API

simmer.add_dark_exp module

For ShARCS, the darks are often produced by an automated script at the end of a night. This module adds these frames to the log sheet.

simmer.add_dark_exp.add_dark_exp(inst, log, raw_dir, tab=None)

Adds dark exposures to the end of log sheet if not specified.

Inputs:

tab:

(string) tab of Excel sheet to be used.

inst:

(Instrument object) instrument for which data is being reduced.

log:

(string path of the logsheet.

raw_dir:

(string) path of the directory containing the raw data.

simmer.add_dark_exp.find_itimes(inst, raw_dir)

Read headers for all darks and make a list showing exposure times.

Inputs:

inst:

(Instrument object) instrument for which data is being reduced.

raw_dir:

(string) path of the directory containing the raw data.

simmer.check_logsheet module

Capability to check whether logsheet format is conducive to creating a config.

simmer.check_logsheet.check_logsheet(inst, log_name, tab=None, add_dark_times=False)

Checks for common typos/type errors in the logsheet. Should be run if an Excel worksheet is sent.

Inputs:

inst:

(Instrument object) instrument for which data is being reduced.

log_name:

(string) path of the logsheet.

tab:

(string) tab of interest,

add_dark_times:

(bool) if true, runs the script within add_dark_exp to add the data from the automated dark script to the log sheet.

Outputs:

failed:

(int) number of failed logsheet checks.

simmer.create_config module

Creates config file from logsheet. Will move into either utils or example folder.

exception simmer.create_config.LogsheetError

Bases: ValueError

For incorrect values within a logsheet that can break create_config with opaque error messages.

simmer.create_config.create_config(log, config_file, tab=None)

Create config csv file out of tab in logsheet.

Inputs:

log:

(string) path of the logsheet.

config_file:

(string) path of the desired concrete file, including the file name.

tab:

(string) tab of logsheet to be turned into.

simmer.create_config.get_filenums(logdf)

Inputs:

logdf: (pd.DataFrame) logsheet in Pandas DataFrame.

Outputs:

filenums: (list) numbers associated with each file

associated with each object.

simmer.create_config.isolate_columns(logdf)

Isolates the subset of columns in a DataFrame log that are relevant to data reduction.

Inputs:

logdf: (pd.DataFrame) logsheet in Pandas DataFrame.

Outputs:

savedf: (pd.DataFrame) Pandas DataFrame to be used in reduction.

simmer.create_config.read_logsheet(log, tab=None)

Reads in a user-defined logsheet, either in CSV or XLSX form.

Inputs:

log: (string) path to logsheet. tab: (string) sheet of a multiple-page logsheet

Outputs:

logdf: (pd.DataFrame) logsheet in Pandas DataFrame.

simmer.darks module

Functions to work with darks.

simmer.darks.create_darks(raw_dir, reddir, darklist, inst)

creates the actual darks from a list of dark file numbers, taking the median and writing to a file. Returns the final dark.

Inputs:

reddir:

(string) directory for the reduced data

darklist:

string) list of dark file numbers

inst:

(Instrument object) instrument for which data is being reduced.

simmer.darks.dark_driver(raw_dir, reddir, config, inst, plotting_yml=None)

Night should be entered in format ‘yyyy_mm_dd’ as string. This will point toward a config file for the night with darks listed.flat

Inputs:

raw_dir:

(string) directory for the raw data

reddir:

(string) directory for the reduced data

config:

(pandas DataFrame) dataframe corresponding to config sheet for data.

inst:

(Instrument object) instrument for which data is being reduced.

plotting_yml:

(string) path to the plotting configuration file.

simmer.drivers module

Module for driving reduction processes. Contains highest-level API.

simmer.drivers.all_driver(inst, config_file, raw_dir, reddir, sep_skies=False, plotting_yml=None, searchsize=10, just_images=False, selected_stars=None, verbose=True)

Runs all drivers, performing an end-to-end reduction.

