def save(self, filename):
"""Save the interpolation data to a file.
This data may not be the same from one version of scipy to
another, and may break with new versions, so is important to
test that this works with new versions of scipy. For now, the
only important values for the spline interpolation are stored
in the spline's _data member, but could change, as could the
structure of _eval_args. To be safe, do not depend on this to
load old interpolations. It is probably safest to create a
new interpolation for a new scipy version. The saved files are
not compatible between Python 2 and Python 3.
"""
data = {'_data': self._data}
if self.ext is not None:
data['ext'] = self.ext
else:
# We are on an older version of SciPy, just use ext=0
# meaning to extrapolate. SciPy versions before 0.15 always
# did this.
data['ext'] = 0
util.pickle(data, filename)
self.filename = filename
def save(self, filename):
"""Save the interpolation data to a file.
This data may not be the same from one version of scipy to
another, and may break with new versions, so is important to
test that this works with new versions of scipy. For now, the
only important values for the spline interpolation are stored
in the spline's _data member, but could change, as could the
structure of _eval_args. To be safe, do not depend on this to
load old interpolations. It is probably safest to create a
new interpolation for a new scipy version. The saved files are
not compatible between Python 2 and Python 3.
"""
util.pickle(self._data, filename)
def save(self, filename):
"""Save the interpolation data to a file.
This data may not be the same from one version of scipy to
another, and may break with new versions, so is important to
test that this works with new versions of scipy. For now, the
only important values for the spline interpolation are stored
in the spline's _data member, but could change, as could the
structure of _eval_args. To be safe, do not depend on this to
load old interpolations. It is probably safest to create a
new interpolation for a new scipy version. The saved files are
not compatible between Python 2 and Python 3.
"""
util.pickle(self._data, filename)
ax.set_ylabel('Depth (m)', fontsize=10)
conc_fig.subplots_adjust(left=0.15, top=0.95)
conc_canvas.print_figure('target_conc.png', dpi=500)
C_true = sim.multiglaciate(con['dz'], con['t_gl'], con['t_int'],
con['t_postgl'], con['sample_depths'],
con['nuclide'], p, con['n_gl'],
postgl_shielding=con['postgl_shielding'])
con['sigma'] = con['nuclide'].measurement_error(C_true)
C_meas = np.random.normal(loc=C_true, scale=con['sigma'])
C_meas_err = con['nuclide'].measurement_error(C_meas)
con['C_meas'] = C_meas
con['C_meas_err'] = C_meas_err
meas_fig = Figure(figsize=(fig_width, fig_height))
meas_canvas = FigureCanvas(meas_fig)
ax = meas_fig.add_subplot(111)
ax.set_title('Synthetic Concentration Measurements', y=1.03, fontsize=12)
ax.errorbar(C_meas, con['sample_depths_m'], xerr=C_meas_err, fmt='k.')
ax.invert_yaxis()
ax.set_xlabel('[$^{10}$Be] (atoms/g)', fontsize=10)
ax.set_ylabel('Depth (m)', fontsize=10)
ax.set_xscale('log')
meas_fig.subplots_adjust(left=0.15) #, top=0.95
meas_canvas.print_figure('meas_conc.png', dpi=500)
util.pickle(con, 'con.dat') # save that input data!
util.pickle(p, 'production_rate.dat')
"n_initial": 500,
"lo_lim": np.array([-50, 0, -100, 0], dtype=float),
"hi_lim": np.array([50, 200, 100, 200], dtype=float),
"d": 4,
"ne": 1000,
"m_true": np.array([20.0, 100.0, 0.0, 100.0], dtype=float),
# for benchmarking
'seed': 10453,
'plot': False,
}
conf['ts'] = np.linspace(2, 20, 1000)
conf['dof'] = conf['ts'].size - conf['d']
sigma_obs = 1.0 # meters
pos_true = positions(conf['m_true'], conf['ts'])
def chi2v(pre, obs, sigma_obs, nu):
return (((obs - pre) / sigma_obs)**2).sum() / nu
def fn(m):
pos = positions(m, conf['ts'])
misfit = chi2v(pos, pos_true, sigma_obs, conf['dof'])
return misfit
if __name__ == "__main__":
util.pickle(conf, 'conf.pkl')
na.search(fn, conf)
C_true = sim.multiglaciate(con['dz'],
con['t_gl'],
con['t_int'],
con['t_postgl'],
con['sample_depths'],
con['nuclide'],
p,
con['n_gl'],
postgl_shielding=con['postgl_shielding'])
con['sigma'] = con['nuclide'].measurement_error(C_true)
C_meas = np.random.normal(loc=C_true, scale=con['sigma'])
C_meas_err = con['nuclide'].measurement_error(C_meas)
con['C_meas'] = C_meas
con['C_meas_err'] = C_meas_err
meas_fig = Figure(figsize=(fig_width, fig_height))
meas_canvas = FigureCanvas(meas_fig)
ax = meas_fig.add_subplot(111)
ax.set_title('Synthetic Concentration Measurements', y=1.03, fontsize=12)
ax.errorbar(C_meas, con['sample_depths_m'], xerr=C_meas_err, fmt='k.')
ax.invert_yaxis()
ax.set_xlabel('[$^{10}$Be] (atoms/g)', fontsize=10)
ax.set_ylabel('Depth (m)', fontsize=10)
ax.set_xscale('log')
meas_fig.subplots_adjust(left=0.15) #, top=0.95
meas_canvas.print_figure('meas_conc.png', dpi=500)
util.pickle(con, 'con.dat') # save that input data!
util.pickle(p, 'production_rate.dat')
"nr": 2,
"n_initial": 500,
"lo_lim": np.array([-50, 0, -100, 0], dtype=float),
"hi_lim": np.array([50, 200, 100, 200], dtype=float),
"d": 4,
"ne": 1000,
"m_true": np.array([20.0, 100.0, 0.0, 100.0], dtype=float),
# for benchmarking
'seed': 10453,
'plot': False,
}
conf['ts'] = np.linspace(2, 20, 1000)
conf['dof'] = conf['ts'].size - conf['d']
sigma_obs = 1.0 # meters
pos_true = positions(conf['m_true'], conf['ts'])
def chi2v(pre, obs, sigma_obs, nu):
return (((obs - pre) / sigma_obs) ** 2).sum() / nu
def fn(m):
pos = positions(m, conf['ts'])
misfit = chi2v(pos, pos_true, sigma_obs, conf['dof'])
return misfit
if __name__ == "__main__":
util.pickle(conf, 'conf.pkl')
na.search(fn, conf)
