coscon.toast_helper module

class coscon.toast_helper.AvesBands(data: Dict[str, float | int | str], validate_schema: bool = True)

Bases: GenericDictStructure

a class describing the bands of Aves.

data: Dict[str, float | int | str]

schema: ClassVar[dict] = {<class 'str'>: {'NET': Or(<class 'float'>, <class 'numpy.float64'>), 'alpha': Or(<class 'float'>, <class 'int'>, <class 'numpy.float64'>, <class 'numpy.int64'>), 'bandpass': <class 'str'>, 'bandwidth': Or(<class 'float'>, <class 'int'>, <class 'numpy.float64'>, <class 'numpy.int64'>), 'center': Or(<class 'float'>, <class 'numpy.float64'>), 'fknee': Or(<class 'float'>, <class 'numpy.float64'>), 'fmin': Or(<class 'float'>, <class 'numpy.float64'>), 'fwhm': Or(<class 'float'>, <class 'numpy.float64'>)}}

class coscon.toast_helper.AvesDetectors(data: Dict[str, str | float | int | np.ndarray | None], validate_schema: bool = True)

Bases: GenericFocalPlane

a class describing the detectors of Aves.

similar to FakeFocalPlane

data: Dict[str, str | float | int | np.ndarray | None]

plot(x_min: float | None = None, x_max: float | None = None, y_min: float | None = None, y_max: float | None = None, width: float | None = None, bleed: float | None = None, fontname: str = 'TeX Gyre Schola', wire: bool = False, wire_TB: bool = False, wire_connectionstyle: str | None = None)

Plot the detectors in Azimuthal equidistant projection with orientation.

For wire_connectionstyle, use ‘arc3’ to draw straight-line. See more in https://matplotlib.org/stable/gallery/userdemo/connectionstyle_demo.html#sphx-glr-gallery-userdemo-connectionstyle-demo-py

schema: ClassVar[dict] = {<class 'str'>: {'UID': Or(<class 'int'>, <class 'numpy.int64'>), 'band': <class 'str'>, 'fwhm': Or(<class 'float'>, <class 'numpy.float64'>), 'orient': And(<class 'str'>, <function <lambda>>), 'pixel': <class 'str'>, 'pixtype': <class 'str'>, 'pol': And(<class 'str'>, <function <lambda>>), 'quat': And(<class 'numpy.ndarray'>, <function <lambda>>), 'wafer': <class 'str'>, Optional('handed'): Or(None, And(<class 'str'>, <function <lambda>>))}}

class coscon.toast_helper.AvesHardware(data: 'Dict[str, dict]', validate_schema: 'bool' = True)

Bases: GenericDictStructure

data: Dict[str, dict]

property dataframe: DataFrame

DataFrame representation of the hardware

property dataframe_with_azimuthal_equidistant_projection_with_orientation: DataFrame

DataFrame representation of the hardware

classmethod from_dataframe(df: DataFrame, validate_schema=True)

Create Hardware from a dataframe representation

reorder_QUAB(validate_schema: bool = False) → AvesHardware

Reorder detector orient & handed in alternate pixel order.

In the following orders:

QA QA UA UA QB QB UB UB …

reorder_TB(pixels: List[int], TB_anti_alignment: bool = False, TB_alternate_anti_alignment: bool = False, validate_schema: bool = False) → AvesHardware

Reorder pixels according its integer assignment

Parameters:

TB_anti_alignment (bool) – if True, assume TB pixels ordering BTTBBTTBBT.. else BTBTBTBT…

reorder_TB_from_csv(path: Path, TB_anti_alignment: bool = False, TB_alternate_anti_alignment: bool = False, validate_schema: bool = False) → AvesHardware

Reorder pixels according its integer assignment

Parameters:

• path (Path) – a csv or txt file that has the pixel numbers per line

• TB_anti_alignment (bool) – if True, assume TB pixels ordering BTTBBTTBBT.. else BTBTBTBT…

schema: ClassVar[dict] = {<class 'str'>: {'bands': <class 'dict'>, 'detectors': <class 'dict'>, 'pixels': <class 'dict'>, 'telescopes': <class 'dict'>, 'wafers': <class 'dict'>}}

property size: int

class coscon.toast_helper.AvesPixels(data: Dict[str, float | List[str]], validate_schema: bool = True)

