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rawplot
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rawplot - pypi Package Compare versions
+3
-0
@@ -164,2 +164,3 @@ # Byte-compiled / optimized / DLL files | ||
| *.csv | ||
| *.ecsv | ||
| *.db | ||
@@ -174,1 +175,3 @@ *.txt | ||
| dataset/ | ||
| .rufff-cache/ | ||
| spectess/ |
+2
-3
| Metadata-Version: 2.1 | ||
| Name: rawplot | ||
| Version: 0.9.8 | ||
| Version: 0.9.9 | ||
| Summary: Plotting tools to analyze and characterize RGB imagers using Bayer raw images | ||
@@ -47,3 +47,3 @@ Author-email: Rafael González <rafael08@ucm.es>, Jaime Zamorano <jzamoran@ucm.es> | ||
| Requires-Dist: astropy | ||
| Requires-Dist: lica[raw] | ||
| Requires-Dist: lica[lica,raw] | ||
@@ -75,3 +75,2 @@ # rawplot | ||
| ls -1 .venv/bin/raw* | ||
| venv/bin/rawplot-filters | ||
| .venv/bin/rawplot-hv | ||
@@ -78,0 +77,0 @@ .venv/bin/rawplot-image |
+3
-3
@@ -30,3 +30,3 @@ | ||
| "astropy", | ||
| "lica[raw]", | ||
| "lica[raw,lica]", | ||
| ] | ||
@@ -50,4 +50,4 @@ | ||
| rawplot-photodiode = "rawplot.photodiode:main" | ||
| rawplot-filters = "rawplot.filters:main" | ||
| [build-system] | ||
@@ -79,2 +79,2 @@ requires = ["setuptools >= 45", "wheel", "setuptools_scm[toml]>=6.2"] | ||
| [tool.setuptools.package-data] | ||
| "rawplot.resources" = ["*.csv", "*.mplstyle"] | ||
| "rawplot.resources" = ["*.mplstyle"] |
+0
-1
@@ -26,3 +26,2 @@ # rawplot | ||
| ls -1 .venv/bin/raw* | ||
| venv/bin/rawplot-filters | ||
| .venv/bin/rawplot-hv | ||
@@ -29,0 +28,0 @@ .venv/bin/rawplot-image |
| [console_scripts] | ||
| rawplot-filters = rawplot.filters:main | ||
| rawplot-hv = rawplot.hv:main | ||
@@ -4,0 +3,0 @@ rawplot-image = rawplot.image:main |
| Metadata-Version: 2.1 | ||
| Name: rawplot | ||
| Version: 0.9.8 | ||
| Version: 0.9.9 | ||
| Summary: Plotting tools to analyze and characterize RGB imagers using Bayer raw images | ||
@@ -47,3 +47,3 @@ Author-email: Rafael González <rafael08@ucm.es>, Jaime Zamorano <jzamoran@ucm.es> | ||
| Requires-Dist: astropy | ||
| Requires-Dist: lica[raw] | ||
| Requires-Dist: lica[lica,raw] | ||
@@ -75,3 +75,2 @@ # rawplot | ||
| ls -1 .venv/bin/raw* | ||
| venv/bin/rawplot-filters | ||
| .venv/bin/rawplot-hv | ||
@@ -78,0 +77,0 @@ .venv/bin/rawplot-image |
@@ -5,2 +5,2 @@ matplotlib | ||
| astropy | ||
| lica[raw] | ||
| lica[lica,raw] |
@@ -19,3 +19,2 @@ .gitignore | ||
| src/rawplot/_version.py | ||
| src/rawplot/filters.py | ||
| src/rawplot/hv.py | ||
@@ -41,4 +40,2 @@ src/rawplot/image.py | ||
| src/rawplot/ptc/variance_curves.py | ||
| src/rawplot/resources/Ham-S2281-04.csv | ||
| src/rawplot/resources/OSI-11-01-004-10D.csv | ||
| src/rawplot/resources/__init__.py | ||
@@ -45,0 +42,0 @@ src/rawplot/resources/global.mplstyle |
@@ -1,3 +0,3 @@ | ||
| from ._version import __version__ | ||
| from ._version import __version__ as __version__ | ||
| __all__ = ["__version__"] |
@@ -15,3 +15,3 @@ # file generated by setuptools_scm | ||
| __version__ = version = '0.9.8' | ||
| __version_tuple__ = version_tuple = (0, 9, 8) | ||
| __version__ = version = '0.9.9' | ||
| __version_tuple__ = version_tuple = (0, 9, 9) |
+117
-91
@@ -13,7 +13,6 @@ # -*- coding: utf-8 -*- | ||
| import csv | ||
| from argparse import Namespace, ArgumentParser | ||
| import logging | ||
| from typing import Tuple | ||
| from importlib.resources import files | ||
| # --------------------- | ||
@@ -23,7 +22,12 @@ # Thrid-party libraries | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| from astropy.table import Table, Column | ||
| import astropy.units as u | ||
| import lica | ||
| from lica.cli import execute | ||
| from lica.photodiode import PhotodiodeModel, COL, BENCH | ||
| # ------------------------ | ||
@@ -33,3 +37,3 @@ # Own modules and packages | ||
| from ._version import __version__ | ||
| from . import __version__ | ||
@@ -40,5 +44,2 @@ # ---------------- | ||
| OSI_PHOTODIODE = "OSI-11-01-004-10D" | ||
| HAMAMATSU_PHOTODIODE = "Ham-S2281-04" | ||
| # ----------------------- | ||
@@ -61,12 +62,27 @@ # Module global variables | ||
| def vbench(x: str) -> float: | ||
| x = float(x) | ||
| if not (BENCH.WAVE_START <= x <= BENCH.WAVE_END): | ||
| raise ValueError(f"{x} outside LICA Optical Test Bench range") | ||
| return x | ||
| def mpl_photodiode_plot_loop(title, wavelength, responsivity, qe, xtitle, ytitle): | ||
| def get_labels(x: Column, y: Column) -> Tuple[str, str]: | ||
| """Get the labels for a table column, using units if necessary""" | ||
| xunit = x.unit | ||
| yunit = y.unit | ||
| xlabel = x.name + f" [{xunit}]" if xunit != u.dimensionless_unscaled else x.name | ||
| ylabel = y.name + f" [{yunit}]" if yunit != u.dimensionless_unscaled else y.name | ||
| return xlabel, ylabel | ||
| def plot_photodiode(table: Table, title: str, marker: str): | ||
| fig, axes = plt.subplots(nrows=1, ncols=1) | ||
| fig.suptitle(title) | ||
| axes.set_xlabel(xtitle) | ||
| axes.set_ylabel(f"{ytitle}") | ||
| xlabel, ylabel = get_labels(table[COL.WAVE], table[COL.RESP]) | ||
| axes.set_xlabel(xlabel) | ||
| axes.set_ylabel(ylabel + " & " + COL.QE) | ||
| axes.grid(True, which="major", color="silver", linestyle="solid") | ||
| axes.grid(True, which="minor", color="silver", linestyle=(0, (1, 10))) | ||
| axes.plot(wavelength, responsivity, marker="o", linewidth=0, label="Responsivity [A/W]") | ||
| axes.plot(wavelength, qe, marker="o", linewidth=0, label="Quantum Efficiency") | ||
| axes.plot(table[COL.WAVE], table[COL.RESP], marker=marker, linewidth=0, label=COL.RESP) | ||
| axes.plot(table[COL.WAVE], table[COL.QE], marker=marker, linewidth=0, label=COL.QE) | ||
| axes.minorticks_on() | ||
@@ -77,66 +93,52 @@ axes.legend() | ||
| def photodiode_export(model, resolution, path): | ||
| log.info("Exporting model %s, resolution %d nm to file %s", model, resolution, path) | ||
| f = files("rawplot.resources").joinpath(model + ".csv") | ||
| with f.open("r") as csvfile: | ||
| lines = csvfile.readlines() | ||
| with open(path, "w") as exportfile: | ||
| exportfile.writelines(lines[0:1]) | ||
| exportfile.writelines(lines[1::resolution]) | ||
| # --------------------------------- | ||
| # Exported non-CLI (i.e to Jupyter) | ||
| # --------------------------------- | ||
| def photodiode_load(model, resolution): | ||
| """Return dictionaries whose keys are the wavelengths""" | ||
| f = files("rawplot.resources").joinpath(model + ".csv") | ||
| with f.open("r") as csvfile: | ||
| reader = csv.DictReader(csvfile, delimiter=";") | ||
| responsivity = dict() | ||
| qe = dict() | ||
| for row in reader: | ||
| responsivity[int(row["Wavelength (nm)"])] = float(row["Responsivity (A/W)"]) | ||
| qe[int(row["Wavelength (nm)"])] = float(row["Quantum Efficiency"]) | ||
| # resample dictionaries if necessary for 5 nm resolution | ||
| responsivity = {key: val for key, val in responsivity.items() if key % resolution == 0} | ||
| qe = {key: val for key, val in qe.items() if key % resolution == 0} | ||
| return responsivity, qe | ||
| def plot( | ||
| model: str, | ||
| resolution: int, | ||
| wave_start: int = BENCH.WAVE_START, | ||
| wave_end: int = BENCH.WAVE_START, | ||
| marker: str = ".", | ||
| ) -> None: | ||
| log.info(" === PHOTODIODE RESPONSIVITY & QE PLOT === ") | ||
| table = lica.photodiode.load(model, resolution, wave_start, wave_end, cross_calibrated=True) | ||
| log.info("Table info is\n%s", table.info) | ||
| plot_photodiode( | ||
| title=f"{model} characteristics @ {resolution} nm", | ||
| table=table, | ||
| marker=marker, | ||
| ) | ||
| def export( | ||
| path: str, | ||
| model: str, | ||
| resolution: int, | ||
| wave_start: int = BENCH.WAVE_START, | ||
| wave_end: int = BENCH.WAVE_START, | ||
| ) -> None: | ||
| lica.photodiode.export(path, model, resolution, wave_start, wave_end) | ||
| # ----------------------- | ||
| # AUXILIARY MAIN FUNCTION | ||
| # ----------------------- | ||
| # ============= | ||
| # CLI INTERFACE | ||
| # ============= | ||
| def export(args): | ||
| def cli_export(args: Namespace) -> None: | ||
| log.info(" === PHOTODIODE RESPONSIVITY & QE EXPORT === ") | ||
| photodiode_export(args.model, args.resolution, args.csv_file) | ||
| export(args.ecsv_file, args.model, args.resolution, args.wave_start, args.wave_end) | ||
| def plot(args): | ||
| def cli_plot(args: Namespace) -> None: | ||
| log.info(" === PHOTODIODE RESPONSIVITY & QE PLOT === ") | ||
| responsivity, qe = photodiode_load(args.model, args.resolution) | ||
| wavelength = np.array([key for key, value in responsivity.items()]) | ||
| responsivity = np.array([value for key, value in responsivity.items()]) | ||
| qe = np.array([value for key, value in qe.items()]) | ||
| mpl_photodiode_plot_loop( | ||
| title=f"{args.model} characteristics", | ||
| wavelength=wavelength, | ||
| responsivity=responsivity, | ||
| qe=qe, | ||
| xtitle="Wavelengnth [nm]", | ||
| ytitle="Responsivity [A/W] & Quantum Efficiency", | ||
| ) | ||
| plot(args.model, args.resolution, args.wave_start, args.wave_end) | ||
| COMMAND_TABLE = { | ||
| "plot": plot, | ||
| "export": export, | ||
| } | ||
| def cli_photodiode(args: Namespace) -> None: | ||
| args.func(args) | ||
| def photodiode(args): | ||
| command = args.command | ||
| func = COMMAND_TABLE[command] | ||
| func(args) | ||
| # =================================== | ||
@@ -147,18 +149,13 @@ # MAIN ENTRY POINT SPECIFIC ARGUMENTS | ||
| def add_args(parser): | ||
| subparser = parser.add_subparsers(dest="command") | ||
| parser_plot = subparser.add_parser("plot", help="Plot Responsivity & Quantum Efficiency") | ||
| parser_expo = subparser.add_parser( | ||
| "export", help="Export Responsivity & Quantum Efficiency to CSV file" | ||
| ) | ||
| parser_plot.add_argument( | ||
| def common_parser() -> ArgumentParser: | ||
| """Common Options for subparsers""" | ||
| parser = ArgumentParser(add_help=False) | ||
| parser.add_argument( | ||
| "-m", | ||
| "--model", | ||
| default=OSI_PHOTODIODE, | ||
| choices=(HAMAMATSU_PHOTODIODE, OSI_PHOTODIODE), | ||
| default=PhotodiodeModel.OSI, | ||
| choices=[p for p in PhotodiodeModel], | ||
| help="Photodiode model. (default: %(default)s)", | ||
| ) | ||
| parser_plot.add_argument( | ||
| parser.add_argument( | ||
| "-r", | ||
@@ -168,23 +165,49 @@ "--resolution", | ||
| default=5, | ||
| choices=(1, 5), | ||
| choices=tuple(range(1, 11)), | ||
| help="Wavelength resolution (nm). (default: %(default)s nm)", | ||
| ) | ||
| parser.add_argument( | ||
| "-w1", | ||
| "--wave-start", | ||
| type=vbench, | ||
| metavar="<W1 nm>", | ||
| default=BENCH.WAVE_START, | ||
| help="Start wavelength in nm (defaults to %(default)d)", | ||
