Source code for psychopy.monitors.calibTools

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""Tools to help with calibrations
"""

# Part of the PsychoPy library
# Copyright (C) 2002-2018 Jonathan Peirce (C) 2019-2021 Open Science Tools Ltd.
# Distributed under the terms of the GNU General Public License (GPL).

from __future__ import absolute_import, division, print_function

# from future import standard_library
# standard_library.install_aliases()
from builtins import str
from builtins import range
from past.builtins import basestring
from past.utils import old_div
from builtins import object
from .calibData import wavelength_5nm, juddVosXYZ1976_5nm, cones_SmithPokorny
from psychopy import __version__, logging, hardware, constants

try:
    import serial
    haveSerial = True
except Exception:
    haveSerial = False
import errno
import os
import time
import glob
import pickle
import sys
from copy import deepcopy, copy

import numpy as np
from scipy import interpolate
import json_tricks  # allows json to dump/load np.arrays and dates

from psychopy import constants

DEBUG = False

# set and create (if necess) the data folder
# this will be the
#   Linux/Mac:  ~/.psychopy2/monitors
#   win32:   <UserDocs>/Application Data/PsychoPy/monitors
join = os.path.join
if sys.platform == 'win32':
    # we used this for a while (until 0.95.4) but not the proper place for
    # windows app data
    oldMonitorFolder = join(os.path.expanduser('~'), '.psychopy3', 'monitors')
    monitorFolder = join(os.environ['APPDATA'], 'psychopy3', 'monitors')
    if os.path.isdir(oldMonitorFolder) and not os.path.isdir(monitorFolder):
        os.renames(oldMonitorFolder, monitorFolder)
else:
    monitorFolder = join(os.environ['HOME'], '.psychopy3', 'monitors')

# HACK for Python2.7! On system where `monitorFolder` contains special characters,
# for example because the Windows profile name does, `monitorFolder` must be
# decoded to Unicode to prevent errors later on. However, this is not a proper
# fix, since *everything* should be decoded to Unicode, and not just this
# specific pathname. Right now, errors will still occur if `monitorFolder` is
# combined with `str`-type objects that contain non-ASCII characters.
if isinstance(monitorFolder, bytes):
    monitorFolder = monitorFolder.decode(sys.getfilesystemencoding())

try:
    os.makedirs(monitorFolder)
except OSError as err:
    if err.errno != errno.EEXIST:
        raise


[docs]class Monitor(object): """Creates a monitor object for storing calibration details. This will be loaded automatically from disk if the monitor name is already defined (see methods). Many settings from the stored monitor can easilly be overridden either by adding them as arguments during the initial call. **arguments**: - ``width, distance, gamma`` are details about the calibration - ``notes`` is a text field to store any useful info - ``useBits`` True, False, None - ``verbose`` True, False, None - ``currentCalib`` is a dictionary object containing various fields for a calibration. Use with caution since the dictionary may not contain all the necessary fields that a monitor object expects to find. **eg**: ``myMon = Monitor('sony500', distance=114)`` Fetches the info on the sony500 and overrides its usual distance to be 114cm for this experiment. These can be saved to the monitor file using :func:`~psychopy.monitors.Monitor.save` or not (in which case the changes will be lost) """ def __init__(self, name, width=None, distance=None, gamma=None, notes=None, useBits=None, verbose=True, currentCalib=None, autoLog=True): """ """ # make sure that all necessary settings have some value super(Monitor, self).__init__() self.__type__ = 'psychoMonitor' self.name = name self.autoLog = autoLog self.currentCalib = currentCalib or {} self.currentCalibName = strFromDate(time.mktime(time.localtime())) self.calibs = {} self.calibNames = [] self._gammaInterpolator = None self._gammaInterpolator2 = None self._loadAll() if len(self.calibNames) > 0: self.setCurrent(-1) # will fetch previous vals if monitor exists if self.autoLog: logging.info('Loaded monitor calibration from %s' % self.calibNames) else: self.newCalib() logging.warning("Monitor specification not found. " "Creating a temporary one...") # overide current monitor settings with the vals given if width: self.setWidth(width) if distance: self.setDistance(distance) if gamma: self.setGamma(gamma) if notes: self.setNotes(notes) if useBits != None: self.setUseBits(useBits)
[docs] def gammaIsDefault(self): """Determine whether we're using the default gamma values """ thisGamma = self.getGamma() # run the test just on this array = np.array return (thisGamma is None or np.alltrue(array(thisGamma) == array([1, 1, 1])))
