Input and target format for multidimentional time-series regression

I'm trying to solve a problem I had intended for tensorflow in Keras.
I've gotten a lot further using Keras, but I'm still unclear on how best to represent my sequence data. The following code works quite well using only one input sample and one target sample:
import numpy as np

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM

# This does work by using only one sample:
data = [[0,0,0,0,0,0,0,0,0,2,1]]
data = np.array(data, dtype=float)
target = [0,0,0,0,0,0,0,0,2,1,0]
target = np.array(target, dtype=float)

data = data.reshape((1, 1, 11)) # Single batch, 1 time steps, 11 dimentions
target = target.reshape((-1, 11)) # Corresponds to shape (None, 11)

# Build Model
model = Sequential()
model.add(LSTM(11, input_shape=(1, 11), unroll=True))
model.add(Dense(11))
model.compile(loss='mean_absolute_error', optimizer='adam')
model.fit(data, target, nb_epoch=1000, batch_size=1, verbose=2)

# Do the output values match the target values?
predict = model.predict(data)
print repr(data)
print repr(predict)

But I can't get it to work with multiple samples:
import numpy as np

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM

# Input sequence
wholeSequence = [[0,0,0,0,0,0,0,0,0,2,1],
                 [0,0,0,0,0,0,0,0,2,1,0],
                 [0,0,0,0,0,0,0,2,1,0,0],
                 [0,0,0,0,0,0,2,1,0,0,0],
                 [0,0,0,0,0,2,1,0,0,0,0],
                 [0,0,0,0,2,1,0,0,0,0,0],
                 [0,0,0,2,1,0,0,0,0,0,0],
                 [0,0,2,1,0,0,0,0,0,0,0],
                 [0,2,1,0,0,0,0,0,0,0,0],
                 [2,1,0,0,0,0,0,0,0,0,0]]

# Preprocess Data: (This does not work)
wholeSequence = np.array(wholeSequence, dtype=float) # Convert to NP array.
data = wholeSequence[:-1] # all but last
target = wholeSequence[1:] # all but first

# This does not work:
# Reshape training data for Keras LSTM model
# The training data needs to be (batchIndex, timeStepIndex, dimentionIndex)
data = data.reshape((1, 9, 11)) # Single batch, 9 time steps, 11 dimentions
target = target.reshape((-1, 11)) # Corresponds to shape (None, 11)

# Build Model
model = Sequential()
model.add(LSTM(11, input_shape=(9, 11), unroll=True))
model.add(Dense(11))
model.compile(loss='mean_absolute_error', optimizer='adam')
model.fit(data, target, nb_epoch=1000, batch_size=1, verbose=2)

# Do the output values match the target values?
predict = model.predict(data)
print repr(data)
print repr(predict)

Due to this error: ValueError: Input arrays should have the same number of samples as target arrays. Found 1 input samples and 9 target samples.
What am I doing wrong with array shape?
It would be really nice if Keras facilitates this use case such that a single data structure holds the sequence and the fitter would know that for each input X_t, the target is X_(t+1). This would provide some benefits such as the following:

There would be no redundancy in storing the data and targets separately.

One would not have to be concerned with the shape of the input and targets separately.

If you want a prediction for each timestep, set return_sequences=True when creating the LSTM.
If you want to run the same dense model after each timestep after the LSTM, use TimeDistributed(Dense(11)).
Run the below so you can see if your shapes all line up:
model.summary()
print "Inputs: {}".format(model.input_shape)
print "Outputs: {}".format(model.output_shape)
print "Actual input: {}".format(data.shape)
print "Actual output: {}".format(target.shape)

Cheers,
Ben

Thank you for the reply Ben.
First, I'm not sure I'm understanding what a "sample" means in the Keras context. I've been thinking one sample = one time-step, but it seems to mean something else, as a sample can have multiple vectors each of multiple dimensions, whereas I expect only one vector of multiple dimensions for each time-step. Maybe sample actually means batch? (a sampling of input vectors?)
Second, in my single vector example above, I see the following shape outputs from your code above:
Inputs: (None, 1, 11)
Outputs: (None, 11)
Actual input: (1, 1, 11)
Actual output: (1, 11)

I don't know how to reshape to (None,11); the following code does not appear to:
target = target.reshape((-1, 11)) # Corresponds to shape (None, 11)
In my multivector example, I see the same pattern of shapes:
Inputs: (None, 9, 11)
Outputs: (None, 11)
Actual input: (1, 9, 11)
Actual output: (9, 11)

But in this case results in the error above, even though (1,11) != (None, 11) and (9,11) != (None, 11)
I don't understand your suggestions regarding return_sequences or TimeDistributed; I'm not even sure I need the final dense layer at all, but saw it in an example somewhere.
To zoom back to the big picture: Once I get the model to fit the data, I'll want to seed the model with a real data time-step, and then use that prediction (eventually with integer clamping) as the next input to recreate the whole sequence:
prediction = data[0] # first input vector
for step in xrange(steps)
prediction = model.predict(prediction)
print step, prediction

The first dimension of your data is the batch dimension. It will show up as None. It can be any size, as long as it is the same for your inputs and targets. When you're dealing with LSTMs, the batch dimension is the number of sequences, not the length of the sequence.
LSTMs in Keras are typically used on 3d data (batch dimension, timesteps, features).
LSTM without return_sequences will output (batch dimension, output features)
LSTM with return_sequences will output (batch dimension, timesteps, output features)
So, if your input shape is (None, 9, 11) and your actual input shape is (1, 9, 11) that means your batch dimension is 1. If your output shape is (None, 11), then your actual targets need to be (1,11).
The loop you're describing isn't the way to do things in Keras. Run a single vector with shape (number of sequences, steps, features) to calculate the entire series in one go. That way errors can backpropagate through time.
Are you trying to do time-series prediction? I'm not sure what you're trying to build.
Basic timeseries data has an input shape (number of sequences, steps, features). Target is (number of sequences, steps, targets). Use an LSTM with return_sequences.
Probably skip the dense layer for now until you have the basics working.
Cheers,
Ben

Following is my working code (with very low loss). Thanks for the help. I still think a fit function for large sequences that does auto random shuffled batches would be a nice feature, rather than storing redundant information.
import numpy as np

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM

# Input sequence
wholeSequence = [[0,0,0,0,0,0,0,0,0,2,1],
                 [0,0,0,0,0,0,0,0,2,1,0],
                 [0,0,0,0,0,0,0,2,1,0,0],
                 [0,0,0,0,0,0,2,1,0,0,0],
                 [0,0,0,0,0,2,1,0,0,0,0],
                 [0,0,0,0,2,1,0,0,0,0,0],
                 [0,0,0,2,1,0,0,0,0,0,0],
                 [0,0,2,1,0,0,0,0,0,0,0],
                 [0,2,1,0,0,0,0,0,0,0,0],
                 [2,1,0,0,0,0,0,0,0,0,0]]

# Preprocess Data: (This does not work)
wholeSequence = np.array(wholeSequence, dtype=float) # Convert to NP array.
data = wholeSequence[:-1] # all but last
target = wholeSequence[1:] # all but first

# Reshape training data for Keras LSTM model
# The training data needs to be (batchIndex, timeStepIndex, dimentionIndex)
# Single batch, 9 time steps, 11 dimentions
data = data.reshape((1, 9, 11))
target = target.reshape((1, 9, 11))

# Build Model
model = Sequential()
model.add(LSTM(11, input_shape=(9, 11), unroll=True, return_sequences=True))
model.add(Dense(11))
model.compile(loss='mean_absolute_error', optimizer='adam')
model.fit(data, target, nb_epoch=2000, batch_size=1, verbose=2)

What if you have a train and test set that is something like:
x_train= x_train.reshape(1, 1870, 14)
y_train= y_train.reshape(1, 1870, 14)
That's my model.fit(x_train, y_train, , epochs=10, batch_size=1, verbose=2)
But my test data is:
x_test= x_test.reshape(1, 200, 14)
y_test= y_test.reshape(1, 200, 14)
It works for training. My data is since the number of my series is 200 it doesnt work anymore since it is expecting the longer time series of 1870 ..
Thanks, Ill appreciate the help

Can you please clarify what you are trying to do with your model?  Thanks.

@galfaroi Ideally you should break out overlapping windows. Instead of one sequence of 1870, you could have many sequences of let's say 20. Your sequences should be overlapping windows [0-20], [1-21], [2-22], etc, so your final shape would be something like (1850, 20, 14).
Same process for your test data. Break into subsequences of the same length as training.

It is extremely important to have many different ways of slicing your data. If you train on just one super long sequence it will probably not learn anything interesting.
Also, depending on what you decide to do, it may be better to generate subsequences as part of a generator function instead of doing it ahead of time. Check the keras docs for how to write a generator.

I came across this searching for a solution to the same problem, but I'm still not 100% sure how to solve my issue.  My input array consists of N timesteps of 2 samples with an embedded lag of 22 timesteps.  My target is a scalar of N timesteps.  When I try to fit I get an error complaining about 2 input samples and 1 target sample, but isn't LSTM used for scalar outputs?
`Using TensorFlow backend.

Layer (type)                 Output Shape              Param #
lstm_1 (LSTM)                (None, 20000, 2)          200

dense_1 (Dense)              (None, 20000, 1)          3
Total params: 203.0
Trainable params: 203
Non-trainable params: 0.0

Inputs: (None, 20000, 22)
Outputs: (None, 20000, 1)
Actual input: (2, 20000, 22)
Actual output: (1, 20000, 1)
`

@ss32 dimensions are (samples, timesteps, features). "Samples" are data that goes together so it doesn't make any sense when you say you have 2 input samples and 1 output sample. Maybe you are saying you have 1 input sample (with 44 features for each timestep). Maybe you are saying you have 2 input samples and 2 output samples, but the 2 output samples are the same.
The data you're describing is impossible, so please explain what you are trying to do with 2 input samples and 1 output sample. What is your data supposed to represent?
Number of input and output samples have to be the same because a "sample" is an input and output combination.
You might also just be confusing samples and features. 2 input features and 1 output feature is just input (1,2000,2) output (1,2000,1).
On a related note, please do not try to pass a 2000 length sequence to your LSTM.  Best strategy is to slice out many subsequences of maybe length 40 (something bigger than your expected time lag) so your shapes are (None, 40, 2) and (None, 40, 1). If you have very many possible different subsequences you will learn a model that works marginalized over all possible positions in the sequence. LSTM will get slow after maybe 10 steps, so 2000 is kind of silly, especially if you don't think you have relationships with 1999 lag. Also, if you only have 1 sample, nothing will generalize. You can break that 1 sample into 1960 subsequences of length 40 and you might learn something more meaningful.
Cheers

a "sample" is an input and output combination.
That helps. I was under the impression that a sample would be a reading of the input at some time.

Let's call my input A and my output B.  I'm trying to predict B given an input of time and A.  A and B are strongly correlated, and B has some pseudo-periodic behavior over long enough time scales, so I would like to predict B for some A at a given time.  I have found that I can predict B when looking back at the past ~22 values for A, hence the (2,22,N) array.

