Module pylars.plotting.plotanalysis
Collection of usefull functions to plot results and analysis intermediate steps.
The file is divided into LED OFF and LED ON sections. Most of the functions provide the plt axis object to facility the integration of these plots in different figures as required.
Expand source code
"""Collection of usefull functions to plot results and analysis intermediate
steps.
The file is divided into LED OFF and LED ON sections. Most of the functions
provide the plt axis object to facility the integration of these plots in
different figures as required.
"""
from typing import Tuple, Union, Optional
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pylars.utils.common import Gaussian, func_linear
from .plotprocessed import *
##### LED ON #####
def plot_area_LED(bv_dataset, voltage, LED_position=300,
log_y=True, full_x=False, ax=None,
color=None):
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
df = bv_dataset.data[voltage]
cut_mask = ((df['position'] > (LED_position - 10)) &
(df['position'] < (LED_position + 20)) &
(df['length'] > 3))
ax = plot_hist_area(df[cut_mask], ax=ax, color=color)
if full_x:
ax.set_xlim(0, 2**14 * 10 * 300)
if log_y:
ax.set_yscale('log')
ax.set_title((f'LED ON\n module {bv_dataset.module} | '
f'channel {bv_dataset.channel[-1]}')
)
med = np.median(df[cut_mask]['area'])
std = np.std(df[cut_mask]['area'])
med_err = std / np.sqrt(len(df[cut_mask]))
return med, med_err, ax
def plot_LED_all_voltages(bv_dataset, cmap='winter', ax=None,):
cm = plt.get_cmap(cmap) # type: ignore
N_lines = len(bv_dataset.voltages)
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
for i, _v in enumerate(bv_dataset.voltages):
med, med_err, ax = plot_area_LED(bv_dataset, 50,
color=cm(i / N_lines),
ax=ax)
ax.axvline(med, color=cm(i / N_lines))
ax.set_title('')
plt.show()
def plot_BV_fit(plot, temperature, voltages, gains,
a, b, _breakdown_v, _breakdown_v_error, ax=None):
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(6, 3), facecolor='white')
ax.plot(gains, voltages,
ls='', marker='x', c='k',
label=(f'{temperature}K: ({_breakdown_v:.2f}'
f'$\\pm${_breakdown_v_error:.2f}) V') # type: ignore
)
_offset_gains = min(gains) * 0.15
_x = np.linspace(min(gains) - _offset_gains,
max(gains) + _offset_gains,
100)
ax.plot(_x, func_linear(_x, a, b), c='r', alpha=0.9)
ax.set_xlabel('Gain')
ax.set_ylabel('Voltage [V]')
ax.legend()
ax.set_title(plot)
ax.ticklabel_format(style='sci', axis='x', scilimits=(0, 0),
useMathText=True)
if isinstance(plot, str):
plt.tight_layout()
plt.savefig(f'figures/{plot}_{temperature}_BV_fit.pdf')
plt.close()
else:
return ax
def plot_BV_results(df_BV_results: pd.DataFrame,
all_channels: Union[list, tuple, np.ndarray],
r2_threshold: float = 0.,
ax=None):
"""Plot the distribution of BV voltages for different channels.
Args:
df_BV_results (pd.DataFrame): df with the BV values, errors, r2 value
of linear fit for eahc temperature and channel.
all_channels (Union[list, tuple, np.ndarray]): the channel names,
in order, to put on the x axis ticks. Usually "(mod, ch)" or
"#xxx" for the MPPC number.
r2_threshold (float, optional): cuts BVs with fits with r2 bellow this
value. Defaults to 0.
ax (plt.axis, optional): the axis to draw into. Defaults to None.
"""
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(6, 4))
temps = df_BV_results.index.levels[0] # type: ignore
for t in temps:
t_mask = ((df_BV_results['temp'] == t) &
(df_BV_results['r2'] > r2_threshold)
)
_df = df_BV_results[t_mask]
ax.errorbar(_df.index.codes[1], # type: ignore
_df['BV'],
yerr=_df['BV_std'],
ls='', marker='.', capsize=4,
label=f'{t:.0f} K')
ax.legend()
ax.set_xticks(df_BV_results.index.levels[1], # type: ignore
all_channels, rotation=30)
return ax
##### LED OFF #####
def plot_DCR_curve(plot: Union[str, bool, None], area_hist_x: np.ndarray,
DCR_values: np.ndarray, _x: np.ndarray,
_y: np.ndarray, min_area_x: float, ax=None):
"""Make the DCR step plot.
