#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tools to help with calibrations
"""
# Part of the PsychoPy library
# Copyright (C) 2002-2018 Jonathan Peirce (C) 2019-2024 Open Science Tools Ltd.
# Distributed under the terms of the GNU General Public License (GPL).
from .calibData import wavelength_5nm, juddVosXYZ1976_5nm, cones_SmithPokorny
from psychopy import __version__, logging, hardware
try:
import serial
haveSerial = True
except (ModuleNotFoundError, ImportError):
haveSerial = False
import errno
import os
import time
import glob
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
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:
"""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 easily 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...")
# override 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.all(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.all(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 with 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
"""
ext = ".json"
# the name of the actual file:
thisFileName = os.path.join(monitorFolder, self.name + ext)
if not os.path.exists(thisFileName):
self.calibNames = []
else:
with open(thisFileName, 'r') as thisFile:
# 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)
self.calibNames = sorted(self.calibs)
[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, str) 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).
"""
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 = self.getLevelsPre() / 255.0
for gun in range(4):
# scale to 0:1
lumsPre[gun, :] = \
(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:
"""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) ** (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 = minLum / 5.0
kGuess = (maxLum - aGuess) ** (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] = 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:
https://www.ryanjuckett.com/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 ** (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=[winSize[0] / 512.0,
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
# assuming the same for the CS100A
if havePhotom and photometer.type == 'LS100':
junk = photometer.getLum()
if havePhotom and photometer.type == 'CS100A':
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:
guns = [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 = (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, 255.0 / float(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, '*.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)
invGamma = 1.0 / float(gamma) # used a lot below, so compute it here
if maxXX > 2.0:
# xx = xx * maxLum / 255.0 + minLum
xx = 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) ** invGamma
yy = a + (b * xx) ** gamma
elif eq == 2:
a = minLum ** invGamma
b = maxLum ** invGamma - a
yy = (a + b * xx) ** gamma
elif eq == 3:
# NB method 3 was an interpolation method that didn't work well
raise ValueError('Parameter `eq` must be one of 1, 2 or 4.')
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 ** invGamma # when y=min, x=0
elif b is None:
if a >= minLum:
b = 0.1 ** invGamma # can't take inv power of -ve
else:
b = (minLum - a) ** invGamma # when y=min, x=0
elif k is None:
k = (maxLum - a) ** invGamma - 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)
else:
raise ValueError('Parameter `eq` must be one of 1, 2 or 4.')
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
invGamma = 1.0 / float(gamma)
if max(yy) == 255:
yy = 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) ** invGamma
elif eq == 2:
yy = np.asarray(yy) * (maxLum - minLum) + minLum
a = minLum ** invGamma
b = maxLum ** invGamma - a
xx = (yy ** invGamma - a) / float(b)
maxLUT = (maxLum ** invGamma - a) / float(b)
minLUT = (minLum ** invGamma - a) / float(b)
xx = (xx / (maxLUT - minLUT)) - minLUT
elif eq == 3:
# NB method 3 was an interpolation method that didn't work well
raise ValueError('Parameter `eq` must be one of 1, 2 or 4.')
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) ** invGamma - b
xx = (((1 - yy) * b**gamma + yy * (b + k)**gamma) ** invGamma - b) / float(k)
else:
raise ValueError('Parameter `eq` must be one of 1, 2 or 4.')
# 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)