On a related note, please do not try to pass a 2000 length sequence to your LSTM.
Maybe LSTM is the wrong layer to use then.   I have the model working as an MLP in Matlab and would like to get it implemented in Keras/Tensorflow.  Regardless, the type of layer doesn't solve my problem of wanting to use a multi-dimensional input.  I found a similar issue here: https://github.com/fchollet/keras/issues/1904 and tried implementing the solution but still ran into the same problem.

@ss32 LSTM is the right layer to use. The problem is that LSTM learns an initial state. If it only ever starts in the same position, it will learn something that might only work from the same starting position. If you train it on different subsequences from different starting positions then it will try to learn something that will work starting from anywhere. This doesn't require any changes to your keras model, but changes to your input and output shaping and preprocessing.
You have a few choices. Easiest option is to flatten everything. If the inputs are (n,depth,3,5) and (n,depth,6), reshape and concatentate into (n,depth,21) then use an LSTM as usual.
I don't see where you are running into problems. Please explain what your data is so we can decide what shape it is supposed to be instead of the other way around.
Standard usage:

input sequences are (n, steps, input features)
output sequences are (n, steps, output features)
Run LSTM with return_sequences=True

If you have only one long sequence instead of short sequences, try breaking them up into reasonably sized chunks.
Cheers

The problem is that LSTM learns an initial state. If it only ever starts in the same position, it will learn something that might only work from the same starting position. If you train it on different subsequences from different starting positions then it will try to learn something that will work starting from anywhere. This doesn't require any changes to your keras model, but changes to your input and output shaping and preprocessing.

You have a few choices. Easiest option is to flatten everything. If the inputs are (n,depth,3,5) and (n,depth,6), reshape and concatentate into (n,depth,21) then use an LSTM as usual.

I see, so I need to break up my N data points into smaller chunks?
I managed to get it working using SimpleRNN but the fit is awful. Wow it's a matter of tuning the model and embedding the time delay again.  My input is now (N,2,1) with target (N, ).
Layer (type)                 Output Shape              Param #
=================================================================
simple_rnn_1 (SimpleRNN)     (None, 1)                 3
_________________________________________________________________
dense_1 (Dense)              (None, 28)                56
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 29
=================================================================
Total params: 88.0
Trainable params: 88
Non-trainable params: 0.0
_________________________________________________________________
Inputs: (None, None, 1)
Outputs: (None, 1)
Actual input: (60000, 2, 1)
Actual output: (60000,)

I'm predicting the solar index, Kp, based on the date and Z component of the Earth's magnetic field.  I've found through a Matlab NARXnet that embedding a time delay of 22 timesteps results in the most accurate predictions, so I'm trying to implement the same network using Keras/TF for validation and it should be less expensive, computationally.
Matlab uses a recurrent network for time series data.  x(t) is my input of [date, bZ] and y(t) is the Kp readings, my targets.

Hopefully that clears things up.  Thanks for your help.

@bstriner do you have an example code that works with the dimensions  (samples, timesteps, features) you described?

@Tchaikovic did you have a dataset or a problem in mind? All LSTM in Keras are (samples, timesteps, features), so any LSTM is an example. The important thing when using them is to understand your dataset so you know what is what and do any necessary preprocessing or reshaping to make the data the right shape.
So, for example, lets say you want to train a language model on 6-word sequences, and you have a vector of words of length n. There are k unique words. First, one-hot encode the words (n,) -> (n, k). Then, roll and concatenate the array to itself (n,k) -> (n-5, 6, k). This array has every 6-word sequence in the data. Each word is encoded as k features (one-hot).

@bstriner ,  I am working on timeseries prediction ( freq DAILY basis)... I am confused with the samples, timesteps, features concept... Lets assume I have 12 features into consideration to predict my output value... and I have 1 year of data ie 365 rows... should I reshape my data as (365,365,12)... should it be interpreted as 1 row or sequence of 12 features represent 1 timestep? appreciated your help in this!

LSTM is probably not going to learn that much over 365 timesteps. Also, you may not be looking for patterns that are 365 timesteps away.
You can shape your data (1,365,12) and run it in one go. The problem is that will probably not generalize or be very meaningful. You are learning a single function that predicts a single batch of data.
Ideally, decide something like 2 weeks is a reasonable amount of data to make a prediction. Reshape your data as (341, 14, 12). That is each 2-week subsequence. There will be repetition. However, you are now learning a function that works on every 2-week subsequence, so the function is more likely to generalize to other data.
Since this involves repetition, it may be more efficient to generate data on the fly using fit_generator. Randomly sample a bunch of starting points, then select those subsequences.

@bstriner Thanks for the explanation :)... I was looking exactly for this core thing... however, could you please tell why you have taken 341? My actual problem is to learn from past 10 years data and make prediction for 1 month window.... however, currently I have data of 1.5 year only... so trying to get intuition on how model works... should I frame like (365, 28, 12) ? does it sound correct?

If you have 5 days of data and look at 3 day windows, there are 3 windows (5-3+1).  If you have 365 days and 14 day windows there should be 352 possible sequences. ignore the 341.
You want to make a 1 month window prediction based on what? The previous 11 months? Then your input should be 365 days of data. If you have 10 years of data that is 36510 possible sequences. So (36510, 365, 12).
If days of the week or which month is part of your model, then you can change things up. The point is that the first dimension is how many sequences, and you want as many as possible so the thing will generalize meaningfully.
As an example of what not to do, make a model that is (1, 365*10, 12). That will learn a function that outputs all 10 years of data, once. The problem is, it will only work on that 10 years of data. If you give it just 9 years of data it might give you something else entirely, because it has only seen one sequence. It might work well on training data but might be junk for other data, because there is in effect only one piece of training data.
So, get creative, and however you can slice things up to make multiple sequences should make a better model.

Hi @bstriner, if we are splitting a sequence into subsequences, does it mean, that one sample has sufficient information for LSTM and we can use shuffling of the dataset during training?

Hi @bstriner, hope you don't mind another question! Earlier in this thread there was a discussion about the dimensions of the input and target arrays. My input array is (1500,50,2) for 1500 samples, 100 timesteps, and 2 features. My target array is just (1500), which consists of binary values (what I'm trying to predict). So far the model I've designed worked. But I was wondering if this makes sense how it's set up.

@nbucklin "worked" is hard to define. You can normally get a model to run but the question is does it do anything meaningful. Do you have any validation data? From your description, you have 1500 independent sequences, each sequence is 100 timesteps, and each sequence predicts a single value.  You are never going to use your model on sequences other than 100 timesteps.
If that is correct, you shouldn't run into the types of issues discussed here. The generalization issues discussed here arise when you have one very long sequence and don't break it up into subsequences. You also run into generalization issues when you want to use the model on sequences that are different sizes than what you trained it on.
If all 1500 are independent sequences, you should set aside a handful as validation.
One minor note: if you don't think the model needs all 100 timesteps to make a decision, you can use return_sequences=True. That way the LSTM will output a guess at each timestep and might get it correct before the 100th timestep. In that case, your targets would be (1500,100).
Cheers

@bstriner Thanks for the advice! That all makes sense to me. Appreciate your continued help in this thread.

Hi bstriner,
My data has n packets (captured from network traffic), each packet has many features f (one of them is time), example (from train_x(4000,41)):
f1 f2 f3 …
pkt1 2   3  3
pkt2 1   3  5
pkt3 2   3  2
pkt4 5   3  1
pkt5 5   3  2
….
And label is train_y(4000,1) for every packets.
I want to use LSTM to classify some adjacent packets into normal or abnormal (timesteps are changed arbitrarily). When I set timesteps=2, each subsequence has 2 rows, and slide window size =1. After shape, like these:
[[[2 3 3]
[3 5 1]]
[[3 5 1]
[2 3 2]]
…. ]
Shape of train_x is: (3998,2,41), easy to shape with code:
train_x=np.array([train_x[i:i+timesteps] for i in range(len(train_x)- timesteps)])
Shape train_y must be (3998,1). I code:
(1)	train_y=np.array(train_y[:train_y.shape[0]-timesteps])
or:
(2)	train_y=np.array(train_y[timesteps:train_y.shape[0]])
which is correct? (1) or (2). Sometime, timesteps is large maybe label is difficult to get exactly.
You see my all processes above whether wrong or correct?
One more question. Sometime, I see data is split separately, like this:
[[[2 3 3]
[3 5 1]]
[[2 3 2]
[5 3 1]]
… ]
When do we split separately?
Thank you.

Hello Everyone,
I am new to keras and I have a question to ask.
I have a traffic prediction problem that might use LSTM to solve.
Basically I have a data that road segments as rows and speed profile of segments as columns and there are 288 columns and each column represent 5 minutes interval speed profile of the segment.
One data represents one day speed profile and I have daily speed profile data for the last 3 months.
The idea is to build keras LSTM model based on these 3 months data and use the model for speed profile prediction. We have real time speed profile updated every 5 minutes and the aim is to use on that day's speed profile update to predict 12 steps ahead speed profile; take in every 5 minutes data and predict the next 12 steps and it needs to continue like that.
Best Regards,
Alim

Hi All,
A fundamental question from my end, I have an input 48672 x 7 which I broke up into overlapping sequences of 96 time steps. So the input to my LSTM is (48577, 96 , 1) and target is (48577, 96, 1), hence I'm trying to predict the next 96 time-steps using a LSTM network.
My first Keras model is
model = Sequential()
model.add(LSTM(24, return_sequences=True, input_shape=(96, 7), implementation=2, unroll=True))
model.add(Dropout(0.5))
model.add(LSTM(12, return_sequences=True, implementation=2, unroll=True))
model.add(Dropout(0.5))
model.add(LSTM(8, return_sequences=True, implementation=2, unroll=True))
model.add(Dropout(0.5))
model.add(TimeDistributed(Dense(1, activation='sigmoid')))
and second model is
model.add(LSTM(96, return_sequences=True, input_shape=(96, 7), implementation=2, unroll=True))
model.add(Dropout(0.5))
model.add(LSTM(48, return_sequences=True, implementation=2, unroll=True))
model.add(Dropout(0.5))
model.add(LSTM(8, return_sequences=True, implementation=2, unroll=True))
model.add(Dropout(0.5))
model.add(TimeDistributed(Dense(1, activation='sigmoid')))
Both models compile and run !
So my questions are:

How is the model able to unroll over 96 time-steps using 24 LSTM cells? Isn't unroll length = sequence length?

Through the TimeDistributed(Dense()) I am making a weight update for each one of the 96 time-steps, am I correct?

Does Keras feed each time-step as a 7-D vector to a LSTM cell, with unroll length = 96 this would correspond to the horizontal LSTM stack of 96 cells capturing the transitions between the time-steps through the states flowing through the layer, right?

Any thoughts? @bstriner @fchollet

In your description I think you meant input is (48577, 96 , 7).

24 units means 24 dimensions in the hidden vector and output vectors in each timestep. Units and steps are unrelated.
You are applying a single matrix over each time step. The output of your lstm will be (n,96,24).  It will apply a 24x1 linear layer. As a side note, due to the broadcasting rules, you should be able to use dense without the timedistributed and it should still work.
basically
Another side note: I think time-locked dropout is becoming standard now if you have sequential data.

