Module pylars.plotting.plotfixwindow
Collection of functions to plot and analyse the fixed window processing.
Expand source code
"""Collection of functions to plot and analyse the fixed window processing.
"""
from typing import Optional, Tuple
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pylars
def plot_LED_window(waveform: np.ndarray, led_window, figax=None):
"""Plot the LED window on top of the waveform.
"""
if figax is None:
fig, ax = plt.subplots(1, 1, figsize=(6, 4), dpi=60)
else:
fig, ax = figax
ax = pylars.plotting.plot_waveform(waveform, ax=ax)
ax.fill_betweenx([0, 16000], led_window[0], led_window[1],
alpha=0.1, color='C1')
return fig, ax
def plot_LED_window_from_file(filename: str,
led_window: Tuple[int, int],
module: Optional[int] = None,
channel: Optional[str] = None,
wf_number: Optional[int] = None,
figax: Optional[Tuple] = None):
"""Plot the LED window on top of the waveform directly from a file.
"""
process = pylars.processing.fixwindowprocessor.window_processor(
baseline_samples=50, led_window=(105, 155))
if module is None:
module = 0
print(f"Loading raw data from {filename}")
process.load_raw_data(path_to_raw=filename, module=module)
if channel is None:
channel = str(np.random.choice(process.raw_data.channels))
print(f"No channel specified, choosing random channel: {channel}")
if wf_number is None:
wf_number = int(np.random.choice(process.raw_data.n_waveforms))
print(f"No waveform number specified, choosing random waveform:"
f"{wf_number}")
channel_data = process.raw_data.get_channel_data(channel)
if figax is None:
fig, ax = plt.subplots(1, 1, figsize=(6, 4), dpi=60)
else:
fig, ax = figax
fig, ax = plot_LED_window(channel_data[wf_number],
led_window=led_window,
figax=(fig, ax))
if figax is None:
plt.show()
else:
return fig, ax
def plot_LED_all_channels(df_processed, figax=None):
"""Plot max ADC counts, amplitude and area for all channels.
"""
if figax is None:
fig, axs = plt.subplots(1, 3, figsize=(13, 4), sharey=True, dpi=60)
else:
fig, axs = figax
plt.subplots_adjust(wspace=0)
for ch in np.unique(df_processed['channel']):
axs[0].hist(df_processed[df_processed['channel'] == ch]['led_ADCcounts'],
bins=100, histtype='step', label=f'{ch}')
axs[1].hist(df_processed[df_processed['channel'] == ch]['led_amplitude'],
bins=100, histtype='step',)
axs[2].hist(df_processed[df_processed['channel'] == ch]['led_area'],
bins=100, histtype='step',)
axs[0].set_xlabel('max ADC counts')
axs[0].set_ylabel('Counts')
axs[1].set_xlabel('Amplitude [ADC counts]')
axs[2].set_xlabel('Area [integrated yADC counts]')
fig.legend(ncol=6, loc='upper center', bbox_to_anchor=(0.5, 1.05))
[_ax.grid(color='lightgray', linestyle='--', linewidth=0.5) for _ax in axs]
if figax is None:
plt.show()
else:
return fig, axs
def plot_light_levels(led_processed_df: pd.DataFrame,
led_width: int,
channel: Optional[str] = None,
module: Optional[int] = None,
figax: Optional[Tuple] = None):
"""Plot the LED area histogram for all the available light levels."""
