Fix 102

Pull Request Checklist:

[x] pre-commit hooks are installed/active in my local clone ($ pre-commit install)
[x] This PR addresses an already opened issue (for bug fixes / features)

This PR fixes #102

[ ] (If applicable) Documentation has been added / updated (for bug fixes / features)
[ ] If a merge request has been made in parallel to this PR in xscen-notebooks, it is merged and the submodules have been updated.
[x] HISTORY.rst has been updated (with summary of main changes)

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

What kind of change does this PR introduce?

Instead of re-converting the calendar for each variable individually, use the conversion that was done on the whole dataset pour variable that interpolate over missing dates.
fixes #102

Does this PR introduce a breaking change?
not sure? The conversion will be done at the same time for many variables instead of one at the time. Hence, the dates that are interpolated over will be the same for all variables, instead of being different for all variables. So, you can't reproduce the old result. But it was already impossible to reproduce it exactly because the dates are random.
Other information:

Well, it doesn't work to just use the conversion done on l.548 for the other version because we want the missing argument to be something else than nan.
I guess I could use it and replace the nan by the missing value wanted, but I thought there might be a risk of  filling nans that are not there because the calendar conversion...
I couldn't think of an example where we needed the variables with a fix missing value to be the same dates for all variables. It might even be better that the missing pr and missing temperature dates are not the same ?

Indeed, filling nans is dangerous. But if the temperature inversion you noticed is a clear-cut physical inconsistency, the one day lag between temperature and pr is also one, even if more subtle. It could yield completely wrong results for indicators like "cold_and_wet_days".

I guess that for the special case where missing_by_var has other values than interpolate and the src (or target) calendar is 360_day, you could:

Convert the dataset, with missing set to some flag value.
When interpolating, replace those flags to nan first.
Replace the flags with the missing_by_var choice for the other case.

I'm not sure what a good flag value would be. It needs to be a float, but  unique. Maybe something like -9999 , which is highly improbable for all common variables we use?
I am aware this change is not elegant... Would you prefer one of the xclim solution I suggested? Then we could leave this PR the current way and update the code once xclim is updated.

ok I see. I can put a unique weird value in the convert on the whole dataset, then use where to fill the missing dates.

I think I'm okay with using the flag.


Can ImageAI use newer Libraries?

Hi I was wanting to try ImageAI but was not able to get it working following the Install Documentation
pip install tensorflow==2.4.0

The first issue was tensorflow 2.4.0 is no longer available so I just installed the latest version (2.9.1), I also tried installing the oldest available version 2.5.0
I also had the same issue with
pip install keras==2.4.3 numpy==1.19.3 pillow==7.0.0 scipy==1.4.1 h5py==2.10.0 matplotlib==3.3.2 opencv-python keras-resnet==0.2.0

For some of these libraries these version weren't available, I started as with tensorflow with the current version, then when I rolled back to tensorflow 2.5.0 I rolled these back to the closest available version
For Reference I'm using a vanilla Debian 11 install with Python 3.9.2
Other issues I had (that might be worth including in the documentation):

The dependancy on libGL.so.1 was missed, so I needed to figure out that I needed to install

apt install libgl1-mesa-glx

tensorflow needs a lot of memory to install (>2GB), for my application I intend to use small ARM SBC's these often only have 512MB of ram so I set my VM up with the same specifications, however the tensorflow install just crashed, I needed to give more than 2GB of swap memory in order for tensorflow to install at all.

My main question is: Will there be a version soon of ImageAI that works with a more recent version of tensorflow and the other dependancies?
Dave.

This issue is quite important because right now anyone who doesn't have the dependencies saved by chance on their computer won't be able to configure the framework. I understand the difficulty of maintaining the software with the new versions of the dependencies, but it is absolutely necessary to keep the project alive.

I agree with the above and am willing to offer any help needed if that is of any interest.

I agree. I cannot install and run directly because it uses too many old libs that don't exist on pip.
I don't want to find the old versions and install them one by one.
I just give up.

You might need to check the tensorflow-2.7.2 branch.


kubectl exec -i echoes passwords

What happened?
kubectl exec -i sets up a terminal that prevents disabling echoing. So when an application ask for a password, it is visible in the terminal.
$ kubectl exec -i pod/test -- passwd
New password: mypassword
BAD PASSWORD: The password fails the dictionary check - it is based on a dictionary word
Retype new password: mypassword
Changing password for user root.
passwd: all authentication tokens updated successfully.

It may seem that adding the -t is the way to help out here, but -t merges stdout and stderr together on purpose, unfortunately. So when you try to redirect the output of such command into a variable (or stdout to a file), you will never see the "Password: " promt because it gets written into stdout because of how  kubectl exec -it sets up the terminal using raw option.
$ STDOUT_RESULT="$( kubectl exec -i pod/test -- passwd )"
New password: mypassword
BAD PASSWORD: The password fails the dictionary check - it is based on a dictionary word
Retype new password: mypassword
$ echo "${STDOUT_RESULT}"
Changing password for user root.
passwd: all authentication tokens updated successfully.

$ STDOUT_RESULT="$( kubectl exec -it pod/test -- passwd )"
# (Without any prompt, I have blindly typed two times `mypassword` into the terminal.)
$ echo "${STDOUT_RESULT}"
Changing password for user root.
New password:
BAD PASSWORD: The password fails the dictionary check - it is based on a dictionary word
Retype new password:
passwd: all authentication tokens updated successfully.

/sig cli
What did you expect to happen?
kubectl exec -i should set up terminal in a way that allows disabling echo. Alternatively, -t should distinguish between stdout and stderr.
How can we reproduce it (as minimally and precisely as possible)?

kubectl run test --image=registry.access.redhat.com/ubi9/ubi:9.3-1610@sha256:66233eebd72bb5baa25190d4f55e1dc3fff3a9b77186c1f91a0abdb274452072 -- sleep infinity
kubectl exec -i pod/test -- passwd
see the password echoed
STDOUT_RESULT="$( kubectl exec -it pod/test -- passwd )"
observe the missing password prompt

Anything else we need to know?
I have used passwd as an example of a tool that ask for password on stderr and is available to everyone to reproduce this. It doesn't return much useful value on stdout, so here is a real word example of a cqlsh query that needs to ask for a password first:
(password echoed)
TOKEN="$( kubectl exec -i "service/scylla-client" -c scylla -- cqlsh --user cassandra -e "SELECT salted_hash from system_auth.roles WHERE role = 'cassandra'" )"
/opt/scylladb/python3/lib64/python3.11/getpass.py:91: GetPassWarning: Can not control echo on the terminal.
Warning: Password input may be echoed.
Password: cassandra

(no output but silently expects the password)
TOKEN="$( kubectl exec -it "service/scylla-client" -c scylla -- cqlsh --user cassandra -e "SELECT salted_hash from system_auth.roles WHERE role = 'cassandra'" )"

Kubernetes version

$ kubectl version
# paste output here

Cloud provider

kubeadm version
kubeadm version: &version.Info{Major:"1", Minor:"28", GitVersion:"v1.28.2", GitCommit:"89a4ea3e1e4ddd7f7572286090359983e0387b2f", GitTreeState:"clean", BuildDate:"2023-09-13T09:34:32Z", GoVersion:"go1.20.8", Compiler:"gc", Platform:"linux/amd64"}

OS version

# On Linux:
$ cat /etc/os-release
PRETTY_NAME="Ubuntu 22.04.4 LTS"
NAME="Ubuntu"
VERSION_ID="22.04"
VERSION="22.04.4 LTS (Jammy Jellyfish)"
VERSION_CODENAME=jammy
ID=ubuntu
ID_LIKE=debian
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
UBUNTU_CODENAME=jammy

$ uname -a
Linux ubuntu-2204 5.15.0-100-generic kubernetes/kubernetes#110-Ubuntu SMP Wed Feb 7 13:27:48 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux

Install tools

Container runtime (CRI) and version (if applicable)

$ crictl version
Version:  0.1.0
RuntimeName:  cri-o
RuntimeVersion:  1.24.6
RuntimeApiVersion:  v1

Related plugins (CNI, CSI, ...) and versions (if applicable)

$ kubectl version
Client Version: v1.30.0-beta.0.7+c9384550cbf771
Kustomize Version: v5.0.4-0.20230601165947-6ce0bf390ce3
Server Version: v1.28.3

What did you expect to happen?
kubectl exec -i should set up terminal in a way that allows disabling echo.