Args:
plot (Optional[str]): string to save the fgiure with a name, None to
return the axis
area_hist_x (np.ndarray): x values on the step plot.
DCR_values (np.ndarray): y values on the step plot.
_x (np.ndarray): x values of the computed 1st derivative
_y (np.ndarray): y values of the computed 1st derivative
min_area_x (float): area of minimum of the derivative function
ax (_type_, optional): Axis where to draw. Creates one if ax=None.
Defaults to None.
Returns:
None or axis: the axis drawn at if `plot` not a str, None if `plot`
is a string
"""
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(12, 5), facecolor='white')
ax.plot(area_hist_x, DCR_values,
marker='x', ls='',
c='k', label='Data points')
ax.set_yscale('log')
ax.set_xlabel('Area [integrated ADC counts]')
ax.set_ylabel('# events')
ax3 = ax.twinx()
ax3.plot(_x, _y, c='r')
ax3.tick_params(axis='y', labelcolor='r')
ax3.axvline(min_area_x, c='r', ls='--', alpha=0.8,
label='1$^{st}$ der. (smoothed)')
ax3.set_ylabel('1$^{st}$ derivative')
if 'fig' in locals():
fig.legend() # type: ignore
if isinstance(plot, str):
fig.savefig(f'figures/{plot}_stepplot.png') # type: ignore
plt.close()
else:
return ax
def plot_SPE_fit(df, length_cut_min, length_cut_max, plot, area_hist_x,
min_area_x, A, mu, sigma, ax=None):
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(12, 5), facecolor='white')
bin_size = area_hist_x[1] - area_hist_x[0]
ax.hist(df[(df['length'] > length_cut_min) &
(df['length'] < length_cut_max)]['area'],
bins=np.linspace(0.5 * min_area_x, 1.5 * min_area_x, 300),
color='gray', alpha=0.8)
_x = np.linspace(area_hist_x[0], area_hist_x[-1], 200)
ax.plot(_x, Gaussian(_x, A, mu, sigma), color='red')
ax.set_xlabel('Area [integrated ADC counts]')
ax.set_ylabel('# events')
for i in range(1, 4):
ax.axvline(mu * i, color='red', ls='--', alpha=0.7)
# plt.yscale('log')
if isinstance(plot, str):
plt.savefig(f'figures/{plot}_1pe_fit.png')
plt.close()
else:
return ax
def plot_found_area_peaks(area_x, area_y, area_filt, area_peaks_x,
plot=None, ax=None):
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(12, 5), facecolor='white')
ax.plot(area_x, area_y, color='k', alpha=0.7) # , range = (100,1e4))
ax.plot(area_x, area_filt, color='blue', ls='-', lw=1, alpha=1)
ax.vlines(area_x[area_peaks_x], 0, 1e6, color='green', alpha=0.5)
ax.set_xlabel('Area')
ax.set_yscale('log')
if isinstance(plot, str):
plt.savefig(f'figures/{plot}_paeks_and_valeys.png')
plt.close()
elif plot is True:
plt.show()
def plot_results_ds(results: pd.DataFrame,
errorbars: bool = True,
figaxs: Union[None, Tuple] = None) -> tuple:
"""Summary plot of the DCR results of run.
Args:
results (pd.DataFrame): results df
errorbars (bool, optional): plot with errorbars. Defaults to True.
figaxs (Union[None, Tuple], optional): where to draw the plots. Creates
if None. Defaults to None.
