Basic Usage — 1D Analyzer

In [4]:

import matplotlib.pyplot as plt

import logging

from geecs_data_utils.config_roots import image_analysis_config
from geecs_data_utils.scan_data import ScanData, ScanPaths
from image_analysis.config import load_line_config
from image_analysis.analyzers.standard_1d_analyzer import Standard1DAnalyzer

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
logging.getLogger("image_analysis").setLevel(logging.WARNING)
logging.getLogger("geecs_data_utils").setLevel(logging.WARNING)

image_analysis_config.set_base_dir(ScanPaths.paths_config.image_analysis_configs_path)

import matplotlib.pyplot as plt import logging from geecs_data_utils.config_roots import image_analysis_config from geecs_data_utils.scan_data import ScanData, ScanPaths from image_analysis.config import load_line_config from image_analysis.analyzers.standard_1d_analyzer import Standard1DAnalyzer logging.basicConfig( level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", ) logging.getLogger("image_analysis").setLevel(logging.WARNING) logging.getLogger("geecs_data_utils").setLevel(logging.WARNING) image_analysis_config.set_base_dir(ScanPaths.paths_config.image_analysis_configs_path)

Out[4]:

PosixPath('/Users/samuelbarber/Desktop/Code/Github_repos/GEECS-Plugins-Configs-loader-api-refactor/scan_analysis_configs/analyzers')

In [2]:

sd = ScanData.from_date(year=2025, month=8, day=7, number=7, experiment="Undulator")

sd = ScanData.from_date(year=2025, month=8, day=7, number=7, experiment="Undulator")

In [5]:

dev_name = "Z_Test_Scope"
line_config = load_line_config(dev_name)
analyzer = Standard1DAnalyzer(line_config=line_config)

file_path = sd.data_frame[f"{dev_name}_expected_path"][1]
result = analyzer.analyze_image_file(file_path)
data = result.line_data

dev_name = "Z_Test_Scope" line_config = load_line_config(dev_name) analyzer = Standard1DAnalyzer(line_config=line_config) file_path = sd.data_frame[f"{dev_name}_expected_path"][1] result = analyzer.analyze_image_file(file_path) data = result.line_data

In [6]:

plt.plot(data[:, 0], data[:, 1])
plt.show()

plt.plot(data[:, 0], data[:, 1]) plt.show()

In [8]:

sd = ScanData.from_date(
    year=2025, month=9, day=24, number=10, experiment="Undulator", append_paths=False
)

dev_name = "U_BCaveICT"

line_config = load_line_config(dev_name)
analyzer = Standard1DAnalyzer(line_config=line_config)

file_path = sd.paths.get_device_shot_path(
    tag=sd.paths.get_tag(), device_name=dev_name, shot_number=2, file_extension="tdms"
)
result = analyzer.analyze_image_file(file_path)
data = result.line_data

plt.plot(data[:, 0], data[:, 1])
plt.show()

sd = ScanData.from_date( year=2025, month=9, day=24, number=10, experiment="Undulator", append_paths=False ) dev_name = "U_BCaveICT" line_config = load_line_config(dev_name) analyzer = Standard1DAnalyzer(line_config=line_config) file_path = sd.paths.get_device_shot_path( tag=sd.paths.get_tag(), device_name=dev_name, shot_number=2, file_extension="tdms" ) result = analyzer.analyze_image_file(file_path) data = result.line_data plt.plot(data[:, 0], data[:, 1]) plt.show()

In [13]:

sd = ScanData.from_date(
    year=2025, month=9, day=24, number=10, experiment="Undulator", append_paths=False
)

config_name = "BcaveMagSpecStitcherSpec"
dev_name = "U_BCaveMagSpec-interpSpec"

line_config = load_line_config(config_name)
analyzer = Standard1DAnalyzer(line_config=line_config)
file_path = sd.paths.get_device_shot_path(
    tag=sd.paths.get_tag(), device_name=dev_name, shot_number=2, file_extension="txt"
)