I think you can do this on your own with stty -echo.

Alternatively, -t should distinguish between stdout and stderr

As far as I understand, this is what allocating a psuedo-tty is inteded for. It creates an "actual" tty device inside the container. It attaches local stdout to the combined remote streams so we can distinguish remote output from local output (kubectl).

I don't think there's anything for us to actually implement here. Feel free to reopen if that's not the case.
/close


Using the VGG Manga model?

A while back, a user posted Deepdreams using a model trained on Manga images. He linked to the model.
The post can be found here:
https://www.reddit.com/r/deepdream/comments/3v9pkt/deepdream_model_trained_on_manga_comics/
Images using the model can be found here: https://imgur.com/a/8EaI8
He posted a link to the model here: https://www.reddit.com/r/deepdream/comments/3vtglt/link_to_manganet_pretrained_model/
The model can be found here:
http://illustration2vec.net/
https://github.com/rezoo/illustration2vec
You mention here that you can use other models. Can you explain how if it's different than the default models?

OK so let's look at the unmodified model first:
ILLUST2VEC = dd.CNNData('illust2vec/illust2vec_tag.prototxt', 'illust2vec/illust2vec_tag_ver200.caffemodel', (127, 127, 127))
cnn = dd.CNN(ILLUST2VEC, cpu_workers=0, gpus=[0])
for layer in cnn.layers():
    print(layer, list(cnn.net.blobs[layer].shape[1:]))

conv1_1 [64, 224, 224]
pool1 [64, 112, 112]
conv2_1 [128, 112, 112]
pool2 [128, 56, 56]
conv3_1 [256, 56, 56]
conv3_2 [256, 56, 56]
pool3 [256, 28, 28]
conv4_1 [512, 28, 28]
conv4_2 [512, 28, 28]
pool4 [512, 14, 14]
conv5_1 [512, 14, 14]
conv5_2 [512, 14, 14]
pool5 [512, 7, 7]
conv6_1 [1024, 7, 7]
conv6_2 [1024, 7, 7]
conv6_3 [1024, 7, 7]
conv6_4 [1539, 7, 7]
pool6 [1539, 1, 1]
prob [1539, 1, 1]

This looks pretty standard and easy to modify. I made a new CNNData tuple containing the deploy prototxt, the model, and since I don't know the per-channel mean used for training yet I just put a reasonable default in.
Looking at the start of the deploy prototxt:
name: "CaffeNet"
input: "data"
input_dim: 50
input_dim: 3
input_dim: 224
input_dim: 224

we need to modify it to force backpropagation on and to lower the minibatch size (since we will only process one image at a time, not 50, and wish to avoid large memory allocations on model load.)
name: "CaffeNet"
force_backward: true
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224

At the end of the stack of layers we find an average pooling layer right before the final sigmoid activation layer that outputs the probability of each tag. Since we'll be using different image sizes than 224x224, we won't have a 7x7 input to pool6 anymore (which expects it) and it will break. We could just truncate the layer stack, discarding any layers after the one that won't work, but caffe provides a better solution:
layer {
  name: "pool6"
  type: "Pooling"
  bottom: "conv6_4"
  top: "pool6"
  pooling_param {
    pool: AVE
    kernel_size: 7
    stride: 1
  }
}

to
layer {
  name: "pool6"
  type: "Pooling"
  bottom: "conv6_4"
  top: "pool6"
  pooling_param {
    pool: AVE
    global_pooling: true
  }
}

telling the pool6 layer to take any size feature map and average it down to a 1x1.

Lastly, we need the per-channel mean for the training data set. They have helpfully provided an image_mean.npy file, which is the per-pixel mean for the training data set (3chx224x224). We don't use per-pixel means since we don't always use 224x224 images. So I brought up ipython / jupyter:
import numpy as np
m = np.load('image_mean.npy')
m.mean(1).mean(1)

and got array([<PHONE_NUMBER>1,<PHONE_NUMBER>7,<PHONE_NUMBER>9]).
which is pretty different from the imagenet per-channel mean. At this point we can enter the mean into the original call to the CNN constructor:
ILLUST2VEC = dd.CNNData('illust2vec/illust2vec_tag.prototxt', 'illust2vec/illust2vec_tag_ver200.caffemodel', (164.76, 167.48, 181.14))

and now I am having weird animated heads show up in the clouds of my images when I run dream(), so it looks like it worked. :)

So I made the modifications to the prototext file and brought it up in ipython / jupyter. I then received the following error:
ILLUST2VEC = dd.CNNData('illust2vec/illust2vec_tag.prototxt', 'illust2vec/illust2vec_tag_ver200.caffemodel', (164.76, 167.48, 181.14))
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-7-2fad972b10a6> in <module>()
----> 1 ILLUST2VEC = dd.CNNData('illust2vec/illust2vec_tag.prototxt', 'illust2vec/illust2vec_tag_ver200.caffemodel', (164.76, 167.48, 181.14))

NameError: name 'dd' is not defined

import deep_dream as dd is a shortcut I often use to reduce typing, sorry. :)

That fixed that issue! Now the only other issue seems to be with cell 4 and 5.
Cell 4:
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-35-5dbcb04f41e0> in <module>()
      1 input_img = Image.open('kodim/img0024.jpg').resize((2385, 1590), Image.LANCZOS)
----> 2 show(input_img)

<ipython-input-34-d9350abe01c7> in show(img)
      2
      3 def show(img):
----> 4     print(cnn.classify(img, 5))
      5     if isinstance(img, np.ndarray):
      6         img = deep_dream.to_image(img)

NameError: name 'cnn' is not defined

Cell 5:
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-36-d32837300cae> in <module>()
----> 1 output_img = cnn.dream(input_img, {'inception_4a/pool_proj': 1},
      2                        scales=7*4, n=2, per_octave=8, step_size=4)
      3 show(output_img)

NameError: name 'cnn' is not defined

I actually just meant to go back and put the mean into the original constructor, resulting in the definition of ILLUST2VEC followed by the definition of cnn.
Also, you're going to have to change the layer name to one of the layers that's actually in this network and get rid of the 'scales' argument.

Thanks! It works now though I do still get this error in cell 4:
100%|█████████████████████████████████████| 47.4M/47.4M [00:52<00:00, 961Kpix/s]
/home/ubuntu/deep_dream/deep_dream/deep_dream.py:340: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
  return [(prob[i], self.categories[i]) for i in indices]
[(array([[[[ 0.]]]], dtype=float32), '0'), (array([[[[ 0.]]]], dtype=float32), '0'), (array([[[[ 0.]]]], dtype=float32), '0'), (array([[[[ 0.]]]], dtype=float32), '0'), (array([[[[ 0.]]]], dtype=float32), '0')]

I also figured I'd upload the modified prototxt for others, here: https://gist.github.com/ProGamerGov/ef5ce8f11f3a8d798a1610d3e9a8ce38

I think it's still trying to classify the images and we haven't provided a list of categories. Just remove the line print(cnn.classify(img, 5)) from the 'show' function.
The first one I tried, conv5_1, gave me faces.