Returns:
tuple: the figure and ax objects (fig,ax)
"""
if figaxs is None:
fig, axs = plt.subplots(4, sharex=True, sharey=False, figsize=(10, 8))
else:
fig, axs = figaxs
fig.subplots_adjust(hspace=0)
temps = np.unique(results['T'])
for _temp in temps:
_select = results['T'] == _temp
if errorbars:
axs[0].errorbar(results[_select]['Gain'],
results[_select]['V'],
xerr=results[_select]['Gain_error'],
ls='', capsize=4,
marker='.') # , label = '180 K')
axs[1].errorbar(results[_select]['Gain'],
results[_select]['SPE_res'],
xerr=results[_select]['Gain_error'],
yerr=results[_select]['SPE_res_error'],
ls='', capsize=4,
marker='.', label=f'{_temp:.0f} K')
axs[2].errorbar(results[_select]['Gain'],
results[_select]['DCR'],
xerr=results[_select]['Gain_error'],
yerr=results[_select]['DCR_error'],
ls='', capsize=4,
marker='.')
axs[3].errorbar(results[_select]['Gain'],
results[_select]['CTP'],
xerr=results[_select]['Gain_error'],
yerr=results[_select]['CTP_error'],
ls='', capsize=4,
marker='.')
else:
axs[0].plot(results[_select]['Gain'],
results[_select]['V'],
ls='',
marker='o', label=f'{_temp:.0f} K')
axs[1].plot(results[_select]['Gain'], results[_select]['SPE_res'],
ls='',
marker='o') # , label = '180 K')
axs[2].plot(results[_select]['Gain'], results[_select]['DCR'],
ls='',
marker='o')
axs[3].plot(results[_select]['Gain'], results[_select]['CTP'],
ls='',
marker='o')
axs[0].set_ylabel('Bias Voltage [V]')
axs[1].set_ylabel('SPE res [%]')
axs[2].set_ylabel('DCR [Hz/mm$^2$]')
axs[3].set_ylabel('CTP [%]')
axs[3].set_ylim(0, 80)
axs[-1].set_xlabel('Gain')
axs[0].grid()
axs[1].grid()
axs[2].grid()
axs[3].grid()
fig.legend(bbox_to_anchor=[0.9, 0.84])
return fig, axs
def plot_BV_DCRresults(df_BV_results: pd.DataFrame,
ch_names: Union[list, tuple, np.ndarray],
errorbars: bool = False,
figax=None) -> tuple:
"""Plot the distribution of BV voltages for different channels.
Args:
df_BV_results (pd.DataFrame): results of BV calculation from a DCR
dataset with
`pylars.analysis.breakdown.compute_BV_DCRds_results`
ch_names(Union[list, tuple, np.ndarray]): names of the channels for
the x axis
errorbars (bool, optional): plot with errorbars. Defaults to True.
figaxs (Union[None, Tuple], optional): where to draw the plots. Creates
if None. Defaults to None.
Returns:
tuple: the figure and ax objects (fig,ax)
"""
if figax is None:
fig, ax = plt.subplots(1, 1, figsize=(6, 4))
else:
fig, ax = figax
temps = np.unique(df_BV_results['T'])
for t in temps:
_df = df_BV_results[df_BV_results['T'] == t]
_df.reset_index(drop=True, inplace=True)
if errorbars:
ax.errorbar(_df.index, _df['BV'],
yerr=_df[f'BV_error'],
label=f'{t} K',
ls='', marker='.', capsize=4)
else:
ax.plot(_df.index, _df['BV'],
label=f'{t} K', ls='',
marker='o')
ax.legend()
ax.set_xticks(_df.index, ch_names, rotation=30) # type: ignore
fig.set_tight_layout(True)
return fig, ax
def plot_parameter_for_V(results_df: pd.DataFrame, V: float,
parameter: str, ch_names: list,
errorbars: bool = False):
"""Plot the results from DCR analysis for a specific parameter
(Gain, SPE_res, DCR, CTP) and applied voltage for all the recorded
channels.
Args:
results_df (pd.DataFrame): DCR analysis results dataset.
V (float): voltage.
parameter (str): parameter to plot.
ch_names (list): list with the names to assign to the channels.
errorbars (bool, optional): include errorbars in plot. Defaults
to False.
Returns:
(fig, ax): The figure and axis objects created.