Inputs:

inst:

(Instrument object) instrument for which data is being reduced.

config_file:

(string) path of the config file containing plotting specifications. Optional.

raw_dir:

(string) path of the directory containing the raw data.

reddir:

(string) path of the directory to contain the reduced data.

sep_skies:

(Boolean) if true, skies for observations of star STAR are recorded with Object = “STAR sky”. If false, observations were taken using a dither pattern and can be used as the skies.

plotting_yml:

(string) path to the plotting configuration file.

selected_stars:

(array of strings; OPTIONAL) list of stars to reduce

simmer.drivers.image_driver(inst, config_file, raw_dir, reddir)

Runs all_drivers, terminating after running image_drivers.

Inputs:

inst:

(Instrument object) instrument for which data is being reduced.

config_file:

(string) path of the config file.

raw_dir:

(string) path of the directory containing the raw data.

reddir:

(string) path of the directory to contain the raw data.

simmer.flats module

Functions to work with flats.

exception simmer.flats.DarkOpeningError

Bases: FileNotFoundError

simmer.flats.create_flats(raw_dir, reddir, flatlist, darkfile, inst, filter_name=None, test=False)

Create a flat from a single list of flat files.

Inputs:

raw_dir:

(str) directory where the raw data is stored.

reddir:

(str) directory where the reduced data is stored.

flatlist:

(list) list of integers corresponding to flats.

inst:

(inst object) instrument for which data is being reduced.

filter_name:

(str) filter name given if head[‘FILT1NAM’] == ‘Unknown’

plotting_yml:

(string) path to the plotting configuration file.

test:

(bool) Boolean flag used for testing purposes.

Outputs:

final_flat:

(array) median-filtered flat.

simmer.flats.flat_driver(raw_dir, reddir, config, inst, plotting_yml=None)

Sets up and runs create_flats.

Inputs:

raw_dir:

(string) directory for the raw data

reddir:

(string) directory for the reduced data

config:

(pandas DataFrame) dataframe corresponding to config sheet for data.

inst:

(Instrument object) instrument for which data is being reduced.

plotting_yml:

(string) path to the plotting configuration file.

simmer.flats.open_darks(darkfile)

Opens dark files. Essentially a wrapper around pyfits.getdata that also includes a descriptive exception if the file doesn’t exist.

Inputs:

darkfile:

(str) path to dark to be opened.

Outputs:

dark:

(array) data from darks FITS file.

simmer.image module

Module to make the image stacking / production occur.

exception simmer.image.FlatOpeningError

Bases: ValueError

simmer.image.create_im(s_dir, ssize1, plotting_yml=None, fdirs=None, method='quick_look', verbose=False)

Take the shifted, cut down images from before, then perform registration and combine. Tests should happen before this, as this is a per-star basis.

Inputs:

s_dir:

(str) directory for the raw data

ssize1:

(int) initial pixel search size of box.

plotting_yml:

(str) path to the plotting configuration file.

fdirs:

(list of str) file directories.

method:

(str) image registration method.

simmer.image.create_imstack(raw_dir, reddir, s_dir, imlist, inst, plotting_yml=None, filter_name=None)

Create the stack of images by performing flat division, sky subtraction.

Inputs:

raw_dir:

(string) path to directory containing raw data

reddir:

(string) path to directory containing reduced data

s_dir:

(string) path to directory corresponding to a specific star.

imlist:

(list) list of strings of paths pointing to image files.

inst:

(Instrument object) instrument for which data is being reduced.

plot:

(bool) determines whether or not intermediate plots should be produced.

filter_name:

(string) name of the filter used for the images in question.

Outputs:

im_array:

(3d array) array of 2d images.

shifts_all:

recording of all the x-y shifts made

simmer.image.image_driver(raw_dir, reddir, config, inst, sep_skies=False, plotting_yml=None, selected_stars=None, verbose=False)

Do flat division, sky subtraction, and initial alignment via coords in header. Returns Python list of each registration method used per star.

Inputs:

raw_dir:

(string) directory for the raw data

reddir:

(string) directory for the reduced data

config:

(pandas DataFrame) dataframe corresponding to config sheet for data.

inst:

(Instrument object) instrument for which data is being reduced.

plotting_yml:

(string) path to the plotting configuration file.

selected_stars:

(array of strings; OPTIONAL) list of stars to reduce

simmer.image.open_flats(flatfile)

Opens flats files. Essentially a wrapper around pyfits.getdata that also includes a descriptive exception if the file doesn’t exist.