| ) | ||
| parser.add_argument( | ||
| "-w2", | ||
| "--wave-end", | ||
| type=vbench, | ||
| metavar="<W2 nm>", | ||
| default=BENCH.WAVE_END, | ||
| help="End wavelength in nm (defaults to %(default)d)", | ||
| ) | ||
| parser_expo.add_argument( | ||
| "-m", | ||
| "--model", | ||
| default=OSI_PHOTODIODE, | ||
| choices=(HAMAMATSU_PHOTODIODE, OSI_PHOTODIODE), | ||
| help="Photodiode model. (default: %(default)s)", | ||
| return parser | ||
| def add_args(parser) -> None: | ||
| subparser = parser.add_subparsers(dest="command") | ||
| parser_plot = subparser.add_parser( | ||
| "plot", parents=[common_parser()], help="Plot Responsivity & Quantum Efficiency" | ||
| ) | ||
| parser_expo.add_argument( | ||
| "-r", | ||
| "--resolution", | ||
| type=int, | ||
| default=5, | ||
| choices=(1, 5), | ||
| help="Wavelength resolution (nm). (default: %(default)s nm)", | ||
| parser_plot.set_defaults(func=cli_plot) | ||
| parser_expo = subparser.add_parser( | ||
| "export", | ||
| parents=[common_parser()], | ||
| help="Export Responsivity & Quantum Efficiency to CSV file", | ||
| ) | ||
| parser_expo.set_defaults(func=cli_export) | ||
| # ------------------------------------------------------------------------------------ | ||
| parser_plot.add_argument( | ||
| "--marker", | ||
| type=str, | ||
| choices=[".", "o", "+", "*"], | ||
| default=".", | ||
| help="Plot Marker", | ||
| ) | ||
| # ------------------------------------------------------------------------------------ | ||
| parser_expo.add_argument( | ||
| "-f", "--csv-file", type=str, required=True, help="CSV file name to export" | ||
| "-f", "--ecsv-file", type=str, required=True, help="ECSV file name to export" | ||
| ) | ||
@@ -200,3 +223,3 @@ | ||
| execute( | ||
| main_func=photodiode, | ||
| main_func=cli_photodiode, | ||
| add_args_func=add_args, | ||
@@ -207,1 +230,4 @@ name=__name__, | ||
| ) | ||
| __all__ = ["plot", "export"] |
+282
-231
@@ -14,4 +14,6 @@ # -*- coding: utf-8 -*- | ||
| import re | ||
| import math | ||
| import logging | ||
| from argparse import ArgumentParser | ||
| from enum import IntEnum | ||
| from typing import Union, Dict, Any, Sequence, Iterable | ||
@@ -25,7 +27,16 @@ # --------------------- | ||
| import astropy.io.ascii | ||
| import astropy.units as u | ||
| from astropy.table import Table | ||
| from astropy.constants import astropyconst20 as const | ||
| from lica import StrEnum | ||
| from lica.cli import execute | ||
| from lica.validators import vdir, vfile, vfloat, vfloat01, vflopath | ||
| from lica.raw.loader.roi import Roi, NormRoi | ||
| from lica.raw.analyzer.image import ImageStatistics | ||
| from lica.csv import read_csv | ||
| import lica.photodiode | ||
| from lica.photodiode import PhotodiodeModel, COL | ||
| # ------------------------ | ||
@@ -36,5 +47,28 @@ # Own modules and packages | ||
| from ._version import __version__ | ||
| from .util.common import common_list_info, make_plot_title_from, export_spectra_to_csv | ||
| from .photodiode import photodiode_load, OSI_PHOTODIODE, HAMAMATSU_PHOTODIODE | ||
| from .util.common import common_list_info, make_plot_title_from | ||
| # ----------------- | ||
| # Additiona classes | ||
| # ----------------- | ||
| BiasType = Union[float, str] | ||
| DarkType = Union[float, str] | ||
| class TBCOL(StrEnum): | ||
| """Additiona columns names for data produced by Scan.exe or TestBench""" | ||
| INDEX = "Index" # Index number 1, 2, etc produced in the CSV file | ||
| CURRENT = "Electrical Current" # | ||
| READ_NOISE = "Read Noise" | ||
| class PhDOption(IntEnum): | ||
| """Photodiode plot options""" | ||
| RAW = 1 | ||
| NORM = 2 | ||
| SNR = 3 | ||
| # ---------------- | ||
@@ -46,6 +80,6 @@ # Module constants | ||
| # Photodiode readings header columns | ||
| WAVELENGTH_CSV_HEADER = "wavelength (nm)" | ||
| CURRENT_CSV_HEADER = "current (A)" | ||
| READ_NOISE_CSV_HEADER = "read noise (A)" | ||
| MONOCROMATOR_FILTERS_LABELS = ( | ||
| {"label": r"$BG38 \Rightarrow OG570$", "wave": 570, "style": "--"}, | ||
| {"label": r"$OG570\Rightarrow RG830$", "wave": 860, "style": "-."}, | ||
| ) | ||
@@ -69,3 +103,27 @@ # ----------------------- | ||
| # Define electron as a unit of charge | ||
| e_ = u.def_unit('e-', 1 * const.e) | ||
| def qe_names(channels: Sequence[str]) -> Sequence[str]: | ||
| return list(map(lambda x: "QE " + x, channels)) | ||
| def qe_units(channels: Sequence[str]) -> Sequence[Any]: | ||
| return list(map(lambda x: u.dimensionless_unscaled, channels)) | ||
| def read_manual_csv(path: str) -> Table: | ||
| """Load CSV files produced by manually copying LICA TestBench.exe into a CSV file""" | ||
| table = astropy.io.ascii.read( | ||
| path, | ||
| delimiter=";", | ||
| data_start=1, | ||
| names=(COL.WAVE, TBCOL.CURRENT, TBCOL.READ_NOISE), | ||
| converters={COL.WAVE: np.float64, TBCOL.CURRENT: np.float64, TBCOL.READ_NOISE: np.float64}, | ||
| ) | ||
| table[COL.WAVE] = np.round(table[COL.WAVE], decimals=0) * u.nm | ||
| table[TBCOL.CURRENT] = np.abs(table[TBCOL.CURRENT]) * u.A | ||
| table[TBCOL.READ_NOISE] = table[TBCOL.READ_NOISE] * u.A | ||
| return table | ||
| def mpl_photodiode_plot_loop(title, x, y, xtitle, ytitle, **kwargs): | ||
@@ -149,3 +207,3 @@ fig, axes = plt.subplots(nrows=1, ncols=1) | ||
| def get_used_wavelengths(file_list, channels): | ||
| def get_used_wavelengths(file_list: Iterable[str], channels: Sequence[str]): | ||
| M = len(channels) | ||
@@ -170,26 +228,24 @@ data = list() | ||
| result = np.tile(result, M).reshape(M, len(data)) | ||
| log.info("Wavelengthss array shape is %s", result.shape) | ||
| log.info("Wavelength array shape is %s", result.shape) | ||
| return result | ||
| def photodiode_readings_to_arrays(csv_path): | ||
| response = read_csv(csv_path) | ||
| wavelength = np.array([int(entry[WAVELENGTH_CSV_HEADER]) for entry in response]) | ||
| current = np.array([math.fabs(float(entry[CURRENT_CSV_HEADER])) for entry in response]) | ||
| read_noise = np.array([float(entry[READ_NOISE_CSV_HEADER]) for entry in response]) | ||
| log.info("Got %d photodiode readings", wavelength.shape[0]) | ||
| return wavelength, current, read_noise | ||
| # --------------------- | ||
| # Exported, non-CLI API | ||
| # --------------------- | ||
| # ----------------------- | ||
| # AUXILIARY MAIN FUNCTION | ||
| # ----------------------- | ||
| def raw_spectrum(args): | ||
| log.info(" === DRAFT SPECTRAL RESPONSE PLOT === ") | ||
| file_list, roi, n_roi, channels, metadata = common_list_info(args) | ||
| def raw_spectrum( | ||
| file_list: Iterable[str], | ||
| roi: Roi, | ||
| n_roi: NormRoi, | ||
| channels=Sequence[str], | ||
| metadata=Dict[str, Any], | ||
| every: int = 1, | ||
| bias: BiasType = None, | ||
| dark: DarkType = None, | ||
| ): | ||
| title = make_plot_title_from("Draft Spectral Response plot", metadata, roi) | ||
| wavelength = get_used_wavelengths(file_list, channels) | ||
| exptime, signal = signal_from(file_list, n_roi, channels, args.bias, args.dark, args.every) | ||
| exptime, signal = signal_from(file_list, n_roi, channels, bias, dark, every) | ||
| mpl_spectra_plot_loop( | ||
@@ -205,67 +261,17 @@ title=title, | ||
| # Optional arguments to be handled by the plotting function | ||
| filters=[ | ||
| {"label": r"$BG38 \Rightarrow OG570$", "wave": 570, "style": "--"}, | ||
| {"label": r"$OG570\Rightarrow RG830$", "wave": 860, "style": "-."}, | ||
| ], # where filters were changesd | ||
| filters=MONOCROMATOR_FILTERS_LABELS, # where filters were changesd | ||
| ) | ||
| def corrected_spectrum(args): | ||
| log.info(" === COMPLETE SPECTRAL RESPONSE PLOT === ") | ||
| responsivity, qe = photodiode_load(args.model, args.resolution) | ||
| log.info( | ||
| "Read %s reference responsivity values at %d nm resolution from %s", | ||
| len(responsivity), | ||
| args.resolution, | ||
| args.model, | ||
| ) | ||
| file_list, roi, n_roi, channels, metadata = common_list_info(args) | ||
| wavelength, current, read_noise = photodiode_readings_to_arrays(args.csv_file) | ||
| qe = np.array( | ||
| [qe[w] for w in wavelength] | ||
| ) # Only use those wavelenghts actually used in the CSV sequence | ||
| current = current / np.max(current) # Normalize photodiode current | ||
| title = make_plot_title_from("Corrected Spectral Response plot", metadata, roi) | ||
| wavelength = np.tile(wavelength, len(channels)).reshape(len(channels), -1) | ||
| exptime, signal = signal_from(file_list, n_roi, channels, args.bias, args.dark, args.every) | ||
| signal = qe * signal / current | ||
| signal = signal / np.max(signal) # Normalize signal to its absolute maxímun for all channels | ||
| if args.export: | ||
| log.info("exporting to CSV file(s)") | ||
| export_spectra_to_csv( | ||
| labels=channels, | ||
| wavelength=wavelength[0], | ||
| signal=signal, | ||
| mode=args.export, | ||
| units=args.units, | ||
| wave_last=args.wavelength_last, | ||
| ) | ||
| mpl_spectra_plot_loop( | ||
| title=title, | ||
| channels=channels, | ||
| plot_func=plot_raw_spectral, | ||
| xtitle="Wavelength [nm]", | ||
| ytitle="Signal (normalized)", | ||
| ylabel="good", | ||
| x=wavelength, | ||
| y=signal, | ||
| # Optional arguments to be handled by the plotting function | ||
| ) | ||
| def photodiode_spectrum(args): | ||
| log.info(" === PHOTODIODE SPECTRAL RESPONSET PLOT === ") | ||
| wavelength, current, read_noise = photodiode_readings_to_arrays(args.csv_file) | ||
| responsivity, qe = photodiode_load(args.model, args.resolution) | ||
| qe = np.array( | ||
| [qe[w] for w in wavelength] | ||
| ) # Only use those wavelenghts actually used in the CSV sequence | ||
| if args.raw_readings: | ||
| def photodiode_spectrum(path: str, model: str, option: PhDOption, resolution: int) -> None: | ||
| readings = read_manual_csv(path) | ||
| reference = lica.photodiode.load(model=model, resolution=resolution) | ||
| if option == PhDOption.RAW: | ||
| title = "Raw Photodiode Signal vs Wavelength" | ||
| y = current | ||
| y = readings[TBCOL.CURRENT] | ||
| ytitle = "Current [A]" | ||
| ylogscale = False | ||
| elif args.normalized: | ||
| elif option == PhDOption.NORM: | ||
| title = "Raw Photodiode Signal vs Wavelength" | ||
| y = current / np.max(current) | ||
| y = readings[TBCOL.CURRENT] / np.max(readings[TBCOL.CURRENT]) | ||
| ytitle = "Current (normalized)" | ||
@@ -275,3 +281,3 @@ ylogscale = False | ||
| title = "Photodiode SNR vs Wavelength" | ||
| y = current / read_noise | ||
| y = readings[TBCOL.CURRENT] / readings[TBCOL.READ_NOISE] | ||
| ytitle = "SNR" | ||
@@ -283,96 +289,131 @@ ylogscale = True | ||
| ytitle=ytitle, | ||
| x=wavelength, | ||
