本文整理汇总了Python中scipy.loadtxt函数的典型用法代码示例。如果您正苦于以下问题:Python loadtxt函数的具体用法?Python loadtxt怎么用?Python loadtxt使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了loadtxt函数的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: time_storage
def time_storage (**cfg):
nStore = int(cfg['MEFourier']['{storage_harmonic}'])
nWriteTime = int(cfg['OCTime']['{write_timecnt}'])
nReadTime = int(cfg['OCTime']['{read_timecnt}'])
nStoreTime = int(cfg['METime']['{storage_timecnt}'])
omega_c = float(cfg['NVSETUP']['{omega_c}'])
name_readwrite = getNameReadWrite(**cfg)
prefix = cfg['FILES']['{prefix}']
filename = name_readwrite+"harmonic{0:0>4}_cavityMode_".format(0)
postfix =cfg['FILES']['{postfix}']
print (" read data : storage and read time")
### reading <down> cavity-amplitudes ###################################################
timeStore,__,__ = sp.loadtxt(prefix+filename+"reg2_store_down"+postfix).T
timeRead ,__,__ = sp.loadtxt(prefix+filename+"reg3_read_stored_down"+postfix).T
time=functionaltimes_readwrite (**cfg)
time['store']=timeStore
time['read'] =timeRead
time['ti'] =timeRead[int(time['idx_ti'])-1]
time['tf'] =timeRead[int(time['idx_tf'])-1]
return time
示例2: setUp
def setUp(self):
sp.random.seed(0)
self.Y = sp.loadtxt(os.path.join(base_folder, 'Y.txt'))
self.XX = sp.loadtxt(os.path.join(base_folder, 'XX.txt'))
self.Xr = sp.loadtxt(os.path.join(base_folder, 'Xr.txt'))
self.N,self.P = self.Y.shape
self.write = False
示例3: compare_mixed_files
def compare_mixed_files(file1,file2,tol=1e-8,delimiter="\t"):
'''
Given two files, compare the contents, including numbers up to absolute tolerance, tol
Returns: val,msg
where val is True/False (true means files to compare to each other) and a msg for the failure.
'''
dat1=sp.loadtxt(file1,dtype='str',delimiter=delimiter,comments=None)
dat2=sp.loadtxt(file2,dtype='str',delimiter=delimiter,comments=None)
ncol1=dat1[0].size
ncol2=dat2[0].size
if ncol1!=ncol2:
return False,"num columns do not match up"
try:
r_count = dat1.shape[0]
c_count = dat1.shape[1]
except:
#file contains just a single column.
return sp.all(dat1==dat2), "single column result doesn't match exactly ('{0}')".format(file1)
for r in xrange(r_count):
for c in xrange(c_count):
val1 = dat1[r,c]
val2 = dat2[r,c]
if val1!=val2:
try:
f1 = float(val1)
f2 = float(val2)
except:
return False, "Values do not match up (file='{0}', '{1}' =?= '{2}')".format(file1, val1, val2)
if abs(f1-f2) > tol:
return False, "Values too different (file='{0}', '{1}' =?= '{2}')".format(file1, val1, val2)
return True, "files are comparable within abs tolerance=%e" % tol
示例4: get_average_column
def get_average_column(path, column=0):
"""
Get the index-based average column for a series of results files.
Args:
path(str): the path containing the results files.
Kwargs:
column (int): the column index in a results file.
Returns:
A numpy.ndarray containing the average values for the specified
column-index of a series of results files.