# functions to set params of current calibration
[docs] def setSizePix(self, pixels): """Set the size of the screen in pixels x,y """ self.currentCalib['sizePix'] = pixels
[docs] def setWidth(self, width): """Of the viewable screen (cm) """ self.currentCalib['width'] = width
[docs] def setDistance(self, distance): """To the screen (cm) """ self.currentCalib['distance'] = distance
[docs] def setCalibDate(self, date=None): """Sets the current calibration to have a date/time or to the current date/time if none given. (Also returns the date as set) """ if date is None: date = time.mktime(time.localtime()) self.currentCalib['calibDate'] = date return date
[docs] def setGamma(self, gamma): """Sets the gamma value(s) for the monitor. This only uses a single gamma value for the three guns, which is fairly approximate. Better to use setGammaGrid (which uses one gamma value for each gun) """ self.currentCalib['gamma'] = gamma
[docs] def setGammaGrid(self, gammaGrid): """Sets the min,max,gamma values for the each gun """ self.currentCalib['gammaGrid'] = gammaGrid
[docs] def setLineariseMethod(self, method): """Sets the method for linearising 0 uses y=a+(bx)^gamma 1 uses y=(a+bx)^gamma 2 uses linear interpolation over the curve """ self.currentCalib['linearizeMethod'] = method
[docs] def setMeanLum(self, meanLum): """Records the mean luminance (for reference only) """ self.currentCalib['meanLum'] = meanLum
[docs] def setLumsPre(self, lums): """Sets the last set of luminance values measured during calibration """ self.currentCalib['lumsPre'] = lums
[docs] def setLumsPost(self, lums): """Sets the last set of luminance values measured AFTER calibration """ self.currentCalib['lumsPost'] = lums
[docs] def setLevelsPre(self, levels): """Sets the last set of luminance values measured during calibration """ self.currentCalib['levelsPre'] = levels
[docs] def setLevelsPost(self, levels): """Sets the last set of luminance values measured AFTER calibration """ self.currentCalib['levelsPost'] = levels
[docs] def setDKL_RGB(self, dkl_rgb): """Sets the DKL->RGB conversion matrix for a chromatically calibrated monitor (matrix is a 3x3 num array). """ self.currentCalib['dkl_rgb'] = dkl_rgb
[docs] def setSpectra(self, nm, rgb): """Sets the phosphor spectra measured by the spectrometer """ self.currentCalib['spectraNM'] = nm self.currentCalib['spectraRGB'] = rgb
[docs] def setLMS_RGB(self, lms_rgb): """Sets the LMS->RGB conversion matrix for a chromatically calibrated monitor (matrix is a 3x3 num array). """ self.currentCalib['lms_rgb'] = lms_rgb self.setPsychopyVersion(__version__)
[docs] def setPsychopyVersion(self, version): """To store the version of PsychoPy that this calibration used """ self.currentCalib['psychopyVersion'] = version
[docs] def setNotes(self, notes): """For you to store notes about the calibration """ self.currentCalib['notes'] = notes
[docs] def setUseBits(self, usebits): """DEPRECATED: Use the new hardware classes to control these devices """ self.currentCalib['usebits'] = usebits
# equivalent get functions
[docs] def getSizePix(self): """Returns the size of the current calibration in pixels, or None if not defined """ size = None if 'sizePix' in self.currentCalib: size = self.currentCalib['sizePix'] # check various invalid sizes if not hasattr(size, '__iter__') or len(size)!=2: return None # make sure it's a list (not tuple) with no None vals sizeOut = [(val or 0) for val in size] return sizeOut
[docs] def getWidth(self): """Of the viewable screen in cm, or None if not known """ return self.currentCalib['width']
[docs] def getDistance(self): """Returns distance from viewer to the screen in cm, or None if not known """ return self.currentCalib['distance']
[docs] def getCalibDate(self): """As a python date object (convert to string using calibTools.strFromDate""" return self.currentCalib['calibDate']
[docs] def getGamma(self): """Returns just the gamma value (not the whole grid) """ gridInCurrent = 'gammaGrid' in self.currentCalib if (gridInCurrent and not np.alltrue(self.getGammaGrid()[1:, 2] == 1)): return self.getGammaGrid()[1:, 2] elif 'gamma' in self.currentCalib: return self.currentCalib['gamma'] else: return None
[docs] def getGammaGrid(self): """Gets the min,max,gamma values for the each gun """ if 'gammaGrid' in self.currentCalib: # Make sure it's an array, so you can look at the shape grid = np.asarray(self.currentCalib['gammaGrid']) if grid.shape != [4, 6]: newGrid = np.zeros([4, 6], 'f') * np.nan # start as NaN newGrid[:grid.shape[0], :grid.shape[1]] = grid grid = self.currentCalib['gammaGrid'] = newGrid return grid else: return None
