diff --git a/README.md b/README.md index c1c8beb..d34aef2 100644 --- a/README.md +++ b/README.md @@ -3,14 +3,14 @@

- - Codecov + + Codecov Documentation Status - - CI + + CI DOI

@@ -38,7 +38,7 @@ pip install pyEDITH Or, if preferred, clone the pyEDITH repository and install locally: ``` -git clone https://github.com/HWO-Project/pyEDITH.git +git clone https://github.com/HabitableWorldsObservatory/pyEDITH.git cd pyEDITH pip install -e . ``` @@ -105,8 +105,6 @@ This mode offers much more flexibility to run the ETC. We refer to our tutorials | npsfratios | Integer | Scalar | Number of PSF ratios | | | nrolls | Integer | Scalar | Number of roll angles | | | nchannels | Integer | Scalar | Number of channels | | -| minimum_IWA | λ/D | Scalar | Minimum Inner Working Angle | | -| maximum_OWA | λ/D | Scalar | Maximum Outer Working Angle | | | coronagraph_optical_throughput | Dimensionless | [nlambda] | Throughput for all coronagraph optics | | | diameter | Length | Scalar | Circumscribed diameter of telescope aperture | | | Area | Length² | Scalar | Effective collecting area of telescope | | @@ -117,7 +115,7 @@ This mode offers much more flexibility to run the ETC. We refer to our tutorials | temperature | Temperature | Scalar | Temperature of the warm optics | | | T_contamination | Dimensionless | Scalar | Effective throughput factor for contamination | | | pixscale_mas | Milliarcsecond | Scalar | Detector pixel scale | | -| npix_multiplier | Dimensionless | [nlambda] | Number of detector pixels per image plane "pixel" | | +| npix_multiplier | Dimensionless | Scalar | Number of detector pixels per image plane "pixel" | | | DC | Electron / (Pixel * Second) | [nlambda] | Dark current | | | RN | Electron / (Pixel * Read) | [nlambda] | Read noise | | | tread | Second | [nlambda] | Read time | | @@ -169,7 +167,7 @@ This mode offers much more flexibility to run the ETC. We refer to our tutorials | photometric_aperture_throughput | [npix, npix, npsfratios] | Dimensionless | fraction of light entering the coronagraph that ends up within the photometric core of the off-axis (planet) PSF assuming perfectly reflecting/transmitting optics, where the core is the solid angle area `Omega` and is set by either `psf_trunc_ratio` or `photometric_aperture_radius`. | No | | omega_lod | [npix, npix, npsfratios] | (λ/D)² | Solid angle of the photometric aperture | No | | skytrans | [npix, npix] | Dimensionless | Sky transmission; the coronagraph’s performance when observing an infinitely extended source | No | -| pixscale | Scalar | λ/D | Pixel scale of the coronagraph model | No | +| pixscale | Scalar | λ/D | Pixel scale of the coronagraph model | Only ToyModel | | npix | Scalar | Dimensionless | length of one side of the coronagraph model images (assuming a square) | No | | xcenter | Scalar | Pixel | X-coordinate of the image center | No | | ycenter | Scalar | Pixel | Y-coordinate of the image center | No | @@ -179,15 +177,13 @@ This mode offers much more flexibility to run the ETC. We refer to our tutorials | npsfratios | Scalar | Dimensionless | Number of PSF truncation ratios (default 1) | No | | nrolls | Scalar | Dimensionless | Number of roll angles performed | Yes | | nchannels | Scalar | Dimensionless | Number of channels in coronagraph | Yes | -| minimum_IWA | Scalar | λ/D | Minimum Inner Working Angle | Yes | -| maximum_OWA | Scalar | λ/D | Maximum Outer Working Angle | Yes | | coronagraph_optical_throughput | [nlambda] | Dimensionless | Throughput for all coronagraph optics | Yes | | coronagraph_spectral_resolution | Scalar | Dimensionless | Spectral resolution of the coronagraph | Yes | -| contrast | Scalar | Dimensionless | Noise floor contrast of coronagraph | Yes | -| noisefloor_factor | Scalar | Dimensionless | Systematic noise floor factor | Yes | -| noisefloor_PPF | Scalar | Dimensionless | Noise floor post-processing factor | Yes | -| Tcore | Scalar | Dimensionless | Core throughput of coronagraph (used in ToyModel only, or if photometric_aperture_radius is specified for omega_lod calculation) | Yes | -| TLyot | Scalar | Dimensionless | Lyot transmission of the coronagraph (used in ToyModel only) | Yes | +| contrast | Scalar | Dimensionless | Noise floor contrast of coronagraph | Yes (ToyModel only) | +| noisefloor_factor | Scalar | Dimensionless | Systematic noise floor factor | Yes (ToyModel only) | +| noisefloor_PPF | Scalar | Dimensionless | Noise floor post-processing factor | Yes (YIP only) | +| Tcore | Scalar | Dimensionless | Core throughput of coronagraph (used in ToyModel only, or if photometric_aperture_radius is specified for omega_lod calculation) | Yes (ToyModel only) | +| TLyot | Scalar | Dimensionless | Lyot transmission of the coronagraph (used in ToyModel only) | Yes (ToyModel only) | | PSFpeak | Scalar | Dimensionless | Peak value of the off-axis PSF | No | ### A note on calculating `omega_lod`: @@ -215,7 +211,7 @@ where `omega_lod` is the solid angle of the photometric aperture. | Variable Name | Length | Unit | Meaning | User Editable | | --------------- | --------- | --------------------------- | ------------------------------------------------- | ------------- | | pixscale_mas | Scalar | Milliarcsecond | Detector pixel scale | Yes | -| npix_multiplier | [nlambda] | Dimensionless | Number of detector pixels per image plane "pixel" | Yes | +| npix_multiplier | Scalar | Dimensionless | Number of detector pixels per image plane "pixel" | Yes | | DC | [nlambda] | Electron / (Pixel * Second) | Dark current | Yes | | RN | [nlambda] | Electron / (Pixel * Read) | Read noise | Yes | | tread | [nlambda] | Second | Read time | Yes | diff --git a/docs/source/conf.py b/docs/source/conf.py index 0716b82..f661489 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -63,7 +63,7 @@ html_static_path = ["_static"] html_theme_options = { "show_toc_level": 2, - "repository_url": "https://github.com/HWO-Project/pyEDITH", + "repository_url": "https://github.com/HabitableWorldsObservatory/pyEDITH", "path_to_docs": "docs/source", "use_issues_button": True, "use_repository_button": True, diff --git a/docs/source/glossary.md b/docs/source/glossary.md index c70ad9a..abc2318 100644 --- a/docs/source/glossary.md +++ b/docs/source/glossary.md @@ -17,8 +17,7 @@ | npsfratios | Integer | Scalar | Number of PSF ratios | | | nrolls | Integer | Scalar | Number of roll angles | | | nchannels | Integer | Scalar | Number of channels | | -| minimum_IWA | λ/D | Scalar | Minimum Inner Working Angle | | -| maximum_OWA | λ/D | Scalar | Maximum Outer Working Angle | | + | coronagraph_optical_throughput | Dimensionless | [nlambda] | Throughput for all coronagraph optics | | | diameter | Length | Scalar | Circumscribed diameter of telescope aperture | | | Area | Length² | Scalar | Effective collecting area of telescope | | @@ -29,7 +28,7 @@ | temperature | Temperature | Scalar | Temperature of the warm optics | | | T_contamination | Dimensionless | Scalar | Effective throughput factor for contamination | | | pixscale_mas | Milliarcsecond | Scalar | Detector pixel scale | | -| npix_multiplier | Dimensionless | [nlambda] | Number of detector pixels per image plane "pixel" | | +| npix_multiplier | Dimensionless | Scalar | Number of detector pixels per image plane "pixel" | | | DC | Electron / (Pixel * Second) | [nlambda] | Dark current | | | RN | Electron / (Pixel * Read) | [nlambda] | Read noise | | | tread | Second | [nlambda] | Read time | | @@ -91,8 +90,6 @@ | npsfratios | Scalar | Dimensionless | Number of PSF truncation ratios (default 1) | No | | nrolls | Scalar | Dimensionless | Number of roll angles performed | Yes | | nchannels | Scalar | Dimensionless | Number of channels in coronagraph | Yes | -| minimum_IWA | Scalar | λ/D | Minimum Inner Working Angle | Yes | -| maximum_OWA | Scalar | λ/D | Maximum Outer Working Angle | Yes | | coronagraph_optical_throughput | [nlambda] | Dimensionless | Throughput for all coronagraph optics | Yes | | coronagraph_spectral_resolution | Scalar | Dimensionless | Spectral resolution of the coronagraph | Yes | | contrast | Scalar | Dimensionless | Noise floor contrast of coronagraph | Yes | @@ -127,13 +124,13 @@ where `omega_lod` is the solid angle of the photometric aperture. | Variable Name | Length | Unit | Meaning | User Editable | | --------------- | --------- | --------------------------- | ------------------------------------------------- | ------------- | | pixscale_mas | Scalar | Milliarcsecond | Detector pixel scale | Yes | -| npix_multiplier | [nlambda] | Dimensionless | Number of detector pixels per image plane "pixel" | Yes | -| DC | [nlambda] | Electron / (Pixel * Second) | Dark current | Yes | -| RN | [nlambda] | Electron / (Pixel * Read) | Read noise | Yes | -| tread | [nlambda] | Second | Read time | Yes | -| CIC | [nlambda] | Electron / (Pixel * Photon) | Clock-induced charge | Yes | -| QE | [nlambda] | Electron / Photon | Quantum efficiency of detector | Yes | -| dQE | [nlambda] | Dimensionless | Effective QE due to degradation | Yes | +| npix_multiplier | Scalar | Dimensionless | Number of detector pixels per image plane "pixel" | Yes (Toymodel only) | +| DC | [nlambda] | Electron / (Pixel * Second) | Dark current | Yes (Toymodel only) | +| RN | [nlambda] | Electron / (Pixel * Read) | Read noise | Yes (Toymodel only) | +| tread | [nlambda] | Second | Read time | Yes (Toymodel only) | +| CIC | [nlambda] | Electron / (Pixel * Photon) | Clock-induced charge | Yes (Toymodel only) | +| QE | [nlambda] | Electron / Photon | Quantum efficiency of detector | Yes (Toymodel only) | +| dQE | [nlambda] | Dimensionless | Effective QE due to degradation | Yes (Toymodel only) | ## Within `observation.py` diff --git a/docs/source/installation.md b/docs/source/installation.md index 7b07e49..d043faa 100644 --- a/docs/source/installation.md +++ b/docs/source/installation.md @@ -27,7 +27,7 @@ pip install pyedith Alternatively, you can clone the pyEDITH repository and install it: ``` -git clone https://github.com/HWO-Project/pyEDITH.git +git clone https://github.com/HabitableWorldsObservatory/pyEDITH.git cd pyEDITH pip install -e . ``` diff --git a/inputs/input_toymodel_advanced.edith b/inputs/input_toymodel_advanced.edith index e883530..8730dd3 100644 --- a/inputs/input_toymodel_advanced.edith +++ b/inputs/input_toymodel_advanced.edith @@ -48,19 +48,17 @@ T_optical = 0.362 ;epswarmTrcold = 0.638 ; 1.0-T_optical {scalar OR array of length NLD} product of cummulative warm emissivity and cummulative cold transmission (we assume throughput == T_optical for toymodel) ;--- (OVERRIDING DEFAULT) CORONAGRAPH PARAMETERS --- -minimum_IWA = 1.0 ; smallest WA to allow (lambda/D) -maximum_OWA = 60.0 ; largest WA to allow (lambda/D) contrast = 1.05e-13 ; contrast of coronagraph (uniform over dark hole and unitless) noisefloor_factor = 0.03 ; 1 sigma systematic noise floor expressed as a multiplicative factor to the contrast (unitless) bandwidth = 0.2 ; fractional bandwidth of coronagraph (unitless) core_throughput = 0.2968371 ; core throughput of coronagraph (uniform over dark hole, unitless) Lyot_transmission = 0.65 ; Lyot transmission of the coronagraph and the factor of 1.6 is just an estimate, used for skytrans nrolls = 1 ; number of rolls -nchannels = 2 ; number of parallel detection channels (channels evaluated as one contiguous channel) +nchannels = 1 ; number of parallel detection channels (channels evaluated as one contiguous channel) PSF_trunc_ratio = 0.3 ;--- (OVERRIDING DEFAULT) DETECTOR PARAMETERS --- -npix_multiplier = 1 ; number of detector pixels per image plane "pixel"# nlambd array, 1 for detections or spectra w/ ERD, ~6*(140/SR) for spectra with IFS +npix_multiplier = 1 ; number of detector pixels per image plane "pixel"# 1 for detections or spectra w/ ERD, ~6*(140/SR) for spectra with IFS DC = 3e-5 ; dark current (counts pix^-1 s^-1) (must have same length as lambda) RN = 0.0 ; read noise (counts pix^-1 read^-1) (must have same length as lambda) tread = 1000. ; read time (seconds) (must have same length as lambda) @@ -86,7 +84,7 @@ secondary_bandwidth = 0.2 ; fractional bandwidth of coronagraph (uni secondary_core_throughput = 1.22E-01 ; core throughput of coronagraph (uniform over dark hole, unitless) ;--- (OVERRIDING DEFAULT) DETECTOR PARAMETERS --- -secondary_npix_multiplier = 1 ; number of detector pixels per image plane "pixel"# nlambd array, 1 for detections or spectra w/ ERD, ~6*(140/SR) for spectra with IFS +secondary_npix_multiplier = 1 ; number of detector pixels per image plane "pixel", 1 for detections or spectra w/ ERD, ~6*(140/SR) for spectra with IFS secondary_DC = 3e-5 ; dark current (counts pix^-1 s^-1) (must have same length as lambda) secondary_RN = 0.0 ; read noise (counts pix^-1 read^-1) (must have same length as lambda) secondary_tread = 1000. ; read time (seconds) (must have same length as lambda) diff --git a/pyproject.toml b/pyproject.toml index 3602888..4746629 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "pyEDITH" -version = "1.6.1" +version = "1.7.0" authors = [ { name = "Eleonora Alei", email = "eleonora.alei@nasa.gov" }, { name = "Miles Currie", email = "miles.h.currie@nasa.gov" }, @@ -31,8 +31,8 @@ dependencies = [ test = ["pytest", "pytest-cov", "pooch"] [project.urls] -Homepage = "https://github.com/HWO-Project/pyEDITH/" -Issues = "https://github.com/HWO-Project/pyEDITH/issues" +Homepage = "https://github.com/HabitableWorldsObservatory/pyEDITH/" +Issues = "https://github.com/HabitableWorldsObservatory/pyEDITH/issues" [build-system] requires = ["setuptools>=61.0"] diff --git a/src/pyEDITH/astrophysical_scene.py b/src/pyEDITH/astrophysical_scene.py index e02a285..f0d42ff 100644 --- a/src/pyEDITH/astrophysical_scene.py +++ b/src/pyEDITH/astrophysical_scene.py @@ -6,6 +6,7 @@ from . import utils from astropy.coordinates import SkyCoord import logging +from pyEDITH import parse_input logger = logging.getLogger("pyEDITH") # def calc_flux_zero_point_synphot(lam: u.Quantity): @@ -204,7 +205,6 @@ def calc_exozodi_flux( This is equivalent to 10^(-0.4*magOmega_EZ). """ - if len(lambd) > 1: if len(lambd) != lambdmag.shape[0]: raise ValueError( @@ -224,16 +224,12 @@ def calc_exozodi_flux( # received at V band nlambd = len(lambd) - flux_exozodi_lambd = np.zeros((nlambd)) * DIMENSIONLESS # modulo factor F0 - - # Adjust flux for each wavelength - for ilambd in range(nlambd): - flux_exozodi_lambd[ilambd] = ( - nexozodis - * vflux_1zodi - * 10.0 ** (-0.4 * (M_V - M_V_sun).value) - * 10.0 ** (-0.4 * (lambdmag[ilambd] - vmag).value) - ) + flux_exozodi_lambd = ( + nexozodis + * vflux_1zodi + * 10.0 ** (-0.4 * (M_V - M_V_sun).value) + * 10.0 ** (-0.4 * (lambdmag - vmag).value) + ) * DIMENSIONLESS return ( flux_exozodi_lambd * INV_SQUARE_ARCSEC @@ -452,9 +448,7 @@ def calc_zodi_flux( # Calculate final zodi flux nlambd = len(lambd) - flux_zodi = u.Quantity(np.zeros((nlambd)), unit=I90fabsfco.unit) - for ilambd in range(nlambd): - flux_zodi[ilambd] = f * I90fabsfco[ilambd] + flux_zodi = f * I90fabsfco return flux_zodi # 1/arcsec^2 (UNITS OF SPECTRAL RADIANCE) @@ -542,10 +536,12 @@ def load_configuration(self, parameters: dict) -> None: Raises ------ - ValueError + KeyError If a required parameter is missing from the input dictionary. """ + parameters = parse_input.parse_parameters(parameters) + # -------- INPUTS --------- # distance to star (pc) # used to be (ntargs array) now scalar @@ -581,7 +577,6 @@ def load_configuration(self, parameters: dict) -> None: # stellar mag (not absolute mag!!) at V band # used to be (ntargs array) now scalar self.vmag = parameters["magV"] * MAGNITUDE - # stellar mag at desired lambd # used to be (ntargs array) now scalar self.mag = parameters["mag"] * MAGNITUDE # difference in mag between planet and host star @@ -632,7 +627,7 @@ def load_configuration(self, parameters: dict) -> None: "FstarV_10pc" not in parameters and parameters["observing_mode"] == "IMAGER" ): - raise ValueError("FstarV_10pc missing in parameters.") + raise KeyError("FstarV_10pc missing in parameters.") else: Fstar_V_10pc = parameters["FstarV_10pc"] * PHOTON_FLUX_DENSITY @@ -663,7 +658,7 @@ def load_configuration(self, parameters: dict) -> None: if param not in parameters ] - raise ValueError( + raise KeyError( f"Insufficient parameters provided. You must provide either:\n" f"1. All magnitude parameters: {', '.join(['magV', 'mag', 'delta_mag'])}\n" f" Missing: {', '.join(missing_mag_params)}\n" @@ -709,7 +704,7 @@ def load_configuration(self, parameters: dict) -> None: * ARCSEC ) else: - raise ValueError( + raise KeyError( "Either separation [arcsec] or semimajor_axis [AU] must be provided." ) @@ -718,18 +713,14 @@ def load_configuration(self, parameters: dict) -> None: # set the exozodi PPF if "ez_PPF" in parameters.keys(): - if not isinstance(parameters["ez_PPF"], (list, np.ndarray)): - self.ez_PPF = parameters["ez_PPF"] * np.ones_like(self.Fp_over_Fs) - else: - assert len(parameters["ez_PPF"]) == len( - self.Fp_over_Fs - ), "length of ez_PPF does not match length of Fp_over_Fs" - self.ez_PPF = np.array(parameters["ez_PPF"]) + # It has already been parsed to be of length nlambda + self.ez_PPF = parameters["ez_PPF"] * DIMENSIONLESS + else: logger.warning( "ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)" ) - self.ez_PPF = np.inf * np.ones_like(self.Fp_over_Fs) + self.ez_PPF = np.inf * np.ones_like(self.Fp_over_Fs) * DIMENSIONLESS def calculate_zodi_exozodi(self, parameters: dict) -> None: """ @@ -758,6 +749,8 @@ def calculate_zodi_exozodi(self, parameters: dict) -> None: If parameters dictionary is missing required keys """ + parameters = parse_input.parse_parameters(parameters) + # Validate that scene has been properly configured required_attributes = ["vmag", "dist", "dec", "ra", "F0", "nzodis", "mag"] missing_attrs = [ @@ -771,8 +764,9 @@ def calculate_zodi_exozodi(self, parameters: dict) -> None: ) # Validate parameters dictionary - if "wavelength" not in parameters: - raise KeyError("parameters dictionary must contain 'wavelength' key") + # if "wavelength" not in parameters: + # raise KeyError("parameters dictionary must contain 'wavelength' key") + # NOTE As of July 06, 2026, this is taken into account into parse_params # calculate flux at zero point for the V band and the prescribed lambda @@ -826,49 +820,30 @@ def validate_configuration(self): } utils.validate_attributes(self, expected_args) - def regrid_spectra(self, parameters, observation): + def regrid_spectra(self, observation): """function to re-grid onto a new wavelength grid if the user specified this option""" # spectra to regrid: F0, Fzodi_list, Fexozodi_list, Fbinary_list, Fp_over_Fs, Fs_over_F0 logger.info("Re-gridding spectra onto ETC wavelength grid...") - self.F0 = utils.regrid_spec_gaussconv( - parameters["wavelength"], - self.F0, - observation.wavelength.value, - observation.delta_wavelength.value, - ) - self.Fzodi_list = utils.regrid_spec_gaussconv( - parameters["wavelength"], - self.Fzodi_list, - observation.wavelength.value, - observation.delta_wavelength.value, - ) - self.Fexozodi_list = utils.regrid_spec_gaussconv( - parameters["wavelength"], - self.Fexozodi_list, - observation.wavelength.value, - observation.delta_wavelength.value, - ) - self.Fbinary_list = utils.regrid_spec_gaussconv( - parameters["wavelength"], - self.Fbinary_list, - observation.wavelength.value, - observation.delta_wavelength.value, - ) - self.Fp_over_Fs = utils.regrid_spec_gaussconv( - parameters["wavelength"], - self.Fp_over_Fs, - observation.wavelength.value, - observation.delta_wavelength.value, - ) - self.Fs_over_F0 = utils.regrid_spec_gaussconv( - parameters["wavelength"], - self.Fs_over_F0, - observation.wavelength.value, - observation.delta_wavelength.value, - ) - self.ez_PPF = utils.regrid_spec_gaussconv( - parameters["wavelength"], - self.ez_PPF, - observation.wavelength.value, - observation.delta_wavelength.value, - ) + for attr_name in [ + "F0", + "Fzodi_list", + "Fexozodi_list", + "Fbinary_list", + "Fp_over_Fs", + "Fs_over_F0", + "ez_PPF", + "mag", + "deltamag", + ]: + setattr( + self, + attr_name, + utils.regrid_to_grid( + getattr(self, attr_name), + observation._input_wavelength, + observation.wavelength.value, + observation.delta_wavelength.value, + name=attr_name, + interpolation="Gaussian", + ), + ) diff --git a/src/pyEDITH/cli.py b/src/pyEDITH/cli.py index 6836e75..a59b47c 100644 --- a/src/pyEDITH/cli.py +++ b/src/pyEDITH/cli.py @@ -194,7 +194,7 @@ def calculate_texp(parameters: dict, ETC_validation: bool = False) -> np.array: parameters["observing_mode"] == "IFS" and parameters["regrid_wavelength"] is True ): - scene.regrid_spectra(parameters, observation) + scene.regrid_spectra(observation) # Create and configure Observatory observatory_config = parse_input.get_observatory_config(parameters) @@ -257,7 +257,7 @@ def calculate_snr(parameters: dict, reference_texp: float): parameters["observing_mode"] == "IFS" and parameters["regrid_wavelength"] is True ): - scene.regrid_spectra(parameters, observation) + scene.regrid_spectra(observation) # Create and configure Observatory observatory_config = parse_input.get_observatory_config(parameters) diff --git a/src/pyEDITH/components/coronagraphs.py b/src/pyEDITH/components/coronagraphs.py index 1c1347e..c05c752 100644 --- a/src/pyEDITH/components/coronagraphs.py +++ b/src/pyEDITH/components/coronagraphs.py @@ -7,6 +7,7 @@ from yippy import Coronagraph as yippycoro from lod_unit import lod import logging +from pyEDITH import parse_input logger = logging.getLogger("pyEDITH") @@ -151,14 +152,14 @@ class Coronagraph(ABC): Number of roll angles. nchannels : int Number of channels. - minimum_IWA : float - Minimum Inner Working Angle (lambd/D) - maximum_OWA : float - Maximum Outer Working Angle (lambd/D) coronagraph_optical_throughput: np.ndarray Throughput for all coronagraph optics in the optical path """ + # Keys that a user is NOT allowed to override for this coronagraph mode. + # Subclasses override this. An empty set means "everything is user-editable". + LOCKED_KEYS: set = set() + @abstractmethod def load_configuration(self) -> None: # pragma: no cover """ @@ -201,8 +202,6 @@ def validate_configuration(self) -> None: "npsfratios": int, "nrolls": int, "nchannels": int, - "minimum_IWA": LAMBDA_D, - "maximum_OWA": LAMBDA_D, "coronagraph_optical_throughput": DIMENSIONLESS, "coronagraph_spectral_resolution": DIMENSIONLESS, } @@ -240,10 +239,11 @@ class ToyModelCoronagraph(Coronagraph): Path to configuration files (not used in toy model) """ + # In toy-model mode EVERY parameter is user-editable, so nothing is locked. + LOCKED_KEYS: set = set() + DEFAULT_CONFIG = { "pixscale": 0.25 * LAMBDA_D, - "minimum_IWA": 2.0 * LAMBDA_D, # smallest WA to allow (lambda/D) (scalar) - "maximum_OWA": 100.0 * LAMBDA_D, # largest WA to allow (lambda/D) (scalar) "contrast": 1.05e-13 * DIMENSIONLESS, # noise floor contrast of coronagraph (uniform over dark hole and unitless) "noisefloor_factor": 0.03 @@ -255,7 +255,7 @@ class ToyModelCoronagraph(Coronagraph): "TLyot": 0.65 * DIMENSIONLESS, # Lyot transmission of the coronagraph and the factor of 1.6 is just an estimate, used for skytrans "nrolls": 1, # number of rolls - "nchannels": 2, # number of channels + "nchannels": 1, # number of channels "coronagraph_optical_throughput": [0.44] * DIMENSIONLESS, # Coronagraph throughput [made up from EAC1-ish] "coronagraph_spectral_resolution": 1 @@ -290,8 +290,11 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: mediator : ObservatoryMediator Mediator object providing access to observation and scene parameters """ + parameters = parse_input.parse_parameters(parameters) # Load parameters, use defaults if not provided - utils.fill_parameters(self, parameters, self.DEFAULT_CONFIG) + utils.fill_parameters( + self, parameters, self.DEFAULT_CONFIG, locked_keys=self.LOCKED_KEYS + ) # Convert to numpy array when appropriate array_params = ["coronagraph_optical_throughput"] @@ -327,11 +330,6 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: # find separations interior to IWA or exterior to OWA # if j[0] ne -1 then photometric_aperture_throughput1[j] = 0.0 - self.photometric_aperture_throughput[self.r < self.minimum_IWA] = ( - 0.0 # index 0 is the - ) - self.photometric_aperture_throughput[self.r > self.maximum_OWA] = 0.0 - # put in the right dimensions (3d arrays), but third dimension # is 1 (number of psf_trunc_ratio) # self.omega_lod = np.array([self.omega_lod]) @@ -408,9 +406,27 @@ class CoronagraphYIP(Coronagraph): Path to the YIP files containing coronagraph response data """ + # In YIP mode these quantities are OWNED by the YIP / yippy and must stay + # consistent with the loaded package. The user is NOT allowed to override + # them; if they try, fill_parameters will warn and keep the YIP value. + # Anything not listed here (e.g. bandwidth, noisefloor_PPF, Tcore, az_avg, + # coronagraph_spectral_resolution) remains user-editable. + LOCKED_KEYS: set = { + "pixscale", + "npix", + "xcenter", + "ycenter", + "skytrans", + "r", + "npsfratios", + "nrolls", + "omega_lod", + "photometric_aperture_throughput", + "Istar", + "noisefloor", + } + DEFAULT_CONFIG = { - "minimum_IWA": 2.0 * LAMBDA_D, # smallest WA to allow (lambda/D) (scalar) - "maximum_OWA": 100.0 * LAMBDA_D, # largest WA to allow (lambda/D) (scalar) # "contrast": 1.05e-13, # noise floor contrast of coronagraph (uniform over dark hole and unitless) "noisefloor_PPF": 30.0, # 30.0 # divide Istar by this to get the noise floor (unitless) "bandwidth": 0.2, # fractional bandwidth of coronagraph (unitless) @@ -420,7 +436,7 @@ class CoronagraphYIP(Coronagraph): "coronagraph_optical_throughput": None, "coronagraph_spectral_resolution": 1 * DIMENSIONLESS, # Set to default. It is used to limit the bandwidth if the coronagraph has a specific spectral window. - "nchannels": 2, # number of channels + "nchannels": 1, # number of channels # "TLyot": 0.65 # * DIMENSIONLESS, # Lyot transmission of the coronagraph and the factor of 1.6 is just an estimate, used for skytrans} "az_avg": True, # azimuthally average the contrast maps and noise floor if True @@ -445,7 +461,7 @@ def __init__(self, path: str = None, yippy_coro: yippycoro = None): self.path = path self.yippy_coro = yippy_coro - def load_configuration(self, parameters, mediator): + def load_configuration(self, parameters: dict, mediator: object) -> None: """ Load configuration parameters from YIP files and user specifications. @@ -471,6 +487,8 @@ def load_configuration(self, parameters, mediator): AssertionError If stellar angular diameter is outside valid bounds (0 <= diameter < 1 λ/D) """ + parameters = parse_input.parse_parameters(parameters) + from eacy import load_instrument, load_telescope # ***** Set the bandwith ***** @@ -598,13 +616,18 @@ def load_configuration(self, parameters, mediator): # ***** Tcore ***** if "Tcore" in parameters.keys(): logger.info("Using user-defined Tcore...") + setattr( + self, + "Tcore", + parameters.get("Tcore") * DIMENSIONLESS, + ) else: logger.info("Using default Tcore...") - setattr( - self, - "Tcore", - parameters.get("Tcore", self.DEFAULT_CONFIG["Tcore"]), - ) + setattr( + self, + "Tcore", + self.DEFAULT_CONFIG["Tcore"], + ) # simple omega calculation, omega = pi * (photometric_aperture_radius)**2, where photometric_aperture_radius is in lambda/D @@ -624,13 +647,6 @@ def load_configuration(self, parameters, mediator): * self.Tcore.unit ) # core throughput at all separations (npix,npix,len(psftruncratio)) - photometric_aperture_throughput[ - self.DEFAULT_CONFIG["r"] < self.DEFAULT_CONFIG["minimum_IWA"] - ] = 0.0 # index 0 is the - photometric_aperture_throughput[ - self.DEFAULT_CONFIG["r"] > self.DEFAULT_CONFIG["maximum_OWA"] - ] = 0.0 - omega_lod = np.maximum(omega_lod, 0) photometric_aperture_throughput = np.maximum(photometric_aperture_throughput, 0) @@ -662,7 +678,6 @@ def load_configuration(self, parameters, mediator): # Load az_avg if the user provided it, otherwise use default setattr(self, "az_avg", parameters.get("az_avg", self.DEFAULT_CONFIG["az_avg"])) - if self.az_avg: # Radial profile projection (replaces 100x rotate-and-average) self.DEFAULT_CONFIG["Istar"] = ( @@ -691,8 +706,10 @@ def load_configuration(self, parameters, mediator): self.DEFAULT_CONFIG["Istar"] / self.DEFAULT_CONFIG["noisefloor_PPF"] ) - # ***** REPLACE PARAMETERS WITH USER-SPECIFIED ONES **** - # for coronagraph, allow replacement only in terms of scaling factors? - # NOTE what should be allowed to be replaced? - # Load parameters, use defaults if not provided - utils.fill_parameters(self, parameters, self.DEFAULT_CONFIG) + # ***** REPLACE PARAMETERS WITH USER-SPECIFIED ONES (no override allowed) **** + utils.fill_parameters( + self, + parameters, + self.DEFAULT_CONFIG, + locked_keys=self.LOCKED_KEYS, + ) diff --git a/src/pyEDITH/components/detectors.py b/src/pyEDITH/components/detectors.py index 9960cba..8babb2d 100644 --- a/src/pyEDITH/components/detectors.py +++ b/src/pyEDITH/components/detectors.py @@ -3,6 +3,7 @@ from .. import utils import astropy.units as u from ..units import * +from pyEDITH import parse_input class Detector(ABC): @@ -16,7 +17,7 @@ class Detector(ABC): ---------- pixscale_mas : float Detector pixel scale in milliarcseconds. - npix_multiplier : ndarray + npix_multiplier : scalar Number of detector pixels per image plane "pixel". DC : ndarray Dark current in counts per pixel per second. @@ -26,12 +27,16 @@ class Detector(ABC): Read time in seconds. CIC : ndarray Clock-induced charge in counts per pixel per photon count. - dQE: ndarray - Quantum efficiency of detector QE: ndarray + Quantum efficiency of detector + dQE: ndarray Effective QE due to degradation, cosmic ray effects, readout inefficiencies """ + # Keys that a user is NOT allowed to override for this detector mode. + # Subclasses override this. An empty set means "everything is user-editable". + LOCKED_KEYS: set = set() + @abstractmethod def load_configuration(self): """ @@ -99,9 +104,11 @@ class ToyModelDetector(Detector): Keyword for configuration selection (not used in toy model) """ + # In toy-model mode EVERY parameter is user-editable, so nothing is locked. + LOCKED_KEYS: set = set() DEFAULT_CONFIG = { "pixscale_mas": None, # Detector pixel scale in milliarcseconds. - "npix_multiplier": [1] + "npix_multiplier": 1 * DIMENSIONLESS, # Number of detector pixels per image plane "pixel". "DC": [3e-5] * DARK_CURRENT, # Dark current (counts pix^-1 s^-1, nlambd array) "RN": [0.0] * READ_NOISE, # Read noise (counts pix^-1 read^-1, nlambd array) @@ -145,8 +152,10 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: mediator : ObservatoryMediator Mediator object providing access to telescope and observation parameters """ + parameters = parse_input.parse_parameters(parameters) - # Calculate default detector pixel scale based on telescope + # Calculate default detector pixel scale based on telescope diameter + # Uses 0.5 * lambda/D at reference wavelength of 0.5 microns self.DEFAULT_CONFIG["pixscale_mas"] = ( 0.5 * lambda_d_to_arcsec( @@ -156,11 +165,8 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: ) ).to(MAS) - # Convert to arrays if lambda > 1: - - # Load parameters, use defaults if not provided - utils.fill_parameters(self, parameters, self.DEFAULT_CONFIG) - + # For IFS, the default config won't work. It needs to be propagated at every wavelength. + # Normalize list shapes just in case. array_params = [ "npix_multiplier", "DC", @@ -170,23 +176,19 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: "QE", "dQE", ] - - for param in array_params: - - if ( - len(getattr(self, param)) == 1 - and len(mediator.get_observation_parameter("wavelength").value) > 1 - ): - setattr( - self, - param, - getattr(self, param)[0] - * np.ones_like( - mediator.get_observation_parameter("wavelength").value - ), + self.DEFAULT_CONFIG.update( + { + key: parse_input.normalize_list_shapes( + self.DEFAULT_CONFIG, + key, + mediator.get_observation_parameter("nlambda"), ) + for key in array_params + } + ) - # # Convert to numpy array when appropriate + # Load parameters from user input, falling back to defaults if not provided + utils.fill_parameters(self, parameters, self.DEFAULT_CONFIG, self.LOCKED_KEYS) utils.convert_to_numpy_array(self, array_params) @@ -206,9 +208,23 @@ class EACDetector(Detector): Keyword for configuration selection (not used in EAC detector) """ + # In EAC mode these quantities are OWNED by the YAML files and must stay + # consistent with the loaded package. The user is NOT allowed to override + # them; if they try, fill_parameters will warn and keep the YAML value. + # Anything not listed here remains user-editable. + LOCKED_KEYS: set = { + "npix_multiplier", + "DC", + "RN", + "tread", + "CIC", + "QE", + "dQE", + } + DEFAULT_CONFIG = { "pixscale_mas": None, # Detector pixel scale in milliarcseconds. - "npix_multiplier": [1] + "npix_multiplier": 1 * DIMENSIONLESS, # Number of detector pixels per image plane "pixel". "DC": None, # Dark current (counts pix^-1 s^-1, nlambd array) "RN": None, # Read noise (counts pix^-1 read^-1, nlambd array) @@ -259,19 +275,17 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: AssertionError If the QE array contains NaN values after processing """ - # Check on possible modes - if parameters["observing_mode"] not in ["IFS", "IMAGER"]: - raise KeyError( - f"Unsupported observing mode: {parameters['observing_mode']}" - ) + parameters = parse_input.parse_parameters(parameters) from eacy import load_detector # ****** Update Default Config when necessary ****** - detector_params = load_detector(parameters["observing_mode"]).__dict__ + raw_detector_params = load_detector( + mediator.get_observation_parameter("observing_mode") + ).__dict__ - if parameters["observing_mode"] == "IMAGER": + if mediator.get_observation_parameter("observing_mode") == "IMAGER": wavelength_range = [ mediator.get_observation_parameter("wavelength") * (1 - 0.5 * mediator.get_coronagraph_parameter("bandwidth")), @@ -280,13 +294,15 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: ] detector_params = utils.average_over_bandpass( - detector_params, wavelength_range + raw_detector_params, wavelength_range ) - elif parameters["observing_mode"] == "IFS": + + elif mediator.get_observation_parameter("observing_mode") == "IFS": detector_params = utils.interpolate_over_bandpass( - detector_params, mediator.get_observation_parameter("wavelength") + raw_detector_params, mediator.get_observation_parameter("wavelength") ) + # scalar values projected to an array of length nlambda dc_arr = np.empty_like(mediator.get_observation_parameter("wavelength").value) dc_arr[mediator.get_observation_parameter("wavelength") < 1 * WAVELENGTH] = ( detector_params["dc_vis"] @@ -298,18 +314,6 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: dc_arr * DARK_CURRENT ) # Dark current (counts pix^-1 s^-1, nlambd array) - # self.DEFAULT_CONFIG["DC"] = ( - # np.array( - # [ - # ( - # float(detector_params["dc_vis"]) - # if mediator.get_observation_parameter("wavelength") < 1 * WAVELENGTH - # else float(detector_params["dc_nir"]) - # ) - # ] - # ) - # * DARK_CURRENT - # ) # Dark current (counts pix^-1 s^-1, nlambd array) rn_arr = np.empty_like(mediator.get_observation_parameter("wavelength").value) @@ -320,27 +324,18 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: detector_params["rn_nir"] ) self.DEFAULT_CONFIG["RN"] = rn_arr * READ_NOISE - # self.DEFAULT_CONFIG["RN"] = [ - # ( - # detector_params["rn_vis"] - # if mediator.get_observation_parameter("wavelength") < 1 * WAVELENGTH - # else detector_params["rn_nir"] - # ) - # ] * READ_NOISE + # array values binned at wavelength points must just be stacked + # combine the vis and nir qe arrays into a single array. + qe_arr = np.empty_like(mediator.get_observation_parameter("wavelength").value) if parameters["observing_mode"] == "IMAGER": - qe_arr = [ - ( - detector_params["qe_vis"] - if mediator.get_observation_parameter("wavelength") < 1 * WAVELENGTH - else detector_params["qe_nir"] - ) - ] + qe_arr[ + mediator.get_observation_parameter("wavelength") < 1 * WAVELENGTH + ] = detector_params["qe_vis"] + qe_arr[ + mediator.get_observation_parameter("wavelength") >= 1 * WAVELENGTH + ] = detector_params["qe_nir"] elif parameters["observing_mode"] == "IFS": - # combine the vis and nir qe arrays into a single array. - qe_arr = np.empty_like( - mediator.get_observation_parameter("wavelength").value - ) qe_arr[ mediator.get_observation_parameter("wavelength") < 1 * WAVELENGTH ] = detector_params["qe_vis"][ @@ -358,17 +353,10 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: assert ~np.isnan(np.sum(qe_arr)), "QE array contains NaN values" self.DEFAULT_CONFIG["QE"] = qe_arr * QUANTUM_EFFICIENCY - # self.DEFAULT_CONFIG["QE"] = [ - # ( - # detector_params["qe_vis"] - # if mediator.get_observation_parameter("wavelength") < 1 * WAVELENGTH - # else detector_params["qe_nir"] - # ) - # ] * QUANTUM_EFFICIENCY dQE_arr = np.empty_like(mediator.get_observation_parameter("wavelength").value) - # for now, hardcoded to 0.75 + # for now, hardcoded to 0.75 TODO change dQE_arr.fill(0.75) self.DEFAULT_CONFIG["dQE"] = dQE_arr * DIMENSIONLESS # self.DEFAULT_CONFIG["dQE"] = [ @@ -386,6 +374,7 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: ).to(MAS) # fill in tread and CIC to match the length of the wavelength array + # TODO read from YAML files self.DEFAULT_CONFIG["tread"] = ( np.full_like( mediator.get_observation_parameter("wavelength").value, @@ -402,34 +391,16 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: ) * CLOCK_INDUCED_CHARGE ) - # Load parameters, use defaults if not provided - utils.fill_parameters(self, parameters, self.DEFAULT_CONFIG) - - # ***** Convert to numpy array when appropriate ***** - array_params = [ - "npix_multiplier", - "DC", - "RN", - "tread", - "CIC", - "QE", - "dQE", - ] - utils.convert_to_numpy_array(self, array_params) - - # Ensure npix_multiplier has the same length as wavelength (the other ones are taken care of by EACy) - if len(self.npix_multiplier) != len( - mediator.get_observation_parameter("wavelength") - ): - self.npix_multiplier = ( - np.full_like( - mediator.get_observation_parameter("wavelength").value, - self.npix_multiplier[0], - dtype=np.float64, - ) - * DIMENSIONLESS - ) + utils.fill_parameters( + self, + parameters, + self.DEFAULT_CONFIG, + self.LOCKED_KEYS, + allow_override=set( + set(parameters.get("overrides", [])) & self.LOCKED_KEYS + ), # finds the keys that should be locked but that the user wants to override + ) # USED ONLY TO VALIDATE ETCs if "t_photon_count_input" in parameters.keys(): diff --git a/src/pyEDITH/components/telescopes.py b/src/pyEDITH/components/telescopes.py index 783b660..6c44905 100644 --- a/src/pyEDITH/components/telescopes.py +++ b/src/pyEDITH/components/telescopes.py @@ -3,6 +3,7 @@ from .. import utils import astropy.units as u from ..units import * +from pyEDITH import parse_input class Telescope(ABC): @@ -30,6 +31,10 @@ class Telescope(ABC): Effective throughput factor to budget for contamination. """ + # Keys that a user is NOT allowed to override for this telescope mode. + # Subclasses override this. An empty set means "everything is user-editable". + LOCKED_KEYS: set = set() + @abstractmethod def load_configuration(self): """ @@ -97,6 +102,9 @@ class ToyModelTelescope(Telescope): Keyword for configuration selection (not used in toy model) """ + # In toy-model mode EVERY parameter is user-editable, so nothing is locked. + LOCKED_KEYS: set = set() + DEFAULT_CONFIG = { "diameter": 7.87 * LENGTH, # circumscribed diameter of aperture (m, scalar) "unobscured_area": (1.0 - 0.121), # unobscured area (percentage,scalar) @@ -140,14 +148,16 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: mediator : ObservatoryMediator Mediator object providing access to other simulation components """ + parameters = parse_input.parse_parameters(parameters) # Load parameters, use defaults if not provided - utils.fill_parameters(self, parameters, self.DEFAULT_CONFIG) + utils.fill_parameters(self, parameters, self.DEFAULT_CONFIG, self.LOCKED_KEYS) # Convert to numpy array when appropriate array_params = [ "telescope_optical_throughput", ] + utils.convert_to_numpy_array(self, array_params) # Derived parameters @@ -171,6 +181,18 @@ class EACTelescope(Telescope): Keyword identifying the specific telescope model to load from EACy files """ + # In EAC mode these quantities are OWNED by the YAML files and must stay + # consistent with the loaded package. The user is NOT allowed to override + # them; if they try, fill_parameters will warn and keep the YAML value. + # Anything not listed here remains user-editable. + LOCKED_KEYS: set = { + "diameter", + "unobscured_area", + "T_contamination", + "temperature", + "telescope_optical_throughput", + } + DEFAULT_CONFIG = { "diameter": None, # circumscribed diameter of aperture (m, scalar) "unobscured_area": 1.0, # unobscured area (percentage,scalar) ### NOTE default for now @@ -224,12 +246,7 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: KeyError If the observing mode is not 'IMAGER' or 'IFS' """ - - # Check on possible modes - if parameters["observing_mode"] not in ["IFS", "IMAGER"]: - raise KeyError( - f"Unsupported observing mode: {parameters['observing_mode']}" - ) + parameters = parse_input.parse_parameters(parameters) from eacy import load_telescope @@ -237,8 +254,7 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: # Load parameters from YAML files telescope_params = load_telescope(self.keyword).__dict__ - - if parameters["observing_mode"] == "IMAGER": + if mediator.get_observation_parameter("observing_mode") == "IMAGER": wavelength_range = [ mediator.get_observation_parameter("wavelength") * (1 - 0.5 * mediator.get_coronagraph_parameter("bandwidth")), @@ -250,7 +266,7 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: telescope_params, wavelength_range ) - elif parameters["observing_mode"] == "IFS": + elif mediator.get_observation_parameter("observing_mode") == "IFS": # interpolate telescope throughput onto native wavelength grid telescope_params = utils.interpolate_over_bandpass( telescope_params, mediator.get_observation_parameter("wavelength") @@ -273,7 +289,15 @@ def load_configuration(self, parameters: dict, mediator: object) -> None: # the coronagraph module needs the telescope module to be initialized first to get the telescope diameter # Load parameters, use defaults if not provided - utils.fill_parameters(self, parameters, self.DEFAULT_CONFIG) + utils.fill_parameters( + self, + parameters, + self.DEFAULT_CONFIG, + self.LOCKED_KEYS, + allow_override=set( + set(parameters.get("overrides", [])) & self.LOCKED_KEYS + ), # finds the keys that should be locked but that the user wants to override + ) # Derived parameters # effective collecting area of telescope (m^2) # scalar diff --git a/src/pyEDITH/exposure_time_calculator.py b/src/pyEDITH/exposure_time_calculator.py index 75a5b07..9448740 100644 --- a/src/pyEDITH/exposure_time_calculator.py +++ b/src/pyEDITH/exposure_time_calculator.py @@ -652,766 +652,611 @@ def calculate_exposure_time_or_snr( raise ValueError("Invalid mode. Use 'exposure_time' or 'signal_to_noise'.") observation.validation_variables = {} - observation.photon_counts = { - "CRp": np.empty(observation.nlambd), - "CRbs": np.empty(observation.nlambd), - "CRbz": np.empty(observation.nlambd), - "CRbez": np.empty(observation.nlambd), - "CRbbin": np.empty(observation.nlambd), - "CRbth": np.empty(observation.nlambd), - "CRbd": np.empty(observation.nlambd), - "CRnf_s": np.empty(observation.nlambd), - "CRnf_ez": np.empty(observation.nlambd), - "CRnf": np.empty(observation.nlambd), - "CRb": np.empty(observation.nlambd), - "omega_lod": np.empty(observation.nlambd), - "PPF_ez": np.empty(observation.nlambd), - } - - for ilambd in range(observation.nlambd): - - # Take the lesser of the desired bandwidth - # and what coronagraph allows - if observatory.observing_mode == "IMAGER": - deltalambda_nm = ( - np.min( - [ - (observation.wavelength[ilambd].to_value(NM)) - / observatory.coronagraph.coronagraph_spectral_resolution, - observatory.coronagraph.bandwidth - * (observation.wavelength[ilambd].to_value(NM)), - ] - ) - * NM - ) # nanometers - if ( - observatory.coronagraph.bandwidth - * observation.wavelength[ilambd].to_value(NM) - >= observation.wavelength[ilambd].to_value(NM) - / observatory.coronagraph.coronagraph_spectral_resolution - ): - logger.warning( - "Bandwidth larger than what the coronagraph allows. Selecting widest possible bandwidth..." - ) - elif observatory.observing_mode == "IFS": - # the effective bandwidth is the width of the spectral element - deltalambda_nm = observation.delta_wavelength[ilambd].to(NM) - else: - raise ValueError("Invalid observation mode. Choose 'IMAGER' or 'IFS'.") - - # Calculate λ/D (dimensionless) - lod = 1 * LAMBDA_D - - # Convert to radians - # NOTE: using LENGTH here ensures that if we change the value of the unit, - # this is still dimensionless - lod_rad = lambda_d_to_radians( - lod, - observation.wavelength[ilambd].to(LENGTH), - observatory.telescope.diameter.to(LENGTH), - ) + observation.photon_counts = {} - # Convert to arcseconds - lod_arcsec = lod_rad.to(ARCSEC) + # ----------------------------------------------------------------------- + # Compute all wavelength-dependent scalars as 1D arrays + # ----------------------------------------------------------------------- - area_cm2 = observatory.telescope.Area.to(AREA) + # --- Telescope collecting area (wavelength-independent) --- + area_cm2 = observatory.telescope.Area.to(AREA) - detpixscale_lod = arcsec_to_lambda_d( - observatory.detector.pixscale_mas.to(ARCSEC), - observation.wavelength[ilambd].to(LENGTH), - observatory.telescope.diameter.to(LENGTH), - ) # LAMBDA_D units + # --- Bandwidth per wavelength channel --- + if observatory.observing_mode == "IMAGER": + coronagraph_limit = ( + observation.wavelength.to_value(NM) + / observatory.coronagraph.coronagraph_spectral_resolution + ) + bandwidth_limit = ( + observatory.coronagraph.bandwidth * observation.wavelength.to_value(NM) + ) + # Warn if bandwidth is wider than what the coronagraph allows + if np.any(bandwidth_limit >= coronagraph_limit): + logger.warning( + "Bandwidth larger than what the coronagraph allows for one or more " + "wavelength channels. Selecting widest possible bandwidth..." + ) + deltalambda_nm = np.minimum(coronagraph_limit, bandwidth_limit) * NM + + elif observatory.observing_mode == "IFS": + deltalambda_nm = observation.delta_wavelength.to(NM) + else: + raise ValueError("Invalid observation mode. Choose 'IMAGER' or 'IFS'.") + + # --- λ/D in radians and arcseconds (one value per wavelength) --- + lod = 1 * LAMBDA_D + lod_rad_arr = lambda_d_to_radians( + lod, + observation.wavelength.to(LENGTH), + observatory.telescope.diameter.to(LENGTH), + ) # shape (nlambd,), units rad + lod_rad_arr = u.Quantity(np.atleast_1d(lod_rad_arr.value), lod_rad_arr.unit) + lod_arcsec_arr = lod_rad_arr.to(ARCSEC) # shape (nlambd,) + + # --- Detector pixel scale in λ/D (one value per wavelength) --- + detpixscale_lod_arr = arcsec_to_lambda_d( + observatory.detector.pixscale_mas.to(ARCSEC), + observation.wavelength.to(LENGTH), + observatory.telescope.diameter.to(LENGTH), + ) # shape (nlambd,), units LAMBDA_D + detpixscale_lod_arr = u.Quantity( + np.atleast_1d(detpixscale_lod_arr.value), detpixscale_lod_arr.unit + ) - stellar_diam_lod = arcsec_to_lambda_d( - scene.stellar_angular_diameter_arcsec, - observation.wavelength[ilambd].to(LENGTH), - observatory.telescope.diameter.to(LENGTH), - ) # LAMBDA_D units + # --- Planet pixel position in the coronagraph image (one per wavelength) --- + # pixscale_rad: [LAMBDA_D] * [rad/LAMBDA_D] = [rad/pix] + pixscale_rad_arr = observatory.coronagraph.pixscale * lod_rad_arr # shape (nlambd,) + oneopixscale_arcsec_arr = (1 * PIXEL) / pixscale_rad_arr.to( + ARCSEC + ) # shape (nlambd,) [pix/arcsec] + + ix_arr = ( + scene.xp * oneopixscale_arcsec_arr + observatory.coronagraph.xcenter + ).value # shape (nlambd,), float pixel indices + iy_arr = ( + scene.yp * oneopixscale_arcsec_arr + observatory.coronagraph.ycenter + ).value # shape (nlambd,), float pixel indices + + ix_arr = np.atleast_1d(ix_arr) + iy_arr = np.atleast_1d(iy_arr) + + # Integer (floored) pixel indices for coronagraph map lookups + ix_int = np.floor(ix_arr).astype(int) # shape (nlambd,) + iy_int = np.floor(iy_arr).astype(int) # shape (nlambd,) + + # --- Pixel validity mask (checks if the planet is within coronagraph pixels) --- + npix = observatory.coronagraph.npix + pixel_valid_mask = np.atleast_1d( + (ix_int >= 0) & (ix_int < npix) & (iy_int >= 0) & (iy_int < npix) + ) # shape (nlambd,), bool + + # Clamp indices so fancy indexing never goes out of bounds on invalid channels + # (values for those channels will be masked out anyway) + ix_safe = np.clip(ix_int, 0, npix - 1) + iy_safe = np.clip(iy_int, 0, npix - 1) + + # --- Vectorized coronagraph map lookups [shape (nlambd,)] --- + + # NOTE: noisefloor_interp: technically the Y axis + # is rows and the X axis is columns, + # that is why they are inverted + # NOTE: Evaluate if int(round(iy)) is better than + # np.floor. Kept np.floor for consistency + Istar_at_planet = np.atleast_1d( + observatory.coronagraph.Istar[iy_safe, ix_safe] + ) # dimensionless + skytrans_at_planet = np.atleast_1d( + observatory.coronagraph.skytrans[iy_safe, ix_safe] + ) # dimensionless + noisefloor_at_planet = np.atleast_1d( + observatory.coronagraph.noisefloor[iy_safe, ix_safe] + ) # dimensionless + + # NOTE: npsfratios == 1 in practice; iratio = 0 throughout. + iratio = 0 # only one psf truncation ratio supported + + # omega_lod at the planet position, shape (nlambd,) + omega_lod_at_planet = u.Quantity( + np.atleast_1d( + observatory.coronagraph.omega_lod[iy_safe, ix_safe, iratio].value + ), + observatory.coronagraph.omega_lod.unit, + ) + # photometric aperture throughput at the planet position, shape (nlambd,) + Upsilon_at_planet = np.atleast_1d( + observatory.coronagraph.photometric_aperture_throughput[ + iy_safe, ix_safe, iratio + ] + ) - """ - WE DO NOT INTERPOLATE ANYMORE, INTERPOLATION IS DONE WITHIN YIPPY - # Interpolate Istar, noisefloor based on angular diameter - # of the star (depends on the target). It reduces dimensionality - # from 3D arrays [npix,npix,angdiam] to 2D arrays [npix,npix]. - # The interpolation is done based on the value of - # stellar_diam_lod (dependence on istar) + # ----------------------------------------------------------------------- + # Measure coronagraph performance close to IWA + # ----------------------------------------------------------------------- + # Compute per wavelength via the existing helper (now vectorized over nlambd + # because measure_coronagraph_performance_at_IWA accepts scalar oneopixscale; + # we call it in a list-comprehension to preserve the existing function signature) #TODO update after validation - Istar_interp, noisefloor_interp = interpolate_arrays( - observatory.coronagraph.Istar, - observatory.coronagraph.noisefloor, - observatory.coronagraph.npix, - observatory.coronagraph.ndiams, - stellar_diam_lod, - observatory.coronagraph.angdiams, - ) - """ - - # Measure coronagraph performance at each IWA - pixscale_rad = observatory.coronagraph.pixscale * lambda_d_to_radians( - lod, - observation.wavelength[ilambd].to(LENGTH), - observatory.telescope.diameter.to(LENGTH), - ) # going from LAMBDA_D to radians - - oneopixscale_arcsec = 1 * PIXEL / pixscale_rad.to(ARCSEC) - - # Measure coronagraph performance at each IWA - ( - det_sep_pix, - det_sep, - det_Istar, - det_skytrans, - det_photometric_aperture_throughput, - det_omega_lod, - ) = measure_coronagraph_performance_at_IWA( - # observatory.coronagraph.psf_trunc_ratio, # this is no longer used in the function + det_results = [ + measure_coronagraph_performance_at_IWA( observatory.coronagraph.photometric_aperture_throughput, observatory.coronagraph.Istar, observatory.coronagraph.skytrans, observatory.coronagraph.omega_lod, - observatory.coronagraph.npix, + npix, observatory.coronagraph.xcenter, observatory.coronagraph.ycenter, - oneopixscale_arcsec, + oneopixscale_arcsec_arr[ilambd], ) + for ilambd in range(observation.nlambd) + ] + + # Convert to Quantity arrays + det_sep_pix_arr = u.Quantity([r[0] for r in det_results]) + det_sep_arr = u.Quantity([r[1] for r in det_results]) + det_Istar_arr = u.Quantity([r[2] for r in det_results]) + det_skytrans_arr = u.Quantity([r[3] for r in det_results]) + det_photometric_aperture_throughput_arr = u.Quantity([r[4] for r in det_results]) + det_omega_lod_arr = u.Quantity([r[5] for r in det_results]) - if ETC_validation: - logger.debug("Fixing det_npix for validation...") - - det_npix = observatory.detector.det_npix_input * PIXEL - else: - # Calculate det_npix - det_npix = ( - observatory.detector.npix_multiplier[ilambd] - * det_omega_lod - / (detpixscale_lod**2) - * observatory.coronagraph.nchannels - ) * PIXEL # number of pixels in detector - - # Here we calculate detector noise, as it may depend on count rates - # We don't know the count rates yet, so we make estimates based on - # values near the IWA - - # Detector noise from signal itself (we budget for 10x - # the planet count rate for the minimum detectable planet) - det_CRp = calculate_CRp( - scene.F0[ilambd], - scene.Fs_over_F0[ilambd], - 10 * scene.Fp_min_over_Fs, + # --- Number of detector pixels + if ETC_validation: + logger.debug("Fixing det_npix for validation...") + + det_npix = observatory.detector.det_npix_input * PIXEL + else: + + # Number of detector pixels (wavelength-independent scalar) + det_npix = ( + observatory.detector.npix_multiplier + * det_omega_lod_arr + / (detpixscale_lod_arr**2) + * observatory.coronagraph.nchannels + ) * PIXEL + + # --- det_* quantities used for t_photon_count estimate --- + # (measured at the IWA, wavelength-dependent because pixscale changes) + # Here we calculate detector noise, as it may depend on count rates + # We don't know the count rates yet, so we make estimates based on + # values near the IWA + + # Detector noise from signal itself (we budget for 10x + # the planet count rate for the minimum detectable planet) + det_CRp_arr = calculate_CRp( + scene.F0, + scene.Fs_over_F0, + 10 * scene.Fp_min_over_Fs, + area_cm2, + det_photometric_aperture_throughput_arr, + observatory.total_throughput, + deltalambda_nm, + observatory.coronagraph.nchannels, + ) + + det_CRbs_arr = calculate_CRbs( + scene.F0, + scene.Fs_over_F0, + det_Istar_arr, + area_cm2, + observatory.coronagraph.pixscale, + observatory.total_throughput, + deltalambda_nm, + observatory.coronagraph.nchannels, + ) + + det_CRbz_arr = calculate_CRbz( + scene.F0, + scene.Fzodi_list, + lod_arcsec_arr, + det_skytrans_arr, + area_cm2, + observatory.total_throughput, + deltalambda_nm, + observatory.coronagraph.nchannels, + ) + + det_CRbez_arr = calculate_CRbez( + scene.F0, + scene.Fexozodi_list, + lod_arcsec_arr, + det_skytrans_arr, + area_cm2, + observatory.total_throughput, + deltalambda_nm, + observatory.coronagraph.nchannels, + scene.dist, + det_sep_arr, + ) + + det_CRbbin_arr = calculate_CRbbin( + scene.F0, + scene.Fbinary_list, + det_skytrans_arr, + area_cm2, + observatory.total_throughput, + deltalambda_nm, + observatory.coronagraph.nchannels, + ) + + det_CRbth_arr = ( + calculate_CRbth( + observation.wavelength, area_cm2, - det_photometric_aperture_throughput, - observatory.total_throughput[ilambd], deltalambda_nm, - observatory.coronagraph.nchannels, + observatory.telescope.temperature, + lod_rad_arr, + observatory.epswarmTrcold, + observatory.detector.QE, + observatory.detector.dQE, ) + * det_omega_lod_arr + ) + + det_CR_arr = ( + det_CRp_arr + + det_CRbs_arr + + det_CRbz_arr + + det_CRbez_arr + + det_CRbbin_arr + + det_CRbth_arr + ) + + # --- t_photon_count estimate --- + t_photon_count_arr = calculate_t_photon_count(det_npix, det_CR_arr) + if ETC_validation: + logger.debug("Fixing t_photon_count for validation...") + t_photon_count_arr = observatory.detector.t_photon_count_input + + # ----------------------------------------------------------------------- + # Photometric aperture size check mask + # ----------------------------------------------------------------------- + # omega_lod must be larger than one detector pixel solid angle + phot_aperture_valid_mask = np.atleast_1d( + omega_lod_at_planet > detpixscale_lod_arr**2 + ) + + # ----------------------------------------------------------------------- + # Vectorized count rate calculations at the planet position + # ----------------------------------------------------------------------- + + # --- PLANET COUNT RATE --- + CRp_arr = calculate_CRp( + scene.F0, + scene.Fs_over_F0, + scene.Fp_over_Fs, + area_cm2, + Upsilon_at_planet, + observatory.total_throughput, + deltalambda_nm, + observatory.coronagraph.nchannels, + ) + observation.photon_counts["CRp"] = CRp_arr.value + + # --- STELLAR LEAKAGE --- + CRbs_arr = calculate_CRbs( + scene.F0, + scene.Fs_over_F0, + Istar_at_planet, + area_cm2, + observatory.coronagraph.pixscale, + observatory.total_throughput, + deltalambda_nm, + observatory.coronagraph.nchannels, + ) + observation.photon_counts["CRbs"] = CRbs_arr.value * omega_lod_at_planet.value + # --- ZODIACAL LIGHT --- + CRbz_arr = calculate_CRbz( + scene.F0, + scene.Fzodi_list, + lod_arcsec_arr, + skytrans_at_planet, + area_cm2, + observatory.total_throughput, + deltalambda_nm, + observatory.coronagraph.nchannels, + ) + observation.photon_counts["CRbz"] = CRbz_arr.value * omega_lod_at_planet.value + + # --- EXOZODIACAL LIGHT --- + CRbez_arr = calculate_CRbez( + scene.F0, + scene.Fexozodi_list, + lod_arcsec_arr, + skytrans_at_planet, + area_cm2, + observatory.total_throughput, + deltalambda_nm, + observatory.coronagraph.nchannels, + scene.dist, + scene.separation, + ) + + observation.photon_counts["CRbez"] = CRbez_arr.value * omega_lod_at_planet.value + observation.photon_counts["omega_lod"] = omega_lod_at_planet.value + + # --- BINARY / NEIGHBORING STARS --- + CRbbin_arr = calculate_CRbbin( + scene.F0, + scene.Fbinary_list, + skytrans_at_planet, + area_cm2, + observatory.total_throughput, + deltalambda_nm, + observatory.coronagraph.nchannels, + ) + + observation.photon_counts["CRbbin"] = CRbbin_arr.value * omega_lod_at_planet.value + + # --- THERMAL BACKGROUND --- + CRbth_arr = calculate_CRbth( + observation.wavelength, + area_cm2, + deltalambda_nm, + observatory.telescope.temperature, + lod_rad_arr, + observatory.epswarmTrcold, + observatory.detector.QE, + observatory.detector.dQE, + ) + observation.photon_counts["CRbth"] = CRbth_arr.value * omega_lod_at_planet.value + + CRbd_arr = calculate_CRbd( + det_npix, + observatory.detector.DC, + observatory.detector.RN, + observatory.detector.tread, + observatory.detector.CIC, + t_photon_count_arr, + ) + + observation.photon_counts["CRbd"] = CRbd_arr.value - det_CRbs = calculate_CRbs( - scene.F0[ilambd], - scene.Fs_over_F0[ilambd], - det_Istar, + # --- NOISE FLOOR --- + # NOTE when calculating the SNR, we set snr_for_nf to 1 to calculate the noise + # factor ratio (i.e. we assume SNR =1 so that we can use it for the snr calculation later) + snr_for_nf = ( + observation.SNR if mode == "exposure_time" else np.ones(observation.nlambd) + ) + + CRnf_s_arr = ( + calculate_CRnf( + scene.F0, + scene.Fs_over_F0, area_cm2, observatory.coronagraph.pixscale, - observatory.total_throughput[ilambd], + observatory.total_throughput, deltalambda_nm, observatory.coronagraph.nchannels, + snr_for_nf, + noisefloor_at_planet, ) + * omega_lod_at_planet + ) + observation.photon_counts["CRnf_s"] = CRnf_s_arr.value - det_CRbz = calculate_CRbz( - scene.F0[ilambd], - scene.Fzodi_list[ilambd], - lod_arcsec, - det_skytrans, - area_cm2, - observatory.total_throughput[ilambd], - deltalambda_nm, - observatory.coronagraph.nchannels, + CRnf_ez_arr = calculate_CRnf_ez( + CRbez_arr * omega_lod_at_planet.value, + snr_for_nf, + scene.ez_PPF, + ) + observation.photon_counts["CRnf_ez"] = CRnf_ez_arr.value + + CRnf_arr = np.sqrt(CRnf_s_arr**2 + CRnf_ez_arr**2) + observation.photon_counts["CRnf"] = CRnf_arr.value + + # ----------------------------------------------------------------------- + # TOTAL BACKGROUND + # ----------------------------------------------------------------------- + + # Parameters that need to be multiplied by omega_lod + CRb_arr = ( + CRbs_arr + CRbz_arr + CRbez_arr + CRbbin_arr + CRbth_arr + ) * omega_lod_at_planet + observation.photon_counts["CRb"] = CRb_arr.value + + # Add detector noise + CRb_arr = CRb_arr + CRbd_arr + observation.photon_counts["CRb+det"] = CRb_arr.value + + # ----------------------------------------------------------------------- + # Compute exposure time or SNR + # ----------------------------------------------------------------------- + if mode == "exposure_time": + cp_arr = ( + (CRp_arr + observation.CRb_multiplier * CRb_arr) + / (CRp_arr * CRp_arr - CRnf_arr * CRnf_arr) + * u.electron ) - det_CRbez = calculate_CRbez( - scene.F0[ilambd], - scene.Fexozodi_list[ilambd], - lod_arcsec, - det_skytrans, - area_cm2, - observatory.total_throughput[ilambd], - deltalambda_nm, - observatory.coronagraph.nchannels, - scene.dist, - det_sep, + exptime_arr = ( + observation.SNR**2 * cp_arr * observatory.telescope.toverhead_multi + + observatory.telescope.toverhead_fixed + ).to(u.s) + + # Enforce limits + exptime_arr = u.Quantity( + np.atleast_1d(np.where(exptime_arr < 0, np.inf, exptime_arr.value)), u.s + ) # set all negative values (if any) to infinity + exptime_arr = u.Quantity( + np.atleast_1d( + np.where(exptime_arr > observation.td_limit, np.inf, exptime_arr.value) + ), + u.s, + ) # set all values higher than the limit to infinity + + if observatory.coronagraph.nrolls != 1: + # multiply by number of required rolls to + # achieve 360 deg coverage + # (after tlimit enforcement) + exptime_arr = exptime_arr * observatory.coronagraph.nrolls + + observation.exptime = exptime_arr.decompose() + + elif mode == "signal_to_noise": + + # cp_arr not used in this mode. Note: This will make the science time in + # validation variables be 0! + cp_arr = 0 + time_factors = ( + observation.obstime / observatory.coronagraph.nrolls + - observatory.telescope.toverhead_fixed + ) / ( + observatory.telescope.toverhead_multi + * (CRp_arr + observation.CRb_multiplier * CRb_arr) ) - - det_CRbbin = calculate_CRbbin( - scene.F0[ilambd], - scene.Fbinary_list[ilambd], - det_skytrans, - area_cm2, - observatory.total_throughput[ilambd], - deltalambda_nm, - observatory.coronagraph.nchannels, + time_factors = time_factors.decompose() + + # UNITS: + # ([s]/[]-[s])/([electron/s]+[]*[electron/s]) + # [s]/[electron/s]=[s^2/electron] + + # Signal-to-noise + # observation.fullsnr[ilambd] = ( + # np.sqrt( + # (time_factors * CRp**2) + # / (1 * ELECTRON + time_factors * CRnf**2) + # ) + # * DIMENSIONLESS + # ) + # rewrote the above equation to properly evaluate the SNR when time = inf + + fullsnr_arr = u.Quantity( + np.atleast_1d( + np.sqrt(CRp_arr.value**2 / (1 / time_factors.value + CRnf_arr.value**2)) + ), + DIMENSIONLESS, + ) + observation.fullsnr = fullsnr_arr + + # UNITS: + # ([s^2/electron]*[electron/s]^2)/([electron]+[s^2/electron]*[electron/s]^2)= + # [electron]/[electron] = [] + + # ----------------------------------------------------------------------- + # Apply validity masks: set invalid channels to infinity + # ----------------------------------------------------------------------- + + # Channels where the planet falls outside the coronagraph pixel grid + out_of_bounds = np.atleast_1d(~pixel_valid_mask) + if np.any(out_of_bounds): + logger.error( + "Planet outside coronagraph YIP image for one or more wavelength " + "channels. Hardcoded infinity results." ) - det_CRbth = ( - calculate_CRbth( - observation.wavelength[ilambd], - area_cm2, - deltalambda_nm, - observatory.telescope.temperature, - lod_rad, - observatory.epswarmTrcold[ilambd], - observatory.detector.QE[ilambd,], - observatory.detector.dQE[ilambd,], - ) - * det_omega_lod + # Channels where the photometric aperture is too small + small_aperture = np.atleast_1d(pixel_valid_mask & ~phot_aperture_valid_mask) + if np.any(small_aperture): + logger.error( + "Photometric aperture is not large enough for one or more wavelength " + "channels. Hardcoded infinity results." ) - det_CR = det_CRp + det_CRbs + det_CRbz + det_CRbez + det_CRbbin + det_CRbth - - # Calculate position of the planet in the image - # (from l/D to pixel) - ix = ( - scene.xp * oneopixscale_arcsec + observatory.coronagraph.xcenter - ).value # this is the "index" of the position in pixel, i.e. the number of the pixel where the planet is - iy = ( - scene.yp * oneopixscale_arcsec + observatory.coronagraph.ycenter - ).value # this is the "index" of the position in pixel, i.e. the number of the pixel where the planet is - - # Calculate separation (from arcsec to l/D) - sp_lod = arcsec_to_lambda_d( - scene.separation, - observation.wavelength[ilambd].to(LENGTH), - observatory.telescope.diameter.to(LENGTH), + # Channels below the noise floor (exposure_time mode only) + below_noise_floor = np.atleast_1d( + (pixel_valid_mask & phot_aperture_valid_mask & (CRp_arr <= CRnf_arr)) + if mode == "exposure_time" + else np.zeros(observation.nlambd, dtype=bool) + ) + if np.any(below_noise_floor): + logger.error( + "Count rate of the planet smaller than the noise floor for one or more " + "wavelength channels. Hardcoded infinity results." ) - # If planet is within the boundaries of the observatory.coronagraph - # simulation and hard IWA/OWA cutoffs... - if ( - (ix >= 0) # check that x pixel is positive - and ( - ix < observatory.coronagraph.npix - ) # check that it is less than the maximum pixel number - and (iy >= 0) # check that the y pixel is positive - and ( - iy < observatory.coronagraph.npix - ) # check that it is less than the maximum pixel number - and ( - sp_lod > observatory.coronagraph.minimum_IWA - ) # check that the separation in l/D is more than the minimum allowed IWA - and ( - sp_lod < observatory.coronagraph.maximum_OWA - ) # check that the separation in l/D is less than the maximum allowed OWA - ): - - for iratio in np.arange(observatory.coronagraph.npsfratios): - # First we just calculate CRp and CRnoisefloor - # to see if CRp > CRnoisefloor - - # PLANET COUNT RATE CRP - CRp = calculate_CRp( - scene.F0[ilambd], - scene.Fs_over_F0[ilambd], - scene.Fp_over_Fs[ilambd], - area_cm2, - observatory.coronagraph.photometric_aperture_throughput[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ], - observatory.total_throughput[ilambd], - deltalambda_nm, - observatory.coronagraph.nchannels, - ) - observation.photon_counts["CRp"][ilambd] = CRp.value - - # Calculate CRbez; this must happen here in order to estimate the exozodi noisefloor - CRbez = calculate_CRbez( - scene.F0[ilambd], - scene.Fexozodi_list[ilambd], - lod_arcsec, - observatory.coronagraph.skytrans[ - int(np.floor(iy)), int(np.floor(ix)) - ], - area_cm2, - observatory.total_throughput[ilambd], - deltalambda_nm, - observatory.coronagraph.nchannels, - scene.dist, - scene.separation, - ) - observation.photon_counts["CRbez"][ilambd] = ( - CRbez.value - * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ].value - ).item() - - observation.photon_counts["omega_lod"][ilambd] = ( - observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ].value - ) - - # NOISE FLOOR CRNF - if mode == "exposure_time": - # NOISE FLOOR CRNF - CRnf_s = calculate_CRnf( - scene.F0[ilambd], - scene.Fs_over_F0[ilambd], - area_cm2, - observatory.coronagraph.pixscale, - observatory.total_throughput[ilambd], - deltalambda_nm, - observatory.coronagraph.nchannels, - observation.SNR[ilambd], - observatory.coronagraph.noisefloor[ - int(np.floor(iy)), int(np.floor(ix)) - ], - ) - - # calculate the exozodi noisefloor to account for imperfect exozodi removal - CRnf_ez = calculate_CRnf_ez( - CRbez - * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ].value, - observation.SNR[ilambd], - scene.ez_PPF[ilambd], - ) - - elif mode == "signal_to_noise": - # NOTE THIS TIME THIS IS JUST THE NOISE - # FACTOR RATIO (i.e. we assume SNR =1 so - # that we can use it for the snr calculation later) - - CRnf_s = calculate_CRnf( - scene.F0[ilambd], - scene.Fs_over_F0[ilambd], - area_cm2, - observatory.coronagraph.pixscale, - observatory.total_throughput[ilambd], - deltalambda_nm, - observatory.coronagraph.nchannels, - 1, - observatory.coronagraph.noisefloor[ - int(np.floor(iy)), int(np.floor(ix)) - ], - ) - CRnf_ez = calculate_CRnf_ez(CRbez, 1, scene.ez_PPF[ilambd]) - - # multiply by omega at that point - CRnf_s *= observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ] - observation.photon_counts["CRnf_s"][ilambd] = CRnf_s.value - - CRnf_ez *= observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ] - observation.photon_counts["CRnf_ez"][ilambd] = CRnf_ez.value.item() - - # total noisefloor - CRnf = np.sqrt(CRnf_s**2 + CRnf_ez**2) - observation.photon_counts["CRnf"][ilambd] = CRnf.value.item() - - # NOTE: noisefloor_interp: technically the Y axis - # is rows and the X axis is columns, - # that is why they are inverted - # NOTE: Evaluate if int(round(iy)) is better than - # np.floor. Kept np.floor for consistency - - # Check if photometric aperture is large enough: - if ( - observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ] - > detpixscale_lod**2 - ): - - # (for exposure time mode) Check if it's above the noise floor - if mode == "exposure_time" and CRp <= CRnf: - logger.error( - "Count rate of the planet smaller than the noise floor. Hardcoded infinity results." - ) - - observation.exptime[ilambd] = np.inf - continue # Skip to next iteration - - # Calculate the rest of the background noise - - # NOTE: WHEN CALCULATING THE COUNT RATES, - # WE NEED TO MULTIPLY BY OMEGA_LOD i.e. - # THE SOLID ANGLE OF THE PHOTOMETRIC APERTURE - - # Calculate CRbs - CRbs = calculate_CRbs( - scene.F0[ilambd], - scene.Fs_over_F0[ilambd], - observatory.coronagraph.Istar[ - int(np.floor(iy)), int(np.floor(ix)) - ], - area_cm2, - observatory.coronagraph.pixscale, - observatory.total_throughput[ilambd], - deltalambda_nm, - observatory.coronagraph.nchannels, - ) - observation.photon_counts["CRbs"][ilambd] = ( - CRbs.value - * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ].value - ) - - # Calculate CRbz - CRbz = calculate_CRbz( - scene.F0[ilambd], - scene.Fzodi_list[ilambd], - lod_arcsec, - observatory.coronagraph.skytrans[ - int(np.floor(iy)), int(np.floor(ix)) - ], - area_cm2, - observatory.total_throughput[ilambd], - deltalambda_nm, - observatory.coronagraph.nchannels, - ) - observation.photon_counts["CRbz"][ilambd] = ( - CRbz.value - * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ].value - ).item() - - # Calculate CRbbin - CRbbin = calculate_CRbbin( - scene.F0[ilambd], - scene.Fbinary_list[ilambd], - observatory.coronagraph.skytrans[ - int(np.floor(iy)), int(np.floor(ix)) - ], - area_cm2, - observatory.total_throughput[ilambd], - deltalambda_nm, - observatory.coronagraph.nchannels, - ) - observation.photon_counts["CRbbin"][ilambd] = ( - CRbbin.value - * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ].value - ) - - # Calculate CRbd - t_photon_count = calculate_t_photon_count( - det_npix, - det_CR, - ) - if ETC_validation: - logger.debug("Fixing t_photon_count for validation...") - # the ETC validation (Stark+2025) fixed the frame rate - # t_photon_count = 1 / (det_CRp.value) * SECOND / FRAME - t_photon_count = observatory.detector.t_photon_count_input - - CRbd = calculate_CRbd( - det_npix, - observatory.detector.DC[ilambd], - observatory.detector.RN[ilambd], - observatory.detector.tread[ilambd], - observatory.detector.CIC[ilambd], - t_photon_count, - ) - - observation.photon_counts["CRbd"][ilambd] = CRbd.value.item() - - CRbth = calculate_CRbth( - observation.wavelength[ilambd], - area_cm2, - deltalambda_nm, - observatory.telescope.temperature, - lod_rad, - observatory.epswarmTrcold[ilambd], - observatory.detector.QE[ilambd,], - observatory.detector.dQE[ilambd,], - ) - observation.photon_counts["CRbth"][ilambd] = ( - CRbth.value - * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ].value - ) - - # TOTAL BACKGROUND NOISE - CRb = ( - CRbs + CRbz + CRbez + CRbbin + CRbth - ) * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), iratio - ] - observation.photon_counts["CRb"][ilambd] = CRb.value.item() - - # Add detector noise - CRb += CRbd - - # EXPOSURE TIME - if mode == "exposure_time": - # count rate term - # NOTE this includes the systematic noise floor - # term a la Bijan Nemati - cp = ( - (CRp + observation.CRb_multiplier * CRb) - / (CRp * CRp - CRnf * CRnf) - * u.electron - ) - - # UNITS: - # ([electron/s]+[electron/s])/([electron/s]^2+[electron/s]^2) = - # [s/electron] - - # Calculate Exposure time - observation.exptime[ilambd] = ( - observation.SNR[ilambd] - * observation.SNR[ilambd] - * cp - * observatory.telescope.toverhead_multi - + observatory.telescope.toverhead_fixed - ).item() # record exposure time with overheads - - # UNITS: - # []^2*[s/electron]*[]+[s] == [s] - if observation.exptime[ilambd] < 0: - # time is past the systematic - # noise floor limit - observation.exptime[ilambd] = np.inf - - if observation.exptime[ilambd] > observation.td_limit: - # treat as unobservable - # if beyond exposure time limit - observation.exptime[ilambd] = np.inf - - if observatory.coronagraph.nrolls != 1: - # multiply by number of required rolls to - # achieve 360 deg coverage - # (after tlimit enforcement) - observation.exptime[ - ilambd - ] *= observatory.coronagraph.nrolls - elif mode == "signal_to_noise": - - # cp not used in this mode. Note: This will make the science time in - # validation variables be 0! - cp = 0 - # SIGNAL-TO-NOISE - # time term - time_factors = ( - observation.obstime / observatory.coronagraph.nrolls - - observatory.telescope.toverhead_fixed - ) / ( - observatory.telescope.toverhead_multi - * ((CRp + observation.CRb_multiplier * CRb)) - ) - # UNITS: - # ([s]*[]-[s])/([electron/s]+[]*[electron/s]) - # [s]/[electron/s]=[s^2/electron] - - # Signal-to-noise - # observation.fullsnr[ilambd] = ( - # np.sqrt( - # (time_factors * CRp**2) - # / (1 * ELECTRON + time_factors * CRnf**2) - # ) - # * DIMENSIONLESS - # ) - # rewrote the above equation to properly evaluate the SNR when time = inf - observation.fullsnr[ilambd] = ( - ( - np.sqrt( - CRp**2 / (1 * ELECTRON / time_factors + CRnf**2) - ) - * DIMENSIONLESS - ) - .decompose() - .item() - ) # Ensure all units are simplified - - # UNITS: - # ([s^2/electron]*[electron/s]^2)/([electron]+[s^2/electron]*[electron/s]^2)= - # [electron]/[electron] = [] - - observation.SNR[ilambd] = observation.fullsnr[ - ilambd - ] # this is the calculated snr now - - # Store the variables of interest - observation.validation_variables[ilambd] = { - "F0": scene.F0[ilambd], - "magstar": scene.mag, - "dist": scene.dist, - "D": observatory.telescope.diameter, - "A_cm": area_cm2, - "wavelength": observation.wavelength[ilambd].to(NM), - "deltalambda_nm": deltalambda_nm, - "snr": observation.SNR[ilambd], - "nzodis": scene.nzodis, - "toverhead_fixed": observatory.telescope.toverhead_fixed, - "toverhead_multi": observatory.telescope.toverhead_multi, - "det_DC": observatory.detector.DC[ilambd], - "det_RN": observatory.detector.RN[ilambd], - "det_CIC": observatory.detector.CIC[ilambd], - "det_tread": observatory.detector.tread[ilambd], - "det_pixscale_mas": observatory.detector.pixscale_mas, - "dQE": observatory.detector.dQE[ilambd], - "QE": observatory.detector.QE[ilambd], - "T_optical": observatory.optics_throughput[ilambd], - "Fs_over_F0": scene.Fs_over_F0[ilambd] * scene.F0[ilambd], - "Fp": scene.Fs_over_F0[ilambd] - * scene.F0[ilambd] - * scene.Fp_over_Fs[ilambd], - "Fzodi": scene.Fzodi_list[ilambd] * scene.F0[ilambd], - "Fexozodi": scene.Fexozodi_list[ilambd] - * scene.F0[ilambd] - / (scene.separation**2 * scene.dist**2), - "sp_lod": arcsec_to_lambda_d( - scene.separation, - observation.wavelength[ilambd], - observatory.telescope.diameter, - ), - "omega_lod": observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), 0 - ], - # "throughput": observatory.total_throughput[ilambd], - "T_core or photometric_aperture_throughput": observatory.coronagraph.photometric_aperture_throughput[ - int(np.floor(iy)), int(np.floor(ix)), 0 - ], - "Istar": observatory.coronagraph.Istar[ - int(np.floor(iy)), int(np.floor(ix)) - ], - "Istar*oneopixscale2 in (l/D)^-2": observatory.coronagraph.Istar[ - int(np.floor(iy)), int(np.floor(ix)) - ] - * (1 / observatory.coronagraph.pixscale) ** 2, - # "contrast * offset PSF peak *oneopixscale2 in (l/D)^-2 (unused)": 0.025 - # * observatory.coronagraph.TLyot - # * observatory.coronagraph.contrast - # * (1 / observatory.coronagraph.pixscale) ** 2, - "skytrans": observatory.coronagraph.skytrans[ - int(np.floor(iy)), int(np.floor(ix)) - ], - "skytrans*oneopixscale2 in (l/D)^-2": observatory.coronagraph.skytrans[ - int(np.floor(iy)), int(np.floor(ix)) - ] - * (1 / observatory.coronagraph.pixscale) ** 2, - "det_npix": det_npix, - "t_photon_count": t_photon_count, - "CRp": CRp, - "CRbs": CRbs - * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), 0 - ], - "CRbz": CRbz.value - * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), 0 - ], - "CRbez": CRbez.value - * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), 0 - ], - "CRbbin": CRbbin - * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), 0 - ], - "CRbth": CRbth - * observatory.coronagraph.omega_lod[ - int(np.floor(iy)), int(np.floor(ix)), 0 - ], - "CRb": CRb, - "CRbd": CRbd, - "CRnf": CRnf, - "sciencetime": observation.SNR[ilambd] - * observation.SNR[ilambd] - * cp, - "exptime": observation.exptime[ilambd], - } - - else: - logger.error( - "Photometric aperture is not large enough. Hardcoded infinity results." - ) - if mode == "exposure_time": - observation.exptime[ilambd] = np.inf - elif mode == "signal_to_noise": - observation.fullsnr[ilambd] = np.inf - - else: - if not ( - ( - sp_lod > observatory.coronagraph.minimum_IWA - ) # check that the separation in l/D is more than the minimum allowed IWA - and ( - sp_lod < observatory.coronagraph.maximum_OWA - ) # check that the separation in l/D is less than the maximum allowed OWA - ): - logger.error( - "Planet outside OWA or inside IWA. Hardcoded infinity results." - ) - - if not ( - (ix >= 0) # check that x pixel is positive - and ( - ix < observatory.coronagraph.npix - ) # check that it is less than the maximum pixel number - and (iy >= 0) # check that the y pixel is positive - and ( - iy < observatory.coronagraph.npix - ) # check that it is less than the maximum pixel number - ): - - logger.error( - "Planet outside coronagraph YIP image. Hardcoded infinity results." - ) - if mode == "exposure_time": - observation.exptime[ilambd] = np.inf - elif mode == "signal_to_noise": - observation.fullsnr[ilambd] = np.inf - - # if logging is at a DEBUG level, print all variables + invalid_mask = np.atleast_1d(out_of_bounds | small_aperture | below_noise_floor) + + if mode == "exposure_time": + exptime_vals = np.atleast_1d(observation.exptime.to_value(u.s)).astype(float) + invalid_mask = np.atleast_1d(np.asarray(invalid_mask, dtype=bool)) + exptime_vals = np.where(invalid_mask, np.inf, exptime_vals) + observation.exptime = u.Quantity(exptime_vals, u.s) + elif mode == "signal_to_noise": + fullsnr_vals = np.atleast_1d( + u.Quantity(observation.fullsnr, DIMENSIONLESS).value + ).astype(float) + invalid_mask = np.atleast_1d(np.asarray(invalid_mask, dtype=bool)) + fullsnr_vals = np.where(invalid_mask, np.inf, fullsnr_vals) + observation.fullsnr = u.Quantity(fullsnr_vals, DIMENSIONLESS) + observation.SNR = observation.fullsnr.copy() + + # ----------------------------------------------------------------------- + # Verbose / validation output + # ----------------------------------------------------------------------- + sciencetime_arr = ( + observation.SNR**2 * cp_arr + if mode == "exposure_time" + else u.Quantity(np.zeros(observation.nlambd), u.s) + ) + + observation.validation_variables = { + # --- scene / star --- + "F0": scene.F0, + "magstar": scene.mag, + "dist": scene.dist, + "nzodis": scene.nzodis, + "Fs_over_F0": scene.Fs_over_F0 * scene.F0, + "Fp": scene.Fs_over_F0 * scene.F0 * scene.Fp_over_Fs, + "Fzodi": scene.Fzodi_list * scene.F0, + "Fexozodi": scene.Fexozodi_list + * scene.F0 + / (scene.separation**2 * scene.dist**2), + # --- telescope / optics --- + "D": observatory.telescope.diameter, + "A_cm": area_cm2, + "toverhead_fixed": observatory.telescope.toverhead_fixed, + "toverhead_multi": observatory.telescope.toverhead_multi, + "T_optical": observatory.optics_throughput, + # --- wavelength grid --- + "wavelength": observation.wavelength.to(NM), + "deltalambda_nm": deltalambda_nm, + "snr": observation.SNR, + # --- geometry --- + "lod_rad": lod_rad_arr, + "lod_arcsec": lod_arcsec_arr, + "detpixscale_lod": detpixscale_lod_arr, + "oneopixscale_arcsec": oneopixscale_arcsec_arr, + "ix": ix_arr, + "iy": iy_arr, + # --- detector --- + "det_DC": observatory.detector.DC, + "det_RN": observatory.detector.RN, + "det_CIC": observatory.detector.CIC, + "det_tread": observatory.detector.tread, + "det_pixscale_mas": observatory.detector.pixscale_mas, + "det_omega_lod": det_omega_lod_arr, + "det_npix": det_npix, + "t_photon_count": t_photon_count_arr, + "dQE": observatory.detector.dQE, + "QE": observatory.detector.QE, + # --- coronagraph maps at planet position --- + "omega_lod": omega_lod_at_planet, + "T_core or photometric_aperture_throughput": Upsilon_at_planet, + "Istar": Istar_at_planet, + "Istar*oneopixscale2 in (l/D)^-2": Istar_at_planet + * (1 / observatory.coronagraph.pixscale) ** 2, + "skytrans": skytrans_at_planet, + "skytrans*oneopixscale2 in (l/D)^-2": skytrans_at_planet + * (1 / observatory.coronagraph.pixscale) ** 2, + # --- count rates (already include omega where appropriate) --- + "CRp": CRp_arr, + "CRbs": CRbs_arr * omega_lod_at_planet, + "CRbz": CRbz_arr.value * omega_lod_at_planet, + "CRbez": CRbez_arr.value * omega_lod_at_planet, + "CRbbin": CRbbin_arr * omega_lod_at_planet, + "CRbth": CRbth_arr * omega_lod_at_planet, + "CRb": CRb_arr, + "CRbd": CRbd_arr, + "CRnf": CRnf_arr, + # --- results --- + "sciencetime": sciencetime_arr, + "exptime": observation.exptime if mode == "exposure_time" else None, + "fullsnr": observation.fullsnr if mode == "signal_to_noise" else None, + } + # Store the full per-wavelength vectors of every intermediate variable. if logger.isEnabledFor(logging.DEBUG): - utils.print_all_variables( - observation, - scene, - observatory, - deltalambda_nm, - lod, - lod_rad, - lod_arcsec, - area_cm2, - detpixscale_lod, - stellar_diam_lod, - pixscale_rad, - oneopixscale_arcsec, - det_sep_pix, - det_sep, - det_Istar, - det_skytrans, - det_photometric_aperture_throughput, - det_omega_lod, - det_CRp, - det_CRbs, - det_CRbz, - det_CRbez, - det_CRbbin, - det_CRbth, - det_CR, - ix, - iy, - sp_lod, - CRp, - CRnf, - CRbs, - CRbz, - CRbez, - CRbbin, - t_photon_count, - CRbd, - CRbth, - CRb, - # cp, - ) + # IF DEBUG, print them into a file + utils.print_all_variables(observation, scene, observatory) + # Save the photon counts for later analysis pickle.dump(observation.photon_counts, open("photon_counts.pk", "wb")) diff --git a/src/pyEDITH/observation.py b/src/pyEDITH/observation.py index dafbe46..ef24a8a 100644 --- a/src/pyEDITH/observation.py +++ b/src/pyEDITH/observation.py @@ -2,6 +2,7 @@ from .units import * from . import utils import logging +from pyEDITH import parse_input logger = logging.getLogger("pyEDITH") @@ -22,13 +23,11 @@ class Observation: nlambd : int Number of wavelength points. SNR : np.ndarray - Signal-to-noise ratio array. - tp : ndarray - Exposure time of every planet (nmeananom x norbits x ntargs array). + Desired bulk SNR. exptime : ndarray - Exposure time for each target and wavelength. + Exposure time per single wavelength datapoint. fullsnr : ndarray - Signal-to-noise ratio for each target and wavelength. + Signal-to-noise ratio per single wavelength datapoint. td_limit : float Limit placed on exposure times. @@ -53,6 +52,20 @@ def load_configuration(self, parameters: dict) -> None: aperture settings. For IFS mode, it can calculate or regrid the wavelength grid based on specified parameters. + Grid model + ---------- + This method is the place where the *resolved* wavelength grid is defined, + and it is designed to be safely re-callable (e.g. inside a loop that + rebins the grid between iterations). To make that safe: + + * ``parameters["wavelength"]`` is stored verbatim as + ``self._input_wavelength`` -- the pristine, pre-regrid source grid. + This is the single source of truth used as ``from_wavelength`` and is + NEVER overwritten with a regridded array. + * Per-wavelength inputs (currently ``snr``) are parsed with ``parse_parameters`` + and then aligned onto the current resolved grid via ``regrid_to_grid``, + always regridding FROM the input wavelength. + Parameters ---------- parameters : dict @@ -66,9 +79,15 @@ def load_configuration(self, parameters: dict) -> None: If required parameters are missing or if regridding is requested without necessary parameters """ + parameters = parse_input.parse_parameters(parameters) self.observing_mode = parameters["observing_mode"] - if self.observing_mode not in ["IMAGER", "IFS"]: - raise KeyError("Invalid observing mode. Must be 'IMAGER' or 'IFS'.") + + # ------------------------------------------------------------------ + # Pristine source grid: the single source of truth for from_wavelength. + # Store it once, before any rebinning, and never overwrite it with a + # regridded array. Everything per-wavelength is regridded FROM this. + # ------------------------------------------------------------------ + self._input_wavelength = np.asarray(parameters["wavelength"], dtype=np.float64) # -------- INPUTS --------- # Observational parameters @@ -76,6 +95,9 @@ def load_configuration(self, parameters: dict) -> None: self.wavelength = ( parameters["wavelength"] * WAVELENGTH ) # wavelength # nlambd array #unit: micron + # IMAGER has no meaningful bin widths for regridding; broadcast-only + self.delta_wavelength = None + elif ( parameters["observing_mode"] == "IFS" and bool(parameters["regrid_wavelength"]) is False @@ -102,21 +124,9 @@ def load_configuration(self, parameters: dict) -> None: and bool(parameters["regrid_wavelength"]) is True ): logger.info("Calculating a new wavelength grid and re-gridding spectra...") - if "spectral_resolution" not in parameters.keys(): - raise KeyError( - "regrid_wavelength is True; you must specify new resolution for each spectral channel: parameters['spectral_resolution']." - ) - if "lam_low" not in parameters.keys(): - raise KeyError( - "regrid_wavelength is True; you must specify the wavelength boundaries between spectral channels: parameters['lam_low']." - ) - if "lam_high" not in parameters.keys(): - raise KeyError( - "regrid_wavelength is True; you must specify the wavelength boundaries between spectral channels: parameters['lam_high']." - ) new_lam, new_dlam = utils.regrid_wavelengths( - parameters["wavelength"], + self._input_wavelength, parameters["spectral_resolution"], parameters["lam_low"], parameters["lam_high"], @@ -126,11 +136,32 @@ def load_configuration(self, parameters: dict) -> None: ) # wavelength # nlambd array #unit: micron self.delta_wavelength = new_dlam * WAVELENGTH - self.SNR = parameters["snr"] * DIMENSIONLESS # signal to noise # nlambd array + # ------------------------------------------------------------------ + # Length of the current resolved grid. Any per-wavelength parameter is + # aligned against this via regrid_to_grid. + # ------------------------------------------------------------------ + self.nlambd = len(self.wavelength) - self.CRb_multiplier = float(parameters["CRb_multiplier"]) + # Target bin widths (as plain floats) for the regrid branch, if defined. + to_delta = ( + None + if self.delta_wavelength is None + else np.asarray(self.delta_wavelength.value, dtype=np.float64) + ) + + # ------------------------------------------------------------------ + # SNR: align onto the current resolved grid. + # ------------------------------------------------------------------ + self.SNR = utils.regrid_to_grid( + parameters["snr"] * DIMENSIONLESS, + from_wavelength=self._input_wavelength, + to_wavelength=self.wavelength.value, + to_delta_wavelength=to_delta, + name="snr", + interpolation="1d", + ) # signal to noise # nlambd array - self.nlambd = len(self.wavelength) + self.CRb_multiplier = float(parameters["CRb_multiplier"]) def set_output_arrays(self): """ @@ -142,10 +173,6 @@ def set_output_arrays(self): """ # Initialize some arrays needed for outputs... - self.tp = 0.0 * TIME # exposure time of every planet - # (nmeananom x norbits x ntargs array), used in c function - # [NOTE: nmeananom = nphases in C code] - # NOTE: ntargs fixed to 1. self.exptime = np.full((self.nlambd), 0.0) * TIME # only used for snr calculation diff --git a/src/pyEDITH/observatory.py b/src/pyEDITH/observatory.py index cc6fa48..1975961 100644 --- a/src/pyEDITH/observatory.py +++ b/src/pyEDITH/observatory.py @@ -10,6 +10,7 @@ from yippy import Coronagraph as yippycoro import logging +from pyEDITH import parse_input logger = logging.getLogger("pyEDITH") @@ -385,6 +386,7 @@ def calculate_optics_throughput(self, parameters: dict, mediator: object) -> Non Mediator object providing access to observation parameters including wavelength array """ + parameters = parse_input.parse_parameters(parameters) if "T_optical" in parameters.keys(): logger.info("Calculating optics_throughput from input...") @@ -402,6 +404,15 @@ def calculate_optics_throughput(self, parameters: dict, mediator: object) -> Non # may also move to elsewhere in code. ifs_eff = u.Quantity(parameters.get("IFS_eff", 1.0), unit=DIMENSIONLESS) + # Rebin to proper wavelength grid + ifs_eff = utils.regrid_to_grid( + ifs_eff, + from_wavelength=parameters["wavelength"], + to_wavelength=mediator.get_observation_parameter("wavelength"), + name="ifs_eff", + interpolation="1d", + ) + self.optics_throughput *= ifs_eff # if optics_throughput is a number and wavelength>1, make it an array of length nlambda @@ -431,6 +442,8 @@ def calculate_warmemissivity_coldtransmission( if "epswarmTrcold" in parameters.keys(): logger.info("Calculating epswarmTrcold from input...") + parameters = parse_input.parse_parameters(parameters) + self.epswarmTrcold = parameters["epswarmTrcold"] * DIMENSIONLESS else: logger.info("Calculating epswarmTrcold as 1 - optics throughput...") diff --git a/src/pyEDITH/parse_input.py b/src/pyEDITH/parse_input.py index 664eae9..81482f3 100644 --- a/src/pyEDITH/parse_input.py +++ b/src/pyEDITH/parse_input.py @@ -44,12 +44,11 @@ def parse_input_file( Raises ------ KeyError - If secondary flag is True but no secondary variables are found in the input file, - or if IMAGER mode is specified with multiple wavelengths + If some parameters are missing FileNotFoundError If a specified spectrum file cannot be found ValueError - If required parameters are missing or if there are issues with the spectrum file + If there are issues with the spectrum file """ @@ -154,7 +153,7 @@ def parse_input_file( variables["nlambda"] = len(spectrum_df["wavelength"].tolist()) else: - raise ValueError( + raise KeyError( "Required parameters 'wavelength', 'Fstar_10pc', and 'Fp/Fs' are not provided. Please write them explicitly or provide a spectrum_file path." ) @@ -167,6 +166,95 @@ def parse_input_file( return variables, secondary_variables +def normalize_list_shapes(parameters, key, default_len): + """ + Normalize the *shape* of a user-supplied parameter without binding it to a + wavelength grid that may later change. + + Parsing here is deliberately grid-agnostic: we handle scalar broadcasting + and preserve ``astropy.Quantity`` units, but we do NOT reject an array whose + length happens to differ from ``default_len``. The final alignment onto the + (possibly rebinned) resolved grid is deferred to ingestion time. + + Parameters + ---------- + key : str + Name of the parameter to look up in ``parameters``. + default_len : int + The expected length based on the *current* input grid. Used only for + scalar broadcasting and (optionally) strict validation. + + """ + + value = parameters[key] + + # Function to convert to float array, preserving Quantity if present + def to_float_array(v): + if isinstance(v, u.Quantity): + return u.Quantity(np.array(v.value, dtype=np.float64), v.unit) + else: + return np.array(v, dtype=np.float64) + + if default_len > 1: + # Case 1 & 1a: default_len > 1 but value is a pure scalar or single-element array + if ( + np.isscalar(value) + or (isinstance(value, u.Quantity) and value.isscalar) + or (isinstance(value, (list, np.ndarray, tuple)) and len(value) == 1) + ): + logger.warning( + f"{key} should be a list of length {default_len}. " + "pyEDITH will create one assuming the input value for all the elements of the list." + ) + if isinstance(value, u.Quantity): + # If it's a single-element quantity array/list, treat it as a scalar + if not value.isscalar and value.size == 1: + value = value[0] + return u.Quantity(np.full(default_len, value.value), value.unit) + else: + # If it's a single-element list, array, or scalar + if (isinstance(value, (list, np.ndarray, tuple))) and len(value) == 1: + value = value[0] + return np.full(default_len, value, dtype=np.float64) + + # Case 2: default_len > 1 and value has length > 1 but != default_len + # This is an error: the user provided a multi-element array that doesn't match + elif ( + isinstance(value, (list, np.ndarray, u.Quantity)) + and len(value) > 1 + and len(value) != default_len + ): + raise ValueError( + f"{key} should be a list of length {default_len}, but it has length {len(value)}." + ) + + # If value is already correct length, just convert to float array + return to_float_array(value) + + else: + # Case 3: default_len == 1 + # A scalar or single-element value becomes a length-1 array. A multi-element + # value throws an error. + if isinstance(value, u.Quantity): + if value.size > 1: + raise ValueError( + f"{key} has length {value.size} but the expected input size is {default_len}. " + ) + else: + # Handle both scalar Quantity and single-element Quantity array + scalar_value = value.value if value.isscalar else value.value.flat[0] + return u.Quantity([scalar_value], value.unit) + elif isinstance(value, (list, np.ndarray, tuple)): + if len(value) > 1: + raise ValueError( + f"{key} has length {len(value)} but the expected input size is {default_len}. " + ) + else: + return to_float_array(value) + else: + return to_float_array([value]) + + def parse_parameters(parameters: dict, nlambda: int = None) -> dict: """ Parse and process input parameters for simulation. @@ -201,58 +289,9 @@ def parse_parameters(parameters: dict, nlambda: int = None) -> dict: The function assumes one target (ntargs = 1) for now. nmeananom and norbits are defaulted to 1. """ - - def parse_list_param(key, default_len): - value = parameters[key] - - # Function to convert to float array, preserving Quantity if present - def to_float_array(v): - if isinstance(v, u.Quantity): - return u.Quantity(np.array(v.value, dtype=np.float64), v.unit) - else: - return np.array(v, dtype=np.float64) - - if default_len > 1: - # Case 1 & 1a: default_len > 1 but value is a pure scalar (including Quantity scalar) - if np.isscalar(value) or (isinstance(value, u.Quantity) and value.isscalar): - logger.warning( - f"{key} should be a list of length {default_len}. pyEDITH will create one assuming the input value for all the elements of the list." - ) - if isinstance(value, u.Quantity): - return u.Quantity(np.full(default_len, value.value), value.unit) - else: - return np.full(default_len, value) - - # Case 2: default_len > 1 but value has a length > 1 and != default_len - elif ( - isinstance(value, (list, np.ndarray, u.Quantity)) - and len(value) != default_len - ): - raise ValueError( - f"{key} should be a list of length {default_len}, but it has length {len(value)}." - ) - - # If value is already correct length, just convert to float array - return to_float_array(value) - - else: - # Case 3: default_len == 1, return a single element array - if isinstance(value, u.Quantity): - if value.size > 1: - logger.warning( - f"{key} should be a list of length 1 but you assigned multiple values. pyEDITH will create a list assuming only the first input value." - ) - return u.Quantity([value[0].value], value.unit) - else: - return u.Quantity([value.value], value.unit) - elif isinstance(value, (list, np.ndarray)) and len(value) > 1: - logger.warning( - f"{key} should be a list of length 1 but you assigned multiple values. pyEDITH will create a list assuming only the first input value." - ) - return to_float_array([value[0]]) - else: - return to_float_array([value]) - + # LEGACY: Protect in case we already passed parsed parameters + if "_parsed" in parameters.keys(): + return parameters parsed_params = {} # NLAMBDA @@ -265,15 +304,14 @@ def to_float_array(v): else: parsed_params["nlambda"] = len(parameters["wavelength"]) - parsed_params["wavelength"] = parse_list_param( - "wavelength", parsed_params["nlambda"] + + parsed_params["wavelength"] = normalize_list_shapes( + parameters, "wavelength", parsed_params["nlambda"] ) - elif nlambda is not None: - parsed_params["nlambda"] = nlambda else: raise ValueError( - "pyEDITH does not have access to wavelength here, you should provide nlambda as an argument to this function." + "pyEDITH does not have access to wavelength here, please review your input." ) # Use the determined or provided nlambda for array standardization @@ -285,7 +323,6 @@ def to_float_array(v): "snr", "T_optical", "epswarmTrcold", - "npix_multiplier", "DC", "RN", "tread", @@ -296,14 +333,17 @@ def to_float_array(v): "mag", # used to be [ntargs x nlambda], now just [nlambda] "Fstar_10pc", "Fp/Fs", + "ez_PPF", "delta_mag", # used to be [nmeananom x norbits x ntargs] "F0", # for validation purposes, the calculation of F0 is different in AYO "det_npix_input", # for validation purposes + "telescope_optical_throughput", + "coronagraph_optical_throughput", ] parsed_params.update( { - key: parse_list_param(key, nlambda) + key: normalize_list_shapes(parameters, key, nlambda) for key in list(set(wavelength_params) & set(parameters.keys())) } ) @@ -333,27 +373,81 @@ def to_float_array(v): "diameter", "toverhead_fixed", "toverhead_multi", - "minimum_IWA", - "maximum_OWA", + "pixscale", # pixel scale of the coronagraph + "pixscale_mas", # pixel scale of the detector "contrast", "noisefloor_factor", "noisefloor_PPF", - "ez_PPF", "bandwidth", "Tcore", "TLyot", + "unobscured_area", "temperature", "T_contamination", "CRb_multiplier", "t_photon_count_input", # only for ETC validation + "npix_multiplier", ] for key in list(set(scalar_params) & set(parameters.keys())): - parsed_params[key] = float(parameters[key]) + value = parameters[key] + # Handle npix_multiplier deprecation: was array, now scalar + if ( + key == "npix_multiplier" + and hasattr(value, "__len__") + and not isinstance(value, str) + ): + import warnings + + warnings.warn( + "Passing 'npix_multiplier' as an array is deprecated and will be removed in a future version. " + "Please provide it as a scalar value instead. Using the first element for now.", + DeprecationWarning, + stacklevel=2, + ) + if isinstance(value, np.ndarray): + parsed_params[key] = float(value.flat[0]) + else: + parsed_params[key] = float(value[0]) + else: + parsed_params[key] = float(parameters[key]) # ---- INTEGERS --- - if "nrolls" in parameters.keys(): - parsed_params["nrolls"] = int(parameters["nrolls"]) + integer_params = ["nrolls", "nchannels"] + + for key in list(set(integer_params) & set(parameters.keys())): + parsed_params[key] = int(parameters[key]) + # ----- BOOLEANS --- + for key in ["az_avg", "regrid_wavelength"]: + if key in parameters.keys(): + value = parameters[key] + if isinstance(value, str): + value_lower = value.lower() + if value_lower in ("true", "1", "yes"): + parsed_params[key] = True + elif value_lower in ("false", "0", "no"): + parsed_params[key] = False + else: + raise ValueError( + f"Invalid value '{value}' for parameter '{key}'. " + f"Expected boolean or one of: 'true', 'false', '1', '0', 'yes', 'no' " + f"(case-insensitive)." + ) + elif isinstance(value, bool): + parsed_params[key] = value + elif isinstance(value, (int, float)): + if value in (0, 1, 0.0, 1.0): + parsed_params[key] = bool(value) + else: + raise ValueError( + f"Invalid numeric value '{value}' for parameter '{key}'. " + f"Expected 0 or 1 for boolean parameters." + ) + else: + raise TypeError( + f"Invalid type {type(value).__name__} for parameter '{key}'. " + f"Expected boolean, string, or numeric (0/1)." + ) # ----- OBSERVATORY SPECS --- for key in [ @@ -366,15 +460,57 @@ def to_float_array(v): if key in parameters.keys(): parsed_params[key] = parameters[key] + if key == "observing_mode" and parameters[key] not in ["IMAGER", "IFS"]: + raise KeyError("Invalid observing mode. Must be 'IMAGER' or 'IFS'.") + + # --- REQUIRED PARAMETERS IN SPECIFIC MODES --- + if parsed_params.get("regrid_wavelength", False): + required_keys = ["spectral_resolution", "lam_low", "lam_high"] + for key in required_keys: + if key not in parameters: + raise KeyError( + f"regrid_wavelength is True, but '{key}' is missing. " + f"Required parameters: {', '.join(required_keys)}" + ) + + # Check that all required parameters are arrays/lists + lengths = [] + for key in required_keys: + val = parameters[key] + if not isinstance(val, (list, np.ndarray, u.Quantity)): + raise ValueError( + f"regrid_wavelength is True, but '{key}' is not an array. " + f"All of {', '.join(required_keys)} must be arrays of the same length." + ) + lengths.append(len(val)) + + # Check that all arrays have the same length + if len(set(lengths)) != 1: + raise ValueError( + f"regrid_wavelength is True, but {', '.join(required_keys)} have different lengths: " + f"{dict(zip(required_keys, lengths))}. All must have the same length." + ) - # Handle boolean parameter - if "regrid_wavelength" in parameters.keys(): - value = parameters["regrid_wavelength"] - if isinstance(value, str): - parsed_params["regrid_wavelength"] = value.lower() in ("true", "1", "yes") + # Add to parsed_params + for key in required_keys: + parsed_params[key] = normalize_list_shapes(parameters, key, lengths[0]) + + # ADVANCED FLAG: dictionary of values to be overwritten (despite being locked) + if "overrides" in parameters.keys(): + overrides_value = parameters["overrides"] + if isinstance(overrides_value, str): + overrides_list = [item.strip() for item in overrides_value.split(",")] + # Filter out empty strings + overrides_list = [item for item in overrides_list if item] + if overrides_list: + parsed_params["overrides"] = overrides_list else: - parsed_params["regrid_wavelength"] = bool(value) + overrides_list = list(overrides_value) + if overrides_list: + parsed_params["overrides"] = overrides_list + # Update _parsed key + parsed_params["_parsed"] = True return parsed_params diff --git a/src/pyEDITH/utils.py b/src/pyEDITH/utils.py index 22cb475..96fdf6a 100644 --- a/src/pyEDITH/utils.py +++ b/src/pyEDITH/utils.py @@ -17,6 +17,17 @@ def average_over_bandpass(params: dict, wavelength_range: list) -> dict: wavelength boundaries. The wavelength array is expected to be stored under the key "lam" in the params dictionary. + Out-of-domain behaviour + ----------------------- + Some curves (e.g. ``qe_vis`` / ``qe_nir``) are only physically defined over + part of the wavelength axis. When a requested ``wavelength_range`` does not + straddle any tabulated point for a given curve, that curve is interpolated + at the *center* of ``wavelength_range``. If that center lies outside the + curve's native domain the interpolation deliberately returns ``NaN`` (via + ``bounds_error=False, fill_value=np.nan``) rather than raising, so the NaN + can be used downstream to mark the region where a curve does not apply + (this is how the vis/nir split is reconstructed). + Parameters ---------- params : dict @@ -37,14 +48,32 @@ def average_over_bandpass(params: dict, wavelength_range: list) -> dict: numpy_array_variables = { key: value for key, value in params.items() if isinstance(value, np.ndarray) } + + lam_values = params["lam"].value + mask = (lam_values >= wavelength_range[0].value) & ( + lam_values <= wavelength_range[1].value + ) + # Center of the requested bandpass, used only for the empty-slice fallback. + center_wavelength = 0.5 * (wavelength_range[0].value + wavelength_range[1].value) + for key, value in numpy_array_variables.items(): if key != "lam": - params[key] = np.mean( - params[key][ - (params["lam"].value >= wavelength_range[0].value) - & (params["lam"].value <= wavelength_range[1].value) - ] - ) + if mask.any(): + params[key] = np.mean(params[key][mask]) + else: + # No tabulated sample points fall inside the requested + # bandpass; interpolate the curve at the band center instead. + # Points outside the curve's native domain return NaN (rather + # than raising) so the NaN can flag where the curve does not + # apply -- this is what lets the vis/nir curves be stitched + # back together element-wise downstream. + interp_func = interp1d( + params["lam"], + params[key], + bounds_error=False, + fill_value=np.nan, + ) + params[key] = interp_func(center_wavelength) return params @@ -57,6 +86,16 @@ def interpolate_over_bandpass(params: dict, wavelengths: list) -> dict: points using 1D linear interpolation. The original wavelength array is expected to be stored under the key "lam" in the params dictionary. + Out-of-domain behaviour + ----------------------- + Interpolation points that fall outside a curve's native wavelength domain + return ``NaN`` (via ``bounds_error=False, fill_value=np.nan``) rather than + raising. This is intentional: curves such as ``qe_vis`` / ``qe_nir`` are + only defined over part of the spectrum, and the resulting NaNs mark the + region where each curve does not apply so the vis/nir arrays can be stitched + together element-wise (e.g. ``qe_vis = [finite, finite, nan]`` and + ``qe_nir = [nan, nan, finite]``). + Parameters ---------- params : dict @@ -80,7 +119,12 @@ def interpolate_over_bandpass(params: dict, wavelengths: list) -> dict: } for key, value in numpy_array_variables.items(): if key != "lam": - interp_func = interp1d(params["lam"], params[key]) + interp_func = interp1d( + params["lam"], + params[key], + bounds_error=False, + fill_value=np.nan, + ) ynew = interp_func( wavelengths ) # interpolates the CG throughput values onto native wl grid @@ -89,46 +133,91 @@ def interpolate_over_bandpass(params: dict, wavelengths: list) -> dict: def fill_parameters( - class_obj: object, parameters: dict, default_parameters: dict + class_obj: object, + parameters: dict, + default_parameters: dict, + locked_keys: set = None, + allow_override: set = None, ) -> None: """ Populate class object attributes with user parameters or default values. - This function sets attributes on a class object by loading user-provided - parameters or falling back to default values. It handles both regular values - and astropy Quantity objects with units, ensuring proper unit consistency - when dealing with physical quantities. - Parameters ---------- class_obj : object - Class instance whose attributes will be set + Class instance whose attributes will be set. parameters : dict - Dictionary of user-provided parameter values + Dictionary of user-provided parameter values. default_parameters : dict - Dictionary of default parameter values with keys matching expected - attribute names. Values can be regular types or astropy Quantities + Dictionary of default (or, for YIP/EAC mode, model-loaded) parameter values. + locked_keys : set, optional + Keys that must NOT be overridden by the user by default. For these keys + the value staged in ``default_parameters`` is always used (e.g. values + loaded from a YIP or EAC YAML), regardless of what the user supplied, + UNLESS the key is also present in ``allow_override``. + allow_override : set, optional + Keys that are normally locked but that the user has explicitly and + intentionally requested to override (e.g. via parameters["overrides"]). + Has no effect on keys that are not in ``locked_keys`` -- those are + already user-editable. Any name in ``allow_override`` that does not + correspond to a key in ``default_parameters`` raises a ValueError, + to catch typos rather than silently ignoring them. """ + if locked_keys is None: + locked_keys = set() + if allow_override is None: + allow_override = set() + + unknown_overrides = allow_override - set(default_parameters.keys()) + if unknown_overrides: + raise ValueError( + f"'overrides' contains unrecognized parameter name(s): " + f"{unknown_overrides}" + ) + + def _coerce(user_value, default_value): + """Match user_value's type/units to default_value's, where applicable.""" + if isinstance(default_value, u.Quantity): + if isinstance(user_value, u.Quantity): + return user_value.to(default_value.unit) + return u.Quantity(user_value, default_value.unit) + return user_value - # Load parameters, use defaults if not provided for key, default_value in default_parameters.items(): - if key in parameters: - # User provided a value - user_value = parameters[key] - if isinstance(default_value, u.Quantity): - # Ensure the user value has the same unit as the default - # TODO Implement conversion of units from the input file - - if isinstance(user_value, u.Quantity): - setattr(class_obj, key, user_value.to(default_value.unit)) - else: - setattr(class_obj, key, u.Quantity(user_value, default_value.unit)) - else: - # For non-Quantity values (like integers), use as is - setattr(class_obj, key, user_value) + is_locked = key in locked_keys + is_overridden = key in allow_override + if key in parameters and is_locked and not is_overridden: + # User tried to set a value that is owned by the model (e.g. YIP/EAC) + # and did not explicitly request an override. + logger.warning( + f"Parameter '{key}' is locked in this mode and " + f"cannot be user-overridden; using the model-provided value " + f"instead of the supplied value {parameters[key]!r}." + ) + setattr(class_obj, key, default_value) + + elif key in parameters and is_locked and is_overridden: + # User explicitly requested to override a normally-locked value. + final_value = _coerce(parameters[key], default_value) + setattr(class_obj, key, final_value) + logger.warning( + f"Parameter '{key}' is normally locked in this mode, but was " + f"explicitly overridden per user request (via 'overrides'). " + f"Model-provided value was {default_value!r}; using " + f"user-supplied value: {final_value!r}." + ) + + elif key in parameters and not is_locked: + # User provided a value and it is allowed to be overridden. + final_value = _coerce(parameters[key], default_value) + setattr(class_obj, key, final_value) + logger.debug(f"Parameter '{key}' set to user-provided value: {final_value}") + else: - # Use default value + # Use default / model-provided value (also the path for locked + # keys the user didn't touch at all). setattr(class_obj, key, default_value) + logger.debug(f"Parameter '{key}' set to default value: {default_value}") def convert_to_numpy_array(class_obj: object, array_params: list) -> None: @@ -307,12 +396,10 @@ def print_array_info( max_val = np.max(arr) min_val = np.min(arr) if has_units: - file.write( f"max value: {max_val.value:.6e}, min value: {min_val.value:.6e}\n" ) else: - file.write(f"max value: {max_val:.6e}, min value: {min_val:.6e}\n") @@ -320,133 +407,30 @@ def print_all_variables( observation: object, scene: object, observatory: object, - deltalambda_nm: np.ndarray, - lod: np.ndarray, - lod_rad: np.ndarray, - lod_arcsec: np.ndarray, - area_cm2: np.ndarray, - detpixscale_lod: np.ndarray, - stellar_diam_lod: np.ndarray, - pixscale_rad: np.ndarray, - oneopixscale_arcsec: np.ndarray, - det_sep_pix: np.ndarray, - det_sep: np.ndarray, - det_Istar: np.ndarray, - det_skytrans: np.ndarray, - det_photometric_aperture_throughput: np.ndarray, - det_omega_lod: np.ndarray, - det_CRp: np.ndarray, - det_CRbs: np.ndarray, - det_CRbz: np.ndarray, - det_CRbez: np.ndarray, - det_CRbbin: np.ndarray, - det_CRbth: np.ndarray, - det_CR: np.ndarray, - ix: int, - iy: int, - sp_lod: float, - CRp: np.ndarray, - CRnf: np.ndarray, - CRbs: np.ndarray, - CRbz: np.ndarray, - CRbez: np.ndarray, - CRbbin: np.ndarray, - t_photon_count: np.ndarray, - CRbd: np.ndarray, - CRbth: np.ndarray, - CRb: np.ndarray, ) -> None: """ Write comprehensive debug information to files for observation calculations. - This function outputs detailed information about all relevant parameters - and calculated variables used in the observation simulation to both validation - and full_info text files. It includes observation parameters, scene properties, - observatory characteristics, and all intermediate calculations. + Reads all intermediate and final arrays from observation.validation_variables + (populated by the vectorized compute path) and writes them to both + pyedith_validation.txt and pyedith_full_info.txt. Parameters ---------- observation : Observation - Observation object containing observation-specific parameters + Observation object containing observation-specific parameters and + a populated `validation_variables` dict. scene : AstrophysicalScene - Scene object containing astrophysical scene parameters + Scene object containing astrophysical scene parameters. observatory : Observatory - Observatory object containing telescope, coronagraph, and detector parameters - deltalambda_nm : np.ndarray - Wavelength intervals in nanometers - lod : np.ndarray - Lambda over D values - lod_rad : np.ndarray - Lambda over D values in radians - lod_arcsec : np.ndarray - Lambda over D values in arcseconds - area_cm2 : np.ndarray - Telescope area in cm² - detpixscale_lod : np.ndarray - Detector pixel scale in λ/D units - stellar_diam_lod : np.ndarray - Stellar diameter in λ/D units - pixscale_rad : np.ndarray - Pixel scale in radians - oneopixscale_arcsec : np.ndarray - Single pixel scale in arcseconds - det_sep_pix : np.ndarray - Detector separation in pixels - det_sep : np.ndarray - Detector separation - det_Istar : np.ndarray - Detector stellar intensity - det_skytrans : np.ndarray - Detector sky transmission - det_photometric_aperture_throughput : np.ndarray - Detector photometric aperture throughput - det_omega_lod : np.ndarray - Detector solid angle in λ/D units - det_CRp : np.ndarray - Detector planet count rate - det_CRbs : np.ndarray - Detector background star count rate - det_CRbz : np.ndarray - Detector zodiacal background count rate - det_CRbez : np.ndarray - Detector exozodiacal background count rate - det_CRbbin : np.ndarray - Detector binary background count rate - det_CRbth : np.ndarray - Detector thermal background count rate - det_CR : np.ndarray - Total detector count rate - ix : int - X pixel coordinate - iy : int - Y pixel coordinate - sp_lod : float - Separation in λ/D units - CRp : np.ndarray - Planet count rate - CRnf : np.ndarray - Noise floor count rate - CRbs : np.ndarray - Background star count rate - CRbz : np.ndarray - Zodiacal background count rate - CRbez : np.ndarray - Exozodiacal background count rate - CRbbin : np.ndarray - Binary background count rate - t_photon_count : np.ndarray - Photon counting time - CRbd : np.ndarray - Detector background count rate - CRbth : np.ndarray - Thermal background count rate - CRb : np.ndarray - Total background count rate + Observatory object containing telescope, coronagraph, and detector parameters. """ logger.debug( "Printing all relevant variables in pyedith_validation.txt and pyedith_full_info.txt." ) + vv = observation.validation_variables # shorthand + for mode in ["validation", "full_info"]: with open("pyedith_" + mode + ".txt", "w") as file: file.write("Input Objects and Their Relevant Properties:\n") @@ -514,15 +498,13 @@ def print_all_variables( ("observatory.coronagraph.pixscale", observatory.coronagraph.pixscale), ( "observatory.coronagraph.psf_trunc_ratio", - getattr( - observatory.coronagraph, "psf_trunc_ratio", None - ), # optional + getattr(observatory.coronagraph, "psf_trunc_ratio", None), ), ( "observatory.coronagraph.photometric_aperture_throughput", getattr( observatory.coronagraph, "photometric_aperture_throughput", None - ), # optional + ), ), ("observatory.coronagraph.skytrans", observatory.coronagraph.skytrans), ( @@ -535,14 +517,6 @@ def print_all_variables( "observatory.coronagraph.nchannels", observatory.coronagraph.nchannels, ), - ( - "observatory.coronagraph.minimum_IWA", - observatory.coronagraph.minimum_IWA, - ), - ( - "observatory.coronagraph.maximum_OWA", - observatory.coronagraph.maximum_OWA, - ), ( "observatory.coronagraph.npsfratios", observatory.coronagraph.npsfratios, @@ -576,13 +550,13 @@ def print_all_variables( file.write("\n1. Initial Calculations:\n") for item_name, item in [ ("Fs_over_F0", scene.Fs_over_F0), - ("deltalambda_nm", deltalambda_nm), - ("lod", lod), - ("lod_rad", lod_rad), - ("lod_arcsec", lod_arcsec), - ("area_cm2", area_cm2), - ("detpixscale_lod", detpixscale_lod), - ("stellar_diam_lod", stellar_diam_lod), + ("deltalambda_nm", vv.get("deltalambda_nm")), + ("lod", vv.get("lod")), + ("lod_rad", vv.get("lod_rad")), + ("lod_arcsec", vv.get("lod_arcsec")), + ("area_cm2", vv.get("area_cm2")), + ("detpixscale_lod", vv.get("detpixscale_lod")), + ("stellar_diam_lod", vv.get("stellar_diam_lod")), ]: print_array_info(file, item_name, item, mode) @@ -595,48 +569,52 @@ def print_all_variables( file.write("\n3. Coronagraph Performance Measurements:\n") for item_name, item in [ - ("pixscale_rad", pixscale_rad), - ("oneopixscale_arcsec", oneopixscale_arcsec), - ("det_sep_pix", det_sep_pix), - ("det_sep", det_sep), - ("det_Istar", det_Istar), - ("det_skytrans", det_skytrans), + ("pixscale_rad", vv.get("pixscale_rad")), + ("oneopixscale_arcsec", vv.get("oneopixscale_arcsec")), + ("det_sep_pix", vv.get("det_sep_pix")), + ("det_sep", vv.get("det_sep")), + ("det_Istar", vv.get("det_Istar")), + ("det_skytrans", vv.get("det_skytrans")), ( "det_photometric_aperture_throughput", - det_photometric_aperture_throughput, + vv.get("det_photometric_aperture_throughput"), ), - ("det_omega_lod", det_omega_lod), + ("det_omega_lod", vv.get("det_omega_lod")), ]: print_array_info(file, item_name, item, mode) file.write("\n4. Detector Noise Calculations:\n") for item_name, item in [ - ("det_CRp", det_CRp), - ("det_CRbs", det_CRbs), - ("det_CRbz", det_CRbz), - ("det_CRbez", det_CRbez), - ("det_CRbbin", det_CRbbin), - ("det_CRbth", det_CRbth), - ("det_CR", det_CR), + ("det_CRp", vv.get("det_CRp")), + ("det_CRbs", vv.get("det_CRbs")), + ("det_CRbz", vv.get("det_CRbz")), + ("det_CRbez", vv.get("det_CRbez")), + ("det_CRbbin", vv.get("det_CRbbin")), + ("det_CRbth", vv.get("det_CRbth")), + ("det_CR", vv.get("det_CR")), ]: print_array_info(file, item_name, item, mode) file.write("\n5. Planet Position and Separation:\n") - for item_name, item in [("ix", ix), ("iy", iy), ("sp_lod", sp_lod)]: + for item_name, item in [ + ("ix", vv.get("ix")), + ("iy", vv.get("iy")), + ("sp_lod", vv.get("sp_lod")), + ]: print_array_info(file, item_name, item, mode) file.write("\n6. Count Rates and Exposure Time Calculation:\n") for item_name, item in [ - ("CRp", CRp), - ("CRnf", CRnf), - ("CRbs", CRbs), - ("CRbz", CRbz), - ("CRbez", CRbez), - ("CRbbin", CRbbin), - ("t_photon_count", t_photon_count), - ("CRbd", CRbd), - ("CRbth", CRbth), - ("CRb", CRb), + ("CRp", vv.get("CRp")), + ("CRnf", vv.get("CRnf")), + ("CRbs", vv.get("CRbs")), + ("CRbz", vv.get("CRbz")), + ("CRbez", vv.get("CRbez")), + ("CRbbin", vv.get("CRbbin")), + ("t_photon_count", vv.get("t_photon_count")), + ("CRbd", vv.get("CRbd")), + ("CRbth", vv.get("CRbth")), + ("CRb", vv.get("CRb")), ]: print_array_info(file, item_name, item, mode) @@ -702,6 +680,7 @@ def synthesize_observation( def wavelength_grid_fixed_res(x_min: float, x_max: float, res: float = -1) -> tuple: """ + LEGACY Generate a wavelength grid at a fixed spectral resolution. This function creates a wavelength grid with constant resolution across @@ -741,6 +720,7 @@ def wavelength_grid_fixed_res(x_min: float, x_max: float, res: float = -1) -> tu def gen_wavelength_grid(x_min: list, x_max: list, res: list) -> tuple: """ + LEGACY Generate a continuous wavelength grid for multiple spectral channels. This function creates wavelength grids at fixed resolution for each spectral @@ -783,6 +763,7 @@ def regrid_wavelengths( input_wls: np.ndarray, res: list, lam_low: list = None, lam_high: list = None ) -> tuple: """ + LEGACY Create a new wavelength grid with specified resolution and channel boundaries. This function generates a new wavelength grid given the resolution and @@ -866,8 +847,6 @@ def regrid_spec_gaussconv( 1D array containing the regridded spectrum with original units preserved """ - input_spec_unit = input_spec.unit - R_arr = new_lam / new_dlam # interpolate original spectrum onto a fine log-lambda grid @@ -909,7 +888,7 @@ def regrid_spec_gaussconv( kernel_segment = kernel[k1:k2] spec_regrid[i] = np.sum(flux_segment * kernel_segment) - return spec_regrid * input_spec_unit + return spec_regrid def regrid_spec_interp( @@ -937,7 +916,105 @@ def regrid_spec_interp( 1D array containing the regridded spectrum with original units preserved """ - input_spec_unit = input_spec.unit interp_func = interp1d(input_wls, input_spec) spec_regrid = interp_func(new_lam) - return spec_regrid * input_spec_unit + return spec_regrid + + +def regrid_to_grid( + values, + from_wavelength, + to_wavelength, + to_delta_wavelength=None, + *, # Forces all subsequent parameters to be keyword-only (must be called as name="value") + name: str = "parameter", + interpolation: str = "1d", # 1d or Gaussian +): + """ + Fit an already-shaped array onto a resolved wavelength grid. + + The rule is centralized here so no consumer re-implements it: + + * length 1 -> broadcast the single value to the grid + * length == len(to_wavelength) -> already on the grid, pass through + * any other length -> regrid onto the grid + + Note that any length > 1 that is neither 1 nor a mismatch requiring regrid + for *user-supplied* params has already been vetted by ``parse_parameters``; + this helper additionally covers *defaults* or values that never passed through it. + + Parameters + ---------- + values : array-like + The values to fit onto the grid (a plain array or Quantity value). + from_wavelength : array-like + The wavelength grid ``values`` currently live on. For consumers this + should be ``parsed_params["input_wavelength"]`` (the pre-regrid grid). + to_wavelength : array-like + The target (resolved) wavelength grid, i.e. ``observation.wavelength``. + to_delta_wavelength : array-like, optional + Bin widths of the target grid, required only when a regrid is needed. + name : str, optional + Name used in log messages. + + Returns + ------- + np.ndarray + A float64 array of length ``len(to_wavelength)``. + """ + if isinstance(values, u.Quantity): + unit = values.unit + values_plain = np.atleast_1d(np.asarray(values.value, dtype=np.float64)) + else: + unit = None + values_plain = np.atleast_1d(np.asarray(values, dtype=np.float64)) + + to_wavelength = np.asarray(to_wavelength, dtype=np.float64) + n_target = len(to_wavelength) + + # length 1 -> broadcast + if len(values_plain) == 1 and n_target > 1: + result = values_plain[0] * np.ones(n_target, dtype=np.float64) + + # already on the grid -> pass through + elif len(values_plain) == n_target: + result = values_plain + + # otherwise -> regrid + else: + logger.info( + f"'{name}' has length {len(values_plain)} but the resolved wavelength grid " + f"has length {n_target}. Rebinning..." + ) + if interpolation == "1d": + result = regrid_spec_interp( + np.asarray(from_wavelength, dtype=np.float64), + values_plain, + to_wavelength, + ) + elif interpolation == "Gaussian": + if to_delta_wavelength is None: + raise ValueError( + f"'{name}' must be regridded onto the resolved grid, but " + f"'to_delta_wavelength' was not provided." + ) + result = regrid_spec_gaussconv( + np.asarray(from_wavelength, dtype=np.float64), + values_plain, + to_wavelength, + to_delta_wavelength, + ) + + else: + raise ValueError( + "Unknown interpolation type. Possible values are '1d' for 1D " + "interpolation and 'Gaussian' for Gaussian kernel interpolation " + "(recommended for spectral quantities)." + ) + + # ------------------------------------------------------------------ + # Unit reattachment: return a Quantity if the input was one. + # ------------------------------------------------------------------ + if unit is not None: + return result * unit + return result diff --git a/tests/test_astrophysical_scene.py b/tests/test_astrophysical_scene.py index cd4c5b6..c0607b9 100644 --- a/tests/test_astrophysical_scene.py +++ b/tests/test_astrophysical_scene.py @@ -24,7 +24,6 @@ ) import logging - # ============================================================================ # Tests for calc_flux_zero_point # ============================================================================ @@ -323,20 +322,22 @@ def test_load_configuration_with_magnitudes(): "ra": 180.0, "dec": 0.0, "separation": 0.1, - "delta_mag": 20.0, + "delta_mag": [20.0, 20.1], "delta_mag_min": 25, } scene.load_configuration(parameters) - assert scene.dist == 10 * DISTANCE assert scene.vmag == 5.0 * MAGNITUDE + assert np.allclose(scene.mag.value, [5.1, 5.2]) + assert scene.mag.unit == MAGNITUDE assert np.isclose(scene.stellar_angular_diameter_arcsec.value, 0.00093009345219) assert scene.nzodis == 3.0 * ZODI assert scene.ra == 180.0 * DEG assert scene.dec == 0.0 * DEG assert scene.separation == 0.1 * ARCSEC - assert scene.deltamag == 20.0 * MAGNITUDE + assert np.allclose(scene.deltamag.value, [20.0, 20.1]) + assert scene.deltamag.unit == MAGNITUDE assert scene.min_deltamag == 25.0 * MAGNITUDE @@ -385,8 +386,8 @@ def test_load_configuration_with_custom_F0(): } scene.load_configuration(parameters) - - assert scene.F0 == parameters["F0"] * PHOTON_FLUX_DENSITY + assert np.allclose(scene.F0.value, [13400, 13400]) # parsed to len(wavelength) + assert scene.F0.unit == PHOTON_FLUX_DENSITY def test_load_configuration_with_semimajor_axis(): @@ -413,7 +414,7 @@ def test_load_configuration_with_semimajor_axis(): def test_load_configuration_missing_separation_and_semimajor_axis(): - """Test that missing both separation and semimajor_axis raises ValueError.""" + """Test that missing both separation and semimajor_axis raises KeyError.""" scene = AstrophysicalScene() parameters = { "wavelength": [0.5, 0.55], @@ -429,7 +430,7 @@ def test_load_configuration_missing_separation_and_semimajor_axis(): } with pytest.raises( - ValueError, + KeyError, match="Either separation \\[arcsec\\] or semimajor_axis \\[AU\\] must be provided.", ): scene.load_configuration(parameters) @@ -587,11 +588,16 @@ def test_load_configuration_insufficient_parameters(): scene = AstrophysicalScene() with pytest.raises(KeyError): - scene.load_configuration({"distance": 1.0}) + scene.load_configuration( + { + "distance": 1.0, + "wavelength": [0.5, 0.55], + } + ) def test_load_configuration_mixed_magnitude_flux_inputs(): - """Test that mixing magnitude and flux inputs raises ValueError.""" + """Test that mixing magnitude and flux inputs raises KeyError.""" scene = AstrophysicalScene() mixed_parameters = { "wavelength": [0.5, 0.55], @@ -603,11 +609,11 @@ def test_load_configuration_mixed_magnitude_flux_inputs(): "ra": 180.0, "dec": 0.0, "separation": 0.1, - "delta_mag": 20.0, + "delta_mag": [20.0, 20.1], "delta_mag_min": 25, } - with pytest.raises(ValueError): + with pytest.raises(KeyError): scene.load_configuration(mixed_parameters) @@ -686,7 +692,7 @@ def test_load_configuration_ifs_mode_fstarv_interpolation(): def test_load_configuration_imager_mode_missing_fstarv(): - """Test that missing FstarV_10pc in IMAGER mode raises ValueError.""" + """Test that missing FstarV_10pc in IMAGER mode raises KeyError.""" scene = AstrophysicalScene() parameters = { "wavelength": 0.5, @@ -702,7 +708,7 @@ def test_load_configuration_imager_mode_missing_fstarv(): "observing_mode": "IMAGER", } - with pytest.raises(ValueError, match="FstarV_10pc missing in parameters."): + with pytest.raises(KeyError, match="FstarV_10pc missing in parameters."): scene.load_configuration(parameters) @@ -771,11 +777,11 @@ def test_load_configuration_ez_ppf_mismatched_length(): "ra": 236.0075773682300, "dec": 02.5151668316500, "separation": 0.1, - "ez_PPF": [100.0], # Wrong length + "ez_PPF": [100.0, 100.0], # Wrong length } with pytest.raises( - AssertionError, match="length of ez_PPF does not match length of Fp_over_Fs" + ValueError, match="ez_PPF should be a list of length 3, but it has length 2." ): scene.load_configuration(parameters) @@ -943,32 +949,11 @@ def test_calculate_zodi_exozodi_missing_parameters(): # Try to calculate without configuring the scene first with pytest.raises(AttributeError, match="must be configured before"): - scene.calculate_zodi_exozodi({}) - - -def test_calculate_zodi_exozodi_missing_wavelength(): - """Test that calculate_zodi_exozodi raises error when wavelength is missing.""" - scene = AstrophysicalScene() - - # Configure the scene properly - parameters = { - "wavelength": [0.5, 0.55, 0.6], - "distance": 14.8, - "magV": 5.84, - "mag": [5.687, 5.632, 5.577], - "stellar_radius": 1, - "nzodis": 3.0, - "ra": 236.0075773682300, - "dec": 02.5151668316500, - "separation": 0.1, - "delta_mag": 25.5, - "delta_mag_min": 25.0, - } - scene.load_configuration(parameters) - - # Now try to calculate with missing wavelength - with pytest.raises(KeyError, match="wavelength"): - scene.calculate_zodi_exozodi({}) + scene.calculate_zodi_exozodi( + { + "wavelength": [0.5, 0.55, 0.6], + } + ) # ============================================================================ @@ -1211,6 +1196,7 @@ class MockObservation: wavelength = np.linspace(0.5, 1.7, 100) * WAVELENGTH nlambd = len(wavelength) delta_wavelength = np.gradient(wavelength) + _input_wavelength = parameters["wavelength"] observation = MockObservation() @@ -1233,7 +1219,11 @@ class MockObservation: ) scene.ez_PPF = 100 * np.ones(len(scene.Fexozodi_list)) * DIMENSIONLESS - scene.regrid_spectra(parameters, observation) + scene.mag = np.random.randn(len(parameters["wavelength"])) * MAGNITUDE + + scene.deltamag = np.random.randn(len(parameters["wavelength"])) * MAGNITUDE + + scene.regrid_spectra(observation) # Check that all arrays match observation wavelength grid assert len(scene.F0) == observation.nlambd @@ -1242,6 +1232,8 @@ class MockObservation: assert len(scene.Fbinary_list) == observation.nlambd assert len(scene.Fp_over_Fs) == observation.nlambd assert len(scene.Fs_over_F0) == observation.nlambd + assert len(scene.mag) == observation.nlambd + assert len(scene.deltamag) == observation.nlambd def test_regrid_spectra_preserves_units(): @@ -1256,6 +1248,7 @@ class MockObservation: wavelength = np.linspace(0.5, 1.7, 100) * WAVELENGTH nlambd = len(wavelength) delta_wavelength = np.gradient(wavelength) + _input_wavelength = parameters["wavelength"] observation = MockObservation() @@ -1271,6 +1264,8 @@ class MockObservation: scene.Fp_over_Fs = np.random.randn(len(parameters["wavelength"])) * DIMENSIONLESS scene.Fs_over_F0 = np.random.randn(len(parameters["wavelength"])) * DIMENSIONLESS scene.ez_PPF = 100 * np.ones(len(scene.Fexozodi_list)) * DIMENSIONLESS + scene.mag = np.random.randn(len(parameters["wavelength"])) * MAGNITUDE + scene.deltamag = np.random.randn(len(parameters["wavelength"])) * MAGNITUDE original_units = { "F0": scene.F0.unit, @@ -1279,9 +1274,11 @@ class MockObservation: "Fbinary_list": scene.Fbinary_list.unit, "Fp_over_Fs": scene.Fp_over_Fs.unit, "Fs_over_F0": scene.Fs_over_F0.unit, + "mag": scene.mag.unit, + "deltamag": scene.deltamag.unit, } - scene.regrid_spectra(parameters, observation) + scene.regrid_spectra(observation) # Check that units are preserved assert scene.F0.unit == original_units["F0"] @@ -1290,3 +1287,5 @@ class MockObservation: assert scene.Fbinary_list.unit == original_units["Fbinary_list"] assert scene.Fp_over_Fs.unit == original_units["Fp_over_Fs"] assert scene.Fs_over_F0.unit == original_units["Fs_over_F0"] + assert scene.mag.unit == original_units["mag"] + assert scene.deltamag.unit == original_units["deltamag"] diff --git a/tests/test_coronagraphs.py b/tests/test_coronagraphs.py index 6a0b7d7..0bb6077 100644 --- a/tests/test_coronagraphs.py +++ b/tests/test_coronagraphs.py @@ -83,6 +83,77 @@ def mock_telescope(): return mock +@pytest.fixture +def imager_toymodel_basic_params(): + """Fixture providing IMAGER observation parameters for the ToyModel.""" + return { + "pixscale": 0.3, + "contrast": 1e-10, + "noisefloor_factor": 0.05, + "bandwidth": 0.1, + "photometric_aperture_radius": 0.6, + "Tcore": 0.3, + "TLyot": 0.7, + "nrolls": 1, + "nchannels": 1, + "wavelength": 0.7, + } + + +@pytest.fixture +def ifs_toymodel_basic_params(): + """Fixture providing IFS observation parameters for the Toy Model coronagraph.""" + return { + "pixscale": 0.3, + "contrast": 1e-10, + "noisefloor_factor": 0.05, + "bandwidth": 0.1, + "photometric_aperture_radius": 0.6, + "Tcore": 0.3, + "TLyot": 0.7, + "nrolls": 1, + "nchannels": 1, + "wavelength": [0.5, 0.6, 0.7], + } + + +@pytest.fixture +def imager_yipcoronagraph_basic_params(): + """Fixture providing IMAGER observation parameters for the YIP coronagraph.""" + return { + "observing_mode": "IMAGER", + "bandwidth": 0.1, + "psf_trunc_ratio": 0.3, + "nchannels": 1, + "az_avg": True, + "wavelength": 0.7, + } + + +@pytest.fixture +def ifs_yipcoronagraph_basic_params(): + """Fixture providing IFS mode observation parameters.""" + return { + "observing_mode": "IFS", + "bandwidth": 0.1, + "psf_trunc_ratio": 0.3, + "nchannels": 1, + "az_avg": True, + "wavelength": [0.5, 0.6, 0.7], + } + + +@pytest.fixture +def single_wavelength_params(): + """Fixture providing single wavelength observation parameters.""" + return { + "wavelength": [0.5], + "snr": [7.0], + "CRb_multiplier": 2.0, + "observing_mode": "IMAGER", + } + + # ============================================================================ # Tests for generate_radii # ============================================================================ @@ -117,350 +188,310 @@ def test_generate_radii_default_square(): def test_toy_model_coronagraph_init(): - """Test ToyModelCoronagraph initializes with None values.""" + """Test ToyModelCoronagraph initializes with None values and Locked Keys are empty.""" coronagraph = ToyModelCoronagraph() assert coronagraph.path is None + assert coronagraph.LOCKED_KEYS == set() + # ============================================================================ -# Tests for ToyModelCoronagraph.load_configuration +# Tests for ToyModelCoronagraph.load_configuration (IMAGER mode) # ============================================================================ -def test_toy_model_load_configuration_basic_parameters(caplog): +def test_toy_model_load_configuration_basic_parameters( + caplog, imager_toymodel_basic_params +): """Test that basic parameters are loaded correctly.""" with caplog.at_level(logging.DEBUG, logger="pyEDITH"): coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "noisefloor_factor": 0.05, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.6, - "Tcore": 0.3, - "TLyot": 0.7 * DIMENSIONLESS, - "nrolls": 2, - "nchannels": 1, - } + parameters = imager_toymodel_basic_params.copy() mediator = MockMediator_IMAGER() coronagraph.load_configuration(parameters, mediator) assert coronagraph.pixscale == 0.3 * LAMBDA_D - assert coronagraph.minimum_IWA == 2.5 * LAMBDA_D - assert coronagraph.maximum_OWA == 90.0 * LAMBDA_D assert coronagraph.contrast == 1e-10 * DIMENSIONLESS assert coronagraph.noisefloor_factor == 0.05 * DIMENSIONLESS assert coronagraph.bandwidth == 0.1 assert coronagraph.photometric_aperture_radius == 0.6 * LAMBDA_D assert coronagraph.Tcore == 0.3 * DIMENSIONLESS assert coronagraph.TLyot == 0.7 * DIMENSIONLESS - assert coronagraph.nrolls == 2 + assert coronagraph.nrolls == 1 assert coronagraph.nchannels == 1 - - -def test_toy_model_load_configuration_default_values(): - """Test that default values are used when not provided.""" + assert coronagraph.coronagraph_optical_throughput == [0.44] * DIMENSIONLESS + assert coronagraph.coronagraph_spectral_resolution == 1 * DIMENSIONLESS + assert hasattr(coronagraph, "npsfratios") + assert hasattr(coronagraph, "npix") + assert hasattr(coronagraph, "xcenter") + assert hasattr(coronagraph, "ycenter") + assert hasattr(coronagraph, "r") + assert hasattr(coronagraph, "omega_lod") + assert hasattr(coronagraph, "skytrans") + assert hasattr(coronagraph, "photometric_aperture_radius") + assert hasattr(coronagraph, "photometric_aperture_throughput") + assert hasattr(coronagraph, "PSFpeak") + assert hasattr(coronagraph, "Istar") + assert hasattr(coronagraph, "noisefloor") + assert coronagraph.npix == 400 + assert coronagraph.xcenter == 200 * PIXEL + assert coronagraph.ycenter == 200 * PIXEL + assert coronagraph.r.shape == (coronagraph.npix, coronagraph.npix) + assert np.isclose(coronagraph.r[0, 0], 84.641) + assert coronagraph.omega_lod.shape == ( + coronagraph.npix, + coronagraph.npix, + coronagraph.npsfratios, + ) + assert np.all( + coronagraph.omega_lod + == np.pi * parameters["photometric_aperture_radius"] ** 2 * LAMBDA_D**2 + ) + assert coronagraph.skytrans.shape == (coronagraph.npix, coronagraph.npix) + assert np.all(coronagraph.skytrans == 0.7 * DIMENSIONLESS) + assert coronagraph.photometric_aperture_throughput.shape == ( + coronagraph.npix, + coronagraph.npix, + coronagraph.npsfratios, + ) + assert np.all( + (coronagraph.photometric_aperture_throughput == 0.3 * DIMENSIONLESS) + | (coronagraph.photometric_aperture_throughput == 0.0 * DIMENSIONLESS) + ) + assert np.isclose(coronagraph.PSFpeak, 0.025 * 0.7 * DIMENSIONLESS) + assert coronagraph.Istar.shape == (coronagraph.npix, coronagraph.npix) + assert np.allclose(coronagraph.Istar.value, 1e-10 * 0.025 * 0.7, rtol=1e-6) + assert coronagraph.Istar.unit == DIMENSIONLESS + assert any( + "Calculating noisefloor by multiplying noisefloor_factor=0.05, contrast=1e-10, PSFpeak=" + + str(0.025 * 0.7) + in record.message + for record in caplog.records + ) + + assert coronagraph.noisefloor.shape == (coronagraph.npix, coronagraph.npix) + assert np.allclose( + coronagraph.noisefloor.value, 0.05 * 1e-10 * 0.025 * 0.7, rtol=1e-6 + ) + assert coronagraph.noisefloor.unit == DIMENSIONLESS + + +def test_toy_model_load_configuration_noisefloor_ppf_raises_error( + imager_toymodel_basic_params, +): + """Test that providing noisefloor_PPF raises appropriate error.""" coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "noisefloor_factor": 0.05, - "bandwidth": 0.1, - "Tcore": 0.3, - "TLyot": 0.7, - "nrolls": 2, - "nchannels": 1, - } - mediator = MockMediator_IMAGER() - - coronagraph.load_configuration(parameters, mediator) - - assert coronagraph.coronagraph_optical_throughput == [0.44] * DIMENSIONLESS - assert coronagraph.coronagraph_spectral_resolution == 1 * DIMENSIONLESS - + parameters = imager_toymodel_basic_params.copy() -def test_toy_model_load_configuration_calculated_attributes(): - """Test that derived attributes are calculated correctly.""" - coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "noisefloor_factor": 0.05, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.6, - "Tcore": 0.3, - "TLyot": 0.7 * DIMENSIONLESS, - "nrolls": 2, - "nchannels": 1, - } + del parameters["noisefloor_factor"] + parameters["noisefloor_PPF"] = 30 mediator = MockMediator_IMAGER() - coronagraph.load_configuration(parameters, mediator) - - assert