I am a beginner in Openmodelica and trying to model a flow system with standard libraries. As shown in the picture below, I would like to simulate a system flow from different sources, each with different fluids (gases).
Example:
source_fluid_1: N2
source_fluid_2: Ar
mixed_fluid: fluid mixture of
source_fluid_1 and source_fluid_2
sink
Example of the flow system
For source_fluid_1 and source_fluid_2 I have set the composition X=1 each. In the volume mixed_fluid I have as X_start {0.5, 0.5}.
According to the error information
Base class Medium not found in scope Fluid_Mixture_Example
andÂ
Class Medium.ExtraProperty not found in scope FluidPort
it seems that I am doing something wrong with the definition of the medium.
But unfortunately I can't get any further here.
With the same fluid (gas) in both sources the model runs without problems.
In addition, I am not sure if I am doing the settings in the composition correctly.
I have looked at the Trace Substances in Modelica's examples, but they don't seem to cover my case.
I am very grateful if someone could help me.
model Fluid_Mixture_Example
Modelica.Fluid.Sources.Boundary_pT source_fluid_1(redeclare package Medium = Modelica.Media.IdealGases.SingleGases.N2, T = system.T_ambient, X = {1}, nPorts = 1, p = 5e5)
Modelica.Fluid.Sources.Boundary_pT source_fluid_2(redeclare package Medium = Modelica.Media.IdealGases.SingleGases.Ar, T = system.T_ambient, X = {1}, nPorts = 1, p = 10e5)
Modelica.Fluid.Sources.FixedBoundary sink(redeclare package Medium = Medium, nPorts = 1)
Modelica.Fluid.Fittings.SimpleGenericOrifice orifice1(redeclare package Medium = Medium, diameter = 0.0015, zeta = 1)
Modelica.Fluid.Fittings.SimpleGenericOrifice orifice2(redeclare package Medium = Medium, diameter = 0.0015, zeta = 1)
Modelica.Fluid.Vessels.ClosedVolume mixed_fluid(V = 1, X_start = {0.5, 0.5}, massDynamics = Modelica.Fluid.Types.Dynamics.FixedInitial, nPorts = 3, use_portsData = false)
end Fluid_Mixture_Example;
OpenModelica v1.18.1 (64-bit)
Related
I'm trying to implement Double DQN (not to be confused with DQN with a slightly delayed Q-target network) in PyTorch to train an agent to play an Atari OpenAI Gym game. Here I discuss the implementation of the following formula:
Update of Q-network, formula taken from Sutton & Barto.
My first implementation is:
Q_pred = self.Q_1.forward(s_now)[T.arange(batch_size), actions.long()]
Q_next_all = self.Q_1.forward(s_next)
maxA_id = T.argmax(Q_next_all, dim=1)
Q_pred2 = self.Q_2.forward(s_next)[T.arange(batch_size), maxA_id]
Q_target = (rewards + (~dones) * self.GAMMA * Q_pred2).detach()
self.Q_1.optimizer.zero_grad()
self.Q_1.loss(Q_target, Q_pred).backward()
self.Q_1.optimizer.step()
(Q_1 and Q_2 are nn.Module classes, and all of the variables involved here are already torch tensors lying in the GPU.)
I noticed that my program ran much slower than a previous implementation which used plain DQN.
I realized that I can combine the batches entering Q_1, so there will be one combined batch being forwarded in the neural network, instead of two batches in sequence. The code becomes:
s_combined = T.cat((s_now, s_next))
Q_combined = self.Q_1.forward(s_combined)
Q_pred = Q_combined[T.arange(batch_size), actions.long()]
Q_next_all = Q_combined[batch_size:]
Q_pred2_all = self.Q_2.forward(s_next)
maxA_id = T.argmax(Q_next_all, dim=1)
Q_pred2 = Q_pred2_all[T.arange(batch_size), maxA_id]
Q_target = (rewards + (~dones) * self.GAMMA * Q_pred2).detach()
self.Q_1.optimizer.zero_grad()
self.Q_1.loss(Q_target, Q_pred).backward()
self.Q_1.optimizer.step()
(This proves that I understand how to do batch training in PyTorch, so don't mark this as a duplicate of this question.)
Furthermore, I realized that Q_1 and Q_2 can process their batches in parallel. So I looked up how to do multiprocessing in PyTorch. Unfortunately, I couldn't find a good example. I tried to adapt a code that looks similar to my scenario, and my code becomes:
def spawned():
s_combined = T.cat((s_now, s_next))
Q_combined = self.Q_1.forward(s_combined)
Q_pred = Q_combined[T.arange(batch_size), actions.long()]
Q_next_all = Q_combined[batch_size:]
mp.set_start_method('spawn', force=True)
p = mp.Process(target=spawned)
p.start()
Q_pred2_all = self.Q_2.forward(s_next)
p.join()
maxA_id = T.argmax(Q_next_all, dim=1)
Q_pred2 = Q_pred2_all[T.arange(batch_size), maxA_id]
Q_target = (rewards + (~dones) * self.GAMMA * Q_pred2).detach()
self.Q_1.optimizer.zero_grad()
self.Q_1.loss(Q_target, Q_pred).backward()
self.Q_1.optimizer.step()
This crashes with the error message:
AttributeError: Can't pickle local object 'Agent.learn.<locals>.spawned'
So how do I make this work?
