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A
Absorption
(in
src.experiments
)
animate2D()
(in
timeElectronicWavefunction
)
APPLY()
(in
zeroNuclearOperator
)
addDiagonalDisorder()
(in
ElectronicHamiltonian
)
animate2D()
(in
timeNuclearWavefunction
)
applyAndExtendOnce()
(in
timeNuclearWavefunction
)
allocateMoreSpace()
(in
timeElectronicWavefunction
)
animateFrequency()
(in
PopulationTimeScan
)
applyOperatorNTimesOnInitialWavefunction()
(in
timeNuclearWavefunction
)
allocateSpace()
(in
timeElectronicWavefunction
)
APPLY()
(in
ElectronicHamiltonian
)
applyOperatorsAndAdvance()
(in
timeElectronicWavefunction
)
allocateSpace()
(in
timeNuclearWavefunction
)
APPLY()
(in
ElectronicPropagator
)
applyOperatorsNTimes()
(in
timeElectronicWavefunction
)
ALPHA
(in
scaledAtomicUnits
)
APPLY()
(in
momentumOperator
)
applyOperatorsNTimesOnInitialWavefunction()
(in
timeElectronicWavefunction
)
AMU_KILOGRAMS
(in
unitHandler
)
APPLY()
(in
nuclearHamiltonian
)
autocorrelation()
(in
timeNuclearWavefunction
)
analyticParitionFunctionValue()
(in
harmonicOscillator
)
APPLY()
(in
nuclearPropagator
)
autocorrelationInFrequencySpace()
(in
timeElectronicWavefunction
)
analyticWavepacket()
(in
harmonicOscillator
)
APPLY()
(in
nuclearPropagatorHIGH_ACCURACY
)
autocorrelationInFrequencySpace()
(in
timeNuclearWavefunction
)
animate1D()
(in
timeElectronicWavefunction
)
APPLY()
(in
nuclearPropagatorLOW_ACCURACY
)
animate1D()
(in
timeNuclearWavefunction
)
APPLY()
(in
positionOperator
)
B
Base
(in
src.experiments.Dynamics
)
Basic_Cluster
(in
src.experiments.PumpProbe
)
BIG_RED_BUTTON()
(in
src.experiments.PumpProbe
)
Base
(in
src.experiments.PumpProbe
)
Basic_One_Gaussian_Excitation
(in
src.experiments.Dynamics
)
BIG_RED_BUTTON()
(in
src.experiments.TransientGrating
)
base_frequency_multiple
(in
TG_Cluster_Helper
)
Basic_Overkill
(in
src.experiments.PumpProbe
)
BIG_RED_BUTTON()
(in
src.experiments.TwoDimensionalSpectrum
)
base_frequency_multiple
(in
overlapHelper
)
bestGuessHamonicOscillator()
(in
userSpecifiedPotential_Hamiltonian
)
BOLTZMANNS_CONSTANT_JOULES_PER_KELVIN
(in
unitHandler
)
base_TG
(in
src.experiments.TransientGrating
)
BIG_RED_BUTTON()
(in
src.experiments.Absorption
)
Basic
(in
src.experiments.PumpProbe
)
BIG_RED_BUTTON()
(in
src.experiments.Dynamics
)
C
C
(in
wavenumberBasedUnits
)
calculate()
(in
TG_Cluster_Helper
)
complexCojugate()
(in
electronicWavefunction
)
C_METERS_PER_SECOND
(in
unitHandler
)
calculate()
(in
WitnessExperiment
)
complexConjugate()
(in
ZeroNuclearWavefunction
)
calculate()
(in
DipoleAbsorption
)
calculate()
(in
base_TG
)
complexConjugate()
(in
nuclearWavefunction
)
calculate()
(in
ThermallyAveragedDipoleAbsorption
)
calculate()
(in
interactionHelper
)
constantMomentumNuclearOperator
(in
src.NuclearOperator
)
calculate()
(in
Basic_One_Gaussian_Excitation
)
calculate()
(in
overlapHelper
)
constantPositionNuclearOperator
(in
src.NuclearOperator
)
calculate()
(in
ClusterScanPulseWidthPulseCenter
)
calculate()
(in
OnePopulationTime
)
correspondingHarmonicOscillator()
(in
morsePotential
)
calculate()
(in
Basic
)
calculate()
(in
PopulationTimeScan
)
cos()
(in
nuclearOperator
)
calculate()
(in
Basic_Cluster
)
calculate()
(in
interactionHelper
)
cosh()
(in
nuclearOperator
)
calculate()
(in
Basic_Overkill
)
checkImplemented()
(in
ElectronicMomentumOperator
)
COULUMB_CONSTANT
(in
scaledAtomicUnits
)
calculate()
(in
PP_Cluster_Helper
)
checkImplemented()
(in
ElectronicOperator
)
currentElectronicWavefunction()
(in
timeElectronicWavefunction
)
calculate()
(in
WitnessExperiment
)
checkImplemented()
(in
ElectronicPositionOperator
)
currentTime()
(in
timeElectronicWavefunction
)
calculate()
(in
PopulationTimeScan
)
ClusterScanPulseWidthPulseCenter
(in
src.experiments.Dynamics
)
calculate()
(in
PopulationTimeScan_cluster
)
coherentThermalState()
(in
harmonicOscillator
)
D
DEFAULT_ZERO_TOLERANCE
(in
Spacetime
)
DipoleAbsorption
(in
src.experiments.Absorption
)
drawEigenstateFromThermalDistribution()
(in
harmonicOscillator
)
deltaFunction
(in
src.TimeFunction
)
distanceUnitsFromSqrtCentimeters()
(in
wavenumberBasedUnits
)
Dynamics
(in
src.experiments
)
E
electricDipole
(in
src.TimePerturbation
)
energyEigenfunction()
(in
morsePotential
)
experiment_type_string
(in
Basic_One_Gaussian_Excitation
)
electricDipoleOneInteraction
(in
src.TimePerturbation
)
energyEigenfunction()
(in
oneDimentionalHamiltonian
)
experiment_type_string
(in
ClusterScanPulseWidthPulseCenter
)
ElectricFieldOverlapWithOtherTimeFunction()
(in
electricDipole
)
energyEigenfunction()
(in
userSpecifiedPotential_Hamiltonian
)
experiment_type_string
(in
Base
)
ElectricFieldOverlapWithOtherTimeFunction()
(in
spaceSavingElectricDipole
)
energyEigenfunctionAmplitude()
(in
nuclearHamiltonian
)
experiment_type_string
(in
Basic
)
ELECTRON_CHARGE
(in
scaledAtomicUnits
)
energyEigenvalue()
(in
harmonicOscillator
)
experiment_type_string
(in
Basic_Cluster
)
ELECTRON_MASS_KILOGRAMS
(in
unitHandler
)
energyEigenvalue()
(in
morsePotential
)
experiment_type_string
(in
Basic_Overkill
)
electronicDimensionMismatch
(in
src.ElectronicWavefunction
)
energyEigenvalue()
(in
nuclearHamiltonian
)
experiment_type_string
(in
WitnessExperiment
)
ElectronicHamiltonian
(in
src.ElectronicOperator
)
energyEigenvalue()
(in
oneDimentionalHamiltonian
)
experiment_type_string
(in
WitnessExperiment_scanCenterFrequency
)
ElectronicMomentumOperator
(in
src.ElectronicOperator
)
energyEigenvalue()
(in
userSpecifiedPotential_Hamiltonian
)
experiment_type_string
(in
PopulationTimeScan
)
ElectronicOperator
(in
src
)
energyUnitsFromJoules()
(in
unitHandler
)
experiment_type_string
(in
PopulationTimeScan_cluster
)
ElectronicOperator
(in
src.ElectronicOperator
)
energyUnitsFromWavenumbers()
(in
unitHandler
)
experiment_type_string
(in
WitnessExperiment
)
ElectronicPositionOperator
(in
src.ElectronicOperator
)
excitationOperator()
(in
ElectronicPositionOperator
)
experimentBase
(in
src.experiments
)
ElectronicPropagator
(in
src.ElectronicOperator
)
exp()
(in
nuclearOperator
)
experiments
(in
src
)
ElectronicWavefunction
(in
src
)
experiment
(in
src.experiments.experimentBase
)
exponential()
(in
ElectronicOperator
)
electronicWavefunction
(in
src.ElectronicWavefunction
)
experiment_type_string
(in
DipoleAbsorption
)
EXPONENTIAL_APPROXIMATION_TRUNCATION
(in
src.ElectronicOperator
)
energyEigenfunction()
(in
harmonicOscillator
)
experiment_type_string
(in
Base
)
F
femtosecondsFromTime()
(in
unitHandler
)
fourierTransform()
(in
timeFunction
)
FT()
(in
timeFunction
)
flatten()
(in
nuclearOperator
)
fourierTransform()
(in
zeroTimeFunction
)
functionalMomentumNuclearOperator
(in
src.NuclearOperator
)
flatten()
(in
zeroNuclearOperator
)
frankCondonOverlapSpectrum()