Inputs:

flatfile:

(str) path to dark to be opened.

Outputs:

dark:

(array) data from darks FITS file.

simmer.insts module

Module for instrument class and subclasses.

class simmer.insts.Instrument(take_skies=False)

Bases: object

Instantiates an object that implements instrument-specific reduction techniques.

bad_pix(image)

Read in bad pixel file, cut down to size, and replace NaN pixels with median of surrounding pixels.

Inputs:

image:

(2D numpy array) image to be filtered for bad pixels.

Outputs:

c_im:

(2D numpy array) image, now filtered for bad pixels. Same dimensions as input image.

name = None

npix = 10

class simmer.insts.PHARO(take_skies=False)

Bases: Instrument

For use on the PHARO instrument at Palomar.

adjust_array(array, nims)

adjust_im(image)

adjust_thisimage(thisimage)

center = nan

file_prefix = 'sph'

filt(nims, head, filter_name)

Given the header of a FITS file, returns its filter.

Inputs:

nims:

(int) number of images.

head:

(astropy.io.fits header object) head of object of interest.

filter_name:

(str) name of filter to use in the event that the filter is unknown in the header.

Outputs:

filt:

(str) name of filter used to observe object of interest.

filter_headtolog = {'Br-gamma': 'BrG', 'J': 'J', 'K-short': 'Ks'}

filter_logtohead = {'BrG': 'Br-gamma', 'BrG+H2': 'Br-gamma', 'J': 'J', 'Ks': 'K_short'}

head(file)

Returns the head of a FITS file.

Inputs:

file:

(str) path to FITS file of interest.

itime(head)

Given a FITS header, returns the true integration time for a file.

Inputs:

head:

(astropy.io.fits header object) head of object of interest.

Outputs:

itime_val:

(float) integration time for object of interest.

name = 'PHARO'

npix = nan

off = (-462, -462)

plate_scale = 0.025

plim = (462, 562)

read_data(raw_dir, new_dir)

Reads data.

Inputs:

rawdir:

(string) absolute path to directory containing raw data. File path should end with ‘/’.

newdir:

(string) absolute path to directory that will contain 4-quadrant data. File path should end with ‘/’.

Outputs:

None

replace_filters = {'BrG-2.16': ['Ks', 'K'], 'H2-2.2122': ['Ks', 'K'], 'J+CH4-1.2': 'J', 'K+CH4-2.4': ['Ks', 'K']}

class simmer.insts.ShARCS(take_skies=False)

Bases: Instrument

For use on the ShARCS camera on the Shane 3m telescope at Lick observatory.

adjust_array(array, nims)

adjust_im(image)

adjust_thisimage(thisimage, rawfile)

bad_pix(image)

Read in bad pixel file, cut down to size, replace bad pixels with median of surrounding pixels.

Inputs:

image:

(2D numpy array) image to be filtered for bad pixels.

Outputs:

iamge:

(2D numpy array) image, now filtered for bad pixels. Same dimensions as input image.

center = (750, 1100)

file_prefix = 's'

filt(nims, head, filter_name)

Given the header of a FITS file, returns its filter.

Inputs:

nims:

(int) number of images.

head:

(astropy.io.fits header object) head of object of interest.

filter_name:

(str) name of filter to use in the event that the filter is unknown in the header.

Outputs:

filt:

(str) name of filter used to observe object of interest.

filter_headtolog = {'BrG-2.16': 'BrG', 'J': 'J+CH4-1.2', 'Ks': 'K'}

filter_logtohead = {'BrG': 'BrG-2.16', 'J+CH4-1.2': 'J', 'K': 'Ks', 'Ks': 'Ks'}

head(file)

Given a FITS file, returns its head.

Inputs:

file:

(str) path to file.

itime(head)

Given a FITS header, returns the true integration time for a file.

Inputs:

head:

(astropy.io.fits header object) head of object of interest.