No particular reason in your input as to why you need to unroll. I'd be interested in what kind of performance differences you get unrolling v not and w/ different implementation modes.

Thanks for your comments @bstriner , yes I meant that my input is (48577, 96, 7). Could you please clarify the following points as well?

As I understand, there is one weight matrix per layer for transforming the input of the previous layer into the new dimension given by the number of units (24 or 96 ). This weight matrix is shared by all time-steps of the input to a particular layer.

I tried implementing with just Dense, I got an error saying that the dimensions of the target were wrong while executing the command model.compile

I will check time-locked dropout and will also post the results of my performance differences.

Could you please clarify an additional point,
How are the weights updated? Are they updated to minimize the error over all time-steps of a single sequence? Or are they updated to minimize the error over a batch of sequences, as with classical mini-batch optimization. Also is Backpropogation Through Time (BPTT) the default optimization strategy in keras LSTM's?

Sorry, I forgot to include a point, unroll creates multiple copies of the LSTM cell for all time-steps. Hence the number of units (24/96) is unrelated to the number of time-steps. Hence enabling/disabling unroll has no relation to the number of time-steps. Am I right?

Hi  @bstriner
I want to ask about how to model my prediction problem ?. I want to predict speed, using lstm of Keras. I used the window method and got the following form.
X
10,20,20,30,30,40,50,50,50,60
Y
60,60,70,70,70
In my training data I have 6000 rows, in my test data 2000 rows, where I have 10 columns for x, to predict in y, the 5 following values.  I do not use reshape.
model.add(lstm(100, input_shape=(1,10))
model.add(Dense(5))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X, Y, epochs=nb_epoch, batch_size=batch_size, verbose=0, shuffle=False)
I put 5 neurons in a dense layer, so that it has 5 outputs that are compared with Y with the MSE. Is everything ok? Or do you see something wrong? Thank you very much for your answer in advance.

@bstriner Hello Ben, I would like to ask you a question, hopefully, you can help me with it.
I am working on a dataset which has multiple time series each time series corresponding to a particular location and particular search query at the location. (particular location corresponds to a Designated Marketing Area and search term could be anything like airconditioner, sunglasses etc). You can see it in a way where location, search query, and the timestamp forms a primary key. I have encoded the search query and location using one-hot encoding. The data is appended further with Weather Parameters (snowfall, precipitation, temperature etc) and google trend data which are time series features (daily) for each of the row and for all the time series available. Now reaching this stage, I am little confused that how should I represent my data so that I can logically feed it to LSTM based neural network. I understand Keras take 3 Dimension Tensor as an input (in format samples, timestamp, features). I also understand what it means to represent single time series in that format. But not exactly sure how can I represent that dataset I explained to feed it to the neural network, is the LSTM based model even a good choice to generalize features over multiple time series as input, I would want to predict google trend data give the historical data for multiple timesteps in future. I am thinking of applying encoder-decoder type LSTM network architecture, but first I would like to create a simple model with one LSTM layer followed by a densely connected layer.
Thanks for the help in advance!

@bstriner How can I modify your sample code to use generator to feed data?
I'm trying to feed images into time-distributed CNN + LSTM, but I cannot get the dimentsion right.
model = Sequential()
model.add(TimeDistributed(Conv2D(16, (7, 7), padding='same'),input_shape=(None, 540, 960, 1)))
model.add(Activation('relu'))

model.add(TimeDistributed(Conv2D(32, (5, 5), padding='same')))
model.add(Activation('relu'))

model.add(TimeDistributed(Flatten()))
model.add(LSTM(num_classes, return_sequences=True))

model.compile(loss='mean_squared_error', optimizer='adam')

The generator
a = readIMG(filenames[start])  # (540, 960, 1)
b = readIMG(filenames[start + 1])  # (540, 960, 1)
c = readIMG(filenames[start + 2])  # (540, 960, 1)
x_train = np.array([[a, b, c]])  # (1, 3, 540, 960, 1)

ValueError: Error when checking target: expected lstm_1 to have 3 dimensions, but got array with shape (1, 3)

@bstriner  Thank you for your effort.

So, for example, lets say you want to train a language model on 6-word sequences, and you have a vector of words of length n. There are k unique words. First, one-hot encode the words (n,) -> (n, k). Then, roll and concatenate the array to itself (n,k) -> (n-5, 6, k). This array has every 6-word sequence in the data. Each word is encoded as k features (one-hot).

I think this is the key point of LSTM  input.
But this come up a question to me: What is the proper way to pad a timestep sequences when step length is larger than shortest one ?
For example :
Have one million sentences with two word2vec model to train.
I want to predict next word by both forward 20 chars and backward 20 chars . So the time_step is 20 .
If a sentence only have 4 chars , or many sentences are less than 20 (it is normal in Chinese )
So , need pad all chucks of a sentence  to 20 , right ?
Pad Flow:
input :  [‘a’, 'b', 'c'，‘d’]
preprocess forward:  [ '', .. .. ,‘a’]  ,  [ '', .. .. ,‘a’ , ‘b’]      [ '', .. .. ,‘a’ , ‘b’,  'c']    , [ '', .. .. ,‘a’ , ‘b’,  'c',  ‘d’]]
A lot of blanks,  I think this is not efficient. Is there any dynamic way to do this?

I do not understand how to format my input matrix like you are describing @bstriner. I have 702 rows of weekly data with 126 features in my training dataset. I would like my lstm to train on data in batches of 12 weeks (quarterly) and from what I am understanding you to say I want an input size of (690, 12, 126) but that is not an allowable size to reshape my original dataset of size (702, 126) into.
Please help. I cannot seem to figure this out!
Thanks so much for the future replies.

how to leverage a GRU to map the input sequence to
a fixed-sized vector. and to  treate the GRU
unfolds along with a series of time slices.

@bstriner  I have a question If there is Dense layer after LSTM. Will output of that Dense layer depend on all timesteps or only in last timestep?


Setup own AVPlayerItem in Player.framework

Hi
I fork your framework and want to setup own AVPlayerItem to this Player.framework.
I want to set AVPlayerItem out of framework as a property, and then Player.framework take this AVPlayerItem object and set to current AVPlayer.

hey @romanmiller nice request. i think i'll be adding something along these lines for caching support.
📎 https://github.com/piemonte/Player/issues/17


Database deletion edit cn=config files

The destroy function mess with files in $server_confdir/cn=config even if the header says:
# AUTO-GENERATED FILE - DO NOT EDIT!! Use ldapmodify.
At least, new checksum should be generated...
It possibly make subsequent fail in the test chain, but this specific error is in :after section and does not trigger the build failure.
debian-6-x64 20:27:19$ puppet apply --strict_variables --verbose --detailed-exitcodes --order=random /tmp/apply_manifest.pp.xMLmc7
  Info: Loading facts
  Notice: Compiled catalog for debian-6-x64.c.eco-emissary-99515.internal in environment production in 1.44 seconds
  Info: Applying configuration version '1447964843'
  Notice: /Stage[main]/Main/Openldap::Server::Database[dc=foo,dc=com]/Openldap_database[dc=foo,dc=com]/ensure: removed
  Error: Could not set 'absent' on ensure: No such file or directory @ unlink_internal - /etc/ldap/slapd.d/cn=config/olcDatabase={2}hdb.ldif at 60:/etc/puppetlabs/code/modules/openldap/manifests/server/database.pp
  Error: Could not set 'absent' on ensure: No such file or directory @ unlink_internal - /etc/ldap/slapd.d/cn=config/olcDatabase={2}hdb.ldif at 60:/etc/puppetlabs/code/modules/openldap/manifests/server/database.pp
  Wrapped exception:
  No such file or directory @ unlink_internal - /etc/ldap/slapd.d/cn=config/olcDatabase={2}hdb.ldif
  Error: /Stage[main]/Main/Openldap::Server::Database[dc=bar,dc=com]/Openldap_database[dc=bar,dc=com]/ensure: change from present to absent failed: Could not set 'absent' on ensure: No such file or directory @ unlink_internal - /etc/ldap/slapd.d/cn=config/olcDatabase={2}hdb.ldif at 60:/etc/puppetlabs/code/modules/openldap/manifests/server/database.pp
  Notice: /Stage[main]/Main/Openldap::Server::Database[cn=config]/Openldap_database[cn=config]/rootdn: rootdn changed 'cn=newadmin,cn=config' to 'cn=admin,cn=config'

@kronos-pbrideau could you please provide a PR for this?


New issue for Number Guess Game.py

Description
(New issue for my old Pull Request Number Guess Game.py)
Additional context
Add any other context, links references or screenshots about the feature request here.
Important Checklist

[x] I have read the contribution guidelines before making this issue.
[x] I have verified this issue program is not already implemented

Languages and Assignees:

[x] Python: @
[] Java: @
[ ] C: @
[ ] C++: @

Notes
Anything you would like to discuss.


Add dekko snap to playpen

Makes a nice example for qml apps that make use of oxide

Fantastic work!


Convert Magnet plugin

This plugin uses libtorrent to convert a magnet URI torrent to a full torrent file by spinning up a small torrent client to pull from the DHT. This way magnets from non-torrent file sites can be processed using the content filters.
Obviously the conversion is time consuming and in situations its possible for invalid torrents to take a lot of time to actually acquire the metadata, a parameter has been added to time out any long hanging queries.
This iteration has been running on my personal machine for a long period of time, suggestions are welcome.

I think it looks interesting and seems like a decent way of not relying on torcache and what have you.
Have there been any particularly annoying problems?

By far the biggest one is invalid torrents which don't exist in the DHT swarm which run the full timeout, hence why I added the configurable timeout. Generally it takes around 3-5sec to acquire a highly shared torrent, which most of the time will be connecting to the DHT in the first place.
I'm sure some further configuration options could be added for the mini-client itself, but the defaults work well for direct connections and simple UPnP firewall setups.

From what I gather this plugin will always rewrite magnet urls unless specifically disabled?

Ahh yes, looks that way. I'm checking for config == False but the default config setup is enabling it. This isn't the intended result, i'll make changes to resolve it.


Editor Not Ready - iPad Lockdown Mode

The editor will not open while iPad is in Lockdown Mode, infact I've even had to enable developer mode to get the editor to work correctly. I have security concerns so I must enable Lockdown mode with developer mode off. Please help fix this. Thank you so much!

Thanks for the heads up. I will investigate this.

This is likely due to JIT not being available in lockdown mode, which the syntax highlighting engine requires. While I am looking for workarounds to this, you might consider disabling lockdown mode for Code App (on a per app basis).

Fixed by disabling syntax highlighting when WebAssembly is not supported. This change will be available on TestFlight soon.

Would it be possible to please add me to TestFlight as a tester. I would love to have access to these features and perhaps provide feedback and additional testing results (free of charge of course lol)

Would it be possible to please add me to TestFlight as a tester. I would love to have access to these features and perhaps provide feedback and additional testing results (free of charge of course lol)

Here's the link: https://testflight.apple.com/join/EgZ8sE2P. But the review for the latest version is taking longer than expected so the feature is not available there yet. If you would like to try it now, you can build the app yourself.


