flow_from_directory seems to find no images

Hello
I'm running into this issue using the latest version of Keras (1.1.0). I also tried to use version 1.0.0 and 1.0.1 and it's the same.
Whenever I try to use the data augmentation ImageDataGenerator, it seems that the method flow_from_directory can't find any image in my folders.
Here's my model code:
img_width, img_height = 150, 150

train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
nb_train_samples = 2000
nb_validation_samples = 800
nb_epoch = 5

model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(3, img_width, img_height,)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))

model.add(Convolution2D(32, 3, 3, dim_ordering="th"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(1))
model.add(Dense(1))
model.add(Activation('sigmoid'))

Here's my data augmentation code: (error is located here)
train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        'data/train',
        target_size=(150, 150),
        batch_size=32,
        class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
        'data/validation',
        target_size=(150, 150),
        batch_size=32,
        class_mode='binary')

model.fit_generator(
        train_generator,
        samples_per_epoch=2000,
        nb_epoch=50,
        validation_data=validation_generator,
        nb_val_samples=800)

I followed this tutorial (even corrected some mistakes with the dimension ordering)
https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
After solving a lot of errors, I'm stuck to the point where the method flow_ffrom_directory finds no image in my folders, thus generating a null output:
$ python ml.py
Using TensorFlow backend.
Found 0 images belonging to 0 classes.
Found 0 images belonging to 0 classes.
Epoch 1/5
Traceback (most recent call last):
  File "ml.py", line 69, in <module>
    nb_val_samples=nb_validation_samples)
  File "/home/clement/ESIEA/5A/Machine_Learning/lib/python3.5/site-packages/Keras-1.1.0-py3.5.egg/keras/models.py", line 874, in fit_generator
    pickle_safe=pickle_safe)
  File "/home/clement/ESIEA/5A/Machine_Learning/lib/python3.5/site-packages/Keras-1.1.0-py3.5.egg/keras/engine/training.py", line 1417, in fit_generator
    'or (x, y). Found: ' + str(generator_output))
Exception: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None

I even tried to modify the number of images in my folder but there seems to be nothing I could do.

My entire bad. Completely forgot to create subdirs. Found my error exploring the source code of Directory Iterator class.

I'm having this problem now...How did you fix it? what was the problem wrt subdirs?

How did you solve it? My path is already a folder; what do you mean?

The path needs to be to a folder, that contains folders, that contains the images.

I have it in a folder that contains folders of pictures. Still, it doesn't read my image. Why is that?

look carefully at the file structure in the beginning of https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html. good luck!

Ok, thank you. I fixed it.
Now, I get this error:
"model.fit_generator(
...         train_generator,
...         samples_per_epoch=12,
...         nb_epoch=1,
...         validation_data=validation_generator,
...         nb_val_samples=4)
Traceback (most recent call last):
File "", line 6, in
File "/Users/Zahra/anaconda/envs/tensorflowTwo/lib/python2.7/site-packages/keras/models.py", line 823, in fit_generator
raise Exception('The model needs to be compiled before being used.')
Exception: The model needs to be compiled before being used.

"

follow the instructions in the link i sent. as the error suggests, you need to compile the model before you can train it(i,e give the net the settings under which to train).

Yes, I followed all the instructions on the link but still I get this error. Here is my code:
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='valid', dim_ordering='tf', input_shape=(150, 200, 4)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25)) #Cannot take float values
model.add(Convolution2D(64, 3, 3, border_mode='valid'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
Note: Keras does automatic shape inference.
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
this is the augmentation configuration we will use for testing:
only rescaling
test_datagen = ImageDataGenerator(rescale=1./255)
this is a generator that will read pictures found in
subfolers of 'data/train', and indefinitely generate
batches of augmented image data
train_generator = train_datagen.flow_from_directory(
'Data/Train',  # this is the target directory
target_size=(150, 200),  # all images will be resized to 150x150
batch_size=32,
class_mode='binary')  # since we use binary_crossentropy loss, we need binary labels
this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
'Data/Validation',
target_size=(150, 200),
batch_size=32,
class_mode='binary')
model.fit_generator(train_generator, 12, 1, validation_generator, 4)
model.save_weights('thesis.h5')  # always save your weights after training or during training
Where's the problem? How can I give the net the settings beforehand?

Yes, I followed all the instructions on the link but still I get this error. Here is my code:
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='valid', dim_ordering='tf', input_shape=(150, 200, 4)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25)) #Cannot take float values
model.add(Convolution2D(64, 3, 3, border_mode='valid'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'Data/Train',  # this is the target directory
target_size=(150, 200),  # all images will be resized to 150x150
batch_size=32,
class_mode='binary')  # since we use binary_crossentropy loss, we need binary labels
validation_generator = test_datagen.flow_from_directory(
'Data/Validation',
target_size=(150, 200),
batch_size=32,
class_mode='binary')
model.fit_generator(train_generator, 12, 1, validation_generator, 4)
model.save_weights('thesis.h5')  # always save your weights after training or during training
Where's the problem? How can I give the net the settings beforehand?

@zahrasorour , you need to compile the model using model.compile before model.fit. model.compile is where you initialize your loss function, optimizer, metrics etc.

Getting the same issue (using Keras and Tensorflow), any help would be greatly appreciated.
My directory is set to be folder/folder/images - for both training and testing data.
I made a loop to test the different depths/nb_layers in a Resnet, as well as some hyper parameters like learning rate, batch size, etc. The test went from 4, 6, 8, 10 - all the way to 20, then gave me output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None
I don't understand why it can work for a handful of the iterations, then fail.
I read here to update keras to 2.0, but was told not to change the version of keras by my boss.
I read here to convert all labels to a numpy array, but keras documentation states this already happens to labels while using the 'categorical' attribute in flow_from_directory
Then I read here to put my train_generator in a function, then create an infinite while loop and yield the results, but this results in the data to be loaded over and over at the start of the program.  "Found 350 images belonging to 7 classes" (repeated 10 times), which then results in an error "output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: <keras.preprocessing.image.DirectoryIterator object at 0x0000000063494BE0>"
Here's my stack trace for the original error:
Traceback (most recent call last):
File "", line 1, in
runfile('K:/Manufacturing Operations/Yield/Tools_Yield/PythonScripts/AI/ISL_DI/Resnet/resISL_Depth.py', wdir='K:/Manufacturing Operations/Yield/Tools_Yield/PythonScripts/AI/ISL_DI/Resnet')
File "C:\Users\paul\AppData\Local\Continuum\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 714, in runfile
execfile(filename, namespace)
File "C:\Users\paul.\AppData\Local\Continuum\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 89, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "K:/Manufacturing Operations/Yield/Tools_Yield/PythonScripts/AI/ISL_DI/Resnet/resISL_Depth.py", line 233, in
callbacks=callbacks_list)
File "C:\Users\paul\AppData\Roaming\Python\Python35\site-packages\keras\engine\training.py", line 1481, in fit_generator
str(generator_output))
ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None
Here's the code other than vars
`rep=0
#####################################
#new model loop
for i in range(retrainings + 1):
#lr_init = [5, 1, .1, .01]
while rep != len(layers) - 1:
lr_init = [5, 1]
for lr_val in lr_init:
        decay_init = .1
        epochs_drop = 20
        patience=60
        # learning rate schedule
        def step_decay(epoch):
            initial_lrate = lr_val
            drop = 0.1
            epochs_drop = 60
            lrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))
            #print('\nLR: {:.6f}\n'.format(lrate))
            return lrate

        momentum_init=0.9
        sgd = SGD(lr=lr_val, decay=decay_init, momentum=momentum_init, nesterov=False)

        ##reduce learning rate when loss has stopped improving
        #lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), cooldown=0, patience=5, min_lr=0.5e-6)
        ##stop training when accuracy has stopped improving
        early_stopper = EarlyStopping(monitor='val_acc', min_delta=0.001, patience=50)
        #csv_logger = CSVLogger('resnet18_cifar10.csv')

        repititions = 3
        #epochs=[105]
        epochs=[200]
        drop_out=[0]
        #batchsize=[2, 4, 8, 10]
        batchsize=[2, 5, 10]
        zoom=[0]
        shear=[0]
        channelshift=[0]
        featurewise=[False]
        samplewise=[False]
        rotation=[0]

        nb_train_samples = 350
        nb_validation_samples = 140

        colormode='rgb'

         # input image dimensions
        img_width, img_height = 224, 224
        nb_classes=7
        img_channels = 3

        for epoch_val in epochs:
            for dropout_val in drop_out:
                for batchsize_val in batchsize:
                    for zoom_val in zoom:
                        for shear_val in shear:
                            for channelshift_val in channelshift:
                                for featurewise_val in featurewise:
                                    for samplewise_val in samplewise:
                                        for rotation_val in rotation:
                                            for r in range(repititions):

            #                                    np.random.seed(7)
            #                                    tf.set_random_seed(7)

                                                train_data_dir = basepath + pathlist[0]
                                                validation_data_dir = basepath + pathlist[1]

                                                #############################################
                                                #############################################

                                                params={}
                                                params['epochs']=epoch_val
                                                params['drop_out']=dropout_val
                                                params['batchsize']=batchsize_val
                                                params['zoom']=zoom_val
                                                params['shear']=shear_val
                                                params['channelshift']=channelshift_val
                                                params['featurewise']=featurewise_val
                                                params['samplewise']=samplewise_val
                                                params['rotation']=rotation_val
                                                params['lr_init']=lr_val
                                                params['momentum_init']=momentum_init
                                                params['decay_init']=decay_init
                                                params['epochs_drop']=epochs_drop
                                                params['img_size']=list([img_width,img_height])
                                                params['patience']=patience

                                                total = 0
                                                currentlayer = [i * 2 for i in layers[rep]]
                                                total = sum(currentlayer) + 2
                                                savefilename='resnet_' + str(total) + '_BKM_lr_' + str(lr_val) + '_batchSize_' + str(batchsize_val) + '_repition' + str((r+1)) + '_Study'
                                                total = 0
                                                with tf.device('/gpu:0'):