(in
nuclearHamiltonian
)
functionalPositionNuclearOperator
(in
src.NuclearOperator
)
fourierTransform()
(in
ZeroNuclearWavefunction
)
freeEvolution()
(in
electricDipole
)
functionSpacetimeZero()
(in
Spacetime
)
fourierTransform()
(in
nuclearWavefunction
)
freeEvolution()
(in
spaceSavingElectricDipole
)
functionSpaceZero()
(in
Spacetime
)
G
GAUSSIAN_FILTER_FACTOR
(in
Spacetime
)
GaussianPulseTooCloseToEdgeOfTimeException
(in
src.TimeFunction
)
groundStateAmplitude()
(in
nuclearHamiltonian
)
GaussianCosinePulse
(in
src.TimeFunction
)
GaussianRazorBladedPulse
(in
src.TimeFunction
)
groundStateElectronicWavefunction()
(in
ElectronicHamiltonian
)
GaussianKMinusPulse
(in
src.TimeFunction
)
genericOneDimensionalFourierTransformFromZero()
(in
Spacetime
)
groundStateEnergy()
(in
nuclearHamiltonian
)
GaussianKPlusPulse
(in
src.TimeFunction
)
goFromTimeWavefunction()
(in
electricDipoleOneInteraction
)
GaussianPulse
(in
src.TimeFunction
)
grabTimeNuclearWavefunction()
(in
timeElectronicWavefunction
)
H
H
(in
scaledAtomicUnits
)
H_JOULE_SECONDS
(in
unitHandler
)
HBAR
(in
scaledAtomicUnits
)
H
(in
wavenumberBasedUnits
)
harmonicOscillator
(in
src.NuclearOperator
)
HBAR
(in
wavenumberBasedUnits
)
H_C
(in
wavenumberBasedUnits
)
hbar
(in
Spacetime
)
HBAR_JOULE_SECONDS
(in
unitHandler
)
I
indentity()
(in
ElectronicOperator
)
integratedTimeOverlap()
(in
timeNuclearWavefunction
)
inverseFourierTransform()
(in
ZeroNuclearWavefunction
)
integrate()
(in
timeFunction
)
integrateInSimulationSpace()
(in
timeFunction
)
inverseFourierTransform()
(in
nuclearWavefunction
)
integrate()
(in
zeroTimeFunction
)
interactionHelper
(in
src.experiments.TransientGrating
)
integratedAmplitude()
(in
nuclearWavefunction
)
interactionHelper
(in
src.experiments.TwoDimensionalSpectrum
)
K
k_MULTIPLIER
(in
GaussianRazorBladedPulse
)
kineticFunction()
(in
oneDimentionalHamiltonian
)
kIntegration()
(in
Spacetime
)
L
length()
(in
timeElectronicWavefunction
)
lengthUnitsFromMeters()
(in
unitHandler
)
load_object_from_file()
(in
TG_Cluster_Helper
)
lengthUnitsFromBohrRadii()
(in
unitHandler
)
load_object_from_file()
(in
PP_Cluster_Helper
)
M
massUnitsFromAmu()
(in
unitHandler
)
momentumOperator
(in
src.NuclearOperator
)
myFourierTransformedFunction()
(in
zeroTimeFunction
)
maxTime()
(in
timeFunction
)
morsePotential
(in
src.NuclearOperator
)
myFourierTransformedFunction_initial()
(in
GaussianRazorBladedPulse
)
metersFromLengthUnits()
(in
unitHandler
)
multivariableFunctionValueInK()
(in
Spacetime
)
myFunction()
(in
GaussianCosinePulse
)
minTime()
(in
timeFunction
)
multivariableFunctionValueInX()
(in
Spacetime
)
myFunction()
(in
GaussianKMinusPulse
)
minusFunction()
(in
GaussianCosinePulse
)
myFourierTransformedFunction()
(in
GaussianCosinePulse
)
myFunction()
(in
GaussianKPlusPulse
)
minusFunction()
(in
GaussianRazorBladedPulse
)
myFourierTransformedFunction()
(in
GaussianKMinusPulse
)
myFunction()
(in
GaussianRazorBladedPulse
)
minusFunction()
(in
zeroTimeFunction
)
myFourierTransformedFunction()
(in
GaussianKPlusPulse
)
myNuclearPropagator()
(in
nuclearHamiltonian
)
MismatchedSpacetimeError
(in
src.Spacetime
)
myFourierTransformedFunction()
(in
GaussianRazorBladedPulse
)
myPropagator()
(in
ElectronicHamiltonian
)
N
next()
(in
timeElectronicWavefunction
)
nuclearHamiltonian
(in
src.NuclearOperator
)
nuclearPropagatorLOW_ACCURACY
(in
src.NuclearOperator
)
next()
(in
timeNuclearWavefunction
)
nuclearHamiltonianDimensionMismatch
(in
src.NuclearOperator
)
NuclearWavefunction
(in
src
)
norm()
(in
electronicWavefunction
)
NuclearOperator
(in
src
)
nuclearWavefunction
(in
src.NuclearWavefunction
)
norm()
(in
nuclearWavefunction
)
nuclearOperator
(in
src.NuclearOperator
)
NUMBER_PROCESSES
(in
src.experiments.Dynamics
)
normSeries()
(in
timeElectronicWavefunction
)
nuclearPropagator
(in
src.NuclearOperator
)
NUMBER_PROCESSES
(in
src.experiments.PumpProbe
)
normSeries()
(in
timeNuclearWavefunction
)
nuclearPropagatorHIGH_ACCURACY
(in
src.NuclearOperator
)
NUMBER_PROCESSES
(in
src.experiments.TransientGrating
)
O
oneDimentionalHamiltonian
(in
src.NuclearOperator
)
overlap()
(in
zeroElectronicWavefunction
)
overlapHelper
(in
src.experiments.TransientGrating
)
OnePopulationTime
(in
src.experiments.TwoDimensionalSpectrum
)
overlap()
(in
oneDimentionalHamiltonian
)
overlapSeies_with_constant_EWF_after_operator_application()
(in
timeElectronicWavefunction
)
openSavedExperiment()
(in
experiment
)
overlap()
(in
ZeroNuclearWavefunction
)
overlap()
(in
electronicWavefunction
)
overlap()
(in
nuclearWavefunction
)
P
peakFinder()
(in
Spacetime
)
plotNormalized()
(in
DipoleAbsorption
)
potentialFunction()
(in
userSpecifiedPotential_Hamiltonian
)
perturbationOperatorAtTime()
(in
electricDipoleOneInteraction
)
plotSpaceFunction()
(in
Spacetime
)
PP_Cluster_Helper
(in
src.experiments.PumpProbe
)
perturbationOperatorAtTime()
(in
Base
)
plotTime()
(in
OnePopulationTime
)
printMe()
(in
ElectronicOperator
)
plot()
(in
electronicWavefunction
)
plotTime()
(in
PopulationTimeScan
)
propagateInitialWavefunctionToEndOfPerturbation()
(in
electricDipole
)
plot()
(in
nuclearWavefunction
)
plusFunction()
(in
GaussianCosinePulse
)
propagateInitialWavefunctionToEndOfPerturbation()
(in
spaceSavingElectricDipole
)
plot()
(in
timeFunction
)
plusFunction()
(in
GaussianRazorBladedPulse
)
proportionAtStateNAndTemperatureT()
(in
harmonicOscillator
)
plot()
(in
DipoleAbsorption
)
plusFunction()
(in
zeroTimeFunction
)
pulseCopyAtNewTime()
(in
GaussianPulse
)
plotAll()
(in
ThermallyAveragedDipoleAbsorption
)
PopulationTimeScan
(in
src.experiments.TransientGrating
)
pulseCopyAtNewTime()
(in
deltaFunction
)
plotAverageNormalized()
(in
ThermallyAveragedDipoleAbsorption
)
PopulationTimeScan
(in
src.experiments.TwoDimensionalSpectrum
)
pulseCopyAtNewTime()
(in
zeroTimeFunction
)
plotFrequency()
(in
OnePopulationTime
)
PopulationTimeScan_cluster
(in
src.experiments.TransientGrating
)
pulseCopyJumpedForward()
(in
GaussianPulse
)
plotFrequency()
(in
PopulationTimeScan
)
positionOperator
(in
src.NuclearOperator
)
pulseCopyJumpedForward()
(in
deltaFunction
)
plotFT()
(in
timeFunction
)
potentialFunction()
(in
harmonicOscillator
)
pulseCopyJumpedForward()
(in
zeroTimeFunction
)
plotInSimulationSpace()
(in
timeFunction
)
potentialFunction()
(in
morsePotential
)
PumpProbe
(in
src.experiments
)
plotLogScaleY()
(in
DipoleAbsorption
)
potentialFunction()
(in
oneDimentionalHamiltonian
)
R
randomRotateTuple()
(in
Spacetime
)
reshapeAndSetAsSurface()
(in
zeroNuclearOperator
)
returnComplexConjugate()
(in
ZeroNuclearWavefunction
)
randomWavefunctionDrawnFromThermalDistributions()
(in
nuclearHamiltonian
)
reshapeAndSetAsSurfaceForOutput()
(in
nuclearOperator
)