Unhandled promise rejection: HttpError: Server Error

TL;DR
I'll try to be as helpful as I can, but the release-please output cuts some of it and I'm not familiar with it's internal logic.
We've been successfully using it in opentelemetry-js-contrib but the last release failed with errors I'm unable to decipher exactly:
 (node:1547) UnhandledPromiseRejectionWarning: HttpError: Server Error
    at /home/runner/work/_actions/google-github-actions/release-please-action/v3/dist/index.js:14510:21
    at runMicrotasks (<anonymous>)
    at processTicksAndRejections (internal/process/task_queues.js:97:5)
    at async /home/runner/work/_actions/google-github-actions/release-please-action/v3/dist/index.js:80188:26
    at async GitHub.commentOnIssue (/home/runner/work/_actions/google-github-actions/release-please-action/v3/dist/index.js:80856:20)
    at async Manifest.createRelease (/home/runner/work/_actions/google-github-actions/release-please-action/v3/dist/index.js:81521:9)
(node:1547) UnhandledPromiseRejectionWarning: Unhandled promise rejection. This error originated either by throwing inside of an async function without a catch block, or by rejecting a promise which was not handled with .catch(). To terminate the node process on unhandled promise rejection, use the CLI flag `--unhandled-rejections=strict` (see https://nodejs.org/api/cli.html#cli_unhandled_rejections_mode). (rejection id: 1)

Expected behavior
A successful build or an more or less helpful error message.
Observed behavior
Number of warnings and a server error ending the CI process.
Action YAML
on:
  push:
    branches:
      - main

name: Run Release Please
jobs:
  release-please:
    runs-on: ubuntu-latest
    steps:
      - uses: google-github-actions/release-please-action@v3
        id: release
        with:
          command: manifest
          token: ${{secrets.RELEASE_PR_TOKEN}}
          default-branch: main

      # The logic below handles the npm publication:
      - name: Checkout Repository
        if: ${{ steps.release.outputs.releases_created }}
        uses: actions/checkout@v3
        with:
          fetch-depth: 0

      - name: Setup Node
        uses: actions/setup-node@v3
        if: ${{ steps.release.outputs.releases_created }}
        with:
          node-version: 14
          registry-url: 'https://registry.npmjs.org'

      - name: Cache Dependencies
        if: ${{ steps.release.outputs.releases_created }}
        uses: actions/cache@v3
        with:
          path: |
            node_modules
            package-lock.json
            detectors/node/*/node_modules
            metapackages/*/node_modules
            packages/*/node_modules
            plugins/node/*/node_modules
            plugins/web/*/node_modules
            propagators/*/node_modules
          key: release-${{ runner.os }}-${{ matrix.container }}-${{ hashFiles('**/package.json') }}

      - name: Build Packages
        if: ${{ steps.release.outputs.releases_created }}
        run: |
          npm install
          npx lerna bootstrap --no-ci

      # Release Please has already incremented versions and published tags, so we just
      # need to publish all unpublished versions to npm here
      # See: https://github.com/lerna/lerna/tree/main/commands/publish#bump-from-package
      - name: Publish to npm
        if: ${{ steps.release.outputs.releases_created }}
        env:
          NODE_AUTH_TOKEN: ${{secrets.NPM_TOKEN}}
        run: npx lerna publish from-package --no-push --no-private --no-git-tag-version --no-verify-access --yes

Log output
Full log output here.
Additional information
No response

Closing as stale


yaml extension is broken

The YAML extension is broken:

nix build .#php82.extensions.yaml -L

An issue has been opened upstream: https://github.com/NixOS/nixpkgs/pull/240662

Update flake.lock when nixpkgs PR has been propagated, track it at https://nixpk.gs/pr-tracker.html?pr=240662

Fixed.


Support different cable drivers/settings inside the same supercluster

What would you like to be added:
Support for different cable drivers inside the same supercluster.
Why is this needed:
Once we start supporting cable drivers with meaningfully different features (sorry if I’m insulting any existing cable driver), e.g. submariner-io/submariner#674, various scenarios involve mixing cable drivers. For example, a supercluster could contain three clusters, one of which should only be connected to using encrypted connections, but two of which may be connected to using unencrypted connections (because they’re connected by a private link). So we’d have three connections, one unencrypted and two encrypted, presumably using different cable drivers.

We're looking for users feedback in order to decide on priority for this issue.


New KISAO term for springsalad / langevin solver

Please provide information about the requested term:
What type of term would you like to create?

[ X] algorithm
[ ] algorithm characteristic
[ ] algorithm parameter
[ ] algorithm output

What is the suggested label of the term?
Langevin springs-connected particle-based, stochastic, biochemical simulation method.
Are there any synonyms for this term?
Please provide a list of synonyms such as below:

NFSim algorithm KISAO_0000263
Smoldyn algorithm KISAO_0000057 (Brownian diffusion Smoluchowski method)

What is the suggested definition of the term?
SpringSaLaD is a particle-based, stochastic, biochemical simulation platform with excluded volume, suitable for modeling mesoscopic systems, which are far too large to be modeled with molecular dynamics but which require more detail than obtainable with macroscopic continuum models.
Are there any citations for the term?
Please provide a list of citations with URLs (preferably using DOIs) such as below:
https://pubmed.ncbi.nlm.nih.gov/26840718/
SpringSaLaD: A Spatial, Particle-Based Biochemical Simulation Platform with Excluded Volume
Paul J Michalski 1, Leslie M Loew 2
PMID: 26840718 PMCID: PMC4744174 DOI: 10.1016/j.bpj.2015.12.026
Which simulation tools support this terms?

springsalad
vcell

What should the parent of this term?
KISAO_0000263
What additional relationships should this new term have to existing terms?
Please provide a list of the relationships that this term should have to other terms such as below:
Is there any additional information that KiSAO should capture about this term?
Please describe any additional information that should be encoded into KiSAO.

Specific terms:
Spring interval (typical 1.0E-9)
Image interval (typical 1.oE-4)
Possible other terms: Num of trials, num of paallel local runs, num of partitions on x, y, z


feat: removing ability to delete resources

What

BREAKING: because it's to easy to mistakenly cause a mass cluster delete we are removing this functionality completely
Because this would break backwards compatibility we are bumping the major version

I like it. I want to make sure it's not going to make life really hard for @wincus and team, and otherwise I'm 👍 .

It does mean whenever we want to remove a service from a cluster going forward, it will have to be manually done via kubectl or another tool.

I think that would be a fairly rare event. We would essentially be retiring a service.

I agree with that ... manual removing a service is not a big deal at all !
👍

Great. Note: We will still be sending this flag from the CLI and Rosie. We should add a task to update these other tools.


[TV] Update ImageSample and FlexContainerSample.

Main changes:
Remove unnecessary Using,
Remove useless commented line,
Replace "Position2D / Size2D" by "Position / Size",
Replace "ClickEvent" by "Clicked",

@dongsug-song please revert it, it has build error

If you use API that added in API8, you should reference tizen80 TFM.
But, tizen80 TFM is not yet released
Finally, please build it before submit


XML comments cause error in parsenator train

The error message can be seen in Travis build 99
Traceback (most recent call last):
  File "/home/travis/virtualenv/python2.7.14/bin/parserator", line 11, in <module>
    sys.exit(dispatch())
  File "/home/travis/virtualenv/python2.7.14/lib/python2.7/site-packages/parserator/main.py", line 58, in dispatch
    args.func(args)
  File "/home/travis/virtualenv/python2.7.14/lib/python2.7/site-packages/parserator/main.py", line 85, in train
    training.train(module, train_file_list, modelfile)
  File "/home/travis/virtualenv/python2.7.14/lib/python2.7/site-packages/parserator/training.py", line 83, in train
    training_data = list(readTrainingData(train_file_list, module.GROUP_LABEL))
  File "/home/travis/virtualenv/python2.7.14/lib/python2.7/site-packages/parserator/training.py", line 62, in readTrainingData
    sequence_xml = etree.fromstring(component_string)
  File "src/lxml/etree.pyx", line 3212, in lxml.etree.fromstring
  File "src/lxml/parser.pxi", line 1876, in lxml.etree._parseMemoryDocument
  File "src/lxml/parser.pxi", line 1764, in lxml.etree._parseDoc
  File "src/lxml/parser.pxi", line 1126, in lxml.etree._BaseParser._parseDoc
  File "src/lxml/parser.pxi", line 600, in lxml.etree._ParserContext._handleParseResultDoc
  File "src/lxml/parser.pxi", line 710, in lxml.etree._handleParseResult
  File "src/lxml/parser.pxi", line 639, in lxml.etree._raiseParseError
  File "<string>", line 1
lxml.etree.XMLSyntaxError: Document is empty, line 1, column 1
make: *** [probablepeople/generic_learned_settings.crfsuite] Error 1

Would please allow XML comments? It could help organize the training and test data sets

It should be possible https://stackoverflow.com/questions/18313818/how-to-not-load-the-comments-while-parsing-xml-in-lxml, it will need to be changed in parserator.

Today XML comments are working fine


Repo sync for protected CLA branch

The pull request is created from main638055364644606110sync_temp to main to fix git push error for protected CLA branch

Learn Build status updates of commit 4b207a2:
:white_check_mark: Validation status: passed
For more details, please refer to the build report.
Note: Broken links written as relative paths are included in the above build report. For broken links written as absolute paths or external URLs, see the broken link report.
For any questions, please:Try searching the learn.microsoft.com contributor guidesPost your question in the Learn support channel


Make it possible to bind lambdas when passed by l-value

Currently, lambdas can only be bound to Opts when passed by r-value. I think it would be nice if lambdas could also be passed by l-value, since it would allow me to separate the parser definition from the logic inside the lambdas. This PR accomplishes that with the help of some SFINAE.
Fixes #58

I see that there are some build failures, I'll look into it


updating file to add services to markdown files

Docs Build status updates of commit 7037f35:
:clock10: Full build: the average full build time is 23 min(s) 2 sec(s), this is based on the last 139 build(s) for this repository.

Docs Build status updates of commit 7037f35:
:white_check_mark: Validation status: passed

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Status
Preview URL
Details

docfx.json
:white_check_mark:Succeeded

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@georgewallace Good to merge?

@sptramer still reviewing the changes to make sure they are good.. Will let you know shortly.

Docs Build status updates of commit f7f4b73:
:clock10: Full build: the average full build time is 23 min(s), this is based on the last 141 build(s) for this repository.

Docs Build status updates of commit f7f4b73:
:white_check_mark: Validation status: passed

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docfx.json
:white_check_mark:Succeeded

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Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.

Docs Build status updates of commit e8f0a3e:
:clock10: Full build: the average full build time is 22 min(s) 56 sec(s), this is based on the last 143 build(s) for this repository.

Docs Build status updates of commit e8f0a3e:
:white_check_mark: Validation status: passed

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@sptramer looks good to me


Specify drawing order in collage, etc

@evancz, this seems useful information to add to the documentation. Merge?

Makes sense, thanks @jvoigtlaender for the ping and @maxsnew for the change!


