def test_ManageData_ReadAndDump(self):
md_dump = ManageData(dump_file=self.file_name)
md_dump.PushData(data=np.random.rand(10), key='paperino')
md_dump.PushData(data='pippo, pluto', key='pappo')
md_dump.Dump()
del md_dump
md_read = ManageData(dump_file=self.file_name)
md_read.Read()
os.rename(self.file_name, self.file_name_mv)
md_read.Dump()
del md_read
file_cmp = filecmp.cmp(self.file_name, self.file_name_mv)
os.remove(self.file_name)
os.remove(self.file_name_mv)
self.assertTrue(file_cmp)
def test_ManageData_Dump(self):
md_dump = ManageData(dump_file=self.file_name)
md_dump.PushData(data=np.random.rand(10), key='paperino')
md_dump.PushData(data='pippo, pluto', key='pappo')
chars_n = md_dump.Dump()
del md_dump
os.remove(self.file_name)
self.assertTrue(True)
def test_ManageData_Read(self):
md_dump = ManageData(dump_file=self.file_name)
md_dump.PushData(data=np.random.rand(10), key='paperino')
md_dump.PushData(data='pippo, pluto', key='pappo')
md_dump.Dump()
del md_dump
md_read = ManageData(dump_file=self.file_name)
read_flag = md_read.Read()
del md_read
os.remove(self.file_name)
self.assertTrue(read_flag)
kde_iso = KernelDensity(bandwidth=0.25, kernel='gaussian')
kde_iso.fit(np.log(norm_2iso_histo)[:, None])
logprob_iso_E12[G] = kde_iso.score_samples(u_bins_iso_E12[G][:, None])
kde_niso = KernelDensity(bandwidth=0.25, kernel='gaussian')
kde_niso.fit(np.log(norm_2niso_histo)[:, None])
logprob_niso_E12[G] = kde_niso.score_samples(u_bins_niso_E12[G][:,
None])
MDH.PushData(data=u_bins_iso_E12, key='u_bins_iso' + 'E12')
MDH.PushData(data=u_bins_niso_E12, key='u_bins_niso' + 'E12')
MDH.PushData(data=logprob_iso_E12, key='logprob_iso' + 'E12')
MDH.PushData(data=logprob_niso_E12, key='logprob_niso' + 'E12')
MDH.Dump()
else:
u_bins_iso_E6 = MDH.PullData('u_bins_iso' + 'E6')
u_bins_niso_E6 = MDH.PullData('u_bins_niso' + 'E6')
logprob_iso_E6 = MDH.PullData('logprob_iso' + 'E6')
logprob_niso_E6 = MDH.PullData('logprob_niso' + 'E6')
u_bins_iso_E8 = MDH.PullData('u_bins_iso' + 'E8')
u_bins_niso_E8 = MDH.PullData('u_bins_niso' + 'E8')
logprob_iso_E8 = MDH.PullData('logprob_iso' + 'E8')
logprob_niso_E8 = MDH.PullData('logprob_niso' + 'E8')
u_bins_iso_E10 = MDH.PullData('u_bins_iso' + 'E10')
u_bins_niso_E10 = MDH.PullData('u_bins_niso' + 'E10')
logprob_iso_E10 = MDH.PullData('logprob_iso' + 'E10')
logprob_niso_E10 = MDH.PullData('logprob_niso' + 'E10')
_u_profiles[_bin_i, _r] = np.sqrt(((UX_spl(_x_cut, _y_cut)) ** 2 +
(UY_spl(_x_cut, _y_cut)) ** 2)/c2)
_bin_i += 1
_u_profile_ave[_str] = np.zeros(_r_range.shape[0])
_u_profile_err[_str] = np.zeros(_r_range.shape[0])
for _r in _r_range:
_u_profile_ave[_str][_r] = np.mean(_u_profiles[:, _r])
_u_profile_err[_str][_r] = np.sqrt(np.var(_u_profiles[:, _r])/(_n_bins - 1))
AveU.PushData(data = _u_profile_ave, key = '_u_profile_ave')
AveU.PushData(data = _u_profile_err, key = '_u_profile_err')
AveU.PushData(data = L, key = 'L')
AveU.Dump()
else:
_u_profile_ave = AveU.PullData('_u_profile_ave')
_u_profile_err = AveU.PullData('_u_profile_err')
L = AveU.PullData('L')
_r_range = np.arange(0, L//2, 1)
'''
Plotting
'''
from matplotlib import rc, rcParams
import matplotlib
import matplotlib.pyplot as plt
rc('font',**{'family':'STIXGeneral'})
rc('mathtext', **{'fontset': 'stix'})
rc('text', usetex=True)