select_mask = ((led_processed_df['LEDwidth'] == led_width) &
(led_processed_df['channel'] == channel) &
(led_processed_df['module'] == module))
_df = led_processed_df[select_mask]
if figax is None:
fig, axs = plt.subplots(2, 1, figsize=(6, 8), dpi=60)
else:
fig, axs = figax
_ledvoltages = np.unique(_df['LEDvoltage'])
_median_amplitudes = []
_std_amplitudes = []
for _v in _ledvoltages:
_mask = _df['LEDvoltage'] == _v
axs[0].hist(_df[_mask]['led_area'],
bins=np.linspace(-1000, 25000, 500), histtype='step', label=f'{_v} V')
_amp = np.median(_df[_mask]['led_area'])
_std = np.std(_df[_mask]['led_area'])
_median_amplitudes.append(_amp)
_std_amplitudes.append(_std)
axs[0].set_yscale('log')
axs[1].errorbar(_ledvoltages, _median_amplitudes,
yerr=_std_amplitudes, marker='o',
ls='-')
axs[1].set_xlabel('LED voltage [V]')
axs[1].set_ylabel('Median Area [ADC counts]')
axs[1].grid(color='lightgray', linestyle='--', linewidth=0.5)
axs[0].set_xlabel('Area [ADC counts]')
axs[0].set_ylabel('Counts')
axs[0].legend(loc='center left', bbox_to_anchor=(1., 0.5),
title='LED voltage')
axs[0].grid(color='lightgray', linestyle='--', linewidth=0.5)
if figax is None:
plt.show()
else:
return fig, axs
def plot_1_pe_fit_led(df_processed, led_voltage, module,
channel, A, mu, sigma, figax=None):
"""Plot the SPE Gaussean fit of the LED area histogram."""
if figax is None:
fig, ax = plt.subplots(1, 1, figsize=(5, 3), dpi=120)
else:
fig, ax = figax
df_processed_mask = ((df_processed['module'] == module) &
(df_processed['channel'] == channel) &
(df_processed['LEDvoltage'] == led_voltage))
hist = ax.hist(df_processed[df_processed_mask]['led_area'],
bins=np.linspace(-2000, 20000, 300), histtype='step')
_x = np.linspace(mu * 0.5, mu * 1.5, 1000)
ax.fill_between(_x, pylars.utils.common.Gaussian(_x, A, mu, sigma),
color='C1', alpha=1, zorder=-1) # , ls = '--', linewidth = '2')
[ax.axvline(mu * n, color='C1', ls='--', alpha=1) for n in range(5)]
# ax.set_yscale('log')
# ax.set_ylabel('Counts')
#ax.set_xlabel('Area (window)')
ax.set_ylim(0, 6000)
if figax is None:
plt.show()
else:
return fig, ax
def plot_gains_occ(df_gains, figaxs=None):
"""Plot the gains and occupancies for all the tiles."""
if figaxs is None:
fig, axs = plt.subplots(2, 1, figsize=(4, 4),
dpi=120, sharex=True,
gridspec_kw={'hspace': 0, 'wspace': 0},
constrained_layout=False)
axs = axs.flatten()
_x = np.arange(len(df_gains))
axs[0].errorbar(_x, df_gains['gain'], yerr=df_gains['gain_err'],
ls='', capsize=4, marker='.')
axs[1].errorbar(_x, df_gains['occ'], yerr=df_gains['occ_err'],
ls='', capsize=4, marker='.')
axs[0].set_xticks(_x, df_gains['tile'])
# axs[0].set_ylim(0,1.5)
axs[0].set_ylabel('Gain [$10^6$]')
axs[1].set_ylabel('Occupancy')
# axs[1].set_ylim(-0.1,3.9)
if figaxs is None:
plt.show()
else:
return (fig, axs)
def plot_gain_evolution(gain_evolution: pd.DataFrame,
tile_list: list, mod,
figax=None):
"""Plot the gain evolution for a list of tiles."""