Snow season length is 0 if there is 0 snow - fix solar_zenith_angle typo

Pull Request Checklist:

[x] This PR addresses an already opened issue (for bug fixes / features)

This PR fixes #1491 and fixes #1493

[x] Tests for the changes have been added (for bug fixes / features)

[ ] (If applicable) Documentation has been added / updated (for bug fixes / features)

[x] CHANGES.rst has been updated (with summary of main changes)

[x] Link to issue (:issue:number) and pull request (:pull:number) has been added

What kind of change does this PR introduce?
snd_season_length and snw_season_length got implemented with the same check as their *_season_start and *_season_end counter parts, with a mask where : not at_least_n_valid(snw.where(snw > 0), n=1, freq=freq).
However, this means the output is NaN if all inputs are 0. This makes sense in the DOY case (can't a have a start/end date if there's no season), but it doesn't in the "length" case. Instead, I think (as does the issue raiser) an all-0 snow timeseries simply means a season length of 0. Thus, I removed the .where(snw > 0) part of the test for those two indicators.
EDIT: I also slipped in another fix for xc.indices.helpers.cosine_of_solar_zenith_angle. There was a typo in the xr.apply_ufunc call.
Does this PR introduce a breaking change?
Yes because it will change the output of two indicators, but I think this is for the best. The previous behaviour was not documented as such in the doc, I believe the new one is actually more intuitive. No need for a warning, IMO.

@Zeitsperre do you know why the coveralls action is still hanging here ?

Coveralls always expects a hook from GitHub with some stats to compare to. The problem is that because we aren't running all tests, the coverage will always be lower than expected, so the statistic is useless for developers until we build with the approved tag.
Maybe there's a way to send along a skip signal to coveralls when we're only building with Python3.9? I'll look into it.

Hum... Ok, but then is that the reason I can't merge ?

Oh, no, it isn't. For some reason, David's PR didn't trigger the approved label. Weird.


fix paths that were messed up with refactor

Ah, sorry I didn't catch this. When I looked at the readthedocs build I was focused on the section headers that were working as early as https://neuroconv--141.org.readthedocs.build/en/141/user_guide/schemas.html but failed to notice the complete disappearance of the galleries.

https://neuroconv--142.org.readthedocs.build/en/142/conversion_examples_gallery/conversion_example_gallery.html Looks good now, though.


Carve our own path?

I have been waiting for this for almost a year. At this point there's nothing wrong if we try not to make it 'official'.

"Unofficial port of" sounds a bit experimental. it should be called simply "based on". That'll give us more freedom.
Merge updates from Headless UI upstream without being restricted to it.
Take inspiration from other projects, i.e., renderless-svelte
Tailwind is not required so it'll be good to keep docs and code examples agnostic and easy to follow for custom/component css, Chota, Picnic, WindiCSS, etc.

Thanks for the feedback!
I agree about keeping the docs generally agnostic to Tailwind CSS, and making the default examples with just normal vanilla CSS. I think this project can provide a lot of value to the broader Svelte ecosystem (which doesn't have a ton of component libraries yet) and it's important that this value doesn't seem like it depends on Tailwind CSS.
At the same time, the project also provides a lot of value to people who want to use Tailwind UI specifically. Making it easy to use Tailwind UI with Svelte is in my mind one of the central use cases for the library. For that reason, I do intend on hewing very close to the upstream library. In particular, I'm overall not interested right now in adding new functionality (much less components) that doesn't exist upstream. From everything I've seen, Tailwind Labs were intending on working on Headless UI more after the new year; I'd rather spend the effort on this project towards keeping up with those changes rather than on doing something totally new--it's far higher ROI. Hope that makes sense.


Add detailed authority management to bread,database,media

I plan to set up a demo app on my server and provide a public demo account, so I need more detailed permission control

The merge failed because of the permission ？


[gopkg] fix insane gopkg situation

What does this PR do?
Fixes gopkg.lock generation, in the previous state generating a gopkg.lock off a fresh gopkg.toml would fail during dependency resolution. The changes introduced allow us to actually compile the lockfile.
Motivation
Not being able to recreate a gopkg.lock from a gopkg.toml is a bad thing. Not being able to safely add new dependencies with guarantees of a being able to compile the updated gopkg.lock may be worse. To make things worse, changes upstream in dependencies, etc.... might also break the dep ensure process.
The goal is to get us back to a sane state where we can manipulate gopkg.{toml,lock} safely.
Additional Notes
There are multiple version changes with an added risk of adding regressions. Maybe we should add additional pins wherever necessary, maybe this is fine. Let's attempt to merge this early in the 6.6.0 cycle.

I've managed to add the required dependencies in the current Gopkg.lock without creating a new one. The change set is now much smaller.
These dependencies were already present in the lock file under slightly different versions so we could pin them in the Gopkg.toml to the current versions not to introduce any changes in the lockfile.
With these changes we are able to create a lock file from scratch and do dep ensure -update without any problem.

Thanks for the investigation @remicalixte ,
Indeed, we moved the imports from github.com/kubernetes/* to k8s.io/*, so the constraints need to be set on that import path instead. We're integrating that in https://github.com/DataDog/datadog-agent/pull/2366 that upgrades the pinned versions

Great, closing this PR then


Unsupported method: NativeArtifact.getSourceFolders()

Getting the following error when Running on Android Studio 3.1.4
Unsupported method: NativeArtifact.getSourceFolders().
The version of Gradle you connect to does not support that method.
To resolve the problem you can change/upgrade the target version of Gradle you connect to.
Alternatively, you can ignore this exception and read other information from the model.

Oops, sorry: last time updated to version 3.3 to use "cmake version" feature.
If you need to downgrade it, might need to do:

use "3.1.4" in project/build.gradle
remove " cmake { version '3.10.2' }" from project/app/build.gradle
this is optional. $project/gradle/wrapper/gradle-wrapper.properties, change gradle version to 4.4

if i downgrade it...
it says

CMake '3.10.2' found in SDK did not match requested version '3.6.0'.


Proper ModuleRun restart if there are errors on startup

Module 'onStartup' hooks are not restarted if error is occured.

Fixed by #280: internal module stated was not cleaned on module disable.


Remove zero mse entry

The regression infrastructure can't really do comparisons with trivially zero fields, so this needs to be removed.

bors r+


Add entries to sitemap based on middleware

Clear and concise description of the problem
In my Nuxt project, I have a middleware for only allowing logged in users on specific pages, specified in the definePageMeta. Right now, I have to remember each time and manually add pages to the sitemap if they are public. I think it would be a great feature to be able to optionally add things to the sitemap depending on what a page's meta is.
Suggested solution
Provide a config value that is a function that takes as input the contents of definePageMeta and outputs a boolean that decides whether to include the page and all of its inner paths in the sitemap or not. I'm not technically familiar with the Nuxt API so perhaps there is a better alternative?
Alternative
No response
Additional context
No response

Hi @jf908, sorry for the delay.
I'd recommend that you use the index: false route rule for any authenticated routes. I'd also recommend putting all of your authenticated routes under some sort of URL pattern then you can match them using wild cards.
For example:
export default defineNuxtConfig({
  routeRules: {
    '/my-account/**': { index: false }
  }
})

You can also provide these at a page level.
defineRouteRules({
   index: false
})

If you're using Nuxt Simple Robots this will also make sure these URLs aren't indexed by search engines.
Will close for now as I think these work arounds are good enough.

Thank you for your response. The problem I am trying to solve is that I would prefer to be able to use 1 line of code to set both the middleware and the routeRules because its possible that a developer will forget to set one or the other so I want someway of implying both.
Since definePageMeta is a macro, you can't add setup a way to do this automatically. After thinking about it though, this is a more an issue with Nuxt's architecture so I don't think this plugin is responsible for addressing this, thanks.


Adding xcscheme for Carthage

Oh... I see you have a branch for that already.
Closing as no longer relevant.


Test-All and Test-Any filters methods dont work with pester

The break statement in the Test-All and Test-Any filter methods was forcing the Pester Should code to exit early. This is a known issue in Pester. https://github.com/pester/Pester/issues/289
The following tests highlight the existing problem. Each test, tests for both true and false output. However all the tests pass. This should not be the case, only one test in each given block should pass, either the output is truthy or it is not.
Import-Module Pester -DisableNameChecking -Force
Import-Module Functional -DisableNameChecking -Force

Describe "Test-Equality" {
  Context "Given leaves" {
    $a = '3'
    $b = 3

    It "Should be false for equal values of different types" {
      $a, $b | Test-Equality | Should -BeFalse
    }
    It "Should be false for equal values of different types but is not" {
      $a, $b | Test-Equality | Should -BeTrue
    }
  }
  Context "Given arrays" {
    $a = @(1, 2, @{a = 1 }, 3)
    $b = @(1, 2, @{a = 2 }, 3)
    It "Should be false for deep inequal values" {
      $a, $b | Test-Equality | Should -BeFalse
    }
    It "Should be false for deep inequal values but is not" {
      $a, $b | Test-Equality | Should -BeTrue
    }
  }
  Context "Given hashtables" {
    $a = @{a = 1; b = @{c = 2 } }
    $b = @{a = 1; b = [pscustomobject]@{c = 2 } }
    It "Should be false for deep inequal values" {
      $a, $b | Test-Equality | Should -BeFalse
    }
    It "Should be false for deep inequal values but is not" {
      $a, $b | Test-Equality | Should -BeTrue
    }
  }

  Context "Given an array of PSCustomObject" {
    $a = @([PSCustomObject]@{ 'Name' = 'Foo'; 'Value' = 'Foo' }, [PSCustomObject]@{ 'Name' = 'Baz'; 'Value' = 'Baz'  } )
    $b = @([PSCustomObject]@{ 'Name' = 'xxx'; 'Value' = 'Foo' }, [PSCustomObject]@{ 'Name' = 'Baz'; 'Value' = 'Baz'  } )
    It "Should be false for deep inequal values" {
      $a, $b | Test-Equality | Should -BeFalse
    }
    It "Should be false for deep inequal values but is not" {
      $a, $b | Test-Equality | Should -BeTrue
    }
  }
}

All of the above tests pass, this is the problem.
Given the below two tests both of which are mutually exclusive only one should pass. $a and $b while different don't change between the tests, only the assertion change. $a and $b can not be both identical and different.
I could not supply tests that would fail due to the problem with the way that pester handles 'break' statements, so I included tests which all pass but should not.
Import-Module Pester -DisableNameChecking -Force
Import-Module Functional -DisableNameChecking -Force

Describe "Test-Equality" {
  Context "Given leaves" {
    $a = '3'
    $b = 3

    It "Should be false for equal values of different types" {
      $a, $b | Test-Equality | Should -BeFalse
    }
    It "Should be false for equal values of different types but is not" {
      $a, $b | Test-Equality | Should -BeTrue
    }
  }
}

OK I just fixed the issue.  I implemented the fix without advanced function methods because I believe the redundant array append operation$results += $_ creates a new array every iteration and therefore causes the function to run O(n^2) rather than O(n).


