = 'F:\\Datasets\\image_data_sets\\faces\\WIDERFace\\'
pattern = "*.jpg"
bboxs = Datasets.load_wider_face(
os.path.join(root, 'wider_face_split', 'wider_face_train_v7.mat'))
for path, subdirs, files in os.walk(root, 'WIDER_train'):
for iname in files:
if fnmatch(iname, pattern):
ipath = os.path.join(path, iname)
img = fr.get_frame(ipath)
H, W = img.shape[:2]
bbox_list = bboxs[iname[:-4]]
for bbox in bbox_list:
label = sp.random.randint(0, 45)
i, j, h, w = [
int(e) for e in inv_calib(bbox[1], bbox[0],
bbox[2], bbox[3], label)
]
face = fr.get_patch(img, i, j, (h, w))
face_r, good_example = Datasets.sample_resize(face, k)
if good_example:
#print('orig:',bbox[1],bbox[0],bbox[2],bbox[3])
#print('inv_calib:',i,j,h,w)
vec_icalib = fr.frame_to_vect(face_r)
X.append(vec_icalib)
y.append(label)
print('face calib:', label, ipath)
y = sp.array(y)
sp.save(y_name, y)
X = sp.array(X)
sp.save(X_name, X)
return X, y
def save(self, dirname):
import json
params = {
"alpha": self.alpha,
"beta": self.beta,
"n_topics": self.n_topics,
"n_docs": self.n_docs,
"n_words": self.n_words,
"B": self.B
}
json.dump(params, open("{}/params.json".format(dirname), "wb"))
sp.save("{}/phi.npy".format(dirname), self.phi)
sp.save("{}/theta.npy".format(dirname), self.theta)
def StateOverlapOutput(t, outinfo, psi):
"""
Output the overlap with psi and a specified state at some timestep.
"""
# Fix up probabilities
idx = sp.array(outinfo["stateoverlap"], dtype=int)[:, 0]
probs = sp.power(abs(psi[idx]), 2).ravel()
# Write to file
fname = outinfo["outdir"] + "/state_overlap_T" + str(t) + ".txt"
if outinfo["binary"]:
sp.save(fname, probs)
else:
sp.savetxt(fname, probs)
def StateOverlapOutput(t, outinfo, psi):
"""
Output the overlap with psi and a specified state at some timestep.
"""
# Fix up probabilities
idx = sp.array(outinfo['stateoverlap'], dtype=int)[:, 0]
probs = sp.power(abs(psi[idx]), 2).ravel()
# Write to file
fname = outinfo['outdir'] + "/state_overlap_T" + str(t) + ".txt"
if outinfo['binary']:
sp.save(fname, probs)
else:
sp.savetxt(fname, probs)
def getTFIDF(path):
texts=[]
processDir(path,defaultS,defaultW,texts)
word,weight=caculateTFIDF.caculateTFIDF(texts)
fp=codecs.open('tfidf_words.txt','w','utf-8')
json.dump(texts,fp)
fp.close()
sp.save('tfidf_weight.npy',weight)
return word,weight
def save_state(self, file_name, userdata=None):
tosave = sp.empty((9), dtype=sp.ndarray)
tosave[0] = self.A
tosave[1] = self.l[0]
tosave[2] = self.uni_l.r
tosave[3] = self.uni_l.K_left
tosave[4] = self.r[self.N]
tosave[5] = self.uni_r.l
tosave[6] = self.uni_r.K
tosave[7] = sp.array([[self.grown_left, self.grown_right],
[self.shrunk_left, self.shrunk_right]])
tosave[8] = userdata
sp.save(file_name, tosave)
def save_state(self, file_name, userdata=None):
tosave = sp.empty((9), dtype=sp.ndarray)
tosave[0] = self.A
tosave[1] = self.l[0]
tosave[2] = self.uni_l.r
tosave[3] = self.uni_l.K_left
tosave[4] = self.r[self.N]
tosave[5] = self.uni_r.l
tosave[6] = self.uni_r.K
tosave[7] = sp.array([[self.grown_left, self.grown_right],
[self.shrunk_left, self.shrunk_right]])
tosave[8] = userdata
sp.save(file_name, tosave)
def execute(self, nprocesses=1):
params = self.params
boxshape = params['boxshape']
boxunit = params['boxunit']
resultf = params['hr'][0]
if len(params['last']) != 0:
resultf = resultf + params['last'][0]
resultf = resultf + '-' + params['hr'][1]
if len(params['last']) != 0:
resultf = resultf + params['last'][1]
FKPweight = params['FKPweight']
in_root = params['input_root']
out_root = params['output_root']
mid = params['mid']
fkpp = params['FKPpk']
WindowF_fname = out_root+'WindowF_'+\
str(boxshape[0])+'x'+str(boxshape[1])+'x'+\
str(boxshape[2])+'x'+str(boxunit)+'_'+resultf
kWindowF_fname = out_root+'k_WindowF_'+\
str(boxshape[0])+'x'+str(boxshape[1])+'x'+\
str(boxshape[2])+'x'+str(boxunit)+'_'+resultf
print WindowF_fname
try:
WindowF = sp.load(WindowF_fname+'.npy')
k = sp.load(kWindowF_fname+'.npy')
except IOError:
print '\tWindow Functin ReMake'
WindowF, k = self.GetWindowFunctionData()
non0 = WindowF.nonzero()
sp.save(WindowF_fname, WindowF)
sp.save(kWindowF_fname, k)
#txtf = open(out_root+'window_for_idl.txt', 'w')
#try:
# for i in range(len(WindowF)):
# if WindowF[i]==0: continue
# print >>txtf, '{0} {1}'.format(k[i], WindowF[i])
#finally:
# txtf.close()
return WindowF, k
def bumpmaps(category, number=None):
"""Uses a NN to generate a perfect image, and caches the result so it'll be fast to load next time"""
