def curve_loader(value):
rs = data[data[independent_variable_name] == value]
result = read_csv(
filename_containing_string_in_dirs(file_key_format % rs[file_key_column].iloc[0], search_dirs)
)
result.columns = file_column_names
if len(rs.index) > 1:
for i, r in rs[1:].iterrows():
d = read_csv(
filename_containing_string_in_dirs(file_key_format % rs[file_key_column].iloc[0], search_dirs)
)
d.columns = file_column_names
result = result.append(d)
return result
def plot_hanle_curve_fits(
data,
key_col="Timestamp",
var_col="W",
A1_col="A1",
A2_col="A2",
k1_col="k1",
k2_col="k2",
L1_col="L1",
L2_col="L2",
y0_col="y0",
Field_col="Field",
FR_col="FR",
file_key_format="%04d",
search_dirs=".",
file_column_names=["Field", "X", "Y", "PDA", "FR", "Timestamp"],
):
for i in range(0, len(data.index)):
d = read_csv(filename_containing_string_in_dirs(file_key_format % data.loc[i, key_col], search_dirs))
d.columns = file_column_names
p = [data.loc[i, A1_col], data.loc[i, k1_col], data.loc[i, A2_col], data.loc[i, k2_col], data.loc[i, y0_col]]
dm = d.groupby("Field")
fig = double_lorentzian_fig(
p,
d[Field_col],
d[FR_col],
xm=dm.Field.mean(),
ym=dm.FR.mean(),
title=(file_key_format + "\n %fnm, %fns, %fns")
% (data.loc[i, key_col], data.loc[i, var_col], data.loc[i, L1_col] / 1e-9, data.loc[i, L2_col] / 1e-9),
)
fig.savefig("./fits/%04d_%04d.pdf" % (data.loc[i, var_col] * 10, data.loc[i, key_col]))
plt.close(fig)
def generate_hanle_params(
data,
file_key_column,
independent_variable_name,
search_dirs,
fit_paths,
file_key_format="%04d",
file_column_names=["Field", "X", "Y", "PDA", "FR", "Timestamp"],
mag_field_col_name="Field",
faraday_rot_col_name="FR",
X_col_name="X",
Y_col_name="Y",
model=double_lorentzian_centered_no_off,
fitter=opt.curve_fit,
):
hanle_model_cols = ["A1", "k1", "A2", "k2", "y0"]
for c in hanle_model_cols:
if not c in data:
data[c] = np.nan
def curve_loader(value):
rs = data[data[independent_variable_name] == value]
result = read_csv(
filename_containing_string_in_dirs(file_key_format % rs[file_key_column].iloc[0], search_dirs)
)
result.columns = file_column_names
if len(rs.index) > 1:
for i, r in rs[1:].iterrows():
d = read_csv(
filename_containing_string_in_dirs(file_key_format % rs[file_key_column].iloc[0], search_dirs)
)
d.columns = file_column_names
result = result.append(d)
return result
for init_p, path in fit_paths:
progressive_fit(
data,
curve_loader,
init_p,
hanle_model_cols,
model,
path,
key_name=independent_variable_name,
x_name=mag_field_col_name,
y_name=faraday_rot_col_name,
fitter=fitter,
)
data["L1"] = data[hanle_model_cols[1]].apply(lambda k: hanle_lifetime_gauss_in_sec(abs(k)))
data["L2"] = data[hanle_model_cols[3]].apply(lambda k: hanle_lifetime_gauss_in_sec(abs(k)))
# Calculate median phase of each Hanle curve and store it in the results
if not "MP" in data:
data["MP"] = np.nan
for i, r in data.iterrows():
d = read_csv(filename_containing_string_in_dirs(file_key_format % r[file_key_column], search_dirs))
d.columns = file_column_names
data.loc[i, "MP"] = np.median(np.arctan2(d[Y_col_name], d[X_col_name]))