√Letter [PCSG00891]

App summary

App name: √Letter ルートレター
App serial: PCSG00891
App version: 1.00

Vita3K summary

Version: v0.1.6
Build number: 2935
Commit hash: https://github.com/vita3k/vita3k/commit/4a50a4e0
CPU backend: Dynarmic
GPU backend: Vulkan

Test environment summary

Tested by: nishinji
OS: Windows 10
CPU: AMD Ryzen 9 3950X
GPU: RTX3060Ti
RAM: 32 GB

Issues
Screenshots

Log
vita3k.log
Recommended labels
Bootable

Vita3K Summary

Version: v0.1.6
Build Number: 3039
Commit Hash: https://github.com/Vita3K/Vita3K/commit/92524b65514c54ccae57f1a64a62876c65d95a7a
CPU Backend: Dynamic
GPU Backend: Vulkan
CPU: Intel Celeron N3350
GPU: Intel Graphics 550

Game Summary

Game Name: √Letter ルートレター
Game Serial: PCSG00891

Issues
Appears with "System data could not be found. Create new system data. Are you sure?" Clicking either "Yes" or "No" results in this message "System data is required for the game to progress. Check the system data again." and returns to the previous message.
Screenshots

Logs
vita3k.log
Recommended Labels

Intro
Savedata bug

App summary

App name: √Letter ルートレター
App serial: PCSG00891
App version: 1.00

Vita3K summary

Version: v0.1.8
Build number: 3160
Commit hash: https://github.com/vita3k/vita3k/commit/1c009661
CPU backend: Dynarmic
GPU backend: Vulkan

Test environment summary

Tested by: nishinji
OS: Windows 10
CPU: AMD Ryzen 9 3950X
GPU: RTX3060Ti
RAM: 32 GB

Issues
doesn't boot with vulkan
and auto mode doesn't work
Screenshots

Log
vita3k.log
Recommended labels
Playable
vulkan issue


Fix typo in docs (index file)

While reading docs I found a small typo in index file. "propertie" instead of "properties", this commit fixes that.

@kacperfronc Thanks for the fix


Pull request #1209 broke nuclear gradients of symmetric molecules in unconventional orientations

Ever since PR #1209, nuclear gradients of symmetric molecules seem to be broken unless the molecules are specified in some 'conventional' choice of axes. This is separate from the issue being addressed by PR #1237. The same molecule gives different gradients or even crashes if I choose the axes differently:
from pyscf import gto, scf

print ("LiH")
mol = gto.M (atom = 'Li 0 0 0; H 0 0 1', basis='sto-3g', symmetry=True,
    verbose=0)
print (scf.RHF (mol).run ().nuc_grad_method ().kernel ())
mol = gto.M (atom = 'Li 0 0 0; H 1 0 0', basis='sto-3g', symmetry=True,
    verbose=0)
print (scf.RHF (mol).run ().nuc_grad_method ().kernel ())
mol = gto.M (atom = 'Li 0 0 0; H 0 1 0', basis='sto-3g', symmetry=True,
    verbose=0)
print (scf.RHF (mol).run ().nuc_grad_method ().kernel ())

print ("\nH2")
mol = gto.M (atom = 'H 0 0 0; H 0 0 1', basis='sto-3g', symmetry=True,
    verbose=0)
print (scf.RHF (mol).run ().nuc_grad_method ().kernel ())
mol = gto.M (atom = 'H 0 0 0; H 1 0 0', basis='sto-3g', symmetry=True,
    verbose=0)
print (scf.RHF (mol).run ().nuc_grad_method ().kernel ())
mol = gto.M (atom = 'H 0 0 0; H 0 1 0', basis='sto-3g', symmetry=True,
    verbose=0)
print (scf.RHF (mol).run ().nuc_grad_method ().kernel ())

The output of this should be (this is from commit 1e874d18e, the last time this worked):
LiH
[[ 0.          0.          0.26651453]
 [ 0.          0.         -0.26651453]]
[[ 2.66514530e-01  0.00000000e+00  5.91781135e-17]
 [-2.66514530e-01  0.00000000e+00 -5.91781135e-17]]
[[ 1.63193083e-17  2.66514530e-01  5.91781135e-17]
 [-1.63193083e-17 -2.66514530e-01 -5.91781135e-17]]

H2
[[ 0.          0.         -0.14857157]
 [ 0.          0.          0.14857157]]
[[-1.48571575e-01  0.00000000e+00 -3.29895166e-17]
 [ 1.48571575e-01  0.00000000e+00  3.29895166e-17]]
[[-9.09738516e-18 -1.48571575e-01 -3.29895166e-17]
 [ 9.09738516e-18  1.48571575e-01  3.29895166e-17]]

Using either 5b3fdd618 or 6eeeff99a, I get this obviously wrong output:
LiH
[[ 0.          0.          0.26651453]
 [ 0.          0.         -0.26651453]]
[[ 0.00000000e+00  0.00000000e+00  1.73688689e-17]
 [ 0.00000000e+00  0.00000000e+00 -1.73688689e-17]]
[[ 0.00000000e+00  0.00000000e+00  1.73688689e-17]
 [ 0.00000000e+00  0.00000000e+00 -1.73688689e-17]]

H2
[[ 0.          0.         -0.14857157]
 [ 0.          0.          0.14857157]]
Traceback (most recent call last):
  File "h2_grad.py", line 20, in <module>
    print (scf.RHF (mol).run ().nuc_grad_method ().kernel ())
  File "/home/herme068/anaconda3/envs/pyscf/lib/python3.7/site-packages/pyscf/grad/rhf.py", line 395, in kernel
    self.de = self.symmetrize(self.de, atmlst)
  File "/home/herme068/anaconda3/envs/pyscf/lib/python3.7/site-packages/pyscf/grad/rhf.py", line 363, in symmetrize
    return symmetrize(self.mol, de, atmlst)
  File "", line 211, in symmetrize
    a_id = pmol.irrep_id.index(0)
ValueError: 0 is not in list

By commenting out lines 394 and 395 of pyscf/grad/rhf.py, I confirmed that the problem for both molecules lies in the "symmetrize" function. But I don't understand what's going on in that function, so I can't offer a bugfix.

Thanks @MatthewRHermes for catching this bug. It's fixed in the latest commit of PR 1237

Thanks, that seems to fix the issue.


Add track_total_hits to search request body

This commit removes track_total_hits as a query string parameter on a search request and instead defines it in the body, allowing track_total_hits to work with individual search requests in multi search API.
Closes #3906

rebased on top of master and force pushed, because the failing CI tests I don't believe are related to this change. Will wait for these to finish running before merging


Request interception doesn't work with redirects

The following script fails for me (since google.com redirects to www.google.com):
(async() => {

const {Browser} = require('puppeteer');
const browser = new Browser();
const page = await browser.newPage();
await page.setRequestInterceptor(request => request.continue());
await page.navigate('https://google.com');
browser.close();

})();

I still have this issue:
const puppeteer = require('puppeteer');

let cookies = [];

(async () => {
  const browser = await puppeteer.launch({
    devtools: true,
  });
  const page = await browser.newPage();
  await page.setRequestInterception(true);
  page.on('request', async interceptedRequest => {
    // console.log(interceptedRequest.protoType);
    //await page.evaluate(`console.log(${interceptedRequest})`);
    // if (interceptedRequest.url().endsWith('.png') || interceptedRequest.url().endsWith('.jpg'))
    //   interceptedRequest.abort();
    // else
    //   interceptedRequest.continue();

    await interceptedRequest.continue();
  });
  await page.goto('http://localhost:4000', {waitUntil: 'networkidle2'});
  await page.type('#session_email', process.env.USER_NAME);
  await page.type('#session_password', process.env.PASSWORD);
  await Promise.all([
    page.click("[type=submit]"),
  ]);
  cookies = await page.cookies();
  console.log('cookies');
  // await browser.close();
})();

@adam-s Did you ever find a solution for intercepting requests?

Interesting my comment is from 2 years ago. Using page.on('request', () => {}) should work. https://pptr.dev/#?product=Puppeteer&version=v5.5.0&show=api-class-httprequest
What are you trying to accomplish?

lol I know. Well, I have a scenario where a virtualhost on a
webserver doesn't resolve (on purpose).  The thought was to set the Host
header and just browse via IP (similar to python request
library/aiohttp/etc).  It works fine until I enter creds and hit enter.
After hitting enter the response is not caught, so the browser just sits
there hung.
Under the dev tools -> network tab, I see the redirect page as "pending".
I'll try your suggestion and keep trying if it doesn't work.
On Fri, Dec 18, 2020 at 1:22 AM Adam S<EMAIL_ADDRESS>wrote:

Interesting my comment is from 2 years ago. Using page.on('request', () =>
{}) should work.
https://pptr.dev/#?product=Puppeteer&version=v5.5.0&show=api-class-httprequest
What are you trying to accomplish?
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub
https://github.com/puppeteer/puppeteer/issues/217#issuecomment-747894869,
or unsubscribe
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.


fix: remove RangeProofs

Closes #584
Closes #284

@tac0turtle, this is the initial PR, please review it. If it is OK for you, I will remove the necessary ones.
Benchmark results
# original
goos: linux
goarch: amd64
pkg: github.com/cosmos/iavl
cpu: Intel(R) Core(TM) i5-9400 CPU @ 2.90GHz
BenchmarkGetNonMembership/fast-6         	    4908	    214837 ns/op	    8729 B/op	     124 allocs/op
BenchmarkGetNonMembership/regular-6      	     162	   7052272 ns/op	 1337791 B/op	   22250 allocs/op

# refactored
goos: linux
goarch: amd64
pkg: github.com/cosmos/iavl
cpu: Intel(R) Core(TM) i5-9400 CPU @ 2.90GHz
BenchmarkGetNonMembership/fast-6         	    6990	    212281 ns/op	    3709 B/op	      15 allocs/op
BenchmarkGetNonMembership/regular-6      	    6045	    213444 ns/op	    3747 B/op	      16 allocs/op

cc @AdityaSripal @colin-axner

could we remove the proto messages https://github.com/cosmos/iavl/blob/master/proto/iavl/proof.proto for things that aren't needed

Nice work! Love to see this cleanup! Would appreciate at least another approval from someone familiar with ics23

cc: @tac0turtle

OK, I removed the isExist param in GetProof, because we have already GetMembershipProof and GetNonMembershipProof.
cc: @AdityaSripal @colin-axner


Ask for codefendant court contact information when we ask for their names

[Use court contact info as default for signature contact info (#64). Really, #64 is the place to be.]
~See #59 and comments in #31.~

Fixed by #68


Can http_server download files?

Is it possible for http_server to download and process files POSTed as a form to given endpoint?

https://www.benthos.dev/docs/components/inputs/http_server#path-defaults-to-post
So, you should be able to combine those messages(raw bytes) into a single one and sent it the way you see fit. But in general it depends on the way you're transmitting the initial message(aka your file)

https://www.benthos.dev/docs/components/outputs/http_client

If the message has multiple parts (is a batch) the request will be sent according to RFC1341

The case is pretty simple, for testing I use post form like this:
    <form action="http://<IP_ADDRESS>:8080/post/" method="POST">

<input type="file" name="photo" required multiple title="Upload one or more photos">

<input type="submit" value="Upload">

    </form>

http_server config:
  http_server:
    # address: ""
    address: <IP_ADDRESS>:8080
    path: /post/
    # ws_path: /post/ws/
    timeout: 10s
    rate_limit: ""

as a result benthos is only able to see filename (that's what come in the body) and does not download file (or files, when there are many in multipart form).

Oh, I forgot enctype="multipart/form-data", let me check again.