                                                    model = resnet_iter.ResnetBuilder.build_resnet_34((img_channels, img_width, img_height), nb_classes, layers[rep])
                                                    model.compile(loss='categorical_crossentropy',
                                                                  optimizer=sgd,
                                                                  metrics=['accuracy'])

                                                    train_datagen = ImageDataGenerator(
                                                        featurewise_center=False,  # set input mean to 0 over the dataset
                                                        samplewise_center=False,  # set each sample mean to 0
                                                        featurewise_std_normalization=featurewise_val,  # divide inputs by std of the dataset
                                                        samplewise_std_normalization=samplewise_val,  # divide each input by its std
                                                        zca_whitening=False,  # apply ZCA whitening
                                                        channel_shift_range=channelshift_val, #VGG set to 0
                                                        fill_mode="reflect", #VGG set to reflect
                                                        rotation_range=rotation_val,  # randomly rotate images in the range (degrees, 0 to 180)
                                                        rescale=1./255, #VGG set to 1./255
                                                        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width) - VGG set to 0
                                                        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height) - VGG set to 0
                                                        shear_range=shear_val, #VGG set to 0
                                                        zoom_range=zoom_val, #VGG set to 0.1
                                                        horizontal_flip=True,  # randomly flip images
                                                        vertical_flip=True)  # randomly flip images VGG set to True

                                                    test_datagen = ImageDataGenerator(rescale=1./255)

                                                    train_generator = train_datagen.flow_from_directory(
                                                        train_data_dir,
                                                        target_size=(img_width, img_height),
                                                        batch_size=batchsize_val,
                                                        shuffle=True,
                                                        color_mode=colormode,
                                                        class_mode='categorical')

                                                    validation_generator = test_datagen.flow_from_directory(
                                                        validation_data_dir,
                                                        target_size=(img_width, img_height),
                                                        batch_size=batchsize_val,
                                                        shuffle=True,
                                                        color_mode=colormode,
                                                        class_mode='categorical')

                                                    lrate = LearningRateScheduler(step_decay)
                                                    callbacks_list = [lrate, early_stopper]

                                                    try:
                                                        A=model.fit_generator(
                                                            train_generator,
                                                            samples_per_epoch=nb_train_samples,
                                                            nb_epoch=epoch_val,
                                                            validation_data=validation_generator,
                                                            nb_val_samples=nb_validation_samples,
                                                            callbacks=callbacks_list)
                                                    except:
                                                        print("train_generator: " + train_generator)
                                                        print("train_data_dir: " + train_data_dir)
                                                        files=os.listdir(train_data_dir)
                                                        print(len(files))`

Maybe someone needs this, it took me quite a lot to find out:
If you are trying to read .gif files with flow_from_directory modify there is a variable in preprocessing/image.py

white_list_formats = {'png', 'jpg', 'jpeg', 'bmp', 'ppm'}

Add 'gif' and it will work smooth.
Do you think a pull request is needed. I think they should warn you in the documentation at least.

Thank you @HectorAnadon .
I was having the same problem with .tif file type.
Fixed the problem by adding it to white_list_formats.

can we view the images generated by data augmentation? if yes then how?

I have a problem as well, the code in keras document is just load 1 image. if I have 10 image in folder how am I going to use this code
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
img = load_img('data/train/cats/cat.0.jpg')  # this is a PIL image
x = img_to_array(img)  # this is a Numpy array with shape (3, 150, 150)
x = x.reshape((1,) + x.shape)  # this is a Numpy array with shape (1, 3, 150, 150)
the .flow() command below generates batches of randomly transformed images
and saves the results to the preview/ directory
i = 0
for batch in datagen.flow(x, batch_size=1,
save_to_dir='preview', save_prefix='cat', save_format='jpeg'):
i += 1
if i > 20:
break  # otherwise the generator would loop indefinitely

To anyone coming here from Google because you're using class_mode=None, you still have to create a subdirectory:

If None, no labels are returned (the generator will only yield batches of image data, which is useful to use with  model.predict_generator(),  model.evaluate_generator(), etc.). Please note that in case of class_mode None, the data still needs to reside in a subdirectory of directory for it to work correctly.

Thank you for replying i need to know how i can display or predict the
image whose path i had provided
On Wed, Jun 27, 2018 at 10:57 AM, Adamya Tripathi<EMAIL_ADDRESS>wrote:

Hi @Hs1000 https://github.com/Hs1000 !!
I guess your path( "C:\Users\user\Downloads\Research\New folder") has the
images.
You need to get those images and store it in a new folder inside the 'New
Folder' so your path must look like - C:\Users\user\Downloads\Research\New
folder\Sub_Dir_Containing_Images' . Then you must pass the same path
again. Remember don't change the path!
validation_generator = test_datagen.flow_from_directory(
r'C:\Users\user\Downloads\Research\New folder', ## No change in the path
target_size=(28,28),
batch_size=32,
class_mode='binary')
Also for deep dive, one must remember that
ImageDataGenerator.flow_from_directory was created keeping in mind the
good ol' image classification problem. So flow_from_directory()
receives a folder name where different folder resides containing the
images(traversal of nested sub directories). What most people tend to do is
directly pass the the folder which contains the images.
Happy Coding!!
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/keras-team/keras/issues/3946#issuecomment-400546787,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AkSY7FrdYAZLUg5Ew3kqM9m59WL-FcgCks5uAxevgaJpZM4KMgpW
.

Maybe someone needs this, it took me quite a lot to find out:
If you are trying to read .gif files with flow_from_directory modify there is a variable in preprocessing/image.py

white_list_formats = {'png', 'jpg', 'jpeg', 'bmp', 'ppm'}

Add 'gif' and it will work smooth.
Do you think a pull request is needed? I think they should warn you in the documentation at least.

for anaconda users, please dont do this in tensorflow/keras folder but in keras' own folder, this happens when you're too lazy to check and search using windows search

My entire bad. Completely forgot to create subdirs. Found my error exploring the source code of Directory Iterator class.
Thanks @cdalbergue ! I got same your issue and your question helps me recognize it.

!mkdir '/content/train/'dog
!mkdir '/content/train/'cat
!bash -c 'mv '/content/train/'cat.{0..12499}.jpg /content/train/cat'
!bash -c 'mv '/content/train/'dog.{0..12499}.jpg /content/train/dog'

I almost pulled all my hairs out for this error and it turns out it needed a new folder inside the given directory.
Are you kidding me?
Anyways thanks guys ....Real lifesaver

How did i solve it? My path is already a folder.I have it in a folder that contains folders of pictures. Still, it doesn't read my image. Why is that?
train_path = 'D:\data\Train_Data\egg'
train_batches = ImageDataGenerator().flow_from_directory(train_path, target_size=(224,224), classes=classes_required, batch_size=batch_size_train)
type(train_batches)
Found 0 images belonging to 106 classes.

The path needs to be to a folder, that contains folders, that contains the images.

It doesn't work in google colab. It gives me you have 0 images but when the directory with images exist

I have the message shows that the image found, and I set the save_to_dir. but I found nothing in my save_to_dir that I set! how could that happen?

The path needs to be to a folder, that contains folders, that contains the images.

Thanks for the clear explanation

give the path of the directory in '--derectory--' rather than directly as an argument.

Does this work with any file extension. Tried reading .pgm images but got the error
Found 0 images belonging to 40 classes.
This works fine with .jpg images

I think this should be fixed: when labels are passed manually with a list, it should not expect a folder that contains a folder with the images. Instead it should just be the specified folder

I'm having this problem now...How did you fix it? what was the problem wrt subdirs?
UPDATE: Also fixed it through subdirs. The path needs to be to a folder (that contains the images) as opposed to directly to the images. Just in case some one else googles this.

True got it sorted this way only was struggling for past 2 days

train_dir="WarsawDataSet\train"
val_dir="WarsawDataSet\val"
learning_rate = 0.0001
img_width = 224
img_height = 224
batch_size = 32
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.1,
zoom_range=0.1,
rotation_range=10.,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip = True,
validation_split = 0.2
)
validation_datagen = ImageDataGenerator(
    rescale=1./255
    )

train_dataset = train_datagen.flow_from_directory(
"WarsawDataSet\train",
target_size = (img_height, img_width),
classes = ('REAL','PRNT'),
batch_size = batch_size,
subset = 'training',
seed=20
)

val_dataset = validation_datagen.flow_from_directory(
"WarsawDataSet\val",
target_size = (img_height, img_width),
classes = ('REAL','PRNT'),
batch_size = batch_size,
subset = 'validation',seed=20
)
Found 0 images belonging to 2 classes.
Found 0 images belonging to 2 classes.
Not able to access images .Dont understand whts going wrong .
C:\Users\ADITI\WarsawDataSet\train
My images are stored in my system with this path.
in train folder there are two class folders containing images .
Plz help me resolve this

train_dir="WarsawDataSet\train" val_dir="WarsawDataSet\val"
learning_rate = 0.0001 img_width = 224 img_height = 224 batch_size = 32
train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.1, zoom_range=0.1, rotation_range=10., width_shift_range=0.1, height_shift_range=0.1, horizontal_flip = True, validation_split = 0.2 ) validation_datagen = ImageDataGenerator(
    rescale=1./255
    )

train_dataset = train_datagen.flow_from_directory( "WarsawDataSet\train", target_size = (img_height, img_width), classes = ('REAL','PRNT'), batch_size = batch_size, subset = 'training', seed=20
)

val_dataset = validation_datagen.flow_from_directory( "WarsawDataSet\val", target_size = (img_height, img_width), classes = ('REAL','PRNT'), batch_size = batch_size, subset = 'validation',seed=20 ) Found 0 images belonging to 2 classes. Found 0 images belonging to 2 classes.
Not able to access images .Dont understand whts going wrong . C:\Users\ADITI\WarsawDataSet\train My images are stored in my system with this path. in train folder there are two class folders containing images .
Plz help me resolve this

