Data Processing

Latest source notebook: pyccapt/calibration/tutorials/jupyter_files/data_processing.ipynb

Google Colab version: pyccapt/calibration/tutorials/colab/data_processing.ipynb

Data Processing Workflow¶

In this workflow within PyCCAPT, we can crop the data, do the voltage and bowl calibration, calculate the 3d reconstruction, and do the ranging.

In [1]:

# Activate intractive functionality of matplotlib
%matplotlib ipympl
# Activate auto reload 
%load_ext autoreload
%autoreload 2
%reload_ext autoreload
# import libraries
import os
import numpy as np
import pandas as pd
import subprocess
from ipywidgets import widgets
from IPython.display import display
from ipywidgets import fixed, interact_manual
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore")

# Local module and scripts
from pyccapt.calibration.calibration import widgets as wd
from pyccapt.calibration.data_tools import data_tools, data_loadcrop, dataset_path_qt
from pyccapt.calibration.tutorials.tutorials_helpers import helper_calibration
from pyccapt.calibration.tutorials.tutorials_helpers import helper_data_loader
from pyccapt.calibration.tutorials.tutorials_helpers import helper_temporal_crop
from pyccapt.calibration.tutorials.tutorials_helpers import helper_special_crop
from pyccapt.calibration.tutorials.tutorials_helpers import helper_t_0_tune
from pyccapt.calibration.tutorials.tutorials_helpers import helper_mc_plot
from pyccapt.calibration.tutorials.tutorials_helpers import helper_3d_reconstruction
from pyccapt.calibration.tutorials.tutorials_helpers import helper_ion_selection
from pyccapt.calibration.tutorials.tutorials_helpers import helper_visualization
from pyccapt.calibration.tutorials.tutorials_helpers import helper_ion_list
from pyccapt.calibration.calibration import ion_selection
from pyccapt.calibration.calibration import share_variables
from pyccapt.calibration.leap_tools.leap_tools import read_rrng

In case of recieving the error about pytable library, you have to install the pytables library with conda command. to do that you can open a new cell and copy the line below in it. Then just run it like other cells. The pytables library will be innstalled.

!conda install --yes --prefix {sys.prefix} pytables

In the cell below we create variable object. This object is used in many of the functions to share the data between functions in as easy way.

In [2]:

variables = share_variables.Variables()

By clicking on the button below, you can select the dataset file you want to work with. The dataset file can be in various formats, including HDF5, EPOS, POS, ATO, and CSV. The cropped data will be saved in the same directory as the original dataset file in a new directory nammed load_crop. The name of the cropped dataset file will be the same as the original dataset file. The figures will be saved in the same directory as the dataset file.

In [3]:

button = widgets.Button(description='load dataset')

@button.on_click
def open_file_on_click_r(b):
    global dataset_path
    folder_path = variables.last_directory
    script = '..//..//data_tools//run_dataset_path_qt.py'
    cmd = f"python {script} {folder_path}"
    result = subprocess.run(cmd, capture_output=True, text=True, shell=True)
    dataset_path = result.stdout.strip()
    variables.last_directory = dataset_path

button

Out[3]:

ROI Selection and Data Cropping¶

From the dropdown lists below, you can select the instrument specifications of the dataset. The instrument specifications are the same as the ones used for the calibration process. Data mode is specify the dataset structure. The dataset can be in raw or calibrated mode. The flight path length is the distance between the sample and the detector. The t0 is the time of flight of the ions with the lowest mass-to-charge ratio. The maximum mass-to-charge ratio is the maximum mass-to-charge ratio of tat you want to plot. You can also change it in te related cells. The detector diameter is the diameter of the detector.