[docs] def getLinearizeMethod(self): """Gets the method that this monitor is using to linearize the guns """ if 'linearizeMethod' in self.currentCalib: return self.currentCalib['linearizeMethod'] elif 'lineariseMethod' in self.currentCalib: return self.currentCalib['lineariseMethod'] else: return None
[docs] def getMeanLum(self): """Returns the mean luminance of the screen if explicitly stored """ if 'meanLum' in self.currentCalib: return self.currentCalib['meanLum'] else: return None
[docs] def getLumsPre(self): """Gets the measured luminance values from last calibration""" if 'lumsPre' in self.currentCalib: return self.currentCalib['lumsPre'] else: return None
[docs] def getLumsPost(self): """Gets the measured luminance values from last calibration TEST""" if 'lumsPost' in self.currentCalib: return self.currentCalib['lumsPost'] else: return None
[docs] def getLevelsPre(self): """Gets the measured luminance values from last calibration""" if 'levelsPre' in self.currentCalib: return self.currentCalib['levelsPre'] else: return None
[docs] def getLevelsPost(self): """Gets the measured luminance values from last calibration TEST""" if 'levelsPost' in self.currentCalib: return self.currentCalib['levelsPost'] else: return None
[docs] def getSpectra(self): """Gets the wavelength values from the last spectrometer measurement (if available) usage: - nm, power = monitor.getSpectra() """ if 'spectraNM' in self.currentCalib: return (self.currentCalib['spectraNM'], self.currentCalib['spectraRGB']) else: return None, None
[docs] def getDKL_RGB(self, RECOMPUTE=False): """Returns the DKL->RGB conversion matrix. If one has been saved this will be returned. Otherwise, if power spectra are available for the monitor a matrix will be calculated. """ if not 'dkl_rgb' in self.currentCalib: RECOMPUTE = True if RECOMPUTE: nm, power = self.getSpectra() if nm is None: return None else: return makeDKL2RGB(nm, power) else: return self.currentCalib['dkl_rgb']
[docs] def getLMS_RGB(self, recompute=False): """Returns the LMS->RGB conversion matrix. If one has been saved this will be returned. Otherwise (if power spectra are available for the monitor) a matrix will be calculated. """ if not 'lms_rgb' in self.currentCalib: recompute = True if recompute: nm, power = self.getSpectra() if nm is None: return None else: return makeLMS2RGB(nm, power) else: return self.currentCalib['lms_rgb']
[docs] def getPsychopyVersion(self): """Returns the version of PsychoPy that was used to create this calibration """ return self.currentCalib['psychopyVersion']
[docs] def getNotes(self): """Notes about the calibration """ return self.currentCalib['notes']
[docs] def getUseBits(self): """Was this calibration carried out witha a bits++ box """ return self.currentCalib['usebits']
# other (admin functions)
[docs] def _loadAll(self): """Fetches the calibrations for this monitor from disk, storing them as self.calibs """ if constants.PY3: ext = ".json" else: ext = ".calib" # the name of the actual file: thisFileName = os.path.join(monitorFolder, self.name + ext) if not os.path.exists(thisFileName): self.calibNames = [] else: if ext == ".json": with open(thisFileName, 'r') as thisFile: if constants.PY3: # Passing encoding parameter to json.loads has been # deprecated and removed in Python 3.9 self.calibs = json_tricks.load( thisFile, ignore_comments=False, preserve_order=False) else: self.calibs = json_tricks.load( thisFile, ignore_comments=False, encoding='utf-8', preserve_order=False) else: with open(thisFileName, 'rb') as thisFile: self.calibs = pickle.load(thisFile) self.calibNames = sorted(self.calibs) if not constants.PY3: # saving for future (not needed if we are IN future!) # save JSON copies of our calibrations self._saveJSON()
[docs] def newCalib(self, calibName=None, width=None, distance=None, gamma=None, notes=None, useBits=False, verbose=True): """create a new (empty) calibration for this monitor and makes this the current calibration """ dateTime = time.mktime(time.localtime()) if calibName is None: calibName = strFromDate(dateTime) # add to the list of calibrations self.calibNames.append(calibName) self.calibs[calibName] = {} self.setCurrent(calibName) # populate with some default values: self.setCalibDate(dateTime) self.setGamma(gamma) self.setWidth(width) self.setDistance(distance) self.setNotes(notes) self.setPsychopyVersion(__version__) self.setUseBits(useBits) newGrid = np.ones((4, 3), 'd') newGrid[:, 0] *= 0 self.setGammaGrid(newGrid) self.setLineariseMethod(1)