if number is None:
return sp.stack([
bumpmaps(category, n)
for n in tqdm(range(1, tools.COUNTS[category] + 1))
])
path = Path(f'cache/nn/output/{category}/{number}.npy')
if not path.exists():
path.parent.mkdir(exist_ok=True, parents=True)
losses, model = load(*MODEL)
bumps = evaluate(category, number, model)[1]
sp.save(path, bumps)
return sp.load(path)
def wifi_info2csv(datas, names, shop_info, root_path):
assert isinstance(datas, list)
assert isinstance(names, list)
mall_ids = shop_info.mall_id.unique()
if os.path.exists(root_path)==False:
os.mkdirs(root_path, mode=0o777)
for _data in datas:
if "basic_wifi_info" not in _data.columns:
preprocess_basic_wifi(_data)
for _mall_id in mall_ids:
print _mall_id
part_datas = [_data[_data.mall_id == _mall_id] for _data in datas]
sorted_wifi = get_sorted_wifi(part_datas)
df = pd.DataFrame(
{
"wifi_name": [wifi[0] for wifi in sorted_wifi],
"wifi_num": [wifi[1] for wifi in sorted_wifi]
}
)
df.index.name = "wifi_rank"
df.to_csv(os.path.join(root_path, "{}_rank.csv".format(_mall_id)))
d = rank_sorted_wifi(sorted_wifi)
for _part_data, name in zip(part_datas, names):
wifi_matrix = np.zeros((_part_data.shape[0], len(sorted_wifi)))
use_wifi_str = []
_part_data.loc[:, "i_index"] = range(_part_data.shape[0])
_part_data[["basic_wifi_info", "i_index"]].apply(
lambda x: basic_wifi_map2matrix(x, wifi_matrix, d, use_wifi_str), axis=1)
a = np.asarray(use_wifi_str)
# 用csv 存读取很慢, 将index, usewifi 和matrix 分开存
np.save(os.path.join(root_path,
"{}_{}_index.csv".format(name, _mall_id)),
_part_data.index)
np.save(os.path.join(root_path,
"{}_{}_use_wifi".format(name, _mall_id)),
a)
# 用稀疏矩阵存取
x = sp.csc_matrix(wifi_matrix)
scipy.save(os.path.join(root_path,
"{}_{}_matrix".format(name, _mall_id)),
x)
def wifi_info2csv_just_train(train, test):
if not os.path.exists("../data/wifi_info_cache2"):
os.mkdir("../data/wifi_info_cache2")
shop_info = load_shop_info()
mall_ids = shop_info.mall_id.unique()
for _data in [train, test]:
if "basic_wifi_info" not in _data.columns:
preprocess_basic_wifi(_data)
for _mall_id in mall_ids:
print _mall_id
train_mall = train[train.mall_id == _mall_id]
test_mall = test[test.mall_id == _mall_id]
sorted_wifi = get_sorted_wifi_just_train(train_mall, test_mall)
df = pd.DataFrame({
"wifi_name": [wifi[0] for wifi in sorted_wifi],
"wifi_num": [wifi[1] for wifi in sorted_wifi]
})
df.index.name = "wifi_rank"
df.to_csv("../data/wifi_info_cache2/{}_rank.csv".format(_mall_id))
d = rank_sorted_wifi(sorted_wifi)
for _part_data, name in zip([train_mall, test_mall],
["train", "test"]):
wifi_matrix = np.zeros((_part_data.shape[0], len(sorted_wifi)))
# wifi_matrix[:] = -115
use_wifi_str = []
_part_data.loc[:, "i_index"] = range(_part_data.shape[0])
_part_data[["basic_wifi_info",
"i_index"]].apply(lambda x: basic_wifi_map2matrix(
x, wifi_matrix, d, use_wifi_str),
axis=1)
a = np.asarray(use_wifi_str)
# 用csv 存读取很慢, 将index, usewifi 和matrix 分开存
np.save(
"../data/wifi_info_cache2/{}_{}_index".format(name, _mall_id),
_part_data.index)
np.save(
"../data/wifi_info_cache2/{}_{}_use_wifi".format(
name, _mall_id), a)
# 用稀疏矩阵存取
x = sp.csc_matrix(wifi_matrix)
scipy.save(
"../data/wifi_info_cache2/{}_{}_matrix".format(name, _mall_id),
x)
def get_train_wider_calib_data(n = None,k = 12):
'''
for calibration net
return X - features
y - labels
cnt - count of examples
'''
X,y = [],[]
sn = (0.83, 0.91, 1.0, 1.10, 1.21)
xn = (-0.17, 0.0, 0.17)
yn = (-0.17, 0.0, 0.17)
prod = [e for e in itertools.product(sn,xn,yn)]
inv_calib = lambda i,j,h,w,n: [ round(i-(-prod[n][1]*w/(prod[n][0]**-1))),round(j-(-prod[n][2]*h/(prod[n][0]**-1))),round(h/prod[n][0]**-1),round(w/prod[n][0]**-1) ]
suff = str(k)
X_name = 'train_data_icalib_'+ suff + '.npy'
y_name = 'labels_icalib_'+ suff + '.npy'
root = 'F:\\Datasets\\image_data_sets\\faces\\WIDERFace\\'
pattern = "*.jpg"
bboxs = Datasets.load_wider_face(os.path.join(root,'wider_face_split','wider_face_train_v7.mat'))
for path, subdirs, files in os.walk(root,'WIDER_train'):
for iname in files:
if fnmatch(iname, pattern):
ipath = os.path.join(path, iname)
img = fr.get_frame(ipath)
H,W = img.shape[:2]
bbox_list = bboxs[iname[:-4]]
for bbox in bbox_list:
label = sp.random.randint(0,45)
i,j,h,w = [int(e) for e in inv_calib(bbox[1],bbox[0],bbox[2],bbox[3],label)]
face = fr.get_patch(img,i,j,(h,w))
face_r,good_example = Datasets.sample_resize(face,k)
if good_example:
#print('orig:',bbox[1],bbox[0],bbox[2],bbox[3])
#print('inv_calib:',i,j,h,w)
