def _saveOneDExtractedData(self, result):
""" Generic function to save 1D spectrum data"""
stamps=result[0]
dat=result[1]
nbpts=dat[0].shape[0]
# Preparation of filenames
i=self.filename.rfind('.')
racine=self.filename[:i]
# Analysis of data
OneD_data=[]
for i in range(len(dat)):
if len(dat[i].shape)==2:
OneD_data.append(i)
# Treatment of spectrum (1D) data
if self.__verbose__ and len(OneD_data)==0:
print(_BLUE+"\t. No Spectrum (1D) data to save"+_RESET)
if self.__verbose__ and len(OneD_data)>0:
print(_BLUE+"\t. Spectrum (1D) data :"+_RESET)
for i in OneD_data:
if stamps[i][1]==None:
stamp=stamps[i][0]
else:
stamp=stamps[i][1]
filename=racine+'_'+stamp.replace( '/', '_')+'.mat'
try:
P.savetxt(filename, N.array(dat[i]))
if self.__verbose__:
print("\t\t-> %s Spectrum (1D) data saved in %s"%(stamp, filename))
except:
print(_RED+'Unable to save Spectrum (1D) data '+stamp)
raise
def PercusYevick(self,dr=0.0005,plot=True,filename="g_of_r.txt", npoints =1024*5):
# number density
rho=6./pi*self.eta
# getting the direct correlation function c(r) from the analytic Percus-Yevick solution
# space discretization dr
r=pl.arange(1,npoints+1,1 )*dr
# reciprocal space discretization (highest available frequency)
dk=1/r[-1]
k=pl.arange(1,npoints+1,1 )*dk
# direct correlation function c(r)
c_direct=self.cc(r)
# getting the Fourier transform
ft_c_direct=spherical_FT(c_direct, k,r,dr)
# using the Ornstein-Zernike equation, getting the structure factor
ft_h=ft_c_direct/(1.-rho*ft_c_direct)
# inverse Fourier transform
h=inverse_spherical_FT(ft_h, k,r,dk)
# print h
# # radial distribution function
gg=h+1.0
# clean the r<1 region
g=pl.zeros(len(gg))
g[r>=1]=gg[r>=1]
# save the cleaned version
# print (g)
pl.savetxt(filename, column_stack((r,g)))
# plots
if plot:
pl.plot(r,g, label='Percus Yevick Equation', color='k', linewidth=2, alpha=0.8)
pl.ylabel("$g(r)$", fontsize=16)
pl.xlabel("$r / \sigma$", fontsize=16)
return r,g
def save(self):
if self.running == False:
filename = QtGui.QFileDialog.getSaveFileName(self, "Save file", self.file_path, "*.txt")
self.file_path = dirname(str(filename))
self.buff.extend([0 for i in range(len(self.v_step) - len(self.buff))])
with open(str(filename),'wb') as f:
pl.savetxt(f, pl.array([self.v_step, self.buff]).transpose(), fmt="%10.5f", delimiter=",")
def meanOfMeans():
initialExperiment = 25
finalExperiment = 35
suffix = "piConc"
experimentFolder = "170"
rootDir = os.path.join("/", "home", "rafaelbeirigo", "ql", "experiments", experimentFolder)
means = []
meanFileName = "W_avg_list_mean.out"
for experiment in range(initialExperiment, finalExperiment + 1):
meanFilePath = os.path.join(rootDir, str(experiment), "PRQL", meanFileName)
# open experiment the file that contains the "individual" mean
# into a list
mean = pl.loadtxt(meanFilePath)
# this must be done because the function meanError.meanMultiDim()
# needs each item in the mean list to be a list itself.
mean = [[item] for item in mean]
# append it to the list of means
means.append(mean)
# obtain the global mean considering the list of individual means
globalMean = meanError.meanMultiDim(means)
## print globalMean
# means2spreadsheet([globalMean])
pl.savetxt(os.path.join(rootDir, "W_avg_list_mean." + suffix + ".out"), globalMean, fmt="%1.6f")
return globalMean
def meanOfMeans():
initialExperiment = 25
finalExperiment = 35
suffix = 'piConc'
experimentFolder = '170'
rootDir = os.path.join('/', 'home', 'rafaelbeirigo', 'ql', 'experiments',
experimentFolder)
means = []
meanFileName = 'W_avg_list_mean.out'
for experiment in range(initialExperiment, finalExperiment + 1):
meanFilePath = os.path.join(rootDir, str(experiment), 'PRQL', meanFileName)
# open experiment the file that contains the "individual" mean
# into a list
mean = pl.loadtxt(meanFilePath)
# this must be done because the function meanError.meanMultiDim()
# needs each item in the mean list to be a list itself.
mean = [[item] for item in mean]
# append it to the list of means
means.append(mean)
# obtain the global mean considering the list of individual means
globalMean = meanError.meanMultiDim(means)
## print globalMean
#means2spreadsheet([globalMean])
pl.savetxt(os.path.join(rootDir, 'W_avg_list_mean.' + suffix + '.out'),
globalMean, fmt='%1.6f')
return globalMean
def saveBackground_Callback(self):
filename = QtGui.QFileDialog.getSaveFileName(None,'Select background file',self.lastBackgroundDir)
filename = str(filename)
if filename:
# save background data
pylab.savetxt(filename,self.backgroundData)
self.backgroundPath = filename
self.lastBackgroundDir = os.path.split(filename)[0]
def save_to_txt(self, file_name, array=None, flatten=True):
if array is None:
array = self.counts
if flatten:
pl.savetxt(file_name, np.array(array).flatten().transpose())
else:
pl.savetxt(file_name, array)
print("\nPower as volts saved in file.")
def save(self):
if self.running == False:
filename = QtGui.QFileDialog.getSaveFileName(
self, "Save file", self.file_path, "*.txt")
self.file_path = dirname(filename)
py.savetxt(filename, [self.v_step, self.buff],
fmt="%10.5f",
delimiter=",")
def save(self):
if self.running == False:
filename = QtGui.QFileDialog.getSaveFileName(
self, "Save file", self.file_path, "*.txt")
self.file_path = dirname(str(filename))
pl.savetxt(str(filename),
pl.array([self.t_step, self.buff0,
self.buff1]).transpose(),
fmt="%10.5f",
delimiter=",")
def normalize_all(path_input, path_output):
for file in path_input.iterdir():
if 'MAT_RAW_realisation' not in file.stem:
continue
mat = loadtxt(file)
norm = normalize_dense(mat)
savetxt(path_output/file.name, norm)
print(file)
return None
def save_to_txt(self, file_name, array=None, flatten=True):
if array is None:
array = self.detector_readout_0
if not (file_name.endswith('.txt')):
file_name = file_name + '.txt'
if flatten:
pl.savetxt(file_name, np.array(array).flatten().transpose())
else:
pl.savetxt(file_name, array)
print("\nPower as volts saved in file.")
def exportData(self):
if bool(self.unit):
self.dir=QFileDialog.getExistingDirectory(self,'Select a directory to save data')
if self.dir!='':
for pv in self.unit.keys():
fheader='Data saved on %s\n'%(time.asctime())
fheader+='col_names=["time[secs]","pv[%s]"]'%self.unit[pv]
fname=pv+'_%s.txt'%(time.strftime('%Y%m%d'))
fname=os.path.join(self.dir,fname.replace(':','_'))
pl.savetxt(fname,self.datafull[pv],header=fheader,comments='#')
else:
QMessageBox.warning(self,'Data Error','No data fetched to save',QMessageBox.Ok)
def save_rsj_params(self):
mat = pl.vstack((pl.array(self.idx), pl.transpose(self.c),\
self.icarr, self.rnarr, self.ioarr, self.voarr,\
self.ic_err_arr, self.rn_err_arr, self.io_err_arr, self.vo_err_arr))
header_c = ''
for n in range(self.cdim):
header_c = header_c + 'Control%d '%(n+1)
header_ = 'Index ' + header_c + \
'Ic Rn Io Vo Ic_err Rn_err Io_err Vo_err'
pl.savetxt(self.get_fullpath(self.fn_rsjparams), pl.transpose(mat),\
header=header_)
def save_Callback(self):
"""
Save acquired data
"""
filepath = os.path.join(self.lastSaveDir, self.lastSaveFile)
filename = QtGui.QFileDialog.getSaveFileName(None, 'Select log file',
filepath)
filename = str(filename)
if filename:
data = pylab.concatenate((self.t, self.data), axis=1)
pylab.savetxt(filename, data)
self.lastSaveDir = os.path.split(filename)[0]
self.lastSaveFile = os.path.split(filename)[1]
def detide(gaugeno):
fname = '%s.txt' % gaugeno
if gaugeno == 21401:
t1fit = -12.*3600.
t2fit = 36.*3600.
t1out = 0.
t2out = 12.*3600.
degree = 15
else:
t1fit = -12.*3600.
t2fit = 36.*3600.
t1out = 0.
t2out = 12.*3600.
degree = 15
fname_notide = os.path.splitext(fname)[0] + '_notide.txt'
t,t_sec,eta = dart.plotdart(fname, t_quake)
c,t_notide,eta_notide = dart.fit_tide_poly(t_sec,eta,degree,\
t1fit,t2fit, t1out,t2out)
# fix bad data value:
if gaugeno==21418:
print "Bad data: ",eta_notide[121:124]
eta_notide[122] = -0.075
print "Fix data: ",eta_notide[121:124]
print "Bad data: ",eta_notide[152:155]
eta_notide[153] = -0.07
print "Fix data: ",eta_notide[152:155]
d = np.vstack([t_notide,eta_notide]).T
pylab.savetxt(fname_notide,d)
print "Created file ",fname_notide
dart.plot_post_quake(t_notide,eta_notide,gaugeno)
pylab.figure(70)
pylab.subplot(211)
pylab.title('DART %s' % gaugeno)
if 0:
pylab.figure(75)
pylab.plot(t_notide,eta_notide,'k')
pylab.xlim(0,3.*3600)
pylab.ylim(-1,2)
return t_notide, eta_notide
def OnExport(self,evt):
arr = self.lignes[0].points[:,:1]
for i in range(len(self.lignes)):
arr=c_[arr,self.lignes[i].points[:,1:2]]
pPath,pName = self.gui.model.getProject();
fname=pPath+sep+pName+self.title+'.txt'
dlg = wx.FileDialog(self.gui,"Sauvegarder","",fname,"*.txt",wx.SAVE)
if dlg.ShowModal() == wx.ID_OK:
fname = dlg.GetDirectory()+sep+dlg.GetFilename()
f1 = open(fname,'w')
f1.write(self.Xtitle)
for n in self.legd: f1.write(' '+n)
f1.write('\n')
savetxt(f1,arr)
f1.close()
def OnExport(self,evt):
arr = self.lignes[0].points[:,:1]
for i in range(len(self.lignes)):
arr=c_[arr,self.lignes[i].points[:,1:2]]
c=self.gui.core
fname = c.fileDir+os.sep+c.fileName+self.title+'.txt'
dlg = wx.FileDialog(self.gui,"Save","",fname,"*.txt",wx.SAVE)
if dlg.ShowModal() == wx.ID_OK:
fname = dlg.GetDirectory()+os.sep+dlg.GetFilename()
f1 = open(fname,'w')
f1.write(self.Xtitle)
for n in self.legd: f1.write(' '+n)
f1.write('\n')
savetxt(f1,arr)
f1.close()
def detide(gaugeno):
fname = '%s.txt' % gaugeno
if gaugeno == 21401:
t1fit = -12. * 3600.
t2fit = 36. * 3600.
t1out = 0.
t2out = 12. * 3600.
degree = 15
else:
t1fit = -12. * 3600.
t2fit = 36. * 3600.
t1out = 0.
t2out = 12. * 3600.