[docs] def setCurrent(self, calibration=-1): """Sets the current calibration for this monitor. Note that a single file can hold multiple calibrations each stored under a different key (the date it was taken) The argument is either a string (naming the calib) or an integer **eg**: ``myMon.setCurrent('mainCalib')`` fetches the calibration named mainCalib. You can name calibrations what you want but PsychoPy will give them names of date/time by default. In Monitor Center you can 'copy...' a calibration and give it a new name to keep a second version. ``calibName = myMon.setCurrent(0)`` fetches the first calibration (alphabetically) for this monitor ``calibName = myMon.setCurrent(-1)`` fetches the last **alphabetical** calibration for this monitor (this is default). If default names are used for calibrations (ie date/time stamp) then this will import the most recent. """ # find the appropriate file # get desired calibration name if necess if (isinstance(calibration, basestring) and calibration in self.calibNames): self.currentCalibName = calibration elif type(calibration) == int and calibration <= len(self.calibNames): self.currentCalibName = self.calibNames[calibration] else: print("No record of that calibration") return False # do the import self.currentCalib = self.calibs[self.currentCalibName] return self.currentCalibName
[docs] def delCalib(self, calibName): """Remove a specific calibration from the current monitor. Won't be finalised unless monitor is saved """ # remove from our list self.calibNames.remove(calibName) self.calibs.pop(calibName) if self.currentCalibName == calibName: self.setCurrent(-1) return 1
[docs] def save(self): """Save the current calibrations to disk. This will write a `json` file to the `monitors` subfolder of your PsychoPy configuration folder (typically `~/.psychopy3/monitors` on Linux and macOS, and `%APPDATA%\\psychopy3\\monitors` on Windows). Additionally saves a pickle (`.calib`) file if you are running Python 2.7. """ if not constants.PY3: # don't ever save pickle files form PY3 thisFileName = os.path.join(monitorFolder, self.name + ".calib") with open(thisFileName, 'wb') as thisFile: pickle.dump(self.calibs, thisFile) # also save as JSON (at the moment) # (When we're sure this works we should ONLY save as JSON) self._saveJSON()
[docs] def saveMon(self): """Equivalent of :func:`~psychopy.monitors.Monitor.save`. """ self.save()
def _saveJSON(self): thisFileName = os.path.join(monitorFolder, self.name + ".json") # convert time structs to timestamps (floats) for calibName in self.calibs: calib = self.calibs[calibName] if isinstance(calib['calibDate'], time.struct_time): calib['calibDate'] = time.mktime(calib['calibDate']) with open(thisFileName, 'w') as outfile: json_tricks.dump(self.calibs, outfile, indent=2, allow_nan=True)
[docs] def copyCalib(self, calibName=None): """Stores the settings for the current calibration settings as new monitor. """ if calibName is None: calibName = strFromDate(time.mktime(time.localtime())) # add to the list of calibrations self.calibNames.append(calibName) self.calibs[calibName] = deepcopy(self.currentCalib) self.setCurrent(calibName)
[docs] def linearizeLums(self, desiredLums, newInterpolators=False, overrideGamma=None): """lums should be uncalibrated luminance values (e.g. a linear ramp) ranging 0:1 """ linMethod = self.getLinearizeMethod() desiredLums = np.asarray(desiredLums) output = desiredLums * 0.0 # needs same size as input # gamma interpolation if linMethod == 3: lumsPre = copy(self.getLumsPre()) if self._gammaInterpolator is not None and not newInterpolators: pass # we already have an interpolator elif lumsPre is not None: if self.autoLog: logging.info('Creating linear interpolation for gamma') # we can make an interpolator self._gammaInterpolator = [] self._gammaInterpolator2 = [] # each of these interpolators is a function! levelsPre = old_div(self.getLevelsPre(), 255.0) for gun in range(4): # scale to 0:1 lumsPre[gun, :] = (old_div((lumsPre[gun, :] - lumsPre[gun, 0]), (lumsPre[gun, -1] - lumsPre[gun, 0]))) self._gammaInterpolator.append( interpolate.interp1d(lumsPre[gun, :], levelsPre, kind='linear')) # interpFunc = Interpolation.InterpolatingFunction( # (lumsPre[gun,:],), levelsPre) # polyFunc = interpFunc.fitPolynomial(3) # self._gammaInterpolator2.append( [polyFunc.coeff]) else: # no way to do this! Calibrate the monitor logging.error("Can't do a gamma interpolation on your " "monitor without calibrating!") return desiredLums # then do the actual interpolations if len(desiredLums.shape) > 1: for gun in range(3): # gun+1 because we don't want luminance interpolator _gammaIntrpGun = self._gammaInterpolator[gun + 1] output[:, gun] = _gammaIntrpGun(desiredLums[:, gun]) else: # just luminance output = self._gammaInterpolator[0](desiredLums) # use a fitted gamma equation (1 or 2) elif linMethod in [1, 2, 4]: # get the min,max lums gammaGrid = self.getGammaGrid() if gammaGrid is not None: # if we have info about min and max luminance then use it minLum = gammaGrid[1, 0] maxLum = gammaGrid[1:4, 1] b = gammaGrid[1:4, 4] if overrideGamma is not None: gamma = overrideGamma else: gamma = gammaGrid[1:4, 2] maxLumWhite = gammaGrid[0, 1] gammaWhite = gammaGrid[0, 2] if self.autoLog: logging.debug('using gamma grid' + str(gammaGrid)) else: # just do the calculation using gamma minLum = 0 maxLumR, maxLumG, maxLumB, maxLumWhite = 1, 1, 1, 1 gamma = self.currentCalib['gamma'] gammaWhite = np.average(gamma) # get the inverse gamma if len(desiredLums.shape) > 1: for gun in range(3): output[:, gun] = gammaInvFun(desiredLums[:, gun], minLum, maxLum[gun], gamma[gun], eq=linMethod, b=b[gun]) else: output = gammaInvFun(desiredLums, minLum, maxLumWhite, gammaWhite, eq=linMethod) else: msg = "Don't know how to linearise with method %i" logging.error(msg % linMethod) output = desiredLums return output
[docs] def lineariseLums(self, desiredLums, newInterpolators=False, overrideGamma=None): """Equivalent of :func:`~psychopy.monitors.Monitor.linearizeLums`. """ return self.linearizeLums(desiredLums=desiredLums, newInterpolators=newInterpolators, overrideGamma=overrideGamma)
[docs]class GammaCalculator(object): """Class for managing gamma tables **Parameters:** - inputs (required)= values at which you measured screen luminance either in range 0.0:1.0, or range 0:255. Should include the min and max of the monitor Then give EITHER "lums" or "gamma": - lums = measured luminance at given input levels - gamma = your own gamma value (single float) - bitsIN = number of values in your lookup table - bitsOUT = number of bits in the DACs myTable.gammaModel myTable.gamma """ def __init__(self, inputs=(), lums=(), gamma=None, bitsIN=8, # how values in the LUT bitsOUT=8, eq=1): # how many values can the DACs output super(GammaCalculator, self).__init__() self.lumsInitial = list(lums) self.inputs = inputs self.bitsIN = bitsIN self.bitsOUT = bitsOUT self.eq = eq # set or or get input levels if len(inputs) == 0 and len(lums) > 0: self.inputs = DACrange(len(lums)) else: self.inputs = list(inputs) # set or get gammaVal # user is specifying their own gamma value if len(lums) == 0 or gamma != None: self.gamma = gamma elif len(lums) > 0: self.min, self.max, self.gammaModel = self.fitGammaFun( self.inputs, self.lumsInitial) if eq == 4: self.gamma, self.a, self.k = self.gammaModel self.b = (lums[0] - self.a)**(old_div(1.0, self.gamma)) else: self.gamma = self.gammaModel[0] self.a = self.b = self.k = None else: raise AttributeError("gammaTable needs EITHER a gamma value" " or some luminance measures")
[docs] def fitGammaFun(self, x, y): """ Fits a gamma function to the monitor calibration data. **Parameters:** -xVals are the monitor look-up-table vals, either 0-255 or 0.0-1.0 -yVals are the measured luminances from a photometer/spectrometer """ import scipy.optimize as optim minGamma = 0.8 maxGamma = 20.0 gammaGuess = 2.0 y = np.asarray(y) minLum = y[0] maxLum = y[-1] if self.eq == 4: aGuess = old_div(minLum, 5.0) kGuess = (maxLum - aGuess)**(old_div(1.0, gammaGuess)) - aGuess guess = [gammaGuess, aGuess, kGuess] bounds = [[0.8, 5.0], [0.00001, minLum - 0.00001], [2, 200]] else: guess = [gammaGuess] bounds = [[0.8, 5.0]] # gamma = optim.fmin(self.fitGammaErrFun, guess, (x, y, minLum, maxLum)) # gamma = optim.fminbound(self.fitGammaErrFun, # minGamma, maxGamma, # args=(x,y, minLum, maxLum)) params = optim.fmin_tnc(self.fitGammaErrFun, np.array(guess), approx_grad=True, args=(x, y, minLum, maxLum), bounds=bounds, messages=0) return minLum, maxLum, params[0]
[docs] def fitGammaErrFun(self, params, x, y, minLum, maxLum): """Provides an error function for fitting gamma function (used by fitGammaFun) """ if self.eq == 4: gamma, a, k = params _m = gammaFun(x, minLum, maxLum, gamma, eq=self.eq, a=a, k=k) model = np.asarray(_m) else: gamma = params[0] _m = gammaFun(x, minLum, maxLum, gamma, eq=self.eq) model = np.asarray(_m) SSQ = np.sum((model - y)**2) return SSQ
[docs]def makeDKL2RGB(nm, powerRGB): """Creates a 3x3 DKL->RGB conversion matrix from the spectral input powers """ interpolateCones = interpolate.interp1d(wavelength_5nm, cones_SmithPokorny) interpolateJudd = interpolate.interp1d(wavelength_5nm, juddVosXYZ1976_5nm) judd = interpolateJudd(nm) cones = interpolateCones(nm) judd = np.asarray(judd) cones = np.asarray(cones) rgb_to_cones = np.dot(cones, np.transpose(powerRGB)) # get LMS weights for Judd vl lumwt = np.dot(judd[1, :], np.linalg.pinv(cones)) # cone weights for achromatic primary dkl_to_cones = np.dot(rgb_to_cones, [[1, 0, 0], [1, 0, 0], [1, 0, 0]]) # cone weights for L-M primary dkl_to_cones[0, 1] = old_div(lumwt[1], lumwt[0]) dkl_to_cones[1, 1] = -1 dkl_to_cones[2, 1] = lumwt[2] # weights for S primary dkl_to_cones[0, 2] = 0 dkl_to_cones[1, 2] = 0 dkl_to_cones[2, 2] = -1 # Now have primaries expressed as cone excitations # get coefficients for cones ->monitor cones_to_rgb = np.linalg.inv(rgb_to_cones) # get coefficients for DKL cone weights to monitor dkl_to_rgb = np.dot(cones_to_rgb, dkl_to_cones) # normalise each col dkl_to_rgb[:, 0] /= max(abs(dkl_to_rgb[:, 0])) dkl_to_rgb[:, 1] /= max(abs(dkl_to_rgb[:, 1])) dkl_to_rgb[:, 2] /= max(abs(dkl_to_rgb[:, 2])) return dkl_to_rgb