returnComplexConjugate()
(in
nuclearWavefunction
)
razorBladeWindow()
(in
GaussianRazorBladedPulse
)
returnAmplitudeFromListOfFunction_handles()
(in
Spacetime
)
rotateTuple()
(in
Spacetime
)
relaxationOperator()
(in
ElectronicPositionOperator
)
returnAmplitudeFromListOfFunctionValues()
(in
Spacetime
)
S
save()
(in
experiment
)
setInitialWavefunction()
(in
timeNuclearWavefunction
)
spaceSavingElectricDipole
(in
src.TimePerturbation
)
save_in_cwd()
(in
experiment
)
setPhase()
(in
timeFunction
)
Spacetime
(in
src
)
SCALE_AMU
(in
scaledAtomicUnits
)
shape()
(in
timeElectronicWavefunction
)
Spacetime
(in
src.Spacetime
)
SCALE_JOULES
(in
scaledAtomicUnits
)
shiftForward()
(in
GaussianRazorBladedPulse
)
spatialFourierTransform()
(in
Spacetime
)
SCALE_METERS
(in
scaledAtomicUnits
)
shiftForward()
(in
timeFunction
)
spatialInverseFourierTransform()
(in
Spacetime
)
SCALE_SECONDS
(in
scaledAtomicUnits
)
shiftForward()
(in
zeroTimeFunction
)
SPEED_OF_LIGHT
(in
scaledAtomicUnits
)
SCALE_WAVENUMBERS
(in
scaledAtomicUnits
)
shiftToCenterTime()
(in
GaussianPulse
)
sqrtCentimetersFromDistanceUnits()
(in
wavenumberBasedUnits
)
scaledAtomicUnits
(in
src.Spacetime
)
sin()
(in
nuclearOperator
)
src
setInitialWavefunction()
(in
timeElectronicWavefunction
)
sinh()
(in
nuclearOperator
)
T
TG_Cluster_Helper
(in
src.experiments.TransientGrating
)
timeFunction
(in
src.TimeFunction
)
totalIntegral()
(in
GaussianPulse
)
ThermallyAveragedDipoleAbsorption
(in
src.experiments.Absorption
)
TimeNuclearWavefunction
(in
src
)
totalIntegral()
(in
GaussianRazorBladedPulse
)
time_domain_multiplier
(in
GaussianRazorBladedPulse
)
timeNuclearWavefunction
(in
src.TimeNuclearWavefunction
)
totalPower()
(in
GaussianPulse
)
time_window_high_frequency_multiplier
(in
GaussianRazorBladedPulse
)
timeOverlap()
(in
timeNuclearWavefunction
)
totalPower()
(in
GaussianRazorBladedPulse
)
TimeElectronicWavefunction
(in
src
)
timeOverlapWithOtherBraEWFOfPolarization()
(in
timeElectronicWavefunction
)
totalPower()
(in
deltaFunction
)
timeElectronicWavefunction
(in
src.TimeElectronicWavefunction
)
timeOverlapWithOtherBraEWFOfPolarizationAndOverlapWithElectricField()
(in
timeElectronicWavefunction
)
totalPower()
(in
zeroTimeFunction
)
timeExpectationValue()
(in
timeElectronicWavefunction
)
TimePerturbation
(in
src
)
TransientGrating
(in
src.experiments
)
timeExpectationValueOfPolarizationAndOverlapWithElectricField()
(in
timeElectronicWavefunction
)
timeUnitsFromSeconds()
(in
unitHandler
)
TwoDimensionalSpectrum
(in
src.experiments
)
TimeFunction
(in
src
)
tIntegration()
(in
Spacetime
)
U
unitHandler
(in
src.Spacetime
)
USE_CLUSTERPOOL
(in
src.experiments.PumpProbe
)
useFilter
(in
overlapHelper
)
unplotableNuclearWavefunction
(in
src.NuclearWavefunction
)
USE_CLUSTERPOOL
(in
src.experiments.TransientGrating
)
userSpecifiedPotential_Hamiltonian
(in
src.NuclearOperator
)
USE_CLUSTERPOOL
(in
src.experiments.Dynamics
)
useFilter
(in
TG_Cluster_Helper
)
userSpecifiedSurface_positionOperator
(in
src.NuclearOperator
)
V
valueAtTime()
(in
deltaFunction
)
valueAtTime()
(in
timeFunction
)
valueAtTime()
(in
zeroTimeFunction
)
W
wavenumberBasedUnits
(in
src.Spacetime
)
WitnessExperiment
(in
src.experiments.PumpProbe
)
WitnessExperiment_scanCenterFrequency
(in
src.experiments.PumpProbe
)
wavenumbersFromEnergyUnits()
(in
unitHandler
)
WitnessExperiment
(in
src.experiments.TransientGrating
)
X
xEmission()
(in
electricDipole
)
xIntegration()
(in
Spacetime
)
xSpectrum()
(in
spaceSavingElectricDipole
)
xEmission()
(in
spaceSavingElectricDipole
)
xSpectrum()
(in
electricDipole
)
Y
yEmission()
(in
electricDipole
)
ySpectrum()
(in
electricDipole
)
yEmission()
(in
spaceSavingElectricDipole
)
ySpectrum()
(in
spaceSavingElectricDipole
)
Z
zEmission()
(in
electricDipole
)
zeroElectronicWavefunction()
(in
electronicWavefunction
)
zeroPositionOperator
(in
src.NuclearOperator
)
zEmission()
(in
spaceSavingElectricDipole
)
zeroMatrix()
(in
ElectronicOperator
)
zeroTimeFunction
(in
src.TimeFunction
)
zero_copy()
(in
timeElectronicWavefunction
)
zeroMomentumOperator
(in
src.NuclearOperator
)
zSpectrum()
(in
electricDipole
)
ZERO_TOLERANCE
(in
GaussianRazorBladedPulse
)
zeroNuclearOperator
(in
src.NuclearOperator
)
zSpectrum()
(in
spaceSavingElectricDipole
)
zeroElectronicWavefunction
(in
src.ElectronicWavefunction
)
ZeroNuclearWavefunction
(in
src.NuclearWavefunction
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--- title: molmapnets keywords: fastai sidebar: home_sidebar summary: "Neural networks for regression and classification for molecular data, using MolMap generated features." description: "Neural networks for regression and classification for molecular data, using MolMap generated features." nb_path: "index.ipynb" ---
This package implements the neural network architects originally presented in the MolMap package, with two important differences:
First you need to install MolMap and ChemBench (you can find the detailed installation guide here), then simply
pip install molmapnets
from chembench import dataset
from molmap import MolMap
from molmapnets.data import SingleFeatureData, DoubleFeatureData
from molmapnets.models import MolMapRegression
And for model training we also need torch
import torch
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader, random_split
torch.set_default_dtype(torch.float64)
Load and process data, using the eSOL
dataset here for regression
data = dataset.load_ESOL()
descriptor = MolMap(ftype='descriptor', metric='cosine',)
descriptor.fit(verbose=0, method='umap', min_dist=0.1, n_neighbors=15,)
feature extraction
X = descriptor.batch_transform(data.x)
Prepare data for model training
esol = SingleFeatureData(data.y, X)
Train, validation, and test split
train, val, test = random_split(esol, [904,112,112], generator=torch.Generator().manual_seed(7))
Batch data loader
train_loader = DataLoader(train, batch_size=8, shuffle=True)
val_loader = DataLoader(val, batch_size=8, shuffle=True)
test_loader = DataLoader(test, batch_size=8, shuffle=True)
Initialise model
model = MolMapRegression()
epochs = 5
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()
Train model. The users are encouraged to tweak the training loop to achieve better performance
for epoch in range(epochs):
running_loss = 0.0
for i, (xb, yb) in enumerate(train_loader):
xb, yb = xb.to(device), yb.to(device)
# zero gradients
optimizer.zero_grad()
# forward propagation
pred = model(xb)
# loss calculation
loss = criterion(pred, yb)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
print('[Epoch: %2d] Training loss: %.3f' %
(epoch + 1, running_loss / (i+1)))
print('Training finished')
Please refer to the package documentation for more detailed usage.