Instantiating ScribblePrompt-UNet

Hi, when I try to instantiate ScribblePrompt-UNet and make predictions, the segmentation results I get are not accurate. Part of my code is as follows. Is there anything wrong? Thank you very much!
def create_scribble_tensor(positive_coords, negative_coords, H, W):
    scribbles = torch.zeros((1, 2, H, W), dtype=torch.float32)
    for coord in positive_coords:
        x, y = coord
        if 0 <= x < W and 0 <= y < H:
            scribbles[0, 0, y, x] = 1

    for coord in negative_coords:
        x, y = coord
        if 0 <= x < W and 0 <= y < H:
            scribbles[0, 1, y, x] = 1
    return scribbles

def binary_mask_to_polygon(binary_mask, tolerance=0):
    padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)
    contours = measure.find_contours(padded_binary_mask, 0.5)
    return contours

def parse_coords(coords_str):
    coords = list(map(int, coords_str.split(',')))
    coords_list = [coords[i:i+2] for i in range(0, len(coords), 2)]
    return coords_list

def main(args: argparse.Namespace) -> None:
    targets = [args.input_dir]

    for index, t in enumerate(targets):
        name = os.path.basename(t)
        image = pydicom.dcmread(t)
        image_w = image.Rows
        image_h = image.Columns
        image = image.pixel_array
        image = (image - image.min()) / (image.max() - image.min())
        image = np.clip(image, 0, 1)
        image = torch.tensor(image, dtype=torch.float32)
        image = image.unsqueeze(0).unsqueeze(0)
        image = image.permute(0, 1, 3, 2)
        image = F.interpolate(image, size=(128,128), mode='bilinear')

        pos_coords = parse_coords(args.pos_box)
        neg_coords = parse_coords(args.neg_box)
        positive_coords = [tuple(point) for point in pos_coords]
        negative_coords = [tuple(point) for point in neg_coords]
        scribbles = create_scribble_tensor(positive_coords, negative_coords, image_h, image_w)
        scribbles = F.interpolate(scribbles, size=(128,128), mode='bilinear')

        sp_unet = ScribblePromptUNet()
        mask = sp_unet.predict(image, None, None, scribbles, None, None)
        mask = F.interpolate(mask, size=(image_h, image_w), mode='bilinear').squeeze()
        mask = mask.cpu().numpy()

        binary_mask = (mask > 0.5).astype(int)
        contours = binary_mask_to_polygon(binary_mask)

Can you show examples of the input image and coordinates you're using and the predictions you get?

Can you show examples of the input image and coordinates you're using and the predictions you get?

The first image is the input image and scribbles, and the second image is the predicted result.

hmm I am not quite sure what's going on. Here are some ideas for troubleshooting:

Could xy be switched between your scribble mask and your image? I see you're swapping the last 2 dimensions of your input image. Have you tried visualizing the scribble mask and input image right before inference to check they are aligned?

Have you tried drawing a similar scribble on your image in our huggingface demo? You can clone the code for the demo here if you prefer to run locally. If you're able to get better results in the gradio demo, it suggests there's a bug in your code.


Enable --config=asan in bazel

Can you try adding this to the github workflow too? It will both test this config, and actually cover our code with asan.

Can you try adding this to the github workflow too? It will both test this config, and actually cover our code with asan.

done

Thanks, this is very nice to have! I've been using valgrind, it is not as nice to use as asan.


Can't open the download menu or download any manga

Your issue may already be reported!
Please search on the issue tracker before creating one.
Expected Behavior

Current Behavior

Possible Solution

Steps to Reproduce (for bugs)

Context

Your Environment

Version used:
Operating System version:

NHentai link (if relevant):

It's working perfectly fine, maybe you download the wrong button then?

Same problem

Download any pre 2.8 version and run it then open the downloads and it'll ask for access to storage then update it

下载任何 2.8 之前的版本并运行它，然后打开下载，它会要求访问存储空间，然后更新它

谢谢好兄弟，你才是真正的英雄

下载任何 2.8 之前的版本并运行它，然后打开下载，它会要求访问存储空间，然后更新它

谢谢好兄弟，你才是真正的英雄

但是试了没有用，还是主页转圈，无法加载任何内容捏。
有什么头绪嘛


Changed sign out button position to the top of the page

I moved sign out button from the bottom to the top inside <ul> after about link. It helps user to see logout button right after his username.

see https://github.com/hoodiehq/hoodie-app-tracker/issues/93#issuecomment-252433980


Haven't found a valid code yet

I've been running it for some bit of time now but still haven't gotten 1 valid code.  Has anyone had any success with this and if so how long did it take? Also, I see people mentioning they added proxies, how do I add a proxy?

Bro i run it on VPS and upgrade script with my 6000+ proxylist that is alive
and upgrade the threading on his script but i run it for about 6days now nothing is valid even now

I've been running it for some bit of time now but still haven't gotten 1 valid code. Has anyone had any success with this and if so how long did it take? Also, I see people mentioning they added proxies, how do I add a proxy?

dont expect a sinlge code to ever work it generates a random string of letters and numbers to try and find a working code but the chance you find a working code is over in 1 in 100 million so dont expect EVER to find a code that works and if you do you have hit the jackpot and wont again for atleast 100 million codes


KeyError: 'com' during dannce-train

Hi,
Upon starting dannce train we get the following error. The com prediction completed successfully and the com3d file has been generated in the ./COM/predict_results