vec_icalib = fr.frame_to_vect(face_r)
X.append(vec_icalib)
y.append(label)
print('face calib:',label,ipath)
y = sp.array(y)
sp.save(y_name,y)
X = sp.array(X)
sp.save(X_name,X)
return X,y
def create_fft(directory):
print(directory)
#os.system("mkdir "+directory+"wav")
for filename in os.listdir(directory):
print(filename)
base_fn, ext = os.path.splitext(filename)
if ext == ".wav":
sample_rate, X = wavfile.read(directory + filename)
fft_features = abs(scipy.fft(X)[:5000])
data_filename = directory_fft + "/" + folder + "/" + filename + ".fft"
# Create target Directory if don't exist
if not os.path.exists(directory_fft + "/" + folder):
os.mkdir(directory_fft + "/" + folder)
print("Directory ", directory_fft + "/" + folder, " Created ")
print(data_filename)
scipy.save(data_filename, fft_features)
print("File", data_filename, "saved")
def main():
"""
朴素贝叶斯实现
"""
# 加载数据
movies_reviews = load_files("./data/tokens")
sp.save('./data/movie_data.npy', movies_reviews.data)
sp.save('./data/movie_target.npy', movies_reviews.target)
movie_data = sp.load('./data/movie_data.npy')
movie_target = sp.load('./data/movie_target.npy')
x = movie_data
y = movie_target
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
count_vec = TfidfVectorizer(binary=False, decode_error='ignore', stop_words="english")
# 训练数据
x_train = count_vec.fit_transform(x_train)
x_test = count_vec.transform(x_test)
# 分类器
clf = MultinomialNB().fit(x_train, y_train)
# doc_pred = clf.predict(x_test)
# print("平均值：", np.mean(doc_pred == y_test))
# 可用 clf.score 代替以上均值
score = clf.score(x_test, y_test)
print("score:",score)
# 准确率 召回率
precision, recall, thresholds = precision_recall_curve(
y_test, clf.predict(x_test))
answer = clf.predict_proba(x_test)[:, 1]
report = answer > 0.5
print(classification_report(y_test, report, target_names=['net', 'pos']))
# 特征名称
# print(count_vec.get_feature_names())
# 保存模型
model_path = "./models/clf_bayes.model"
joblib.dump(clf, model_path, compress=0)
def execute(self, nprocesses=1) :
params = self.params
model = params["model"]
kiyopy.utils.mkparents(params['output_root'])
parse_ini.write_params(params, params['output_root'] + 'params.ini',
prefix=prefix)
# Loop over files to process.
for file_middle in params['file_middles'] :
input_fname = (params['input_root'] + file_middle +
params['input_end'])
Reader = core.fitsGBT.Reader(input_fname, feedback=self.feedback)
output_fname = params["output_root"] + file_middle + ".npy"
if model == "scan_var" :
n_scans = len(Reader.scan_set)
n_IFs = len(Reader.IF_set)
first_block = True
for jj in range(n_IFs) :
# These all become arrays on the first iteration.
var = 0.0
mean = 0.0
counts = 0
for ii in range(n_scans) :
Data = Reader.read(ii, jj)
if first_block :
out_shape = (n_IFs,) + Data.dims[1:]
out_arr = sp.empty(out_shape, dtype=float)
first_block = False
var += ma.sum(Data.data**2, 0).filled(0)
mean += ma.sum(Data.data, 0).filled(0)
counts += ma.count(Data.data, 0)
# If we didn't get at least 5 good hits, throw aways the
# scan.
counts[counts < 5] = -1
var = var/counts - (mean/counts)**2
var[counts < 5] = 1.0e10
out_arr[jj, ...] = var
sp.save(output_fname, out_arr)
if self.feedback > 1 :
print ("Wrote noise parameters to file: "
+ utils.abbreviate_file_path(output_fname))
else :
raise ValueError("Invalid noise model: " + model)
def main(A, ktrunc, matName, subfolder, inneriter, Tvec):
print 'Tests using matrix : ', matName
if matName[0:7] == 'SDlarge':
RunOracle = False
else:
RunOracle = True
# Save sketch budgets Tvec:
sp.save(subfolder + '/' + 'Tvec.npy', Tvec)
# Evaluate Frobenius norm difference between A and optimal rank-p approximation:
FrobOptRankP = optFrobErrRankp(A, ktrunc)
temptime = time.clock()
# Evaluate performance of baseline 1-view method proposed by
# Tropp et al. (2017) (Algorithm 7 in Bjarkason (2018) with ellCut = ell1):
print '####################################################################'
print 'Running baseline Tropp 1-view experiments'
print '####################################################################'
runStandard1viewTroppTests(A, ktrunc, matName, subfolder, inneriter, Tvec,
FrobOptRankP, RunOracle)
print matName, 'Time spent on baseline Tropp', time.clock() - temptime
temptime = time.clock()
# Evaluate performance of 1-view Algorithm 7 in Bjarkason (2018)
# using ellCut and ell1=ell2 (sometimes ell1=ell2-1):
print '####################################################################'
print 'Running Lcut Alg. 7 1-view experiments'