"""
fig, ax = plt.subplots(1, 1, figsize=(8, 5))
temps = np.unique(results_df['T'])
for t in temps:
_df = results_df[(results_df['V'] == V) & (results_df['T'] == t)]
_df.reset_index(drop=True, inplace=True)
if len(_df) == 0:
print(f'Found no points for {V} V at {t} K.')
continue
if errorbars:
ax.errorbar(_df.index, _df[parameter],
yerr=_df[f'{parameter}_error'],
label=f'{t} K',
ls='', marker='.', capsize=4)
else:
ax.plot(_df.index, _df[parameter],
label=f'{t} K', ls='',
marker='o')
ax.legend(title=f'@{V} V')
ax.set_ylabel(parameter)
ax.set_xticks(_df.index, ch_names, rotation=30) # type: ignore
fig.set_tight_layout(True)
return fig, axFunctions
def plot_BV_DCRresults(df_BV_results: pandas.core.frame.DataFrame, ch_names: Union[list, tuple, numpy.ndarray], errorbars: bool = False, figax=None) ‑> tuple-
Plot the distribution of BV voltages for different channels.
Args
df_BV_results:pd.DataFrame- results of BV calculation from a DCR
dataset with
compute_BV_DCRds_results() - ch_names(Union[list, tuple, np.ndarray]): names of the channels for
- the x axis
errorbars:bool, optional- plot with errorbars. Defaults to True.
figaxs:Union[None, Tuple], optional- where to draw the plots. Creates if None. Defaults to None.
Returns
tuple- the figure and ax objects (fig,ax)
Expand source code
def plot_BV_DCRresults(df_BV_results: pd.DataFrame, ch_names: Union[list, tuple, np.ndarray], errorbars: bool = False, figax=None) -> tuple: """Plot the distribution of BV voltages for different channels. Args: df_BV_results (pd.DataFrame): results of BV calculation from a DCR dataset with `pylars.analysis.breakdown.compute_BV_DCRds_results` ch_names(Union[list, tuple, np.ndarray]): names of the channels for the x axis errorbars (bool, optional): plot with errorbars. Defaults to True. figaxs (Union[None, Tuple], optional): where to draw the plots. Creates if None. Defaults to None. Returns: tuple: the figure and ax objects (fig,ax) """ if figax is None: fig, ax = plt.subplots(1, 1, figsize=(6, 4)) else: fig, ax = figax temps = np.unique(df_BV_results['T']) for t in temps: _df = df_BV_results[df_BV_results['T'] == t] _df.reset_index(drop=True, inplace=True) if errorbars: ax.errorbar(_df.index, _df['BV'], yerr=_df[f'BV_error'], label=f'{t} K', ls='', marker='.', capsize=4) else: ax.plot(_df.index, _df['BV'], label=f'{t} K', ls='', marker='o') ax.legend() ax.set_xticks(_df.index, ch_names, rotation=30) # type: ignore fig.set_tight_layout(True) return fig, ax def plot_BV_fit(plot, temperature, voltages, gains, a, b, _breakdown_v, _breakdown_v_error, ax=None)-
Expand source code
def plot_BV_fit(plot, temperature, voltages, gains, a, b, _breakdown_v, _breakdown_v_error, ax=None): if ax is None: fig, ax = plt.subplots(1, 1, figsize=(6, 3), facecolor='white') ax.plot(gains, voltages, ls='', marker='x', c='k', label=(f'{temperature}K: ({_breakdown_v:.2f}' f'$\\pm${_breakdown_v_error:.2f}) V') # type: ignore ) _offset_gains = min(gains) * 0.15 _x = np.linspace(min(gains) - _offset_gains, max(gains) + _offset_gains, 100) ax.plot(_x, func_linear(_x, a, b), c='r', alpha=0.9) ax.set_xlabel('Gain') ax.set_ylabel('Voltage [V]') ax.legend() ax.set_title(plot) ax.ticklabel_format(style='sci', axis='x', scilimits=(0, 0), useMathText=True) if isinstance(plot, str): plt.tight_layout() plt.savefig(f'figures/{plot}_{temperature}_BV_fit.pdf') plt.close() else: return ax def plot_BV_results(df_BV_results: pandas.core.frame.DataFrame, all_channels: Union[list, tuple, numpy.ndarray], r2_threshold: float = 0.0, ax=None)-
Plot the distribution of BV voltages for different channels.