Try giving path till \DataSet, also just fiddle a bit with folder structure and the path you give, you'll get it eventually, and check version of keras this is a really old thread

Traceback (most recent call last):
File "C:\Users\Mary_Sowmya\Downloads\Emotion-detection-master\Emotion-detection-master\src\emotions.py", line 56, in
train_generator = train_datagen.flow_from_directory(
File "C:\Users\Mary_Sowmya\AppData\Roaming\Python\Python311\site-packages\keras\preprocessing\image.py", line 1651, in flow_from_directory
return DirectoryIterator(
File "C:\Users\Mary_Sowmya\AppData\Roaming\Python\Python311\site-packages\keras\preprocessing\image.py", line 563, in init
for subdir in sorted(os.listdir(directory)):
FileNotFoundError: [WinError 3] The system cannot find the path specified: 'data/train'
I'm getting this error , i'm not able to find this error

from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
train_data_dir = 'data/train'
val_data_dir = 'data/test'
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'data/train',
target_size = (48,48),
batch_size = 64,
color_mode = "grayscale",
class_mode = 'categorical'
)
val_generator = val_datagen.flow_from_directory(
'data/val',
target_size = (48,48),
batch_size = 64,
color_mode = "grayscale",
class_mode = 'categorical'
)
emotion_model = Sequential()
emotion_model.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape = (48,48,1)))
emotion_model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2,2)))
emotion_model.add(Dropout(0.25))
emotion_model.add(Conv2D(128, kernel_size=(3,3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2,2)))
emotion_model.add(Conv2D(128, kernel_size=(3,3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2,2)))
emotion_model.add(Dropout(0.25))
emotion_model.add(Flatten())
emotion_model.add(Dense(1024, activation='relu'))
emotion_model.add(Dropout(0.5))
emotion_model.add(Dense(7, activation='softmax'))
emotion_model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.0001, decay=1e-6),metrics=['accuracy'])
emotion_model_info = emotion_model.fit_generator(
train_generator,
steps_per_epoch = 28709 // 64,
epochs=75,
validation_data = val_generator,
validation_steps = 7178 // 64
)
emotion_model.save_weights('model.h5')
this is my code
plz rectify this error


fix(layers/prometheus-client): remove duplicated increment_request_total of write operation

Which issue does this PR close?
No
Rationale for this change
Repeatedly record the requests_total of the write operation.
What changes are included in this PR?

Remove duplicated increment_request_total metric of write operation.
Update example of prometheus-client layer doc

Are there any user-facing changes?
No

The main fix is  d4c926270944c83e78773ce121bc2aa6e1141dfc


feat: default allowed_gas

Feature Request
Add an attribute that sets the available_gas for every #[test] in a module.
Describe Preferred Solution
Introduce a new module attribute default_available_gas that would set the available gas for each test case in the decorated module. If a test case has its specific available_gas attribute, the test runner would use this specific value to set the gas.
#[cfg(test)]
#[default_available_gas(1000000000)]
mod tests {
    #[test]
    fn test_1() {
        // uses the mod default available gas of<PHONE_NUMBER>
    }

    #[test]
    #[available_gas(1000)]
    fn test_perf() {
        // uses the test specific available gas of 1000
    }
}

Describe Alternatives
An alternative would be for the test runner to accept a --available-gas cmdline argument that would act in the same way as the attribute described above. It could also work in tandem where the cmdline arg would be a less specific value. The available gas resolution would then be from most to least specific - test case attribute, module attribute, command line arg, global default.
Related Code

Additional Context
Pretty much everyone just amends their test cases with available_gas and a random big number to have their test cases running. Examples from Kakarot, Renegade finance or this repo. It's annoying and having a way how to globally specify the number would result in a much better devex.
If the feature request is approved, would you be willing to submit a PR?
(Help can be provided if you need assistance submitting a PR)

[x] Yes

I'd need some guidance.

Should user be unaware for their tests that they take gas?

IMO the most common use case is "I just want to ensure my tests run, I want to test my code." 95% of the time, these tests are not performance tests, but unit or integration tests. So I'd say it's fine if users (developers) are not aware that tests take gas. In which case, your proposed solution of infinity available gas is great.
For the remaining cases where we want to test the efficiency of a function, the current available_gas attribute works fine.

so we also probably need to add some option for "const_gas" - to enable ensuring a function does not take gas.
suggestions for such a name?


Ideas / More Examples: Token Issuer Page demo to show how the tokens can be utilised inside Devcon

This ticket is to create a new example/s of how the token negotiator can work with a third party website:
Ideas include:

Gaming, how tokens could be loaded and interact together off chain
Beer token, whereby attending an event your token earns you a first free beer with a local bar
Cross token attestations e.g. POAP Token + Opensea NFT = Super Powers inside a DAPP or website.

Closing this ticket. Idea's are logged inside Jira.


Reorganize: Move test_d* unit tests into files/directories that mimic source code being tested

Housekeeping

[X] I am a maintainer of dbt-core

Short description
As part of our Robust Testing Initiative we want to re-organize our unit tests to mimic the source code directory and files. Doing one file is generally smaller than what we'd rank as one unit of work, so this is a grouping of multiple. Here we want take unit tests in unit test files starting with test_d* and reorganize them into appropriate files within appropriate directories. This includes: test_deprecations.py, test_deps.py, test_doc_blocks.py, and test_docs_generate.py. These files contain 36 unit tests.
Acceptance criteria
The unit tests in test_deprecations.py, test_deps.py, test_doc_blocks.py, and test_docs_generate.py have been moved to files within a directory structure under tests/unit/ that mirror the organization of the source code being tested.
Suggested Tests
The work is the tests
Impact to Other Teams
None
Will backports be required?
Narp
Context
https://github.com/dbt-labs/dbt-core/issues/9649

Duplicate of https://github.com/dbt-labs/dbt-core/issues/9961


Introduction to packing and building applications with Webpack 4

Hello,
I have one web-related presentation I held not long ago at .NET meetup in Vodnjan and which was very-well received, so I am interested in holding it in Zagreb as well. I think that it would be good to hold it because it's something necessary for a lot of Web developers. I am also working a lot on it in Infobip, so I am very interested in sharing at least some of my knowledge to others. :)
Ante Tomić, Software Engineer (Infobip)
Title: Introduction to packing and building applications with Webpack 4
Description: Are you familiar with developing of applications and having to be careful to „include that script before your own because the application won't work otherwise“? Or have you ever used Webpack or something similar and ever wonder how it works? Here we will look at the basic example of how we can set up generation of our app output from no setup code to a basic example where we can look at the concepts you will need the most. We will also look into how Webpack relates to other module bundlers like Parcel or task runner like Gulp, Grunt and more.
I have materials from .NET meetup here: http://bit.ly/WebpackDotNetMeetup.
I am planning to change some things a little bit for 2nd version of presentation (and maybe change order of some stuff), but this interactive notebook is a bit more detailed version of presentation that I held. Also, presentation I held on .NET meetup was in Croatian and it was 45 minutes long, but here I would go with English version, while the presentation length is up to discussion.

Great! :) Get back with description by the end of this month so I can create event. Cheers!

@vblazenka Here is the description: :)
Title: Introduction to packing and building applications with Webpack 4
Presenter: Ante Tomić, Software Engineer (Infobip)
Description: Are you familiar with developing apps and having to be careful to „include that script before your own because the application won't work“? Or have you ever used Webpack or similar solutions and wondered how they work? Here we will check out how to build the application from no setup code to the most common concepts. We will also see how Webpack relates to the other popular solutions like Parcel, Gulp or Grunt.
I didn't remove any part in the end (since all of them are important), but I simplified the description. :)
Do you maybe know when will the exact date be known?


npm flow-bin still is 0.95.1 (0.95.2 is 4 days old)

https://www.npmjs.com/package/flow-bin

version
0.95.1

and, as of now,

v0.95.2
@facebook-github-bot facebook-github-bot released this 4 days ago

It is vital for my well-being, world peace, the end of the end of the Mueller investigation and also for our successful earth-defense against extraterrestrial invasion that the npm package is updated ASAP (the JS code on the armed satellites is checked with Flow)!

It is already 0.96.0

Should be fixed soon as part of the 0.96.0 release.

@avikchaudhuri Well, still nothing, no 0.96 on npm.... and not all versions where built yet, right now Linux still is missing.

Yeah we’re having some problems with the Linux build, there’s a circleci
issue.
On Mon, Apr 1, 2019 at 2:00 PM KittenWithHerbs<EMAIL_ADDRESS>wrote:

@avikchaudhuri https://github.com/avikchaudhuri Well, still nothing, no
0.96 on npm.... and not all versions where built yet, right now Linux still
is missing.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/facebook/flow/issues/7591#issuecomment-478681869, or mute
the thread
https://github.com/notifications/unsubscribe-auth/AFFNMf_oZ5a7wizB51hIEEmpKx8bNxpQks5vckkygaJpZM4cLB-a
.