[docs]def makeLMS2RGB(nm, powerRGB): """Creates a 3x3 LMS->RGB conversion matrix from the spectral input powers """ interpolateCones = interpolate.interp1d(wavelength_5nm, cones_SmithPokorny) coneSens = interpolateCones(nm) rgb_to_cones = np.dot(coneSens, np.transpose(powerRGB)) cones_to_rgb = np.linalg.inv(rgb_to_cones) return cones_to_rgb
def makeXYZ2RGB(red_xy, green_xy, blue_xy, whitePoint_xy=(0.3127, 0.329), reverse=False): """Create a linear RGB conversion matrix. Returns a matrix to convert CIE-XYZ (1931) tristimulus values to linear RGB given CIE-xy (1931) primaries and white point. By default, the returned matrix transforms CIE-XYZ to linear RGB coordinates. Use 'reverse=True' to get the inverse transformation. The chromaticity coordinates of the display's phosphor 'guns' are usually measured with a spectrophotometer. The routines here are based on methods found at: http://www.ryanjuckett.com/programming/rgb-color-space-conversion/ Parameters ---------- red_xy : tuple, list or ndarray Chromaticity coordinate (CIE-xy) of the 'red' gun. green_xy: tuple, list or ndarray Chromaticity coordinate (CIE-xy) of the 'green' gun. blue_xy : tuple, list or ndarray Chromaticity coordinate (CIE-xy) of the 'blue' gun. whitePoint_xy : tuple, list or ndarray Chromaticity coordinate (CIE-xy) of the white point, default is D65. reverse : bool Return the inverse transform sRGB -> XYZ. Default is `False`. Returns ------- ndarray 3x3 conversion matrix Examples -------- Construct a conversion matrix to transform CIE-XYZ coordinates to linear (not gamma corrected) RGB values:: # nominal primaries for sRGB (or BT.709) red = (0.6400, 0.3300) green = (0.300, 0.6000) blue = (0.1500, 0.0600) whiteD65 = (0.3127, 0.329) conversionMatrix = makeXYZ2RGB(red, green, blue, whiteD65) # The value of `conversionMatrix` should have similar coefficients to # that presented in the BT.709 standard. # # [[ 3.24096994 -1.53738318 -0.49861076] # [-0.96924364 1.8759675 0.04155506] # [ 0.05563008 -0.20397696 1.05697151]] # """ # convert CIE-xy chromaticity coordinates to xyY and put them into a matrix mat_xyY_primaries = np.asarray(( (red_xy[0], red_xy[1], 1.0 - red_xy[0] - red_xy[1]), (green_xy[0], green_xy[1], 1.0 - green_xy[0] - green_xy[1]), (blue_xy[0], blue_xy[1], 1.0 - blue_xy[0] - blue_xy[1]) )).T # convert white point to CIE-XYZ whtp_XYZ = np.asarray( np.dot(1.0 / whitePoint_xy[1], np.asarray(( whitePoint_xy[0], whitePoint_xy[1], 1.0 - whitePoint_xy[0] - whitePoint_xy[1]) ) ) ) # compute the final matrix (sRGB -> XYZ) u = np.diag(np.dot(whtp_XYZ, np.linalg.inv(mat_xyY_primaries).T)) to_return = np.matmul(mat_xyY_primaries, u) if not reverse: # for XYZ -> sRGB conversion matrix (we usually want this!) return np.linalg.inv(to_return) return to_return def getLumSeries(lumLevels=8, winSize=(800, 600), monitor=None, gamma=1.0, allGuns=True, useBits=False, autoMode='auto', stimSize=0.3, photometer=None, screen=0): """Automatically measures a series of gun values and measures the luminance with a photometer. :Parameters: photometer : a photometer object e.g. a :class:`~psychopy.hardware.pr.PR65` or :class:`~psychopy.hardware.minolta.LS100` from hardware.findPhotometer() lumLevels : (default=8) array of values to test or single value for n evenly spaced test values gamma : (default=1.0) the gamma value at which to test autoMode : 'auto' or 'semi'(='auto') If 'auto' the program will present the screen and automatically take a measurement before moving on. If set to 'semi' the program will wait for a keypress before moving on but will not attempt to make a measurement (use this to make a measurement with your own device). Any other value will simply move on without pausing on each screen (use this to see that the display is performing as expected). """ import psychopy.event import psychopy.visual from psychopy import core if photometer is None: havePhotom = False elif not hasattr(photometer, 'getLum'): msg = ("photometer argument to monitors.getLumSeries should be a " "type of photometer object, not a %s") logging.error(msg % type(photometer)) return None