Yes, I can confirm, it works. Thanks. 🙂


Separate Protocol class into CoreProtocol, ServerProtocol, and ClientProtocol

At the time of writing, there is a single Protocol class which holds both client and server methods, despite it living inside the codegen folder. The codegen folder is supposed to be server agnostic.
We should separate Protocol interface into CoreProtocol, ServerProtocol, and ClientProtocol interfaces.

CoreProtocol holds methods common to both the server and client.
ServerProtocol holds an instance of CoreProtocol and lives in the codegen-server.
ClientProtocol should work similarly to ServerProtocol - encapsulating all client specific methods and living in an appropriate space.

As a result, the classes implementing Protocol should also be separated. For example:

class CoreRestJsonProtocol: CoreProtocol
class ServerRestJsonProtocol: ServerProtocol
class ClientRestJsonProtocol: ClientProtocol

and methods should be added to Server{Protocol}Protocol and Client{Protocol}Protocol to retrieve Core{Protocol}Protocol.

Progress towards this goal: https://github.com/awslabs/smithy-rs/pull/1697

The ServerProtocol class has been introduced in https://github.com/awslabs/smithy-rs/pull/1693. Further work needs to be done to move existing methods from the implementations of Protocol onto the implementations of ServerProtocol.

and methods should be added to Server{Protocol}Protocol and Client{Protocol}Protocol to retrieve Core{Protocol}Protocol.

Rather than have this be the case, it's probably more ergonomic to have ServerProtocol : ClientProtocol and Server{Protocol}Protocol : Client{Protocol}Protocol.
Changes have been made in https://github.com/awslabs/smithy-rs/pull/1693 to do this for the server side.


Suggestion tab

How can I make a suggestion box with a prediction or auto-correct feature?
i.e.
first section = user's word
second section = predicted word
third section = auto correct word

How can I make a suggestion box with a prediction or auto-correct feature? i.e.
first section = user's word second section = predicted word third section = auto correct word

this is so challenging, i need some reasearch for solve this feature request, because i never create feature like this

How can I make a suggestion box with a prediction or auto-correct feature? i.e.
first section = user's word second section = predicted word third section = auto correct word

this is so challenging, i need some research for solve this feature request, because i never create feature like this

I can help you to develop suggestions and predictive text.

when will you give the new version with suggestions?

@Pify can u help me for solve this issue ? it would be an honor to be able to help

i'll see what i can do @amirisback

thank u very much @Pify

im a little busy lately so i cant promise to deliver it asap in the mean time you can refer to this answer to implement suggestion tab @guptaAbhinav1993

Hi @Pify   @amirisback
I have developed my suggestions using room DB

wow, its so cool, can u make some pull request @guptaAbhinav1993 i'm waiting for you 😄

Hi,
I am sorry for the late response.
I would like to help you for suggestion part.

I am not able to create a pull request on master branch.

and I want to join here with my other email ID. Can I request from there?

SENT :)

I dont receive any email


Using gRPC with cosmjs

Does cosmjs support calls via gRPC endpoint? If so, could someone provide an example of how it should work. The library is mentioned here https://docs.cosmos.network/master/run-node/interact-node.html#using-grpc but it doesn't actually state if it's already supported.

All clients in this library connect to Tendermint RPC endpoints. This allows us to use the grpc services. But we do not communicate with the gRPC or grpc-web endpoints directly (yet). See also https://github.com/cosmos/cosmjs/issues/885


Fix the naming of event rule target id

Description

This is a fix for #1545
GitHub Issues

#1545

Coverage remained the same at 74.018% when pulling 26c33635e9dd5d820b01cfdf7a44104e0a09cce2 on jaykay:master into d99c193e32733946fb52a4f9b2bdfd1d2929ba49 on Miserlou:master.

Coverage remained the same at 74.018% when pulling 26c33635e9dd5d820b01cfdf7a44104e0a09cce2 on jaykay:master into d99c193e32733946fb52a4f9b2bdfd1d2929ba49 on Miserlou:master.

Coverage remained the same at 74.018% when pulling 26c33635e9dd5d820b01cfdf7a44104e0a09cce2 on jaykay:master into d99c193e32733946fb52a4f9b2bdfd1d2929ba49 on Miserlou:master.

Coverage remained the same at 74.018% when pulling 26c33635e9dd5d820b01cfdf7a44104e0a09cce2 on jaykay:master into d99c193e32733946fb52a4f9b2bdfd1d2929ba49 on Miserlou:master.

Coverage remained the same at 74.018% when pulling 26c33635e9dd5d820b01cfdf7a44104e0a09cce2 on jaykay:master into d99c193e32733946fb52a4f9b2bdfd1d2929ba49 on Miserlou:master.

Coverage remained the same at 74.018% when pulling 26c33635e9dd5d820b01cfdf7a44104e0a09cce2 on jaykay:master into d99c193e32733946fb52a4f9b2bdfd1d2929ba49 on Miserlou:master.


how to best write a rule that corrects missing space before subcommand e.g. npminstall, gitclone

As far as I can tell thefuck is unable to correct gitclone http://... or npminstall some-long-package...
I'm sure I could easily create individual rules that work only for npm or git but I'm curious if anyone has any tips how I could make a generic rule that works for a bunch of programs with subcommands.

Nice idea, it's very easy to implement rule like that. Look at the implementation and tests.

Added in 3.15.

@nvbn awesome, thank you!


Game.destroy(true) throws Error in version 3.5.1

In #3569, fixed a Error, but still throws Error when the game has multi scenes.
Version: 3.5.1
https://jsfiddle.net/xonw21q6/6/
Uncaught TypeError: Cannot read property 'events' of null
  at DataManagerPlugin.destroy

Want to back this issue? Post a bounty on it! We accept bounties via Bountysource.

Here's an example using multiple Scenes running at once and it still destroys properly:
http://labs.phaser.io/view.html?src=src\game config\game destroy with multi scenes.js
What do I need to do to the above example to replicate the error you're seeing?

OK, I know what happen. If I do not set active: true to the Scene, error throws when destroy the game.
@photonstorm

http://labs.phaser.io/edit.html?src=src\scenes\launch parallel scene.js
Try this example. add
setTimeout(function () {
  game.destroy(true)
}, 3000)

To the bottom.


Adjust installer

Description
Changes proposed in this pull request:

...
...
...

Related issue(s)

/test all


Add support for row- and column-span in tables

Bug/issue #, if applicable: rdar://98017880
Summary
This PR loads the new Markdown support for row- and column- span in tables and emits it in Render JSON. For more information about the Markdown syntax, see the swift-cmark PR linked below.
Dependencies

https://github.com/apple/swift-cmark/pull/46 (already merged)
https://github.com/apple/swift-markdown/pull/74
https://github.com/apple/swift-docc-render/pull/420

Testing
Use the following sample Markdown to test the new feature:
| one | two | three |
| --- | --- | ----- |
| big      || small |
| ^        || small |

Steps:

Add the above Markdown to Swift-DocC's top-level article.
Build the above-linked Swift-DocC-Render PR and add the resulting dist directory to the DOCC_HTML_DIR environment variable.
swift run docc preview Sources/SwiftDocC/SwiftDocC.docc --fallback-display-name SwiftDocC --fallback-bundle-identifier org.swift.SwiftDocC --fallback-bundle-version 1.0.0
Compare the added content to the below screenshot.

Checklist
Make sure you check off the following items. If they cannot be completed, provide a reason.

[x] Added tests
[x] Ran the ./bin/test script and it succeeded
[x] Updated documentation if necessary

@swift-ci Please test

@swift-ci Please test

Rebased.
@swift-ci Please test


Fix nil pointer exception for not yet initialised memberList

The lock obviously needs to come after the nil check.
Sorry for the premature initial PR.

Just a friendly comment to push this PR ✌:)


NetflixOSS images return 404 even though they show up in search results

Search results return all the netflixoss docker images but trying to pull any image returns a 404.

Taking a look. Thanks for the issue1

@jwalant are you able to pull these on the command line? I get:
$ docker pull netflixoss/hello-netflix-oss
Pulling repository netflixoss/hello-netflix-oss
FATA[0004] Tag latest not found in repository netflixoss/hello-netflix-oss

Same here I am not able to pull them.
On May 31, 2015 4:59 PM, "Jeffrey Morgan"<EMAIL_ADDRESS>wrote:

@jwalant https://github.com/jwalant are you able to pull these on the
command line? I get:
$ docker pull netflixoss/hello-netflix-oss
Pulling repository netflixoss/hello-netflix-oss
FATA[0004] Tag latest not found in repository netflixoss/hello-netflix-oss
—
Reply to this email directly or view it on GitHub
https://github.com/kitematic/kitematic/issues/572#issuecomment-107261076
.

Seems like there's no latest tag on these repos.
Kitematic should fetch the tags before pulling and default to the first one if latest doesn't exist.
Will also investigate for the Docker CLI + Index

Yes, there is no latest tag.

Changing this issue to be a feature request, where Kitematic should prompt the user regarding which tag to use if no latest is found.

Kitematic should prompt the user regarding which tag to use if no latest is found.

+1 on this needing some sort of resolution. I guess the UI requirements are:

default is latest
allow user to select an arbitrary tag instead of latest
offer some sort of alternative in the case of going with default an latest not being available?

Would one option be to have a drop down list of tags displayed when an image is selected, defaulting to latest (if it exists) or most recent tag if it doesn't?

Ideally the Registry API would include the most-recent tag as part of their response. This would allow us to set the 'default' as the tag provided by the registry hub.
The docker command line could also benefit from this, since the following doesn't work:
$ docker pull netflixoss/hello-netflix-oss
Pulling repository netflixoss/hello-netflix-oss
FATA[0003] Tag latest not found in repository netflixoss/hello-netflix-oss

There are some containers which will never have a latest tag. In addition to the container with the real tag (loaded through the command line), kitematic also shows a cloud symbol for the container with the latest tag and the following error:
no such container
404
No such image: andypetrella/spark-notebook:latest (tag: latest)

Kitematic should fetch the tags before pulling and default to the first one if latest doesn't exist.

and;

Ideally the Registry API would include the most-recent tag as part of their response. This would allow us to set the 'default' as the tag provided by the registry hub.

The most recent tag may not be the most recent release of an image (e.g. :v1-legacy being pushed/built after :v2-brand-new was)
TL;DR; I'd be very careful here; I think that asking the user to make the decision would really be the best option here, not "automagically" trying to decide

And the issues still exit !!! Tag issue which I'm already facing.

@abhinavkorpal Same thing happens at the CLI level. Feel free to open an issue with the image owner and letting them know how frustrating it is that they don't have a "latest" tag.

@FrenchBen Sure Will do


mw update fails

mw update
    Error: ENOENT: no such file or directory, open '/opt/homebrew/Cellar/mw/1.0.0-alpha.29/lib/client/bin/mw'
    Code: ENOENT
@mittwald/cli: Updating CLI from 1.0.0-alpha.29 to 1.0.0-alpha.41-2c79e9d... !
╭──────────────────────────────────────────────────────────────────────────────╮
│ ERROR                                                                        │
│                                                                              │
│ An error occurred while executing this command:                              │
│                                                                              │
│   Error: ENOENT: no such file or directory, open                             │
│   '/Users/twestpfahl/.local/share/mw/channel'                                │
│                                                                              │
│ If you believe this to be a bug, please open an issue at                     │
│ https://github.com/mittwald/cli/issues/new.                                  │
╰──────────────────────────────────────────────────────────────────────────────╯

  ERROR STACK TRACE

  Please provide this when opening a bug report.