Same issue again now with 0.96.1 — two days since release, npm version still is 0.96.,0
Something broken in the process that it keeps happening?


test-marist-ubuntu1604-s390x-3 agent process crash

https://ci.adoptopenjdk.net/view/Test_openjdk/job/Test_openjdk15_hs_sanity.openjdk_s390x_linux/4/console suggested test-marist-ubuntu1604-s390x-3 is offline and the jenkins UI is complaining that the jenkins agent failed to start on it This agent is offline because Jenkins failed to launch the agent process on it. See log for more details

The log actually says :
Evacuated stdout
Agent successfully connected and online

However is still apparently loading - I think the agent has hanged so I'm going to disable it, attempt to ssh into it, kill the jenkins agent process and restart it. However, sshing is currently timing out ...

https://adoptopenjdk.slack.com/archives/C53GHCXL4/p1596639847367300?thread_ts=1596636363.360900&cid=C53GHCXL4
There's a power outage problem where the machines are based. I'll keep the issue open until the problem is resolved :-)

It's back up now :-)


device_plugins: Updated the latest operator version to v0.28.0

Signed-off-by: chaitanya1731<EMAIL_ADDRESS>

LGTM


chore: release main

:robot: I have created a release beep boop
@libp2p/multidim-interop: 2.0.7
Dependencies

The following workspace dependencies were updated

dependencies

@libp2p/webrtc bumped from ^4.0.6 to ^4.0.7
@libp2p/webtransport bumped from ^4.0.6 to ^4.0.7
libp2p bumped from ^1.0.6 to ^1.0.7

@libp2p/webrtc: 4.0.7
Dependencies

The following workspace dependencies were updated

devDependencies

libp2p bumped from ^1.0.6 to ^1.0.7

@libp2p/webtransport: 4.0.7
Dependencies

The following workspace dependencies were updated

devDependencies

libp2p bumped from ^1.0.6 to ^1.0.7

libp2p: 1.0.7
1.0.7 (2023-12-04)
Bug Fixes

allow no transports in config (#2293) (16588d2)

This PR was generated with Release Please. See documentation.

:robot: Release is at https://github.com/libp2p/js-libp2p/releases/tag/multidim-interop-v2.0.7 :sunflower:

:robot: Release is at https://github.com/libp2p/js-libp2p/releases/tag/webtransport-v4.0.7 :sunflower:


Download the results of SQL query as Excel or CSV

download the results of SQL query as Excel or CSV
example:

https://github.com/livebook-dev/livebook/discussions/1652

Thak you. I added an issue to the more specific https://github.com/livebook-dev/kino_explorer/issues/16


Live Metrics Filtering: Part 1 of 5 - port filtering functionality

Description
This PR includes:

Port functionality from filter.ts in node repo to Filter.java. This is responsible for filtering any telemetry item.
New types: TelemetryColumns & it's child classes. These exist to make filtering easier - instead of using TelemetryItems directly, needed some fields to be boolean or numeric.
Added a function for parsing timestamps from filtering configuration (it's in a different format than whats in FormattedDuration.java)
Added util function for converting FormattedDuration string back to microseconds, for filtering of durations.
Added unit tests for Filter.java & the util function

None of this new code is actually used in the QuickPulseDataCollector yet - that's for a future PR.
Spec: https://github.com/aep-health-and-standards/Telemetry-Collection-Spec/blob/main/ApplicationInsights/live-metrics-filtering.md
All SDK Contribution checklist:

[x] The pull request does not introduce [breaking changes]
[ ] CHANGELOG is updated for new features, bug fixes or other significant changes.
[x] I have read the contribution guidelines.

General Guidelines and Best Practices

[x] Title of the pull request is clear and informative.
[] There are a small number of commits, each of which have an informative message. This means that previously merged commits do not appear in the history of the PR. For more information on cleaning up the commits in your PR, see this page.

Testing Guidelines

[x] Pull request includes test coverage for the included changes.

API change check
API changes are not detected in this pull request.

I have approved with some comments.
It would be great if you could address them in this PR or a follow up one.
As discussed, we may revisit this code later after other PRs are merged.

I will merge for now and address comments in following PR - we can always go back and reflect on content from this PR when we need to.


HMS Push Notification with type "data" does not come to the application when app in killed state (Ionic-Capacitor)

Description
Hello, help me please with my issue.😇
HMS Push Notification with type "data" does not come to the application when app in killed state. I copied the code from your example for my project, and set this.hmsPush.setBackgroundFile('public/assets/background.js'), but background file is not working for create local notification. Maybe I missed something?
Expected behavior
When a notification with the "data" type arrives, this code should work in a file background.js:
onBackgroundRemoteMessageReceived((remoteMessage)=>{
  const jsonData = JSON.parse(remoteMessage.data);
  const headlessNotification = {
              "title": "[Headless] " + jsonData.title,
              "message": jsonData.message.replace("{{name}}","YourName")
          };
  const notification = JSON.stringify(headlessNotification);
  HmsLocalNotification.backgroundLocalNotification(notification);
});

Current behavior
Local notification is not created in app killed state.
Environment

Platform: Ionic-Capacitor @ionic/angular 6.2.6
Kit: Push
Kit Version 6.3.0-304
OS Version Android 12

Please see if this guide solves your issue: https://forums.developer.huawei.com/forumPortal/en/topic/0201622509719960128

Please see if this guide solves your issue: https://forums.developer.huawei.com/forumPortal/en/topic/0201622509719960128

Unfortunately, this method does not work😥

I found a solution for Ionic/Capacitor.
It was necessary to upload the file via this.hmsPush.setBackgroundFile('public/assets/background.js');
And create a file in the directory  www/assets/background.js.
Thanks for help! 😇


give back the getter for container property

This PR fixes #12106
the getter for the container property of the dashboardInput was changed unexpectedly by https://github.com/microsoft/azuredatastudio/commit/d8dcc9085731df0ff6bb9dc336c0a76e6e1a0dd0#diff-fa11a5cd769e38a19da55711f5836079L131
this is causing the container property to be undefined.

Pull Request Test Coverage Report for Build 238341680

0 of 0   changed or added relevant lines in 0 files are covered.
103 unchanged lines in 1 file lost coverage.
Overall coverage decreased (-0.01%) to 39.048%

Files with Coverage Reduction
New Missed Lines
%

extensions/azdata/src/azdata.ts
103
37.63%

Totals

Change from base Build 238265202:
-0.01%

Covered Lines:
18952

Relevant Lines:
44359

💛  - Coveralls

Why would this not cause a compile error? I removed it because we had get container and getContainer doing the exact same thing.

I agree it is a surprise for me we didn't get compilation error, maybe I shouldn't since it is TS/JS :). there is an issue talking about why not give compilation error when reading a write-only property: https://github.com/Microsoft/TypeScript/issues/11596


SQL Server: export task in new table ignores options "Selected columns(rows) only"

System information:

Win10, DBeaver CE 21.2.4

Connection specification:

MS SQL Server (AWS RDS)

Describe the problem you're observing:
I want run export task to the new table with checked options "Selected columns only" and/or "Selected rows only" but they are ignored.
Steps to reproduce, if exist:
Open table with several columns.
Highlight some columns and rows.
Begin export.
Select Database export type.
Select target table as new table (Mapping = create)
Check "Selected columns only" and save task.
Close Data transfer dialog and run created task.
Expected: only selected columns were exported
Actual: all columns and rows of the source table are copied to the target table despite on the enabled options "Selected columns(rows) only"

Unfortunately, we can't fix this behavior because the tables mapping page in the Data Transfer Wizard is before the Extraction settings (with setting "Selected columns only").
The best solution in this situation will be a change of columns mapping to "skip" on the "Tables mapping page".


Consider renaming buildUrl and buildRelativeUrl functions

Current names are misleading because these functions don't generate full URLs. Instead, they generate URL parts which are called paths in React Router. There already are buildPath and buildRelativePath functions, but they actually generate pathnames, so they can also be renamed.
These are the possible renamings:

buildUrl => buildPath
buildRelativeUrl => buildRelativePath
buildPath => buildPathname
buildRelativePath => buildRelativePathname

It won't even be a breaking change, because buildUrl can be safely used instead of buildPath.

buildPathname and buildRelativePathname seem redundant, so it's better to simply have buildPath and buildRelativePath.


feat: add C ndarray interface and refactor implementation for stats/base/dnanmaxabs

Resolves None
Description

What is the purpose of this pull request?

This pull request:

adds C ndarray interface for stats/base/dnanmaxabs
refactor JavaScript implementation
refactor examples, tests, docs and benchmarks

Related Issues

Does this pull request have any related issues?

This pull request:

resolves None

Questions

Any questions for reviewers of this pull request?

No.
Other

Any other information relevant to this pull request? This may include screenshots, references, and/or implementation notes.

No.
Checklist

Please ensure the following tasks are completed before submitting this pull request.

[x] Read, understood, and followed the contributing guidelines.

@stdlib-js/reviewers

/stdlib merge

same question here as #4206, what to use for generating random arrays with NaN values in JS benchmarks and examples

this one's ready for review!


deps: update tekton to latest versions

Changes
Fixes: https://github.com/knative/func/issues/1716

:broom: Bumps tekton/cli and tekton/pipelines to 0.31.0 and 0.47.0 respectively.

/cc @jrangelramos

/kind techdebt

@lance this probably requires Go higher than 1.18.

Hi @lance maybe we could upgrade CI scripts as well and see how it goes... https://github.com/knative/func/blob/main/hack/tekton.sh#L25

btw it works fine with OSH Pipelines 1.10

/approve
/lgtm
/hold for others

@knative/func-reviewers ptal

/unhold

@lance you might need to override the linter

/override "style / suggester / shell"

/override "style / suggester / github_actions"


Release cobigen-javaplugin-v2.2.3

This issue has been automatically created. It serves as a container for all release related commits

Error during release process


BUG: No way to close the more dropdown list

Is there an existing issue for this?

[X] I have searched the existing issues

What happened?
The more dropdown list does not close when any other page is opened. Also there is not other way to close it except reloading the page.
I would like to resolve this issue to close the dropdown list as required

Record

[X] I agree to follow this project's Code of Conduct
[ ] I'm a GSSOC'24 contributor
[X] I want to work on this issue

Is there an existing issue for this?

