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document print_qr_info and logscale_hist

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markus 2021-02-09 13:56:02 +01:00
parent 3ac0de206b
commit 25b9bf56f0
1 changed files with 6 additions and 1 deletions
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7
cara/montecarlo.py
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@ -381,7 +381,7 @@ def logscale_hist(x: typing.Iterable, bins: int) -> None:
Plots the data of x as a log-scale histogram Plots the data of x as a log-scale histogram
:param x: An array containing the data to be plotted :param x: An array containing the data to be plotted
:param bins: The number of bins to be used in the histogram (number of bars) :param bins: The number of bins to be used in the histogram (number of bars)
:return: Nothing :return: Nothing, a graph is displayed
""" """
hist, bins = np.histogram(x, bins=bins) hist, bins = np.histogram(x, bins=bins)
logscale_bins = np.logspace(np.log10(bins[0]), np.log10(bins[-1]), len(bins)) logscale_bins = np.logspace(np.log10(bins[0]), np.log10(bins[-1]), len(bins))
@ -390,6 +390,11 @@ def logscale_hist(x: typing.Iterable, bins: int) -> None:
def print_qr_info(log_qr: np.ndarray) -> None: def print_qr_info(log_qr: np.ndarray) -> None:
"""
Prints statistical parameters of a given (logarithmic) distribution of qR-values
:param log_qr: A numpy-array of the logarithms of qR-values
:return: Nothing, parameters are printed
"""
qr_values = 10 ** log_qr qr_values = 10 ** log_qr
print(f"MEAN of log_10(qR) = {np.mean(log_qr)}\n" print(f"MEAN of log_10(qR) = {np.mean(log_qr)}\n"
f"SD of log_10(qR) = {np.std(log_qr)}\n" f"SD of log_10(qR) = {np.std(log_qr)}\n"