(Achieving this in CUDA programming is trivial. One simply launches two device kernels using a sequential host code, and the two kernels are automatically computed in parallel in the GPU.)
I have recently started exploring the QuantLib option pricing libraries for python and have come across an error that I don't seem to understand. Basically, I am trying to price an Up&Out Barrier option using the Heston model. The code that I have written has been taken from examples found online and adapted to my specific case. Essentially, the problem is that when I run the code below I get an error that I believe is triggered at the last line of the code, i.e. the european_option.NPV() function
*** RuntimeError: wrong argument type
Can someone please explain me what I am doing wrong?
# option inputs
maturity_date = ql.Date(30, 6, 2020)
spot_price = 969.74
strike_price = 1000
volatility = 0.20
dividend_rate = 0.0
option_type = ql.Option.Call
risk_free_rate = 0.0016
day_count = ql.Actual365Fixed()
calculation_date = ql.Date(26, 6, 2020)
ql.Settings.instance().evaluationDate = calculation_date
# construct the option payoff
european_option = ql.BarrierOption(ql.Barrier.UpOut, Barrier, Rebate,
ql.PlainVanillaPayoff(option_type, strike_price),
ql.EuropeanExercise(maturity_date))
# set the Heston parameters
v0 = volatility*volatility # spot variance
kappa = 0.1
theta = v0
hsigma = 0.1
rho = -0.75
spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))
# construct the Heston process
flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date,
risk_free_rate, day_count))
dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date,
dividend_rate, day_count))
heston_process = ql.HestonProcess(flat_ts, dividend_yield,
spot_handle, v0, kappa,
theta, hsigma, rho)
# run the pricing engine
engine = ql.AnalyticHestonEngine(ql.HestonModel(heston_process),0.01, 1000)
european_option.setPricingEngine(engine)
h_price = european_option.NPV()
The problem is that the AnalyticHestonEngine is not able to price Barrier options.
Check here https://www.quantlib.org/reference/group__barrierengines.html for a list of Barrier Option pricing engines.
I'm new to python. I'm searching on google about python code to get street sign detection, I found some code but I can't understand what the code means.
elif dominant_color[0] > 80:
zone_0 = square[square.shape[0]*3//8:square.shape[0]
* 5//8, square.shape[1]*1//8:square.shape[1]*3//8]
cv2.imshow('Zone0', zone_0)
zone_0_color = warnadominan(zone_0, 1)
zone_1 = square[square.shape[0]*1//8:square.shape[0]
* 3//8, square.shape[1]*3//8:square.shape[1]*5//8]
cv2.imshow('Zone1', zone_1)
zone_1_color = warnadominan(zone_1, 1)
zone_2 = square[square.shape[0]*3//8:square.shape[0]
* 5//8, square.shape[1]*5//8:square.shape[1]*7//8]
cv2.imshow('Zone2', zone_2)
zone_2_color = warnadominan(zone_2, 1)
Thanks in advance
Assuming square has shape (8, 8)
I think this diagram might help:
Edit: The code is trying to extract the zones shown in the image
I'm trying to make tensorflow mfcc give me the same results as python lybrosa mfcc
i have tried to match all the default parameters that are used by librosa
in my tensorflow code and got a different result
this is the tensorflow code that i have used :
waveform = contrib_audio.decode_wav(
audio_binary,
desired_channels=1,
desired_samples=sample_rate,
name='decoded_sample_data')
sample_rate = 16000
transwav = tf.transpose(waveform[0])
stfts = tf.contrib.signal.stft(transwav,
frame_length=2048,
frame_step=512,
fft_length=2048,
window_fn=functools.partial(tf.contrib.signal.hann_window,
periodic=False),
pad_end=True)
spectrograms = tf.abs(stfts)
num_spectrogram_bins = stfts.shape[-1].value
lower_edge_hertz, upper_edge_hertz, num_mel_bins = 0.0,8000.0, 128
linear_to_mel_weight_matrix =
tf.contrib.signal.linear_to_mel_weight_matrix(
num_mel_bins, num_spectrogram_bins, sample_rate, lower_edge_hertz,
upper_edge_hertz)
mel_spectrograms = tf.tensordot(
spectrograms,
linear_to_mel_weight_matrix, 1)
mel_spectrograms.set_shape(spectrograms.shape[:-1].concatenate(
linear_to_mel_weight_matrix.shape[-1:]))
log_mel_spectrograms = tf.log(mel_spectrograms + 1e-6)
mfccs = tf.contrib.signal.mfccs_from_log_mel_spectrograms(
log_mel_spectrograms)[..., :20]
the equivalent in librosa:
libr_mfcc = librosa.feature.mfcc(wav, 16000)
the following are the graphs of the results:
I'm the author of tf.signal. Sorry for not seeing this post sooner, but you can get librosa and tf.signal.stft to match if you center-pad the signal before passing it to tf.signal.stft. See this GitHub issue for more details.