Outputs:

itime_val:

(float) integration time for object of interest.

name = 'ShARCS'

npix = 1000

off = (-250, -250)

plate_scale = 0.033

plim_inds = (250, 350)

read_data(night, rawfilename, newfilename)

replace_filters = {'BrG-2.16': ['Ks', 'K'], 'H2-2.2122': ['Ks', 'K'], 'J+CH4-1.2': 'J', 'K+CH4-2.4': ['Ks', 'K']}

simmer.registration module

Module containing all functions related to image center-finding and stacking.

simmer.registration.calc_shifts(dat, x_initial, y_initial, xrad, yrad, find='max', method='radon')

Do the radon search and then translate back to image coordinates.

simmer.registration.find_wide_binary(image)

Performs the first step of image registration for a science image that contains a wide binary. User input selects which target is the primary star of interest in the first frame of the target.

inputs:

image:

(2-d array) photon counts at each pixel of each science image.

outputs:

rough_center:

(2-element tuple) rough center of image, as determined by the user.

simmer.registration.fit_psf(im, source_find='photutils')

Performs a basic, flexible PSF fit.

simmer.registration.gaus2d3(x=0, y=0, mx=0, my=0, sx=1, sy=1, theta=0)

simmer.registration.log_likelihood(theta, X, Y, im1, noise)

simmer.registration.log_prior(theta)

simmer.registration.log_probability(theta, x, y, im1, noise)

simmer.registration.register_bruteforce(image, rough_center=None)

Performs the default image registration scheme. Shifts the center of the image to the peak.

inputs:

image:

(2-d array) photon counts at each pixel of each science image.

rough_center:

(2-d array, default None) location of primary star. This argument is only passed in the wide binary case.

outputs:

image_centered : (2-d array) image cenered by the rotations method.

simmer.registration.register_psf_fit(frames)

Constructs an empirical PSF for a single target, fitting for all the positions.

Inputs

frames:

(np.ndarray, n_image x n_x x n_y) input array containing all the images at once.

Outputs

frames_centered:

(np.ndarray, n_image x n_x x n_y) same as frames, but centered on the target.

simmer.registration.register_saturated(image, searchsize1, newshifts1, rough_center=None)

Performs image registration when a saturated star is present in the science image.

inputs:

image:

(2-d array) photon counts at each pixel of each science image.

searchsize1:

(int) initial size of search for center of image.

newshifts1:

(list) keeps tracks of x-y shifts.

rough_center:

(2-d array, default None) location of primary star. This argument is only passed in the wide binary case.

outputs:

image_centered:

(2-d array) image centered by the rotations method.

rot:

the rotation

newshifts1:

(list) keeps tracks of x-y shifts.

simmer.registration.roll2d(dat, xshift, yshift)

Essentially performs numpy roll function in 2 dimensions.

inputs:

dat:

(2d array) image data.

xshift:

(int) shift in the x direction.

yshift:

(int) shift in the y direction.

simmer.registration.roll_shift(image, shifts, cval=0.0)

Rolls and shifts image.

inputs:

image:

(2-d array) photon counts at each pixel of each science image.

shifts:

(1-d array of tuples) Enter shifts as (drow, dcol).

simmer.registration.rot_search(dat, x_initial, y_initial, xrad, yrad)

Perform rotational search of an image.

Inputs:

dat:

(2d array) image data.

x_initial:

(int) the initial guess for the x-coordinate of the center.

y_initial:

(int) the initial guess for the y-coordinate of the center.

xrad:

(int) radius in x to search.

yrad:

(int) radius in y to search.

outputs:

(xshift, yshift):

(tuple) record of how much the image was shifted in x and y

out:

(2d array) output shifted image

simmer.registration.rotate_sub(dat)

Rotate the image about the center point, then subtract from original and record residuals. Does this at set angles.

inputs:

dat:

(2d array) image data.

outputs:

total_residuals:

(float) the summed total residuals.

simmer.registration.run_rot(image, searchsize, center, newsize)

Runs all rotations.

simmer.registration.run_starfinder(im, **kwargs)

Outputs

central:

(tuple) x, y coordinates of the central source

simmer.registration.shift_bruteforce(image, base_position=None, max_shift=350, verbose=False)

This will shift the maximum pixel to base_position (i.e. the center of image). Make sure base_position is entered as (int,int).

max_shift: set pixels farther than max_shift from base_position to 0

to avoid selecting brightened edges as the target star.

simmer.registration.zoom_image(image, rough_center)

Zooms in around a smaller portion of the image for further registration.

inputs:

image:

(2-d array) photon counts at each pixel of each science image.

rough_center:

(2-d array, default None) location of primary star. This argument is only passed in the wide binary case.

outputs:

zoomed_image : (2-d array) image zoomed around the provided center.

simmer.search_headers module

Search for issues in FITS file headers similar to s0512.fits from night 2015_10_26, which did not contain either/both DATAFILE or/and FRAMENUM in the header. These issues are only known to occur with ShARCS data.

simmer.search_headers.search_headers(raw_dir, write_dir=None)

Function to perform search of FITS headers.