"""
files = [f for f in listdir(path) if isfile(join(path, f))
and f.endswith(".txt")]
col_seq = column,
sum_col = loadtxt(join(path, files[0]), usecols=col_seq, unpack=True)
for file in files[1:]:
sum_col = sum_col + loadtxt(join(path, file), usecols=col_seq,
unpack=True)
return sum_col / len(files)
示例5: functionaltimes_readwrite
def functionaltimes_readwrite (**cfg):
nWrite = int(cfg['OCFourier']['{write_harmonic}'])
nTime = int(cfg['OCTime']['{read_timecnt}'])
prefix =cfg['FILES']['{prefix}']
postfix =cfg['FILES']['{postfix}']
name_readwrite=getNameReadWrite(**cfg)
name_optimized=cfg['FILES']['{name_optimized}']
print (" read data : functional time")
timeRead =sp.zeros([nTime],float)
# load time for reading section memory - part of reg2
filename=prefix+"harmonic{0:0>4}_cavityMode_reg2_memory".format(nWrite)+postfix
timeRead ,__,__ = sp.loadtxt(filename).T
filename=prefix+"harmonic{0:0>4}_cavityMode_reg1_write".format(nWrite)+postfix
timeWrite,__,__ = sp.loadtxt(filename).T
# read funtional times t2, t3
configParser2 = cp.ConfigParser()
configParser2.read(prefix+name_readwrite+name_optimized+"FunctionalTimes"+postfix)
time=configParser2.__dict__['_sections']['functime'] # in seconds*wc
time['read'] =timeRead # in seconds*wc
time['write']=timeWrite # in seconds*wc
# read funtional times t2, t3
cfg['METime']['{fidelity_ti}'] = time['idx_ti']
cfg['METime']['{fidelity_tf}'] = time['idx_tf']
return time
示例6: harmonics_readwrite
def harmonics_readwrite (**cfg):
nWrite = int(cfg['OCFourier']['{write_harmonic}'])
nRead = int(cfg['OCFourier']['{read_harmonic}'])
nTime = int(cfg['OCTime']['{read_timecnt}'])
wTime = int(cfg['OCTime']['{write_timecnt}'])
prefix =cfg['FILES']['{prefix}']
postfix =cfg['FILES']['{postfix}']
print (" read data : cavity modes")
cavityWrite=sp.zeros([nWrite,wTime],complex)
cavityMemo =sp.zeros([nWrite,nTime],complex)
cavityRead =sp.zeros([nRead,nTime],complex)
# load memory - part of reg2
for iMemo in range(nWrite):
filename=prefix+"harmonic"+"{0:0>4}".format(iMemo+1)+"_cavityMode_reg1_write"+postfix
__,real,imag = sp.loadtxt(filename).T
# time,real,imag=sp.loadtxt(filename,unpack=True)
cavityWrite[iMemo,:] = real[:]+1j*imag[:]
filename=prefix+"harmonic"+"{0:0>4}".format(iMemo+1)+"_cavityMode_reg2_memory"+postfix
__,real,imag = sp.loadtxt(filename).T
# time,real,imag=sp.loadtxt(filename,unpack=True)
cavityMemo[iMemo,:] = real[:]+1j*imag[:]
# load memory - part of reg2
for iRead in range(nRead):
filename=prefix+"harmonic"+"{0:0>4}".format(iRead+1)+"_cavityMode_reg2_read"+postfix
__,real,imag = sp.loadtxt(filename).T
# time,real,imag=sp.loadtxt(filename,unpack=True)
cavityRead[iRead,:] = real[:]+1j*imag[:]
return cavityWrite,cavityMemo,cavityRead
示例7: convert_g012
def convert_g012(self,hdf,g012_file,chrom,start,end):
"""convert g012 file to LIMIX hdf5
hdf: handle for hdf5 file (target)
g012_file: filename of g012 file
chrom: select chromosome for conversion
start: select start position for conversion
end: select end position for conversion
"""
if ((start is not None) or (end is not None) or (chrom is not None)):
print "cannot handle start/stop/chrom boundaries for g012 file"
return
#store
if 'genotype' in hdf.keys():
del(hdf['genotype'])
genotype = hdf.create_group('genotype')
col_header = genotype.create_group('col_header')
row_header = genotype.create_group('row_header')
#load position and meta information
indv_file = g012_file + '.indv'
pos_file = g012_file + '.pos'
sample_ID = sp.loadtxt(indv_file,dtype='str')
pos = sp.loadtxt(pos_file,dtype='str')
chrom = pos[:,0]
pos = sp.array(pos[:,1],dtype='int')
row_header.create_dataset(name='sample_ID',data=sample_ID)
col_header.create_dataset(name='chrom',data=chrom)
col_header.create_dataset(name='pos',data=pos)
M = sp.loadtxt(g012_file,dtype='uint8')
snps = M[:,1::]
genotype.create_dataset(name='matrix',data=snps,chunks=(snps.shape[0],min(10000,snps.shape[1])),compression='gzip')
pass
示例8: setUp
def setUp(self):
SP.random.seed(0)
self.Y = SP.loadtxt('./data/Y.txt')
self.XX = SP.loadtxt('./data/XX.txt')
self.Xr = SP.loadtxt('./data/Xr.txt')
self.N,self.P = self.Y.shape
self.write = False
示例9: main
def main(argv):
import scipy
from sklearn import metrics
from sklearn.multiclass import OneVsOneClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.cross_validation import cross_val_score
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import preprocessing
import similarity
class ScaledSVC(SVC):
def _scale(self, data):
return preprocessing.scale(data)
def fit(self, X, Y):
return super(ScaledSVC, self).fit(self._scale(X), Y)
def predict(self, X):
return super(ScaledSVC, self).predict(self._scale(X))
data, labels = scipy.loadtxt(argv[1]), scipy.loadtxt(argv[2])
if len(argv) > 3:
features = np.array([int(s) for s in argv[3].split(',')])
data = data[:, features]
def ovo(model, adj_strat):
return OneVsOneClassifier(BinaryTiloClassifier(model, adj_strat))
classifiers = [
('TILO/PRC/Gaussian',
ovo(PinchRatioCutStrategy(),
similarity.Gaussian())),
("TILO/Nearest/Gaussian",
ovo(NearestCutStrategy(),
similarity.Gaussian())),
("TILO/PRC/KNN",
ovo(PinchRatioCutStrategy(),
similarity.KNN())),
("TILO/Nearest/KNN",
ovo(NearestCutStrategy(),
similarity.KNN())),
("SVC", ScaledSVC()),
("Gaussian Naive Bayes", GaussianNB()),
("K Neighbors", KNeighborsClassifier()),
("Decision Tree", DecisionTreeClassifier())]
format_str = '{:<30} {} {} {}'
print '{:<30} {:<10} RAND Accuracy'.format('method', 'accuracy')
for name, c in classifiers:
scores = cross_val_score(c, data, labels, cv=5)
#scores = np.array([1., 1.])