(dannce) E:\DANNCE_test_210608>dannce-train C:\Users\realtime\dannce\configs\dannce_mouse_config.yaml
2021-07-14 14:00:37.660334: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
io_config not found in io.yaml file, falling back to main config
new_n_channels_out not found in io.yaml file, falling back to main config
batch_size not found in io.yaml file, falling back to main config
epochs not found in io.yaml file, falling back to main config
net_type not found in io.yaml file, falling back to main config
train_mode not found in io.yaml file, falling back to main config
num_validation_per_exp not found in io.yaml file, falling back to main config
vol_size not found in io.yaml file, falling back to main config
nvox not found in io.yaml file, falling back to main config
max_num_samples not found in io.yaml file, falling back to main config
dannce_finetune_weights not found in io.yaml file, falling back to main config
mono not found in io.yaml file, falling back to main config
com_train_dir set to: .\COM\train_results
com_predict_dir set to: .\COM\predict_results
dannce_train_dir set to: .\DANNCE\train_results\AVG
dannce_predict_dir set to: .\DANNCE\predict_results
dannce_predict_model set to: .\DANNCE\train_results\AVG\weights.1200-12.77642.hdf5
exp set to: [{'label3d_file': 'E:/DANNCE_test_210608/20210610_091000_Label3D_dannce.mat'}]
io_config set to: io.yaml
new_n_channels_out set to: 22
batch_size set to: 4
epochs set to: 1200
net_type set to: AVG
train_mode set to: finetune
num_validation_per_exp set to: 4
vol_size set to: 100
nvox set to: 64
max_num_samples set to: max
dannce_finetune_weights set to: C:\Users\realtime\dannce\demo\markerless_mouse_1\DANNCE\train_results
mono set to: True
base_config set to: C:\Users\realtime\dannce\configs\dannce_mouse_config.yaml
viddir set to: videos
crop_height set to: None
crop_width set to: None
camnames set to: None
n_channels_out set to: 20
sigma set to: 10
verbose set to: 1
net set to: None
gpu_id set to: 0
immode set to: vid
mirror set to: False
loss set to: mask_nan_keep_loss
num_train_per_exp set to: None
metric set to: ['euclidean_distance_3D']
lr set to: 0.001
augment_hue set to: False
augment_brightness set to: False
augment_hue_val set to: 0.05
augment_bright_val set to: 0.05
augment_rotation_val set to: 5
data_split_seed set to: None
valid_exp set to: None
com_fromlabels set to: False
medfilt_window set to: None
com_file set to: None
new_last_kernel_size set to: [3, 3, 3]
n_layers_locked set to: 2
vmin set to: None
vmax set to: None
interp set to: nearest
depth set to: False
comthresh set to: 0
weighted set to: False
com_method set to: median
cthresh set to: None
channel_combo set to: None
predict_mode set to: torch
n_views set to: 6
rotate set to: True
augment_continuous_rotation set to: False
drop_landmark set to: None
use_npy set to: False
rand_view_replace set to: True
n_rand_views set to: 0
multi_gpu_train set to: False
start_batch set to: 0
n_channels_in set to: None
extension set to: None
vid_dir_flag set to: None
chunks set to: None
lockfirst set to: None
load_valid set to: None
raw_im_h set to: None
raw_im_w set to: None
n_instances set to: 1
start_sample set to: None
write_npy set to: None
expval set to: None
com_thresh set to: None
cam3_train set to: None
debug_volume_tifdir set to: None
downfac set to: None
from_weights set to: None
dannce_predict_vol_tifdir set to: None
Using the following *dannce.mat files: .\20210610_091000_Label3D_dannce.mat
Setting vid_dir_flag to True.
Setting extension to .avi.
Setting chunks to {'Camera1': array([0]), 'Camera2': array([0]), 'Camera3': array([0]), 'Camera4': array([0]), 'Camera5': array([0])}.
Setting n_channels_in to 3.
Setting raw_im_h to 600.
Setting raw_im_w to 960.
Setting expval to True.
Setting net to finetune_AVG.
Setting crop_height to [0, 600].
Setting crop_width to [0, 960].
Setting maxbatch to max.
Setting start_batch to 0.
Setting vmin to -50.0.
Setting vmax to 50.0.
Fine-tuning from C:\Users\realtime\dannce\demo\markerless_mouse_1\DANNCE\train_results\weights.12000-0.00014.hdf5
Experiment 0 using videos in E:/DANNCE_test_210608\videos
Experiment 0 using camnames: ['Camera1', 'Camera2', 'Camera3', 'Camera4', 'Camera5']
{'0_Camera1': array([0]), '0_Camera2': array([0]), '0_Camera3': array([0]), '0_Camera4': array([0]), '0_Camera5': array([0])}
E:/DANNCE_test_210608/20210610_091000_Label3D_dannce.mat
The length of the camnames list must divide evenly into 6. Duplicate a subset of the views starting from the first camera (y/n)?y
Duping camnames. Changed from ['Camera1', 'Camera2', 'Camera3', 'Camera4', 'Camera5'] to ['Camera1', 'Camera2', 'Camera3', 'Camera4', 'Camera5', 'Camera1']
Traceback (most recent call last):
File "C:\Users\realtime\anaconda3\envs\dannce\Scripts\dannce-train-script.py", line 33, in
sys.exit(load_entry_point('dannce', 'console_scripts', 'dannce-train')())
File "c:\users\realtime\dannce\dannce\cli.py", line 66, in dannce_train_cli
dannce_train(params)
File "c:\users\realtime\dannce\dannce\interface.py", line 737, in dannce_train
) = do_COM_load(exp, expdict, n_views, e, params)
File "c:\users\realtime\dannce\dannce\interface.py", line 1685, in do_COM_load
c3dfile = io.load_com(exp["com_file"])
File "c:\users\realtime\dannce\dannce\engine\io.py", line 91, in load_com
d = sio.loadmat(path)["com"]
KeyError: 'com'

Thanks for the help!

You can specify the com_file used for prediction in the io.yaml.
com_file: /path/to/com3d.mat

Hey,
same error even if I add the line specified above. I have attached the io.yaml I used and the config file.
io.txt
dannce_mouse_config.txt

2021-07-14 18:42:27.600576: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
io_config not found in io.yaml file, falling back to main config
new_n_channels_out not found in io.yaml file, falling back to main config
batch_size not found in io.yaml file, falling back to main config
epochs not found in io.yaml file, falling back to main config
net_type not found in io.yaml file, falling back to main config
train_mode not found in io.yaml file, falling back to main config
num_validation_per_exp not found in io.yaml file, falling back to main config
vol_size not found in io.yaml file, falling back to main config
nvox not found in io.yaml file, falling back to main config
max_num_samples not found in io.yaml file, falling back to main config
dannce_finetune_weights not found in io.yaml file, falling back to main config
mono not found in io.yaml file, falling back to main config
com_train_dir set to: .\COM\train_results
com_file set to: E:\DANNCE_test_210608\COM\predict_results\com3d.mat
dannce_train_dir set to: .\DANNCE\train_results\AVG
dannce_predict_dir set to: .\DANNCE\predict_results
dannce_predict_model set to: .\DANNCE\train_results\AVG\weights.1200-12.77642.hdf5
exp set to: [{'label3d_file': 'E:/DANNCE_test_210608/20210610_091000_Label3D_dannce.mat'}]
io_config set to: io.yaml
new_n_channels_out set to: 22
batch_size set to: 4
epochs set to: 1200
net_type set to: AVG
train_mode set to: finetune
num_validation_per_exp set to: 4
vol_size set to: 100
nvox set to: 64
max_num_samples set to: max
dannce_finetune_weights set to: C:\Users\realtime\dannce\demo\markerless_mouse_1\DANNCE\train_results
mono set to: True
base_config set to: C:\Users\realtime\dannce\configs\dannce_mouse_config.yaml
viddir set to: videos
crop_height set to: None
crop_width set to: None
camnames set to: None
n_channels_out set to: 20
sigma set to: 10
verbose set to: 1
net set to: None
gpu_id set to: 0
immode set to: vid
mirror set to: False
loss set to: mask_nan_keep_loss
num_train_per_exp set to: None
metric set to: ['euclidean_distance_3D']
lr set to: 0.001
augment_hue set to: False
augment_brightness set to: False
augment_hue_val set to: 0.05
augment_bright_val set to: 0.05
augment_rotation_val set to: 5
data_split_seed set to: None
valid_exp set to: None
com_fromlabels set to: False
medfilt_window set to: None
new_last_kernel_size set to: [3, 3, 3]
n_layers_locked set to: 2
vmin set to: None
vmax set to: None
interp set to: nearest
depth set to: False
comthresh set to: 0
weighted set to: False
com_method set to: median
cthresh set to: None
channel_combo set to: None
predict_mode set to: torch
n_views set to: 6
rotate set to: True
augment_continuous_rotation set to: False
drop_landmark set to: None
use_npy set to: False
rand_view_replace set to: True
n_rand_views set to: 0
multi_gpu_train set to: False
start_batch set to: 0
n_channels_in set to: None
extension set to: None
vid_dir_flag set to: None
chunks set to: None
lockfirst set to: None
load_valid set to: None
raw_im_h set to: None
raw_im_w set to: None
n_instances set to: 1
start_sample set to: None
write_npy set to: None
expval set to: None
com_thresh set to: None
cam3_train set to: None
debug_volume_tifdir set to: None
downfac set to: None
from_weights set to: None
dannce_predict_vol_tifdir set to: None
Using the following *dannce.mat files: .\20210610_091000_Label3D_dannce.mat
Setting vid_dir_flag to True.
Setting extension to .avi.
Setting chunks to {'Camera1': array([0]), 'Camera2': array([0]), 'Camera3': array([0]), 'Camera4': array([0]), 'Camera5': array([0])}.
Setting n_channels_in to 3.
Setting raw_im_h to 600.
Setting raw_im_w to 960.
Setting expval to True.
Setting net to finetune_AVG.
Setting crop_height to [0, 600].
Setting crop_width to [0, 960].
Setting maxbatch to max.
Setting start_batch to 0.
Setting vmin to -50.0.
Setting vmax to 50.0.
Fine-tuning from C:\Users\realtime\dannce\demo\markerless_mouse_1\DANNCE\train_results\weights.12000-0.00014.hdf5
Experiment 0 using videos in E:/DANNCE_test_210608\videos
Experiment 0 using camnames: ['Camera1', 'Camera2', 'Camera3', 'Camera4', 'Camera5']
{'0_Camera1': array([0]), '0_Camera2': array([0]), '0_Camera3': array([0]), '0_Camera4': array([0]), '0_Camera5': array([0])}
E:/DANNCE_test_210608/20210610_091000_Label3D_dannce.mat
The length of the camnames list must divide evenly into 6. Duplicate a subset of the views starting from the first camera (y/n)?y
Duping camnames. Changed from ['Camera1', 'Camera2', 'Camera3', 'Camera4', 'Camera5'] to ['Camera1', 'Camera2', 'Camera3', 'Camera4', 'Camera5', 'Camera1']
Traceback (most recent call last):
File "C:\Users\realtime\anaconda3\envs\dannce\Scripts\dannce-train-script.py", line 33, in
sys.exit(load_entry_point('dannce', 'console_scripts', 'dannce-train')())
File "c:\users\realtime\dannce\dannce\cli.py", line 66, in dannce_train_cli
dannce_train(params)
File "c:\users\realtime\dannce\dannce\interface.py", line 737, in dannce_train
) = do_COM_load(exp, expdict, n_views, e, params)
File "c:\users\realtime\dannce\dannce\interface.py", line 1685, in do_COM_load
c3dfile = io.load_com(exp["com_file"])
File "c:\users\realtime\dannce\dannce\engine\io.py", line 91, in load_com
d = sio.loadmat(path)["com"]
KeyError: 'com'