print '####################################################################'
runLcut1viewTroppTests(A, ktrunc, matName, subfolder, inneriter, Tvec,
FrobOptRankP, RunOracle)
print matName, 'Time spent on Lcut Alg. 7', time.clock() - temptime
temptime = time.clock()
# Evaluate performance of extended SRFT schemes, see Section 6.7:
print '####################################################################'
print 'Running Bwz 1-view experiments'
print '####################################################################'
run1viewBwzTests(A, ktrunc, matName, subfolder, inneriter, Tvec,
FrobOptRankP, RunOracle)
print matName, 'Time spent on Bwz', time.clock() - temptime
def get_train_data(n_pos=31929, n_neg=164863, k=12):
'''
megre positive and negative examples
'''
suff = str(k)
X_name = 'train_data_' + suff + '.npy'
y_name = 'labels_' + suff + '.npy'
if not (os.path.exists(X_name) and os.path.exists(y_name)):
X_train_face, y_train_face = Datasets.get_train_face_wider_data(
k=k)
#X_pos = X_train_face[y_train_face==1]
X_pos = X_train_face
X_aflw, y_train_face_aflw = Datasets.get_aflw_face_data(k=k)
X_pos = sp.vstack([X_pos, X_aflw])
X_train_non_face, y_train_non_face = Datasets.get_train_non_face_data(
k=k)
print('c1_pos:', len(X_pos))
if len(X_train_face[y_train_face == 0]) > 0:
X_neg = sp.vstack(
(X_train_face[y_train_face == 0], X_train_non_face))
else:
X_neg = X_train_non_face
X_pos = shuffle(X_pos, random_state=42)
X_neg = shuffle(X_neg, random_state=42)
X_pos = X_pos[:n_pos]
X_neg = X_neg[:n_neg]
n_neg = len(X_neg)
n_pos = len(X_pos)
y_pos = sp.ones(n_pos, int)
y_neg = sp.zeros(n_neg, int)
X = sp.vstack((X_pos, X_neg))
y = sp.hstack((y_pos, y_neg))
X, y = shuffle(X, y, random_state=42)
sp.save(X_name, X)
sp.save(y_name, y)
else:
X = sp.load(X_name)
y = sp.load(y_name)
print("Done", "Positive examples count, Negative exapmples count:",
len(y[y == 1]), len(y[y == 0]))
def save_analysis_objects(self, output_dir, site_tag):
(event_set, event_activity, source_model, sites, motion,
eqrm_flags) = self.create_analysis_objects()
# 2. Save test objects to file
event_set.save(os.path.join(output_dir, '%s_event_set' % site_tag))
event_activity.save(os.path.join(output_dir,
'%s_event_set' % site_tag))
source_model.save(os.path.join(output_dir, '%s_event_set' % site_tag))
sites.save(os.path.join(output_dir, '%s_sites' % site_tag))
# Motion is an numpy.ndarray so save manually
os.mkdir(os.path.join(output_dir, '%s_motion' % site_tag))
save(
os.path.join(output_dir, '%s_motion' % site_tag, 'bedrock_SA.npy'),
motion)
save(
os.path.join(output_dir, '%s_motion' % site_tag,
'atten_periods.npy'), eqrm_flags['atten_periods'])
# ... and eqrm_flags
eqrm_flags_to_control_file(os.path.join(output_dir, 'eqrm_flags.py'),
eqrm_flags)
def save_analysis_objects(self, output_dir, site_tag):
(event_set,
event_activity,
source_model,
sites,
motion,
eqrm_flags) = self.create_analysis_objects()
# 2. Save test objects to file
event_set.save(os.path.join(output_dir, '%s_event_set' % site_tag))
event_activity.save(os.path.join(output_dir, '%s_event_set' % site_tag))
source_model.save(os.path.join(output_dir, '%s_event_set' % site_tag))
sites.save(os.path.join(output_dir, '%s_sites' % site_tag))
# Motion is an numpy.ndarray so save manually
os.mkdir(os.path.join(output_dir, '%s_motion' % site_tag))
save(os.path.join(output_dir, '%s_motion' % site_tag, 'bedrock_SA.npy'),
motion)
save(os.path.join(output_dir, '%s_motion' % site_tag, 'atten_periods.npy'),
eqrm_flags['atten_periods'])
# ... and eqrm_flags
eqrm_flags_to_control_file(os.path.join(output_dir, 'eqrm_flags.py'),
eqrm_flags)
def get_train_data(n_pos = 31929, n_neg = 164863,k=12):
'''
megre positive and negative examples
'''
suff = str(k)
X_name = 'train_data_'+ suff + '.npy'
y_name = 'labels_'+ suff + '.npy'
if not(os.path.exists(X_name) and os.path.exists(y_name)):
X_train_face,y_train_face = Datasets.get_train_face_wider_data(k = k)
#X_pos = X_train_face[y_train_face==1]
X_pos = X_train_face
X_aflw,y_train_face_aflw = Datasets.get_aflw_face_data(k = k)
X_pos = sp.vstack( [X_pos,X_aflw] )
X_train_non_face,y_train_non_face = Datasets.get_train_non_face_data(k = k)
print('c1_pos:',len(X_pos))
if len(X_train_face[y_train_face==0]) > 0:
X_neg = sp.vstack( (X_train_face[y_train_face==0],X_train_non_face) )
else:
X_neg = X_train_non_face
X_pos = shuffle(X_pos,random_state=42)
X_neg = shuffle(X_neg,random_state=42)
X_pos = X_pos[:n_pos]
X_neg = X_neg[:n_neg]