Mixpanel tools
A debugger tool helps to intercept mixpanel events and display event properties
List of API hosts configured before,


[Ann 1] Yang, X. and Tian Y., Robust Door Detection in Unfamiliar Environments by Combining Edge and Corner Features. IEEE Computer Society Workshop on Computer Vision Applications for the Visually Impaired, 2010.
Synopsis: In this paper, they go over the current limitations and room for research in the area of helping visually-impaired people explore unfamiliar indoor environments. Indoor door-detection is difficult because of large intra-class variations of appearance and design and relatively small inter-class variations of different object models.
The researchers develop a geometric model by combining edges and corners to determine the concavity of the object. Concave objects (doors and elevators) are separated from convex objects (bookshelves and cabinets) by integrating the lateral information of a detected doorframe to obtain the depth information with respect to the wall. The researchers wanted to direct the user by using audio queues. To obtain the relative position of the door to the user, they use the angle formed by the horizontal bar (of the detected doorframe) and the horizontal axis of the image.
Reliability: These researchers are at the Department of Electrical Engineering, The City College, City University of New York. IEEE is the world's largest technical organization.
[Ann 2] Chen, Zhichao and Birchfield, Stanley T., Visual Detection of Lintel-Occluded Doors from a Single Image. Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on.
Synopsis: As an introduction to their paper, they go over two visual cues that have been used by previous papers: intensity edges to detect the sides (posts) and top (lintel) of the door, and the average door color which is assumed to be different from that of the surrounding wall.
Their approach uses a single image and five features. Weak classifiers based on these 5 features are combined using Adaboost to produce a strong classifier. Their assumptions are that both door posts and visible in the image, the camera is oriented so that the posts appear nearly vertical, and the door is at least a certain width. The features they look for are pairs of vertical lines, the concavity of the detected shape, the gap below the door, the color, and a kick plate.
Their algorithm detect 20 doors out of 22 in the corridor they tested, with 100% accuracy for the ones detected (10/10 times each). The ones that weren’t detected were due to an occluding water fountain and the other wasn’t detected because it was flush with the wall.
Reliability: Zhichao Chen is in the Electrical and Computer Engineering Department at Clemson University, SC. IEEE is the world's largest technical organization.
[Ann 3] Kim, S., Cheong, H., Kim, D. H., Park, S., Context-based Object Recognition for Door Detection. 15th International Conference on Advanced Robotics (ICAR), 2011.
Synopsis: In this paper, they go over context-based door-finding for a robot. They use the robotic context (height of the stereo cameras) and the average height of doorknobs to help them. They also detect two different types of doorknobs, which were straight and round ones. As an introduction to their paper, they went over both range sensor-based door detection methods and vision-based door detection methods.
For their methodology, they create a region of interest to look for doors. They want to find vertical lines so to extract vertical lines, they used a moving window to scan an edge image along the x axis to build a histogram of edges. The width of the moving window is set wide enough to cover inclined vertical lines. Next, they look for the doorknob. To recognize the doorknob they used an affine category shape model, which uses object shapes only. They trained the model and used a feature matching algorithm to recognize the doorknobs.
Their results reported a 100% door detection rate, though some false positives were present (1/102 for handled doors and 5/189 for knobbed doors).
Reliability: These researchers are from The Australian National University. ICAR is co-sponsored by the IEEE Robotics and Automation Society.
[Ann 4] Anguelov, D., Koller, D., Parker, E., Thrun, S.: Detecting and modeling doors with mobile robots. In: Proceedings of the IEEE International Conference on Robotics and Automation (2004).
Synopsis: This is a journal article that talks about research in which a laser rangefinder is used to determine the concavity of doors.
Reliability: These researchers are from the CS department at Stanford. IEEE is the world's largest technical organization.
[Ann 5] Kim, D., Nevatia, R.: A method for recognition and localization of generic objects for indoor navigation. ARPA Image Understanding Workshop (1994).
Synopsis: A journal article which uses multiple cameras to employ stereo vision to obtain depth information.
Reliability: Dongsung Kim wrote this while at the Institute for Robotics & Intelligent Systems, University of Southern California (USC).
[Ann 6] Munoz-Salinas, R., Aguirre, E., Garcia-Silvente, M., Gonzalez, A.: Door-detection using computer vision and fuzzy logic. In: Proceedings of the 6th WSEAS International Conference on Mathematical Methods & Computational Techniques in Electrical Engineering (2004).
Synopsis: This paper uses fuzzy logic to implement door-detection. Features of the segments like size, direction or the distance between them are measured and analyzed using fuzzy logic in order to establish a membership degree of the segments on the defined fuzzy concepts.
Reliability: These researchers are from the Department of Computer Science and Artificial Intelligence at the University of Granada.
[Ann 7] Murillo, A.C., Kosecka, J., Guerrero, J.J., Sagues, C.: Visual door detection integrating appearance and shape cues. Robotics and Autonomous Systems (2008).
Synopsis: This article attempts to do door detection using 2 classifiers based on color and shape, which is dependent on all doors being the same.
Reliability: These researchers are from the University of Zaragoza, Spain and the Department of Computer Science, George Mason University, Fairfax, USA.
[Ann 8] OpenCV Canny Edge Detector Documentation. Accessed 05/12/2016. http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.html
Synopsis: This is the documentation for using OpenCV's Canny Edge Detector. OpenCV is an open-source, cross-platform library for Computer Vision, originally developed by Intel.
Reliability: OpenCV is a well-established library. It has over 47 thousand in its user community with over 9 million downloads.
[Ann 9] Javascript Quiz Code. Accessed 05/14/2016. http://www.javascriptsource.com/miscellaneous/basic-javascript-quiz.html
Synopsis: This article goes over how to create a simple quiz on a webpage.
Reliability: The JavaScript Source is an excellent JavaScript resource with tons of "cut and paste" JavaScript examples for your Web pages.
[Ann 10] He, X., Yung, N.: Corner detector based on global and local curvature properties. Optical Engineering 47(5) (2008).
Synopsis: This paper goes over a curvature-based detection algorithm for corners.
Reliability: He is from the CS department at the University of Hong Kong and Yung is from the Department of Electrical and Electronic Engineering at the same university.
[Img 1] - Yang, X. and Tian Y., Robust Door Detection in Unfamiliar Environments by Combining Edge and Corner Features. IEEE Computer Society Workshop on Computer Vision Applications for the Visually Impaired, 2010.
[Img 2] - How Do Rangefinders Work. Accessed 05/02/2016. http://www.rangefinder101.com/wp-content/uploads/how-do-rangefinders-work.jpg
[Img 3] - OpenCV Forum. Accessed 05/10/2016. http://answers.opencv.org/question/57688/does-camera-rotation-help-with-a-stereo-depth-map/
[Img 4] - Gaussian Smoothing. Accessed 05/10/2016. http://homepages.inf.ed.ac.uk/rbf/HIPR2/gsmooth.htm