[x]  I have searched the existing issues

What happened?
The more dropdown list does not close when any other page is opened. Also there is not other way to close it except reloading the page. I would like to resolve this issue to close the dropdown list as required
Record

[x]  I agree to follow this project's Code of Conduct
[ ]  I'm a GSSOC'24 contributor
[x]  I want to work on this issue

Assigned!
make it better in dark mode and take care of responsive and other elements shouldn't be affected!!
Make sure to attach before & after screenshots in your PR. Also, star the repo before working on it. While creating your PR, ensure you pull all existing changes beforehand to avoid conflicts.
Happy open-source contributing!

Thanks...working on it!!


pattern matching on symlinks use the final path of symlink ?

hi,
I have this folder with a bunch of folders and only one or 2 relevant to ghcide, e.g:
$ ls
_generated/
bazel-bin/
...
project/

What i would like to do is ignore all folders but "project". Hence I've written the following hie.yaml:
# Configuration for hie/ghcide.
# See https://github.com/mpickering/hie-bios/blob/master/README.md for the
# documentation
cradle:
    multi:
        # we default to none because of all the non-haskell folders at the root
        - path: "./"
          config: { cradle: { none:  } }
        - path: "./project"
          config:
              cradle:
                  bios:
                      program: ".hie-bios"
                      dependency-program: ".hie-bios-dependencies"

Now if I run $ hie-bios debug _generated/, I get the message Cradle failed to load. It looks like a real error since I see "No prefixes matched" but I would expec "./" to match ./_generated".
hie-bios debug _generated/
Cradle failed to load
Exit Code: ExitSuccess
Stderr: Multi Cradle: No prefixes matched
pwd: /home/teto/nova/jinko2
filepath: /nix/store/llxkyzbl2z06d5ndm2yp2cvl8fyfrxha-jinko-generated-folder
prefixes:
("./",None)
("./project",Bios {prog = ".hie-bios", depsProg = Just ".hie-bios-dependencies"})

Now relooking at the error, seems like it compares prefixes against the resolved filepath and because ./_generated is a symlink to /nix/store/llxkyzbl2z06d5ndm2yp2cvl8fyfrxha-jinko-generated-folder, it won't match the prefix. Is that correct ? I would expect to match against the symlink path rather its resolved path.

Can confirm, looks like a bug to me.
I think that we are too loosely using canonicalizePath here: https://github.com/mpickering/hie-bios/blob/master/src/HIE/Bios/Cradle.hs#L245
Maybe a makeAbsolute is sufficient?

If I modify the path to
        - path: "./_generated"
          config: { cradle: { none:  } }

it works better:
hie-bios debug _generated/
No cradle

though I would prefer the message to be "Matched the None cradle" or sthg similar.

Should be fixed in current master!

nice thank you !


Fix inconsistency in container path

Thanks for your work on this! This PR fixes a minor inconsistency in the README.md which had me hung up for a second.

So sorry it took me so long to get to this!
Gonna go ahead and merge this, thanks for the catch!


Maintenance - Brings fork up to date as of November 25, 2023, at 5:05 PM (EST)

Title
There's two hard-to-find-in-the-sea-of-commits changes this PR makes: A portion of _Citadel.Thalers was moved into shared for compatibility with minds now existing primarily within shared, and instances of MindComponent (and MindComponent EntityUids) within parameter args have been changed to instead use Entity<T>. Everything else here is more or less just a fast-forward to match mainline SS14

Self-merging at the request of other maintainers


Update Licences at Notice.txt to reflect dependency changes.

/release-note-none

Will try to auto merge this PR once all tests and checks are passing. This might take up to an hour.

Error trying doing the automated Cherry picking. Please do this manually
+++ Updating remotes...
Fetching upstream
Failed to add the RSA host key for IP address '<IP_ADDRESS>' to the list of known hosts (/app/.ssh/known_hosts).
From github.com:mattermost/mattermost-webapp
   616ce7d052..e03716ccad  master     -> upstream/master
Fetching origin
Failed to add the RSA host key for IP address '<IP_ADDRESS>' to the list of known hosts (/app/.ssh/known_hosts).
+++ Updating remotes done...
!!! 'upstream/release-7.6' not found. The second argument should be something like upstream/release-0.21.
    (In particular, it needs to be a valid, existing remote branch that I can 'git checkout'.)

+++ Returning you to the master branch and cleaning up.

/cherry-pick cloud

Cherry pick is scheduled.


update dockerfile and tests

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Comparison is base (7556042) 88.05% compared to head (c756f3c) 88.14%.

:exclamation: Your organization needs to install the Codecov GitHub app to enable full functionality.
Additional details and impacted files
@@            Coverage Diff             @@
##             main      #54      +/-   ##
==========================================
+ Coverage   88.05%   88.14%   +0.09%
==========================================
  Files          24       24
  Lines        1063     1063
==========================================
+ Hits          936      937       +1
+ Misses        127      126       -1

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


jekyll serve error on M1

When I run bundle exec jekyll serve command, I got this error: [BUG] Bus Error at 0x0000000102afc000
It is related to latest MacOS on M1.

The solution is:
gem install --user-install ffi -- --enable-libffi-alloc


Update to tfjs-core 0.13.7

This change is

@nkreeger 0.13.7 has some issues that caused layers test to fail when layers depends on it. @annxingyuan has swiftly fixed those issues and we will release 0.13.8 soon. Are you okay with skipping 0.13.7 as a dep for tfjs-node?


Receive amount never went through

Prior to update , I’ve been able to send and receive bitcoin from cashapp. Yesterday I sent 100,000 sats from cashapp to my mutiny wallet and it never came in. I contacted cashapp and it shows pending. They then I turn told me to contact mutiny because it’s an error on your end. As of now those 100,000 sats are just stuck somewhere waiting on mutiny to receive them.

How to upload the logs ? I'm attaching two screenshots to last transactions.

in the emergency kit there is a download logs button. if you could attach that file here

There is no receive of the 100,000 sats from cashapp which is still stuck in the unknown land while trying to send to my mutiny wallet. Yes it's insignificant amount, but to have it occur after an update doesn't sit well in trying to learn the wallet and its functions.

in the emergency kit there is a download logs button. if you could attach that file here

Thank you. I found that and uploading now.

in the emergency kit there is a download logs button. if you could attach that file here

How to attach via mobile because when I download it on iOS to files, it doesn't upload here.

You can click the photo icon and select it from your files

You can click the photo icon and select it from your files

I thought that too originally and tried that and it didn't work.

can you just email them to<EMAIL_ADDRESS>then

Yes just emailed them. Thank you.

mutiny-logs.txt

I am seeing a lot of connection errors in your logs, might be that your node can't connect to the LSP to receive the payment.

I am seeing a lot of connection errors in your logs, might be that your node can't connect to the LSP to receive the payment.

What does that mean, and 2. Is it a fix for it I can do via mobile?

I would make sure you have a stable wifi connection and not on a spotty internet.

I've only sent while connected to wifi. That's why I'm puzzled that I was able to send and receive before, and after the update it gets lost in transit. When home, all devices are connected to stable wifi. Disappointed because the BTC community raved about this wallet and after this experience it's not pleasant one.

Lightning payment failures and stuck transactions happen. It'll show back up on the cashapp side after a few days when it switches from pending to failed, nothing we can do about it.


[Bug] identifiableAttribute method expected even when options are explicitly defined

Bug report
What I did
Added a select2_from_array field for a BelongsTo relationship where I wanted to control which options are available.
// Models\Notification:
...
public function market() {
    return $this->belongsTo("App\Models\Market");
}
...

// NotificationCrudController:
$this->crud->addField([
[
    'label'	=>	'Market',
    'name'	=>	'market_id',
    'type' => 'select2_from_array',
    'options' => Market::whereIn("slug", $this->getAllowedMarkets())->get()->pluck("name", "id")->all(),
    'allows_null' => true,
]);

What I expected to happen
Because this field is not explicitly a relationship field, I did not expect backpack to have any trouble with only defining the options and not the entity, model, and attribute options.
What happened
Backpack automatically detects the relationship method and tries to call identifiableAttribute on the model, causing an exception.
What I've already tried to fix it
I have added the attribute option, but this shouldn't be necessary for fields with explicit options.
Backpack, Laravel, PHP, DB version
When I run php artisan backpack:version the output is:
PHP VERSION:
PHP 7.3.27 (cli) (built: Feb 27 2021 18:04:51) ( NTS )
Copyright (c) 1997-2018 The PHP Group
Zend Engine v3.3.27, Copyright (c) 1998-2018 Zend Technologies
with Zend OPcache v7.3.27, Copyright (c) 1999-2018, by Zend Technologies
LARAVEL VERSION:
v6.20.20@664a9432bcceb67871c5c9252f920d28f21dfc23
BACKPACK VERSION:
4.1.41@44770347b918e42c052d0a138c24535b43e32472

Hello @nbolender , thanks for the report.
Does entity => false solves your problem ?
Best,
Pedro

I'v tested with entity => false and it works.
But also I think there is no sense trying to call a function when it does not exist in the model so I'v submitted #3643
I am going to close this, let's keep any discussion in the PR thread.
Thanks again,
Pedro


Editor v2: Plantuml captions with emphasis generates invalid .conf file

Uml directive with emphasis text in the caption leads to invalid page generated for editor v2.
 tag is not inserted at the correct place.
Tested with:
(system)
 platform: Linux-<IP_ADDRESS>-microsoft-standard-WSL2-x86_64-with-glibc2.35
   python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
   sphinx: 7.2.6
 docutils: 0.18.1
 requests: 2.31.0
  builder: 2.3.0

(configuration)
confluence_disable_notifications: True
confluence_editor: v2
confluence_full_width: True
confluence_page_hierarchy: True
confluence_parent_page: (set)
confluence_publish: True
confluence_server_url: https://<removed>
confluence_space_key: (set; upper)
confluence_timeout: 10

(confluence instance)
 connected: no

sphinxcontrib.plantuml 0.27

Simple reproducer:
=============================
UML with caption and emphasis
=============================

.. uml::
    :caption: *simple* test
    :align: center

    @startuml
    actor Client
    participant Server
    Client -> Server
    @enduml

Generates:

<ac:image ac:align="center" ac:alt="@startuml
actor Client
participant Server
Client -> Server
@enduml">
<ri:attachment ri:filename="plantuml-c6673cc5b0adb11b77423984194011565ae365e7.png"></ri:attachment>
<ac:caption>simple test</ac:caption>
</ac:image>

<ac:image ac:alt="*simple* test">
<ri:attachment ri:filename="atlassian.png"></ri:attachment>
</ac:image>

I'll submit a really simple PR that fixes it, even though I don't really understand why this part of the code specific to v2 was needed ^^ (so the fix could be wrong).