degree = 15
fname_notide = os.path.splitext(fname)[0] + '_notide.txt'
t, t_sec, eta = dart.plotdart(fname, t_quake)
c,t_notide,eta_notide = dart.fit_tide_poly(t_sec,eta,degree,\
t1fit,t2fit, t1out,t2out)
# fix bad data value:
if gaugeno == 21418:
print "Bad data: ", eta_notide[121:124]
eta_notide[122] = -0.075
print "Fix data: ", eta_notide[121:124]
print "Bad data: ", eta_notide[152:155]
eta_notide[153] = -0.07
print "Fix data: ", eta_notide[152:155]
d = np.vstack([t_notide, eta_notide]).T
pylab.savetxt(fname_notide, d)
print "Created file ", fname_notide
dart.plot_post_quake(t_notide, eta_notide, gaugeno)
pylab.figure(70)
pylab.subplot(211)
pylab.title('DART %s' % gaugeno)
if 0:
pylab.figure(75)
pylab.plot(t_notide, eta_notide, 'k')
pylab.xlim(0, 3. * 3600)
pylab.ylim(-1, 2)
return t_notide, eta_notide
def img_binaryzation(img_filename):
import os
import numpy as np
from PIL import Image
import pylab
# 修改图片的路径
img_filename = os.path.join(os.path.dirname(os.path.dirname(__file__)),
img_filename)
# 调整图片的大小为 32*32px
img = Image.open(img_filename)
out = img.resize((32, 32), Image.ANTIALIAS)
out.save(img_filename)
# RGB 转为二值化图
img = Image.open(img_filename)
lim = img.convert('1')
lim.save(img_filename)
img = Image.open(img_filename)
# 将图像转化为数组并将像素转换到0-1之间
img_ndarray = np.asarray(img, dtype='float64') / 256
# 将图像的矩阵形式转化成一位数组保存到 data 中
data = np.ndarray.flatten(img_ndarray)
# 将一维数组转化成矩阵
a_matrix = np.array(data).reshape(32, 32)
# 将矩阵保存到 txt 文件中转化为二进制0，1存储
img_filename_list = img_filename.split('.') # type:list
img_filename_list[-1] = 'jpg'
txt_filename = '.'.join(img_filename_list)
pylab.savetxt(txt_filename, a_matrix, fmt="%.0f", delimiter='')
# 把 .txt 文件中的0和1调换
with open(txt_filename, 'r') as fr:
data = fr.read()
data = data.replace('1', '2')
data = data.replace('0', '1')
data = data.replace('2', '0')
with open(txt_filename, 'w') as fw:
fw.write(data)
return txt_filename
def OnExport(self, evt):
arr = self.lignes[0].points[:, :1]
for i in range(len(self.lignes)):
arr = c_[arr, self.lignes[i].points[:, 1:2]]
pPath, pName = self.gui.model.getProject()
fname = pPath + sep + pName + self.title + '.txt'
dlg = wx.FileDialog(self.gui, "Sauvegarder", "", fname, "*.txt",
wx.SAVE)
if dlg.ShowModal() == wx.ID_OK:
fname = dlg.GetDirectory() + sep + dlg.GetFilename()
f1 = open(fname, 'w')
f1.write(self.Xtitle)
for n in self.legd:
f1.write(' ' + n)
f1.write('\n')
savetxt(f1, arr)
f1.close()
def save_ah_params(self, n):
n += 1
mat = pl.vstack((pl.array(self.idx)[:n], pl.transpose(self.c[:n]),\
self.icarr[:n], self.rnarr[:n], self.ioarr[:n], self.voarr[:n],\
self.tnarr[:n],\
self.ic_err_arr[:n], self.rn_err_arr[:n], self.io_err_arr[:n],\
self.vo_err_arr[:n], self.tn_err_arr[:n]))
n -= 1
header_c = ''
for m in range(self.cdim):
header_c = header_c + 'Control%d '%(m+1)
header_ = 'Index ' + header_c + \
'Ic Rn Io Vo Tn Ic_err Rn_err Io_err Vo_err Tn_err'
pl.savetxt(self.get_fullpath(self.fn_ahparams), pl.transpose(mat),\
header=header_)
def saveResults(self,filename=None):
#save the results to a file
H_theory=self.H_theory(self.H.getfreqs(),[self.n[:,1].real,self.n[:,1].imag],self.l_opt)
#built the variable that should be saved:
savetofile=py.column_stack((
self.H.getfreqs(), #frequencies
self.n[:,1].real,-self.n[:,1].imag, #the real and imaginary part of n
self.n[:,2].real,-self.n[:,2].imag, #the real and imaginary part of the smoothed n
self.H.getFReal(),self.H.getFImag(),#H_measured
self.H.getFRealUnc(),self.H.getFImagUnc(),#uncertainties
self.H.getFAbs(),self.H.getFPh(),#absH,ph H measured
H_theory.real,H_theory.imag, #theoretic H
))
headerstr=('freq, '
'ref_ind_real, ref_ind_imag, '
'ref_ind_sreal, ref_ind_simag, '
'H_measured_real, H_measured_imag, '
'Hunc_real, Hunc_imag, '
'abs(H_measured), angle(H_measured), '
'H_theory_real, H_theory_imag')
if filename==None:
fname=self.getFilenameSuggestion()
else:
fname=filename+"_"
fname+='analyzed_' + 'D=' + str(self.l_opt/1e-6) +'.txt'
py.savetxt(fname,savetofile,delimiter=',',header=headerstr)
# Save in a separate file the results of the simplified calculation of n, k, alpha end epsilon along with their unc
headerstr=('freq, '
'ref_ind_real, ref_ind_imag, '
'u(ref_ind_real), u(ref_ind_imag), '
'std(l)_n, std(Esam)_n, std(Eref)_n, f(Theta)_n, f(k<<<)_n, f(FP)_n, f(n0)_n, '
'std(l)_k, std(Esam)_k, std(Eref)_k, f(Theta)_k, f(k<<<)_k, f(FP)_k, f(n0)_k, '
'Epsilon_1, Epsilon_2, '
'u(Epsilon_1), u(Epsilon_2), '
'alpha, u(alpha), alpha_max, ')
if filename==None:
fname=self.getFilenameSuggestion()
else:
fname=filename+"_"
fname+='SimplifiedAnalysis_' + 'D=' + str(self.l_opt/1e-6) +'.txt'
py.savetxt(fname,self.n_with_unc,delimiter=',',header=headerstr)
def __init__(self, filename, start_index=0, export=None, autoexport=False):
"""Object used to read SPE files, plot them and export them to a text file
Args:
filename (str): path to the SPE file
start_index (int, optional): index of the frame you want to export or the plot to start.
export (str, optional): path to the exported text file. If None, will plot the spectra, if not None, will export.
autoexport (bool, optional): if True, will export with an automated 'filename + frame number' named text file.
"""
self.data = self.open_spe(filename)
self.curr_pos = start_index
if export is None:
self.init_plot(start_index)
else:
save_array = pl.array([self.data.wavelength, self.data.data[start_index][0]]).transpose()
pl.savetxt(export, save_array)
def _saveSingleTwoDExtractedData(self, dir, stamp, num, mat):
""" Generic function to save a single 2D image data
Input : dir : directory where image have to be stored
stamp : sensor attached to the image
num : point (image) number
mat : the matrix of the image itself
"""
# Preparation of filenames
i=self.filename.rfind('.')
racine=self.filename[self.filename.rfind('/')+1:self.filename.rfind('.')]
filename=dir+racine+'_'+stamp.replace('/', '_')
try:
sys.stdout.write('save image %d \r'%(num))
sys.stdout.flush()
P.savetxt(filename+'_'+str(num)+'.imag', mat)
sys.stdout.write(' \r')
sys.stdout.flush()
except:
print(_RED+'Unable to save image (2D) data '+stamp)
raise
def post_processing(self):
"""
Post-processing at the end of each time iteration
"""
pp = self.parameters.post_processing
# Plot or save fields
if pp.plot_u and self.comm_size == 1:
plot(self.u, mode="displacement")
if pp.plot_alpha and self.comm_size == 1:
plot(self.alpha, mode="color")
if pp.save_u:
#self.file_u.write(self.u, self.t)
self.file_u << (self.u, self.t)
if pp.save_alpha:
#self.file_alpha.write(self.alpha, self.t)
self.file_alpha << (self.alpha, self.t)
if pp.save_V:
self.file_V.write(self.V, self.t)
if pp.save_Beta:
self.file_Beta.write(self.Beta, self.t)
# Calculate energies
if pp.save_energies:
self.elastic_energy_value = assemble(self.elastic_energy)
self.dissipated_energy_value = assemble(self.dissipated_energy)
if self.comm_rank == 0:
self.energies.append([
self.t, self.elastic_energy_value,
self.dissipated_energy_value
])
pl.savetxt(self.save_dir + pp.file_energies,
pl.array(self.energies), "%.5e")
# User post-processing
self.set_user_post_processing()
def proc_shot(self):
shot.sweeps_average = 10
initial_sweep = 0
last_sweep = len(shot.points)
initial_time = 58
last_time = 95
initial_sweep = shot.time2sweep(initial_time)
last_sweep = shot.time2sweep(last_time)
#'all_shot' set to 1 avoids printing unnecessary information.
shot.reference_gd(all_shot=1)
print("time for reading files: {0} s".format(time.time() - time0))
time1 = time.time()
for sweep in p.arange(initial_sweep, last_sweep, sweeps_average):
# print sweep
shot.plasma_gd(sweep, sweeps_average, all_shot=1)
shot.overlap_gd()
shot.init_gd()
# choose poly fit order
shot.profile_poly(order=7, all_shot=1)
# save profile in file with time in microsseconds
p.savetxt(
path.join(prof_folder,
"%06d.dat" % (shot.sweep2time(sweep) * 1e3)), shot.r)
# separate density in other file, to save space.
p.savetxt(path.join(prof_folder, "ne.dat"), shot.ne_poly)
# save info file with parameters used to evaluate profiles.
p.savetxt(path.join(prof_folder, "prof_info.dat"),
[sweeps_average, initial_time, last_time])
print("time for Processing: {0} s".format(time.time() - time1))
def proc_shot(self):
shot.sweeps_average = 10
initial_sweep = 0
last_sweep = len(shot.points)
initial_time = 58
last_time = 95
initial_sweep = shot.time2sweep(initial_time)
last_sweep = shot.time2sweep(last_time)
#'all_shot' set to 1 avoids printing unnecessary information.
shot.reference_gd(all_shot=1)
print("time for reading files: {0} s".format(time.time() - time0))
time1 = time.time()
for sweep in p.arange(initial_sweep, last_sweep, sweeps_average):
# print sweep
shot.plasma_gd(sweep, sweeps_average, all_shot=1)
shot.overlap_gd()
shot.init_gd()
# choose poly fit order
shot.profile_poly(order=7, all_shot=1)
# save profile in file with time in microsseconds
p.savetxt(path.join(prof_folder, "%06d.dat" % (shot.sweep2time(sweep) * 1e3)), shot.r)
# separate density in other file, to save space.
p.savetxt(path.join(prof_folder, "ne.dat"), shot.ne_poly)
# save info file with parameters used to evaluate profiles.
p.savetxt(path.join(prof_folder, "prof_info.dat"), [sweeps_average, initial_time, last_time])
print("time for Processing: {0} s".format(time.time() - time1))
def PercusYevickHS(phi,plot=True,filename="g_of_r.txt",x=pl.arange(0,5,0.05)):
# number density
rho=6./pi*phi
# getting the direct correlation function c(r) from the analytic Percus-Yevick solution
# vectorizing the function
c=pl.vectorize(cc)
# space discretization
dr=0.005
r=pl.arange(1,1024*2+1,1 )*dr
# reciprocal space discretization (highest available frequency)
dk=1/r[-1]
k=pl.arange(1,1024*2+1,1 )*dk
# direct correlation function c(r)
c_direct=c(r,phi)
# getting the Fourier transform
ft_c_direct=spherical_FT(c_direct, k,r,dr)
# using the Ornstein-Zernike equation, getting the structure factor
ft_h=ft_c_direct/(1.-rho*ft_c_direct)
# inverse Fourier transform
h=inverse_spherical_FT(ft_h, k,r,dk)
# print h
# # radial distribution function
gg=h+1
# clean the r<1 region
g=pl.zeros(len(gg))
g[r>=1]=gg[r>=1]
# save the cleaned version
pl.savetxt(filename, zip(r,g))
from scipy.interpolate import InterpolatedUnivariateSpline
spl=InterpolatedUnivariateSpline(r, g)
# plots
if plot:
pl.plot(r,abs((g-1)*r))
# pl.plot(r,g-1)
pl.ylabel("|(g(r)-1)r|")
pl.xlabel("r")
# return abs((spl(x)-1)*x)
return spl(x)
def _saveFileXY(self, savePathStr, x, y):
try:
if self.times:
pl.savetxt(savePathStr, pl.array([x, y, self.times]).transpose(), fmt="%10.5f", delimiter=",")
else:
pl.savetxt(savePathStr, pl.array([x, y]).transpose(), fmt="%10.5f", delimiter=",")
pl.savetxt(savePathStr, pl.array([x, y]).transpose(), fmt="%10.5f", delimiter=",")
except IOError:
self.updateStatusFileField('The file could not be saved. Is the path and name valid?')