Crystal Butterfly is an HTML5 game.
The goal of this game is to get the highest score.
You have match minimum 3 items vertically or horizontally to win points.
When you arrive to a specific amount of points your level is increased. You win more points and time decrease faster.
There are 3 modes of difficulty.
Each week the score board will be reveiwed and the highest scorer will win a 25% discount. Be sure to fill out our form so we can varify your win.


Copyright © 2009 Jacob Torrey<EMAIL_ADDRESS>Authors: Jacob Torrey (torreyji@clarkson.edu).
| accept/1 | Returns a function to accept a socket or connection. |
| close/1 | Returns a function to close the socket. |
| set_control/1 | Returns a function that sets the controlling process of Sock to Pid. |
| start/1 | Returns a function (arity 1) for starting a server socket with default socket options. |
| start/2 | Returnes a function (arity 1) for starting a server socket with passed socket options. |
accept(X1) -> any()
Returns a function to accept a socket or connection
close(X1) -> any()
Returns a function to close the socket
set_control(X1::tcp | udp | sctp) -> 'fun'()
Returns a function that sets the controlling process of Sock to Pid
start(X1::tcp | udp | sctp) -> 'fun'()
Returns a function (arity 1) for starting a server socket with default socket options
start(X1::tcp | udp | sctp, Options::list()) -> 'fun'()
Returnes a function (arity 1) for starting a server socket with passed socket options
Generated by EDoc, Nov 5 2009, 11:34:37.


Bibliography¶
- BNO04
Albert-Laszlo Barabasi and Zoltan N Oltvai. Network biology: understanding the cell’s functional organization. Nature reviews. Genetics, 5:101–13, 03 2004. doi:10.1038/nrg1272.
- BBS15
Anas Belahcen, Monica Bianchini, and Franco Scarselli. Web Spam Detection Using Transductive-Inductive Graph Neural Networks, pages 83–91. Springer International Publishing, Cham, 2015. URL: https://doi.org/10.1007/978-3-319-18164-6_9, doi:10.1007/978-3-319-18164-6_9.
- BM13
Monica Bianchini and Marco Maggini. Supervised neural network models for processing graphs. In Handbook on Neural Information Processing, volume 49 of Intelligent Systems Reference Library, pages 67–96. Springer, 2013.
- New10
Mark Newman. Networks: An Introduction. Oxford University Press, Inc., New York, NY, USA, 2010. ISBN<PHONE_NUMBER>,<PHONE_NUMBER>650.
- RTD+18
Alberto Rossi, Matteo Tiezzi, Giovanna Maria Dimitri, Monica Bianchini, Marco Maggini, and Franco Scarselli. Inductive-transductive learning with graph neural networks. In ANNPR. 2018.
- SGT+09a
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. Computational capabilities of graph neural networks. IEEE Transactions on Neural Networks, 20:81–102, 2009.
- SGT+09b
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE Transactions on Neural Networks, 20:61–80, 2009.
- Spo03
Olaf Sporns. Graph Theory Methods for the Analysis of Neural Connectivity Patterns, pages 171–185. Springer US, Boston, MA, 2003. URL: https://doi.org/10.1007/978-1-4615-1079-6_12, doi:10.1007/978-1-4615-1079-6_12.


This page uses the amp-user-notification to display a cookie concent banner at the bottom of the page.
When user dismisses the notification, the amp-analytics component is used to post a 'cookie-concent-ping' request to an analytics server.
The analytics server doesn't exist so you will see an error in developer console.