| x=readings[COL.WAVE], | ||
| y=y, | ||
| # Optional arguments to be handled by the plotting function | ||
| ylogscale=ylogscale, | ||
| filters=[ | ||
| {"label": r"$BG38 \Rightarrow OG570$", "wave": 570, "style": "--"}, | ||
| {"label": r"$OG570\Rightarrow RG830$", "wave": 860, "style": "-."}, | ||
| ], # where filters were changesd | ||
| qe=qe if args.normalized else None, | ||
| photodiode=args.model if args.normalized else None, | ||
| filters=MONOCROMATOR_FILTERS_LABELS, # where filters were changesd | ||
| qe=reference[COL.QE] if option == PhDOption.NORM else None, | ||
| photodiode=model if option == PhDOption.NORM else None, | ||
| ) | ||
| COMMAND_TABLE = { | ||
| "raw": raw_spectrum, | ||
| "corrected": corrected_spectrum, | ||
| "photodiode": photodiode_spectrum, | ||
| } | ||
| def corrected_spectrum( | ||
| file_list: Iterable[str], | ||
| roi: Roi, | ||
| n_roi: NormRoi, | ||
| channels: Sequence[str], | ||
| metadata: Dict[str, Any], | ||
| photod_path: str, | ||
| model: str, | ||
| resolution: int, | ||
| every: int = 1, | ||
| bias: BiasType = None, | ||
| dark: DarkType = None, | ||
| normalize: bool = False, | ||
| gain: float = 1.0, | ||
| pixel_area: float = 1.0, | ||
| export_path: str = None, | ||
| ) -> None: | ||
| readings = read_manual_csv(photod_path) | ||
| reference = lica.photodiode.load(model=model, resolution=resolution) | ||
| # This quirk is because the reference photodiode data actaully goes until 1049 and not 1050 nm | ||
| file_list = file_list[:-1] | ||
| assert len(file_list) == len(readings) == len(reference) | ||
| title = make_plot_title_from("Corrected Spectral Response plot", metadata, roi) | ||
| photod_qe = reference[COL.QE] | ||
| wavelength = reference[COL.WAVE] | ||
| photod_current = readings[TBCOL.CURRENT] | ||
| wavelength = np.tile(wavelength, len(channels)).reshape(len(channels), -1) | ||
| exptime, signal = signal_from(file_list, n_roi, channels, bias, dark, every) | ||
| gain = gain * (e_ / u.adu) | ||
| detector_current = (((signal * u.adu * gain)) / (metadata["exposure"] * u.s)).decompose() | ||
| area_ratio = (reference.meta['Photosensitive area'] / (pixel_area * (u.um ** 2))).decompose() | ||
| log.info("AREA RATIO = %s / %s = %s", reference.meta['Photosensitive area'], (pixel_area * (u.um ** 2)), area_ratio) | ||
| detector_qe = photod_qe * area_ratio * (detector_current / photod_current) | ||
| if normalize: | ||
| normalization_factor = np.max(detector_qe) | ||
| detector_qe = detector_qe / normalization_factor # Normalize QE to its absolute maxímun for all channels | ||
| if export_path: | ||
| log.info("exporting to ECSV file(s)") | ||
| columns = [wavelength[0],] | ||
| columns.extend(np.unstack(detector_qe)) | ||
| table = Table( | ||
| data = columns, | ||
| names = [COL.WAVE,] + qe_names(channels), | ||
| units = [u.nm] + qe_units(channels) | ||
| ) | ||
| table.meta["Detector Gain"] = gain | ||
| table.meta["Diode Photosensitive area"] = reference.meta['Photosensitive area'] | ||
| table.meta["Dectector pixel area"] = pixel_area * (u.um ** 2) | ||
| table.meta["Integration time"] = metadata["exposure"] * u.s | ||
| if normalize: | ||
| table.meta["Normalized"] = True | ||
| table.meta["Normalization Factor"] = normalization_factor | ||
| table.write(export_path, delimiter=",", overwrite=True) | ||
| mpl_spectra_plot_loop( | ||
| title=title, | ||
| channels=channels, | ||
| plot_func=plot_raw_spectral, | ||
| xtitle="Wavelength [nm]", | ||
| ytitle="QE (normalized)" if normalize else "QE", | ||
| ylabel="good", | ||
| x=wavelength, | ||
| y=detector_qe, | ||
| # Optional arguments to be handled by the plotting function | ||
| ) | ||
| def spectral(args): | ||
| command = args.command | ||
| func = COMMAND_TABLE[command] | ||
| func(args) | ||
| # =================================== | ||
| # MAIN ENTRY POINT SPECIFIC ARGUMENTS | ||
| # Command Line Interface Entry Points | ||
| # =================================== | ||
| def add_args(parser): | ||
| subparser = parser.add_subparsers(dest="command") | ||
| def cli_raw_spectrum(args): | ||
| log.info(" === DRAFT SPECTRAL RESPONSE PLOT === ") | ||
| file_list, roi, n_roi, channels, metadata = common_list_info(args) | ||
| raw_spectrum( | ||
| file_list, | ||
| roi, | ||
| n_roi, | ||
| channels, | ||
| metadata, | ||
| ) | ||
| parser_raw = subparser.add_parser("raw", help="Raw spectrum") | ||
| parser_corr = subparser.add_parser("corrected", help="Correced spectrum") | ||
| parser_diode = subparser.add_parser("photodiode", help="Photodiode readings") | ||
| # --------------------------------------------------------------------------------------------------------------- | ||
| parser_raw.add_argument( | ||
| "-i", | ||
| "--input-dir", | ||
| type=vdir, | ||
| required=True, | ||
| help="Input directory with RAW files", | ||
| def cli_corrected_spectrum(args): | ||
| log.info(" === COMPLETE SPECTRAL RESPONSE PLOT === ") | ||
| file_list, roi, n_roi, channels, metadata = common_list_info(args) | ||
| corrected_spectrum( | ||
| file_list=file_list, | ||
| roi=roi, | ||
| n_roi=n_roi, | ||
| channels=channels, | ||
| metadata=metadata, | ||
| photod_path=args.photodiode_file, | ||
| model=args.model, | ||
| resolution=args.resolution, | ||
| every=args.every, | ||
| bias=args.bias, | ||
| dark=args.dark, | ||
| normalize=args.normalize, | ||
| gain=args.gain, | ||
| pixel_area=args.pixel_area, | ||
| export_path=args.export, | ||
| ) | ||
| parser_raw.add_argument( | ||
| "-f", | ||
| "--image-filter", | ||
| type=str, | ||
| required=True, | ||
| help="Images filter, glob-style (i.e. flat*, dark*)", | ||
| ) | ||
| parser_raw.add_argument( | ||
| "-x", | ||
| "--x0", | ||
| type=vfloat01, | ||
| help="Normalized ROI start point, x0 coordinate [0..1]", | ||
| ) | ||
| parser_raw.add_argument( | ||
| "-y", | ||
| "--y0", | ||
| type=vfloat01, | ||
| help="Normalized ROI start point, y0 coordinate [0..1]", | ||
| ) | ||
| parser_raw.add_argument( | ||
| "-wi", | ||
| "--width", | ||
| type=vfloat01, | ||
| default=1.0, | ||
| help="Normalized ROI width [0..1] (default: %(default)s)", | ||
| ) | ||
| parser_raw.add_argument( | ||
| "-he", | ||
| "--height", | ||
| type=vfloat01, | ||
| default=1.0, | ||
| help="Normalized ROI height [0..1] (default: %(default)s) ", | ||
| ) | ||
| parser_raw.add_argument( | ||
| "-c", | ||
| "--channels", | ||
| default=("R", "Gr", "Gb", "B"), | ||
| nargs="+", | ||
| choices=("R", "Gr", "Gb", "G", "B"), | ||
| help="color plane to plot. G is the average of G1 & G2. (default: %(default)s)", | ||
| ) | ||
| parser_raw.add_argument( | ||
| "--every", | ||
| type=int, | ||
| metavar="<N>", | ||
| default=1, | ||
| help="pick every n `file after sorting", | ||
| ) | ||
| parser_raw.add_argument( | ||
| def cli_photodiode_spectrum(args): | ||
| log.info(" === PHOTODIODE SPECTRAL RESPONSET PLOT === ") | ||
| if args.raw_readings: | ||
| option = PhDOption.RAW | ||
| elif args.normalized: | ||
| option = PhDOption.NORM | ||
| else: | ||
| option = PhDOption.SNR | ||
| photodiode_spectrum(args.photodiode_file, args.model, option, args.resolution) | ||
| def prs_aux_files() -> ArgumentParser: | ||
| parser = ArgumentParser(add_help=False) | ||
| parser.add_argument( | ||
| "-bi", | ||
@@ -383,3 +424,3 @@ "--bias", | ||
| ) | ||
| parser_raw.add_argument( | ||
| parser.add_argument( | ||
| "-dk", | ||
@@ -390,4 +431,8 @@ "--dark", | ||
| ) | ||
| # --------------------------------------------------------------------------------------------------------------- | ||
| parser_corr.add_argument( | ||
| return parser | ||
| def prs_images() -> ArgumentParser: | ||
| parser = ArgumentParser(add_help=False) | ||
| parser.add_argument( | ||
| "-i", | ||
@@ -399,3 +444,3 @@ "--input-dir", | ||
| ) | ||
| parser_corr.add_argument( | ||
| parser.add_argument( | ||
| "-f", | ||
@@ -407,3 +452,3 @@ "--image-filter", | ||
| ) | ||
| parser_corr.add_argument( | ||
| parser.add_argument( | ||
| "-x", | ||
@@ -414,3 +459,3 @@ "--x0", | ||
| ) | ||
| parser_corr.add_argument( | ||
| parser.add_argument( | ||
| "-y", | ||
@@ -421,3 +466,3 @@ "--y0", | ||
| ) | ||
| parser_corr.add_argument( | ||
| parser.add_argument( | ||
| "-wi", | ||
@@ -429,3 +474,3 @@ "--width", | ||
| ) | ||
| parser_corr.add_argument( | ||
| parser.add_argument( | ||
| "-he", | ||
@@ -437,3 +482,3 @@ "--height", | ||
| ) | ||
| parser_corr.add_argument( | ||
| parser.add_argument( | ||
| "-c", | ||
@@ -446,3 +491,3 @@ "--channels", | ||
| ) | ||
| parser_corr.add_argument( | ||
| parser.add_argument( | ||
| "--every", | ||
@@ -454,17 +499,10 @@ type=int, | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-bi", | ||
| "--bias", | ||
| type=vflopath, | ||
| help="Bias, either a single value for all channels or else a 3D FITS cube file (default: %(default)s)", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-dk", | ||
| "--dark", | ||
| type=vfloat, | ||
| help="Dark count rate in DN/sec. (default: %(default)s)", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-cv", | ||
| "--csv-file", | ||
| return parser | ||
| def prs_photod() -> ArgumentParser: | ||
| parser = ArgumentParser(add_help=False) | ||
| parser.add_argument( | ||
| "-ph", | ||
| "--photodiode-file", | ||
| type=vfile, | ||
@@ -474,10 +512,10 @@ required=True, | ||
| ) | ||
| parser_corr.add_argument( | ||
| parser.add_argument( | ||
| "-m", | ||
| "--model", | ||
| default=OSI_PHOTODIODE, | ||
| choices=(HAMAMATSU_PHOTODIODE, OSI_PHOTODIODE), | ||
| default=PhotodiodeModel.OSI, | ||
| choices=[p for p in PhotodiodeModel], | ||
| help="Photodiode model. (default: %(default)s)", | ||
| ) | ||
| parser_corr.add_argument( | ||
| parser.add_argument( | ||
| "-r", | ||
@@ -487,33 +525,61 @@ "--resolution", | ||
| default=5, | ||
| choices=(1, 5), | ||
| choices=tuple(range(1, 11)), | ||
| help="Wavelength resolution (nm). (default: %(default)s nm)", | ||
| ) | ||
| return parser | ||
| def cli_main(args): | ||
| args.func(args) | ||
| def add_args(parser): | ||
| subparser = parser.add_subparsers(dest="command") | ||
| parser_raw = subparser.add_parser( | ||
| "raw", parents=[prs_images(), prs_aux_files()], help="Raw spectrum" | ||
| ) | ||
| parser_raw.set_defaults(func=cli_raw_spectrum) | ||
| parser_corr = subparser.add_parser( | ||
| "corrected", | ||
| parents=[prs_images(), prs_aux_files(), prs_photod()], | ||
| help="Corrected spectrum", | ||
| ) | ||
| parser_corr.set_defaults(func=cli_corrected_spectrum) | ||
| parser_diode = subparser.add_parser( | ||
| "photodiode", parents=[prs_photod()], help="Photodiode readings" | ||
| ) | ||