hasattr(coronagraph, "npsfratios") - assert hasattr(coronagraph, "npix") - assert hasattr(coronagraph, "xcenter") - assert hasattr(coronagraph, "ycenter") - assert hasattr(coronagraph, "r") - assert hasattr(coronagraph, "omega_lod") - assert hasattr(coronagraph, "skytrans") - assert hasattr(coronagraph, "photometric_aperture_radius") - assert hasattr(coronagraph, "photometric_aperture_throughput") - assert hasattr(coronagraph, "PSFpeak") - assert hasattr(coronagraph, "Istar") - assert hasattr(coronagraph, "noisefloor") + with pytest.raises( + KeyError, + match="Noisefloor_PPF mode not implemented in ToyModel coronagraph", + ): + coronagraph.load_configuration(parameters, mediator) -def test_toy_model_load_configuration_pixel_grid(): - """Test that pixel grid parameters are calculated correctly.""" +def test_toy_model_load_configuration_default_noisefloor_factor( + caplog, imager_toymodel_basic_params +): + """Test that default noisefloor_factor is used when not provided.""" coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "noisefloor_factor": 0.05, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.6, - "Tcore": 0.3, - "TLyot": 0.7 * DIMENSIONLESS, - "nrolls": 2, - "nchannels": 1, - } - mediator = MockMediator_IMAGER() - - coronagraph.load_configuration(parameters, mediator) - - assert coronagraph.npix == 400 - assert coronagraph.xcenter == 200 * PIXEL - assert coronagraph.ycenter == 200 * PIXEL + parameters = imager_toymodel_basic_params.copy() - -def test_toy_model_load_configuration_radial_grid(): - """Test that radial separation grid is calculated correctly.""" - coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "noisefloor_factor": 0.05, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.6, - "Tcore": 0.3, - "TLyot": 0.7, - "nrolls": 2, - "nchannels": 1, - } + del parameters["noisefloor_factor"] mediator = MockMediator_IMAGER() - coronagraph.load_configuration(parameters, mediator) - - assert coronagraph.r.shape == (coronagraph.npix, coronagraph.npix) - assert np.isclose(coronagraph.r[0, 0], 84.641) - + caplog.clear() + with caplog.at_level(logging.INFO, logger="pyEDITH"): + coronagraph.load_configuration(parameters, mediator) -def test_toy_model_load_configuration_omega_lod(): - """Test that omega_lod is calculated with correct shape and values.""" - coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "noisefloor_factor": 0.05, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.6, - "Tcore": 0.3, - "TLyot": 0.7, - "nrolls": 2, - "nchannels": 1, - } - mediator = MockMediator_IMAGER() + assert any( + "noisefloor_factor value not provided. Using the default value: 0.03" + in record.message + for record in caplog.records + ) + assert any( + "Calculating noisefloor by multiplying noisefloor_factor=0.03, contrast=1e-10, PSFpeak=" + + str(0.025 * 0.7) + in record.message + for record in caplog.records + ) - coronagraph.load_configuration(parameters, mediator) + assert coronagraph.noisefloor.unit == DIMENSIONLESS - assert coronagraph.omega_lod.shape == (coronagraph.npix, coronagraph.npix, 1) - assert np.all( - coronagraph.omega_lod - == np.pi * parameters["photometric_aperture_radius"] ** 2 * LAMBDA_D**2 - ) +# ============================================================================ +# Tests for ToyModelCoronagraph.load_configuration (IFS mode) +# ============================================================================ -def test_toy_model_load_configuration_skytrans(): - """Test that sky transmission is set correctly.""" - coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "noisefloor_factor": 0.05, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.6, - "Tcore": 0.3, - "TLyot": 0.7, - "nrolls": 2, - "nchannels": 1, - } - mediator = MockMediator_IMAGER() - coronagraph.load_configuration(parameters, mediator) +def test_toy_model_load_configuration_ifs_basic_parameters( + caplog, ifs_toymodel_basic_params +): + """Test that basic parameters are loaded correctly in IFS mode. + + This is the IFS analogue of + ``test_toy_model_load_configuration_basic_parameters``. The key point is + that IFS mode does NOT change the shape of the geometry arrays: per-position + quantities keep their trailing ``npsfratios`` axis, and the 2D products keep + their ``(npix, npix)`` shape. The number of wavelengths only affects the + spectral iteration, not these arrays. + """ + with caplog.at_level(logging.DEBUG, logger="pyEDITH"): + coronagraph = ToyModelCoronagraph() + parameters = ifs_toymodel_basic_params.copy() + mediator = MockMediator_IFS() - assert coronagraph.skytrans.shape == (coronagraph.npix, coronagraph.npix) - assert np.all(coronagraph.skytrans == 0.7 * DIMENSIONLESS) + coronagraph.load_configuration(parameters, mediator) + # --- Scalar parameters --- + assert coronagraph.pixscale == 0.3 * LAMBDA_D + assert coronagraph.contrast == 1e-10 * DIMENSIONLESS + assert coronagraph.noisefloor_factor == 0.05 * DIMENSIONLESS + assert coronagraph.bandwidth == 0.1 + assert coronagraph.photometric_aperture_radius == 0.6 * LAMBDA_D + assert coronagraph.Tcore == 0.3 * DIMENSIONLESS + assert coronagraph.TLyot == 0.7 * DIMENSIONLESS + assert coronagraph.nrolls == 1 + assert coronagraph.nchannels == 1 + assert coronagraph.coronagraph_optical_throughput == [0.44] * DIMENSIONLESS + assert coronagraph.coronagraph_spectral_resolution == 1 * DIMENSIONLESS + + # --- Attributes exist --- + assert hasattr(coronagraph, "npsfratios") + assert hasattr(coronagraph, "npix") + assert hasattr(coronagraph, "xcenter") + assert hasattr(coronagraph, "ycenter") + assert hasattr(coronagraph, "r") + assert hasattr(coronagraph, "omega_lod") + assert hasattr(coronagraph, "skytrans") + assert hasattr(coronagraph, "photometric_aperture_radius") + assert hasattr(coronagraph, "photometric_aperture_throughput") + assert hasattr(coronagraph, "PSFpeak") + assert hasattr(coronagraph, "Istar") + assert hasattr(coronagraph, "noisefloor") + + # --- Grid geometry (independent of number of wavelengths) --- + assert coronagraph.npix == 400 + assert coronagraph.xcenter == 200 * PIXEL + assert coronagraph.ycenter == 200 * PIXEL + assert coronagraph.r.shape == (coronagraph.npix, coronagraph.npix) + assert np.isclose(coronagraph.r[0, 0], 84.641) + + # --- Per-position arrays carry a trailing axis of length npsfratios --- + # IFS does NOT change this shape (it is not sized by nwave). + npsfratios = coronagraph.npsfratios + assert coronagraph.omega_lod.shape == ( + coronagraph.npix, + coronagraph.npix, + npsfratios, + ) + assert np.all( + coronagraph.omega_lod + == np.pi * parameters["photometric_aperture_radius"] ** 2 * LAMBDA_D**2 + ) + + assert coronagraph.skytrans.shape == (coronagraph.npix, coronagraph.npix) + assert np.all(coronagraph.skytrans == 0.7 * DIMENSIONLESS) + + assert coronagraph.photometric_aperture_throughput.shape == ( + coronagraph.npix, + coronagraph.npix, + npsfratios, + ) + assert np.all( + (coronagraph.photometric_aperture_throughput == 0.3 * DIMENSIONLESS) + | (coronagraph.photometric_aperture_throughput == 0.0 * DIMENSIONLESS) + ) + + # --- PSF peak / stellar intensity / noisefloor (2D, wavelength-agnostic) --- + assert np.isclose(coronagraph.PSFpeak, 0.025 * 0.7 * DIMENSIONLESS) + + assert coronagraph.Istar.shape == (coronagraph.npix, coronagraph.npix) + assert np.allclose(coronagraph.Istar.value, 1e-10 * 0.025 * 0.7, rtol=1e-6) + assert coronagraph.Istar.unit == DIMENSIONLESS + + assert any( + "Calculating noisefloor by multiplying noisefloor_factor=0.05, contrast=1e-10, PSFpeak=" + + str(0.025 * 0.7) + in record.message + for record in caplog.records + ) + + assert coronagraph.noisefloor.shape == (coronagraph.npix, coronagraph.npix) + assert np.allclose( + coronagraph.noisefloor.value, 0.05 * 1e-10 * 0.025 * 0.7, rtol=1e-6 + ) + assert coronagraph.noisefloor.unit == DIMENSIONLESS + + +def test_toy_model_load_configuration_ifs_array_shapes_match_npsfratios( + ifs_toymodel_basic_params, +): + """Test that IFS mode sizes per-position arrays by npsfratios, not nwave. + + This guards the primary shape contract: even though the mediator provides + three wavelengths, the geometry arrays (``omega_lod``, + ``photometric_aperture_throughput``) carry a trailing axis of length + ``npsfratios`` and are therefore identical in shape to IMAGER mode. The + trailing axis must NOT equal the number of wavelengths (unless they happen + to coincide, which we explicitly rule out here since nwave==3). + """ + mediator = MockMediator_IFS() + nwave = len(mediator.get_observation_parameter("wavelength")) + assert nwave == 3 # guard against fixture drift -def test_toy_model_load_configuration_photometric_aperture_throughput(): - """Test that photometric aperture throughput respects IWA and OWA.""" coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "noisefloor_factor": 0.05, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.6, - "Tcore": 0.3, - "TLyot": 0.7, - "nrolls": 2, - "nchannels": 1, - } - mediator = MockMediator_IMAGER() + parameters = ifs_toymodel_basic_params.copy() coronagraph.load_configuration(parameters, mediator) - assert coronagraph.photometric_aperture_throughput.shape == ( + npsfratios = coronagraph.npsfratios + + # Per-position arrays are sized by npsfratios (see original implementation). + assert coronagraph.omega_lod.shape == ( coronagraph.npix, coronagraph.npix, - 1, + npsfratios, ) - assert np.all( - (coronagraph.photometric_aperture_throughput == 0.3 * DIMENSIONLESS) - | (coronagraph.photometric_aperture_throughput == 0.0 * DIMENSIONLESS) - ) - assert np.all( - coronagraph.photometric_aperture_throughput[ - coronagraph.r < coronagraph.minimum_IWA - ] - == 0.0 * DIMENSIONLESS - ) - assert np.all( - coronagraph.photometric_aperture_throughput[ - coronagraph.r > coronagraph.maximum_OWA - ] - == 0.0 * DIMENSIONLESS + assert coronagraph.photometric_aperture_throughput.shape == ( + coronagraph.npix, + coronagraph.npix, + npsfratios, ) + # The trailing (npsfratios) axis is NOT the wavelength axis. + assert coronagraph.omega_lod.shape[-1] == npsfratios + assert coronagraph.photometric_aperture_throughput.shape[-1] == npsfratios -def test_toy_model_load_configuration_psf_peak(): - """Test that PSF peak is calculated correctly.""" - coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "noisefloor_factor": 0.05, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.6, - "Tcore": 0.3, - "TLyot": 0.7, - "nrolls": 2, - "nchannels": 1, - } - mediator = MockMediator_IMAGER() - - coronagraph.load_configuration(parameters, mediator) - - assert np.isclose(coronagraph.PSFpeak, 0.025 * 0.7 * DIMENSIONLESS) - - -def test_toy_model_load_configuration_istar(): - """Test that stellar intensity is calculated correctly.""" - coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "noisefloor_factor": 0.05, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.6, - "Tcore": 0.3, - "TLyot": 0.7, - "nrolls": 2, - "nchannels": 1, - } - mediator = MockMediator_IMAGER() - - coronagraph.load_configuration(parameters, mediator) - + # Sanity check against the IMAGER path: the 2D (non-spectral) products keep + # their shape regardless of the number of wavelengths. assert coronagraph.Istar.shape == (coronagraph.npix, coronagraph.npix) - assert np.allclose(coronagraph.Istar.value, 1e-10 * 0.025 * 0.7, rtol=1e-6) - assert coronagraph.Istar.unit == DIMENSIONLESS - - -def test_toy_model_load_configuration_noisefloor_with_factor(caplog): - """Test noisefloor calculation using noisefloor_factor.""" - coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "noisefloor_factor": 0.05, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.6, - "Tcore": 0.3, - "TLyot": 0.7, - "nrolls": 2, - "nchannels": 1, - } - mediator = MockMediator_IMAGER() - - with caplog.at_level(logging.INFO, logger="pyEDITH"): - coronagraph.load_configuration(parameters, mediator) - - assert any( - "Calculating noisefloor by multiplying noisefloor_factor=0.05, contrast=1e-10, PSFpeak=" - + str(0.025 * 0.7) - in record.message - for record in caplog.records - ) - assert coronagraph.noisefloor.shape == (coronagraph.npix, coronagraph.npix) - assert np.allclose( - coronagraph.noisefloor.value, 0.05 * 1e-10 * 0.025 * 0.7, rtol=1e-6 - ) - assert coronagraph.noisefloor.unit == DIMENSIONLESS + assert coronagraph.skytrans.shape == (coronagraph.npix, coronagraph.npix) -def test_toy_model_load_configuration_noisefloor_ppf_raises_error(): - """Test that providing noisefloor_PPF raises appropriate error.""" +def test_toy_model_load_configuration_ifs_noisefloor_ppf_raises_error( + ifs_toymodel_basic_params, +): + """Test that noisefloor_PPF is rejected in IFS mode too. + + The ToyModel does not implement the PPF noise-floor path in either mode, so + supplying ``noisefloor_PPF`` must raise regardless of observing_mode. This + is the IFS counterpart of + ``test_toy_model_load_configuration_noisefloor_ppf_raises_error``. + """ coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "bandwidth": 0.1, - "Tcore": 0.3, - "TLyot": 0.7, - "nrolls": 2, - "nchannels": 1, - "noisefloor_PPF": 30, - } - mediator = MockMediator_IMAGER() + parameters = ifs_toymodel_basic_params.copy() + + del parameters["noisefloor_factor"] + parameters["noisefloor_PPF"] = 30 + mediator = MockMediator_IFS() with pytest.raises( KeyError, @@ -469,21 +500,19 @@ def test_toy_model_load_configuration_noisefloor_ppf_raises_error(): coronagraph.load_configuration(parameters, mediator) -def test_toy_model_load_configuration_default_noisefloor_factor(caplog): - """Test that default noisefloor_factor is used when not provided.""" +def test_toy_model_load_configuration_ifs_default_noisefloor_factor( + caplog, ifs_toymodel_basic_params +): + """Test that default noisefloor_factor is used when not provided in IFS mode. + + IFS counterpart of + ``test_toy_model_load_configuration_default_noisefloor_factor``. + """ coronagraph = ToyModelCoronagraph() - parameters = { - "pixscale": 0.3, - "minimum_IWA": 2.5, - "maximum_OWA": 90.0, - "contrast": 1e-10, - "bandwidth": 0.1, - "Tcore": 0.3, - "TLyot": 0.7, - "nrolls": 2, - "nchannels": 1, - } - mediator = MockMediator_IMAGER() + parameters = ifs_toymodel_basic_params.copy() + + del parameters["noisefloor_factor"] + mediator = MockMediator_IFS() caplog.clear() with caplog.at_level(logging.INFO, logger="pyEDITH"): @@ -515,6 +544,20 @@ def test_coronagraph_yip_init_with_path(): assert coronagraph.path == "test_path" assert coronagraph.yippy_coro is None + assert coronagraph.LOCKED_KEYS == { + "pixscale", + "npix", + "xcenter", + "ycenter", + "skytrans", + "r", + "npsfratios", + "nrolls", + "omega_lod", + "photometric_aperture_throughput", + "Istar", + "noisefloor", + } def test_coronagraph_yip_init_with_yippy_coro(yippy_coronagraph): @@ -548,99 +591,129 @@ def test_coronagraph_yip_init_not_both_path_and_yippy(yippy_coronagraph): @patch("eacy.load_instrument") @patch("eacy.load_telescope") -def test_coronagraph_yip_load_configuration_imager_basic_parameters( +def test_coronagraph_yip_warns_when_user_overrides_locked_key( mock_load_telescope, mock_load_instrument, + caplog, yippy_coronagraph, mock_instrument, mock_telescope, + imager_yipcoronagraph_basic_params, ): - """Test that basic YIP parameters are loaded correctly in IMAGER mode.""" + """ + A user-supplied value for a LOCKED (YIP-owned) key must: + 1. emit a warning that names the locked key, and + 2. be ignored in favour of the YIP/model value. + + We use ``nrolls`` here: the user asks for 2, but the YIP forces 1. + ``nrolls`` is in ``CoronagraphYIP.LOCKED_KEYS``, so the override is + rejected with a warning. + """ mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope + # Sanity check that the key we are testing really is locked, so this test + # stays meaningful if LOCKED_KEYS is ever refactored. + assert "nrolls" in CoronagraphYIP.LOCKED_KEYS + coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } + parameters = imager_yipcoronagraph_basic_params.copy() + parameters["nrolls"] = 2 # <-- attempt to override a locked key + mediator = MockMediator_IMAGER() - coronagraph.load_configuration(parameters, mediator) + with caplog.at_level(logging.WARNING, logger="pyEDITH"): + coronagraph.load_configuration(parameters, mediator) - assert coronagraph.pixscale == yippy_coronagraph.header.pixscale.value * LAMBDA_D - assert coronagraph.minimum_IWA == 2.0 * LAMBDA_D - assert coronagraph.maximum_OWA == 90.0 * LAMBDA_D - assert coronagraph.bandwidth == 0.1 - assert coronagraph.nrolls == 2 - assert coronagraph.nchannels == 1 - assert coronagraph.az_avg == True + # 1) The locked value from the YIP wins, not the user's 2. + assert coronagraph.nrolls == 1 + + # 2) A warning was emitted that mentions the locked key and that it is locked. + locked_warnings = [ + rec.message + for rec in caplog.records + if rec.levelno == logging.WARNING and "nrolls" in rec.message + ] + assert len(locked_warnings) == 1, ( + f"Expected exactly one lock warning for 'nrolls', " f"got: {locked_warnings}" + ) + assert "locked" in locked_warnings[0].lower() + # The rejected user value should be surfaced in the message for transparency. + assert "2" in locked_warnings[0] @patch("eacy.load_instrument") @patch("eacy.load_telescope") -def test_coronagraph_yip_load_configuration_imager_pixel_grid( +def test_coronagraph_yip_no_warning_when_user_sets_unlocked_key( mock_load_telescope, mock_load_instrument, + caplog, yippy_coronagraph, mock_instrument, mock_telescope, + imager_yipcoronagraph_basic_params, ): - """Test that pixel grid parameters are loaded from YIP.""" + """ + The opposite case: a user-supplied value for an UNLOCKED key (``bandwidth``) + must be applied and must NOT trigger a lock warning. + """ mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope + assert "bandwidth" not in CoronagraphYIP.LOCKED_KEYS + coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } + parameters = imager_yipcoronagraph_basic_params.copy() + mediator = MockMediator_IMAGER() - coronagraph.load_configuration(parameters, mediator) + with caplog.at_level(logging.WARNING, logger="pyEDITH"): + coronagraph.load_configuration(parameters, mediator) - assert coronagraph.npix == yippy_coronagraph.header.naxis1 - assert coronagraph.xcenter == yippy_coronagraph.header.xcenter * PIXEL - assert coronagraph.ycenter == yippy_coronagraph.header.ycenter * PIXEL + # User value is applied. + assert coronagraph.bandwidth == 0.1 + + # No "locked" warning about bandwidth. + bandwidth_lock_warnings = [ + rec.message + for rec in caplog.records + if rec.levelno == logging.WARNING + and "bandwidth" in rec.message + and "locked" in rec.message.lower() + ] + assert bandwidth_lock_warnings == [] @patch("eacy.load_instrument") @patch("eacy.load_telescope") -def test_coronagraph_yip_load_configuration_imager_has_required_attributes( +def test_coronagraph_yip_load_configuration_imager_basic_parameters( mock_load_telescope, mock_load_instrument, yippy_coronagraph, mock_instrument, mock_telescope, + imager_yipcoronagraph_basic_params, ): - """Test that all required attributes are created in IMAGER mode.""" + """Test that basic YIP parameters are loaded correctly in IMAGER mode.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } + parameters = imager_yipcoronagraph_basic_params.copy() + mediator = MockMediator_IMAGER() coronagraph.load_configuration(parameters, mediator) + assert coronagraph.pixscale == yippy_coronagraph.header.pixscale.value * LAMBDA_D + assert coronagraph.bandwidth == 0.1 + assert coronagraph.nrolls == 1 + assert coronagraph.nchannels == 1 + assert coronagraph.az_avg == True + assert coronagraph.npix == yippy_coronagraph.header.naxis1 + assert coronagraph.xcenter == yippy_coronagraph.header.xcenter * PIXEL + assert coronagraph.ycenter == yippy_coronagraph.header.ycenter * PIXEL + assert hasattr(coronagraph, "npix") assert hasattr(coronagraph, "xcenter") assert hasattr(coronagraph, "ycenter") @@ -651,34 +724,6 @@ def test_coronagraph_yip_load_configuration_imager_has_required_attributes( assert hasattr(coronagraph, "Istar") assert hasattr(coronagraph, "noisefloor") - -@patch("eacy.load_instrument") -@patch("eacy.load_telescope") -def test_coronagraph_yip_load_configuration_imager_array_shapes( - mock_load_telescope, - mock_load_instrument, - yippy_coronagraph, - mock_instrument, - mock_telescope, -): - """Test that arrays have correct shapes in IMAGER mode.""" - mock_load_instrument.return_value = mock_instrument - mock_load_telescope.return_value = mock_telescope - - coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } - mediator = MockMediator_IMAGER() - - coronagraph.load_configuration(parameters, mediator) - assert coronagraph.r.shape == (coronagraph.npix, coronagraph.npix) assert coronagraph.omega_lod.shape == (coronagraph.npix, coronagraph.npix, 1) assert coronagraph.skytrans.shape == (coronagraph.npix, coronagraph.npix) @@ -689,129 +734,14 @@ def test_coronagraph_yip_load_configuration_imager_array_shapes( ) assert coronagraph.Istar.shape == (coronagraph.npix, coronagraph.npix) assert coronagraph.noisefloor.shape == (coronagraph.npix, coronagraph.npix) - - -@patch("eacy.load_instrument") -@patch("eacy.load_telescope") -def test_coronagraph_yip_load_configuration_imager_non_zero_values( - mock_load_telescope, - mock_load_instrument, - yippy_coronagraph, - mock_instrument, - mock_telescope, -): - """Test that arrays contain non-zero values where expected.""" - mock_load_instrument.return_value = mock_instrument - mock_load_telescope.return_value = mock_telescope - - coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } - mediator = MockMediator_IMAGER() - - coronagraph.load_configuration(parameters, mediator) - assert not np.all(coronagraph.omega_lod == 0) assert not np.all(coronagraph.skytrans == 0) assert not np.all(coronagraph.photometric_aperture_throughput == 0) assert not np.all(coronagraph.Istar == 0) - - -@patch("eacy.load_instrument") -@patch("eacy.load_telescope") -def test_coronagraph_yip_load_configuration_imager_correct_units( - mock_load_telescope, - mock_load_instrument, - yippy_coronagraph, - mock_instrument, - mock_telescope, -): - """Test that arrays have correct units in IMAGER mode.""" - mock_load_instrument.return_value = mock_instrument - mock_load_telescope.return_value = mock_telescope - - coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } - mediator = MockMediator_IMAGER() - - coronagraph.load_configuration(parameters, mediator) - assert coronagraph.omega_lod.unit == LAMBDA_D**2 assert coronagraph.noisefloor.unit == DIMENSIONLESS - - -@patch("eacy.load_instrument") -@patch("eacy.load_telescope") -def test_coronagraph_yip_load_configuration_imager_skytrans_from_yip( - mock_load_telescope, - mock_load_instrument, - yippy_coronagraph, - mock_instrument, - mock_telescope, -): - """Test that sky transmission matches YIP values.""" - mock_load_instrument.return_value = mock_instrument - mock_load_telescope.return_value = mock_telescope - - coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } - mediator = MockMediator_IMAGER() - - coronagraph.load_configuration(parameters, mediator) - assert np.all(coronagraph.skytrans == yippy_coronagraph.sky_trans() * DIMENSIONLESS) - -@patch("eacy.load_instrument") -@patch("eacy.load_telescope") -def test_coronagraph_yip_load_configuration_imager_optical_throughput( - mock_load_telescope, - mock_load_instrument, - yippy_coronagraph, - mock_instrument, - mock_telescope, -): - """Test that coronagraph optical throughput is loaded correctly.""" - mock_load_instrument.return_value = mock_instrument - mock_load_telescope.return_value = mock_telescope - - coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } - mediator = MockMediator_IMAGER() - - coronagraph.load_configuration(parameters, mediator) - assert len(coronagraph.coronagraph_optical_throughput) == 1 assert np.isclose(coronagraph.coronagraph_optical_throughput.value, 0.394770896) @@ -825,21 +755,15 @@ def test_coronagraph_yip_load_configuration_imager_default_noisefloor_ppf( mock_instrument, mock_telescope, caplog, + imager_yipcoronagraph_basic_params, ): """Test that default noisefloor_PPF is used when not provided.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } + parameters = imager_yipcoronagraph_basic_params.copy() + mediator = MockMediator_IMAGER() with caplog.at_level(logging.WARNING, logger="pyEDITH"): @@ -869,22 +793,15 @@ def test_coronagraph_yip_load_configuration_imager_custom_noisefloor_ppf( mock_instrument, mock_telescope, caplog, + imager_yipcoronagraph_basic_params, ): """Test that custom noisefloor_PPF is used correctly.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - "noisefloor_PPF": 35, - } + parameters = imager_yipcoronagraph_basic_params.copy() + parameters["noisefloor_PPF"] = 35 mediator = MockMediator_IMAGER() caplog.clear() @@ -915,22 +832,15 @@ def test_coronagraph_yip_load_configuration_imager_noisefloor_factor_raises_erro yippy_coronagraph, mock_instrument, mock_telescope, + imager_yipcoronagraph_basic_params, ): """Test that noisefloor_factor raises appropriate error in YIP mode.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - "noisefloor_factor": 1e-10, - } + parameters = imager_yipcoronagraph_basic_params.copy() + parameters["noisefloor_factor"] = 1e-10 mediator = MockMediator_IMAGER() with pytest.raises( @@ -948,19 +858,15 @@ def test_coronagraph_yip_load_configuration_imager_missing_aperture_raises_error yippy_coronagraph, mock_instrument, mock_telescope, + imager_yipcoronagraph_basic_params, ): """Test that missing both aperture parameters raises error.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "nrolls": 2, - "nchannels": 1, - } + parameters = imager_yipcoronagraph_basic_params.copy() + del parameters["psf_trunc_ratio"] mediator = MockMediator_IMAGER() with pytest.raises( @@ -978,31 +884,37 @@ def test_coronagraph_yip_load_configuration_imager_az_avg_false( yippy_coronagraph, mock_instrument, mock_telescope, + imager_yipcoronagraph_basic_params, ): """Test that az_avg=False uses full 2D stellar intensity map.""" + from unittest.mock import MagicMock + mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope + # Wrap the methods as mocks while preserving their return values + original_stellar_intens = yippy_coronagraph.stellar_intens + original_core_mean = yippy_coronagraph.core_mean_intensity_map + + yippy_coronagraph.stellar_intens = MagicMock(side_effect=original_stellar_intens) + yippy_coronagraph.core_mean_intensity_map = MagicMock( + side_effect=original_core_mean + ) + coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": False, - } + parameters = imager_yipcoronagraph_basic_params.copy() + parameters["az_avg"] = False mediator = MockMediator_IMAGER() coronagraph.load_configuration(parameters, mediator) + # Verify stellar_intens was called (else branch), not core_mean_intensity_map + yippy_coronagraph.stellar_intens.assert_called_once() + yippy_coronagraph.core_mean_intensity_map.assert_not_called() + assert coronagraph.Istar.shape == (coronagraph.npix, coronagraph.npix) assert coronagraph.Istar.unit == DIMENSIONLESS assert not np.all(coronagraph.Istar == 0) - - # Verify it's 2D (not azimuthally averaged) - # The stellar intensity should vary across the 2D map assert coronagraph.Istar.ndim == 2 @@ -1013,39 +925,61 @@ def test_coronagraph_yip_load_configuration_imager_az_avg_false( @patch("eacy.load_instrument") @patch("eacy.load_telescope") -def test_coronagraph_yip_load_configuration_ifs_optical_throughput( +def test_coronagraph_yip_load_configuration_ifs_basic_parameters( mock_load_telescope, mock_load_instrument, yippy_coronagraph, mock_instrument, mock_telescope, caplog, + ifs_yipcoronagraph_basic_params, ): """Test that IFS mode correctly handles multiple wavelengths.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IFS", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "photometric_aperture_radius": 0.8, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } - mediator_ifs = MockMediator_IFS() + parameters = ifs_yipcoronagraph_basic_params.copy() + mediator = MockMediator_IFS() - with caplog.at_level(logging.WARNING, logger="pyEDITH"): - coronagraph.load_configuration(parameters, mediator_ifs) + coronagraph.load_configuration(parameters, mediator) + + assert coronagraph.pixscale == yippy_coronagraph.header.pixscale.value * LAMBDA_D + assert coronagraph.bandwidth == 0.1 + assert coronagraph.nrolls == 1 + assert coronagraph.nchannels == 1 + assert coronagraph.az_avg == True + assert coronagraph.npix == yippy_coronagraph.header.naxis1 + assert coronagraph.xcenter == yippy_coronagraph.header.xcenter * PIXEL + assert coronagraph.ycenter == yippy_coronagraph.header.ycenter * PIXEL + + assert hasattr(coronagraph, "npix") + assert hasattr(coronagraph, "xcenter") + assert hasattr(coronagraph, "ycenter") + assert hasattr(coronagraph, "r") + assert hasattr(coronagraph, "omega_lod") + assert hasattr(coronagraph, "skytrans") + assert hasattr(coronagraph, "photometric_aperture_throughput") + assert hasattr(coronagraph, "Istar") + assert hasattr(coronagraph, "noisefloor") - assert any( - "Both 'photometric_aperture_radius' and 'psf_trunc_ratio' provided" - in record.message - for record in caplog.records + assert coronagraph.r.shape == (coronagraph.npix, coronagraph.npix) + assert coronagraph.omega_lod.shape == (coronagraph.npix, coronagraph.npix, 1) + assert coronagraph.skytrans.shape == (coronagraph.npix, coronagraph.npix) + assert coronagraph.photometric_aperture_throughput.shape == ( + coronagraph.npix, + coronagraph.npix, + 1, ) + assert coronagraph.Istar.shape == (coronagraph.npix, coronagraph.npix) + assert coronagraph.noisefloor.shape == (coronagraph.npix, coronagraph.npix) + assert not np.all(coronagraph.omega_lod == 0) + assert not np.all(coronagraph.skytrans == 0) + assert not np.all(coronagraph.photometric_aperture_throughput == 0) + assert not np.all(coronagraph.Istar == 0) + assert coronagraph.omega_lod.unit == LAMBDA_D**2 + assert coronagraph.noisefloor.unit == DIMENSIONLESS + assert np.all(coronagraph.skytrans == yippy_coronagraph.sky_trans() * DIMENSIONLESS) assert len(coronagraph.coronagraph_optical_throughput) == 3 assert np.isclose( @@ -1056,69 +990,59 @@ def test_coronagraph_yip_load_configuration_ifs_optical_throughput( @patch("eacy.load_instrument") @patch("eacy.load_telescope") -def test_coronagraph_yip_load_configuration_ifs_psf_trunc_ratio_warning( +def test_coronagraph_yip_load_configuration_ifs_prioritize_psf_trunc_ratio( mock_load_telescope, mock_load_instrument, yippy_coronagraph, mock_instrument, mock_telescope, caplog, + ifs_yipcoronagraph_basic_params, ): - """Test warning when both aperture methods provided in IFS mode.""" + """Test that IFS mode correctly handles multiple wavelengths.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IFS", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "photometric_aperture_radius": 0.8, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } + parameters = ifs_yipcoronagraph_basic_params.copy() + parameters["photometric_aperture_radius"] = 0.3 mediator_ifs = MockMediator_IFS() with caplog.at_level(logging.INFO, logger="pyEDITH"): coronagraph.load_configuration(parameters, mediator_ifs) assert any( - "Using psf_trunc_ratio to calculate Omega..." in record.message + "Both 'photometric_aperture_radius' and 'psf_trunc_ratio' provided" + in record.message for record in caplog.records ) - -# ============================================================================ -# Tests for CoronagraphYIP with photometric_aperture_radius -# ============================================================================ + assert any( + "Using psf_trunc_ratio to calculate Omega..." in record.message + for record in caplog.records + ) @patch("eacy.load_instrument") @patch("eacy.load_telescope") -def test_coronagraph_yip_photometric_aperture_with_custom_tcore( +def test_coronagraph_yip_photometric_aperture_tcore_calculations( mock_load_telescope, mock_load_instrument, yippy_coronagraph, mock_instrument, mock_telescope, caplog, + ifs_yipcoronagraph_basic_params, ): """Test photometric aperture calculation with user-defined Tcore.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.85, - "nrolls": 2, - "nchannels": 1, - "Tcore": 0.5 * DIMENSIONLESS, - } + parameters = ifs_yipcoronagraph_basic_params.copy() + del parameters["psf_trunc_ratio"] + parameters["photometric_aperture_radius"] = 0.3 + parameters["Tcore"] = 0.5 mediator = MockMediator_IMAGER() with caplog.at_level(logging.INFO, logger="pyEDITH"): @@ -1131,90 +1055,23 @@ def test_coronagraph_yip_photometric_aperture_with_custom_tcore( assert any( "Using user-defined Tcore..." in record.message for record in caplog.records ) - - -@patch("eacy.load_instrument") -@patch("eacy.load_telescope") -def test_coronagraph_yip_photometric_aperture_omega_calculation( - mock_load_telescope, - mock_load_instrument, - yippy_coronagraph, - mock_instrument, - mock_telescope, -): - """Test that omega_lod is calculated correctly with photometric aperture.""" - mock_load_instrument.return_value = mock_instrument - mock_load_telescope.return_value = mock_telescope - - coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.85, - "nrolls": 2, - "nchannels": 1, - "Tcore": 0.5 * DIMENSIONLESS, - } - mediator = MockMediator_IMAGER() - - coronagraph.load_configuration(parameters, mediator) - assert coronagraph.omega_lod.shape == (coronagraph.npix, coronagraph.npix, 1) assert np.all( coronagraph.omega_lod == np.pi * parameters["photometric_aperture_radius"] ** 2 * LAMBDA_D**2 ) - - -@patch("eacy.load_instrument") -@patch("eacy.load_telescope") -def test_coronagraph_yip_photometric_aperture_throughput_iwa_owa( - mock_load_telescope, - mock_load_instrument, - yippy_coronagraph, - mock_instrument, - mock_telescope, -): - """Test that photometric aperture throughput respects IWA and OWA bounds.""" - mock_load_instrument.return_value = mock_instrument - mock_load_telescope.return_value = mock_telescope - - coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "photometric_aperture_radius": 0.85, - "nrolls": 2, - "nchannels": 1, - "Tcore": 0.5 * DIMENSIONLESS, - } - mediator = MockMediator_IMAGER() - - coronagraph.load_configuration(parameters, mediator) - assert coronagraph.photometric_aperture_throughput.shape == ( coronagraph.npix, coronagraph.npix, 1, ) assert np.all( - (coronagraph.photometric_aperture_throughput == 0.5 * DIMENSIONLESS) + ( + coronagraph.photometric_aperture_throughput + == parameters["Tcore"] * DIMENSIONLESS + ) | (coronagraph.photometric_aperture_throughput == 0.0 * DIMENSIONLESS) ) - assert np.all( - coronagraph.photometric_aperture_throughput[ - coronagraph.r < coronagraph.minimum_IWA - ] - == 0.0 * DIMENSIONLESS - ) - assert np.all( - coronagraph.photometric_aperture_throughput[ - coronagraph.r > coronagraph.maximum_OWA - ] - == 0.0 * DIMENSIONLESS - ) @patch("eacy.load_instrument") @@ -1226,20 +1083,16 @@ def test_coronagraph_yip_photometric_aperture_default_tcore( mock_instrument, mock_telescope, caplog, + ifs_yipcoronagraph_basic_params, ): """Test that default Tcore is used when not provided.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "photometric_aperture_radius": 0.85, - "bandwidth": 0.1, - "nrolls": 2, - "nchannels": 1, - } + parameters = ifs_yipcoronagraph_basic_params.copy() + del parameters["psf_trunc_ratio"] + parameters["photometric_aperture_radius"] = 0.3 mediator = MockMediator_IMAGER() caplog.clear() @@ -1282,21 +1135,15 @@ def test_coronagraph_yip_load_from_path( mock_instrument, mock_telescope, coronagraph_path, + ifs_yipcoronagraph_basic_params, ): """Test that CoronagraphYIP can be constructed from a path.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(path=coronagraph_path) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } + parameters = ifs_yipcoronagraph_basic_params.copy() + mediator = MockMediator_IMAGER() coronagraph.load_configuration(parameters, mediator) @@ -1305,9 +1152,9 @@ def test_coronagraph_yip_load_from_path( assert hasattr(coronagraph, "Istar") -# ============================================================================ -# Tests for CoronagraphYIP with pre-constructed yippy_coro -# ============================================================================ +# # ============================================================================ +# # Tests for CoronagraphYIP with pre-constructed yippy_coro +# # ============================================================================ @patch("eacy.load_instrument") @@ -1318,21 +1165,14 @@ def test_coronagraph_yip_preconstruced_yippy_coro( mock_instrument, mock_telescope, yippy_coronagraph, + ifs_yipcoronagraph_basic_params, ): """Test that pre-constructed yippy_coro is used directly.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } + parameters = ifs_yipcoronagraph_basic_params.copy() mediator = MockMediator_IMAGER() coronagraph.load_configuration(parameters, mediator) @@ -1351,21 +1191,15 @@ def test_coronagraph_yip_preconstruced_yippy_trunc_ratio_mismatch_warning( mock_telescope, yippy_coronagraph, caplog, + ifs_yipcoronagraph_basic_params, ): """Test warning when yippy_coro psf_trunc_ratio differs from parameters.""" mock_load_instrument.return_value = mock_instrument mock_load_telescope.return_value = mock_telescope coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.99, # Different from yippy_coro - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } + parameters = ifs_yipcoronagraph_basic_params.copy() + mediator = MockMediator_IMAGER() caplog.clear() @@ -1387,6 +1221,7 @@ def test_coronagraph_yip_nrolls_from_yippy_object( mock_telescope, yippy_coronagraph, monkeypatch, + ifs_yipcoronagraph_basic_params, ): """Test that nrolls is read from yippy object when available.""" mock_load_instrument.return_value = mock_instrument @@ -1395,15 +1230,7 @@ def test_coronagraph_yip_nrolls_from_yippy_object( monkeypatch.setattr(yippy_coronagraph, "nrolls", 4, raising=False) coronagraph = CoronagraphYIP(yippy_coro=yippy_coronagraph) - parameters = { - "observing_mode": "IMAGER", - "maximum_OWA": 90.0, - "bandwidth": 0.1, - "psf_trunc_ratio": 0.3, - "nrolls": 2, - "nchannels": 1, - "az_avg": True, - } + parameters = ifs_yipcoronagraph_basic_params.copy() coronagraph.load_configuration(parameters, MockMediator_IMAGER()) @@ -1415,10 +1242,10 @@ def test_coronagraph_yip_nrolls_from_yippy_object( # ============================================================================ -def test_validate_configuration_valid_setup(): - """Test that validate_configuration passes with valid configuration.""" +@pytest.fixture +def valid_coronagraph(): + """Fixture providing a coronagraph with valid configuration.""" coronagraph = ToyModelCoronagraph() - coronagraph.Istar = np.ones((100, 100)) * DIMENSIONLESS coronagraph.noisefloor = np.ones((100, 100)) * DIMENSIONLESS coronagraph.psf_trunc_ratio = 0.3 * DIMENSIONLESS @@ -1433,205 +1260,69 @@ def test_validate_configuration_valid_setup(): coronagraph.npsfratios = 1 coronagraph.nrolls = 1 coronagraph.nchannels = 1 - coronagraph.minimum_IWA = 2 * LAMBDA_D - coronagraph.maximum_OWA = 10 * LAMBDA_D coronagraph.coronagraph_optical_throughput = np.array([0.5]) * DIMENSIONLESS coronagraph.coronagraph_spectral_resolution = 1 * DIMENSIONLESS + return coronagraph + +def test_validate_configuration_valid_setup(valid_coronagraph): + """Test that validate_configuration passes with valid configuration.""" # Should not raise any exception - coronagraph.validate_configuration() + valid_coronagraph.validate_configuration() -def test_validate_configuration_missing_istar(): +def test_validate_configuration_missing_istar(valid_coronagraph): """Test that missing Istar attribute raises AttributeError.""" - coronagraph = ToyModelCoronagraph() - - coronagraph.noisefloor = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.psf_trunc_ratio = 0.3 * DIMENSIONLESS - coronagraph.photometric_aperture_throughput = np.ones((100, 100, 1)) * DIMENSIONLESS - coronagraph.omega_lod = np.ones((100, 100, 1)) * LAMBDA_D**2 - coronagraph.skytrans = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.pixscale = 0.1 * LAMBDA_D - coronagraph.npix = 100 - coronagraph.xcenter = 50 * PIXEL - coronagraph.ycenter = 50 * PIXEL - coronagraph.bandwidth = 0.1 - coronagraph.npsfratios = 1 - coronagraph.nrolls = 1 - coronagraph.nchannels = 1 - coronagraph.minimum_IWA = 2 * LAMBDA_D - coronagraph.maximum_OWA = 10 * LAMBDA_D - coronagraph.coronagraph_optical_throughput = np.array([0.5]) * DIMENSIONLESS - coronagraph.coronagraph_spectral_resolution = 1 * DIMENSIONLESS - + delattr(valid_coronagraph, "Istar") with pytest.raises(AttributeError, match="Coronagraph is missing attribute: Istar"): - coronagraph.validate_configuration() + valid_coronagraph.validate_configuration() -def test_validate_configuration_incorrect_npix_type(): +def test_validate_configuration_incorrect_npix_type(valid_coronagraph): """Test that non-integer npix raises TypeError.""" - coronagraph = ToyModelCoronagraph() - - coronagraph.Istar = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.noisefloor = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.psf_trunc_ratio = 0.3 * DIMENSIONLESS - coronagraph.photometric_aperture_throughput = np.ones((100, 100, 1)) * DIMENSIONLESS - coronagraph.omega_lod = np.ones((100, 100, 1)) * LAMBDA_D**2 - coronagraph.skytrans = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.pixscale = 0.1 * LAMBDA_D - coronagraph.npix = 100.0 # Should be int - coronagraph.xcenter = 50 * PIXEL - coronagraph.ycenter = 50 * PIXEL - coronagraph.bandwidth = 0.1 - coronagraph.npsfratios = 1 - coronagraph.nrolls = 1 - coronagraph.nchannels = 1 - coronagraph.minimum_IWA = 2 * LAMBDA_D - coronagraph.maximum_OWA = 10 * LAMBDA_D - coronagraph.coronagraph_optical_throughput = np.array([0.5]) * DIMENSIONLESS - coronagraph.coronagraph_spectral_resolution = 1 * DIMENSIONLESS - + valid_coronagraph.npix = 100.0 # Should be int with pytest.raises( TypeError, match="Coronagraph attribute npix should be an integer" ): - coronagraph.validate_configuration() + valid_coronagraph.validate_configuration() -def test_validate_configuration_incorrect_bandwidth_type(): +def test_validate_configuration_incorrect_bandwidth_type(valid_coronagraph): """Test that non-float bandwidth raises TypeError.""" - coronagraph = ToyModelCoronagraph() - - coronagraph.Istar = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.noisefloor = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.psf_trunc_ratio = 0.3 * DIMENSIONLESS - coronagraph.photometric_aperture_throughput = np.ones((100, 100, 1)) * DIMENSIONLESS - coronagraph.omega_lod = np.ones((100, 100, 1)) * LAMBDA_D**2 - coronagraph.skytrans = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.pixscale = 0.1 * LAMBDA_D - coronagraph.npix = 100 - coronagraph.xcenter = 50 * PIXEL - coronagraph.ycenter = 50 * PIXEL - coronagraph.bandwidth = "0.1" # Should be float - coronagraph.npsfratios = 1 - coronagraph.nrolls = 1 - coronagraph.nchannels = 1 - coronagraph.minimum_IWA = 2 * LAMBDA_D - coronagraph.maximum_OWA = 10 * LAMBDA_D - coronagraph.coronagraph_optical_throughput = np.array([0.5]) * DIMENSIONLESS - coronagraph.coronagraph_spectral_resolution = 1 * DIMENSIONLESS - + valid_coronagraph.bandwidth = "0.1" # Should be float with pytest.raises( TypeError, match="Coronagraph attribute bandwidth should be a float" ): - coronagraph.validate_configuration() + valid_coronagraph.validate_configuration() -def test_validate_configuration_incorrect_pixscale_units(): +def test_validate_configuration_incorrect_pixscale_units(valid_coronagraph): """Test that incorrect pixscale units raise ValueError.""" - coronagraph = ToyModelCoronagraph() - - coronagraph.Istar = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.noisefloor = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.psf_trunc_ratio = 0.3 * DIMENSIONLESS - coronagraph.photometric_aperture_throughput = np.ones((100, 100, 1)) * DIMENSIONLESS - coronagraph.omega_lod = np.ones((100, 100, 1)) * LAMBDA_D**2 - coronagraph.skytrans = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.pixscale = 0.1 * u.m # Incorrect unit - coronagraph.npix = 100 - coronagraph.xcenter = 50 * PIXEL - coronagraph.ycenter = 50 * PIXEL - coronagraph.bandwidth = 0.1 - coronagraph.npsfratios = 1 - coronagraph.nrolls = 1 - coronagraph.nchannels = 1 - coronagraph.minimum_IWA = 2 * LAMBDA_D - coronagraph.maximum_OWA = 10 * LAMBDA_D - coronagraph.coronagraph_optical_throughput = np.array([0.5]) * DIMENSIONLESS - coronagraph.coronagraph_spectral_resolution = 1 * DIMENSIONLESS - + valid_coronagraph.pixscale = 0.1 * u.m # Incorrect unit with pytest.raises( ValueError, match="Coronagraph attribute pixscale has incorrect units" ): - coronagraph.validate_configuration() + valid_coronagraph.validate_configuration() -def test_validate_configuration_pixscale_not_quantity(): +def test_validate_configuration_pixscale_not_quantity(valid_coronagraph): """Test that pixscale without units raises TypeError.""" - coronagraph = ToyModelCoronagraph() - - coronagraph.Istar = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.noisefloor = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.psf_trunc_ratio = 0.3 * DIMENSIONLESS - coronagraph.photometric_aperture_throughput = np.ones((100, 100, 1)) * DIMENSIONLESS - coronagraph.omega_lod = np.ones((100, 100, 1)) * LAMBDA_D**2 - coronagraph.skytrans = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.pixscale = 0.1 # Missing unit - coronagraph.npix = 100 - coronagraph.xcenter = 50 * PIXEL - coronagraph.ycenter = 50 * PIXEL - coronagraph.bandwidth = 0.1 - coronagraph.npsfratios = 1 - coronagraph.nrolls = 1 - coronagraph.nchannels = 1 - coronagraph.minimum_IWA = 2 * LAMBDA_D - coronagraph.maximum_OWA = 10 * LAMBDA_D - coronagraph.coronagraph_optical_throughput = np.array([0.5]) * DIMENSIONLESS - coronagraph.coronagraph_spectral_resolution = 1 * DIMENSIONLESS - + valid_coronagraph.pixscale = 0.1 # Missing unit with pytest.raises( TypeError, match="Coronagraph attribute pixscale should be a Quantity" ): - coronagraph.validate_configuration() + valid_coronagraph.validate_configuration() -def test_validate_configuration_with_psf_trunc_ratio(): +def test_validate_configuration_with_psf_trunc_ratio(valid_coronagraph): """Test that validation passes with psf_trunc_ratio instead of aperture radius.""" - coronagraph = ToyModelCoronagraph() - - coronagraph.Istar = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.noisefloor = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.photometric_aperture_throughput = np.ones((100, 100, 1)) * DIMENSIONLESS - coronagraph.omega_lod = np.ones((100, 100, 1)) * LAMBDA_D**2 - coronagraph.skytrans = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.pixscale = 0.1 * LAMBDA_D - coronagraph.npix = 100 - coronagraph.xcenter = 50 * PIXEL - coronagraph.ycenter = 50 * PIXEL - coronagraph.bandwidth = 0.1 - coronagraph.npsfratios = 1 - coronagraph.nrolls = 1 - coronagraph.nchannels = 1 - coronagraph.minimum_IWA = 2 * LAMBDA_D - coronagraph.maximum_OWA = 10 * LAMBDA_D - coronagraph.coronagraph_optical_throughput = np.array([0.5]) * DIMENSIONLESS - coronagraph.coronagraph_spectral_resolution = 1 * DIMENSIONLESS - coronagraph.psf_trunc_ratio = 0.3 * DIMENSIONLESS - + # valid_coronagraph already has psf_trunc_ratio set # Should not raise - coronagraph.validate_configuration() + valid_coronagraph.validate_configuration() -def test_validate_configuration_missing_both_aperture_params(): +def test_validate_configuration_missing_both_aperture_params(valid_coronagraph): """Test that missing both aperture parameters raises AttributeError.""" - coronagraph = ToyModelCoronagraph() - - coronagraph.Istar = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.noisefloor = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.photometric_aperture_throughput = np.ones((100, 100, 1)) * DIMENSIONLESS - coronagraph.omega_lod = np.ones((100, 100, 1)) * LAMBDA_D**2 - coronagraph.skytrans = np.ones((100, 100)) * DIMENSIONLESS - coronagraph.pixscale = 0.1 * LAMBDA_D - coronagraph.npix = 100 - coronagraph.xcenter = 50 * PIXEL - coronagraph.ycenter = 50 * PIXEL - coronagraph.bandwidth = 0.1 - coronagraph.npsfratios = 1 - coronagraph.nrolls = 1 - coronagraph.nchannels = 1 - coronagraph.minimum_IWA = 2 * LAMBDA_D - coronagraph.maximum_OWA = 10 * LAMBDA_D - coronagraph.coronagraph_optical_throughput = np.array([0.5]) * DIMENSIONLESS - coronagraph.coronagraph_spectral_resolution = 1 * DIMENSIONLESS - + delattr(valid_coronagraph, "psf_trunc_ratio") with pytest.raises(AttributeError, match="photometric_aperture_radius"): - coronagraph.validate_configuration() + valid_coronagraph.validate_configuration() diff --git a/tests/test_detectors.py b/tests/test_detectors.py index 32a4384..4c9d248 100644 --- a/tests/test_detectors.py +++ b/tests/test_detectors.py @@ -19,7 +19,6 @@ FRAME, ) - # ============================================================================ # Mock Objects and Fixtures # ============================================================================ @@ -47,6 +46,8 @@ def get_observation_parameter(self, param): return np.array([0.5, 0.7, 1.2]) * WAVELENGTH elif self.observing_mode == "IMAGER": return np.array([0.5]) * WAVELENGTH + elif param == "observing_mode": + return self.observing_mode return 1.0 def get_coronagraph_parameter(self, param): @@ -135,11 +136,13 @@ def mock_detector(): def _create_mock(detector_type): mock = MagicMock() - mock.lam = np.array([0.2, 0.8, 1.1, 1.6]) * u.um + mock.lam = np.array([0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8]) * u.um mock.verbose = False - qe_vis = np.array([0.9, 0.9, np.nan, np.nan]) - qe_nir = np.array([np.nan, np.nan, 0.85, 0.85]) + qe_vis = np.array([0.9, 0.9, 0.9, 0.9, 0.9, np.nan, np.nan, np.nan, np.nan]) + qe_nir = np.array( + [np.nan, np.nan, np.nan, np.nan, np.nan, 0.85, 0.85, 0.85, 0.85] + ) common_dict = { "lam": mock.lam, @@ -177,18 +180,47 @@ def _create_mock(detector_type): @pytest.fixture -def toy_detector_parameters(): +def imager_toy_detector_parameters(): """Fixture providing standard parameters for ToyModelDetector testing.""" return { "pixscale_mas": 10, - "npix_multiplier": [2], - "DC": [4e-5], - "RN": [1.0], - "tread": [1100], - "CIC": [1.5e-3], + "npix_multiplier": 2, + "DC": 4e-5, + "RN": 1.0, + "tread": 1100, + "CIC": 1.5e-3, + "wavelength": 0.5, + "observing_mode": "IMAGER", } +@pytest.fixture +def ifs_toy_detector_parameters(): + """Fixture providing standard parameters for ToyModelDetector testing.""" + return { + "pixscale_mas": 10, + "npix_multiplier": 2, + "DC": 4e-5, + "RN": 1.0, + "tread": 1100, + "CIC": 1.5e-3, + "wavelength": [0.5, 0.7, 1.2], + "observing_mode": "IFS", + } + + +@pytest.fixture +def imager_eac_detector_parameters(): + """Fixture providing standard parameters for EACDetector testing.""" + return {"wavelength": 0.5, "observing_mode": "IMAGER"} + + +@pytest.fixture +def ifs_eac_detector_parameters(): + """Fixture providing standard parameters for EACDetector testing.""" + return {"wavelength": [0.5, 0.7, 1.2], "observing_mode": "IFS"} + + # ============================================================================ # Tests for ToyModelDetector initialization # ============================================================================ @@ -208,39 +240,23 @@ def test_toy_model_detector_init(): def test_toy_model_detector_load_configuration_imager_user_params( - toy_detector_parameters, + imager_toy_detector_parameters, ): """Test loading ToyModelDetector configuration with user parameters in IMAGER mode.""" detector = ToyModelDetector() mediator = MockMediator("IMAGER") + parameters = imager_toy_detector_parameters.copy() - detector.load_configuration(toy_detector_parameters, mediator) + detector.load_configuration(parameters, mediator) assert detector.pixscale_mas == 10 * MAS - assert np.all(detector.npix_multiplier == [2] * DIMENSIONLESS) + assert detector.npix_multiplier == 2 * DIMENSIONLESS assert np.all(detector.DC == [4e-5] * DARK_CURRENT) assert np.all(detector.RN == [1.0] * READ_NOISE) assert np.all(detector.tread == [1100] * READ_TIME) assert np.all(detector.CIC == [1.5e-3] * CLOCK_INDUCED_CHARGE) - - -def test_toy_model_detector_load_configuration_imager_default_qe(): - """Test that default QE and dQE values are used in IMAGER mode.""" - detector = ToyModelDetector() - mediator = MockMediator("IMAGER") - parameters = { - "pixscale_mas": 10, - "npix_multiplier": [2], - "DC": [4e-5], - "RN": [1.0], - "tread": [1100], - "CIC": [1.5e-3], - } - - detector.load_configuration(parameters, mediator) - - assert np.all(detector.QE == [0.9] * QUANTUM_EFFICIENCY) - assert np.all(detector.dQE == [0.75] * DIMENSIONLESS) + assert np.all(detector.QE == [0.9] * QUANTUM_EFFICIENCY) # default + assert np.all(detector.dQE == [0.75] * DIMENSIONLESS) # default def test_toy_model_detector_load_configuration_imager_defaults(): @@ -248,9 +264,16 @@ def test_toy_model_detector_load_configuration_imager_defaults(): detector = ToyModelDetector() mediator = MockMediator("IMAGER") - detector.load_configuration({}, mediator) + detector.load_configuration({"wavelength": 0.5}, mediator) assert np.isclose(detector.pixscale_mas, 6.4457752 * MAS) + assert detector.npix_multiplier == 1 * DIMENSIONLESS + assert np.all(detector.DC == [3e-5] * DARK_CURRENT) + assert np.all(detector.RN == [0.0] * READ_NOISE) + assert np.all(detector.tread == [1000] * READ_TIME) + assert np.all(detector.CIC == [1.3e-3] * CLOCK_INDUCED_CHARGE) + assert np.all(detector.QE == [0.9] * QUANTUM_EFFICIENCY) # defaults + assert np.all(detector.dQE == [0.75] * DIMENSIONLESS) # defaults # ============================================================================ @@ -258,38 +281,23 @@ def test_toy_model_detector_load_configuration_imager_defaults(): # ============================================================================ -def test_toy_model_detector_load_configuration_ifs_user_params(toy_detector_parameters): +def test_toy_model_detector_load_configuration_ifs_user_params( + ifs_toy_detector_parameters, +): """Test loading ToyModelDetector configuration with user parameters in IFS mode.""" detector = ToyModelDetector() mediator = MockMediator("IFS") - detector.load_configuration(toy_detector_parameters, mediator) + detector.load_configuration(ifs_toy_detector_parameters, mediator) assert detector.pixscale_mas == 10 * MAS - assert np.all(detector.npix_multiplier == [2, 2, 2] * DIMENSIONLESS) + assert detector.npix_multiplier == 2 * DIMENSIONLESS assert np.all(detector.DC == [4e-5, 4e-5, 4e-5] * DARK_CURRENT) assert np.all(detector.RN == [1.0, 1.0, 1.0] * READ_NOISE) assert np.all(detector.tread == [1100, 1100, 1100] * READ_TIME) assert np.all(detector.CIC == [1.5e-3, 1.5e-3, 1.5e-3] * CLOCK_INDUCED_CHARGE) - - -def test_toy_model_detector_load_configuration_ifs_default_qe(): - """Test that default QE and dQE values are broadcast in IFS mode.""" - detector = ToyModelDetector() - mediator = MockMediator("IFS") - parameters = { - "pixscale_mas": 10, - "npix_multiplier": [2], - "DC": [4e-5], - "RN": [1.0], - "tread": [1100], - "CIC": [1.5e-3], - } - - detector.load_configuration(parameters, mediator) - - assert np.all(detector.QE == [0.9, 0.9, 0.9] * QUANTUM_EFFICIENCY) - assert np.all(detector.dQE == [0.75, 0.75, 0.75] * DIMENSIONLESS) + assert np.all(detector.QE == [0.9, 0.9, 0.9] * QUANTUM_EFFICIENCY) # defaults + assert np.all(detector.dQE == [0.75, 0.75, 0.75] * DIMENSIONLESS) # defaults def test_toy_model_detector_load_configuration_ifs_defaults(): @@ -297,49 +305,43 @@ def test_toy_model_detector_load_configuration_ifs_defaults(): detector = ToyModelDetector() mediator = MockMediator("IFS") - detector.load_configuration({}, mediator) + detector.load_configuration({"wavelength": [0.5, 0.7, 1.2]}, mediator) assert np.isclose(detector.pixscale_mas, 6.4457752 * MAS) + assert detector.npix_multiplier == 1 * DIMENSIONLESS + assert np.all(detector.DC == [3e-5, 3e-5, 3e-5] * DARK_CURRENT) + assert np.all(detector.RN == [0.0, 0.0, 0.0] * READ_NOISE) + assert np.all(detector.tread == [1000, 1000, 1000] * READ_TIME) + assert np.all(detector.CIC == [1.3e-3, 1.3e-3, 1.3e-3] * CLOCK_INDUCED_CHARGE) + assert np.all(detector.QE == [0.9, 0.9, 0.9] * QUANTUM_EFFICIENCY) # defaults + assert np.all(detector.dQE == [0.75, 0.75, 0.75] * DIMENSIONLESS) # defaults -# ============================================================================ -# Tests for EACDetector.load_configuration - IMAGER mode -# ============================================================================ +# # ============================================================================ +# # Tests for EACDetector.load_configuration - IMAGER mode +# # ============================================================================ @patch("eacy.load_detector") @patch("eacy.load_instrument") def test_eac_detector_load_configuration_imager_basic( - mock_load_instrument, mock_load_detector, mock_instrument, mock_detector + mock_load_instrument, + mock_load_detector, + mock_instrument, + mock_detector, + imager_eac_detector_parameters, ): """Test basic EACDetector configuration loading in IMAGER mode.""" mock_load_instrument.return_value = mock_instrument mock_load_detector.return_value = mock_detector("IMAGER") - + parameters = imager_eac_detector_parameters.copy() detector = EACDetector() - parameters = {"observing_mode": "IMAGER"} mediator = MockMediator("IMAGER") detector.load_configuration(parameters, mediator) assert detector.pixscale_mas is not None - assert np.all(detector.npix_multiplier == 1 * DIMENSIONLESS) - - -@patch("eacy.load_detector") -@patch("eacy.load_instrument") -def test_eac_detector_load_configuration_imager_units( - mock_load_instrument, mock_load_detector, mock_instrument, mock_detector -): - """Test that all detector parameters have correct units in IMAGER mode.""" - mock_load_instrument.return_value = mock_instrument - mock_load_detector.return_value = mock_detector("IMAGER") - - detector = EACDetector() - parameters = {"observing_mode": "IMAGER"} - mediator = MockMediator("IMAGER") - - detector.load_configuration(parameters, mediator) + assert detector.npix_multiplier == 1 * DIMENSIONLESS assert detector.DC.unit == DARK_CURRENT assert detector.RN.unit == READ_NOISE @@ -347,46 +349,15 @@ def test_eac_detector_load_configuration_imager_units( assert detector.CIC.unit == CLOCK_INDUCED_CHARGE assert detector.QE.unit == QUANTUM_EFFICIENCY assert detector.dQE.unit == DIMENSIONLESS - - -@patch("eacy.load_detector") -@patch("eacy.load_instrument") -def test_eac_detector_load_configuration_imager_shapes( - mock_load_instrument, mock_load_detector, mock_instrument, mock_detector -): - """Test that detector parameter arrays have correct shapes in IMAGER mode.""" - mock_load_instrument.return_value = mock_instrument - mock_load_detector.return_value = mock_detector("IMAGER") - - detector = EACDetector() - parameters = {"observing_mode": "IMAGER"} - mediator = MockMediator("IMAGER") - - detector.load_configuration(parameters, mediator) - expected_shape = (1,) assert detector.DC.shape == expected_shape assert detector.RN.shape == expected_shape assert detector.QE.shape == expected_shape - -@patch("eacy.load_detector") -@patch("eacy.load_instrument") -def test_eac_detector_load_configuration_imager_values( - mock_load_instrument, mock_load_detector, mock_instrument, mock_detector -): - """Test that detector parameters have correct values in IMAGER mode.""" - mock_load_instrument.return_value = mock_instrument - mock_load_detector.return_value = mock_detector("IMAGER") - - detector = EACDetector() - parameters = {"observing_mode": "IMAGER"} - mediator = MockMediator("IMAGER") - - detector.load_configuration(parameters, mediator) - - assert np.allclose(detector.DC.value, 3e-05) - assert np.allclose(detector.RN.value, 0.1) + assert np.allclose(detector.DC.value, 3e-05) # vis channel + assert np.allclose(detector.RN.value, 0.1) # vis channel + assert np.allclose(detector.QE.value, 0.9) # vis channel + assert np.allclose(detector.dQE.value, 0.75) # hardcoded # ============================================================================ @@ -397,37 +368,24 @@ def test_eac_detector_load_configuration_imager_values( @patch("eacy.load_detector") @patch("eacy.load_instrument") def test_eac_detector_load_configuration_ifs_basic( - mock_load_instrument, mock_load_detector, mock_instrument, mock_detector + mock_load_instrument, + mock_load_detector, + mock_instrument, + mock_detector, + ifs_eac_detector_parameters, ): """Test basic EACDetector configuration loading in IFS mode.""" mock_load_instrument.return_value = mock_instrument mock_load_detector.return_value = mock_detector("IFS") detector = EACDetector() - parameters = {"observing_mode": "IFS"} + parameters = ifs_eac_detector_parameters.copy() mediator = MockMediator("IFS") detector.load_configuration(parameters, mediator) assert detector.pixscale_mas is not None - assert np.all(detector.npix_multiplier == 1 * DIMENSIONLESS) - - -@patch("eacy.load_detector") -@patch("eacy.load_instrument") -def test_eac_detector_load_configuration_ifs_units( - mock_load_instrument, mock_load_detector, mock_instrument, mock_detector -): - """Test that all detector parameters have correct units in IFS mode.""" - mock_load_instrument.return_value = mock_instrument - mock_load_detector.return_value = mock_detector("IFS") - - detector = EACDetector() - parameters = {"observing_mode": "IFS"} - mediator = MockMediator("IFS") - - detector.load_configuration(parameters, mediator) - + assert detector.npix_multiplier == 1 * DIMENSIONLESS assert detector.DC.unit == DARK_CURRENT assert detector.RN.unit == READ_NOISE assert detector.tread.unit == READ_TIME @@ -435,44 +393,12 @@ def test_eac_detector_load_configuration_ifs_units( assert detector.QE.unit == QUANTUM_EFFICIENCY assert detector.dQE.unit == DIMENSIONLESS - -@patch("eacy.load_detector") -@patch("eacy.load_instrument") -def test_eac_detector_load_configuration_ifs_shapes( - mock_load_instrument, mock_load_detector, mock_instrument, mock_detector -): - """Test that detector parameter arrays have correct shapes in IFS mode.""" - mock_load_instrument.return_value = mock_instrument - mock_load_detector.return_value = mock_detector("IFS") - - detector = EACDetector() - parameters = {"observing_mode": "IFS"} - mediator = MockMediator("IFS") - - detector.load_configuration(parameters, mediator) - expected_shape = (3,) assert detector.DC.shape == expected_shape assert detector.RN.shape == expected_shape assert detector.QE.shape == expected_shape assert detector.CIC.shape == expected_shape - -@patch("eacy.load_detector") -@patch("eacy.load_instrument") -def test_eac_detector_load_configuration_ifs_wavelength_dependent_values( - mock_load_instrument, mock_load_detector, mock_instrument, mock_detector -): - """Test that detector parameters vary correctly with wavelength in IFS mode.""" - mock_load_instrument.return_value = mock_instrument - mock_load_detector.return_value = mock_detector("IFS") - - detector = EACDetector() - parameters = {"observing_mode": "IFS"} - mediator = MockMediator("IFS") - - detector.load_configuration(parameters, mediator) - # VIS wavelengths (< 1 μm) assert np.allclose(detector.DC[:2].value, 3e-05) assert np.allclose(detector.RN[:2].value, 0.0) @@ -482,54 +408,32 @@ def test_eac_detector_load_configuration_ifs_wavelength_dependent_values( assert np.allclose(detector.RN[2:].value, 0.4) -# ============================================================================ -# Tests for EACDetector.load_configuration - Error handling -# ============================================================================ - - -def test_eac_detector_load_configuration_unsupported_mode(): - """Test that unsupported observing mode raises KeyError.""" - detector = EACDetector() - parameters = {"observing_mode": "test"} - mediator = MockMediator("test") - - with pytest.raises(KeyError, match="Unsupported observing mode: test"): - detector.load_configuration(parameters, mediator) - - -def test_eac_detector_load_configuration_invalid_mode(): - """Test that invalid observing mode raises KeyError.""" - detector = EACDetector() - parameters = {"observing_mode": "INVALID"} - mediator = MockMediator("IMAGER") - - with pytest.raises(KeyError, match="Unsupported observing mode: INVALID"): - detector.load_configuration(parameters, mediator) - - -# ============================================================================ -# Tests for EACDetector validation inputs -# ============================================================================ +# # ============================================================================ +# # Tests for EACDetector validation inputs +# # ============================================================================ @pytest.mark.parametrize("observing_mode", ["IMAGER", "IFS"]) -def test_eac_detector_etc_validation_inputs(observing_mode): +def test_eac_detector_etc_validation_inputs( + observing_mode, ifs_eac_detector_parameters, imager_eac_detector_parameters +): """Test that ETC validation inputs are correctly loaded.""" detector = EACDetector() mediator = MockMediator(observing_mode) - - parameters = { - "observing_mode": observing_mode, - "t_photon_count_input": 0.7, - "det_npix_input": 200, - } + parameters = ( + imager_eac_detector_parameters + if observing_mode == "IMAGER" + else ifs_eac_detector_parameters + ) + parameters["t_photon_count_input"] = 0.7 + parameters["det_npix_input"] = 200 detector.load_configuration(parameters, mediator) assert hasattr(detector, "t_photon_count_input") assert hasattr(detector, "det_npix_input") assert detector.t_photon_count_input == 0.7 * SECOND / FRAME - assert detector.det_npix_input == 200 * DIMENSIONLESS + assert np.allclose(detector.det_npix_input, 200 * DIMENSIONLESS) # ============================================================================ @@ -537,11 +441,11 @@ def test_eac_detector_etc_validation_inputs(observing_mode): # ============================================================================ -def test_detector_validate_configuration_all_valid(toy_detector_parameters): +def test_detector_validate_configuration_all_valid(imager_toy_detector_parameters): """Test that validation passes with all correct attributes.""" detector = ToyModelDetector() parameters = { - **toy_detector_parameters, + **imager_toy_detector_parameters, "QE": [0.95], "dQE": [0.8], } @@ -553,12 +457,14 @@ def test_detector_validate_configuration_all_valid(toy_detector_parameters): detector.validate_configuration() -def test_detector_validate_configuration_missing_pixscale(toy_detector_parameters): +def test_detector_validate_configuration_missing_pixscale( + imager_toy_detector_parameters, +): """Test that missing pixscale_mas attribute raises AttributeError.""" detector = ToyModelDetector() mediator = MockMediator() - detector.load_configuration(toy_detector_parameters, mediator) + detector.load_configuration(imager_toy_detector_parameters, mediator) delattr(detector, "pixscale_mas") with pytest.raises( @@ -567,12 +473,14 @@ def test_detector_validate_configuration_missing_pixscale(toy_detector_parameter detector.validate_configuration() -def test_detector_validate_configuration_pixscale_not_quantity(toy_detector_parameters): +def test_detector_validate_configuration_pixscale_not_quantity( + imager_toy_detector_parameters, +): """Test that non-Quantity pixscale_mas raises TypeError.""" detector = ToyModelDetector() mediator = MockMediator() - detector.load_configuration(toy_detector_parameters, mediator) + detector.load_configuration(imager_toy_detector_parameters, mediator) detector.pixscale_mas = 10 # Not a Quantity with pytest.raises( @@ -582,13 +490,13 @@ def test_detector_validate_configuration_pixscale_not_quantity(toy_detector_para def test_detector_validate_configuration_incorrect_pixscale_units( - toy_detector_parameters, + imager_toy_detector_parameters, ): """Test that pixscale_mas with incorrect units raises ValueError.""" detector = ToyModelDetector() mediator = MockMediator() - detector.load_configuration(toy_detector_parameters, mediator) + detector.load_configuration(imager_toy_detector_parameters, mediator) detector.pixscale_mas = 10 * u.arcsec # Wrong unit with pytest.raises( @@ -597,9 +505,9 @@ def test_detector_validate_configuration_incorrect_pixscale_units( detector.validate_configuration() -# ============================================================================ -# Tests for parameter broadcasting in IFS mode -# ============================================================================ +# # ============================================================================ +# # Tests for parameter broadcasting in IFS mode +# # ============================================================================ def test_toy_model_detector_scalar_to_array_broadcasting(): @@ -612,6 +520,8 @@ def test_toy_model_detector_scalar_to_array_broadcasting(): "RN": [1.0], "tread": [1100], "CIC": [1.5e-3], + "wavelength": [0.5, 0.6, 0.7], + "observing_mode": "IFS", } detector.load_configuration(parameters, mediator) diff --git a/tests/test_exposure_time_calculator.py b/tests/test_exposure_time_calculator.py index e49b5ae..16027f5 100644 --- a/tests/test_exposure_time_calculator.py +++ b/tests/test_exposure_time_calculator.py @@ -30,7 +30,6 @@ from pyEDITH.components.coronagraphs import ToyModelCoronagraph from pyEDITH.components.detectors import ToyModelDetector - # ============================================================================ # Fixtures - Mock Objects # ============================================================================ @@ -47,8 +46,10 @@ def mock_observation(): observation.CRb_multiplier = 2 observation.nlambd = 1 observation.tp = 0.0 * u.s - observation.exptime = u.Quantity([0.0], u.s) - observation.fullsnr = u.Quantity([0.0], DIMENSIONLESS) + # Initialize some arrays needed for outputs... + observation.exptime = np.full((observation.nlambd), 0.0) * TIME + observation.fullsnr = np.full((observation.nlambd), 0.0) * DIMENSIONLESS + return observation @@ -74,8 +75,8 @@ def mock_scene(): scene.xp = 0.0628 * u.arcsec scene.yp = 0.0 * u.arcsec scene.M_V = 4.98869142 * u.mag - scene.Fzodi_list = (u.Quantity([6.11055505e-10], 1 / u.arcsec**2),) - scene.Fexozodi_list = (u.Quantity([2.97724302e-09], 1 / u.arcsec**2),) + scene.Fzodi_list = u.Quantity([6.11055505e-10], 1 / u.arcsec**2) + scene.Fexozodi_list = u.Quantity([2.97724302e-09], 1 / u.arcsec**2) scene.ez_PPF = u.Quantity([np.inf], DIMENSIONLESS) scene.Fbinary_list = u.Quantity([0], DIMENSIONLESS) return scene @@ -110,7 +111,7 @@ def mock_observatory(): observatory.detector.path = None observatory.detector.keyword = "ToyModel" observatory.detector.pixscale_mas = 6.55224925 * u.mas - observatory.detector.npix_multiplier = u.Quantity([1.0], DIMENSIONLESS) + observatory.detector.npix_multiplier = u.Quantity(1.0, DIMENSIONLESS) observatory.detector.DC = u.Quantity([3.0e-05], DARK_CURRENT) observatory.detector.RN = u.Quantity([0.0], READ_NOISE) observatory.detector.tread = u.Quantity([1000.0], READ_TIME) @@ -123,8 +124,6 @@ def mock_observatory(): observatory.coronagraph.path = None observatory.coronagraph.keyword = "ToyModel" observatory.coronagraph.pixscale = 30.0 * LAMBDA_D - observatory.coronagraph.minimum_IWA = 1.0 * LAMBDA_D - observatory.coronagraph.maximum_OWA = 60.0 * LAMBDA_D observatory.coronagraph.contrast = 1.05e-13 * DIMENSIONLESS observatory.coronagraph.noisefloor_factor = 0.03 * DIMENSIONLESS observatory.coronagraph.bandwidth = 0.2 @@ -431,9 +430,9 @@ def test_calculate_exposure_time_etc_validation( mock_observation, mock_scene, mock_observatory, ETC_validation=True ) - assert np.isclose(mock_observation.validation_variables[0]["det_npix"].value, 100) + assert np.isclose(mock_observation.validation_variables["det_npix"].value, 100) assert np.isclose( - mock_observation.validation_variables[0]["t_photon_count"].value, 1.0 + mock_observation.validation_variables["t_photon_count"].value, 1.0 ) @@ -468,83 +467,6 @@ def test_calculate_exposure_time_verbose_output( assert args[2] == mock_observatory -# ============================================================================ -# Tests for calculate_exposure_time_or_snr - Infinity cases -# ============================================================================ - - -def test_calculate_exposure_time_planet_outside_owa( - mock_observation, mock_scene, mock_observatory, caplog -): - """Test that planet outside OWA returns infinity.""" - mock_observatory.coronagraph.maximum_OWA = 0.5 * LAMBDA_D - - with caplog.at_level(logging.WARNING, logger="pyEDITH"): - calculate_exposure_time_or_snr(mock_observation, mock_scene, mock_observatory) - - assert np.isinf(mock_observation.exptime[0]) - assert any( - "Planet outside OWA or inside IWA" in record.message - for record in caplog.records - if record.levelno == logging.ERROR - ) - - -def test_calculate_snr_planet_outside_owa( - mock_observation, mock_scene, mock_observatory, caplog -): - """Test that planet outside OWA returns infinity in SNR mode.""" - mock_observatory.coronagraph.maximum_OWA = 0.5 * LAMBDA_D - - with caplog.at_level(logging.WARNING, logger="pyEDITH"): - calculate_exposure_time_or_snr( - mock_observation, mock_scene, mock_observatory, mode="signal_to_noise" - ) - - assert np.isinf(mock_observation.fullsnr[0]) - assert any( - "Planet outside OWA or inside IWA" in record.message - for record in caplog.records - if record.levelno == logging.ERROR - ) - - -def test_calculate_exposure_time_planet_inside_iwa( - mock_observation, mock_scene, mock_observatory, caplog -): - """Test that planet inside IWA returns infinity.""" - mock_observatory.coronagraph.minimum_IWA = 10.0 * LAMBDA_D - - with caplog.at_level(logging.WARNING, logger="pyEDITH"): - calculate_exposure_time_or_snr(mock_observation, mock_scene, mock_observatory) - - assert np.isinf(mock_observation.exptime[0]) - assert any( - "Planet outside OWA or inside IWA" in record.message - for record in caplog.records - if record.levelno == logging.ERROR - ) - - -def test_calculate_snr_planet_inside_iwa( - mock_observation, mock_scene, mock_observatory, caplog -): - """Test that planet inside IWA returns infinity in SNR mode.""" - mock_observatory.coronagraph.minimum_IWA = 10.0 * LAMBDA_D - - with caplog.at_level(logging.WARNING, logger="pyEDITH"): - calculate_exposure_time_or_snr( - mock_observation, mock_scene, mock_observatory, mode="signal_to_noise" - ) - - assert np.isinf(mock_observation.fullsnr[0]) - assert any( - "Planet outside OWA or inside IWA" in record.message - for record in caplog.records - if record.levelno == logging.ERROR - ) - - def test_calculate_exposure_time_photometric_aperture_too_small( mock_observation, mock_scene, mock_observatory, caplog ): @@ -553,7 +475,7 @@ def test_calculate_exposure_time_photometric_aperture_too_small( mock_observatory.coronagraph.omega_lod = u.Quantity( np.zeros_like(original_omega_lod), LAMBDA_D**2 ) - + mock_observation.obstime = 10 * u.hr with caplog.at_level(logging.WARNING, logger="pyEDITH"): calculate_exposure_time_or_snr(mock_observation, mock_scene, mock_observatory) @@ -569,6 +491,9 @@ def test_calculate_snr_photometric_aperture_too_small( mock_observation, mock_scene, mock_observatory, caplog ): """Test that insufficient photometric aperture returns infinity in SNR mode.""" + + mock_observation.obstime = 10 * u.hr + original_omega_lod = mock_observatory.coronagraph.omega_lod.copy() mock_observatory.coronagraph.omega_lod = u.Quantity( np.zeros_like(original_omega_lod), LAMBDA_D**2 @@ -669,9 +594,7 @@ def test_calculate_exposure_time_bandwidth_restriction_warning( for record in caplog.records if record.levelno == logging.WARNING ) - assert np.isclose( - mock_observation.validation_variables[0]["deltalambda_nm"].value, 50 - ) + assert np.isclose(mock_observation.validation_variables["deltalambda_nm"].value, 50) def test_calculate_exposure_time_ifs_mode( diff --git a/tests/test_observation.py b/tests/test_observation.py index fd3e995..139d4d7 100644 --- a/tests/test_observation.py +++ b/tests/test_observation.py @@ -6,7 +6,6 @@ from pyEDITH.observation import Observation from pyEDITH.units import WAVELENGTH, DIMENSIONLESS, LAMBDA_D, TIME, MAGNITUDE - # ============================================================================ # Fixtures # ============================================================================ @@ -28,7 +27,7 @@ def ifs_observation_params(): """Fixture providing IFS mode observation parameters.""" return { "wavelength": np.linspace(0.2, 1.8, 1000), - "snr": [7.0, 7.0, 7.0], + "snr": [7.0] * np.ones(1000), "spectral_resolution": [140, 40], "lam_low": [0.5, 1.0], "lam_high": [1.0, 1.7], @@ -107,6 +106,9 @@ def test_observation_load_configuration_ifs_mode(ifs_observation_params): obs.wavelength[channel_2_mask] / obs.delta_wavelength[channel_2_mask] == ifs_observation_params["spectral_resolution"][1] ) + assert len(obs.SNR) == len(obs.wavelength) + assert len(obs.SNR) != len(ifs_observation_params["wavelength"]) + assert obs.SNR.unit == DIMENSIONLESS def test_observation_load_configuration_ifs_missing_spectral_resolution(): @@ -114,7 +116,7 @@ def test_observation_load_configuration_ifs_missing_spectral_resolution(): obs = Observation() params = { "wavelength": np.linspace(0.5, 1.7, 1000), - "snr": [7.0, 7.0, 7.0], + "snr": 7.0, "lam_low": [0.5, 1.0], "lam_high": [1.0, 1.7], "regrid_wavelength": True, @@ -131,7 +133,7 @@ def test_observation_load_configuration_ifs_missing_lam_low(): obs = Observation() params = { "wavelength": np.linspace(0.5, 1.7, 1000), - "snr": [7.0, 7.0, 7.0], + "snr": 7.0, "lam_high": [1.0, 1.7], "spectral_resolution": [140, 40], "regrid_wavelength": True, @@ -148,7 +150,7 @@ def test_observation_load_configuration_ifs_missing_lam_high(): obs = Observation() params = { "wavelength": np.linspace(0.5, 1.7, 1000), - "snr": [7.0, 7.0, 7.0], + "snr": 7.0, "lam_low": [0.5, 1.0], "spectral_resolution": [140, 40], "regrid_wavelength": True, @@ -210,7 +212,7 @@ def test_observation_load_configuration_invalid_key(): obs = Observation() with pytest.raises(KeyError): - obs.load_configuration({"invalid_key": 0}) + obs.load_configuration({"wavelength": [0.5, 0.55, 0.6], "invalid_key": 0}) def test_observation_load_configuration_invalid_observing_mode(): @@ -220,7 +222,9 @@ def test_observation_load_configuration_invalid_observing_mode(): with pytest.raises( KeyError, match="Invalid observing mode. Must be 'IMAGER' or 'IFS'." ): - obs.load_configuration({"observing_mode": "Invalid"}) + obs.load_configuration( + {"wavelength": [0.5, 0.55, 0.6], "observing_mode": "Invalid"} + ) # ============================================================================ @@ -234,7 +238,6 @@ def test_observation_set_output_arrays(basic_observation_params): obs.load_configuration(basic_observation_params) obs.set_output_arrays() - assert obs.tp == 0.0 * TIME assert obs.exptime.shape == (3,) assert obs.fullsnr.shape == (3,) assert np.all(obs.exptime == 0.0 * TIME) diff --git a/tests/test_observatory.py b/tests/test_observatory.py index 202e59d..4cd48ea 100644 --- a/tests/test_observatory.py +++ b/tests/test_observatory.py @@ -63,7 +63,7 @@ class MockDetector(Detector): def load_configuration(self, parameters, mediator): self.path = None self.pixscale_mas = 6.55224925 * u.mas - self.npix_multiplier = u.Quantity([1.0], DIMENSIONLESS) + self.npix_multiplier = u.Quantity(1.0, DIMENSIONLESS) self.DC = u.Quantity([3.0e-05], DARK_CURRENT) self.RN = u.Quantity([0.0], READ_NOISE) self.tread = u.Quantity([1000.0], READ_TIME) @@ -78,8 +78,6 @@ class MockCoronagraph(Coronagraph): def load_configuration(self, parameters, mediator): self.path = None self.pixscale = 30.0 * LAMBDA_D - self.minimum_IWA = 1.0 * LAMBDA_D - self.maximum_OWA = 60.0 * LAMBDA_D self.contrast = 1.05e-13 * DIMENSIONLESS self.noisefloor_factor = 0.03 * DIMENSIONLESS self.bandwidth = 0.2 @@ -168,7 +166,7 @@ def mock_observatory(): @pytest.fixture -def mock_observation(): +def mock_observation_imager(): """Fixture providing a mock observation.""" obs = Observation() obs.observing_mode = "IMAGER" @@ -183,6 +181,21 @@ def mock_observation(): return obs +@pytest.fixture +def mock_observation_ifs(): + """Fixture providing a mock observation.""" + obs = Observation() + obs.observing_mode = "IFS" + obs.td_limit = 1.0e20 * u.s + obs.wavelength = u.Quantity([0.5, 0.6], u.micron) + obs.SNR = u.Quantity([7, 7], DIMENSIONLESS) + obs.CRb_multiplier = 2 + obs.nlambd = 2 + obs.exptime = u.Quantity([0.0, 0.0], u.s) + obs.fullsnr = u.Quantity([0.0, 0.0], DIMENSIONLESS) + return obs + + @pytest.fixture def mock_scene(): """Fixture providing a mock astrophysical scene.""" @@ -666,12 +679,12 @@ def test_observatory_validate_configuration_incorrect_units( def test_calculate_optics_throughput_with_t_optical( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ): """Test calculation of optics throughput with explicit T_optical parameter.""" - parameters = {"T_optical": [0.8], "observing_mode": "IMAGER"} + parameters = {"T_optical": 0.8, "observing_mode": "IMAGER", "wavelength": 0.5} mediator = ObservatoryMediator( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ) configured_mock_observatory.calculate_optics_throughput(parameters, mediator) @@ -680,26 +693,38 @@ def test_calculate_optics_throughput_with_t_optical( def test_calculate_optics_throughput_ifs_mode( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_ifs, mock_scene ): """Test calculation of optics throughput in IFS mode with IFS efficiency.""" - parameters = {"T_optical": [0.8], "observing_mode": "IFS", "IFS_eff": [0.9]} + parameters = { + "T_optical": [0.8, 0.84], + "observing_mode": "IFS", + "IFS_eff": [0.9, 0.92], + "wavelength": [0.5, 0.6], + } mediator = ObservatoryMediator( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_ifs, mock_scene ) configured_mock_observatory.calculate_optics_throughput(parameters, mediator) - assert configured_mock_observatory.optics_throughput.value == [0.8 * 0.9] + assert np.allclose( + configured_mock_observatory.optics_throughput.value, + [ + 0.8 * 0.9, + 0.84 * 0.92, + ], + rtol=1e-5, + ) def test_calculate_optics_throughput_from_components( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ): """Test calculation of optics throughput from component throughputs.""" - parameters = {"observing_mode": "IMAGER"} + parameters = {"observing_mode": "IMAGER", "wavelength": 0.5} mediator = ObservatoryMediator( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ) configured_mock_observatory.calculate_optics_throughput(parameters, mediator) @@ -719,12 +744,12 @@ def test_calculate_optics_throughput_from_components( def test_calculate_warmemissivity_coldtransmission_explicit( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ): """Test calculation with explicit epswarmTrcold parameter.""" - parameters = {"epswarmTrcold": 0.3} + parameters = {"epswarmTrcold": 0.3, "wavelength": 0.5} mediator = ObservatoryMediator( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ) configured_mock_observatory.calculate_warmemissivity_coldtransmission( @@ -735,13 +760,13 @@ def test_calculate_warmemissivity_coldtransmission_explicit( def test_calculate_warmemissivity_coldtransmission_calculated( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ): """Test calculation derived from optics throughput.""" - parameters = {} + parameters = {"wavelength": 0.5} configured_mock_observatory.optics_throughput = [0.8] * DIMENSIONLESS mediator = ObservatoryMediator( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ) configured_mock_observatory.calculate_warmemissivity_coldtransmission( @@ -775,11 +800,13 @@ def test_calculate_total_throughput(mock_observatory): # ============================================================================ -def test_observatory_load_configuration(mock_observatory, mock_observation, mock_scene): +def test_observatory_load_configuration( + mock_observatory, mock_observation_imager, mock_scene +): """Test loading complete observatory configuration.""" - parameters = {"observing_mode": "IMAGER", "T_optical": [0.8]} + parameters = {"observing_mode": "IMAGER", "T_optical": 0.8, "wavelength": 0.5} - mock_observatory.load_configuration(parameters, mock_observation, mock_scene) + mock_observatory.load_configuration(parameters, mock_observation_imager, mock_scene) assert mock_observatory.observing_mode == "IMAGER" assert mock_observatory.optics_throughput.value == [0.8] @@ -793,11 +820,11 @@ def test_observatory_load_configuration(mock_observatory, mock_observation, mock def test_observatory_mediator_get_telescope_parameter( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ): """Test getting telescope parameter through mediator.""" mediator = ObservatoryMediator( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ) result = mediator.get_telescope_parameter("diameter") @@ -806,11 +833,11 @@ def test_observatory_mediator_get_telescope_parameter( def test_observatory_mediator_get_telescope_parameter_nonexistent( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ): """Test getting non-existent telescope parameter returns None.""" mediator = ObservatoryMediator( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ) result = mediator.get_telescope_parameter("nonexistent") @@ -824,11 +851,11 @@ def test_observatory_mediator_get_telescope_parameter_nonexistent( def test_observatory_mediator_get_coronagraph_parameter( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ): """Test getting coronagraph parameter through mediator.""" mediator = ObservatoryMediator( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ) result = mediator.get_coronagraph_parameter("contrast") @@ -842,11 +869,11 @@ def test_observatory_mediator_get_coronagraph_parameter( def test_observatory_mediator_get_detector_parameter( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ): """Test getting detector parameter through mediator.""" mediator = ObservatoryMediator( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ) result = mediator.get_detector_parameter("pixscale_mas") @@ -860,16 +887,16 @@ def test_observatory_mediator_get_detector_parameter( def test_observatory_mediator_get_observation_parameter( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ): """Test getting observation parameter through mediator.""" mediator = ObservatoryMediator( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ) result = mediator.get_observation_parameter("wavelength") - assert result == mock_observation.wavelength + assert result == mock_observation_imager.wavelength # ============================================================================ @@ -878,11 +905,11 @@ def test_observatory_mediator_get_observation_parameter( def test_observatory_mediator_get_scene_parameter( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ): """Test getting scene parameter through mediator.""" mediator = ObservatoryMediator( - configured_mock_observatory, mock_observation, mock_scene + configured_mock_observatory, mock_observation_imager, mock_scene ) result = mediator.get_scene_parameter("vmag") diff --git a/tests/test_parse_input.py b/tests/test_parse_input.py index 5bb7c3f..5c1d1c8 100644 --- a/tests/test_parse_input.py +++ b/tests/test_parse_input.py @@ -196,7 +196,7 @@ def test_parse_input_file_secondary_flag_no_secondary_vars( def test_parse_input_file_ifs_missing_keys(ifs_input_file_missing_keys): """Test that IFS mode with missing required keys raises ValueError.""" with pytest.raises( - ValueError, + KeyError, match="Required parameters 'wavelength', 'Fstar_10pc', and 'Fp/Fs' are not provided", ): parse_input_file(ifs_input_file_missing_keys, secondary_flag=False) @@ -291,6 +291,209 @@ def test_parse_input_file_with_valid_spectrum_file(valid_spectrum_file): os.unlink(tmp.name) +# ============================================================================ +# Tests for normalize_list_shapes +# ============================================================================ + + +def test_normalize_list_shapes_scalar_single_wavelength(): + """Test scalar value with single wavelength (default_len=1).""" + parameters = {"snr": 10.0} + result = normalize_list_shapes(parameters, "snr", default_len=1) + + np.testing.assert_array_equal(result, np.array([10.0])) + assert isinstance(result, np.ndarray) + assert result.dtype == np.float64 + + +def test_normalize_list_shapes_scalar_multiple_wavelengths(caplog): + """Test scalar value broadcast to multiple wavelengths (default_len>1).""" + parameters = {"snr": 10.0} + + with caplog.at_level(logging.WARNING, logger="pyEDITH"): + result = normalize_list_shapes(parameters, "snr", default_len=3) + + np.testing.assert_array_equal(result, np.array([10.0, 10.0, 10.0])) + assert isinstance(result, np.ndarray) + assert result.dtype == np.float64 + assert any( + "snr should be a list of length 3" in record.message + for record in caplog.records + if record.levelno == logging.WARNING + ) + + +def test_normalize_list_shapes_single_element_list_broadcast(caplog): + """Test single-element list broadcast to multiple wavelengths.""" + parameters = {"snr": [10.0]} + + with caplog.at_level(logging.WARNING, logger="pyEDITH"): + result = normalize_list_shapes(parameters, "snr", default_len=3) + + np.testing.assert_array_equal(result, np.array([10.0, 10.0, 10.0])) + assert isinstance(result, np.ndarray) + assert result.dtype == np.float64 + assert any( + "snr should be a list of length 3" in record.message + for record in caplog.records + if record.levelno == logging.WARNING + ) + + +def test_normalize_list_shapes_matching_length_list(): + """Test list with correct length passes through.""" + parameters = {"snr": [10.0, 20.0, 30.0]} + result = normalize_list_shapes(parameters, "snr", default_len=3) + + np.testing.assert_array_equal(result, np.array([10.0, 20.0, 30.0])) + assert isinstance(result, np.ndarray) + assert result.dtype == np.float64 + + +def test_normalize_list_shapes_mismatched_length_error(): + """Test list with wrong length raises ValueError.""" + parameters = {"snr": [10.0, 20.0]} + + with pytest.raises( + ValueError, match="snr should be a list of length 3, but it has length 2" + ): + normalize_list_shapes(parameters, "snr", default_len=3) + + +def test_normalize_list_shapes_quantity_scalar_single_wavelength(): + """Test Quantity scalar with single wavelength.""" + parameters = {"DC": 10.0 * DARK_CURRENT} + result = normalize_list_shapes(parameters, "DC", default_len=1) + + assert isinstance(result, u.Quantity) + np.testing.assert_array_equal(result.value, np.array([10.0])) + assert result.unit == DARK_CURRENT + + +def test_normalize_list_shapes_quantity_scalar_broadcast(caplog): + """Test Quantity scalar broadcast to multiple wavelengths.""" + parameters = {"DC": 10.0 * DARK_CURRENT} + + with caplog.at_level(logging.WARNING, logger="pyEDITH"): + result = normalize_list_shapes(parameters, "DC", default_len=3) + + assert isinstance(result, u.Quantity) + np.testing.assert_array_equal(result.value, np.array([10.0, 10.0, 10.0])) + assert result.unit == DARK_CURRENT + assert any( + "DC should be a list of length 3" in record.message + for record in caplog.records + if record.levelno == logging.WARNING + ) + + +def test_normalize_list_shapes_quantity_single_element_broadcast(caplog): + """Test single-element Quantity array broadcast to multiple wavelengths.""" + parameters = {"DC": u.Quantity([10.0], DARK_CURRENT)} + + with caplog.at_level(logging.WARNING, logger="pyEDITH"): + result = normalize_list_shapes(parameters, "DC", default_len=3) + + assert isinstance(result, u.Quantity) + np.testing.assert_array_equal(result.value, np.array([10.0, 10.0, 10.0])) + assert result.unit == DARK_CURRENT + assert any( + "DC should be a list of length 3" in record.message + for record in caplog.records + if record.levelno == logging.WARNING + ) + + +def test_normalize_list_shapes_quantity_matching_length(): + """Test Quantity array with correct length passes through.""" + parameters = {"DC": u.Quantity([10.0, 20.0, 30.0], DARK_CURRENT)} + result = normalize_list_shapes(parameters, "DC", default_len=3) + + assert isinstance(result, u.Quantity) + np.testing.assert_array_equal(result.value, np.array([10.0, 20.0, 30.0])) + assert result.unit == DARK_CURRENT + + +def test_normalize_list_shapes_quantity_mismatched_length(): + """Test Quantity array with wrong length raises ValueError.""" + parameters = {"DC": u.Quantity([10.0, 20.0], DARK_CURRENT)} + + with pytest.raises( + ValueError, match="DC should be a list of length 3, but it has length 2" + ): + normalize_list_shapes(parameters, "DC", default_len=3) + + +def test_normalize_list_shapes_excess_length_single_wavelength(caplog): + """Test multi-element array preserved when default_len=1 for downstream regridding.""" + parameters = {"snr": [10.0, 20.0, 30.0]} + + with pytest.raises( + ValueError, match="snr has length 3 but the expected input size is 1" + ): + normalize_list_shapes(parameters, "snr", default_len=1) + + +def test_normalize_list_shapes_excess_length_quantity_single_wavelength(caplog): + """Test multi-element Quantity preserved when default_len=1 for downstream regridding.""" + parameters = {"DC": u.Quantity([10.0, 20.0, 30.0], DARK_CURRENT)} + + with pytest.raises( + ValueError, match="DC has length 3 but the expected input size is 1" + ): + normalize_list_shapes(parameters, "DC", default_len=1) + + +def test_normalize_list_shapes_numpy_array(): + """Test that numpy arrays are properly converted.""" + parameters = {"snr": np.array([10.0, 20.0, 30.0])} + result = normalize_list_shapes(parameters, "snr", default_len=3) + + np.testing.assert_array_equal(result, np.array([10.0, 20.0, 30.0])) + assert isinstance(result, np.ndarray) + assert result.dtype == np.float64 + + +def test_normalize_list_shapes_tuple_input(): + """Test that tuples are properly converted to arrays.""" + parameters = {"snr": (10.0, 20.0, 30.0)} + result = normalize_list_shapes(parameters, "snr", default_len=3) + + np.testing.assert_array_equal(result, np.array([10.0, 20.0, 30.0])) + assert isinstance(result, np.ndarray) + assert result.dtype == np.float64 + + +def test_normalize_list_shapes_integer_values(): + """Test that integer values are converted to float64.""" + parameters = {"snr": [10, 20, 30]} + result = normalize_list_shapes(parameters, "snr", default_len=3) + + np.testing.assert_array_equal(result, np.array([10.0, 20.0, 30.0])) + assert isinstance(result, np.ndarray) + assert result.dtype == np.float64 + + +def test_normalize_list_shapes_mixed_numeric_types(): + """Test that mixed int/float values are converted to float64.""" + parameters = {"snr": [10, 20.5, 30]} + result = normalize_list_shapes(parameters, "snr", default_len=3) + + np.testing.assert_array_equal(result, np.array([10.0, 20.5, 30.0])) + assert isinstance(result, np.ndarray) + assert result.dtype == np.float64 + + +def test_normalize_list_shapes_preserves_quantity_unit(): + """Test that Quantity units are preserved through conversion.""" + parameters = {"wavelength": u.Quantity([0.5, 0.6, 0.7], u.um)} + result = normalize_list_shapes(parameters, "wavelength", default_len=3) + + assert isinstance(result, u.Quantity) + np.testing.assert_array_equal(result.value, np.array([0.5, 0.6, 0.7])) + assert result.unit == u.um + + # ============================================================================ # Tests for parse_parameters - Wavelength handling # ============================================================================ @@ -339,14 +542,12 @@ def test_parse_parameters_wavelength_dependent_single(): "snr", "T_optical", "epswarmTrcold", - "npix_multiplier", "DC", "RN", "tread", "CIC", "QE", "dQE", - "IFS_eff", "mag", "Fstar_10pc", "Fp/Fs", @@ -417,34 +618,20 @@ def test_parse_parameters_wavelength_dependent_mismatched_length(): def test_parse_parameters_wavelength_dependent_excess_length_scalar(caplog): - """Test that excess length triggers warning and uses first value.""" - with caplog.at_level(logging.DEBUG, logger="pyEDITH"): - parsed = parse_parameters({"wavelength": 0.5, "snr": [10, 20, 30]}) - - assert any( - "snr should be a list of length 1 but you assigned multiple values" - in record.message - for record in caplog.records - if record.levelno == logging.WARNING - ) - assert parsed["snr"] == np.array([10]) + """Test that excess length triggers error for single wavelength.""" + with pytest.raises( + ValueError, match="snr has length 3 but the expected input size is 1" + ): + parse_parameters({"wavelength": 0.5, "snr": [10, 20, 30]}) def test_parse_parameters_wavelength_dependent_excess_length_quantity(caplog): """Test that excess length triggers warning and uses first value.""" - with caplog.at_level(logging.DEBUG, logger="pyEDITH"): - parsed = parse_parameters( - {"wavelength": 0.5, "DC": [10, 20, 30] * DARK_CURRENT} - ) - assert any( - "DC should be a list of length 1 but you assigned multiple values" - in record.message - for record in caplog.records - if record.levelno == logging.WARNING - ) - assert parsed["DC"] == np.array([10]) * DARK_CURRENT - assert isinstance(parsed["DC"], u.Quantity) + with pytest.raises( + ValueError, match="DC has length 3 but the expected input size is 1." + ): + parse_parameters({"wavelength": 0.5, "DC": [10, 20, 30] * DARK_CURRENT}) def test_parse_parameters_wavelength_dependent_with_quantity(): @@ -464,16 +651,7 @@ def test_parse_parameters_wavelength_dependent_with_quantity(): # ============================================================================ -def test_parse_parameters_nlambda_provided(): - """Test parsing with nlambda provided instead of wavelength.""" - parsed = parse_parameters({"snr": 1.5}, nlambda=3) - - assert parsed["nlambda"] == 3 - assert np.all(parsed["snr"] == np.array([1.5, 1.5, 1.5])) - assert isinstance(parsed["snr"], np.ndarray) - - -def test_parse_parameters_nlambda_not_provided(): +def test_parse_parameters_wavelength_not_provided(): """Test that missing both wavelength and nlambda raises ValueError.""" with pytest.raises( ValueError, match="pyEDITH does not have access to wavelength here" @@ -500,6 +678,7 @@ def test_parse_parameters_target_params(): "Fp_min/Fs", "separation", "semimajor_axis", + "npix_multiplier", ] for param in target_params: @@ -522,12 +701,9 @@ def test_parse_parameters_scalar_params(): "diameter", "toverhead_fixed", "toverhead_multi", - "minimum_IWA", - "maximum_OWA", "contrast", "noisefloor_factor", "noisefloor_PPF", - "ez_PPF", "bandwidth", "Tcore", "TLyot", @@ -569,7 +745,6 @@ def test_parse_parameters_observatory_specs(): "telescope_type", "coronagraph_type", "detector_type", - "observing_mode", ] for spec in observatory_specs: @@ -577,47 +752,629 @@ def test_parse_parameters_observatory_specs(): assert parsed[spec] == "TestSpec" assert isinstance(parsed[spec], str) + parsed = parse_parameters({"wavelength": 0.5, "observing_mode": "IMAGER"}) + assert parsed["observing_mode"] == "IMAGER" + assert isinstance(parsed["observing_mode"], str) -def test_parse_parameters_regrid_wavelength_bool(): - """Test parsing regrid_wavelength as boolean.""" - parsed = parse_parameters({"wavelength": 0.5, "regrid_wavelength": True}) + +# ============================================================================ +# Tests for parse_parameters - Boolean parameters +# ============================================================================ + + +def test_parse_parameters_boolean_true(): + """Test parsing boolean parameter as True.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": True, + "spectral_resolution": [100], + "lam_low": [0.4], + "lam_high": [1.0], + } + ) + assert parsed["az_avg"] is True + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_false(): + """Test parsing boolean parameter as False.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": False, + } + ) + assert parsed["az_avg"] is False + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_string_true(): + """Test parsing boolean from 'true' string (case-insensitive).""" + for true_string in ["true", "True", "TRUE"]: + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": true_string, + } + ) + assert parsed["az_avg"] is True + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_string_false(): + """Test parsing boolean from 'false' string (case-insensitive).""" + for false_string in ["false", "False", "FALSE"]: + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": false_string, + } + ) + assert parsed["az_avg"] is False + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_string_one(): + """Test parsing boolean from '1' string.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": "1", + } + ) + assert parsed["az_avg"] is True + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_string_zero(): + """Test parsing boolean from '0' string.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": "0", + } + ) + assert parsed["az_avg"] is False + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_string_yes(): + """Test parsing boolean from 'yes' string (case-insensitive).""" + for yes_string in ["yes", "Yes", "YES"]: + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": yes_string, + } + ) + assert parsed["az_avg"] is True + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_string_no(): + """Test parsing boolean from 'no' string (case-insensitive).""" + for no_string in ["no", "No", "NO"]: + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": no_string, + } + ) + assert parsed["az_avg"] is False + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_int_one(): + """Test parsing boolean from integer 1.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": 1, + } + ) + assert parsed["az_avg"] is True + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_int_zero(): + """Test parsing boolean from integer 0.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": 0, + } + ) + assert parsed["az_avg"] is False + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_float_one(): + """Test parsing boolean from float 1.0.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": 1.0, + } + ) + assert parsed["az_avg"] is True + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_float_zero(): + """Test parsing boolean from float 0.0.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": 0.0, + } + ) + assert parsed["az_avg"] is False + assert isinstance(parsed["az_avg"], bool) + + +def test_parse_parameters_boolean_invalid_string(): + """Test that invalid boolean string raises ValueError with helpful message.""" + with pytest.raises( + ValueError, + match=r"Invalid value 'maybe' for parameter 'az_avg'\. " + r"Expected boolean or one of: 'true', 'false', '1', '0', 'yes', 'no' " + r"\(case-insensitive\)\.", + ): + parse_parameters( + { + "wavelength": 0.5, + "az_avg": "maybe", + } + ) + + +def test_parse_parameters_boolean_invalid_numeric(): + """Test that invalid numeric boolean value raises ValueError.""" + with pytest.raises( + ValueError, + match=r"Invalid numeric value '2' for parameter 'az_avg'\. " + r"Expected 0 or 1 for boolean parameters\.", + ): + parse_parameters( + { + "wavelength": 0.5, + "az_avg": 2, + } + ) + + +def test_parse_parameters_boolean_invalid_numeric_float(): + """Test that invalid float boolean value raises ValueError.""" + with pytest.raises( + ValueError, + match=r"Invalid numeric value '0\.5' for parameter 'az_avg'\. " + r"Expected 0 or 1 for boolean parameters\.", + ): + parse_parameters( + { + "wavelength": 0.5, + "az_avg": 0.5, + } + ) + + +def test_parse_parameters_boolean_invalid_type(): + """Test that invalid type for boolean raises TypeError.""" + with pytest.raises( + TypeError, + match=r"Invalid type list for parameter 'az_avg'\. " + r"Expected boolean, string, or numeric \(0/1\)\.", + ): + parse_parameters( + { + "wavelength": 0.5, + "az_avg": [True], + } + ) + + +def test_parse_parameters_boolean_invalid_type_dict(): + """Test that dict type for boolean raises TypeError.""" + with pytest.raises( + TypeError, + match=r"Invalid type dict for parameter 'az_avg'\. " + r"Expected boolean, string, or numeric \(0/1\)\.", + ): + parse_parameters( + { + "wavelength": 0.5, + "az_avg": {"value": True}, + } + ) + + +def test_parse_parameters_boolean_both_params(): + """Test parsing both boolean parameters (az_avg and regrid_wavelength).""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": True, + "regrid_wavelength": "yes", + "spectral_resolution": [100], + "lam_low": [0.4], + "lam_high": [1.0], + } + ) + assert parsed["az_avg"] is True assert parsed["regrid_wavelength"] is True + assert isinstance(parsed["az_avg"], bool) assert isinstance(parsed["regrid_wavelength"], bool) - parsed = parse_parameters({"wavelength": 0.5, "regrid_wavelength": False}) + +def test_parse_parameters_boolean_mixed_formats(): + """Test parsing booleans with different format inputs.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "az_avg": "TRUE", + "regrid_wavelength": 0, + "spectral_resolution": [100], + "lam_low": [0.4], + "lam_high": [1.0], + } + ) + assert parsed["az_avg"] is True assert parsed["regrid_wavelength"] is False - assert isinstance(parsed["regrid_wavelength"], bool) -def test_parse_parameters_regrid_wavelength_string(): - """Test parsing regrid_wavelength from string.""" - # Test true-like strings - for true_string in ["true", "True", "TRUE", "1", "yes", "Yes", "YES"]: - parsed = parse_parameters({"wavelength": 0.5, "regrid_wavelength": true_string}) - assert parsed["regrid_wavelength"] is True - assert isinstance(parsed["regrid_wavelength"], bool) +def test_parse_parameters_regrid_wavelength_invalid_string_value(): + """Test that invalid string for regrid_wavelength raises ValueError.""" + with pytest.raises( + ValueError, + match=r"Invalid value 'invalid' for parameter 'regrid_wavelength'\. " + r"Expected boolean or one of: 'true', 'false', '1', '0', 'yes', 'no' " + r"\(case-insensitive\)\.", + ): + parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": "invalid", + "spectral_resolution": [100], + "lam_low": [0.4], + "lam_high": [1.0], + } + ) - # Test false-like strings - for false_string in ["false", "False", "FALSE", "0", "no", "No", "NO"]: - parsed = parse_parameters( - {"wavelength": 0.5, "regrid_wavelength": false_string} + +def test_parse_parameters_regrid_wavelength_invalid_numeric(): + """Test that invalid numeric for regrid_wavelength raises ValueError.""" + with pytest.raises( + ValueError, + match=r"Invalid numeric value '-1' for parameter 'regrid_wavelength'\. " + r"Expected 0 or 1 for boolean parameters\.", + ): + parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": -1, + "spectral_resolution": [100], + "lam_low": [0.4], + "lam_high": [1.0], + } + ) + + +def test_parse_parameters_boolean_empty_string(): + """Test that empty string for boolean raises ValueError.""" + with pytest.raises( + ValueError, + match=r"Invalid value '' for parameter 'az_avg'\. " + r"Expected boolean or one of: 'true', 'false', '1', '0', 'yes', 'no' " + r"\(case-insensitive\)\.", + ): + parse_parameters( + { + "wavelength": 0.5, + "az_avg": "", + } + ) + + +def test_parse_parameters_boolean_whitespace_string(): + """Test that whitespace-only string for boolean raises ValueError.""" + with pytest.raises( + ValueError, + match=r"Invalid value ' ' for parameter 'az_avg'\. " + r"Expected boolean or one of: 'true', 'false', '1', '0', 'yes', 'no' " + r"\(case-insensitive\)\.", + ): + parse_parameters( + { + "wavelength": 0.5, + "az_avg": " ", + } + ) + + +def test_parse_parameters_boolean_none_type(): + """Test that None type for boolean raises TypeError.""" + with pytest.raises( + TypeError, + match=r"Invalid type NoneType for parameter 'az_avg'\. " + r"Expected boolean, string, or numeric \(0/1\)\.", + ): + parse_parameters( + { + "wavelength": 0.5, + "az_avg": None, + } ) - assert parsed["regrid_wavelength"] is False - assert isinstance(parsed["regrid_wavelength"], bool) + + +def test_parse_parameters_regrid_wavelength_bool(): + """Test parsing regrid_wavelength as boolean.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": True, + "spectral_resolution": [100], + "lam_low": [0.4], + "lam_high": [1.0], + } + ) + assert parsed["regrid_wavelength"] is True + assert isinstance(parsed["regrid_wavelength"], bool) + + parsed = parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": False, + "spectral_resolution": [100], + "lam_low": [0.4], + "lam_high": [1.0], + } + ) + assert parsed["regrid_wavelength"] is False + assert isinstance(parsed["regrid_wavelength"], bool) def test_parse_parameters_regrid_wavelength_int(): """Test parsing regrid_wavelength from integer.""" - parsed = parse_parameters({"wavelength": 0.5, "regrid_wavelength": 1}) + parsed = parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": 1, + "spectral_resolution": [100], + "lam_low": [0.4], + "lam_high": [1.0], + } + ) assert parsed["regrid_wavelength"] is True assert isinstance(parsed["regrid_wavelength"], bool) - parsed = parse_parameters({"wavelength": 0.5, "regrid_wavelength": 0}) + parsed = parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": 0, + "spectral_resolution": [100], + "lam_low": [0.4], + "lam_high": [1.0], + } + ) assert parsed["regrid_wavelength"] is False assert isinstance(parsed["regrid_wavelength"], bool) +def test_parse_parameters_regrid_wavelength_missing_lam_low(): + """Test that regrid_wavelength=True with missing lam_low raises ValueError.""" + with pytest.raises( + KeyError, + match="regrid_wavelength is True, but 'lam_low' is missing. " + "Required parameters: spectral_resolution, lam_low, lam_high", + ): + parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": True, + "spectral_resolution": [100], + "lam_high": [1.0], + } + ) + + +def test_parse_parameters_regrid_wavelength_missing_lam_high(): + """Test that regrid_wavelength=True with missing lam_high raises ValueError.""" + with pytest.raises( + KeyError, + match="regrid_wavelength is True, but 'lam_high' is missing. " + "Required parameters: spectral_resolution, lam_low, lam_high", + ): + parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": True, + "spectral_resolution": [100], + "lam_low": [0.4], + } + ) + + +def test_parse_parameters_regrid_wavelength_missing_all_required(): + """Test that regrid_wavelength=True with all required parameters missing raises ValueError.""" + with pytest.raises( + KeyError, + match="regrid_wavelength is True, but 'spectral_resolution' is missing. " + "Required parameters: spectral_resolution, lam_low, lam_high", + ): + parse_parameters({"wavelength": 0.5, "regrid_wavelength": True}) + + +def test_parse_parameters_regrid_wavelength_spectral_resolution_not_array(): + """Test that regrid_wavelength=True with scalar spectral_resolution raises ValueError.""" + with pytest.raises( + ValueError, + match="regrid_wavelength is True, but 'spectral_resolution' is not an array. " + "All of spectral_resolution, lam_low, lam_high must be arrays of the same length.", + ): + parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": True, + "spectral_resolution": 100, # scalar instead of array + "lam_low": [0.4], + "lam_high": [1.0], + } + ) + + +def test_parse_parameters_regrid_wavelength_lam_low_not_array(): + """Test that regrid_wavelength=True with scalar lam_low raises ValueError.""" + with pytest.raises( + ValueError, + match="regrid_wavelength is True, but 'lam_low' is not an array. " + "All of spectral_resolution, lam_low, lam_high must be arrays of the same length.", + ): + parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": True, + "spectral_resolution": [100], + "lam_low": 0.4, # scalar instead of array + "lam_high": [1.0], + } + ) + + +def test_parse_parameters_regrid_wavelength_lam_high_not_array(): + """Test that regrid_wavelength=True with scalar lam_high raises ValueError.""" + with pytest.raises( + ValueError, + match="regrid_wavelength is True, but 'lam_high' is not an array. " + "All of spectral_resolution, lam_low, lam_high must be arrays of the same length.", + ): + parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": True, + "spectral_resolution": [100], + "lam_low": [0.4], + "lam_high": 1.0, # scalar instead of array + } + ) + + +def test_parse_parameters_regrid_wavelength_mismatched_lengths_two_params(): + """Test that regrid_wavelength=True with two parameters of different lengths raises ValueError.""" + with pytest.raises( + ValueError, + match="regrid_wavelength is True, but spectral_resolution, lam_low, lam_high have different lengths: " + ".*spectral_resolution.*2.*lam_low.*2.*lam_high.*3.* All must have the same length.", + ): + parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": True, + "spectral_resolution": [100, 200], + "lam_low": [0.4, 0.5], + "lam_high": [1.0, 1.5, 2.0], # different length + } + ) + + +def test_parse_parameters_regrid_wavelength_mismatched_lengths_all_different(): + """Test that regrid_wavelength=True with all parameters of different lengths raises ValueError.""" + with pytest.raises( + ValueError, + match="regrid_wavelength is True, but spectral_resolution, lam_low, lam_high have different lengths: " + ".*spectral_resolution.*1.*lam_low.*2.*lam_high.*3.* All must have the same length.", + ): + parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": True, + "spectral_resolution": [100], + "lam_low": [0.4, 0.5], + "lam_high": [1.0, 1.5, 2.0], + } + ) + + +def test_parse_parameters_regrid_wavelength_valid_lists(): + """Test that regrid_wavelength=True with valid lists succeeds.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": True, + "spectral_resolution": [100, 200], + "lam_low": [0.4, 0.5], + "lam_high": [1.0, 1.5], + } + ) + + assert parsed["regrid_wavelength"] is True + assert np.all(parsed["spectral_resolution"] == np.array([100, 200])) + assert np.all(parsed["lam_low"] == np.array([0.4, 0.5])) + assert np.all(parsed["lam_high"] == np.array([1.0, 1.5])) + + +def test_parse_parameters_regrid_wavelength_valid_numpy_arrays(): + """Test that regrid_wavelength=True with numpy arrays succeeds.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": True, + "spectral_resolution": np.array([100, 200]), + "lam_low": np.array([0.4, 0.5]), + "lam_high": np.array([1.0, 1.5]), + } + ) + + assert parsed["regrid_wavelength"] is True + assert np.all(parsed["spectral_resolution"] == np.array([100, 200])) + assert np.all(parsed["lam_low"] == np.array([0.4, 0.5])) + assert np.all(parsed["lam_high"] == np.array([1.0, 1.5])) + + +def test_parse_parameters_regrid_wavelength_valid_quantities(): + """Test that regrid_wavelength=True with Quantity arrays succeeds.""" + parsed = parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": True, + "spectral_resolution": [100, 200] * u.dimensionless_unscaled, + "lam_low": [0.4, 0.5] * u.um, + "lam_high": [1.0, 1.5] * u.um, + } + ) + + assert parsed["regrid_wavelength"] is True + assert isinstance(parsed["spectral_resolution"], u.Quantity) + assert isinstance(parsed["lam_low"], u.Quantity) + assert isinstance(parsed["lam_high"], u.Quantity) + + +def test_parse_parameters_regrid_wavelength_false_no_validation(): + """Test that regrid_wavelength=False skips validation of required parameters.""" + # Should not raise even though required parameters are missing + parsed = parse_parameters( + { + "wavelength": 0.5, + "regrid_wavelength": False, + } + ) + + assert parsed["regrid_wavelength"] is False + + +def test_parse_parameters_regrid_wavelength_absent_no_validation(): + """Test that absence of regrid_wavelength skips validation of required parameters.""" + # Should not raise even though required parameters are missing + parsed = parse_parameters( + { + "wavelength": 0.5, + } + ) + + assert "regrid_wavelength" not in parsed + + # ============================================================================ # Tests for parse_parameters - Complete parameter set # ============================================================================ @@ -763,3 +1520,218 @@ def test_get_observatory_config_missing_component(): with pytest.raises(ValueError, match="Detector type not specified"): get_observatory_config(parameters) + + +def test_parsed_flag_prevents_reprocessing(): + """Test that _parsed flag prevents reprocessing of already-parsed parameters.""" + + # Create a minimal set of parameters + raw_params = { + "wavelength": [1.0, 2.0, 3.0], + "snr": [10.0, 15.0, 20.0], + "distance": 10.0, + } + + # Parse once + parsed_once = parse_parameters(raw_params) + + # Verify _parsed flag is set + assert "_parsed" in parsed_once + assert parsed_once["_parsed"] is True + + # Store a reference to verify identity + parsed_once_id = id(parsed_once) + + # Parse again - should return immediately without reprocessing + parsed_twice = parse_parameters(parsed_once) + + # Should return the same object (not a copy) + assert id(parsed_twice) == parsed_once_id + assert parsed_twice is parsed_once + + # Verify all original parsed values are unchanged + assert parsed_twice["nlambda"] == 3 + assert "_parsed" in parsed_twice + + +def test_parsed_flag_not_present_initially(): + """Test that _parsed flag is not present in raw parameters.""" + + raw_params = {"wavelength": [1.0], "distance": 10.0} + + # Verify _parsed is not in raw params + assert "_parsed" not in raw_params + + # Parse + parsed = parse_parameters(raw_params) + + # Now it should be present + assert "_parsed" in parsed + assert parsed["_parsed"] is True + + +def test_parsed_flag_set_after_single_parse(): + """Test that _parsed flag is set after a single parsing operation.""" + + raw_params = {"wavelength": 1.5, "snr": 25.0, "distance": 5.0} + + parsed = parse_parameters(raw_params) + + # Check the flag exists and is True + assert parsed.get("_parsed") is True + + +# ============================================================================ +# Tests for parse_parameters - overrides parameter +# ============================================================================ + + +def test_parse_parameters_overrides_string(): + """Test parsing overrides parameter as a comma-separated string.""" + parsed = parse_parameters({"wavelength": 0.5, "overrides": "DC,RN,QE"}) + + assert "overrides" in parsed + assert parsed["overrides"] == ["DC", "RN", "QE"] + assert isinstance(parsed["overrides"], list) + + +def test_parse_parameters_overrides_string_with_spaces(): + """Test parsing overrides parameter with spaces around commas.""" + parsed = parse_parameters({"wavelength": 0.5, "overrides": "DC, RN, QE"}) + + assert parsed["overrides"] == ["DC", "RN", "QE"] + assert isinstance(parsed["overrides"], list) + + +def test_parse_parameters_overrides_string_single_value(): + """Test parsing overrides parameter with single value string.""" + parsed = parse_parameters({"wavelength": 0.5, "overrides": "DC"}) + + assert parsed["overrides"] == ["DC"] + assert isinstance(parsed["overrides"], list) + + +def test_parse_parameters_overrides_list(): + """Test parsing overrides parameter as a list.""" + parsed = parse_parameters({"wavelength": 0.5, "overrides": ["DC", "RN", "QE"]}) + + assert parsed["overrides"] == ["DC", "RN", "QE"] + assert isinstance(parsed["overrides"], list) + + +def test_parse_parameters_overrides_tuple(): + """Test parsing overrides parameter as a tuple.""" + parsed = parse_parameters({"wavelength": 0.5, "overrides": ("DC", "RN")}) + + assert parsed["overrides"] == ["DC", "RN"] + assert isinstance(parsed["overrides"], list) + + +def test_parse_parameters_overrides_empty_string(): + """Test parsing overrides parameter as an empty string.""" + parsed = parse_parameters({"wavelength": 0.5, "overrides": ""}) + + assert "overrides" not in parsed + + +def test_parse_parameters_overrides_empty_list(): + """Test parsing overrides parameter as an empty list.""" + parsed = parse_parameters({"wavelength": 0.5, "overrides": []}) + + assert "overrides" not in parsed + + +def test_parse_parameters_overrides_not_present(): + """Test that absence of overrides parameter doesn't create key.""" + parsed = parse_parameters({"wavelength": 0.5}) + + assert "overrides" not in parsed + + +def test_parse_parameters_overrides_string_extra_whitespace(): + """Test parsing overrides with extra whitespace.""" + parsed = parse_parameters({"wavelength": 0.5, "overrides": " DC , RN , QE "}) + + assert parsed["overrides"] == ["DC", "RN", "QE"] + assert isinstance(parsed["overrides"], list) + + +# ============================================================================ +# Tests for parse_parameters - npix_multiplier deprecation +# ============================================================================ + + +def test_parse_parameters_npix_multiplier_scalar(): + """Test that scalar npix_multiplier works without warnings.""" + parameters = {"wavelength": [1.0], "npix_multiplier": 2.5} + import warnings + + with warnings.catch_warnings(): + warnings.simplefilter("error") # Turn warnings into errors + parsed = parse_parameters(parameters) + + assert parsed["npix_multiplier"] == 2.5 + assert isinstance(parsed["npix_multiplier"], float) + + +def test_parse_parameters_npix_multiplier_list_raises_deprecation_warning(): + """Test that list npix_multiplier raises DeprecationWarning and uses first element.""" + parameters = {"wavelength": [1.0, 1.2], "npix_multiplier": [2.5, 3.0]} + + with pytest.warns( + DeprecationWarning, + match="Passing 'npix_multiplier' as an array is deprecated", + ): + parsed = parse_parameters(parameters) + + assert parsed["npix_multiplier"] == 2.5 + assert isinstance(parsed["npix_multiplier"], float) + + +def test_parse_parameters_npix_multiplier_numpy_array_raises_deprecation_warning(): + """Test that numpy array npix_multiplier raises DeprecationWarning and uses first element.""" + parameters = {"wavelength": [1.0, 1.2], "npix_multiplier": np.array([2.5, 3.0])} + + with pytest.warns( + DeprecationWarning, + match="Passing 'npix_multiplier' as an array is deprecated", + ): + parsed = parse_parameters(parameters) + + assert parsed["npix_multiplier"] == 2.5 + assert isinstance(parsed["npix_multiplier"], float) + + +def test_parse_parameters_npix_multiplier_single_element_array(): + """Test that single-element array npix_multiplier raises warning and uses first element.""" + parameters = {"wavelength": [1.0], "npix_multiplier": [2.5]} + + with pytest.warns(DeprecationWarning): + parsed = parse_parameters(parameters) + + assert parsed["npix_multiplier"] == 2.5 + assert isinstance(parsed["npix_multiplier"], float) + + +def test_parse_parameters_npix_multiplier_string_converts_to_float(): + """Test that string npix_multiplier is converted to float without warning.""" + parameters = {"wavelength": [1.0], "npix_multiplier": "2.5"} + import warnings + + with warnings.catch_warnings(): + warnings.simplefilter("error") + parsed = parse_parameters(parameters) + + assert parsed["npix_multiplier"] == 2.5 + assert isinstance(parsed["npix_multiplier"], float) + + +def test_parse_parameters_npix_multiplier_integer_array(): + """Test that integer arrays are also deprecated and converted to float.""" + parameters = {"wavelength": [1.0, 1.2], "npix_multiplier": [3, 4]} + + with pytest.warns(DeprecationWarning): + parsed = parse_parameters(parameters) + + assert parsed["npix_multiplier"] == 3.0 + assert isinstance(parsed["npix_multiplier"], float) diff --git a/tests/test_telescopes.py b/tests/test_telescopes.py index 477d06b..b24712c 100644 --- a/tests/test_telescopes.py +++ b/tests/test_telescopes.py @@ -7,7 +7,6 @@ from pyEDITH.utils import average_over_bandpass, interpolate_over_bandpass from copy import deepcopy - # ============================================================================ # Mock Objects and Fixtures # ============================================================================ @@ -25,6 +24,13 @@ def get_observation_parameter(self, param): return np.array([0.5, 0.7, 1.1]) * WAVELENGTH elif self.observing_mode == "IMAGER": return np.array([0.7]) * WAVELENGTH + if param == "delta_wavelength": + if self.observing_mode == "IFS": + return np.array([0.5 / 140, 0.7 / 140, 1.1 / 140]) * WAVELENGTH + elif self.observing_mode == "IMAGER": + return np.array([0.7 / 140]) * WAVELENGTH + elif param == "observing_mode": + return self.observing_mode return 1.0 def get_coronagraph_parameter(self, param): @@ -47,28 +53,32 @@ def __init__(self): @pytest.fixture -def toy_telescope_parameters(): - """Fixture providing standard parameters for ToyModelTelescope testing.""" +def full_telescope_parameters_imager(): + """Fixture providing complete parameters for ToyModelTelescope testing.""" return { "diameter": 8.0, "unobscured_area": 0.9, "toverhead_fixed": 9000, "toverhead_multi": 1.2, - "telescope_optical_throughput": [0.85], + "telescope_optical_throughput": 0.85, + "temperature": 280, + "T_contamination": 0.98, + "wavelength": 0.7, # must be provided ALWAYS } @pytest.fixture -def full_toy_telescope_parameters(): +def full_telescope_parameters_ifs(): """Fixture providing complete parameters for ToyModelTelescope testing.""" return { "diameter": 8.0, "unobscured_area": 0.9, "toverhead_fixed": 9000, "toverhead_multi": 1.2, - "telescope_optical_throughput": [0.85], + "telescope_optical_throughput": 0.85, "temperature": 280, "T_contamination": 0.98, + "wavelength": [0.5, 0.7, 1.1], # must be provided ALWAYS } @@ -86,58 +96,102 @@ def test_toy_model_telescope_init(): # ============================================================================ -# Tests for ToyModelTelescope.load_configuration - Basic parameters +# Tests for ToyModelTelescope.load_configuration - IMAGER # ============================================================================ -def test_toy_model_telescope_load_configuration_user_params(toy_telescope_parameters): +def test_toy_model_telescope_load_configuration_user_params( + full_telescope_parameters_imager, +): """Test loading ToyModelTelescope configuration with user parameters.""" telescope = ToyModelTelescope() mediator = MockMediator() - telescope.load_configuration(toy_telescope_parameters, mediator) + telescope.load_configuration(full_telescope_parameters_imager, mediator) assert telescope.diameter == 8.0 * LENGTH assert telescope.unobscured_area == 0.9 assert telescope.toverhead_fixed == 9000 * TIME assert telescope.toverhead_multi == 1.2 * DIMENSIONLESS assert np.all(telescope.telescope_optical_throughput == [0.85] * DIMENSIONLESS) + assert telescope.temperature == 280 * TEMPERATURE + assert telescope.T_contamination == 0.98 * DIMENSIONLESS + assert np.isclose(telescope.Area.value, 45.2389, rtol=1e-4) + assert telescope.Area.unit == LENGTH**2 -def test_toy_model_telescope_load_configuration_default_temperature( - toy_telescope_parameters, +def test_toy_model_telescope_load_configuration_default_values( + full_telescope_parameters_imager, ): - """Test that default temperature is used when not provided.""" + """Test that defaults are used when parameters not provided.""" telescope = ToyModelTelescope() mediator = MockMediator() - telescope.load_configuration(toy_telescope_parameters, mediator) + parameters = {"wavelength": 0.5} + + telescope.load_configuration(parameters, mediator) + assert telescope.diameter == 7.87 * LENGTH + assert telescope.unobscured_area == 0.879 + assert telescope.toverhead_fixed == 8.25e3 * TIME + assert telescope.toverhead_multi == 1.1 * DIMENSIONLESS + assert np.all(telescope.telescope_optical_throughput == [0.823] * DIMENSIONLESS) assert telescope.temperature == 290 * TEMPERATURE + assert telescope.T_contamination == 0.95 * DIMENSIONLESS + assert np.isclose( + telescope.Area.value, np.single(np.pi) / 4.0 * 7.87**2.0 * 0.879, rtol=1e-4 + ) + assert telescope.Area.unit == LENGTH**2 -def test_toy_model_telescope_load_configuration_default_contamination( - toy_telescope_parameters, +# ============================================================================ +# Tests for ToyModelTelescope.load_configuration - IFS +# ============================================================================ + + +def test_toy_model_telescope_load_configuration_user_params( + full_telescope_parameters_ifs, ): - """Test that default contamination factor is used when not provided.""" + """Test loading ToyModelTelescope configuration with user parameters.""" telescope = ToyModelTelescope() - mediator = MockMediator() + mediator = MockMediator("IFS") - telescope.load_configuration(toy_telescope_parameters, mediator) + telescope.load_configuration(full_telescope_parameters_ifs, mediator) - assert telescope.T_contamination == 0.95 * DIMENSIONLESS + assert telescope.diameter == 8.0 * LENGTH + assert telescope.unobscured_area == 0.9 + assert telescope.toverhead_fixed == 9000 * TIME + assert telescope.toverhead_multi == 1.2 * DIMENSIONLESS + assert np.all( + telescope.telescope_optical_throughput == [0.85, 0.85, 0.85] * DIMENSIONLESS + ) + assert telescope.temperature == 280 * TEMPERATURE + assert telescope.T_contamination == 0.98 * DIMENSIONLESS + assert np.isclose(telescope.Area.value, 45.2389, rtol=1e-4) + assert telescope.Area.unit == LENGTH**2 -def test_toy_model_telescope_load_configuration_calculated_area( - toy_telescope_parameters, -): - """Test that telescope area is calculated correctly from diameter and obscuration.""" +def test_toy_model_telescope_load_configuration_default_values(): + """Test that defaults are used when parameters not provided.""" telescope = ToyModelTelescope() - mediator = MockMediator() + mediator = MockMediator("IFS") - telescope.load_configuration(toy_telescope_parameters, mediator) + parameters = {"wavelength": mediator.get_observation_parameter("wavelength")} - assert np.isclose(telescope.Area.value, 45.2389, rtol=1e-4) + telescope.load_configuration(parameters, mediator) + + assert telescope.diameter == 7.87 * LENGTH + assert telescope.unobscured_area == 0.879 + assert telescope.toverhead_fixed == 8.25e3 * TIME + assert telescope.toverhead_multi == 1.1 * DIMENSIONLESS + assert np.all( + telescope.telescope_optical_throughput == [0.823, 0.823, 0.823] * DIMENSIONLESS + ) + assert telescope.temperature == 290 * TEMPERATURE + assert telescope.T_contamination == 0.95 * DIMENSIONLESS + assert np.isclose( + telescope.Area.value, np.single(np.pi) / 4.0 * 7.87**2.0 * 0.879, rtol=1e-4 + ) assert telescope.Area.unit == LENGTH**2 @@ -147,39 +201,32 @@ def test_toy_model_telescope_load_configuration_calculated_area( @patch("eacy.load_telescope") -def test_eac_telescope_load_configuration_imager_basic( - mock_load_telescope, mock_telescope_params +def test_eac_telescope_load_configuration_user_params( + mock_load_telescope, + mock_telescope_params, + full_telescope_parameters_imager, + caplog, ): - """Test basic EACTelescope configuration loading in IMAGER mode.""" - mock_load_telescope.return_value = deepcopy(mock_telescope_params) + """Test loading EACTelescope configuration with user parameters.""" + import logging + mock_load_telescope.return_value = deepcopy(mock_telescope_params) telescope = EACTelescope(keyword="EAC1") - parameters = {"observing_mode": "IMAGER"} mediator = MockMediator("IMAGER") - telescope.load_configuration(parameters, mediator) + with caplog.at_level(logging.DEBUG): + telescope.load_configuration(full_telescope_parameters_imager, mediator) + # These values can be changed by the user + assert telescope.toverhead_fixed == 9000 * TIME + assert telescope.toverhead_multi == 1.2 * DIMENSIONLESS + + # LOCKED PARAMETERS: EVEN IF WE LOAD USER PARAMETERS, WE WANT THE DEFAULTS assert telescope.diameter == 8.0 * LENGTH assert telescope.unobscured_area == 1.0 - assert telescope.toverhead_fixed == 8.25e3 * TIME - assert telescope.toverhead_multi == 1.1 * DIMENSIONLESS + assert telescope.temperature == 290 * TEMPERATURE assert telescope.T_contamination == 1.0 * DIMENSIONLESS - - -@patch("eacy.load_telescope") -def test_eac_telescope_load_configuration_imager_throughput_averaging( - mock_load_telescope, mock_telescope_params -): - """Test that telescope throughput is averaged over bandpass in IMAGER mode.""" - mock_load_telescope.return_value = deepcopy(mock_telescope_params) - - telescope = EACTelescope(keyword="EAC1") - parameters = {"observing_mode": "IMAGER"} - mediator = MockMediator("IMAGER") - - telescope.load_configuration(parameters, mediator) - wavelength = mediator.get_observation_parameter("wavelength") bandwidth = mediator.get_coronagraph_parameter("bandwidth") wavelength_range = [ @@ -199,64 +246,103 @@ def test_eac_telescope_load_configuration_imager_throughput_averaging( expected_throughput, rtol=1e-5, ) - - -@patch("eacy.load_telescope") -def test_eac_telescope_load_configuration_imager_calculated_area( - mock_load_telescope, mock_telescope_params -): - """Test that telescope area is calculated correctly in IMAGER mode.""" - mock_load_telescope.return_value = deepcopy(mock_telescope_params) - - telescope = EACTelescope(keyword="EAC1") - parameters = {"observing_mode": "IMAGER"} - mediator = MockMediator("IMAGER") - - telescope.load_configuration(parameters, mediator) - assert np.isclose(telescope.Area.value, 50.2655, rtol=1e-4) assert telescope.Area.unit == LENGTH**2 - -# ============================================================================ -# Tests for EACTelescope.load_configuration - IFS mode -# ============================================================================ + # Check that warning messages were logged for locked parameters using default values + warning_messages = [ + record.message for record in caplog.records if record.levelname == "WARNING" + ] + locked_keys = { + "diameter", + "unobscured_area", + "T_contamination", + "temperature", + "telescope_optical_throughput", + } + for key in locked_keys: + assert any( + key in msg and "is locked in this mode" in msg for msg in warning_messages + ), f"Expected warning message for locked key '{key}' not found" @patch("eacy.load_telescope") -def test_eac_telescope_load_configuration_ifs_basic( +def test_eac_telescope_load_configuration_default_values( mock_load_telescope, mock_telescope_params ): - """Test basic EACTelescope configuration loading in IFS mode.""" + """Test that defaults are used when parameters not provided.""" mock_load_telescope.return_value = deepcopy(mock_telescope_params) + mediator = MockMediator("IMAGER") telescope = EACTelescope(keyword="EAC1") - parameters = {"observing_mode": "IFS"} - mediator = MockMediator("IFS") + parameters = { + "wavelength": mediator.get_observation_parameter("wavelength"), + } telescope.load_configuration(parameters, mediator) + # Defaults assert telescope.diameter == 8.0 * LENGTH assert telescope.unobscured_area == 1.0 assert telescope.toverhead_fixed == 8.25e3 * TIME assert telescope.toverhead_multi == 1.1 * DIMENSIONLESS assert telescope.temperature == 290 * TEMPERATURE assert telescope.T_contamination == 1.0 * DIMENSIONLESS + wavelength = mediator.get_observation_parameter("wavelength") + bandwidth = mediator.get_coronagraph_parameter("bandwidth") + wavelength_range = [ + wavelength * (1 - 0.5 * bandwidth), + wavelength * (1 + 0.5 * bandwidth), + ] + expected_throughput = average_over_bandpass( + { + "lam": mock_telescope_params.lam, + "total_tele_refl": mock_telescope_params.total_tele_refl.copy(), + }, + wavelength_range, + )["total_tele_refl"] + + assert np.isclose( + telescope.telescope_optical_throughput[0].value, + expected_throughput, + rtol=1e-5, + ) + assert np.isclose(telescope.Area.value, 50.2655, rtol=1e-4) + assert telescope.Area.unit == LENGTH**2 + + +# # ============================================================================ +# # Tests for EACTelescope.load_configuration - IFS mode +# # ============================================================================ @patch("eacy.load_telescope") -def test_eac_telescope_load_configuration_ifs_throughput_interpolation( - mock_load_telescope, mock_telescope_params +def test_eac_telescope_load_configuration_user_params( + mock_load_telescope, + mock_telescope_params, + full_telescope_parameters_ifs, + caplog, ): - """Test that telescope throughput is interpolated onto wavelength grid in IFS mode.""" - mock_load_telescope.return_value = deepcopy(mock_telescope_params) + """Test loading EACTelescope configuration with user parameters.""" + import logging + mock_load_telescope.return_value = deepcopy(mock_telescope_params) telescope = EACTelescope(keyword="EAC1") - parameters = {"observing_mode": "IFS"} mediator = MockMediator("IFS") - telescope.load_configuration(parameters, mediator) + with caplog.at_level(logging.DEBUG): + telescope.load_configuration(full_telescope_parameters_ifs, mediator) + + # These values can be changed by the user + assert telescope.toverhead_fixed == 9000 * TIME + assert telescope.toverhead_multi == 1.2 * DIMENSIONLESS + # LOCKED PARAMETERS: EVEN IF WE LOAD USER PARAMETERS, WE WANT THE DEFAULTS + assert telescope.diameter == 8.0 * LENGTH + assert telescope.unobscured_area == 1.0 + + assert telescope.temperature == 290 * TEMPERATURE + assert telescope.T_contamination == 1.0 * DIMENSIONLESS wavelengths = mediator.get_observation_parameter("wavelength") expected_throughput = interpolate_over_bandpass( { @@ -265,70 +351,98 @@ def test_eac_telescope_load_configuration_ifs_throughput_interpolation( }, wavelengths, )["total_tele_refl"] - + print(expected_throughput, telescope.telescope_optical_throughput) assert np.allclose( telescope.telescope_optical_throughput.value, expected_throughput, rtol=1e-5, ) + assert np.isclose(telescope.Area.value, 50.2655, rtol=1e-4) + assert telescope.Area.unit == LENGTH**2 + + # Check that warning messages were logged for locked parameters using default values + warning_messages = [ + record.message for record in caplog.records if record.levelname == "WARNING" + ] + locked_keys = { + "diameter", + "unobscured_area", + "T_contamination", + "temperature", + "telescope_optical_throughput", + } + for key in locked_keys: + assert any( + key in msg and "is locked in this mode" in msg for msg in warning_messages + ), f"Expected warning message for locked key '{key}' not found" @patch("eacy.load_telescope") -def test_eac_telescope_load_configuration_ifs_calculated_area( +def test_eac_telescope_load_configuration_default_values( mock_load_telescope, mock_telescope_params ): - """Test that telescope area is calculated correctly in IFS mode.""" + """Test that defaults are used when parameters not provided.""" mock_load_telescope.return_value = deepcopy(mock_telescope_params) + mediator = MockMediator("IFS") telescope = EACTelescope(keyword="EAC1") - parameters = {"observing_mode": "IFS"} - mediator = MockMediator("IFS") + parameters = { + "wavelength": mediator.get_observation_parameter("wavelength"), + } telescope.load_configuration(parameters, mediator) + # Defaults + assert telescope.diameter == 8.0 * LENGTH + assert telescope.unobscured_area == 1.0 + assert telescope.toverhead_fixed == 8.25e3 * TIME + assert telescope.toverhead_multi == 1.1 * DIMENSIONLESS + assert telescope.temperature == 290 * TEMPERATURE + assert telescope.T_contamination == 1.0 * DIMENSIONLESS + wavelengths = mediator.get_observation_parameter("wavelength") + expected_throughput = interpolate_over_bandpass( + { + "lam": mock_telescope_params.lam, + "total_tele_refl": mock_telescope_params.total_tele_refl.copy(), + }, + wavelengths, + )["total_tele_refl"] + + assert np.allclose( + telescope.telescope_optical_throughput.value, + expected_throughput, + rtol=1e-5, + ) assert np.isclose(telescope.Area.value, 50.2655, rtol=1e-4) assert telescope.Area.unit == LENGTH**2 -# ============================================================================ -# Tests for EACTelescope.load_configuration - Error handling -# ============================================================================ - - -def test_eac_telescope_load_configuration_invalid_mode(): - """Test that invalid observing mode raises KeyError.""" - telescope = EACTelescope() - parameters = {"observing_mode": "INVALID"} - mediator = MockMediator("IMAGER") - - with