Thanks!

Sorry about that, I misread and thought you were doing prediction. For training you can add the same com_file: ... line to the experiment dictionary in the io.yaml like this:
...
exp:
    - label3d_file: 'E:/DANNCE_test_210608/20210610_091000_Label3D_dannce.mat'
      com_file: E:/DANNCE_test_210608/COM/predict_results/com3d.mat

Keep the other line though, because you'll eventually need it for prediction.


Write Add Funds to Credit Card API route

User Story
The user add funds to a virtual credit card
Description
The user needs to add funds to a credit card before it can be used.
Acceptance Criteria
User is able to add funds to a credit card.
Testing
Try to add funds to a credit card.

Refreshing a page is to use vanilla JavaScript 'window.location.reload(false);'

Tested and complete


Have simple JS object for configuration

I am wondering if you don't think that's nicer to have a simple and usual JS object for the configurations rather than having to wrap the string in double quotes because that object is provided as it's to the Webpack define plugin, but we could provide pass the JSON representation of it before assigning to process.env
I had that modification in my project and it was easy to make, however I created the project when this template was for webpack v1 and last week I did the update and found that it's hard and tedious to synchronize changes if you have modifications in the template build folder, so I rolled back them to have the less changes as possible in order to have a simple upgrades in the future.
Thanks for considering.

Can you specify what exactly you mean with "the configurations":

a simple and usual JS object for the configurations

It seems you are talking about environment variables(?), but that's not really clear to me.

Sorry, my fault.
I'm talking about the environment variables which are in config folder, specified with the files ended with .env.js

...and what does your propose change would look like? I don't really see how we would get around the need for quoting strings twice - or do you suggest to turn .env.js files into JSON files?
If so, that's not a good idea, as it would be very limiting.

No, I don't suggest to turn them into JSON.
Those environment files are read in a few limited places, we only have to make a simple change in those.
I wanted to check if there is a preference to keep them as they are, because I was thinking that you prefer that people understand that those values are injected in the web application by the Webpack DefinePlugin, but I see that there aren't any such preferences, so if it's fine for you I'll try to send a PR in a couple of weeks with such update and it will be more clear what changes are needed.

Sure!

I'm, working on improving config with #1178


feat/ snake water gun game

add a new game of snake water gun  .
https://github.com/vignesh-cloud-prog/SnakeWaterGun-Game
In this game you have to choose between snake,water and gun. Snake kills the water, water kills the gun and gun kills the snake. In this game winner will get 1 point. if both choose the same then both will get 1 points. in total of 10 rounds who gets the highest points will be considered as winner.

@ashitaprasad I want to add this game please assign me .


Multiple inscriptions in a single transaction

Now that #1201 has been merged, it would be interesting to explore how to inscribe multiple sats (collections) in the same transaction. I think it ultimately boils down to two options: either make an inscription per output or put all the inscriptions in the same output. Each has pros and cons, which I think can be summarized as follows:

1 inscription per utxo:
Pros: cleaner structure, more streamlined ordinal management and likely way easier to implement. No need to fragment a utxo when sending an inscription from a new collection.
Cons: fees for minting are proportional to the number of items in a collection; these need to be payed upfront, and collections made of thousands of items might get very expensive for creators to inscribe. Not sure if this is actually a positive feature compared to other platforms like eth (along the lines of: more expensive hence less frivolous?), but at a first glance it looks like a downside.

Multiple inscriptions per utxo:
Pros: fees for minting are independent of the size of a collection, because only one utxo is created. This means much lower upfront costs for creators, especially when it comes to large collections.
Cons: the utxo containing the whole collection will need to be fragmented whenever an item is transferred from it. Assuming 1 change utxo (with all the remaining items) for each transfer, creators costs are comparable to the 1inscription=1utxo model; they’re just spread thin over time. But one upside is that fees are lower when transferring a subset of (consecutive) items.

Not sure if there are other more efficient ways to organize inscriptions inside utxos.

Good summary! This is a dupe of #876, I'll reply there.


Make app channel address configurable instead of hard coded

Closes https://github.com/dapr/dapr/issues/6177.

@yaron2 this requires a change in CLI and the run template also ...

As FYI, our e2e test infrastructure didn't really support apps with the sidecar disabled, so fixed that along the way.


Add tifffile to project dependencies

Closes #174
Add tifffile to the project dependencies in pyproject.toml.

Specify the minimum version of tifffile as 2023.8.12.

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

Project coverage is 46.15%. Comparing base (54da345) to head (3270803).
Report is 155 commits behind head on main.

Additional details and impacted files
@@             Coverage Diff             @@
##             main     #185       +/-   ##
===========================================
+ Coverage   35.91%   46.15%   +10.23%
===========================================
  Files          19       22        +3
  Lines        1715     2119      +404
===========================================
+ Hits          616      978      +362
- Misses       1099     1141       +42

see 4 files with indirect coverage changes


Add dag into the website UI

Hi someone there,
I'am new to this project,I want add new dag to the website UI like this :

I read the document ,and didn't find the way ,anyone can help me ?