Inputs:

raw_dir:

(string) absolute path to directory containing raw data.

write_dir:

(string) absolute path to directory containing raw data. Defaults to None; if this is the case, it’s reassigned to the raw_dir directory.

simmer.sky module

Functions to work with skies.

simmer.sky.create_skies(raw_dir, reddir, s_dir, skylist, inst, plotting_yml=None, filter_name=None)

Create a sky from a single list of skies. sf_dir is the reduced directory for the specific star and filter.

Inputs:

raw_dir:

(string) directory for the raw data

reddir:

(string) directory for the reduced data

s_dir:

(string) directory corresponding to a specific star.

skylist:

(list) list of strings of paths pointing to sky files.

inst:

(Instrument object) instrument for which data is being reduced.

plotting_yml:

(string) path to the plotting configuration file.

Outputs:

final_sky:

(2D array) medianed sky image.

simmer.sky.sky_driver(raw_dir, reddir, config, inst, sep_skies=False, plotting_yml=None)

Night should be entered in format ‘yyyy_mm_dd’ as string. This will point toward a config file for the night with flats listed.

Inputs:

raw_dir:

(string) directory for the raw data

reddir:

(string) directory for the reduced data

config:

(pandas DataFrame) dataframe corresponding to config sheet for data.

inst:

(Instrument object) instrument for which data is being reduced.

sep_skies:

(Boolean) if true, skies for observations of star STAR are recorded with Object = “STAR sky”. If false, observations were taken using a dither pattern and can be used as the skies.

plotting_yml:

(string) path to the plotting configuration file.

simmer.utils module

This module provides utility functions for the reduction pipeline.

simmer.utils.find_angle(loc1, loc2)

Calculated the angle between two locations on a grid.

Inputs:

loc1:

(tuple) first location.

relative:

(tuple) second location.

Outputs:

:angle : (float) real-valued angle between loc1 and loc2.

simmer.utils.header_subsection(input_image_file, npix, center)

Reads out the header of a subsection.

Inputs:

input_image:

(2D array) image of which a subsection is desired.

center:

(tuple) center of image, with format (x, y)

npix:

(float) value for size of return image. If non-square image desired, enter as list.

Outputs:

header:

(FITS header) header of the image file, adjusted accordingly.

simmer.utils.image_subsection(input_image, npix, center)

reads in a full image array, selects the relevant subsection of the array, and returns the new array, transposed for use with Python. input_image must be 2D

Inputs:

input_image:

(2D array) image of which a subsection is desired.

center:

(tuple) center of image, with format (x, y)

npix:

(float) value for size of return image. If non-square image desired, enter as list.

default:

center = (750, 1100)

npix:

= 800 for inscribed region or npix = 1000 for circumscribing region (was 600 for inscribed region; CDD changed to 800)

transposed:

= np.rot90(input_image.T,2)

transposed:

= np.rot90(input_image,2)

center:

= (2047-center[0],2047-center[1]

Outputs:

subsection:

(2d array) subsection of original image.

)

simmer.utils.make_filelist(directory, numlist, inst)

Turn a list of numbers into a list of properly formatted filenames.

Inputs:

directory:

(string) path leading to directory of interest.

numlist:

(list) list of numbers corresponding to fits files.

inst:

(Instrument object) instrument for which data is being reduced.

Outputs:

filelist:

(list) list of strings pertaining to files of interest

simmer.utils.read_imcube(filelist)

Reads a stack of fits files into an image cube of dimensions (nims, xpix, ypix).

Inputs:

filelist:

(list) list of strings pertaining to files of interest.