n_neg = len(X_neg)
n_pos = len(X_pos)
y_pos = sp.ones(n_pos,int)
y_neg = sp.zeros(n_neg,int)
X = sp.vstack((X_pos,X_neg))
y = sp.hstack( (y_pos,y_neg) )
X,y = shuffle(X,y,random_state=42)
sp.save(X_name,X)
sp.save(y_name,y)
else:
X = sp.load(X_name)
y = sp.load(y_name)
print("Done","Positive examples count, Negative exapmples count:",len(y[y==1]),len(y[y==0]))
def save_andor_load_arrays(endog, exog, true_params, save_arrays, load_old_arrays):
if save_arrays:
sp.save("endog.npy", endog)
sp.save("exog.npy", exog)
sp.save("true_params.npy", true_params)
if load_old_arrays:
endog = sp.load("endog.npy")
exog = sp.load("exog.npy")
true_params = sp.load("true_params.npy")
return endog, exog, true_params
def save_andor_load_arrays(
endog, exog, true_params, save_arrays, load_old_arrays):
if save_arrays:
sp.save('endog.npy', endog)
sp.save('exog.npy', exog)
sp.save('true_params.npy', true_params)
if load_old_arrays:
endog = sp.load('endog.npy')
exog = sp.load('exog.npy')
true_params = sp.load('true_params.npy')
return endog, exog, true_params
def splitTrainTestBasedTime(filename):
print("============splitTrainTestBasedTime============:"+filename)
if filename.find('.txt')!=-1: filename = filename.split('.txt')[0]
trainName = filename+'_Train'
f = open(trainName+'.txt', 'r')
data = np.loadtxt(f)
x = data[:, :-1]
y = data[:, -1]
sp.save(trainName+'_data.npy', x.data)
sp.save(trainName+'_target.npy', y.data)
f.close()
testName = filename+'_Test'
f = open(testName+'.txt', 'r')
data = np.loadtxt(f)
x = data[:, :-1]
y = data[:, -1]
sp.save(testName+'_data.npy', x.data)
sp.save(testName+'_target.npy', y.data)
def compute_matrices(base,prefix): """ Computes and saves the matrices from the Singular Value Decomposition (SVD) of the target base : A = U*S*V^t The suffixes "_U.npy", "_S.npy", "_S.npy" are added to the prefix. """ if base.ndim == 3 : ntargets,nvertices,dim = base.shape base = base.reshape(ntargets,nvertices*3) else : ntargets,nvertices = base.shape nvertices/=3 U,S,Vt = svd(base.T,full_matrices = False, overwrite_a = True) #We keep only significant singular values Scum = S.cumsum()/S.sum() save(prefix+'_U',U[:,Scum<1.]) save(prefix+'_S',S) # we still save the full singular values just in case... save(prefix+'_V',Vt.T[:,Scum<1.])
BH = statsmodels.stats.multitest.multipletests(pvals=PVals,
alpha=Alpha,
method='fdr_bh',
returnsorted=False)
DataDict[Trait]['Alpha_'+str(Alpha)] = Alpha
DataDict[Trait]['AlphaBonf_'+str(Alpha)] = BH[3]
BHPVals = scipy.array(['BHp_value_alpha='+str(Alpha)])
BHPVals = scipy.append(BHPVals,BH[1].astype(str))
Data = scipy.vstack((Data,BHPVals))
BHAccept = scipy.array(['BHAccept_alpha='+str(Alpha)])
BHAccept = scipy.append(BHAccept,BH[0].astype(str))
DataDict[Trait]['GeneSetAtAlpha_'+str(Alpha)] = scipy.compress(condition=BH[0],
a=Genes).tolist()
Data = scipy.vstack((Data,BHAccept))
OutFile = os.path.join('Data',os.path.basename(DecomprFile))
scipy.save(file=OutFile,
arr=Data)
os.system('lbzip2 -f '+OutFile)
print OutFile
fw = open('Data/DataDict.json','w')
json.dump(obj=DataDict,
fp=fw)
fw.close()
os.system('lbzip2 -f Data/DataDict.json')
AllGenesFile = 'Data/UniqGenesOverAllTraits.npy'
scipy.save(file=AllGenesFile,
arr=AllGenes)
os.system('lbzip2 -f '+AllGenesFile)
if(False):
AllGenesFile = 'Data/UniqGenesOverAllTraits.npy'
if('-p' in sys.argv):
fr = open('BackgroundGeneSet.csv','r')
BGSHeader = fr.readline().strip().split('|')
fr.close()
os.system('recode HTML BackgroundGeneSet.csv')
BGSData = scipy.genfromtxt(fname='BackgroundGeneSet.csv',
dtype=str,
comments=None,
delimiter='|',
skip_header=1,
unpack=True)
scipy.save(file='BackgroundGeneSet.npy',
arr=BGSData)
os.system('lbzip2 BackgroundGeneSet.csv')
os.system('lbzip2 BackgroundGeneSet.npy')
if('-e' in sys.argv):
os.system('lbzip2 -d BackgroundGeneSet.csv.bz2')
fr = open('BackgroundGeneSet.csv','r')
BGSHeader = fr.readline().strip().split('|')
fr.close()
os.system('lbzip2 BackgroundGeneSet.csv')
# os. system('lbzip2 -d BackgroundGeneSet.npy.bz2')
BGSData = scipy.load('BackgroundGeneSet.npy')
# os. system('lbzip2 BackgroundGeneSet.npy.bz2')
print BGSHeader
print len(BGSData),len(BGSData[0])
def execute(self, nprocesses=1):
params = self.params
boxshape = params['boxshape']
boxunit = params['boxunit']
resultf = params['hr'][0]
if len(params['last']) != 0:
resultf = resultf + params['last'][0]
resultf = resultf + '-' + params['hr'][1]
if len(params['last']) != 0:
resultf = resultf + params['last'][1]