I spent a whole 1 day trying to make them match. Even the rryan's solution didn't work for me (center=False in librosa), but I finally found out, that TF and librosa STFT's match only for the case win_length==n_fft in librosa and frame_length==fft_length in TF. That's why rryan's colab example is working, but you can try that if you set frame_length!=fft_length, the amplitudes are very different (although visually, after plotting, the patterns look similar). Typical example - if you choose some win_length/frame_length and then you want to set n_fft/fft_length to the smallest power of 2 greater than win_length/frame_length, then the results will be different. So you need to stick with the inefficient FFT given by your window size... I don't know why it is so, but that's how it is, hopefully it will be helpful for someone.
The output of contrib_audio.decode_wav should be DecodeWav with { audio, sample_rate } and audio shape is (sample_rate, 1), so what is the purpose for getting first item of waveform and do transpose?
transwav = tf.transpose(waveform[0])
No straight forward way, since librosa stft uses center=True which does not comply with tf stft.
Had it been center=False, stft tf/librosa would give near enough results. see colab sniff
But even though, trying to import the librosa code into tf is a big headache. Here is what I started and gave up. Near but not near enough.
def pow2db_tf(X):
amin=1e-10
top_db=80.0
ref_value = 1.0
log10 = 2.302585092994046
log_spec = (10.0/log10) * tf.log(tf.maximum(amin, X))
log_spec -= (10.0/log10) * tf.log(tf.maximum(amin, ref_value))
pow2db = tf.maximum(log_spec, tf.reduce_max(log_spec) - top_db)
return pow2db
def librosa_feature_like_tf(x, sr=16000, n_fft=2048, n_mfcc=20):
mel_basis = librosa.filters.mel(sr, n_fft).astype(np.float32)
mel_basis = mel_basis.reshape(1, int(n_fft/2+1), -1)
tf_stft = tf.contrib.signal.stft(x, frame_length=n_fft, frame_step=hop_length, fft_length=n_fft)
print ("tf_stft", tf_stft.shape)
tf_S = tf.matmul(tf.abs(tf_stft), mel_basis);
print ("tf_S", tf_S.shape)
tfdct = tf.spectral.dct(pow2db_tf(tf_S), norm='ortho'); print ("tfdct", tfdct.shape)
print ("tfdct before cut", tfdct.shape)
tfdct = tfdct[:,:,:n_mfcc];
print ("tfdct afer cut", tfdct.shape)
#tfdct = tf.transpose(tfdct,[0,2,1]);print ("tfdct afer traspose", tfdct.shape)
return tfdct
x = tf.placeholder(tf.float32, shape=[None, 16000], name ='x')
tf_feature = librosa_feature_like_tf(x)
print("tf_feature", tf_feature.shape)
mfcc_rosa = librosa.feature.mfcc(wav, sr).T
print("mfcc_rosa", mfcc_rosa.shape)
For anyone still looking for this: I had a similar problem some time ago: Matching librosa's mel filterbanks/mel spectrogram to a tensorflow implementation. The solution was to use a different windowing approach for the spectrogram and librosa's mel matrix as constant tensor. See here and here.
from fipy import *
nx = 50
dx = 1.
mesh = Grid1D(nx=nx, dx=dx)
phi = CellVariable(name="solution variable",
mesh=mesh,
value=0.)
D = 1.
valueLeft = 1
valueRight = 0
phi.constrain(valueRight, mesh.facesRight)
phi.constrain(valueLeft, mesh.facesLeft)
eqX = TransientTerm() == ExplicitDiffusionTerm(coeff=D)
timeStepDuration = 0.9 * dx**2 / (2 * D)
steps = 100
phiAnalytical = CellVariable(name="analytical value",
mesh=mesh)
viewer = Viewer(vars=(phi, phiAnalytical),
datamin=0., datamax=1.)
viewer.plot()
x = mesh.cellCenters[0]
t = timeStepDuration * steps
try:
from scipy.special import erf
phiAnalytical.setValue(1 - erf(x / (2 * numerix.sqrt(D * t))))
except ImportError:
print "The SciPy library is not available to test the solution to \
the transient diffusion equation"
for step in range(steps):
eqX.solve(var=phi,
dt=timeStepDuration)
viewer.plot()
I am trying to implement an example from the fipy examples list which is the 1D diffusion problem. but I am not able to view the result as a plot.
I have defined viewer correctly as suggested in the code for the example. Still not helping.
The solution vector runs fine.
But I am not able to plot using the viewer function. can anyone help? thank you!
Your code works fine on my computer. Probably is a problem with a plotting library used by FiPy.
Check if the original example works (just run this from the fipy folder)
python examples/diffusion/mesh1D.py
If not, download FiPy from the github page of the project https://github.com/usnistgov/fipy and try the example again. If not, check if the plotting libraries are correctly installed.
Anyways, you should specify the platform you are using and the errors you are getting. The first time I had some problems with the plotting libraries too.