| parser_diode.set_defaults(func=cli_photodiode_spectrum) | ||
| # --------------------------------------------------------------------------------------------------------------- | ||
| parser_corr.add_argument( | ||
| "--export", | ||
| type=str, | ||
| choices=("combined", "individual"), | ||
| help="Export to CSV file(s)", | ||
| metavar="<FILE>", | ||
| help="Export to ECSV file", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-u", | ||
| "--units", | ||
| type=str, | ||
| choices=("nm", "angs"), | ||
| default="nm", | ||
| help="Exported wavelength units. (default: %(default)s)", | ||
| "-n", | ||
| "--normalize", | ||
| action="store_true", | ||
| help="Normalize spectrum with respect to its maximum", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-wl", | ||
| "--wavelength-last", | ||
| action="store_true", | ||
| help="Wavelength is last column in exported file", | ||
| "-g", | ||
| "--gain", | ||
| type=float, | ||
| default = 1.0, | ||
| help="Camera gain [e-/DN] (defaults to %(default)f)", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-pa", | ||
| "--pixel-area", | ||
| type=float, | ||
| default = 1.0, | ||
| help="Pixel area in \u03BCm^2 (defaults to %(default)f)", | ||
| ) | ||
| # --------------------------------------------------------------------------------------------------------------- | ||
| parser_diode.add_argument( | ||
| "-cv", | ||
| "--csv-file", | ||
| type=vfile, | ||
| required=True, | ||
| help="CSV file with photdiode readings", | ||
| ) | ||
| dioex1 = parser_diode.add_mutually_exclusive_group(required=True) | ||
@@ -534,21 +600,6 @@ dioex1.add_argument( | ||
| "-s", | ||
| "--signal-to-noise", | ||
| "--snr", | ||
| action="store_true", | ||
| help="Plot Raw Signal to Noise Ratio", | ||
| ) | ||
| parser_diode.add_argument( | ||
| "-m", | ||
| "--model", | ||
| default=OSI_PHOTODIODE, | ||
| choices=(HAMAMATSU_PHOTODIODE, OSI_PHOTODIODE), | ||
| help="Photodiode model. (default: %(default)s)", | ||
| ) | ||
| parser_diode.add_argument( | ||
| "-r", | ||
| "--resolution", | ||
| type=int, | ||
| default=5, | ||
| choices=(1, 5), | ||
| help="Wavelength resolution (nm). (default: %(default)s nm)", | ||
| ) | ||
@@ -563,3 +614,3 @@ | ||
| execute( | ||
| main_func=spectral, | ||
| main_func=cli_main, | ||
| add_args_func=add_args, | ||
@@ -566,0 +617,0 @@ name=__name__, |
@@ -187,41 +187,1 @@ # -*- coding: utf-8 -*- | ||
| return xc, yc | ||
| def export_spectra_to_csv(labels, wavelength, signal, mode, units, wave_last=False): | ||
| wave_exported = wavelength * 10 if units == "angs" else wavelength | ||
| COLS = len(labels) | ||
| if mode == "combined": | ||
| if not wave_last: | ||
| header = [ | ||
| f"Wavelength [{units}]", | ||
| ] + labels | ||
| else: | ||
| header = labels + [f"Wavelength [{units}]"] | ||
| path = "combined_" + "_".join(labels) + ".csv" | ||
| with open(path, "w", newline="") as csvfile: | ||
| writer = csv.writer(csvfile, delimiter=";") | ||
| writer.writerow(header) | ||
| for row in range(wave_exported.shape[0]): | ||
| data = [signal[lab][row] for lab in range(COLS)] | ||
| if not wave_last: | ||
| data = [wave_exported[row]] + data | ||
| else: | ||
| data = data + [wave_exported[row]] | ||
| writer.writerow(data) | ||
| else: | ||
| if not wave_last: | ||
| header = (f"Wavelength [{units}]", "Relative value") | ||
| else: | ||
| header = ("Relative value", f"Wavelength [{units}]") | ||
| for i, label in enumerate(labels): | ||
| path = label + ".csv" | ||
| selected_signal = signal[i] | ||
| with open(path, "w", newline="") as csvfile: | ||
| writer = csv.writer(csvfile, delimiter=";") | ||
| writer.writerow(header) | ||
| for j in range(wave_exported.shape[0]): | ||
| if not wave_last: | ||
| data = (wave_exported[j], selected_signal[j]) | ||
| else: | ||
| data = (selected_signal[j], wave_exported[j]) | ||
| writer.writerow(data) |
| # -*- coding: utf-8 -*- | ||
| # ---------------------------------------------------------------------- | ||
| # Copyright (c) 2021 | ||
| # | ||
| # See the LICENSE file for details | ||
| # see the AUTHORS file for authors | ||
| # ---------------------------------------------------------------------- | ||
| # -------------------- | ||
| # System wide imports | ||
| # ------------------- | ||
| import re | ||
| import csv | ||
| import logging | ||
| # --------------------- | ||
| # Thrid-party libraries | ||
| # --------------------- | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| from lica.cli import execute | ||
| from lica.validators import vfile | ||
| # ------------------------ | ||
| # Own modules and packages | ||
| # ------------------------ | ||
| from ._version import __version__ | ||
| from .util.common import export_spectra_to_csv | ||
| from .photodiode import photodiode_load, OSI_PHOTODIODE, HAMAMATSU_PHOTODIODE | ||
| # ---------------- | ||
| # Module constants | ||
| # ---------------- | ||
| WAVELENGTH_REG_EXP = re.compile(r"(\w+)_(\d+)nm_g(\d+)_(\d+)_(\d+)_(\w+).jpg") | ||
| # Photodiode readings header columns | ||
| WAVELENGTH_CSV_HEADER = "wavelength (nm)" | ||
| CURRENT_CSV_HEADER = "current (A)" | ||
| READ_NOISE_CSV_HEADER = "read noise (A)" | ||
| # ----------------------- | ||
| # Module global variables | ||
| # ----------------------- | ||
| log = logging.getLogger(__name__) | ||
| # ----------------- | ||
| # Matplotlib styles | ||
| # ----------------- | ||
| # Load global style sheets | ||
| plt.style.use("rawplot.resources.global") | ||
| # ------------------ | ||
| # Auxiliary fnctions | ||
| # ------------------ | ||
| def mpl_filters_plot_loop(title, x, y, xtitle, ytitle, plot_func, ylabels, **kwargs): | ||
| fig, axes = plt.subplots(nrows=1, ncols=1) | ||
| fig.suptitle(title) | ||
| axes.set_xlabel(xtitle) | ||
| axes.set_ylabel(ytitle) | ||
| filters = kwargs.get("filters") | ||
| diode = kwargs.get("diode") | ||
| model = kwargs.get("model") | ||
| # labels = kwargs.get('labels') | ||
| Z, _ = y.shape | ||
| for i in range(Z): | ||
| plot_func(axes, i, x, y, ylabels, **kwargs) | ||
| if filters is not None: | ||
| for filt in filters: | ||
| axes.axvline(filt["wave"], linestyle=filt["style"], label=filt["label"]) | ||
| if diode is not None: | ||
| axes.plot(x, diode, marker="o", linewidth=0, label=model) | ||
| axes.grid(True, which="major", color="silver", linestyle="solid") | ||
| axes.grid(True, which="minor", color="silver", linestyle=(0, (1, 10))) | ||
| axes.minorticks_on() | ||
| axes.legend() | ||
| plt.show() | ||
| # This is incomplete for 5 filter banks | ||
| def guess_color(label): | ||
| label = label.lower() | ||
| red = re.compile(r"red") | ||
| green = re.compile(r"green") | ||
| blue = re.compile(r"blue") | ||
| if red.search(label): | ||
| return "red" | ||
| if green.search(label): | ||
| return "green" | ||
| if blue.search(label): | ||
| return "blue" | ||
| return "magenta" | ||
| def plot_filter_spectrum(axes, i, x, y, ylabels, **kwargs): | ||
| wavelength = x | ||
| signal = y[i] | ||
| marker = "o" | ||
| color = guess_color(ylabels[i]) | ||
| axes.plot(wavelength, signal, marker=marker, color=color, linewidth=1, label=ylabels[i]) | ||
| def csv_readings_to_array(csv_path): | ||
| log.info("reading CSV file %s", csv_path) | ||
| with open(csv_path, newline="") as csvfile: | ||
| reader = csv.reader(csvfile, delimiter="\t") | ||
| contents = {int(round(float(row[1]), 0)): float(row[2]) for row in reader} | ||
| wavelength = np.array(sorted(contents.keys())) | ||
| signal = np.array(tuple(contents[key] for key in sorted(contents.keys()))) | ||
| return wavelength, signal | ||
| def validate_lists(args): | ||
| if len(args.filters) != len(args.labels): | ||
| raise ValueError("Number of labels must match number of filter files") | ||
| if len(args.filters) < 1: | ||
| raise ValueError("Missing filter CSV files") | ||
| if len(args.diodes) < 1: | ||
| raise ValueError("Missing calibration diode CSV files") | ||
| def get_info_from(args): | ||
| accum = list() | ||
| labels = list() | ||
| diodes = list() | ||
| for filt, label in zip(args.filters, args.labels): | ||
| wavelength, signal = csv_readings_to_array(filt) | ||
| accum.append(signal) | ||
| labels.append(label) | ||
| signal = np.vstack(accum) | ||
| for diode in args.diodes: | ||
| _, diode = csv_readings_to_array(diode) | ||
| diodes.append(diode) | ||
| # diode = np.vstack(diodes) | ||
| # log.info("Before: Diode shapes is %s", diode.shape) | ||
| diode = np.mean(np.vstack(diodes), axis=0) | ||
| log.info("Diode shapes is %s", diode.shape) | ||
| return wavelength, signal, labels, diode, args.model | ||
| # ----------------------- | ||
| # AUXILIARY MAIN FUNCTION | ||
| # ----------------------- | ||
| def raw_spectrum(args): | ||
| log.info(" === DRAFT SPECTRAL RESPONSE PLOT === ") | ||
| validate_lists(args) | ||
| wavelength, signal, labels, diode, model = get_info_from(args) | ||
| mpl_filters_plot_loop( | ||
| title=f"Raw response for {args.title}", | ||
| plot_func=plot_filter_spectrum, | ||
| xtitle="Wavelength [nm]", | ||
| ytitle="Signal [A]", | ||
| ylabels=labels, | ||
| x=wavelength, | ||
| y=signal, | ||
| # Optional arguments to be handled by the plotting function | ||
| diode=diode, | ||
| model=model, | ||
| filters=[ | ||
| {"label": r"$BG38 \Rightarrow OG570$", "wave": 570, "style": "--"}, | ||
| {"label": r"$OG570\Rightarrow RG830$", "wave": 860, "style": "-."}, | ||
| ], # where filters were changesd | ||
| ) | ||
| def corrected_spectrum(args): | ||
| log.info(" === COMPLETE SPECTRAL RESPONSE PLOT === ") | ||
| validate_lists(args) | ||
| wavelength, signal, labels, diode, model = get_info_from(args) | ||
| log.info | ||
| responsivity, qe = photodiode_load(args.model, args.resolution) | ||
| log.info( | ||
| "Read %s reference responsivity values at %d nm resolution from %s", | ||
| len(responsivity), | ||
| args.resolution, | ||
| args.model, | ||
| ) | ||
| qe = np.array( | ||
| [qe[w] for w in wavelength] | ||
| ) # Only use those wavelenghts actually used in the CSV sequence | ||
| diode = diode / np.max(diode) # Normalize photodiode current | ||
| signal = qe * signal / diode | ||
| signal = signal / np.max(signal) # Normalize signal to its absolute maxímun for all channels | ||
| if args.export: | ||
| log.info("exporting to CSV file(s)") | ||
| export_spectra_to_csv( | ||
| labels=labels, | ||
| wavelength=wavelength, | ||
| signal=signal, | ||
| mode=args.export, | ||
| units=args.units, | ||
| wave_last=args.wavelength_last, | ||
| ) | ||
| mpl_filters_plot_loop( | ||
| title=f"Corrected response for {args.title}", | ||
| plot_func=plot_filter_spectrum, | ||
| xtitle="Wavelength [nm]", | ||
| ytitle="Normalized signal level", | ||
| ylabels=labels, | ||
| x=wavelength, | ||
| y=signal, | ||
| filters=[ | ||
| {"label": r"$BG38 \Rightarrow OG570$", "wave": 570, "style": "--"}, | ||
| {"label": r"$OG570\Rightarrow RG830$", "wave": 860, "style": "-."}, | ||
| ], # where filters were changesd | ||
| ) | ||
| COMMAND_TABLE = { | ||
| "raw": raw_spectrum, | ||
| "corrected": corrected_spectrum, | ||
| } | ||
| def filters(args): | ||
| command = args.command | ||
| func = COMMAND_TABLE[command] | ||
| func(args) | ||
| # =================================== | ||
| # MAIN ENTRY POINT SPECIFIC ARGUMENTS | ||
| # =================================== | ||
| def add_args(parser): | ||
| subparser = parser.add_subparsers(dest="command") | ||
| parser_raw = subparser.add_parser("raw", help="Raw spectrum") | ||
| parser_corr = subparser.add_parser("corrected", help="Correced spectrum") | ||
| # --------------------------------------------------------------------------------------------------------------- | ||
| parser_raw.add_argument( | ||
| "-t", | ||
| "--title", | ||
| type=str, | ||
| help='Filters set model (ie. "Astronomik L-RGB Type 2c"', | ||
| ) | ||
| parser_raw.add_argument( | ||
| "-f", | ||
| "--filters", | ||
| required=True, | ||
| metavar="<CSV>", | ||
| nargs="+", | ||
| type=vfile, | ||
| help="Filter CSV files", | ||
| ) | ||
| parser_raw.add_argument( | ||
| "-l", | ||
| "--labels", | ||
| required=True, | ||
| metavar="<LABEL>", | ||
| nargs="+", | ||
| type=str, | ||
| help="CSV file labels", | ||
| ) | ||
| parser_raw.add_argument( | ||
| "-d", | ||
| "--diodes", | ||
| required=True, | ||
| metavar="<CSV>", | ||
| nargs="+", | ||
| type=vfile, | ||
| help="reference photodiode CSV files", | ||
| ) | ||
| parser_raw.add_argument( | ||
| "-m", | ||
| "--model", | ||
| default=OSI_PHOTODIODE, | ||
| choices=(HAMAMATSU_PHOTODIODE, OSI_PHOTODIODE), | ||
| help="Reference photodiode model. (default: %(default)s)", | ||
| ) | ||
| # --------------------------------------------------------------------------------------------------------------- | ||
| parser_corr.add_argument( | ||
| "-t", | ||
| "--title", | ||
| type=str, | ||
| help='Filters set model (ie. "Astronomik L-RGB Type 2c"', | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-f", | ||
| "--filters", | ||
| required=True, | ||
| metavar="<CSV>", | ||
| nargs="+", | ||
| type=vfile, | ||
| help="Filter CSV files", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-l", | ||
| "--labels", | ||
| required=True, | ||
| metavar="<LABEL>", | ||
| nargs="+", | ||
| type=str, | ||
| help="CSV file labels", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-d", | ||
| "--diodes", | ||
| required=True, | ||
| metavar="<CSV>", | ||
| nargs="+", | ||
| type=vfile, | ||
| help="reference photodiode CSV files", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-m", | ||
| "--model", | ||
| default=OSI_PHOTODIODE, | ||
| choices=(HAMAMATSU_PHOTODIODE, OSI_PHOTODIODE), | ||
| help="Reference photodiode model. (default: %(default)s)", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-r", | ||
| "--resolution", | ||
| type=int, | ||
| default=5, | ||
| choices=(1, 5), | ||
| help="Wavelength resolution (nm). (default: %(default)s nm)", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-x", | ||
| "--export", | ||
| type=str, | ||
| choices=("combined", "individual"), | ||
| help="Export to CSV file(s)", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-u", | ||
| "--units", | ||
| type=str, | ||
| choices=("nm", "angs"), | ||
| default="nm", | ||
| help="Exported wavelength units. (default: %(default)s)", | ||
| ) | ||
| parser_corr.add_argument( | ||
| "-wl", | ||
| "--wavelength-last", | ||
| action="store_true", | ||
| help="Wavelength is last column in exported file", | ||
| ) | ||
| # --------------------------------------------------------------------------------------------------------------- | ||
| # ================ | ||
| # MAIN ENTRY POINT | ||
| # ================ | ||
| def main(): | ||
| execute( | ||
| main_func=filters, | ||
| add_args_func=add_args, | ||
| name=__name__, | ||
| version=__version__, | ||
| description="Filters spectral response", | ||
| ) |
| Wavelength (nm);Responsivity (A/W);Quantum Efficiency | ||
| 350;0.15060;0.53349 | ||
| 351;0.15044;0.53140 | ||
| 352;0.15028;0.52933 | ||
| 353;0.15012;0.52727 | ||
| 354;0.14996;0.52522 | ||
| 355;0.14980;0.52318 | ||
| 356;0.14964;0.52115 | ||
| 357;0.14948;0.51914 | ||
| 358;0.14932;0.51713 | ||
| 359;0.14916;0.51514 | ||
| 360;0.14900;0.51316 | ||
| 361;0.14884;0.51119 | ||
| 362;0.14868;0.50923 | ||
| 363;0.14852;0.50728 | ||
| 364;0.14836;0.50534 | ||
| 365;0.14820;0.50341 | ||
| 366;0.14804;0.50149 | ||
| 367;0.14788;0.49959 | ||
| 368;0.14772;0.49769 | ||
| 369;0.14756;0.49580 | ||
| 370;0.14740;0.49393 | ||
| 371;0.14862;0.49667 | ||
| 372;0.14984;0.49940 | ||
| 373;0.15106;0.50212 | ||
| 374;0.15228;0.50482 | ||
| 375;0.15350;0.50751 | ||
| 376;0.15472;0.51018 | ||
| 377;0.15594;0.51284 | ||
| 378;0.15716;0.51549 | ||
| 379;0.15838;0.51812 | ||
| 380;0.15960;0.52073 | ||
| 381;0.16082;0.52334 | ||
| 382;0.16204;0.52593 | ||
| 383;0.16326;0.52850 | ||
| 384;0.16448;0.53107 | ||
| 385;0.16570;0.53362 | ||
| 386;0.16692;0.53615 | ||
| 387;0.16814;0.53867 | ||
| 388;0.16936;0.54118 | ||
| 389;0.17058;0.54368 | ||
| 390;0.17180;0.54617 | ||
| 391;0.17282;0.54800 | ||
| 392;0.17384;0.54983 | ||
| 393;0.17486;0.55165 | ||
| 394;0.17588;0.55346 | ||
| 395;0.17690;0.55526 | ||
| 396;0.17792;0.55705 | ||
| 397;0.17894;0.55883 | ||
| 398;0.17996;0.56061 | ||
| 399;0.18098;0.56237 | ||
| 400;0.18200;0.56413 | ||
| 401;0.18302;0.56587 | ||
| 402;0.18404;0.56761 | ||
| 403;0.18506;0.56934 | ||
| 404;0.18608;0.57106 | ||
| 405;0.18710;0.57278 | ||
| 406;0.18812;0.57448 | ||
| 407;0.18914;0.57618 | ||
| 408;0.19016;0.57786 | ||
| 409;0.19118;0.57954 | ||
| 410;0.19220;0.58121 | ||
| 411;0.19303;0.58229 | ||
| 412;0.19385;0.58336 | ||
| 413;0.19468;0.58442 | ||
| 414;0.19550;0.58548 | ||
| 415;0.19633;0.58653 | ||
| 416;0.19715;0.58758 | ||
| 417;0.19798;0.58863 | ||
| 418;0.19880;0.58967 | ||
| 419;0.19963;0.59070 | ||
| 420;0.20045;0.59173 | ||
| 421;0.20128;0.59275 | ||
| 422;0.20210;0.59377 | ||
| 423;0.20293;0.59479 | ||
| 424;0.20375;0.59580 | ||
| 425;0.20458;0.59680 | ||
| 426;0.20540;0.59780 | ||
| 427;0.20623;0.59880 | ||
| 428;0.20705;0.59979 | ||
| 429;0.20788;0.60077 | ||
| 430;0.20870;0.60176 | ||
| 431;0.20943;0.60246 | ||
| 432;0.21016;0.60316 | ||
| 433;0.21089;0.60386 | ||
| 434;0.21162;0.60455 | ||
| 435;0.21235;0.60524 | ||
| 436;0.21308;0.60593 | ||
| 437;0.21381;0.60661 | ||
| 438;0.21454;0.60730 | ||
| 439;0.21527;0.60797 | ||
| 440;0.21600;0.60865 | ||
| 441;0.21673;0.60932 | ||
| 442;0.21746;0.60999 | ||
| 443;0.21819;0.61066 | ||
| 444;0.21892;0.61132 | ||
| 445;0.21965;0.61198 | ||
| 446;0.22038;0.61264 | ||
| 447;0.22111;0.61329 | ||
| 448;0.22184;0.61394 | ||
| 449;0.22257;0.61459 | ||
| 450;0.22330;0.61524 | ||
| 451;0.22398;0.61573 | ||
| 452;0.22465;0.61622 | ||
| 453;0.22533;0.61671 | ||
| 454;0.22600;0.61719 | ||
| 455;0.22668;0.61767 | ||
| 456;0.22735;0.61815 | ||
| 457;0.22803;0.61863 | ||
| 458;0.22870;0.61911 | ||
| 459;0.22938;0.61958 | ||
| 460;0.23005;0.62006 | ||
| 461;0.23073;0.62053 | ||
| 462;0.23140;0.62099 | ||
| 463;0.23208;0.62146 | ||
| 464;0.23275;0.62193 | ||
| 465;0.23343;0.62239 | ||
| 466;0.23410;0.62285 | ||
| 467;0.23478;0.62331 | ||
| 468;0.23545;0.62376 | ||
| 469;0.23613;0.62422 | ||
| 470;0.23680;0.62467 | ||
| 471;0.23745;0.62504 | ||
| 472;0.23809;0.62541 | ||
| 473;0.23874;0.62578 | ||
| 474;0.23938;0.62615 | ||
| 475;0.24003;0.62651 | ||
| 476;0.24067;0.62688 | ||
| 477;0.24132;0.62724 | ||
| 478;0.24196;0.62760 | ||
| 479;0.24261;0.62796 | ||
| 480;0.24325;0.62832 | ||
| 481;0.24390;0.62867 | ||
| 482;0.24454;0.62903 | ||
| 483;0.24519;0.62938 | ||
| 484;0.24583;0.62973 | ||
| 485;0.24648;0.63008 | ||
| 486;0.24712;0.63043 | ||
| 487;0.24777;0.63078 | ||
| 488;0.24841;0.63113 | ||
| 489;0.24906;0.63147 | ||
| 490;0.24970;0.63181 | ||
| 491;0.25032;0.63209 | ||
| 492;0.25094;0.63237 | ||
| 493;0.25156;0.63265 | ||
| 494;0.25218;0.63292 | ||
| 495;0.25280;0.63320 | ||
| 496;0.25342;0.63347 | ||
| 497;0.25404;0.63374 | ||
| 498;0.25466;0.63401 | ||
| 499;0.25528;0.63428 | ||
| 500;0.25590;0.63455 | ||
| 501;0.25652;0.63482 | ||
| 502;0.25714;0.63509 | ||
| 503;0.25776;0.63535 | ||
| 504;0.25838;0.63562 | ||
| 505;0.25900;0.63588 | ||
| 506;0.25962;0.63614 | ||
| 507;0.26024;0.63640 | ||
| 508;0.26086;0.63666 | ||
| 509;0.26148;0.63692 | ||
| 510;0.26210;0.63718 | ||
| 511;0.26271;0.63740 | ||
| 512;0.26331;0.63762 | ||
| 513;0.26392;0.63784 | ||
| 514;0.26452;0.63806 | ||
| 515;0.26513;0.63828 | ||
| 516;0.26573;0.63849 | ||
| 517;0.26634;0.63871 | ||
| 518;0.26694;0.63893 | ||
| 519;0.26755;0.63914 | ||
| 520;0.26815;0.63935 | ||
| 521;0.26876;0.63957 | ||
| 522;0.26936;0.63978 | ||
| 523;0.26997;0.63999 | ||
| 524;0.27057;0.64020 | ||
| 525;0.27118;0.64041 | ||
| 526;0.27178;0.64062 | ||
| 527;0.27239;0.64082 | ||
| 528;0.27299;0.64103 | ||
| 529;0.27360;0.64124 | ||
| 530;0.27420;0.64144 | ||
| 531;0.27479;0.64161 | ||
| 532;0.27538;0.64178 | ||
| 533;0.27597;0.64195 | ||
| 534;0.27656;0.64212 | ||
| 535;0.27715;0.64228 | ||
| 536;0.27774;0.64245 | ||
| 537;0.27833;0.64262 | ||
| 538;0.27892;0.64278 | ||
| 539;0.27951;0.64295 | ||
| 540;0.28010;0.64311 | ||
| 541;0.28069;0.64327 | ||
| 542;0.28128;0.64344 | ||
| 543;0.28187;0.64360 | ||
| 544;0.28246;0.64376 | ||
| 545;0.28305;0.64392 | ||
| 546;0.28364;0.64408 | ||
| 547;0.28423;0.64424 | ||
| 548;0.28482;0.64440 | ||
| 549;0.28541;0.64456 | ||
| 550;0.28600;0.64472 | ||
| 551;0.28659;0.64486 | ||
| 552;0.28717;0.64501 | ||
| 553;0.28776;0.64515 | ||
| 554;0.28834;0.64530 | ||
| 555;0.28893;0.64544 | ||
| 556;0.28951;0.64559 | ||
| 557;0.29010;0.64573 | ||
| 558;0.29068;0.64587 | ||
| 559;0.29127;0.64602 | ||
| 560;0.29185;0.64616 | ||
| 561;0.29244;0.64630 | ||
| 562;0.29302;0.64644 | ||
| 563;0.29361;0.64658 | ||
| 564;0.29419;0.64672 | ||
| 565;0.29478;0.64686 | ||
| 566;0.29536;0.64700 | ||
| 567;0.29595;0.64713 | ||
| 568;0.29653;0.64727 | ||
| 569;0.29712;0.64741 | ||
| 570;0.29770;0.64755 | ||
| 571;0.29828;0.64766 | ||
| 572;0.29885;0.64777 | ||
| 573;0.29943;0.64789 | ||
| 574;0.30000;0.64800 | ||
| 575;0.30058;0.64811 | ||
| 576;0.30115;0.64823 | ||
| 577;0.30173;0.64834 | ||
| 578;0.30230;0.64845 | ||
| 579;0.30288;0.64856 | ||
| 580;0.30345;0.64867 | ||
| 581;0.30403;0.64878 | ||
| 582;0.30460;0.64889 | ||
| 583;0.30518;0.64900 | ||