--- layout: post title: "MacOSX Theme for Win7 Laptop" comments: true ---
I really like MacOSX and am not a fan that it cannot run natively on my laptop. There my laptop now looks as follows:
After seeing this a few people have asked where I got the theme from. Here are the steps to recreate it:
1. Basically I took a MaxOSX Win 7 theme that was already created - available here - (rehosted by me as first was available after you took surveys etc)
2. After that was installed and working I installed RocketDock
3. Configure RocketDock
4. Enjoy MacOSX theme :)
(PS i have got a full max OSX theme including shell extensions and new icon packs for Windows but its a risky install process – if you want it then let me know)


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@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import controllers.ileQuery.routes.FindConsignmentController @import models.notifications.queries.{MucrInfo, UcrInfo} @import uk.gov.hmrc.govukfrontend.views.html.components.{GovukTag, Tag, Text} @import views.components.BackButton @import views.html.components.gds.gds_main_template @import views.html.components.ilequery._ @import views.html.components.print_page_button @import views.Title @this( govukLayout: gds_main_template, summarySection: response_mucr_summary, parentSection: response_parent, previousMovementsSection: response_previous_movements, associatedConsignmentsSection: response_associated_consignments, responseButtons: response_buttons, govukTag: GovukTag, printButton: print_page_button ) @(info: MucrInfo, parentMucr: Option[MucrInfo], associatedConsignments: Seq[UcrInfo])(implicit request: Request[_], messages: Messages) @govukLayout( useCustomContentWidth = true, title = Title("ileQueryResponse.mucr.title"), backButton = Some(BackButton(messages("site.back"), FindConsignmentController.displayPage)) ) {


"Simplicity is the ultimate sophistication." - Leonardo da Vinci
"It's not the length of my life that matters, it's the depth." - চাঁদের পাহাড়, বিভূতিভূষণ বন্দ্যোপাধ্যায় (Translated)
"We draw the new map, son. The rule of expedition." - চাঁদের পাহাড়, বিভূতিভূষণ বন্দ্যোপাধ্যায় (Translated)
"It is better to travel well, than to arrive." - চাঁদের পাহাড়, বিভূতিভূষণ বন্দ্যোপাধ্যায় (Translated)
"Listen more, talk less and be decisive when the time comes." - Satya Nadella
"One of the greatest tragedies in life is to lose your own sense of self and accept the version of you that is expected by everyone else." - ???
"Hard work without talent is a shame, but talent without hard work is a tragedy." - Robert Half
"Despise chaos, create order." - Origin, Dan Brown
"When the going gets tough, the tough get going." - Joseph P. Kennedy
“Human beings are funny. They long to be with the person they love but refuse to admit openly. Some are afraid to show even the slightest sign of affection because of fear. Fear that their feelings may not be recognized, or even worst, returned. But one thing about human beings puzzles me the most is their conscious effort to be connected with the object of their affection even if it kills them slowly within.” - Sigmund Freud
"The interpretation of dreams is the royal road to a knowledge of the unconscious activities of the mind." - The interpretation of dreams, Sigmund Freud
"Illusions commend themselves to us because they save us pain and allow us to enjoy pleasure instead. We must therefore accept it without complaint when they sometimes collide with a bit of reality against which they are dashed to pieces." - Sigmund Freud
"If you really need a mystery, I recommend the human heart." - Harold Finch, Person Of Interest
"Experience is what you get when you didn't get what you wanted." - Randy Pausch , Professor, Carnegie Mellon University
"It does not matter how slowly you go as long as you do not stop." - Confucius
“Words have a magical power. They can bring either the greatest happiness or deepest despair; they can transfer knowledge from teacher to student; words enable the orator to sway his audience and dictate its decisions. Words are capable of arousing the strongest emotions and prompting all men's actions.” - Sigmund Freud
"Memories are always special. Sometimes we laugh by remembering the days we cried, And we cry by remembering the days we laughed." - ???
"I suppose that everyone feels that he is the hero in his own story. But there are no heroes; no villains. Just people doing the best they can." - Harold Finch, Person Of Interest
"The journey of thousand miles begins with one step." - Lao Tzu
“Perhaps our eyes need to be washed by our tears once in a while, so that we can see life with a clearer view again.” - ???


Implements the methodology of "Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Stat. Soc., Ser. B. (with discussion), 79, 959–1035". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.
| Version: | 0.4 |
| Depends: | R (≥ 3.4.2), MASS, parallel |
| Imports: | class, stats |
| Published: | 2017-10-07 |
| Author: | Timothy I. Cannings and Richard J. Samworth |
| Maintainer: | Timothy I. Cannings <cannings at marshall.usc.edu> |
| License: | GPL-3 |
| URL: | http://arxiv.org/abs/1504.04595, http://www-bcf.usc.edu/~cannings/ |
| NeedsCompilation: | no |
| CRAN checks: | RPEnsemble results |
| Reference manual: | RPEnsemble.pdf |
| Package source: | RPEnsemble_0.4.tar.gz |
| Windows binaries: | r-devel: RPEnsemble_0.4.zip, r-release: RPEnsemble_0.4.zip, r-oldrel: RPEnsemble_0.4.zip |
| OS X binaries: | r-release: RPEnsemble_0.4.tgz, r-oldrel: RPEnsemble_0.4.tgz |
| Old sources: | RPEnsemble archive |
Please use the canonical form https://CRAN.R-project.org/package=RPEnsemble to link to this page.


Ruby Documentation vs. JavaScript Documentation
When I first started working on JavaScript and I wanted to take a peek at the document, I noticed that it was not anywhere as comprehensive as the Ruby documentation. The fact that Ruby's docs might be so good is one of the main reasons that Ruby is recommended for beginners. It is incredibly important to be able to read and understand documentation easily in the beginning. If the documentation is too complicated for a beginner, they will be put off thinking that programming is much harder than it actually is. JavaScript is not a language that is recommended for beginners.
When I was working on the numbers comma challenge with my partner, we hard more difficulty with the JavaScript documentation then we did with the Ruby documentation. It took us longer to find functions and it also took us longer to figure out how to use the functions. We had to search all over the Internet for documentation, but with Ruby all we needed was the Ruby documentation.
I think that Ruby's documentation is easier to read is because that a lot of beginner's learn Ruby and people that are using JavaScript are usually more experienced with programming in general if they are dealing with concepts such as front-end and back-end. JavaScript has different frameworks as well, the popular ones being Node.js, Angular.js, BackBone.js and Ember.js. Each of these frameworks are slightly different and they have have slightly different documentation. The only differences between Ruby is between the previous versions.