pytest.raises(KeyError, match="Unsupported observing mode: INVALID"): - telescope.load_configuration(parameters, mediator) - - -# ============================================================================ +# # ============================================================================ # Tests for Telescope.validate_configuration # ============================================================================ -def test_telescope_validate_configuration_all_valid(full_toy_telescope_parameters): +def test_telescope_validate_configuration_all_valid( + full_telescope_parameters_imager, +): """Test that validation passes with all correct attributes.""" telescope = ToyModelTelescope() mediator = MockMediator() - telescope.load_configuration(full_toy_telescope_parameters, mediator) + telescope.load_configuration(full_telescope_parameters_imager, mediator) # Should not raise telescope.validate_configuration() def test_telescope_validate_configuration_missing_diameter( - full_toy_telescope_parameters, + full_telescope_parameters_imager, ): """Test that missing diameter attribute raises AttributeError.""" telescope = ToyModelTelescope() mediator = MockMediator() - telescope.load_configuration(full_toy_telescope_parameters, mediator) + telescope.load_configuration(full_telescope_parameters_imager, mediator) delattr(telescope, "diameter") with pytest.raises( @@ -338,13 +452,13 @@ def test_telescope_validate_configuration_missing_diameter( def test_telescope_validate_configuration_diameter_not_quantity( - full_toy_telescope_parameters, + full_telescope_parameters_imager, ): """Test that non-Quantity diameter raises TypeError.""" telescope = ToyModelTelescope() mediator = MockMediator() - telescope.load_configuration(full_toy_telescope_parameters, mediator) + telescope.load_configuration(full_telescope_parameters_imager, mediator) telescope.diameter = 8.0 # Not a Quantity with pytest.raises( @@ -354,13 +468,13 @@ def test_telescope_validate_configuration_diameter_not_quantity( def test_telescope_validate_configuration_incorrect_diameter_units( - full_toy_telescope_parameters, + full_telescope_parameters_imager, ): """Test that diameter with incorrect units raises ValueError.""" telescope = ToyModelTelescope() mediator = MockMediator() - telescope.load_configuration(full_toy_telescope_parameters, mediator) + telescope.load_configuration(full_telescope_parameters_imager, mediator) telescope.diameter = 8.0 * u.s # Wrong unit with pytest.raises( @@ -382,6 +496,7 @@ def test_toy_model_telescope_throughput_array_conversion(): parameters = { "diameter": 8.0, "telescope_optical_throughput": [0.85, 0.90], # List input + "wavelength": [0.5, 0.6], } telescope.load_configuration(parameters, mediator) @@ -403,6 +518,7 @@ def test_toy_model_telescope_area_with_no_obscuration(): parameters = { "diameter": 10.0, "unobscured_area": 1.0, + "wavelength": mediator.get_observation_parameter("wavelength"), } telescope.load_configuration(parameters, mediator) @@ -411,42 +527,6 @@ def test_toy_model_telescope_area_with_no_obscuration(): assert np.isclose(telescope.Area.value, expected_area, rtol=1e-6) -def test_toy_model_telescope_area_with_partial_obscuration(): - """Test area calculation with partial obscuration.""" - telescope = ToyModelTelescope() - mediator = MockMediator() - - parameters = { - "diameter": 10.0, - "unobscured_area": 0.8, - } - - telescope.load_configuration(parameters, mediator) - - expected_area = np.pi * (10.0**2) / 4.0 * 0.8 - assert np.isclose(telescope.Area.value, expected_area, rtol=1e-6) - - -# ============================================================================ -# Tests for default values -# ============================================================================ - - -def test_toy_model_telescope_all_defaults(): - """Test that all default values are correctly applied.""" - telescope = ToyModelTelescope() - mediator = MockMediator() - - telescope.load_configuration({}, mediator) - - assert telescope.diameter == 7.87 * LENGTH - assert telescope.unobscured_area == (1.0 - 0.121) - assert telescope.toverhead_fixed == 8.25e3 * TIME - assert telescope.toverhead_multi == 1.1 * DIMENSIONLESS - assert telescope.temperature == 290 * TEMPERATURE - assert telescope.T_contamination == 0.95 * DIMENSIONLESS - - @patch("eacy.load_telescope") def test_eac_telescope_default_contamination( mock_load_telescope, mock_telescope_params @@ -455,8 +535,11 @@ def test_eac_telescope_default_contamination( mock_load_telescope.return_value = deepcopy(mock_telescope_params) telescope = EACTelescope(keyword="EAC1") - parameters = {"observing_mode": "IMAGER"} mediator = MockMediator("IMAGER") + parameters = { + "observing_mode": "IMAGER", + "wavelength": mediator.get_observation_parameter("wavelength"), + } telescope.load_configuration(parameters, mediator) @@ -469,8 +552,11 @@ def test_eac_telescope_default_temperature(mock_load_telescope, mock_telescope_p mock_load_telescope.return_value = deepcopy(mock_telescope_params) telescope = EACTelescope(keyword="EAC1") - parameters = {"observing_mode": "IMAGER"} mediator = MockMediator("IMAGER") + parameters = { + "observing_mode": "IMAGER", + "wavelength": mediator.get_observation_parameter("wavelength"), + } telescope.load_configuration(parameters, mediator) @@ -490,8 +576,11 @@ def test_eac_telescope_throughput_shape_imager( mock_load_telescope.return_value = deepcopy(mock_telescope_params) telescope = EACTelescope(keyword="EAC1") - parameters = {"observing_mode": "IMAGER"} mediator = MockMediator("IMAGER") + parameters = { + "observing_mode": "IMAGER", + "wavelength": mediator.get_observation_parameter("wavelength"), + } telescope.load_configuration(parameters, mediator) @@ -504,9 +593,13 @@ def test_eac_telescope_throughput_shape_ifs(mock_load_telescope, mock_telescope_ mock_load_telescope.return_value = deepcopy(mock_telescope_params) telescope = EACTelescope(keyword="EAC1") - parameters = {"observing_mode": "IFS"} mediator = MockMediator("IFS") + parameters = { + "observing_mode": "IFS", + "wavelength": mediator.get_observation_parameter("wavelength"), + } + telescope.load_configuration(parameters, mediator) wavelengths = mediator.get_observation_parameter("wavelength") diff --git a/tests/test_utils.py b/tests/test_utils.py index 5fba5b4..ba21a27 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -110,15 +110,13 @@ def mock_observatory(): observatory.coronagraph.xcenter = 50 * u.pix observatory.coronagraph.ycenter = 50 * u.pix observatory.coronagraph.nchannels = 2 - observatory.coronagraph.minimum_IWA = 2 * LAMBDA_D - observatory.coronagraph.maximum_OWA = 10 * LAMBDA_D observatory.coronagraph.npsfratios = 1 observatory.coronagraph.nrolls = 1 observatory.detector.pixscale_mas = 100 * u.mas observatory.detector.QE = np.array([0.8, 0.8, 0.8]) * QUANTUM_EFFICIENCY observatory.detector.dQE = np.array([1.0, 1.0, 1.0]) * DIMENSIONLESS - observatory.detector.npix_multiplier = [1.0, 1.0, 1.0] + observatory.detector.npix_multiplier = 1.0 * DIMENSIONLESS observatory.detector.DC = [1e-3, 1e-3, 1e-3] * u.electron / u.s / u.pix observatory.detector.RN = [3, 3, 3] * u.electron / u.pix observatory.detector.tread = [100, 100, 100] * u.s @@ -541,20 +539,27 @@ def test_print_array_info_none_scalar_validation(): # ============================================================================ -@pytest.mark.parametrize("mode", ["validation", "full_info"]) def test_print_all_variables_creates_files( - mode, mock_observation, mock_scene, mock_observatory, validation_kwargs + mock_observation, mock_scene, mock_observatory ): - """Test that print_all_variables creates output files with expected structure.""" + """Test that print_all_variables creates both output files.""" with tempfile.TemporaryDirectory() as tmpdirname: original_dir = os.getcwd() os.chdir(tmpdirname) try: - print_all_variables( - mock_observation, mock_scene, mock_observatory, **validation_kwargs - ) + mock_observation.validation_variables = { + "deltalambda_nm": np.array([10.0]), + "lod": np.array([0.05]), + "CRp": np.array([1.0]), + "CRbs": np.array([0.5]), + "CRb": np.array([2.0]), + } + + print_all_variables(mock_observation, mock_scene, mock_observatory) - assert os.path.exists(f"pyedith_{mode}.txt") + # A single call writes BOTH files via the internal `for mode` loop. + assert os.path.exists("pyedith_validation.txt") + assert os.path.exists("pyedith_full_info.txt") finally: os.chdir(original_dir) @@ -562,16 +567,22 @@ def test_print_all_variables_creates_files( @pytest.mark.parametrize("mode", ["validation", "full_info"]) def test_print_all_variables_file_structure( - mode, mock_observation, mock_scene, mock_observatory, validation_kwargs + mode, mock_observation, mock_scene, mock_observatory ): """Test that output file contains all expected sections.""" with tempfile.TemporaryDirectory() as tmpdirname: original_dir = os.getcwd() os.chdir(tmpdirname) try: - print_all_variables( - mock_observation, mock_scene, mock_observatory, **validation_kwargs - ) + mock_observation.validation_variables = { + "deltalambda_nm": np.array([10.0]), + "lod": np.array([0.05]), + "CRp": np.array([1.0]), + "CRbs": np.array([0.5]), + "CRb": np.array([2.0]), + } + + print_all_variables(mock_observation, mock_scene, mock_observatory) with open(f"pyedith_{mode}.txt", "r") as file: content = file.read() @@ -592,16 +603,22 @@ def test_print_all_variables_file_structure( @pytest.mark.parametrize("mode", ["validation", "full_info"]) def test_print_all_variables_includes_attributes( - mode, mock_observation, mock_scene, mock_observatory, validation_kwargs + mode, mock_observation, mock_scene, mock_observatory ): """Test that output file includes specific object attributes.""" with tempfile.TemporaryDirectory() as tmpdirname: original_dir = os.getcwd() os.chdir(tmpdirname) try: - print_all_variables( - mock_observation, mock_scene, mock_observatory, **validation_kwargs - ) + mock_observation.validation_variables = { + "deltalambda_nm": np.array([10.0]), + "lod": np.array([0.05]), + "CRp": np.array([1.0]), + "CRbs": np.array([0.5]), + "CRb": np.array([2.0]), + } + + print_all_variables(mock_observation, mock_scene, mock_observatory) with open(f"pyedith_{mode}.txt", "r") as file: content = file.read() @@ -619,16 +636,23 @@ def test_print_all_variables_includes_attributes( @pytest.mark.parametrize("mode", ["validation", "full_info"]) def test_print_all_variables_includes_calculated_vars( - mode, mock_observation, mock_scene, mock_observatory, validation_kwargs + mode, mock_observation, mock_scene, mock_observatory ): - """Test that output file includes calculated variables.""" + """Test that output file includes calculated variables read from + observation.validation_variables.""" with tempfile.TemporaryDirectory() as tmpdirname: original_dir = os.getcwd() os.chdir(tmpdirname) try: - print_all_variables( - mock_observation, mock_scene, mock_observatory, **validation_kwargs - ) + mock_observation.validation_variables = { + "deltalambda_nm": np.array([10.0]), + "lod": np.array([0.05]), + "CRp": np.array([1.0]), + "CRbs": np.array([0.5]), + "CRb": np.array([2.0]), + } + + print_all_variables(mock_observation, mock_scene, mock_observatory) with open(f"pyedith_{mode}.txt", "r") as file: content = file.read() @@ -644,16 +668,22 @@ def test_print_all_variables_includes_calculated_vars( def test_print_all_variables_mode_specific_output_full_info( - mock_observation, mock_scene, mock_observatory, validation_kwargs + mock_observation, mock_scene, mock_observatory ): """Test that full_info mode includes shape and unit information.""" with tempfile.TemporaryDirectory() as tmpdirname: original_dir = os.getcwd() os.chdir(tmpdirname) try: - print_all_variables( - mock_observation, mock_scene, mock_observatory, **validation_kwargs - ) + mock_observation.validation_variables = { + "deltalambda_nm": np.array([10.0]), + "lod": np.array([0.05]), + "CRp": np.array([1.0]), + "CRbs": np.array([0.5]), + "CRb": np.array([2.0]), + } + + print_all_variables(mock_observation, mock_scene, mock_observatory) with open("pyedith_full_info.txt", "r") as file: content = file.read() @@ -666,16 +696,22 @@ def test_print_all_variables_mode_specific_output_full_info( def test_print_all_variables_mode_specific_output_validation( - mock_observation, mock_scene, mock_observatory, validation_kwargs + mock_observation, mock_scene, mock_observatory ): """Test that validation mode includes value information.""" with tempfile.TemporaryDirectory() as tmpdirname: original_dir = os.getcwd() os.chdir(tmpdirname) try: - print_all_variables( - mock_observation, mock_scene, mock_observatory, **validation_kwargs - ) + mock_observation.validation_variables = { + "deltalambda_nm": np.array([10.0]), + "lod": np.array([0.05]), + "CRp": np.array([1.0]), + "CRbs": np.array([0.5]), + "CRb": np.array([2.0]), + } + + print_all_variables(mock_observation, mock_scene, mock_observatory) with open("pyedith_validation.txt", "r") as file: content = file.read() @@ -853,7 +889,7 @@ def test_regrid_wavelengths_single_resolution(): def test_regrid_spec_gaussconv_basic(): """Test Gaussian convolution regridding produces correct output length.""" input_wls = np.linspace(0.4, 2.0, 100) - input_spec = np.random.rand(100) * PHOTON_FLUX_DENSITY + input_spec = np.random.rand(100) new_lam = np.linspace(0.5, 1.9, 50) new_dlam = np.gradient(new_lam) @@ -862,41 +898,697 @@ def test_regrid_spec_gaussconv_basic(): assert len(spec_regrid) == len(new_lam) -def test_regrid_spec_gaussconv_preserves_units(): - """Test that Gaussian convolution regridding preserves units.""" +# ============================================================================ +# Tests for regrid_spec_interp +# ============================================================================ + + +def test_regrid_spec_interp_basic(): + """Test interpolation regridding produces correct output length.""" input_wls = np.linspace(0.4, 2.0, 100) - input_spec = np.random.rand(100) * PHOTON_FLUX_DENSITY + input_spec = np.random.rand(100) new_lam = np.linspace(0.5, 1.9, 50) - new_dlam = np.gradient(new_lam) - spec_regrid = regrid_spec_gaussconv(input_wls, input_spec, new_lam, new_dlam) + spec_regrid = regrid_spec_interp(input_wls, input_spec, new_lam) - assert spec_regrid.unit == input_spec.unit + assert len(spec_regrid) == len(new_lam) # ============================================================================ -# Tests for regrid_spec_interp +# Tests for regrid_to_grid # ============================================================================ -def test_regrid_spec_interp_basic(): - """Test interpolation regridding produces correct output length.""" - input_wls = np.linspace(0.4, 2.0, 100) * WAVELENGTH - input_spec = np.random.rand(100) * PHOTON_FLUX_DENSITY - new_lam = np.linspace(0.5, 1.9, 50) * WAVELENGTH +def test_regrid_to_grid_broadcast_scalar(): + """Test that a single value is broadcast to the entire grid.""" + values = np.array([1.5]) + from_wavelength = np.array([1.0]) + to_wavelength = np.linspace(0.5, 2.0, 100) - spec_regrid = regrid_spec_interp(input_wls, input_spec, new_lam) + result = regrid_to_grid(values, from_wavelength, to_wavelength) - assert isinstance(spec_regrid, u.Quantity) - assert len(spec_regrid) == len(new_lam) + assert len(result) == len(to_wavelength) + assert np.all(result == 1.5) -def test_regrid_spec_interp_preserves_units(): - """Test that interpolation regridding preserves units.""" - input_wls = np.linspace(0.4, 2.0, 100) * WAVELENGTH - input_spec = np.random.rand(100) * PHOTON_FLUX_DENSITY - new_lam = np.linspace(0.5, 1.9, 50) * WAVELENGTH +def test_regrid_to_grid_passthrough(): + """Test that values already on the target grid pass through unchanged.""" + to_wavelength = np.linspace(0.5, 2.0, 50) + values = np.random.rand(50) + from_wavelength = to_wavelength.copy() - spec_regrid = regrid_spec_interp(input_wls, input_spec, new_lam) + result = regrid_to_grid(values, from_wavelength, to_wavelength) + + assert len(result) == len(to_wavelength) + np.testing.assert_array_equal(result, values) + + +def test_regrid_to_grid_interpolation_1d(): + """Test regridding using 1D interpolation.""" + from_wavelength = np.linspace(0.4, 2.0, 100) + values = np.random.rand(100) + to_wavelength = np.linspace(0.5, 1.9, 50) + + result = regrid_to_grid(values, from_wavelength, to_wavelength, interpolation="1d") + + assert len(result) == len(to_wavelength) + assert result.dtype == np.float64 + + +def test_regrid_to_grid_interpolation_gaussian(): + """Test regridding using Gaussian convolution.""" + from_wavelength = np.linspace(0.4, 2.0, 100) + values = np.random.rand(100) + to_wavelength = np.linspace(0.5, 1.9, 50) + to_delta_wavelength = np.gradient(to_wavelength) + + result = regrid_to_grid( + values, + from_wavelength, + to_wavelength, + to_delta_wavelength, + interpolation="Gaussian", + ) + + assert len(result) == len(to_wavelength) + assert result.dtype == np.float64 + + +def test_regrid_to_grid_gaussian_missing_delta_wavelength(): + """Test that Gaussian interpolation raises error without delta_wavelength.""" + from_wavelength = np.linspace(0.4, 2.0, 100) + values = np.random.rand(100) + to_wavelength = np.linspace(0.5, 1.9, 50) + + with pytest.raises(ValueError, match="to_delta_wavelength.*not provided"): + regrid_to_grid(values, from_wavelength, to_wavelength, interpolation="Gaussian") + + +def test_regrid_to_grid_invalid_interpolation(): + """Test that invalid interpolation method raises error.""" + values = np.array([1.0, 2.0, 3.0]) + from_wavelength = np.array([0.5, 1.0, 1.5]) + to_wavelength = np.linspace(0.5, 1.5, 10) + + with pytest.raises(ValueError, match="Unknown interpolation type"): + regrid_to_grid( + values, from_wavelength, to_wavelength, interpolation="invalid_method" + ) + + +def test_regrid_to_grid_with_quantity(): + """Test that astropy Quantity units are preserved.""" + values = np.array([1.0, 2.0, 3.0]) * u.Jy + from_wavelength = np.array([0.5, 1.0, 1.5]) + to_wavelength = np.linspace(0.5, 1.5, 10) + + result = regrid_to_grid(values, from_wavelength, to_wavelength, interpolation="1d") + + assert isinstance(result, u.Quantity) + assert result.unit == u.Jy + assert len(result) == len(to_wavelength) + + +def test_regrid_to_grid_broadcast_quantity(): + """Test that a single Quantity value is broadcast correctly.""" + values = np.array([2.5]) * u.erg / u.s / u.cm**2 + from_wavelength = np.array([1.0]) + to_wavelength = np.linspace(0.5, 2.0, 50) + + result = regrid_to_grid(values, from_wavelength, to_wavelength) + + assert isinstance(result, u.Quantity) + assert result.unit == u.erg / u.s / u.cm**2 + assert len(result) == len(to_wavelength) + assert np.all(result.value == 2.5) + + +def test_regrid_to_grid_name_parameter(): + """Test that custom name appears in log messages (requires log capture).""" + # This test ensures the name parameter is accepted + values = np.random.rand(10) + from_wavelength = np.linspace(0.5, 1.5, 10) + to_wavelength = np.linspace(0.5, 1.5, 20) + + result = regrid_to_grid( + values, from_wavelength, to_wavelength, name="custom_flux", interpolation="1d" + ) + + assert len(result) == len(to_wavelength) + + +def test_regrid_to_grid_dtype_conversion(): + """Test that output is always float64.""" + values = np.array([1, 2, 3], dtype=np.int32) + from_wavelength = np.array([0.5, 1.0, 1.5]) + to_wavelength = np.linspace(0.5, 1.5, 10) + + result = regrid_to_grid(values, from_wavelength, to_wavelength, interpolation="1d") + + assert result.dtype == np.float64 + + +# ============================================================================ +# Tests for fill_parameters +# ============================================================================ + + +def test_fill_parameters_basic_default_values(test_object): + """Test that default parameters are correctly assigned when no user params provided.""" + parameters = {} + default_parameters = { + "param1": 10, + "param2": 20.5, + "param3": "test_string", + } + + fill_parameters(test_object, parameters, default_parameters) + + assert test_object.param1 == 10 + assert test_object.param2 == 20.5 + assert test_object.param3 == "test_string" + + +def test_fill_parameters_user_override(test_object): + """Test that user-provided parameters override defaults.""" + parameters = { + "param1": 100, + "param2": 200.5, + } + default_parameters = { + "param1": 10, + "param2": 20.5, + "param3": "default_string", + } + + fill_parameters(test_object, parameters, default_parameters) + + assert test_object.param1 == 100 + assert test_object.param2 == 200.5 + assert test_object.param3 == "default_string" + + +def test_fill_parameters_locked_keys_prevent_override(test_object, caplog): + """Test that locked keys cannot be overridden by user parameters.""" + parameters = { + "locked_param": 999, + "unlocked_param": 50, + } + default_parameters = { + "locked_param": 42, + "unlocked_param": 25, + } + locked_keys = {"locked_param"} + + with caplog.at_level(logging.WARNING): + fill_parameters(test_object, parameters, default_parameters, locked_keys) + + # Locked parameter should retain default value + assert test_object.locked_param == 42 + # Unlocked parameter should use user value + assert test_object.unlocked_param == 50 + # Warning should be logged + assert "locked_param" in caplog.text + assert "locked in this mode" in caplog.text + + +def test_fill_parameters_quantity_unit_conversion(test_object): + """Test that Quantity parameters with matching units are converted correctly.""" + parameters = { + "distance": 5.0 * u.pc, # parsecs + } + default_parameters = { + "distance": 10.0 * u.m, # meters (different unit) + } + + fill_parameters(test_object, parameters, default_parameters) + + # Should be converted to meters (the default unit) + assert isinstance(test_object.distance, u.Quantity) + assert test_object.distance.unit == u.m + # 5 pc = 1.54285714e+17 m + assert np.isclose(test_object.distance.value, 1.54285714e17, rtol=1e-4) + + +def test_fill_parameters_quantity_without_units(test_object): + """Test that unitless user value gets assigned default units from Quantity default.""" + parameters = { + "length": 100.0, # No units + } + default_parameters = { + "length": 1.0 * u.m, # Has units + } + + fill_parameters(test_object, parameters, default_parameters) + + assert isinstance(test_object.length, u.Quantity) + assert test_object.length.unit == u.m + assert test_object.length.value == 100.0 + + +def test_fill_parameters_quantity_user_override_with_units(test_object): + """Test that user Quantity with units correctly overrides default.""" + parameters = { + "wavelength": 500 * u.nm, + } + default_parameters = { + "wavelength": 1.0 * u.um, # Different value and unit + } + + fill_parameters(test_object, parameters, default_parameters) + + assert isinstance(test_object.wavelength, u.Quantity) + # Should be converted to microns (the default unit) + assert test_object.wavelength.unit == u.um + assert np.isclose(test_object.wavelength.value, 0.5) + + +def test_fill_parameters_mixed_locked_and_unlocked_quantities(test_object, caplog): + """Test handling of both locked and unlocked Quantity parameters.""" + parameters = { + "locked_distance": 100 * u.m, + "unlocked_distance": 200 * u.m, + } + default_parameters = { + "locked_distance": 10 * u.m, + "unlocked_distance": 20 * u.m, + } + locked_keys = {"locked_distance"} + + with caplog.at_level(logging.WARNING): + fill_parameters(test_object, parameters, default_parameters, locked_keys) + + # Locked should use default + assert test_object.locked_distance == 10 * u.m + # Unlocked should use user value + assert test_object.unlocked_distance == 200 * u.m + # Warning should be logged only for locked parameter + assert "locked_distance" in caplog.text + + +def test_fill_parameters_empty_locked_keys(test_object): + """Test that passing None for locked_keys works correctly.""" + parameters = { + "param1": 100, + } + default_parameters = { + "param1": 10, + "param2": 20, + } + + # Should not raise any errors with locked_keys=None (default) + fill_parameters(test_object, parameters, default_parameters, locked_keys=None) + + assert test_object.param1 == 100 + assert test_object.param2 == 20 + + +def test_fill_parameters_all_locked_keys(test_object, caplog): + """Test behavior when all parameters are locked.""" + parameters = { + "param1": 999, + "param2": 888, + } + default_parameters = { + "param1": 10, + "param2": 20, + } + locked_keys = {"param1", "param2"} + + with caplog.at_level(logging.WARNING): + fill_parameters(test_object, parameters, default_parameters, locked_keys) + + # All should use defaults + assert test_object.param1 == 10 + assert test_object.param2 == 20 + # Should have warnings for both + assert "param1" in caplog.text + assert "param2" in caplog.text + + +# ============================================================================ +# Tests for fill_parameters - Override functionality +# ============================================================================ + + +def test_fill_parameters_override_locked_key(test_object, caplog): + """Test that locked keys can be overridden when explicitly allowed.""" + parameters = { + "locked_param": 999, + } + default_parameters = { + "locked_param": 42, + } + locked_keys = {"locked_param"} + allow_override = {"locked_param"} + + with caplog.at_level(logging.WARNING): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + # Locked parameter should now use user value + assert test_object.locked_param == 999 + # Warning should indicate explicit override + assert "locked_param" in caplog.text + assert "explicitly overridden" in caplog.text + + +def test_fill_parameters_override_locked_quantity(test_object, caplog): + """Test that locked Quantity parameters can be overridden.""" + parameters = { + "locked_distance": 100 * u.m, + } + default_parameters = { + "locked_distance": 10 * u.m, + } + locked_keys = {"locked_distance"} + allow_override = {"locked_distance"} + + with caplog.at_level(logging.WARNING): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + assert test_object.locked_distance == 100 * u.m + assert "locked_distance" in caplog.text + assert "explicitly overridden" in caplog.text + + +def test_fill_parameters_override_with_unit_conversion(test_object, caplog): + """Test that overridden Quantity is converted to default units.""" + parameters = { + "locked_distance": 5.0 * u.pc, + } + default_parameters = { + "locked_distance": 10.0 * u.m, + } + locked_keys = {"locked_distance"} + allow_override = {"locked_distance"} + + with caplog.at_level(logging.WARNING): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + # Should be converted to meters (the default unit) + assert isinstance(test_object.locked_distance, u.Quantity) + assert test_object.locked_distance.unit == u.m + assert np.isclose(test_object.locked_distance.value, 1.54285714e17, rtol=1e-4) + + +def test_fill_parameters_override_without_unit(test_object, caplog): + """Test that overridden unitless value gets default units.""" + parameters = { + "locked_length": 100.0, + } + default_parameters = { + "locked_length": 1.0 * u.m, + } + locked_keys = {"locked_length"} + allow_override = {"locked_length"} + + with caplog.at_level(logging.WARNING): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + assert isinstance(test_object.locked_length, u.Quantity) + assert test_object.locked_length.unit == u.m + assert test_object.locked_length.value == 100.0 + + +def test_fill_parameters_override_unrecognized_parameter(): + """Test that unknown override keys raise ValueError.""" + test_object = type("TestObject", (), {})() + parameters = {} + default_parameters = { + "param1": 10, + "param2": 20, + } + locked_keys = {"param1"} + allow_override = {"param1", "nonexistent_param"} + + with pytest.raises(ValueError, match="unrecognized parameter name"): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + +def test_fill_parameters_override_multiple_unrecognized(): + """Test that multiple unknown override keys are reported.""" + test_object = type("TestObject", (), {})() + parameters = {} + default_parameters = { + "param1": 10, + } + locked_keys = {"param1"} + allow_override = {"nonexistent1", "nonexistent2"} + + with pytest.raises(ValueError) as excinfo: + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + # Both names should appear in the error message + assert "nonexistent1" in str(excinfo.value) + assert "nonexistent2" in str(excinfo.value) + + +def test_fill_parameters_override_non_locked_key(test_object, caplog): + """Test that override of non-locked key has no special effect.""" + parameters = { + "unlocked_param": 100, + } + default_parameters = { + "unlocked_param": 10, + } + locked_keys = set() + allow_override = {"unlocked_param"} + + with caplog.at_level(logging.WARNING): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + # Should use user value (not locked anyway) + assert test_object.unlocked_param == 100 + # Should not log override warning since it wasn't locked + assert "explicitly overridden" not in caplog.text + + +def test_fill_parameters_override_mixed_locked_unlocked(test_object, caplog): + """Test overriding some locked parameters but not others.""" + parameters = { + "locked_param1": 100, + "locked_param2": 200, + "unlocked_param": 300, + } + default_parameters = { + "locked_param1": 10, + "locked_param2": 20, + "unlocked_param": 30, + } + locked_keys = {"locked_param1", "locked_param2"} + allow_override = {"locked_param1"} + + with caplog.at_level(logging.WARNING): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + # First locked param should use user value (explicitly overridden) + assert test_object.locked_param1 == 100 + # Second locked param should use default (not overridden) + assert test_object.locked_param2 == 20 + # Unlocked param should use user value + assert test_object.unlocked_param == 300 + + # Check log messages + assert "locked_param1" in caplog.text + assert "explicitly overridden" in caplog.text + assert "locked_param2" in caplog.text + assert "locked in this mode" in caplog.text + + +def test_fill_parameters_override_empty_set(test_object): + """Test that empty allow_override set behaves correctly.""" + parameters = { + "locked_param": 999, + } + default_parameters = { + "locked_param": 42, + } + locked_keys = {"locked_param"} + allow_override = set() + + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + # Should use default (not overridden) + assert test_object.locked_param == 42 + + +def test_fill_parameters_override_none_value(test_object, caplog): + """Test that None for allow_override is handled correctly.""" + parameters = { + "locked_param": 999, + } + default_parameters = { + "locked_param": 42, + } + locked_keys = {"locked_param"} + + with caplog.at_level(logging.WARNING): + fill_parameters( + test_object, + parameters, + default_parameters, + locked_keys, + allow_override=None, + ) + + # Should use default (not overridden) + assert test_object.locked_param == 42 + # Should have standard locked warning + assert "locked_param" in caplog.text + assert "locked in this mode" in caplog.text + + +def test_fill_parameters_override_without_user_parameter(test_object, caplog): + """Test override specification when user didn't provide the parameter.""" + parameters = {} + default_parameters = { + "locked_param": 42, + } + locked_keys = {"locked_param"} + allow_override = {"locked_param"} + + with caplog.at_level(logging.WARNING): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + # Should use default (user didn't override it) + assert test_object.locked_param == 42 + # Should not log any override warning + assert "explicitly overridden" not in caplog.text + + +def test_fill_parameters_override_all_locked_keys(test_object, caplog): + """Test overriding all locked parameters.""" + parameters = { + "locked_param1": 100, + "locked_param2": 200, + "locked_param3": 300, + } + default_parameters = { + "locked_param1": 10, + "locked_param2": 20, + "locked_param3": 30, + } + locked_keys = {"locked_param1", "locked_param2", "locked_param3"} + allow_override = {"locked_param1", "locked_param2", "locked_param3"} + + with caplog.at_level(logging.WARNING): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + # All should use user values + assert test_object.locked_param1 == 100 + assert test_object.locked_param2 == 200 + assert test_object.locked_param3 == 300 + + # All should have override warnings + assert caplog.text.count("explicitly overridden") == 3 + + +def test_fill_parameters_override_logs_both_values(test_object, caplog): + """Test that override warning includes both model and user values.""" + parameters = { + "locked_param": 999, + } + default_parameters = { + "locked_param": 42, + } + locked_keys = {"locked_param"} + allow_override = {"locked_param"} + + with caplog.at_level(logging.WARNING): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + # Warning should mention both values + assert "42" in caplog.text + assert "999" in caplog.text + assert "Model-provided value" in caplog.text + assert "user-supplied value" in caplog.text + + +def test_fill_parameters_override_quantity_logs_units(test_object, caplog): + """Test that override warning for Quantity shows units.""" + parameters = { + "locked_distance": 100 * u.m, + } + default_parameters = { + "locked_distance": 10 * u.m, + } + locked_keys = {"locked_distance"} + allow_override = {"locked_distance"} + + with caplog.at_level(logging.WARNING): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + # Warning should mention units + assert "10.0 m" in caplog.text or "10. m" in caplog.text + assert "100.0 m" in caplog.text or "100. m" in caplog.text + + +# ============================================================================ +# Tests for fill_parameters - Edge cases with overrides +# ============================================================================ + + +def test_fill_parameters_override_superset_of_locked(): + """Test that allow_override can be a superset of locked_keys.""" + test_object = type("TestObject", (), {})() + parameters = { + "locked_param": 100, + "unlocked_param": 200, + } + default_parameters = { + "locked_param": 10, + "unlocked_param": 20, + } + locked_keys = {"locked_param"} + # Override set includes both locked and unlocked + allow_override = {"locked_param", "unlocked_param"} + + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) + + # Both should use user values + assert test_object.locked_param == 100 + assert test_object.unlocked_param == 200 + + +def test_fill_parameters_override_disjoint_sets(): + """Test error when allow_override contains keys not in defaults.""" + test_object = type("TestObject", (), {})() + parameters = {} + default_parameters = { + "param1": 10, + } + locked_keys = {"param1"} + allow_override = {"completely_different_param"} - assert spec_regrid.unit == input_spec.unit + with pytest.raises(ValueError, match="unrecognized parameter name"): + fill_parameters( + test_object, parameters, default_parameters, locked_keys, allow_override + ) diff --git a/tutorials/imaging_tutorial.ipynb b/tutorials/imaging_tutorial.ipynb index 5907c40..8577346 100644 --- a/tutorials/imaging_tutorial.ipynb +++ b/tutorials/imaging_tutorial.ipynb @@ -28,10 +28,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:11.693617Z", - "iopub.status.busy": "2026-05-19T21:31:11.693328Z", - "iopub.status.idle": "2026-05-19T21:31:13.895037Z", - "shell.execute_reply": "2026-05-19T21:31:13.894806Z" + "iopub.execute_input": "2026-07-13T17:35:15.136322Z", + "iopub.status.busy": "2026-07-13T17:35:15.136026Z", + "iopub.status.idle": "2026-07-13T17:35:16.780659Z", + "shell.execute_reply": "2026-07-13T17:35:16.780364Z" } }, "outputs": [ @@ -47,7 +47,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:13,891] Logging level set to: INFO\n" + "[pyEDITH] INFO [2026-07-13 13:35:16,777] Logging level set to: INFO\n" ] } ], @@ -93,10 +93,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:13.896487Z", - "iopub.status.busy": "2026-05-19T21:31:13.896332Z", - "iopub.status.idle": "2026-05-19T21:31:13.898325Z", - "shell.execute_reply": "2026-05-19T21:31:13.898123Z" + "iopub.execute_input": "2026-07-13T17:35:16.790505Z", + "iopub.status.busy": "2026-07-13T17:35:16.790354Z", + "iopub.status.idle": "2026-07-13T17:35:16.792465Z", + "shell.execute_reply": "2026-07-13T17:35:16.792235Z" } }, "outputs": [], @@ -127,7 +127,7 @@ "source": [ "### 1.3 Running the ETC\n", "\n", - "We can now package the parameters so that they will be ingested by the ETC. " + "We can now calculate the exposure time. " ] }, { @@ -135,34 +135,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:13.899447Z", - "iopub.status.busy": "2026-05-19T21:31:13.899365Z", - "iopub.status.idle": "2026-05-19T21:31:13.900765Z", - "shell.execute_reply": "2026-05-19T21:31:13.900579Z" - } - }, - "outputs": [], - "source": [ - "# Make the parameters be the shape that the code desires \n", - "parsed_parameters= parse_input.parse_parameters(imaging_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, we can calculate the exposure time:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-19T21:31:13.901889Z", - "iopub.status.busy": "2026-05-19T21:31:13.901813Z", - "iopub.status.idle": "2026-05-19T21:31:14.797275Z", - "shell.execute_reply": "2026-05-19T21:31:14.797030Z" + "iopub.execute_input": "2026-07-13T17:35:16.793677Z", + "iopub.status.busy": "2026-07-13T17:35:16.793595Z", + "iopub.status.idle": "2026-07-13T17:35:17.620595Z", + "shell.execute_reply": "2026-07-13T17:35:17.620260Z" } }, "outputs": [ @@ -170,202 +146,202 @@ "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:13,909] Flux zero point calculated at 5.5e-05 cm in units of ph / (s cm3)\n" + "[pyEDITH] INFO [2026-07-13 13:35:16,798] Flux zero point calculated at 5.5e-05 cm in units of ph / (s cm3)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:13,911] Flux zero point calculated at [5.e-05] cm in units of ph / (s cm3)\n" + "[pyEDITH] INFO [2026-07-13 13:35:16,800] Flux zero point calculated at [5.e-05] cm in units of ph / (s cm3)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-05-19 17:31:13,912]\u001b[0m ez_PPF not set. 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Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 17:31:14,035] \u001b[0meac1_optimal_order_6_1d is radially symmetric\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:35:16,913] \u001b[0meac1_optimal_order_6_1d is radially symmetric\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 17:31:14,232] \u001b[0mLoading performance metrics from /Users/ealei/Library/Caches/yippy/eac1_optimal_order_6_1d.zip.unzip/eac1_optimal_order_6_1d/yippy_cache/performance/trunc_0.30_v2.7.3.fits\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:35:17,077] \u001b[0mLoading performance metrics from /Users/ealei/Library/Caches/yippy/eac1_optimal_order_6_1d.zip.unzip/eac1_optimal_order_6_1d/yippy_cache/performance/trunc_0.30_v2.7.3.fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 17:31:14,233] \u001b[0mLoading throughput and contrast from trunc_0.30_v2.7.3.fits\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:35:17,078] \u001b[0mLoading throughput and contrast from trunc_0.30_v2.7.3.fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 17:31:14,248] \u001b[0mSuccessfully loaded performance data from trunc_0.30_v2.7.3.fits\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:35:17,084] \u001b[0mSuccessfully loaded performance data from trunc_0.30_v2.7.3.fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 17:31:14,248] \u001b[0mComputing core area curve (PSF trunc ratio = 0.3)...\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:35:17,084] \u001b[0mComputing core area curve (PSF trunc ratio = 0.3)...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 17:31:14,579] \u001b[0mComputing occulter transmission curve...\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:35:17,405] \u001b[0mComputing occulter transmission curve...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 17:31:14,670] \u001b[0mComputing core mean intensity curve...\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:35:17,495] \u001b[0mComputing core mean intensity curve...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 17:31:14,770] \u001b[0mOWA set to max_offset_in_image: 32.00 lam/D\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:35:17,591] \u001b[0mOWA set to max_offset_in_image: 32.00 lam/D\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 17:31:14,770] \u001b[0mCreated eac1_optimal_order_6_1d\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:35:17,592] \u001b[0mCreated eac1_optimal_order_6_1d\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:14,771] Using psf_trunc_ratio to calculate Omega...\n" + "[pyEDITH] INFO [2026-07-13 13:35:17,593] Using psf_trunc_ratio to calculate Omega...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-05-19 17:31:14,782]\u001b[0m noisefloor_PPF value not provided. Using the default value: 30.0\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:35:17,605]\u001b[0m noisefloor_PPF value not provided. Using the default value: 30.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:14,785] Calculating optics throughput from preset...\n" + "[pyEDITH] INFO [2026-07-13 13:35:17,609] Calculating optics throughput from preset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:14,786] Calculating epswarmTrcold as 1 - optics throughput...\n" + "[pyEDITH] INFO [2026-07-13 13:35:17,609] Calculating epswarmTrcold as 1 - optics throughput...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Calculated exposure time: [2.91103904] h\n" + "Calculated exposure time: [3.53041142] h\n" ] } ], "source": [ "# Calculate Exposure time\n", - "texp, validation_output = calculate_texp(parsed_parameters)\n", + "texp, validation_output = calculate_texp(imaging_params)\n", "print(f\"Calculated exposure time: {texp.to(u.hr)}\")" ] }, @@ -378,13 +354,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:14.798533Z", - "iopub.status.busy": "2026-05-19T21:31:14.798440Z", - "iopub.status.idle": "2026-05-19T21:31:15.068691Z", - "shell.execute_reply": "2026-05-19T21:31:15.068369Z" + "iopub.execute_input": "2026-07-13T17:35:17.621920Z", + "iopub.status.busy": "2026-07-13T17:35:17.621836Z", + "iopub.status.idle": "2026-07-13T17:35:17.867505Z", + "shell.execute_reply": "2026-07-13T17:35:17.867257Z" } }, "outputs": [ @@ -392,196 +368,196 @@ "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:14,800] Flux zero point calculated at 5.5e-05 cm in units of ph / (s cm3)\n" + "[pyEDITH] INFO [2026-07-13 13:35:17,624] Flux zero point calculated at 5.5e-05 cm in units of ph / (s cm3)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:14,802] Flux zero point calculated at [5.e-05] cm in units of ph / (s cm3)\n" + "[pyEDITH] INFO [2026-07-13 13:35:17,626] Flux zero point calculated at [5.e-05] cm in units of ph / (s cm3)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-05-19 17:31:14,803]\u001b[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:35:17,626]\u001b[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:14,806] Observatory Configuration:\n" + "[pyEDITH] INFO [2026-07-13 13:35:17,629] Observatory Configuration:\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:14,806] Using preset: EAC1\n" + "[pyEDITH] INFO [2026-07-13 13:35:17,629] Using preset: EAC1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:14,806] \n" + "[pyEDITH] INFO [2026-07-13 13:35:17,629] \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-05-19 17:31:14,806]\u001b[0m Coronagraph 'eac1_optimal_order_6_1d' not found locally. Attempting to fetch from remote database...\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:35:17,630]\u001b[0m Coronagraph 'eac1_optimal_order_6_1d' not found locally. 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Using the default value: 30.0\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:35:17,853]\u001b[0m noisefloor_PPF value not provided. Using the default value: 30.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:15,056] Calculating optics throughput from preset...\n" + "[pyEDITH] INFO [2026-07-13 13:35:17,855] Calculating optics throughput from preset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 17:31:15,056] Calculating epswarmTrcold as 1 - optics throughput...\n" + "[pyEDITH] INFO [2026-07-13 13:35:17,856] Calculating epswarmTrcold as 1 - optics throughput...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Calculated snr: [7.48584728]\n" + "Calculated snr: [5.29329344]\n" ] } ], @@ -589,7 +565,7 @@ "\n", "# Calculate SNR given a specific exposure time\n", "texp = 3*u.hr\n", - "snr, validation_output = calculate_snr(parsed_parameters, texp)\n", + "snr, validation_output = calculate_snr(imaging_params, texp)\n", "print(f\"Calculated snr: {snr}\")" ] }, @@ -602,71 +578,78 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:15.070087Z", - "iopub.status.busy": "2026-05-19T21:31:15.070003Z", - "iopub.status.idle": "2026-05-19T21:31:15.074135Z", - "shell.execute_reply": "2026-05-19T21:31:15.073921Z" + "iopub.execute_input": "2026-07-13T17:35:17.868886Z", + "iopub.status.busy": "2026-07-13T17:35:17.868807Z", + "iopub.status.idle": "2026-07-13T17:35:17.873333Z", + "shell.execute_reply": "2026-07-13T17:35:17.873116Z" } }, "outputs": [ { "data": { "text/plain": [ - "{'F0': ,\n", + "{'F0': ,\n", " 'magstar': ,\n", " 'dist': ,\n", + " 'nzodis': ,\n", + " 'Fs_over_F0': ,\n", + " 'Fp': ,\n", + " 'Fzodi': ,\n", + " 'Fexozodi': ,\n", " 'D': ,\n", " 'A_cm': ,\n", - " 'wavelength': ,\n", - " 'deltalambda_nm': ,\n", - " 'snr': ,\n", - " 'nzodis': ,\n", " 'toverhead_fixed': ,\n", " 'toverhead_multi': ,\n", - " 'det_DC': ,\n", - " 'det_RN': ,\n", - " 'det_CIC': ,\n", - " 'det_tread': ,\n", + " 'T_optical': ,\n", + " 'wavelength': ,\n", + " 'deltalambda_nm': ,\n", + " 'snr': ,\n", + " 'lod_rad': ,\n", + " 'lod_arcsec': ,\n", + " 'detpixscale_lod': ,\n", + " 'oneopixscale_arcsec': ,\n", + " 'ix': array([156.02519783]),\n", + " 'iy': array([128.]),\n", + " 'det_DC': ,\n", + " 'det_RN': ,\n", + " 'det_CIC': ,\n", + " 'det_tread': ,\n", " 'det_pixscale_mas': ,\n", - " 'dQE': ,\n", - " 'QE': ,\n", - " 'T_optical': ,\n", - " 'Fs_over_F0': ,\n", - " 'Fp': ,\n", - " 'Fzodi': ,\n", - " 'Fexozodi': ,\n", - " 'sp_lod': ,\n", - " 'omega_lod': ,\n", - " 'T_core or photometric_aperture_throughput': ,\n", - " 'Istar': ,\n", - " 'Istar*oneopixscale2 in (l/D)^-2': ,\n", - " 'skytrans': ,\n", - " 'skytrans*oneopixscale2 in (l/D)^-2': ,\n", - " 'det_npix': ,\n", - " 't_photon_count': ,\n", - " 'CRp': ,\n", - " 'CRbs': ,\n", - " 'CRbz': ,\n", - " 'CRbez': ,\n", - " 'CRbbin': ,\n", - " 'CRbth': ,\n", - " 'CRb': ,\n", - " 'CRbd': ,\n", - " 'CRnf': ,\n", - " 'sciencetime': ,\n", - " 'exptime': }" + " 'det_omega_lod': ,\n", + " 'det_npix': ,\n", + " 't_photon_count': ,\n", + " 'dQE': ,\n", + " 'QE': ,\n", + " 'omega_lod': ,\n", + " 'T_core or photometric_aperture_throughput': ,\n", + " 'Istar': ,\n", + " 'Istar*oneopixscale2 in (l/D)^-2': ,\n", + " 'skytrans': ,\n", + " 'skytrans*oneopixscale2 in (l/D)^-2': ,\n", + " 'CRp': ,\n", + " 'CRbs': ,\n", + " 'CRbz': ,\n", + " 'CRbez': ,\n", + " 'CRbbin': ,\n", + " 'CRbth': ,\n", + " 'CRb': ,\n", + " 'CRbd': ,\n", + " 'CRnf': ,\n", + " 'sciencetime': ,\n", + " 'exptime': None,\n", + " 'fullsnr': }" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "validation_output[0]" + "validation_output" ] }, { @@ -678,13 +661,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:15.075342Z", - "iopub.status.busy": "2026-05-19T21:31:15.075265Z", - "iopub.status.idle": "2026-05-19T21:31:15.529191Z", - "shell.execute_reply": "2026-05-19T21:31:15.528875Z" + "iopub.execute_input": "2026-07-13T17:35:17.874573Z", + "iopub.status.busy": "2026-07-13T17:35:17.874488Z", + "iopub.status.idle": "2026-07-13T17:35:18.331619Z", + "shell.execute_reply": "2026-07-13T17:35:18.331343Z" } }, "outputs": [ @@ -692,98 +675,98 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-05-19 17:31:15,080]\u001b[0m ez_PPF not set. 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Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-05-19 17:31:15,347] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-07-13 13:35:18,151] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reference exposure time: [2.91103904] h\n" + "Reference exposure time: [3.53041142] h\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-05-19 17:31:15,513]\u001b[0m noisefloor_PPF value not provided. Using the default value: 30.0\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:35:18,318]\u001b[0m noisefloor_PPF value not provided. Using the default value: 30.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "SNR at the secondary lambda: [5.19067343]\n" + "SNR at the secondary lambda: [5.19067342]\n" ] } ], @@ -802,126 +785,19 @@ " secondary_imaging_params[key] = imaging_params[key]\n", "\n", "# Calculating texp from primary lambda \n", - "parsed_parameters= parse_input.parse_parameters(imaging_params)\n", - "\n", - "texp, _ = calculate_texp(parsed_parameters)\n", + "texp, _ = calculate_texp(imaging_params)\n", "print(\"Reference exposure time: \", texp.to(u.hr))\n", "\n", "# Calculating SNR at secondary lambda\n", "if np.isfinite(texp).all():\n", " \n", - " parsed_secondary_parameters= parse_input.parse_parameters(secondary_imaging_params)\n", "\n", - " snr, _ = calculate_snr(parsed_secondary_parameters, texp)\n", + " snr, _ = calculate_snr(secondary_imaging_params, texp)\n", " print(\"SNR at the secondary lambda: \", snr)\n", "else:\n", " raise ValueError(\"Returned exposure time is infinity.\")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Advanced Usage\n", - "\n", - "In the preceding examples, we utilized the premade `calculate_texp` and `calculate_snr` functions. While this approach is straightforward, it can lead to significant performance overhead in scenarios involving repeated calculations with large parameter spaces or numerous iterations. This is because each function call reinitializes the entire calculation process.\n", - "\n", - "For improved computational efficiency, particularly when dealing with extensive parameter sweeps, it is advisable to implement the loop logic within the `calculate_texp` function itself. This approach allows for targeted iteration over specific parameters while maintaining the state of other computationally intensive components.\n", - "\n", - "To illustrate this optimization technique, we can examine the internal structure of the `calculate_texp` function. Refer to the `pyEDITH` workflow picture for details." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-19T21:31:15.530547Z", - "iopub.status.busy": "2026-05-19T21:31:15.530453Z", - "iopub.status.idle": "2026-05-19T21:31:15.759874Z", - "shell.execute_reply": "2026-05-19T21:31:15.759610Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-05-19 17:31:15,535]\u001b[0m ez_PPF not set. Assuming EZ subtraction to Poisson limit (ez_PPF = inf)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-05-19 17:31:15,537]\u001b[0m Coronagraph 'eac1_optimal_order_6_1d' not found locally. Attempting to fetch from remote database...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-05-19 17:31:15,584] \u001b[0mUnhandled header fields: {'TMULCHAR', 'TMULDET'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-05-19 17:31:15,584] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-05-19 17:31:15,585] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-05-19 17:31:15,748]\u001b[0m noisefloor_PPF value not provided. Using the default value: 30.0\n" - ] - } - ], - "source": [ - "params = imaging_params.copy()\n", - "\n", - "# Parse the desired parameters\n", - "parsed_parameters= parse_input.parse_parameters(params)\n", - "\n", - "# Define Observation and load relevant parameters\n", - "observation = Observation()\n", - "observation.load_configuration(parsed_parameters)\n", - "observation.set_output_arrays()\n", - "observation.validate_configuration()\n", - "\n", - "# Define Astrophysical Scene and load relevant parameters,\n", - "# then calculate zodi/exozodi\n", - "scene = AstrophysicalScene()\n", - "scene.load_configuration(parsed_parameters)\n", - "scene.calculate_zodi_exozodi(parsed_parameters)\n", - "scene.validate_configuration()\n", - "\n", - "# Create and configure Observatory\n", - "observatory_config = parse_input.get_observatory_config(parsed_parameters)\n", - "observatory = Observatory()\n", - "observatory.create_observatory(observatory_config)\n", - "observatory.load_configuration(parsed_parameters, observation, scene\n", - ")\n", - "observatory.validate_configuration()\n", - "\n", - "# EXPOSURE TIME CALCULATION\n", - "calculate_exposure_time_or_snr(\n", - " observation,\n", - " scene,\n", - " observatory,\n", - " mode=\"exposure_time\",\n", - ")" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -934,7 +810,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. Parameter Space Exploration\n", + "## 2. Parameter Space Exploration\n", "\n", "Now, let's explore how various parameters affect the exposure time and SNR.\n", "\n" @@ -942,13 +818,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:15.761496Z", - "iopub.status.busy": "2026-05-19T21:31:15.761404Z", - "iopub.status.idle": "2026-05-19T21:31:15.763193Z", - "shell.execute_reply": "2026-05-19T21:31:15.762946Z" + "iopub.execute_input": "2026-07-13T17:35:18.333079Z", + "iopub.status.busy": "2026-07-13T17:35:18.332998Z", + "iopub.status.idle": "2026-07-13T17:35:18.334788Z", + "shell.execute_reply": "2026-07-13T17:35:18.334498Z" } }, "outputs": [], @@ -961,26 +837,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3.1 Planet-to-star contrast\n", + "### 2.1 Planet-to-star contrast\n", "Let's explore how the exposure time changes with planet-to-star contrast:\n", "\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:15.764488Z", - "iopub.status.busy": "2026-05-19T21:31:15.764420Z", - "iopub.status.idle": "2026-05-19T21:31:18.212887Z", - "shell.execute_reply": "2026-05-19T21:31:18.212623Z" + "iopub.execute_input": "2026-07-13T17:35:18.336016Z", + "iopub.status.busy": "2026-07-13T17:35:18.335919Z", + "iopub.status.idle": "2026-07-13T17:35:21.042500Z", + "shell.execute_reply": "2026-07-13T17:35:21.042179Z" } }, "outputs": [ { "data": { - "image/png": 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", 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77hPbrT7wwANhG35kP3/+fDEc4JJLLqErrrhCrLv6/vvvJ2XpB/tAZ5V02XxZGxV71XJe1+mnbibrqYuWQdZTxYem6elEM+ipB9AzOT5MFsXLft8MoOcIf4cAaD/B6oknnoiZzrtUffrpp62W5d2qXn31VfKCeKsPJJoumy9ro2KvWs7rOv3UzWQ9ddEyyHqq+NA0PZ1oBj31AHomx4fJwhoCoMN41UD2rAIAAAAAAG8mV/X0eY1VBsEqAAAAAABo0bOanZ9Jnfu1XOrKa7SeYKUTmGAFAAAAgFSnuqyWZv3hP+J93z260XkvTf09DxOsgot9BxaVdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQfQMzk+TAbFS38fAtBLg8lVDIYBuIB9azaVdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQfQMzk+TPZKAL1G+j9elUGwCgAAAAAAWkyuQs8qAAAAAADQiuLtPavpmWnUfXBH0gH0rLpAhw4dXEmXzZe1UbFXLed1nX7qZrKeumgZZD1VfGiank40g556AD2T40O3qa9ppJJVZeJ9t0GFlJWrxxbMCFZdoLGx0ZV02XxZGxV71XJe1+mnbibrqYuWQdZTxYem6elEM+ipB9AzOT50m40/llJTQ0irIQAMglUX0H07Ryf2quW8rtOk7Tmd2KuW86o+E/XEdqvyfsF2q6l1f0JPdR8mdXKVBpsBWCBYBQAAAAAApOOyVQyCVQAAAAAAQDouW8UgWHWB/Px8V9Jl82VtVOxVy3ldp5+6maynLloGWU8VH5qmpxPNoKceQM/k+NBNmppCtH55c89qxz75lFeYQ7qAYNUF0tLSXEmXzZe1UbFXLed1nX7qZrKeumgZZD1VfGiank40g556AD2T40M32fJLOdVVNoj3vUbqMwSAQbDqAhUVFa6ky+bL2qjYq5bzuk4/dTNZT120DLKeKj40TU8nmkFPPYCeyfGhm6yP2AxAnyEADIJVAAAAAADDKVpqH6+KnlUAAAAAAKAR6yOWrUKwmnLk5ua6ki6bL2ujYq9azus6/dTNZD110TLIeqr40DQ9nWgGPfUAeibHh25StH3ZqrzCbCrs3Y50AsMAXCA7O9uVdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQfQMzk+dIvyjdVUUVIdHgKg02RLBsGqC2zbts2VdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQfQMzk+dAtrySodJ1cxCFYBAAAAAAymSOPJVQyCVQAAAAAAg1kfMbkKPaspie5j4pzYq5bzuk6Txjg6sVct51V9JuqJMavyfsGY1dS6P6Gnug/donj7GquZ2enUdWAh6QZ6Vl0gLy/PlXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkH0DM5PnSD2sp62ry6eTxsj2GdKCNTv9BQvxYFEN0ncDixVy3ndZ0mTchxYq9azqv6TNQTE6zk/YIJVql1f0JPdR+6wYYVWykUan7fU8MhAAyCVRcIWSorpsvmy9qo2KuW87pOP3UzWU9dtAyynio+NE1PJ5pBTz2AnsnxoZtDAJjeGk6uYhCsAgAAAAAYSpF9JQD0rKYumZmZrqTL5svaqNirlvO6Tj91M1lPXbQMsp4qPjRNTyeaQU89gJ7J8aEbrN/es8r7APCYVR1JC+n03Edjqqurady4ceL9okWLtJqIAAAAAACQKI0NTXTriOeooa6Juu5UQJfPP1LLWAjDAFygsrLSlXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkH0DM5PlRl009lIlDVeQgAg2DVBRoaGlxJl82XtVGxVy3ndZ1+6maynrpoGWQ9VXxomp5ONIOeegA9k+NDNydX9Ryh5+QqBsEqAAAAAICBFAdgchWDYNUF0nhUsgvpsvmyNir2quW8rtNP3UzWUxctg6ynig9N09OJZtBTD6BncnxowrJVDCZYSYIJVgAAAABIFUKhEN2+23+ouqyO2nfLo2s/P67NMphgFWBYPDfSZfNlbVTsVct5Xaefupmspy5aBllPFR+apqcTzaCnHkDP5PhQhbKiKhGoMr1H6jsEgMEwABeoq6tzJV02X9ZGxV61nNd1+qmbyXrqomWQ9VTxoWl6OtEMeuoB9EyOD1UoXrYlEJOrGASrAAAAAAAGT67qrfF4VQbBKgAAAACAYRRHLFul9zAATLCSpLVBxTxIOdYsvUTTZfNlbVTsVct5Xaeb7YCe6j70g6DqqeJD0/R0ohn0hJ6JXhNObd0s60V9d+/3CpWuq6Ts/Ey68buTKD297boxwSrA6D4mzom9ajmv6zRpjKMTe9VyXtVnop4YsyrvF4xZTa37E3qq+9Ap1WW1IlBleg7vJBWo+gmGAbhATU2NK+my+bI2Kvaq5byu00/dTNZTFy2DrKeKD03T04lm0FMPoGdyfOiU4qW/DwHopfnkKgbBKgAAAACAQRTbVgLopfmyVQyCVQAAAAAAQydX9ULPqhm0b9/elXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkH0DM5PlTtWU3PTKPugzuS7qBn1aUZem6ky+bL2qjYq5bzuk4/dTNZT120DLKeKj40TU8nmkFPPYCeyfGhE+prGqlkVZl4321QIWXlZpDuIFh1gcrKSlfSZfNlbVTsVct5Xaefupmspy5aBllPFR+apqcTzaCnHkDP5PjQCRt/LKWmhlBghgAwCFYBAAAAAEycXDVC/8lVDIJVAAAAAABDKA7YslUMglUXaNeunSvpsvmyNir2quW8rtNP3UzWUxctg6ynig9N09OJZtBTD6BncnxowrJVDIJVF8jIyHAlXTZf1kbFXrWc13X6qZvJeuqiZZD1VPGhaXo60Qx66gH0TI4PE6WpKUTrlzf3rHbsk095hTkUBBCsukB5ebkr6bL5sjYq9qrlvK7TT91M1lMXLYOsp4oPTdPTiWbQUw+gZ3J8mChbfimnusoG8b7XyGAMAWAQrAIAAAAAGMD6iM0AgjEEgEGwCgAAAABgAEVL7eNV0bNqFDk5Oa6ky+bL2qjYq5bzuk4/dTNZT120DLKeKj40TU8nmkFPPYCeyfFhoqyPWLYKwapR5ObmupIumy9ro2KvWs7rOv3UzWQ9ddEyyHqq+NA0PZ1oBj31AHomx4eJUrR92aq8wmwq7K3P6h9tgWEALlBWVuZKumy+rI2KvWo5r+v0UzeT9dRFyyDrqeJD0/R0ohn01APomRwfJkL5xmqqKKkODwFIS0ujoIBgFQAAAAAgxVm/fcmqoE2uYhCsAgAAAACkOEUBnVwV2GC1vr6e/vKXv9Dw4cMpPz+fhgwZQrfccgvV1taGbRYvXkzjxo0T+b169aJLL72UKisrk9KerKwsV9Jl82VtVOxVy3ldp5+6maynLloGWU8VH5qmpxPNoKceQM/k+ND55Kpg9axmOi3Y1NRE3333Ha1YsYI2btwoxlJ06tSJunXrRoMHD6ZRo0ZRsrj66qvpn//8J1133XW0yy670Oeff0633nor/fbbb/TII4+INk2ePJn22msvmj17NpWUlNDNN99Mq1atorffftv19ui+naMTe9VyXtdp0vacTuxVy3lVn4l6YrtVeb9gu9XUuj+hp7oPE6F4+xqrmdnp1HVgIaV0sPrxxx/Tv//9b3rjjTeotLRUpIVCoXC+NWC3S5cudNBBB9H5559PY8eOda3BDQ0NIlC9+OKL6aabbhJpxxxzjOhVvf/+++nvf/87zZo1izp37kxz5swJz57r2bOnsFu4cCGNHz+e3KSiooLat2+vnC6bL2ujYq9azus63WwH9FT3oR8EVU8VH5qmpxPNoCf0TPSacGrrZlm366utrKfNq7eJ9z2GdaKMzPTUDFZXrlxJV1xxBb377rvUoUMH2meffWjfffelHXbYQQSGBQUFwpEcwK5du5b+97//0dy5c+nZZ5+liRMn0l//+lcaPXq0K9uNcWDavXv3iHRuAweydXV1Ikg955xzIpZ54MA5Oztb5LkdrDY2NrqSLpsva6Nir1rO6zr91M1kPXXRMsh6qvjQND2daAY99QB6JseHsmxYsZWsfsWeARsCIB2sXnXVVfTAAw/QgQceSG+99RZNnTqV0tPbjsq5x/W9994TZffee2+aPn266JVVgYcaHHDAAXTvvffSH/7wB9p9991pyZIldN9999ERRxxBmzdvpq1bt9Kuu+7aovt8wIABIuhujXjjWqurm5d7AAAAAAAIEsW2bVZ7B2xylXSwun79evryyy9pxIgRCVXOQwKmTJkiXhwk3nnnneQGTz31lBiPevDBB4fTeNgBDwPgtlqfYwW6ba1TFq+LnYPzeONwMzIyXEmXzZe1UbFXLed1nU5wSzeT9dRFyyDrqeJD0/R0ohn01APomRwfylIc4MlV0sHqM888o3ygoUOH0mOPPaZcDw8BOPzww8VwAB5awD2oy5cvp9tuu0086n/ooYfiluWA040B5xzw8nADhodE8PACexDMKxBwoM5d9lZ6Xl6emMm3bVvzmBFO53KczmnWuN/MzEwRMHMPLw9rsNrNx+HeXeu4DJfhzzU1NeE0LsuT36qqqsJpfM78sreRt23jYRL2NG4f27FvuQ77zcH18SoMDJ8bD/vg49pXYOA2cpvtvdDsi+geaz5nbrvlCyaeL7g8Dy+xHn/E80VhYaFoi4wvuA6u09KB/cD+sPuCy8TzhV2beL7gNPaX5QuuO54v2O/2a8XSpi1fcNvYF3Zt2vKF/TjRvmBi+cLSJtoXXCefv2Vr+SJam2hftHZd8HnycSxi+cK6TmNdF7F8wXXaz4ftuFxrvrCuCz736OuUz9vuC0ub6OuCkfEF18flZH0Rfc8ydl9Y2sTyRax7lsvZ7xFuC7c/+rqI5Yt492yseySWL2J9f3Fdli/4XFkbPifLF5wWyxfWPevEF1xPvO+vtr7LY/mCbWP5It73l+UL+3HifX/ZfdHadcG2Vlvi+cKN7/K2fGFdF3xs2e9yrtN+PtG+sK6LWN9fbCv775rsdzm3z4kvrO1RZf9dk/kub2xsbPPftVjf5Wu/3bT9PInyd8hqcV0kcs/6QsghTU1Noffeey9UVFQUTrvrrrtChx56aOiss84KLV26NJQMnnvuOVY8NHv27Ij0d955R6TPmjVL/H322WdblB0xYkRo2rRprdZfUVER81VSUhLafffdxauqqiqiTGVlZcy6Ek2XzZe1UbFXLed1nU5wSzeT9dRFyyDrqeJD0/R0ohn01APomRwfytBQ3xi6afDToRv6PRm6d//XQipw/BMvFkomjqaD8bhQHivKPZm8fJW1nNQ111wjloZ6/PHHxRjVr776yu3YmlavXi3+8vHtjBkzRvzlXyX8i+Kbb76JyOdfAz///HObS2rxL554r3jYfwmppMvmy9qo2KuW87pOJ7ilm8l66qJlkPVU8aFpejrRDHrqAfRMjg9l2PRTGTXUNQV2CADjKFi98cYb6euvvxarA3DQyA7817/+Rbvtthtt2LCB3nnnHZE2c+ZM1xvMk6SYjz76KCL9iy++EH+5DRxEv/jiixHd1fzZGkIAAAAAAGDa5KqeI4I3ucrxpgBvvvmmWLbq7rvvFp8/+OADMSbjwgsvFJsCcLDIKwZEB5RucNRRR9HIkSPpyiuvpHXr1omeUu5tveuuu2iPPfagww47TAS0PAGLVwc499xzRT4Hzvx+4MCBrrcJAAAAAEBHiu3brJrUs8rDAHjnKIsPP/wwPPPfghfhtw80dgt+xM9B8GWXXUYvvPACnXzyyWKi1ZFHHinWdeXBwjzpit/zAOLTTjtNLGvF263yElrJgAdBu5Eumy9ro2KvWs7rOv3UzWQ9ddEyyHqq+NA0PZ1oBj31AHomx4cmLFvlOFjljQCKi4sjelp32mkn6tevXzht2bJlMZePcoOOHTvS7bffLpbD4lly3JaHH35YLE1lwQv/f/rpp2I2G29SwLta8Sy8ZGCfqaeSLpsva6Nir1rO6zr91M1kPXXRMsh6qvjQND2daAY99QB6JseHbcGrEFjLVrXvlidexgSrPO6TJ1L9+c9/pjPPPFNMpDruuONEHgeHvN4p935OmDCBTMC+zIVKumy+rI2KvWo5r+v0UzeT9dRFyyDrqeJD0/R0ohn01APomRwftkVZURVVlzbP3+k9MphDABhHXY0zZsyg+fPni95Kq6eVd7liTjnlFHr99ddFmpUPAAAAAAC8pdi2GUBQJ1c5Dlb5MTxvcbpgwQIqLS2lyZMnizSGJzjx2NVp06a1utwTAAAAAADwZnJV74COV2XSeLHVRMdM3HDDDTR27Fg6+uijyRR4eMO4cePE+0WLFondKix4NwnePSKaRNNl82VtVOxVy3ldp5vtgJ7qPvSDoOqp4kPT9HSiGfSEnoleE05t3SzrVn3PnPcBLZ/7m3h/+YIjqeuAgqTFQskk3cls/Oeee07MxAfNWFuUqabL5svaqNirlvO6Tj91M1lPXbQMsp4qPjRNTyeaQU89gJ7J8aFsz2p2fiZ17teBgoqjkJ+XgeKF/5cvX+5+iwKIfc9glXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkH0DM5PmyN6rJaKl1XKd73HN6J0tPTyKgxq8OHD6chQ4aIhfenT59OO++8M2VnZ8e0Peuss1TbCAAAAAAAHK6v2ivAk6scB6v2saoPPvhgi3zeIICHwvJfBKsAAAAAAD7uXDUyuMtWOQ5WH3/8cfdbEmDirXqQaLpsvqyNir1qOa/r9FM3k/XURcsg66niQ9P0dKIZ9NQD6JkcH7aG8T2r/Ogf/A73ILuRLpsva6Nir1rO6zr91M1kPXXRMsh6qvjQND2daAY99QB6JseHMmuspmemUffBzcuLGjXBipdPkH2ZQEVFhSvpsvmyNir2quW8rtNP3UzWUxctg6ynig9N09OJZtBTD6BncnwYj/qaRipZVSbedxtUSFm5GWTcMIDMzEzp3gWdlkIBAAAAAEh1Nv5YSk0NoZSYXOU4WB0/fnyLYJWD0i1bttAPP/xAjY2NtN9++1H//v3daicAAAAAAEhwm9VeI4I9ucpxsPrBBx/EzSsrK6Prr7+e/vOf/9BDDz1EJhBvB4dE02XzZW1U7FXLeV2nn7qZrKcuWgZZTxUfmqanE82gpx5Az+T4MB7rU2jZKsb1fdgKCwvpgQceoB133JGuueYaMoGsrCxX0mXzZW1U7FXLeV2nn7qZrKcuWgZZTxUfmqanE82gpx5Az+T4MB5FKbRsFZO0TYN32WUX+vjjj8kEtm3b5kq6bL6sjYq9ajmv6/RTN5P11EXLIOup4kPT9HSiGfTUA+iZHB/GoqkpROuXN/esduyTT3mFORR0khKs8pZgixcvpvT0pMXCAAAAAAAgii2/lFNdZfPk9l4jgz8EwPGY1RkzZrQaqL7zzju0atUqOuGEE1TaBgAAAAAAEiByvGrwhwA4DlZvu+22Nm3Gjh1L9913H5lAdna2K+my+bI2Kvaq5byu00/dTNZTFy2DrKeKD03T04lm0FMPoGdyfNj2eFWDe1YXLFjQqjN5ctUOO+xApqD7bGMn9qrlvK7TpNnjTuxVy3lVn4l6YjUAeb9gNYDUuj+hp7oPY7E+Ytmq1AhWHQ0qnTBhQtzXmDFjjApUgzCBw4m9ajmv6zRpQo4Te9VyXtVnop6YYCXvF0ywSq37E3qq+zAWRUubhwHkFWZTYe92ZGzPqsW3335Lzz//PC1btoxqamqoW7dutPfee9NJJ51EXbt2JVMIhUKupMvmy9qo2KuW87pOP3UzWU9dtAyynio+NE1PJ5pBTz2AnsnxYTTlG6upoqQ6PARAZrfRlA5Wb7nlFpo5c2YL5z3zzDNiU4D/9//+H51zzjlutBEAAAAAALSBtWRVKk2ucjwM4JVXXqGbb76Z9txzT3rrrbdow4YNVF5eTitXrqS//e1v1LNnTzr//PPp7bffJhPIzMx0JV02X9ZGxV61nNd1+qmbyXrqomWQ9VTxoWl6OtEMeuoB9EyOD02YXMWkhRw89+GZ/r/++iutWLGC2rVrOR6itLSURo4cScOGDaP333+fUgFekmvcuHHi/aJFi7QauA4AAAAA8MIlC+m7t34Rjvjjfw+jHkM7pUQs5Khn9euvv6YpU6bEDFSZjh070tSpU+mLL74gE6isrHQlXTZf1kbFXrWc13X6qZvJeuqiZZD1VPGhaXo60Qx66gH0TI4PoynevsZqZnY6dR1YSKmCo2A1IyODGhsb27Srr68nE2hoaHAlXTZf1kbFXrWc13X6qZvJeuqiZZD1VPGhaXo60Qx66gH0TI4P7dRW1tPm1c0rBvQY1okyMlNnF1FHZ7LHHnvQnDlzaPPmzTHzefzqu+++S8OHD1dtHwAAAAAAaIMNK7aSNbCzZwpNrnIcrPJsfw5UeYLVAw88QD/99JMYx7B27Vp67rnnaN9996X169fTJZdcQiaQnp7uSrpsvqyNir1qOa/r9FM3k/XURcsg66niQ9P0dKIZ9NQD6JkcH8YaAsD0TqHJVY4nWDGzZ8+miy++WASpsbj22mtp1qxZlCpgghUAAAAAdOW16z6lJc+tEu/Pe/kg6rt7d9ePEagJVsz06dNFj+pf//pXOuGEE+iAAw6gww47TASp33zzTUoFqm0RL2BPNF02X9ZGxV61nNd1+qmbyXrqomWQ9VTxoWl6OtEMeuoB9EyOD+0Ub9+5ivcB4DGrqYTSono9evSgK6+8kkynrq4u5q+LRNNl82VtVOxVy3ldp5vtgJ7qPvSDoOqp4kPT9HSiGfSEnoleE05t3SybaH2NDU1izCrTZUAB5eRnUSrhOFi1JlGtWbOGKioqYm4Dxtt88S5XAAAAAAAgOWz6qYwa6ppSbucqpWD1yy+/pIMPPpg2bdrU6l61CFYBAAAAAJJLsW1yVc8RqTW5ynGwesUVV1BJSYmYYHXQQQdRhw4dyGQKCgpcSZfNl7VRsVct53Wdfupmsp66aBlkPVV8aJqeTjSDnnoAPZPjQ4ti+zar6Flthnemmjx5Mt1///1h55gMjyPJyclRTpfNl7VRsVct53WdbrYDeqr70A+CqqeKD03T04lm0BN6JnpNOLV1s2yi9RWn8LJVjlcD4AG+ffr0cb81AaWmpsaVdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQfQMzk+ZHg4ZvGy5p7V9t3yxCvVcBSs8njVefPmUVVVlfstAgAAAAAAUpQVVVF1aZ1433tk6k2ukg5Wm5qaIl68tmqnTp3oqKOOok8++USsDBBtY70AAAAAAEByKN7eq5qqk6ukJ1hlZmaKmf3RfPfdd/T+++/HLcdlGhoaKNVp3769K+my+bI2Kvaq5byu00/dTNZTFy2DrKeKD03T04lm0FMPoGdyfBg9uSoVx6tKB6vjx4+PGayCZrgHOSMjQzldNl/WRsVetZzXdbrZDuip7kM/CKqeKj40TU8nmkFP6JnoNeHU1s2yidRXHLFsVSdzg9UPPvgg+S0JMDx2t7CwUDldNl/WRsVetZzXdbrZDuip7kM/CKqeKj40TU8nmkFP6JnoNeHU1s2yidRXvL1nNTs/kzr362DumNVt27a5crAtW37vqgYAAAAAAM6pLqul0nWV4n3P4Z0oPT3N3GC1b9++dMMNN9D69esdHaSoqIj+7//+jwYNGuSoPAAAAAAAiD8EoFeKTq6SDlbnzJlDc+fOpf79+9PJJ59Mb7zxhthqtTXWrVtHL7zwAh1//PE0YMAA+uyzz2jBggWUirRr186VdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQfQMzk+LLbvXJWiy1ZJj1ndb7/96PPPP6dnnnmGbr31VhGE8oSrfv360Q477EAdO3YUs9R4CSt+1P/bb7+JYJUXqh06dCg9/vjjdMopp1CqEm8AdaLpsvmyNir2quW8rtNP3UzWUxctg6ynig9N09OJZtBTD6BncnxYjJ7Vlpx66qm0fPly+u9//0uXX3652KeWt15966236Pnnnxc9sF9//TX16NGDrrjiClq4cKGwT+VAleEg3Y102XxZGxV71XJe1+mnbibrqYuWQdZTxYem6elEM+ipB9AzOT4s3r7GanpmGnUf3JGM7lm1wz2qkydPFi+L6upq2rp1q+ii5l5WAAAAAACQPOprGqlkVZl4321QIWXl6vNkxPdgNRZ5eXniBQAAAAAAks/GH0upqSGU8pOrpCdYgdbJyclxJV02X9ZGxV61nNd1+qmbyXrqomWQ9VTxoWl6OtEMeuoB9HTfh8W2bVZ7pehmABYIVl0gNzfXlXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkH0NN9H643ZHIVg2DVBcrKylxJl82XtVGxVy3ndZ1+6maynrpoGWQ9VXxomp5ONIOeegA93fdhkSHLVjEIVgEAAAAAAkRTU4jWL2/uWe3YJ5/yCvUZwpMMEKwCAAAAAASILb+UU11lg3jfa2RqDwFwJVjdsGEDLVq0iN5++23xedu2bWQaWVlZrqTL5svaqNirlvO6Tj91M1lPXbQMsp4qPjRNTyeaQU89gJ7u+nB9xHjV1B4CoBSs8uL/++67L/Xu3ZsmTpxIRxxxhEi/+OKLxY5X33//PZmC7ts5OrFXLed1nSZtz+nEXrWcV/WZqCe2W5X3C7ZbTa37E3o692FRxHhV9KzGZM2aNTRhwgT66quv6OSTT6YxY8aIrVUZDmA5kB0/fjz9/PPPZAK675DjxF61nNd1mrTjkRN71XJe1WeintjBSt4v2MEqte5P6Onch+sjlq1CsBqTmTNnUkNDA3366af09NNP05577hnOu/DCC2nevHlUUVEh7JIFB8RTp04VO2Z16NCBJk2aJNIsFi9eTOPGjaP8/Hzq1asXXXrppVRZWZmUtjQ1NbmSLpsva6Nir1rO6zr91M1kPXXRMsh6qvjQND2daAY99QB6uuvD4u3DAPIKs6mwtz496FoNA3jnnXfo0EMPpd122y1mPve08nas8+fPp2Twww8/0P777y9+ZTz00EN033330W+//UaHH364CEhXrFghjp+dnU2zZ8+mGTNm0AsvvEDHH398UtoDAAAAAOAFFSXVVL6xOjwEIC0tLeUd72i7VZ5EVVhY2KoN92YmK1i96aabxPG5B9fa5pV7d6dMmUKfffYZPf7449S5c2eaM2dOeBHdnj170jHHHEMLFy4UQxTcJCMjw5V02XxZGxV71XJe1+mnbibrqYuWQdZTxYem6elEM+ipB9DTPR8WGza5ynHP6ogRI0TQx0MB4nVVc9DYt29fcpu6ujp6/fXX6ZxzzhGBKh+Lx8vuuuuutH79ejHZi4PUE088MWK3h4MOOkj0tHKe27Rv396VdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQfQ0z0fFhk2ucpxsHrRRRfRjz/+SKeddhoVFxdH5PHj+OnTp9OyZcvojDPOILf55ptvqLq6WvSs8goEvFcuj1nl9z/99JOY/LV161YRvEbPohswYACtXLmy1fp5GEG8VzyqqqpcSZfNl7VRsVct53WdTnBLN5P11EXLIOup4kPT9HSiGfTUA+jpng/XR0yuMqNn1dEwgLPOOktMZvrHP/5BL774ImVmNlfTpUsXKi0tFT2dBx98MF199dVutzccHF933XV09NFHi15W7lH985//LFYiePPNN8NtiaZTp05tbr0X71d4eno6jRo1KmZefX29K+my+bI2Kvaq5byu0wlu6WaynrpoGWQ9VXxomp5ONIOeegA93fNh8fZhAJnZ6dR1YOtDMo0OVpm///3vdNhhh9GDDz4oHvlv3rxZPJIfO3YsnX766XT22WcnZdCvtekAz/R/6qmnwumDBw8WY1Hfe++9uGU54HRj3TkOeHk4AsO9ujwcwh4E8woEfO723lgessAL+nL7rXQelsDpnGYt/WUF/mxjDbPgdvNxuEfZOi7nFxQUiM81NTURwTbrYP8Vy+fMafY2co80D5Owp3H72JYnrlmzDq1ebK7PulH43PjYfNza2tpwecsXXMbuC6u9FlwPn699A4l4vuDyvLJEY2NjXF8w3EZui4wvuA6u02oT+4H9YfmC01vzhV0b6xqP9gX7h8+Ty1jHieUL67qwp1natOYLtrd8YdemLV/Yj2P3hUW0L+za2H3BY6e4TvsxrOsiWhu7L1q7LvgYfJ725Vli+cLSJvq6YGL5gttiPx/2meXDtq4LPvfo65Sx+8LSxn5dcDv42DK+4LZwOVlf2M/FuubsvrC0ieWLWPcsl7PuEW6D9f0VfV3E8kW8ezb6Hol3XcT6/uK67E+zWBs+J/aFlRbtC/s9G+2LeNeF5QurznjfX619l8fyhWUbyxfxvr8sX9iPE+/7y/JFa9eF5Qv7ucS6LlS/y9vyRfS/a7Hu2Vjf5dw++/nYfWG/LmJ9fzGy/67Fuk6jfcHnwO1z4gvrvGT/XWvru7ySr/O0HNq0uvkYXQcXUEVluSvf5Yncs74QChgvvfQSKx667777WuR16NAhNHnyZJH/7LPPtsgfMWJEaNq0aa3WX1FREfNVUlIS2n333cWrqqoqokxZWVnMuhJNl82XtVGxVy3ndZ1OcEs3k/XURcsg66niQ9P0dKIZ9NQD6OmOD39ZsjF0Q78nxeuVaz4JeQ3HP/FioWTiuGeV4V8AJSUlEb+Mo9lpp53ITXjcKRMd3fOvFm7PyJEjxfavPLaVNyywYHvepODcc89ttX7rF1M01q/AWPCvDTfSZfNlbVTsVct5XacT3NLNZD110TLIeqr40DQ9nWgGPfUAerrjw+XLfp9309uQyVWOJ1ht2bKFTjjhBOG4HXfcUTyCj/UaMmSI6w3miVO8DBWPlbW61Zl3331XBM0HHnigmPnP+faAlj9zdzqvxeo28YL1RNNl82VtVOxVy3ldp5+6maynLloGWU8VH5qmpxPNoKceQE93fFhs4OQqJtPpagAvvfQS7bzzzmJjAC/X5uPxHnfeeadYcYA3JuDxsdy7y7tlHXDAAXTIIYeIAHqvvfYSKwRwT+rq1atFPr8fOHCg623iINi+TJbTdNl8WRsVe9VyXtfpZjugp7oP/SCoeqr40DQ9nWgGPaFnoteEU1s3y8arr3hp8+Qqni7RYxiC1TZ3sNpnn33o448/9mXnhGnTpokBwbfeeqtYHos3ADjllFPojjvuEO3h3te5c+fSNddcI5bX4pUBeLvVZG7/CgAAAACQLJoammjDiuZgtcuAAsrJzzLG2Y56Vjkg5LGhfm7xxbtR8SsevDLAp59+6mmbAAAAAACSwebVFdRQ12TcEADHY1Z5TOj777+v1fgmP+ElJ9xIl82XtVGxVy3ndZ1+6maynrpoGWQ9VXxomp5ONIOeegA91Sn/9fd5OD1HmDO5ynHP6r333iu2NeWhABdffDH1799frOcVr4cz1eG1yaz1F1XSZfNlbVTsVct5Xaeb7YCe6j70g6DqqeJD0/R0ohn0hJ6JXhNObd0sG4t1328OvzetZzXT6WoAvJjtt99+SxdccEGrttait6kML5wb64JMNF02X9ZGxV61nNd1utkO6KnuQz8Iqp4qPjRNTyeaQU/omeg14dTWzbKxKLIFqyYtW+U4WL3kkktozZo1ond1jz32iNurCgAAAAAA1AiFQrRxZfNuVO275YmXSTgKVpcsWUITJkyg+fPnu98iAAAAAAAQpqyoimrKmrdi7T3SrCEAjidYdevWTaxlClrf9SrRdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQfQU41i22YApk2uchysXnjhhTRnzhxav369+y0CAAAAAABhipduMXa8quNgdfTo0WInKF4NgHeTeuutt8SQgFgvE6isrHQlXTZf1kbFXrWc13X6qZvJeuqiZZD1VPGhaXo60Qx66gH0VKN4WfNmAExPw1YCcDxmdcqUKWJDAB7we/3115PpqwEAAAAAACSL4u3DALLzM6lzP33WQ9Y6WJ0xY4avu1cBAAAAAJhAdVktla5tfnrQc3gnSk83L/5yFKzefPPN7rckwOTl5bmSLpsva6Nir1rO6zr91M1kPXXRMsh6qvjQND2daAY99QB6ujMEoJeBk6scj1kFkWRlZbmSLpsva6Nir1rO6zr91M1kPXXRMsh6qvjQND2daAY99QB6ujO5qpeBy1ZJB6t9+/alW2+9NeKzzKtfv35kAtu2bXMlXTZf1kbFXrWc13X6qZvJeuqiZZD1VPGhaXo60Qx66gH0dE4xelblhgGkp6dHjFGN/gwAAAAAAJI3uSo9M426D+5opIulglXeWrW1zwAAAAAAwF3qaxqpZFXzNqudB7SnrNwMI13saMzqk08+KbZcbY3PP/+cXn31VTKB7OxsV9Jl82VtVOxVy3ldp5+6maynLloGWU8VH5qmpxPNoKceQE9nbPyxlJoaQuJ9r+Fmjld1HKyeccYZ9Nxzz7Vq8+9//5tOOeUUMgHdZxs7sVct53WdJs0ed2KvWs6r+kzUE6sByPsFqwGk1v0JPRPbZnWHXbuRqUgHq5MmTQq/mBdffDEizf4aP348zZ49mzp2NGNshe4TOJzYq5bzuk6TJuQ4sVct51V9JuqJCVbyfsEEq9S6P6Fn26y3Ta4q6J9DpiK9zuoHH3wQfs+Tq9auXSte8ejVqxfdcccdZAK8k5cb6bL5sjYq9qrlvK7TT91M1lMXLYOsp4oPTdPTiWbQUw+gpzOKbMtWdR9aQKYiHazW19eHLzgee3LZZZfR3XffHdMWqwUAAAAAADinqSlE65c396x27JNPuQX6jCnXNljNyPh9Btrjjz9OI0eOjEgzmczMTFfSZfNlbVTsVct5Xaefupmspy5aBllPFR+apqcTzaCnHkDPxNnySznVVTaI971Gdtbq/vSatJBOz300prq6msaNGyfeL1q0SKuB6wAAAABILb6f8ws9f/FC8X7S5bvSpMv/YGwshO1WXaCiosKVdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQfQU228aq+RnbW6P70GwaoLNDY2upIumy9ro2KvWs7rOv3UzWQ9ddEyyHqq+NA0PZ1oBj31AHomznrbslW9RnTW6v70GgSrAAAAAACaUbx92aq8wmwq7N2OTAbBqhtOTE93JV02X9ZGxV61nNd1+qmbyXrqomWQ9VTxoWl6OtEMeuoB9EyMipJqKt9YHR4CkJaWptX9GbgJVk1NTbRu3ToqLy+nESNGUKqCCVYAAAAA8IIfPyyi2dPfF