Args
df_BV_results:pd.DataFrame- df with the BV values, errors, r2 value of linear fit for eahc temperature and channel.
all_channels:Union[list, tuple, np.ndarray]- the channel names, in order, to put on the x axis ticks. Usually "(mod, ch)" or "#xxx" for the MPPC number.
r2_threshold:float, optional- cuts BVs with fits with r2 bellow this value. Defaults to 0.
ax:plt.axis, optional- the axis to draw into. Defaults to None.
Expand source code
def plot_BV_results(df_BV_results: pd.DataFrame, all_channels: Union[list, tuple, np.ndarray], r2_threshold: float = 0., ax=None): """Plot the distribution of BV voltages for different channels. Args: df_BV_results (pd.DataFrame): df with the BV values, errors, r2 value of linear fit for eahc temperature and channel. all_channels (Union[list, tuple, np.ndarray]): the channel names, in order, to put on the x axis ticks. Usually "(mod, ch)" or "#xxx" for the MPPC number. r2_threshold (float, optional): cuts BVs with fits with r2 bellow this value. Defaults to 0. ax (plt.axis, optional): the axis to draw into. Defaults to None. """ if ax is None: fig, ax = plt.subplots(1, 1, figsize=(6, 4)) temps = df_BV_results.index.levels[0] # type: ignore for t in temps: t_mask = ((df_BV_results['temp'] == t) & (df_BV_results['r2'] > r2_threshold) ) _df = df_BV_results[t_mask] ax.errorbar(_df.index.codes[1], # type: ignore _df['BV'], yerr=_df['BV_std'], ls='', marker='.', capsize=4, label=f'{t:.0f} K') ax.legend() ax.set_xticks(df_BV_results.index.levels[1], # type: ignore all_channels, rotation=30) return ax def plot_DCR_curve(plot: Union[str, bool, NoneType], area_hist_x: numpy.ndarray, DCR_values: numpy.ndarray, _x: numpy.ndarray, _y: numpy.ndarray, min_area_x: float, ax=None)-
Make the DCR step plot.
Args
plot:Optional[str]- string to save the fgiure with a name, None to return the axis
area_hist_x:np.ndarray- x values on the step plot.
DCR_values:np.ndarray- y values on the step plot.
_x:np.ndarray- x values of the computed 1st derivative
_y:np.ndarray- y values of the computed 1st derivative
min_area_x:float- area of minimum of the derivative function
ax:_type_, optional- Axis where to draw. Creates one if ax=None. Defaults to None.
Returns
Noneoraxis- the axis drawn at if
plotnot a str, None ifplotis a string
Expand source code
def plot_DCR_curve(plot: Union[str, bool, None], area_hist_x: np.ndarray, DCR_values: np.ndarray, _x: np.ndarray, _y: np.ndarray, min_area_x: float, ax=None): """Make the DCR step plot. Args: plot (Optional[str]): string to save the fgiure with a name, None to return the axis area_hist_x (np.ndarray): x values on the step plot. DCR_values (np.ndarray): y values on the step plot. _x (np.ndarray): x values of the computed 1st derivative _y (np.ndarray): y values of the computed 1st derivative min_area_x (float): area of minimum of the derivative function ax (_type_, optional): Axis where to draw. Creates one if ax=None. Defaults to None. Returns: None or axis: the axis drawn at if `plot` not a str, None if `plot` is a string """ if ax is None: fig, ax = plt.subplots(1, 1, figsize=(12, 5), facecolor='white') ax.plot(area_hist_x, DCR_values, marker='x', ls='', c='k', label='Data points') ax.set_yscale('log') ax.set_xlabel('Area [integrated ADC counts]') ax.set_ylabel('# events') ax3 = ax.twinx() ax3.plot(_x, _y, c='r') ax3.tick_params(axis='y', labelcolor='r') ax3.axvline(min_area_x, c='r', ls='--', alpha=0.8, label='1$^{st}$ der. (smoothed)') ax3.set_ylabel('1$^{st}$ derivative') if 'fig' in locals(): fig.legend() # type: ignore if isinstance(plot, str): fig.savefig(f'figures/{plot}_stepplot.png') # type: ignore plt.close() else: return ax def plot_LED_all_voltages(bv_dataset, cmap='winter', ax=None)-