# Make parent directory and write parameter file.
kiyopy.utils.mkparents(params['output_root'])
parse_ini.write_params(params, params['output_root']+'params.ini',prefix='nl_' )
in_root = params['input_root']
out_root = params['output_root']
cambin_root = params['camb_input_root']
all_out_fname_list = []
all_in_fname_list = []
#### Process ####
kiyopy.utils.mkparents(params['output_root'])
PKcamb_fname = cambin_root + 'PKcamb.npy'
PKcamb = algebra.load(PKcamb_fname)
N = len(params['boxunitlist'])
yy = np.ndarray(shape=(N, 10))
xx = np.ndarray(shape=(N, 10))
for params['boxshape'], params['boxunit'], i\
in zip(params['boxshapelist'], params['boxunitlist'], range(N)):
params['plot'] = False
parse_ini.write_params(params,
params['output_root']+'params.ini',prefix='wd_' )
WindowF, k = \
windowf.WindowFunctionMaker(params['output_root']+'params.ini',
feedback=self.feedback).execute()
if yy.shape[1] != WindowF.shape[0]:
yy.resize((N,WindowF.shape[0]))
xx.resize((N,WindowF.shape[0]))
yy[i] = WindowF.copy()
xx[i] = k.copy()
def chisq(A, y, x, e):
err = (y - windowfunction(x, A))**2/e**2
return err
non0 = yy[0].nonzero()
y = yy[0].take(non0)[0][:-10]
x = xx[0].take(non0)[0][:-10]
non0 = yy[-1].nonzero()
y = np.append(y, yy[-1].take(non0)[0][10:-4])
x = np.append(x, xx[-1].take(non0)[0][10:-4])
err = y.copy()*10.
err[5:] = err[5:]*1.e-8
print x.min(), x.max()
ki = np.logspace(log10(0.01), log10(1.5), num=300)
A1 = 1.
A2 = 1.
A3 = 1.8
A0 = np.array([A1, A2, A3])
A, status = leastsq(chisq, A0, args=(y, x, err), maxfev=20000)
window = windowfunction(PKcamb[0], A)
#boxinf = str(boxshape[0])+'x'\
# +str(boxshape[1])+'x'+str(boxshape[2])+'x'+str(boxunit)
sp.save(out_root+'window_fit_'+resultf, window)
CC = 1.
# CC = romberg(lambda k2: K(ki,k2)*k2*k2, PKcamb[0].min(), PKcamb[0].max())
# # CC = romberg(lambda k2: K(ki,k2)*k2*k2, 1.e-10, 1.e10)
print A
aaa = A[1]*1.e-3
bbb = A[2]*1.e-3
if bbb**4<4*aaa**4:
CC = 1./(pi*bbb*(2.-(bbb/aaa)**2)**(0.5))
def g(x):
return atan((bbb**4 + 2.*aaa**2*x**2)/
(bbb**2*(4.*aaa**4-bbb**4)**0.5))
def K(k1, k2):
return CC/(k1*k2)*(g(k1+k2)-g(k1-k2))
else:
mu = bbb**2*(bbb**4-4.*aaa**4)**0.5
CC = aaa/(pi*2**0.5*((bbb**4+mu)**0.5-(bbb**4-mu)**0.5))
def g(x):
return (mu+bbb**4+2*aaa**2*x**2)/(mu-bbb**4-2*aaa**2*x**2)
def K(k1, k2):
return CC/(k1*k2)*log(g(k1-k2)/g(k1+k2))
#def K(k1,k2):
# uplim = k1+k2
# downlim = np.fabs(k1-k2)
# C = 8*pi**2/(k1*k2)*CC
# return C*romberg(lambda Q: windowfunction(Q,A)*Q, downlim, uplim)
# print CC
P = interp1d(PKcamb[0], PKcamb[1], kind='cubic')
#print PKcamb[0].min(), PKcamb[0].max()
Pnl = np.zeros(len(ki))
Pnl_err = np.zeros(len(ki))
for i in range(len(ki)):
#Pnl[i] = romberg(lambda k1: k1**2*P(k1)*K(k1,ki[i]),
Pnl[i], Pnl_err = quad(lambda k1: k1**2*P(k1)*K(k1,ki[i]),
PKcamb[0].min(), PKcamb[0].max(), limit=200)
#Pnl = sp.load(out_root+'nonlPK_'+resultf+'.npy')
CCC = romberg(lambda k1: k1**2*K(k1, 0.01), ki.min(), ki.max())
print CCC
#Pnl = Pnl/CCC
OmegaHI = params['OmegaHI']
Omegam = params['Omegam']
OmegaL = params['OmegaL']
z = params['z']
a3 = (1+z)**(-3)
Tb = 0.3e-3 * (OmegaHI/1.e-3) * ((Omegam + a3*OmegaL)/0.29)**(-0.5)\
* ((1.+z)/2.5)**0.5
#Pnl = Pnl*(Tb**2)
#PKcamb[1] = PKcamb[1]*(Tb**2)
#print Pnl
sp.save(out_root+'nonlPK_'+resultf, Pnl)
sp.save(out_root+'k_nonlPK_'+resultf, ki)
if self.plot==True:
#plt.figure(figsize=(6,6))
##print k
#plt.subplot('111')
##kj = sp.linspace(0,PKcamb[0][-1], num=500)
##KI = np.zeros(500)
##for j in sp.linspace(ki.min(),ki.max(), num=20):
## for i in range(500):
## KI[i] = K(j, kj[i])
## #plt.plot(kj, KI, label=str(ki))
## plt.plot(kj, KI, 'r-', linewidth=1)
#plt.semilogy()
##plt.loglog()
##plt.ylim(ymin=1.e-0)
#plt.xlim(xmin=0, xmax=ki.max())
#plt.title('Coupling Kernels')
#plt.xlabel('$k$')
#plt.ylabel('$K(k, k_i)$')
##plt.legend()
#plt.savefig(out_root+'Ki.eps', format='eps')
plt.figure(figsize=(8,4))
plt.subplot('111')
#plt.plot(PKcamb[0], window, 'b--', linewidth=1,
# label='Fitted Window Function')
plt.plot(PKcamb[0], PKcamb[1], 'g-', linewidth=1,
label='Camb Power Spectrum')
plt.plot(ki, Pnl, 'r-', linewidth=1,
label='Power Spectrum')
plt.loglog()
plt.xlim(xmin=ki.min(), xmax=ki.max())
plt.legend()
plt.savefig(out_root+'nonlPK.eps', format='eps')
plt.show()
#print 'Finished @_@ '
return PKcamb
def save(self, kernel, R, dx=0, dy=0, **kw):
rescaled = (255.0 * kernel).astype('uint8')
name = "kernels/Airy/kernel.Airy.R%d.dx%1.2f.dy%1.2f" % (R, dx, dy)
image = fromarray(rescaled)
image.save(name+".png")
scipy.save(name+".npy", kernel)
V = potV(X, Y, xyMax, a, b, V0)
# Creating or loading operator matrix
if (platform.system() == 'Windows'):
mPath = 'Operator_Matrices\\delta_' + str(delta) + 'V0_' + str(V0) + '.npy'
else:
mPath = 'Operator_Matrices/delta_' + str(delta) + 'V0_' + str(V0) + '.npy'
try:
M = sp.load(mPath)
print 'Matrix will be loaded'
except:
start = time.time()
print 'Creating operator matrix. Sit back, this may take a while :)'
M = H(X, Y, xyMax, spacing, nm, nmIndexes, V)
sp.save(mPath, M)
print 'Matrix ready'
print 'Took ' + str(time.time() - start) + ' seconds!'