model = c.fit(data, labels)
guesses = model.predict(data)
acc = metrics.zero_one_score(guesses, labels)
rand = metrics.adjusted_rand_score(guesses, labels)
print '{:<30} {:.4f} +/- {:.4f} {: .4f} {:.4f}'.format(name, scores.mean(),
scores.std() / 2,
rand, acc)
示例10: calc_loss_deagg_suburb
def calc_loss_deagg_suburb(bval_path_file, total_building_loss_path_file, site_db_path_file, file_out):
""" Given EQRM ouput data, produce a csv file showing loss per suburb
The produced csv file shows total building loss, total building
value and loss as a percentage. All of this is shown per suburb.
bval_path_file - location and name of building value file produced by EQRM
total_building_loss_path_file - location and name of the total building
loss file
site_db_path_file - location and name of the site database file
Note: This can be generalised pretty easily, to get results
deaggregated on other columns of the site_db
"""
aggregate_on = ["SUBURB"]
# Load all of the files.
site = csv_to_arrays(site_db_path_file, **attribute_conversions)
# print "site", site
bvals = loadtxt(bval_path_file, dtype=scipy.float64, delimiter=",", skiprows=0)
# print "bvals", bvals
# print "len(bvals", len(bvals)
total_building_loss = loadtxt(total_building_loss_path_file, dtype=scipy.float64, delimiter=" ", skiprows=1)
# print "total_building_loss", total_building_loss
# print "total_building_loss shape", total_building_loss.shape
site_count = len(site["BID"])
assert site_count == len(bvals)
assert site_count == total_building_loss.shape[1]
# For aggregates
# key is the unique AGGREGATE_ON combination .eg ('Hughes', 2605,...)
# Values are a list of indices where the combinations are repeated in site
aggregates = {}
for i in range(site_count):
assert site["BID"][i] == int(total_building_loss[0, i])
marker = []
for name in aggregate_on:
marker.append(site[name][i])
marker = tuple(marker)
aggregates.setdefault(marker, []).append(i)
# print "aggregates", aggregates
handle = csv.writer(open(file_out, "w"), lineterminator="\n")
handle.writerow(["percent losses (building and content) by suburb"])
handle.writerow(["suburb", "loss", "value", "percent loss"])
handle.writerow(["", " ($ millions)", " ($ millions)", ""])
keys = aggregates.keys()
keys.sort()
for key in keys:
sum_loss = 0
sum_bval = 0
for row in aggregates[key]:
sum_loss += total_building_loss[1][row]
sum_bval += bvals[row]
handle.writerow([key[0], sum_loss / 1000000.0, sum_bval / 1000000.0, sum_loss / sum_bval * 100.0])
示例11: plotSummary
def plotSummary(mctraj_filename, ratespec_filename, nskip=0):
"""Read in the MC trajectory data and make a plot of it.
Skip the first nskip points (as these may be far from the mean)"""
data = scipy.loadtxt(mctraj_filename) # step w sigma tau neglogP
ratespec_data = scipy.loadtxt(ratespec_filename)
figure()
# plot the lambda trajectory
subplot(2,2,1)
plot(data[nskip:,0], data[nskip:,1])
xlabel('accepted steps')
ylabel('$\lambda$')
#title(mctraj_filename)
# try a contour plot of sigma and tau
subplot(2,2,2)
myhist, myextent = histBin( data[nskip:,2], data[nskip:,3], 20)
# convert to log scale
myhist = np.log(np.array(myhist) + 1.)