def store_state(self, result_dir, index=None):
""" Stores all generated plots in the given directory *result_dir* """
if self.store:
node_dir = os.path.join(result_dir, self.__class__.__name__)
if not index == None:
node_dir += "_%d" % int(index)
create_directory(node_dir)
if (self.ts_plot != None):
name = 'timeseries_sp%s.pdf' % self.current_split
self.ts_plot.savefig(os.path.join(node_dir, name),
bbox_inches="tight")
if (self.histo_plot != None):
name = 'histo_sp%s.pdf' % self.current_split
self.histo_plot.savefig(os.path.join(node_dir, name),
bbox_inches="tight")
for label in self.labeled_corr_matrix.keys():
name = 'Feature_Correlation_%s_sp%s.txt' % (label,
self.current_split)
pylab.savetxt(os.path.join(node_dir, name),
self.labeled_corr_matrix[label],
fmt='%s',
delimiter=' ')
name = 'Feature_Development_%s_sp%s.pdf' % (label,
self.current_split)
self.feature_development_plot[label].savefig(
os.path.join(node_dir, name))
for label in self.corr_plot.keys():
name = 'Feature_Correlation_%s_sp%s.pdf' % (label,
self.current_split)
self.corr_plot[label].savefig(os.path.join(node_dir, name))
pylab.close("all")
def store_state(self, result_dir, index=None):
""" Stores all generated plots in the given directory *result_dir* """
if self.store:
node_dir = os.path.join(result_dir, self.__class__.__name__)
if not index == None:
node_dir += "_%d" % int(index)
create_directory(node_dir)
if (self.ts_plot != None):
name = 'timeseries_sp%s.pdf' % self.current_split
self.ts_plot.savefig(os.path.join(node_dir, name),
bbox_inches="tight")
if (self.histo_plot != None):
name = 'histo_sp%s.pdf' % self.current_split
self.histo_plot.savefig(os.path.join(node_dir, name),
bbox_inches="tight")
for label in self.labeled_corr_matrix.keys():
name = 'Feature_Correlation_%s_sp%s.txt' % (label,
self.current_split)
pylab.savetxt(os.path.join(node_dir, name),
self.labeled_corr_matrix[label], fmt='%s',
delimiter=' ')
name = 'Feature_Development_%s_sp%s.pdf' % (label,
self.current_split)
self.feature_development_plot[label].savefig(
os.path.join(node_dir, name))
for label in self.corr_plot.keys():
name = 'Feature_Correlation_%s_sp%s.pdf' % (label,
self.current_split)
self.corr_plot[label].savefig(os.path.join(node_dir, name))
pylab.close("all")
def _saveTwoDExtractedData(self, result):
""" Generic function to save 2D image data"""
stamps=result[0]
dat=result[1]
nbpts=dat[0].shape[0]
# Preparation of filenames
i=self.filename.rfind('.')
racine=self.filename[:i]
# Analysis of data
TwoD_data=[]
for i in range(len(dat)):
if len(dat[i].shape)==3:
TwoD_data.append(i)
# Treatment of image (2D) data
if self.__verbose__ and len(TwoD_data)==0:
print(_BLUE+"\t. No Image (2D) data to save"+_RESET)
for i in TwoD_data:
if self.__verbose__:
print(_BLUE+"\t. Images (2D) data :"+_RESET)
if stamps[i][1]==None:
stamp=stamps[i][0]
else:
stamp=stamps[i][1]
filename=racine+'_'+stamp.replace('/', '_')
try:
for num in range(nbpts):
sys.stdout.write('image %d/%d \r'%(num, nbpts))
sys.stdout.flush()
P.savetxt(filename+'.'+str(num)+'.imag', dat[i][num, :,:])
sys.stdout.write(' \r')
sys.stdout.flush()
if self.__verbose__:
print("\t\t-> %s Images (2D) data saved in %s from 0 to %d"%(stamps, filename+""".##.imag""", nbpts-1))
except:
print(_RED+'Unable to save image (2D) data '+stamps)
raise
outside_temperature = temperatures[i]
# Leak heat from the house (if it is indeed colder outside than inside)
# Also allow sunlight to shine in and heat the house
house.exchange_heat_with_outside(outside_temperature, np.mean(solar_heat))
# Calculate the demand
current_heat_demand = house.get_heat_demand()
if current_heat_demand > 0.0:
heat_demand_profile.append(current_heat_demand)
else:
heat_demand_profile.append(0.0)
i += 1
# scale the demand_profile
demand_profile = np.array(heat_demand_profile)
mini = 0 * 4 * 24
maxi = int(7 * 4 * 24)
plt.plot(demand_profile[mini:maxi])
plt.xlabel('time (15 minutes)')
plt.ylabel('Heat demand (kW)')
plt.show()
print "../output_data/hp_space_heating_" + str(floor_area) + "m2"
plt.savetxt("../output_data/space_heating_demand_" + str(floor_area) + "m2.csv", demand_profile, fmt='%.3f', delimiter=',')
os.path.walk(os.getcwd(), LoadGenomeMeanSize, GrandMeansData)
GrandMeansData = sorted(GrandMeansData, key=lambda data_sort: data_sort[2])
# ---- loading and cheking data ---
TheMeans = p.zeros((len(GrandMeansData), 3))
i = 0
for item in GrandMeansData:
TheMeans[i, 0] = item[0]
TheMeans[i, 1] = item[1]
TheMeans[i, 2] = item[2]
i += 1
print TheMeans
TheMeans = TheMeans[~p.isnan(TheMeans).any(1)] # removing NaN from the output
p.savetxt("GenomeSizes.dat", TheMeans, fmt='%1.6f')
p.figure(10, figsize=(10, 6))
#p.get_current_fig_manager().window.wm_geometry("1000x600+200+110")
p.plot(TheMeans[:, 2], TheMeans[:, 0], 'ko-')
#p.plot(TheMeans[:,2],TheMeans[:,0]-TheMeans[:,1], 'k--' )
#p.plot(TheMeans[:,2],TheMeans[:,0]+TheMeans[:,1], 'k--' )
#p.errorbar(TheMeans[:,2],TheMeans[:,0], yerr=TheMeans[:,1], fmt='o-', ecolor='k')
p.fill_between(TheMeans[:, 2],
TheMeans[:, 0] + TheMeans[:, 1],
TheMeans[:, 0] - TheMeans[:, 1],
color=(0.75, 0.75, 0.75, 0.75))
#p.axis([0, 0.5, 2, 20])
p.xlabel('turbulence level ($ T $)', fontsize=16)
p.ylabel('number of genes', fontsize=16)
p.grid(True)
p.show()
data = np.load(filename) # pl.plot(data['favgs'], data['rchan']) # pl.show() x = data['favgs'] y = data['rchan'] yerr = np.sqrt(data['xchanerr']**2 + data['ychanerr']**2) p0t = [-113, 0.01, -64, 0.01, 0, 0.05, 82, 0.09, 1, 1, 1, 0.001] # # yh = tw.multipletripple(p0t, x) # # pl.subplot(211) # # pl.plot(x, y, '.') # # pl.plot(x, yh, '-') # # pl.subplot(212) # # pl.plot(x, y-yh, '.') yh, outthree = tw.completefit(tw.multipletripple, x, y, p0t, filename, 'tripple', toplot=True, tosave=True) pl.subplot(211) pl.plot(x, y, '.') pl.plot(x, yh, '-') pl.subplot(212) pl.plot(x, y-yh, '.') pl.savetxt(filename+".csv", zip(x, y, yerr, yh), delimiter=',') pl.show()
sys.exit(1)
F = [];
F.append(watch_procs(proc, totaltime=options.totaltime));
for child in get_children(proc):
F.append(watch_procs(child, totaltime=options.totaltime));
for f in F:
f.start()
for f in F:
f.join()
time.sleep(1)
if options.saveas.upper()=='TXT':
header = "time cputimes cpu ram"
print f.name
for f in F:
x, cpu, ram, z = f.get_result()
pl.savetxt(proc.name+'.'+f.proc.name+'.'+str(f.proc.pid)+'.txt', zip(x, z, cpu, ram), header=header, fmt='%10.10f');
else:
for f in F:
x, cpu, ram, z = f.get_result()
pl.step(x-pl.amin(x), cpu, label=f.proc.name)
pl.step(x-pl.amin(x), ram);
pl.legend(loc=2)
pl.xlabel('Wall Clock (s)');
pl.ylabel('% Usage')
pl.savefig(options.proc+'.eps');
#!/usr/bin/python
import glob, pylab
eta = 0.3
data = pylab.loadtxt("figs/mc/a1/wallsMC-a1-pair-%02.1f-%05.3f.dat" %(eta,2.005))
z = data[:,0]
dadz = pylab.zeros_like(z)
dr = 0.01
rmax = 5
for r in pylab.arange(2.005, rmax, dr):
data = pylab.loadtxt("figs/mc/a1/wallsMC-a1-pair-%02.1f-%05.3f.dat" %(eta,r))
rmax = r + dr/2
rmin = r - dr/2
dadz += data[:,1]*dr/r**6 # *4*pylab.pi/3*(rmax**3 - rmin**2)
data[:,1] = dadz # /(4*pylab.pi*rsquare_well**3/3 - 4*pylab.pi*2.0**3/3)
pylab.savetxt("figs/mc/a1/inverse-sixth-%.1f-rmax-%g.dat" % (eta, rmax), data, fmt='%.3g')
GrandMeansData = []
os.path.walk(os.getcwd(), LoadEnvCond, GrandMeansData)
GrandMeansData = sorted(GrandMeansData, key=lambda data_sort: data_sort[2])
TheMeans = p.zeros((len(GrandMeansData), 4))
i = 0
for item in GrandMeansData:
TheMeans[i,0] = item[0]
TheMeans[i,1] = item[1]
TheMeans[i,2] = item[2]
TheMeans[i,3] = item[4]
i += 1
xx = '# %(num)1.6f'%{"num":GrandMeansData[0][3]}
TheMeans = TheMeans[~p.isnan(TheMeans).any(1)] # removing NaN's from the output
p.savetxt("VarianceOfEnvEtAl.dat", TheMeans, fmt='%1.6f')
doc = open('VarianceOfEnvEtAl.dat', 'a')
doc.write(xx)
print TheMeans
p.figure(9, figsize=(1000,600))
p.plot(TheMeans[:, 2], TheMeans[:, 0], 'ko--')
p.fill_between(TheMeans[:, 2], TheMeans[:, 0] + TheMeans[:, 1],
TheMeans[:, 0] - TheMeans[:, 1], color=(0.75, 0.75, 0.75, 0.75))
p.plot(TheMeans[:, 2], TheMeans[:, 3], 'ko-')
p.xlabel('turbulence level ($ T $)', fontsize=LabelFontSize)
p.ylabel("variance of the env conditions",
fontsize=LabelFontSize)
p.ylim(ymin=0)
p.xticks(size=TickSize)
chgcarfiles=sys.argv[1].split(",")
bondLengths=map(float,sys.argv[2].split(","))
normalize=False
if len(sys.argv)>3:
if sys.argv[3][0] in ["n","N"]:
normalize=True
verbose=True
Ninterps=[50,50]
if len(sys.argv)>4:
Ninterps=map(int,sys.argv[5].split(","))
for chgcarfile in chgcarfiles:
avgGrids,grids,bondCounts=chgcarBondAnalysis(chgcarfile,bondLengths,normalize,verbose,Ninterps=Ninterps,loc="center")
chgcar=open(chgcarfile,"r").readlines()
poscardata,gridSize,chg = chgcarIO.read(chgcar)
"""
for avgGrid,bondCount,cutoff in zip(avgGrids,bondCounts,bondLengths):
#vtkfname=chgcarfile+"_cut%2.2f_NBond%d.vtk"%(cutoff,bondCount)
#chgcar.writeVTK(vtkfname,poscardata,avgGrid.shape,avgGrid)
pl.imshow(avgGrid[0])
pl.show()
"""
for i,grid in enumerate(grids):
pl.savetxt("grid%d_%s.data"%(i,chgcarfile),grid)
pl.imshow(grid)
pl.show()
import mypy
import pylab as pl
res = mypy.sps.read_res('filters.res')
pl.savetxt('./data/HST/WFC/F435W.txt', res[1-1].data)
pl.savetxt('./data/HST/WFC/F475W.txt', res[2-1].data)
pl.savetxt('./data/HST/WFC/F555W.txt', res[3-1].data)
pl.savetxt('./data/HST/WFC/F606W.txt', res[4-1].data)
pl.savetxt('./data/HST/WFC/F775W.txt', res[5-1].data)
pl.savetxt('./data/HST/WFC/F814W.txt', res[6-1].data)
pl.savetxt('./data/HST/WFC/F850LP.txt', res[7-1].data)
pl.savetxt('./data/HST/WFPC2/F300W.txt', res[10-1].data)
pl.savetxt('./data/HST/WFPC2/F336W.txt', res[11-1].data)
pl.savetxt('./data/HST/WFPC2/F450W.txt', res[12-1].data)
pl.savetxt('./data/HST/WFPC2/F555W.txt', res[13-1].data)
pl.savetxt('./data/HST/WFPC2/F606W.txt', res[14-1].data)
pl.savetxt('./data/HST/WFPC2/F702W.txt', res[15-1].data)
pl.savetxt('./data/HST/WFPC2/F814W.txt', res[16-1].data)
pl.savetxt('./data/HST/WFPC2/F850LP.txt', res[17-1].data)
pl.savetxt('./data/HST/WFC3/F098M.txt', res[201-1].data)
pl.savetxt('./data/HST/WFC3/F105W.txt', res[202-1].data)
pl.savetxt('./data/HST/WFC3/F125W.txt', res[203-1].data)
pl.savetxt('./data/HST/WFC3/F140W.txt', res[204-1].data)
pl.savetxt('./data/HST/WFC3/F160W.txt', res[205-1].data)
##################################################REGRIDDING & PLOTTING
xi, yi, zi = toRegGrid(sol, nx=50, ny=50)
pl.matplotlib.pyplot.autumn()
pl.contourf(xi, yi, zi, 10)
pl.xlabel("Horizontal Displacement (m)")
pl.ylabel("Depth (m)")
pl.savefig(os.path.join(save_path, "Ucontour.png"))
print("Solution has been plotted ...")