Thanks for reporting and setting up the PR. It all looks great. I was planning on maybe adding a crude unit test to sanity check this in the future, hence why I haven't immediately added it in (hope that doesn't block you).
For your comment on v2-specific, the fix is still good. For v2, images have a caption tag which we try to process there, so it is the only editor type which has this issue. The original/fallback caption logic would build it's own paragraph after an image for the caption text.

Thank you for the clarifications.
And no problem, I'm not blocked for now :)

v2.4 is now available on PyPI -- marking as closed.


Start Building ARM VMs

This is the final PR to realize support for ARM, and by final I really mean that is will work end-to-end.  I've logged issues to cover the defects found in code review as well as necessary enhancements to make this a real solution.
Part of the changes needed for #94

Hi @boumenot, I'm your friendly neighborhood Microsoft Pull Request Bot (You can call me MSBOT). Thanks for your contribution!
You've already signed the contribution license agreement. Thanks!
The agreement was validated by Microsoft and real humans are currently evaluating your PR.
TTYL, MSBOT;

How do you feel with the -arm- in the ARM builder vs. the -sm- in the ASM post-processor?

I don't like that we have to surface this distinction to the users or label the tools in such a way, but it isn't clear to me how to erase that difference given the state of the world.  Some tools do not have the -sm- because -arm- came afterwards and needed to distinguish itself.  Should we go back and correct that, or leave it as is?
So, I fine with the labels, and we can eventually remove them I would vote it favor of that.


Add support for unregistered projections to PreviewMap

Description
This enhances the PreviewMap example with some sample data. The sample data is in projection "EPSG:3857" to demonstrate the use of data  and dataProjection.

Closes #2202
Do you introduce a breaking change?

[ ] Yes
[x] No
[ ] I am unsure (no worries, we'll find out)

:tada: This PR is included in version 13.0.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Remove extra type aliases

Follow-up of https://github.com/neondatabase/neon/pull/2231#discussion_r942313342
Removes both timeline-related type aliases, simplifying some type declarations:
Before: local_timelines: HashMap<ZTimelineId, Arc<<RepositoryImpl as Repository>::Timeline>>,
After   : local_timelines: HashMap<ZTimelineId, Arc<LayeredTimeline>>,

+1. I think I made most of these changes in https://github.com/neondatabase/neon/pull/2279 too

Before: local_timelines: HashMap<ZTimelineId, Arc<::Timeline>>,
After : local_timelines: HashMap<ZTimelineId, Arc>,

+1 on the above, but I think the RepositoryImpl/TimelineImpl aliases still make the code nicer to read. It makes it more clear when code deals with an implementation of the Repository/Timeline trait, and isn't tied to the LayeredTimeline implementation. In practice, it compiles to the same of course.
Alternatively, we could rename LayeredRepository to RepositoryImpl and LayeredTimeline to TimelineImpl.

It makes it more clear when code deals with an implementation of the Repository/Timeline trait

I think I'm treating the traits differently, that's why reluctant to the idea: trait can be implemented for many things, so I never care if "N implements a trait", for me it's "a certain trait(s) is implemented for N" and that's why I'd like to avoid having "TraitImpl" in the struct name: we have two timeline-related traits already (albeit one extends another) and might have more.
So, I'd leave entities named as they are, but maybe @LizardWizzard can break a tie and find a compromise 🙂
Either way, I'm fine with closing this and seeing an alternative version in the overhaul PR.

Hmm, I think I'm leaning towards @SomeoneToIgnore position. Repository/Timeline traits contain fair amount of implementation details of LayeredRepo and frequently we access Layered{Timeline, Repository} directly. Previously I said that it would be better to keep these traits as an API boundary, but now I think that we can achieve the same thing by properly using pub modifier (. Outside code wont be able to call internal methods, everything is simple. No need to workaround issues like this. Timeline and Repository traits look somewhat like a header file for the only implementation. So almost every time you add something new you need to add it to both places.
To summarize I think it wont harm to remove these traits altogether and use pub to show which API's are expected to be used by other modules. This PR is a step in that direction.
Another thought is that Repository and Timeline carry too much weight, they're overloaded with different responsibilities, like gc, compaction stuff, datadir mapping etc. (Maybe this is only my feeling). So in the future I think we'll end up splitting them into several objects/modules with their own api boundaries. Though it would take some time to find the right balance.
If we come up with another storage engine implementation it will also help to form better abstraction when it will be needed
Thoughts?

To summarize I think it wont harm to remove these traits altogether and use pub to show which API's are expected to be used by other modules. This PR is a step in that direction.

Ok, let's merge this.


How to apply custom themes / styles in the grid

HI Team,
I am looking for apply custom themes on the grid. Need some tutorials on this topic.
the grid has properties of theme in it. Can you please post some help on this?

Thanks for your input! We will post a wiki on "themes" in the next 24-48 hrs and will let you know when available.

@prabhur1983 The themes wiki is up https://github.com/openfin/fin-hypergrid/wiki/Themes


JsTree 2nd level child not able to select on load

Hi, I am taking selected nodes and storing in session storage. on page refresh I am selecting previously selected nodes using "select_node" and passing either selected objects or id, the node is not selected if the parent has more than one child node (i.e for level 2 child ) but getting selected if the parent has only one child node. I tried in timeout but not worked. when I execute $('tree').jstree("select_node", selectedNodes); after sometime in browser the node is getting selected. please help me to solve this issue. For your information I am loading 100 data for the tree.

Activate the state plugin in your instance - there is more info in the docs - it does exactly what you need.
Adding "state" to your plugins array should be enough.

Thanks for that suggestion. for page refresh scenario it works but I have one more scenario. I have an option to search. On clicking search button I am calling jstree("refresh") and data are dynamically retrieved in $.jstree.defaults.core.data where I have mentioned url, type and returned data to $.jstree.defaults.core.data on success. In this scenario "state" plugin completely failed to retain state so  I felt storing selected data in session and making the node to select by using jstree("select_node") in 'refresh.jstree' callback function but it failed to select 2nd level node. Please help me to solve this issue.

Look at what the state plugin does so that you can implement this. My guess would be that you should call get_state and set_state instead of only storing the selected values. The idea is that you need to restore opened nodes before selecting any children (if you have a tree where nodes are loaded on demand). The *_state functions mentioned above should do the trick for you.

Thank again for the solution. Let me check whether it works in my scenarios.


Fix bug with setting a title on spinner in Firefox

Pull Request
🤨 Rationale
Resolves #1825
👩💻 Implementation
I created a transparent overlay on top of the spinning bits in the spinner's template. This is enough to get the title to display correctly in Firefox and not flicker when the bits move.
🧪 Testing
Manually verified that the title now displays correctly in Firefox and still displays correctly in Edge.
✅ Checklist

[ ] I have updated the project documentation to reflect my changes or determined no changes are needed.

@msmithNI, can you buddy this PR for me?


Service did not respond to the start or control request in a timely fashion

I am having issues starting my service on Windows Server 2008 R2.
Installed it via
dotnet .\MyWindowsServiceApplication.dll action:install name:MyWindowsServiceApplication displayName:MyWindowsServiceApplication description:"My windows service application."
And it shows up, when i try to start it I instantly get Error 1053 service did not respond to the start or control request in a timely fashion
When I run in manually through powershell via "dotnet .\MyWindowsServiceApplication.dll action:start" I get the following error through my logging around the ServiceRunner.Run:
 2017-03-23 18:51:16.1887 ERROR Program - System.InvalidOperationException: Cannot start service MyWindowsServiceApplication on computer '.'. ---> System.ComponentModel.Win32Exception: The service did not respond to the start or control request in a timely fashion    --- End of inner exception stack trace ---    at System.ServiceProcess.ServiceController.Start(String[] args)    at PeterKottas.DotNetCore.WindowsService.ServiceRunner1.StartService(HostConfiguration1 config, ServiceController sc)    at PeterKottas.DotNetCore.WindowsService.ServiceRunner1.ConfigureService(HostConfiguration1 config)    at PeterKottas.DotNetCore.WindowsService.ServiceRunner1.Run(Action1 runAction)
I can't tell what is going on as I get no other exception anywhere, any ideas?
I have managed to get this running locally on my computer which is the annoying part, just not on the server.

Hey mate. 2 things come to mind afk. Try to omit "." when calling the service. Second thing to try would be running cmd/powershell as administrator. If that doesn't help, give this a read https://github.com/PeterKottas/DotNetCore.WindowsService/issues/12  . Tou might find some good points there. If nothing helps, I'll get back to to u tomorrow ;-)

Yeah tried both of this things with no luck, I have simplified my app down to this, and again working locally not on the server:
        public static void Main(string[] args)
        {
            LogManager.Configuration = new XmlLoggingConfiguration(
                Path.Combine(AppContext.BaseDirectory, "NLog.config"), true);
            var logger = LogManager.GetCurrentClassLogger();

            Console.WriteLine("I am outputing.");
            ServiceRunner<MyWindowsServiceApplicationService>.Run(config =>
            {
                config.SetName("MyWindowsServiceApplication");
                var name = config.GetDefaultName();

                config.Service(serviceConfig =>
                {
                    serviceConfig.ServiceFactory(extraArguments => new MyWindowsServiceApplicationService());
                    Console.WriteLine("I am outputing.");

                    serviceConfig.OnStart((service, extraParams) =>
                    {
                        Console.WriteLine("I am not outputing.");
                    });

                    serviceConfig.OnStop(service =>
                    {
                    });

                    serviceConfig.OnError(e =>
                    {
                        Console.WriteLine(e);
                        logger.Error(e);
                    });

                });
            });
        }

    public class MyWindowsServiceApplicationService : IMicroService
    {

        public void Start()
        {

        }

        public void Stop()
        {
        }
    }

I looks kinda similar to that issue I've send you previously. Try to get the int that the run method returns. It should be 0 if it works properly.