if figax is None:
fig, ax = plt.subplots(1, 1, figsize=(6, 4), dpi=120)
else:
fig, ax = figax
for _tile in tile_list:
_df = gain_evolution[gain_evolution['tile'] == _tile]
ax.errorbar(_df['start'], _df['gain'], yerr=_df['gain_err'],
ls='--',
marker='.', label=_tile)
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left',
title='Tile',
labelspacing=1.0)
ax.set_ylim(0, None) # type:ignore
ax.set_title(f'Gain evolution - ADC #{mod}')
ax.tick_params(axis='x', rotation=45)
ax.set_ylabel('SiPM Gain [M]')
# plt.twinx()
# plt.plot(tt09['datetime'], tt09['TT09'], 'k--', alpha = 0.3,
# zorder = -10,label='Gast temperature')
# plt.axvline(np.datetime64('2024-06-14T17:00:00'),ls = '--', color = 'gray', alpha = 0.8)
# plt.text(np.datetime64('2024-06-14T08:00:00'), 0.78,
# 'Filling complete', color = 'gray',rotation=90)
if figax is None:
fig.savefig(f'21062024_mod{mod}.png')
plt.show()
else:
return fig, ax
def plot_gains_occ_ledvoltage(LED_calib, module, channel, labels):
"""Plot the gains and occupancies for a given module and channel as a
function of the LED voltage."""
fig, ax = plt.subplots(1, 1, figsize=(6, 3))
_df = LED_calib.results_df
_df = _df[(_df['module'] == module) &
(_df['channel'] == channel) &
(_df['LEDvoltage'] > 1)]
ax.errorbar(_df['LEDvoltage'], _df['gain'], yerr=_df['gain_err'], fmt='o')
ax.set_xlabel('LED voltage [V]')
ax.set_ylabel('Gain [ADC/PE/10^6]')
ax.set_title(labels[f"mod{module}"][channel])
ax1 = ax.twinx()
ax1.errorbar(_df['LEDvoltage'], _df['occ'],
yerr=_df['occ_err'], fmt='o',
color='C1')
ax1.set_ylabel('Occupancy [PE]', color='C1')
ax.grid()
plt.show()Functions
def plot_1_pe_fit_led(df_processed, led_voltage, module, channel, A, mu, sigma, figax=None)-
Plot the SPE Gaussean fit of the LED area histogram.
Expand source code
def plot_1_pe_fit_led(df_processed, led_voltage, module, channel, A, mu, sigma, figax=None): """Plot the SPE Gaussean fit of the LED area histogram.""" if figax is None: fig, ax = plt.subplots(1, 1, figsize=(5, 3), dpi=120) else: fig, ax = figax df_processed_mask = ((df_processed['module'] == module) & (df_processed['channel'] == channel) & (df_processed['LEDvoltage'] == led_voltage)) hist = ax.hist(df_processed[df_processed_mask]['led_area'], bins=np.linspace(-2000, 20000, 300), histtype='step') _x = np.linspace(mu * 0.5, mu * 1.5, 1000) ax.fill_between(_x, pylars.utils.common.Gaussian(_x, A, mu, sigma), color='C1', alpha=1, zorder=-1) # , ls = '--', linewidth = '2') [ax.axvline(mu * n, color='C1', ls='--', alpha=1) for n in range(5)] # ax.set_yscale('log') # ax.set_ylabel('Counts') #ax.set_xlabel('Area (window)') ax.set_ylim(0, 6000) if figax is None: plt.show() else: return fig, ax def plot_LED_all_channels(df_processed, figax=None)-
Plot max ADC counts, amplitude and area for all channels.
Expand source code
def plot_LED_all_channels(df_processed, figax=None): """Plot max ADC counts, amplitude and area for all channels. """ if figax is None: fig, axs = plt.subplots(1, 3, figsize=(13, 4), sharey=True, dpi=60) else: fig, axs = figax plt.subplots_adjust(wspace=0) for ch in np.unique(df_processed['channel']): axs[0].hist(df_processed[df_processed['channel'] == ch]['led_ADCcounts'], bins=100, histtype='step', label=f'{ch}') axs[1].hist(df_processed[df_processed['channel'] == ch]['led_amplitude'], bins=100, histtype='step',) axs[2].hist(df_processed[df_processed['channel'] == ch]['led_area'], bins=100, histtype='step',) axs[0].set_xlabel('max ADC counts') axs[0].set_ylabel('Counts') axs[1].set_xlabel('Amplitude [ADC counts]') axs[2].set_xlabel('Area [integrated yADC counts]') fig.legend(ncol=6, loc='upper center', bbox_to_anchor=(0.5, 1.05)) [_ax.grid(color='lightgray', linestyle='--', linewidth=0.5) for _ax in axs] if figax is None: plt.show() else: return fig, axs def plot_LED_window(waveform: numpy.ndarray, led_window, figax=None)-
Plot the LED window on top of the waveform.