Define a new dag in a python file, for example tutorial and paste it in the dags_folder (by default <user_home>/airflow/dags ). When a task in DAG runs for next time (via scheduler), it will show up in the UI.


Spelling

Generated by https://github.com/jsoref/spelling f; to maintain your repo, please consider fchurn

this is the only commit I am not willing to accept because of function name changes. The risk of potential side effects and amount of work for testing are to high in relation to the benefit of the new name.

Even before rebasing/folding, GitHub wouldn't explain which changes this comment was about.
I've left comments for the things that might match your review, if you indicate which it was for, I'll drop them.

Oh I'm sorry for that, I didn't realise that it is not clear in github which commit I meant, but you spotted it correctly (fb50b4e74887a9e5d942aaddf821f84f289b4efb)
Probably you are right with your assumption that there are only local names changed. But I still don't like the idea to change function names. I don't see any reason for doing it. Let's say we would change the function name callGithub to callGitHub, then we should also change the variable name github to gitHub in order to remain conform with the new "naming convention"... and this would definitely cause unexpected side effects since the githubService is used everywhere in the app.
So my proposal would be to skip this commit and takeover all other changes.

I've stripped github to text changes d25c923. And to address a comment I made initially, I've added 7ac40a6.

Thanks again!


Trying to get in contact with you about this script

Your email is bouncing - please message me about changing the output of this script to XML<EMAIL_ADDRESS>

Hi,
I'm sorry I am late in responding. I am not the original author of this script. It is just here for archival purpose. Hence I have no plans to change it in any way. Sorry.
Regards,
Jeroen


Upgrade Sentry client in server to v6.11.0

Supersedes #318, #319.

Welp, it seems like something in this version of sentry is causing tests to hang in CI, but not locally. :\

Alright, I understand this pretty clearly now; it’s a regression caused by autoSessionTracking being turned on by default in Sentry 6.9+. The session tracking infrastructure runs forever until you explicitly shut down Sentry, and therefore keeps the tests open.
The best solution here is probably to explicitly set autoSessionTracking: false, since that reverts the same behavior we had before v6.9.

Oof. How would you feel about skipping Sentry initialization in tests?

I’m actually not sure what would happen if we didn’t call Sentry.init() at all. 🤔
But since autoSessionTracking used to default to false, I think just explicitly setting it to false now is probably the best solution. It makes for less fuss in the code, at least.

As a side note, can you repro the hang locally (using the first commit of this PR), @astonm? I could not, even though looking at the Sentry code I can’t see what would be happening differently. Maybe some environment variable in CI.

@Mr0grog I can't get the hang in tests to repro locally either.

Nevermind, I see what’s happening now — if you don’t specify a release option, Sentry automatically turns off autoSessionTracking. But it detects the value from release from a huge variety of possible env vars, including… GITHUB_SHA, which is set in the GitHub Actions environment.
(And experimentally, if I set that locally, tests hang. So Sentry was implicitly turning the feature on in test and off in my local environment. Ugh.)


The system can not find the path specified

When I run:
grunt watch
and modify the files, console warns me as above for Windows Ultimate 64.
I tried to change --post-file location with backslashes for Windows compatibility but did not help.
Also getting" failed: unknown error" while connecting to localhost.
Any ideas please?

@JesuanT Sadly I don't have a PC with Windows. I've found this [issue](https://github.com/palant/autoinstaller/issues/2 issue) with Extension Auto-Installer and Windows. Can you check this answer and tell me if it works?

@dgil Thank you very much for your help. I thought running "grunt watch" would automatically start Firefox. I opened Firefox first and then run the "grunt watch". This fixed the issue.
But when I modify package.json for the add-on, say if the name and description of the add-on is changed, the changes are not grabbed. Any thoughts on this please?
Thanks again.

@JesuanT many thanks for reporting these bugs.
I've just released a new version (v0.3.1) with a fix for grunt watch task to watch all files (instead of only js and html). Also I've updated the readme file with Firefox running requirement in grunt watch.

@dgil I thank you for your prompt help and fix for the issue. Keep up the great work.


How can this happen, throw an exception after CallEnd

I got a stacktrace that show a request has callEnd,  and receive an exception.  Why not call callFail, but call callEnd and then throw this exception.
FYI, this request factly not success because the response body not receive completely.

I can't reproduce with https://github.com/square/okhttp/pull/4069

👍,nice job for ：
Fix: Don't drop a call to EventListener.callEnd() when the response body is consumed inside an interceptor.


security/tinc: add support for "StrictSubnets" variable

In our setup we need to set the "StrictSubnets" variable to "yes", in order to prevent traffic to hosts not directly known (please see more extensive explanation here).
This commit allows to set the value, defaulting to "no", thus without impacting existing configurations.

@andrewhotlab can you try https://github.com/opnsense/plugins/commit/1490e00a691073e46602dad4bfb80492c9cc04bc on top? shouldn't change much, just some minor style fixes. Thanks!

Thanks @AdSchellevis. I know nothing about Python, but it looks like the changes in the file objects.py from commit 1490e00a691073e46602dad4bfb80492c9cc04bc break something... after applying it, we are unable to start/stop tinc service from web GUI.

@andrewhotlab It does look normal on my end (at least not different than before), can you try with a clean install using the steps below:
opnsense-revert os-tinc
opnsense-patch -c plugins 94fcded 1490e00

Ok, just tried the commands you suggested and it's working for me too (OPNsense v23.1.11_1). Since I was applying manually the changes (no development skills here), I likely did some mistakes in writing... sorry, and thank you!
Now I have another problem to solve with tinc plugin, which we hadn't in the previous pfSense setup we migrated from: it does not insert in the routing table all subnets declared in the host files... I'll start working on it ASAP and maybe I'll be able to propose you a new pull request!
Thanks once more for your wonderful work with OPNsense!!


Update lazyLoading option to accept a hash

Updates the lazyLoading option to accept a hash as its value. This is in preparation for a future PR to enable further options to be added to the lazy loading functionality.
Backwards compatibility has been retained by converting boolean values into a hash, e.g.
lazyLoading: true

// Would be converted to

lazyLoading: {
  enabled: true
}

Not sure if this needs tests and if so, where they should actually be located as I had a quick look but couldn't find any existing tests for the flag.
cc/ @rwjblue

restarted CI

@trentmwillis Thanks for the review, I've addressed your issue.
Also can't figure out why CI would be failing?


documents github monitor analytic

creates a folder for feeds + analytics and documents github monitor analytic

Fixed some of the structure / makrdown that was used. Thanks for your contribution!


callbackOnChange not fired when value set by setValueByChoice

When I programmatically set select value with setValueByChoice method the onChange is not fired.
Only the onAddItem callback is triggered.
Quote from doc :

Usage: Function to run each time an item is added/removed by a user.

Hi @jbanety,
That is intentional, the onChange is only fired by a user change, not a programmatic change. As you are setting it pragmatically, you'll know that the state has changed so can run your code immediately after.


[dx12] Check for WARP driver

The "Microsoft Basic Render Driver" is the assigned name for a WARP driver, which is the Microsoft provided software renderer. The code simply checks if the device name is set to this to determine if WARP was enabled.
Fixes #issue
PR checklist:

[x] make succeeds (on *nix)
[x] make reftests succeeds
[x] tested examples with the following backends: dx12

Updated to use the appropriate flag for detecting if it software rendering is being enabled.