Outputs:

im_array:

(3D array) array of 2D arrays pertaining to the files in filelist.

simmer.schemas.custom_validator module

Includes all custom (i.e., not provided by Cerberus) validators used by SImMER.

class simmer.schemas.custom_validator.SimmerValidator(*args, **kwargs)

Bases: Validator

Validator class. Normalizes and/or validates any mapping against a validation-schema which is provided as an argument at class instantiation or upon calling the validate(), validated() or normalized() method. An instance itself is callable and executes a validation.

All instantiation parameters are optional.

There are the introspective properties types, validators, coercers, default_setters, rules, normalization_rules and validation_rules.

The attributes reflecting the available rules are assembled considering constraints that are defined in the docstrings of rules’ methods and is effectively used as validation schema for schema.

Parameters:

• schema (any mapping) – See schema. Defaults to None.

• ignore_none_values (bool) – See ignore_none_values. Defaults to False.

• allow_unknown (bool or any mapping) – See allow_unknown. Defaults to False.

• require_all (bool) – See require_all. Defaults to False.

• purge_unknown (bool) – See purge_unknown. Defaults to to False.

• purge_readonly (bool) – Removes all fields that are defined as readonly in the normalization phase.

• error_handler (class or instance based on BaseErrorHandler or tuple) – The error handler that formats the result of errors. When given as two-value tuple with an error-handler class and a dictionary, the latter is passed to the initialization of the error handler. Default: BasicErrorHandler.

checkers = ()

coercers = ()

default_setters = ()

normalization_rules = {'coerce': {'oneof': [{'type': 'callable'}, {'schema': {'oneof': [{'type': 'callable'}, {'allowed': (), 'type': 'string'}]}, 'type': 'list'}, {'allowed': (), 'type': 'string'}]}, 'default': {'nullable': True}, 'default_setter': {'oneof': [{'type': 'callable'}, {'allowed': (), 'type': 'string'}]}, 'purge_unknown': {'type': 'boolean'}, 'rename': {'type': 'hashable'}, 'rename_handler': {'oneof': [{'type': 'callable'}, {'schema': {'oneof': [{'type': 'callable'}, {'allowed': (), 'type': 'string'}]}, 'type': 'list'}, {'allowed': (), 'type': 'string'}]}}

rules = {'allof': {'logical': 'allof', 'type': 'list'}, 'allow_unknown': {'oneof': [{'type': 'boolean'}, {'check_with': 'bulk_schema', 'type': ['dict', 'string']}]}, 'allowed': {'type': 'container'}, 'anyof': {'logical': 'anyof', 'type': 'list'}, 'check_with': {'oneof': [{'type': 'callable'}, {'schema': {'oneof': [{'type': 'callable'}, {'allowed': (), 'type': 'string'}]}, 'type': 'list'}, {'allowed': (), 'type': 'string'}]}, 'coerce': {'oneof': [{'type': 'callable'}, {'schema': {'oneof': [{'type': 'callable'}, {'allowed': (), 'type': 'string'}]}, 'type': 'list'}, {'allowed': (), 'type': 'string'}]}, 'contains': {'empty': False}, 'default': {'nullable': True}, 'default_setter': {'oneof': [{'type': 'callable'}, {'allowed': (), 'type': 'string'}]}, 'dependencies': {'check_with': 'dependencies', 'type': ('dict', 'hashable', 'list')}, 'empty': {'type': 'boolean'}, 'excludes': {'schema': {'type': 'hashable'}, 'type': ('hashable', 'list')}, 'forbidden': {'type': 'list'}, 'items': {'check_with': 'items', 'type': 'list'}, 'keysrules': {'check_with': 'bulk_schema', 'forbidden': ['rename', 'rename_handler'], 'type': ['dict', 'string']}, 'max': {'nullable': False}, 'maxlength': {'type': 'integer'}, 'meta': {}, 'min': {'nullable': False}, 'minlength': {'type': 'integer'}, 'noneof': {'logical': 'noneof', 'type': 'list'}, 'nullable': {'type': 'boolean'}, 'oneof': {'logical': 'oneof', 'type': 'list'}, 'purge_unknown': {'type': 'boolean'}, 'readonly': {'type': 'boolean'}, 'regex': {'type': 'string'}, 'rename': {'type': 'hashable'}, 'rename_handler': {'oneof': [{'type': 'callable'}, {'schema': {'oneof': [{'type': 'callable'}, {'allowed': (), 'type': 'string'}]}, 'type': 'list'}, {'allowed': (), 'type': 'string'}]}, 'require_all': {'type': 'boolean'}, 'required': {'type': 'boolean'}, 'schema': {'anyof': [{'check_with': 'schema'}, {'check_with': 'bulk_schema'}], 'type': ['dict', 'string']}, 'type': {'check_with': 'type', 'type': ['string', 'list']}, 'valuesrules': {'check_with': 'bulk_schema', 'forbidden': ['rename', 'rename_handler'], 'type': ['dict', 'string']}}