cut = int(len(xi) // 2)
pl.clf()
r = np.linspace(
0.0000001, mx / 2, 100
) # starting point would be 0 but that would cause division by zero later
m = 2 * pl.pi * 10 * 10 * 200 * -G / (r * r)
pl.plot(xi, zi[:, cut])
#pl.plot(r+2500,m)
pl.title("Potential Profile")
pl.xlabel("Horizontal Displacement (m)")
pl.ylabel("Potential")
pl.savefig(os.path.join(save_path, "Upot00.png"))
out = np.array([xi, zi[:, cut]])
pl.savetxt('profile1.asc', out.transpose())
pl.clf()
####################################################ITERATION VARIABLES
n = 0 # iteration counter
t = 0 # time counter
##############################################################ITERATION
while t < tend:
g = grad(u)
pres = csq * g # get current pressure
mypde.setValue(X=-pres) # set values in pde
accel = mypde.getSolution() # get new acceleration
u_p1 = (2.0 * u - u_m1) + h * h * accel # calculate the displacement for the next time step
u_m1 = u
u = u_p1 # shift values back one time step for next iteration
# save current displacement, acceleration and pressure
if t >= rtime:
saveVTK(
os.path.join(savepath, "ex07b.%i.vtu" % n),
displacement=length(u),
acceleration=length(accel),
tensor=pres,
)
rtime = rtime + rtime_inc # increment data save time
u_rec0.append(rec.getValue(u)) # location specific recording
# increment loop values
t = t + h
n = n + 1
print("time step %d, t=%s" % (n, t))
# save location specific recording to file
pl.savetxt(os.path.join(savepath, "u_rec.asc"), u_rec0)
def process():
options, inFilenames = parse_options()
options.port = bool(options.psort)
# open files
if options.psort == 1:
inFilenames = psorted(inFilenames)
afs = [al.Sndfile(f) for f in inFilenames]
if (options.mixingmatrix.strip() == "") and \
(options.unmixingmatrix.strip() == ""):
# subsample
if options.method == 'simple':
if len(options.subsample) == 0:
args = [44100, ] # set defaults
elif len(options.subsample) == 1:
try:
args = [int(options.subsample[0]), ]
except ValueError:
raise ValueError("Could not convert subsample argument "
"to int[%s]" % options.subsample[0])
else:
raise ValueError("Wrong number of subsample arguments, "
"expected 1: %s" % str(options.subsample))
data = subsample(afs, *tuple(args))
elif options.method == 'random':
if len(options.subsample) == 0:
args = [44100, ]
elif len(options.subsample) == 1:
try:
args = [int(options.subsample[0]), ]
except ValueError:
raise ValueError("Could not convert subsample argument "
"to int[%s]" % options.subsample[0])
else:
raise ValueError("Wrong number of subsample arguments, "
"expected 1: %s" % str(options.subsample))
data = random_subsample(afs, *tuple(args))
elif options.method == 'range':
# only accept two arguments, a start and stop
if len(options.subsample) == 2:
try:
args = [int(options.subsample[0]), \
int(options.subsample[1])]
except ValueError:
raise ValueError("Could not convert subsample arguments "
"to int[%s,%s]" % tuple(options.subsample))
else:
raise ValueError("Wrong number of subsample arguments, "
"expected 2: %s" % str(options.subsample))
data = range_subsample(afs, *tuple(args))
elif options.method == 'multi':
if (len(options.subsample) % 2) or (len(options.subsample) == 0):
raise ValueError("Wrong number of subsample arguments, "
"expected at least or multiples of 2: %s" % \
str(options.subsample))
else:
ranges = []
for (s, e) in zip(options.subsample[::2], \
options.subsample[1::2]):
ranges.append((s, e))
data = multi_range_subsample(afs, ranges)
else:
raise ValueError("Unknown subsample method: %s" % options.method)
# ica
ica = run_ica(data, options.ncomponents)
# clean
MM = clean_ica(ica, options.threshold, options.count)
UM = pl.matrix(ica.unmixing_matrix_)
# save M
mmfilename = '%s/mixingmatrix' % options.output
pl.savetxt(mmfilename, MM)
umfilename = '%s/unmixingmatrix' % options.output
pl.savetxt(umfilename, UM)
# add meta info
for fn in [mmfilename, umfilename]:
with open(fn, 'a') as f:
f.write('# method: %s\n' % options.method)
f.write('# subsample: %s\n' % str(options.subsample))
f.write('# threshold: %s\n' % str(options.threshold))
f.write('# count: %i\n' % options.count)
for (i, ifn) in enumerate(inFilenames):
f.write('# %i %s\n' % (i, ifn))
else:
logging.debug("Loading matrix from file: %s" % options.mixingmatrix)
MM = pl.matrix(pl.loadtxt(options.mixingmatrix))
logging.debug("Loading matrix from file: %s" % options.unmixingmatrix)
UM = pl.matrix(pl.loadtxt(options.unmixingmatrix))
if not options.short:
ofs = make_output_files(inFilenames, options.output, \
afs[0].format, afs[0].samplerate)
#clean_files(afs, ofs, ica, MM, options.chunksize)
clean_files(afs, ofs, UM, MM, options.chunksize)
# close
logging.debug("Closing files")
[ofile.close() for ofile in ofs]
if options.plot:
pl.figure()
#pl.imshow(ica.get_mixing_matrix(), interpolation='none')
pl.imshow(MM, interpolation='none')
pl.colorbar()
pl.suptitle("Mixing matrix (pre-cleaning)")
pl.figure()
pl.imshow(MM, interpolation='none')
pl.colorbar()
pl.suptitle("Mixing matrix (post-cleaning)")
# plot components?
pl.show()
return UM, MM
def switch_function(threshold_temperature, temp_array):
return [1 if x < threshold_temperature else 0 for x in temp_array]
# Importing the data
file_name = "../input_data/temperatures_de Bilt_2013_fifteen_minutes.csv"
temperature_file = open(file_name)
# Data for temperature (15 minutes)
data = genfromtxt(temperature_file, delimiter=",")
# Applying the function
threshold = 4.0
switch_profile = switch_function(threshold, data)
plt.savetxt("../output_data/hhp_switch_according_to_" + file_name.split('/')[-1], switch_profile, fmt='%.3f')
min_x = 0
max_x = 5000
plt.figure(figsize=[10,7])
plt.plot(([threshold]*35040)[min_x: max_x],'r--')
plt.plot(data[min_x: max_x],'k', label='Temperature')
plt.plot(10*np.array(switch_profile[min_x: max_x]), label='HP/Gas')
plt.xlabel('time (15 minutes)')
plt.ylabel('Temperature, HP/Gas')
plt.legend()
plt.show()
plt.close()
def main():
# assign variables, define constants
args = parseCMD()
T,H,N = float(args.temp), float(args.field), int(args.nodes)
J, s = float(args.exchange), int(args.sweeps)
k_B = 1 # = 1.3806503 * pow(10,-23)
upColor = 'Fuchsia'
downColor = 'Black'
if args.evolve:
findMencoder()
if args.align:
print 'Chose to start all spins in up position'
K = 3
m = 5
# http://networkx.github.com/documentation/latest/tutorial/
# tutorial.html#adding-attributes-to-graphs-nodes-and-edges
#G = nx.complete_graph(N)
#G = nx.karate_club_graph()
#z=[K for i in range(N)]
#G=nx.expected_degree_graph(z)
G = nx.barabasi_albert_graph(N, m, seed=None)
# keep track of spins of nodes
spinUp, spinDown = [], []
# assign each node a spin (\pm 1)
for node in G:
if args.align:
r = 1.0
else:
r = 2.0*random.randint(0,1)-1.0
if r == 1.0:
spinUp.append(node)
else:
spinDown.append(node)
for neighbor in range(len(G)):
if neighbor in G[node]:
G[neighbor][node] = r
else:
pass
# compute initial quantities
totSpin = len(spinUp) - len(spinDown)
spinSum = 0.0
for i in range(len(G)):
if i in spinUp:
tempSpin = 1.0
else:
tempSpin = -1.0
for j in G[i]:
spinSum += tempSpin*G[i][j]
E = - 0.5 * J * spinSum # divide by two because of double counting
M = 1.0*totSpin/(1.0*N)
# define arrays for mcSteps, Energies, Magnetism
mcSteps, Es, Ms = pl.arange(s), pl.array([E]), pl.array([M])
E2 = pl.array([E*E])
# keep track of acceptance
a = 0 # accepted moves
r = 0 # rejected moves
if args.showHist:
pl.ion()
fig = pl.figure(1)
ax = fig.add_subplot(111)
position = nx.circular_layout(G)
for step in mcSteps:
# randomly choose a node to try to flip the spin
R = random.randint(0,len(G)-1)
# compute change in energy from flipping that spin
delE = EnergyChange(spinUp, G, J, R)
# calculate Boltzmann factor
Boltz = pl.exp(-1.0*delE/(k_B*T))
if (delE <= 0):
reject = False
E += delE
spinUp, spinDown, totSpin = swapSpin(spinUp,
spinDown, R, totSpin)
else:
n = random.random()
if (n <= Boltz):
E += delE
reject = False
spinUp, spinDown, totSpin = swapSpin(spinUp,
spinDown, R, totSpin)
else:
reject = True
if reject==True:
r += 1
else:
a += 1
# calculate magnetism (not absolute value)
M = 1.0*totSpin/(1.0*N)
# store observables in array
Es = pl.append(Es, E)
Ms = pl.append(Ms, M)
E2 = pl.append(E2, E*E)
if args.showHist:
updatePlot(G, position, spinUp, spinDown, upColor, downColor, ax,
args.evolve, step)
if args.showHist:
pl.close()
pl.ioff()
if args.evolve:
os.chdir('./data/animate/')
# set up bash commands to run mencoder
command = ('mencoder', 'mf://*.png', '-mf', 'type=png:w=800:h=600:fps=25',
'-ovc', 'lavc', '-lavcopts', 'vcodec=mpeg4', '-oac', 'copy',
'-o', 'fightingNodes.avi')
print "\n\nabout to execute:\n%s\n\n" % ' '.join(command)
subprocess.check_call(command)
print os.getcwd()
for image in glob.glob('*png*'):
os.remove(image)
os.chdir('../..')