Cleaned up logic and packed into a single commit.

bors r=msiglreith


[BUG] generate template with data doesn't use default storage class

Describe the bug

When you generate a template with data it doesn't use the default storage class. It uses harvester-longhorn
To Reproduce
Steps to reproduce the behavior:

Create a VM with data in it
Create a new storage class
Change the new storage class to be the default
Click the triple dot menu to the right of the VM name in the list on the virtual machines page
Click generate template
Check the With Data checkbox
Create a template
Check the images page
Edit the config of the new exporting image
Check the storage class in storage

Expected behavior

The template should use the default storage class
Support bundle

supportbundle_314d015f-bac6-449f-a676-14fd3732e576_2024-05-03T22-17-35Z.zip
Environment

Harvester ISO version: v1.3.0
Underlying Infrastructure (e.g. Baremetal with Dell PowerEdge R630): Bare metal DL360

Additional context
Add any other context about the problem here.
There isn't an option to specify storage class in the menu for generate template. Also this happens in both the Harvester web UI and when adminstering Harvester from an upstream Rancher cluster through virtualization management.

Hi @noahgildersleeve, I think this is expected. When we use VolumeSnapshot to backup a volume, Longhorn doesn't really copy the data to somewhere. It's more like a pointer to the volume data. So we have to use export-from-volume to save data as VMImage.
https://github.com/harvester/harvester/blob/5ab43f02144fb8c193005905945d60cc65a73133/pkg/api/vm/handler.go#L682-L686
https://github.com/harvester/harvester/blob/5ab43f02144fb8c193005905945d60cc65a73133/pkg/api/vm/handler.go#L853-L858


[Bug] Issue with Multiple Account Payable

Is there an existing issue for this?

[X] I have searched the existing issues

Describe the issue
In case a quickbook account has multiple accounts payable it will cause an issue in the general ledger, multiplying the data.
I am talking about the model: "int_quickbooks__bill_payment_double_entry.sql"
In this model, you check for the active Account Payable, but sometimes there are multiple such accounts:
`ap_accounts as (
select
    account_id
from accounts

where account_type = 'Accounts Payable'
    and is_active
    and not is_sub_account

),`
Afterwards, there is a cross-join, which in the case of multiple accounts, causes duplicate data in the general ledger:
bill_payment_join as (
select
bill_payments.bill_payment_id as transaction_id,
bill_payments.source_relation,
row_number() over(partition by bill_payments.bill_payment_id order by bill_payments.transaction_date) - 1 as index,
bill_payments.transaction_date,
bill_payments.total_amount as amount,
coalesce(bill_payments.credit_card_account_id,bill_payments.check_bank_account_id) as payment_account_id,
ap_accounts.account_id,
bill_payments.vendor_id,
bill_payments.department_id
from bill_payments
**cross join ap_accounts**

),
Relevant error log or model output
No response
Expected behavior
I think it would be good to give the option to define which one is the real account payable in the project yaml, maybe,, and have a note about this issue.
maybe raise an alert if there are multiple accounts!
dbt Project configurations
quickbooks dbt project.yml:
config-version: 2
name: 'quickbooks'
version: '0.9.1'
require-dbt-version: [">=1.3.0", "<2.0.0"]
models:
quickbooks:
+materialized: table
+schema: quickbooks
double_entry_transactions:
+schema: quickbooks_intermediate
+materialized: table
transaction_lines:
+materialized: ephemeral
intermediate:
+materialized: ephemeral
vars:
quickbooks:
account: "{{ ref('stg_quickbooks__account') }}"
address: "{{ ref('stg_quickbooks__address') }}"
bill_line: "{{ ref('stg_quickbooks__bill_line') }}"
bill_linked_txn: "{{ ref('stg_quickbooks__bill_linked_txn') }}"
bill_payment_line: "{{ ref('stg_quickbooks__bill_payment_line') }}"
bill_payment: "{{ ref('stg_quickbooks__bill_payment') }}"
bill: "{{ ref('stg_quickbooks__bill') }}"
bundle_item: "{{ ref('stg_quickbooks__bundle_item') }}"
bundle: "{{ ref('stg_quickbooks__bundle') }}"
credit_memo_line: "{{ ref('stg_quickbooks__credit_memo_line') }}"
credit_memo: "{{ ref('stg_quickbooks__credit_memo') }}"
credit_card_payment_txn: "{{ ref('stg_quickbooks__credit_card_payment_txn') }}"
customer: "{{ ref('stg_quickbooks__customer') }}"
department: "{{ ref('stg_quickbooks__department') }}"
deposit_line: "{{ ref('stg_quickbooks__deposit_line') }}"
deposit: "{{ ref('stg_quickbooks__deposit') }}"
estimate: "{{ ref('stg_quickbooks__estimate') }}"
estimate_line: "{{ ref('stg_quickbooks__estimate_line') }}"
invoice_line: "{{ ref('stg_quickbooks__invoice_line') }}"
invoice_line_bundle: "{{ ref('stg_quickbooks__invoice_line_bundle') }}"
invoice_linked_txn: "{{ ref('stg_quickbooks__invoice_linked_txn') }}"
invoice: "{{ ref('stg_quickbooks__invoice') }}"
item: "{{ ref('stg_quickbooks__item') }}"
journal_entry_line: "{{ ref('stg_quickbooks__journal_entry_line') }}"
journal_entry: "{{ ref('stg_quickbooks__journal_entry') }}"
payment_line: "{{ ref('stg_quickbooks__payment_line') }}"
payment: "{{ ref('stg_quickbooks__payment') }}"
purchase_line: "{{ ref('stg_quickbooks__purchase_line') }}"
purchase: "{{ ref('stg_quickbooks__purchase') }}"
refund_receipt_line: "{{ ref('stg_quickbooks__refund_receipt_line') }}"
refund_receipt: "{{ ref('stg_quickbooks__refund_receipt') }}"
sales_receipt_line: "{{ ref('stg_quickbooks__sales_receipt_line') }}"
sales_receipt: "{{ ref('stg_quickbooks__sales_receipt') }}"
transfer: "{{ ref('stg_quickbooks__transfer') }}"
vendor_credit_line: "{{ ref('stg_quickbooks__vendor_credit_line') }}"
vendor_credit: "{{ ref('stg_quickbooks__vendor_credit') }}"
vendor: "{{ ref('stg_quickbooks__vendor') }}"
financial_statement_ordinal: []
cash_flow_statement_type_ordinal: []

using_address: true
using_bill: true
using_credit_memo: true
using_department: true
using_deposit: true
using_estimate: true
using_invoice: true
using_invoice_bundle: true
using_journal_entry: true
using_payment: true
using_refund_receipt: true
using_transfer: true
using_vendor_credit: true
using_sales_receipt: true
using_credit_card_payment_txn: false

analysis-paths: ["analysis"]
clean-targets:

target
dbt_modules
dbt_packages

Package versions
packages:

package: fivetran/quickbooks
version: [">=0.9.0", "<0.10.0"] # we recommend using ranges to capture non-breaking changes automatically

What database are you using dbt with?
bigquery
dbt Version
1.5.1
Additional Context
No response
Are you willing to open a PR to help address this issue?

[ ] Yes.
[ ] Yes, but I will need assistance and will schedule time during our office hours for guidance
[X] No.

hi there @bernhardF1984 👋 thanks for taking the time to open this issue, we really appreciate it and agree with your proposed approach! i also really like the idea of raising a warning if there are multiple Accounts Payable (perhaps it could be automatically disabled if you provide the "real" accounts payable n the dbt_project.yml).
Couple of follow up questions for ya:

On your end, how do you determine which is the appropriate Accounts Payable to use? is it something you just know internally, or could the package somehow programmatically figure it out? I imagine it's probably the former, but wanted to check first

We have similar logic in the following models, where we assume there is only one account of the respective type and perform a cartesian join. I assume we would also introduce variables to configure the "real" accounts of these types as well -- are you seeing duplicates from these models?

int_quickbooks__credit_memo_double_entry - Accounts Payable
int_quickbooks__deposit_double_entry - UndepositedFunds
int_quickbooks__payment_double_entry - Accounts Receivable
int_quickbooks__invoice_double_entry - Accounts Receivable

We'll get started on this next sprint, which begins mid next week 🤠

Hi jamie,
thanks for getting back to me.