else: havePhotom = True if useBits: # all gamma transforms will occur in calling the Bits++ LUT # which improves the precision (14bit not 8bit gamma) bitsMode = 'fast' else: bitsMode = None if gamma == 1: initRGB = 0.5**(old_div(1, 2.0)) * 2 - 1 else: initRGB = 0.8 # setup screen and "stimuli" myWin = psychopy.visual.Window(fullscr=0, size=winSize, gamma=gamma, units='norm', monitor=monitor, allowGUI=True, winType='pyglet', bitsMode=bitsMode, screen=screen) instructions = ("Point the photometer at the central bar. " "Hit a key when ready (or wait 30s)") message = psychopy.visual.TextStim(myWin, text=instructions, height=0.1, pos=(0, -0.85), rgb=[1, -1, -1]) noise = np.random.rand(512, 512).round() * 2 - 1 backPatch = psychopy.visual.PatchStim(myWin, tex=noise, size=2, units='norm', sf=[old_div(winSize[0], 512.0), old_div(winSize[1], 512.0)]) testPatch = psychopy.visual.PatchStim(myWin, tex='sqr', size=stimSize, rgb=initRGB, units='norm') # stay like this until key press (or 30secs has passed) waitClock = core.Clock() tRemain = 30 while tRemain > 0: tRemain = 30 - waitClock.getTime() backPatch.draw() testPatch.draw() instructions = ("Point the photometer at the central white bar. " "Hit a key when ready (or wait %iss)") message.setText(instructions % tRemain, log=False) message.draw() myWin.flip() if len(psychopy.event.getKeys()): break # we got a keypress so move on if autoMode != 'semi': message.setText('Q to quit at any time') else: message.setText('Spacebar for next patch') # LS100 likes to take at least one bright measurement if havePhotom and photometer.type == 'LS100': junk = photometer.getLum() # what are the test values of luminance if type(lumLevels) in (int, float): toTest = DACrange(lumLevels) else: toTest = np.asarray(lumLevels) if allGuns: guns = [0, 1, 2, 3] # gun=0 is the white luminance measure else: allGuns = [0] # this will hold the measured luminance values lumsList = np.zeros((len(guns), len(toTest)), 'd') # for each gun, for each value run test for gun in guns: for valN, DACval in enumerate(toTest): lum = old_div(DACval, 127.5) - 1 # get into range -1:1 # only do luminanc=-1 once if lum == -1 and gun > 0: continue # set the patch color if gun > 0: rgb = [-1, -1, -1] rgb[gun - 1] = lum else: rgb = [lum, lum, lum] backPatch.draw() testPatch.setColor(rgb) testPatch.draw() message.draw() myWin.flip() # allowing the screen to settle (no good reason!) time.sleep(0.2) # take measurement if havePhotom and autoMode == 'auto': actualLum = photometer.getLum() print("At DAC value %i\t: %.2fcd/m^2" % (DACval, actualLum)) if lum == -1 or not allGuns: # if the screen is black set all guns to this lum value! lumsList[:, valN] = actualLum else: # otherwise just this gun lumsList[gun, valN] = actualLum # check for quit request for thisKey in psychopy.event.getKeys(): if thisKey in ('q', 'Q', 'escape'): myWin.close() return np.array([]) elif autoMode == 'semi': print("At DAC value %i" % DACval) done = False while not done: # check for quit request for thisKey in psychopy.event.getKeys(): if thisKey in ('q', 'Q', 'escape'): myWin.close() return np.array([]) elif thisKey in (' ', 'space'): done = True myWin.close() # we're done with the visual stimuli if havePhotom: return lumsList else: return np.array([])
[docs]def getLumSeriesPR650(lumLevels=8, winSize=(800, 600), monitor=None, gamma=1.0, allGuns=True, useBits=False, autoMode='auto', stimSize=0.3, photometer='COM1'): """DEPRECATED (since v1.60.01): Use :class:`psychopy.monitors.getLumSeries()` instead """ logging.warning( "DEPRECATED (since v1.60.01): Use monitors.getLumSeries() instead") val = getLumSeries(lumLevels, winSize, monitor, gamma, allGuns, useBits, autoMode, stimSize, photometer) return val
[docs]def getRGBspectra(stimSize=0.3, winSize=(800, 600), photometer='COM1'): """ usage: getRGBspectra(stimSize=0.3, winSize=(800,600), photometer='COM1') :params: - 'photometer' could be a photometer object or a serial port name on which a photometer might be found (not recommended) """ import psychopy.event import psychopy.visual if hasattr(photometer, 'getLastSpectrum'): photom = photometer else: # setup photom photom = hardware.Photometer(photometer) if photom != None: havephotom = 1 else: havephotom = 0 # setup screen and "stimuli" myWin = psychopy.visual.Window(fullscr=0, rgb=0.0, size=winSize, units='norm') instructions = ("Point the photometer at the central square. " "Hit a key when ready") message = psychopy.visual.TextStim(myWin, text=instructions, height=0.1, pos=(0.0, -0.8), rgb=-1.0) message.draw() testPatch = psychopy.visual.PatchStim(myWin, tex=None, size=stimSize * 2, rgb=0.3) testPatch.draw() myWin.flip() # stay like this until key press (or 30secs has passed) psychopy.event.waitKeys(30) spectra = [] for thisColor in [[1, -1, -1], [-1, 1, -1], [-1, -1, 1]]: # update stimulus testPatch.setColor(thisColor) testPatch.draw() myWin.flip() # make measurement photom.measure() spectra.append(photom.getLastSpectrum(parse=False)) myWin.close() nm, power = photom.parseSpectrumOutput(spectra) return nm, power