Bases: GenericDictStructure

a class describing the pixels of Aves.

data: Dict[str, float | List[str]]

schema: ClassVar[dict] = {<class 'str'>: {'bands': And(<class 'list'>, <function <lambda>>), 'sizemm': <class 'float'>}}

class coscon.toast_helper.AvesTelescopes(data: Dict[str, float | List[str]], validate_schema: bool = True)

Bases: GenericDictStructure

a class describing the telescopes of Aves.

data: Dict[str, float | List[str]]

schema: ClassVar[dict] = {<class 'str'>: {'platescale': Or(<class 'float'>, <class 'numpy.float64'>), 'wafers': And(<class 'list'>, <function <lambda>>), 'waferspace': Or(<class 'float'>, <class 'numpy.float64'>)}}

class coscon.toast_helper.AvesWafers(data: Dict[str, float | int | str | List[str] | None], validate_schema: bool = True)

Bases: GenericDictStructure

a class describing the wafers of Aves.

data: Dict[str, float | int | str | List[str] | None]

schema: ClassVar[dict] = {<class 'str'>: {'npixel': Or(<class 'int'>, <class 'numpy.int64'>), 'pixels': And(<class 'list'>, <function <lambda>>), 'position': Or(<class 'int'>, <class 'numpy.int64'>), 'telescope': <class 'str'>, 'type': <class 'str'>, Optional('handed'): Or(None, <class 'float'>, <class 'int'>, <class 'numpy.float64'>, <class 'numpy.int64'>)}}

class coscon.toast_helper.CrosstalkMatrix(names: np.ndarray['S'], data: np.ndarray[np.float64], name: str | None = None, freq: np.ndarray[np.float64] | None = None)

Bases: GenericMatrix

Crosstalk square matrix where column and row share the same names

compare_plot_diag(others: List[CrosstalkMatrix])

compare_plot_nearest_neighbor(others: List[CrosstalkMatrix])

property dataframe: DataFrame

property dataframe_diag_only: DataFrame

property dataframe_nearest_neighbor_only: DataFrame

freq: np.ndarray[np.float64] | None = None

classmethod from_geometric(names: List[str] | np.ndarray, r: float = 0.01) → CrosstalkMatrix

Generate a crosstalk matrix with coefficients in geometric series.

classmethod from_joshian_matrix(names: List[str] | np.ndarray, nearest: float, next_nearest: float, floor: float) → CrosstalkMatrix

Generate a crosstalk matrix with structure based on Josh’s simple assumption

classmethod from_unique_matrix(names: List[str] | np.ndarray) → CrosstalkMatrix

generate a crosstalk matrix with each entry to be unique.

Mainly for debug use to confirm the matrix multiplication is correct.

plot(annot: bool = True, figsize: Tuple[float, float] | None = None, log: bool = True)

to_random_normal() → CrosstalkMatrix

Generate a random normal matrix based on original matrix except for diagonal

with mean and std given by the original matrix.

class coscon.toast_helper.DictValueEqualsKey

Bases: object

A fake dictionary where its values always equal their keys

class coscon.toast_helper.FakeFocalPlane(data: Dict[str, float | np.ndarray], validate_schema: bool = True)

Bases: GenericFocalPlane

a class describing the output of fake_focalplane

data: Dict[str, float | np.ndarray]

plot(width: float = 4.0, height: float = 4.0, outfile: Path | None = None, facecolor: Dict[str, str | Tuple[float, float, float]] | None = None, polcolor: Dict[str, str | Tuple[float, float, float]] | None = None)

schema: ClassVar[dict] = {<class 'str'>: {'NET': Or(<class 'float'>, <class 'numpy.float64'>), 'alpha': Or(<class 'float'>, <class 'numpy.float64'>), 'epsilon': Or(<class 'float'>, <class 'numpy.float64'>), 'fknee': Or(<class 'float'>, <class 'numpy.float64'>), 'fmin': Or(<class 'float'>, <class 'numpy.float64'>), 'fwhm_arcmin': Or(<class 'float'>, <class 'numpy.float64'>), 'quat': And(<class 'numpy.ndarray'>, <function <lambda>>), 'rate': Or(<class 'float'>, <class 'numpy.float64'>)}}

class coscon.toast_helper.GenericDictStructure(data: 'Dict[str, Any]', validate_schema: 'bool' = True)