Also get the app path this way : PlatformServices.Default.Application.ApplicationBasePath . That's what I use so it's worth trying.

Updated the path, the run method is returning a -1.

Amazing what a good nights sleep can do, I figured it out in my nightmares. The problem is the path and the command that gets generated.
Locally the command thats getting setup for the service is  C:\Program Files\dotnet\dotnet.exe C:\Projects\MyWindowsServiceApplication\MyWindowsServiceApplication\bin\Debug\netcoreapp1.1\MyWindowsServiceApplication.dll action:run Notice no spaces to the DLL.
However on the server C:\Program Files\dotnet\dotnet.exe C:\Program Files\My Windows Service\MyWindowsServiceApplication.dll action:run has spaces in Program Files and My Windows Service which is making them invalid.
Is there anyway to fix this, could we wrap the second parameter in quotes through dotnet .\MyWindowsServiceApplication.dll action:install I am not sure if thats valid?

Hi Andrew, don't worry, you're not spamming at all. On the contrary, good catch about the error! I'll make the change now and will upload new version so that you can give it a try. Will let you know once it's done

Ok that's done and uploaded in v1.0.17 Let me know if it helps ;)

Just tested up on the server works perfect, thank you so much :+1:

Awesome, glad we fixed it mate. Thanks for making this lib better! Spread the work and let me know if you need anything else ;)


Improve compatibility for updating host trusted certificates

What does this PR change?
These changes aim to improve compatibility with other distributions as the container host operating system.
The CopyCaCertificate function fails to copy and/or update the host's trusted certificates on certain distributions such as AlmaLinux 9 (RHEL9) and Arch Linux without these changes.
I've tested the changes with Podman on openSUSE Leap Micro 5.5, AlmaLinux 9 and Arch Linux.
Test coverage

No tests

[x] DONE

Links
Issue(s): #

[x] DONE

Changelogs
Make sure the changelogs entries you are adding are compliant with https://github.com/uyuni-project/uyuni/wiki/Contributing#changelogs and https://github.com/uyuni-project/uyuni/wiki/Contributing#uyuni-projectuyuni-repository
If you don't need a changelog check, please mark this checkbox:

[ ] No changelog needed

If you uncheck the checkbox after the PR is created, you will need to re-run changelog_test (see below)
Before you merge
Check How to branch and merge properly!

Hi, thanks for the feedback. I've rebased and removed the locale changes from the PR.


Logical_disk collectors for Disk Read latency and Write latency

Do you have the repo which has the Logical_disk collectors for Disk Read latency and Write latency.
Can anyone help me to get the updated Logical _disk collectors MSI file as it is very much needed for our clients.
Please help me ASAP

This should be resolved as of 853d615

Looks like the metrics added in 853d615 weren't multiplied by ticksToSecondsScaleFactor but this should be fixed in https://github.com/martinlindhe/wmi_exporter/pull/400.
For now I'm using rate(wmi_logical_disk_read_latency_seconds_total[1m]) / 1e7 to get disk latency in seconds.

@robodair You shouldn't use rate() on a gauge. What you probably want is instead something like the following:
wmi_logical_disk_read_latency_seconds_total{volume=~".:"} / 1e7

I don't understand why wmi_logical_disk_read_latency_seconds_total is a counter and not a gauge.
# HELP wmi_logical_disk_read_latency_seconds_total Shows the average time, in seconds, of a read operation from the disk (LogicalDisk.AvgDiskSecPerRead)
# TYPE wmi_logical_disk_read_latency_seconds_total counter
wmi_logical_disk_read_latency_seconds_total{volume="C:"}<PHONE_NUMBER>
wmi_logical_disk_read_latency_seconds_total{volume="D:"}<PHONE_NUMBER>9999998
wmi_logical_disk_read_latency_seconds_total{volume="HarddiskVolume1"} 0.004719
wmi_logical_disk_read_latency_seconds_total{volume="HarddiskVolume2"} 0.015182699999999999

Installed today, version is:
wmi_exporter_build_info{branch="master",goversion="go1.12.3",revision="012b938b5451e5d10e2bb364876aac66cd85c54e",version="0.9.0"} 1

If this is really meant to be a counter, then how does it compare to wmi_logical_disk_read_seconds_total, which would make sence as rate() would be essentially dividing that over the time difference, and you would have an average. eg. compare the following
rate(wmi_logical_disk_read_seconds_total[1m])
rate(wmi_logical_disk_read_latency_seconds_total[1m])

Perhaps worthy of a note in the docs at least.
Thanks,
Cameron

Good spot Cameron!
My understanding is that the wmi_logical_disk_read_seconds_total metric is the total number of seconds the disk has spent reading, while the wmi_logical_disk_read_latency_seconds_total metric is the total number of seconds the disk has spent waiting to perform a read operation.
This does not appear to be the case as both metrics are returning the same value.

Looking at the values returned directly by Perflib via Get-Counter, it looks like the Avg. Disk sec/Read metric should be a gauge type, returning the average read request latency.
The % Disk Read Time should also be a gauge type returning a percentage. There doesn't appear to be a read_seconds_total value returned at all.
I think the underlying perflib values need to be fixed, and the wmi_logical_disk_read_seconds_total needs to be re-named to wmi_logical_disk_read_percentage.
The above would also need to be applied to the wmi_logical_disk_write_seconds_total and wmi_logical_disk_write_latency_seconds_total metrics.

@breed808 When we pulled the data with WMI, I'm fairly sure the values were seconds. Could it be the perflib data has a _base value corresponding to it?
It would be preferable to keep the seconds-counter rather than a percentage gauge (if possible, of course!), and it seems strange WMI and perflib would differ here.

@carlpett It's been a while so I'll need to check that the counters were returning the same value prior to the perflib rewrite (I.E. v0.8.3 or older). My testing during the perflib rewrite was to ensure that the perflib metrics returned the same value as the WMI metrics.
I'll check the above and also see if there are any base perflib counters that could be used.

Ok, I've checked both v0.8.3 and master and there is a discrepancy with wmi_logical_disk_read_latency_seconds_total. Note that <IP_ADDRESS>:9182 is v0.8.3.

I see two issues:

The WMI version (v0.8.3) reports an incredible large value for the wmi_logical_disk_latency_seconds_total metric.

The Perflib version reports the same value for both metrics.

wmi_logical_disk_read_seconds_total is sourced from the perflib PercentDiskReadTime counter, while wmi_logical_disk_read_latency_seconds_total is sourced from the perflib AvgDiskSecPerRead counter.
I will look to see if another perflib counter/base counter may be used or is applicable for either of these two metrics.

I've checked the base counter for perflib:Avg. Disk sec/Read" and perflib:Avg. Disk sec/Read" with no luck, results are still the same.
Perhaps we should consider retiring one of the two identical metrics?


Model Usage

How to use the model without uploading to HuggingFace? [Granmmar Corrector] Basically I want to use your model and then finetune it and use the new model locally.

Got it!


[backport] Remove '--pre' from 'pip install' commands in ChainerX installation document

Backport of #8527

[automatic post] Jenkins, test this please.

Jenkins CI test (for commit a7bf81063763fc29a904b734c190aed8071991f8, target branch v6) succeeded!


Update the hesa codes for 2023

Context
We have the HESA new codes for 2023 cycle

This PR adds the HESA data for 2023 (and keeps old data for old cycles)
Guidance to review

Are the codes correct?
Does it work posting the diversity questionnaire and see in the HESA export on provider interface?

 Update the HESA post-submission questionnaire to reflect new disability codes


error while data collection

Hi,
I want to create dataset and for that I run like this:
./datagen.sh /home/carla /home/transfuser-2022
But I got this error.
can't open file '/home/transfuser-2022/leaderboard/leaderboard/leaderboard_evaluator_local.py': [Errno 2] No such file or directory
I have check my folders, file is there but it is still showing this error. How to solve this?
~/transfuser-2022/leaderboard/leaderboard$ ls
autoagents  envs  init.py  leaderboard_evaluator_local.py  leaderboard_evaluator.py
Thanks.

No, It is actually given /home/username/transfuser-2022. I deleted it.

No CARLA 0.9.14 is not compatible with this codebase/any codebase using leaderboard 1.0.
You need to use CARLA version <IP_ADDRESS>
This script will download the correct version for you.
To use CARLA 0.9.14 you need to use leaderboard 2.0, but there are no publicly available repositories for it right now.
(You can adapt existing leaderboard 1.0 codebases to become compatible with 2.0, it will require some work though).

Thanks for your reply and suggestion.


test: Distribute template.test.in in source release tarballs

We'll need this if we want to build with --enable-installed-tests
from an official source release, which I'd like to do in Debian.

Please apply to test/Makefile.in too

Please apply to test/Makefile.in too

I'd been leaving that for people who have the right "blessed" build environment to update generated files, since I don't know what environment that is (it certainly doesn't match mine).
I've edited the generated file as requested, since this particular line maps relatively obviously from the source.

since this particular line maps relatively obviously from the source.