+/3O2c4HXzjHlo4PnATrHhDAN5OlXep6t+/P+26664i/eyzz6ZTTz2VioqKyBRYPDfSZfNlbVTsVct5Xaefupmspy5aBllPFR+apqcTzaCnHkBPtclVut2fXuMoWN20aRPts88+9Pzzz4sglddctTpo+/XrJ9LHjBlDJSUlZAJ1dXWupMvmy9qo2KuW87pOP3UzWU9dtAyynio+NE1PJ5pBTz2AniqTqzrF9aEpOApWb7nlFtqwYQO9/fbb9NFHH9EBBxwQzpsxYwa99NJL9Ntvv9HMmTPdbCsAAAAAgDGTqzKz06nrwEIyHUfB6quvvkoHH3wwTZ06NWb+0UcfTfvvv78IZgEAAAAAgBy1lfW0efU28b7HsE6UkWnuxCrlYQC9e/du1WbgwIFUXFxMJlBYWOhKumy+rI2KvWo5r+v0UzeT9dRFyyDrqeJD0/R0ohn01APoKc+GFaVkTX3vuX0IgG73ZyCC1Z122ok+++yzVm2++eYb6tGjB5lAbW2tK+my+bI2Kvaq5byu00/dTNZTFy2DrKeKD03T04lm0FMPoKc8xbbxqr23T67S7f4MRLB65plnimD0qquuovr6+og8nq12/fXX05IlS+jEE08kE6ipqXElXTZf1kbFXrWc13X6qZvJeuqiZZD1VPGhaXo60Qx66gH0dBasWpOrdLs/vSbTSaErrrhCTKy699576ZFHHqGcnByRvvvuu9OqVavEmqujRo0Sk60AAAAAAIAcxUubJ1elpTWPWQUOe1YzMjLotddeo4ceekgMCdiyZYtYuuqrr74SYyq4Z5WD2fz8fPgYAAAAAECCxoYm2rCiOVjtMqCAcvKz4DcnPasclHLPKfemnnfeeeLFj/63bt0qglMTBwC3b9/elXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkH0FOOTT+VUUNdU4shALrdn9r3rDY0NIiJUzwUwIK32+LVAUwMVK0tZ91Il82XtVGxVy3ndZ1+6maynrpoGWQ9VXxomp5ONIOeegA9E1tflek5onObPjSFhIPVrKwssTtVW6sBmERVVZUr6bL5sjYq9qrlvK7TT91M1lMXLYOsp4oPTdPTiWbQUw+gZ+LBanTPapVG92cgxqz+85//pHXr1tHZZ59tzJaqAAAAAADJpHhp7GWrTMfRagAnn3wy5ebm0hNPPCFe3bp1o+zs7BZ2aWlp9Msvv7jRTgAAAACAlIXnBFnLVrXvlideQCFY5dn/HIj27dvXSfGUo127dq6ky+bL2qjYq5bzuk4/dTNZT120DLKeKj40TU8nmkFPPYCebVNWVEXVpXXife+RnbS+PwMRrK5Zs8b9lgSY9PR0V9Jl82VtVOxVy3ldp5+6maynLloGWU8VH5qmpxPNoKceQM/ENgOInlyl2/3pNeaeuYtUVFS4ki6bL2ujYq9azus6/dTNZD110TLIeqr40DQ9nWgGPfUAeqqPV63Q6P4MRM+q7M5UPFRg5syZTg4BAAAAAGAMkctWYecq5WD1tttuazVAtQYKI1gFAAAAAJAfBpCdn0md+3WAy1SD1QULFsTcLIAnXi1evJgee+wxmjhxIt15551kArwyghvpsvmyNir2quW8rtNP3UzWUxctg6ynig9N09OJZtBTD6Bn61SX1VLp2krxvufwTpSeniblQ1NwFKxOmDAhbt7xxx9PF1xwAY0ePZreeOMN+tOf/kSpDm8960a6bL6sjYq9ajmv6/RTN5P11EXLIOup4kPT9HSiGfTUA+iZyGYAnbW/P1NigtXgwYPp4IMPpn/9619kAmVlZa6ky+bL2qjYq5bzuk4/dTNZT120DLKeKj40TU8nmkFPPYCe8pOresVYtkq3+zNlVgOoqamhtWvXJqt6AAAAAICUQKZn1WQcDQNoamqKm1ddXU0vv/wyzZ07lwYOHKjSNgAAAAAAYyZXpWemUffBHf1uTmoEq5mZmeFZ//Hg1QCuueYaMoGsrCxX0mXzZW1U7FXLeV2nn7qZrKcuWgZZTxUfmqanE82gpx5Az/jU1zRSyarmR/zdBhVSVm6GtA9NwVGwOn78+LjBanZ2Nu244450yimn0KRJk8gEdN/O0Ym9ajmv6zRpe04n9qrlvKrPRD2x3aq8X7Ddamrdn9CzmZJVpdTUEGpzCEA7jfQMRLD6wQcfuN+SAFNeXk4dOnRQTpfNl7VRsVct53WdbrYDeqr70A+CqqeKD03T04lm0BN6JnpNOLV1WrbIPrmqlc0AyjW6PwM9wYonVS1ZsoR++eUXMol4Y3gTTZfNl7VRsVct53Wdfupmsp66aBlkPVV8aJqeTjSDnnoAPeOzXnJyVZNG92dggtVXX32Vxo4dS4sWLRKfOUjt27cv7b333rTTTjvRmWeeSY2NjW62FQAAAAAgpSiSWLbKdDKdBqrHHnsstW/fXoxRZS655BIqLS0VGwJ899139OSTT9KQIUPouuuuo1QnIyPDlXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkH0DM2TU0hWr+8uWe1Y598yivMCcT96TVpIZ62nyBjxoyhH3/8UQSlvXr1ol9//ZX69+9Pl156Kd13331i69WRI0eKVQOWLl1KqQAvyTVu3DjxnnuT8/Ly/G4SAAAAAALMptXb6L79Xxfvh0/ZkU7910TSmWqfYiFHwwC+//57mjJlighUmfnz54vVAY488kjxmYNU3pL1559/Ji+48MILxfE5SLZYvHixcGh+fr5oJwfSlZXN++66Tbx6E02XzZe1UbFXLed1nX7qZrKeumgZZD1VfGiank40g556AD1lxqt2Csz96TWOglV+9F9QUBD+vGDBArFn7X777Rcxa82LLmvefOChhx6KSFuxYgVNnjxZtHP27Nk0Y8YMeuGFF+j4449PShvsQbJKumy+rI2KvWo5r+v0UzeT9dRFyyDrqeJD0/R0ohn01APo2fpmAEyvkZ0Dc38GYswqP+KfN28eVVRU0Pr168UY1v333z88fnXLli0igB08eDAlEx4je/bZZ1O/fv0iViCYNWsWde7cmebMmUO5ubkirWfPnnTMMcfQwoULxTqxAAAAAAB+UhyxbBW2WXW1Z/WGG24Qj/h79OhBI0aMEF3TF110kch77LHHxJjWjRs30jnnnEPJ5I9//KPY0nX69OkRSztwkHriiSeGA1XmoIMOEsE057lNvA0SEk2XzZe1UbFXLed1nX7qZrKeumgZZD1VfGiank40g556AD1jU7x9GEBeYTYV9m4XmPszEMEqB37PP/88/eEPfxC9p3fddRcdeuihIu/NN98UPZ480eriiy+mZMG9ufzi4Ngu4Jo1a2jr1q206667ttj5YcCAAbRy5cpW6+XAO94rHvYhESrpsvmyNir2quW8rtMJbulmsp66aBlkPVV8aJqeTjSDnnoAPVtSUVJN5Rurw0MA2gpGCzS6PwOzzuoJJ5xAn3zyiZjtf9VVV4XTn376adqwYYOY0JQsSkpK6Pzzz6c777xTrOlqZ9OmTeJvly5dWpTr1KkTlZU1778bD16OK9aLe5Fb2wzBjXTZfFkbFXvVcl7X6QS3dDNZT120DLKeKj40TU8nmkFPPYCe8XtVZSZX6XZ/BmLMqkVdXZ3YenXZsmXCid26daO99tqLdtllF0omHKjyuFlr6IGd+vr6uOXS09Nd2VuXA14+d4a3PuNe19ra2nA+r0DAv5B47C6/Z3h5h6ysLNq2bZuw53QelsDpnGatIMYrKfAgat5QwRpMze3m4/CSEdZxuQ5e5YA/2y9gDqx5KERVVVU4jc+Zy9rbyBPieJiEPXjn9rEtT46zdsrgcjzel+uzfMvnxr/w+Lj2OrmN3GYuY/eF1V4LroePz+dtEc8XXJ7HRlsbTMTyBVNYWCjaIuMLroPrtHRgP3B7LF9weseOHeP6gvMtbdgXVnvtvmD/8HlyGes4sXxhXRf2a8XSpjVfcB3sB37ZtWnLF/bj2H1hEe0LuzZ2X/DkSa6T7azztq6LaG3svmjtuuBj8HnycSxi+cK6TqOvC24bv6J9wfVFX6dcLp4v7NcFn3v0dcrnxsewfGFpY78uWBO+P2V8wW3hOmR9YdeGz4vT7L6wtIl1XcS6Z7mcdY9wG7p37y60jL4uYvki3j0bfY/Euy5ifX9xXdbTLK6LteFzYl9YadG+sN+z0b7g97GuC8sXVp3xvr9a+y6P5Qs+PtvG8kW87y/LF/bjxPv+snzR2nVh3SP8lNNaWijWdaH6Xd6WL6L/XbP+bbN8wefHNtHf5exDe3vsvrBfF7G+v7icvWxr/67Fuk6jfcHt4+M48QWfV2vf5au/KArbcs9qW9/llZWVouOstX/XnH6XJ3LP+kLIIXPnzg317t07lJ6eHkpLSwu/+PPee+8d+uqrr0LJ4Nlnnw3l5eWFvv3221B5ebl4XXfddXwVhEpLS0NLly4V79kumhEjRoSmTZvWav0VFRUxXyUlJaHdd99dvKqqqiLK8HFjkWi6bL6sjYq9ajmv63SCW7qZrKcuWgZZTxUfmqanE82gpx5Az5Y8f/GHoRv6PSle61dsceRDr+H4J14slEwc9ax+8803dMQRR4gInYcA7LnnniKyLyoqonfffVdMYpo4caIYJsATsNzks88+E9F+9JhUhn9ZT506Vfyi4DaefPLJ4Tz+NcCTws4999xW67d+MUXDv1oAAAAAANzAGgaQmZ1OXQcWwqlu72DFS0Dx0lVffvklDRo0qEX+V199RWPHjqXDDz9cTMRyk9WrV9O6desi0niS1eOPPy6GJPAjLN7ilXfXWr58eXg5rWeeeYZOP/10sfMWryDg5q4N7MJYA6MTTZfNl7VRsVct53WdbrYDeqr70A+CqqeKD03T04lm0BN6JnpNOLVNpGxtZT3dtvPzxBFYn1270IVvHJLUthi5g9WHH35IhxxySMxAlRk1ahQddthhInh0G57Rz4Gw/dW3b1+Rx5sSDB8+nG655RYR0HLv78svv0x33303XXDBBaJX1Umg2hbxxskmmi6bL2ujYq9azus6/dTNZD110TLIeqr40DQ9nWgGPfUAekayYUWpCFSZnhKTq3S7P70m3WlkzTPrW4Nn4/Pgbj/gIQK8sxUPLD7ttNPEMlq8OsEDDzyQlOPZB16rpMvmy9qo2KuW87pOP3UzWU9dtAyynio+NE1PJ5pBTz2AnvF3rurdxs5VOt6fXuNozOrQoUNF7yrPJos3lvPjjz+mHXbYgbzg5ptvFi87vEvVp59+6snxAQAAAABkidhmVbJn1WQc9azy4/QVK1aIsau8zqqdtWvX0hlnnEHff/99xAQnAAAAAADA26w2T67iIag9hiFYTUrPKq9v+sUXX4hJTbxjVe/evcVr8+bN9Ouvv4q1urhnk7dlNYF4Kwgkmi6bL2ujYq9azus6/dTNZD110TLIeqr40DQ9nWgGPfUAev5OY0MTbVjRHKx2GVBAOflZjn1oCo7XY3r00Ufp9ddfF9uscnDKKwDwzlK8jNX9999P77//vlhCCgAAAAAANLPp523UUNe8KD+GAMihtHgoL031xhtvUHFxsVjHlCc08VjViy++WOyKYAr2XSxU0mXzZW1U7FXLeV2nn7qZrKcuWgZZTxUfmqanE82gpx5Az98pXvr7eNWeIzor+dAUlLZb5UX/X3nlFbF2KW8JxisA7LbbbmIsazKWiAIAAAAASIXNABj0rCYxWOVVAHiS1RNPPBHe79biueeeE2NVr732WrHeKQAAAAAAaNmzKrtslek4GgZw1113iclVo0ePFuNWN2zYQBUVFfTDDz/Qv//9b7FI/6xZs+jJJ58kE4i3g0Oi6bL5sjYq9qrlvK7TT91M1lMXLYOsp4oPTdPTiWbQUw+gZzPcwWctW9W+W554qfjQFBxtt7rTTjuJv8uWLYs5iWrLli20yy67iK1PeeJVKoDtVt1Dhy3jgrw9pxN71XJe1WeinthuVd4v2G41te5PE/UsXVdJd+/3ing/ZGJvmvbEAUr1eU2gtltdv349TZ48Oe5s/86dO4vtVnksqwls27bNlXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkH0LPlZgCJTK7S7f70GkfB6siRI2njxo2t2nDvqlc7WAEAAAAA6A7Gq3oYrM6YMYPefvtt8YoFL1/FmwVcddVVDpsFAAAAAJC6KwH0xDaryV0NYPny5WKJKl5ndcyYMeLVtWtXMZaBd7Z69913xSSr3377TQS2FjzWYubMmZRq5OTkuJIumy9ro2KvWs7rOv3UzWQ9ddEyyHqq+NA0PZ1oBj31AHpGDgPIzs+kzv06KPvQFBxNsEpPd7aXAAerjY2NFET8GlQMAAAAgOBTXVZLs/7wH/G+7x7d6LyXplLQqPYpFnLUs7pgwQL3WxJgeNBzQUGBcrpsvqyNir1qOa/rdLMd0FPdh34QVD1VfGiank40g57QM9FrwqltW2UjNwPoHOj702scBascVcv0rvL2q4WFhZTqxOucTjRdNl/WRsVetZzXdfqpm8l66qJlkPVU8aFpejrRDHrqAfSMnFzVa2QnV3xoCo6e5++xxx5trp/6wgsv0PDhw522CwAAAAAgZVDtWTUZR8Hq119/TXvvvTddffXVVFNTE5G3du1aMfHqlFNOoU2bNpEJZGZmupIumy9ro2KvWs7rOv3UzWQ9ddEyyHqq+NA0PZ1oBj31AHr+PrkqPTONug/u6IoPTcFRsMqDaocNG0Z333232Klq/vz5onv6b3/7G40YMYLmzJkjel8/++wzMoH8/HxX0mXzZW1U7FXLeV2nn7qZrKcuWgZZTxUfmqanE82gpx6Yrmd9TSOVrCoT77sNKqSs3Ayl+kzDUbC63377iWEAd911V3g3q4EDB9KVV14pIv8HH3yQFi9eTKNGjSITqKiocCVdNl/WRsVetZzXdfqpm8l66qJlkPVU8aFpejrRDHrqgel6lqwqpaaGkNIQgAqN7k+vcbYGFRFlZGTQ5ZdfTmeccYboVV2zZo2YdPXEE0/QBRdc4Pv+tV4SbzmuRNNl82VtVOxVy3ldp5+6maynLloGWU8VH5qmpxPNoKcemK5nkX1ylcPNABo1uj8DE6zOmzdPDAHgXtTu3bvT2WefLQLUY445RrwvKSlxt6UAAAAAAAFkPSZXeR+snnDCCXTQQQfRypUrafr06WJHq0ceeYSWLFlCu+++Oz3++OM0dOhQuv/++8kE4i3jlWi6bL6sjYq9ajmv6/RTN5P11EXLIOup4kPT9HSiGfTUA9P1LFJctkq3+zMwO1gNGjSIHn74Ydp///0j8rg6DlJvuOEGqqqqSplua+xgBQAAAIBEaWoK0W27PE91lQ3UsU8+/enjYwLrxGqfdrByFKZfe+219N1337UIVBkeCnDppZfSsmXL6LDDDiMT4KDcjXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkHJuu55ZdyEagyvUZ2Ton702scLdp1++23t2mz44470uuvv04mUF9f70q6bL6sjYq9ajmv6/RTN5P11EXLIOup4kPT9HSiGfTUA5P1jByv6mwIgG73p9dI9ayOHj2a7rnnnhbpxcXF9O2338Ysc/3111OXLl3UWwgAAAAAEFCszQBUe1ZNJl12x6p169a1SOd1VuOtpcrjGkpLS9VbCAAAAAAQUIojlq1CsOoEc6eWuUhhYaEr6bL5sjYq9qrlvK7TT91M1lMXLYOsp4oPTdPTiWbQUw9M1rN4+zCAvMJsKuzdTrk+E0Gw6gK1tbWupMvmy9qo2KuW87pOP3UzWU9dtAyynio+NE1PJ5pBTz0wVc+Kkmoq31gdHgKgsmFSrUb3p9cgWHWBmpoaV9Jl82VtVOxVy3ldp5+6maynLloGWU8VH5qmpxPNoKcemKqn1auqOrlKt/vTaxCsAgAAAAAkgcjNADBe1SkIVgEAAAAAksB6+0oAij2rJoNg1QXat2/vSrpsvqyNir1qOa/r9FM3k/XURcsg66niQ9P0dKIZ9NQDU/W0hgFkZqdT14GFKXN/arspwNKlS+mxxx5rkcY8/vjjYpvVWHkm0NTURBkZGcrpsvmyNir2quW8rtPNdkBPdR/6QVD1VPGhaXo60Qx6Qs9ErwmnttHUlNfS5tXbxPsewzpRRmZ6ytyf2gar7733nnjF4uyzz474zLPdOHhVmfUWJHgLtFhLSiSaLpsva6Nir1rO6zrdbAf0VPehHwRVTxUfmqanE82gJ/RM9JpwahvNL99uIKsfr6cLQwCqNLo/tQxWuecUAAAAAADIsXFFWfh9b0yuSn6wOn36dLWjAAAAAAAYxMaVzUMAGEyuUgMTrFygXbt2rqTL5svaqNirlvO6Tj91M1lPXbQMsp4qPjRNTyeaQU89MFHPTT+Ui788IpLHrKbS/ek1CFbdcGJ6uivpsvmyNir2quW8rtNP3UzWUxctg6ynig9N09OJZtBTD0zTs7GhiTauLBXvuwwooJz8LEf1uNGWVMDcM3eRiooKV9Jl82VtVOxVy3ldp5+6maynLloGWU8VH5qmpxPNoKcemKbnpp+3UUNdk6tDACo0uj+9BsEqAAAAAICLFNt2ruo5AjtXqYJgFQAAAADARazNABhMrlIHwaoL5ObmupIumy9ro2KvWs7rOv3UzWQ9ddEyyHqq+NA0PZ1oBj31wDQ97T2rbi1blavR/ek1CFZdICcnx5V02XxZGxV71XJe1+mnbibrqYuWQdZTxYem6elEM+ipBybpyZsiFS9rDlbbd8sTr1S7P70GwaoLlJWVuZIumy9ro2KvWs7rOv3UzWQ9ddEyyHqq+NA0PZ1oBj31wCQ9y4qqqLq0TrzvPdKdyVW63Z9eg2AVAAAAAMAlrF5VBpOr3AHBKgAAAABAEiZXYZtVd0Cw6gLZ2dmupMvmy9qo2KuW87pOP3UzWU9dtAyynio+NE1PJ5pBTz0wSc/IZas6peT96TUIVl0gLy/PlXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkHJulpDQPIzs+kzv06JFzezbakCghWXaC8vNyVdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQem6FldVkulayvF+25DCig9PS2h8m62JZVAsOoCTU1NrqTL5svaqNirlvO6Tj91M1lPXbQMsp4qPjRNTyeaQU89MEVP+3jV7kMKEirrdltSCQSrAAAAAAAuYB+v2n1YIXzqEghWXSAjI8OVdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQem6JnMbVYzNLo/vSYtxFstgDaprq6mcePGifeLFi0yeqAzAAAAAFryj6lv0foVWyk9M43+/P3JlJWbWgFmtU+xEHpWXaCystKVdNl8WRsVe9VyXtfpp24m66mLlkHWU8WHpunpRDPoqQcm6NlQ20gbV5WK990GFVJdY410WbfbkmogWHWBhoYGV9Jl82VtVOxVy3ldp5+6maynLloGWU8VH5qmpxPNoKcemKDnxh9Lqamh+WF1rxGdU/r+9BoEqwAAAAAAihTZJle5PV7VdBCsukBaWpor6bL5sjYq9qrlvK7TT91M1lMXLYOsp4oPTdPTiWbQUw9M0HN9xOSqzil9f3pNIIPVxsZGuuuuu2jo0KGUk5ND3bt3p+nTp1NJSUnYZvHixWIQcH5+PvXq1YsuvfTSpI33KCgocCVdNl/WRsVetZzXdfqpm8l66qJlkPVU8aFpejrRDHrqgQl6RvSsjuyU0ven1wQyWL3xxhvp2muvpYMPPpheeOEFuvzyy+mVV16hI488knhxgxUrVtDkyZPFPrqzZ8+mGTNmCLvjjz8+abPj3EiXzZe1UbFXLed1nX7qZrKeumgZZD1VfGiank40g556kOp6NjWFaP3y5p7Vjn3yKa8wJ6XvT68JXLDKYv3jH/+gs846i+677z466qij6Prrr6d7772XPv30U5o7dy7NmjWLOnfuTHPmzKHjjjuOLrzwQnrooYfonXfeoYULF7reprq6OlfSZfNlbVTsVct5Xaefupmspy5aBllPFR+apqcTzaCnHqS6nlt/Lae6yuYJUL1Gdk74OG62JRUJXLD6448/UkVFBU2dOjUifcyYMeLv999/L4LUE088kXJzc8P5Bx10kOhp5TwAAAAAALcoXpq8zQAAUWbQnNC3b1967733aPTo0RHpPEbVGs+6detW2nXXXSPy27VrRwMGDKCVK1e2Wn+8ca0md78DAAAAID7Fy+zjVZt7VoHBwWrHjh3pwAMPjEibN28eXXXVVdS/f3/ae++9RVqXLl1alO3UqROVlZW1Wn/79u1jpqenp9OoUaMCOYHDib1qOa/rNGlCjhN71XJe1WeinphgZe6EHDfLelGfCqmuZ3HEslWdU15PrwlcsGqHZ/9fd9119Nhjj1G/fv3o9ddfp/Ly8rj2HHByD6sqHPBaY0c6dOggel3ti/XyCgS8xAT38GZlZYk03pKM32/bto3q6+vFex6WwOmcZu16m5mZKfLYxqqT220dxzou53NAzp9ramoigu2mpiaqqqoKp/E5c49zbW1tOI1XUeBhEvbgnY/LtuxDroPhvxzkc318TIbPjW8aPq69Tm4jt9neC82+iO6x5v2NOZ3P2yKeL9iOh31w++P5giksLBRtkfEF18F1WjqwH9gfli84ne3i+YLPxdKGfcHlOc/uC/YP18PttI4TyxfWdbF58+bwtWJp05ovuE62Y1/YtWnLF3yO1nHsvrCI9oVdG7svWEOuk+2s5VSs6yJaG7svWrsu+Bh8nvZ7OJYvrOs0+rpgW9Yl2hdsZ7WbYZ9xuXi+sF8XfO7R1ymfO/vXqtPSxn5dcP08bl7GF1wnH0/WF9E/uK1ztHxhaRPruoh1z3I56x5he+4QYC2jr4tYvoh3z0bfI/Gui1jfX1wXl7d0YG34nNgXVlq0L+z3bLQvuJ2xrgvLF1ad8b6/Wvsuj+ULPifOj+WLeN9fli/4HK3jxPv+snzR2nVh+a20tFQcM951ofpd3pYvov9d43OMvmf5c/R3OR/Dsov2hf26iPX9xedkb3dr/67Fuk6jfRGrzli+KNres5pbkEWUXx/+buQ6Zf9da+u7vL6+XrS7tX/XnH6XJ3LP+kIooDz33HOhTp06hTIzM0OXX355qLy8XKQvXbqUr4jQs88+26LMiBEjQtOmTWu13oqKipivkpKS0O677y5eVVVVEWVKS0tj1pVoumy+rI2KvWo5r+t0glu6maynLloGWU8VH5qmpxPNoKcepLKe5RurQjf0e1K8Hj15rqPjuNWWZMPxT7xYKJkEboIV8/DDD9PJJ59MQ4YMoW+++UasBGA9vh84cKD4RcHpdvjXwM8//xz3Ub4F/+KJ9wIAAAAAsFMcsRkAJlclg8AFq9x9zeNTx48fL5ahGjFiREQ+d3vzzP8XX3wxoruaP3N3+uGHH+5DqwEAAACQimByVfIJ3JhVXkeVx1YccsghNH/+/Bb5gwYNoltuuYX22msvOuKII+jcc8+l1atX08yZM8V77nl1m3i9rommy+bL2qjYq5bzuk4/dTNZT120DLKeKj40TU8nmkFPPUhlPSMnV3Uy4v70msAFqxx4MryDVSxuuukmuvnmm0VQe80119Bpp50mJiLxdqscsAIAAAAAuD0MIDM7nboOLIRjk0DghgH86U9/ErPq4r04UGV4mADvaMWz2dauXSt2teJZeMkg3tqsiabL5svaqNirlvO6Tj91M1lPXbQMsp4qPjRNTyeaQU89SFU9ayvrafPq5tUQegzrRBmZ6Ubcn14TuGAVAAAAAEAHNqwope2rUlFPTK5KGghWAQAAAAAUJ1f1xs5VSQPBqgvwIrtupMvmy9qo2KuW87pOP3UzWU9dtAyynio+NE1PJ5pBTz1IVT0jVgKI6llN5fvTaxCsukC8sbCJpsvmy9qo2KuW87pOP3UzWU9dtAyynio+NE1PJ5pBTz1IVT2LlzZPruKN/HjMqtPjuNGWVAbBqgvE2+I10XTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkHqahnY0MTbVjRHKx2GVBAOflZjo+j2pZUB8EqAAAAAECCbPp5GzXUNYn32LkquSBYBQAAAABIEPtmAD1HdIb/kgiCVRfgLV7dSJfNl7VRsVct53Wdfupmsp66aBlkPVV8aJqeTjSDnnqQinpamwHE61lN5fvTaxCsukBubq4r6bL5sjYq9qrlvK7TT91M1lMXLYOsp4oPTdPTiWbQUw9SUU97z2qsZatS+f70GgSrLrBt2zZX0mXzZW1U7FXLeV2nn7qZrKcuWgZZTxUfmqanE82gpx6kmp68Y6a1bFX7bnnipXIclbaYAIJVF+CL1o102XxZGxV71XJe1+mnbibrqYuWQdZTxYem6elEM+ipB6mmZ1lRFVWX1on3vUd2Uj6OSltMAMEqAAAAAEAC2DcDwOSq5INg1QWysrJcSZfNl7VRsVct53Wdfupmsp66aBlkPVV8aJqeTjSDnnqQanraJ1fF22Y1le9Pr0Gw6gLt2rVzJV02X9ZGxV61nNd1+qmbyXrqomWQ9VTxoWl6OtEMeupBqukZuWxVJ+PuT69BsOoCFRUVrqTL5svaqNirlvO6Tj91M1lPXbQMsp4qPjRNTyeaQU89SDU9rWEA2fmZ1LlfB+XjqLTFBBCsukBjY6Mr6bL5sjYq9qrlvK7TT91M1lMXLYOsp4oPTdPTiWbQUw9SSc/qsloqXVsp3vcc3onS09OUj+O0LaaAYBUAAAAAQJLIzQCwc5UXIFh1w4np6a6ky+bL2qjYq5bzuk4/dTNZT120DLKeKj40TU8nmkFPPUglPe3jVXvFWbYq0eM4bYsppIVMXrgrAaqrq2ncuHHi/aJFiygvr+UCwAAAAABIbZ69dD59/upyyknPpT++dRT12aULmUK1T7GQuWG6i1RVVbmSLpsva6Nir1rO6zr91M1kPXXRMsh6qvjQND2daAY99SDoevJ40ZdffpkOOOAAOvX+A+i+tZfQnb+eQ6dferxIjzWeNJXvT6/J9PyIKUh9fb0r6bL5sjYq9qrlvK7TT91M1lMXLYOsp4oPTdPTiWbQUw+CrCdvc3rsscfSvHnzWuQv+GCBeB144IEiaC0oKHB0HNm2mAqCVQAAAACAGHCP6YknnigC1ay0HNqrw0G0W/sJVJjZhcoaNtPXFR/S/8r/K/I5oH333XcpIyMDvnQZDAMAAAAAAIjBW2+9FQ5UT+txHU3sdBx1zOpGaWnp4i9/5nTOZ7vXX38dfkwCmGAlCSZYAQAAAGbBY1Tnz59P+xUcIQLTeCzY+iJ9su1NYR9ruECqUI0JVsGlpqbGlXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KkHQdST7ThQZfjRf2uMaj9R/H3//ffD9afy/ek1GAbgArW1ta6ky+bL2qjYq5bzuk4/dTNZT120DLKeKj40TU8nmkFPPQiinsU/bwy/5zGqrVFgyy8vL0/oOEG8P70GE6wAAAAAAIiIl55f+9Um+nT2CvrmrVVhn/BkKh6jGo9tDZvD7zt06ABfugyCVQAAAAAYTUNtI3331hpaPHslrfu2OfBMo0zqnzuC1tQsE7P+Wxuz+lXFB+Ivj1nNzc31rN2mgAlWLgwqbmpqirkNWqLpsvmyNir2quW8rtPNdkBPdR/6QVD1VPGhaXo60Qx6Qs/Wromy4kr63zM/0JLnfqTKzZGP2dt1yqHqkWvotmeuCa8G0Dtnpxb1FdX+TE9vuIPqQ7VirdVjjjlG6toL4v1Z7dMEK/SsurQOW6wLKNF02XxZGxV71XJe1+lmO6Cnug/9IKh6qvjQND2daAY9oWc0DQ0NVPT1VtGLuuzdX6mpMXLX+V4jO9M+04fSrkf0p0ZqoMUb3hMz/Dkg3bPDFDGZylpnlXtUPy+fKwJV3hjgyCOPlL72gnx/eo2ZZ+0yum/n6MRetZzXdZq0PacTe9VyXtVnop7YbjU1t+d0gmn3p9d61tc00JIXfqR/Hv4O/fuEufT9nF/CgWp6Zhrtclg/Ovelg+iitw6h3U8YRFm5mWJSE/eWciDKASkvT/VA0VV0+69niL/82QpU2c6+IUAq6+k16FkFAAAAQMqydW0F/e+pH0SgWl1aF5GX3zWX9jxlMO116hAq6NEuZnneQpV3puIF/x988EGxPJUFj1G96KKLRI8qdq5KHghWAQAAAJBys/p//nQ9LX5iJa2Yt5ZCTZGP+nf4Qxfa54xhtPMh/Sgzp+3tUTkQ5bGo/OL1Tnl5Kp71j8lU3oBg1QXatWvnSrpsvqyNir1qOa/r9FM3k/XURcsg66niQ9P0dKIZ9DRLz0zKpv89/QMtfnIFbfyhLCIvIyuddj6sH+156iDqv0dPx+3mAFUmSE3l+9NrEKy6QLwBz4mmy+bL2qjYq5bzuk4/dTNZT120DLKeKj40TU8nmkFPM/Tc/Es5ffbkSvryP6uoprw+Iq9D9zza67QhtOfJg6l9tzwxUUml3V6U9aK+IGHumbtIRUWFK+my+bI2Kvaq5byu00/dTNZTFy2DrKeKD03T04lm0DN19WxqCtGPC4voqbPn030TX6NPHl0eEaj23aMbnXj/OPrTx8fQ/pfuKgLVtupPpC3JLOtFfUECPasAAAAACAy1FfX01cs/iaWnNv28LSIvIzud/nDkALH0VO+dW98iFQQHBKsAAAAA0B4OTBc+8h0tfXOtCFjtFPZuJ2b0Dzm0B/Xq3923NoLkgGDVBeINtE40XTZf1kbFXrWc13X6qZvJeuqiZZD1VPGhaXo60Qx6BltP8aj/g3ViVj8/8o+m/949aMwZQ2nY5B0pIzNdrIvqpC1u2LpZ1ov6ggSCVRfIzs52JV02X9ZGxV61nNd1+qmbyXrqomWQ9VTxoWl6OtEMegZTz+qyOvryxVX02VM/0JZfyiPys3Iz6A9HD6B9pg2jnsM7SdWZqI0TWzfLelFfkMAEKxfYtm2bK+my+bI2Kvaq5byu00/dTNZTFy2DrKeKD03T04lm0DNYem78sZReunoR/XXMy/TObV9EBKodd8inCVeOoP9bfCwddceYFoFqvDqd2DixdbOsF/UFCfSsAgAAAMA3mhqbxML9PGHq50/Wt8gfOLYn7TN9GA2d1IfKK8qpXWGOL+0E/oFgFQAAAACew4/6v31uKX321EoqXVcZkZfdLpNGHbsT7T19GHUfVAh1DAfBqgvoPibOib1qOa/rNGmMoxN71XJe1WeinhgTJ+8XjFlNjfuzeNkW0Yv6zWs/U0NtU4Rdl/4daPTJO9HeJw+j3AL5a0A2X9bGia2bZb2oL0ikhXgDXdAm1dXVNG7cOPF+0aJFlJfXvLgwAAAAAFqnsaGJls/9jT59YgX98r+NLfKHTOwtHvUPmtCb0tPT4E5NqfYpFsIEKxcoLy93JV02X9ZGxV61nNd1+qmbyXrqomWQ9VTxoWl6OtEMerpLTU0NlZSUiL8yVG6uoQ8e+I7u3u8Vev6ihRGBanb7TBpz5jC6fMGRNO2JA2jI/n1EoAo99b8/vQbDAFygqanJlXTZfFkbFXvVcl7X6aduJuupi5ZB1lPFh6bp6UQz6KlOY2Mjvfbaa/Tggw/S/Pnzw+mTJk2iiy66iI466ijKyMiIKLPuu820+IkV9N2ba6ihLlKfbgMLaJ8zhtFOB3Slbr1b7jIFPZ1dy6kMglUAAAAAxF0u6dhjj6V58+a1yOPAlV8HHnggvfzyy9QuN5+WvvOrGI/625clkcZpRMMO2EFsgzpwbC9KS0ujsrIyeB1IgWDVBTIzM11Jl82XtVGxj1WOH/vwY4gOHToo7aThtC1uk6g+DQ0N4vFX9PkHVU8nRF8Dumjp5n3Y2jnx+W/dupVycnJavQcS8YuKD932f1v1ufUd4LQtTjRz+/5sywdB0lOmR9UKVLPScmivDgfRbu0nUGFmFypr2ExfV3xI/yv/r8jff88pdGyHK6hqU11EHTxJavcTB9Eux+5IOwzrrp2ebtq6WdaL+oIExqw64NBDDxW/IvlGZvLz82PaJZoumy9ro2JvwV/GfK4HHHCAGEjdvXt38Zc/233gRVvcRkYfPj/r/Lt27Rrz/IOkp5Nydh9EXwPvvvuuo2sgGbh1H0anR5//gAED2rwHEvGzyv3g9r0Uq77W9Hf6HeC0LU6+U924PxP5HtRdz0TgR/9WoHpaj+toYqfjqGNWN0pLSxd/+TOnc/6XP3xGX/76cbhsj6Ed6cjb96arFx9DB9+we4tA1U89U+n+NAZeDQC0TVVVVWj33XcXr/T0dF5BIXTggQeGysrKQhUVFTHLJJoumy9ro2LP8Lntv//+4lzjvSwfJLstyaAtffi8+PzaOv+ioiJHx0nURsXeaTlZHyR6DSQDt+5De7rT80/Ezyr3g9v3UnR9fuof69ycfKeqft8m+j2os56JMmnSJHF++xUcEbqh35NxX/sWHC7s+ueODD1z/gehnz4pDjU1NWmpp6yNE1s3y3pRn2osxO+9wtw+ZQX26XAIfb7tPfGLkx+RvPDCCzF/8fBj41jES5fNl7VRsbce/yxYsKDNxz9sxz1s0QPs3WpLsmhNn0Qef51yyinib7zz10FPJ+US8UGi10AySPR+aytd5fwT8bPK/eD2vWSvz2/9Y52bk+9Ule9bJ9+DXurJK0821DZSfXUj1VU3UEMNv2+g+hp+NVLppm2UnZEj8q00kS9s7GmNkWnVDVRVVU3zP26eTMXn3Bqj2k+kT7a9SWtqltIx9+0dc3iEDnomYuPE1s2yXtQXJBCsOmBcx6NpaN4e9PSGO8SX1Jw5c+j000+nVCL68U/vnJ3CedbjnyHtRod98Prrr9MxxxxDJp7/Bx98kHLnz+AawD1g8neA03uAZ8g31DU2B44RASJ/jgwmwwHi9r8VZVWU3pQRN5jkwJSDUvFe5CdvCE5l4+/70HNw3hoFtnwez5vs8czAPBCsOoS/tPbsMEX8mvz7nQ/Szvn7tbCpqa6m3LzN0umy+bI2KvZ3zrxH/OWeBPsXdDwf3HnzPTSAdktKW5JFa/rceaeD8w/9Ie4iynl5m1pti4yNir2TcnxOifqgf1NsH7SJC1uTVNdUU17u7+dm7XdSU1NNubktF65uK/3OWxM7/7/cdDf1rd+luY7qGsrNy417XvatWJqvw+3tiLNHS7ytW4SeMc4hkb1eItpi88kdf7k7ofO//Ya/UreSoeKc+fiiXv4rDrK9TeLv9vTtD9HZItTUMq+2trZ5xx5bem1tHWVnZf1edrttXW0tZWVZttvrJiuvjrKyssL1iGNZPuL8unrKysyMbNf2/Flv3J6QD/5v2k10So8qCjUGd6+dtDSirLxMysouJFrbnMa9yBycx2Nbw+/fozzxLHa9aVJpraW3lSeTL2vjxNbNsl7UFyQQrCpgPfpYsnQxvXDFAspMS42t0BpCdfT5r58m9Pjnf999kjI+cHz+V36QEuev4oP/XJUaPnBy/p9//ym9dPXClDn/L35dnND5f7FiMc25fXFKnL/lg6XrvkzIBz9Xfk/1DbVJ8UFmdjpl5mZSVl4GZedlUmZuBmVt/xzxl9Pzov62ZmdLz8zJpMyc9HBQtPCAx8XSVDzcgXuR4/FVxQfiL084i9erWlBQIJXWWnpbeTL5sjZObN0s60V9QQLBqgL2Rx+1TTWUmZEaX9J8Lk4e/6SKD0w/f8Z0H+D8zdZf5RooHJxLXTt3payclsFgVs72v3GCyaa0RmrfsZ34zMEoB6XNQWQGpWekO3yS4nw7TF7wn4NVHpfLwx1i9S4X1f5Mn5fPDdsn0pZ47Wut3W2dk8w5J+IXFR+q+j/Z9QUJBKsK2B99HH3zOMrJzlG+EWXyZW2c2tfW1dJ95yT++CeWD1TbkkzitaNsWyndd4l759/8eLoNPSVsVOwTLef0Gjhm5nipa0CJOE/CEr3fWkvnwOC+sxI//2NvmyDO36o77lM7W3p1VTXltctz9GiyurqK8vLatajzd7u4FcRMqqqqonbt2jXrf1Li53/6Pw6knJy85qakbW+n+Nt8gPBfW35auj2P/2suU1VVSfnt20eUrayspPbb09J4//jtdVRUVlCHDu1FBeF6th+7oqKCOhR0iKjH3q7yigoqKOgQcWzOr6mtofsGXpKwD8565kDq0aMHOYEXyS8sLCS3qKurU/qu5Z2peMF/Ho/L43J5uAP3IlsTzLhHlQPV+lCtsDvyyCMTaku89rXW7rbOSeacE/GLig9V/Z/s+oJEygers2fPpr/85S+0Zs0a6tu3L11yySX0xz/+0ZW6rUcfEyZMoPFn7yr9xdPWF5LMF1aiX2qJ2k96dlLCj39i+cCNtiSL1vSZ9Ip756+Dnk7KObkGxp3VPGbTDxK939pKn/R04uc/dvrOrdadSLuTXbat+ib9K/HzH33k0KS0pbW05vTMuH4oK0tv1UeZZSEqLIw1zjJfTJZy6zF4EOHVHXgdWWtVCB7qwK9orB2s/FwNBKQ2Kb0pwNNPP01nnHEGTZw4kZ577jk68cQT6YorrqA777xTuW77o4+zzz6bUg3rcQ4//uFzjYXs458gYvr5M6b7AOdvtv6M6deANU6Sl+SyNkWwY22KwPkmj6cEySeNF1ulFKSpqUnsNLP33nvTf/7zn3D6ZZddRo8//jgVFxcntBsEP9YbN26ceJ+9qict2TYv/OjjnXfeibkNGrs21qO9eOmy+bI2Kva8vuDUqVPDy7a09fgnkTUWE21LsmhNH75+3Dp/HfR0Ui6Z10AySPR+aytd5fwT8bPK/eD2vWSvz2/9Y52bk+9Ule9bJz5IT/99gpJOerqF0y13ddAzERsntm6W9aI+J9hjoUWLFnk3LCGUonz55ZdiIZI5c+ZEpL/33nsi/e2333ZtB6va2tqYZRJNl82XtVGxZ/jcrB1M3Ny9xklbkkFb+sju3lNSUuLoOInaqNg7LRekHazcug/t6U7PPxE/q9wPbt9L0fX5qX+sc3Pynar6fZvo96DOevqJLnrK2jixdbOsF/UFaQerlB0G8NVXX4m/u+4aOY5w552bx5StXLkyZjkewB/vZcHDCuyPPviXRiwSTZfNl7VRsWf43F588UXXH/84aUsyaEsf2cdfYg1HB8dJ1EbF3mm5tnzw5JNPavMI0K370J7u9BFoIn5WuR/cvpei6/PzEXCsc3Pynar6fZvo96DOevqJLnrK2jixdbOsF/UFiZSdYLVpU/Pi4F26RC450qlTp/BA/VjwTNNY8KOdUaNGifdvvfWWMTPy+LEe70rDL6ePf1Ll/Dds2CCuA5POv61rgO+jVJ9UEX3+69atoz59+hhzDZj+HcDABwD4S8oGq/X19THTOdhgeHkWp/A/0LyEBMNf2rxfrz345bGwPK7E3hvLwS33wG3bti2czruzcDqnWUOHrbGvbGPtA2wFSPyryjou5/Mvef7M/4DYg20eb8lL0FjwuXKavY05OTnhYMOC28e2/I8R2zN8TJ5Jy/WxT7nNfEwuy8flXWYsLF/Yf/1Z44LtvuB6+Hz5vC3i+YLL89IzPHYsni8YbiO3RcYXXAfXabWJz4X9YfmC02P5gtvVrVs3kW/ZWuOHon3B2vB5cjut48TyhXVd2NMsbVrzBdtbvrC0kfGF/Th2X1hE+8KujeUL/mxdm/ZjsC+sJw12bey+aO264GPwefJxWvOFpU30dcHE8gW3xX4+7DPLh21dF3yu0ddp165dxTEsvS1t7Pcst4OPLeMLbguXk/WF/VysNth9wYEV6x3LF7HuWS5n3SPcBuv7K/q64HO3dpXiv6xHvHvW7ovWrotY319cl/1pFmvD58S+sNKifWG/Z6N9Ee+6sHxh1Rnv+yvWdzlfA3zeli/t31+WbSxfxPv+snxhP0687y/LF61dF5Yv7OcS67qwtHH6XW7/d82itX/XYt2zsb7LuX3287H7wn5dxPr+YmT/XYt1nUb7gs+B2+fEF9Z5yf671tZ3eeX2pdta+3ctke9y+3WRyD3rByk7werBBx+kiy++mIqKiqhXr17h9JKSEurevTs99thjdOaZZ7YoZ7/47LBoPNA+1qBivuBi9S4lmi6bL2ujYq9azus63WwH9FT3oR8EVU8VH5qmpxPNoCf0TPSacGrrZlkv6gvSBKuU7VkdOXKk+PvNN99EBKvLly8Xf61H+tHEWyHA+hUYi3jxfqLpsvmyNir2quW8rtMJbulmsp66aBlkPVV8aJqeTjSDnnoAPZPjQ1NI2QlWY8aMEY9sn3nmmYj0p556SmwOsNtuu7l2rHi9sYmmy+bL2qjYq5bzuk4/dTNZT120DLKeKj40TU8nmkFPPYCeyfGhKaRszyqPzbjtttvo/PPPF5OseAb/+++/T48++miLABYAAAAAAOhJygarzHnnnSe6ze+55x566KGHaODAgaJn9eSTT/a7aQAAAAAAwPRgleGeVX4lk3gDjBNNl82XtVGxVy3ndZ1+6maynrpoGWQ9VXxomp5ONIOeegA9k+NDU0jZMateEmurVSfpsvmyNir2quW8rtNP3UzWUxctg6ynig9N09OJZtBTD6BncnxoCghWXcC+5p1Kumy+rI2KvWo5r+v0UzeT9dRFyyDrqeJD0/R0ohn01APomRwfmoK5YXoC8LhX+4LA0Vue8Wee0BVNoumy+bI2Kvaq5byu0812QE91H/pBUPVU8aFpejrRDHpCz0SvCae2bpb1oj4n2OMfL5fSStlNAZK1CC4AAAAAgOnMnTuXOnfu7MmxMAxAApPXNgMAAAAAiMa+5WuyQc+qBLzfLu/Ry7tYrV69WvySsPaE//rrr2ns2LH00UcfRWw0kGi6bL6sjYq9ajmv63Ty46NHjx7i/YYNGyJ2LYOewdIy6Hqq+NBt/+uupxPNoCf0jAb3p3o81Lt3b/F+y5Yt1KFDB/ICjFmVgAPTpqYm8erUqRO1a9cunJeTkyPS+a99WYlE02XzZW1U7FXLeV1nolhaMtwGFd1M1lMHLYOup4oPTdPTiWbQE3pGg/tTDb7XGhoa2tyG3m0wDAAAAAAAAGgLglUAAAAAAKAtCFYBAAAAAIC2IFhVpFevXnTTTTeJvyrpsvmyNir2quW8rtNNoKe6r3RCdz1VfGiank40g57QM9Frwqmtm2W9qC9oYDUAydmp7du3D8+Es882BsEDeqYW0DO1gJ6pBfRMLSp9iocQrAIAAAAAAG3BMAAAAAAAAKAtCFYBAAAAAIC2IFgFAAAAAADagmAVAAAAAABoC4LVJDF37lwaPnw49e/fnyZPnkzr169P1qFAkrnrrrto2LBh4dfQoUOpW7duVFxcDN8HlHvuuYeGDBlCO+20Ex122GG0ceNGv5sEHBAKheiaa66hwYMHU79+/eiCCy6guro6+DKAsG477rgjlZaWhtOeeeYZcY/26dOHTjrpJDH7HARb09bSWwPBahLYtGkTnXDCCfTwww/TmjVraNddd6WZM2cm41DAA66++mpasWJF+DVt2jTxMnW9u6Dz9ddf03333UdLliyhn3/+WQQ5vH4hCB5PP/00ffrpp/Ttt9/SqlWrxA/I+++/3+9mgQS59dZbad9996W1a9eG03766Se68sorad68eSI9NzeXZs2aBd8GWNPW0tvC+GCVA8oddtghpnNmz54tekfz8vJEb5rsl+Crr75Ke+65J40fP158njFjBl166aUJCQP00TM60OF/IO+44w5IFFA909PTqampiaqqqqixsVH01hQWFrrccuCFlp999hkdfPDBolxWVhYddNBB9Prrr8P5Aft+3WOPPejGG2+MSHvttdfEUw/uWU1LSxO95i+//LLyOQD/NG0tvU1CBvPbb7+FBg8eHOrTp0+LvKeeeirE7rngggtCr776aujPf/5zKCMjI/SXv/ylzXovv/zy0LHHHhs66qijQkOHDg0dc8wx4lggmHraGTduXOill15ysdXADz2nTZsWysrKCnXs2DHUrVu3UFFREYQIoJb/+te/QpMmTQpVVVWFKioqQlOmTBHHAcHR0A7XsXXrVvH+wgsvDN12223hvOLi4lBeXp4LZwL80lQmPW49IQOZP39+aJdddhGOZ4dFC9XY2Bjq27dv6Pjjj49Iv/TSS0MdOnQQX4qtcfbZZ4d69OgRWrVqVaipqSl0/fXXh6ZOnZqUcwHJ19Pio48+EscBwdbz6aefDu2xxx6hzZs3h+rr60OXXXZZ6NBDD03KuZhOsrVsaGgIXX311aHhw4eH9tprL/EP6+jRo5NyLqbi1fdrdABz0UUXhe68885w3oYNG0R9ILiayqTHw8hhAN27d6dTTjmFbrvtNho9enSL/G+++YZ+/fVXOuOMMyLSDz/8cCovL6eFCxfS7bffLrYci349+OCDYvINDwEYOHCgeHxx6qmn0vfff+/hGZpFsvW0T8o599xzPTknk0m2np988gkdffTR1LlzZ8rMzBSa8vAOEDwtf/nlF6HfsmXLxJCAvn370qhRoyBlgDSMB48l53otfvvtNxo0aBC0DbCmSoQMZ/r06S1+VTz66KMi6o9+dM+PITj93nvvbbXOTz75JNSrV6/QunXrxGd+lHHSSSclofXACz2Z2traULt27ULr16+H0wOu52OPPRbab7/9wr0Dt99+e+i4445LQutBsrWcPXt2aN999xX3J3/f9uvXL7Rw4UI4PmDfr7F623766SdxLK6He/pOO+20hB9BA700lUmPR2ZyQuDgz+ZnunTpEpHeqVMn8besrKzV8mPGjKE777yTDjjgADGBY5dddqGHHnooiS0GydST4RnHvXv3ph49esDZAdeTewtWr15Nu+22m/g8YsQIMckABE9L7h368MMPxdJV1sod48aNS1p7QXK+X2PBE6u452/s2LFUU1NDU6dOpSuuuAISBFhTFRCsxqC+vj7uLGKmXbt2bTr29NNPFy+QGnpOmDCBfvzxR9fbBrzXk4fm3HLLLeIFgq0lD+N49NFHk9I24N33q0Vzh1vkD8voR9Eg2Jq2lR4PI8estoX16yF6wVrrc9euXX1pF3AG9EwtoGfqAC2DDzRMPTppGAMhWI3ByJEjw4OM7Sxfvlz8xQD+YAE9UwvomTpAy+ADDVOPkRrGQAhW44w55Rn9vNWbnaeeekrMNrXGuoFgAD1TC+iZOkDL4AMNU48xGsZAGLMag+zsbDGw+/zzzxcDjCdOnEjvv/++GBsVLR7QH+iZWkDP1AFaBh9omHpkaxgDIViNw3nnnScGAPPamjyTn9dM5V8VJ598srcKAVeAnqkF9EwdoGXwgYapx3maxUBp29e7AgAAAAAAQDswZhUAAAAAAGgLglUAAAAAAKAtCFYBAAAAAIC2IFgFAAAAAADagmAVAAAAAABoC4JVAAAAAACgLQhWAQAAAACAtiBYBQAAAAAA2oJgFQAAAAAAaAuCVQCAI5544glKS0uL+SosLKRDDz2Uvv7664gy/fv3px122AEeBwAAIA2CVQCAEnvvvTdddtll4ddFF11Ee+65J73zzju03377tQhYdYODZw6iZWhoaBDB+MSJE8kvTjvtNNGGNWvWUKpz4403inP94IMPfDn+qlWrxPHPOOMMX44PAGgmc/tfAABwxNSpU+nmm29ukf7vf/+bzj33XJoxYwa98cYb8C4AAABHoGcVAJAUzjzzTMrPz6ePPvoIHtaYpqYmqquroyBTXV3tdxMAAEkEwSoAIClkZGRQbm4uVVZWtmr322+/0cUXX0wDBgwQ9jvuuCOddNJJ9MUXX0TY8aP3jh07UmlpqRhq0KNHD8rLy6N99tmHPv744xb1fvfdd3TMMcdQ586dqX379jR27Fh65ZVXwvn8aJkf8a5bt45++eUX8T5WD7F9jG5WVpZ4/+GHHwp7TrPg9h5xxBHieNyu0aNH07/+9S8KhUJS/mpsbKR//OMftOuuu4ogn+vh+r755puwDR/zmWeeEe/ZX/bhCIn6saioiI466ijhm2XLlrXatjfffJPGjBlD7dq1o549e9Lhhx/eot7a2lq6/fbbadiwYeL4rM/pp59OP//8c4QdP1Ln81i/fj3dcMMNop1s/4c//IFef/31sB0PzZg1a5Z4v//++4eHalhjpV999VX6y1/+Qr169aLrr78+XO65554TWvM58mvUqFF0zz33tAjIV6xYQccffzz16dNHHJ/bze1nHRi+FgYPHizez54929fhCAAYTwgAABzw+OOPcxQWuummm2Lmr1mzRuSPGDEinNavX79Qnz59wp/LyspCvXv3FnaTJ08OnX/++aEpU6aE0tPTQ9nZ2aGlS5eGbSdMmBBq165daLfddgsNGDAgdPbZZ4cOO+wwUbZjx46hTZs2hW0XLlwobPl17LHHhs4555xQ//79he0NN9wgbH788cfQZZddFmrfvn2oQ4cO4v0777wT93w/++yz0KWXXirq4HNge05j5s6dG8rJyRF1nXTSSaFzzz03NHjwYGE7ffp0KX9edNFFwn6fffYRfuBzy8jIEG1bvXq1sOFjDhs2TNideeaZofvvv9+RH/Pz80U9Q4YMEb757bff4rbr4YcfFvVy/exz9mdWVpY436+++krYNDQ0iOOy3ejRo0MXXHBB6IgjjhB2hYWFYTuG/cF2++23X6h79+7i84knnijOle2XL18u7GbOnBnaa6+9hC0fkz/br7vdd99d+Pu4444LvfTSSyLvr3/9q8jbYYcdQtOmTQudccYZ4j2nnXbaaeE2rFq1Svi1oKBApLPdoEGDhN0ll1wibPha4HROGz58uPA9XzMAAO9BsAoAcDVYra6uDn3yySehPffcU+RzABEvWH3mmWeEzb333htRB3/m9Ntvvz0iyOK0cePGhaqqqsLp5513nkjnupi6ujpxnC5duoR++OGHiHaNHz8+lJaWFlq2bFk4ndvD9jLU19eLY3FbLGpqakK9evUSgc/KlSvD6dyOI488Uti/9tprrdZbUVERyszMDI0ZMyYi/Y033hDlr7vuunDaqaeeKtKsANapHzm45La3xrp160K5ubmhoUOHhrZu3RpO/+9//yvqOP3008Xnv//97+IzB6l2PvroIxGAjho1qkWwynXaf2BwGzl91qxZ4TT+YcFpCxYsaHHd8Q+R7777LuJ4/AOBtSwtLQ2nlZeXi4CVg2sL9ifXwedhUVtbK34osA6sB8PBaSI/OAAAyQHDAAAASsycOTNi2Sp+BL7vvvvS559/TgcffLBYISAeAwcOpFtvvZWmT58ekc6PwplYQwjuuusucQyLQw45RPzlR/nMnDlzxPsrrrgi/BiX4Ue9PLucf6TbHzerwscrLi6ms88+m4YMGRJO5yED1mNsfjTdGnyevNLA1q1bqaqqKuLceFWFk08+udXyTvzI9jk5Oa3Wy+2uqamha6+9VjxSt5gyZQodffTRVF9fLz4/8sgjlJ2dTbfccktEeV4N4sgjj6SvvvpKPHa3w4/Zu3TpEnGudh3bYtq0abTzzjtHpPGEvr/97W9i6TQLHubAQyN4mAL7mOGhJMzatWvDdtx+HmLAQx7S0/FPIwA6gdUAAADKS1fxuFELDlg7dOggxjjySgH8ubWy/OIg7b333qPVq1eLJZlefvnluGWiAxQrMLGCvMWLF4u/n3zyCV1++eURtlbQtnLlylaXK+Kxo3b4PPgVi//973/iLwfm0YwcOVKM87TGhD777LNhewteLaF79+5iXVoOfIcOHUrHHnssTZgwQYzVjHdcVT/usssubda7ZMkS8TfWUl3W+F/2+/fffy/07tatWws7XsbspZdeEj7gcaGyOrZFrPb/3//9n/gx8u2334rgmH3AbYue5MfLfz366KMiuOW2cfDN57jbbrsJ/wMA9ALBKgAgKUtXycA9XBdeeCH95z//EbPSeWLRiBEjaI899qAffvghZhnuIY2FNZFp8+bN4u/bb78d97jl5eVx87i3jXvn7HCvYryg0Tpe7969Y+ZzLzD3TjJz584Vk3XscEDNk6k4+Hv44Yfpqaeeor///e+iDZmZmWIyE6fHCgRV/Mg9iW1hnRsH0/HgAJl9H+/8OVhnLB/I6tgWsdr/2muviUlmPHmMfyTx5C0OornnmX+EWFg9/zzx6q233hK910y/fv1E7zD32gIA9AHPOgAAvsFDBJ5//nnRI8a9YBUVFaLn8ZxzznFcJwdqVuCyfVx+ixf3psWDe9ii7VsLxq0hCVZgZ4cfPW/ZsiUcaPJj5ui6rVnuHHz98Y9/FOdfUlIiAk9+NM6z3tsKnpLhR8YaJrBt27YWeTy7ns+vtfNnOHBkWgu23YB7Uk888UQRHL///vuiF52HFLBfYgXSPESCfziwr3llg9tuu00sgcWrFXz66adJbSsAIDEQrAIAfIMfe/M4T16CiHu1LPgxtlOscZqxds7iR8L8CJjHJbqF9Th70aJFLfK4J5UD0r322qvVOnjoAi8j9dlnn4nPPJaTl1XisbXcQ8pLZXntR/u5RQ9d4OWdOADkMancK8zLR3355Zcxx8b+97//FcuY8VJeyWTevHkigOae0UmTJoWDaPZ/9DhY7s3mMc0Mj0/ltvEyWvfff7+wj6UlAMA/EKwCAHyDexP5MbL9sTyvy2lNTHKCtXboP//5z4gtScvKyuiSSy4Rk4Z22mmncDoHK9bEm7awJt7Y7XmiEQdGHOjwmq0W3OvIk8G4DG+Q0BoFBQUiMOXH0va6eS1S7pm0P4aP1YZk+JHhiV38OJ3Pw5pMxdx7772iJ5XH2TKnnHKK8O8dd9zRIojmIJZ9ZJ9MJUusc21rWMCvv/4aTuPAkyeSRQer7FMelxy9VixPBGMsfydyfABAEknSKgMAAMPXWY1F9NJV1157raiD17jktUEPPfRQsS4oL4nE6QMHDhTHsS+5xMtH2eFljezrpzJPPfWUWGO0U6dOYh1OXu6Jl5eK1V5eLspah/Ptt99u8xy4Hl6OiddSXbx4sUh76KGHwuu9cj2ct9NOO7VYdqo1jjrqKGHP65/y+p5cT+fOnUXao48+2mLZpYkTJ4olo9zyYzyuuuqq8FJTXPeBBx4oPvO6tdZyVrzOq7X+K/vzwgsvFOus8jJQPXv2FGvuRi9dFb1mKS/FxemsVfQar7ym6s033xxx3T3yyCMR5YuKioTevF7r0UcfHTrrrLOEP3r06CHWnOUyvAZuSUlJ6IsvvhB2vJwVr2fL5zV27NjweVpLo/ESVqw1L0vG6+Dal0IDAHgHglUAgG/BKq9FeuONN4Z23HFHsW7m3nvvHXruuedEHgcHHExceeWVCQerzPvvvx/eSIAXpudF6F944YUWbXr33XfF8Tmw+n//7/9JnTcvZs/BoLUYPfP666+H9t13X3E8XnSfF7R/7LHHpH3D64HywvMcBHKAxPXwup/RbebAjxfeZxsOytzyYzyamppCf/vb30QQx/VwEH7xxReHNm7cGGHHa6ZyOuvLbeNNBHjjgl9++SXCLpFglYNhDsr5uNZarfGCVWbJkiViLV32P/+o4DV4i4uLQ99++63wDQedvHYsw2ussi84jbXndvMGCRs2bIiok9d95SA4Ly8vvAkEAMBb0vh/yey5BQAAAAAAwCkYswoAAAAAALQFwSoAAAAAANAWBKsAAAAAAEBbEKwCAAAAAABtQbAKAAAAAAC0BcEqAAAAAADQFgSrAAAAAABAWxCsAgAAAAAAbUGwCgAAAAAAtAXBKgAAAAAA0BYEqwAAAAAAQFsQrAIAAAAAAG1BsAoAAAAAAEhX/j+1w9bGCuXmRQAAAABJRU5ErkJggg==", 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" ] @@ -998,8 +874,7 @@ " params = imaging_params.copy()\n", " params['Fp/Fs'] = contrast\n", " # make the parameters be the shape that the code desires \n", - " parsed_parameters= parse_input.parse_parameters(params)\n", - " texp, validation_output = calculate_texp(parsed_parameters)\n", + " texp, validation_output = calculate_texp(params)\n", " exposure_times.append(texp.to(u.hr).value)\n", "\n", "\n", @@ -1028,96 +903,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3.2 Telescope Diameter\n", - "\n", - "Let's explore the impact of telescope diameter on exposure time:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "execution": { - "iopub.execute_input": "2026-05-19T21:31:18.214149Z", - "iopub.status.busy": "2026-05-19T21:31:18.214046Z", - "iopub.status.idle": "2026-05-19T21:31:28.075466Z", - "shell.execute_reply": "2026-05-19T21:31:28.075156Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "\n", - "contrasts=np.logspace(-9, -11, 10)\n", - "\n", - "for idx, diam in enumerate([6,8,10,12]):\n", - " exposure_times=[]\n", - " for contrast in np.logspace(-9, -11, 10):\n", - " params = imaging_params.copy()\n", - " params['Fp/Fs']= contrast\n", - " params['diameter'] = diam\n", - " parsed_parameters= parse_input.parse_parameters(params)\n", - " texp, validation_output = calculate_texp(parsed_parameters)\n", - "\n", - " exposure_times.append(texp.to(u.hr).value)\n", - "\n", - " plt.loglog(contrasts, exposure_times, marker='o', label=f'{diam} m',\n", - " markersize=7, linewidth=2.5, color=colors[idx],\n", - " markeredgewidth=1, markeredgecolor='black', alpha=0.9)\n", - "\n", - "plt.xlabel('Planet-to-star contrast', fontsize=14, fontweight='bold')\n", - "plt.ylabel('Exposure time (hours)', fontsize=14, fontweight='bold')\n", - "plt.tick_params(axis='both', which='major', labelsize=12, width=1.5, length=6)\n", - "plt.tick_params(axis='both', which='minor', width=1, length=3)\n", - "plt.xlim(1e-9,1e-11)\n", - "plt.grid(True, which='both', alpha=0.3, linestyle='--', linewidth=0.8)\n", - "plt.legend(title='Diameter', fontsize=11, title_fontsize=12, frameon=True,\n", - " fancybox=True, shadow=True, loc='best')\n", - "\n", - "ax = plt.gca()\n", - "for spine in ax.spines.values():\n", - " spine.set_visible(True)\n", - " spine.set_linewidth(1.5)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.3 Distance and contrast\n", + "### 2.2 Distance and contrast\n", "Let's change distance from the star and planet contrast. " ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:28.076937Z", - "iopub.status.busy": "2026-05-19T21:31:28.076827Z", - "iopub.status.idle": "2026-05-19T21:31:37.893925Z", - "shell.execute_reply": "2026-05-19T21:31:37.893631Z" + "iopub.execute_input": "2026-07-13T17:35:21.043806Z", + "iopub.status.busy": "2026-07-13T17:35:21.043707Z", + "iopub.status.idle": "2026-07-13T17:35:30.836248Z", + "shell.execute_reply": "2026-07-13T17:35:30.835960Z" } }, "outputs": [ { "data": { - "image/png": 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", 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K0IEfwV+TMkMJ+XId80kTjjmbOeqBoiLQi0J60SMhIREBLWJ55ZPOK7e2uxzKrrm13eWAL0hubXpPyPVd0Nza7vKeS5lb29N80kI/C6ML6qe7vOeOunCXAz4/XbjLAU/XFUQX1Hd3ec8ddeEuB7y73Np56YL0SH0rqC6EsaHPu9OFuxzwgi5IFsexdacL6gvJ76oLktHdvHA3T/PShWMfqYzkcZ2nrrqg/lGfXHVBuLtnC6oLd/NCWMuF9t3pwvWeLYwuhHnqOi/omoLes+504TgvHHXh7p511EVe6xfJQ/IVdv26ly7c3bOuupB6/SL5CKXXcsexUftanoUsDP/mHdxMsmfCrFaqKiZ1mYCczBwkZSbdpQtvRVjgPqtudlYdQzbRhKGDSAT3WdWOz6rackarTR4xaKkvWtGvmDGRYjylnBNyzi+l567a7hW1ySMWrfVHShLTE/H2dyNxKf6SraxidEXMf34uIoLz1pm3fFb5zqoLpUuXZi/HgeFoD6VuMF+VRwxa6otW9CtmTKQYTynnhJzzS+m5q7Z7RW3yiEVr/ZGKlMwUjFo91slQLRtZFnN6zMzXUPUm3GeVUyShRzJqQm3yiEFLfdGKfsWMiRTjKeWckHN+KT131XavqE0esWitP1KQlpWGMWvG4VzcOVtZyfCSmPvcbESHRkOtcGOVUyRRm4+x2uQRg5b6ohX9ihkTKcZTyjkh5/xSeu6q7V5Rmzxi0Vp/xJKRnYFxayfi5M1TtjIyUMlQLRFeHGqGG6scDofD4XA4GibblI2JP03G0av2dKmRwZF477nZKBNpd31UK9xY5XA4HA6Hw9EoOeYcTF43DQcuHbCVhQWGYU6PWShfrDx8Ae7QwbknriFlBChkBoXO8EWEEDhqQW3yiEFLfdGKfsWMiRTjKeWckHN+KT131XavqE0esWitP55gMpsw8+fZ2HV+l60s2D8Es7vPRJXileEr8J1Vzj3ZvXs3iwXn+qpevbrPao9i0akJtckjBi31RSv6FTMmUoynlHNCzvml9NxV272iNnnEorX+FBaKqTp303xsO73dVkaB/mc+Ow3VS1WDL8F3VlUMxXjdsGEDNm3ahISkJERFRKBTp07o2rWrot8Yz507h6ioKKxfv14z31rVFn9PbfKIQUt9USM8ziqPs6qmuaVmtNafwj4RXfD7Qmw5vsVW5mfwx9SnJ6NO2drwNbixqlI2b96MAUOG4kZcHKyVYoDwCODCFXz30zqUnjARSxZ+gPbt2ytmrNIuaqtWrRRpj8PhcDgcjueG6uI/P8Ivh3+1lRn1Rkx+aiIaVmzgk2rlbgAqNVR7vfIKbhQvjeDRUxExeDQier/FftLfVN7z5ZfZ55QyVqtVy31k4JgSLj8uXrzI0th99tlnGDhwIKKjo9k33L59+zqlpSNWrFiB2rVrs0c2lStXxoQJE5zS3nE4HA6HwymYofrZthX4af86W5leZ8CEJ8ahaZUm8FW4saoyyEijHdXMarUR1qc/jCVKOb1Pf1M5vU+fU8KoI2P10qVLqFGjBssxTP6qr732mlPu4bwYOXIkEhIS8Mknn2DcuHH47rvvnNKeLly4EH369EGHDh3w7bff4qWXXsKsWbPYdXIi5I1WC2qTRwxa6otW9CtmTKQYTynnhJzzS+m5q7Z7RW3yiEVr/SkIK3d8i+92r3Io0WHM4yPxYLWW8GW4G4CMJGfn4GSC8y7ivdiyYT2u3bqFwFcHwgQdeUi7+ZQO/l264drsd/H+19/ikS5dC9VGzahwhPv7FcpYTU1NxbRp01CrVi38+++/mD17No4dO4a///4bBoMhz2tpx3TlypW2v8PDw/HWW2+xnMIPPPAAxo8fj1dffRULFixg7z/99NOIi4vD119/jQ8++AByobYoBmqTRwxa6otW9CtmTKQYTynnhJzzS+m5q7Z7RW3yiEVr/bkXq/d8j8//+cKp7J1Ow/Fwrbbwdbix6sKNGzfYS0DMziUZqk/9Zj+FVxASv/oGGeUrIyuiGJCVnfcHI4rBUq4ipny5Egv9IgvVxk8dW6NpyYKnVZs7dy4zLMlQJchXtly5cmx3lQ6AOe6UuuL6Hv1NxuquXbuQnZ3NMow899xzTp+ZP38+3n33XcgJGd+hoaFQC2qTRwxa6otW9CtmTKQYTynnhJzzS+m5q7Z7RW3yiEVr/cmPdQfW4+OtnziVDXp0ADrV6wAtwI1VF5YtW4bJkyc7xRJt2LAh+50ee5OBRdCjcJPJhIyMDEnTulkz0nMPUxWEiMjcz8vMCy+8cFfZM888w4zVo0eP5muslilTxulviipApKenIzY2lv1evrxzUOKgoCD2kgLysaUXhfAwm81s8SIobiw9IqKIBo7uDFRG38ZpPOkagnaOacGja2jMCfLHpV3izMxMpy80VEbtCfOCCAkJsbXp2EdqS/DfpfcojzX57VIZ+R0RVEbXk9wkvzAnaf6RDh19iMknmGQhmQRIbuoHfVaA+kd1CLogqF1XXZDLB8lZUF1Qn+n+oPdI1sLogvrpeA+RLK66EMamoLpwHRv6HF1XEF1Q3x19q93pQhib/HSR37wgPbrGMM5PF8LY0OeFGMeOuhDGxp0uqF3hc3npgvpC8rvqgvrmbl64m6d56YI+K7RPZaQb13nqqgvqH/XJVRdUj7t7tqC6cDcvhLVcmLvudOF6zxZGF8I8dZ0XdE1B71l3unCcF466cHfPOuoir/WL5CH5Crt+3UsX7u5ZV11IvX6RfNSm0mu549gotZav27sBH25dYuuPTge83OxFtK3ShunE07XcnS4c9aEk3Fh14c0338QTTzxh+5smzKBBg2wTyNGIoglAr8IcPLoXuqBgICmxYB9OSoQuMtf4k4uzZ89i586dzCB1/IYqTGSaxPnhepjq+vXr7GeJEiVQsmRJ9jv5tDpy4cIFHD9+HI8//rho+WkRoRfd0HRzOoYxEUJvuQttQguIK8JC5QgtSK6x/BznhSPu2nEsE+pxp1N3uwPuHnFRn1xDirn2u6DyFFYXjl8yhHo81YWAGF24GxuaB0rrQkCsLhzLhP4WVBfUrmudBdUF/cMs6NjkpQtaL1zvPXeh7woiD63J7j4nZl44jo1j3WLmaUHmBRkSSt2zBdGFYNgocc+6u0ek1gX1xxtruYASa/mfJ/7Ckm1LodfrbGW9H3wFL7bsJcv65U4fSsCNVRdKly7NXgJivkWQbyg9ci8MW7ITMHXUCAQm3YHB5XCVI+ZbN5F59RImDh6IRwrZBslVUGj3kw49LV++nPmWCnz55Zfs56OPPprv9eQmQF8ABAT/VQqDVapUKbY4rFu3Dg8++KDtM0OGDMHly5clMVbzIj8/W2+gNnnEoKW+aEW/YsZEivGUck7IOb+Unrtqu1fUJo9YtNYfV/4+/Q9mbZxLe+K2sp7NnsMLLXpCa3BjVUboEFNhfEOJ+1/qheXz5uLGzz+wU/86/d03m9ViRsbPP6BsieIY9mJPWYPzN2vWjBmW/fv3x82bN1GzZk220zpv3jx2ip8OUOXHli1bWLiqLl26YO/evZgzZw5zIahbty57/+2338bMmTPZLk+TJk3w+++/MwP3q6++gpyozY9JbfKIQUt90Yp+xYyJFOMp5ZyQc34pPXfVdq+oTR6xaK0/juw+vwfTNsyAxWp373n6gafQp/Wr7HG+1uChq1QGGZ4U8D/wzHGkLF8C062bTu+bYm+wcnqfPid3Finys6GdzzfeeAMfffQRnn/+eaxduxYTJ05k/r33gvx/yUfn5ZdfZvFUBw8e7GSITpkyhdVFp/9pB3fHjh344osv8OKLL8raL0e/HzWgNnnEoKW+aEW/YsZEivGUck7IOb+Unrtqu1fUJo9YtNYfgQOXDuLdn6bAZMn1NSUer/8Y+j/ST5OGKsF3VlUInbb/5osvcjNYzZ4Aa4UYcoghBxzoLp9D6fuKY8mXXyqWwapYsWIsjJQnoaTo2lWrHGO+OUM7qnTyX+7T/65I5WOsVXnEoKW+aEW/YsZEivGUck7IOb+Unrtqu1fUJo9YtNYf4ti145jw47vIMdsPQD1aux2GtB+kWUOV4MaqSqEg+UcPHsDPP/+MX3/9FQlJSYiqUh6dx45gj9Tl3lHlcDgcDoejHk7fPIMx349HZo49SsBD1VuzWKq08aNluLGqYsgg7datG3txOBwOh8MpmpyPu4BRa8YgPdseNqt5TDOM6TIKRoP2TTnt95DjFSpVqmSLL6dG3IUZ8SZqk0cMWuqLVvQrZkykGE8p54Sc80vpuau2e0Vt8ohFK/25cucKRq4ejZRMe5zdhhUbYuIT4+FnKBopZbW9b8zh5IFjsGU1oDZ5xKClvmhFv2LGRIrxlHJOyDm/lJ67artX1CaPWLTQn+uJN/DOqlFITLfHX69bri6mPPUu/I3eiXnqDbixyimSiEmjWxTkEYOW+qIV/YoZEynGU8o5Ief8Unruqu1eUZs8Rb0/t5LjMGLVKMSnxtvKapaqgenPTEGQvzRZHn0FbqxyOBwOh8PhqAgyUMlQjU3OTUtOxBSPwczu0xEScHfWKa3DjVUOh8PhcDgclUCP/EeuHoNriddsZRWjK2J2jxkIC7w7ZWpRgBurnCKJuxzJ3kRt8ohBS33Rin7FjIkU4ynlnJBzfik9d9V2r6hNnqLYHzpENWr1WFyKv2QrKxNZBnN6zERkcCSKKtxY5RRJzGZ7ijo1oDZ5xKClvmhFv2LGRIrxlHJOyDm/lJ67artX1CZPUetPelY6i6N6Lu6craxEeEnMfW42okMLl7pda3BjlVMkUVsaPrXJIwYt9UWN8HSrPN2qmuaWmvGl/lCg/3FrJ+LkjZO2smIh0ZjbYxZKhpdAUYcbqxwOh8PhcDheItuUjYk/TsaRq0dsZRFBEXjvudkoG1WGjws3VtUfduP777/Ha6+9hu7dnmU/6W9vhuNYtmwZypUr5/a9nTt3onXr1ggJCUHp0qUxePBgpKXZs21wOBwOh8Oxk2POwZT107D/0n5bWWhAKHv0XyG6PFfVf/AMVi7cuHGDvQS8ZRhu3rwZQwcORvyNWNT0i0a0PhBnLJnYsOoHTCxdEh8sXoT27dsrKtPVq1cxb948t++dPHmSydO0aVN88cUXiIuLw6RJk3D27Fn88ssvUBtkUKsJtckjBi31RSv6FTMmUoynlHNCzvml9NxV272iNnm03h+T2YSZG+dg57ldtrJg/xDM7jETVYpXhtrIysrCjz/+6JW2ubHqZudw8uTJtr/1ej0aNmzIfk9KSkJ2drbtlKHJZEJGRgb7OyXFngZNCkO1d6+X8ACiMa1cF5QNsKeMu5aVhOVx+/BKzxfx+TdfoUOHDpCbv/76C0OGDMHx48eZw3rZsmXv+sz06dNRrFgxbNy4EYGBgaysVKlSeOaZZ7Bt2zY89NBD8AY5OTnsZbFYmOypqamsnH6nhSwgIICNq4Cfnx+Cg4PZeNI1hMFgQGhoKNslpjEndDodwsPDWYYUxy80VEbtCfPCccF03GUOCgpibSUnJ9vkoXZJd1QmpKo1Go3sepJbOCxAc5LmH/ljUVuOqQVJFsesLSQ39cPRd4vaoToEXRDUrqsu/P39mZwF1QX1me4PkpM+VxhdUD8d7yGSxVUXwtgUVBeuY0Ofo+sKogvquzA2eelCGJv8dJHfvCA9Ut8KqgthbKgP9LerLoSxcacLksWxHXe6oL6Q/K66oHrdzQt38zQvXdCL6hF0QfK4zlNXXVD/6BpXXdA8cHfPFlQX7uaFsJYLc9edLlzv2cLoQpinrvOCPlfQe9adLhznhaMu3N2zjrrIa/2ia0m+wq5f99KFu3vWVRdSr18kH5UpvZY7jk1+urBYLfhw60fYdmYb+wx9LMDoj7GdRqFGqeqqWMsFqIw2nUa8PQSJd26hTIXqUBqdVc0J3FWyszpo0CD2+/bt29mAu+P27dtYu3YtM87uu+8+j9un9hrWux9V7ugxtmxbGHR3uxWbrRbMuLYV54tZcODIITYx5eTYsWPYsGED+33NmjWIjY1lu6wCNPmpz+SmMGfOHFs53VRRUVEYOnQoZs+e7bbuSpUqoVOnTqhduzbmz5/P6m7VqhUWLVqEmjVr2j63Y8cOjBo1Cnv27GGLGu3izpo1y63h7DgeNF5UZ9WqVfHUU0/Z3qcbWU15o9Umjxi01Bet6FfMmEgxnlLOCTnnl9JzV233itrk0Wp/yOxa8PsibDy00VbmZ/DD9G5T8UDF3M0xNbF582b0eeUFtKtjwLCnyuG1z3FPm0hq+M6qC+RrSS8Bx29VhcWUko6Ms3bDtyD89MevuH3tBqaU6QKdyQoL7g69oQPwSlQDDLj2C9Ys+hRPtutUqDaCqpaGMSy4wJ+vU6cOewmP+8n4c+TixYtISEhA/fr1ncrpW1/lypVx6tSpfOsnAzgmJgYzZ85k+p4xYwbatm2LI0eOoHjx4ti9ezcefvhh9vr6669Z+1OmTGHvHzx4sFB953A4HA7Hm4bqR38tczJUjXojJj05UZWGalZWFoYPHcgM1aX9Y5DNTBLl3SO5sSojZKgef/X9Ql2z5sqfqGEJR8l0PUzp9q19V0rBgOqWMKyeuRjVvj5RqDZqrxiGsIYxkAraxSSio++OA0c7q46PI9xBj1p+++039lmiRYsWbKd16dKlmDBhAsaMGYOKFSuy3V16lCK09fzzz+Pw4cN3GckcDofD4aiRFX9/gbX77H6fep0B47qOQbOYplAjGzZswJ24mxgzpDoMBh35IXlFDh66SmWkmbNRTF+wbfVofRD7vLdx9AlzhfxpaIc1P9q0aWMzVIlatWqhRo0a2LVrF/tWRz6v5F4hGKrEs88+y9w1HF0FCoPgV6sW1CaPGLTUF63oV8yYSDGeUs4JOeeX0nNXbfeK2uTRWn9W7vgG3+z81qFEh1GPvYPW1VtBrWzatAmNqwagSinSpQXIlu58TmHgxqrKCDH4446lYK4H8ZYM9nlvIxiaiYmJd71HZffy4S1TpozbOsnnNT4+njl/ly/vHMKDnMPpABc5jnuCp9fJhdrkEYOW+qIV/YoZEynGU8o5Ief8Unruqu1eUZs8WurPmj0/sF1VR97uNAztaj8CNZOclICSEXQ40pprqFpyD2MpDXcDkBHyDaVH7oWh+x818fakcYgNtqCsf3ien7uanYTTiSmYP2Y0anvgsyol5G9K32APHTqEnj172srpNOH58+fx+uuv53u94ylKgevXrzN3ADJ0yTAln1jXa2jHlT7jzv3gXtD1anK8V5s8YtBSX7SiXzFjIsV4Sjkn5JxfSs9dtd0rapNHK/1Zf2ADlm392KlsYLsB6FyvI9ROeEQUYq+YgexkwJL3U1S54caqnMoNCy60b2iP2q9j5icf4vM7B/KNBvBF7EHcV7YUug96TfZoAPeC2u/YsSM7KEUHn4Rvs/Q3Pcbv2rVrvtf/+eefbBdVcBf4+++/cfnyZYwYMYLVRaHD1q9fj3Hjxtmu+fTTT9n7t27dkrl3HA6Hw+F4xm9HN2PhHx86lb3R5jU89cATPqHSjh0exZAfv8b5G2GoUtJ7O9XcDUBlkOFHAf/3I56Fp6K4qo5czUpi5fQ+fc7bhqoAGanXrl3DE088gR9++AHvvfce+vXrx3ZVaec1P+gAFoWvWrVqFRYvXsz8U+lAVe/evdn7FB2AQlaRn+rq1atZ1AA6ePXqq696tKvK4XA4HI7cbD35P7y3yfmQ9cstX0KPpt19QvlWSw4eK7MPUSF6zFwbD7PFSsFYvSIL31lVIRRDlAL+Uwart67+jJp+xdhhKvJRPZlzB9GlS+KLxV8rnsEqP+hEPsVio1ioL774IjMiKd2qY4KFvCDjtEqVKiyeLbkOtGvXjmXKooDFBPWTDFmq66WXXmKxVYcNG4bx48drwpdJjfKIQUt9USPcZ5X7rKppbqkZb/bnnzP/YubG2bBac4PwE8817YGXWr4AX8BqMcG8fyr84ndgbt8qeGPhafRbFothz1T0ijw8KcA9oLiflO9eqaQAjtAj9J9//hm//vorkhISEREVic6dO6NLly6q2VEVCyUFoCQAFD9VSu6VFIDD4XA4HDnYc2EvJqx9FyaHw0hPNXwSA9q9xbJEqR2rxQzzwRmwXPnNVvb7oVSMWpmAhIRElK5QjZXxpAAcBhmk3bp1Yy+O9I73lEJOLahNHjFoqS9a0a+YMZFiPKWcE3LOL6XnrtruFbXJ44v9OXj5ECb+ONnJUO1crxP6P9LPNwxVqwXmw3OdDFXo/dD5rU/w6Kj6bBOInnwqDfdZ5RRJ1JZlWG3yiEFLfdGKfsWMiRTjKeWckHN+KT131XavqE0eX+vP8esnMH7tROQ4xD9/pNYjGNphMIs57gv6Mh9ZAMuln+2FeiOMTadDX7wJ20Dz1hNK7rPK8SqUqpXD4XA4HF/mTOwZjPl+HDJzMm1lFOx/ZOe3YdBTnFL1G6qWYx/CcmGtvVBvgLHxVOhLtvSmaLmieFsADscbOGbDUgNqk0cMWuqLVvQrZkykGE8p54Sc80vpuau2e0Vt8vhKfy7EXcDI1WORlpVmK2tapSnGdhkNo8HoG4bqiWUwn1tlL9TpYHxgIvSlc8/seBturHKKJCEhIVATapNHDFrqi1b0K2ZMpBhPKeeEnPNL6bmrtntFbfL4Qn+u3rmKkavHICXTntymYYUGmPTkBPgZ/OALWE6vgPmMwyFnHWBsOB76su2gFtRv8nM4MpCWlqaqhVlt8ohBS33Rin7FjIkU4ynlnJBzfik9d9V2r6hNHjX1h6LzbNiwAZs2bUJCYgKiIqPQrHUzbE7agoQse4bFumXrYMrTk+Bv9I0wYOYzX8N8crlTmeH+0dCXV1d2LW6scookJpN38hv7ijxi0FJftKJfMWMixXhKOSfknF9Kz1213Stqk0ct/aEY4gOHDELsrZsIKhMMY4gROSdz8NXqr2AM9kP1rjURXT0aNUrVwPRuUxHk7z7Epdown1sF8/GlTmWG+sNhqNgFaoMbqxwOh8PhcDh5GKovvPwC/MsHoO6T9REUHQyLxYKkzGREx5dA7LbrOL76CNq93h4zB05HSIBv7EybL6yF+egipzJD3YEwVH4GaoT7rHKKJGqLd6c2ecSgpb5oRb9ixkSK8ZRyTsg5v5Seu2q7V9Qmj7f7Q4/+aUeVDNWaz9bONVStViRnpjCDNSAqAOW7VkJYlQic3nASAXofefR/+WeYD893KjPUfhOGmOehVrixypGM9PR0pKam3vVSY+w+tQW+Vps8YtBSX7SiXzFjIsV4Sjkn5JxfSs9dtd0rapPH2/0hH1V69F+pXWXo9LpcQzUjGWaL2fYZg8GAau2rIe52HMs4qXYsV3+D+eAspzJDjVdhqPYS1Aw3VlUMfav7/vvv8dprr6FH927sJ/1N5d5i2bJlKFeu3F3lZJBSmtmwsLC7XufOnYMa0+iqCbXJIwYt9UUr+hUzJlKMp5RzQs75pfTcVdu9ojZ5xCK2P3SYinxUhR3VFBdDVa/TIzwoHCHFQ9nnKDW6mrFc+xOm/dMAh/0jQ7UXoK/RB2qH+6yq2E9m+NCBuBN3E42rBqBkhAGxV80YvG41Jk0shfc/WIz27dsrKtPVq1fzTLN248YNtjB8/fXXqFixotN77oxbb5OdnY2gIPU4watNHjFoqS9a0a+YMZFiPKWcE3LOL6XnrtruFbXJ4+3+0Kl/OkxFmzEpGSkwORiq5GJAhqrhv8xU9LnExESoFcuN7TDtn0w7S7YyfUx36Gv5RhpYbqyq1FDt88oLaFfHgDFDqqNKqUDbe+dvZmLmmqt49eVeWP7FSnTo0EF2ef766y8MGTIEx48fh9lsRtmyZe/6DO2e0oR/5plnNLXYcTgcDqdoQuGpTKdymI+qyWKPLED/6yIcDFXClGZCZGQk1IgldgdMeycAjrvClZ+Coc5gnzBUCe4GoDLoET/tqJKhurR/jJOhStDfVE7v0+eUcAkoUaIEevXqhWnTpuGBBx5w+xkyVsmIJUOVDFpyPi8IlSpVQr9+/bBw4UL2O11PO8YnT550+tyOHTvw0EMPsfdLlSqFl156CdeuXZOkfxwOh8PhuPLIo48g8XIS0m6nudlRtadQTY9PR8b1dHTu3Fl1SrTE7YVp91jAwdjWV3gchnrDfcZQJfjOqoxYc1JhTS6cv+a69b/jTtx1jBkUA4POBLix+Qw6YHS3kmg38RzWf/shunV9tFBt6MJjoPMLLfDn69Spw14EGZGxsbFujVV/f388/PDD2LZtG/R6PR555BEsWLAAtWrVyrf+NWvWICYmBjNnzmSuBDNmzEDbtm1x5MgRFC9eHLt372b10ovcDKj9KVOmsPcPHjwILRwkUJs8YtBSX9QIP2DFD1ipaW6pGTH9yTZlY5/lIAxBBhaeik796w16hAeGw+hgqFotVlzacgElS5RCly7qik9quX0Qpl0jAUuOrUxfrgMM94+ETudbe5XcWHXje0kvATE7l2Somv4eUKhrNn1zBo0r6VClWBaQnXfbMcWARpV0+HXlHDwZ9WOh2jC2Wgxd9P2QEjJWz58/jyeeeAJjx47FhQsX2E5s69atsX//flSoUCHPa3NycvDbb78hKiqK/d2iRQvUrl0bS5cuxYQJEzBmzBjmB0snM4Vcz9HR0Xj++edx+PBh1K9f3yNfpoCAAKgFtckjBi31RSv6FTMmUoynlHNCzvml9NxV272iNnm81Z8cUw4mrZuKQ9cOsYD/FEf1ys+XULV9dRhDnHdUyVDNvpKFz79aoSrdWe4chWnXCMCcbSvTl3kYhoZjoXMwtn0Fbqy6Oe0+efJk29+0Q9iwYUP2e1JSEpv8BJ1yp+wYwmnDlJQUSQYkOd2MkpEFm0ilIg1ISLf7oHiTPn364I033mA7ogL02J4Myfnz57Md1rxo06aNzVAlaCe2Ro0a2LVrF/uyQDu177zzjs1QJZ599ln2paJYsWL5ykWGML3ILYHcEyiUlpCGjwxeWlxoXAX8/PwQHBzMxlNwZaDQJKGhoewaISMKexQUHo7MzEynLzRURu05nkIV0v3R9QLkzkBtJScn296jvgQGBrIyIdwX9ZmuJ7lJfmFO0vyjUGHUlkBERASThWQSILmpH/RZAeof1SHogqB2XXVBO+UkZ0F1QX2m+0NIcVgYXVA/He8hksVVF8LYFFQXrmNDn6PrCqIL6rswNnnpQhib/HSR37wgPVLfCqoLYWzo8+Qb56oLYWzc6YL6Qn3ITxfUF5LfVRcks+OcEnThbp7mpYuEhATb2FMZ6cZ1nrrqgvpHfXLVhescdzcv8tOFu3khrOXC3HWnC9d7tjC6EOap67wQrinIPetOF47zwlEX7u5ZR13ktX6RPPReYdeve+nC3T3rqgup1y+Sj/pLbRRmLTeZTZjz+zzsu7yPnUWKqloMDXo+gJu/X8eJz44iqHQwjKEGmFJN7NF/qRKlsOLL5WjatKmtfXfrl5JruV/GBWT/Oww6kzBmOhhKt0ZG9WGwpKQVeC13t355K2IEN1ZdePPNN9nuoABNmEGDBtkmkOPhIbo5hX8AjhNNDOHBBsQmFmw392aiGcUj1fFN7tFH73ZFqFmzJjM8jx49mu+1ZcqUuauMjFe6KePj49mNVL58eaf36UYj39V7QQsWveiGpmtoDAWEb8GOZQK0gLjiLsc0LQz0csRxXjjirh3HMqEed4+uaFFxhRYqV6hPrt/uXftdUHkKqwu6N4T7Q6jHU10IiNGFu7GheaC0LgTE6sKxTOhvQXVB7brWWVBd0D/Mgo5NXrqgf3yu9567XaiCyENrsrvPiZkXjmPjWLeYeVqQeUHGjVL3bEF0QfIodc+6u0ek1gX1pzBrucHPgBk/z2KGKkEuncH+wVg0YQGqfhDD4qhSeCo69U9fGMlHlR7957Wj6o213Jp0Fjn/DoXenAH896hfX7I5DI2nIMzgL3r9cjc3lIAbqy6ULl2avQTEfIsg31B65F4YOt35HUNHTsL5OwF3Ha5y5NzNTOy7aMUHc0fC2KrwPqtSQt/i161bxx7fk++pI/RP6l5+Q47fzgWuX7/O6qPYrXSD0s6M6zW040qfoR1SDofD4XA8hXZUZ/8yF9tP/20rCzAGYHq3qahTtjb7u1u3buylVqwpF2D6dwiQY99p1RdvBEOT6dC5MVR9CW6syggdYiqsb+iTvWpiytyPMfOHWHbq30CnqVwwm62Y9UMsihUvgyd6DoTey34y9I2UdqTp0fwXX3xhK6eDUXQga/Dgwfle/+eff7JdVOHb5d9//43Lly9jxIgR7FscuWGsX78e48aNs13z6aefsvdv3brlkczuvt16E7XJIwYt9UUr+hUzJlKMp5RzQs75pfTcVdu9ojZ5lOoPBfp/b9N8bD35P1uZn8Ef056Zgvrl68EXsKZeZoaqNdvuCkH2h6HpLOgM6ngCKwZurKoM2vangP8UR7XfknMY072c0w7ruRsZmPX9NWw5ZsaKLxerwqGbHrOTQTpr1iwmz2OPPcYOWNGpfjoo9eqrr+Z7PT2q6dSpEwYMGIDbt28zn2E6UNW7d2/2PtXTsWNHZgz36NGDHeaiw1tUr6e7qmpLAas2ecSgpb5oRb9ixkSK8ZRyTsg5v5Seu2q7V9QmjxL9IT/Q+b8twB/Ht9jK/Ax+mPr0JDSs2AC+gDXtGkz/DIY1846tTFesDozN5kBn1Ebcc26sqhCKM0oB/ymO6iPjT6NRTAA7TEU+qvvOZaFYcXLoVj6DVX6Q8ViyZEl8/PHH+PLLL5nPKfn+kqHp6h/mCiUSqFKlCvMNJreBdu3asUxZwrdi6ueqVauYEUvxVSme67BhwzB+/HiP5SWncne+P95CbfKIQUt90Yp+xYyJFOMp5ZyQc34pPXfVdq+oTR65+0PG7Ae/L8JvRzfbyox6IyY9ORGNKzeCL2BNvwnTv2So3raV6SJrwNh8HnR+d/uj+ircWFUplJlq34EjNofupKRElCwXiUUj8nfolpvPP//cbTn5lQ4dOpS9Cgsd9CBjl1550b17d/bicDgcDkcsZKh+uGUJNh7+xVZGgf4nPjkezWKa+oSCrZm3cx/9p9tjn+vCq8DY4v1CxVL3BbixqmLIIFW7QzeHw+FwOL5mqH701zKsO7DeVqbXGTCuyxi0rNoCvoA1807ujmqaPZOjLqwSjC0/gM5fW8kdCN9KYcDhSIS7UCHeRG3yiEFLfdGKfsWMiRTjKeWckHN+KT131XavqE0eOfpDhuon//sMa/fZk+lQNqexXUbhoRqt4QtYsxJh2jEU1pTLtjJdaLlcQzXAHrNcS/CdVY5XuXjxolfaJbcFNaE2ecSgpb5oRb9ixkSK8ZRyTsg5v5Seu2q7V9Qmjxz9+fzvL7F6zxqHEh1GdX4HbWu2gS9gzU7JNVSTz9vKdCFlYGy5ELpA7YZx5DurnCKJVBnHtCqPGLTUF63oV8yYSDGeUs4JOeeX0nNXbfeK2uSRuj9f/bsSK3d+41T2TqfheLROO/gC1pxUmHYOY4H/BXRBJXIN1aAS0DLcWOVwOBwOh6Npvt21Cl/886VT2dAOQ9CpXgf4AlZTOkw7R8CacNJWRjupxgcXQhd872yOvg43VjkcDofD4WiW1Xu+x2fbljuVDWw3AF3ufwy+gNWUCdOuUbDeOWIrI99UtqMaUg5FAW6scookakimoGZ5xKClvmhFv2LGRIrxlHJOyDm/lJ67artX1CaPFP1Zu+8nfLz1E6fytx5+E0898AR8Aas5G+Y9Y2C9fcBWRqf9jS0XQBdWEUUFbqxyiiT3SlRQ1OURg5b6ohX9ihkTKcZTyjkh5/xSeu6q7V5Rmzxi2Xzidyz58yOnstfb9EW3xs/AF7BacmDeMw6WW3vshX4hMLRYAF14DIoS3FjlFEkoxauaUJs8YtBSX7SiXzFjIsV4Sjkn5JxfSs9dtd0rapNHDL8e3oQFmxc5lb3aujeea9oDPmOo7n0Xltgd9kJjMAv4r4+sjqIGN1Y5HA6Hw+FoBkqfOu+3BU5lL7V8ES807wlfwGoxw7x/Giw3ttkLjYEwNn8P+qjaKIpwY5VzT8xmM+bMmYMaNWowH6ASJUrglVdeQVxcnNPndu7cidatWyMkJASlS5fG4MGDWW5mDofD4XCUYMvxPzH31/lk8tnKejZ7Di+3fNEnBsBqtcB8cCYs17bYCw3+MDabA310fRRVeFIAFZOVlYUNGzZg06ZNSEhMQFRkFDp16oSuXbsq6gg/fvx4zJ49mxmfbdu2xfHjxzFz5kycOXMG//zzD3Q6HU6ePIn27dujadOm+OKLL5ghO2nSJJw9exa//GLPvawW/Pz8oCbUJo8YtNQXrehXzJhIMZ5Szgk555fSc1dt94ra5CksW09uw6xf5toMVZ0O6N7kWfRp/Sr7P6V2KLuW+dBcWK5sshfq/WBsOhv6+x5AUcZjY9ViseDIkSPMSLl16xbzdYmKikLx4sVRrVo1NGzYUFpJixibN2/GwCGDEHvrJoLKBMMYYoTplAlrflyDkhNKYfHCD5lxKDcZGRn48MMP0adPHyxYkPtY5amnnmK7q6+//jqTs2PHjpg+fTqKFSuGjRs32pz0S5UqhWeeeQbbtm3DQw89BDWhtrSCapNHDFrqixrh6VZ5ulU1zS218PfpfzDj51lsZ1LgmUZP4402r/mOoXrkfVgubbAX6o0wNpkGfYkmKOoU2lilnbRPP/0U69evR2Jiok3JAsKkiI6OZkbMm2++iVatWkkps+YhA/CFl1+Af/kA1H2yPoKi7QtIRnw6Lm65gF4v9cLKL1eiQwd5AxrT7mlqairb0XWkRYsW7OfRo0eZ0UxG6muvveZ0mpTG39/fn72Xl7FaqVIlVnft2rUxf/58xMbGsvmyaNEi1KxZ0/a5HTt2YNSoUdizZw8iIiJYm7NmzULZsmU96hf1KTQ0FGpBbfKIQUt90Yp+xYyJFOMp5ZyQc34pPXfVdq+oTZ6CsuPsTkzbMAMWq9lW1rVBF7zc9EWfMVQtxxfDcmGtvVCnh7HxZOhLPehN0XzPWD116hSGDRvGHkmHhYWhefPmaNmyJcqVK8d21MLDw9lEJwP26tWr2L17NzO6vvnmG/boeO7cuXjggaK9jV3QR/+0o0qGas1na0Ond77RyHCl8pPfH2efO3LwsKwuARUqVMDvv/9+19iRfypRvnx5XLx4EQkJCahfv/5d39IrV67M5k5+rFmzBjExMcy1gHZyZ8yYweYM7dzTTj3NpYcffpi9vv76a2bQTpkyhb1/8OBBj/1w1YTa5BGDlvqiFf2KGRMpxlPKOSHn/FJ67qrtXlGbPAVh9/k9mLxuKkwWk62sc71OGNRugM+kj7Wc/ATms9/ZC3Q6GBu9C33pNt4Uy/eM1bfffhuLFy/Go48+ip9//pnthOn1+gJ9WyBDh65t1qwZO5RDu7JFhdTMVFy4fbFQ12zeuBk3Yq+jVtd6MNG3xDzWjrJty+PEZ0ex+IslaP9Y4dwBKt9XCaGBBfv2HBkZycbdkT/++IPNCdoVffzxx3Hs2DHbbror5Bpyr3AoOTk5+O2339hnhV1b2mldunQpJkyYgDFjxqBixYrMf9doNNraev7553H48OG7jGQOh8PhaJ/9Fw/g3Z8mOxmqHet2wLAOQwpko6gB86nPYT7tkAZWBxgbjoW+bDtviuWbxurNmzexf/9+ZkAUBtp+p8fU9KLdNTqkU5QgQ3XYt28X6poTa45BX8KInKAc5GTk5P3BYEBf3Ih5n87DL0kOztgF4P2e81CvXF0UFjo0RYbj8uXLmfG4bt06dvKfjM28oAXjXn5Qbdq0sRmqRK1atVjkgV27drGdZvJ5feedd2yGKvHss8/ixo0bbFffEwwGA9SE2uQRg5b6ohX9ihkTKcZTyjkh5/xSeu6q7V5Rmzz5cfDyIYz/8V3kmO3/fx6p9TCGdxxqM1TV3h/zmZUwn3TewDPUHwl9+c5ek8mnjdWVK1eKboiMDzJyOPljyjTBL7RgJzL9wvzY55Xgu+++Q//+/dljlSFDhmDq1Kk23ybB0BR8mB2hsqpVq+Zbd5kyZe4qozrT09MRHx8Pk8nE3A0coUWIDnB5itr8stQmjxi01Bet6FfMmEgxnlLOCTnnl9JzV233itrkyYsjV49i/NqJyDZl2cra1GiDkZ3fgUFv8In+mM+vgfm4c3YtQ71hMFTyjTSwSuPxPjk94qfHwbS7JUB+qV26dEHfvn1ZeCNO4TEGGpGTms+OqgM5KTns83KzbNky9OzZE9WrV8ehQ4fw/vvvOy0C5G9KB6voPUeys7Nx/vz5e0aGSE5Ovqvs+vXrLOLAfffdxwxT8ol1vYZcUsiY9QQyhNWE2uQRg5b6ohX9ihkTKcZTyjkh5/xSeu6q7V5RmzzuOH79BMZ+Px6ZOZm2sgerPYjRj4+A0WD0if6YL/4E85EPnMoMdQbCUKWb12RSOx5ZOmQg0GlsMk5+/fVXFgB+5MiReO+992yfWb16NXt8W5RDWJFvKD1yLwybwzdj0ph34Zfhh6BiQXl+jqICWOJMzHfUE5/VgkI7qdQGneYn/2M63e8KHfCik/90UIoOPgmfob/pMT7Fhc2PP//8ky0qgrvA33//jcuXL2PEiBGsLppDFH1i3LhxtmvI95nep7BpnpCf64I3UJs8YtBSX7SiXzFjIsV4Sjkn5JxfSs9dtd0rapPHlVM3T2P0mnHIyMmwlTWPaYbxXcfAz+DnE/0xX94I8yG7rUQYar0BQ9XnvSaTZo1VChJPp7CHDx+ORo0asQnx8ccfo0GDBuygDPm3Pvnkk5g8eTJ++uknFFXoEFNhfUOr966GZQuW4dpfl91GAyCsFiuubb2C0iXLYMAr/WWNBkARHSgL1WOPPcaMSlfoET+9yEilhABPPPEEi7964cIFNv70O+285gcdwKJDewMGDMDt27fZdeQT27t3b/Y+RQcgY5j8VHv06IFz585h2rRpePXVV90e6uJwOByOtjgTexYjV49GerY9K2KTyo3x7hMT3BqqasRydTPLTuWIocarMFR/2WsyadpYpVPZFLZK2EndunUreyz71ltvsVBDZFiQ8UE7ZJzCQYYnBfynOKoUnqpSu8pOcVbT49NxacsFZF/JwudfrZA9kxUZncTo0aPdvv/uu++yTFV0Ip8MW4qF+uKLLzIjkjJekeF5LyhxQJUqVTBo0CDmOtCuXTvMmzfP5mpAu/irVq1idb300ksstiqFUaMvTRwOh8PRNufjLjBDNS3Lbqg2rNgQk56cCD+jjxiq1/+Caf80xyywMFTtBX2NPt4US/tuAI6Pdv/3v//ZTv4L0OEXd76InHtDxhkF/Kc4qkc/OWzPYJVmQsb1dJQsUYoZqkpksKJT+PQqCOQqQMH7Cwud3KSdUnrlRffu3dlLKiixgJpQmzxi0FJftKJfMWMixXhKOSfknF9Kz1213Stqk4e4ePsSRqwahZRMe8zU+8vfj6lPT0KAX4BP9Mdy8x+Y9k2ix6K2Mn2V7tDXfssnkhb4rLFKiQAcD1bRTivtjNGjWwE6YMUf0XoOGf4U8J8OEZFfMJ2qp5innTt3ZofY5N5R1TqZmZlO2ba8jdrkEYOW+qIV/YoZEynGU8o5Ief8Unruqu1eUZs8V+5cYYZqUoY9VnfdsnUw9ZnJCPQL9In+WGJ3wrRnHGCxB03XV3oShrqDuaEqt7FKu6qUL54CtlO2qgMHDrADVgRlIKLDL+QCQP6FHM8hg7Rbt27sxZEWOvjl7UVMzfKIQUt90Yp+xYyJFOMp5ZyQc34pPXfVdq+oSZ5rCdfx9nejkJBujwRTq3QtzOg2DcH+eR8+VlN/LHF7YdozFnBIWqCv8BgM9d/mhqoSxurEiRPZYZvp06fbdlrpxDjRq1cvFiyeyoT3OZy8oFStHA6Hw+EI3Ei8gXdWjcSdNHtowuolq2Pms9MQHJB/khm1YIk/BNOuUYA521amL/coDPePgk7nG9m1fN5YpcfRe/fuxV9//cUeT5PvJJUR9IiaHmG//PLLLLsRh8PhcDgcTkGITb6Ft1eNQlxKnK0spkQMZveYUeA04d7GcucYTDtHAGZ70gJ9mTYwNBwPnUPSAo6Mxir5gFC8y1atWuHpp5++631KCMDhqJ2wsDCoCbXJIwYt9UUr+hUzJlKMp5RzQs75pfTcVdu94m15bqfcxjvfjcSt5FhbWeX7KmNuj1kIC1R23nuKJfEUTDuHAyZ7QgJ9qZYwNJoEnV7+JD5apdB70eT/8e2337JQQhyOr0IpXNWE2uQRg5b6ohX9ihkTKcZTyjkh5/xSeu6q7V7xpjzxqfF4+7uRuJFkP7xdMboi5vSYifCgcJ/ojzX5LEw7hgI59hBb+hLNYGg8DTq9b4TYUiseOU5Q/Ew6oX7ixAnpJfJRhPATZrP9xB/He9xrHOggoJpQmzxi0FJftKJfMWMixXhKOSfknF9Kz1213SvekudOWgLeWTUK1xKv2crKRZXDnB6zEBUS5RP9saZchOnfoUC2PcSWvngjGJrOgM5wd+ZHXyQrKws//vijV9r2aE+6Vq1aLE88ZSx65ZVXULduXbdpOIk+fYpGwFshgD2F9CpZsqS3xSnyCKHVKIYrh8PhcNRJUnoSC/hPYaoEykSWwbzn5yA6tBh8AWvqFZj+HQJrVqKtTBddD4ams6AzaCPM5ObNmzF04GDcuXkL5apX8Q1j1dFXdcmSJW53Ga1WK/tZVIxVCjNVs2ZN7N69m/1dunRpGAzckdobO6pkqNI4GI1G2443D7zM4XA46iI5IwUjVo/Gxdv2qDClIkrhvefIUPWNVNrWtOsw/TsY1kx75AJdVG0Ym82FzliwEFu+YKj27vUSHkA0JpbtjPdwyjeM1RUrVkgviQZo3bo185ERDFaO9yBDVUicQGPi6mivtkgVapNHDFrqi1b0K2ZMpBhPKeeEnPNL6bmrtntFSXlSM1Mxas0YnI87bysrEVYC7z03GyXCi6uuP/QInBIgbdq0CclJCQiPiELHR5qjc8RG+JvskQt0kdVhbDEfOj/fiFxQkH7TjioZqmPLtoVJ55AvVu3GKj369wXmz5+PBQsWwM/Pj2XYomQFjlm2pIZ27x555BGkpqayDF4lSpRw2t0rLMLutKeIvV7u+uSqmx79C7v7SUlJbIe7fPnyTp9R206r2uQRg5b6ohX9ihkTKcZTyjkh5/xSeu6q7V5RSp60rDSMXjMWZ2LP2MpoJ3Xuc7PZzqra+kM7i8OHDsSduJtoXDUAJSMMiL1iwpC1X2JSqBFz+1bBow0ioQuvAmOL9zVjqBJkoMffiMW0cl1g0OmRY87x0HIUh0dNWiz2/Lb3wls+g3v27MHcuXPZLicZKnPmzMGAAQNY+lIldlhTUlJw7do1FuqLjGVPoB1BMnY9Rez1ctcnZ92CsRoUFMR8q+nLiiP0hUIteaPVKI8YtNQXrehXzJhIMZ5Szgk555fSc1dt94oS8qRnZ2DM9+Nx8qb9UXKxkGjMe34uykaVUV1/yFDt88oLaFfHgDFDqqNKKcqIZQGyk3D+Rjhmro3HGwtP4+NRrdB5yALo/NUznlJAO8k1/aJRNiACVrMFpsR0wAvHcjyyDgq6W0ifERs6YtmyZZg6dSpL6+rKF198gVmzZrEsSBUqVMDAgQMxaNAg9t7Zs2dZYgJhR61z585YtGgRlCAqKgrdu3dnMl+/fp0ZTJ6QlpYm6jGG2Ovlrk/uummeku9w8eLFVbeDweFwOEWNjOwMjPthAo5fP24riwyOxNznZqFcVFmo8RE47aiSobq0fwwMBp3NUIXFjCol/bH0zVLo93EsRq64hHZDQqCN41R2khISEa0PzDVUk9JgtRZ8s9LrxupDDz101z9/Mkrv3LmD06dPs0MuDz74ICpVqiRKODL25s2b5/a9r7/+Gr1790a/fv3QsWNH7N+/H8OGDUN6ejpGjRqFnj17spcQvmLSpEkss5ZSUHQE2s2Ljo72+JsdPcIW861Q7PVy16dU3RwOh8PxLlk5WZj44yQcuXrEVhYeFMEC/leMrgC1PgKnR/+0o+pqqAqQm9noHpXRbuI59uS2W7du0BIRUZE4Y87INVTNFsBL+z4eGatbt27N1+gYO3YsVq9ejaVLl3okFKVxHTJkCPP7JMO3bNmyd7khUBYt2r386KOPWNlTTz3F2p4+fTrbYRV26dasWYPRo0czA1upnVVHPN1VFXutFNfLXZ9SdauhPV+TRwxa6otW9OvNdUiqOuSoS8m61dCet+TJNmVj4k+TceDyQVtZaEAoM1QrF68MtfaHHoGTj6r90X+yk6EKnR7wj0BMGQMaxQSw+PNaM1bbNW+FdV98i2v+SShj9F6GM8kdSml3bPHixezxO+1wegIdTOrVqxemTZuGBx544K73Dx06hMuXL7OdVUe6du3KfEW3bdvG/BVffPFFzJgxA19++SWLYBAcHJzvY+i8XmLw1F9V7LVSXC93fUrVrYb2fE0eMWipL1rRrzfXIanqkKMuJetWQ3vekCfHlINJ66Zi38V9trKQgBAW8D+mRBVV94dO/dNhKruharrLUIUuN0RlqUgDkpPscVa1QHZsAmLWnkO4IQBfpByB2WqBzkvnkGRrtV69evjnn388urZOnTpsN5ReVI8rBw4cYD/r16/vVE7JCYhTp06xdLDHjh3Djh07mEtCQYL6u3uJDfCfnJzslWuluF7u+pSqWw3t+Zo8YtBSX7SiX2+uQ1LVIUddStathvaUlodOjk/ZMB27z9vDOQb5B2NW9xmoXqoa1N4fCk8Vm2j+79F/3oYqcTPRjPCISGiF7FuJOP7aQlivJ2BI2dbYmxOLOcm7cCvYO1k6ZTnaTT6iO3fulC0SwO3bt9lP8gd1PdhEkDvAuXPn2MErwYAl6LDN9u3bPW6X6s3Ozma/U9xO8tN1TOdGrgfky0snEAVycnLYLq/jTUP+rPR4gsroPeEwEF1PO7nCoTTyv6WdampDaJcIDw9nf1OkAQEyrMk9gq4RoHqojHabBSj2aGBgIOuL47dP2nWmzwmRHsgPh+qk+qgPjvJQu+R4LpCXLgjHnWnqM7XlTheOcgu6ID0KaVNpLlE7rrogeUiWe+mC+kd1CGNDcpEeSB8F1YXj2NA40zi46oLKSF+F1QW9J4yNu3nhTheOYyNGF4Q7XQhjU1BdCGMj9LMwuqB+upunjroQxqagunA3T+m6guiC+u5unjrqwt0966qL/OYF6ZH6VlBdCGNDn3enC3f3rKALksVxbN3pwt36Re0KIeAKu3456sKxj3mtX666oP5Rn1x1Qbi7Zwuqi/zWL6F9d7rIb/26ly7yWr/omoLes+504elantf6RfKQfGLWckEXJosZC/5ciB3nd8BiydVNoF8gJj42FrVK15RsLWf15rGWE2LW8kfbtsSwtV/h3I0wdpiKuWvq9LD60eNwHUA7jTodzt3IxN5zmZjd/yEmg6+v5cmXb+LMm4uRfe0OK28WVh6TGz2ND2L/xdDrv6J89Rgojc4qaLIQTJw4Mc/3aAKS38aJEyfQo0cPfPvtt6IEpEf9f/zxh1M0APJLHT9+PBs0R58UGmwaFApTNWLEiEK1k9fjfupPp06d2O9k6BbWB0bMwSF+wEo+1HagS23yiEFLfdGKfr25DklVhxx1KVm3GtpTSh6zxYyZG2dj68n/2coCjAGY+ex01C9/99NSNfbHmp2MtK0D0ey179G4ih879c+yUrrsqJrNVvRbcg77rodj34Ejti9UvryjeuK1D5B52Z7oILBCCdT6bAis4YFYu3at7eC7JzaRojur5Et6L1q1asUC8suBsIOamJjopCj6m7jvvvsKXWdeYZPE7g6T8eyNa6W4Xu76lKpbDe35mjxi0FJftKJfb65DUtUhR11K1q2G9pSQhwzVub/OczJU/Qz+mPrMZEUNVTH9IUPV9O8Q+GecZwH/KY5qv2WxGPN8ZVQpbTdUz93IwKzvr2HLMTNWfLnY9w3VuCSceG2hi6FanBmq/sUjbIfZ84rSpDpjlU7r5zc56HBVuXLlIBfk0yoctKJH+wK0m0s0bNgQaoFHA5BfT55QVE7hegMt9UWN8GgAPBqAmuaWI/SIef5vH+CP41tsZX4GP0x9ehIeqNjQJ/ojGKrWpNzsWpSZ6uO3G2Hkl7F4ZMI5duqfDlORj+q+c1koVrwUM1Tbt28PnzdU+9KO6i1bWUD54qj1qd1Q9SYeGatt2rSBN2nRogUL9L5y5UrbI3riq6++YskBGjRoALVAPivkA6L0tVJcL3d9StWthvZ8TR4xaKkvWtGvN9chqeqQoy4l61ZDe3LKQx6FH/zxIX47+putzKg3YtKTE9G4ciP4Qn9cDVVCFxiNzoMXod3bJVkcVXJzTEpKRMlykVg0ojO6dOmikR3VD+4yVGt/Ohj+JdRxaEzUAavDhw/ju+++Y/FQyRGYDMhmzZrh+eef9+hRfEGh3VtyRXjzzTfZIau2bdtiy5Yt+Oyzz5gBqyY8cAmW5Foprpe7PqXqVkN7viaPGLTUF63o15vrkFR1yFGXknWroT255KHrPtyyBBsPbbSVGfQGTHxyPJrFNJVQwsLLVeDPZif9Z6iedTJUjQ8ugi60AstMRTFUtRZHNft2Ek68vhCZl9wYqiVzXS592lidMmUKJk+efNdkIGORkgLMnz8fr732GuTijTfeYG2T7wQlH4iJiWE7q0LWKk+5ceMGewk4ng7kcDgcDodjh/4PL/3rY6w7sN5WptcZMK7LGLSs2sInVHUvQ1WrZN9OxonXFyHzYqytLKDcfaj1iboMVY+jAdBpsGeffRZNmzZlkQGaNGnCQihcv36dZXygTFEUOopSlT322GPwJSgtKxnhjgesBB9YegQg+MAUNHQVqZdOI3oS7oQMZUqQ4GnoKkFOqcKdCPLIEbrq1q1btkcpSoSuos9HRkaqJnQV1UN98eVwJ8LYUPtUHw9dJU/oKtIvjX9hwjUlJCQ4HRYtTOgqqktow9PQVfRZqkuK0FUki9CG1KGrhLmrVOgqkpPqVEvoKvos9amg6xe1/dWulfjx4DpQBnaWht0KDG03BK2rPqjIWp5f6CoaE7o+3/XLlIKgI+NhSaRH/7ljaPUvBv/WS2AKKOXR/zVfCF1lSUzHlWGfIf3cDQhWoH+ZKNRdMRyWyKA8Q+/Ri1wflI4G4JGxSif9KYPUyZMn3WaFolP5dAiqZs2a7PG8L+FuZ3XQoEGKDwyHw+FwOGqB/hfSBhRtSCUkJiAqMgoBFYNwwngSeqPwRUiH0Y+NwKN12sG3d1Q/hC60PLRKTnwKTrz+ATLO37SVBZSNZoepAkoXy/daMlZbt27tG6GrDh48yPxS80pfSjtWdPDphx9+gK9B0QUcIww4fqvyBPoGlldYLDmvleJ6uetTqm41tOdr8ohBS33Rin69uQ5JVYccdSlZtxra81SezZs3Y+CQQYi9dRNBZYJhDDEi80QmklcnwS/YH9W71kR09Wi802m4qgzV/PSba6gOhjXpXNEzVN9Y6Gyololmj/7vZah6E4+MVdpKd3wslBeO29pFFcfHVUpeK8X1ctenVN1qaM/X5BGDlvqiFf16cx2Sqg456lKybjW054k8ZKi+8PIL8C8fgLpP1kdQdDAysjOQnp2BrIQsxG67juOrj2DcrAnoVK8D1ERe+i2yhuqdFJx4cxEyztmfHvuXLpZrqJZxzgiqNjyKeN+4cWNs3LgR8fHxbt8nvwh6VFCrVi2x8nE4HA6Hw/HSo3/aUSVDteaztf8zVDOZoUoERAWgfNdKiKpaDF8v/MonDiRbsxLdGKr3FR1D9ex1W5l/qSjUpkf/ZdVtqHpsrNJpfzJU6WDV4sWL2WEqelxOKVEpvWrLli1x8+ZNDBw4EEUdMRmwxGbPEnu93PUpVbca2vM1ecSgpb5oRb/eXIekqkOOupSsWw3tFVYe8lGlR/+V2lWGTq/7z1B1PsQbEhiCKu2rss/RIWQ194cZqjuGuDFUF2nbUE1IzTVUzzgbqrV8xFD12A2gXbt2WL58OQYMGIDBgwe7/czo0aPRu3dvFHXopJ83rpXiernrU6puNbTna/KIQUt90Yp+vbkOSVWHHHUpWbca2iusPPSElHxU7TuqzoZqsH8wgvwDgWiwz1HAfDXFIXXsT5E1VBPTcn1UHQ3VklGo9ckQBJaTLx6+auKsvvLKK+wQFcVV3bNnD9tppVNhdevWZbFO6acvInWcVdpx9vS0nJhrpbhe7vqUqlsN7fmaPGLQUl+0ol9vrkNS1SFHXUrWrYb2CisPnfqnw1SCj6ojQf5BuYbqf9DnKBKQmhD6Y3v0n3ze2VBt9SF0IfKlhleDoXrS1VAtEcl2VAPL+46hKjqDVcmSJTF8+HBoiWXLluUZZ5XilgmxxwoaZ5UOmQnx1gobm4/iqdHnPI2zSvVQvDWpYvMJ8sgRZ5UWOaGPSsTmI7nob7XEWaX3qE4txFml9+gnj7MqT5xV+jxdV5jYovQ5x/umMHFWaU45XutJnFWK8yrUITbOKl3rKI+UcVaFuatUnFW6ht5TS5xVkoc+L8zTkOAQZCbn+qha/4tByuTxD0awfxAsFntZTmoOiwRU0PVLiTir1F9TejyM+0dCn3bxv77rWRzVzPozYTWFQfdfSlap1nLHsfHmWh5/+QYuD/sMmWdu2GLgGu4LQ7n3+yAr3A+m1NR7ruXudCE2QpKicVYdD1FdvHiRKcpdNdRxR8OvKMZZpZuHJp4niLlWiuvlrk+putXQnq/JIwYt9UUr+vXmOiRVHXLUpWTdamivMPLQ//RB0wfj4znLULV3TXaYyunRvwPp8ek49slhLP94uarcAJLiLiP46Pgit6NqSqJH/4uQfuqqrcyveARqLx+KwPLFRdXtU3FW9+/fj86dO+P27dv55t71RWNV6jirHA6Hw+H4EvR/ffn2z3HccJLFUaXwVHTqnw5TuRqqVosVl7ZcQMkSpWyZjdQAPfr3Pzga1vTLtjJdUPFcH1WtG6pvfni3ofrZENGGqjfxyFgdNmwY4uLi2AGrjh07qs5JXE3Q9rk3rpXiernrU6puNbTna/KIQUt90Yp+vbkOSVWHHHUpWbca2iuIPGSofvK/z7B6zxqWmYoC/lMc1Zu/XENM+6rsMJXjjioZqtlXsvD5VytsKbO9jeCjaki/xLJqFRlDNTkdJ/t9iPSTV+42VCuUgC/jkbG6b98+tG/fHosWLZJeIo1Bfh+e3sBirpXiernrU6puNbTna/KIQUt90Yp+vbkOSVWHHHUpWbca2rsX5Pa24t8v8MO+tbYyykw1btY4fL1wJY5+ctiWwcqUZkLG9XS2o0qGKtkEasDxMJX1P1O1SBiqKbmGatoJZ0OVHabycUPVY2OVfBTKli0rvTQahBykPV2MxFwrxfVy16dU3Wpoz9fkEYOW+qIV/XpzHZKqDjnqUrJuNbSXH7Sj+uGWJdh0/DeHUh2GdxyCx+p3xqg+o1gcVQpPRQdi6TAVuQPSo3/V9CErAaZ/h9h9VK1W6IJLFA1D9c0PkXb8srOh+slgBFX0fUPVY2OVJugff/zBTqXRaTQOh8PhcDi+CZ0yX7RlMX45ugl6vc5mqL7TabgthSoZpHR4Sk0HqPI1VKks4L6iYaj2W+xsqN4XnmuoVioJrVAgY1UIgSEwd+5cdOjQAU899RQmTZqEevXq2UI6qD1Dh9JxVjkcDofDUSv0//39zR/g1yObbGUU3mlU53fwaJ128AXcGar06D+73gwEat1QfWsx0o6Rb24uftHaM1QLbKxSfDA62e/KkSNHsGXLljyvo2uE+F1FNc4qxeGjxyuexOYT3vM0zip9E6YyqWLzCe/JEWeV+irIpERsPqqXrlNLnFWSh+rSQpxVkpPq4XFW5YmzKsRHLUxsUZprjmNbmDirVO5uXhQmzqowJ6SIs0qfdXfPShFnVZBTqTir9LmC3rNyxFlNS09jhuqfp7baynXQY8jDg9CkXGObLgqzliseZzU7EYGHx0OXegFWa26frQHFYXlgDkKiqii+ljuOjZxruTk1A9dGfsUMVYrIwNooFoqYxf0QVLmUJGu5z8VZbdu2rVtjtSD89ddfKMpxVmmAaSJ7gphrpbhe7vqUqlsN7fmaPGLQUl+0ol9vrkNS1SFHXUrWrYb2HDFbzJjz6zxsOW7fcNLrDBjbZTTa1nwIvrujSj6qC9mjf62uRabUDJzqvwSphy/YyozFwtiOanCMPfSmHKg6zurWrbnfuooCUsdZpW9BngZ9FnOtFNfLXZ9SdauhPV+TRwxa6otW9OvNdUiqOuSoS8m61dCegMlswqxf5mDryf/Zyox6I4a1G6IZQ1Wra5HJnaEaFYpaHw+S3VD1JgVyKHXc3hbDnTt3JKmHw+FwOBxO4ckx52DGz7PuMlTffXICmldu5uOGqrYPU5nTMnFqgBtDlXZUq5aBlimQsVqhQgWMGzcON2/e9KiR69evY8SIEahatapH13M4HA6HwxFvqE7bMBPbTm+3lfkZ/DD5qXfRompzn1CvNetOPoZqWU0bqifJUD3kYKhG0o6q9g3VAhurGzduxObNm1GpUiX07NkT69evZ6lW8+PatWtYtWoVunfvjsqVK2PXrl0+578qBWJCe4kNCyZ1WDE5w5QpHQJNbSHX1CaPGLTUF63o15vrkFR1yFGXknV7u70cUw4mr5uKf878YyvzM/hj6tOT0SymqeLyeG6oDi2woar2/hRuR/UjpB4872KoDkJwNe0bqgX2WX3wwQexZ88erFy5ElOnTmVGKB24qlixIsqVK8eCA9OpMjppRo/6r1y5woxVOrtVo0YNrFixAr169UJRhE7ceeNaKa6Xuz6l6lZDe74mjxi01Bet6Neb65BUdchRl5J1e7O9bFM2Jq2bit3nd9vK/I0BmPbMZDxQsaHi8ihhqKq9PwXFnJ6JUwM/QsrBc7YyY0RIrqFaXbs7yaKSArzwwgvM6KSEAJs2bWJhqyj1quMhJDoZVrt2bfTo0YPFYW3VqhWKMmTAe+rgLeZaKa6Xuz6l6lZDe74mjxi01Bet6Neb65BUdchRl5J1e6u9rJwsTPxpMvZd3GcrC/QLxLRnpqBBhfsVl0fco/8LhXr0r9b+FM5QXYqUA86Gak169F+EDFWPMljRjirlAHbMA0zGakJCAttyp11WDofD4XA43iUzJxMT1r6LA5cP2sqC/IIw49lpqFeurldlk9tQ9XXM6Vm5hur+s7YyQ3gwM1RDami335KmW3WFdlOVirXF4XA4HA4nf9KzMzB+7UQcvnLYVhbkH4zZ3WegdplaPm6ofghdSBltG6qDXQzVsGB26r8oGqqSGataQup0q5TJwhvXSnG93PUpVbca2vM1ecSgpb5oRb/eXIekqkOOupSsW8n20rPSMfaH8Th67ZitLCQgBLO6z0Ct0jUVl8dbhqqa+lNQzBnZOD1kGVL2nnExVAchpIZ2w3LdC26sypxuVUgl6mmKPkrZ5mm6VXLLkDLdKvWNrpUj3Sr1R6hTkRR9//VHLelWBbSQbpUgOXi6VXnSrRI0HoVJMUp4mm41r3lRmHSrjve32HSrea1fUqRbFeauUulWSRdSp1u16C0YsWo0Tt08bW8nKAxTnngXZYJLs8/ntX6RjCSfmLVcknSr2YnwPzAKhowr7D2qj1KoZtefiSD/4tCbzfecp95cyz1Nt2rJzMaVUV8i4yCljrWCLjWEBaLcvN4IrFaGfUbptdyn0q0WJaROt0oTxlMHbzHXSnG93PUpVbca2vM1ecSgpb5oRb/eXIekqkOOupSsW4n2UjNTMXrNWJy8ecpWFhYYjrnPzULVEjGKy+Pxjuo/g2FNuWgr0wWXhLHlokI/+ldDfwq3o7oUybvtXzIMoUHs1H9I7QpQC6pOt1qUkDrdKofD4XA4cpOckYJRa8bgTKz98XF4UATee242qhSv7BMDYM2kR/9uDFV69B+s3VSitKN6euiyuwzVmssGqspQ9SbcWOVwOBwOx4dJSk/CyNVjcC7OHuIoKjiK7ahWuq8SfIGibKieGrIMybtOORuqSwcitE5Fr8rmcxms8iM2NpZtBf/yyy/sb1cfvKIO+bJ441oprpe7PqXqVkN7viaPGLTUF63o15vrkFR1yFGXknXL1V5CWgLeWTXKyVAtFhKNec/PLbSh6q17Vy5DVe1rETNUh37sYqgGouZHAxBalxuqkhirBw8eRMuWLVGmTBm0bdsWTzzxBCsfMGAAy3h19OhRT6vWFDzdqvx68gS1peFTmzxi0FJf1AhPt8rTrQrc+c9QvXDbfmI+OjQa85+fgwrR5RWZW2reUVXzWmTJysHp4Z8geedJF0N1IELr+cZuuOqN1YsXL6JNmzY4cOAAevbsiRYtWthOvJEBS4bsQw89hPPn7WnRiiqOpzeVvFaK6+WuT6m61dCer8kjBi31RSv69eY6JFUdctSlZN1StxefGo+3vxuBS/GXbGXFw4pj/vPvoVyxcj7R/1xDdZBsj/7VuhYxQ3XYJ0j694StzBASiJpLBnBDVUpjlUI7UaiDHTt24Ouvv0aTJk1s77311lssHSuFUnAMAVVUEcJEKH2tFNfLXZ9SdauhPV+TRwxa6otW9OvNdUiqOuSoS8m6pWwvLiUOw78dgSt3ckM7ESXCS2L+83NRNqqMT/TfbqjajW1dcClJfVTVuBYJO6pJ/x53MlRrkKFa3zcOwvnMAatff/0Vjz/+OBo0aOD2fdpppXSsf/75p1j5OBwOh8Ph/Eds8i28891I3Eiyh1gsHVGanfovGVHSJ/RkzYz/79G/q6G6SNuHqbJNOPP2p0j6525DNex+bqhKvrNKh6juFbuMwj/dvn0bRR0KvOuNa6W4Xu76lKpbDe35mjxi0FJftKJfb65DUtUhR11K1i1FezeTbmL4t+84GaplIstgfs+5khiqSvRfSUNVTWuRYKgm/m3PKqYPDkCNxf25oSqXsVq7dm1s27bNlvXA3db7rl27UKECjw9GGSK8ca0U18tdn1J1q6E9X5NHDFrqi1b06811SKo65KhLybrFtnct4Tp79B+bHGsrKxtVFu/3fI/5qiotj7SGqjzhqdSyFjFD9Z1Pkbj9qJOhWnNJf4Q1qOJV2TRtrPbv3x9nzpzBiy++6JTtibhy5QpeeeUVHD9+HL1794avQf3Zv3+/7UWHxcTgmBpNyWuluF7u+pSqWw3t+Zo8YtBSX7SiX2+uQ1LVIUddStYtpr2rCdfYYapbKbdsZRWKVWCHqej0v9LyeG6o5uWjWkqza5HNUN3mYKgG+aMm7ag2uHdWMY4In9U+ffowI+7DDz/EmjVrWM5bIjo6GomJiSwyQOfOnTFy5Ej4GsuWLXM6GEZ5dxs2bGhL3Sbky3XNJ024yydN+XSFfMmFzSdNNxqF3nCXp9ddbm3XfNJUD+UJLmhubXd5gx1zawvy3Cu3tqCLwuSTJnmEdjzOJ52PLlxzKJNc1Ee15JOm90g+T/JJi9WF1Pmk6T2SqzC6oH66m6eOunCXAz4/Xbibp3RdQXRBfXc3Tx114e6eLUhubcexob4VVBfC2Aifd9WFu3tW0IUwJvnpwt36Re1SPxyvLej65agL+r8g1JHX+uWqC+of9clVF1SPu3u2oLrIb/0S9OROF/mtX/fSRV7rF11DsuZ3z15LvIZ3f56KO2l3bG1UKFYeM56eiujQYh6t5XmtX1Q/yS9mLXe7fsVfhmHfCOjTcw+E6XR6WANLIKP+DCAniP65Sr5+kXzUX6XXcqd7Nj4BV8Z/jZR/TkKny20LAX4oN/tlWKrcx2RT81ruThfeyuqpswqzygM2b96MJUuWsEf+8fHxbCDr16+Pl156CX379s0dGB/cWXXcLaYJM2jQII/z4HozJ7fUeZF5Tm758KUc1kWpL1rRrzfXIanqkKMuJev2pL2Lty9hxKpRSEhPsJVVKV4Fc3vMQkRwhE/0376jetlWRo/8c31U5dlRVcNaZMkx4eyIz5Cw9YjTjmqND/sjvFFV+CoZGRlo3bq1xzaRV9KtdujQgb20BB0Mo5eA2G8RYgx2sca+1F8W5PzyofQXG7V9kVKbPGLQUl+0ol9vrkNS1SFHXUrWXdj2LsRdYAH/kzLsO2QxJWKYoRoeFK64PJ5gzbz9n4+q8oaqN9ciZqiOXO5sqAb6o8ait3zaUPUmooxV2haOi4tz2qp2pUqVou08TNvn3rhWiuvlrk+putXQnq/JIwYt9UUr+vXmOiRVHXLUpWTdhWnv3K3zzFBNybQ/Wq5RqgZmdZ+OsMAwxeXxRUPVW2sRM1RHrUDCX4ddDNV+CG9cTXF5irSxeufOHfTr1w8bNmxw8mtw960mr4gBRQUy5MmHROlrpbhe7vqUqlsN7fmaPGLQUl+0ol9vrkNS1SFHXUrWXdD2zsSewcjVY5CSaffVrVW6FmY+Ow2hgfKebpeq/2owVL0xnsxQHb0CCX8espXpA/xQfWE/hDeprpgcWsToaTSA77//HnXr1mWJAdQUy0xtkM+rpzeLmGuluF7u+pSqWw3t+Zo8YtBSX7SiX2+uQ1LVIUddStZdkPZO3jiFUWvGIC3LfoCnTtk6mNFtKkICQhSXx2ND9Z9BsKZe8aqhqvR4kqF6bvTnSNhyt6Ea0ZQbql7LYNW8eXP8888/3D+Nw+FwOByRHL9+AqPXjEN6tt1QrVeuHqZ3m4pgf2UOsYhFTYaqkjBDdewXuLPFHupS5/+fodqshldlK9JxVunxfp06dbihyuFwOByOSI5eO4ZRa8Y6GaoNKtzPdlS5oapurCZzrqH6+wEnQ7XGwje5oertndWOHTtiy5Yt3DetAFAMNW9cK8X1ctenVN1qaM/X5BGDlvqiFf16cx2Sqg456lKy7rzaO3zlCMb+MB6ZOfaDyg9UfABTnn4XgX7KutN42n+3O6ohZWBsudCrO6pSjie5FNA5nU2bNiEpIRERUZHo2L4Dau9KQOqfR5wN1Q/eRETzmpK1zfHQWH3//ffRtm1b5gowYMAAVKpUiQWhdcdDDz1UpPVMB8woIK/S10pxvdz1KVW3GtrzNXnEoKW+aEW/3lyHpKpDjrqUrNsde8/vw+QNU5Flsgejb1K5MSY/9S78jcrfQ570P9dQHQhr6lVVGapSjifFlB86cDDib8Sipl80ovWBOGPJxLqvViFc54chZVujWVh56PyMqL7gDUS04IaqaqIBUKaQw4cPs6gA+SFkFCmqUJxWT28WMddKcb3c9SlVtxra8zV5xKClvmhFv95ch6SqQ466lKzblX0X92PCT5Ngttoj5jSr0gyTnpwAP6MfvEF+/XfcWUxOSkB4RBQ6PtISnaN+hX/2DdUZqlKNJxmqvXu9hAcQjWnluqBsQARgBUwp6bjqdwdfpBzBtMtbML5ye7z8+XREtqwlmfwckcbqwIEDcfHiRba72rhx4zx3VTkcDofD4Tiz+/wevPvTZOSYc6DX5waub1G1BSY+MQ5+Bu8Yqvcy2IYPHYg7cTfRuGoASkYYEHvFhCFrv8SkUCPm9q2CRxtE5hqqdJgqqCS0ABnotKNKhurYsm1h0OUe8yFD1ZKVgzLGMIyMbI45SbuwJOMQ3mwU422RNYtHxurevXvRpk0b/Pnnn9JLxOFwOByORtlxdicmr5sKk8W+o9q6eiuM7TJatYZqn1deQLs6BowZUh1VSgUCVguQk4TzN8Ixc2083lh4Gh+PaI7OQ7RjqBK0k0yP/mlHlRmqVruhKkDlr5ZugoGxm/Dzzz+jW7duXpVZq3gUDaB48eIoX7689NJokJCQEK9cK8X1ctenVN1qaM/X5BGDlvqiFf16cx2Sqg456lKybuKfM/86GaoUWadNjTaqMVRd+087i7SjSobq0v4xToYqLGZUKemPpW+WQrv6wRj5+TVk6yOhJsSOJ7k8kI8qPfq3kqGa7GyoEsaIEFQIjUZNv2IsrCdHRcbqW2+9hY0bN+LmzZvQGjdu3MD+/fttr4MH7XHTOBwOh8PxhG2ntmPK+mlOO6qP1GqLMY+PVIWhmtfOIj36H9O9HAwGnZOhKkBJgUb3qIw78XFsZ1FL0Kl/OkxltVphTkqDJftuQ1Xvn/uAOlofxD7PUZEbwAMPPICYmBgWDYAMV4q5Ghwc7PazjzzyCHyJZcuWYfLkyba/9Xo9GjZsyH5PSkqypZelkBh00pAcuB2/xdE35dTUVFtZTk4OoqOjkZxsz/FMDt9BQUGsjG4Cwmg0suvT0tJsKWrT09NRunRp1oZjWlvKd0x/Uyo5gdDQUFgsFnaNANUTFRWFlBR72j7yL6aMHtQXAT8/PzZ+9DmqQ1iAqE6qj/rgKA+1S9+4BfLSBUH9EaA+U1vudBEXF2ebQ4IuSI/CAT0aB2rHVRcRERFMlnvpguqmOoSxIbloXEgfBdWF49jQONM4uOqCykhfhdUFvVesWDE2Nu7mhTtdOI6NGF0Q1K6rLoSxKaguhLGh90jmwuiC+ulunjrqQhibgurC3Tyl6wqiC+q7u3nqqAt396yrLvKbF6RH6ltBdSGMDX0+MjLyLl24u2cFXSQkJDgdNHGnC3frF7VLMlN5YdcvR11Q+8LY57V+ueqC+kd9ctWF45jmNS/y00V+65cwd93pIr/16166+Pf8Dry/ZSHMFhPboSMertEG/R58A2aTGWmpafe8Z93pwtO1PK/1i/pA1wvzdP369WgcE4AqpQJgteQAOSnMYBVmg5UejfuFo0ppPRpVCWA7i+3bty/Q+iXVWp7X+kW6oP7SZz1dy8MiwnHOnAFTYhqLp+qIITwYMBpYX6mO2+YMVAoJZjK4W798cS13t345rmFKorMKmiwEpBzqlDBIWooGQDur9BKgCTNo0CD2+/bt29mAFwaaMDTxPEHMtVJcL3d9StWthvZ8TR4xaKkvWtGvN9chqeqQoy4l6t5y/E/M+mUurLQr+R+d6nXE8A5DmfGgpnvFtf89undDRMo/WNyvEpCdnLuzKkCGqn8EoMtNtz7go7NICmuF1Wu+h1oQO57fLf8Sg/r1x4dRj7LDVAydDsaIYOj97Ht9V7OS0P/qz1jy+aea91nNyMhA69atPbaJFN1ZnThxomazV9HOIb0EvPUtgsPhcDi+zeajv2PupvlOhurj9z+OIY8OZJs+aofCU9Gpf2Qn0bar/Q29ge2oCoYqcTPRjJLl1OWzKoas6/GI+eEMwvX+LDwVnfo36g0wRoZAZ7T322y1YEXcPkSXLokuXbp4VWYt45GxOmnSJOkl0ShivnWI/cYi9TceOb9BKfXtzFvt+Zo8YtBSX7SiX2+uQ1LVIUddctb965HfMG/T+/Sw3Fb2ZMMnMLBdf9tmj9ruFVd5OjSvgqFrk3D+Zjg7TMXQGwH/cKcjL+duZGDfuSwsGtEZasJT/WZcjMXJNz+EJTaBBfynOKoUnqpvueYo52Co0o4qGar7EY8vFn/Nw3iqzVjlFBzym/HGtVJcL3d9StWthvZ8TR4xaKkvWtGvN9chqeqQoy656v750C9YsPkDp7JnGj2Ntx5+0+mppNruFUd5LNe2oHOxTZgc6sfCU9GpfwNl1fIPczJUzWYrZn1/DcWKl1LdzqIn+k07eQUn31oMU0KuHyhlpprc6GksvLUT/a9vZKf+6TBVvCUDJ3PusB1VMlTJV5cjHwV6DlGhQgVMnTrV6e+CvCpWrIiijqMzvpLXSnG93PUpVbca2vM1ecSgpb5oRb/eXIekqkOOuuSo+6f96+8yVLs3efYuQ1Wq9qREkMd88SeY9r2LAIOFBfzfciQD/T6Ow/k7/nftqPZbcg5bjpnx/geLVbezWFj9phw8hxOvLbQZqkRQtTLo+/OHOHjiKPNJrfbMwzC0qcl+0t8HjhzihqpadlaFA1V5/c3hcDgcTlFn7b4fseTPpU5lPZs9hz6tX/WN/5kUoun0lzCf+NhWRJmpPpnSCyM+/BePjD+DRjEBKBVpYD6q9OifdlRXfLnY5w22xH9P4MzwT2DJtJ+CD61XCTU+fIuFqCLo8JTWD1D5tLFKqVXz+5vD4XA4nKLM6j3f4+OtnziVvdTiBbz84Es+YahSdB/juc9gvvqjU7k+pjs6PTEIj7yWw+KoUniqpKREdpiKfFTp0b/adlQLy50/DuDs6M+dwlOFN62O6gvegCE40KuycUT4rH755ZeoXbs2GjdunOdn9uzZg6tXr+Lpp59GUcYxtqGS10pxvdz1KVW3GtrzNXnEoKW+aEW/3lyHpKpDjrqkqvvbXavw2bblTmUvt3wJLz/4oiztSY3VYoL50BwYr2xkoZkEDDVfg776K8zYJoPU13YWC6LfuHU7cX7ySsBiPwgX1bYeqs7uA32AunyKizIexc7o3bs3vv3223w/8+mnn6JXr14o6vBoAPLryRPUfgrXl9FSX9QIjwagrmgAX/278i5DlR7738tQ9bQ9qbGas2HeOxGWy7/Yd4B1gKH+cBhq9PaJXWFP9Xtz5V84/+7XToZq9ONNUHVuX26o+urOqmsmqjVr1uDAgQNuP0vZEHbv3s2yJxV1yMGbsj8ofa0U18tdn1J1q6E9X5NHDFrqi1b06811SKo65KiroHVTchhKPUq54hMSExAVGYWOHTsiKToZ3+1b7fTZN9q8hh5Nu4tqTymsOWkw7xkLS9y+3L8pO5XBD8aG46Av1wG+Tl76JZeHa8t+xbWlvziVl3zuIVQc9Sx0PhADt6hRYGN169attt/pmxY94qdXXlBg/ZkzZ6Ko40GCMEmuleJ6uetTqm41tOdr8ohBS33Rin69uQ5JVYccdRWk7s2bN2PgkEGIvXUTQWWCYQwxwnTKhG++/wa6QD2qd62J6OrR7LN04r9b42dEtacU1uwkmHa+A2vCCXuZ3h9+TaZDX+pBaAF3+qWyy/PW4ubXfzmVl+nbAeUGdvXpnWQtU2BjVchbSwNNfiBDhgzBe++95/azPFoAh8PhcHwdMlRfePkF+JcPQN0n6yMoOpiVp2WlI+lWEmK3Xcfx1UdQu0ddvPvWu3jqgSfgC1gz4mDaMQzWFIfD0n4hyK4zEYEaMVTdYTVbcGHKN8xP1ZHyQ59Emd6+Hc1A6xTYWDUY7FkbVqxYgTp16jiVcfJQsNHolWuluF7u+pSqWw3t+Zo8YtBSX7SiX2+uQ1LVIUdd+dVNj/5pR5UM1ZrP1oZOr7MZqpk5mQiICkD5rpVwZcNF3NoSi84fdhTVnlJYU6/AtGMorOmxtjJdQBQMzefB4FcWWsJRv5ZsE86N/YKd/Leh06HyuOdQ4tlW3hGQU2A8csx45ZVX8o0EwLETEpIbn03pa6W4Xu76lKpbDe35mjxi0FJftKJfb65DUtUhR1351U0+qvTov1K7ysxQpSfKaVlpzFAVoPJK7WKQmpTCwjqJaU8JLElnYPq7v7OhGlwSxlZLoI+srrl7V+iPOSMbp4cuczJUdQYDqs7szQ1VH4F7EctMamqqV66V4nq561OqbjW052vyiEFLfdGKfr25DklVhxx15Vc3HaYiH1V69E+GampWKjJzspw+HxIQgqhSkexzFH9UTHtyY4k/BNM/A2HNSrCV6cIqwtjqI+hCyysujxJQf0wp6Tj11mIk/Wv3zdX5+6Ha+68julMjr8rHKTj8eZ3MmM1mr1wrxfVy16dU3Wpoz9fkEYOW+qIV/XpzHZKqDjnqyq9uOvVPh6nonEZKZgpyzCanz4YGhCLALzeOJ30uMTFRVHtyYondAdOecYDZnp1JF1UTxmbvQRcQqbg8SpF1OwkXRn2F9FP2w+D64ADUWNgP4Y2reVU2TuHgxiqHw+FwOC5QeKqckzlIykiG2eJsxIUGhiLAaA84b0ozITLSbvSpCcvV32E6MA1w6IO+eCMYmsyAzk9bj/0dybqZgIsDP4bparytzBAejJpLBiC0bkWvysYpPNxYdeHGjRvs5ehkLwaKjOCNa6W4Xu76lKpbDe35mjxi0FJftKJfb65DUtUhR1351d24VWN8tforRMeXYIepCAprFBYYBj+D/V9nenw6Mq6no3PnzqLakwPzhbUwH5kPOERw0pd+CIZGk6Az+Gv23s28fAsn3liE7Bt3oP8vFJVf8QjU/GgAgquW8bZ4HG8YqxaLBdeuXUNKSgpLwerrLFu2DJMnT3a6eRs2bMh+T0pKQnZ27mOUsLAwlvwgIyPDyZmbFjNHvx/KoEGPkSg4sQCF/qJyKhPiwNGpRbo+LS2N1Su0TVAbQrsEBTmmvzMz7Y7+oaGhbCzS09NtZcHBwayMxkaAUuYFBgayvgj4+fmxz9Ln6PMERXqgOqk+IWyZEH+O2nU04vPSBUH9cdQFteVOF6QHQSZBF6RH4bEU6YLacdVFREQEk6UguqA6HMeGriN9FFQXjmNDuqBxcNUFlZG+PNEF1UVj425euNOF49iI1QW166oLYWwKqgvHsaF6CqML6qe7eeqoC2FsCqoLd/OUriuILqjv7uapoy7c3bPudJHXvCA9Ut8KqgvHsaE+uOrC3T0r6IJ053i9O124W7+oXarT3by41/rlqAvSmVBHXuuXqy6of9QnV12Q7O7u2YLq4l7rF9V9OekyfrnzG4xBfiw8VbmuFVmd4YHh0Ov0sAgZj6xWXNpyAcXvK47WrVuza/Nay92tX6SLgt6z7nSR51qenAzdhW/gd+HL/8ZCz/RrLvUocqoNhi4tE+Hh/m51QfKJWcsLun5JuZY7rl+Z527i8vDlMCekMkPVQuE2SxdD+fmvwlIqN0GA3Gu5u/XLV9dyV1046kNJdFYPoxJTQoCRI0eyE5DUWeokdbhv375MwXPnzkWZMmU0sbM6aNAg9vv27dsLnR6PBtbTlHpirpXiernrU6puNbTna/KIQUt90Yp+vbkOSVWHHHW5q/vgtUOYumEGsk1ZiD8dz+KohlWJQNX21RBaPNRpR5UM1ewrWfjmq2/Qvn17VfSFslCZj30IyznnzFqGqs9DX3tAvkHvff3eTTl0AacGLIE5NdegIvOGdlJrLh0I/+IR3hZPE2RkZLAvZp7aRIrurN6+fRvNmzfH9evX0bJlS/bt4dixY+y9ihUrsp3Jv//+G3v37kXx4sXhS1DmLXoJiP0WQd9OPB1MMddKcb3c9SlVtxra8zV5xKClvmhFv95ch6SqQ466XNl46Fcs+/sTZvARlJmq81tdcPTHwzjx2VF7Bqs0E3v0X7JEKXz+1QqPDFU5+mK1mGA+NAeWy85pRA213oC+2kv3zM7ky/du0s6TOD3sY1gy7DuCgTXKotbHQ+AXqV3fXCWhzbsff/wR3sAjB5UpU6YgNjYWv/zyCzNK27VrZ3tv4sSJ+P7773HlyhWnx+kcDofD4agR2oH76t+VWPK/pTZDlWhZtSVWTf0Gxw4fxfKPl6NLs8fRvFwz9pP+PnLwsMeGqtRYzdkw753gbKjqAEP94TBUf1nTaUTv/HkIpwZ+5GSohjWqhgrv9+WGqoTZ3Oo2aIg3hwyFN/BoZ5Usa3Im79Spk9v3n376aTz88MPMmOVwOBwOR63QSf+FfyzGxkMbncq7NuiCge36w6DPzdTYrVs39lIj1pw0mHaPgfX2fnuh3gDjAxOgL/sotEzcz7tx/t2vaSBtZZEP1UW1OX2QkuUd/0otGqq9XnkFmdVqI+SVt4Dvv1BcBo/dAO7ljxoTE4N///0XRR1ylPbGtVJcL3d9StWthvZ8TR4xaKkvWtGvN9chqeqQo66snCxM+3kmdpzdwf7W/5dS9dVWr6BX856y70ZK0RdrViJMO9+GNfGUvdDgD2OT6dCXbKG4PEpy87v/4dKsNU5lFOi/ytSXoPczIiLw7ogHnMI/+h8wZCgzVMP69IfeS7F4PXIDqFKlCnbt2pXvZw4dOoSSJUuiqCMm9JXYsFlir5e7PqXqVkN7viaPGLTUF63o15vrkFR1SF1XckYKRqwebTNUCR30eLvjMLzQopcij83F9sWacQumfwY4G6p+ITC2WFBoQ1UKeZR027j26W93GaolurdCzPRXmKHqS/1RMxs2bMCNuDgEP9EdOr0BaTnOyTFUbay++uqrzBh9++23ncItCAeSxo4dyw5XPffccyjqOIaeUPJaKa6Xuz6l6lZDe74mjxi01Bet6Neb65BUdUhZV2xSLIZ8MwzHrx+3lfkbAzC64wh0ru/evU0OxPTFmnoZpu39YE25ZCvTBUTB78HF0EfXV1weJQ3VKwvW4eqHG5zKS7/yKCqNfQ46g96n+qN2Nm3aBGulGBhLlEJKtglp/4W58gk3gGHDhrGDVe+//z4++eQTFtOLaNSoEc6ePctieVFsUjpsxeFwOByOWjgfdwGj14zDnTR7ZqOwwHDM6DYFZUJ8I9yiJfE0zDuHMxcAAV1wKRhbLoAupBy0itVswYXp3yFurbOLYfnBT6BMnw5ek0vLJFC81vAIJGXnINNs9myHUwI8apeCyf70009YunQpcwm4c+cO+7Zz4MAB5vNCO6tkzAoBdDkcDofD8TYHLx/C0G/edjJUS4aXxKIX3ketMrXgC1jiD8H07yBnQzW8MoytPtK0oWrJMeHsmM/vMlQrjenBDVUZCQ0LQ1ZCAjNUvUmhd1bJKKWdU9pNfeONN9iLHv0nJCQw49TXHLTlhjJEeONaKa6Xuz6l6lZDe74mjxi01Bet6Neb65BUdYita+vJbZi1cTZMFvtjzJjiMZjx7FREh0ZLLmdBKGx7lpv/wLR3AmC2h2jSRdWGsfl70PmHKy6PUlgys3FmxHIkbj9qLzToETP5RdzXpanP9cdXiMvIxMGSFWG58BMQFwtd8ZLQUTw0X9hZpSxVdHCKXAEEKIgwRQfghurdCKnNlL5Wiuvlrk+putXQnq/JIwYt9UUr+vXmOiRVHWLqWrvvJ0zbMMPJUG1YoQHm95xrM1SllrMgFKY9y9XfYNoz1slQ1RdvBPboXwJDtbDyKIUpNQMn+y9xMlR1fkZUn/d6voaqWvvjK1xKScOTm7bjVtXa0JPR/8uPgMWKyAA/3zBWKQduixYt7hkNgJOLYx5fJa+V4nq561OqbjW052vyiEFLfdGKfr25DklVhyd10ZPAT/73GZb8+RH9ZSt/pNbDmPHsNIQEhPjEOmQ+/wNM+6YCFvujWH2ZNjA0mwudMVhxeZQiJyEVJ19fiJT9Z21l+iB/1PjwLUS1redz/fEVjt5JxBObtuNiShp0fn6I6NUH+pNHEfDNp9DFx/mOz+pHH32Ea9euoW/fvoiL847gHA6Hw+HkRY45B7N/mYtVu1c7lT/buBtGPzYSfgbv7BAVBjK2zaeWw3zkfadyfYXHYWg0BTqDduOIZt9KxPE+C5B24oqtzBAWjFrLBiGiWQ2vyqZl/rkRh2d++4e5AAjUbNEKny9fjrK3byB9/lTfiQbQs2dPBAYG4vPPP2ev4sWLw9//7puG4tRdumQPq8HhcDgcjtykZ6Vj8vpp2Hdxn1P5m23fQPcm6sxC5QqlfTUfXQTLeedYooaqvaCv/Zam06dmXrmNk/0WIeua/SCc333hqLlkAIKrl/WqbFrm50vXMGD7PuQ4uE/cHx2Fr9s1R3RgALp37oi1a9di3rx5vrGzSqf/6UapUKECe5HPKkUIcH3p9d4KcqAegoODvXKtFNfLXZ9SdauhPV+TRwxa6otW9OvNdUiqOgpa1520BLy9aqSToWrUGzG2y+h7GqpqWYesFhPMB2bcbajW7gdDnf6yGapquHfTz1zH8T7vOxmq/qWLofaKYYU2VNXQH1/hi1MX8Ob/9joZqm3KlMD3HR5khipBB+ufeuop39lZvXjxovSSaBQxBrtYY1/qLwtyfvlQ+ouN2r5IqU0eMWipL1rRrzfXIanqKEhd1xKuY/SasbiRdMNWFuQfjMlPTcQDFRsqKmdBcNee1ZwN894J7OS/DR1gqD8ChkpPKi6PkqQeuYiTA5bAnGz3NQ2sVBK1lg2Ef8kon+uPL2C1WjHv0CnMP3zSqfypSuXwwYMPwM8hyYI3UYcUGiY1NdUr10pxvdz1KVW3GtrzNXnEoKW+aEW/3lyHpKrjXnWdunkag1cOdTJUi4UUw/s93yuQoSq1nJ60Z81JhWnn286Gqt4IY6PJshuq7uRRkqTdp3HizUVOhmpwzfKovXyoR4Yqwdei/DFbLBi189BdhurrtWLwYetGqjFUPd5ZLWhmKnpUMXnyZE+a4HA4HA6nQOw+vwdT1k9DZo79UEjZqLKY9ex0lI4s7RNatGYlwLTzHVgTT9kLDQEwNp0BfYlm0DIJ/zuCM+98BqtD3vmwhjGovvBNGMP4o3w5yDKb0X/7Xvx62f7ljhj3QB30r1NVdT7RHhmr06ZNy/M9oYO0tcyNVQ6Hw+HIyW9HN2P+bwtgdgjrVLN0TUx/Zgoign0jSY01Ixamf4fCmmo/+Q6/EBibz4O+WF1omdu/7sX58V/B6pAhKeLB2qj23mswBGk32oE3Sc7OQe+/dmFn7G1bmUGnw3stGuK5qhWgRjwyVv/66y+3yQLo4NXOnTuxfPlytG3bFrNnz0ZRh6ImeONaKa6Xuz6l6lZDe74mjxi01Bet6Neb65BUdbjWRRsi3+5aheXbVzi93zymGcZ1GYMg/yCvylnQ9qypl3MN1YxbtnJdYDEYW8yHLryq4vIoSezq7bg4czUNpq2sWIcHEDP9Zej9PDJPnOBr0d3Epmei15Z/cSIh2VYWaDBg2UNN0L58KagVj2ZDmzZt8nyve/fu6NevHx544AGsX78e77zzDooydHrOG9dKcb3c9SlVtxra8zV5xKClvmhFv95ch6Sqw7Eu2kVd8udSrDuw3um9TvU6Ymj7wTAajD4xd/0yLsK0Yzis2Um2Ml1IGRhbvA9diPIhmpTs//UVv+PKB+ucyoo/3RKVxz8PnUS+knwtcuZ8cip6/vEvrqTa/YIj/P3x1SPN0bhEMagZWbxnq1Wrhs6dO+Pjjz9GUScpKckr10pxvdz1KVW3GtrzNXnEoKW+aEW/3lyHpKpDIC4+DtM3zLzLUH2xxQt4u+Mwjw1Vpeeu5fYBZG8f4GyohleBsdUSrxiqSvWfdsQvf7DuLkO11MvtUHliT8kMVYKvRXYO3k7AE79udzJUSwcHYV2nVqo3VAnx++x5kJmZiatXr8pVPYfD4XCKGKmZqZi8cRpOxtpPL+t0egx+dCC6NngcvgKd9jftGQ+dOQvQ5RpnumJ1YGz2HnT+YdAqVosFF2euwa01253Ky/XvgjKvd1TdoR6t8L/rt9B3626km+wH2KpFhOHbR1uiTEjh3WV8xli1OASNdSUjIwM//PADNm/ejJiYGDGycTgcDofDiEuJw5jvx+P8rQvQ63ONGj+DP8Z3HYMHq7X0GS1ZrvwG04HpZLnZyvQlmsDQZAZ0Rt8wHDzBkmPC+XdXIv6XPU7lFUd1R6meebsWcsTx44WrGPLPfpgc7LZGxYvhy0eaIyrAdw6weWSsGo3Ge34Doq3+UaNGoajj5+fnlWuluF7u+pSqWw3t+Zo8YtBSX7SiX2+uQ1LUcfH2JRbs/3bqbQj/ekIDQjGt2xTULVsHvjJ3zefXwHzkA4cSHfRlHobhgQnQGbxvOMjVf0tWDs6MXI7E/x2xF+p1qDL5RRTvKl9YrqK+Fn164hwm7nHQOYBHy5XCsocaI8go24N1WfBI2oceeihPY9Xf3x/ly5dHr1698Mgjj8DXuHHjBnsJZGVliaqPp1uVX0+eoLY0fGqTRwxa6osaKWrpVo9cPYoJa99FalZuwHr631M8rDhmPjsdle6rCF+Yu7R5Yzm1HOZTzpELDJWegKH+O9DpDVADcvTfnJaJ00M/RvKe07YyndGAqrP7oFi7+yEnRXUtslqtmHngOD48esapvEdMBcxt3kBVwf5lNVa3bt0KrbJs2TKnRAaUrq1hw4Y2Z+3s7Gz2e1hYGAvXRW4PAiEhIWwhdcyaYTabERUVheTkZCeDPigoiJXRpBJ2q+n6tLQ0Vq/g91uyZEnWhtAuER4ezv6m9wVCQ0OZe0Z6ut15mv6OiIhASkqK0+lICufh6HhO3z7ppqbPCS4eBoOB1Un15eTksDKSo1SpUqxdRyM+L10Q1B8B6jO15U4XN2/eZD8ddUF6JP0J40DtuOqC+key3EsX1D+qQxgbeq9YsWJMHwXVhePY0DjTOLjqgspIX4XVBclD84TGxt28cKcLx7ERowuC2nXVhTA2BdWFMDbUFrVRGF1QP93NU0ddCGNTUF24m6d0XUF0QX13N08ddeHunnXVRX7zgvRIfSuoLoSxIfmpf666cHfPCrqIj49n9eanC3frF7VLOhTGoDDrl6MuEhMTbYZDXuuXqy7o8zvO78T0DbOQY87tD+2RVCxWEeMfG4sov0imE3fzIj9d5Ld+CXPXnS7yW7/y1YXVAr+zH8N4bcN/n8n9nKlCD5grv4qAHBMyM1Pvec9Sn6Ray/Nav6ic6hSzljvqAunZuDLic6QcuSh0G/pAP9T4oB/8G1R0kl3q9YvkE+aDkmu549h4Yy03+vtj/P4T+O7sJTgysG51DKpWHumpKQVey93pwlEfSqKzOq5CIiHFHj16FMWLF0fFitJ+4/XmzuqgQYPY79u3b7cZVAWFbh6aeJ4g5loprpe7PqXqVkN7viaPGLTUF63o15vrkKd1rD+wAYu2LIHVwbezfvn6eOeRYShTogx8Ye5aLTkwH5gJy9XNTuWG2m/BUO0F1d0rUsqTHZeEk28tRsbZ67YyQ2gQanz4FsIaVIESqE2/cpNhMqHftr34/epNp/JJjevijdrSxOwlY7V169Ye20Se4vFe8I8//ohWrVoxYYm9e/eiQoUKaNasGapUqYJXX33V9u3BlyhdujSLESu8GjRo4G2ROBwOp8hA+ycrtn+BhX986GSoPlS9NUufGhKQu7uldqzmLJj3jHM2VHU6GBqMYoaqlsm6Fo/jfRY4GarGYmGo9dlQxQzVokZiVjZ6/P6vk6Fq1OvxYatGkhmqPucGQIZqt27d2BYybf0TAwcOZI96KCHAkSNH8OWXX6J69eoYM2YMijK01e6Na6W4Xu76lKpbDe35mjxi0FJftKJfb65DhanDZDbh/c0L8dvR35zKn2r4JN565E0Y9AZkG+yPJ9U6d605qTDtGgVr/CF7od4IY6N32YEqqduTCinkyTh/EyfeXIScOAeXopJRqPnxIARVLAElUZt+5eJ6WgYL9n8mye4mEmw04tM2TdC2bEloAY/cAFq0aIEzZ84wo5R2Ii9fvoxKlSph8ODBWLBgAfN/qFOnDvPROHbsGHwZb215czgcTlEiIzsD0zbMxK7zu5zKX3uoL55r2t1nYnBas+7AtONtWJMcDrcYA2FsOhP64k2gZdKOX2aP/k1Jdp/OwAolUHPZQASUVn/geV/kTGIKnv/jX9xIt/uSFgsIwFftmqPhfVGSt+dTbgDkl9qhQwdmqBJ//vknW0iefPJJ9jcZqZSS9fz58yjqODpiK3mtFNfLXZ9SdauhPV+TRwxa6otW9OvNdaggdSSmJ2LEqlFOhirtoo56bASeb9bDyVBV8zpkTb8J09/9nQ1Vv1AYW3zg1lBV270iRp7kfWdx4vWFToZqcI1yqL1imNcMVbXpV2r2xd3Bk79tdzJUy4UEs6xUchiqPucGQI/+6XSYwF9//cVOvz344IO2MjqNWFS24PNDOGWn9LVSXC93fUrVrYb2fE0eMWipL1rRrzfXoXvVcSPxBkavGYdriddsZYF+gXj3yQloUrmxLPJ4Iue9sKZcgmnHUFgz4mxlusBoGFu8z9KoSt2eHHgqT+Lfx3B6+KewZttPtIfeX5kdpjKGeS98lNr0KyV/XL2JN/63B5kOZ4NqRYXjm3YtUTI4EFrDI2OVHvH/8ccfLFwChRwiH9aHH37Y5r96584dZsBWq1ZNank5HA6HoxHOxJ7F2O/HIyE9wVYWERSBGc9OQ41S1eErWBJOwLzzHViz7X6aupCyuYZqiDyRC5SGIuNs2LABmzZtQlJCIiKiItGpUye0DCiLq5O+g9XBaIpoURPV5r0OQ3CAV2XWKqvPXsbbOw7A7ODF2axENL54pDnC/bWZCMEjY3XcuHHo3LkziwFKsbfo1H///v3Ze8uXL8fs2bNx69YtTJgwAUUdMX5WYn20pPbxktNnTGl/NLX5v6lNHjFoqS9a0a8316G86th/8QDeXTcFGdn2uJFlIsuwYP9lo8r4zDpkidsH0+7RgMn+KJZ2UpmhGhgteXtykpc8lD596MDBiL8Ri5p+0YjWB+KMJRPrv1mNMIsRQ8q2RrOw8uyzxdo1QMzM3tD7ez9Dktr0Kxar1YqPjp3FtP3OZ4E6lS+NJa0bI9Co3afZHs2mjh074rvvvmOHqSiOWZ8+ffD444+z9+ibF0UFoPcGDBiAoo6ju4SS10pxvdz1KVW3GtrzNXnEoKW+aEW/3lyH3NWx5fifmPvrPJgs9se01UtWx/RuUxAVEqW6dchxVzE5KQHhEVFsV/HxxlEwHJlGie9tn9UVqwtjs7nQ+Yd53J63cCcPGaq9e72EBxCNaeW6oGxAbtxSc3oWriTF4YuUI5h2eQvGV3gEj7/QA1Um9mQZqtSA2vQrBovViil7j+LjE+ecyl+oVgkzm9VnYaq0jKRJAQSHZiHLgxYQe/KNEiVQJgtPEHOtFNfLXZ9SdauhPV+TRwxa6otW9OvNdci1jtV7vsfHWz9xer9xpUaY+OQEBPsHqW4dImNt+NCBuBN3E42rBqBkhAGxSWbsPZOOqGAr5vatgkcbRLLP6ks0g6HJNOiMQT55r7jKQ0Z6w3r3o8odPcaWbQuDTm9LoUrGKvvdasGcxJ04G5KFw5fPIFBFEXPUpl9PyTFbMPTf/fjxwlWn8mH1a+Cd+2squoPsrWgAovbpKR0XpV49fvw4mxSUuapp06aoV6+edBL6OHSze3qziLlWiuvlrk+putXQnq/JIwYt9UUr+vXmOiTUQWcalm39BD/sW+v0Xvs6j2J4x6HwM/ipbh0iQ7XPKy+gXR0Dxgypjiql/nvPnIHzVxMwc2083lh4GssGVUPHLt1geGACdHo/n71XXOWh3WR69E87qoKhakrNgCXDHuuWynuXaIxB8Zux8ZdfWAx2taA2/XpCWo4Jr/9vN7Zev2Ur00GHaU3r4dWaRSfBgsfG6u+//47evXuzA1aOm7Nk4Tdp0gRLly7l2Z84HA6HgxxzDmZunI2/Tm510sbzTZ9D34deVaVvIRk6tKNKhurS/jEwGEhGK2BKZ/6pVUr6Y+mbpdBv2U2M/OoW2o0aDWMhDFVfgNweyEeVHv3T/3lzSgYsWXaXB8IQEoiKwRGomVwMv/76q6qMVV8nPjMLL23ZiYPx9gOIfv9lpepaqSyKEh4Zq4cOHcITTzzBtn/ffvttZpwGBwfj+vXrbHJv3LgRbdu2xb///ovatWtLLzWHw+FwfIK0rDRM+2UGjt5wPBSiw8B2/fHUA09ArdCuIj36px1Vu6GaBpgybZ8x6HUY3b0s2k26io2/aM9Qo1P/dJjKarbAlJTudOKfMIQFwRCYGwUoWh/EPs+Rhiupaej5xw6cT061lYX6+WF526ZoVbp4kVOzR8bq5MmT4efnh927d6NqVeecs6+//joOHDiAVq1aYcqUKewgVlGGH7CSX09acLxXmzxi0FJf1IgvHbCKT43HmO/H43ycPUGMUW/EmC6j0KbGQ4rLU5i6aeOFfFTZo3+rBchJcTpIxfALQUy5IDSKifNoV1Ft94qrPBSe6kxOOnISUukousM7OhjDg6APsO8kx1syUC0q13dXLahNvwXlREISev2xA7EZ9i9G9wUGYGW7FqgXrS4dK4VHx8f+97//4bHHHrvLUBVo2LAhunTpwvxZizoU2ssb10pxvdz1KVW3GtrzNXnEoKW+aEW/3liHrty5gkErhzFDVbBzQgJCMKv7DI8NVTHyFLZuOvVPh6mYgZqd6MZQDQUMuYdLSkUakJyU6PP3iqM89Ni/iV9pHEu7ies5ybZynV4PY2SIk6F6NSsJJ3PusJCWakJt+i0Iu2Lj8fRvfzsZqhXDQrC+U+sia6h6bKzSabCoqPzDi0RHR7MQVkUd0pU3rpXiernrU6puNbTna/KIQUt90Yp+lV6Hjl8/gcErh+NWcqzN8CkWEo33e85Dgwr3eyyLp/J4UndYeCRiE7KBnKTcnVUB8q/1DwcM9oM7NxPNCI+I9Pl7RZDHnJ6JsyOWo/Y/txFhDGThqejUv87PCGNUKPR+9tBUVL4ibh+iS5dkm1RqQm36vRebLt/Ac7//g2SHTGB1i0UwQ7VSeCiKMh4ZqzVq1GC7qxaLww3swj///INy5cqJkY3D4XA4PsaOszvxzqpRSMm078aViyqLRS8uQJXileELWM1Z6FAzA3vPpOD8TfvJd+gNgH8koM/10yTO3cjAvnNZqttV9JTMK3E49vJ83PnjAPz1Bhbwf29OLOam7kFsoAk6vc5pR3XGta3Yj3h8sHgRS7vO8YyVpy/itf/tRraDXdWqVHH80KEVigf5dkQDrxmr5Jd68uRJPPPMMzh2zDmTwtWrV1mUgKNHj6Jnz55SycnhcDgclfPL4V8x8afJyDblxuAk6pStgxlPTkPJ8BLwBazpN2H6+y08VvkiokL9WHgqs8UKGAJyDVWdw66i2YpZ319DseKlVLer6Ampu07jaK+5yDh73VbWPKoSPhw3HRdK6dH/2s8YeflXzL66lf3sf/VnnC9mwRfffo327dt7VXZfhZ46LDh8CiN2HmSB/wW6ViyLr9s1R5hG06cqlhSgb9++WLFiBQs5UqZMGfaKj4/H5cuXYTKZ8NBDDzEHdV+PcSY2AC7pwmj0LEKYmGuluF7u+pSqWw3t+Zo8YtBSX7SiX7nXIfo38tW/K/Hlv185lbes2hLjuoyGQWeQbE7IOb9ybuwEDk6BNTt3V/iPg4ksjmq7+8Mx5rmKqFIqyGlHlQzVLcfMWPHlNx4Za2q5V2j8bnz+B64sXO90kMqveASqz38dofUqsVBeP//8MztIRqf+6fAV7SaTka7WHVW16DcvzBYLJu45ihWn7AcQiVdrVMGUJnVhUGFWqgwvJQUQlcGKQnt88skn2LNnDzNUSei6deuiV69e6NevHwwGdaRcEwM3Vu1wY7XoLqpFtS9qRG3GqtlixsI/FmPjoY1O5V3ufxyDHh0Ag94g6ZyQY37Rv0HL2a9hPvGxk7GmC4jClpTOeOfdBSyMVaOYAHaYinxU6dE/7ai+/8Fij3cV1XCvUCaq85NW4s7m/azrQsjbsAYxqPpeH/jfl5te1RdRg37zIttsxqC/92PDpWtO5SMb1MKQetVVGXvYZzNYde3alb04+aefjYiIUPxaKa6Xuz6l6lZDe74mjxi01Bet6FeudSgrJwvTfp6JHWd3OJX3fvAVvNCip+0frpRzQur5Zc1JhfnADFhubIOVDhH9l6lJV6wOjI2nolNQCTz8RF/7rmJSIkqWi8SiEeJ3Fb19r2ReuY3Twz9GxpnrNqOdxqxE99aoOLIb9H7qNPR8Rb95kZKdg75bd+Pvm3G2Mr1Oh9nN7scL1St5VTa1ImomUtD/tWvX4syZM+wRAUUAaNCgAfNljYmJkU5KDofD4XgNWt/pSRq5diUkJiAqMgoPPdIGu7J341Tcadvn9DoDhnUYjM71O/nEaFlTLsK0ewysqVecyvWVnoSh7hDoDLkHqcggpRiqWgr6n7TjJM6MXA5zSrqtTOenR+XxPVHi6ZZelU3LxGVk4oUtO3D0TpKtzF+vx9KHmqBThdJelU1zxipFAaBDVp9//rlTqlXi22+/xbhx4zB69GiWFIDD4XA4vsuff/6JUWNHI/bWTQSVCYYxxIickzn4avXXMAYbUb1rTURXj4a/MQATuo5Fi6rN4QtYrv8F04EZLHWqgFXnB2PDkTBUeBxahfmnfvGffyodHHPwTy096XmUeLCeV+XTMheTU9Fryw5cTEmzlYX7++Hzh5uhecn7vCqbJo3VOXPmsMNVjRo1wsSJE9G8eXOEhISwdKvbtm3DjBkzMH36dJY04OWXX0ZRRow/h1hfEKl9SeT0TVHK78Vb7fmaPGLQUl+0ol9Px2Tz5s14o98b8K8QgLpP1kdQdDBMFjOSM5IRfacEYrddx/HVR9D4haZYOH4BapepJWn7ctRltZhgOfkJzGdWOpXrgktC32ASDMXrafZeYf6pk7/Bnd/2OZWH3l8Z1ea9BoRr695V01p09E4iy0p1O9MeKaNkUCC+ebQFakWpz1VBbXh0wKpKlSrs5/Hjx92e9r9z5w7q1auHEiVKsNSrvoxYZ2LBB8gTxFwrxfVy16dU3Wpoz9fkEYOW+qIV/XpyDT36r9egPtJC01Dz2dosvmaOOQcpGamwIvffhtVixdWfLyE8LQwnjpzI039Tyjkhak3NSoB53yRY4pyNNX3xRjA0mgz4R2h2Hcq8ehunh9n9UwVKdG+FiiOfZf6pWrt31dKfv2/Eoc/W3Uh1yKhVJTwU3z7aAuVDQ+BLZHjpgJVHcRFu3rzJTj/mFZaqWLFizPGcfFmLOsnJyV65Vorr5a5PqbrV0J6vySMGLfVFK/r15BryUaVH/xUersQM1cycLCRnpNgMVYJOWldrX51Fg6EDSFK2L3VdloQTMP2v712GqqHaizA0nw9dQKRm16GknSdxtNccJ0NVZzSg8oSeqDzuedtBKq3du2roz/qL15iPqqOh2iA6Cus6tfY5Q9Xn3ADq1KmDW7du5fsZ2l3lGaw4HA7HN6HDVOSjGhgdhJTMVGSbHDI5kY+jwYiwwDDognXsc3RSXq0HkMyXf4b50DzA4pAr3hgEY8Nx0JdpC037p365BVc+WHeXf2q1915D2P2+kVHMV1lx8jzG7z7i9AWvbZkS+KRNU4T4eKQFpfFIW+Sn+uyzz+KXX37BY4895jbVKn0rX7RokRQycjgcDkdh6NS/PtjA/FMtVufU2v5Gf4QGhNpictKhq8TERNWNkdWcDfPRBbBcXO9UrgurAGOTmdCFVYRWMWdk48LklYjf5OKfWj/XP9W/OPeTlPNLwtyDJ7HgyCmn8mcql8f7LRvCz6C+YP+aNFZPnDjBQlRRjNUWLVqw13333cd8Gfbt28e+kVeoUAFXrlxhhq0A+Y5MnjwZRQkxMfjEZgWROquInFlKlM6AoraMK2qTRwxa6otW9FvYa+ifbZIpCelJabBYzbR6294L9g9CoF+QzVAlTGkmREZGSiqz2LqsGbdg2jMO1oQTTuX6Mm1gaDAWOr8Qza5DWdficXr4J0g/ddWpvES3B1FxVHfo/Y1F4t71Rn9MFgvG7DqMlWcuOpW/USsGExvXZfFUOQodsNJ7mAKMjFWzmRY+38FbzsQcDofjDZLSkzB30zys+3EdTq07gaq9ayIgKgB6nR6hgaHs8b8j6fHpOPbJYSz/eLlq3ADIL9W8711Ysxx2e3U6GGr1g75qL1UcupGLpF2ncHbkcpiS0pz8UyuN7o4Sz7byqmxaJ9NkRv/te7Hpyg2n8vEP1MFbdapqYt5l+FIGq7/++kt6STQKOXiHh4crfq0U18tdn1J1q6E9X5NHDFrqi1b0W9BrDl85guk/z0R8ajyK1y6B87+fY+GpYp6ujtCg0Lt2hSgawKUtF1CyRCl2qFZKmT2pi6VNPfcdzMc/IuFs5Tr/cBgaT4G+eGOP65YDKdujvt/86k9cXvCTs3/qfeG5/qkNqigqjxpQsj/J2Tl45c+d2HUr3lZm0Okwr0VD9KhaQREZtIxHxipZ1QXZXU1KSlJlqjMl8WDjWpJrpbhe7vqUqlsN7fmaPGLQUl+0ot97XWO2mLFyx7f4asdKlnKU0Bv1qP1kHZz+/iSu/XwZldpVZnFWHXdUyVDNvpKFz79ake8jVynnRF51WU3pMB+cBcu1P53KdZE1YGwyHbrgUh7XLRdStcf8U6d+i/hf9ojyT9XavatUf2LTM9Fry784kWCPPhBoMODjNk3waLl7zzuOTMZq48aN8dlnn6Fhw4Z5fmbVqlUYNmwYSxTA4XA4HHVyO+U2ZmycjcNXDjuVly9WHh9PW4pDnQ6yDFZHPzlsy2BFPqoZ19PZjioZqhTK0JtYUy/n+qcmX3Aq11d4HIb6b9vSpmqRrOvxOD3sbv/U4k+3RKUxPfL0T+WISDmclISoiAh06tQJtR9qi97b9+Nqmj1tbYS/P75u1xyNihfjapYIj2bxwYMH0axZMwwdOpSlVHWMt3r16lW89dZbLFKAwWBAUYfiEHrjWimul7s+pepWQ3u+Jo8YtNQXreg3r2t2ntuFOb/OQ3KGPU850bFuRwxs9xaC/INQskMJFvWF4qhSeCo69U+HqTp37swe/RfkEIuUc8K1LsvNv2HaPxXIsftoQm+Eod5wGCo9IapuuRHbXl7+qXSIqmT3wvunau3elao/lMltwJChuBEXB2ulGCA8ArhwBd/8tA6moGCE9XwVAXUbsM+WDg5iwf6rR2rHncJnD1hRaCoySI8ePYqYmBgsW7YMDz/8MBYuXIgJEyYgNTUVTZo0wdKlS/PdffUF+AErDoejNSgT1WfbVuD7vT84lQf5BWFoh8FoV/sRqB2rxQzLqeUwn/7CqVwXeB8MTaZDX6wOtArzT125FZff/5F8OGzlftHhqDavL8IaxHhVPi1BhmqvV15BZrXaCH6iO4wlch/rZ5stSLh2FdZf1gInjyCq7yDUa9Ua3zzaEmVCtHsQO8OXMlg9+OCDLI3qnDlzbNmsyGgdPnw4+yazZMkS7Ny50+cNVSkgw90b10pxvdz1KVW3GtrzNXnEoKW+aEW/jtdcS7iOISuH32WoVi1RFUtfWXKXoSrFeEo5J6gua3YyzLtG3m2o3tcQxrbLPTZUfWEdsmRm49z4L3H5vR+cDNXQepVQ99uRogxVrd27YvtDj/5pR5UM1bA+/W2GaqbZjITsHKB4CeClN4Ca9ZC5+guserippg1Vb+JxZFp6xE9uAL1792bf8i5evMgOXX3++efo16+fJkI0SIGYUF1iw3xJHSZMzrBjSoc0U1sINbXJIwYt9UUr+hWu+evEVvT7cgBOx552er9bo2ew6IUFKBtVRpL25KhDwJJ4Ojdt6q1dTuWGqs/D2OJ96AKKaXYdyrpxB8d6v4/4jc4HqYo/1QK1PhsK/xKRisqjdsT2h3xU6dE/7ajq9LlujekmM5LIUP0vK5VOr4f/492gS0vF379vlkRujoTG6h9//IF69eqxXdQSJUqgb9++zEB95pln2O9xcXGeVs3hcDgcCcnMycR7m+azsFQZ2faDIGGB4Zj69GS89cib8DP6qV7nliub4L9/OKzpDnEsjYEwNp4MQ52B0Om15XPpSPKe0zjacw7ST16xlekMBlQa2wOV3+3FD1LJAB2mIh9V2lGlaGCJWTlIyclxdp0xGFCsfHlYK8Ywn26OiozVHj16oGPHjjh16hReeeUVltHqk08+wd69e9GoUSOsWLECNWrUUE261ezsbJQvX94r6QA9TaAg9loprpe7PqXqVkN7viaPGLTUFy3o93zcBYxcOwabjvzmVF6vXD183HsJWlRtLml7ctRhteTAdHg+TPunQWfJtpXrQsrBr/XH0JdtB62uQ7n+qX/hRL8PYUpMdfJPrfXpYJTs8ZBkTzK1du+K7Q+d+qfDVOSfGp+ZhSyL805tiNGIcP//vuRFROR+niMLHo3k999/z3xUt2zZguXLlyMqKoqV169fn/mqLliwADk5OcxNwNtMnToVLVu2ZFEKvEFYWJhXrpXiernrU6puNbTna/KIQUt98WX9kpGz4eDPGPDVYFxLumYr1+n0eKnli3jvudkoHlZcsvbkqsOaeRumfwbBcmGtTX5CX+pBGNt8Cl34vYPdKyGnHO2Rf+r5CV/h0lxn/9SQuhVR95sRCGso7UEqrd27YvsTER6OnMQEJGRnw/LfY39CBx3C/fwQ6uewk/9fOCuOiozV0aNH48iRIywCgCv0DW/w4ME4fvx4vhlNCgNFGyhXrpzb97744gvUqlWLnUhzt5tLMWHHjx8Pb5Genu6Va6W4Xu76lKpbDe35mjxi0FJffFW/qZmpmLp+Oj74fRFyzNm24OjFQqLxXo/ZeOXBl2D4zwdPivbkqsMSfwim//WB9c5RW5kVVhhqvQ5D05nQ+YVCq+sQ+acef/V93P55t1P5fU80R23yTy0Zpag8voiY/lxMTsWBEhVgOn8W1rhYW7lRp0exAH8EGe33jyn2BnSXz7Fwbhx58MjBZ8aMGff8DD12X7duHcRCO6Lz5s1z+97XX3/NDnjRgS5yS9i/fz9LREATdNSoUewz3p48tMPsjWuluF7u+pSqWw3t+Zo8YtBSX3xRv8evn8C0DTNxK9n+D5Zs1eYxzTCy89uICI5QfDwLWwdLm3rhB5iPLQIcH736hSK75ggEVJHmsb9a16HkvWdwZsRnMCWkOvmnVhjxDEo+J91j/4LK46t42p/vz13BmF2HkFq1NvShobD88iOsL72BED9/hPoboXMJoZa+4XuUvq+4ZBt0HA93Vh944AG3BuONGzdw+LBz1hOBsWPHIjo6Gp7y119/MbeCSpUq4cyZM3e9b7FYMG7cOHTv3h0fffQRnnrqKZagYMCAAZg+fTrS0hwCRHM4HI7GoTVx1e7VGPbt206GqlFvRJ+Wr2DaM5MLbah6A6spE+b9U2A+ssDJUNVFVIVfm89giW4MrcL8U7/9H068ucjJUDUWC0PNjweh1PNteKQdGUnJzsHA7fsw+J99SDOZoPPzQ0SvPtCfPIqAbz5FUOJtJ0OVdlRTli9B4JnjWLLwgwIlyODIuLNKGavatm17VznFWaVEAO7CQ1DgWDEHmijCQK9evdjva9asQWysffElDh06hMuXLzND1ZGuXbsymbZt21aoXdW8jFvqB4fD4aiZO2kJmP3LXOy7uM+pvExkGYzrOgalgkr6hJFjTbsG056xsCadcyrXl+8IQ/0R0BkDmW+gFrFk5eDCtO9we4NzSK6Q2hVQbf7rCCgl/WN/jp19cXcwYPs+XE51tgUefvRRdGvVCBNHjcKN2RNgrRDDDlPRPKRH/7SjuuTLL72ecljrqDbOR506ddiLOHny5F3GKiUlIGj31ZG6deuynxSpoDDGamhoaJ6nCcUkN4gQ4XAt5loprpe7PqXqVkN7viaPGLTUF1/Q7/6LBzDrlzm4k3bHqfzhmm1ZNqqQgBBJ25OrDkvsDpj2TXJJm2qAoc5g6Cs/YzO2tbgOZd1MwJnhnyDt+OW7/FMrj3sO+gBlwopp7d4tSH/MFgsWHzuLuQdPwOyQ0NOg02FEg1oYUKcqDHo9nunYwZZymE79R1Upj85jRxQ45TBHo8bqvbh9+zb76epqIEQmSJLh2zfVSWGwhFOGJpPJaec1JCSELaiOWTPI2CVDODk52Vbm7+/PDoRRmXDwgTJ/0fW0w0v1EvST+kdtCO0S4eHh7O/MzExbGbVBjwEdHcopcUNwcDBSUlJsZXRTBQYGOunHz8/P9jmqQ7iW6qT6BL8f+nnfffexdimzh0BeunDdsaY+U1vudBEfH2/L4yzogvQo7NqTHqkdV13QYkSy3EsX1D+qQxgb6gvVR/ooqC4cx4bGmcbBVRdURnUXVhd0DbVBY+NuXrjThePYiNEFQe266kIYm4LqQhgbkon6VhhdUD/dzVNHXQhjU1BduJundF1BdEF9dzdPHXXh7p511UV+84L0SH0rqC6EsaF6SG7/AH988tdn+H7/D8wnNVcfOvjp/fB6q754pMbDMGWagAA46TEvXbhbv6hdus5RjwVdvxx1QeU0fm7XL6sFwTfWwnp6BaxWi3Pa1MZTkepXiTpg0wXV6zjH3c0Ld+tXfvNCWL+EuetOF/mtX/fSRV7rF/Un7cB5nB/9OUwJufNAp9exV4mBjyPqmeZIyUxHsD6Y9UmqtTyv9Yv6Q58Xs5YXdP2Sai3Pa/0i+agv1EZe69fNjEyM3HcCe+IT/7uHcuUuFxyIpW2aol5kGFIddP7kk0+yl6MuSJfUX0dduFu/fHEtd7d+eetps1FrjtNCXDUaREccF+rCpGWjgenUqZNtAjnmwaUJQK/8vs3RhKEJ4O4bHk0AV4SFQbiWoDZd8+/SZHT9NkeTz7Vt0oe7tt2VuQvz4ahHQR66GejlSEF0kV8Z3ciu5e52uz3VhWvb1BfhmoLqwnFsBKTSBckj1ONuXrjThesc91QX9yorqC6EsaG+CPWInRdidOFubApzP0ihC0fE6sJxrqSYUjH9x5k4fv04W1+EJ/yV76uM8U+MRcXoCnetf651FlQXjuNZmPVLgPRA//gc6xDmqTUnFeb902G5+a9TWCpddD0YG09lBqtry3nJI2ZeCGPjWreYeXqvecEyP674DXGLf4XVbGZfNAhjVCiqze2L8MbVCtS2J2t5XroQ+q/EPSvVWp5fWV5zhcZm0+UbGL7jABKzco2y3HtIh2cql8fMZvUR9l/8VG+tX2pYy93dI+70oQQ+a6wKO6jkF+uoVMFPlnYAC4O7AdNikGQOh+Pb7LywC0u2LUValrNvXdcGXdCv7RsI8PONR5LW5LMw7R7H/FQd0Vd5FoY6A6DTqz+jlij/1OmrcOunHTYjlQipVR7V3n+D+6fKSIbJhEl7j+Kr0xfvCvA/s9n9eDamvJzNc4qasSr4s9JBq9KlS9vKKZsWIcbPlMPhcNRGtikby7Z+gh/3rXM2cAJCMLzjULSp8RB8Bcu1P2A6MBMw2x9Bw+AP4/2j2GEqLZMdm4DTwz9F2rFLTuX3dWmKyuOfhz7QOztXRYETCUl4a9tenE6yP9onGt4XhcWtGqFSuLRxeznS4bPGaosWLVC8eHGsXLnS9pie+Oqrr1ChQgU0aNAAaiCvg1tyXyvF9XLXp1TdamjP1+QRg5b6ohau3LmCqetn4HzceadT/bVK18K4rqNRKqKUatchxzoobar5+EewnFvt9L4upAyMTWaw8FRKyOONuonk/Wdx5p3PYLqTayyRbyoMelR8+xmU7On9sFRau3dt885qxYpTFzBl71Fk/+ezKWSiGlC3KkbcXwt+Bv4UVRPG6rFjx1hqVdcyYsWKFXf5hArvyQX5TUybNg1vvvkmO4REobUo/etnn33GDFi1QM7M5Gei9LVSXC93fUrVrYb2fE0eMWipL2rgt6ObseiPxcjMsR+2IJ5v+hxeafUS/Ax+ql6HhDr0OUkw7ZsI6+2DTu/pSzaH4YF3ofMP0+w6RP8fb63ejktzfmD+qQLGiBBUf68vwptUhxrQ2r1L/UnMMWHYvwfwx9WbTu+VDArEolaN0Kr0vVMOc3zIWP3999/Zyx19+/Z1+ls4HSf3t8Q33niDtUMJC5YuXYqYmBi2s9qzZ0+P66REB/QScDwp6Ql0cs/TcCBirpXiernrU6puNbTna/KIQUt98SbpWelY+MeH+OP4Fqfy8MBwtpvauHIjn1iHiIwb+xB0YhasmblRXAQMNV6FvsartoNVSsmjZN3kn3pxxirErdvpVB5cszxKT3ke4dUrQi1o7d79/eJVjD14CrcynL/odShXCvNbNkSxQN/w7+YU0FilnVNv8vnnn+f5Hu2s0ksqli1bhsmTJ7uNs+pJ6CqKWuAa1qKg4U6EhcPT0FVUD5VJFe5EkEeO0FWOcisR7oTkyivciTdCV9F7Wgl3IvSTh67yPHTVwfOHMO+P93Ej6SY7pUw6tlisqF+2Ht5o0ReVSlZiny9ouCYaF8exLWzoKnfzoiChq6idrDNr4Hd8ESww2zczjCHIrvkOLPc1A5JT3OqC5qm7cE2Eu3tWitBVQvtSha7KuBGPq+NXIuPEVej/28Shj0V0aIDSI5+Gzt9Y4HtWidBVJB/NFV8PXZVjsWDRqUv4+NQF22eYzvR6TGpSD8+WKwFzViaSsjIlX8sdx8bX13I1ha7SWe8V06mI4W5nddCgQez37du33xXO4V7kFTpD7muluF7u+pSqWw3t+Zo8YtBSX5SGluMf96/Dx1s/gcmS+w+E0OsMeLX1K3iuSXf2D6ew+vXGOmQ1Z8F8eB4sl39h8VNtYanCq8DYZDp0oeU1vQ6lHDjH/FNz4u1GDPNPHf40SvZqywwDtd0rapPHEy4kp+Kt7XtxmMVOtT/hrREZjo9aN0bNqLvDSXEKDhmrrVu39tgmKnIHrOSCIgs4RhcQ+y3CXfw0Ja6V4nq561OqbjW052vyiEFLfVGS5IxkzPn1Pew855xus0RYCZYytU7Z2h7rV+l1yJp+MzdtauJp9rdgMOjLtoOhwWjojEGaXYeYf+qav3Fp9vfO/qmRoag6pw8imlZX7b2iNnkKq/fV565g3O7DSHfYKSRerVEFExrVQaBRO/64RQ1urMqMmDitYmO8Sh0jVs6Ys0rHs1Vb/Fy1ySMGLfVFKQ5fOYIZP8/C7VRnn84Hqz2IdzoNQ1hgmCj9KrkOWW7tgXnfu7BmO+wo6iht6gDoq/QQfZZBzeuQJduEizNXI+7H3CQHAsE1yqH6+68joEy0qu8VtclTUJKzczBq5yGsu3jVqTzK3x/vP9gQHcrbN6A4volvzkwfIq/MWHJfK8X1ctenVN1qaM/X5BGDlvoiN2aLGV/9uxJvrxrpZKjSCf9Bjw7ApCcnOBmqnupXiXWIdrbMZ76CaedwJ0NVFxCFrPrTYYh5TpJDt2pdh7LjknDitQ/uMlSjH2uC2p8Pv8tQFdueHKhNnoKwL+4O2v/8112GaqtSxfFj28bcUNUIfGeVw+FwvEB8ajxmbpyNg5cPOZWXiyqHCU+MQ0yJKj4zLixt6oEZsNzY5lSuK1aHpU21ZGv71HXKwfM4886nyLnt7J9aYehTKPXiw16Pn6pFzBYLFh45g/mHT8LscPTGqNdjZIOaeKt2VaQ6HEjj+DbcWOVwOByF2XV+N2b/8h6SM+wndomOdTtgYLv+CPJX5tCCFFhTLsK0ewysqVecyvWVnoSh7hDoDP5AtnM/fQ06aLthwwZs2rQJSQmJiIiKZMlounbtisT1e3Bp9hpYTc7xU5l/arMaXpVbq1xPy8DAv/dhZ6yz20ylsBAsbt2YZaTiaAturMocZ5XCSHjjWimul7s+pepWQ3u+Jo8YtNQXqckx5+CzbSvw/d4fnMqD/IIwpP0gPFqnnSz6FbsOORpryUkJCI+IYsba443CYDj+HmByOIiq94Ph/ndgqPC4JO27k0cu3NW9efNmDB04GPE3YlHTLxrR+kCcsWRiw6ofMDYgGAMjGqNZWHln/9T5ryOgbLRH7XkTtcnjjl8uXcfbOw4iySG0EvFslfKY0aw+Qv38fKo/nILBjVWZ46xSWAdP46xS2xQvzdM4q3SyU8o4q9Q3ul6OOKuOMeWUis0n6EMNcVYJmgtaic1HMvA4q8737OW4y5i+cRbO3jpn0xtROboS3m4/DGUiSjP90Zjf656luVCY2KKusVILE2d169atGDZkABLiY9E4JhClIgyIvWLGoB9X4t0QHeb0qYRHG0Sy0FSWgOLIrjsO1rBq0CUnO80LYV7mtX4VNM5qXuuXFHFWhbkr6GL9+vXo/9qbaIRoTC37OMoFRrJ4t7BYcCXhFj5POIhpSVswttzDaBZWARGP1kf5cT0QEB1111rubv0iXagpzirJqNY4q+kmM2YeOYPvL+duJglthBgNmN64Lp6rUYXpIkkFazmPsyo9PM6qCzzOqu/HN1Rje74mjxi01Bep2Hryf5j/2wdIz7b/kyOeafQ0Xn+oL/yM906ZKka/no4J7Sq++vL/2zsPOKeq9O//7k2bTO+dNkPvHURQVwVF7NiVYl1FwbJ/V111d9ni6u66vooNO2IXe1ewYUGl987AML239OS+n+ckN5NkMi1t7mTOl0+Y5Oaeem9OfjnnOc9zBU4frcY9F+ejIJtmqhyApQmHy1rwr3drsG6HESuXDsEZc06HatJyCLrkkJUfyrZ0N28SLhPGjENBrYg/5Z0ClctPrMNqh73RAMnhgF1y4N/1G7DPUYd1j7+KAdee2S37VKV9VpRWH5ldtQ3Md+rBBm8b1InpqXhi1iQMSHCKyN7Snt6MkftZjU4/qxwOp+9isprw5NdP49Ptn3kdpx3+f5z7fzhh8HQoFRJrd9x2C04bpcbTSwqhUgm0k4oJVUgOFGRp8fTvs3HjynLc9Uo1TrvrX1Dreq+fTl/I7IGW/v+Rf7ZbqNqNFtibW78T6PjCxLFY1vQ1NqUYMJBvpAopNHv63J7D+MfmXSwqlYwAAcvGDMUdY4dBo+JOjfoC/CpzOBxOGDhSdQQ3r17WRqiOzh+NZxY/pWihKou12qpy3HNxLlTkS91mcG6UklpFg0ol4u5L+6O2wYhPPvVuZ2+H7HPJRjVPl8RmU611zV5ClRDUKgzMyMEIXTo++yy62t/TVBvNWPD1Bvxl4w4voZodq8dbs2fgrgkjuFDtQ3Cb1TBDdjw9kTYU6cOdX6TyVkJ5va0+wRBNbQl0NogE6uPrnoLV7rkJRMCCGVfiyumXQ61SR7R/A0lDYm3yYB0KMkXAXOclUhmiCtAkoDBXjUmFOibW5s+fH7Lye3ocol3/qYIOtkYDHOZWu1cZUaeBKkHPlv3TRD07P5jylIBS6vNtSQVu/WkLqoyt9qvEmf1y8PCMCUjRaXtVezjBw8VqmAkmbm6wMXdDHbM3nDGAIxVfuKfK6231CYZoakt3aTY145EvH8N3+77zOp4al4Y/nX0Xxvcf1yP9G0iahqpjyIq3Q7D6cRRP7qg08e7FuexkFRoa6iNyT0RiHCJxqq5sQbmhCY4YH6EqCFDFxUClbxVCNQ4jhqQkB1yeUujp+ljsdvxryx6s3H3Q67hOpcLyyaOxYOjAbtkE93R7OKGDmwGEGc/dm5FMG4r04c4vUnkrobzeVp9giKa2dIc9ZXtx48s3txGqUwum4pnFT4ZEqAbav91JIxkrYdv0NyQYtqGi3grJS6iJTpGqSfD6+iivtyMxKTki90Q47y/a0V33zXZsn/9PjC2WsMdajVJba3lijBaa1AQvoXrc3IC91lrMnTu3139WerI+hxqacM5n69sI1REpifh83slYOGxQt4MrKK1/OYHDZ1bDjOwyI9JpQ5E+3PlFKm8llNfb6hMM0dSWrrb37Y1r8ML6l1j4VBm1qMa1J12D+ZMuCGnM9UD6tytpJJsJjoOvwn7wVcBuwZyJKbjj2VocrrCgMFsLqPSAmjZQeQuGQ2VGbDpkxoo750bkngjX/WU4VIaiB96AcZPTtdjMxEF4Tv0rVjXtwF3pJ0KbEAdRQ8a7rZA3gBerNiEtJwtnn312r/+s9ER9yGzmzUPHcO+v22H0CKxAXDO8APdPGsVmVgNBaf3LCRwuVsMcFIDD4fRuPB3i19XXISU5xR29yGAz4N+f/Re/HdnolSYnKQf3nnMPhucoP4KRJDkgHf8K9j1PQzJWuY/Pm5KKv79+DP96rx4rbxkKlbrt14XdLuHBNSVIzcgOSKwpAVuTAcef+hQVb37PNlKJolOMa0UV7hg+B38//CX+Y9iIa+ImIQ9JXjOqJFQ3owarnniF+dbkdI9GixV/3LAVHxaVeB1P1enwyIwJmN0vm3cph8HFapiDAtAvu0CDAshCOdCgAJR3KIMCUHldcaot90V3HEl7tiUSQQEoH3+O8HvKkTSdHy2OpOW2R0NQgK+//hp33nUnKmsqEZsbB1WcCra9Nrz17ltITknGoLMKoe6ngUdocpw68hTcOOv30Ahqd3/66wt/jvA76gs5L8qju47w6Rx/QQGMZZugObgSYuM+9xKr+95LHYZ//+cq3HDrn/H7J4uYVwCnn1WBnXuw1IAH15Ri3S4bnn72YVbP9u4Lz3YHGxSAjoUiKIBkd6D+k42oeX4t2+lPzXaOmRSES4Wsy0/G5ZefiNxff8Y9d96Fm45/jOGaVKQKMahxmLDXVoO0rEyseuoVTJs2zV2n9gK8+Bu/6LiSggJQfSIVFGB9cSlu/XkbylybqNj9JwHTM5Lx0MSRyNTrWJpAxy+qn9xnPCiANuAAL77jV0+58+RBAcIcFIDD4fROyCH+lQuvhLafDgNPGwR9mtOHKH0P15XX4ujXR9B8pAkjLxmNtKHp0Kp1WHraEpw55oxu29ZFGslQDvvup+EoWdvmPSEmHaqRN0HIn82iUlE/kL9VcmNFu/5pMxXZqNLSP82oPvLoE5g9ezZ6E01bDqHowbdh2He8zXtJJ47EgDvnQz8wy+t74OOPP2YeD2jXf1JKMrNRpdlkPqPaPWwOBx7dsR+PbN8Hh8evPLUo4u7xI3DjqMEQFf756csYeygoABerYb4w9KtF/mXaXYJJG4r04c4vUnkrobzeVp++3hYSJ2PGj0VLfAuGXzQSgmtp2O5woNncDJvdBskhofijIpjKTbjkb5fhLxfej4HpAxTZv3IayWaA48CrsB96ndmleqHSQjX4SoiDr4Cg1nulpRkyWaw1NtSzzVTdEWuhvCeCyctcXodjj7yP2i82tXkvpn8GMm+Zh5w5k9FXPyvhrs/xZgNu+WETfq2s8To+MCEOT82ajHHpKVHdv9GAkUewik7k6fVIpw1F+nDnF6m8lVBeb6tPX28L2ahWVJZj9Hlj3ULVbLOgxdziXuKk41mzclC0+hDOSDo9IkI10P61WS2wH/sOjj0rIZm8hQIh5s+GauSNEPRZfsujL3zyodqeH9Vw1DmUeTlMFpS9/DVKX/iSPfeE3FDlXn8Gsq/8HZoM3uFw+9pnJZz1+aioBHdu2MrsVD25pLA//jF1DOI1XQ853Fv7lxM43GaVw+FwfKDNVPrcWLb0b7FZYbAYvHb6E8wZfG46avtVY+1Xa3HZpZcpsh8dNdug3fow7M2H27wnpI6CatQyiKmjEI3QD4u6r7fh2MPvwVzaVqSnnzsd/ZadA206jx8fLgxWG+7/bQdeP3jU63iCRoOHpo/D+YPyw1Y2J3rgYjXMBGO7FqzdW6jt5sJphxdpGz+l2RQqrT59vS2061+IFdFgaITN0XZ2htxSxcfEQyWKUMepUV/f/ehF4e5fqaUU9t1PwVH6DUSKPuWKb8/y0Gc67VLzTu80v1Bcz1DeE13Ny7C/BEf/+w4af93f5r34MQMx4I8Xsb/hqmdv/KyEuj47aupx0/qNONzoHVhiUkYqnpg5Cf0T4vpU/3ICh4vVMEO76HoibSjShzu/SOWthPJ6W336clu2HN2KfTX7YWgw+BWqek0M9NpYCmTEsLXYkJzc/ehF4epfydoCx4HVsB96E3DIu79dQlWlg2rIAoiFl0FQx4SkvEjl0dW8bA0tOP7kJ6h4ez3g8AppAE16Ivrdeh7S502B4Mf3LR+HQnOdaOPUs3sO4YHNu2H18HVKG6duHTMUt48dxjZUhZvePhZxWuFiNQLGyIHulgsmbSjShzu/SOWthPJ6W336Ylt2HN+JVT++jK3HtkHIF2Hc1AJznRm6FOcGIp1GB71Gz2ZTZQw1BhhLDQFFLwp1/0oOOxzFnzntUs113u9JElT9z4JqxA0Q9BkhKS/SeXSWF7miqnznBxx/4hMmWD0R1CpkX3Uq8q4/g9moRqKevfGzEor6VBlNuPXHzfi2tNLreE6sHk/MmoTpWenoq/3LCRwuVsMM+SoL9MMSTNpQpA93fpHKWwnl9bb69KW27C7dg1U/rsamotYd4hkjM3H4q0Oo+L4Ugy8YhtiYWC+RSpA3gKPrjiArM7IO8f31r6N6C+w7H4XU4B2qkhBSx8A08Bok9psSsvJ6Io+O8mrceABFD70N44HSNucnnzQaA/7vQsT0z4xoPXvjZyXY+nxTUsGEarXJO5jOWf1z8d8TxiNZ1xqmti/2LydwuFj1gUew4nD6BvvLD+ClH1/Gr4d/bfOeSqPG5Usvx3uPvYvjHx/18rMqz6iSULUUm/HS6hd7zNem1HIc9l1PwlH2fZv3hNgsqEYugZB7KiQP5+3RBG2aYq6ovtrS5r2YAZnMLjX5xJE9Ure+hMVuxz8372ZL/57EqFT4+5QxuGLIAG4/ygkKLlbDHMGKoj4EGsGKonZQxJVAI1hRPqGMYCXXJxwRrDzrHYkIVlQvJUWwoveiJYKV3E6lRrDaXbwbr//2Jn4t2tgmahO9PHnoSVg8axGS1ImYN/IsFsFqx7PbWiNYNdvY0n9WRhYTqjNmzHD3W1ciwAQbwYrO1wgW6Irfhu3QG4CHXS3ZpkoqHaz9L4W93wWAqEWSILC6+Itg5Xlf+Bu/qFyqo7/7orPxy7MvPNsYbAQroq6iCjWvfY/qV78HrDZWFnMwLwFinA5Z15yO/ovnwGT1bndn45dcvr++6G7Upq5EsKI0SopgRfVpL4JVbW0t85Kxdu1aNLW0IDU5GSeffDKGnTgLd28/iL2NzSxfuR+GJcbj0eljMS4nK6xjOdHeWE7wCFYIeCyX4RGs+kAEK/rgBrojMZi0oUgf7vwilbcSyutt9YnGthRVF+HlH1/B9/vX+33/pKGzsPDEqzAw3XuHuGf0Itr1T5upeip6EbNLPfYRHHufg2T28UAgAGI/l11qTLpixqFQ5SHnU/PFZhT/v/dhKfe2y6VfGhnnTUf+LeSKKrCNNXwc8n+dKIrZzbfehrKqKkgDC4HEJKCxAfYjB2HXxyHxiquhGz3eff51Iwpx78SR0KlU6EmUOhb1Zow8KIAyyMnJYQ+ZYOPg0i9Z+lUT6bShSB/u/CKVtxLK6231iaa2FNcWM5vUb/fSUrn37nBixuAZWHTiAhRmFvhNT4I0GIf4ocJRtRH2nY9BaqSlVu8vYCFtHFSjb4WYPFRx41Co8mjZV4Kj/16Dxo372wiQ+HGDMPCuixE3sn+P11PJ5QVSHxKqVyxaBNOQkYi9dhnUmdks5HCj1QpLeSnw6Xuoe24FUq5ditwp0/D/ZkzAafnZUAJK619O4HAzgDBDYjfQD0swaUORPtz5RSpvJZTX2+oTDW0pqSvFKz+/irW7v4ZEfkZ9mFYwjYnUodlDoGSk5mLYdz0BR/kPbWaLhNgcqEbdDCHn5A5nkHpyHAo2D2tdM3NFRTv9yRUVCSW5qZqMJPS/7XyknTU5JDNofBzyvk60qkAzqiRUE65ZAkFUMVdUDRYr7HQfZmRBWnADsPoZWN5ahU/vvR39kpUTYEEpYxEneLhY5XA4UUV5QzlW//Qavtq1Fg7JO+oUMWngJCw+cQFG5I6AkpGszXDsexH2I+942aUy1LFQDV0EseBiCKro/DKWbHZUvP0DE6r2plabRdkVVc6i05B77RyoYrvmL5YTWNhhWvqnGVU7RLRYrDC5bE9lRFEF/bkXQ/rvX/HrurXo18MrEJzohItVDocTFVQ2VuHVDa/j8x2ftwmNSkzoPx4LT1yAMfmjoWQkhw2Oox/Csfd5SJbWDSMMAbDlzEXs2JshxKQiWmn4ZR+O/ucdGA+2dUWVMGsEBt99GWL6Rc5fZ1+FNlTZBxSgOSnNayOWjEoQkazVQJ2bh4YBhcyuu6fNZTjRCRerYUbeSRnptKFIH+78IpW3EsrrbfXpTW2paa7BaxvewCfbPvUbcWp03igsnrkI4/uPg9JxVP4K+64VkBqPtHlPSJ8I9ehlEOMGQlCre8041J08TMerceyR91C3blub9/QF2cwVVdzkwWzHfTjg41Cc29Tkm9JKfLrvEExxCRD9/PjTq1RI0GparaeTklDnsRtfCUTTuNrX4WKVw+H0Smpb6vDGL2/io62fwGpvdbMiMzxnOK6euQgTB0xQ/I5gqeko7Lseh6Pi5zbvCXF5UI26BUL2TGc7XG5mogm7wYzSF79C2UtrIVm926eK1yP/prOQecksiBq1280OJwzXwSHh06ISrNixH7vqGlAvqoEGb68TWlGFeI0KGt9wqQ0NSCnoxy8LJyxwsRpmyIcZ+Y+LdNpQpA93fpHKWwnl9bb6KLktDYYGvPnr23h/y4ew2NouTQ7NGopFMxdg6qApyheplkbY970IR9G7gO/slSYOqqGLIQ6a72WXGkj/9uQ41FEezBXV55tQ/Mj7sFT6uuISkDn/ROQvmQdNakJI69Pdekb7556c+r99qBiPbd+LYkOrn1Pd2AkwrX4OUlUF9Nm5iFOroRbbfqZsFWUQjh3C3D/dCSWhlP7lBA8XqxwOp1fQaGzC27+twfubP4DR2talXEFGARbPXIgTCqcrX6Q6rHAUfQD7vucBS6ujd4YgQBx4HlTDroWgS0G00rK3GEcfXIOmrd5Rj4iE8YUYcPdFiBvOZ+rCSbPVilf2H8XK3QdRYTS18UuaMHEa8PE7MH/xIRKvJW8Agl/fv4aP1iAnPSOiYYc5fQsuVjkcjqJpNjXjnU3v4Z2N78FgaY1wJDMgbQBzQTVzyIksso3SoaV+ZpfadKzNe2LGZKhGL4WQWIhoxVrbhOLHP0LVez/T1KrXe9rMZPS/4wKknjFR8T84ejO1JjNe2HsEz+89jAaPSEUyerUKVw0ZiBtHDsbO/ERcvnAhml54ErHnXsz8rHrOqJJQjTmwG0++/HKPhR3mRD9crHYhglUwdDfiVajShiJ9uPOLVN5KKK+31UcJbTGYDXhv8/t467c1aDG3Fan5KflMpJ487KReIVKlpiOw73wcjspf2rwnxOdDNWophKwZnYq0QPq3J8chOQ+H1YaKN9ej5OlPYW/2nhkXtBrkLj4NOYtnQxXbseDh41DglLYY8fTug3j1QBGMtrabppK0Wlw/shBXDxuE1BjndciZPRuvrVrljGD10P2Q+heyzVRko0pL/zSjSkJ19uzZUBrRNK72dbhY9WHlypVYvny5+zV9CU6YMIE9p7jDcrxcz3jSMv7iSdMvTVpaCSSetHyuvzi9/mJr+8ZQpnjJdCxU8aSprlSfjmJre/YF4Rv3vL3Y2lSOnF7ui3DGk6b3qT3+4km31xf+Yij79oW/GPBd6Qsqix50bfzdF/76wvPaBNMX8r0SqnjSdC49705fUDvl+9RkNeHLvWvx7pb3mX2qDGk4KisrIQuXTLoIswbPhEatYW3x1xf+7lPqw670BbXd333q2Rf+PrN+Y2tbGhBXtgaOovfYkmlrewRAHQ/LgCtgzzsbENXQW61efeHvM0vlU3lUd8/7wt9nVu4L38+nv77wN35RuVQff/dFZ+OXZ1/UrN+ByhWfwny0CqLojB8vT6omnjIKg+68CNqcVDTTfdHgvH+pfdQm376g8v19ZrvaFx2NX/K9668vOhq/OuuL9sYvqmNXP7P++qKrY3mFXcIjW/fgg6OlsLnqSe1zPpWQodPimiH9cfXIIdCrRFbPBrOzTtSWU089FT9+9y1zZbV27Vo0tbQgdVA+Tr51Cc4880xWF0rTXl+EcyyX71N/Yzkdj/RY7nltevtY7q8vgo3qGSiCJPckp92Z1aVLl7Ln69ev7/YvNbphAjXwDiZtKNKHO79I5a2E8npbfXqiLWarGR9u/Rhv/voW6g0+m20AZCVmYcGMq3D6yFOhVin/dzazSz3yLuz7XgCsPjPDgghx4PlQDb8WgjYp7P3bU+OQqbgKR//7Lmq/2c5Eqif6wbkYcOd8JE0bFrH69GTePVHe9pp6trP/02NlkPyEGh6YEIdbRg/BRQX9oFWpomocIqKtPUqAxOqsWbMC1kSBovwRP8Lk5OSwh0xP/YrgcPoKFpsFn2z/DK9veAO1LbVt3s9IyMCVJ1yBM0bPhkalgdJhs4YVPzntUpuPt3lfzJwG1ehbICQMQrRiN5hQ+tyXKHt5HYtE5YkqIRb5N89D1kUzWSQqTujvv5/Kq7Fi5wF8X1bp95xRKUlYOmYo5vXPgaoXmNBwOFyscjicHsFqt+LzHV/g1Z9fR3VzdZv3U+PScMX0yzBv7Fy23N8bkBoPwr5zBRxVm9q8JyQMYP5SxawTEK0wV1Sf/IZjj34Aa5WPg3hRYAI1b8nZ0CRzZ+2hxiFJ+Op4OVbsOIDN1W1/9BHTs9KxdPQQnJKbyTewcXoVXKyGmWB2Rwa7szLUOzPDudMz0rtIlbZrVWn1CWdbSKSu3bUOq39+DZWNFW3eT45NxuXTLsXZ4+ZBp+n5fiFTIIqRTvZ6jQ11SExKYXZ655xzjrutkrkO9r3PsTCpvjvcoU2Aath1EAeeC0HU9Mi9EolxqHnXURx9aA2at7eNvpUwaTAK7rkEsUPzAq5Hd+ujtLzDVZ7V7sD7RcfxxM4D2N/g4wbNxez8bCwdPRSTM1P7zDgUje3py3CxGmbI2Lkn0oYifbjzi1TeSiivt9UnHG2x2W1Yt+drvPLTayhraLULl0nUJ+GyqRfjnPFnQ69Vxi7eL7/8Enfcdgtqq8oxebAOWUkqVBy3Y9kHb+Gvf87G//73/3Da4HrY969qa5cqqiAOvBCqYVdD0Cb26L0S7DjkKdgb6uqRlJLsFuxCkxnHV3yIqg9/aeuKKjsF/f9wIVJPHx+ymTw+Djkx2ex4/eBRPLXrII63tG66kREFAecNzGM2qSNSkvrcOBSN7enLcLEaZmg3IO2ki3TaUKQPd36RylsJ5fW2+oSyLXaHHd/u/Q4v//QKSupK2pwfr4vHJVMvxvkTzkWsLhZKgYTqNYuuxGmjVLjn1qEoyG794jtcbsS/3jqGa666ECtvKcTsCd7O+8XsGc4QqfH9FXGvBHN/vffee7j3rntQU1aB4Zo0pIkxOOAw4aM338G9cQlYlj4Vk9WtvjcJUadBztWzkbv4dIgx2pDe3319HGq0WPHSviN4ds8h1JjaulbUiiIuGzwAN40ajAEJcX12HIrG9vRluFgNM8E4WwjWUUOoHT2E03FEpJ1SKM0JhtLqE4q2kIuU7/f/gNU/vYKjNUfbnBeni8P8SRfiwknnIz4mHkqCZhJpRpWE6tNLCqFSecwKSjYUpFrw9HWpuHGlBX984Qg2/C8JOo0IIXEQ85cqZk5V1L0S6P1Fgn3Jdb/HJCEd/8g/G3k65wydw2JDcX0lXqrfhuU1n+O+/qdiWoJTmKfOmYj+t58PXU5qWO7vvjoOVRpNeHb3Iby8vwhNHm6OZOI1GiwaOpD5Sc3UBzajGE3jUDS2py/DxSqHwwn5F8SPB37CSz+8jCPVbe0X9dpYzJ90AeZPvgAJMa0x35UELXnT0j/NqLqFquQAbAbA7vR9qBIF3H1BGk5bXoxPt5hw0XX3QxxwDgQxOoZVEuy33bIMk5CGP+WdApUgQrI7YG82wWGxIkeIwx+Tp+Pf9RvwaMkPeGve7Rhyz2VInDykp6seVRxrasFTuw/i9QNHYXH5yPQkLUaH60cUYvGwQUjU9o6NiBxOd4mOUVXBkLPgnkgbivThzi9SeSuhvN5Wn0BF6i+Hf8UL37+Iw35Eqk6twwUTz8clUy9Col7ZS3Nkm0k2qgXZWsBuBOwWwNF2NqswR4tJg+PxZdkYXDroAsXeK4GkIcFOS/9/y5sHwWyHzWxkM6qekIBdlDgWSxvX4chlwzGpHaEayvu7r4xDe+sa8fjOA/ig6DjsfmYI8+JisWTUYFw2uD/06tB8lUfDOBTN7enLcLEaZijCRE+kDUX6cOcXqbyVUF5vq48nnptr6urrkJLsvRueROrGok1Y9cPL2Fu+r016jUqL8yacg0unXoyUOG/bTiVCO/sbynYjK94OmPy7AGKotIA6Dtmp9WhoahsOVkn3SnfT2I0WvP/cKxgmJSKrSYQNbTfwEKJeiwGxuRhhycDnX3yBiy6+OGR17qvj0MbKWqzYuZ+5ofLHkKQEtmnq/IH50KjEPjMOBUK0tacvw8VqmKEwaBTSLNJpQ5E+3PlFKm8llNfb6uNps3jLrUtRUVkOfW4s1HFq2PbZ8PZ7byPr/mzcdu9t2CcewK6SXe40DofEohWRA/95485ibqjS4tOgZCRLAxxl30MqWQdH9WYkWA6hor7tTCpDVDGRClHLXpbX25GVn6zoe6UraWjWtOHH3aj+fBPqv9+Bsj07kSKR65+2s3qiRg1VfIzbqX+aqGdeAkJZ50jkFcm8OyqPfvB9W1rJHPlvqGjrc5iYkJ7C3E/N6ZfNdvqHsz7RQrS1py/DxWqYkWMARzptKNKHO79I5a2E8npbfWSheuXCK6Htp8Po88ZCn9Y6S9FY1YQjaw/i9ptvw8hLRiNtaLr7PbWowrzxZzGH/hR9SqlIliY4ykmgfg1H9UbA0XoN5kxMwR3P1uJwhQUFWVoKmg2odLTNHWA2qU6xcKjMiE2HzFhx51xF3yvtpXFYbWj8dT9qvtiMuq+3wd7sEQddpUWt2cNvpyCwXf7sofX+6qhxGDEkpX3BHsr7O5rGIYvVhg+LSvD4zv3YWesTRMHFrJwMJlJPzE4PuyN/JY5DwRBt7enLcLHK4XD8Lv3TjCoJ1eEXjYTgiututdtgtBhg1duQN68/HB8VYf9H+zDt1lRotBqcMXoOzhl1NobkD1Zkr0rWZucMaunXcFSRQPW2wZSZNyUVf3/jOP71fgOeXlIAlYZmUb2Fgt0u4cE1JUjNyMbZZ5+N3gJtkmrcdBC1X25G7VdbYGvwb8JwQkJ/PNywHmWCCf0SUiFoNUyz+3Lc3IC91losmxs5wd7bsdjtWHO4GI9t24tjBueGPU8ECJjbP4ct949PV77pDIcTbrhY9aGsrIw9PL+0g0EMIu5yMGlDkT7c+UUqbyWU19vqQzaqtPRPM6qSAJitZlhsFhZ9SoYEbNasHBxctQ/5zbl48M4HkZucg6Ym/1F0elKgSuU/wlG6Do7KX9sVqAxtAsSckxGXdxoeeaYG1yxegBufLsY9F+d7+VmlGVUSqut22fHiy09ENFJOIPcKacymrYdR88UmJlCt1Y3tn6tRI3nWKCz63ZVYfftivFy3C39KO4WipbbBLjnwYtUmpOVkdSjYQ3l/9+ZxqMVqwysHirBy9yGUG4xs+d9ztlQtipg/KB83jx6CwUmR95ShtHEoWKKtPX0ZLlZ9WLlyJZYvX+51s0+YMIE9b2hogMViYc8TEhJgs9lgNHosm8XFsYGH7GRk9Ho9G5DIObGMVqtlx+mY7AdOrVaz9C0tLSxfuWyCypDLJcjJMb02mVp/kZNdDvm1NBgMXsbldMxTPNCXKkX1oLZ47pikc+k8Op9QqVQsT8rP6vLpJw+qVK6niG+vLwhqj2dfUFn++oL6Qa6T3BfUj/IyDvUFlePbF0lJSawuXekLysPz2lA66o+u9oXntaG+oOvg2xd0jPorkL6gvOja+Lsv/PWF57UJti+oXLkvHJIDr7z5CjRZGlj0VrS0tLaFtZ1JH2f9EjISkNwvBdoKDVJ0ye6+pL/d6Qtqp7/71LMv5GvTlb5IjFXDXPwtE6hizSYIktV9/3r6XmTHNAmwpU6HPfMkOFLGISY2HiqtFtOmN2LFk8/g3nvuxO/u24/JhTpkJ6mYjerGwyakpGXhxZefwowZM9ztlu8Lf59Z376QbRW72hee9ym117cvfD+zlLdpfyksP+5H1ecbYSmra203KU+ptS8EtYikE4YzH6mayYOgindGEPvPo//D9YuuwQPHv8HijEnI0yax2VVq5zFjPVZVb8Im1OCZh59n9fT3GaE20z0o17+98cu3L6h91CbfvqDr7e8z21FfeH5GOhu/KO/2xvL2xq/OxvIGqx1vl1Tiud0HUW/x+MFHnSmRI38BlwzMxdWD+2NoZrpXf3XUF8GM5f7GL+oLujbBjOVdHb9COZZ7jl+efUHl9MRY7m/86omx3PfadLUvOhq/PPsjkggS95rb6czq0qVL2fP169ezC94d5C+VQAgmbSjShzu/SOWthPKUXp8mUxM2FW1mrqd+Pfwbfnjme9i1DvSbN8Dv+Vq1loVEJfvUve/sxvT8aVizZk2PtkWyGSBV/AwHbZKq3OB0N9Uemjg2gyrm/g5CxmQIYvsubmgM+Pjjj/HZZ5+hsaEeiUnJmDt3LptJ7InY4531r+FgKbNBrfl8E8zFVeyY7wweQxSYT9S0MyYh5bTx0CT7j3b04Ycf4u7/+6MrglUq20xFNqq09E8zqo8+sQKzZ88Oqs7ROg6VtRixcvdBvHLgKAwuQeBJglqN60YW4trhBUiN6fk49j09DoWaaGuPEiCxOmvWrIA1UaDwmVUfcnJy2MPzwgSD56+lSKYNRfpw5xepvJVQntLqQ+LlSHURE6f02F2yBw6pdTOCOkYNU2NLmxlVjVrjFqkythYbkpOTe6Qtks3oFKhkg1rxU8cCVR0LMWcWxNxTIWRMgUCup7oACdL58+ezhxLw17+mY5Wo+XwzW+Y3Hmr9sS1DkzuyVk0YX4jUMyci9fTx0KZ3HjP+5JNPxpYd29yCnXb902aqZd0Q7KG8J3rDOHS4sRlP7DzA7FKtfhz5Z+ljcMPIQpyblYq8dOV4ylDauBgs0daevgwXqxxOH8FgMWLL0S1s5pQEanWzfxc5ROqwNFR9UAlbvRXxGQlsJlUtqttssDHUGGAsNbCZxkgh2c2tArX8R8DegV25Wg8xeybEvNMgZEztskDtDZhLa1DzJc2gboZhb3GH5+qH5yFr3jSknjERuuzub9hRmmDvUV/DDQ1ISUry8jUss6OmnvlI/eRoGSQ/Lr8GJsRhyaghuLiwH3QqlddSLYfDaR8uVjmcKOZ47XHX7Olv2F68HbaONhex5X0dJg4Yj4knT8Cdv/wfan+qRuZFmW5vAJ5IDglH1x1BVmb4d8NLdgukyg1wkJupih8AW9sd1G7UMRCzToSYdyqEzOkQyOVUlGCpakDthz/h+Pe70bytbZQwT/RDctkSf9oZE2FO1DI7OE5gLtxuvvU2lFVVQRpYCCQmAUeK8cb7HyDn/j/jiUf/H+LHTmQi9bvSSr95jEhJxLLRQzFvQC7bRMXhcLoHF6thJpgviGC/XEL95RTOL7tIf5Eq7Ys7VPWhHfvbire7bU9L60s7TZObnIupBVMwrWAqxvUby2ZRiYQn4nHFgiuwd81uDDxtkJefVZpRJaFqKTbjpdUves0uhaotTKBW/ea0QS0ngeo/ihJDpYOYfaLTBjXzBAjq1p37vR1rXTPq1m1ldqiNGw841/TbIaZ/JtLmTmIbpWILW82ZgumNUFzPUH7eIjkOkVC9YtEimIaMROy1y6DOzHa/Z6ssx/EP3sLZl1+JhGtuhm7M+Db5Tc1Mw9LRQ3BqXpZfH6nROg4phWhrT1+Gi9UwI+/wjnTaUKQPd36RylsJ5YWzPhWNlfjVZXu65ehWmG0du1uj5fwx/cZgesFUJlDzUvL8fpHSpplXX36V+Vvd+ez21ghWLTa29E8zqiRUfTfXBNMWyWGFVPkrHKXfwFG+HrB2EMZUpYWYdYLTBjVrBgR1ZAz9I4GtyYC6b7azJf6GX/YC9la7R9/NUtqcVOcM6txJiB3q/1r25DgUqjzCkVdHedPSP82oklBNuGYJBA87bZPdjpbEVFivuA5Y/QwaXn8BGcMfhuCKRX9aXhZz5D81K63PjENKJNra05fhYjXM0IAX6IclmLShSB/u/CKVtxLKC2V9yNcpbYhis6dHfkNRdVGnaSjc6fSCaWwGdcKACYjVdk3YzZkzBzu2bndvrqmvr2ebqTraDd/dvmUCtWqT0wa17LuOBaqocQrUPFmgRs9OX7vBjPrvd6Lm842o/2E3JJv/6Ds0sarNTELanIlsiT9uzMBOIxv15DgUqjzCkVdHeZONKi3904wqCVXaKGWxO2C022GXXX6JIqSzLoDjv8th2bYRl1x0MZtJHZmaFPXjUG8g2trTl+FilcPpBdQ01+K3IxuZON1UtAkt5g4EHfOXJ2JU7ghMK5zGZk8HpXcuaCK5uYYJ1OotriX+7wFLB4EERDXEzGkugXoiBE30xPp2mK1MmNIu/vrvdrDX7aFOjkfq7AnQnTgUObPGQVBx28dwYbLZserd92HpPwj2pFRYjWa/G6YIMSMbwqBCnF5fjqdOmhy2OnE4fRkuVjkcBWJ32LG/fD+zO91w+FccqDjQaZpEfRKmDpqM6YXTMGngRCTERD4CTkdIDhukGhKoX0Mq+w6SpbETgTrVucSfPTO6BKrVhoYNe1H7xWa21G9vaX+zGDnnTzl1HNLOnISkqUMhqJ07yLlQDS1Gmw0bq2rxTVEJtjQ0Y3N1HSoPHoE1PhF2P66nZFdusWoVYimIQ0oqbMYObKo5HE5QcLEaZig6RU+kDUX6cOcXqbyVUF5X6tNoJMf8m1zL+xvRaOzcrc3QrKGYVjiVLe8PyxqqiPCCnn0rOeyQarayJX4mUM317ScUVRDJ/2nuqRBzTooqgSrZHWxzFDnqr123FfbG9oWNqNci5ZSxToF6wgiIWnXQ925PjkOhyiOUeVHYUxKnP1dU46fyamyrqW/jD1XQxwINbe9XtSAiRiUykeperCB3VgX9omIciiairT19GS5WwwyFVQtUQASTNhTpw51fpPJWQnn+oI0yh6uOMHH688EN2Fu+D5LkfxZHJk4Xh8kDJ2NqwWRMGTQFqXHd95kZjI/JxoY6JCal+PUxKWOzWaBu3MtCnUql30Iyt4b5bIMgQqQIUnmnQsw+CYI2EdGC5HAw91JMoK7dCmtN+zPJglaDlFmjkHrmJCTPHAWVXhvSe7cnx6FQ5RFMXs1WK36rrGXCdENlDROntnZmTGV0YyfAtPo5qKqroMvKgUYlQiuKFPjLC1tFGYRjhzD3T3dGpC3hRGn1CZZoa09fhovVCIR7C9R9RjBpQ5E+3PlFKm8llOcu12zAlmNb3ZGjappr2HGHQ4Lox5cpMTB9ILM7pceI3OHQqNoPDRpqyHXPHbfdgtqqckwerENWkgoVx+1Y9sFb+Oufs/HIo08wbwAksqXaHc5NUsXrYLN2MIMqCBDTJ0LIOx1i9iwIutboV0rEU6xT9KaklOR2xTqLb7/rGHPWX/v5Jlgq2+8HWtJPmjHCGe70lDFQxcWE7d7tyXEoVHl0J69GixW/VtZgQ0UNE6g7auvdm6I6gq7fiJQknJCVjsnTx+IPX32Eyi/eRzzzBtBW9NDKgeGjNchJzwjI13BPjUO9pT7BEm3t6ctwscrhhBH68jteR475f8Mvh37BjuM7O3XMr1PrMGHAeExz7d7PSszskWtEQvWaRVfitFEq3HPrUBRkt4qpw+Um/Ovt47h6waV4dvmlOH1gKSSTU3gLNDssiP4Fau7vIOacDEEX/hnhUPXBbbcsQ01ZBYZr0pAmxuCAw4SP3nwHf87JwqNPrMDpp58O44FStkmKfKGaj7cfGYym5RKnDmNL/KmnjoM6MXq8GfQkJE5/qahxLutXVGNnbQMcXRCnZHdKDvtPyEpjAnWkXoOBmRnu9+NXPIbLFy5E0wtPIvbci739rFaUMaEac2A3nnz55S6FneVwOIHBxSqH09Uwi/V1SEnueAmcpbGavRzzlzW0jdXuS3ZiFk4cOoPNno7tN8btmL8n200zqiRUn15SCJVKnvWVAIcNBel2PH1dCm58uhT/989V2PC/8dBpfAUqIKSNZ5ukxJxTIMSkojdBQnXxFQswEWn4R/7ZyNO1ztCUmBvwQuVGLLjwEvxl7HmY2NKBfa0gIGFCIdLOnIjU0yZAk8bt6IKl3mzBL5XOWVMSqLtqG9vdre91KSBgVGoiZmSl44TsdEzLTEOyrvWz5hv+lFYNXlu1yhnB6qH7IfUvJE/zzEaVlv5pRpWEqq+vYQ6HE1oEiaZ+OO1iNBoxa9Ys9nz9+vXQ67vneNxqtULjchTdXYJJG4r04c4vUnkHWh6JFXKIX1FZ7tch/hOPPe7+kqpoqHCHNaVlfksXHPOP6z8O0wqmYGrBVGTFZ0a0/Z2xZs0aLLvpGnz9jyEoyFIDDjsg2QC7hdY+3ecdKrfgtOXFeOT6QlwwI50JVCSPgSr/dIi5J0OISUdvhMT6hDHjUFAr4k95p0DlmimmjVLkXooeNqsV/67fgH2OOrw09FJoPZzGE/FjBiKVnPXPmQBtZnKPflZ6chwKRR61JjNb0idh+mNZFfY1NHdJnIqCgDGpye6Z02lZaUjUarpdT7ofZF/DdbSZKimpQ1/DSh33elt9giXa2hMNmihQ+MxqmAnGuDtYw/BQG5aH01A90kbwnZVHQvXKhVdC20+H0eeN9Qo1aqwxoGjdEVx65aVYcOdCNKcZcLTmaKdlpsenu/yeTsH4/uO9HPPTRoCeRLKZIDUXAU1HIDUewacvP43JA4GC5BagA91dmK3FpMIYfLFLwEW/v5XNoDq0qVCpvIVbb+ODd95DdWk5/pY5FzBYYLM5INntTKzKkIBdmDAGt9R8iR8ai3BqciFih+UzR/0U7jQmP10xn5WeHIcCyaPaaMaGymr8XO4UqHvrO3Bz5oFKEDA2jcRpOhOoUzI7FqddrWc4fA13VF5PobT6BEu0tacvw8VqmGlubg7YwDuYtKFIH+78IpV3d8ujWRSaUSWhOvyikRBcG58cLIKNFbY4BzLPzMGxj47guYefw7RbT4CobjsoioIKI/NGuCNHdeSYP1Ltl+xmSE0uUep+FEEylLIVfpnGmlJkJXcy0ItqQKVDdmosGvSjoCq4mB1ubmjoNZsaHCYLjEUVMB4sg+FQGYyHy9njlZ/ewjB7ArLMatg7UOt56gSM0KZje54Dt71yH/SDWm0aw0Ug90pPjkNdyaPKaMLPrpnTn8ursb+h/SARnuFm1aKIcWmtM6dTMlMRH8RMmpLGoZ5AafUJlmhrT1+Gi1UfysrK2MNTuHD6DhabBc+/+gJKy0sw7JqRaLEa4HDYmWNwh6dbKRHImpWDg6v2oXpPFTLHZLHDybHJmDqIlvan9KhjfslugdR8FGg6zGZKW0VpiZcobY/EWBUq6n3ufdIHglOgQtQCgnP2tLzBgax+yt7RT8v2xqJKGA+Vwkii9BCJ03KYS6oBR9sOabGbkSq2v7xFTvlFnYY9MsyJsGUnRESoRgsVBhKnTntTsjs91NjcpXQaUcSopHiclJ/N7E4nZaQiTsO/xjicaId/yn1YuXIlli9f7rWMMGHCBLfxvcVicTsbttlszH5DJi4ujv3ip19znjYzNBPQ2Ni6jKXVapmdBx2TTYbVajVL39LSwvL1dLtBZcjlEomJiey1ydQa+SY+Pp7N/FEaGcqHjjU1NXktZ1GsZM+NBGTTExsby86j8wlaxqU8KT9qg2d9qFxPEd9eXxDUHhlqM5Xlry886y33BfWjvDxO14HK8e0Lqg/VpbO+oPZRHpQnCdIjFUUwlBhQ2VyJwxVFKG8oQ1lDOapbarB3zS6osjSwxztgt5rcGzNI5XnKGl1qDPQ5cbAUmXHxooswqf9ETCgYz+pM9XSYHWgwN3SpL+g9+dr4uy/89QW7NuYWCIbjEFqOItZeAVv9QTgaD0M0VpBEc89AeZqm+ztGm4DoH3NBJahx+vQh+L8nN+JwjQqDsvVOYeoSpxTKlfmDlRw4VGbCxkNmPH7nXPe1kdtJ9yndO125L6id/u5Tz76Q79N2+8JqhcNig6W4GurKZjTuPYaWg6UwH6mAtaTWmbHkpy8EaornMSBOpUWtuclLnJKrKdBfjRqCWmRp6VFtN2JgXKwzspQgsHb7+8z69gXdp8zVVRf7Qv7M0vn++sLfZ1b+jFBdPD/z1GeUzvMz4m/8onKpjpWVlWyT4dq1a9HY3Iz01FScdNJJbLMh1c/f+OXZF4drarGzuBy/Vtdjc10jjjS2eH2a2rtPSZyOSU7AtPRkTElPxrTsTIj2Vm8aNkMLDN3si47GL/la+OuLjsavzsby9sYvStPV8YvaFKqxXL42vn1B9aH6BTOWd3X8CsVYLkP9QP3h2xeEv77wd5/69kV3xi/fvghoLPe5T0PZF1rXtelqX3Q0fnn2RyThG6y6MLO6dOnSgI2JKX2gBvjBpA1F+nDnF868SZCW1pehpK7E+agvQyl7XorKpirXUqL/tDtWb4Nd60C/eQP8vs++RFUaaFUaHP7wIE7In842JIWz/TRTipZjztlR90zpYUgtNFMa4B5JUQ0hvh+EhEEQEga6/hYAcXmwWO2YNGEMJuU2+ngDaMVul3Djk4ewqTQRm7bscNc/nPeJZ8hSE82UHpZnScvYUr6J3EZ52JV2B0GlQsyADOgLc/B1wyHc/8oTeCr/HOTpk9u9V46bG7Dk+Md48qXnQm7P2B6B9G8w14Q2Ft1+5x9RVlUFaWAhkJgENDZAKDqEnIwMPPnYo212w5c0G9iyPrmR2lBRjaLGFufMfCeQ0/3JmalsSX96ZhqbOY2hHwkhaktnROLe7cnyelt9giXa2qME+AYrhZCTk8MeMsH+iqBfND2RNhTpw51fsHk7BWkpE6D+BGlH693tiQ9CHaOGib5cXcJUJaigosg1ooqJVNrJL6e3t9iRnJwcsvZLDivQ7BKlbLPTYedfJkoDE2EUxlSI8xGliYOAuHwIon/7Pp1OzRz+X73wCiZI77k438vP6qEyIx5cU4J1u+x48eUnvL4QQnmfMFF6rMq5dE/2pC7bUvOxKrbhKSBUImL6ZSC2MAf6wTnQF2QzgRozIBOia0m5v9mMR799Fy/WbnZ6AyC7Dx/skgMvVm1CWk5WQA7hAyWQ/g30mjD3XddfD9OQkYi9dpm3n9HKcpR9+DbzQ/ro0yuhHjWeCVMSqMXNPuFk2/m86VQqTMlIxfSsNMzITseE9BR2LBxt6QrhzFsJ5fW2+gRLtLWnL8PNAMIMLQMEauAdTNpQpA93fl3JOxhB2hGeEaMS9UnIS85FXgo98nBQcwD/+9vDiDHpEJsW51weMpngcFhhEemXegxbajHWGpgbK3Jh012corTYLUrNNfugMZdAajnudBMVCIIIIT7fPUMqC1PQ7Gk7orQjaLbshVWvMn+rp963H5MKdchOVqG83o5Nh8xIzchmQtV3Vi2Q+4REqbm42jlDKgvTQ2UwHa2EZAtClOanM0HKhCk9BmUjZmAWRG3HQx+Jb3L4v+jyq/BAybe4JmOSl59VmlEloboZNVj1xCsRnb0JpH8DSUOzUuRf1Dh4JBJZBKdWEUnRoKypGZCuugF1Lz2FRTffgozlD0NoZ3OTvClKr1Zhckaq089pVjrGpydD203PET0xDoWLSJfX2+oTLNHWnr4MF6ucLjvFr6qqQkZGRqdO8bsLCdJjtcfQWNkUUkHqia8gTdGlYEjeYHYsPsbbobt5khmvr3wdR746hIzfZTHbOtI25LPRYZPQYDKhsUFA1TcVzN9qR7NqTJS2lHjMkrpmTFuKvUSpitmKil0XpXF5rhnSApc4HQDE9YegCu1Mwpw5c/DTho3429/+xu6DbSUt0Mcm4YpF5+DPf/4zs2XqDiQ8aameCVKPHfimoorARakoOEWpLEgLneK0K6K0I0iEv/TaahbB6qbjH2O4JhVpoh41DiP2WmvZjCoJ1Wh0CE8bCleveQclVVXQLL4FRgfdrjanSHU4vEKXSmddAOm/y2HauhH6KSd45ROrVmNqZirGJ8XhtIH9MDY1GRoVdyfE4XC6BxernC47xVfFqWA/bMfb772NrPu9neJ3RnszpMfrSlDVVM2MvuWZzlAJ0vyUPOTScz+ClIzP2/vFTSL86oWLcf9f7ofNakbhqbmIT24V5s1VZhz6phT1+xpx6/Jl7HyJQqjKotTTJVTzMRbxKSBoA09sHluydwpSefk+9KK06+FGk1HTYsLqlc/j0w8/ZrOP/u4B8kfqFqXu3fdlMB0JQpQKAnT56YgtzIa+wCVMaRmfRKkuPI6/Saxv2bHN7RC+oa4eQ1KSsSwEDuF7CpvDgUqjGWUGI0pbjCg1GFHWYkSZweR8bjCy3fo1z70IY/5AmJNSaadou/kJGVlwDCiAeftmZMw4iYlTmjmlpf0xLnHa0eeNw+FwOoOL1TDTW21W/TnFl5fyZKf4Vyy4Aq++/Cr7Qu+KIA3UhjRQQRpo39Bs8qurX8TvRiWgqNqEPS/td0awitfA1mx1RrBKUGHCyFi88uzDWDpxJ7SWkiBEKW0pzYOYVOghTAuA+MiJ0oDCjVZtYsvkzzy6AifmDncv4TfvL4GFbEqtgYt0XV6a25Y01lOUxkS+P8LlED5QOrp3rXYHKowmJjiZ+HSJ0eLGZlSZrex5pdEERxc25UlGg3MzVQeQBwnaFGVPScWMRD0+v3Qu833anTp3F26zGj6izcYz2trTl+FiNcwEE4os2DBmgaZvzyk+CVX6jtOk6FBw/mDsf3cfrrpuAa584CqUN1d0Kkg7wtNZvixI81PzkEt/AxSkXe0btlRvaWAPydKAD979ELWVx7Hmb4OQl6rCpxsb8cWWRtQbLEjOEXDG3AycNTEOx2tsLNToJ19vcoYa7bSRcM6UJgxoFaSuJXyBfJcqCLoHaEaVhCptMBIlgc2WsofNjiybBnfGTsZDNT/h1mtv9BtutCs4Ralrk5PLtjRmUDZUeq0iTWHkUJuhNoXpCiREy400G2pCWXkNE6JsdpTNjDoFKs2YdiUMaVcQ9LFAQ717cxQJU4oQRQ+tSmQiVe0aGxqaGjFwcH+/QpUIZUjGcIZ3jFToyJ4qr7fVJ1iirT19GS5Wu8GSJUswb968bn1JkV8z8qMWCMGk7Up6q82KRlMTms3NaDY1o4mem5rxxSdf4njZceYUv9ni9MFmd9id/uJIcLn8S6bNSGdO8dd9uc7tFL+r+ArStJhUFOYWBi1ImYsnayNgqYdklgVoPWBpZEJUFqVWYy3U9mbnMZv3zuXP3z6AyYNUKEizQ4Id50/R4bzJ6T7CWmgNNbq5ro1YFWJzvJbvmSiNHwBB3bqjPpTXuqvYjRbYGlqcj3p6NLueG7yOf7r9Z1QdKcZf0ubAXt0Efwv3JEsWxo/2CjdK0Kwd2fd6os1Jbd3kRMv4rs1OqlhliXR/s8u0ycjLbdORYrzx/gfIuf/Pft02BYLFbmczofLSvDwr6hajBhOL8uQbwamrdJYmQaNBTmwMcuL0yInVIy9Oj2MXXYBn/vpn6OtrocvObXf1w1ZRBuHYIcz9053t5h/K+zucn5VIfQ57qrzeVp9gibb29GW4WO0Gn2/8Au9++G637DVlB7yB0Fla5lDaZnYLzSZTM1rMLU7RaW5GVX01rLCy9+k1ve8813m+lYSdH/as2QVNthZSvASjxcgmS136zFUvp2jVpuiYU/yavdV+xWqSPsk5M9qFGVJ/Nm0UGtQpLhtdgtM588mEqMUpSFtFqOuYrWuuxsQONjQ1GuzISlaxGSrWVkiupjsDAziXUAVmY5udrEadIwmqwVcAbl+lAyGou/eLvrv3Cc1w2pqMfkSnS3A2GNzPrfQee26AZGnf9tCTb4o3Yrg6Fbmqjn84ULhRsmX9uekoE6varBSo+6chccQApzilGdOCbKji/It0pQvVKxYt6tRt02urVrlNYfxhstlRzkRnqwD1shcl+1BT+CLlJWo1yNRq0C8xATlxMW4xSn+ZQI3VI0Hb1ubXPHwQPn3ycZR+/A501y5xB4XwRHLYYfhoDXLSMzrcaBjMOBjOvCKZtxLK6231CZZoa09fhovVbpB3Vj4kM1D+Y2kbe81AIcFptJpaBaXRKTRJUFbWVcIu2FuFpsf7skC1dWAn6emeqTvYTDZo4tTsg84C/PhoOjpGeo3e1yRooIces0ed3qEglWymVkHZuAeOmgbXzCcda4CmuQo2GN2zn+wvidUewBlqlFxVUZQmAYJEHUCNpi9ngfmcN9rsMDvsKKkXkDvuJKhGLQlqttNaUY+W8qZWsekWnfJz18xnfTOs9S2wN9GPiNAs9/qjxW7xCjdK96mzOKdQJ9MQ8kkqqlVI18VDmDgAk977D9Tx+qjYTCO7bSKhmuDjtokg4UrHG194EjcsXYYXvliLGrvDPSvqKUbrzP5/FIaCJK0WuXExyGXC0/nIjaOHU4Rmx8YgXqMJ6JrQ6hHNHF+6YAGaXngSsede7C3YK8qYUI05sBtPvvxyr9xsxuFwegdcrHYDu80Mq0pC6knpzG0R2XXu2LqdGXHTjCYTkTSz6SEoqxtrYHFYXILTucwuz246z29mS+z+CGS5z5NAk6rIKX6Dic0jqp3RKJ0PAaCvbBEkggGDyQFLnRkzJw3C/w0f5BKZm4DjXwOHG2D1Ep4df2GT6yZHV103BQK1Q5MIaOmRDIcYC1VsOgRtsvuYwP4mYXbtd1iy9A5sP2zDqKw4p0j1DA0JAXpBhUOVzfhpdyOe/sPp7LjkcMDWGNhsZ7DXOvj+EaBK0EOTHAd1cjxSrVmoOFoCSauC0WSGg66PKEISBdglOyS7DSpJZLPndaIFQwbkMaEqhxXsDbCVCbsDLTYbWqw2GGx29txgs+GLDz5AcWUltC63TdReugNoop1m1sl1E/s751wc++9yzH94RRu3TcGSotP6nQVNUQGFaanI1scg1hXEoDMCvSbMfdczz+APd92Nsofuh9S/kGJB0lIIW/qnGVUSqp2tMoXyngjn/RXpe1dpnxWl1SdYoq09fRl+JbuBJkYFivxntUtImpaColcPYdaSU5A0ItkZKz3EdFu8uGfanEvXKkFEvFaHeG0MErQ0w6Jjsyy05BevUiNOrUKCWoV4tYg4FZCgAuJF4KGt9XjqnU3QNZoQn6ZrDT7jUx1zixWGkmakjtkF+47/F2RbuyFUyWZWm+QSmUkQNPQ8yXUsCdCR8PQ5po73mh3TuJbSHWZnPHkSjOx5sxWmasBskfDYJ3V4YqGefMt7NZ6EK4UafeyTelisEo7+ew02Prk5qNnOUApVQauBOikW6uQ4aJLjoU4iARrnPOZ+7hSl7ucJegge/i8vXTMANy68BnuaK5BF/RkTR4GsW8sgX5smI3ZXH8cuUxVz5eQbPzscopKEZIvVKSppdtt5zM7EJnvues/ge9zjuTOdnZ3T3q74+tffgrnfIFiTu+62qTtiNS1Gx8Rnrmsm1GtWNDYG2bH6NmFGgyGYa3LuuefijDPOcLvvYpvMCvoxG9Wuuu8K5T0R6vsrUnkrobzeVp9gibb29GW4WO0GFvl7TSUgJj0Gumw9ju8oRuJwP47RXV+CJGJlGeKcnWPTdG5B2Xqun/ckie2uJUEZpxaRoBIRrxaYoIxXSYgXHYgX7YgXXH9JbKoE1wOIEch3qU/YQ0/srofPantD+RHoVRJK1ldg2Hn9mNP1Ns1zSCj5oQIxKqC6qWt2kH4hkapOgF2Ig0qbDEmIgyTEwwFylRULyRELu10Ph00Huy0GDosODosKDotLaJqtkCw293OH2QzJchwO8xE4zDY4XCJUoudMmDrPt5ss7caQf6/4axQgFWs3N+EWqQx3nZ2OQRmtu9MPV1nw74+rsW6LiZ3349HdOKVf6/JoIPidWaXZzni9S3TKAtP1lz2PbSs6k+JCspOezFuMdiveMOzF0uTfsR8+XogiHHo93qzbwM6jmTVqg4V8eDY0ks8Yt0Ckv0xkul+3FZyeM5u+aToSleGiK26b3CQlO893kaGPcc+CtopR1+s459J8ZyFFO6KlpaXbX8KBpPFNG4z7rmDKD2dekcxbCeX1tvoES7S1py/DxWo38JISAqCJV8PS3ALJXNdmVtMTjeAUj7KITHD9jVP5HCch6nEsTpQQI5KIkVVlR8hffq1lM9tyWQjLutj7P58kzvOami2YOTQGG4sase+DYgw4OQv61NbZE0OtGce+q4ClqBGzhsagqckMe7MRkiTCQYLSqoOdHmYt7CYNbPQwqmFrUcHerIK1RYS1SYStUYDd6DQyaLWvJeFb53qEj47secles0CdjMUxY/DU1k04ffsxTKZQoylqlNfZsPGQGfGOGPwxdga+MxWz8ztD0KidgtIlKjU+wtOiFZCYm+EtSBNj2WwniUCbQ4JNcsDqkNxRhOiYyRVNSH5tbWmBvbm59bXrr5yWHMI78/B97ZGHw4FtX30Bs1aDnx21QOUPuDR1HLLJjMJFmaURb9Vtw0+ogVWjxvC/PAhx4jQmKnvcpCGUbps8j9E/Zgrj3Fwnu3EyNTdi9oiheOLC2cjW68MeoYm8c0QiTSjShjKPcOQVybyVUF5vq0+wRFt7+jJcrHYD+voVPWw4bU1W0BzgFcl2JIhAHJvxdC6lx8uvBQk60TXL6v7P44nnaxKXDpdec9lJso+al/ZtFZ7Olz4zTl4alHaxdywamJS1kcgUnX/tKkhWCSpRwIs3ZOH2V6qw88VGp1P8OJdT/DIDsuJVePqaHKz4qga7DgnY+egUNuMp75v320QfEe953Cly2l9F9+hB/3n4094dpGeCinyH+jlZK4modRgwJSYX43RZ+NlUgl8OluIwLIiFFkti8jAtJg8aUYU1hn0Q4lOw++QRMMfpYI7VwRgXA2OsFsY4LQx6LVpitTCpRdgkZ/Qgm0sYsrCVTBySVwcLHFWlsFd6i0abT2jLSFH/6aewFw6D4+TZ+PGNVfi1/HOMUFMEqxjUOEzYY6uBNTERtstugfTtl6jf/BuSJ0xFT0Mus+LUasSqVSzUZ5zG9dfnuef77LXGaRYT63pvvdSIP/3hD9A31kOb1f6sOW0yshUfwaJ770K/eD6Dw+FwOOGAi9VukKyW3N5baHbRVNKCIdkaXBLjs45O2kKeDCWB0sX82Q57l3B02F1/bSLs1rbH2HH6a/U5bnUdtwut6eS/HnnIaQVTjLIAAByySURBVGl3O0lvTzm0t+gIKlV1+NeFemz4UwE+39GML3c2o8FoRVK6iDmnZOPMMfEoqbdh40EzMu2xKK2nVkb2V6xDFGBVq2DViLCqVLCpRVhIFNIxteh8z+uv8zk7TyXCqqHnrvdcr+n5odc3oPzoMexXm5GlScC4uMEYh8FMiEsuLU4Oso5YG7HdVgNbYhqKJuX51I4uphlooUfnbVHabKS8DC6MHAvb/Q/CtmMzNu3aBtHQAkdsLoRRFwBjJkLQaCBt+dVrGTxcotL511tU0mv21/UeOaoPRT+OuuxSPP7ggyj76G1o/HgD6I7bplATSPuC26gZfH+G1CY7jJ+TSH8GlfSZV2J9giXa2tOX4WK1O8gr6g6JLYNrBAnxahVKt2e0CkdPIekrLH1Fpk1oFY8ewjHyeM/c5akSUG6rx0MfV+OJRTk4b2Iie3hCM4Jkt0ltyNLEoylOx0SgLPwsHkLQRkJQrYJF4xKL7LVLRJJAdL2m8y0egtK/4GzN0xGm5dbalBg4ytR4rWEbbs44sa29JrVfcrBlcKtWDTE2LioGVdG1rK0RRRji42GurYVIc/MUsnDSdAiTnBuIqKrsuGuVwdLUgFED+uPKcSO6JCrpmE4VGlEZLmS3TeRHVWlumxITEyOSJhRpQ5lHOPKKZN5KKK+31SdYoq09fRkuVruBJAkw1phxbH05jIfJAT1QezQOu74awmbc7LTpRBTgEAQ4VM6/dnotPzxf6wQ4YkXnufJ7rjTy+XYKb0p/Wb5wneORRqSZPmeedvk55UHnU31Yemed3OX6Kae1bs5zjz73M8zHBXy+xYglQhn+eHaG9wajSgv+80kVvthihMku4Of+Ohy4PbgoPpGeWeyoPDEuHuacXPxUWQlU/YhLUrztNcutLntNRyWsOblITkxEpj6GhZ5UC84QlCT41C7hp/J5zc5zH3OFrLTboddpW9N4nOvvtVyWhuXl/Nt6ju/r1vKd7/nm6zzuGXVqjdaExTfehNimOi+R5guJNhwvwj333Y3544axY0ajMSrCHNKmMXL4zyJYBeG2KdQE0r/BXJNQXM9Q3hPhvL8ife8q7bOitPoES7S1py/DxWo32LPmCAylzciMV2FonhYbDhhRPjIPb/xnkdunqbwJwzlHKsBmtUKr1biFkSwHnH5LnRalzvNb35dfW60W5sOV5SmnE7zLoBku2ReqjJyvxWJBjE7XtgyP+nm9dtXxw18Go7SiCPmqZPy4tRmztx1ts8EoVopBvioNu1GP8aNG4qoJI5k9rzzjRsKHnrNjrvqwY4LnsdbnRoMBCXFxrA5yPvK5nud5H/M+t7Vcf+d6HKMwfM3NSE5M8CjHlR4CPlYbcfOyZVBfthAbPnoXv1V8jhGaNKQJetRIRuyx1sCelAT9OQuhef91PHfzvzB//plBfRCV5kifQgpTOFGK0uTPKX5Hy+B030XLFwR5Rdi5dUtQbptCTSD9G8w1CcX1DOU9Ec77K9L3rtI+K0qrT7BEW3v6MlysdoPT0wWMn5iOjYeN+HRLC2wOEY/d80dcNufEsIiQYAVMoOnHLl6ABd9/i32OZkxTZ2KYLgOHj9Zgf5EFekGL82PTsN9ShV/slVDp1Lhr4ZWYP2ZowPUMpq4BlyfZkdTOhpjL51+If/797yjbtwtJf/8vzNu3YNf2zRAMLZBi86Ebeynix05A8+pnI26vGCmUvAweaahtwbht4nA4HE5wcLHaDWoa7XjogxrYbAJb/s7My8EFF1yAaINm1fLz8lCs1uKXqmpsbNjhNbP4WvMONrOIjALk28xRJ9Y8hRoJUhJqng7fSajR8WgXakpdBudwOBxO30KQyHiP06HNy6xZs9jzYzv2o9FhgUOtQoxeh3fefLPTL+pgbDGDteMMJv1XX33FxJqxcDjUBUNgPXrENbMYB82AQbAdPgD9ob14PURiRUk2qzJffvmlU6hVV/kXao89GjKhpjRvAJ6YzWbvZfCkJMydO7fdZXAltyUaCKR/e3IcClUe4cgrknkrobzeVp9gibb2KE0TrV+/PmJmFlysduPCbD9aDNFsRH5OLp56fEWXhArZzJDdaSAEkzYU6duINYro0xgesRZsXcNVXneFWrjr0xuIprZES//25DgUqjzCkVck81ZCeb2tPsESbe3py2KVmwF0gyvPPouJlO4IFbqwgX5YgkkbivS+m0sqq6uRWRiezSXB1jVc5UXKXjHS7Q8n0dSWaOnfnhyHQpVHOPKKZN5KKK+31SdYoq09fRkuVrvBk08+2ed2FnqKNaXtWOdwOBwOhxP9cLHqQ1lZGXt4LgNzOBwOh8PhcHoGLlZ9WLlyJZYvX+5+LYoiJkyYwJ7TzCLZwBAJCQmw2WxsmUEmzuUntLm52X2MliDIyLuxsdHrGM3Q0jF5f5uaov7ExaGlpYXlS8jvURlyuXJUDnptMpncx+Lj4+FwOGAwGLxmRelYU1OT17GYmBjWFhmNRoPY2Fh2Hp1PqFQqliflZ7Va2TH5PSrXU8S31xcEtUeG2kxl+esLaqtcJ7kvqB/tdrv7OlA5vn1BM71Ul876gtpHecjXhvKldNQfXe0Lz2tD15mug29f0DHqr+72BdWH8qJr4+++8NcXntcmmL4gqFzfvpCvTVf7Qr42VE/Kpzt9Qe30d5969oV8bbraF/7uU0rXlb6gtvu7Tz37wt9n1rcvOrovqB+pbV3tC/nayG3w7Qt/n1m5L6jvPK+tv77wN35RuXTc333R2fjl2RfyPdHR+OXbF9Q+apNvX9C5/j6zXe2LjsYvuZ7++qKj8auzvmhv/KLzuvqZ9dcXgY7l7Y1flJbqF8xY3tXxK1RjeXvjF9WP0kd6LPe8Nr19LPfXF579EUn4BqsuzKwuXbo0YGNiugnoJg2EYNKGIn2484tU3koor7fVJxiiqS3R0r89OQ6FKo9w5BXJvJVQXm+rT7BEW3v68gar8ARX78Xk5ORg4sSJ7sf48eODys/zF1gk04Yifbjzi1TeSiivt9UnGKKpLdHSvz05DoUqj3DkFcm8lVBeb6tPsERbe/oyXKxyOBwOh8PhcBQLF6scDofD4XA4HMXCxWqYCcaeI1hbkFDbkoTTNiXSLsGU5oJMafUJhmhqS7T0b0+OQ6HKIxx5RTJvJZTX2+oTLNHWnr4MF6thJhjj7mANw0NtWB5OQ/VIG8ErzeheafUJhmhqS7T0b0+OQ6HKIxx5RTJvJZTX2+oTLNHWnr4MF6thxtPVSCTThiJ9uPOLVN5KKK+31ScYoqkt0dK/PTkOhSqPcOQVybyVUF5vq0+wRFt7+jL8Z0cnyD7SiED8i/XmcKvhzi9SeSuhvN5Wn2CIprYoER5ulY9DSrq3lEy0tUcJeOogT30UbrhY7QRPp7xz5swJ9/XgcDgcDofD6RX6KDY2NiJlcTOATvCMFsHhcDgcDofDQUT1EY9g1QWbl9TUVPa8tLSUhSfrKlu3bsXMmTPxww8/dDu4QDBpQ5E+3PlFKm8llNfb6hMM0dQWJTo4z8rKYs8rKircoR87oyfHoVDlEY68Ipm3EsrrbfUJlmhrj1Kg0K65ubnseW1tLQsXGwm4GUAnUNxdOW4uTXd3xxUGxeelOLz0t7suNIJJG4r04c4vUnkrobzeVp9giKa2KA3qVzmuN/VtV/u3J8ehUOURjrwimbcSyutt9QmWaGuPUqA+lTUR6aNIwc0AOBwOh8PhcDiKhYtVDofD4XA4HI5i4WKVw+FwOBwOh6NYuFgNIzk5OfjLX/7C/kYybSjShzu/SOWthPJ6W32CIZraEi305DgUqjzCkVck81ZCeb2tPsESbe3p63BvAF3YgSt7AKBdcF3dgcvhcDihhI9FHA6nr45DXKxyOBwOh8PhcBQLNwPgcDgcDofD4SgWLlY5HA6Hw+FwOIqFi1UOh8PhcDgcjmLhYpXD4XA4HA6Ho1i4WA0xkiThrrvuwpAhQzBgwADceOONsFgsoS6Gw+Fw/ELjTb9+/VBfX+8+9uqrr6KgoAB5eXm47LLL2C5eDofDifRY1NHxjuBiNcS88sor+Pnnn7F9+3YcPHgQZWVlWLFiRaiL4XA4nDb8/e9/x4wZM3D8+HH3sUOHDuGOO+7A2rVr2fGYmBj885//5L3H4XAiOhZ1dLwz+rRYXblyJfLz8/2+t2rVKowYMQJ6vR7Dhg3rsuD85ZdfMHfuXJZOo9HgjDPOwAcffBDimnM4nN5MOMYeYvLkybjvvvu8jr3//vs4++yz2cyqIAhsteedd94Jug0cDqf3szKCY1FHxztDjT4KqfqHH3643dnRxYsXs0GdxObmzZtx++23w2AwsCX+jhg3bhzeeOMN3HbbbXA4HEyolpeXh6kVHA6ntxGusYegH8q+0MwqCVWZgQMHdntWg8PhRB/HIzwWdXS8U6Q+xtdffy2NGTNGUqlUEjU/Ly/P63273S71799fuvjii72OL1u2TEpISJCam5s7zN9ms0l//OMfpREjRkhTp06VbrzxRmnixIlhaQuHw+k9hHvs8YTyr6urY8+XLFkiPfTQQ+73KioqWH4cDqdv8nUPjUVdOd4efc4MIDMzE1dccQX+8Y9/YOLEiW3e37ZtG44dO8Z+UXhyzjnnoKmpCd9//z0eeOABFm7M9/Hkk0/i6NGjuP7667F7925mEtC/f39MmDAhgi3kcDh9cexpD9roSfnKFBcXY/DgwSFuHYfD6S1k9tBYFBRSH2bRokVtflE8//zzTPEXFxd7HS8rK2PHH3nkkQ7zXLVqlTRjxgzJbDZLJSUl0oABA6Tvv/8+LPXncDi9k3CMPe3NWhw6dIiVRfnQjMlVV10lPfjggyFqCYfD6c0siuBY1JXj7dHnZlY7o7q6mv1NS0vzOp6SksL+NjQ0dJiefq0MHz6cua464YQTcOedd2LWrFlhrDGHw4kGgh172oPsVWkGZebMmWylR6fTMdszDofDieRYFAx9doNVe1itVr/HRdGp62NjYztMr1ar8fzzz4elbhwOJ3oJduzxxDlx0Qot5/ku6XE4HE6kx6LOjrcHn1n1Qf7l4OusVn6dnp7erQ7mcDicrsDHHg6HowRSFKiDuFj1YdSoUW4DY0/27NnD/vLNUhwOJxzwsYfD4SiBUQrUQVys+kB2phkZGSw8oSerV69m9l7jx4+P5PXhcDh9BD72cDgcJXCCAnUQt1n1QavVss0Iv//975lx8SmnnIJ169YxO1TfC8fhcDihgo89HA5HCWgVqIO4WPXDDTfcwIx/KbLD008/jcLCQvaL4vLLL4/8FeJwOH0GPvZwOBwlcIPCdJDg8nfF4XA4HA6Hw+EoDm6zyuFwOBwOh8NRLFyscjgcDofD4XAUCxerHA6Hw+FwOBzFwsUqh8PhcDgcDkexcLHK4XA4HA6Hw1EsXKxyOBwOh8PhcBQLF6scDofD4XA4HMXCxSqHw+FwOBwOR7FwscrhcDgcDofDUSxcrHI4nG7z0ksvQRAEv4+kpCTMmzcPW7du9UozcOBA5Ofn897mcDgcTrfgYpXD4QTMtGnTcOutt7ofS5YswZQpU/DZZ5/hxBNPbCNYlQaJZxLRXcFmszExfsopp6CnuOqqq1gdioqKEO3cd999rK3ffvttj5R/8OBBVv7ixYt7pHwOh9OK2uM5h8PhdIszzzwTf/3rX9scf+6553D99dfjz3/+Mz788EPeqxwOh8MJGD6zyuFwQs7VV1+NuLg4/PDDD7x3FYzD4YDFYkFvxmg09nQVOBxOmOFilcPhhByVSoWYmBi0tLR0eF5xcTFuvvlmDBo0iJ3fr18/XHbZZdi0aZPXebT0npycjPr6emZqkJWVBb1ej+nTp+PHH39sk++OHTtw4YUXIjU1FfHx8Zg5cybeffdd9/u0tExLvCUlJTh69Ch77m+G2NNGV6PRsOffffcdO5+OyVB9zz33XFYe1WvixIl45plnIElSl/rLbrfj8ccfx9ixY5nIp3wov23btrnPoTJfffVV9pz6y9Mcobv9WFpaivPPP5/1ze7duzus20cffYQTTjgBsbGxyM7OxjnnnNMmX7PZjAceeADDhw9n5dP1WbBgAQ4fPux1Hi2pUzvKy8tx7733snrS+ePGjcMHH3zgPo9MM/75z3+y57/73e/cphqyrfR7772HBx98EDk5OfjTn/7kTvf666+za01tpMeECRPw8MMPtxHke/fuxcUXX4y8vDxWPtWb6k/XgaB7YciQIez5qlWretQcgcPhADSYcjgcTrd48cUXSYVJf/nLX/y+X1RUxN4fOXKk+9iAAQOkvLw89+uGhgYpNzeXnTd79mzp97//vTRnzhxJFEVJq9VKu3btcp978sknS7GxsdL48eOlQYMGSddee6109tlns7TJyclSdXW1+9zvv/+enUuP+fPnS9ddd500cOBAdu69997Lzjlw4IB06623SvHx8VJCQgJ7/tlnn7Xb3l9++UVatmwZy4PaQOfTMeLLL7+UdDody+uyyy6Trr/+emnIkCHs3EWLFnWpP5csWcLOnz59OusHaptKpWJ1O3LkCDuHyhw+fDg77+qrr5ZWrFgRUD/GxcWxfIYOHcr6pri4uN16rVy5kuVL+VOfU39qNBrW3i1btrBzbDYbK5fOmzhxonTjjTdK5557LjsvKSnJfR5B/UHnnXjiiVJmZiZ7femll7K20vl79uxh5y1fvlyaOnUqO5fKpNee992kSZNYf1900UXSmjVr2Hv/+c9/2Hv5+fnSwoULpcWLF7PndOyqq65y1+HgwYOsXxMTE9lxOm/w4MHsvFtuuYWdQ/cCHadjI0aMYH1P9wyHw+kZuFjlcDghE6tGo1H66aefpClTprD3SUC0J1ZfffVVds4jjzzilQe9puMPPPCAl8iiY7NmzZIMBoP7+A033MCOU16ExWJh5aSlpUn79+/3qtdJJ50kCYIg7d69232c6kPndwWr1crKorrImEwmKScnhwmfffv2uY9TPc477zx2/vvvv99hvs3NzZJarZZOOOEEr+MffvghS3/PPfe4j1155ZXsmCxgA+1HEpdU944oKSmRYmJipGHDhkl1dXXu41988QXLY8GCBez1Y489xl6TSPXkhx9+YAJ0woQJbcQq5en5A4PqSMf/+c9/uo/RDws69s0337S57+iHyI4dO7zKox8IdC3r6+vdx5qamphgJXEtQ/1JeVA7ZMxmM/uhQNeBrgdB4rQ7Pzg4HE744GYAHA4nYJYvX+7ltoqWwGfMmIHffvsNc+fOZR4C2qOwsBB///vfsWjRIq/jtBRO+DMh+Pe//83KkDnrrLPYX1rKJz755BP2/Pbbb3cv4xK01Eu7y+kHuudyc7BQeWVlZbj22msxdOhQ93EyGZCXsWlpuiOoneRpoK6uDgaDwatt5FXh8ssv7zB9IP1I5+t0ug7zpXqbTCbcfffdbEldZs6cObjgggtgtVrZ62effRZarRZ/+9vfvNKTN4jzzjsPW7ZsYcvuntAye1pamldbPa9jZyxcuBCjR4/2OkYb+h599FHmOk2GzBzINILMFKiPCTIlIY4fP+4+j+pPJgZk8iCK/GuRw1Ea3BsAh8MJynUV2Y3KkGBNSEhgNo7kKYBed5SWHiTSvvrqKxw5coS5ZHrnnXfaTeMrUGRhIou8DRs2sL8//fQTbrvtNq9zZdG2b9++Dt0Vke2oJ9QOevjj119/ZX9JmPsyatQoZucp24S+9tpr7vNlyFtCZmYm80tLwnfYsGGYP38+Tj75ZGar2V65wfbjmDFjOs1348aN7K8/V12y/S/1+86dO9n1zsjIaHMeuTFbs2YN6wOyC+3qdewMf/W/88472Y+R7du3M3FMfUB1893kR+6/nn/+eSZuqW4kvqmN48ePZ/3P4XCUBxerHA4n5K6rugLNcN10001466232K502lg0cuRITJ48Gfv37/ebhmZI/SFvZKqpqWF/P/3003bLbWpqavc9mm2j2TlPaFaxPdEol5ebm+v3fZoFptlJ4ssvv2SbdTwhQU2bqUj8rVy5EqtXr8Zjjz3G6qBWq9lmJjruTwgG0480k9gZcttITLcHCWTq+/baT2KdkPugq9exM/zV//3332ebzGjzGP1Ios1bJKJp5pl+hMjIM/+08erjjz9ms9fEgAED2OwwzdpyOBxlwdc7OBxOj0AmAm+88QabEaNZsObmZjbzeN111wWcJwk1Wbi4bPLbPGg2rT1ohs33/I7EuGySIAs7T2jpuba21i00aZnZN295lzuJr6VLl7L2V1VVMeFJS+O0670z8RSOfiRkM4HGxsY279HuempfR+0nSDgSHYntUEAzqZdeeikTx+vWrWOz6GRSQP3iT0iTiQT9cKC+Js8G//jHP5gLLPJW8PPPP4e1rhwOp/twscrhcHoEWvYmO09yQUSzWjK0jB0osp2mv8hZtCRMS8Bklxgq5OXs9evXt3mPZlJJkE6dOrXDPMh0gdxI/fLLL+w12XKSWyWyraUZUnKVFel+9Gybr+kCuXciAUg2qTQrTO6jNm/e7Nc29osvvmBuzMiVVzhZu3YtE9A0M3rqqae6RTT1v68dLM1mk00zQfapVDdyo7VixQp2vr9ryeFwehYuVjkcTo9As4m0jOy5LE9+OeWNSYEg+w596qmnvEKSNjQ04JZbbmGbhgoKCtzHSazIG286Q95443k+bTQiYURCh3y2ytCsI20GozQUIKEjEhMTmTClZWnPvMkXKc1Mei7D+6tDOPqRoI1dtJxO7ZA3UxGPPPIIm0klO1viiiuuYP37r3/9q42IJhFLfeS5maqr+GtrZ2YBx44dcx8j4UkbyXzFKvUp2SX7+oqljWCE3N/dKZ/D4YSZMHoa4HA4fdTPqj98XVfdfffdLA/ycUm+QefNm8f8gpJLJDpeWFjIyvF0uUTuozwht0ae/lOJ1atXMx+jKSkpzA8nuXsi91L+6kvuomQ/nJ9++mmnbaB8yB0T+VLdsGEDO/b000+7/b1SPvReQUFBG7dTHXH++eez88n/Kfn3pHxSU1PZseeff76N26VTTjmFuYwKVT+2xx/+8Ae3qynK+/TTT2evyW+t7M6K/LzK/l+pP2+66SbmZ5XcQGVnZzOfu76uq3x9lpIrLjpO18rXxyv5VP3rX//qdd89++yzXulLS0vZ9SZ/rRdccIF0zTXXsP7IyspiPmcpDfnAraqqkjZt2sTOI3dW5M+W2jVz5kx3O2XXaOTCiq41uSUjP7iertA4HE5k4WKVw+H0iFglX6T33Xef1K9fP+Y3c9q0adLrr7/O3iNxQGLijjvu6LZYJdatW+cOJECO6ckJ/ZtvvtmmTp9//jkrn4TV//73vy61m5zZkxiUndETH3zwgTRjxgxWHjndJ4f2L7zwQpf7hvyBkuN5EoEkkCgf8vvpW2cSfuR4n84hURaqfmwPh8MhPfroo0zEUT4kwm+++WapsrLS6zzymUrH6fpS3SiIAAUuOHr0qNd53RGrJIZJlFO5sq/W9sQqsXHjRuZLl/qfflSQD96ysjJp+/btrG9IdJLvWIJ8rFJf0DG69lRvCpBQUVHhlSf5fSURrNfr3UEgOBxO5BHov3DP3nI4HA6Hw+FwOIHAbVY5HA6Hw+FwOIqFi1UOh8PhcDgcjmLhYpXD4XA4HA6Ho1i4WOVwOBwOh8PhKBYuVjkcDofD4XA4ioWLVQ6Hw+FwOByOYuFilcPhcDgcDoejWLhY5XA4HA6Hw+EoFi5WORwOh8PhcDiKhYtVDofD4XA4HI5i4WKVw+FwOBwOh6NYuFjlcDgcDofD4SgWLlY5HA6Hw+FwOFAq/x/ZmpTSDFAojwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -1133,11 +937,11 @@ "for idx, dist in enumerate([5,10,15,20]):\n", " exposure_times=[]\n", " for contrast in np.logspace(-9, -11, 10):\n", + " params=imaging_params.copy()\n", " params['Fp/Fs']=contrast\n", " params['distance']=dist\n", " # make the parameters be the shape that the code desires \n", - " parsed_parameters= parse_input.parse_parameters(params)\n", - " texp, validation_output = calculate_texp(parsed_parameters)\n", + " texp, validation_output = calculate_texp(params)\n", " exposure_times.append(texp.to(u.hr).value)\n", "\n", "\n", @@ -1166,20 +970,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3.4 Separation\n", + "### 2.3 Separation\n", "\n", "Now let's change the separation of the planet and see how the exposure time changes. Note: there will be some errors!" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:37.895469Z", - "iopub.status.busy": "2026-05-19T21:31:37.895356Z", - "iopub.status.idle": "2026-05-19T21:31:43.307984Z", - "shell.execute_reply": "2026-05-19T21:31:43.307681Z" + "iopub.execute_input": "2026-07-13T17:35:30.839890Z", + "iopub.status.busy": "2026-07-13T17:35:30.839767Z", + "iopub.status.idle": "2026-07-13T17:35:35.415317Z", + "shell.execute_reply": "2026-07-13T17:35:35.415040Z" } }, "outputs": [ @@ -1187,42 +991,42 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:38,158]\u001b[0m Planet outside OWA or inside IWA. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:31,076]\u001b[0m Count rate of the planet smaller than the noise floor for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:42,487]\u001b[0m Planet outside coronagraph YIP image. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:34,664]\u001b[0m Planet outside coronagraph YIP image for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:42,714]\u001b[0m Planet outside coronagraph YIP image. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:34,889]\u001b[0m Planet outside coronagraph YIP image for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:42,957]\u001b[0m Planet outside coronagraph YIP image. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:35,109]\u001b[0m Planet outside coronagraph YIP image for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:43,209]\u001b[0m Planet outside coronagraph YIP image. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:35,341]\u001b[0m Planet outside coronagraph YIP image for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -1237,9 +1041,8 @@ "for sep in separations:\n", " params = imaging_params.copy()\n", " params['separation'] = sep\n", - " parsed_parameters= parse_input.parse_parameters(params)\n", "\n", - " texp, _ = calculate_texp(parsed_parameters)\n", + " texp, _ = calculate_texp(params)\n", " exposure_times.append(texp.to(u.hr)[0].value)\n", "\n", "fig, ax1 = plt.subplots()\n", @@ -1266,34 +1069,6 @@ " ax1.spines[spine].set_visible(True)\n", " ax1.spines[spine].set_linewidth(1.5)\n", "\n", - "# Secondary x-axis (lambda/D)\n", - "separations_lod = arcsec_to_lambda_d(separations*ARCSEC,\n", - " observation.wavelength[0].to(LENGTH),\n", - " observatory.telescope.diameter.to(LENGTH))\n", - "\n", - "num_ticks = 5 \n", - "tick_indices = np.linspace(0, len(separations) - 1, num_ticks, dtype=int)\n", - "reduced_separations = [separations[i] for i in tick_indices]\n", - "reduced_separations_lod = [separations_lod[i] for i in tick_indices]\n", - "\n", - "ax2 = ax1.twiny()\n", - "ax2.set_xticks(reduced_separations)\n", - "ax2.set_xticklabels([f'{m:.1f}' for m in reduced_separations_lod],color=colors.purple)\n", - "ax2.set_xlim(ax1.get_xlim())\n", - "ax2.set_xlabel(r'Separation ($\\lambda$/D)', fontsize=14, fontweight='bold',color=colors.purple)\n", - "ax2.tick_params(axis='x', which='major', labelsize=12, direction='in',\n", - " width=1.5, length=6,color=colors.purple)\n", - "\n", - "\n", - "for spine in ['top']:\n", - " ax2.spines[spine].set_visible(True)\n", - " ax2.spines[spine].set_linewidth(1.5)\n", - " ax2.spines[spine].set_color(colors.purple)\n", - "\n", - "\n", - "\n", - "\n", - "\n", "\n", "legend = ax1.legend(loc='best', frameon=True, fancybox=False, shadow=False,\n", " fontsize=11, edgecolor='#333333', framealpha=0.95)\n", @@ -1311,13 +1086,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:43.309547Z", - "iopub.status.busy": "2026-05-19T21:31:43.309443Z", - "iopub.status.idle": "2026-05-19T21:31:45.786974Z", - "shell.execute_reply": "2026-05-19T21:31:45.786707Z" + "iopub.execute_input": "2026-07-13T17:35:35.416886Z", + "iopub.status.busy": "2026-07-13T17:35:35.416778Z", + "iopub.status.idle": "2026-07-13T17:35:37.732690Z", + "shell.execute_reply": "2026-07-13T17:35:37.732405Z" } }, "outputs": [ @@ -1325,19 +1100,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:43,537]\u001b[0m Planet outside OWA or inside IWA. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:35,637]\u001b[0m Count rate of the planet smaller than the noise floor for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:45,709]\u001b[0m Planet outside coronagraph YIP image. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:37,658]\u001b[0m Planet outside coronagraph YIP image for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1355,9 +1130,8 @@ " params = imaging_params.copy()\n", " del params['separation']\n", " params['semimajor_axis'] = a \n", - " parsed_parameters= parse_input.parse_parameters(params)\n", "\n", - " texp, _ = calculate_texp(parsed_parameters)\n", + " texp, _ = calculate_texp(params)\n", " exposure_times.append(texp.to(u.hr)[0].value)\n", "\n", "\n", @@ -1394,19 +1168,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3.5 PSF Truncation Ratio\n", + "### 2.4 PSF Truncation Ratio\n", "Also, we can test the effect of varying the PSF trunction ratio. There should exist a PSF truncation ratio that minimizes exposure time and maximizes SNR (and it should be around 0.3). Let's test this:" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:45.788400Z", - "iopub.status.busy": "2026-05-19T21:31:45.788269Z", - "iopub.status.idle": "2026-05-19T21:31:50.721664Z", - "shell.execute_reply": "2026-05-19T21:31:50.721369Z" + "iopub.execute_input": "2026-07-13T17:35:37.734259Z", + "iopub.status.busy": "2026-07-13T17:35:37.734153Z", + "iopub.status.idle": "2026-07-13T17:35:42.436755Z", + "shell.execute_reply": "2026-07-13T17:35:42.436483Z" } }, "outputs": [ @@ -1414,33 +1188,33 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:49,889]\u001b[0m Photometric aperture is not large enough. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:41,700]\u001b[0m Photometric aperture is not large enough for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:50,146]\u001b[0m Photometric aperture is not large enough. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:41,924]\u001b[0m Photometric aperture is not large enough for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:50,400]\u001b[0m Photometric aperture is not large enough. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:42,142]\u001b[0m Photometric aperture is not large enough for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:50,644]\u001b[0m Photometric aperture is not large enough. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:42,367]\u001b[0m Photometric aperture is not large enough for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -1457,8 +1231,7 @@ "for ratio in psfratios:\n", " params = imaging_params.copy()\n", " params['psf_trunc_ratio'] = ratio\n", - " parsed_parameters= parse_input.parse_parameters(params)\n", - " texp, _ = calculate_texp(parsed_parameters)\n", + " texp, _ = calculate_texp(params)\n", " exposure_times.append(texp.to(u.hr)[0].value)\n", "\n", "fig = plt.figure(dpi=100)\n", @@ -1498,13 +1271,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T21:31:50.722942Z", - "iopub.status.busy": "2026-05-19T21:31:50.722838Z", - "iopub.status.idle": "2026-05-19T21:31:55.338663Z", - "shell.execute_reply": "2026-05-19T21:31:55.338384Z" + "iopub.execute_input": "2026-07-13T17:35:42.438321Z", + "iopub.status.busy": "2026-07-13T17:35:42.438205Z", + "iopub.status.idle": "2026-07-13T17:35:46.939437Z", + "shell.execute_reply": "2026-07-13T17:35:46.939129Z" } }, "outputs": [ @@ -1512,33 +1285,33 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:54,630]\u001b[0m Photometric aperture is not large enough. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:46,202]\u001b[0m Photometric aperture is not large enough for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:54,846]\u001b[0m Photometric aperture is not large enough. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:46,443]\u001b[0m Photometric aperture is not large enough for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:55,068]\u001b[0m Photometric aperture is not large enough. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:46,665]\u001b[0m Photometric aperture is not large enough for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-05-19 17:31:55,295]\u001b[0m Photometric aperture is not large enough. Hardcoded infinity results.\n" + "\u001b[38;2;156;29;16m[pyEDITH] ERROR [2026-07-13 13:35:46,893]\u001b[0m Photometric aperture is not large enough for one or more wavelength channels. Hardcoded infinity results.