validation_rules = {'allof': {'logical': 'allof', 'type': 'list'}, 'allow_unknown': {'oneof': [{'type': 'boolean'}, {'check_with': 'bulk_schema', 'type': ['dict', 'string']}]}, 'allowed': {'type': 'container'}, 'anyof': {'logical': 'anyof', 'type': 'list'}, 'check_with': {'oneof': [{'type': 'callable'}, {'schema': {'oneof': [{'type': 'callable'}, {'allowed': (), 'type': 'string'}]}, 'type': 'list'}, {'allowed': (), 'type': 'string'}]}, 'contains': {'empty': False}, 'dependencies': {'check_with': 'dependencies', 'type': ('dict', 'hashable', 'list')}, 'empty': {'type': 'boolean'}, 'excludes': {'schema': {'type': 'hashable'}, 'type': ('hashable', 'list')}, 'forbidden': {'type': 'list'}, 'items': {'check_with': 'items', 'type': 'list'}, 'keysrules': {'check_with': 'bulk_schema', 'forbidden': ['rename', 'rename_handler'], 'type': ['dict', 'string']}, 'max': {'nullable': False}, 'maxlength': {'type': 'integer'}, 'meta': {}, 'min': {'nullable': False}, 'minlength': {'type': 'integer'}, 'noneof': {'logical': 'noneof', 'type': 'list'}, 'nullable': {'type': 'boolean'}, 'oneof': {'logical': 'oneof', 'type': 'list'}, 'readonly': {'type': 'boolean'}, 'regex': {'type': 'string'}, 'require_all': {'type': 'boolean'}, 'required': {'type': 'boolean'}, 'schema': {'anyof': [{'check_with': 'schema'}, {'check_with': 'bulk_schema'}], 'type': ['dict', 'string']}, 'type': {'check_with': 'type', 'type': ['string', 'list']}, 'valuesrules': {'check_with': 'bulk_schema', 'forbidden': ['rename', 'rename_handler'], 'type': ['dict', 'string']}}

simmer.schemas.read_yml module

simmer.schemas.read_yml.get_plotting_args(yml_filename=None)

Gets plotting args.

Inputs:

yml_filename:

(string) path of the plotting yml to be used. Defaults to None.

Outputs:

plotting_arg:

(dictionary) all arguments that are related to plotting. See the plotting_schema.yml schema for documentation of keys and values.

simmer.schemas.read_yml.normalize(yml_dict, validator, schema, plot_types)

Inputs:

yml_dict:

(dictionary) the dictionary to be normalized against a schema

validator:

(SimmerValidator) the validator object used.

schema:

the schema against which the yml_dict is normalized.

plot_types:

(list of strings) the basic plot_types that must be in the uppermost keys.

Outputs:

normalized:

normalized dictionary.

simmer.schemas.read_yml.read_yml(yml_filename)

Reads in a yaml file.

Inputs:

yml_filename:

(string) path to the yml.

Outputs:

parsed_yml_file:

(dictionary) key-value pairs as read from the yaml file.

simmer.schemas.read_yml.validate_yml(schema_filename, yml_filename)

Ensures that a given yml file is in accordance with the provided schema. In essence, this ensures that no odd keys or fields are provided to the yml.

Inputs:

schema_filename:

(string) path to schema yaml.

yml_filename:

(string) path to yml yaml.

Outputs:

validated:

(bool) whether or not the yaml was successfully validated.