#contour(myhist, extent = myextent, interpolation = 'nearest')
contourf(myhist, extent = myextent, interpolation = 'nearest')
# plot mean +/- std spectrum
ax = subplot(2,2,3)
Timescales = ratespec_data[:,0]
maxLikA = ratespec_data[:,1]
meanA = ratespec_data[:,2]
stdA = ratespec_data[:,3]
ci_5pc = ratespec_data[:,4]
ci_95pc = ratespec_data[:,5]
#matplotlib.pyplot.errorbar(Timescales, meanA, yerr=stdA)
PlotStd = False
plot(Timescales, meanA, 'k-', linewidth=2)
hold(True)
if PlotStd:
plot(Timescales, meanA+stdA, 'k-', linewidth=1)
hold(True)
plot(Timescales, meanA-stdA, 'k-', linewidth=1)
else:
plot(Timescales, ci_5pc, 'k-', linewidth=1)
hold(True)
plot(Timescales, ci_95pc, 'k-', linewidth=1)
ax.set_xscale('log')
xlabel('timescale (s)')
# plot mean +/- std spectrum
subplot(2,2,4)
wcounts, wbins = np.histogram(data[nskip:,1], bins=30)
plot(wbins[0:-1], wcounts, linestyle='steps', linewidth=2)
xlabel('$\lambda$')
show()
示例12: load_dataset
def load_dataset(path):
sortedfilesbyglob = lambda x: sorted(glob.glob(os.path.join(path, '%s*' % x)))
inptfiles = sortedfilesbyglob('input')
targetfiles = sortedfilesbyglob('target')
data = []
for infn, targetfn in itertools.izip(inptfiles, targetfiles):
inpt = scipy.loadtxt(infn)
target = scipy.loadtxt(targetfn)
target.shape = scipy.size(target), 1
data.append((inpt, target))
return data
示例13: read_CavityMemory
def read_CavityMemory (**cfgFiles):
filename = cfgFiles['{prefix}']+cfgFiles['{name_readwrite}']+ \
cfgFiles['{name_optimized}']+cfgFiles['{name_cavity}']
print ("### read initial value for cavity up")
cavity = sp.loadtxt(filename+"up"+cfgFiles['{postfix}'] )
cavity_up = cavity[0] + 1j*cavity[1]
print ("### read initial value for cavity down")
cavity = sp.loadtxt(filename+"down"+cfgFiles['{postfix}'] )
cavity_down = cavity[0] + 1j*cavity[1]
return cavity_down, cavity_up
示例14: compare_files
def compare_files(file1,file2,tol=1e-8,delimiter="\t"):
'''
Given two files, compare the contents, including numbers up to absolute tolerance, tol
Returns: val,msg
where val is True/False (true means files to compare to each other) and a msg for the failure.
'''
dat1=sp.loadtxt(file1,dtype='str',delimiter=delimiter,comments=None)
dat2=sp.loadtxt(file2,dtype='str',delimiter=delimiter,comments=None)
ncol1=dat1[0].size
ncol2=dat2[0].size
if ncol1!=ncol2:
return False,"num columns do not match up"
try:
head1=dat1[0,:]
head2=dat2[0,:]
except:
#file contains just a single column.
return sp.all(dat1==dat2), "single column result doesn't match exactly ('{0}')".format(file1)
#logging.warn("DO headers match up? (file='{0}', '{1}' =?= '{2}')".format(file1, head1,head2))
if not sp.all(head1==head2):
return False, "headers do not match up (file='{0}', '{1}' =?= '{2}')".format(file1, head1,head2)
for c in range(ncol1):
checked=False
col1=dat1[1:,c]
col2=dat2[1:,c]
try:
#if it is numeric
col1=sp.array(col1,dtype='float64')
col2=sp.array(col2,dtype='float64')
except Exception:
# if it is a string
pass
if not sp.all(col1==col2):
return False, "string column %s does not match" % head1[c]
checked=True
#if it is numeric
if not checked:
absdiff=sp.absolute(col1-col2)
if sp.any(absdiff>tol):
try:
return False, "numeric column %s does diff of %e not match within tolerance %e" % (head1[c],max(absdiff), tol)
except:
return False, "Error trying to print error message while comparing '{0}' and '{1}'".format(file1,file2)
return True, "files are comparable within abs tolerance=%e" % tol
示例15: plotmonetvspg
def plotmonetvspg():
x1=s.linspace(0,22,22,endpoint=False)
y1=s.loadtxt('average-monet.log')
y2=s.loadtxt('average-pg.log')
y3=s.loadtxt('result-mysql.log')
p1=py.bar(x1,y1,width=0.35)
p2=py.bar(x1+0.4,y2,width=0.4,color='green')
p3=py.bar(x1+0.8,y3,width=0.4,color='magenta')
py.xlabel('queries')
py.xlim(0,22)
py.ylabel('reponse time in seconds')
#py.xticks((p1,p2),('m','p'))
py.legend((p1,p2,p3),('monetdb','postgresql','mysql'),loc='upper left')
py.title('TPC-H benchmark with Postgresql and MonetDB')
py.savefig('monetvspg_mysql.jpg')