print "\n The movie was written to 'fightingNodes.avi'"
print 'acceptance ratio: ', 1.0*a/(r+a)
if os.path.exists('./data/'):
os.chdir('./data/')
else:
os.mkdir('./data/')
os.chdir('./data/')
filename = 'ising2D_L%s_s%s_Temp%s.dat'%(int(N), s, T)
fid = open(filename, 'w')
fid.write('# temp: %s\n'%T)
fid.write('# nodes: %s\n'%N)
fid.write('# field: %s\n'%H)
fid.write('# %15s\t%15s\t%15s\t%15s\n'%('mcSteps','Energies',
'Magnetism','Energy^2'))
zipped = zip(mcSteps, Es, Ms, E2)
pl.savetxt(fid, zipped, fmt='%5.9f\t%5.9f\t%5.9f\t%5.9f')
fid.close()
print 'Data has been saved to: ',filename
No_overlaps=Data[picked]
if(mode==2):
print "- Cutting out particles with at least two overlaps at distance <", cutoff,"..."
picked=[]
for p in range(N):
# removing only double overlaps
if(pl.sum(test[p][p:])>1):
pass
else:
picked.append(p)
No_double_overlaps=Data[picked]
# Do it again, now for the single overlaps
dists=squareform(pdist(No_double_overlaps))
# exclude the case of self-distance
pl.fill_diagonal(dists, pl.inf)
print "- Cutting out particles with overlaps at distance <", cutoff,"..."
test= (dists<cutoff)
picked_again=[]
for p in range(No_double_overlaps.shape[0]):
if(pl.any(test[p][p:])):
pass
else:
picked_again.append(p)
No_overlaps=No_double_overlaps[picked_again]
print "Saving file "+F.CYAN+"Trimmed_"+filename+F.RESET
pl.savetxt("Trimmed_"+filename,No_overlaps, fmt="%g")
print "\n====> Removed"+F.GREEN,N-No_overlaps.shape[0],F.RESET+"of the initial", N,"particles. "+F.RED,No_overlaps.shape[0],F.RESET+"remaining."
tmp = cities[0]
cities[0] = cities[21]
cities[21] = tmp
tmpname = citynames[0]
citynames[0] = citynames[21]
citynames[21] = tmpname
print "Starting point: %s" % (citynames[0])
print "Building the distances table"
dist_table = hm.salesman.build_distance_table(cities, type="geographic")
pylab.savetxt("DMAT.txt", dist_table)
print "Solving..."
tstart = time.time()
result = hm.salesman.solve_ga(
dist_table, ncities, pop_size=500, kill_percent=0.4, mutation_prob=0.01, crossover_prob=0.75, max_gen=1000
)
tend = time.time()
print "Done in %g seconds" % (tend - tstart)
best_routes, best_dists, dists = result
dists=squareform(pdist(Positions))
# exclude the case of self-distance
pl.fill_diagonal(dists, pl.inf)
# first in, first served
test= (dists<cutoff)
print "- Cutting overlaps"
picked=[]
for p in range(N):
if pl.any(test[p,:]):
test[:,p]=False
test[p,:]=False
else:
picked.append(p)
No_overlaps=Positions[picked]
print "\n====> Detected"+F.GREEN,No_overlaps.shape[0],F.RESET+"particles.\n"
# ======================== SAVING THE RESULT =====================
# reorder the columns
z,y,x=No_overlaps[:,0],No_overlaps[:,1],No_overlaps[:,2]
outfile="Detected_"+filename.split('_')[0][-3:]+".txt"
pl.savetxt(outfile,zip(x,y,z), fmt="%g")
print "Saved the output in ", outfile
print "Total execution time:", time.time()-start
for i in range(num_stages): if i < 1250: t = ((score_1250 - score_0)*i/1250) + score_0 else: t = ((score_2000 - score_1250)*(i - 1250)/(2000 - 1250)) + score_1250 thresholds[i] = t else: # 2012 cascade (ECCV workshop submission time) from scipy.interpolate import interp1d as interpolate_1d curve_samples_x = [0, 6, 11, 45, 60, 180, 370, 500, 1000, 1350, 1500, 1750, 2000] curve_samples_y = [-0.002, -0.0078, -0.007, -0.011, -0.013, -0.018, -0.02, -0.023, -0.0227, -0.0215, -0.0245, -0.0145, -0.006] spline_order = 1 curve = interpolate_1d(curve_samples_x, curve_samples_y, kind=spline_order) for i in range(num_stages): thresholds[i] = curve(i) data = [thresholds] * num_cascades data = pylab.array(data) filename = "synthetic_cascades_thresholds.txt" pylab.savetxt(filename, data) print "Created", filename
def main():
import optparse
from numpy import sum
# Parse command line
parser = optparse.OptionParser(usage=USAGE)
parser.add_option("-p",
"--plot",
action="store_true",
help="Generate pdf with IR-spectrum")
parser.add_option("-i",
"--info",
action="store_true",
help="Set up/ Calculate vibrations & quit")
parser.add_option("-s",
"--suffix",
action="store",
help="Call suffix for binary e.g. 'mpirun -n 4 '",
default='')
parser.add_option("-r",
"--run",
action="store",
help="path to FHI-aims binary",
default='')
parser.add_option("-x",
"--relax",
action="store_true",
help="Relax initial geometry")
parser.add_option("-m",
"--molden",
action="store_true",
help="Output in molden format")
parser.add_option("-w",
"--distort",
action="store_true",
help="Output geometry distorted along imaginary modes")
parser.add_option("-t",
"--submit",
action="store",
help="""\
Path to submission script, string <jobname>
will be replaced by name + counter, string
<outfile> will be replaced by filename""")
parser.add_option("-d",
"--delta",
action="store",
type="float",
help="Displacement",
default=0.0025)
options, args = parser.parse_args()
if options.info:
print __doc__
sys.exit(0)
if len(args) != 2:
parser.error("Need exactly two arguments")
AIMS_CALL = options.suffix + ' ' + options.run
hessian_thresh = -1
name = args[0]
mode = args[1]
delta = options.delta
run_aims = False
if options.run != '': run_aims = True
submit_script = options.submit is not None
if options.plot:
import matplotlib as mpl
mpl.use('Agg')
from pylab import figure
if options.plot or mode == '1':
from pylab import savetxt, transpose, eig, argsort, sort,\
sign, pi, dot, sum, linspace, argmin, r_, convolve
# Constant from scipy.constants
bohr = constants.value('Bohr radius') * 1.e10
hartree = constants.value('Hartree energy in eV')
at_u = constants.value('atomic mass unit-kilogram relationship')
eV = constants.value('electron volt-joule relationship')
c = constants.value('speed of light in vacuum')
Ang = 1.0e-10
hbar = constants.value('Planck constant over 2 pi')
Avo = constants.value('Avogadro constant')
kb = constants.value('Boltzmann constant in eV/K')
hessian_factor = eV / (at_u * Ang * Ang)
grad_dipole_factor = (eV / (1. / (10 * c))) / Ang #(eV/Ang -> D/Ang)
ir_factor = 1
# Asign all filenames
inputgeomerty = 'geometry.in.' + name
inputcontrol = 'control.in.' + name
atomicmasses = 'masses.' + name + '.dat'
xyzfile = name + '.xyz'
moldenname = name + '.molden'
hessianname = 'hessian.' + name + '.dat'
graddipolename = 'grad_dipole.' + name + '.dat'
irname = 'ir.' + name + '.dat'
deltas = array([-delta, delta])
coeff = array([-1, 1])
c_zero = -1. / (2. * delta)
f = open('control.in', 'r') # read control.in template
template_control = f.read()
f.close
if submit_script:
f = open(options.submit, 'r') # read submission script template
template_job = f.read()
f.close
folder = '' # Dummy
########### Central Point ##################################################
if options.relax and mode == '0':
# First relax input geometry
filename = name + '.out'
folder = name + '_relaxation'
if not os.path.exists(folder): os.mkdir(folder) # Create folder
shutil.copy('geometry.in', folder + '/geometry.in') # Copy geometry
new_control = open(folder + '/control.in', 'w')
new_control.write(template_control +
'relax_geometry trm 1E-3\n') # Relax!
new_control.close()
os.chdir(folder) # Change directoy
print 'Central Point'
if run_aims:
os.system(AIMS_CALL + ' > ' +
filename) # Run aims and pipe the output
# into a file named 'filename'
if submit_script: replace_submission(template_job, name, 0, filename)
os.chdir('..')
############################################################################
# Check for relaxed geometry
if os.path.exists(folder + '/geometry.in.next_step'):
geometry = open(folder + '/geometry.in.next_step', 'r')
else:
geometry = open('geometry.in', 'r')
# Read input geometry
n_line = 0
struc = structure()
lines = geometry.readlines()
for line in lines:
n_line = n_line + 1
if line.rfind('set_vacuum_level') != -1: # Vacuum Level
struc.vacuum_level = float(split_line(line)[-1])
if line.rfind('lattice_vector') != -1: # Lattice vectors and periodic
lat = split_line(line)[1:]
struc.lattic_vector = append(struc.lattic_vector,
float64(array(lat))[newaxis, :],
axis=0)
struc.periodic = True
if line.rfind('atom') != -1: # Set atoms
line_vals = split_line(line)
at = Atom(line_vals[-1], line_vals[1:-1])
if n_line < len(lines):
nextline = lines[n_line]
if nextline.rfind(
'constrain_relaxation') != -1: # constrained?
at = Atom(line_vals[-1], line_vals[1:-1], True)
else:
at = Atom(line_vals[-1], line_vals[1:-1])
struc.join(at)
geometry.close()
n_atoms = struc.n()
n_constrained = n_atoms - sum(struc.constrained)
# Atomic mass file
mass_file = open(atomicmasses, 'w')
mass_vector = zeros([0])
for at_unconstrained in struc.atoms[struc.constrained == False]:
mass_vector = append(mass_vector,
ones(3) * 1. / sqrt(at_unconstrained.mass()))
line = '{0:10.5f}'.format(at_unconstrained.mass())
for i in range(3):
line = line + '{0:11.4f}'.format(at_unconstrained.coord[i])
line = line + '{0:}\n'.format(at_unconstrained.kind)
mass_file.writelines(line)
mass_file.close()
# Init
dip = zeros([n_constrained * 3, 3])
hessian = zeros([n_constrained * 3, n_constrained * 3])
index = 0
counter = 1
# Set up / Read folders for displaced atoms
for atom in arange(n_atoms)[struc.constrained == False]:
for coord in arange(3):
for delta in deltas:
filename=name+'.i_atom_'+str(atom)+'.i_coord_'+str(coord)+'.displ_'+\
str(delta)+'.out'
folder=name+'.i_atom_'+str(atom)+'.i_coord_'+str(coord)+'.displ_'+\
str(delta)
if mode == '0': # Put new geometry and control.in into folder
struc_new = copy.deepcopy(struc)
struc_new.atoms[atom].coord[coord]=\
struc_new.atoms[atom].coord[coord]+delta
geoname='geometry.i_atom_'+str(atom)+'.i_coord_'+str(coord)+\
'.displ_'+str(delta)+'.in'
if not os.path.exists(folder): os.mkdir(folder)
new_geo = open(folder + '/geometry.in', 'w')
newline='#\n# temporary structure-file for finite-difference '+\
'calculation of forces\n'
newline=newline+'# displacement {0:8.4f} of \# atom '.format(delta)+\
'{0:5} direction {1:5}\n#\n'.format(atom,coord)
new_geo.writelines(newline + struc_new.to_str())
new_geo.close()
new_control = open(folder + '/control.in', 'w')
template_control = template_control.replace(
'relax_geometry', '#relax_geometry')
new_control.write(template_control+'compute_forces .true. \n'+\
'final_forces_cleaned '+\
'.true. \noutput dipole \n')
new_control.close()
os.chdir(folder) # Change directoy
print 'Processing atom: '+str(atom+1)+'/'+str(n_atoms)+', coord.: '+\
str(coord+1)+'/'+str(3)+', delta: '+str(delta)
if run_aims:
os.system(AIMS_CALL + ' > ' +
filename) # Run aims and pipe the output
# into a file named 'filename'
if submit_script:
replace_submission(template_job, name, counter,
filename)
# os.system('qsub job.sh') # Mind the environment variables
os.chdir('..')