| 584;0.30575;0.64911 | ||
| 585;0.30633;0.64922 | ||
| 586;0.30690;0.64933 | ||
| 587;0.30748;0.64944 | ||
| 588;0.30805;0.64955 | ||
| 589;0.30863;0.64965 | ||
| 590;0.30920;0.64976 | ||
| 591;0.30977;0.64986 | ||
| 592;0.31034;0.64995 | ||
| 593;0.31091;0.65005 | ||
| 594;0.31148;0.65014 | ||
| 595;0.31205;0.65024 | ||
| 596;0.31262;0.65033 | ||
| 597;0.31319;0.65043 | ||
| 598;0.31376;0.65052 | ||
| 599;0.31433;0.65062 | ||
| 600;0.31490;0.65071 | ||
| 601;0.31547;0.65080 | ||
| 602;0.31604;0.65090 | ||
| 603;0.31661;0.65099 | ||
| 604;0.31718;0.65108 | ||
| 605;0.31775;0.65117 | ||
| 606;0.31832;0.65126 | ||
| 607;0.31889;0.65136 | ||
| 608;0.31946;0.65145 | ||
| 609;0.32003;0.65154 | ||
| 610;0.32060;0.65163 | ||
| 611;0.32117;0.65171 | ||
| 612;0.32173;0.65179 | ||
| 613;0.32230;0.65187 | ||
| 614;0.32286;0.65195 | ||
| 615;0.32343;0.65203 | ||
| 616;0.32399;0.65210 | ||
| 617;0.32456;0.65218 | ||
| 618;0.32512;0.65226 | ||
| 619;0.32569;0.65234 | ||
| 620;0.32625;0.65242 | ||
| 621;0.32682;0.65249 | ||
| 622;0.32738;0.65257 | ||
| 623;0.32795;0.65265 | ||
| 624;0.32851;0.65273 | ||
| 625;0.32908;0.65280 | ||
| 626;0.32964;0.65288 | ||
| 627;0.33021;0.65295 | ||
| 628;0.33077;0.65303 | ||
| 629;0.33134;0.65310 | ||
| 630;0.33190;0.65318 | ||
| 631;0.33246;0.65325 | ||
| 632;0.33302;0.65331 | ||
| 633;0.33358;0.65338 | ||
| 634;0.33414;0.65344 | ||
| 635;0.33470;0.65350 | ||
| 636;0.33526;0.65357 | ||
| 637;0.33582;0.65363 | ||
| 638;0.33638;0.65370 | ||
| 639;0.33694;0.65376 | ||
| 640;0.33750;0.65382 | ||
| 641;0.33806;0.65389 | ||
| 642;0.33862;0.65395 | ||
| 643;0.33918;0.65401 | ||
| 644;0.33974;0.65407 | ||
| 645;0.34030;0.65414 | ||
| 646;0.34086;0.65420 | ||
| 647;0.34142;0.65426 | ||
| 648;0.34198;0.65432 | ||
| 649;0.34254;0.65438 | ||
| 650;0.34310;0.65445 | ||
| 651;0.34366;0.65450 | ||
| 652;0.34421;0.65455 | ||
| 653;0.34477;0.65460 | ||
| 654;0.34532;0.65465 | ||
| 655;0.34588;0.65470 | ||
| 656;0.34643;0.65475 | ||
| 657;0.34699;0.65480 | ||
| 658;0.34754;0.65486 | ||
| 659;0.34810;0.65491 | ||
| 660;0.34865;0.65496 | ||
| 661;0.34921;0.65501 | ||
| 662;0.34976;0.65506 | ||
| 663;0.35032;0.65511 | ||
| 664;0.35087;0.65516 | ||
| 665;0.35143;0.65521 | ||
| 666;0.35198;0.65525 | ||
| 667;0.35254;0.65530 | ||
| 668;0.35309;0.65535 | ||
| 669;0.35365;0.65540 | ||
| 670;0.35420;0.65545 | ||
| 671;0.35476;0.65551 | ||
| 672;0.35532;0.65557 | ||
| 673;0.35588;0.65562 | ||
| 674;0.35644;0.65568 | ||
| 675;0.35700;0.65574 | ||
| 676;0.35756;0.65580 | ||
| 677;0.35812;0.65585 | ||
| 678;0.35868;0.65591 | ||
| 679;0.35924;0.65597 | ||
| 680;0.35980;0.65602 | ||
| 681;0.36036;0.65608 | ||
| 682;0.36092;0.65613 | ||
| 683;0.36148;0.65619 | ||
| 684;0.36204;0.65625 | ||
| 685;0.36260;0.65630 | ||
| 686;0.36316;0.65636 | ||
| 687;0.36372;0.65641 | ||
| 688;0.36428;0.65647 | ||
| 689;0.36484;0.65652 | ||
| 690;0.36540;0.65658 | ||
| 691;0.36595;0.65661 | ||
| 692;0.36650;0.65665 | ||
| 693;0.36705;0.65669 | ||
| 694;0.36760;0.65672 | ||
| 695;0.36815;0.65676 | ||
| 696;0.36870;0.65680 | ||
| 697;0.36925;0.65683 | ||
| 698;0.36980;0.65687 | ||
| 699;0.37035;0.65690 | ||
| 700;0.37090;0.65694 | ||
| 701;0.37145;0.65697 | ||
| 702;0.37200;0.65701 | ||
| 703;0.37255;0.65705 | ||
| 704;0.37310;0.65708 | ||
| 705;0.37365;0.65712 | ||
| 706;0.37420;0.65715 | ||
| 707;0.37475;0.65719 | ||
| 708;0.37530;0.65722 | ||
| 709;0.37585;0.65726 | ||
| 710;0.37640;0.65729 | ||
| 711;0.37695;0.65733 | ||
| 712;0.37750;0.65736 | ||
| 713;0.37805;0.65739 | ||
| 714;0.37860;0.65743 | ||
| 715;0.37915;0.65746 | ||
| 716;0.37970;0.65750 | ||
| 717;0.38025;0.65753 | ||
| 718;0.38080;0.65757 | ||
| 719;0.38135;0.65760 | ||
| 720;0.38190;0.65763 | ||
| 721;0.38245;0.65767 | ||
| 722;0.38300;0.65770 | ||
| 723;0.38355;0.65773 | ||
| 724;0.38410;0.65777 | ||
| 725;0.38465;0.65780 | ||
| 726;0.38520;0.65783 | ||
| 727;0.38575;0.65787 | ||
| 728;0.38630;0.65790 | ||
| 729;0.38685;0.65793 | ||
| 730;0.38740;0.65797 | ||
| 731;0.38796;0.65801 | ||
| 732;0.38851;0.65805 | ||
| 733;0.38907;0.65809 | ||
| 734;0.38962;0.65813 | ||
| 735;0.39018;0.65817 | ||
| 736;0.39073;0.65821 | ||
| 737;0.39129;0.65825 | ||
| 738;0.39184;0.65829 | ||
| 739;0.39240;0.65833 | ||
| 740;0.39295;0.65837 | ||
| 741;0.39351;0.65841 | ||
| 742;0.39406;0.65845 | ||
| 743;0.39462;0.65849 | ||
| 744;0.39517;0.65853 | ||
| 745;0.39573;0.65857 | ||
| 746;0.39628;0.65861 | ||
| 747;0.39684;0.65865 | ||
| 748;0.39739;0.65869 | ||
| 749;0.39795;0.65873 | ||
| 750;0.39850;0.65877 | ||
| 751;0.39905;0.65879 | ||
| 752;0.39959;0.65881 | ||
| 753;0.40014;0.65884 | ||
| 754;0.40068;0.65886 | ||
| 755;0.40123;0.65888 | ||
| 756;0.40177;0.65890 | ||
| 757;0.40232;0.65893 | ||
| 758;0.40286;0.65895 | ||
| 759;0.40341;0.65897 | ||
| 760;0.40395;0.65899 | ||
| 761;0.40450;0.65901 | ||
| 762;0.40504;0.65904 | ||
| 763;0.40559;0.65906 | ||
| 764;0.40613;0.65908 | ||
| 765;0.40668;0.65910 | ||
| 766;0.40722;0.65912 | ||
| 767;0.40777;0.65914 | ||
| 768;0.40831;0.65917 | ||
| 769;0.40886;0.65919 | ||
| 770;0.40940;0.65921 | ||
| 771;0.40996;0.65925 | ||
| 772;0.41051;0.65928 | ||
| 773;0.41107;0.65932 | ||
| 774;0.41162;0.65936 | ||
| 775;0.41218;0.65940 | ||
| 776;0.41273;0.65943 | ||
| 777;0.41329;0.65947 | ||
| 778;0.41384;0.65951 | ||
| 779;0.41440;0.65954 | ||
| 780;0.41495;0.65958 | ||
| 781;0.41551;0.65962 | ||
| 782;0.41606;0.65965 | ||
| 783;0.41662;0.65969 | ||
| 784;0.41717;0.65973 | ||
| 785;0.41773;0.65976 | ||
| 786;0.41828;0.65980 | ||
| 787;0.41884;0.65983 | ||
| 788;0.41939;0.65987 | ||
| 789;0.41995;0.65991 | ||
| 790;0.42050;0.65994 | ||
| 791;0.42105;0.65997 | ||
| 792;0.42160;0.66000 | ||
| 793;0.42215;0.66002 | ||
| 794;0.42270;0.66005 | ||
| 795;0.42325;0.66008 | ||
| 796;0.42380;0.66011 | ||
| 797;0.42435;0.66013 | ||
| 798;0.42490;0.66016 | ||
| 799;0.42545;0.66019 | ||
| 800;0.42600;0.66022 | ||
| 801;0.42655;0.66024 | ||
| 802;0.42710;0.66027 | ||
| 803;0.42765;0.66030 | ||
| 804;0.42820;0.66032 | ||
| 805;0.42875;0.66035 | ||
| 806;0.42930;0.66038 | ||
| 807;0.42985;0.66040 | ||
| 808;0.43040;0.66043 | ||
| 809;0.43095;0.66046 | ||
| 810;0.43150;0.66048 | ||
| 811;0.43205;0.66050 | ||
| 812;0.43259;0.66052 | ||
| 813;0.43314;0.66054 | ||
| 814;0.43368;0.66056 | ||
| 815;0.43423;0.66058 | ||
| 816;0.43477;0.66060 | ||
| 817;0.43532;0.66061 | ||
| 818;0.43586;0.66063 | ||
| 819;0.43641;0.66065 | ||
| 820;0.43695;0.66067 | ||
| 821;0.43750;0.66069 | ||
| 822;0.43804;0.66071 | ||
| 823;0.43859;0.66072 | ||
| 824;0.43913;0.66074 | ||
| 825;0.43968;0.66076 | ||
| 826;0.44022;0.66078 | ||
| 827;0.44077;0.66080 | ||
| 828;0.44131;0.66081 | ||
| 829;0.44186;0.66083 | ||
| 830;0.44240;0.66085 | ||
| 831;0.44295;0.66087 | ||
| 832;0.44349;0.66089 | ||
| 833;0.44404;0.66090 | ||
| 834;0.44458;0.66092 | ||
| 835;0.44513;0.66094 | ||
| 836;0.44567;0.66096 | ||
| 837;0.44622;0.66097 | ||
| 838;0.44676;0.66099 | ||
| 839;0.44731;0.66101 | ||
| 840;0.44785;0.66103 | ||
| 841;0.44840;0.66105 | ||
| 842;0.44894;0.66106 | ||
| 843;0.44949;0.66108 | ||
| 844;0.45003;0.66110 | ||
| 845;0.45058;0.66111 | ||
| 846;0.45112;0.66113 | ||
| 847;0.45167;0.66115 | ||
| 848;0.45221;0.66117 | ||
| 849;0.45276;0.66118 | ||
| 850;0.45330;0.66120 | ||
| 851;0.45384;0.66121 | ||
| 852;0.45438;0.66122 | ||
| 853;0.45492;0.66123 | ||
| 854;0.45546;0.66124 | ||
| 855;0.45600;0.66125 | ||
| 856;0.45654;0.66126 | ||
| 857;0.45708;0.66127 | ||
| 858;0.45762;0.66128 | ||
| 859;0.45816;0.66129 | ||
| 860;0.45870;0.66130 | ||
| 861;0.45924;0.66131 | ||
| 862;0.45978;0.66132 | ||
| 863;0.46032;0.66133 | ||
| 864;0.46086;0.66134 | ||
| 865;0.46140;0.66134 | ||
| 866;0.46194;0.66135 | ||
| 867;0.46248;0.66136 | ||
| 868;0.46302;0.66137 | ||
| 869;0.46356;0.66138 | ||
| 870;0.46410;0.66139 | ||
| 871;0.46464;0.66140 | ||
| 872;0.46518;0.66141 | ||
| 873;0.46572;0.66142 | ||
| 874;0.46626;0.66143 | ||
| 875;0.46680;0.66144 | ||
| 876;0.46734;0.66145 | ||
| 877;0.46788;0.66146 | ||
| 878;0.46842;0.66147 | ||
| 879;0.46896;0.66147 | ||
| 880;0.46950;0.66148 | ||
| 881;0.47004;0.66149 | ||
| 882;0.47058;0.66150 | ||
| 883;0.47112;0.66151 | ||
| 884;0.47166;0.66152 | ||
| 885;0.47220;0.66153 | ||
| 886;0.47274;0.66154 | ||
| 887;0.47328;0.66155 | ||
| 888;0.47382;0.66156 | ||
| 889;0.47436;0.66157 | ||
| 890;0.47490;0.66157 | ||
| 891;0.47544;0.66158 | ||
| 892;0.47598;0.66159 | ||
| 893;0.47652;0.66160 | ||
| 894;0.47706;0.66161 | ||
| 895;0.47760;0.66162 | ||
| 896;0.47814;0.66163 | ||
| 897;0.47868;0.66164 | ||
| 898;0.47922;0.66164 | ||
| 899;0.47976;0.66165 | ||
| 900;0.48030;0.66166 | ||
| 901;0.48084;0.66167 | ||
| 902;0.48138;0.66168 | ||
| 903;0.48192;0.66169 | ||
| 904;0.48246;0.66170 | ||
| 905;0.48300;0.66171 | ||
| 906;0.48354;0.66171 | ||
| 907;0.48408;0.66172 | ||
| 908;0.48462;0.66173 | ||
| 909;0.48516;0.66174 | ||
| 910;0.48570;0.66175 | ||
| 911;0.48625;0.66177 | ||
| 912;0.48680;0.66179 | ||
| 913;0.48735;0.66181 | ||
| 914;0.48790;0.66184 | ||
| 915;0.48845;0.66186 | ||
| 916;0.48900;0.66188 | ||
| 917;0.48955;0.66190 | ||
| 918;0.49010;0.66192 | ||
| 919;0.49065;0.66195 | ||
| 920;0.49120;0.66197 | ||
| 921;0.49175;0.66199 | ||
| 922;0.49230;0.66201 | ||
| 923;0.49285;0.66203 | ||
| 924;0.49340;0.66205 | ||
| 925;0.49395;0.66208 | ||
| 926;0.49450;0.66210 | ||
| 927;0.49505;0.66212 | ||
| 928;0.49560;0.66214 | ||
| 929;0.49615;0.66216 | ||
| 930;0.49670;0.66218 | ||
| 931;0.49712;0.66203 | ||
| 932;0.49754;0.66188 | ||
| 933;0.49796;0.66173 | ||
| 934;0.49838;0.66158 | ||
| 935;0.49880;0.66143 | ||
| 936;0.49922;0.66128 | ||
| 937;0.49964;0.66113 | ||
| 938;0.50006;0.66098 | ||
| 939;0.50048;0.66083 | ||
| 940;0.50090;0.66068 | ||
| 941;0.50132;0.66053 | ||
| 942;0.50174;0.66038 | ||
| 943;0.50216;0.66023 | ||
| 944;0.50258;0.66008 | ||
| 945;0.50300;0.65994 | ||
| 946;0.50342;0.65979 | ||
| 947;0.50384;0.65964 | ||
| 948;0.50426;0.65950 | ||
| 949;0.50468;0.65935 | ||
| 950;0.50510;0.65920 | ||
| 951;0.50526;0.65871 | ||
| 952;0.50541;0.65822 | ||
| 953;0.50557;0.65773 | ||
| 954;0.50572;0.65725 | ||
| 955;0.50588;0.65676 | ||
| 956;0.50603;0.65627 | ||
| 957;0.50619;0.65579 | ||
| 958;0.50634;0.65530 | ||
| 959;0.50650;0.65482 | ||
| 960;0.50665;0.65434 | ||
| 961;0.50681;0.65386 | ||
| 962;0.50696;0.65338 | ||
| 963;0.50712;0.65290 | ||
| 964;0.50727;0.65242 | ||
| 965;0.50743;0.65194 | ||
| 966;0.50758;0.65147 | ||
| 967;0.50774;0.65099 | ||
| 968;0.50789;0.65052 | ||
| 969;0.50805;0.65005 | ||
| 970;0.50820;0.64957 | ||
| 971;0.50780;0.64840 | ||
| 972;0.50740;0.64722 | ||
| 973;0.50700;0.64604 | ||
| 974;0.50660;0.64487 | ||
| 975;0.50620;0.64370 | ||
| 976;0.50580;0.64253 | ||