Expand source code
def plot_LED_window(waveform: np.ndarray, led_window, figax=None): """Plot the LED window on top of the waveform. """ if figax is None: fig, ax = plt.subplots(1, 1, figsize=(6, 4), dpi=60) else: fig, ax = figax ax = pylars.plotting.plot_waveform(waveform, ax=ax) ax.fill_betweenx([0, 16000], led_window[0], led_window[1], alpha=0.1, color='C1') return fig, ax def plot_LED_window_from_file(filename: str, led_window: Tuple[int, int], module: Union[int, NoneType] = None, channel: Union[str, NoneType] = None, wf_number: Union[int, NoneType] = None, figax: Union[Tuple, NoneType] = None)-
Plot the LED window on top of the waveform directly from a file.
Expand source code
def plot_LED_window_from_file(filename: str, led_window: Tuple[int, int], module: Optional[int] = None, channel: Optional[str] = None, wf_number: Optional[int] = None, figax: Optional[Tuple] = None): """Plot the LED window on top of the waveform directly from a file. """ process = pylars.processing.fixwindowprocessor.window_processor( baseline_samples=50, led_window=(105, 155)) if module is None: module = 0 print(f"Loading raw data from {filename}") process.load_raw_data(path_to_raw=filename, module=module) if channel is None: channel = str(np.random.choice(process.raw_data.channels)) print(f"No channel specified, choosing random channel: {channel}") if wf_number is None: wf_number = int(np.random.choice(process.raw_data.n_waveforms)) print(f"No waveform number specified, choosing random waveform:" f"{wf_number}") channel_data = process.raw_data.get_channel_data(channel) if figax is None: fig, ax = plt.subplots(1, 1, figsize=(6, 4), dpi=60) else: fig, ax = figax fig, ax = plot_LED_window(channel_data[wf_number], led_window=led_window, figax=(fig, ax)) if figax is None: plt.show() else: return fig, ax def plot_gain_evolution(gain_evolution: pandas.core.frame.DataFrame, tile_list: list, mod, figax=None)-
Plot the gain evolution for a list of tiles.
Expand source code
def plot_gain_evolution(gain_evolution: pd.DataFrame, tile_list: list, mod, figax=None): """Plot the gain evolution for a list of tiles.""" if figax is None: fig, ax = plt.subplots(1, 1, figsize=(6, 4), dpi=120) else: fig, ax = figax for _tile in tile_list: _df = gain_evolution[gain_evolution['tile'] == _tile] ax.errorbar(_df['start'], _df['gain'], yerr=_df['gain_err'], ls='--', marker='.', label=_tile) ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', title='Tile', labelspacing=1.0) ax.set_ylim(0, None) # type:ignore ax.set_title(f'Gain evolution - ADC #{mod}') ax.tick_params(axis='x', rotation=45) ax.set_ylabel('SiPM Gain [M]') # plt.twinx() # plt.plot(tt09['datetime'], tt09['TT09'], 'k--', alpha = 0.3, # zorder = -10,label='Gast temperature') # plt.axvline(np.datetime64('2024-06-14T17:00:00'),ls = '--', color = 'gray', alpha = 0.8) # plt.text(np.datetime64('2024-06-14T08:00:00'), 0.78, # 'Filling complete', color = 'gray',rotation=90) if figax is None: fig.savefig(f'21062024_mod{mod}.png') plt.show() else: return fig, ax def plot_gains_occ(df_gains, figaxs=None)-
Plot the gains and occupancies for all the tiles.