if mode == '1': # Read output
forces_reached = False
atom_count = 0
data = open(folder + '/' + filename)
for line in data.readlines():
if line.rfind(
'Dipole correction potential jump') != -1:
dip_jump = float(split_line(line)[-2]) # Periodic
if line.rfind('| Total dipole moment [eAng]') != -1:
dip_jump = float64(
split_line(line)[-3:]) # Cluster
if forces_reached and atom_count < n_atoms: # Read Forces
struc.atoms[atom_count].force = float64(
split_line(line)[2:])
atom_count = atom_count + 1
if atom_count == n_atoms:
forces_reached = False
if line.rfind('Total atomic forces') != -1:
forces_reached = True
data.close()
if struc.periodic:
dip[index, 2] = dip[
index,
2] + dip_jump * coeff[deltas == delta] * c_zero
else:
dip[index, :] = dip[index, :] + dip_jump * coeff[
deltas == delta] * c_zero
forces = array([])
for at_unconstrained in struc.atoms[struc.constrained ==
False]:
forces = append(
forces,
coeff[deltas == delta] * at_unconstrained.force)
hessian[index, :] = hessian[index, :] + forces * c_zero
counter = counter + 1
index = index + 1
if mode == '1': # Calculate vibrations
print 'Entering hessian diagonalization'
print 'Number of atoms = ' + str(n_atoms)
print 'Name of Hessian input file = ' + hessianname
print 'Name of grad dipole input file = ' + graddipolename
print 'Name of Masses input file = ' + atomicmasses
print 'Name of XYZ output file = ' + xyzfile
print 'Threshold for Matrix elements = ' + str(hessian_thresh)
if (hessian_thresh < 0.0): print ' All matrix elements are taken'+\
' into account by default\n'
savetxt(hessianname, hessian)
savetxt(graddipolename, dip)
mass_mat = mass_vector[:, newaxis] * mass_vector[newaxis, :]
hessian[abs(hessian) < hessian_thresh] = 0.0
hessian = hessian * mass_mat * hessian_factor
hessian = (hessian + transpose(hessian)) / 2.
# Diagonalize hessian (scipy)
print 'Solving eigenvalue system for Hessian Matrix'
freq, eig_vec = eig(hessian)
print 'Done ... '
eig_vec = eig_vec[:, argsort(freq)]
freq = sort(sign(freq) * sqrt(abs(freq)))
ZPE = hbar * (freq) / (2.0 * eV)
freq = (freq) / (200. * pi * c)
grad_dipole = dip * grad_dipole_factor
eig_vec = eig_vec * mass_vector[:, newaxis] * ones(
len(mass_vector))[newaxis, :]
infrared_intensity = sum(dot(transpose(grad_dipole),eig_vec)**2,axis=0)*\
ir_factor
reduced_mass = sum(eig_vec**2, axis=0)
norm = sqrt(reduced_mass)
eig_vec = eig_vec / norm
# The rest is output, xyz, IR,...
print 'Results\n'
print 'List of all frequencies found:'
print 'Mode number Frequency [cm^(-1)] Zero point energy [eV] '+\
'IR-intensity [D^2/Ang^2]'
for i in range(len(freq)):
print '{0:11}{1:25.8f}{2:25.8f}{3:25.8f}'.format(
i + 1, freq[i], ZPE[i], infrared_intensity[i])
print '\n'
print 'Summary of zero point energy for entire system:'
print '| Cumulative ZPE = {0:15.8f} eV'.format(sum(ZPE))
print '| without first six eigenmodes = {0:15.8f} eV\n'.format(
sum(ZPE) - sum(ZPE[:6]))
print 'Stability checking - eigenvalues should all be positive for a '+\
'stable structure. '
print 'The six smallest frequencies should be (almost) zero:'
string = ''
for zz in ZPE[:6]:
string = string + '{0:25.8f}'.format(zz)
print string
print 'Compare this with the largest eigenvalue, '
print '{0:25.8f}'.format(freq[-1])
nums = arange(n_atoms)[struc.constrained == False]
nums2 = arange(n_atoms)[struc.constrained]
newline = ''
newline_ir = '[INT]\n'
if options.molden:
newline_molden = '[Molden Format]\n[GEOMETRIES] XYZ\n'
newline_molden = newline_molden + '{0:6}\n'.format(n_atoms) + '\n'
for i_atoms in range(n_constrained):
newline_molden = newline_molden + '{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline_molden = newline_molden + '{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord])
newline_molden = newline_molden + '\n'
newline_molden = newline_molden + '[FREQ]\n'
for i in range(len(freq)):
newline_molden = newline_molden + '{0:10.3f}\n'.format(freq[i])
newline_molden = newline_molden + '[INT]\n'
for i in range(len(freq)):
newline_molden = newline_molden + '{0:17.6e}\n'.format(
infrared_intensity[i])
newline_molden = newline_molden + '[FR-COORD]\n'
newline_molden = newline_molden + '{0:6}\n'.format(n_atoms) + '\n'
for i_atoms in range(n_constrained):
newline_molden = newline_molden + '{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline_molden = newline_molden + '{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord] / bohr)
newline_molden = newline_molden + '\n'
newline_molden = newline_molden + '[FR-NORM-COORD]\n'
for i in range(len(freq)):
newline = newline + '{0:6}\n'.format(n_atoms)
if freq[i] > 0:
newline = newline + 'stable frequency at '
elif freq[i] < 0:
newline = newline + 'unstable frequency at '
if options.distort and freq[i] < -50:
struc_new = copy.deepcopy(struc)
for i_atoms in range(n_constrained):
for i_coord in range(3):
struc_new.atoms[i_atoms].coord[i_coord]=\
struc_new.atoms[i_atoms].coord[i_coord]+\
eig_vec[(i_atoms)*3+i_coord,i]
geoname = name + '.distorted.vibration_' + str(
i + 1) + '.geometry.in'
new_geo = open(geoname, 'w')
newline_geo = '#\n# distorted structure-file for based on eigenmodes\n'
newline_geo=newline_geo+\
'# vibration {0:5} :{1:10.3f} 1/cm\n#\n'.format(i+1,freq[i])
new_geo.writelines(newline_geo + struc_new.to_str())
new_geo.close()
elif freq[i] == 0:
newline = newline + 'translation or rotation '
newline = newline + '{0:10.3f} 1/cm IR int. is '.format(freq[i])
newline = newline + '{0:10.4e} D^2/Ang^2; red. mass is '.format(
infrared_intensity[i])
newline = newline + '{0:5.3f} a.m.u.; force const. is '.format(
1.0 / reduced_mass[i])
newline = newline + '{0:5.3f} mDyne/Ang.\n'.format(
((freq[i] *
(200 * pi * c))**2) * (1.0 / reduced_mass[i]) * at_u * 1.e-2)
if options.molden: newline_molden=newline_molden+\
'vibration {0:6}\n'.format(i+1)
for i_atoms in range(n_constrained):
newline = newline + '{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline = newline + '{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord])
for i_coord in range(3):
newline = newline + '{0:10.4f}'.format(
eig_vec[(i_atoms) * 3 + i_coord, i])
if options.molden:
newline_molden = newline_molden + '{0:10.4f}'.format(
eig_vec[(i_atoms) * 3 + i_coord, i] / bohr)
newline = newline + '\n'
if options.molden: newline_molden = newline_molden + '\n'
for i_atoms in range(n_atoms - n_constrained):
newline = newline + '{0:6}'.format(
struc.atoms[nums2[i_atoms]].kind)
for i_coord in range(3):
newline = newline + '{0:10.4f}'.format(
struc.atoms[nums2[i_atoms]].coord[i_coord])
for i_coord in range(3):
newline = newline + '{0:10.4f}'.format(0.0)
newline = newline + '\n'
newline_ir = newline_ir + '{0:10.4e}\n'.format(
infrared_intensity[i])
xyz = open(xyzfile, 'w')
xyz.writelines(newline)
xyz.close()
ir = open(irname, 'w')
ir.writelines(newline_ir)
ir.close()
if options.molden:
molden = open(moldenname, 'w')
molden.writelines(newline_molden)
molden.close()
if mode == '1' and options.plot:
x = linspace(freq.min() - 500, freq.max() + 500, 1000)
z = zeros(len(x))
for i in range(len(freq)):
z[argmin(abs(x - freq[i]))] = infrared_intensity[i]
window_len = 150
gauss = signal.gaussian(window_len, 10)
s = r_[z[window_len - 1:0:-1], z, z[-1:-window_len:-1]]
z_convolve = convolve(gauss / gauss.sum(), s,
mode='same')[window_len - 1:-window_len + 1]
fig = figure(0)
ax = fig.add_subplot(111)
ax.plot(x, z_convolve, 'r', lw=2)
ax.set_xlim([freq.min() - 500, freq.max() + 500])
ax.set_ylim([-0.01, ax.get_ylim()[1]])
ax.set_yticks([])
ax.set_xlabel('Frequency [1/cm]', size=20)
ax.set_ylabel('Intensity [a.u.]', size=20)
fig.savefig(name + '_IR_spectrum.pdf')
print '\n Done. '
N += 1;
if f.proc.is_running()==False:
pname = str(proc.pid);
fname = str(proc.pid);
fid = proc.pid;
else:
pname = proc.name;
fname = f.proc.name;
fid = f.proc.pid;
x, cpu, ram, z, i0, i1, i2, i3 = f.get_result()
if options.tag!=None:
outname = options.tag+'.'+pname+'.'+fname+'.'+str(fid)+str(N)+'.dat';
else:
outname = pname+'.'+fname+'.'+str(fid)+str(N)+'.dat';
pl.savetxt(outname, zip(x, z, cpu, ram, i0, i1, i2, i3), fmt='%10.10f');
else:
for f in F:
x, cpu, ram, z = f.get_result()
pl.step(x-pl.amin(x), cpu, label=f.proc.name)
pl.step(x-pl.amin(x), ram);
pl.legend(loc=2)
pl.xlabel('Wall Clock (s)');
pl.ylabel('% Usage')
pl.savefig(proc+'.eps');
if fout!=None:
fout.close()
os.system('mv temp.txt '+pname+'.stdout.txt')
def key(self, event):
'''
# x = identify line
# i = skip to next line
# z = zoom in
# a = zoom out
# r = reset
'''
k = event.key
x = event.xdata
y = event.ydata
sp = event.inaxes
if k == 'x' and sp.get_subplotspec() == self.sp1.get_subplotspec():
self.sp1.axvline(x, color='r', lw=1)
self.show_lines[self.counter][0].set_ls('-')
self.counter += 1
if self.counter == len(self.ref_lines):
self.sp1.text(0.35, 0.35, "\n Complete: close \n this window \n",
transform=self.sp1.transAxes, size=22, bbox=dict(fc='w'))
self.sp2.text(0.35, 0.35, "\n Complete: close \n this window \n",
transform=self.sp2.transAxes, size=22, bbox=dict(fc='w'))
pl.savetxt('initial_lambda_soln.dat', self.soln_data)
else:
self.soln_data.append([self.ref_lines[self.counter], x])
self.show_lines.append([self.sp2.axvline(self.ref_lines[self.counter],
color='r', lw=1, ls='--'),
self.sp2.annotate(str(int(self.ref_lines[self.counter]+0.5)),
(self.ref_lines[self.counter], self.ytext),
rotation='vertical', size=11)])
if k == 'i':
self.show_lines[self.counter][0].set_lw(0)
self.show_lines[self.counter][1].set_visible(False)
self.counter += 1
if self.counter == len(self.ref_lines):
self.sp1.text(0.35, 0.35, "\n Complete: close \n this window \n",
transform=self.sp1.transAxes, size=22, bbox=dict(fc='w'))
self.sp2.text(0.35, 0.35, "\n Complete: close \n this window \n",
transform=self.sp2.transAxes, size=22, bbox=dict(fc='w'))
pl.savetxt('initial_lambda_soln.dat', self.soln_data)
else:
self.show_lines.append([self.sp2.axvline(self.ref_lines[self.counter],
color='r', lw=1, ls='--'),
self.sp2.annotate(str(int(self.ref_lines[self.counter]+0.5)),
(self.ref_lines[self.counter], self.ytext),
rotation='vertical', size=11)])
if k == 'z':
self.zooms.append([self.sp1.axis()[:2], self.sp2.axis()[:2]])
axis = sp.axis()
dx = (axis[1] - axis[0])/10.