| 977;0.50540;0.64137 | ||
| 978;0.50500;0.64020 | ||
| 979;0.50460;0.63904 | ||
| 980;0.50420;0.63789 | ||
| 981;0.50380;0.63673 | ||
| 982;0.50340;0.63558 | ||
| 983;0.50300;0.63443 | ||
| 984;0.50260;0.63328 | ||
| 985;0.50220;0.63213 | ||
| 986;0.50180;0.63099 | ||
| 987;0.50140;0.62984 | ||
| 988;0.50100;0.62871 | ||
| 989;0.50060;0.62757 | ||
| 990;0.50020;0.62643 | ||
| 991;0.49908;0.62440 | ||
| 992;0.49796;0.62237 | ||
| 993;0.49684;0.62035 | ||
| 994;0.49572;0.61832 | ||
| 995;0.49460;0.61631 | ||
| 996;0.49348;0.61429 | ||
| 997;0.49236;0.61229 | ||
| 998;0.49124;0.61028 | ||
| 999;0.49012;0.60828 | ||
| 1000;0.48900;0.60628 | ||
| 1001;0.49054;0.60759 | ||
| 1002;0.48925;0.60538 | ||
| 1003;0.48795;0.60317 | ||
| 1004;0.48666;0.60097 | ||
| 1005;0.48536;0.59878 | ||
| 1006;0.48407;0.59659 | ||
| 1007;0.48277;0.59440 | ||
| 1008;0.48148;0.59222 | ||
| 1009;0.47994;0.58974 | ||
| 1010;0.47799;0.58677 | ||
| 1011;0.47605;0.58381 | ||
| 1012;0.47411;0.58085 | ||
| 1013;0.47216;0.57789 | ||
| 1014;0.47008;0.57478 | ||
| 1015;0.46801;0.57168 | ||
| 1016;0.46594;0.56859 | ||
| 1017;0.46398;0.56564 | ||
| 1018;0.46204;0.56272 | ||
| 1019;0.46009;0.55981 | ||
| 1020;0.45815;0.55690 | ||
| 1021;0.45587;0.55358 | ||
| 1022;0.45334;0.54997 | ||
| 1023;0.45081;0.54637 | ||
| 1024;0.44829;0.54278 | ||
| 1025;0.44535;0.53870 | ||
| 1026;0.44147;0.53348 | ||
| 1027;0.43778;0.52851 | ||
| 1028;0.43520;0.52488 | ||
| 1029;0.43261;0.52125 | ||
| 1030;0.43002;0.51762 | ||
| 1031;0.42742;0.51400 | ||
| 1032;0.42483;0.51039 | ||
| 1033;0.42224;0.50679 | ||
| 1034;0.41872;0.50208 | ||
| 1035;0.41484;0.49694 | ||
| 1036;0.41095;0.49181 | ||
| 1037;0.40707;0.48669 | ||
| 1038;0.40318;0.48158 | ||
| 1039;0.39929;0.47648 | ||
| 1040;0.39541;0.47139 | ||
| 1041;0.39152;0.46631 | ||
| 1042;0.38710;0.46059 | ||
| 1043;0.37959;0.45123 | ||
| 1044;0.37570;0.44618 | ||
| 1045;0.37181;0.44114 | ||
| 1046;0.36793;0.43611 | ||
| 1047;0.36404;0.43110 | ||
| 1048;0.36016;0.42609 | ||
| 1049;0.35628;0.42109 | ||
| 1050;0.35239;0.41610 |
| Wavelength (nm);Responsivity (A/W);Quantum Efficiency | ||
| 350;0.04196;0.14864 | ||
| 351;0.04120;0.14554 | ||
| 352;0.04047;0.14256 | ||
| 353;0.03987;0.14005 | ||
| 354;0.03919;0.13724 | ||
| 355;0.03842;0.13419 | ||
| 356;0.03814;0.13283 | ||
| 357;0.03771;0.13096 | ||
| 358;0.03720;0.12885 | ||
| 359;0.03689;0.12740 | ||
| 360;0.03646;0.12557 | ||
| 361;0.03609;0.12395 | ||
| 362;0.03593;0.12306 | ||
| 363;0.03557;0.12149 | ||
| 364;0.03531;0.12029 | ||
| 365;0.03514;0.11937 | ||
| 366;0.03497;0.11847 | ||
| 367;0.03478;0.11749 | ||
| 368;0.03458;0.11652 | ||
| 369;0.03455;0.11608 | ||
| 370;0.03442;0.11535 | ||
| 371;0.03472;0.11604 | ||
| 372;0.03499;0.11662 | ||
| 373;0.03532;0.11739 | ||
| 374;0.03578;0.11863 | ||
| 375;0.03619;0.11967 | ||
| 376;0.03663;0.12080 | ||
| 377;0.03717;0.12226 | ||
| 378;0.03765;0.12350 | ||
| 379;0.03830;0.12529 | ||
| 380;0.03887;0.12681 | ||
| 381;0.03954;0.12868 | ||
| 382;0.04023;0.13058 | ||
| 383;0.04094;0.13255 | ||
| 384;0.04171;0.13467 | ||
| 385;0.04253;0.13695 | ||
| 386;0.04337;0.13931 | ||
| 387;0.04424;0.14174 | ||
| 388;0.04515;0.14428 | ||
| 389;0.04606;0.14682 | ||
| 390;0.04708;0.14966 | ||
| 391;0.04799;0.15219 | ||
| 392;0.04895;0.15482 | ||
| 393;0.04996;0.15760 | ||
| 394;0.05098;0.16043 | ||
| 395;0.05206;0.16341 | ||
| 396;0.05312;0.16631 | ||
| 397;0.05421;0.16930 | ||
| 398;0.05531;0.17229 | ||
| 399;0.05636;0.17513 | ||
| 400;0.05757;0.17843 | ||
| 401;0.05875;0.18163 | ||
| 402;0.05997;0.18495 | ||
| 403;0.06119;0.18826 | ||
| 404;0.06240;0.19150 | ||
| 405;0.06363;0.19479 | ||
| 406;0.06497;0.19840 | ||
| 407;0.06618;0.20159 | ||
| 408;0.06745;0.20496 | ||
| 409;0.06872;0.20833 | ||
| 410;0.07005;0.21182 | ||
| 411;0.07131;0.21512 | ||
| 412;0.07252;0.21825 | ||
| 413;0.07380;0.22155 | ||
| 414;0.07512;0.22497 | ||
| 415;0.07635;0.22810 | ||
| 416;0.07767;0.23149 | ||
| 417;0.07886;0.23448 | ||
| 418;0.08022;0.23793 | ||
| 419;0.08150;0.24116 | ||
| 420;0.08281;0.24446 | ||
| 421;0.08407;0.24758 | ||
| 422;0.08552;0.25127 | ||
| 423;0.08678;0.25436 | ||
| 424;0.08813;0.25772 | ||
| 425;0.08942;0.26085 | ||
| 426;0.09082;0.26433 | ||
| 427;0.09218;0.26765 | ||
| 428;0.09341;0.27060 | ||
| 429;0.09482;0.27405 | ||
| 430;0.09617;0.27729 | ||
| 431;0.09746;0.28035 | ||
| 432;0.09879;0.28353 | ||
| 433;0.10014;0.28675 | ||
| 434;0.10138;0.28961 | ||
| 435;0.10273;0.29281 | ||
| 436;0.10403;0.29584 | ||
| 437;0.10530;0.29877 | ||
| 438;0.10664;0.30186 | ||
| 439;0.10793;0.30483 | ||
| 440;0.10920;0.30770 | ||
| 441;0.11052;0.31072 | ||
| 442;0.11183;0.31368 | ||
| 443;0.11308;0.31649 | ||
| 444;0.11443;0.31953 | ||
| 445;0.11568;0.32229 | ||
| 446;0.11703;0.32533 | ||
| 447;0.11834;0.32824 | ||
| 448;0.11961;0.33101 | ||
| 449;0.12092;0.33391 | ||
| 450;0.12226;0.33686 | ||
| 451;0.12348;0.33947 | ||
| 452;0.12476;0.34222 | ||
| 453;0.12604;0.34496 | ||
| 454;0.12725;0.34751 | ||
| 455;0.12855;0.35028 | ||
| 456;0.12991;0.35321 | ||
| 457;0.13110;0.35566 | ||
| 458;0.13240;0.35841 | ||
| 459;0.13371;0.36116 | ||
| 460;0.13501;0.36389 | ||
| 461;0.13623;0.36637 | ||
| 462;0.13757;0.36918 | ||
| 463;0.13880;0.37169 | ||
| 464;0.14007;0.37427 | ||
| 465;0.14138;0.37697 | ||
| 466;0.14262;0.37945 | ||
| 467;0.14387;0.38196 | ||
| 468;0.14517;0.38460 | ||
| 469;0.14642;0.38708 | ||
| 470;0.14765;0.38950 | ||
| 471;0.14894;0.39206 | ||
| 472;0.15017;0.39446 | ||
| 473;0.15141;0.39689 | ||
| 474;0.15269;0.39939 | ||
| 475;0.15390;0.40171 | ||
| 476;0.15516;0.40414 | ||
| 477;0.15638;0.40648 | ||
| 478;0.15758;0.40872 | ||
| 479;0.15876;0.41094 | ||
| 480;0.16005;0.41341 | ||
| 481;0.16127;0.41569 | ||
| 482;0.16253;0.41808 | ||
| 483;0.16375;0.42033 | ||
| 484;0.16499;0.42264 | ||
| 485;0.16618;0.42482 | ||
| 486;0.16750;0.42731 | ||
| 487;0.16868;0.42945 | ||
| 488;0.16994;0.43175 | ||
| 489;0.17113;0.43389 | ||
| 490;0.17242;0.43627 | ||
| 491;0.17362;0.43841 | ||
| 492;0.17483;0.44058 | ||
| 493;0.17609;0.44285 | ||
| 494;0.17728;0.44493 | ||
| 495;0.17847;0.44702 | ||
| 496;0.17972;0.44924 | ||
| 497;0.18090;0.45128 | ||
| 498;0.18214;0.45347 | ||
| 499;0.18341;0.45571 | ||
| 500;0.18460;0.45776 | ||
| 501;0.18578;0.45977 | ||
| 502;0.18702;0.46191 | ||
| 503;0.18821;0.46392 | ||
| 504;0.18946;0.46608 | ||
| 505;0.19070;0.46819 | ||
| 506;0.19197;0.47037 | ||
| 507;0.19312;0.47226 | ||
| 508;0.19439;0.47444 | ||
| 509;0.19557;0.47637 | ||
| 510;0.19683;0.47851 | ||
| 511;0.19797;0.48034 | ||
| 512;0.19919;0.48236 | ||
| 513;0.20036;0.48423 | ||
| 514;0.20158;0.48624 | ||
| 515;0.20278;0.48819 | ||
| 516;0.20404;0.49028 | ||
| 517;0.20520;0.49209 | ||
| 518;0.20639;0.49401 | ||
| 519;0.20767;0.49611 | ||
| 520;0.20889;0.49805 | ||
| 521;0.21014;0.50008 | ||
| 522;0.21136;0.50202 | ||
| 523;0.21253;0.50383 | ||
| 524;0.21378;0.50583 | ||
| 525;0.21498;0.50770 | ||
| 526;0.21625;0.50972 | ||
| 527;0.21744;0.51156 | ||
| 528;0.21864;0.51342 | ||
| 529;0.21987;0.51533 | ||
| 530;0.22102;0.51703 | ||
| 531;0.22220;0.51882 | ||
| 532;0.22342;0.52068 | ||
| 533;0.22466;0.52259 | ||
| 534;0.22583;0.52434 | ||
| 535;0.22705;0.52618 | ||
| 536;0.22821;0.52788 | ||
| 537;0.22941;0.52966 | ||
| 538;0.23064;0.53152 | ||
| 539;0.23176;0.53312 | ||
| 540;0.23304;0.53505 | ||
| 541;0.23421;0.53675 | ||
| 542;0.23548;0.53867 | ||
| 543;0.23663;0.54030 | ||
| 544;0.23782;0.54203 | ||
| 545;0.23898;0.54367 | ||
| 546;0.24017;0.54538 | ||
| 547;0.24141;0.54719 | ||
| 548;0.24256;0.54878 | ||
| 549;0.24373;0.55044 | ||
| 550;0.24489;0.55205 | ||
| 551;0.24610;0.55377 | ||
| 552;0.24721;0.55526 | ||
| 553;0.24845;0.55702 | ||
| 554;0.24961;0.55863 | ||
| 555;0.25088;0.56045 | ||
| 556;0.25202;0.56198 | ||
| 557;0.25325;0.56373 | ||
| 558;0.25439;0.56524 | ||
| 559;0.25552;0.56674 | ||
| 560;0.25671;0.56836 | ||
| 561;0.25795;0.57009 | ||
| 562;0.25908;0.57155 | ||
| 563;0.26025;0.57313 | ||
| 564;0.26144;0.57472 | ||
| 565;0.26267;0.57640 | ||
| 566;0.26387;0.57801 | ||
| 567;0.26500;0.57947 | ||
| 568;0.26616;0.58097 | ||
| 569;0.26734;0.58253 | ||
| 570;0.26850;0.58402 | ||
| 571;0.27159;0.58972 | ||
| 572;0.27260;0.59088 | ||
| 573;0.27366;0.59213 | ||
| 574;0.27468;0.59331 | ||
| 575;0.27583;0.59476 | ||
| 576;0.27664;0.59547 | ||
| 577;0.27785;0.59704 | ||
| 578;0.27910;0.59868 | ||
| 579;0.28010;0.59979 | ||
| 580;0.28122;0.60115 | ||
| 581;0.28235;0.60253 | ||
| 582;0.28353;0.60401 | ||
| 583;0.28461;0.60527 | ||
| 584;0.28569;0.60652 | ||
| 585;0.28689;0.60803 | ||
| 586;0.28804;0.60943 | ||
| 587;0.28932;0.61110 | ||
| 588;0.29031;0.61213 | ||
| 589;0.29149;0.61358 | ||
| 590;0.29265;0.61499 | ||
| 591;0.29376;0.61628 | ||
| 592;0.29485;0.61752 | ||
| 593;0.29604;0.61896 | ||
| 594;0.29717;0.62027 | ||
| 595;0.29833;0.62165 | ||
| 596;0.29961;0.62326 | ||
| 597;0.30066;0.62441 | ||
| 598;0.30182;0.62578 | ||
| 599;0.30294;0.62704 | ||
| 600;0.30407;0.62834 | ||
| 601;0.30519;0.62961 | ||
| 602;0.30637;0.63099 | ||
| 603;0.30753;0.63231 | ||
| 604;0.30875;0.63378 | ||
| 605;0.30990;0.63509 | ||
| 606;0.31108;0.63645 | ||
| 607;0.31223;0.63775 | ||
| 608;0.31338;0.63905 | ||
| 609;0.31453;0.64034 | ||
| 610;0.31571;0.64169 | ||
| 611;0.31680;0.64284 | ||
| 612;0.31799;0.64420 | ||
| 613;0.31913;0.64546 | ||
| 614;0.32029;0.64675 | ||
| 615;0.32150;0.64815 | ||
| 616;0.32264;0.64939 | ||
| 617;0.32380;0.65066 | ||
| 618;0.32489;0.65181 | ||
| 619;0.32613;0.65322 | ||
| 620;0.32716;0.65423 | ||
| 621;0.32832;0.65550 | ||
| 622;0.32949;0.65679 | ||
| 623;0.33069;0.65811 | ||
| 624;0.33185;0.65936 | ||
| 625;0.33303;0.66064 | ||
| 626;0.33425;0.66202 | ||
| 627;0.33532;0.66307 | ||
| 628;0.33651;0.66436 | ||
| 629;0.33759;0.66543 | ||
| 630;0.33874;0.66665 | ||
| 631;0.33991;0.66788 | ||
| 632;0.34103;0.66901 | ||
| 633;0.34226;0.67038 | ||
| 634;0.34339;0.67152 | ||
| 635;0.34467;0.67298 | ||
| 636;0.34578;0.67408 | ||
| 637;0.34686;0.67512 | ||
| 638;0.34805;0.67638 | ||
| 639;0.34912;0.67740 | ||
| 640;0.35022;0.67847 | ||
| 641;0.35136;0.67961 | ||
| 642;0.35249;0.68074 | ||
| 643;0.35368;0.68197 | ||
| 644;0.35481;0.68308 | ||
| 645;0.35599;0.68429 | ||
| 646;0.35705;0.68528 | ||
| 647;0.35819;0.68639 | ||
| 648;0.35932;0.68750 | ||
| 649;0.36046;0.68862 | ||
| 650;0.36157;0.68969 | ||
| 651;0.36270;0.69078 | ||
| 652;0.36389;0.69198 | ||
| 653;0.36489;0.69281 | ||
| 654;0.36603;0.69392 | ||
| 655;0.36718;0.69503 | ||
| 656;0.36841;0.69630 | ||
| 657;0.36956;0.69741 | ||
| 658;0.37074;0.69856 | ||
| 659;0.37189;0.69967 | ||
| 660;0.37302;0.70074 | ||
| 661;0.37409;0.70169 | ||
| 662;0.37514;0.70260 | ||
| 663;0.37623;0.70357 | ||
| 664;0.37732;0.70454 | ||
| 665;0.37846;0.70561 | ||