# analyzer.line_config.roi.x_min = 0
# analyzer.line_config.roi.x_max = 0.1

file_path = file_path.parent / "Scan010_U_BCaveMagSpec_001.txt"
result = analyzer.analyze_image_file(file_path)
data = result.line_data

plt.plot(data[:, 0], data[:, 1])
# plt.xlabel(f"{result['analyzer_input_parameters']} ({result.x_units})")
# plt.ylabel(f"{result.y_label} ({result.y_units})")
plt.show()
result.metadata

sd = ScanData.from_date( year=2025, month=9, day=24, number=10, experiment="Undulator", append_paths=False ) config_name = "BcaveMagSpecStitcherSpec" dev_name = "U_BCaveMagSpec-interpSpec" line_config = load_line_config(config_name) analyzer = Standard1DAnalyzer(line_config=line_config) file_path = sd.paths.get_device_shot_path( tag=sd.paths.get_tag(), device_name=dev_name, shot_number=2, file_extension="txt" ) # analyzer.line_config.roi.x_min = 0 # analyzer.line_config.roi.x_max = 0.1 file_path = file_path.parent / "Scan010_U_BCaveMagSpec_001.txt" result = analyzer.analyze_image_file(file_path) data = result.line_data plt.plot(data[:, 0], data[:, 1]) # plt.xlabel(f"{result['analyzer_input_parameters']} ({result.x_units})") # plt.ylabel(f"{result.y_label} ({result.y_units})") plt.show() result.metadata

Out[13]:

{'line_name': 'U_BCaveMagSpec-interpSpec',
 'data_type': <Data1DType.TSV: 'tsv'>,
 'config_name': 'U_BCaveMagSpec-interpSpec',
 'data_format': 'Charge density vs Energy',
 'x_units': 'MeV',
 'y_units': None,
 'x_label': 'Momentum_GeV/c',
 'y_label': 'ChargeDen_pC/GeV'}

Below is an example of using the ICT_1d_analyzer. The plot just shows the prepocessed trace (before applying the ICT analysis algorithm. The subsequent cells show how to how to use utilities in ict_algorithms to inspect and fine tune the relevant parameters to ensure the analysis result is being calculated as desired

In [22]:

from image_analysis.analyzers.ict_1d_analyzer import ICT1DAnalyzer

config_name = "U_BCaveICT"
dev_name = "U_BCaveICT"
line_config = load_line_config(config_name)

analyzer = ICT1DAnalyzer(line_config=line_config)
sd = ScanData.from_date(year=2025, month=11, day=13, number=2, experiment="Undulator")
file_path = sd.data_frame[f"{dev_name}_expected_path"][34]

from image_analysis.analyzers.ict_1d_analyzer import ICT1DAnalyzer config_name = "U_BCaveICT" dev_name = "U_BCaveICT" line_config = load_line_config(config_name) analyzer = ICT1DAnalyzer(line_config=line_config) sd = ScanData.from_date(year=2025, month=11, day=13, number=2, experiment="Undulator") file_path = sd.data_frame[f"{dev_name}_expected_path"][34]

In [23]:

# run the analyzer using the ict_analsis_parameters from the config
result = analyzer.analyze_image_file(file_path)
data = result.line_data
plt.plot(data[:, 0], data[:, 1])
plt.show()

# run the analyzer using the ict_analsis_parameters from the config result = analyzer.analyze_image_file(file_path) data = result.line_data plt.plot(data[:, 0], data[:, 1]) plt.show()

In [24]:

# detailed view of the analysis, tune the parameters of ict_analysis
from image_analysis.algorithms.ict_algorithms import apply_ict_analysis_with_details

res = apply_ict_analysis_with_details(
    data[:, 1],
    dt=4e-9,
    butterworth_order=1,
    butterworth_crit_f=0.75,
    calibration_factor=0.1,
)

plt.plot(
    res[1]["raw_data"][res[1]["signal_region_start"] : res[1]["signal_region_end"]]
)
plt.plot(
    res[1]["cleaned_data"][res[1]["signal_region_start"] : res[1]["signal_region_end"]]
)
plt.show()
res[0]

# detailed view of the analysis, tune the parameters of ict_analysis from image_analysis.algorithms.ict_algorithms import apply_ict_analysis_with_details res = apply_ict_analysis_with_details( data[:, 1], dt=4e-9, butterworth_order=1, butterworth_crit_f=0.75, calibration_factor=0.1, ) plt.plot( res[1]["raw_data"][res[1]["signal_region_start"] : res[1]["signal_region_end"]] ) plt.plot( res[1]["cleaned_data"][res[1]["signal_region_start"] : res[1]["signal_region_end"]] ) plt.show() res[0]

Out[24]:

5.617502363979413

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