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def plot_LED_all_voltages(bv_dataset, cmap='winter', ax=None,): cm = plt.get_cmap(cmap) # type: ignore N_lines = len(bv_dataset.voltages) if ax is None: fig, ax = plt.subplots(1, 1, figsize=(12, 6)) for i, _v in enumerate(bv_dataset.voltages): med, med_err, ax = plot_area_LED(bv_dataset, 50, color=cm(i / N_lines), ax=ax) ax.axvline(med, color=cm(i / N_lines)) ax.set_title('') plt.show() def plot_SPE_fit(df, length_cut_min, length_cut_max, plot, area_hist_x, min_area_x, A, mu, sigma, ax=None)-
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def plot_SPE_fit(df, length_cut_min, length_cut_max, plot, area_hist_x, min_area_x, A, mu, sigma, ax=None): if ax is None: fig, ax = plt.subplots(1, 1, figsize=(12, 5), facecolor='white') bin_size = area_hist_x[1] - area_hist_x[0] ax.hist(df[(df['length'] > length_cut_min) & (df['length'] < length_cut_max)]['area'], bins=np.linspace(0.5 * min_area_x, 1.5 * min_area_x, 300), color='gray', alpha=0.8) _x = np.linspace(area_hist_x[0], area_hist_x[-1], 200) ax.plot(_x, Gaussian(_x, A, mu, sigma), color='red') ax.set_xlabel('Area [integrated ADC counts]') ax.set_ylabel('# events') for i in range(1, 4): ax.axvline(mu * i, color='red', ls='--', alpha=0.7) # plt.yscale('log') if isinstance(plot, str): plt.savefig(f'figures/{plot}_1pe_fit.png') plt.close() else: return ax def plot_area_LED(bv_dataset, voltage, LED_position=300, log_y=True, full_x=False, ax=None, color=None)-
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def plot_area_LED(bv_dataset, voltage, LED_position=300, log_y=True, full_x=False, ax=None, color=None): if ax is None: fig, ax = plt.subplots(1, 1, figsize=(12, 6)) df = bv_dataset.data[voltage] cut_mask = ((df['position'] > (LED_position - 10)) & (df['position'] < (LED_position + 20)) & (df['length'] > 3)) ax = plot_hist_area(df[cut_mask], ax=ax, color=color) if full_x: ax.set_xlim(0, 2**14 * 10 * 300) if log_y: ax.set_yscale('log') ax.set_title((f'LED ON\n module {bv_dataset.module} | ' f'channel {bv_dataset.channel[-1]}') ) med = np.median(df[cut_mask]['area']) std = np.std(df[cut_mask]['area']) med_err = std / np.sqrt(len(df[cut_mask])) return med, med_err, ax def plot_found_area_peaks(area_x, area_y, area_filt, area_peaks_x, plot=None, ax=None)-
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def plot_found_area_peaks(area_x, area_y, area_filt, area_peaks_x, plot=None, ax=None): if ax is None: fig, ax = plt.subplots(1, 1, figsize=(12, 5), facecolor='white') ax.plot(area_x, area_y, color='k', alpha=0.7) # , range = (100,1e4)) ax.plot(area_x, area_filt, color='blue', ls='-', lw=1, alpha=1) ax.vlines(area_x[area_peaks_x], 0, 1e6, color='green', alpha=0.5) ax.set_xlabel('Area') ax.set_yscale('log') if isinstance(plot, str): plt.savefig(f'figures/{plot}_paeks_and_valeys.png') plt.close() elif plot is True: plt.show() def plot_parameter_for_V(results_df: pandas.core.frame.DataFrame, V: float, parameter: str, ch_names: list, errorbars: bool = False)-
Plot the results from DCR analysis for a specific parameter (Gain, SPE_res, DCR, CTP) and applied voltage for all the recorded channels.
Args
results_df:pd.DataFrame- DCR analysis results dataset.
V:float- voltage.
parameter:str- parameter to plot.
ch_names:list- list with the names to assign to the channels.
errorbars:bool, optional- include errorbars in plot. Defaults to False.
Returns
(fig, ax): The figure and axis objects created.