\n" ] }, { "data": { - "image/png": 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QW//S2ioNDfk5cOCA+uEg6RXykDQG0UCMs6QKuBuaVYPgF3fpOjTd+p/JwMTim9Kq4WFoXC6vIZXn6pHh8C/AoD30wANWs7psvtUkF3RMh/Zb1wN5co2cJcDfH5HyKCTJXS5e6gJazC+QCq3aIrhsGZxMTc9rLC0W7LqQqB7TccReXiE02G5eW140sE2L0Qrr7Hi0usM6St18Nd7kNu+yElz/ZHt3IbmYkoMqqQBqt4cOXTYKgCyvW7dOdc4W0yfDWcr1sjgti/nHWxeKGorLNtuZXNfymzox0I7XRbk9bzvm/DhuK8isYzZzKseTf71tm3LlyqnXkjMrt+vluivT1EuKgaQyyO36gnCc7c1G/vOWcxPDKy2sBaVoyvZivqXFWNINJH9V0h86dOhwWYd3V0OzagASkAX9cvFV5LZxSTs0Yc92ax6nQ45osL8/GpSNsptSmyGVltMywSXr5SgtgWKwlAGbMrnQtARkZKjtPPHLUgyx0qmYXyD//c/TGDFiGM6mpWPruXhsORdvf955IfGyFuhz6ZlYduKMeji2woqOjga2VYVyuCrcOjpDcdM3bMNpFZa+oRuso9TNl+NNrmfSUqfyH69w/ZPtJLfUndqIgbKZVRlfVSYDsq2TjtiyTm5H20YHkFvV0lnqSkinILndvnr16jyjGYj5Kwy5ZS9I/mb79u3t5XJLX1pPpXVXEOMs6Qpy3ZYUhsGDB+fpJCadxsToOe7Tdktfbu/LMTny119/qelubSkHtg5eYtTlMXbsWNXSKevknPIb1J07d+bRtLBzkxbUxo0b52ltlZlCJV1AUgF+/PFHla8q+5OHtAbL/qWjmWMrrauhWTUAuTVhGyaCowFYLyzO5GOOrlEZrWvXspvT2lERTg0JVRBiqKQl0G7ExCDLIx+2FkNPGDBnDLTEWtkAf/SqVhm9q1exv1dWTi72JCRiS1w8tp63Glh5nCqgFVaMrTx+3Je3FVaMa/jWTU4Np6U7rKPUzZfjTa5n0unGPjFAIdc/2wxW7r7+iRmSHFQZIstxWnXRLTIyUrU2imlq1KiRmmpWet2LltIBSKb4LAoxYtK5qXbt2srsSYOApBQU1tlN8jRlVBrpWCS3wuVWuhg4uR0vU4YKkmsqPfEXLVqk3vP3339XnZ8kp9SWxy/byHtJaoPkocpDJiMQJk2apFIRZB+SwxoXF6eOUXJhxahLi61cT8UQy0NySaUzl5hjSUUQTaSDmGgit+mllVdGRrC1pBaWHiH7lU5bYrgltUL0k1ECxCDL+8l3tZynmHyJDen4JZMzyQ8I22gBPjEpgC9RUJO8L3IoMRlv7DpYYL7llTs0dcdjrRtjSK1qqF82ymVG1YbcspaWQDGjjsN/CLIs5bLeU7e2bQZajKgYUvXl8ewE4IHR1mdZvmhUHQ10QbEWFOCP5uWjMbphbbzeqTUWDO6Bk7cPw+kxw7BoSA+81bk1bm9YWxlSGWYrP9IKu/T4Gcz97uvip284pnuYANZR6ubL8SbXtblz5yozmv+WuixLuaz3xPVPTJ108pEcU8fWSEGMmphFybWU3E3pHCQGSwyXlP/yyy9FvrekMMiwV2JwxajJbXR5XRQyYlH//v1x1113KS2k9VGGgRJTJ2PEy/6l1bFbt27qOiw5q9JiKT3rbUyYMEFpKMcsrcJixMUACvI+YjTlWOQ97rjjDnWrXbYTxJxPnz5dtZbKd5Ecy6lTp5Thlc9DTKm0um7ZskWZaTG5t99+u33Yq8KQlmL5TGV0Ahm9QNIMZIgwOX5BjLCYbDHfctzSGi3DV0l/BXf/yPKzeMtYUE4iQ/vIhyLIryLpfehuRHK5hSC/3HT4Fe1pci0WLDx6ElN37MO8wyegAnDKS8CeHdZZmIrKx5wzQ5kyqaAF5Ui6E116ucutd2mhFOPnOEyJaCK3nKRF1WZUXRFr0gorHdgc0wjkcVqmdH14jHUjmUGrqE5fcWeshvpindN9lABfr6POQt300E2Mi7R4SaucGBR5z9K+ry7Xv/y6mbF+2sY7FUPr7bqdPn1aGWfJeT1//rxqwZY7liX1X0wDIB5Dxjn9avdBfLwzFgcSU/KsC+jRHzliVj3YoamkyAVah4u0GFG5lS4PT3yBSCtsiwrR6uHIjsNH0fzhkqdvXEhIwFUa6EgIMd/1j/gmNKtGCR/oO9L/deYcPtoei+n7j1w2lmiNyHDc26Qe7hx9Hcbs36pVhyZv+QJxV6zVq+yQTF+C4bTa/PYnRjdtgNsuphno2jLiS3XUlVA36kaIq+HV2ADky1kSoL2ZtOxsTN93RN3q33j20hifNvpVr4LxzepjcK2q9lxT3To0eQPujDUxySUdTguNmuN0di7e2bpHPZqXL4vbGtTG6Aa1UC0yHLrgC3XUHVA36ubpeDMjMqugkfiZUDd2sDIoX0Ru33pjuvC+hCQ8tnYzqn33K8Yu35DHqEYHB+GRlo2w5+ZBWDikB4bWqZ6nU9SVOjTJ8CSSyK3TEC++Hmv2dAxp9ZY0jYJwSN/ocMPNeTpsbT+fgH+v34Ia3/+Kvr8tw7d7DiL54tiHRuLNddSdUDfq5ul4I76hG1tWDUK+CKUXnTcgMy/9fuQkPtoRi4VHT122vk3FcnigWX3cUr8WwoMCnc7HtHVCIPrEWkmH01rw/BOIz8rG//YfwXd7D2HtaeuwMnLpXHz8tHrcv3IjhteprtIE+lSrrCZLMAJvqqOehLoZr5ut5UxmWiLESIqaYKEk0KwSpzmTlo4vdh3AJzv34Uhy3mFXQgL8cVO9mhjfrAE6xJR36rYDE/r1x5nxaCsEBOD+Zg3UQ1riv997CN/FHrJ3upNpZn+IPaweVcJDVYqApAq0qmiduYUQUjS2MUJlznZ3DtROyJWQsVqloam0uew0q0RR3F7lEnTSGjZ1Ryx+2n/0slmQZGD++5vWx9jGdVExjK1SvoAtfaO4w2k5IuPjPt++BZ67ujnWnIpTpnXGviOIz7SmAsgkBW9v2aMeLSS/taHkt9ZG1Ygw0w+7Q4i7kBiXcUmXL1+uWrZkulJv7Phm1qGrfEG3nJwcZVRlzFbJ/5frv+w3/xSvxYXjrBo0zqrsV/ZlZEWTYJIcUDEY9ulOLw74LAZDbvHaDEZKVjZ+iD2kOkzJGJuOyBkMrHkVHmjWAP1rVHHbbVtddDMTRmhWWmOYkZOjxt+VNIF5R04iK98PIn8/P/SuFqOMq6QLRAYFlTq+88NYcw7qpo9u8p7yA3Lbtm0uGWeVkJJim2HM1rp/6NAhNTOXbZKJkvgvmlUDzKoOyPRr9lu3hSC3bl/9/Ct8d/QMvt57EIkXW7scp9y8q3Fd3Nu0PuqWYc9p4p6xeaWlVVpc113Mb3UkPDAAIy7mt/Z2yG8tbnxLaoKnZiAjxNPIDzaZbWjr1q0qNUByYmlaiSeQOJPWfGlJlTg8e/YswsPDcd1116FmzZpqG5rVEuCLLasSOAMGDLg0p3sRnWLQuDnw0JOAQ2tpx5gKatipG+vVRGig54aQMlo3M+JNmsXGJ+H72EOqxfVgUt5JJYSq4WH4V4Na+Fe9Gnh89M3Fim/V6WvBgstaWL1JN09C3fTTLSsrS9UFmY5UhkwyY0/wor7LOIyh/rrJvuROW79+/VCnTh17Oc1qCfDF6ValNWnUqFHWL/KJzxY+W5T07hbDes8jCGvfGf+qXwv3N6uPdpXKwwiM1s2MeKNmck6rJb917yE1qoAtv9XOpvXAZ1OKHd9SH2T0CW/XzRNQNz11E3MSHx+PlJTLf+SZWTNbuhHrqN66SQur3MHKP3Y1p1slRSI5fAppcSroi1yQclm/YDYabFmH9e+/hnIhwVSWGI5cYK+9qpJ6vNelLeYdsea3/m7Lb12xqETxLfUhv1klxJtQo3BUqKAe3gJ/GPmWbpwUwMeQzi/2ziZya7QoLq6PXb8WYRaO10f0Q9JQRtatgdkDuuLEbUPxbvvmwJ4dJYpvGb1A6gUhhBA9oVk1CKOGz5HmfzuSw1cUDuvz/J+BcNghalYYMlTa6JqVXRbfjDXnoG7UzVMw1nxHN5pVA5CmdwkWI5rg80xVKp1NisJhvQ5TnBqpm1nxNc2cje+TORaf1s1VUDfqxljTGz+TXttoVg3KGUlOTjakV+aCk3EIlB7+gvSKLoqL62Vgdx1+iRmpm1nxNc0kTm1j+BU3vtGoOVrNXorevy3FT/uPIDMnx+d0cxXUjbox1vTGYtJrG82qQWRnZ3t0f/IFPHH1JgxfuArZXftYC2X4HukVXRBSLusBNYC6r+rmDfiaZvZ4LWZ8o3tf9bT0+BncuGgNan7/G57esBUH4hM9dcheha/Fm6ugbtSMsVY4NKs+wIHEZHSZvQTvbdtrLWjdHjFtrrYOSyXD98yZAcSdAaQntTzL8sVhfWQcSpkqkxCzIDNTSdwWJ7679+qFN+69Cw3KXkofOJ2Wjlc370LruSswZP4K/HboOHLyzaJFCCHEc3jfZMEkDzMPHMXY5Rvss08F+/vj3WvbYfToxWqsVTVw+oLZ1kchM/xw0GViJiReJW7tM1hdIb5l/L9JbZpi2fHT+GTnfsw+dAzZuRbITTIZDkseNSLDMa5JXdzVuB6qRvjGLHeEEKILbFk1CJl2zJ2kZ+fgwZV/Y9Qfq+1GtX6ZSKwb0RfjmzdQY6zJzD3yZS05qY7IspTLet2monS3bt6IL2omcVuS+Pb380Pv6lXwU78uODL6ekxu3wI1HUzp0eRU/Oev7aj5/a8YuXAVFh09hVyT5Xx5Cl+MN1dA3agZY61w/Cxmy7L1ghms3M2+hCSVe7c57oK97Ob6NfFpt/YoExxU4P/IOJO2WS106ExFiCtxJr7l1v+Co6fwyc59mHf4hGppdUR+/N3btD7uaFRHDZtFCCHEPf6LLasGTnfmjt8JM/YdQdufF9qNakiAPz7tdjWm9e5cqFEV5Au8UqVKWhtVd+rmrVAz5+JbdEtNScGgmlfht4HdcHD0dXimbVNUCb/0//sSk/F/6/5Bte/m4NYla7Hq5NkSxaYY6LNnz3rVhASMN+rGWNMbi0m/R2lWDULmanYladnZuG/FX7h58RokZVl74zYsG4UNI/rhnqb1TTemmqd08wWoWel1qxUVgckdWqoUgZ/7dUGfapcmH8jMzcUPsYfRdc4StPjfAny4fS8SMjILfU9baoK0IsTExKhnW2qCN3xW3nAORkDdqBljrXDYwcoL2BOfqG77bz0Xby+7tUEtfNztakQGFd6aSggpGUEB/mp6V3nExifh01378NXugzh/0ZzuuJCAh1Ztwr/XbcEt9Wvhvqb1cXVMebUuMTHxUqevfMgUyPJw7PRFCCHECs2qyflh7yHcu2IjUi6ObRgWGIAPr22HOxvV8ZrWVEJ0pEF0FN7q3AYvtW+Jnw8cVbmtq09ZZ8VKzc7BF7sPqEe7SuVwT6M6mD5xPJYtWQKEhAA9BwJdelqnfJWZtGSCgmXzlZEVQyudvzgKByGEWGEHKwM6WEmuiAwAHRgY6LShTM3KxoTVm/D57gP2siblyuB/fa9B8/LR8EZcoZuvQc08q9u2c/H4dOd+fLv3oD0dR7FpPfDZFKtRnfgsULtewRMVXBz/VVpXR4wYAbPBeKNujDW9sWj0PcoOVpojARIUFOR0oOy6kICOvyzKY1THNKyNv0b081qj6grdfBFq5lndWlSIxodd2+HE7UPxWff2aFuxnHXFikXWZ2lRLcioClIu6wFMnToVZoTxRt0Ya3rjZ9LvUXawMuiXTUJCglO98b7dcxBXz/wD288nqOXwwAB83bMjvu7VCRFB3p3VURrdfBVqZoxukit+d5N6+HtUf6wa1A3Ys8O6Qm79F8XF9UuWLDHlKAGMN+rGWNMbi0m/R73b3WhMSQMlJSsbD676G1/vOWgva1aurLrt37R8WfgKZqtgOkDNjNWtYZhDJ0fJUS0Kh/UyvIzOQ8kVBuONujHW9MZiwu9RmlUTsON8Am5ctBo7LyTay+5qXBfvd2mLcC9vTSXE7MhEBHakM1XFmMI3lvUF/R8hhPgwTAPQ/NfPl7sPoP2sP+xGNSIwEN/36oTPe3SgUSXEBEjraK9evawL0uu/KC6u79ituylbVQkhxB3QrBrElVpNkrOycPvSdbhr+QakZVsH2W5ZIRp/j+qH0Q1rw1dhaxM1M2OsjR8/3vpi2Xxrr/+CkHJZD+CvJu3w9PqtarIPs8E6St0Ya3oTZcK7NjSrBiC98Pz9/QvtjSeD+0snqu9jD9vL7m1aD+uG90GjaN8dLPxKuhFqpmusDRs2TA34L8NSqeGp5swA4s4AubnWZ1m+OGwVGjdHbst2eGXzTrT83wIsOXYKZoF1lLox1vTGz6TfozSrBo0ttn//fvWc/7b/f3fuQ8dZi7AnPkmVRQUF4sc+nfFJt/YIC/Tt/FSz9mI0Emqmh24ywL+MnWo3rAtmA89OAB4YbX2W5YwM9OrdG49P/S+CLtb1fYnJ6DN3OcYsXYe4tAzoDuONujHW9MZi0u9RmlUP4TgneHh4OBo0aKCebXOCX0hLx7+WrFWzUaVfnFu7TcVy+Htkf9xcv5anDpMQ4iZkClWZmcp2HXDEdh34Y+FCvN6zM/4Z1R/XVrk0MsC3ew+h8Yx5+GbPQdN9yRBCSGnx7aY6D1GcOcHDmrdG2tiHgLBwVf5AswZ4q3NrhAYGGHDEhBB3IC2sMjOVPGQcVRmeSvLH8nemkuHo/hzaG1/sOoD/W/cPEjKzcC49E3csW69mx/qka3s13SshhPgCbFn1QIuq3ajKVIsDhgGT3wM++sH6LMshIUjb/g/w33cRFeiPn/p2UbPg0KgS4r2IQa1UqVKhvf79/fwwrmk97L55EG6qV9NevvT4GbT4aT5e/nsHMi/ehSGEEG+GZtXNzJ49+5JRlTnBh95kHWfR39/6LMtSLut3b8drIVkYVa+Guw/LlEhCeNmyZU2XGG4k1Mz8ulUJD8P0vtdg3sBuqBVpvfOSkZOLZ/7ahrY//4E1py6NzWo0OulmJqgbNWOsFQ3Nqpuxz/FdzDnBZ33zlbsPybRIrl5ubi5z9qiZT8baoFpVseOmQZjUqpFqdRV2XEhAl9mLcf+KjYjPyDT6ELXUzQxQN2rGWCsamlU3Ijlpko/qC3OCewrJ8SPUzFdjLSIoEG91boO/RvRFu0rl7OWf7NyHJjN+x0/7jxhuFHXUzQxQN2rGWCscmlVPXXxKOCc4IYQURttK5bFueF+8e00bNaudcCo1HTcuWoPrF6zEkaQUikcI8RpoVj05J3hRcE5wQkgJCPT3x8SWjbDzpoEYUquqvXzu4RNoOmM+pmzdgxyZdIAQQkwOzapmc4LLeIucE7xw2HGj5FAz79atZlQEfh3QVY0iclW4dWSBlOxsPLJmMzr+sgibzp736PGYRTfdoG7UjLFWODSrbqakc4LbtyeXwR6zJYea+YZucpwyisiumwbh/qb1YTvqv89eQPtZizBpzWYkZ2V55DjMpJsuUDdqxlgrGppVN1OSOcFlu6FDh7r7kEyLdBzJysoyvAOJmaBmvqVb2ZBgTO12NVYN64Nm5cqqslyLBe9s3YPmM+bj98Mnivx/6dx59uxZpzt5mlU3o6Fu1IyxVjQ0q26muHOCy3rZTrYnhZOSwo4jJYWa+Z5u11SpiE2j+uHlDi0QEmC9zB9OTsXg+Stw06LVOJWaVuBU0GFhYYiJiVHPtilgZb2v6GYk1I2aMdZMZFZTU1Px6KOPonr16ip3s169enjssceQkJBg32b37t0YOHCgmmu7YsWKuP3221VrgJnnBJf1sh0hhLiC4IAAPNW2GbbdMBC9qsXYy/+3/ygaT/8d/925D/EJCRgwYABGjRp1aZi9i8iylMt6mTKaEEKMwjrmiUbcd999+OGHHzBp0iR06NAB27Ztw2uvvYZ9+/ap2aDi4uJUp6UqVargk08+QWZmJl5++WX07dsXGzZsQHBwMHSfEzwtLQ3Hjx9HtWrVVAsGIYS4iwbRUVg8pCe+23sIj67djHPpmUjIzMK9yzfg8f++jYQtm6wz6MnEJDLeswyjJ6OTSKfPZfPVDHwyZbT8oOadH0IIfN2sihGdNm0ann76abz44ouqTH7ZSw7U66+/rm6TvPfee+pX/pYtW9S82kKrVq3Qtm1bTJ8+XbWy6o60GFeuXJm9/p2AX5bUzFN4U6xJB57bG9XBoJpVMWntZny79xDwz1+XjKpM+ew4w55tKuhWV6ucejGsc+bMUT+2fUk3T0LdqBljzSRpAGJWO3XqpG475R+vVKbwy8jIwK+//opBgwbZjarQpk0b1Uo5b968Qt9bjG5hDyO+OOSc2GOWujHW9MRb62jFsBB806sTFg/pgbA1S0s0FbR96mgf1M3dUDdqxlgzUctq48aNsWrVKvVaWlPFSG7cuBEffPABhgwZonI6JS3ghhtuuOx/mzdvjj179hT63pGRkQWW+/v7K7Nr65Hp2ItVLiAF9Wotbbmt52dBKQvu2qeR5a56b0HSPoKCgvJ8GZr5nNxdXhLNdDt2Iz8nWx216eYN5+TINRXKIm3H1uJPBb1gtpoKWlKY8o8D7fj+jrrJtdXb6pO7PidZzs7ORmBg4GVG36zn5O5yV2mm0zm5qtyviG2l4c/x2mbksZvWrDoit5xsprRChQqYPHkyzp8/r05YlvNTrly5Is1qcZBOXPLFLoiRDA8PVxdnW5kgF2p5iJGWimJDtpX/SU5OztN7NiIiQgWFpC44XtDlYSt3RMYolGBynHLVNgaf7M+xJVhuG0krhgSedEyzIZVXzLm0RDsOQePOcxLkWOQLyrEznCvPSfYr7+1Yycx+Tu7+nGQfcsdCtrFpZvZz8sTnJOVyXnIcsj9vOCfHz+nChQtOTQV96tQpda0t7Jzk+GU/cudLyr2tPrnrc5L/l22lD4Mcpzeck7s/J5tWNu284Zw89TnFOXwnGHlOJcHPoumAeOfOncM///yD2NhYvPvuuzhx4gTmzp2LHj16qNtR999/f57tR48erbbfsWNHge9X2O1+EdyWdrBixYo8HZ7c2bIqwRUdHe3y99ax3JW/wuLj4y8bdNzM5+SJX67F1Uy3Yze6ZVXqqE03bzgnR+TLR75EFJPfs+aoFoaMBy3D7F0creVKLas23diyWrJWQjEicveQLaue1Uynuu2pltUEh2ubkccu16GuXbuq1ytXriyyw7m2LavSeirDOslDclRr1aqlRgOwffnmR8pkGKvCKMzFywXVhu1LyZH8y64qdwwSV7+3juWueA8Jdttn5KnPqahynfR1lWY6Hburyp19j/y6ecM52ZAvBRlVRQ1XJb3+pTNVMaaCLuzLJP8PIWc000lfV5U78x66a6PTsdjKS3ucOp5Tacv9rqBXft2MOnZTdrB655131JBUp0+fzlNes2ZN9StAXHjt2rXVSAD52bVrlz331AxIEzuhbow1ffH2OlrSqaDvvOeeYr2vt+vmLqgbNWOsmcSsygQAYlQXLlyYp/zvv/9WzdZiRmU60vnz5+fJuVq9ejUOHjxomqlK5ReG5IKU9peGr0HdqBljzZipoNG4OV7OCcOe+KInB2AddQ7qRs08hZ9J/YdWOauSS9GuXTvVUerf//43WrRogf379+Ott95SCft//fWXMqktW7ZE3bp11cQB0unqhRdeUGOt5je5xUFyVoubM+EqRHJJWg4JCTFdwBgJdaNmjDXXIjl/MuC/jKNaGAFNWyLn7glAWDiiggLxba9OGFanOuuoC+G1jZr5YqyllcB/adWyKvmjMlaqDDz94Ycf4qabbsL777+vLqa2zk9Vq1bF8uXLVVL12LFj8dxzz2H48OGYNWsWzIRj7zpC3Rhr+uELdbQ4U0FvW7EczapepcqSsrIxfOEqPL1+K3IcemD7mm7ugLpRM8Za4WiXXCRm9Pvvvy9yGxlTtaiWAEIIISWfCloMkwx3I0PZOPb6XzeiD+5e/hdm7D+ill/ZvBMbz57HtD6dUSE0hFITQtyKVi2rhBBCjEMMqqRc5R+eKjIoCD/26Yy3O7dGwMVbh38cO4WrZ/6BTWfPG3S0hBBfgWbVIAqavYpQN8aaPrCO5kXy2x5t1VhN1VrpYmvqoaQUdJm9BN/sOUjdGG+soyYh2IT+g2bVoIu+44xChLox1vSCdbRwelSrjE2j+qNDTHm1nJ6TgzuWrccDKzciKzeX1zbGG+uoxviZ1H/QrBqAbUpCjQZiMAXUjZox1vSgemQ4VgztjXub1rOXTd2xDz1+XYp9ced4bSshvLaVHGrmW7rRrBqE45y6hLox1vSDdbRoQgIC8Em39viiRweEBFi/StaePocuc//EihNnPPIZeROMN2rGWCscmlVCCCFOM7ZxXawa2gc1I8PV8pn0TPSeuxzvbd1jutYbQoie0KwSQggpFVfHlMffI/ujd7XKajnHYsHENZtx65J1SMnKprqEkFJBs2oQ+YeGIdSNsaYXrKMlo2JYCOYP6oZJzRvYy6btO4zOvyzC/oQkl38+3gbjjZox1gqHZtUApBeeXJjM1hvPaKgbNWOs6U1QQADeurYdfu7XBZFB1jlntp1PUOOxzjt8wujD0xZe26gZY61oaFYNQPK4kpOTmc9F3RhrmsI6WjrdRtSpjg0j+qJxdBlVHp+ZhSHzV+CFjduRyzxWxhvrqGFYTOo/aFYNIjubeVzUjbGmM6yjpdOtSbmyWD+iL4bXqW5f9/zG7bh+/kpcyOBoKIw31lGjyDah/6BZJYQQ4hbKBAdhZr8ueK1jK/hfTHuad+QE2s/8A1vPxVN1QkixoFklhBDi1nzMf7dpggWDuqNCqHWax/2Jyej0yyJMiz1E5QkhV4Rm1SBkujNC3Rhr+sI66lrd+taoooa3alepnFpOy87B6CXrMHH1JmTl5MLXYbxRM8Za4dCsGtTSEBwczNEAqBtjTVNYR92jW62oCDWBwJ2N6tjL3tu2F73nLsOp1LTLtk9PT8fZs2fVszfDeKNmjLWioVk1AOmFl5SUZLreeEZD3agZY838dTQ0MEBN0fpJt6sR5G/9Clp58iza/fwH1p6KQ05ODmbOnInevXsjLCwMMTEx6lmWpVzWexu8tlEzxlrR0KwahDdecD0BdaNmjDXz11FpSby3aX2sGNoL1SLCVNmJ1DR0mzEXza/thlGjRmHp0qV5/keWpXzAgAFITEyEt8FrGzVjrBUOzSohhBBD6FS5ospj7X5VJSA3F9mfvI3d69YAISHAgGHA5PeAj36wPstySAgWL16MkSNH0twR4kPQrBJCCDGMyuGhWDSkJ4ZcOAHs3m41qhOfBYbeBFSMASRVQJ5lWcovGtY5c+bwUyPER6BZNYiIiAijdm1qqBs1Y6x5Xx0NCvBH6tL51oWeA4Ha9QreUMplPYCpU6fCm+C1jZox1gqHZtUAJF8rKCiIowFQN8aaprCOelY36e1vz1Ht0rPojS+uX7JkideMEsB4o2aMtaKhWTWo52dCQgJHA6BujDVNYR31rG4ygoCd8hWL3thhfZ7/MzGMN2rGWCsamlWD4LBV1I2xpjeso57TLSoq6tLC+biiN3ZYn+f/TA7jjZox1gqHZpUQQoihhIaGolevXtaF1cuK3vjiehl3Vf6PEOL90KwSQggxnPHjx1tfLJsPHNpf8EZSLusB9Lh5tAePjhBiJDSrBuFNt688CXWjZow176yjw4YNQ58+fYCMDGDKZGDODCDujBp/VT3LspTL+sbN8UJ2GL7dcxDeAq9t1IyxVjiBRawjbuz56e/vz9EAqJvbYaxRN7PEW0BAgJpOVQb8l3FUsWC29ZGP8q3b4fzt45ENYMyy9dibkIQX27eAvxP71AXWU2rGWCsatqwaAHt+UjfGmt6wjhqjW5kyZbBgwQJlWiUn1RFZlvITG9bi/nYt7eUvb9qJWxavQVq22FdzwnijZoy1omHLKiGEEG2QFtYRI0aoh4yjKsNTyS1yx85UH3Vth0bRUXh07T/ItVjwv/1HcTgpFXMGdFUzYhFCvAu2rBJCCNESMaiVKlW6rNe/3Daf0LIR5gy4FhGB1jaX9WfOoeOsP7D9fLxBR0sIcRc0q4QQQkzJkFrVsHpYb1SPCFPLh5NTcc0vi7HgyEmjD40Q4kJoVg1AWgXKli3LDlbUjbGmKayj5tGtVcVy2DCiH66uVF4tJ2VlY/D8FfhoeyzMAuONmjHWioZm1aBk+tzcXM5YQt0Ya5rCOmou3a6KCMOf1/fCiDrV1bLksT646m9MWLUJOTL0leYw3qgZY61oaFYNwlvmtPY01I2aMdb0xqg6Gh4UiJ/6dcG/Wzexl72/fS+GLliFpMws6A6vbdSMsVY4NKuEEEK8Ahlr9bVOrfBFjw4I9LemIsw7cgLXzlmCI0kpRh8eIcRJaFYJIYR4FWMb18Ufg3ugXEiwWt56Lh4dZi3CX2fOGX1ohBAnoFk1CE92QPAmqBs1Y6zpjS51tGe1ylg7vA/qlYlUy6fT0tH916WYeeAodEQX3cwENfMd3WhWDYA9P6kbY01vWEe9Q7dG0WWwbnhfdL2qklpOy87BqD9W47XNO7Xq4KqbbmaAmvmWbjSrBiAXyaysLK0ulmaAulEzxpre6FhHK4aFYNGQHri9YW172ZPrt+Ku5RuQmZMDHdBRN92hZr6lG82qQaSkMNmfujHWdIZ11Ht0CwkIwNc9O+Kl9i3sZV/tOYh+c5fjfHoGdEBH3XSHmvmObjSrhBBCvB657fl0u2aY0ecahAYEqLI/T55Fp18WIzaeQwkSojM0q4QQQnyGG+vXxPLreyEmLEQtxyYkodMvi/DniTNGHxohpBBoVg0i4OIve0LdGGt6wjrqvbp1rFwB64f3RfPyZdXy+YxM9J27HN/sOWjYMZlBN92gZr6jG82qQbejoqKiTNcbz2ioGzVjrOmNmepo7TKRWD2sDwbUuEotZ+Xm4o5l6/H0+q1qulZPYibddIGa+ZZuNKsGIL3wMjMzTdcbz2ioGzVjrOmN2epomeAg/DawKx5o1sBe9srmnbhp0RqkZWd77DjMppsOUDPf0o1m1SBSU1ON2rWpoW7UjLGmN2aro4H+/viwazu836Wtmq5V+PnAUfT4dSlOpaZ57DjMppsOUDPf0Y1mlRBCiM/zUIuG+HVAV0QGBSotNpw5j46zFmHbufg82qSnp+Ps2bPqmRDiGWhWCSGEEACDa1VVeaw1IsOVHkeSU9Fl9mLMPXgUM2fORO/evREWFoaYmBj1LMtSnqPJ5AKEeCs0qwYRGGj99U6oG2NNT1hHfVO3lhWi1UgB7SuVV8tJiYm4btBgjBo1CkuXLs2zrSxL+YABA5CYmOjTuhkBNfMd3cx3xF6A9MKLjIw0+jBMB3WjZow1vfGWOnpVRJgai/W2xasx69EHgd3bgZAQoOdAoEtPoHxF4HwcsHoZsGw+Fi9ejJEjR2LBggVODQvkLbp5EmrmW7qxZdUApBee5DuZrTee0VA3asZY0xtvqqPhQYG4OeXsJaM68Vlg6E1AxRjA39/6LMtSHhKiDOucOXPg67p5CmrmW7rRrBoEk/OpG2NNb1hHqdsnH39sFUFaVGvXK1gQKZf1AKZOncp48yCso76jG80qIYQQUsAXuj1HVW79F8XF9UuWLDGlESBEd2hWCSGEkHwkJSVdWpAc1aJwWJ/n/wgh3mlWZQiQN954A40aNUJISIgaImTMmDFqXDsbv/zyC9q3b6+mDKtevTruuOMOnDp1CmYiODjY6EMwJdSNmjHW9MZb6qh8v9iRzlRF4bA+z//5oG6ehJr5jm7amdVnnnkGTzzxBAYOHIgZM2Zg4sSJmDVrFoYOHaoSgiWBfcSIEWjWrBm+/vprPP7445g/fz769u1rmtsv0hsvPDzcdHPzGg11o2aMNb3xpjoaGhqKXr16WRek139RXFwv467K//mybp6CmvmWbloNXZWWloYPP/wQY8eOxZQpU1TZsGHDVOvquHHj8Mcff+D9999Hu3btlFG1Ubt2bWVmV61ahT59+kB3xHTLucqg0mYLGCOhbtSMsaY33lZHx48fb81bXTYfaHV1wZ2sDu23rgdw5z33OLUfb9PNE1Az39JNq5bV2NhYJCcnqwGWHencubN63r59O+Li4lCpUqU868uXtw7eLB9AYaSkpBT6MILMzExD9mt2qBs1Y6zpjTfVUWksUQ0gGRnAlMnAnBlA3BkgN9f6LMtSLusbN8dnQeWQmpUNX9fNU1Az39FNq5bVmjVrYtGiRWjbtm2e8nXr1qnnGjVq4IYbbsDzzz+Pjz76SM0ccuLECTzyyCOoWrUqevYsvMdmYYPg+vv7o02bNvZfHI5jj8mvjoLGIittueN+8m/vrn0aWe6q9y7oMzL7Obm7vCSa6XbsRn5Ojg9vOSdPlDvq5g3nJN8PP//8s/qukXFUsWC29ZGPwKYtkX33BPx5+hyGL1yF2f2vRVhQYLH3Wdj3gQ4aGL1Pd2um0zm5qtyvmNc3o4+9JPhZNB8ZVi4QcqEoV66calmVfKBBgwaplABHISSX9brrriv0fQpr7nY0q7/99ptqGrclIEteR2pqap5fIbJ/eUgLcHb2pV/Qsq38j/QEdZwnOiIiAkFBQUhISLjsgh4dHX3ZFH1ly5ZFbm5unh6lcuxSnpWVlaclWGZKkWR+OT45Tsep1MScSw6vYx6vO89JkGMRPaXcHeck+5WOdrJv2+dp9nNy9+ck+5AfdI45SmY/J098TlIuxyrHIfvzhnPyxOckxy//K3e/pNwbzkmQZxmW6r///a96ttG9e3c8+OCDqHZNV/T/fQWSsq3bD6haCbMHdUegn1+xzkk0kfOX7x/HO4TeUp/c8TnZtLJp5w3n5InPKTMzU32P2r4TjDwn2a5r165qeeXKlXb/ZSqzKmI++eST+PLLL1GrVi1lRlu2bKkuDDLw8sMPP4z+/fvj3LlzePPNN7Fv3z5s3LgRTZo0KfD9CrvdL8FuSztYsWJFHrHc2bKakZFRYCK+Tr/OXFXuyl9hUmlklAjHHx9mPidP/HItrma6HbvRLatSR226ecM5eaLcUTf5EvWGc8pfLt8Z8sUrX/C2a7iUrzp5BgPmrUDKxS/qUXVrYFrvTgiU2a6u8N6yLF/88qWev2FFJw10OhZXaabTObmq3K+IbcXEOl7bjDx2+W4ytVmdPn26SmyXC4KY08mTJyuHLy1EkgogQ1mJibUheax16tTBLbfcon75lgS58BRXLEIIIaQwlh0/jUG/r0D6xRak0Q1q4ZueHRGQz7ASQlAi/6VdDfr000+V6WzYsCG2bNmCd999155vevjwYfWrQEYDcKRixYqoX78+jhw5AjMgvw+kqVzD3wlaQ92oGWNNb3y9jvasVhm/9L8WwRfN6Q+xh3Hvio3IvYIevq6bM1Az39JNK7MqLamTJk1Ct27d1C35pk2b5lkvQ1Sp2y2rVuUpj4+Px4EDBwpNAdARx5wOQt0Ya/rBOkrdnGFAzavwv77XINDfeov1i90H8PCqTVc0B4w31lFPkW1C/6HVaADSaUpyS6UDlX1OZgek9VTGYP3iiy9UrpBMHCDbf/DBByo/SiYQIIQQQoxkaJ3q+KFXZ9yyZK1qVf1oRyxCA/3xZqfWhXb2JYSYxKwePHhQPcsMVgXx3HPPqTSBjh074pNPPsFPP/2kerJJS+y3336rOmIRQgghRnNj/ZrIyM3BmKXrIW2qb2/Zg7CAAEzu0NLoQyPEdGjZwcqTGNHBSiSXITEch2Ai1I2xpg+so9TNVXy2cz/uWfGXffnlDi3wVNtmjLdSwjpqft1M3cHKF5AAKWi4DULdGGt6wDpK3VzFuKb18H6XSxPdPL1hG97ZspvxVkpYR31LN5pVg37ZSGcyH2/ULjHUjZox1vSGdbRgHmrREG90amVfnrT2H0zdHkvdGGsex2JS/0GzahCOMzkQ6sZY0w/WUermSv6vdRO8cHVz+/IDq/7Gl7sPMN5KAeuo7+hGs0oIIYR4gGfbNcOTbS4NsXj38g2YFnuI2hNiptEACCGEEG9F8gRf7tASadk5mLJtrxol4Pal69UkAn0qlDH68AjRFrasGkRERIRRuzY11I2aMdb0hnX0yob1nWva4L6m9dVyjsWixmNdfi7RI5+PN8FY8x3daFYNuljpMGyE2aBu1Iyxpjeso8XX6aOu7XBHozpqOTvXgpuWrMOiY6fc+vl4E4w139KNZtUApBdeQkKC6XrjGQ11o2aMNb1hHS0+/n5++Lx7e9xSv6ZazszNxbCFq/DniTNu+3y8Ccaab+lGs2oQZgsUXaBu1Iyxpjeso8UnwN8f3/TshOG1q6llyWUd/PsKrD0V57bPx5tgrPmObjSrhBBCiEEEBfjjxz6d0a9qJbWckp2NAb//ib/PnudnQshFaFYJIYQQAwkOCMA3XVqjT7XKajkxMwv95i7H1nPx/FwIoVk1jqioKAYgdWOsaQzrKHXzJJWio/FL/2vR9SprC+v5jEz0+W0Zdl1I8OhxmAnWUd/RjS2rBiC98Pz9/U3XG89oqBs1Y6zpDeto6XSLDA7CvIHd0DGmgio/m56B3r8tw76EJJd+Tt4AY823dKNZNQCz9sYzGupGzRhresM6WnrdooKDsGBwd7StWE6tO5margzr4aQUl35WZoex5lu60awSQgghGhEdEow/hvRA8/Jl1fKR5FT0+m0pjienGn1ohBgCzSohhBCiGRVCQ7B4SE80irbmFx5ITEHvuctwOjXd6EMjxOPQrBJCCCEaUjk8FEuG9ETdMtbpMffEJ6HP3GWIS8sw+tAI8Sg0qwYgic1ly5Y1XYKz0VA3asZY0xvWUdfrVi0yHEuv64WakeFqefv5BPSbtxwXMjLt26Snp+Ps2bPq2VdgrPmWbjSrBiCJzbm5uaZLcDYa6kbNGGt6wzrqHt1qRUVgyXU9cVV4qFreHHcBA35diu9nzEDv3r0RFhaGmJgY9SzLM2fORE5ODrwZxppv6UazahBJSRyKhLox1nSGdZS66RRv9ctGqRbWmLAQIC0VG/7zf7jt5puxdOnSPNvJ8qhRozBgwAAkJibCm2Ed9R3dAo0+AEIIIYRcmcblymDhwG5o37MXsndvB0JCgJ4DgS49gfIVgfNxwOplwLL5WLx4MUaOHIkFCxYgICCA8hJTw5ZVQgghxCTsX7kc2Tu3Wo3qxGeBoTcBFWMAf3/rsyxLeUiIMqxz5swx+pAJKTU0qwZhtuRmXaBu1Iyxpjeso+7VberUqdYX0qJau17BG0m5rHfc3gthrPmObjSrBmDW3nhGQ92oGWNNb1hH3aub9Pa356jKrf+iuLh+yZIlXjlKAGPNt3SjWTUA6YWXlZVlut54RkPdqBljTW9YR92rW56OMZKjWhQO683YoeZKMNZ8SzeaVYNISeE8z9SNsaYzrKPUTbd4i4qyzmalkM5UReGwPs//eRGso76jG80qIYQQYgJCQ0PRq1cv64L0+i+Ki+tl3FX5P0LMDM0qIYQQYhLGjx9vfbFsPnBof8EbSbmsd9yeEBNDs2oQHPeOujHW9IZ1lLrpGG/Dhg1Dnz59gIwMYMpkYM4MIO4MkJtrfZZlKc/IQM9evTF06FB4K6yjvqMbJwUwAOmF5605RO6EulEzxpresI66XzcxGjKdqgz4L+OoYsFs6yM/jZuj9mNPm9KYFAfGmm/pxpZVA5BeeJmZmabrjWc01I2aMdb0hnXUM7qVKVNGzUwlplVyUh25pnsPhNz/GPDQk/jq8GksPHoS3ghjzbd0o1k1iNTUVKN2bWqoGzVjrOkN66hndJMW0xEjRqjW1bS0NJw5c0Y9r16+DFMeuNc6oxWAscs24EJGJrwRxprv6EazSgghhJgY6e1fqVIle6//e5vWQ7/qVdTrE6lpeHjV3wYfISGlg2aVEEII8bK8xC96dEDZ4CC1/H3sYcw6cNTowyLEaWhWDSIwkH3bqBtjTWdYR6mbmeOtemQ4Pri2rX35vhUbcSbNu6ZdZR31Hd3cblYTExPdvQtT/uqNjIw03dy8RkPdqBljTW9YR/XS7dYGtTGsdjX1+mx6Bu5fsdF0HWsKg7HmW7qVyF7LfLI//fQTNm7ciOzsbDRv3hy33HKLGgZBKsCKFStw/PhxtU6W4+Li8PHHH2Pfvn3uOwMTItpkZGQgJCTEdAFjJNSNmjHW9IZ1VC/d5L0+6dYeq07FIS49A7MOHsO02MMY3bA2zA5jzbd0K7ZZTU5ORo8ePbB58+Y8v8xee+01NYTGuHHjsGrVqjz/I9uZSQxPkp6eroKFUDfGmp6wjlI3b4i3yuGh+KTb1Rj1x2q1/OCqv9GjagyqRYbD7LCO+o5uxTarL7/8MjZt2oT+/fvjrrvuQlhYGP7++2+8+eab6Ny5My5cuIAmTZqgX79+iI6OVv/j7++P+vXru/P4CSGEEFIEI+vWwOgGtfBD7GHEZ2bhrj83YP6g7mxMIt5nVmfPno2mTZti3rx5yoQKgwcPRp06dTBmzBg0atRIpQeIiSWEEEKIPnxwbTssO35GDWW18OgpfLZrP+5pysYk4mUdrI4ePYquXbvajaqNQYMGqWdZR6NafIKDg0uwNaFuzsNYo26ehPGmp27lQoLxeY/29uVH1/yDA4nJMDOMNd/Rzb8kMx4UZEYrVKignqV3GSkekscbHh7OWzAlhLqVHGrmHNSNunljvA2sWRXjmtRVr1Oys3HnsvXINenoAKyjvqUbx1k1AOl4JubfW4YQ8RTUjZox1vSGdVR/3d7u3Aa1oyLU6xUnz+K9rXthRhhrvqUbzapBZGZ651zN7oa6UTPGmt6wjuqtW1RwEL7u2dG+/OSGLdh1IQFmhLHmO7rRrBJCCCE+RPeqMZjYoqF6nZGTizFL1yM7N9fowyLENZMCrFu3Di+++GKJ1klexLPPPluS3RBCCCHEjbzSsSXmHz2JPfFJ+Ovseby+eReebteMmhMt8bMUM3Eh/ygAxd6Bnx9ycnKgK2lpaWokA2HlypUeGdHArDNIGA11o2aMNb1hHTWXbutPn8M1sxerTlZB/v7YMKIvWlcsBzPAWDO/biXxX8VuWf3qq69cc3REBUhoaCiVKCHUreRQM+egbtTNF+KtY+UKeKJ1E7yyeSeycnMxZtl6ZVhDAgKgO6yjvqVbsc2qDPxPXPfLJiUlBREREYb/sjET1I2aMdb0hnXUfLo9d3UzzD1yAlvPxavHCxu345WOraA7jDXf0o0drAwiOzvbqF2bGupGzRhresM6ai7dggMC8G3PjioNQHj9n91YdzoOZoCx5ju6Fbtl9csvv3R6J2PHjnX6fwkhhBDiPlpVLIfnr26GpzdsU/mrMjrA5lH9ER5Uoj7YhLiNYkfi3XffrZqMi9Mfy7FpWV7TrBJCCCH68njrJphz6Dg2nDmPvQlJeGrDVkzp0tbowyLEfR2sVqxYga+//loZ23LlzNGz0NPIdGeEujHW9IV1lLr5UrwF+vvjm56d0ObnhUjPycF72/ZiaO1q6FmtMnTFaM3MSrgJdXNpB6tjx47h//7v//C///1Ptajed999eOmll0p7jF6HaBMcHGz0YZgO6kbNGGt6wzpqbt0alyuD1zq2xMQ1m9XyncvWY+uNA1EmOAi6oYtmZsPPpLq5pIOVjNk1efJkNG7cGDNmzECXLl3w999/46OPPipxy6qMyfrGG2+gUaNGahywmJgYZZTPnj1r30Ze33XXXahSpYoagqFly5Zqv2ZBWpyTkpJMNzev0VA3asZY0xvWUfPr9lCLhuh+VSX1+nByKiZdNK66oZNmZsJiUt1KbVZ//vlnZVKfe+45REdH4/vvv1dpAK1aOTf0xTPPPIMnnngCAwcOVAZ04sSJmDVrFoYOHarElTlt+/fvjz/++AMvvPACvvnmG1x11VX417/+hc2b9axUBaHzRAk6Q92oGWNNb1hHza2bv58fvurZEZEXO1d9vvsAfj98Ajqii2ZmI8eEujnd1W/btm2YMGEC/vzzTwQFBeHxxx9X06rK2F2lmc3gww8/VB2ypkyZosqGDRumWlfHjRunDOqhQ4fwzz//qP03a2adGm748OFo3bo15s2bhzZt2ji9f0IIIcTXqVMmEu90boN7Vvyllu/+cwO23zgQ5UNDjD404qOU2KyeP39etX5+/vnnaqyuAQMG4L333kODBg1KfTCxsbFITk5W7+lI586d1fP27dvx+++/o1evXnajKr8QJP9i586dRb63DIJbmEEmhBBCyCXublIXvxw8hvlHT+JkajoeWrUJP/SxfhcTom0aQG5urmr1FFP6ySefoFatWpgzZ44yj64wqkLNmjWxaNEiZUYdWbdunXquUaMG1q9fj/r166tW3bJly6q5ZNu3b4+lS5cW+d6RkZEFPipXvtTTUdIMHB8Flbmq3NYbz5P7NKrcle8tunnbObm7vLia6XjsRn5Ojrp5yzl5otymmzedkyc+J7krqcuxy0M64nzWvT3KhVg740zbdxg/7z+q1TG6QjNfjL3wfN8JRh67y1tWJQdVWi8lgO+880489thjqgPUgQMHrvi/devWLdY+JOe1T58+ecoWL16MSZMmoXbt2ipXVVpIJS9WWla/+OILdcLSstu3b18sWbIEPXr0gLMkJCSonFhBWmvlA5WWV1uZIB265CHH4TgLhGwr/yMtw475IFKZJE0iMTExz4cTFRVl36cjYsDlh4EkQNsQzaVc9ufYQhwQEKDeJysrC6mpqfbywMBAZcSl41t6erq93BPn5O/v79ZzkveR/XrTObn7c5L38bZz8sbPydvOSY7d287J3Z+THJ9O5xSRnYnX2zTGPeu2qvL7Vv6Fa2LKIzwnS5vPSfT1hfoU5KJzkvd1PHYjz6kk+FmKaW9FEMeTLvYO/PycmtpLevw/+eSTauYsWytu+fLlVeuq5LBKykCZMmXUtvKBSKtst27d1HYlTQOwpR1IxzBpqXU89oLkKW25vJagk8Bx9XvrWO6q9xakUsrnnn/iCbOek7vLS6KZbsdu5Odkq6M23bzhnDxR7qibfGd4wzk5W16SbWVZvsfENOT/fjX6nORx46I1mHnwmCq/vlZV/NL/WsOvwa7SzGh93VHuV8S2tgYfx+8Eo45dTHDXrl3V65UrV+bxX063rN5+++0lMqmlYfr06Rg/frwKRLndL8NiicM/d+6cWn/NNdfYjaogwXrttddiz549hb5nYS4+vwkvKOgLwp3lRuzT3eWueA9bsPNzcp9mOsWMq8pL8x6OunnLOXmi3BnNdDl2V5aX9PpWUD119zFeqVweH3e7GitOnsXZ9Az8evgEvo89jNsb1TH8GF2lmU4x46pyvytsm183o469uBTbrMqMVJ7g008/VZMJdOzYUbWqNm3a1L5OWlbFpDo2PduQJmwzzspACCGE6EylsFD8t3t7DF+4Si0/vHoTelaLQY1I50f/IcSt46zKYP8vvviiGkbKEZkAoEmTJqoZV27VP/zww5flUVwJaUmV/FS5nS+35B2Nqs2ZS26qDJd1+vRpe3lcXBxWrVqF3r17l/R0CCGEEHIFhtWpjtsb1lavEzKzcNfyDSXuJEOIR4aukoH/bdOnyq35fv36qdcySoCYUzGTMjKA5KhK2caNG5WJdLzVXhRigCW3dNCgQQX27pdRAMQoL1y4EN27d1edvCTp+N1331WpADLVq1mwdbAi1I2xpieso9SN8ZaX97q0xZLjp3E8JQ2Ljp3GJzv34f5mrhkNyBlYR31Ht2K3rEprphjUevXqqd749957ryqXZF0pF6Mqt+13796Nffv24eWXX1bDTE2bNq3YB3Pw4EH1bJvBKv9D9iutratXr1ajA4hBloe8luRc6XhlBkQrMfCeygH2FqgbNWOs6Q3rqHfrFh0SjC97dLAvP7b2H+xPuNTj3JOYRTPd8DOpbsUeDeCGG27A/PnzVSematWq2cvFOEpvLhlKSmaVsiFvKyZSzKX8n67IaADF7Y3mKkQbSZGQ0QDMFjBGQt2oGWNNb1hHfUO3+1dsVK2qwrVVKmL59b0QUMw7qL6qmS5YNNKtJP6r2NG1Zs0alRPqaFSF5cuXq+frr78+T7mIID30N23aVNLjJ4QQQoimvNm5FeqWsXauWnUqDlO27TX6kIiX41+ScU+l41R+xA2LMZUc0vxI7/0LFy6U/igJIYQQogWRQUH4umdH2Nrlnt6wFTvPl6xDNSFuMasyzqkY1vzDRUkagOQ/dO58+ZzB0ktf/o8QQggh3kPXq2LwaKtG6nVGTi7GLFuPrJxcow+L+LpZbdOmjerZLzkGNmR6U+m9365du8t6l8kUXcuWLVPTtJK8SEu0DvkiZoO6UTPGmt6wjvqWbi+1b4km5awT9Gw8ex6vbt7psX2bVTOj8TOpbsU2qzKj1MmTJzF8+HCVvypG9cEHH1QnfPPNN+fZVuZ/lV760hJ72223ueO4TZ/gLKMocIw66sZY0xPWUerGeLsyoYEB+KZnRwRcND6TN+3AprPn7VNpigdwnFeeddR4LCb1H8U2qyNHjsS///1vNRaq9N6SMVYPHDiAli1bqhmnHLerWbMmPvvsM7XN2LFj3XXspkYmQCDUjbGmL6yj1I3xdmXax1TAU22tE/hkZ+dg2Ktvo2evXqpntwwnKc/SOXvmzJmqIYt11HiSTOg/SjQpwKuvvopRo0Zh1qxZOHfunJqx6u6770ZoaKh9mzlz5qBixYp46qmn8J///Mcdx0wIIYQQTXimbVPM2RWLra8+h6O7t+NovvUyyY88+vTpo0yrTJtOiNvMqiD5qfIojFOnTimzSgghhBDvJwBA+JcfALu3AyEhQM+BQJeeQPmKwPk4YPUyYNl8LF68WN19XbBgAQIC5L8IKR4uH8WXRrV4mC25WReoGzVjrOkN66jv6TZ79mys+3O51ahOfBYYehNQMQaQiQLkWZalPCREGVa5A+vrmhmJnwl18+yUE8TUvfGMhrpRM8aa3rCO+qZuU6dOtb6QFtXa9QreSMplveP2PqyZUfiZVDeaVQOQXngyRq3ZeuMZDXWjZow1vWEd9T3dpLe/5KMq5NZ/UVxcL6MJlXaUADNrZiQWk+pGs2oQMj4toW6MNX1hHaVujLcS9iyXHNWicFjvih7prKO+oxvNKiGEEEKcIs+EQNKZqigc1uefSIiQoqBZJYQQQohTyNCVvXr1si5Ir/+iuLhexl11HPKSkCtBs2oQHLaDujHW9IZ1lLox3oo/w6Vi2Xzg0P6CN5JyWe+4fSlhHfUd3WhWDUB64cktELP1xjMa6kbNGGt6wzrqm7oNGzZMDfiPjAxgymRgzgwg7gyQm2t9lmUpz8hAuVZtMWDIEPi6ZkbhZ1LdSjwpAHFdb7ygoCDTBYyRUDdqxljTG9ZR39RNWupkZioZ8F/GUcWC2dZHfho3x4UxD+CuFRvxQ+/O8C/FuZpdM6OwmFQ3tqwaRGpqqlG7NjXUjZox1vSGddQ3dZMpVGVmKjGtkpPqiCy/+sXXCH3kGSAsHNP3HcG/122Br2tmFKkm1I0tq4QQQghxSQvriBEj1EPGUZXhqeSWs60zVfPDxzF0wSrkWix4a8tu1IgMx8MtGlJ5ckXYskoIIYQQlyIGtVKlSnl6/Q+pVQ0fd21nX564ehNmHjhK5ckVoVk1iMBANmpTN8aazrCOUjfGm+u5p2l9PNO2qXotcyiNXrIWK0+eceq9WEfhM7rRrBqAJDVHRkaaKrlZB6gbNWOs6Q3rKHUrDi+2b4E7GtVRrzNyclVqwK4LCYw1D+BnUv9Bs2pQbzzJ5zHb3LxGQ92oGWNNb1hHqVtxEKP0327t0b9GFbV8ISMTA+b9iRMpaYw1N2Mxqf+gWTUICRZC3Rhr+sI6St0Yb+4jKMAfP/XtgrYVy6nlI8mpGPT7n0jMzCr2e7COOocZdaNZJYQQQojHiQoOwrxB3VA7KkItbzkXjxELVyEzJ4efBskDzSohhBBCDKFKeBgWDO6O8iHBannJ8dO4a/kG092mJu6FZtUggoOtFZNQN8aanrCOUjfGm2doFF0Gvw3shtCLc9Z/H3sYT23YesX/Yx11DjPqRrNqUHJ5eHi46XrjGQ11o2aMNb1hHaVuznJNlYr4sc+lKVhf27wLU7fHMtZcjJ9J/QfNqgHI7Q2Z7oy3OagbY01PWEepG+PN8wyrUx0fdGlrX35w1d+YffBYgduyjjqHWXWjWTWIzMxMo3ZtaqgbNWOs6Q3rKHUrDeObN8ATbZqo12Knblm8FmtOxTHWfLyO0qwSQgghRBte6dAStzaopV6n5+TguvkrsCc+0ejDIgZCs0oIIYQQbZB8yi96dECfapXV8vmLkwacSi3+pAHEu6BZNYjQ0FCjdm1qqBs1Y6zpDesodXMFwQEBmNn/WrSqEK2WDyWlYPDvK5DkMGkAY805zKgbzapBvxolWMzWG89oqBs1Y6zpDesodXMlZYKD8Pug7qgZGa6WN8VdwA2LViMrJ5ex5mN1lGbVAKQXXnJysul64xkNdaNmjDW9YR2lbq6makQY5g/qjujgILW88OgpjPtzA3Jzc/k96kN1lGbVILKzs43atamhbtSMsaY3rKPUzdU0LV8Wvw7sipAAq2X5Zu8h/GfjdsaaD9VRmlVCCCGEaE3Xq2Lwfa/OsN28fnnTTny174jBR0U8Bc0qIYQQQrRnVL0aePeaNvblx/7eid8OHTf0mIhnoFk1CJnujFA3xpq+sI5SN8abfkxo2QiTWjVSr3MtwM1L1mL96XNGH5apCDeh/6BZNQDphRccHGy63nhGQ92oGWNNb1hHqZsneKNTa9xcv6Z6nZadgyHzVyA2Pskj+zY7fib1HzSrBiC98JKSkkzXG89oqBs1Y6zpDesodfME/n5++KpHB3StXF4tx6VnYODvf+JMWrpH9m9mLCb1HzSrBpGTk2PUrk0NdaNmjDW9YR2lbp4gJCAA33Vpg+bly6rl/YnJGPL7CqRkma+nu6fJMaH/oFklhBBCiOkoK5MGDOyG6hFhavmvs+dx46LVyM7NNfrQiIuhWSWEEEKIKakeGY75g7sr4yr8fuQk7l+x0XS3uUnR0KwaREREhFG7NjXUjZox1vSGdZS6eTrWmpePxuz+1yLY32ppPt99AC/+vcNjx2E2IkzoP2hWDUB64QUFBZmuN57RUDdqxljTG9ZR6mZUrPWoVhnf9OpoX//8xu34Ytf+PP+Tnp6Os2fPqmdfxc+k/oNm1QDk9kRCQgJvU1A3xpqmsI5SN8ab+erozfVr4a3Ore3L967YiN8OHMXMmTPRu3dvhIWFISYmRj3LspSbsbORL17bAo0+AF/FbIGiC9SNmjHW9IZ1lLoZGWuPtmyEo8mpeG/bXuSkpmDYkMHI3bXtsu2WLl2qHn369FGmtUyZMvAVLCb0HzSrhBBCCPEK5Pb2O9e0wbHEZMx89AHk7t4OhIQAPQcCXXoC5SsC5+OA1cuAZfOxePFijBw5EgsWLEBAQIDRh08KgWkAhBBCCPGqSQNGJp0GbEZ14rPA0JuAijGAdMKSZ1mW8pAQZVjnzJlj9GGTIqBZNYioqCijdm1qqBs1Y6zpDesoddMh1j7/9BPrC2lRrV2v4I2kXNYDmDp1KnyFKBP6D5pVg25T+Pv7m643ntFQN2rGWNMb1lHqpkOsSW9/yUdVyK3/ori4fsmSJT4xSoCfSf0HzaoBmLU3ntFQN2rGWNMb1lHqpkOsJSUlXVqQHNWicFif5/+8FItJ/QfNKiGEEEK88za3dKYqCof1Zrw97itoZ1ZlzLM33ngDjRo1QkhIiBoTbcyYMWog34KYMWOGas6WBGlCCCGE+DahoaHo1auXdUF6/RfFxfUy7qr8H9ET7czqM888gyeeeAIDBw5URnTixImYNWsWhg4delmz9alTpzB+/HjDjpUQQggh+mH3BsvmA4fyzmRlR8plveP2REu0Gmc1LS0NH374IcaOHYspU6aosmHDhqnW1XHjxuGPP/5A//797dtLmTTbnz9/HmZCWoLLli1rugRno6Fu1Iyxpjeso9RNl1gT7yAD/qu7rlMmFzrOKjIyUK1dB1x//fXwBfxM6j+0almNjY1FcnIyBgwYkKe8c+fO6nn79u32si+++ALr1q1TKQPFISUlpdCHp5EW4tzcXNMlOBsNdaNmjDW9YR2lbrrEmgzwLzNTiWEVQ4oFs4FnJwAPjLY+y7KUN26O46PvxaPrtvjEd7LFpP5Dq5bVmjVrYtGiRWjbtm2ecjGlQo0aNdTz4cOH8eijj+Kzzz5Tra7FITIyssByGcKhTZs26rV8eI4foPzyKOgDLW25vE5MTER0dPRl27trn0aWu+q9BdFNfhV6yzm5u7wkmul27EZ+TrY6atPNG87JE+WOunnLOTlbXpJtZVl6ohc05adZz8nd5cXRTO68zp8/Xw34//HHH6vhqWxIjmrzYaPwnn8ZNVHAB9tjEejvh7c6tVb/r9O5uvpzyv+dYOT3kynNqpg39SvIAWnCnzRpEmrXro3BgwerE77zzjvRr18/3HjjjVi+fLnL9i/DOWRmZqrXwcHBCA8PV6kJtjJBErDlIS2y2dnZ9nLZVv5HWoalk5iNiIgIBAUFqeBwvKA7fiE6IgEkv3och9CwNdvL/hxbguWXo1TGrKwspKam2ssDAwOVOc/IyMgzbpw7z0mQYxHzLzq645xkv1Iu72+7hWH2c3L35yT7sO3TppnZz8kTn5OU285L9ucN5+SJz0mOX/Yj5yTl3nBOnvic5P8FOSc5Tm84J3d/TmFhYepZzkmOqahzEmMqaQHyvsePH1f7t3Wman3qPMYu3wDZ8t2te5GblYXnWzVSfsRbYy/V4TvByHMqCX4WTduCpff/k08+iS+//BK1atVSv4xatmyJ999/Hy+99BJ27NiBSpUqKbPas2dP1SKb3+g6UtjtfhHclnawYsUKewVwd8uqBJdUBle/t47lrvwVFh8ff1m+jZnPyRO/XIurmW7HbnTLqtRRm27ecE6eKHfUTb5EveGcnC0vaSuhGBFpJXSsp2Y+J3eXu0ozKfts5z7cs2KjveyZtk0xuUNLbc7VlZ+TmFjHa1tJ39uV5WKCu3btql6vXLkyj//SumXVxvTp01XPPPlVMGHCBEyePFk5/AMHDqiRAj766CN1UuLUbb9C5aTFkBbm1gsrt/2iFWxfSo7kX3ZVuW2/BW3vrn0aWe6K95Bgt8284anPqahynfR1lWY6Hburyp19j/y6ecM5eaLccXYcbzknZ8tLum1B9dTdx+jMcep0LK7QbFzT+si2WDB+5d9q+aVNOxEc4I9n2zV327G7u9yviG0L+k4w6thN27L66aef4r777kPHjh1Vq2rTpk3t62bPno3hw4cX+r/yS0FakUqCmN3iOntCCCGEeCfvb9uLCas32Zdf7dgST7S55EGIaymJ/9KqZVVaUiU/tVu3buq2vuQ5OCLlckKObN68GQ8//DDeffdddOrUCWZAfh9ITofkhJT214YvQd2oGWNNb1hHqZuZY+3hFg2RlZuLx9b+o5afXL8Vwf7+eLRVY3gLFpP6D63MqoyjKrfyBw0ahKVLl162vn79+rj22mvzlNkSeZs3b24asyrIeebvoU2oG2NNH1hHqRvjzffq6KRWjZVhFaOqltf+gyB/fzzUoiG8hRQT+g+tzOrBgwfVs+SlFsRzzz2H559/3sNHRQghhBBfQW79i2H9z1/Wsd0fXr1JGdb7mtU3+tB8Fq3M6mOPPaYeJaFHjx6lHr+LEEIIIcSGdK7KzMlVna2E+1duRJC/H+5qUo8i+foMVr6EjG1GqBtjTV9YR6kb48236+iL7Vvg362b2JfH/fkXvtljvQNsZgJM6D+0aln1FSSpWQbhJdSNsaYnrKPUjfGmN56oo7IPGREgMzcX727doyYOuHPZetXC+q8GtWFG/EzqP9iyagCStiAzPzB9gbox1vSEdZS6Md70xlN1VMzd251b48HmDaz7BXDb0vX4af8RmBGLSf0HzapBOE5rRqgbY00/WEepG+NNbzxVR8Wwvt+lLe5tas1XzbVYcMvitfjl4DGYkVQT+g+aVUIIIYSQKxjWqV2vxtjGddRyjsWCmxatwdzDx6mbB6BZJYQQQgi5kmHy88N/u7XHbQ2t+aoyvNXIhaux4MhJaudmaFYNQmaPINSNsaYvrKPUjfGmN0bU0QB/f3zVowNuqV9TLUvnq2ELV2LxsVMwC4Em9B80qwbdToiMjDTVVGc6QN2oGWNNb1hHqZsvxJoY1m97dcKoujXUckZOLq5fsBLLj5+G7viZ1H/QrBqA9MJLT083XW88o6Fu1Iyxpjeso9TNV2It0N8f03p3xrDa1dRyWnYOBs9fgZUnz0BnLCb1HzSrBiHBQqgbY01fWEepG+NNb4yuo0EB/pjR9xoMrllVLadm52DQ7yuw9lQcdCbdhP6DZpUQQgghxAmCAwLwc78u6F+jilpOzsrGgN//xF9nzlFPF0KzSgghhBDiJKGBAfil/7XoXa2yWk7MzEK/ucux6ex5auoiaFYNIjg42KhdmxrqRs0Ya3rDOkrdfDHWwgID8euAruh+VSW1HJ+Zhb5zl2PruXjoRrBGuhUXmlUDkF544eHhpuuNZzTUjZox1vSGdZS6+XKshQcFYu6gbuhSpaJaPp+Rid6/LcOO8wnQBT8NdSsONKsGIL3wZLozs/XGMxrqRs0Ya3rDOkrdfD3WIoOC8Pug7uhUuYJajkvPUIZ194VE6IBFU92uBM2qQWRmZhq1a1ND3agZY01vWEepm6/HWpngICwY1B1XVyqvlk+npaPXb0sRG58EHcjUVLeioFklhBBCCHEhZUOC8ceQHmhdIVotn0y1GtYDicmXDSN19uxZUw4n5UloVgkhhBBCXEy5kGAsvq4nWpQvq5aPpaSh569LcSA+ETNnzkTv3r0RFhaGmJgY9SzLUp6Tk8PPIh80qwYRGhpq1K5NDXWjZow1vWEdpW6MtUtUCA1RhrVpuTJq+cjZODTr2g2jRo3C0qVL80gly1I+YMAAJCa6L8c11IT+g2bVAKQXngSL2XrjGQ11o2aMNb1hHaVujLXLiQkLxZLreqJhmXDgv+8iffsWICQEGDAMmPwe8NEP1mdZDgnB4sWLMXLkSLe0sPqZ1H/QrBqA9MJLTk42XW88o6Fu1Iyxpjeso9SNsVYwVcLD8JglBdi93WpUJz4LDL0JqBgD+Ptbn2VZyi8a1jlz5rCOXoRm1SCys7ON2rWpoW7UjLGmN6yj1I2xVjDTv/zc+qLnQKB2vYI3knJZD2Dq1KlukTLbhP6DZpUQQgghxI1Ib397jmqXnkVvfHH9kiVLOErARWhWCSGEEELcSFKSwxir5a0zXBWKw/o8/+fD0KwahEx3RqgbY01fWEepG+NNb8xUR6Oioi4tnI8remOH9Xn+zwd1s0GzagDSCy84ONh0vfGMhrpRM8aa3rCOUjfGWsFID/xevXpZF1YvK1qmi+tl3FVXDzPlZ1L/QbNqUI9ZadrnaADUjbGmJ6yj1I3xpjdmrKPjx4+3vlg2Hzi0v+CNpFzWO27v47oJNKsGwRkqqBtjTW9YR6kb401vzFZHhw0bhj59+gAZGcCUycCcGUDcGSA31/osy1KekaG2Gzp0qFuOI8dkugmBRh8AIYQQQoi3ExAQoKZTlQH/ZRxVLJhtfeRDjKpsJ9sTK2xZJYQQQgjxAGXKlMGCBQuUGZWc1Dw0ao7ohx7HnHnz1HbkEmxZNYiIiAijdm1qqBs1Y6zpDesodWOsFY20mI4YMUI9ZPxVySG9Z/1WzD52FvEAfjp4HGMa1WEddYAtqwYgvfCCgoJM1xvPaKgbNWOs6Q3rKHVjrJUM6e1fqVIl/N/Vrexlb2/Z7bYOUH4m9R80qwYgQZiQkGC63nhGQ92oGWNNb1hHqRtjzTk6V66AjjEV1Ott5xOw5Phpt0hpMan/oFk1CLMFii5QN2rGWNMb1lHqxlgrOdLSOalVI/vyO1v2uE1Giwn9B80qIYQQQojBDK9THbUirbNLzT96EjvPJxh9SNpAs0oIIYQQYjCB/v6Y0OJS6+q7W93Xumo2aFYNwh3z/foC1I2aMdb0hnWUujHWnOeuJnVRJjhIvf4u9hDOpKW7XE4z1lGaVYNyU/z9/U3XG89oqBs1Y6zpDesodWOslQ4xquOa1FWvM3JyMXV7rEslNWsdpVk1ALP2xjMa6kbNGGt6wzpK3Rhrpefh5g0RcNFMTt2xD2nZ2fD1OkqzSgghhBCiCTWjInBDvRrq9dn0DPwQexi+Ds0qIYQQQohGPNrSYRirrXuQa7KWUFdDs0oIIYQQohHtYyqg61WV1OtdFxKx8OhJ+DI0qwYgic1ly5Y1XYKz0VA3asZY0xvWUerGWHNP6+rbLpokwKx1lGbVACSxOTc313QJzkZD3agZY01vWEepG2PNdVxXqyrqlYlUr2X61S1xF3y2jtKsGkRSUpJRuzY11I2aMdb0hnWUujHWXEOAvz8eaen6SQLMWEdpVgkhhBBCNOSORnVQLiRYvZ627whOpKTBF6FZJYQQQgjRkIigQNzbtJ56nZWbi49cPEmAWaBZNQizJTfrAnWjZow1vWEdpW6MNdfyUPOGCPK32rVPdu5DSla2z9VRmlUDMGtvPKOhbtSMsaY3rKPUjbHmeqpGhOHm+jXV6/MZmfhmz0Gfq6M0qwYgvfCysrJM1xvPaKgbNWOs6Q3rKHVjrLl/GKt3SzFJgFnrKM2qQaSkpBi1a1ND3agZY01vWEepG2PN9bSuWA69qsWo1/sSkzH38AmfqqM0q4QQQgghmvNoy8b2129v2Q1fgmaVEEIIIURzBta8Co2jy6jXK06excYz5+Er0KwaREBAgFG7NjXUjZox1vSGdZS6Mdbcg7+fHx5p2bDUkwSYsY5qZ1ZzcnLwxhtvoFGjRggJCUFMTAzGjBmDs2fP2reZM2cOOnbsiIiICERFRaFv377YtGkTzIL0wpPjNltvPKOhbtSMsaY3rKPUjbHmXm5rWBsVQ0PU6xn7j+BocopP1FHtzOozzzyDJ554AgMHDsSMGTMwceJEzJo1C0OHDlW91xYsWIDhw4ejQoUK+Prrr/HOO+/g4MGD6NWrF06fPg0zIOeRmZlput54RkPdqBljTW9YR6kbY829hAUGYnyz+up1jsWCD7bF+kQd1cqspqWl4cMPP8TYsWMxZcoUDBs2DE899RTeffddrF27Fn/88QfefPNNNGzYEL/99htuuOEGjBs3ThnYhIQE9T9mITU11ehDMCXUjZox1vSGdZS6Mdbcy/hmDRB8cZKA/+7aj6TMLK+vo1qZ1djYWCQnJ2PAgAF5yjt37qyet2/fjs2bN6NPnz55ci7q16+PihUrqvVFDdVQ2IMQQgghxAxUDg/FrQ1rqdcJmVn4cvcBeDuB0IiaNWti0aJFaNu2bZ7ydevWqecaNWrgp59+Qu3atfOs37dvH+Li4tT6woiMjCyw3N/fH23atFGvpVncsWlccjoKaiovbbnjfvJv7659Glnuqvcu6DMy+zm5u7wkmul27EZ+To4PbzknT5Q76uYt5+RseUm2Lez7wMzn5O5yV2mm0zmVpPyRFo3w5W7rTFZTtu3FA83qI+Bia2txr29Gn5NpzWp0dLRqNXVk8eLFmDRpkjKogwcPVp2qHDlw4ABGjBiBwMBA3HvvvaXav6QSSC6HEBwcjPDwcJWaYCsTQkND1UNaZLOzL83PK9vK/0jLsHQSsyHHGxQUhMTExDyVS1qG5VnKHZFp0HJzc5GUlHTZ9GiyP8eWYHkPSZSW2Sgcm/VFCzHnGRkZSE9Pt5e785wEORYx/6KjO85J9iv7k/e3JYeb/Zzc/TnJPuT9HTUz+zl54nOScptusj9vOCdPfE5y/PKeso2Ue8M5eeJzkv+X45dzkuP0hnNy9+cUFham3kvOSY7JG86pJJ9T9QCgX/XK+OPYaRxKSsEPO2MxtEaVYp2T43eCkedUEvwspbW7bkJ6/z/55JP48ssvUatWLTUCQMuWLe3rRVjJZX3xxRfVh/fFF1/g5ptvLvT9CrvdL4Lb0g5WrFihKoANX//lynPi52T2cp2OhefEz8ns5TodC88JWHTsFPrP+1Np0blyBawe1kc7fYsqFxPctWtX9XrlypV5/JfWLas2pk+fjvHjx6tfBRMmTMDkyZPz3MaXvNXRo0dj165dGDRokOqUVadOnSLfszAXL7+0HAW1tT45lhVEacptrQ8yNFdB27tjn0aXu+I9itLNrOfk7vKSaqbTsbuq3Jn3KEg3s5+TJ8oddSvp+xh97O4oL+62opt8cZvhO0GXY3GlZrqcU0nL+1avgubly2L7+QSsPX0O606fQ+cqFYt8D6Gg7wSjzsmUHayETz/9FLfccovq8b9lyxbVeupoVGU8VXHi0oT966+/Yt68eVc0qjri2KxOqBtjTT9YR6kb401vfL2O+vn54dGWjezL7xRzkgAz6qaVWZWWVMlP7datm7ol37Rp08u2kXFXxbyuWbMG1113nSHHSQghhBBiNP9qUAuVw0LV61kHj+FgYjK8Ea3SAGQcVcktlVv7S5cuvWy9DE+1atUq1fIqKQDycKR8+fLo0KGDB4+YEEIIIcQYQgIC8GDzBnj2r23ItVjw/ra9eLdL3hGVvAGtzKrMRCXIDFYFIaMBSJ7KtGnT1CM/3bt3x/Lly2EGpHccoW6MNX1hHaVujDe9YR21cl/T+nhl806kZefg890H8NzVzREdEuxVummVBvDYY4/lGQMs/2Pu3LlFrjeLUZU8ExnOobQJx74GdaNmjDW9YR2lbow1z1MxLARjGlr77iRnZePzXQe8ro5qZVZ9BTHWMp6ZpqOGaQt1o2aMNb1hHaVujDVjmNiyof31+9v3Iivn0tiz3lBHaVYNwnEwXULdGGv6wTpK3RhvesM6eolG0WUwpFZV9fpocip+PnAU3qQbzSohhBBCiMmZlG8YK7O1nhYFzSohhBBCiMnpXjUGbSqWU683nj2PlSfPwlugWTUImUOXUDfGmr6wjlI3xpvesI7mRTpN5W9d9RbdaFYNCigJFrP1xjMa6kbNGGt6wzpK3RhrxnJDvRqoFhGmXv966Dhi45O8oo7SrBqA5JEkJyd7VT6JJ6Bu1Iyxpjeso9SNsWYswQEBeKi5dWQAcRhTtu3xijpKs2oQ2dnZRu3a1FA3asZY0xvWUerGWDOWe5rWQ0Sgdc6nr/YcxPn0DNPXUZpVQgghhBAvoVxIMMY2tk4SILNafbpzP8wOzSohhBBCiBcxoUVD2LJSP9i+F5k5OTAzNKsGIdOdEerGWNMX1lHqxnjTG9bRwqlXNgrD61RXr0+mpmP6viOm1o1m1QCkF15wcLDpeuMZDXWjZow1vWEdpW6MNX14tIBJAsxaR2lWDUACJikpyXS98YyGulEzxpresI5SN8aaPlxTpSI6xlRQr7eci8fS46dNW0dpVg0ix+T5I0ZB3agZY01vWEepG2NND/z8/C5rXTVrHaVZJYQQQgjxQkbUrY5akdYc1d+PnMSuC4kwIzSrhBBCCCFeSKC/Pya0uNS6+m4hU7DqDs2qQURERBi1a1ND3agZY01vWEepG2NNL+5qUhdRQdZJAr6LPYRUf+trM0GzalAeSVBQkOl64xkNdaNmjDW9YR2lbow1/SgTHIRxTeqp1xk5ufhs7yHT+Q+aVQOQXngJCQmm641nNNSNmjHW9IZ1lLox1vTk4RYNEXDRoH64bS/Sssw15SrNqkHQqFI3xpresI5SN8ab3rCOFp9aUREYVbeGeh2XkYkf9h2GmaBZJYQQQgjxch5tmbejlZnMPs0qIYQQQoiX06FyBVxbpaJ6vfNCIhYePQWzQLNqEFFRUUbt2tRQN2rGWNMb1lHqxljTl0fyTBKwG2aBZtUApBeev7+/6XrjGQ11o2aMNb1hHaVujDW9GVq7GuqViVSvFx07ja3n4mEGaFYNgD1mqRtjTW9YR6kb401vWEedw9/PD/fUt3a0MtMkATSrhBBCCCE+wr/qVEN0cJB6/UPsYZxMSYPu0KwSQgghhPgIkUGBuLdpffU6KzcXH+2Ihe7QrBJCCCGE+BAPNq+PQH9rv5mPd+xDquaTBNCsGtQJoWzZsuxgRd0Ya5rCOkrdGG96wzpaOt2qR0bg5no1Vdn5jEx8u/cQdIZm1aDE8NzcXFMNyKsD1I2aMdb0hnWUujHWzFNHH23VOE9Hq1yNPQnNqkEkJSUZtWtTQ92oGWNNb1hHqRtjzRx1tE3FcuhZNUa93puQhHmHT0BXaFYJIYQQQnyQSQ6tq29v0XeSAJpVQgghhBAfZGDNq9Ao2jqj5p8nz2LN0RM4e/Ys0tPToRM0qwbB2auoG2NNb1hHqRvjTW9YR0uvm0wSMKFZfWDTemDKS+hSsxpiYmIQFhaG3r17Y+bMmcjJyYHRBBp9AL7cG49QN8aanrCOUjfGm96wjrpGt8TERPz8yAPAkiWXbbt06VL16NOnjzKtZcqUgVHQrBqA9MLLzs5GYGAgfxlSN8aahrCOUjfGm96wjpZeNxkVYOTIkVgqRjUkBOg5EOjSEyhfETgfB6xeBiybj8WLF6vtFixYgICAABgB0wAMIiUlxahdmxrqRs0Ya3rDOkrdGGvmqKOzZ89WRlQZ1YnPAkNvAirGAP7+1mdZlvKQELXdnDlzDDtmmlVCCCGEEB9j6tSp1hfSolq7XsEbSbmsd9zeAGhWCSGEEEJ8iPT0dJWPqpBb/0Vxcf2SJUsMGyWAZtUgjMr7MDvUjZox1vSGdZS6Mdb0r6NJjhMTSY5qUTisN2rSD3awMqg3XlSUdVwzQt0Ya/rBOkrdGG96wzpaOt2CgoIuFUpnKslRLQxZfxGjvAtbVg3qjZeZmameCXVjrOkH6yh1Y7zpDeto6XQLCQlBr169rIXS678oLq6XcVdDQ0NhBDSrBpGammrUrk0NdaNmjDW9YR2lbow1c9TR8ePHWwuWzQcO7S94YymX9Y7bGwDNKiGEEEKIjzFs2DA14D8yMoApk4E5M4C4M0BurvVZlqU8I0NtN3ToUMOOlTmrhBBCCCE+2NFq5syZasB/Nd7qgtnWRz5sM1gZ2XmSLasGIbNHEOrGWNMX1lHqxnjTG9bR0usmU6jKzFRiRiUn1RFZlnJZb+RUqwIdk0G98SIjI43YtamhbtSMsaY3rKPUjbFmvjoaEBCAESNGqIeMoyrDU0mvf6M6UxUEW1YN6o0nAcHRAKgbY01PWEepG+NNb1hH3aObGNRKlSppZVQFmlWDMGoWCLND3agZY01vWEepG2NNb9JN6D9oVgkhhBBCiLbQrBJCCCGEEG2hWTWI4OBgo3ZtaqgbNWOs6Q3rKHVjrOlNsAn9B0cDMKg3Xnh4uBG7NjXUjZox1vSGdZS6Mdb0xs+k/oMtqwYgvfBkujOOBkDdGGt6wjpK3RhvesM66lu60awaRGZmplG7NjXUjZox1vSGdZS6Mdb0JtOE/sPn0wAcf12kpaV5bJ+yL8kbkSZ5Qt0Ya3rBOkrdGG96wzpqft0cPdeVWnp93qw6jjfWr18/t34whBBCCCHkci9WVC4t0wAIIYQQQoi2+FnMlmXrYnJzcxEfH69ey/Ri7m4WT0lJQeXKldXr06dPIyIiwq378xaoGzVjrOkN6yh1Y6zpTYpm/sM29asQHR0Nf//C2099Pg1AxClfvrxHzbE8hLCwMPUg1I2xpg+so9SN8aY3rKPeo1txh9FiGgAhhBBCCNEWmlVCCCGEEKItNKuEEEIIIURbaFYJIYQQQoi20KwSQgghhBBt8fmhqwghhBBCiL6wZZUQQgghhGgLzSohhBBCCNEWmlVCCCGEEKItNKuEEEIIIURbfH66VUIIIe4hJycHaWlpl5UHBARoMdUjIcQcsGXVDXzzzTdo0qSJuhg3atQIH3zwQbH/98knn8S1114LX6SkuqWnp+OJJ55A7dq1ERISgmrVquHhhx9GamoqfIWSanbu3DmMHTsWMTExak7mxo0b46WXXkJmZiZ8idLUUWHQoEGoXr06fI2S6vbzzz8jKirqskffvn3hSzgTb8uWLVPfBZGRkYiOjsb111+PQ4cOwVcoiWZy/UpOTi70kZGRAV/hmxLGWkpKCv7v//4P9evXR0REBJo3b46PPvoIubm50AoLcSnfffedRWS97777LL/88ovl2WeftQQEBFhee+21K/7vtm3bLBUrVrR06dLF5z4VZ3S7+eabLcHBwZbnnnvOMmvWLMsTTzxhCQoKstx0000WX8AZzbp27WoJCwuzvPjii5aff/7ZMmHCBPUe8uwrlKaOCp9++qn6/2rVqll8CWd0e/nlly1NmjSxrFy5Ms9j69atFl/BGd1EI7m2DRkyRNXTDz74wFK5cmVLu3btLDk5ORZvp6SayXeAbF/YY8yYMRZf4DsnYm3YsGGWsmXLWt5++20Va/fff796j5deesmiEzSrLkQuIjVr1rTccMMNecoffvhhS1RUlCU5ObnA/5s2bZqlcePG9orla2bVGd0OHz5s8fPzs0yePDlP+dNPP6003L17t8WbcUazzZs3K22++OKLPOW33HKL+iL0BZytozYOHDigtqtVq5ZPmVVndRs7dqzP/Hh0pW6dOnVSD0djOnfuXFVP9+7da/FmnP0+yP+DSB4vvPCCasBYunSpxdvJcUK3I0eOqO+E999/P0/58OHDtbu+MQ3AhWzZsgVHjhzBHXfckaf8uuuuQ1JSElasWFHg/9WqVQtjxozBq6++ijp16sDXcEa3f/75R35oYcCAAXnKO3furJ63b98Ob8YZzS5cuIAuXbqgR48eecrltqyvpE44W0cFibc777wTw4cPv0xDb8dZ3fbv348GDRqo176WauKsbseOHcO6devwwAMPwN/fX+X9CoMHD8apU6fsenorzmhWs2ZNlTLh+JAyuQX+xhtvoGfPnvB2tjihW1xcnHqWtDBHypcvX2CuuZHQrLqQzZs3q+eWLVvmKZccEGHPnj0F/t8111yjci/lIRXM13BGtw4dOmDRokVo2rRpnnK5yAs1atSAN+OMZnLBXrVqFerWrauMw/nz5/HTTz/hxx9/VD+WfAFn66gwZcoUxMbG4r333oOv4axuYlb/+usvVR8lr7xChQp4/PHHfca4OqPb2rVr7a+7d++O4OBglCtXDrfddhtOnz4Nb6c0ddSRu+++W+VhTpgwAb7AZid0k3WS3/rcc8+p74YzZ86o74Pvv/8et956K3SCowG4ENuvFLkgOyIXGiEhIcGVu/Np3apUqaIejvzwww/qV3SnTp1w9dVXw5spbay9//77KqleEPM6adIk+ALO6rZ792489dRTytxLZxdfwxndpAPk8ePHkZ2drTrxSYe0hQsX4p133sHBgweVlt6OM7qdPHlSPY8fP1615Esjxr59+/DMM89g48aNqgVNDKy34orv0W+//RaLFy/G+vXr4efnB18gzgndgoKCVD2UO25du3a1l9erVw8vvvgidIJm1YVkZWUVWC63cgTpfU1cr5v0kJ04cSLmzJmD1q1bY8aMGfb/9VZKq9ktt9yCVq1aqXSJt956S7Xu79ixw35h81ac0U3M1u23346bbroJQ4YMgS/ijG7Sm1hMQ7du3ex3jPr37696HIt5lVQeqa/ejDO6JSYmqufRo0fnacWXH0kSh/PmzVOpKN5Kaa9t8iNJjL3cDm/fvj18hSwndBODK9c0qZOShigmdcOGDXjttdcwYsQILFmyBLrg3d/oHsb2RR8fH5+n3LZcsWJFQ47Lm3WTW7OSCiApAfIFKBXNF1IpShtrMsyXDB/0yCOPYPr06ao1R4YZ8nac0e3NN9/E4cOHVXzZhsIRAys5rPLaF25pO6ObfDnKrcT89VG+BH0hr9xZ3Wzjz+bPx5fh0kpyG9xXr21fffWVyvuVFmlfopwTun3++eeqsUdu/d9///3o16+fMvrSgLF06VKsWbMGukCz6kKaNWumnuU2jSO7du1Sz23atHHl7uDrusmYtGK2JBdTbtM+/fTT6raGL+CMZqKVjEkrJsuRFi1aqGfJYfV2nNFNbiVKLpfkXdrGCZWUkxMnTqjXkoPp7Tijm2wrOuWPN5u5L1OmDLwdZ3SzdbLN/yPI1nLm7XfoSvs9Kp2q5DuhYcOG8CWaOaGbpOMI7dq1K7CjsnTY0gajhyPwJjIyMiyVKlWy3HrrrXnK7777bjWkRHHo3r27zw1d5YxusbGxFn9/fzXsUm5ursXXcEazKVOmqGFK/vzzzzzlMraelC9cuNDi7Tij286dOy8bEmfgwIHqfeS1DGfl7Tij2/fff6/iKv+wQePHj7eEhoZazp07Z/F2nNEtLi7OEhISYhk1alSe8qlTpyo9ZTxub6Y036ObNm1SGn3yyScWXyPDCd1effVVpdeCBQvylMvwhlL+zz//WHSBZtXF2AYMl0HWZVDeBx98UI0HKmOpCseOHbPMnz9fPReEL5pVZ3R766231PZfffWVKs//KExfX9YsKSnJctVVV1kqVKhgefPNNy0zZ85Ug2nLGHy9e/f2GdNf2joqyCDjuo1DqJtuCQkJloYNG1rKly+vBrX/6aefLPfcc496D5mUwldwJt5EH/kfMR4zZsxQkyuIwZdxa30BZ+uojKsq/3fw4EGLL/JpCXU7c+aM/Tvh9ddfV5PrSOzJd4JMFqATNKtuQH7VNWjQQP06btq0qWphsCHmymayCsJXzWpJdXvggQeKnLGkMH19PdZ27dpluf7669WMJTJYdt26dS1PPvmkJTU11eJLlKaO+qpZdUY3GXRctKpSpYr6n2bNminj6ms4E29iPBo1aqTqqbSMyWxEWVlZFl/BGc369+/vk/WyNLqdOHFCzVpVp04dNWuaxNrjjz9uSUtLs+iEn/wxOhWBEEIIIYSQgmAHK0IIIYQQoi00q4QQQgghRFtoVgkhhBBCiLbQrBJCCCGEEG2hWSWEEEIIIdpCs0oIIYQQQrSFZpUQQgghhGgLzSohhBBCCNEWmlVCiMv5+uuv4efnV+CjbNmyGDx4MP7555/L/k/mKPnss89wzTXXoEyZMggODkbt2rUxZswYbN68+bLt77jjjkL34/iIj48v8Dh79OhRrP+Xh+zLm5FzvPbaaw3Z9/LlywvVPTQ0FHXr1sX48eNx+vRpl+73+eefV/tYvHixS9+XEOJaAl38foQQYqdjx47o1KmTfTkrKwt79uzB/PnzlUFZvXo1WrdubTeqo0ePxo8//og6deooQxsdHY39+/dj2rRp+P777/HJJ59g3Lhxlyk8cuRIVK9evVDlQ0JCCiwfNWqUff823nvvPURFRWHs2LF5yjt06OAVn+y+ffvQoEED9QNAflTYmDBhgjKFRtKkSRP069cvT1lGRgb+/vtvfPzxx5g3b556XbFixRK/96233ooffvgBBw8eVD+ABIlNOe+aNWu67BwIIW7A6PleCSHeh20O6ueee67A9Z999plaf91119nLfvrpJ1V2++23WzIzM/Nsv3XrVkvFihXVfNfHjh2zl8u88/I/y5Ytc9mxy/vVqlXL4q3ExsaqcxTtdEE+vysd01133aW2mTx5slP7GD16tPr/gwcPluJICSFGwDQAQojHufPOOxEREYFVq1bZy2bNmqWeX3rpJQQFBeXZvkWLFnjkkUdUK9uiRYugE5mZmcjNzTX6MLyee++9Vz1v3LjR6EMhhHgYmlVCiMcJCAhQuYgpKSn2snPnzqnn9PT0Av/npptuwuTJk9GoUSMYidxCltSB3bt3o1evXiplIDExscj8x/z5oLZc21OnTuHpp59GjRo1lB6tWrXCnDlzLvv/NWvWoE+fPoiMjFS3wGW/S5YsybNNamoqXnvtNTRv3hzh4eGoXLkyevfujZkzZ9q3kWOUFADhm2++Uccg6RgFHaMgx3ffffehWrVqKpWiXr16ePLJJ5GUlFSgJsePH1epHOXLl1fHKvvftWsXXEFYWJh6zs7OzlO+bt06DB8+HFWqVFHnLef36KOP4uTJk/Zt5NwkBUCQFBPJVbbpUdBnNmPGDHTu3Fn9oJLc6b59+9p1IoR4HppVQojHOXz4sDKn9evXz5OvKNxyyy1YsWIFcnJy8vyPGKVnnnlGmQijOX/+PLp3744LFy4o41lYTuyVkJzZzz//XJm6YcOGYceOHbjhhhuUEbbx+++/K3O1adMmtb3kdP7111/KQMk6GzfeeKMyktIqLfmZ8j8bNmxQ//PFF1/YczRtHcVEb8nXLCzXV4yn5BxLh7eWLVuqHN6rrrpKGeIuXboog+6IfJ7SMW7Lli1qn+3bt8fSpUsxaNCgywymM8j5C82aNctjVLt27Yo//vhDPd92223KXL777rvKeEurtyDn2bhxY3urvhxfYcgPoptvvhnHjh1TsTh06FC1b/mMvvzyy1KfByHECQxJPiCE+GTOalpammXNmjWW9u3bq/VvvvmmfZ3kotaoUUOVyyM6OlrltMo2GzdutOTm5l62H1vO6siRIy0TJkwo8HHu3DmX5qzKOlt+ZU5Ojr1czlXKFy1aVOB7dunS5bLjbtSokSUuLs5e/sorr6jyl19+2a5X5cqVVb7ukSNH7Ntt27bNEhwcbOnatataPn78uPq/ESNG5DmmzZs3q/J+/fpdMWc1/zHKe0nZjz/+mGe7119/XZVPnDjxMk1uvPHGPPvv37+/Kl+9erXF2ZzVCxcuWObMmWOpUqWKpWzZsnlyTseNG2fx8/OzbNq0Kc//DB06VL2fxFpROav5P7N//vnH4u/vb2nXrp0lOTnZvt2pU6csderUsYSGhqrXhBDPQrNKCHGbWS3qMXDgwMs6Uolxe/755y3Nmze/bHsxRNOnT8+zvc30FfUoaYea4prV/KbFGbOa3wiKWZLye+65Ry3//PPPhXZUGz9+vKV79+7q9cmTJ1XHIzGnjoi+Yr4c910cs3rmzBn1f23atLlsv2JGxUDLI78mchyOTJkyRZVPmzbNUhyzWtQjKCjIsmLFijz/N2PGDMt777132fs9++yzl30WxTGrDz30kFoWc5yfDz/8UK37+OOPizwXQojr4dBVhBCPDV0l+YGS4ym38gcMGKCWHalQoQKee+459Th79qzK1Vy2bJnK4zx06JC6PSt5idddd12e/5NtbHmInqBSpUoqJ7S0SH6pIzIGrS3/1LEzUUHn9tFHH9lfS76mpEhIBzTRTIanEr0kz9KZzl8yPJT838CBAy9b5+/vj7Zt26rhxyQdQvJTbZ+dHEdR5+PM0FVpaWkq7UHG2b3nnnvUrX/b+0rqgy2tRNIP5Jzl3L/77js4g6RNSEz279//snWS1iDs3LnTqfcmhDgPzSohxG2IIZVOLM4aQskXlMfbb7+NV155Bf/5z3/w4YcfXmZWPY1MVlBcisrXlE5VBWFt6LzU6SwmJuaK+3nxxRdVPqmYO8lblXxg6ZQlRr6k2PZbtWrVAtfLD4b8neEKOxfH87kSMpbtlClTLiuX/GUxsZIDK6NGSN6pIDm+8lrMrCCdz6Sjl5hp2daZ8xbTXVAOckHnTAjxDOxgRQgxHDEbgYGBaniqwkYPeOqpp1SPcGk9MxPS+ugsNtOUvzOTbYIFm3H66quvVGu0jBIgM4OJYZUWQJngoDQ9722mNT8nTpxQLay2VlV3I5+/7QeKdPyynb/8kBHDKiMbxMXFqdZ4GdrM2Zm45LxltrOCWqPlnG0/ogghnoVmlRBiOA0bNlS3X1euXFnoNtK6Ji10zsxe5MnWVjGKjqxfv77UaQJye7qg29K2nvwys5MgPfdl+Csxd4LM1lSa/Rb0eYj5lvQEGSGgqNZUVyOzmTm2VMtMaDK7mYx8cPvtt6sWURulOW95/7Vr1162buHChV41kxkhZoJmlRBiOJKDKPmHkiv5wgsvXDZsleRiytiZ0pIoQzzpiIyVKjiOayrjyMrteWeR8UPFEH7wwQd5Wld//vlnlaMpU9I6GuUjR47Yt0lOTlZDNuVHWkSvlJ4gPx7atWunxh/NPwnDq6++qlo17777bhiBLaWgoHO2mcr//e9/Tp33v/71L/X87LPP2oe9su1DpqaVtIiC8ngJIe6FOauEEC2QXFQZQF5yXOW2toybKSb29OnT+PPPP9UtXjFnBRkwHZDxRKX1T25Jx8bGom7duipfVExscXJOC0L+780338RDDz2kWv0kB1j0mDt3LsqVK6du/QvSsvjjjz+qjkEjRoxQpl5Mm3RYkkHwxdg+8cQTKqdVOoZJTutvv/2GBx54ABMnTrRPFODIp59+qjp2yXnJQyYGkBQDaXWUz8Y2o5SnsJlNud0vyDFLR70FCxao2/6iz9atW1Wrr4yjKnrIbGhya1/GhbW1Qo8bN05pJJrmZ8iQIWpsVflf0U5yfqVzmOgt5v/bb78tUb4yIcQ1sGWVEKIFYr6kJ/tbb72lDJWMACCGSaZklcHmpWONGCxdzYLchhbjJMZIeq5Li6SYPDE6+aePLQkPPvigmn1J8kPFLMl7SwugDFQvhlgQEztt2jRlKMVoiWGTlmjpZCTmX4yebcYrmZVJyiRVQH4UyMQGBSEtq2JMxcDJJA0yIL7ksEqro5yn5Bh7EtsEEtOnT1d5y5I28ssvvyhzKZMoSLl8BhJD//3vf9XnICMHnDlzRv2fmGvpeLV69eoiO53JSALyA0HiTFpTJcVCTLGMrGBrySaEeBY/Gb/Kw/skhBBCCCGkWLBllRBCCCGEaAvNKiGEEEII0RaaVUIIIYQQoi00q4QQQgghRFtoVgkhhBBCiLbQrBJCCCGEEG2hWSWEEEIIIdpCs0oIIYQQQrSFZpUQQgghhGgLzSohhBBCCNEWmlVCCCGEEKItNKuEEEIIIQS68v+lJLBf4MofuQAAAABJRU5ErkJggg==", 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", 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" ] @@ -1557,8 +1330,7 @@ "for ratio in psfratios:\n", " params = imaging_params.copy()\n", " params['psf_trunc_ratio'] = ratio\n", - " parsed_parameters= parse_input.parse_parameters(params)\n", - " snr,_ = calculate_snr(parsed_parameters,exptime)\n", + " snr,_ = calculate_snr(params,exptime)\n", " snrs.append(snr[0].value)\n", "\n", "# MAKE PLOT\n", @@ -1588,11 +1360,245 @@ "\n", "# SNR maximized at 0.3, just like it should! " ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Advanced Usage\n", + "\n", + "### 3.1 Using `pyEDITH` iteratively\n", + "In the preceding examples, we utilized the premade `calculate_texp` and `calculate_snr` functions. While this approach is straightforward, it can lead to significant performance overhead in scenarios involving repeated calculations with large parameter spaces or numerous iterations. This is because each function call reinitializes the entire calculation process.\n", + "\n", + "For improved computational efficiency, particularly when dealing with extensive parameter sweeps, it is advisable to implement the loop logic within the `calculate_texp` function itself. This approach allows for targeted iteration over specific parameters while maintaining the state of other computationally intensive components.\n", + "\n", + "To illustrate this optimization technique, we can examine the internal structure of the `calculate_texp` function. Refer to the `pyEDITH` workflow picture for details." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-13T17:35:46.940835Z", + "iopub.status.busy": "2026-07-13T17:35:46.940729Z", + "iopub.status.idle": "2026-07-13T17:35:47.174671Z", + "shell.execute_reply": "2026-07-13T17:35:47.174375Z" + } + }, + "outputs": [], + "source": [ + "params = imaging_params.copy()\n", + "\n", + "# Define Observation and load relevant parameters\n", + "observation = Observation()\n", + "observation.load_configuration(params)\n", + "observation.set_output_arrays()\n", + "observation.validate_configuration()\n", + "\n", + "# Define Astrophysical Scene and load relevant parameters,\n", + "# then calculate zodi/exozodi\n", + "scene = AstrophysicalScene()\n", + "scene.load_configuration(params)\n", + "scene.calculate_zodi_exozodi(params)\n", + "scene.validate_configuration()\n", + "\n", + "# Create and configure Observatory\n", + "observatory_config = parse_input.get_observatory_config(params)\n", + "observatory = Observatory()\n", + "observatory.create_observatory(observatory_config)\n", + "observatory.load_configuration(params, observation, scene\n", + ")\n", + "observatory.validate_configuration()\n", + "\n", + "# EXPOSURE TIME CALCULATION\n", + "calculate_exposure_time_or_snr(\n", + " observation,\n", + " scene,\n", + " observatory,\n", + " mode=\"exposure_time\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2. Vary observatory parameters\n", + "\n", + "By default, using a preset telescope or detector (e.g. `EACTelescope`, `EACDetector`) loads all parameters directly from the HWO specification files. Some parameters are **locked** by default, meaning they are owned by the specification and cannot be changed accidentally — this ensures that a standard EAC run is always internally self-consistent.\n", + "\n", + "An advanced user may want to unlock specific parameters to explore the trade space (e.g. \"what if the telescope were larger?\" or \"what if the detector had lower dark current?\"). This is done by explicitly listing the parameter names in `parameters['overrides']`:\n", + "\n", + "```python\n", + "params['overrides'] = ['diameter'] # only this key is unlocked\n", + "params['diameter'] = 8.0 # values without units are assumed to be\n", + " # in the default unit for that parameter (meters here)\n", + "```\n", + "\n", + "A few rules worth knowing before you start:\n", + "\n", + "- **Only locked parameters need to appear in `overrides`.** Parameters that are already user-editable (e.g. `toverhead_fixed`) can be set directly without listing them. See the [Glossary](https://pyedith.readthedocs.io/en/latest/glossary.html) for which parameters are locked for each component.\n", + "- **If you set a locked parameter without listing it in `overrides`**, the value you supplied is silently ignored and the specification value is kept. A warning will appear in the logs — if your change seems to have no effect, increase the verbosity level and check there first. To see the full provenance of every parameter (i.e. whether each value came from the specification default or from your own input), increase the logging verbosity to DEBUG. At this level, every parameter is logged individually as it is assigned.\n", + "- **If you mistype a name in `overrides`** (e.g. `'diamter'`), an error is raised immediately. This is intentional — it prevents typos from silently doing nothing.\n", + "- **This mechanism applies to Telescope and Detector parameters only.** For coronagraph parameters that are locked by the YIP, the only way to change them is to provide a custom YIP file. See the [Coronagraph (YIP) Guide](https://pyedith.readthedocs.io/en/latest/yippy_guide.html) for how to do that.\n", + "\n", + ".. danger:: Overriding locked parameters breaks the internal self-consistency of the observatory configuration. Changing one parameter (e.g. `diameter`) can have physical implications for other parameters that are *not* automatically recalculated. Make sure you understand these dependencies before relying on the results. If you want to double check exactly which values were used in a given run, increase the log verbosity to `DEBUG` (see above) to see every parameter's assignment logged individually.\n", + "\n", + "### Example: impact of telescope diameter on exposure time\n", + "\n", + "Here we loop over four telescope diameters and compute exposure time as a function of planet-to-star contrast. Note that `overrides` is set once outside both loops, since it does not change across iterations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-13T17:35:47.176413Z", + "iopub.status.busy": "2026-07-13T17:35:47.176333Z", + "iopub.status.idle": "2026-07-13T17:35:56.408419Z", + "shell.execute_reply": "2026-07-13T17:35:56.408135Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "set_verbosity(level='quiet')\n", + "\n", + "\n", + "fig = plt.figure()\n", + "\n", + "contrasts=np.logspace(-9, -11, 10)\n", + "params = imaging_params.copy()\n", + "params['overrides'] = ['diameter']# Defining here that this parameter will change.\n", + "\n", + "for idx, diam in enumerate([6,8,10,12]):\n", + " exposure_times=[]\n", + " params['diameter'] = diam\n", + "\n", + " for contrast in np.logspace(-9, -11, 10):\n", + " params['Fp/Fs']= contrast\n", + " parsed_parameters= parse_input.parse_parameters(params)\n", + " texp, validation_output = calculate_texp(parsed_parameters)\n", + "\n", + " exposure_times.append(texp.to(u.hr).value)\n", + "\n", + " plt.loglog(contrasts, exposure_times, marker='o', label=f'{diam} m',\n", + " markersize=7, linewidth=2.5, color=colors[idx],\n", + " markeredgewidth=1, markeredgecolor='black', alpha=0.9)\n", + "\n", + "plt.xlabel('Planet-to-star contrast', fontsize=14, fontweight='bold')\n", + "plt.ylabel('Exposure time (hours)', fontsize=14, fontweight='bold')\n", + "plt.tick_params(axis='both', which='major', labelsize=12, width=1.5, length=6)\n", + "plt.tick_params(axis='both', which='minor', width=1, length=3)\n", + "plt.xlim(1e-9,1e-11)\n", + "plt.grid(True, which='both', alpha=0.3, linestyle='--', linewidth=0.8)\n", + "plt.legend(title='Diameter', fontsize=11, title_fontsize=12, frameon=True,\n", + " fancybox=True, shadow=True, loc='best')\n", + "\n", + "ax = plt.gca()\n", + "for spine in ax.spines.values():\n", + " spine.set_visible(True)\n", + " spine.set_linewidth(1.5)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Example: impact of detector dark current on exposure time\n", + "\n", + "To show that the same mechanism works for detector parameters, here is an analogous sweep over dark current values. Note that `DC` appears in `overrides` this time, and `diameter` does not — the telescope stays at its EAC-default value throughout.\n", + "\n", + "\n", + ".. note::\n", + "`overrides` is a flat list shared across all components. A key name like `diameter` unambiguously refers to the Telescope (no Detector or Coronagraph key shares that name). If you are unsure which component owns a given parameter, consult the [Glossary](https://pyedith.readthedocs.io/en/latest/glossary.html).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-13T17:35:56.410111Z", + "iopub.status.busy": "2026-07-13T17:35:56.410012Z", + "iopub.status.idle": "2026-07-13T17:36:05.460130Z", + "shell.execute_reply": "2026-07-13T17:36:05.459845Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "set_verbosity(level='quiet')\n", + "\n", + "fig = plt.figure()\n", + "params = imaging_params.copy()\n", + "params['overrides'] = ['DC'] # unlocking a detector locked key this time\n", + "\n", + "dark_currents = [1e-6,1e-4,1e-2,0.1] # counts/pix/s\n", + "contrasts=np.logspace(-9, -11, 10)\n", + "\n", + "for idx, dc in enumerate(dark_currents):\n", + " exposure_times = []\n", + " params['DC'] = dc # interpreted as default DC unit (counts/pix/s)\n", + "\n", + " for contrast in contrasts:\n", + " params['Fp/Fs'] = contrast\n", + " parsed_parameters = parse_input.parse_parameters(params)\n", + " texp, _ = calculate_texp(parsed_parameters)\n", + " exposure_times.append(texp.to(u.hr).value)\n", + "\n", + " plt.loglog(contrasts, exposure_times, marker='o', label=f'DC={dc}',\n", + " markersize=7, linewidth=2.5, color=colors[idx],\n", + " markeredgewidth=1, markeredgecolor='black', alpha=0.9)\n", + "\n", + "plt.xlabel('Planet-to-star contrast', fontsize=14, fontweight='bold')\n", + "plt.ylabel('Exposure time (hours)', fontsize=14, fontweight='bold')\n", + "plt.tick_params(axis='both', which='major', labelsize=12, width=1.5, length=6)\n", + "plt.tick_params(axis='both', which='minor', width=1, length=3)\n", + "plt.xlim(1e-9,1e-11)\n", + "plt.grid(True, which='both', alpha=0.3, linestyle='--', linewidth=0.8)\n", + "plt.legend(title='DC', fontsize=11, title_fontsize=12, frameon=True,\n", + " fancybox=True, shadow=True, loc='best')\n", + "\n", + "ax = plt.gca()\n", + "for spine in ax.spines.values():\n", + " spine.set_visible(True)\n", + " spine.set_linewidth(1.5)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] } ], "metadata": { "kernelspec": { - "display_name": "pyedith-dist-venv (3.12.0)", + "display_name": ".venv (3.12.4)", "language": "python", "name": "python3" }, diff --git a/tutorials/spectroscopy_tutorial.ipynb b/tutorials/spectroscopy_tutorial.ipynb index 6ad88ed..386c50b 100644 --- a/tutorials/spectroscopy_tutorial.ipynb +++ b/tutorials/spectroscopy_tutorial.ipynb @@ -27,10 +27,10 @@ "id": "3a4ccd3b", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:10.707911Z", - "iopub.status.busy": "2026-05-19T15:18:10.707179Z", - "iopub.status.idle": "2026-05-19T15:18:12.316645Z", - "shell.execute_reply": "2026-05-19T15:18:12.316416Z" + "iopub.execute_input": "2026-07-13T17:36:07.708587Z", + "iopub.status.busy": "2026-07-13T17:36:07.708212Z", + "iopub.status.idle": "2026-07-13T17:36:09.282863Z", + "shell.execute_reply": "2026-07-13T17:36:09.282571Z" } }, "outputs": [ @@ -46,7 +46,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:12,312] Logging level set to: INFO\n" + "[pyEDITH] INFO [2026-07-13 13:36:09,278] Logging level set to: INFO\n" ] } ], @@ -86,17 +86,17 @@ "id": "ee94eec7-70b0-4572-93bd-e3ae7f8a7336", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:12.318040Z", - "iopub.status.busy": "2026-05-19T15:18:12.317894Z", - "iopub.status.idle": "2026-05-19T15:18:12.319463Z", - "shell.execute_reply": "2026-05-19T15:18:12.319276Z" + "iopub.execute_input": "2026-07-13T17:36:09.284749Z", + "iopub.status.busy": "2026-07-13T17:36:09.284592Z", + "iopub.status.idle": "2026-07-13T17:36:09.286414Z", + "shell.execute_reply": "2026-07-13T17:36:09.286199Z" }, "scrolled": true }, "outputs": [], "source": [ "parameters = {}\n", - "parameters[\"observing_mode\"] = \"IFS\" # tells ETC to use spectroscopy (IFS) mode\n" + "parameters[\"observing_mode\"] = \"IFS\" # tells ETC to use spectroscopy (IFS) mode" ] }, { @@ -114,10 +114,10 @@ "id": "316e54db-f871-4f7b-8129-0e2c54bd13fd", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:12.320591Z", - "iopub.status.busy": "2026-05-19T15:18:12.320516Z", - "iopub.status.idle": "2026-05-19T15:18:12.322164Z", - "shell.execute_reply": "2026-05-19T15:18:12.321978Z" + "iopub.execute_input": "2026-07-13T17:36:09.287755Z", + "iopub.status.busy": "2026-07-13T17:36:09.287676Z", + "iopub.status.idle": "2026-07-13T17:36:09.289605Z", + "shell.execute_reply": "2026-07-13T17:36:09.289409Z" } }, "outputs": [], @@ -150,10 +150,10 @@ "id": "ebc37f57", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:12.323213Z", - "iopub.status.busy": "2026-05-19T15:18:12.323143Z", - "iopub.status.idle": "2026-05-19T15:18:12.324801Z", - "shell.execute_reply": "2026-05-19T15:18:12.324624Z" + "iopub.execute_input": "2026-07-13T17:36:09.290907Z", + "iopub.status.busy": "2026-07-13T17:36:09.290768Z", + "iopub.status.idle": "2026-07-13T17:36:09.292643Z", + "shell.execute_reply": "2026-07-13T17:36:09.292416Z" } }, "outputs": [], @@ -187,10 +187,10 @@ "id": "d9374c81", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:12.325976Z", - "iopub.status.busy": "2026-05-19T15:18:12.325904Z", - "iopub.status.idle": "2026-05-19T15:18:12.328243Z", - "shell.execute_reply": "2026-05-19T15:18:12.327987Z" + "iopub.execute_input": "2026-07-13T17:36:09.293804Z", + "iopub.status.busy": "2026-07-13T17:36:09.293720Z", + "iopub.status.idle": "2026-07-13T17:36:09.296639Z", + "shell.execute_reply": "2026-07-13T17:36:09.296414Z" } }, "outputs": [ @@ -198,7 +198,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:12,326] Calculating a new wavelength grid and re-gridding spectra...\n" + "[pyEDITH] INFO [2026-07-13 13:36:09,294] Calculating a new wavelength grid and re-gridding spectra...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,295] 'snr' has length 1000 but the resolved wavelength grid has length 154. Rebinning...\n" ] } ], @@ -227,10 +234,10 @@ "id": "879901cc-6838-48ac-88be-01b47d4390be", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:12.329572Z", - "iopub.status.busy": "2026-05-19T15:18:12.329470Z", - "iopub.status.idle": "2026-05-19T15:18:12.624974Z", - "shell.execute_reply": "2026-05-19T15:18:12.624631Z" + "iopub.execute_input": "2026-07-13T17:36:09.297917Z", + "iopub.status.busy": "2026-07-13T17:36:09.297816Z", + "iopub.status.idle": "2026-07-13T17:36:09.583857Z", + "shell.execute_reply": "2026-07-13T17:36:09.583572Z" } }, "outputs": [ @@ -342,10 +349,10 @@ "id": "cdbee4d5-1179-4072-840e-416cc876c777", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:12.626522Z", - "iopub.status.busy": "2026-05-19T15:18:12.626381Z", - "iopub.status.idle": "2026-05-19T15:18:12.628420Z", - "shell.execute_reply": "2026-05-19T15:18:12.628206Z" + "iopub.execute_input": "2026-07-13T17:36:09.585362Z", + "iopub.status.busy": "2026-07-13T17:36:09.585242Z", + "iopub.status.idle": "2026-07-13T17:36:09.587250Z", + "shell.execute_reply": "2026-07-13T17:36:09.587027Z" } }, "outputs": [], @@ -394,10 +401,10 @@ "id": "549bfe42", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:12.629684Z", - "iopub.status.busy": "2026-05-19T15:18:12.629590Z", - "iopub.status.idle": "2026-05-19T15:18:12.670922Z", - "shell.execute_reply": "2026-05-19T15:18:12.670677Z" + "iopub.execute_input": "2026-07-13T17:36:09.588498Z", + "iopub.status.busy": "2026-07-13T17:36:09.588406Z", + "iopub.status.idle": "2026-07-13T17:36:09.601674Z", + "shell.execute_reply": "2026-07-13T17:36:09.601467Z" } }, "outputs": [ @@ -405,14 +412,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:12,632] Flux zero point calculated at 5.5e-05 cm in units of ph / (s cm3)\n" + "[pyEDITH] INFO [2026-07-13 13:36:09,591] Flux zero point calculated at 5.5e-05 cm in units of ph / (s cm3)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:12,636] Flux zero point calculated at [2.00000000e-05 2.01601602e-05 2.03203203e-05 2.04804805e-05\n", + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:09,592]\u001b[0m ez_PPF should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,596] Flux zero point calculated at [2.00000000e-05 2.01601602e-05 2.03203203e-05 2.04804805e-05\n", " 2.06406406e-05 2.08008008e-05 2.09609610e-05 2.11211211e-05\n", " 2.12812813e-05 2.14414414e-05 2.16016016e-05 2.17617618e-05\n", " 2.19219219e-05 2.20820821e-05 2.22422422e-05 2.24024024e-05\n", @@ -668,7 +682,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-05-19 11:18:12,637]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:09,597]\u001b[0m `FstarV_10pc` not specified in parameters. Calculating internally...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:09,597]\u001b[0m ez_PPF should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" ] } ], @@ -696,10 +717,10 @@ "id": "1dd438c2", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:12.672330Z", - "iopub.status.busy": "2026-05-19T15:18:12.672256Z", - "iopub.status.idle": "2026-05-19T15:18:12.717553Z", - "shell.execute_reply": "2026-05-19T15:18:12.717299Z" + "iopub.execute_input": "2026-07-13T17:36:09.602977Z", + "iopub.status.busy": "2026-07-13T17:36:09.602908Z", + "iopub.status.idle": "2026-07-13T17:36:09.663768Z", + "shell.execute_reply": "2026-07-13T17:36:09.663500Z" } }, "outputs": [ @@ -707,13 +728,76 @@ "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:12,672] Re-gridding spectra onto ETC wavelength grid...\n" + "[pyEDITH] INFO [2026-07-13 13:36:09,603] Re-gridding spectra onto ETC wavelength grid...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,603] 'F0' has length 1000 but the resolved wavelength grid has length 154. Rebinning...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,610] 'Fzodi_list' has length 1000 but the resolved wavelength grid has length 154. Rebinning...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,617] 'Fexozodi_list' has length 1000 but the resolved wavelength grid has length 154. Rebinning...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,623] 'Fbinary_list' has length 1000 but the resolved wavelength grid has length 154. Rebinning...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,629] 'Fp_over_Fs' has length 1000 but the resolved wavelength grid has length 154. Rebinning...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,636] 'Fs_over_F0' has length 1000 but the resolved wavelength grid has length 154. Rebinning...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,643] 'ez_PPF' has length 1000 but the resolved wavelength grid has length 154. Rebinning...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,649] 'mag' has length 1000 but the resolved wavelength grid has length 154. Rebinning...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,656] 'deltamag' has length 1000 but the resolved wavelength grid has length 154. Rebinning...\n" ] } ], "source": [ "if parameters[\"regrid_wavelength\"] is True:\n", - " scene.regrid_spectra(parameters, observation)\n" + " scene.regrid_spectra(observation)\n" ] }, { @@ -722,10 +806,10 @@ "id": "eebf5c71", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:12.718821Z", - "iopub.status.busy": "2026-05-19T15:18:12.718748Z", - "iopub.status.idle": "2026-05-19T15:18:12.826403Z", - "shell.execute_reply": "2026-05-19T15:18:12.826073Z" + "iopub.execute_input": "2026-07-13T17:36:09.665152Z", + "iopub.status.busy": "2026-07-13T17:36:09.665081Z", + "iopub.status.idle": "2026-07-13T17:36:09.770335Z", + "shell.execute_reply": "2026-07-13T17:36:09.770065Z" } }, "outputs": [ @@ -798,10 +882,10 @@ "id": "43f784aa-63a4-4269-9f39-518afa3ac9fd", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:12.828031Z", - "iopub.status.busy": "2026-05-19T15:18:12.827928Z", - "iopub.status.idle": "2026-05-19T15:18:12.829695Z", - "shell.execute_reply": "2026-05-19T15:18:12.829449Z" + "iopub.execute_input": "2026-07-13T17:36:09.771731Z", + "iopub.status.busy": "2026-07-13T17:36:09.771631Z", + "iopub.status.idle": "2026-07-13T17:36:09.773412Z", + "shell.execute_reply": "2026-07-13T17:36:09.773188Z" } }, "outputs": [], @@ -813,7 +897,7 @@ "parameters[\"IFS_eff\"] = 1.\n", "\n", "# number of detector pixels per spectral bin\n", - "parameters[\"npix_multiplier\"] = np.ones_like(parameters[\"wavelength\"]) \n", + "parameters[\"npix_multiplier\"] =1.\n", "\n", "# post processing factor (30 is a good realistic value)\n", "parameters[\"noisefloor_PPF\"] = 30 \n", @@ -828,10 +912,10 @@ "id": "c2af66d3", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:12.830922Z", - "iopub.status.busy": "2026-05-19T15:18:12.830842Z", - "iopub.status.idle": "2026-05-19T15:18:13.735838Z", - "shell.execute_reply": "2026-05-19T15:18:13.735589Z" + "iopub.execute_input": "2026-07-13T17:36:09.774778Z", + "iopub.status.busy": "2026-07-13T17:36:09.774681Z", + "iopub.status.idle": "2026-07-13T17:36:10.667952Z", + "shell.execute_reply": "2026-07-13T17:36:10.667750Z" } }, "outputs": [ @@ -839,168 +923,238 @@ "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:12,831] Observatory Configuration:\n" + "[pyEDITH] INFO [2026-07-13 13:36:09,775] Observatory Configuration:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,775] Using preset: EAC1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,775] \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:09,776]\u001b[0m Coronagraph 'eac1_optimal_order_6_1d' not found locally. Attempting to fetch from remote database...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:09,776] \u001b[0mFetching YIP 'eac1_optimal_order_6_1d' (cache: /Users/ealei/Library/Caches/yippy)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:09,821] \u001b[0mYIP 'eac1_optimal_order_6_1d' available at /Users/ealei/Library/Caches/yippy/eac1_optimal_order_6_1d.zip.unzip/eac1_optimal_order_6_1d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[pyEDITH] INFO [2026-07-13 13:36:09,821] Successfully downloaded coronagraph to: /Users/ealei/Library/Caches/yippy/eac1_optimal_order_6_1d.zip.unzip/eac1_optimal_order_6_1d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:09,822]\u001b[0m ez_PPF should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:09,822]\u001b[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:09,838] \u001b[0mCreating eac1_optimal_order_6_1d coronagraph\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-07-13 13:36:09,839] \u001b[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:12,831] Using preset: EAC1\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-07-13 13:36:09,840] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:12,832] \n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-07-13 13:36:09,840] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-05-19 11:18:12,832]\u001b[0m Coronagraph 'eac1_optimal_order_6_1d' not found locally. Attempting to fetch from remote database...\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:09,885] \u001b[0meac1_optimal_order_6_1d is radially symmetric\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:12,832] \u001b[0mFetching YIP 'eac1_optimal_order_6_1d' (cache: /Users/ealei/Library/Caches/yippy)\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:10,041] \u001b[0mLoading performance metrics from /Users/ealei/Library/Caches/yippy/eac1_optimal_order_6_1d.zip.unzip/eac1_optimal_order_6_1d/yippy_cache/performance/trunc_0.30_v2.7.3.fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:12,878] \u001b[0mYIP 'eac1_optimal_order_6_1d' available at /Users/ealei/Library/Caches/yippy/eac1_optimal_order_6_1d.zip.unzip/eac1_optimal_order_6_1d\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:10,042] \u001b[0mLoading throughput and contrast from trunc_0.30_v2.7.3.fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:12,879] Successfully downloaded coronagraph to: /Users/ealei/Library/Caches/yippy/eac1_optimal_order_6_1d.zip.unzip/eac1_optimal_order_6_1d\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:10,048] \u001b[0mSuccessfully loaded performance data from trunc_0.30_v2.7.3.fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:12,896] \u001b[0mCreating eac1_optimal_order_6_1d coronagraph\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:10,048] \u001b[0mComputing core area curve (PSF trunc ratio = 0.3)...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-05-19 11:18:12,897] \u001b[0mUnhandled header fields: {'TMULDET', 'TMULCHAR'}\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:10,390] \u001b[0mComputing occulter transmission curve...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-05-19 11:18:12,898] \u001b[0mUsing default unit for D: m. Could not extract unit from comment: \"circumscribed diameter of the telescope in mete\"\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:10,489] \u001b[0mComputing core mean intensity curve...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[33mWARNING [2026-05-19 11:18:12,899] \u001b[0mUsing default unit for D_INSC: m. Could not extract unit from comment: \"inscribed diameter of the telescope in meters\"\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:10,646] \u001b[0mOWA set to max_offset_in_image: 32.00 lam/D\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:12,982] \u001b[0meac1_optimal_order_6_1d is radially symmetric\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:10,647] \u001b[0mCreated eac1_optimal_order_6_1d\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:13,137] \u001b[0mLoading performance metrics from /Users/ealei/Library/Caches/yippy/eac1_optimal_order_6_1d.zip.unzip/eac1_optimal_order_6_1d/yippy_cache/performance/trunc_0.30_v2.7.3.fits\n" + "[pyEDITH] INFO [2026-07-13 13:36:10,648] Using psf_trunc_ratio to calculate Omega...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:13,138] \u001b[0mLoading throughput and contrast from trunc_0.30_v2.7.3.fits\n" + "[pyEDITH] INFO [2026-07-13 13:36:10,658] Setting the noise floor via user-supplied noisefloor_PPF...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:13,148] \u001b[0mSuccessfully loaded performance data from trunc_0.30_v2.7.3.fits\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:10,660]\u001b[0m ez_PPF should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:13,148] \u001b[0mComputing core area curve (PSF trunc ratio = 0.3)...\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:10,660]\u001b[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:13,477] \u001b[0mComputing occulter transmission curve...\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:10,662]\u001b[0m ez_PPF should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:13,620] \u001b[0mComputing core mean intensity curve...\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:10,662]\u001b[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:13,719] \u001b[0mOWA set to max_offset_in_image: 32.00 lam/D\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:10,664]\u001b[0m Parameter 'npix_multiplier' is locked in this mode and cannot be user-overridden; using the model-provided value instead of the supplied value 1.0.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:13,720] \u001b[0mCreated eac1_optimal_order_6_1d\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:10,665]\u001b[0m ez_PPF should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:13,721] Using psf_trunc_ratio to calculate Omega...\n" + "\u001b[38;2;226;147;0m[pyEDITH] WARNING [2026-07-13 13:36:10,666]\u001b[0m IFS_eff should be a list of length 1000. pyEDITH will create one assuming the input value for all the elements of the list.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:13,730] Setting the noise floor via user-supplied noisefloor_PPF...\n" + "[pyEDITH] INFO [2026-07-13 13:36:10,666] Calculating optics throughput from preset...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:13,734] Calculating optics throughput from preset...\n" + "[pyEDITH] INFO [2026-07-13 13:36:10,666] 'ifs_eff' has length 1000 but the resolved wavelength grid has length 154. Rebinning...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:13,734] Calculating epswarmTrcold as 1 - optics throughput...\n" + "[pyEDITH] INFO [2026-07-13 13:36:10,666] Calculating epswarmTrcold as 1 - optics throughput...\n" ] } ], @@ -1029,10 +1183,10 @@ "id": "80787a1f-4112-40f4-81a7-8fcf53b1bb0d", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:13.737230Z", - "iopub.status.busy": "2026-05-19T15:18:13.737124Z", - "iopub.status.idle": "2026-05-19T15:18:15.006392Z", - "shell.execute_reply": "2026-05-19T15:18:15.006083Z" + "iopub.execute_input": "2026-07-13T17:36:10.669260Z", + "iopub.status.busy": "2026-07-13T17:36:10.669174Z", + "iopub.status.idle": "2026-07-13T17:36:10.725668Z", + "shell.execute_reply": "2026-07-13T17:36:10.725411Z" } }, "outputs": [], @@ -1054,16 +1208,16 @@ "id": "b8cf072d", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:15.008109Z", - "iopub.status.busy": "2026-05-19T15:18:15.007996Z", - "iopub.status.idle": "2026-05-19T15:18:15.120252Z", - "shell.execute_reply": "2026-05-19T15:18:15.119916Z" + "iopub.execute_input": "2026-07-13T17:36:10.727173Z", + "iopub.status.busy": "2026-07-13T17:36:10.727096Z", + "iopub.status.idle": "2026-07-13T17:36:10.833367Z", + "shell.execute_reply": "2026-07-13T17:36:10.833049Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1120,10 +1274,10 @@ "id": "92fb8e76", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:15.121748Z", - "iopub.status.busy": "2026-05-19T15:18:15.121643Z", - "iopub.status.idle": "2026-05-19T15:18:16.386670Z", - "shell.execute_reply": "2026-05-19T15:18:16.386378Z" + "iopub.execute_input": "2026-07-13T17:36:10.834827Z", + "iopub.status.busy": "2026-07-13T17:36:10.834613Z", + "iopub.status.idle": "2026-07-13T17:36:10.893001Z", + "shell.execute_reply": "2026-07-13T17:36:10.892728Z" } }, "outputs": [], @@ -1151,16 +1305,16 @@ "id": "c7dbb61e", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:16.388397Z", - "iopub.status.busy": "2026-05-19T15:18:16.388318Z", - "iopub.status.idle": "2026-05-19T15:18:16.445369Z", - "shell.execute_reply": "2026-05-19T15:18:16.445117Z" + "iopub.execute_input": "2026-07-13T17:36:10.894353Z", + "iopub.status.busy": "2026-07-13T17:36:10.894268Z", + "iopub.status.idle": "2026-07-13T17:36:10.952574Z", + "shell.execute_reply": "2026-07-13T17:36:10.952337Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1199,16 +1353,16 @@ "id": "e79158e8", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:16.446887Z", - "iopub.status.busy": "2026-05-19T15:18:16.446791Z", - "iopub.status.idle": "2026-05-19T15:18:20.559981Z", - "shell.execute_reply": "2026-05-19T15:18:20.559690Z" + "iopub.execute_input": "2026-07-13T17:36:10.953969Z", + "iopub.status.busy": "2026-07-13T17:36:10.953889Z", + "iopub.status.idle": "2026-07-13T17:36:11.208634Z", + "shell.execute_reply": "2026-07-13T17:36:11.208345Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1342,10 +1496,10 @@ "id": "ec22e704", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:20.561642Z", - "iopub.status.busy": "2026-05-19T15:18:20.561531Z", - "iopub.status.idle": "2026-05-19T15:18:20.616506Z", - "shell.execute_reply": "2026-05-19T15:18:20.616185Z" + "iopub.execute_input": "2026-07-13T17:36:11.210107Z", + "iopub.status.busy": "2026-07-13T17:36:11.210009Z", + "iopub.status.idle": "2026-07-13T17:36:11.259877Z", + "shell.execute_reply": "2026-07-13T17:36:11.259631Z" } }, "outputs": [ @@ -1391,10 +1545,10 @@ "id": "56dbf0bd", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:20.618151Z", - "iopub.status.busy": "2026-05-19T15:18:20.618009Z", - "iopub.status.idle": "2026-05-19T15:18:20.893599Z", - "shell.execute_reply": "2026-05-19T15:18:20.893323Z" + "iopub.execute_input": "2026-07-13T17:36:11.261078Z", + "iopub.status.busy": "2026-07-13T17:36:11.260995Z", + "iopub.status.idle": "2026-07-13T17:36:11.530554Z", + "shell.execute_reply": "2026-07-13T17:36:11.530254Z" } }, "outputs": [ @@ -1491,16 +1645,16 @@ "id": "9ada7958", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:20.896205Z", - "iopub.status.busy": "2026-05-19T15:18:20.896090Z", - "iopub.status.idle": "2026-05-19T15:18:20.988828Z", - "shell.execute_reply": "2026-05-19T15:18:20.988560Z" + "iopub.execute_input": "2026-07-13T17:36:11.532606Z", + "iopub.status.busy": "2026-07-13T17:36:11.532499Z", + "iopub.status.idle": "2026-07-13T17:36:11.624552Z", + "shell.execute_reply": "2026-07-13T17:36:11.624248Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1569,10 +1723,10 @@ "id": "d6c1045c-203e-4d34-ac29-da1b490439fa", "metadata": { "execution": { - "iopub.execute_input": "2026-05-19T15:18:20.990186Z", - "iopub.status.busy": "2026-05-19T15:18:20.990105Z", - "iopub.status.idle": "2026-05-19T15:18:22.503840Z", - "shell.execute_reply": "2026-05-19T15:18:22.503569Z" + "iopub.execute_input": "2026-07-13T17:36:11.625938Z", + "iopub.status.busy": "2026-07-13T17:36:11.625837Z", + "iopub.status.idle": "2026-07-13T17:36:11.852570Z", + "shell.execute_reply": "2026-07-13T17:36:11.852257Z" } }, "outputs": [ @@ -1580,26 +1734,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] INFO [2026-05-19 11:18:20,992] Logging level set to: DEBUG\n" + "[pyEDITH] INFO [2026-07-13 13:36:11,627] Logging level set to: DEBUG\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-05-19 11:18:20,992] \u001b[0mLogging level set to: DEBUG\n" + "\u001b[38;5;229m\u001b[48;5;16m[yippy]\u001b[0m \u001b[32mINFO [2026-07-13 13:36:11,628] \u001b[0mLogging level set to: DEBUG\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[pyEDITH] DEBUG [2026-05-19 11:18:22,277] Printing all relevant variables in pyedith_validation.txt and pyedith_full_info.txt.\n" + "[pyEDITH] DEBUG [2026-07-13 13:36:11,686] Printing all relevant variables in pyedith_validation.txt and pyedith_full_info.txt.\n" ] }, { "data": { - "image/png": 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", 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", 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