def calc(gen: Union[PDFGenerator, DebyePDFGenerator], data_file: str,
rlim: Tuple[float, float]) -> None:
"""
Calculate the value of generator and compare it with the data in the file.
Parameters
----------
gen
The PDFGenerator or DebyePDFGenerator.
data_file
The path to the data file.
rlim
The limit of calculation range.
Returns
-------
None
"""
r, g_data = loadData(data_file).T
msk = np.logical_and(r >= rlim[0], r <= rlim[1])
r, g_data = r[msk], g_data[msk]
g_calc = gen(r)
plt.figure()
plt.plot(r, g_data, label="data")
plt.plot(r, g_calc, label="calculation")
plt.xlabel(r"r ($\AA$)")
plt.ylabel(r"G ($\AA^{-2}$)")
plt.legend()
plt.show()
return
def calc(gen: Union[PDFGenerator, DebyePDFGenerator], data_file: str,
rlim: Tuple[float, float]) -> None:
"""
calculate PDF according to generator in recipe and compare with the data.
:param gen: generator used to calculate PDF.
:param data_file: path to the data file.
:param rlim: limit of r-range.
:return: None.
"""
r, g_data = loadData(data_file).T
msk = np.logical_and(r >= rlim[0], r <= rlim[1])
r, g_data = r[msk], g_data[msk]
g_calc = gen(r)
plt.plot(r, g_data, label="data")
plt.plot(r, g_calc, label="calculation")
plt.xlabel(r"r ($\AA$)")
plt.ylabel(r"G ($\AA^{-2}$)")
plt.legend()
return
import matplotlib.pyplot as plt
from diffpy.utils.parsers.loaddata import loadData
#if use stylesheet remotely
path = 'https://raw.githubusercontent.com/Billingegroup/mpl-stylesheets/master/bg_style'
#if use stylesheet locally
# path = '../bg_style'
plt.style.use(path)
#load PDF data
r, gcalc, dr, dg, gdiff = loadData('./CdSe.fgr').T
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
offset = -0.5 # the offset to plot difference curve
ax.plot(r, gcalc + gdiff) # Experimental
ax.plot(r, gcalc) # Calculated
ax.plot(r, gdiff + offset) # Difference
ax.axhline(y=offset, color='k', linestyle='--')
ax.set_xlim(2.0, 20.0) # set x-axis lower and upper limits
ax.set_xlabel('r ($\mathrm{\AA}$)')
ax.set_ylabel('G ($\mathrm{\AA}$$^{-2}$)')
plt.show()
# fig.savefig('plot.png')
def load_array(data_file: str, minrows=10, **kwargs) -> np.ndarray:
"""Load data columns from the .txt file and turn columns to rows and return the numpy array."""
return loadData(data_file, minrows=minrows, **kwargs).T