In [4]:

# create an object for selection of instrument specifications of the dataset
tdc, pulse_mode, flight_path_length, t0, max_mc, det_diam = wd.dataset_instrument_specification_selection()

# Display lists and comboboxes to selected instrument specifications
display(tdc, pulse_mode, flight_path_length, t0, det_diam, max_mc)

In [5]:

helper_data_loader.load_data(dataset_path, max_mc.value, flight_path_length.value, pulse_mode.value, tdc.value, variables)
data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)
display(variables.data)
display(variables.range_data)

The maximum possible TOF is: 5010 ns
=============================
The data will be saved on the path: T:/Monajem/APT_data/2382_Jan-10-2025_15-12_NiC9_Al/2382_Jan-10-2025_15-12_NiC9_Al/data_processing/
=============================
The dataset name after saving is: 2382_Jan-10-2025_15-12_NiC9_Al
=============================
The figures will be saved on the path: T:/Monajem/APT_data/2382_Jan-10-2025_15-12_NiC9_Al/2382_Jan-10-2025_15-12_NiC9_Al/data_processing/
=============================
{'apt': ['experiment_chamber_vacuum', 'id', 'num_events', 'num_raw_signals', 'temperature', 'timestamps'], 'dld': ['high_voltage', 'laser_pulse', 'start_counter', 't', 'voltage_pulse', 'x', 'y'], 'tdc': ['channel', 'high_voltage', 'laser_pulse', 'start_counter', 'time_data', 'voltage_pulse']}
The data is loaded in raw mode
The number of data that is removed: 43356
Total number of Ions: 23050746
The maximum possible time of flight is: 5010

	x (nm)	y (nm)	z (nm)	mc (Da)	mc_uc (Da)	high_voltage (V)	pulse	t (ns)	t_c (ns)	x_det (cm)	y_det (cm)	delta_p	multi	start_counter
0	0.0	0.0	0.0	0.0	0.0	500.00	328.000	1560.517326	0.0	1.211429	0.786939	0.0	0.0	12877
1	0.0	0.0	0.0	0.0	0.0	500.00	328.000	3862.638198	0.0	1.315918	-0.538776	0.0	0.0	12897
2	0.0	0.0	0.0	0.0	0.0	500.00	328.000	4725.038556	0.0	1.795918	-0.884898	0.0	0.0	328
3	0.0	0.0	0.0	0.0	0.0	500.00	328.000	4286.572326	0.0	1.126531	-1.453061	0.0	0.0	5238
4	0.0	0.0	0.0	0.0	0.0	500.00	328.000	2820.023316	0.0	-3.983673	-0.153469	0.0	0.0	6942
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
23050741	0.0	0.0	0.0	0.0	0.0	4200.38	840.076	682.816770	0.0	2.390204	-2.259592	0.0	0.0	19720
23050742	0.0	0.0	0.0	0.0	0.0	4200.38	840.076	4706.048754	0.0	1.776327	-1.191837	0.0	0.0	19721
23050743	0.0	0.0	0.0	0.0	0.0	4200.38	840.076	3621.936114	0.0	-2.279184	1.423673	0.0	0.0	19852
23050744	0.0	0.0	0.0	0.0	0.0	4200.38	840.076	183.910986	0.0	2.491429	-2.357551	0.0	0.0	19921
23050745	0.0	0.0	0.0	0.0	0.0	4200.38	840.076	3537.884466	0.0	1.345306	-1.240816	0.0	0.0	19951

23050746 rows × 14 columns

	name	ion	mass	mc	mc_low	mc_up	color	element	complex	isotope	charge
0	unranged0	un	0.0	0.0	0.0	400.0	#000000	[unranged]	[0]	[0]	0

Load a pre saved range file if you want from cell below

In [6]:

button_r = widgets.Button(description='load range dataset')

@button_r.on_click
def open_file_on_click_r(b):
    global range_path
    folder_path = variables.last_directory
    script = '..//..//data_tools//run_dataset_path_qt.py'
    cmd = f"python {script} {folder_path}"
    result = subprocess.run(cmd, capture_output=True, text=True, shell=True)
    range_path = result.stdout.strip()
    variables.last_directory = range_path

button_r

Out[6]:

In [7]:

if 'range_path' in globals():
    _, file_extension = os.path.splitext(range_path)
    if file_extension == '.rrng':
        variables.range_data = read_rrng(range_path)
    else:
        variables.range_data = pd.read_hdf(range_path, mode='r')
display(variables.range_data)

	name	ion	mass	mc	mc_low	mc_up	color	element	complex	isotope	charge
0	unranged0	un	0.0	0.0	0.0	400.0	#000000	[unranged]	[0]	[0]	0

Temporal crop¶

Select the data by drawing a rectangle over the experiment history. Experiment history is a 2D histogram of the time of flight of the ions versus sequence of evaporation. The experiment history is plotted by clicking on the button below te cell.

In [8]:

helper_temporal_crop.call_plot_crop_experiment(variables, pulse_mode.value)

Spatial crop¶

Select the region of maximum concentration of Ions in the below plotted graph to utilize relevant data. To crop you can draw a circle over the filed desorption map. The field desorption map is a 2D histogram of the time of flight of the ions versus the position of the ions on the detector. The field desorption map is plotted by clicking on the button below the cell.

In [9]:

helper_special_crop.call_plot_crop_fdm(variables)

Calculate pulses since the last event pulse and ions per pulse. The percentage of loss in ROI selection process will be printed.

In [10]:

# maximum value of the tdc counter
if tdc.value == 'pyccapt':
    if not (variables.data['delta_p'] != 0).any() or not (variables.data['multi'] != 0).any():
        if (variables.data['start_counter'] != 0).any():
            max_start_counter = max(variables.data['start_counter'])
            # if (variables.data['pulse_pi'] == 0).all() or (variables.data['ion_pp'] == 0).all():
            pulse_pi, ion_pp = data_loadcrop.calculate_ppi_and_ipp(variables.data, max_start_counter)
            # add two calculated array to the croped dataset
            variables.data['delta_p'] = pulse_pi.astype(np.uintc)
            variables.data['multi'] = ion_pp.astype(np.uintc)
    
    # save the cropped data
    print('tof Crop Loss {:.2f} %'.format((100 - (len(variables.data) / len(variables.data_backup)) * 100)))
#percentage of double event per pulse
ion_pp = variables.data['multi'].to_numpy()
print('percentage of multihit event per pulse', len(ion_pp[ion_pp != 1]) / float(len(ion_pp)) * 100)

Progress: 0% complete
Progress: 19% complete
Progress: 39% complete
Progress: 59% complete
Progress: 79% complete
Progress: 99% complete
tof Crop Loss 27.82 %
percentage of multihit event per pulse 3.4706176948758403

In the next cell by changing the t₀ value you can correct the position of H₁ or any other known peak. this correction would be helpful for the position of the peaks in the m/c calibration process.

In [11]:

data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)
variables.data

The maximum possible time of flight is: 5010

Out[11]:

	x (nm)	y (nm)	z (nm)	mc (Da)	mc_uc (Da)	high_voltage (V)	pulse	t (ns)	t_c (ns)	x_det (cm)	y_det (cm)	delta_p	multi	start_counter
0	0.0	0.0	0.0	0.0	0.0	3680.84	736.168	723.306402	0.0	0.943673	-2.778776	0	1	18197
1	0.0	0.0	0.0	0.0	0.0	3680.84	736.168	742.426506	0.0	2.432653	1.812245	39	1	18236
2	0.0	0.0	0.0	0.0	0.0	3680.84	736.168	721.633050	0.0	1.737143	0.300408	400	1	18636
3	0.0	0.0	0.0	0.0	0.0	3680.84	736.168	731.357694	0.0	0.212245	1.782857	37	1	18673
4	0.0	0.0	0.0	0.0	0.0	3680.84	736.168	726.989148	0.0	1.564082	-2.527347	405	1	19078
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16636947	0.0	0.0	0.0	0.0	0.0	5234.12	1046.824	611.843328	0.0	1.469388	-2.383673	307	1	4887
16636948	0.0	0.0	0.0	0.0	0.0	5234.12	1046.824	610.910640	0.0	2.063673	-1.753469	86	1	4973
16636949	0.0	0.0	0.0	0.0	0.0	5234.12	1046.824	609.154992	0.0	-0.862041	-2.726531	15	1	4988
16636950	0.0	0.0	0.0	0.0	0.0	5234.12	1046.824	618.481872	0.0	1.596735	1.126531	406	1	5394
16636951	0.0	0.0	0.0	0.0	0.0	5234.12	1046.824	608.729796	0.0	1.338776	0.535510	383	1	5777