Expand source code
def plot_parameter_for_V(results_df: pd.DataFrame, V: float, parameter: str, ch_names: list, errorbars: bool = False): """Plot the results from DCR analysis for a specific parameter (Gain, SPE_res, DCR, CTP) and applied voltage for all the recorded channels. Args: results_df (pd.DataFrame): DCR analysis results dataset. V (float): voltage. parameter (str): parameter to plot. ch_names (list): list with the names to assign to the channels. errorbars (bool, optional): include errorbars in plot. Defaults to False. Returns: (fig, ax): The figure and axis objects created. """ fig, ax = plt.subplots(1, 1, figsize=(8, 5)) temps = np.unique(results_df['T']) for t in temps: _df = results_df[(results_df['V'] == V) & (results_df['T'] == t)] _df.reset_index(drop=True, inplace=True) if len(_df) == 0: print(f'Found no points for {V} V at {t} K.') continue if errorbars: ax.errorbar(_df.index, _df[parameter], yerr=_df[f'{parameter}_error'], label=f'{t} K', ls='', marker='.', capsize=4) else: ax.plot(_df.index, _df[parameter], label=f'{t} K', ls='', marker='o') ax.legend(title=f'@{V} V') ax.set_ylabel(parameter) ax.set_xticks(_df.index, ch_names, rotation=30) # type: ignore fig.set_tight_layout(True) return fig, ax def plot_results_ds(results: pandas.core.frame.DataFrame, errorbars: bool = True, figaxs: Union[NoneType, Tuple] = None) ‑> tuple-
Summary plot of the DCR results of run.
Args
results:pd.DataFrame- results df
errorbars:bool, optional- plot with errorbars. Defaults to True.
figaxs:Union[None, Tuple], optional- where to draw the plots. Creates if None. Defaults to None.
Returns
tuple- the figure and ax objects (fig,ax)
Expand source code
def plot_results_ds(results: pd.DataFrame, errorbars: bool = True, figaxs: Union[None, Tuple] = None) -> tuple: """Summary plot of the DCR results of run. Args: results (pd.DataFrame): results df errorbars (bool, optional): plot with errorbars. Defaults to True. figaxs (Union[None, Tuple], optional): where to draw the plots. Creates if None. Defaults to None. Returns: tuple: the figure and ax objects (fig,ax) """ if figaxs is None: fig, axs = plt.subplots(4, sharex=True, sharey=False, figsize=(10, 8)) else: fig, axs = figaxs fig.subplots_adjust(hspace=0) temps = np.unique(results['T']) for _temp in temps: _select = results['T'] == _temp if errorbars: axs[0].errorbar(results[_select]['Gain'], results[_select]['V'], xerr=results[_select]['Gain_error'], ls='', capsize=4, marker='.') # , label = '180 K') axs[1].errorbar(results[_select]['Gain'], results[_select]['SPE_res'], xerr=results[_select]['Gain_error'], yerr=results[_select]['SPE_res_error'], ls='', capsize=4, marker='.', label=f'{_temp:.0f} K') axs[2].errorbar(results[_select]['Gain'], results[_select]['DCR'], xerr=results[_select]['Gain_error'], yerr=results[_select]['DCR_error'], ls='', capsize=4, marker='.') axs[3].errorbar(results[_select]['Gain'], results[_select]['CTP'], xerr=results[_select]['Gain_error'], yerr=results[_select]['CTP_error'], ls='', capsize=4, marker='.') else: axs[0].plot(results[_select]['Gain'], results[_select]['V'], ls='', marker='o', label=f'{_temp:.0f} K') axs[1].plot(results[_select]['Gain'], results[_select]['SPE_res'], ls='', marker='o') # , label = '180 K') axs[2].plot(results[_select]['Gain'], results[_select]['DCR'], ls='', marker='o') axs[3].plot(results[_select]['Gain'], results[_select]['CTP'], ls='', marker='o') axs[0].set_ylabel('Bias Voltage [V]') axs[1].set_ylabel('SPE res [%]') axs[2].set_ylabel('DCR [Hz/mm$^2$]') axs[3].set_ylabel('CTP [%]') axs[3].set_ylim(0, 80) axs[-1].set_xlabel('Gain') axs[0].grid() axs[1].grid() axs[2].grid() axs[3].grid() fig.legend(bbox_to_anchor=[0.9, 0.84]) return fig, axs