sp.set_xlim(x-dx, x+dx)
if k == 'a':
if len(self.zooms):
self.sp1.set_xlim(self.zooms[-1][0])
self.sp2.set_xlim(self.zooms[-1][1])
self.zooms.pop(-1)
if k == 'r':
self.fig.clf()
self.soln_data = []
self.counter = 0
self.sp1 = self.fig.add_subplot(211)
self.sp2 = self.fig.add_subplot(212)
self.zooms = []
self.sp1.plot(self.xaxis, self.flux/max(self.flux))
self.sp1.set_ylim(-0.03, 1.2)
self.sp1.set_title('ArcLamp Spectrum: x = identify line, i = skip line, z/a = zoom in/out, r = reset')
self.sp1.set_xlabel('Pixel')
self.sp2.plot(self.ref_spec[:,0], self.ref_spec[:,1]/max(self.ref_spec[:,1]))
self.sp2.set_ylim(-0.03, 1.2)
x0, x1, y0, y1 = self.sp2.axis()
self.ytext = y0 + (y1-y0)*0.85
self.show_lines = [[self.sp2.axvline(self.ref_lines[0], color='r', lw=1, ls='--'),
self.sp2.annotate(str(int(self.ref_lines[0]+0.5)),
(self.ref_lines[0], self.ytext),
rotation='vertical', size=11)]]
self.sp2.set_title('Reference Spectrum')
self.sp2.set_xlabel('Wavelength (Angstroms)')
pl.draw()
maxi = int(7 * 4 * 24)
plt.plot(demand_profile[mini:maxi])
plt.plot(availability_profile[mini:maxi])
plt.xlabel("time (15 minutes)")
plt.ylabel("Fraction of buffer extracted")
plt.show()
print "../output_data/hp_space_heating_" + str(heat_output_capacity_in_kW) + "kW_" + str(floor_area) + "m2_" + str(
liters
) + "liter"
plt.savetxt(
"../output_data/hp_space_heating_"
+ str(heat_output_capacity_in_kW)
+ "kW_"
+ str(floor_area)
+ "m2_"
+ str(liters)
+ "liter_use.csv",
demand_profile,
fmt="%.3f",
delimiter=",",
)
plt.savetxt(
"../output_data/hp_space_heating_"
+ str(heat_output_capacity_in_kW)
+ "kW_"
+ str(floor_area)
+ "m2_"
+ str(liters)
+ "liter_availability.csv",
availability_profile,
fmt="%.3f",
bath = 'bath_' profile = individual_DHW_profile(n, b, i) use_filename = '../output_data/DHW_demand_profile_' + str(n) + 'p_' + bath + str(i) + '.csv' # aggregated profiles # # average number of people per household # n = 2.2 # profile = aggregated_DHW_profile(n) # use_filename = '../output_data/DHW_demand_profile_aggregated.csv' print use_filename plt.savetxt(use_filename, profile, fmt='%.3e', delimiter=',') plt.plot(profile) plt.show() # # Appendix: plot probability curves for documentation # # probability throughout the day # plt.figure(1) # plt.plot(prob_day_AB / sum(prob_day_AB), label="types A and B") # plt.plot(prob_day_C / sum(prob_day_C), label="type C") # plt.plot(prob_day_D / sum(prob_day_D), label="type D") # plt.xlim(0, 96)
t1fit = 16500.
t2fit = 18000.
t1out = 16800
t2out = 17500.
degree = 15
fname_notide = os.path.splitext(fname)[0] + '_notide.txt'
t,eta = dart.plotdart(fname)
t = pylab.flipud(t)
eta = pylab.flipud(eta)
c,t_notide,eta_notide = dart.fit_tide_poly(t,eta,degree,t1fit,t2fit, t1out,t2out)
# Time of quake: 05:48:15 UTC on March 12, 2011
# Convert to minutes after start of March:
t_quake = 11*24*60 + 5*60 + 48 + 15/60.
#print "Time of quake = %7.2f minutes after start of March" % t_quake
t_sec = (t_notide-t_quake)*60.
d = pylab.vstack([t_sec,eta_notide]).T
pylab.savetxt(fname_notide,d)
print "Created file ",fname_notide
#dart.plot_postquake(t_notide,eta_notide,t_quake,gaugeno)
pylab.figure(70)
pylab.subplot(211)
pylab.title('DART %s' % gaugeno)
currentTime = data[0][0]
currentIndex = 0
while currentIndex < columns:
while currentIndex < columns and (currentTime > data[currentIndex][0]):
currentIndex += 1
if currentIndex < columns-1:
aTime = data[currentIndex][0]
bTime = data[currentIndex+1][0]
pTime = (currentTime - aTime) / (bTime - aTime)
row = [currentTime]
for i in range(1, rows):
currentValue = data[currentIndex][i]
nextValue = data[currentIndex+1][i]
interp = currentValue + (nextValue-currentValue)*pTime
row.append( interp )
r.append( row )
currentTime += samplingPeriod
return r
sensorFile = raw_input('data file ')
resampleRate = int(raw_input('sampling rate '))
sensorFile = os.path.realpath(sensorFile)
data = pylab.loadtxt(sensorFile)
data = resampleUniform(data, resampleRate)
pylab.savetxt(sensorFile + ".txt", data,'%d')
y1, y2 = extent[0], extent[1]
z1, z2 = extent[5]+0.1, -1*extent[4]
mesh = fatiando.mesh.prism_mesh(x1=x1, x2=x2, y1=y1, y2=y2, z1=z1, z2=z2,
nx=100, ny=50, nz=10)
# Set the seeds and save them for later use
log.info("Getting seeds from mesh:")
spoints1 = []
sdens1 = []
dx, dy = 5000, 3000
for x in numpy.arange(15000 + dx, 85000, dx):
for y in numpy.arange(15000 + dy, 35000, dy):
spoints1.append((x, y, 500))
sdens1.append(300)
pylab.savetxt("seeds1.txt", numpy.array(spoints1))
spoints2 = []
sdens2 = []
dx = 3000
for x in numpy.arange(25000 + dx, 55000, dx):
spoints2.append((x, 39500, 500))
sdens2.append(400)
pylab.savetxt("seeds2.txt", numpy.array(spoints2))
spoints = []
spoints.extend(spoints1)
spoints.extend(spoints2)
sdens = []
sdens.extend(sdens1)
grav_pot=sol,g_field=g_field,g_fieldz=g_fieldz,gz=gz)
##################################################REGRIDDING & PLOTTING
xi, yi, zi = toRegGrid(sol, nx=50, ny=50)
pl.matplotlib.pyplot.autumn()
pl.contourf(xi,yi,zi,10)
pl.xlabel("Horizontal Displacement (m)")
pl.ylabel("Depth (m)")
pl.savefig(os.path.join(save_path,"Ucontour.png"))
print("Solution has been plotted ...")
cut=int(len(xi)//2)
pl.clf()
r=np.linspace(0.0000001,mx/2,100) # starting point would be 0 but that would cause division by zero later
m=2*pl.pi*10*10*200*-G/(r*r)
pl.plot(xi,zi[:,cut])
#pl.plot(r+2500,m)
pl.title("Potential Profile")
pl.xlabel("Horizontal Displacement (m)")
pl.ylabel("Potential")
pl.savefig(os.path.join(save_path,"Upot00.png"))
out=np.array([xi,zi[:,cut]])
pl.savetxt('profile1.asc',out.transpose())
pl.clf()
def main():
import optparse
from numpy import sum
# Parse command line
parser = optparse.OptionParser(usage=USAGE)
parser.add_option("-p", "--plot", action="store_true",
help="Generate pdf with IR-spectrum, broadened with Lorentzian")
parser.add_option("-i", "--info", action="store_true",
help="Set up/ Calculate vibrations & quit")
parser.add_option("-s", "--suffix", action="store",
help="Call suffix for binary e.g. 'mpirun -n 4 '",
default='')
parser.add_option("-r", "--run", action="store",
help="path to FHI-aims binary",default='')
parser.add_option("-x", "--relax", action="store_true",
help="Relax initial geometry")
parser.add_option("-m", "--molden", action="store_true",
help="Output in molden format")
parser.add_option("-w", "--distort", action="store_true",
help="Output geometry distorted along imaginary modes")
parser.add_option("-t", "--submit", action="store",
help="""\
Path to submission script, string <jobname>
will be replaced by name + counter, string
<outfile> will be replaced by filename""")
parser.add_option("-d", "--delta", action="store", type="float",
help="Displacement", default=0.0025)
parser.add_option("-b", "--broadening", action="store", type="float",
help="Broadening for IR-spectrum in cm^{-1}", default=5)
options, args = parser.parse_args()
if options.info:
print __doc__
sys.exit(0)
if len(args) != 2:
parser.error("Need exactly two arguments")
AIMS_CALL=options.suffix+' '+options.run
hessian_thresh = -1
name=args[0]
mode=args[1]
delta=options.delta
broadening=options.broadening
run_aims=False
if options.run!='': run_aims=True
submit_script = options.submit is not None
if options.plot:
import matplotlib as mpl
mpl.use('Agg')
from pylab import figure
if options.plot or mode=='1' or mode=='2':
from pylab import savetxt, transpose, eig, argsort, sort,\
sign, pi, dot, sum, linspace, argmin, r_, convolve
# Constant from scipy.constants
bohr=constants.value('Bohr radius')*1.e10
hartree=constants.value('Hartree energy in eV')
at_u=constants.value('atomic mass unit-kilogram relationship')
eV=constants.value('electron volt-joule relationship')
c=constants.value('speed of light in vacuum')
Ang=1.0e-10
hbar=constants.value('Planck constant over 2 pi')
Avo=constants.value('Avogadro constant')
kb=constants.value('Boltzmann constant in eV/K')
pi=constants.pi
hessian_factor = eV/(at_u*Ang*Ang)
grad_dipole_factor=(eV/(1./(10*c)))/Ang #(eV/Ang -> D/Ang)
ir_factor = 1
# Asign all filenames
inputgeomerty = 'geometry.in.'+name
inputcontrol = 'control.in.'+name
atomicmasses = 'masses.'+name+'.dat';
xyzfile = name+'.xyz';
moldenname =name+'.molden';
hessianname = 'hessian.'+name+'.dat';
graddipolename = 'grad_dipole.'+name+'.dat';
irname = 'ir.'+name+'.dat';
deltas=array([-delta,delta])
coeff=array([-1,1])
c_zero = - 1. / (2. * delta)
f=open('control.in','r') # read control.in template
template_control=f.read()
f.close
if submit_script:
f=open(options.submit,'r') # read submission script template
template_job=f.read()
f.close
folder='' # Dummy
########### Central Point ##################################################
if options.relax and (mode=='0' or mode=='2'):
# First relax input geometry
filename=name+'.out'
folder=name+'_relaxation'
if not os.path.exists(folder): os.mkdir(folder) # Create folder
shutil.copy('geometry.in', folder+'/geometry.in') # Copy geometry
new_control=open(folder+'/control.in','w')
new_control.write(template_control+'relax_geometry trm 1E-3\n') # Relax!
new_control.close()
os.chdir(folder) # Change directoy
print 'Central Point'
if run_aims:
os.system(AIMS_CALL+' > '+filename) # Run aims and pipe the output
# into a file named 'filename'
if submit_script: replace_submission(template_job, name, 0, filename)
os.chdir('..')