Bases: object

data: Dict[str, Any]

property dataframe: DataFrame

DataFrame representation of the focal plane

This has opposite key-ordering from the dict i.e. self.data[some_key][‘quat’] == self.dataframe[‘quat’][some_key] == self.dataframe.loc[some_key, ‘quat’]

dump(path: Path | str, overwrite: bool = False, compress: bool = None)

Write hardware config to a TOML/YAML/JSON file.

classmethod load(path: Path | str)

Read data from a TOML/YAML/JSON file.

schema: ClassVar[dict] = {}

validate()

validate_schema: bool = True

class coscon.toast_helper.GenericFocalPlane(data: 'Dict[str, Any]', validate_schema: 'bool' = True)

Bases: GenericDictStructure

property Quat: Quaternion

property azimuthal_equidistant_projection_with_orientation: ndarray[floating]

property bleed: float

Bleeding for plotting from FWHM.

property dataframe_with_azimuthal_equidistant_projection_with_orientation: DataFrame

property dist_spherical_pairwise: ndarray[floating]

Pair-wise great circle distances between detector quaternions.

Assume input is a 1-dim array of quarternions (2d-array) and return pairwise distance in 1d-array, ordered in “row-major” and “j>i” directions. E.g. for 3 detectors, [(0, 1), (0, 2), (1, 2)] ordering.

iplot(scale: float = 1.0, height: int = 1000, width: int = 1000) → Figure

property quat: ndarray[floating]

property x_max: float

property x_min: float

property y_max: float

property y_min: float

class coscon.toast_helper.GenericMatrix(names: np.ndarray['S'], data: np.ndarray[np.float64], name: str | None = None)

Bases: object

Generic matrix that has row names in ASCII.

data: np.ndarray[np.float64]

dump(path: Path, compress_level: int = 9)

classmethod load(path: Path, name: str | None)

name: str | None = None

names: np.ndarray['S']

property names_str: List[str]

names in list of str

property shape: Tuple[int, ...]

property size: int

class coscon.toast_helper.NaiveTod(names: np.ndarray['S'], data: np.ndarray[np.float64], name: str | None = None)

Bases: GenericMatrix

Naive TOD matrix that has all data in contiguous array

apply_crosstalk(crosstalk_matrix: CrosstalkMatrix) → NaiveTod

property dataframe: DataFrame

class coscon.toast_helper.SQUID(R_TES: float | np.ndarray[np.float64], r_s: float | np.ndarray[np.float64], L: float | np.ndarray[np.float64], L_com: float, C: np.ndarray[np.float64] | None = None, omega: np.ndarray[np.float64] | None = None)

Bases: object

A model of the SQUID readout.

See fig. 4 in Montgomery2020.

Montgomery2020: Montgomery et al., “Performance and Characterization of the SPT-3G Digital Frequency Multiplexed Readout System Using an Improved Noise and Crosstalk Model.”

C: np.ndarray[np.float64] | None = None

L: float | np.ndarray[np.float64]

L_com: float

R_TES: float | np.ndarray[np.float64]

dataframe(include_omega: bool = False, include_freq: bool = False) → DataFrame

property freq

omega: np.ndarray[np.float64] | None = None

r_s: float | np.ndarray[np.float64]

to_crosstalk_matrix(names: List[str] | np.ndarray | None = None) → CrosstalkMatrix

Compute the crosstalk matrix using Montgomery2020 approximations

to_crosstalk_matrix_exact(names: List[str] | np.ndarray | None = None) → CrosstalkMatrix

Compute the crosstalk matrix using Montgomery2020 formulism without the approximations

to_dict(include_omega: bool = False, include_freq: bool = False) → dict

coscon.toast_helper.fake_focalplane(samplerate: float = 20.0, epsilon: float = 0.0, net: float = 1.0, fmin: float = 0.0, alpha: float = 1.0, fknee: float = 0.05, fwhm: float = 30.0, npix: int = 7, fov: float = 3.0) → Dict[str, float | np.ndarray]

Create a set of fake detectors.

This generates 7 pixels (14 dets) in a hexagon layout at the boresight and with a made up polarization orientation.

This function is copied from TOAST workshop with the following license:

BSD 2-Clause License

Copyright (c) 2019, hpc4cmb All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

coscon.toast_helper.na_to_none(value)

Convert pandas null value to None