16636952 rows × 14 columns

In [12]:

helper_t_0_tune.call_fine_tune_t_0(variables, flight_path_length, pulse_mode, t0)

Remove the data with m/c greater than max m/c and x, y, t = 0. Also add the needed colums for calibration. The data types of the final cropped dataset is displayed below.

In [13]:

# add the columns in the dataset for x, y, z, mc, mc calibrated, and t calibrated
helper_data_loader.add_columns(variables, max_mc)
# save data temporarily
data_tools.save_data(variables.data, variables, hdf=True, epos=False, pos=False, csv=False, temp=True)
display(variables.data)
display(variables.data.dtypes)

The number of data over max_mc: 0
The number of data with having t, x, and y equal to zero is: 0

	x (nm)	y (nm)	z (nm)	mc (Da)	mc_uc (Da)	high_voltage (V)	pulse	t (ns)	t_c (ns)	x_det (cm)	y_det (cm)	delta_p	multi	start_counter
0	0.0	0.0	0.0	0.0	29.403503	3680.84	736.168	723.306402	0.0	0.943673	-2.778776	0	1	18197
1	0.0	0.0	0.0	0.0	30.936210	3680.84	736.168	742.426506	0.0	2.432653	1.812245	39	1	18236
2	0.0	0.0	0.0	0.0	30.556896	3680.84	736.168	721.633050	0.0	1.737143	0.300408	400	1	18636
3	0.0	0.0	0.0	0.0	31.408325	3680.84	736.168	731.357694	0.0	0.212245	1.782857	37	1	18673
4	0.0	0.0	0.0	0.0	29.671481	3680.84	736.168	726.989148	0.0	1.564082	-2.527347	405	1	19078
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16636947	0.0	0.0	0.0	0.0	29.424786	5234.12	1046.824	611.843328	0.0	1.469388	-2.383673	307	1	4887
16636948	0.0	0.0	0.0	0.0	29.444546	5234.12	1046.824	610.910640	0.0	2.063673	-1.753469	86	1	4973
16636949	0.0	0.0	0.0	0.0	29.071973	5234.12	1046.824	609.154992	0.0	-0.862041	-2.726531	15	1	4988
16636950	0.0	0.0	0.0	0.0	31.084819	5234.12	1046.824	618.481872	0.0	1.596735	1.126531	406	1	5394
16636951	0.0	0.0	0.0	0.0	30.466620	5234.12	1046.824	608.729796	0.0	1.338776	0.535510	383	1	5777

16636952 rows × 14 columns

x (nm)              float64
y (nm)              float64
z (nm)              float64
mc (Da)             float64
mc_uc (Da)          float64
high_voltage (V)    float64
pulse               float64
t (ns)              float64
t_c (ns)            float64
x_det (cm)          float64
y_det (cm)          float64
delta_p              uint32
multi                uint32
start_counter        uint32
dtype: object

---

---

Time-of-Flight/Mass-to-charge Calibration¶

Below you can plot the ToF histogram of the dataset. You can select the peak range of the data you want to plot by drawing a rectangle over peak with holding left click. After that you should apply the voltage and bowl calibration. These three steps should be repeated until you see no improvement in the peak resolution. You can also save the calibration by clicking on the save button. The saved calibration will be used for the next steps.