Expand source code
def plot_gains_occ(df_gains, figaxs=None): """Plot the gains and occupancies for all the tiles.""" if figaxs is None: fig, axs = plt.subplots(2, 1, figsize=(4, 4), dpi=120, sharex=True, gridspec_kw={'hspace': 0, 'wspace': 0}, constrained_layout=False) axs = axs.flatten() _x = np.arange(len(df_gains)) axs[0].errorbar(_x, df_gains['gain'], yerr=df_gains['gain_err'], ls='', capsize=4, marker='.') axs[1].errorbar(_x, df_gains['occ'], yerr=df_gains['occ_err'], ls='', capsize=4, marker='.') axs[0].set_xticks(_x, df_gains['tile']) # axs[0].set_ylim(0,1.5) axs[0].set_ylabel('Gain [$10^6$]') axs[1].set_ylabel('Occupancy') # axs[1].set_ylim(-0.1,3.9) if figaxs is None: plt.show() else: return (fig, axs) def plot_gains_occ_ledvoltage(LED_calib, module, channel, labels)-
Plot the gains and occupancies for a given module and channel as a function of the LED voltage.
Expand source code
def plot_gains_occ_ledvoltage(LED_calib, module, channel, labels): """Plot the gains and occupancies for a given module and channel as a function of the LED voltage.""" fig, ax = plt.subplots(1, 1, figsize=(6, 3)) _df = LED_calib.results_df _df = _df[(_df['module'] == module) & (_df['channel'] == channel) & (_df['LEDvoltage'] > 1)] ax.errorbar(_df['LEDvoltage'], _df['gain'], yerr=_df['gain_err'], fmt='o') ax.set_xlabel('LED voltage [V]') ax.set_ylabel('Gain [ADC/PE/10^6]') ax.set_title(labels[f"mod{module}"][channel]) ax1 = ax.twinx() ax1.errorbar(_df['LEDvoltage'], _df['occ'], yerr=_df['occ_err'], fmt='o', color='C1') ax1.set_ylabel('Occupancy [PE]', color='C1') ax.grid() plt.show() def plot_light_levels(led_processed_df: pandas.core.frame.DataFrame, led_width: int, channel: Union[str, NoneType] = None, module: Union[int, NoneType] = None, figax: Union[Tuple, NoneType] = None)-
Plot the LED area histogram for all the available light levels.
Expand source code
def plot_light_levels(led_processed_df: pd.DataFrame, led_width: int, channel: Optional[str] = None, module: Optional[int] = None, figax: Optional[Tuple] = None): """Plot the LED area histogram for all the available light levels.""" select_mask = ((led_processed_df['LEDwidth'] == led_width) & (led_processed_df['channel'] == channel) & (led_processed_df['module'] == module)) _df = led_processed_df[select_mask] if figax is None: fig, axs = plt.subplots(2, 1, figsize=(6, 8), dpi=60) else: fig, axs = figax _ledvoltages = np.unique(_df['LEDvoltage']) _median_amplitudes = [] _std_amplitudes = [] for _v in _ledvoltages: _mask = _df['LEDvoltage'] == _v axs[0].hist(_df[_mask]['led_area'], bins=np.linspace(-1000, 25000, 500), histtype='step', label=f'{_v} V') _amp = np.median(_df[_mask]['led_area']) _std = np.std(_df[_mask]['led_area']) _median_amplitudes.append(_amp) _std_amplitudes.append(_std) axs[0].set_yscale('log') axs[1].errorbar(_ledvoltages, _median_amplitudes, yerr=_std_amplitudes, marker='o', ls='-') axs[1].set_xlabel('LED voltage [V]') axs[1].set_ylabel('Median Area [ADC counts]') axs[1].grid(color='lightgray', linestyle='--', linewidth=0.5) axs[0].set_xlabel('Area [ADC counts]') axs[0].set_ylabel('Counts') axs[0].legend(loc='center left', bbox_to_anchor=(1., 0.5), title='LED voltage') axs[0].grid(color='lightgray', linestyle='--', linewidth=0.5) if figax is None: plt.show() else: return fig, axs