# Calculating eigen energies
values = linalg.eig(M)[0]
values = values[values.argsort()]
e0Array.append(values[0].real)
e1Array.append(values[1].real)
e2Array.append(values[2].real)
e3Array.append(values[3].real)
e4Array.append(values[4].real)
e0Array = sp.array(e0Array)
e1Array = sp.array(e1Array)
e2Array = sp.array(e2Array)
def save_state(self, file):
sp.save(file, self.A)
def execute(self, nprocesses=1) :
"""Worker funciton."""
params = self.params
# Make parent directory and write parameter file.
kiyopy.utils.mkparents(params['output_root'])
parse_ini.write_params(params, params['output_root'] + 'params.ini',
prefix=prefix)
save_noise_diag = params['save_noise_diag']
in_root = params['input_root']
all_out_fname_list = []
all_in_fname_list = []
# Figure out what the band names are.
bands = params['bands']
if not bands:
map_files = glob.glob(in_root + 'dirty_map_' + pol_str + "_*.npy")
bands = []
root_len = len(in_root + 'dirty_map_')
for file_name in map_files:
bands.append(file_name[root_len:-4])
# Loop over files to process.
for pol_str in params['polarizations']:
for band in bands:
if band == -1:
band_str = ''
else:
band_str = "_" + repr(band)
dmap_fname = (in_root + 'dirty_map_' + pol_str +
band_str + '.npy')
all_in_fname_list.append(
kiyopy.utils.abbreviate_file_path(dmap_fname))
# Load the dirty map and the noise matrix.
dirty_map = algebra.load(dmap_fname)
dirty_map = algebra.make_vect(dirty_map)
if dirty_map.axes != ('freq', 'ra', 'dec') :
msg = ("Expeced dirty map to have axes ('freq',"
"'ra', 'dec'), but it has axes: "
+ str(dirty_map.axes))
raise ce.DataError(msg)
shape = dirty_map.shape
# Initialize the clean map.
clean_map = algebra.info_array(sp.zeros(dirty_map.shape))
clean_map.info = dict(dirty_map.info)
clean_map = algebra.make_vect(clean_map)
# If needed, initialize a map for the noise diagonal.
if save_noise_diag :
noise_diag = algebra.zeros_like(clean_map)
if params["from_eig"]:
# Solving from eigen decomposition of the noise instead of
# the noise itself.
# Load in the decomposition.
evects_fname = (in_root + 'noise_evects_' + pol_str +
+ band_str + '.npy')
if self.feedback > 1:
print "Using dirty map: " + dmap_fname
print "Using eigenvectors: " + evects_fname
evects = algebra.open_memmap(evects_fname, 'r')
evects = algebra.make_mat(evects)
evals_inv_fname = (in_root + 'noise_evalsinv_' + pol_str
+ "_" + repr(band) + '.npy')
evals_inv = algebra.load(evals_inv_fname)
evals_inv = algebra.make_mat(evals_inv)
# Solve for the map.
if params["save_noise_diag"]:
clean_map, noise_diag = solve_from_eig(evals_inv,
evects, dirty_map, True, self.feedback)
else:
clean_map = solve_from_eig(evals_inv,
evects, dirty_map, False, self.feedback)
# Delete the eigen vectors to recover memory.
del evects
else:
# Solving from the noise.
noise_fname = (in_root + 'noise_inv_' + pol_str +
band_str + '.npy')
if self.feedback > 1:
print "Using dirty map: " + dmap_fname
print "Using noise inverse: " + noise_fname
all_in_fname_list.append(
kiyopy.utils.abbreviate_file_path(noise_fname))
noise_inv = algebra.open_memmap(noise_fname, 'r')
noise_inv = algebra.make_mat(noise_inv)
# Two cases for the noise. If its the same shape as the map
# then the noise is diagonal. Otherwise, it should be
# block diagonal in frequency.
if noise_inv.ndim == 3 :
if noise_inv.axes != ('freq', 'ra', 'dec') :
msg = ("Expeced noise matrix to have axes "
"('freq', 'ra', 'dec'), but it has: "
+ str(noise_inv.axes))
raise ce.DataError(msg)
# Noise inverse can fit in memory, so copy it.
noise_inv_memory = sp.array(noise_inv, copy=True)
# Find the non-singular (covered) pixels.
max_information = noise_inv_memory.max()
good_data = noise_inv_memory < 1.0e-10*max_information
# Make the clean map.
clean_map[good_data] = (dirty_map[good_data]
/ noise_inv_memory[good_data])
if save_noise_diag :
noise_diag[good_data] = \
1/noise_inv_memory[good_data]
elif noise_inv.ndim == 5 :
if noise_inv.axes != ('freq', 'ra', 'dec', 'ra',
'dec'):
msg = ("Expeced noise matrix to have axes "
"('freq', 'ra', 'dec', 'ra', 'dec'), "
"but it has: " + str(noise_inv.axes))
raise ce.DataError(msg)
# Arrange the dirty map as a vector.
dirty_map_vect = sp.array(dirty_map) # A view.