############################################################################
# Check for relaxed geometry
if os.path.exists(folder+'/geometry.in.next_step'):
geometry=open(folder+'/geometry.in.next_step','r')
else:
geometry=open('geometry.in','r')
# Read input geometry
n_line=0
struc=structure()
lines=geometry.readlines()
for line in lines:
n_line= n_line+1
if line.rfind('set_vacuum_level')!=-1: # Vacuum Level
struc.vacuum_level=float(split_line(line)[-1])
if line.rfind('lattice_vector')!=-1: # Lattice vectors and periodic
lat=split_line(line)[1:]
struc.lattice_vector=append(struc.lattice_vector,float64(array(lat))
[newaxis,:],axis=0)
struc.periodic=True
if line.rfind('atom')!=-1: # Set atoms
line_vals=split_line(line)
at=Atom(line_vals[-1],line_vals[1:-1])
if n_line<len(lines):
nextline=lines[n_line]
if nextline.rfind('constrain_relaxation')!=-1: # constrained?
at=Atom(line_vals[-1],line_vals[1:-1],True)
else:
at=Atom(line_vals[-1],line_vals[1:-1])
struc.join(at)
geometry.close()
n_atoms= struc.n()
n_constrained=n_atoms-sum(struc.constrained)
# Atomic mass file
mass_file=open(atomicmasses,'w')
mass_vector=zeros([0])
for at_unconstrained in struc.atoms[struc.constrained==False]:
mass_vector=append(mass_vector,ones(3)*1./sqrt(at_unconstrained.mass()))
line='{0:10.5f}'.format(at_unconstrained.mass())
for i in range(3):
line=line+'{0:11.4f}'.format(at_unconstrained.coord[i])
line=line+'{0:}\n'.format(at_unconstrained.kind)
mass_file.writelines(line)
mass_file.close()
# Init
dip = zeros([n_constrained*3,3])
hessian = zeros([n_constrained*3,n_constrained*3])
index=0
counter=1
# Set up / Read folders for displaced atoms
for atom in arange(n_atoms)[struc.constrained==False]:
for coord in arange(3):
for delta in deltas:
filename=name+'.i_atom_'+str(atom)+'.i_coord_'+str(coord)+'.displ_'+\
str(delta)+'.out'
folder=name+'.i_atom_'+str(atom)+'.i_coord_'+str(coord)+'.displ_'+\
str(delta)
if mode=='0' or mode=='2': # Put new geometry and control.in into folder
struc_new=copy.deepcopy(struc)
struc_new.atoms[atom].coord[coord]=\
struc_new.atoms[atom].coord[coord]+delta
geoname='geometry.i_atom_'+str(atom)+'.i_coord_'+str(coord)+\
'.displ_'+str(delta)+'.in'
if not os.path.exists(folder): os.mkdir(folder)
new_geo=open(folder+'/geometry.in','w')
newline='#\n# temporary structure-file for finite-difference '+\
'calculation of forces\n'
newline=newline+'# displacement {0:8.4f} of \# atom '.format(delta)+\
'{0:5} direction {1:5}\n#\n'.format(atom,coord)
new_geo.writelines(newline+struc_new.to_str())
new_geo.close()
new_control=open(folder+'/control.in','w')
template_control=template_control.replace('relax_geometry',
'#relax_geometry')
new_control.write(template_control+'compute_forces .true. \n'+\
'final_forces_cleaned '+\
'.true. \n')
new_control.close()
os.chdir(folder) # Change directoy
print 'Processing atom: '+str(atom+1)+'/'+str(n_atoms)+', coord.: '+\
str(coord+1)+'/'+str(3)+', delta: '+str(delta)
if run_aims:
os.system(AIMS_CALL+' > '+filename)# Run aims and pipe the output
# into a file named 'filename'
if submit_script: replace_submission(template_job, name, counter,
filename)
# os.system('qsub job.sh') # Mind the environment variables
os.chdir('..')
if mode=='1' or mode=='2': # Read output
forces_reached=False
atom_count=0
data=open(folder+'/'+filename)
for line in data.readlines():
if line.rfind('Dipole correction potential jump')!=-1:
dip_jump = float(split_line(line)[-2]) # Periodic
if line.rfind('| Total dipole moment [eAng]')!=-1:
dip_jump = float64(split_line(line)[-3:]) # Cluster
if forces_reached and atom_count<n_atoms: # Read Forces
struc.atoms[atom_count].force=float64(split_line(line)[2:])
atom_count=atom_count+1
if atom_count==n_atoms:
forces_reached=False
if line.rfind('Total atomic forces')!=-1:
forces_reached=True
data.close()
if struc.periodic:
pass
#dip[index,2]=dip[index,2]+dip_jump*coeff[deltas==delta]*c_zero
else:
dip[index,:]=dip[index,:]+dip_jump*coeff[deltas==delta]*c_zero
forces=array([])
for at_unconstrained in struc.atoms[struc.constrained==False]:
forces=append(forces,coeff[deltas==delta]*at_unconstrained.force)
hessian[index,:]=hessian[index,:]+forces*c_zero
counter=counter+1
index=index+1
if mode=='1' or mode=='2': # Calculate vibrations
print 'Entering hessian diagonalization'
print 'Number of atoms = '+str(n_atoms)
print 'Name of Hessian input file = '+hessianname
print 'Name of grad dipole input file = '+graddipolename
print 'Name of Masses input file = '+atomicmasses
print 'Name of XYZ output file = '+xyzfile
print 'Threshold for Matrix elements = '+str(hessian_thresh)
if (hessian_thresh < 0.0): print ' All matrix elements are taken'+\
' into account by default\n'
savetxt(hessianname,hessian)
savetxt(graddipolename,dip)
mass_mat=mass_vector[:,newaxis]*mass_vector[newaxis,:]
hessian[abs(hessian)<hessian_thresh]=0.0
hessian=hessian*mass_mat*hessian_factor
hessian=(hessian+transpose(hessian))/2.
# Diagonalize hessian (scipy)
print 'Solving eigenvalue system for Hessian Matrix'
freq, eig_vec = eig(hessian)
print 'Done ... '
eig_vec=eig_vec[:,argsort(freq)]
freq=sort(sign(freq)*sqrt(abs(freq)))
ZPE=hbar*(freq)/(2.0*eV)
freq = (freq)/(200.*pi*c)
grad_dipole = dip * grad_dipole_factor
eig_vec = eig_vec*mass_vector[:,newaxis]*ones(len(mass_vector))[newaxis,:]
infrared_intensity = sum(dot(transpose(grad_dipole),eig_vec)**2,axis=0)*\
ir_factor
reduced_mass=sum(eig_vec**2,axis=0)
norm = sqrt(reduced_mass)
eig_vec = eig_vec/norm
# The rest is output, xyz, IR,...
print 'Results\n'
print 'List of all frequencies found:'
print 'Mode number Frequency [cm^(-1)] Zero point energy [eV] '+\
'IR-intensity [D^2/Ang^2]'
for i in range(len(freq)):
print '{0:11}{1:25.8f}{2:25.8f}{3:25.8f}'.format(i+1,freq[i],ZPE[i],
infrared_intensity[i])
print '\n'
print 'Summary of zero point energy for entire system:'
print '| Cumulative ZPE = {0:15.8f} eV'.format(sum(ZPE))
print '| without first six eigenmodes = {0:15.8f} eV\n'.format(sum(ZPE)-
sum(ZPE[:6]))
print 'Stability checking - eigenvalues should all be positive for a '+\
'stable structure. '
print 'The six smallest frequencies should be (almost) zero:'
string=''
for zz in ZPE[:6]: string=string+'{0:25.8f}'.format(zz)
print string
print 'Compare this with the largest eigenvalue, '
print '{0:25.8f}'.format(freq[-1])
nums=arange(n_atoms)[struc.constrained==False]
nums2=arange(n_atoms)[struc.constrained]
newline=''
newline_ir='[INT]\n'
if options.molden:
newline_molden='[Molden Format]\n[GEOMETRIES] XYZ\n'
newline_molden=newline_molden+'{0:6}\n'.format(n_atoms)+'\n'
for i_atoms in range(n_constrained):
newline_molden=newline_molden+'{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline_molden=newline_molden+'{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord])
newline_molden=newline_molden+'\n'
newline_molden=newline_molden+'[FREQ]\n'
for i in range(len(freq)):
newline_molden=newline_molden+'{0:10.3f}\n'.format(freq[i])
newline_molden=newline_molden+'[INT]\n'
for i in range(len(freq)):
newline_molden=newline_molden+'{0:17.6e}\n'.format(
infrared_intensity[i])
newline_molden=newline_molden+'[FR-COORD]\n'
newline_molden=newline_molden+'{0:6}\n'.format(n_atoms)+'\n'
for i_atoms in range(n_constrained):
newline_molden=newline_molden+'{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline_molden=newline_molden+'{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord]/bohr)
newline_molden=newline_molden+'\n'
newline_molden=newline_molden+'[FR-NORM-COORD]\n'
for i in range(len(freq)):
newline=newline+'{0:6}\n'.format(n_atoms)
if freq[i]>0:
newline=newline+'stable frequency at '
elif freq[i]<0:
newline=newline+'unstable frequency at '
if options.distort and freq[i]<-50:
struc_new=copy.deepcopy(struc)
for i_atoms in range(n_constrained):
for i_coord in range(3):
struc_new.atoms[i_atoms].coord[i_coord]=\
struc_new.atoms[i_atoms].coord[i_coord]+\
eig_vec[(i_atoms)*3+i_coord,i]
geoname=name+'.distorted.vibration_'+str(i+1)+'.geometry.in'
new_geo=open(geoname,'w')
newline_geo='#\n# distorted structure-file for based on eigenmodes\n'
newline_geo=newline_geo+\
'# vibration {0:5} :{1:10.3f} 1/cm\n#\n'.format(i+1,freq[i])
new_geo.writelines(newline_geo+struc_new.to_str())
new_geo.close()
elif freq[i]==0:
newline=newline+'translation or rotation '
newline=newline+'{0:10.3f} 1/cm IR int. is '.format(freq[i])
newline=newline+'{0:10.4e} D^2/Ang^2; red. mass is '.format(
infrared_intensity[i])
newline=newline+'{0:5.3f} a.m.u.; force const. is '.format(
1.0/reduced_mass[i])
newline=newline+'{0:5.3f} mDyne/Ang.\n'.format(((freq[i]*(200*pi*c))**2)*
(1.0/reduced_mass[i])*at_u*1.e-2)
if options.molden: newline_molden=newline_molden+\
'vibration {0:6}\n'.format(i+1)
for i_atoms in range(n_constrained):
newline=newline+'{0:6}'.format(struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord])
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(eig_vec[(i_atoms)*3+i_coord,i])
if options.molden: newline_molden=newline_molden+'{0:10.4f}'.format(
eig_vec[(i_atoms)*3+i_coord,i]/bohr)
newline=newline+'\n'
if options.molden: newline_molden=newline_molden+'\n'
for i_atoms in range(n_atoms-n_constrained):
newline=newline+'{0:6}'.format(struc.atoms[nums2[i_atoms]].kind)
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(
struc.atoms[nums2[i_atoms]].coord[i_coord])
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(0.0)
newline=newline+'\n'
newline_ir=newline_ir+'{0:10.4e}\n'.format(infrared_intensity[i])
xyz=open(xyzfile,'w')
xyz.writelines(newline)
xyz.close()
ir=open(irname,'w')
ir.writelines(newline_ir)
ir.close()
if options.molden:
molden=open(moldenname,'w')
molden.writelines(newline_molden)
molden.close()
if (mode=='1' or mode=='2') and options.plot:
x=linspace(freq.min()-500,freq.max()+500,1000)
z=zeros(len(x))
for i in range(len(freq)):
z[argmin(abs(x-freq[i]))]=infrared_intensity[i]
window_len=150
lorentzian=lorentz(pi,broadening,arange(250))#signal.gaussian(window_len,broadening)
s=r_[z[window_len-1:0:-1],z,z[-1:-window_len:-1]]
z_convolve=convolve(lorentzian/lorentzian.sum(),s,mode='same')[
window_len-1:-window_len+1]
fig=figure(0)
ax=fig.add_subplot(111)
ax.plot(x,z_convolve,'r',lw=2)
ax.set_xlim([freq.min()-500,freq.max()+500])
ax.set_ylim([-0.01,ax.get_ylim()[1]])
ax.set_yticks([])
ax.set_xlabel('Frequency [1/cm]',size=20)
ax.set_ylabel('Intensity [a.u.]',size=20)
fig.savefig(name+'_IR_spectrum.pdf')
print '\n Done. '