  Error: ENOENT: no such file or directory, open '/Users/twestpfahl/.local/share/mw/channel'

Context:
❯ mw --version
@mittwald/cli/1.0.0-alpha.29 darwin-arm64 node-v18.11.0

Not sure if this is a layer 8 problem or if this has been fixed already, but on my machine an mw update from 1.0.0-alpha.29 to 1.0.0-alpha.41 did not work.
I did the "update" now manually via
brew uninstall mittwaldw/cli/mw
brew install mittwaldw/cli/mw

No clue why this happens. It is possible that you originally installed some ancient version, when automatic weren't supported yet (so, sometime before #21?)

I think thats unlikely, but since i cannot replicate it, I will just close this issue for now.


Allow oneways and foot-only roads for bike

http://graphhopper.com/maps/?point=49.952383%2C11.578732&point=49.953391%2C11.578404&vehicle=bike
Allow opposite oneways and foot-only roads and set get_off_bike to true. But assign not only low speed like 5km/h but also low priority.

This is an important issue for urban use cases. I am not sure whether the link above is still demonstrating the problem.
I used this one (where the suggested route is more than twice as long): https://graphhopper.com/maps/?point=52.496249%2C13.420917&point=52.497328%2C13.415364&locale=en-GB&elevation=true&profile=bike&use_miles=false&selected_detail=Elevation&layer=Omniscale
From reading the discussions it seems that workarounds are possible, but none is given. Perhaps it would make sense to link a workaround from here, if it exists.

I'm trying to solve that here. Can you take a look when you can @karussell ? Still some tests missing, but to know if is the good approach. Thanks


[WIP] reduction in status/log calls

no longer fetch all simulation taskflows on first load.
only fetch taskflow information when a simulation has been opened.

_

still improvements to be made

should only fetch items from a folder if that folder has been opened
entirely get rid of updateAllTaskflows(), replace it with something smarter

@cjh1 PTAL to see if it cleans calls up somewhat. Could be merged but would like to see if I can get any improvements on the two that I mentioned first.

LGTM, this seems better, lets merge it. Lets also add you other ideas as issues and I think we can still do better.


suggest to add the article type of “link”

Overview

Can you give more details about "link" type?


Incompatibility with other extensions

I'm unsure if this is an issue which can be resolved. However, the other major extensions for markdown (BibTeX autocomplete, autocomplete-plus, and nvatom) do not seem to detect the language "Markdown" as suitable. It's quite frustrating as I am unsure of where all these extensions have compatibility issues, but it appear they are all based off the original gfm package.
Are you able to integrate a method such that other extensions which depend on launguage-gfm simply revert to your package? Or do those extensions need to change to you? It's pretty game changing for me, because I would rather have nvatom, BibTeX autocomplete, and simpler language detection in exchange for slightly less glossy highlighting.

I think those packages need a small change. Documents are either detected by their extension or a base grammar scope. I've changed the latter from source.gfm to text.md and it's possible that those packages tie in to it. I think that's why it might not work. If you could link all affected packages here (preferably a link to the repo), I'll have a look.

Cheers for having a look much appreciated.

The autocomplete-bibtex actually does appear to work with your package on my Linux machine. Not sure why I was getting the errors on Windows.

nvatom actually functions with Markdown, if you modify the interlink.coffee and change the         editor.setGrammar(atom.grammars.grammarForScopeName('source.gfm.nvatom')) line to text.md, and then change loadGrammarSync method to specify language-markdown package. However, a side effect is that this will break the interlink capabilities. Interlink is basically a way to make markup styled as [[foo]] to link to a note called foo.md. The specifications for interlink seem quite embedded in gfm.

I had an attempt at compiling the interlink grammar into the language-markdown package, but I'm not experienced enough to actually get it to work. Gist nvatom.cson was my futile attempt. I assumed I had to modify and .ass.
3. Not really important, but it would be nice if the pandoc flavoured markdown package could somehow be integrated too. However, I haven't had any compability issues
Thanks again for your time, it makes a huge difference to my workflow :)

Thanks.
I'll double-check autocomplete-bibtex myself. Adding the interlinks shouldn't be an issue, kudos for the gist. Do you know if the interlink syntax comes from some standardized specifications? I've never seen it before. The .ass thing is only for tests btw. This package compiles its grammar file, unlike literally all other packages, so that's probably why you didn't get it to work.
Pandoc is on the todo-list for the future. I've been adding a few features here and there, but haven't had time yet to fully look in to it. One day!!
@seongjaelee See the comment above. Your package nvatom is tied into the language-gfm package. Can you think of an easy way so our packages play along nicely?

The actual interlink function appears to be unique to the nvatom package. However, the actual syntax seems to be derrived from Example 511 on the spec.commonmark.org. Sorry I can't be much more help!

No problem, thanks. I haven't gotten around to proper nested brackets yet, but I'll add a quick fix just for the interlinks.

I've looked into this issue today, and there's no easy fix. nvatom is build around language-gfm, and while changing the former to work around language-markdown shouldn't be that difficult, supporting both packages will require more effort. Perhaps the best course of action would be to report this as an issue over at nvatom, and let @seongjaelee decide if and how to implement this. I won't mind helping out though.

Thanks heaps for your time regardless. I've raised it as an issue and may fork it myself at some point. I managed to do a hacky work around for my local machine but it's not a suitable fix for public release. Cheers.

Good luck! Let me know if I can help.


Add Vietnamese translation into README.md

Could you add this change to #37? I'll close this PR and we can keep all the changes there.


Error when running run_baseline_parallel_fast.py

I followed the Video instructions and am running in a conda environment. The run_baseline_parallel.py seams to work fine, but when I run python run_baseline_parallel_fast.py I end up running into this error.
Traceback (most recent call last):   File "/home/eru/PokemonRedExperiments/baselines/run_baseline_parallel_fast.py", line 51, in <module>     callbacks = [checkpoint_callback, TensorboardCallback()] TypeError: TensorboardCallback.__init__() missing 1 required positional argument: 'log_dir'

Do you have the same version of tensorboard and wandb as the requirements.txt?

i have same problem, and i have the same version of tensorboard and wandb as the requirements.txt

I've got the same issue

Hm it seems like this could be due to mismatched versions of tensorboard and sb3, but these versions have not been updated from the original requirements.txt so I'm not sure why that would only be an issue now.
You can also try out of the requirements.txt / code in the v2 folder which has many dependencies updated as and some other improvements.


unrecognized selector

Hello I'm trying to use the library but I got this issue Terminating app due to uncaught exception 'NSInvalidArgumentException', reason: '-[newsPagerViewController topViewController]: unrecognized selector sent to instance
In this line
 newsPagerViewController *twitterPaggingViewer = (newsPagerViewController *)navigationController.topViewController;

newsPagerViewController is subclass of XHTwitterPaggingViewer and in storyboard I've embded newsPagerViewController in navigation controller.

@chlebta
Hi! i miss this issue,
The XHTwitterPaggingViewer subclass of UIViewController.  Because UIViewController unrecognized property of topViewController. TopViewController property in UINavigationController.
Hope to have help
Jack

@xhzengAIB
Hello, sorry but I can't get the point XHTwitterPaggingViewer is already subclass of UIViewController so what I've to change or add ?

@chlebta
Hi!  Look this point https://github.com/duowan/TwitterPaggingViewer/blob/master/Example/XHTwitterPaggingViewerStoryboradExample/XHTwitterPaggingViewerStoryboradExample/AppDelegate.m#L19
or look this example
https://github.com/duowan/TwitterPaggingViewer/tree/master/Example/XHTwitterPaggingViewerStoryboradExample

@xhzengAIB  I've checked that many time but I can't figure the problem please can you explain more , I' new on IOS development .

@chlebta
Yep！ You can see XHTwitterPaggingViewer is subclasse UIViewController, now! You must init rootViewController is XHTwitterPaggingViewer(or subclasse XHTwitterPaggingViewer) on UINavigationController

@xhzengAIB Can you please tell me the step to follow for using this Library,  this how I use  it  :
** 1 I've subclassed XHTwitterPagingViewr Class , the subclass name 'XHTPagingViewController'
** 2 In StoryBoard I've added new ViewController and then make it subclass of XHTPagingViewController
** 3 I've created different View then I defined for each view the subclass (paging view) and StoryBoardID like your storyboard example (TableViewController2, TableViewController1)
** 4 then I've added this code to my appDelegate :
UITabBarController *tabBarController = (UITabBarController *)self.window.rootViewController;

    UINavigationController *navigationController = [[(UINavigationController *)tabBarController viewControllers] firstObject];

    XHTPagingViewController *twitterPaggingViewer = (XHTPagingViewController *)navigationController.topViewController;

    UIStoryboard *storyboard = [UIStoryboard storyboardWithName:@"Main" bundle:nil];

    MainCategoryVC *tableViewController1 = [storyboard instantiateViewControllerWithIdentifier:@"main"];
    AkhbarCategoryVC *tableViewController2 = [storyboard instantiateViewControllerWithIdentifier:@"akhbar"];

    twitterPaggingViewer.viewControllers = @[tableViewController1, tableViewController2];

And Her's the error that I got : **[XHTPagingViewController topViewController]: unrecognized selector sent to instance **

@chlebta
yet！got it, your window rootViewController topviewControoler is SWRevealViewController, XHTPagingViewController in navigationController viewControllers is not top, you can found XHTPagingViewController in navigationController viewController property.

@xhzengAIB  didn't help but I found another solution it's very easy .
In my Subclass XHTPagingViewController I've added those couple of lines in viewDidLoad Method:
 UIStoryboard *storyboard = [UIStoryboard storyboardWithName:@"Main" bundle:nil];
    MainCategoryVC *tableViewController1 = [storyboard instantiateViewControllerWithIdentifier:@"main"];
    tableViewController1.title = @"Home";
    AkhbarCategoryVC *tableViewController2 = [storyboard instantiateViewControllerWithIdentifier:@"akhbar"];
    tableViewController2.title = @"Akhbar";
    self.viewControllers = @[tableViewController1, tableViewController2];
    [self reloadData];

but I got display issue the navigation bar doesn't looks like twitter (The dots under the title are missing and also the title color is black I've changed the title color via storyboard but haven't help)  :

Also there's a littel bug if the user forget to set title property for one or more view then the app will crash.

Hi! @chlebta
This open source will be help you(https://github.com/StefanLage/SLPagingView).

@chlebta
Because of the open source repository changes color and manage multiple pages, but there is no reuse mechanism.
Jack

@xhzengAIB
I've solved some part of it but the problem when I opened first time my app (before any swipe or navigation between pages) the titles views doesn't appear

I've loved this problem by adding :
self.paggingNavbar.contentOffset = self.paggingScrollView.contentOffset;
at
- (void)viewWillAppear:(BOOL)animated {

}