In [14]:

# exctract needed data from Pandas data frame as an numpy array
data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)

The maximum possible time of flight is: 5010

In [15]:

helper_calibration.call_voltage_bowl_calibration(variables, det_diam.value, flight_path_length.value, pulse_mode.value)

In [16]:

variables.dld_t_calib_backup = np.copy(variables.dld_t_calib)
variables.mc_calib_backup = np.copy(variables.mc_calib)

In [17]:

helper_ion_list.call_ion_list(variables, selector='peak')

In [18]:

variables.data['mc (Da)'] = variables.mc_calib
variables.data['t_c (ns)'] = variables.dld_t_calib
# Remove negative mc
threshold = 0
mc_t = variables.data['mc (Da)'].to_numpy()
mc_t_mask = (mc_t <= threshold)
print('The number of ions with negative mc are:', len(mc_t_mask[mc_t_mask==True]))
variables.data.drop(np.where(mc_t_mask)[0], inplace=True)
variables.data.reset_index(inplace=True, drop=True)
# save data temporarily
data_tools.save_data(variables.data, variables, hdf=True, epos=False, pos=False, csv=False, temp=True)

The number of ions with negative mc are: 2489

In [19]:

helper_mc_plot.call_mc_plot(variables, selector='None')

---

---

3D Reconstruction¶

After bowl and voltage calibration we are ready to calculate the 3d reconstruction. In this workflow we calculate the reconstructed x,y,z and then plot the 3d, heatmap, projection plots and mass-to-charge histogram.

In [20]:

# exctract needed data from Pandas data frame as a numpy array
data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)

display(variables.data)

The maximum possible time of flight is: 5010

	x (nm)	y (nm)	z (nm)	mc (Da)	mc_uc (Da)	high_voltage (V)	pulse	t (ns)	t_c (ns)	x_det (cm)	y_det (cm)	delta_p	multi	start_counter
0	0.0	0.0	0.0	27.023671	29.403503	3680.84	736.168	723.306402	656.477023	0.943673	-2.778776	0	1	18197
1	0.0	0.0	0.0	26.935078	30.936210	3680.84	736.168	742.426506	655.460971	2.432653	1.812245	39	1	18236
2	0.0	0.0	0.0	26.913140	30.556896	3680.84	736.168	721.633050	655.209121	1.737143	0.300408	400	1	18636
3	0.0	0.0	0.0	27.041236	31.408325	3680.84	736.168	731.357694	656.678267	0.212245	1.782857	37	1	18673
4	0.0	0.0	0.0	27.171525	29.671481	3680.84	736.168	726.989148	658.169010	1.564082	-2.527347	405	1	19078
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16634458	0.0	0.0	0.0	27.188185	29.424786	5234.12	1046.824	611.843328	658.359375	1.469388	-2.383673	307	1	4887
16634459	0.0	0.0	0.0	26.975280	29.444546	5234.12	1046.824	610.910640	655.922243	2.063673	-1.753469	86	1	4973
16634460	0.0	0.0	0.0	27.040801	29.071973	5234.12	1046.824	609.154992	656.673286	-0.862041	-2.726531	15	1	4988
16634461	0.0	0.0	0.0	27.423863	31.084819	5234.12	1046.824	618.481872	661.046094	1.596735	1.126531	406	1	5394
16634462	0.0	0.0	0.0	27.048862	30.466620	5234.12	1046.824	608.729796	656.765623	1.338776	0.535510	383	1	5777

16634463 rows × 14 columns

You have to select the main element in your sample from the from dropdown below.

In [21]:

element_selected = wd.density_field_selection()
display(element_selected)

In case that yopu face error about plotly library, like javascripts error. You have check your jupyter lab version is compatibale with the plotly extenstion. Sometimes running jupyter lab build command fix the proble.