Cacti (T. pachanoi)
Hey kids what's up? I've found this page to be an excellent resource so I decided to add a San Pedro Cactus experience. I ordered the cactus from a online mail order company. I received three 12 inch long and about 4 inch wide cuttings of Trichocerus pachanoi amazingly, 4 days after I placed in the order. I spent a significant amount of time researching different methods of preparing the cactus for ingestion and decided on making juice with the blender.
First, I notched out all the short spines with a knife which was easier than I had expected. Next, I began the task of removing the plastic like skin. In all the reports I've read folks talk about the ease of peeling san Pedro's, for some reason I found it to be extremely difficult. I couldn't seem to get the skin to peel in large pieces like so many people write about.
After about 2 hours and a huge mess later I was able to cut it into strips and remove the core. I stuck the whole thing in the freezer for about 1.5 hours. I then began dropping the frozen pieces into the blender on slow setting. The blending caused the juice to foam up so I put in on the stove on low heat to make the bubbles dissipate. I then strained the juice through an old sweater and blended the remaining pulp again with a little water and strained again. There was only about 1 cup of pulp left after all the straining so I just tossed it.
I have to tell you I have a lot of experience with psycadelic drugs and I have a pretty high tolerance to most of them. I couldn't have picked a better setting for my first san Pedro experience. I was seeing Yonder Mountain String Band at a beautiful venue called Mishawaka. Its located next to a river in the beautiful poudre canyon. The vibe was very happy to say the least. Many of the folks in attendance had eaten their psycadelic wheaties as well. I started by taking a little pepto to avoid nausea. I then slammed 2x 16 or 18 ounce cups of the tasty brew.
Everybody says it tastes so bad but I found it to taste quite well kinda resembling tea.  I then chased the poison with a cold delicious beer which has never tasted better let me tell ya. After about 15 minutes I could already feel the nausea coming on. for about 1.5 hours I didn't much feel like doing anything but sit next to the river. It took a full 2 hours for the mescaline to take effect. the effects weren't very strong but they were very enjoyable. Colors were very vibrant, the lights and smoke on the stage were very entertaining and the music sounded great. I remember feeling perfectly at peace and having a thought that everything was right.
After I came down I couldn't remember what it was I was thinking, it was really weird. After the show I decided I wanted to be a little higher so I munched some mushrooms and tripped and drank beer all night. This about the time everything went nut-balls, usually a direct effect of psilocybin mushrooms. But this report is about san perdo so we'll stop there.
My next run in with the gag juice was only a couple days later. My girlfriend really wanted to try it so we split what was left (probably about 28 inches). We decided to stay home because it was already 11:00. The nausea wasn't as bad this time for me but it seemed to effect her pretty strongly (she has a weak stomach). She threw up about 1.5 hours after ingestion. This trip was stronger than the first but I think it would have been stronger if I had waited a few more days so my tolerance could have gone down a bit. She said she was tripping pretty hard and her pupils were huge. I would compare both trips with eating about 1.5 hits of blotter acid. I didn't have any strong visuals but colors were VERY intense and fun to look at. Also, a friend brought over a huge digital projector and we played x-box all night on the wall (a very entertaining trip toy).
Well, it wasn't as strong as I had hoped but it was still awesome. I will definitely try it again but next time I will probably try to eat as much as possible (18inches+) to see what San Pedro really has to offer. I think a Juicer would be ideal for making the slime, it might not make it so foamy. It was a lot of work, but I was drinking beer and rocking out to music while I was concocting the funk that is cactus juice. The nausea was more like motion sickness so I don't believe the pepto really helped much.
I enjoyed the trip better than LSD or psilocybin. It seemed more mellow comparable to very low does of MDMA in combination with acid or mushrooms. Another thing I would suggest is to eat the potion at home and let the nausea take its toll before leaving the house because you'll probably want to lay down. I tried to add the answers to all the questions that I still had after doing research, I hope this is useful to someone. Have fun and stay safe y'all.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


class ChangeText : Transition
This transition tracks changes to the text in TextView targets. If the text changes between the start and end scenes, the transition ensures that the starting text stays until the transition ends, at which point it changes to the end text. This is useful in situations where you want to resize a text view to its new size before displaying the text that goes there.
ChangeText()
This transition tracks changes to the text in TextView targets. If the text changes between the start and end scenes, the transition ensures that the starting text stays until the transition ends, at which point it changes to the end text. This is useful in situations where you want to resize a text view to its new size before displaying the text that goes there. |
var changeBehavior: Int
The type of fading animation to use when this transition is run. One of CHANGE_BEHAVIOR_KEEP, CHANGE_BEHAVIOR_OUT, CHANGE_BEHAVIOR_IN, and CHANGE_BEHAVIOR_OUT_IN. |
fun captureEndValues(transitionValues: TransitionValues): Unit |
|
fun captureStartValues(transitionValues: TransitionValues): Unit |
|
fun createAnimator(sceneRoot: ViewGroup, startValues: TransitionValues?, endValues: TransitionValues?): Animator? |
|
fun getTransitionProperties(): Array<String> |
const val CHANGE_BEHAVIOR_IN: Int
Flag specifying that the text changing animation should fade in the end text into the affected target view(s). This transition is typically used in conjunction with an earlier .CHANGE_BEHAVIOR_OUT transition, possibly with other transitions running as well, such as a sequence to fade out, then resize the view, then fade in. |
|
const val CHANGE_BEHAVIOR_KEEP: Int
Flag specifying that the text in affected/changing TextView targets will keep their original text during the transition, setting it to the final text when the transition ends. This is the default behavior. |
|
const val CHANGE_BEHAVIOR_OUT: Int
Flag specifying that the text changing animation should first fade out the original text completely. The new text is set on the target view at the end of the fade-out animation. This transition is typically used with a later .CHANGE_BEHAVIOR_IN transition, allowing more flexibility than the .CHANGE_BEHAVIOR_OUT_IN by allowing other transitions to be run sequentially or in parallel with these fades. |
|
const val CHANGE_BEHAVIOR_OUT_IN: Int
Flag specifying that the text changing animation should first fade out the original text completely and then fade in the new text. |


We would like you back with us at Metisa
As an early user of Metisa, we would like to invite you to use Metisa again. This time, with many fantastic features available to you for free.
Over the past few months, we have learned a lot about what our users want from an e-commerce data-to-sales solution. Since the last time we spoke, Metisa has become easier to use and a lot more powerful.
Even though we haven't seen you in a while, we'll always remember you as an early user of Metisa. That's why we'd like to invite you back to use a new and improved Metisa. You will be getting Free features and a renewed Premium Trial!
We hope you'll find value in using Metisa again. If there’s anything you’d like to comment on, please be honest with us at<EMAIL_ADDRESS>Every engineer and data scientist on our team reads your emails, so we can continue to build the best intelligent marketing product, ever.
Sincerely,
The Metisa team
No more 30-days-only trial. Now, access a suite of useful features for Free, and upgrade only when you need Premium features.
Give every customer personalised recommendations powered by data. Place them on your site, even emails. For a limited time, our powerful Widgets are also Free!


Tuning Profile
Editor
New
Load…
Drive
Recup
Ramps
Speed
Max speed:
Warn speed:
Power
Current:
Torque:
Power low:
Power high:
Drive
Drive level:
Auto 100% ref:
Auto min level:
Torque/Speed-Map: Drive
Trq level 1:
…at speed:
Trq level 2:
…at speed:
Trq level 3:
…at speed:
Trq level 4:
…at speed:
Recup
Neutral level:
Brake level:
Auto 100% ref:
Auto min level:
Torque/Speed-Map: Neutral
Trq level 1:
…at speed:
Trq level 2:
…at speed:
Trq level 3:
…at speed:
Trq level 4:
…at speed:
Torque/Speed-Map: Brake
Trq level 1:
…at speed:
Trq level 2:
…at speed:
Trq level 3:
…at speed:
Trq level 4:
…at speed:
General
Limit up:
Limit down:
Smoothing:
Ramp Speeds
Start/reverse:
Accelerate:
Decelerate:
Neutral:
Brake:
Label:
Title:
Reset
Save as…
Save


OpenSpot is an app that allows customers to make reservations for businesses. The business owner needs to fill out our simple onboarding form, following which they will receive a unique reservation form that they can post on social media for their customers. Business owners can view their reservations in the admin dashboard.
During the COVID-19 pandemic, a lot of businesses want to open up, but they don't want to have too many people
at a time, and don't want any queues. This app is aimed at solving this problem as business owners can set the
maximum amount of people they want in their store and the amount of time per person.
As a result, customers can simply book some time online and show up without having to queue!
OpenSpot is Free and Open Source Software. For support inquiries, contact<EMAIL_ADDRESS>OpenSpot data is stored on Google Cloud. OpenSpot source code is available at Github.