Yes, that's exactly why I wanted it in the PR :)


Release version 1.0.0

We need to make a release of the project so that it can be used as a node-module.
https://docs.npmjs.com/getting-started/publishing-npm-packages

Travis-ci has been integrated such that it will deploy to npm a new module any time that a github release is created.  0.0.1 was released to demonstrate this.
https://github.com/singletoncoding/java-utils/releases


Tree element not found: [object Object]: Error: Tree element not found: [object Object]

Version: 1.33.0-insider (user setup)
Commit: 1876bd1d3805d632a9aa78fad47b2c3c555f6eec
Date: 2019-03-25T14:53:36.371Z
Electron: 3.1.6
Chrome: 66.0.3359.181
Node.js: 10.2.0
V8: 6.6.346.32
OS: Windows_NT x64 10.0.17763
[2019-03-25 23:47:57.303] [renderer3] [error] Tree element not found: [object Object]: Error: Tree element not found: [object Object]
    at s.getElementLocation (file:///F:/Users/Paulo/AppData/Local/Programs/Microsoft VS Code Insiders/resources/app/out/vs/workbench/workbench.main.js:91:107)
    at s.isCollapsed (file:///F:/Users/Paulo/AppData/Local/Programs/Microsoft VS Code Insiders/resources/app/out/vs/workbench/workbench.main.js:90:451)
    at j.isCollapsed (file:///F:/Users/Paulo/AppData/Local/Programs/Microsoft VS Code Insiders/resources/app/out/vs/workbench/workbench.main.js:939:572)
    at B.revealMarkersForCurrentActiveEditor (file:///F:/Users/Paulo/AppData/Local/Programs/Microsoft VS Code Insiders/resources/app/out/vs/workbench/workbench.main.js:4602:105)
    at B.autoReveal (file:///F:/Users/Paulo/AppData/Local/Programs/Microsoft VS Code Insiders/resources/app/out/vs/workbench/workbench.main.js:4601:961)
    at B.onActiveEditorChanged (file:///F:/Users/Paulo/AppData/Local/Programs/Microsoft VS Code Insiders/resources/app/out/vs/workbench/workbench.main.js:4599:350)
    at B._register.e (file:///F:/Users/Paulo/AppData/Local/Programs/Microsoft VS Code Insiders/resources/app/out/vs/workbench/workbench.main.js:4598:360)
    at d.fire (file:///F:/Users/Paulo/AppData/Local/Programs/Microsoft VS Code Insiders/resources/app/out/vs/workbench/workbench.main.js:74:728)
    at setTimeout (file:///F:/Users/Paulo/AppData/Local/Programs/Microsoft VS Code Insiders/resources/app/out/vs/workbench/workbench.main.js:71:135)

Steps to Reproduce:

Have 2 typescript files opened, have some errors on one (or two of them)
Switch between them

Does this issue occur when all extensions are disabled?: No

I'm seeing this too (though with Dart). As I switch between these two files, note the count of errors counting up in the console.

@DanTup Can you easily reproduce this issue?

I'm not sure about easily! I do still see it a lot, but I don't seem to be able to repro it on-demand right now with either TS or Dart.
If I get to the point where I do see it again, is there anything I can do to try and get more info about what's going on?

Probably the steps that you followed every time this issue appears. May be I can diagnose it from that.

Reopening it as it is quite often reported in our error telemetry

I have the same error with version 1.36.1
Sadly I have not found a way to reproduce it. The only thing that I have noticed is that is has happened only on the test folder of a python project. But that may just be coincidence

Fixed by b8d5f4cbf469e54586fcc7ce5a4fadfd32e57d32.
Above fix reuses the resource markers model, which means that if resource markers are updated, existing resource markers is reused instead of creating a new one. This will prevent tree and markers model to be out of sync.

@sandy081 I'm still seeing what I think is this issue a lot in current insiders, for example:
https://travis-ci.org/Dart-Code/Dart-Code/jobs/597702388
TreeError [MarkersPanel] Tree element not found: [object Object]: Error: TreeError [MarkersPanel] Tree element not found: [object Object]
    at t.ObjectTreeModel.getElementLocation (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:135:801)
    at t.ObjectTreeModel.isCollapsed (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:135:14)
    at W.isCollapsed (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:1032:72)
    at z.revealMarkersForCurrentActiveEditor (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:5849:628)
    at z.autoReveal (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:5849:485)
    at z.onActiveEditorChanged (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:5846:776)
    at file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:5845:754
    at file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:114:390
    at c.fire (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:117:984)
    at e.doEmitActiveEditorChangeEvent (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:4852:364)
    at e.handleActiveEditorChange (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:4852:160)
    at file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:4852:474
    at c.fire (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:117:984)
    at file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:5495:736
    at processTicksAndRejections (internal/process/task_queues.js:89:5)
    at async e.openEditor (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:5494:750)
    at async e.doOpenEditor (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:4854:479)
    at async e.$tryShowTextDocument (file:///Users/travis/build/Dart-Code/Dart-Code/.vscode-test/vscode-insiders/Visual Studio Code - Insiders.app/Contents/Resources/app/out/vs/workbench/workbench.desktop.main.js:3461:517)


Vision encoding

I tested your library and used the GPT-4 Vision example from the readme. However, it encodes to an invalid JSON schema, that wraps the image object in an extra _0 object, causing OpenAI to respond with a 400 error
JSON:
    {
      "role" : "user",
      "content" : [
        {
          "image" : {
            "_0" : { // <---
              "type" : "image_url",
              "image_url" : {
                "detail" : "auto",
                "url" : "data:image\/jpeg;base64,\/9j\/4AAQSkZJRgAAAABAAAAGgAAAAAAA..."
              }
            }
          }
        }
      ]
    }

Hey @terkelg, thanks for reporting this. Sorry for not getting back to you sooner. I hadn't fully tested this yet myself but will try to get a fix up soon.

Hi @terkelg, I pushed a fix which resolved the issue and adds a test to cover it with commit 5264097f752360b867771efb857228dcffaed24b


DotNetCorePublish OutputDirectory is not relative to WorkingDirectory

DotNetCorePublishSettings has a property WorkingDirectory which I set to my project dir relative to repository root (where my cake.build file is). eg, ./source/MyProject.
There is also a property OutputDirectory which I set to ./obj/Docker/publish.
Now I would assume, that output directory would be relative to working dir, but it's not - it is relative to cake.build file (aka repository root).

The documentation for dotnet publish states that if --output is relative, the output path is relative to the project directory.  The code in DotNetCorePublisher always makes the path absolute. This makes it impossible to publish the whole solution because the cli will combine the artifacts for all projects into a single folder, making it impossible to discern the dlls from one executable to another.
I don't think the publisher code should make the output path absolute, I think this should be left to the consumer so they can treat this API the same as the dotnet core cli.


Bid Strategy Types: Add ManualCpm

Docs Build status updates of commit b9f869f:
:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

Advertising/bingads-13/guides/budget-bid-strategies.md
:white_check_mark:Succeeded
View (=bingads-13)

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 docs.microsoft.com contributor guidesPost your question in the Docs support channel

Disregard, will update in another commit.


Large file flag

Adds a flag to ignore recommended file size limits.
Also fixes reviewer recommendations in  #566.

thank you @PurplProto !


Order assets in overviews

should be merged after https://github.com/threefoldtech/wallet-next/pull/128 into development


Create twilio index.md

Added Docs for Twilio integration with ASP.NET and C#.

@KhushalGupta305 	we don't want to create a new doc for this link. You could probably add the link to https://docs.microsoft.com/en-us/aspnet/mvc/overview/security/aspnet-mvc-5-app-with-sms-and-email-two-factor-authentication


fix(angular): datepicker detect changed options

#748

👋 Thanks for creating a pull request! 🚀 Checkout the storybook we've created for it: angular


[Question] How do I mock with a method that returns :self?

Here's a snippet of source:
    public function play(string $url) : self
    {
        $this->addStep(new Play($url));

        return $this;
    }

    public function hangup() : self
    {
        $this->addStep(new Hangup());

        return $this;
    }

When using this class, I'm writing my mocks like:
        $option = Mockery::mock(Option::class);
        $option->shouldReceive('hangup')->once();
        $option->shouldReceive('play')->andReturn(Mockery::self());

I've also tried andReturn($option) but either way I end up with the error:
atal error: Cannot use 'self' as class name as it is reserved in /private/var/folders/m2/y4sdt3ks3_x206vw2gv37pgc0000gp/T/Mockery55S8Oy on line 1

@bkuhl I think that's a bug! The return type hints are fairly new and I doubt we (I) considered self when implementing...

@bkuhl or maybe not, which version of mockery are you using? I think you'll want the master branch or the 1.0.0-alpha1 release.

Bingo!  I was not using the alpha build.  This works great now, thanks!


equals can not handle circular Map objects

var a = new Map
var b = new Map
a.set('x',a)
b.set('x',b)
console.log(R.equals(a,b)) //RangeError: Maximum call stack size exceeded

Though I don't think anyone would do like this

Though I don't think anyone would do like this

... and perhaps stack exhaustion is an appropriate punishment.  :smiling_imp:


feat: ArticleTeaser component

References https://autoricardo.atlassian.net/browse/DM-362
Motivation and context
Implements ArticleTeaser component
Before
No article teaser 😱
After

How to test
Play around with the article teaser in storybook
Notes

we want to give flexibility on image sizing, but we want to have some control of the allowed sizes, hence new size values
existing designs will be adapted to use provided values
the entire teaser is a link, this was clarified with Enrique

According to figma it's column on mobile (with image on top) and row on desktop (with image on the left)

According to figma it's column on mobile (with image on top) and row on desktop (with image on the left)

Mmm strange I see this in figma:

That means that tablet should be column as well, right?

Maybe it could be good if we add some space between the image and the text on small devices. What doy you think? is it possible that you doble check with the designer?

@GutierrezGo thanks, clarified with Enrique and adapted the spacing. Good eye!


add staff information and picture

add staff from the UCC google sheet and email

A few of these have errors, but I'll fix them up in a commit. Good job for now.