In [22]:

helper_3d_reconstruction.call_x_y_z_calculation(variables, flight_path_length, element_selected=element_selected)

In [23]:

variables.data['x (nm)'] = variables.x
variables.data['y (nm)'] = variables.y
variables.data['z (nm)'] = variables.z
# save data temporarily
data_tools.save_data(variables.data, variables, hdf=True, epos=False, pos=False, csv=False, temp=True)

---

---

Ion Selection and Rangging¶

This tutorial outlines a comprehensive workflow for ion selection and organization. Users can choose ions using peak and element finders, manually add ions, customize ion colors, and create histograms. Histograms can be generated for selected ranges and areas, and figures can be saved. Additionally, users have the option to save both figures and data in CSV and HDF5 formats.

In [24]:

# exctract needed data from Pandas data frame as an numpy array
data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)

display(variables.data)

The maximum possible time of flight is: 5010

	x (nm)	y (nm)	z (nm)	mc (Da)	mc_uc (Da)	high_voltage (V)	pulse	t (ns)	t_c (ns)	x_det (cm)	y_det (cm)	delta_p	multi	start_counter
0	5.544648	-16.326975	3.173361	27.023671	29.403503	3680.84	736.168	723.306402	656.477023	0.943673	-2.778776	0	1	18197
1	14.248317	10.614518	3.376773	26.935078	30.936210	3680.84	736.168	742.426506	655.460971	2.432653	1.812245	39	1	18236
2	10.519766	1.819208	1.191354	26.913140	30.556896	3680.84	736.168	721.633050	655.209121	1.737143	0.300408	400	1	18636
3	1.284474	10.789582	1.234681	27.041236	31.408325	3680.84	736.168	731.357694	656.678267	0.212245	1.782857	37	1	18673
4	9.179021	-14.832072	3.250077	27.171525	29.671481	3680.84	736.168	726.989148	658.169010	1.564082	-2.527347	405	1	19078
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16634458	12.327791	-19.998417	203.316053	27.188185	29.424786	5234.12	1046.824	611.843328	658.359375	1.469388	-2.383673	307	1	4887
16634459	17.361259	-14.751576	203.062549	26.975280	29.444546	5234.12	1046.824	610.910640	655.922243	2.063673	-1.753469	86	1	4973
16634460	-7.219228	-22.833542	203.482930	27.040801	29.071973	5234.12	1046.824	609.154992	656.673286	-0.862041	-2.726531	15	1	4988
16634461	13.695249	9.662292	201.255914	27.423863	31.084819	5234.12	1046.824	618.481872	661.046094	1.596735	1.126531	406	1	5394
16634462	11.595761	4.638304	200.327160	27.048862	30.466620	5234.12	1046.824	608.729796	656.765623	1.338776	0.535510	383	1	5777

16634463 rows × 14 columns

In [25]:

display(variables.data.dtypes)

x (nm)              float64
y (nm)              float64
z (nm)              float64
mc (Da)             float64
mc_uc (Da)          float64
high_voltage (V)    float64
pulse               float64
t (ns)              float64
t_c (ns)            float64
x_det (cm)          float64
y_det (cm)          float64
delta_p              uint32
multi                uint32
start_counter        uint32
dtype: object

In [26]:

helper_ion_selection.call_ion_selection(variables)

Now we can also show the color in the range dataframe

In [28]:

display(variables.range_data.style.applymap(ion_selection.display_color, subset=['color']))

	name	ion	mass	mc	mc_low	mc_up	color	element	complex	isotope	charge
0	H1	${}^{1}H^{+}$	1.010000	1.002499	0.847339	1.178480	#b2aa2d	['H']	[1]	[1]	1.000000
1	Al1	${}^{27}Al^{2+}$	13.490000	13.433483	13.253794	14.185893	#e7e0d1	['Al']	[1]	[27]	2.000000
2	Al1	${}^{27}Al^{+}$	26.980000	26.967215	26.542237	28.995128	#e7e0d1	['Al']	[1]	[27]	1.000000

In [29]:

variables.range_data.dtypes

Out[29]:

name        object
ion         object
mass       float64
mc         float64
mc_low     float64
mc_up      float64
color       object
element     object
complex     object
isotope     object
charge     float64
dtype: object

Save the range in the hdf5 and csv format.