--- layout: work permalink: /work/example title: Example description: This is an example published-url: https://google.com cdn: example tags: ['example'] require: ['modal', 'menu', 'nav-bar', 'arrow-up'] --- {% include work-head.html %} {% include work-about.html %}
{% include work-modal.html %} {% include work-version.html %}


Philadelphia Eagles to host HS football game after shooting
PLEASANTVILLE, N.J. (AP) - The Philadelphia Eagles have offered to host a high school football game that was interrupted by a shooting that left a 10-year-old boy in critical condition.
The New Jersey State Interscholastic Athletic Association says in a release Monday that the game between Pleasantville High School and Camden High School will resume at Lincoln Financial Field on Wednesday.
The Eagles offered up the stadium to play out the remaining 17 minutes of the playoff game that was cut short when three people were shot Friday in Pleasantville.
Six men have been charged in connection to the shooting, including a 27-year-old man who was shot.
A 15-year-old male was also grazed in the shooting.
The governing organization says the game will be closed to the general public but will be streamed live and free-of-charge on NJ.com.
Copyright 2019 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.


Your personal grade book
| Student ID | Student Name | Grade Level | Grade Average |
|---|---|---|---|
| 1 | Placeholder | Placeholder2 | Placeholder3 |
| 2 | Placeholder | Placeholder2 | Placeholder3 |
| 3 | Placeholder | Placeholder2 | Placeholder3 |
| 4 | Placeholder | Placeholder2 | Placeholder3 |
| 5 | Placeholder | Placeholder2 | Placeholder3 |
| 6 | Placeholder | Placeholder2 | Placeholder3 |
| 7 | Placeholder | Placeholder2 | Placeholder3 |
| 8 | Placeholder | Placeholder2 | Placeholder3 |
| 9 | Placeholder | Placeholder2 | Placeholder3 |
| 10 | Placeholder | Placeholder2 | Placeholder3 |


Try your hand at the memory game. It`s very simple - turn over one tile and then try to find a matching picture


What? This web application does a fuzzy lookup of wireline log mnemonics. Given any string, it returns the best guess from its database.
Whence? The database was compiled and curated from the resources on the SPWLA's website.
How? There are two ways to use the app: go to the home page and use the form; or use the web API.
Why? The app is an experiment to see if we could improve on existing curve search, especially providing a machine-readable output, and to help us learn to program.
Built by @kwinkunks and @EvanBianco in December 2012.
The data were downloaded, concatenated, and curated from the SPWLA's useful cross-company resources.
This web app is running on the amazing Google App Engine.
Thank you to the following people for inspiration, comments, and testing: Martin, Alexander, Chris, Matthew, and Donatella.


Sorry about missing class yesterday!
Tomorrow, we’ll have a short practice quiz to see how prepared you are for the first fundamental’s assessment, which will happen on Friday or Monday. On that assessment, you’ll have to write (in chorale or keyboard style) chord progressions that move from various DP chords to D chords.
To prepare:
-
If you haven’t already done the workbook assignment that I asked you to complete for Monday, please do that!
-
I’d like you to skim Chapter 9 (Text and Examples) for information about ii6, ii6/5, IV6, and ii4/2. Don’t read the whole chapter. Just look for information about these chords. What sorts of generalizations can you make?
Finally, I made a short video (below) that you should watch before tomorrow. It’s an introduction to the keyboard tutorial that you’ll have to attempt next week.


Projects
Contact Me
Shade:
The Portfolio
fCC Project 5:
Personal Portfolio
fCC Project 4:
Technical Documentation Page
fCC Project 3:
Product Landing Page
fCC Project 2:
Feedback Form
fCC Project 1:
Tribute Page


Procedure for APK installation from the web
On your Android device; Check the option under "Settings" / "Applications" or
"Security" / "Unknown sources".
|
Procedure for APK installation directly from a PC
Short video of the process
- Connect Android device to PC via USB cable and turn on USB storage.
- Copy .apk file to attached device's storage.
- Turn off USB storage and disconnect it from PC.
- Check the option Settings / Applications / Unknown sources.
- Open FileManager app and click on the copied .apk file.
It will ask you whether to install this app or not. Click Yes or OK.
This procedure works even if ADB is not available.
|


This tool allows you to tweek your dice to find the perfect difficulty setting for your need.
The table has 4 columns showing the amount of Success, Advantage, Triumph and Despair you can expect per Roll on average.
A negative Success/Advantage indicates a number of Failure/Threat instead.
For example a value of :
The value computed are the Expected Values, see Wikipedia article
The scale of color is the following:
-4
-3
-2
-1
0
1
2
3
4
5
6
7


Plus-F
ZX Spectrum over IP
HOME
DOWNLOAD
PLAY
NETWORK
GAMES
BLOG
CREDITS
Version 1.4
Support for loading TAP and TZX files inside ZIP files.
Integration with World Of Spectrum archive.


I expected him to pay no attention to my entry, and not even to glance in my direction, as if a million dollars were a trifling matter, until we were alone.
Before I could stop myself I opened my mouth and blurted the truth.


Here you can view all registered drivers.
Here you can add a new driver.
Here you can view all registered vehicles.
Here you can add a new vehicle.
Here you view custom reports.
Distance traveled by vehicle: km
From date:
To date:
Distance traveled by driver: km
From date:
To date:


Used to specify the existing type that is aliased by means of the typedef.
The optional <cxxTypedefDeclaredType> element contains a CDATA field and an <apiRelation> element.
The CDATA field may be used to show any C++ keywords associated with the typedef (e.g. const or static). The <apiRelation> element provides a links to the documentation of the existing type referred to by the typedef.
<cxxTypedefDeclaredType>const <apiRelation href="GUID-FC2F2B93-3D18-3BCC-9FD6-6BC6B240B667.xml">TUint</apiRelation></cxxTypedefDeclaredType>
<cxxTypedefDeclaredType> <apiRelation href="GUID-12C65B99-553E-35CA-B36F-2F77BFC972B3.xml">CArrayFixFlat</apiRelation>< <apiRelation href="GUID-C8DA9E1C-33A6-33B3-8D75-6B5F6F3BA20F.xml">CProtocolBase</apiRelation> * > *</cxxTypedefDeclaredType>


Frequently Asked Questions
What is the difference between
"Structure Revision"
,
"Structure Data"
and
"XYZ Coordinates"
data types?
What is the difference between
"Ensemble"
and
"XYZ Coordinates"
data types?


Web Dev 101 - Day 2
Welcome to Web Dev 101 series!
Heading 1
Heading 2
Heading 3
Heading 4
Heading 5
Heading 6
This text is bold
This text is important!
This text is italic
This text is emphasized
Welcome to Web Dev 101.
This is some smaller text.
I love Mountains Beaches.
This is subscripted text.
This is superscripted text.
- Item one
- Item two
Google Developers
Google Developers (Open in New Tab)
| First Name |
Last Name |
| Kartik |
Derasari |
| John |
Doe |


Programming Alpha is a machine question-answering AI system.
You would prefer to ask some concrete questions here.
Some supplementary description about the system.
The service currently support programming related topics, which serves for relative
engineers and learners.
Our system currently DO NOT SUPPORT MATHEMATIC or CODE comprehension,
so 2 special words, the [CODE] (refers to a code snippet) and [MATH](refers to a mathematic
expression), might appear in the generated answers.
The first block above(blow the "Ask Programming Alpha" button) is the generated answer, and the rest
several
block are the brief question title of the relevant useful posts the system read
for answer generating. You can search them via Google search engine if you wish to attend more detailed
information.


Thanks to all your contributions, mainly through Speedsolving forum (and Stefan who did a summay of some of the queries done there), here you are some examples of queries to introduce and edit in the input. Someone may not work, I haven't checked all of them.