| 666;0.37957;0.70662 | ||
| 667;0.38071;0.70767 | ||
| 668;0.38179;0.70862 | ||
| 669;0.38298;0.70977 | ||
| 670;0.38414;0.71085 | ||
| 671;0.38501;0.71140 | ||
| 672;0.38618;0.71250 | ||
| 673;0.38737;0.71364 | ||
| 674;0.38844;0.71455 | ||
| 675;0.38953;0.71549 | ||
| 676;0.39063;0.71644 | ||
| 677;0.39179;0.71752 | ||
| 678;0.39293;0.71854 | ||
| 679;0.39402;0.71947 | ||
| 680;0.39511;0.72039 | ||
| 681;0.39619;0.72130 | ||
| 682;0.39728;0.72224 | ||
| 683;0.39844;0.72328 | ||
| 684;0.39949;0.72412 | ||
| 685;0.40057;0.72502 | ||
| 686;0.40179;0.72617 | ||
| 687;0.40284;0.72702 | ||
| 688;0.40394;0.72793 | ||
| 689;0.40500;0.72878 | ||
| 690;0.40609;0.72969 | ||
| 691;0.40715;0.73054 | ||
| 692;0.40818;0.73133 | ||
| 693;0.40922;0.73214 | ||
| 694;0.41030;0.73300 | ||
| 695;0.41143;0.73397 | ||
| 696;0.41244;0.73470 | ||
| 697;0.41345;0.73546 | ||
| 698;0.41450;0.73626 | ||
| 699;0.41560;0.73716 | ||
| 700;0.41663;0.73793 | ||
| 701;0.41770;0.73877 | ||
| 702;0.41873;0.73954 | ||
| 703;0.41969;0.74018 | ||
| 704;0.42084;0.74116 | ||
| 705;0.42186;0.74190 | ||
| 706;0.42291;0.74269 | ||
| 707;0.42389;0.74337 | ||
| 708;0.42499;0.74424 | ||
| 709;0.42601;0.74498 | ||
| 710;0.42701;0.74567 | ||
| 711;0.42811;0.74654 | ||
| 712;0.42911;0.74724 | ||
| 713;0.43012;0.74794 | ||
| 714;0.43113;0.74865 | ||
| 715;0.43212;0.74932 | ||
| 716;0.43306;0.74989 | ||
| 717;0.43420;0.75082 | ||
| 718;0.43513;0.75138 | ||
| 719;0.43619;0.75216 | ||
| 720;0.43727;0.75297 | ||
| 721;0.43812;0.75339 | ||
| 722;0.43923;0.75425 | ||
| 723;0.44027;0.75500 | ||
| 724;0.44127;0.75567 | ||
| 725;0.44239;0.75654 | ||
| 726;0.44340;0.75723 | ||
| 727;0.44435;0.75781 | ||
| 728;0.44538;0.75852 | ||
| 729;0.44644;0.75929 | ||
| 730;0.44741;0.75989 | ||
| 731;0.44840;0.76054 | ||
| 732;0.44948;0.76132 | ||
| 733;0.45041;0.76185 | ||
| 734;0.45144;0.76255 | ||
| 735;0.45242;0.76318 | ||
| 736;0.45346;0.76389 | ||
| 737;0.45441;0.76444 | ||
| 738;0.45539;0.76506 | ||
| 739;0.45643;0.76577 | ||
| 740;0.45737;0.76631 | ||
| 741;0.45842;0.76703 | ||
| 742;0.45933;0.76752 | ||
| 743;0.46027;0.76805 | ||
| 744;0.46129;0.76873 | ||
| 745;0.46224;0.76926 | ||
| 746;0.46313;0.76972 | ||
| 747;0.46416;0.77039 | ||
| 748;0.46519;0.77107 | ||
| 749;0.46615;0.77163 | ||
| 750;0.46701;0.77203 | ||
| 751;0.46802;0.77267 | ||
| 752;0.46890;0.77309 | ||
| 753;0.46978;0.77350 | ||
| 754;0.47069;0.77398 | ||
| 755;0.47173;0.77466 | ||
| 756;0.47259;0.77506 | ||
| 757;0.47355;0.77560 | ||
| 758;0.47441;0.77598 | ||
| 759;0.47532;0.77644 | ||
| 760;0.47636;0.77713 | ||
| 761;0.47724;0.77753 | ||
| 762;0.47820;0.77808 | ||
| 763;0.47903;0.77840 | ||
| 764;0.47994;0.77886 | ||
| 765;0.48086;0.77933 | ||
| 766;0.48172;0.77970 | ||
| 767;0.48260;0.78012 | ||
| 768;0.48346;0.78048 | ||
| 769;0.48440;0.78099 | ||
| 770;0.48524;0.78133 | ||
| 771;0.48609;0.78168 | ||
| 772;0.48697;0.78209 | ||
| 773;0.48790;0.78255 | ||
| 774;0.48881;0.78300 | ||
| 775;0.48960;0.78327 | ||
| 776;0.49050;0.78368 | ||
| 777;0.49126;0.78389 | ||
| 778;0.49214;0.78429 | ||
| 779;0.49300;0.78465 | ||
| 780;0.49394;0.78514 | ||
| 781;0.49478;0.78547 | ||
| 782;0.49561;0.78578 | ||
| 783;0.49645;0.78610 | ||
| 784;0.49727;0.78640 | ||
| 785;0.49810;0.78671 | ||
| 786;0.49894;0.78703 | ||
| 787;0.49984;0.78744 | ||
| 788;0.50071;0.78782 | ||
| 789;0.50153;0.78811 | ||
| 790;0.50229;0.78831 | ||
| 791;0.50316;0.78868 | ||
| 792;0.50391;0.78884 | ||
| 793;0.50470;0.78909 | ||
| 794;0.50556;0.78944 | ||
| 795;0.50631;0.78962 | ||
| 796;0.50710;0.78985 | ||
| 797;0.50800;0.79026 | ||
| 798;0.50882;0.79054 | ||
| 799;0.50970;0.79092 | ||
| 800;0.51052;0.79121 | ||
| 801;0.51130;0.79142 | ||
| 802;0.51213;0.79173 | ||
| 803;0.51288;0.79190 | ||
| 804;0.51375;0.79225 | ||
| 805;0.51451;0.79244 | ||
| 806;0.51536;0.79277 | ||
| 807;0.51623;0.79311 | ||
| 808;0.51691;0.79318 | ||
| 809;0.51766;0.79335 | ||
| 810;0.51868;0.79393 | ||
| 811;0.51940;0.79405 | ||
| 812;0.52007;0.79410 | ||
| 813;0.52092;0.79441 | ||
| 814;0.52164;0.79453 | ||
| 815;0.52244;0.79477 | ||
| 816;0.52325;0.79503 | ||
| 817;0.52405;0.79527 | ||
| 818;0.52478;0.79540 | ||
| 819;0.52553;0.79557 | ||
| 820;0.52633;0.79581 | ||
| 821;0.52711;0.79602 | ||
| 822;0.52794;0.79630 | ||
| 823;0.52873;0.79653 | ||
| 824;0.52942;0.79659 | ||
| 825;0.53015;0.79673 | ||
| 826;0.53102;0.79707 | ||
| 827;0.53188;0.79740 | ||
| 828;0.53264;0.79757 | ||
| 829;0.53340;0.79774 | ||
| 830;0.53418;0.79795 | ||
| 831;0.53491;0.79808 | ||
| 832;0.53565;0.79823 | ||
| 833;0.53637;0.79833 | ||
| 834;0.53708;0.79844 | ||
| 835;0.53790;0.79870 | ||
| 836;0.53865;0.79886 | ||
| 837;0.53940;0.79901 | ||
| 838;0.54025;0.79931 | ||
| 839;0.54087;0.79928 | ||
| 840;0.54167;0.79951 | ||
| 841;0.54235;0.79955 | ||
| 842;0.54322;0.79989 | ||
| 843;0.54385;0.79987 | ||
| 844;0.54455;0.79994 | ||
| 845;0.54534;0.80016 | ||
| 846;0.54608;0.80030 | ||
| 847;0.54671;0.80027 | ||
| 848;0.54761;0.80065 | ||
| 849;0.54827;0.80067 | ||
| 850;0.54906;0.80087 | ||
| 851;0.54971;0.80089 | ||
| 852;0.55038;0.80091 | ||
| 853;0.55107;0.80099 | ||
| 854;0.55165;0.80088 | ||
| 855;0.55252;0.80122 | ||
| 856;0.55313;0.80117 | ||
| 857;0.55388;0.80130 | ||
| 858;0.55460;0.80142 | ||
| 859;0.55526;0.80143 | ||
| 860;0.55588;0.80141 | ||
| 861;0.55690;0.80194 | ||
| 862;0.55761;0.80202 | ||
| 863;0.55827;0.80204 | ||
| 864;0.55899;0.80214 | ||
| 865;0.55965;0.80217 | ||
| 866;0.56026;0.80212 | ||
| 867;0.56088;0.80208 | ||
| 868;0.56160;0.80218 | ||
| 869;0.56222;0.80215 | ||
| 870;0.56291;0.80221 | ||
| 871;0.56347;0.80208 | ||
| 872;0.56431;0.80236 | ||
| 873;0.56484;0.80219 | ||
| 874;0.56543;0.80211 | ||
| 875;0.56622;0.80232 | ||
| 876;0.56690;0.80236 | ||
| 877;0.56749;0.80228 | ||
| 878;0.56820;0.80237 | ||
| 879;0.56877;0.80225 | ||
| 880;0.56942;0.80226 | ||
| 881;0.57004;0.80222 | ||
| 882;0.57062;0.80214 | ||
| 883;0.57126;0.80212 | ||
| 884;0.57183;0.80201 | ||
| 885;0.57267;0.80229 | ||
| 886;0.57321;0.80213 | ||
| 887;0.57382;0.80208 | ||
| 888;0.57443;0.80203 | ||
| 889;0.57514;0.80212 | ||
| 890;0.57566;0.80194 | ||
| 891;0.57632;0.80197 | ||
| 892;0.57690;0.80186 | ||
| 893;0.57751;0.80182 | ||
| 894;0.57809;0.80172 | ||
| 895;0.57875;0.80175 | ||
| 896;0.57939;0.80173 | ||
| 897;0.58000;0.80169 | ||
| 898;0.58066;0.80170 | ||
| 899;0.58125;0.80163 | ||
| 900;0.58180;0.80148 | ||
| 901;0.58234;0.80134 | ||
| 902;0.58293;0.80126 | ||
| 903;0.58360;0.80130 | ||
| 904;0.58411;0.80111 | ||
| 905;0.58481;0.80119 | ||
| 906;0.58539;0.80109 | ||
| 907;0.58605;0.80111 | ||
| 908;0.58655;0.80091 | ||
| 909;0.58711;0.80079 | ||
| 910;0.58772;0.80074 | ||
| 911;0.58830;0.80066 | ||
| 912;0.58888;0.80057 | ||
| 913;0.58963;0.80071 | ||
| 914;0.59019;0.80060 | ||
| 915;0.59080;0.80054 | ||
| 916;0.59137;0.80044 | ||
| 917;0.59206;0.80051 | ||
| 918;0.59273;0.80054 | ||
| 919;0.59316;0.80024 | ||
| 920;0.59379;0.80022 | ||
| 921;0.59448;0.80028 | ||
| 922;0.59511;0.80027 | ||
| 923;0.59555;0.79998 | ||
| 924;0.59613;0.79990 | ||
| 925;0.59676;0.79989 | ||
| 926;0.59732;0.79976 | ||
| 927;0.59781;0.79956 | ||
| 928;0.59837;0.79945 | ||
| 929;0.59890;0.79929 | ||
| 930;0.59955;0.79930 | ||
| 931;0.60001;0.79906 | ||
| 932;0.60050;0.79885 | ||
| 933;0.60088;0.79849 | ||
| 934;0.60136;0.79827 | ||
| 935;0.60165;0.79780 | ||
| 936;0.60214;0.79760 | ||
| 937;0.60243;0.79713 | ||
| 938;0.60292;0.79693 | ||
| 939;0.60350;0.79685 | ||
| 940;0.60378;0.79638 | ||
| 941;0.60428;0.79619 | ||
| 942;0.60468;0.79587 | ||
| 943;0.60513;0.79561 | ||
| 944;0.60564;0.79544 | ||
| 945;0.60599;0.79506 | ||
| 946;0.60643;0.79479 | ||
| 947;0.60688;0.79454 | ||
| 948;0.60732;0.79428 | ||
| 949;0.60764;0.79386 | ||
| 950;0.60814;0.79368 | ||
| 951;0.60813;0.79283 | ||
| 952;0.60826;0.79217 | ||
| 953;0.60845;0.79159 | ||
| 954;0.60857;0.79091 | ||
| 955;0.60861;0.79013 | ||
| 956;0.60880;0.78955 | ||
| 957;0.60889;0.78885 | ||
| 958;0.60903;0.78820 | ||
| 959;0.60917;0.78756 | ||
| 960;0.60925;0.78685 | ||
| 961;0.60941;0.78624 | ||
| 962;0.60959;0.78565 | ||
| 963;0.60967;0.78494 | ||
| 964;0.60989;0.78440 | ||
| 965;0.61002;0.78376 | ||
| 966;0.61008;0.78303 | ||
| 967;0.61026;0.78245 | ||
| 968;0.61032;0.78172 | ||
| 969;0.61043;0.78105 | ||
| 970;0.61046;0.78028 | ||
| 971;0.60985;0.77870 | ||
| 972;0.60938;0.77729 | ||
| 973;0.60880;0.77576 | ||
| 974;0.60833;0.77437 | ||
| 975;0.60777;0.77285 | ||
| 976;0.60720;0.77135 | ||
| 977;0.60672;0.76995 | ||
| 978;0.60603;0.76828 | ||
| 979;0.60551;0.76684 | ||
| 980;0.60492;0.76531 | ||
| 981;0.60429;0.76374 | ||
| 982;0.60368;0.76219 | ||
| 983;0.60318;0.76078 | ||
| 984;0.60247;0.75911 | ||
| 985;0.60181;0.75752 | ||
| 986;0.60120;0.75597 | ||
| 987;0.60053;0.75437 | ||
| 988;0.59972;0.75259 | ||
| 989;0.59906;0.75100 | ||
| 990;0.59840;0.74941 | ||
| 991;0.59681;0.74668 | ||
| 992;0.59529;0.74402 | ||
| 993;0.59358;0.74113 | ||
| 994;0.59193;0.73834 | ||
| 995;0.59032;0.73558 | ||
| 996;0.58862;0.73272 | ||
| 997;0.58686;0.72980 | ||
| 998;0.58509;0.72688 | ||
| 999;0.58334;0.72397 | ||
| 1000;0.58151;0.72098 | ||
| 1001;0.58285;0.72191 | ||
| 1002;0.58079;0.71865 | ||
| 1003;0.57875;0.71542 | ||
| 1004;0.57634;0.71173 | ||
| 1005;0.57430;0.70850 | ||
| 1006;0.57203;0.70499 | ||
| 1007;0.56986;0.70162 | ||
| 1008;0.56765;0.69822 | ||
| 1009;0.56514;0.69443 | ||
| 1010;0.56198;0.68987 | ||
| 1011;0.55874;0.68521 | ||
| 1012;0.55559;0.68068 | ||
| 1013;0.55242;0.67612 | ||
| 1014;0.54880;0.67104 | ||
| 1015;0.54541;0.66622 | ||
| 1016;0.54181;0.66118 | ||
| 1017;0.53856;0.65657 | ||
| 1018;0.53514;0.65176 | ||
| 1019;0.53158;0.64678 | ||
| 1020;0.52798;0.64178 | ||
| 1021;0.52405;0.63637 | ||
| 1022;0.51979;0.63059 | ||
| 1023;0.51542;0.62467 | ||
| 1024;0.51092;0.61861 | ||
| 1025;0.50622;0.61232 | ||
| 1026;0.50003;0.60425 | ||
| 1027;0.49422;0.59665 | ||
| 1028;0.48961;0.59051 | ||
| 1029;0.48485;0.58420 | ||
| 1030;0.48022;0.57806 | ||
| 1031;0.47540;0.57170 | ||
| 1032;0.47058;0.56535 | ||
| 1033;0.46592;0.55921 | ||
| 1034;0.45987;0.55142 | ||
| 1035;0.45371;0.54351 | ||
| 1036;0.44722;0.53521 | ||
| 1037;0.44109;0.52737 | ||
| 1038;0.43476;0.51930 | ||
| 1039;0.42843;0.51125 | ||
| 1040;0.42218;0.50330 | ||
| 1041;0.41566;0.49506 | ||
| 1042;0.40889;0.48653 | ||
| 1043;0.39868;0.47392 | ||
| 1044;0.39249;0.46611 | ||
| 1045;0.38634;0.45837 | ||
| 1046;0.38003;0.45045 | ||
| 1047;0.37392;0.44279 | ||
| 1048;0.36778;0.43510 | ||
| 1049;0.36164;0.42743 | ||
| 1050;0.35551;0.41978 |
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Worsened metrics
- Total package byte prevSize
1820152
-1.35%- Number of package files
47
-6%- Lines of code
4154
-7.03%