In [30]:

interact_manual_range = interact_manual.options(manual_name="save range")
interact_manual_range(data_tools.save_range, variables=fixed(variables));

Save the cropped dataset. You can specify te output format from list below. The output formats are HDF5, EPOS, POS, ATO, and CSV. The output file will be saved in the same directory as the original dataset file in a new directory nammed load_crop.

In [31]:

interact_manual_data = interact_manual.options(manual_name="save data")
interact_manual_data(data_tools.save_data, data=fixed(variables.data), variables=fixed(variables),
                name=widgets.Text(value=variables.result_data_name),
                hdf=widgets.Dropdown(options=[('True', True), ('False', False)]),
                epos=widgets.Dropdown(options=[('False', False), ('True', True)]),
                pos=widgets.Dropdown(options=[('False', False), ('True', True)]),
                ato_6v=widgets.Dropdown(options=[('False', False), ('True', True)]),
                csv=widgets.Dropdown(options=[('False', False), ('True', True)]),
               temp=fixed(False));

---

---

Visualization¶

In [32]:

variables.data

Out[32]:

	x (nm)	y (nm)	z (nm)	mc (Da)	mc_uc (Da)	high_voltage (V)	pulse	t (ns)	t_c (ns)	x_det (cm)	y_det (cm)	delta_p	multi	start_counter
0	5.544648	-16.326975	3.173361	27.023671	29.403503	3680.84	736.168	723.306402	656.477023	0.943673	-2.778776	0	1	18197
1	14.248317	10.614518	3.376773	26.935078	30.936210	3680.84	736.168	742.426506	655.460971	2.432653	1.812245	39	1	18236
2	10.519766	1.819208	1.191354	26.913140	30.556896	3680.84	736.168	721.633050	655.209121	1.737143	0.300408	400	1	18636
3	1.284474	10.789582	1.234681	27.041236	31.408325	3680.84	736.168	731.357694	656.678267	0.212245	1.782857	37	1	18673
4	9.179021	-14.832072	3.250077	27.171525	29.671481	3680.84	736.168	726.989148	658.169010	1.564082	-2.527347	405	1	19078
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16634458	12.327791	-19.998417	203.316053	27.188185	29.424786	5234.12	1046.824	611.843328	658.359375	1.469388	-2.383673	307	1	4887
16634459	17.361259	-14.751576	203.062549	26.975280	29.444546	5234.12	1046.824	610.910640	655.922243	2.063673	-1.753469	86	1	4973
16634460	-7.219228	-22.833542	203.482930	27.040801	29.071973	5234.12	1046.824	609.154992	656.673286	-0.862041	-2.726531	15	1	4988
16634461	13.695249	9.662292	201.255914	27.423863	31.084819	5234.12	1046.824	618.481872	661.046094	1.596735	1.126531	406	1	5394
16634462	11.595761	4.638304	200.327160	27.048862	30.466620	5234.12	1046.824	608.729796	656.765623	1.338776	0.535510	383	1	5777

16634463 rows × 14 columns

helper_visualization.call_visualization(variables)¶

In [33]:

data_tools.extract_data(variables.data, variables, flight_path_length.value, max_mc.value)

The maximum possible time of flight is: 5010

In [34]:

helper_visualization.call_visualization(variables)

In [35]:

variables.plotly_3d_reconstruction.show()

In [ ]:

from IPython.display import display, HTML
display(HTML(variables.animation_detector_html))