dirty_map_vect.shape = (shape[0], shape[1]*shape[2])
frequencies = dirty_map.get_axis('freq')/1.0e6
# Allowcate memory only once.
noise_inv_freq = sp.empty((shape[1], shape[2],
shape[1], shape[2]), dtype=float)
if self.feedback > 1 :
print "Inverting noise matrix."
# Block diagonal in frequency so loop over frequencies.
for ii in xrange(dirty_map.shape[0]) :
if self.feedback > 1:
print "Frequency: ", "%5.1f"%(frequencies[ii]),
if self.feedback > 2:
print ", start mmap read:",
sys.stdout.flush()
noise_inv_freq[...] = noise_inv[ii, ...]
if self.feedback > 2:
print "done, start eig:",
sys.stdout.flush()
noise_inv_freq.shape = (shape[1]*shape[2],
shape[1]*shape[2])
# Solve the map making equation by diagonalization.
noise_inv_diag, Rot = sp.linalg.eigh(
noise_inv_freq, overwrite_a=True)
if self.feedback > 2:
print "done",
map_rotated = sp.dot(Rot.T, dirty_map_vect[ii])
# Zero out infinite noise modes.
bad_modes = (noise_inv_diag
< 1.0e-5 * noise_inv_diag.max())
if self.feedback > 1:
print ", discarded: ",
print "%4.1f" % (100.0 * sp.sum(bad_modes)
/ bad_modes.size),
print "% of modes",
if self.feedback > 2:
print ", start rotations:",
sys.stdout.flush()
map_rotated[bad_modes] = 0.
noise_inv_diag[bad_modes] = 1.0
# Solve for the clean map and rotate back.
map_rotated /= noise_inv_diag
map = sp.dot(Rot, map_rotated)
if self.feedback > 2:
print "done",
sys.stdout.flush()
# Fill the clean array.
map.shape = (shape[1], shape[2])
clean_map[ii, ...] = map
if save_noise_diag :
# Using C = R Lambda R^T
# where Lambda = diag(1/noise_inv_diag).
temp_noise_diag = 1/noise_inv_diag
temp_noise_diag[bad_modes] = 0
# Multiply R by the diagonal eigenvalue matrix.
# Broadcasting does equivalent of mult by diag
# matrix.
temp_mat = Rot*temp_noise_diag
# Multiply by R^T, but only calculate the
# diagonal elements.
for jj in range(shape[1]*shape[2]) :
temp_noise_diag[jj] = sp.dot(
temp_mat[jj,:], Rot[jj,:])
temp_noise_diag.shape = (shape[1], shape[2])
noise_diag[ii, ...] = temp_noise_diag
# Return workspace memory to origional shape.
noise_inv_freq.shape = (shape[1], shape[2],
shape[1], shape[2])
if self.feedback > 1:
print ""
sys.stdout.flush()
elif noise_inv.ndim == 6 :
if save_noise_diag:
# OLD WAY.
#clean_map, noise_diag, chol = solve(noise_inv,
# dirty_map, True, feedback=self.feedback)
# NEW WAY.
clean_map, noise_diag, noise_inv_diag, chol = \
solve(noise_fname, noise_inv, dirty_map,
True, feedback=self.feedback)
else:
# OLD WAY.
#clean_map, chol = solve(noise_inv, dirty_map,
# False, feedback=self.feedback)
# NEW WAY.
clean_map, noise_inv_diag, chol = \
solve(noise_fname, noise_inv, dirty_map,
False, feedback=self.feedback)
if params['save_cholesky']:
chol_fname = (params['output_root'] + 'chol_'
+ pol_str + band_str + '.npy')
sp.save(chol_fname, chol)
if params['save_noise_inv_diag']:
noise_inv_diag_fname = (params['output_root'] +
'noise_inv_diag_' + pol_str + band_str
+ '.npy')
algebra.save(noise_inv_diag_fname, noise_inv_diag)
# Delete the cholesky to recover memory.
del chol
else :
raise ce.DataError("Noise matrix has bad shape.")
# In all cases delete the noise object to recover memeory.
del noise_inv
# Write the clean map to file.
out_fname = (params['output_root'] + 'clean_map_'
+ pol_str + band_str + '.npy')
if self.feedback > 1:
print "Writing clean map to: " + out_fname
algebra.save(out_fname, clean_map)
all_out_fname_list.append(
kiyopy.utils.abbreviate_file_path(out_fname))
if save_noise_diag :
noise_diag_fname = (params['output_root'] + 'noise_diag_'
+ pol_str + band_str + '.npy')
algebra.save(noise_diag_fname, noise_diag)
all_out_fname_list.append(
kiyopy.utils.abbreviate_file_path(noise_diag_fname))
# Check the clean map for faileur.
if not sp.alltrue(sp.isfinite(clean_map)):
n_bad = sp.sum(sp.logical_not(sp.isfinite(clean_map)))
msg = ("Non finite entries found in clean map. Solve"
" failed. %d out of %d entries bad"
% (n_bad, clean_map.size))
raise RuntimeError(msg)