def DACrange(n): """Returns an array of n DAC values spanning 0-255 """ # NB python ranges exclude final val return np.arange(0.0, 256.0, old_div(255.0, (n - 1))).astype(np.uint8)
[docs]def getAllMonitors(): """Find the names of all monitors for which calibration files exist """ monitorList = glob.glob(os.path.join(monitorFolder, '*.calib')) if constants.PY3: monitorList = glob.glob(os.path.join(monitorFolder, '*.json')) split = os.path.split splitext = os.path.splitext # skip the folder and the extension for each file monitorList = [splitext(split(thisFile)[-1])[0] for thisFile in monitorList] return monitorList
[docs]def gammaFun(xx, minLum, maxLum, gamma, eq=1, a=None, b=None, k=None): """Returns gamma-transformed luminance values. y = gammaFun(x, minLum, maxLum, gamma) a and b are calculated directly from minLum, maxLum, gamma **Parameters:** - **xx** are the input values (range 0-255 or 0.0-1.0) - **minLum** = the minimum luminance of your monitor - **maxLum** = the maximum luminance of your monitor (for this gun) - **gamma** = the value of gamma (for this gun) """ # scale x to be in range minLum:maxLum xx = np.array(xx, 'd') maxXX = max(xx) if maxXX > 2.0: # xx = xx * maxLum / 255.0 + minLum xx = old_div(xx, 255.0) else: # assume data are in range 0:1 pass # xx = xx * maxLum + minLum # eq1: y = a + (b*xx)**gamma # eq2: y = (a + b * xx)**gamma # eq4: y = a + (b + k*xx)**gamma # Pelli & Zhang 1991 if eq == 1: a = minLum b = (maxLum - a)**(old_div(1, gamma)) yy = a + (b * xx)**gamma elif eq == 2: a = minLum**(old_div(1, gamma)) b = maxLum**(old_div(1, gamma)) - a yy = (a + b * xx)**gamma elif eq == 3: # NB method 3 was an interpolation method that didn't work well pass elif eq == 4: nMissing = sum([a is None, b is None, k is None]) # check params if nMissing > 1: msg = "For eq=4, gammaFun needs 2 of a, b, k to be specified" raise AttributeError(msg) elif nMissing == 1: if a is None: a = minLum - b**(old_div(1.0, gamma)) # when y=min, x=0 elif b is None: if a >= minLum: b = 0.1**(old_div(1.0, gamma)) # can't take inv power of -ve else: b = (minLum - a)**(old_div(1.0, gamma)) # when y=min, x=0 elif k is None: k = (maxLum - a)**(old_div(1.0, gamma)) - b # when y=max, x=1 # this is the same as Pelli and Zhang (but different inverse function) yy = a + (b + k * xx)**gamma # Pelli and Zhang (1991) return yy
[docs]def gammaInvFun(yy, minLum, maxLum, gamma, b=None, eq=1): """Returns inverse gamma function for desired luminance values. x = gammaInvFun(y, minLum, maxLum, gamma) a and b are calculated directly from minLum, maxLum, gamma **Parameters:** - **xx** are the input values (range 0-255 or 0.0-1.0) - **minLum** = the minimum luminance of your monitor - **maxLum** = the maximum luminance of your monitor (for this gun) - **gamma** = the value of gamma (for this gun) - **eq** determines the gamma equation used; eq==1[default]: yy = a + (b * xx)**gamma eq==2: yy = (a + b*xx)**gamma """ # x should be 0:1 # y should be 0:1, then converted to minLum:maxLum # eq1: y = a + (b * xx)**gamma # eq2: y = (a + b * xx)**gamma # eq4: y = a + (b + kxx)**gamma if max(yy) == 255: yy = old_div(np.asarray(yy, 'd'), 255.0) elif min(yy) < 0 or max(yy) > 1: logging.warning( 'User supplied values outside the expected range (0:1)') else: yy = np.asarray(yy, 'd') if eq == 1: xx = np.asarray(yy)**(old_div(1.0, gamma)) elif eq == 2: yy = np.asarray(yy) * (maxLum - minLum) + minLum a = minLum**(old_div(1, gamma)) b = maxLum**(old_div(1, gamma)) - a xx = old_div((yy**(old_div(1, gamma)) - a), b) maxLUT = old_div((maxLum**(old_div(1, gamma)) - a), b) minLUT = old_div((minLum**(old_div(1, gamma)) - a), b) xx = old_div(xx, (maxLUT - minLUT)) - minLUT elif eq == 3: # NB method 3 was an interpolation method that didn't work well pass elif eq == 4: # this is not the same as Zhang and Pelli's inverse # see https://www.psychopy.org/general/gamma.html for derivation a = minLum - b**gamma k = (maxLum - a)**(old_div(1., gamma)) - b xx = old_div((((1 - yy) * b**gamma + yy * (b + k)**gamma)**(old_div(1, gamma)) - b), k) # then return to range (0:1) # xx = xx / (maxLUT - minLUT) - minLUT return xx
def strFromDate(date): """Simply returns a string with a std format from a date object """ if type(date) == float: date = time.localtime(date) return time.strftime("%Y_%m_%d %H:%M", date)

Back to top