Yes, this is mainly something internal. The only thing that could help
recognize it, is if one account balance is 0 and the other one not. But
again I think having an alert to make the user know about the issue would
be great.
Yes definitely I would do that, as it can also happen here, that the
accountant didn't disable the secondary account.

Cheers,
Bernhard
On Tue, Jul 18, 2023 at 10:46 PM Jamie Rodriguez @.***>
wrote:

hi there @bernhardF1984 https://github.com/bernhardF1984 👋 thanks for
taking the time to open this issue, we really appreciate it and agree with
your proposed approach! i also really like the idea of raising a warning if
there are multiple Accounts Payable (perhaps it could be automatically
disabled if you provide the "real" accounts payable n the dbt_project.yml).
Couple of follow up questions for ya:

On your end, how do you determine which is the appropriate Accounts
Payable to use? is it something you just know internally, or could the
package somehow programmatically figure it out? I imagine it's probably the
former, but wanted to check first
2.
We have similar logic in the following models, where we assume there
is only one account of the respective type and perform a cartesian join. I
assume we would also introduce variables to configure the "real" accounts
of these types as well -- are you seeing duplicates from these models?

int_quickbooks__credit_memo_double_entry
https://github.com/fivetran/dbt_quickbooks/blob/main/models/double_entry_transactions/int_quickbooks__credit_memo_double_entry.sql#L39
Accounts Payable
int_quickbooks__deposit_double_entry
https://github.com/fivetran/dbt_quickbooks/blob/main/models/double_entry_transactions/int_quickbooks__deposit_double_entry.sql#L34
UndepositedFunds
int_quickbooks__payment_double_entry
https://github.com/fivetran/dbt_quickbooks/blob/main/models/double_entry_transactions/int_quickbooks__payment_double_entry.sql#L33
Accounts Receivable
int_quickbooks__invoice_double_entry
https://github.com/fivetran/dbt_quickbooks/blob/main/models/double_entry_transactions/int_quickbooks__invoice_double_entry.sql#L93
Accounts Receivable

We'll get started on this next sprint, which begins mid next week 🤠
—
Reply to this email directly, view it on GitHub
https://github.com/fivetran/dbt_quickbooks/issues/97#issuecomment-1640902201,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/A7XPLJ2KGCVAT7574DOK523XQ3RYHANCNFSM6AAAAAA2L4QP4U
.
You are receiving this because you were mentioned.Message ID:
@.***>

Hi @bernhardF1984 ! We're in the process of deploying a fix. If you want to test this before we deploy it live, try pointing to this branch in your packages.yml rather than the default dbt package, and try configuring your account type values in your dbt_project.yml by following the instructions in the new README?
packages
  - git: https://github.com/fivetran/dbt_quickbooks.git
     revision: bugfix/set-account-type-variables
     warn-unpinned: false

If not, we should be deploying this live by next week!

Thanks, I'll check it out!
On Wed, Aug 2, 2023 at 2:48 AM Avinash Kunnath @.***>
wrote:

Hi @bernhardF1984 https://github.com/bernhardF1984 ! We're in the
process of deploying a fix. If you want to test this before we deploy it
live, try pointing to this branch in your packages.yml rather than the
default dbt package, and try configuring your account type values in your
dbt_project.yml by following the instructions in the new README?
packages

git: https://github.com/fivetran/dbt_quickbooks.git
revision: bugfix/set-account-type-variables
warn-unpinned: false

If not, we should be deploying this live by next week!
—
Reply to this email directly, view it on GitHub
https://github.com/fivetran/dbt_quickbooks/issues/97#issuecomment-1661253054,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/A7XPLJ3LIZDKRNVSKZWEFM3XTGITTANCNFSM6AAAAAA2L4QP4U
.
You are receiving this because you were mentioned.Message ID:
@.***>


Do you sanitize the strings?

Hello,
Everything is in the title, do you sanitize the strings or should it be done using a third party way?
Keep up the good work,
Cheers

It doesn't make any sense to sanitize on client size since an attack of man in the middle will bypass this sanitization
Do it on server side or, at least, in the jump before to write your data without any interference possible (meaning when the data riches the final server)

noted. Thanks for answering :)
2015-04-26 22:15 GMT+02:00 Garito<EMAIL_ADDRESS>
It doesn't make any sense to sanitize on client size since an attack of
man in the middle will bypass this sanitization
Do it on server side or, at least, in the jump before to write your data
without any interference possible (meaning when the data riches the final
server)
—
Reply to this email directly or view it on GitHub
https://github.com/mindmup/bootstrap-wysiwyg/issues/193#issuecomment-96430511
.


Expose parameters offered by robot

The robot currently offers the following parameters:
$ ros2 param list
/motion_control:
max_speed (read only, set when saftey_override is set)
reflexes.REFLEX_BUMP
reflexes.REFLEX_CLIFF
reflexes.REFLEX_DOCK_AVOID
reflexes.REFLEX_GYRO_CAL
reflexes.REFLEX_PANIC
reflexes.REFLEX_PROXIMITY_SLOWDOWN
reflexes.REFLEX_STUCK
reflexes.REFLEX_VIRTUAL_WALL
reflexes.REFLEX_WHEEL_DROP
reflexes_enabled
safety_override
/static_transform:
wheel_base
wheels_encoder_resolution
wheels_radius
For example:
wheel_base
$ ros2 param get /static_transform wheel_base
Double value is: 0.2329999953508377
safety_override
$ ros2 param get /motion_control safety_override
String value is: none

All the reflexes and safety_override should be implemented with recent code pushes.  I believe all that remains from the list above is exposing the read only params:
wheel_base
wheels_encoder_resolution
wheels_radius

These can be hard coded using values observed from the robot


x/pkgsite: navigation bar loses heading structure from README

What is the URL of the page with the issue?
https://pkg.go.dev/github.com/mattn/go-sqlite3
What is your user agent?
Mozilla/5.0 (X11; CrOS x86_64 13505.73.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.109 Safari/537.36

Screenshot

Compare to the README as rendered on GitHub:

What did you do?
Examine the headings on the left navigation bar.
What did you expect to see?
A clear distinction between topics (h1) and subtopics (h2).
What did you see instead?
The pkg.go.dev navigation bar does not visibly distinguish between topics and subtopics.

Thanks for the feedback!
We decided to flatten the hierarchy because users like to do a lot of different things with their README headings.
Some examples:

skip levels (github.com/google/uuid has only h1s and h6s, github.com/sirupsen/logrus has only h1s and h4s)
multiple h1s (github.com/spf13/cobra and github.com/mattn/go-sqlite3 have multiple h1s)
no h1s (github.com/googleapis/google-cloud-go/tree/master/storage starts at h2)

We currently show the highest and second highest levels in the TOC for the README section.
If you have suggestions on how to better surface this information, let us know!
/cc  @jamalc @georgehu @fflewddur

I agree that it does make sense to compress the observed heading range and prune out levels that appear to be too fine-grained.
But, rather than flattening the levels that are included, I would like them to be visually distinct in some manner. That could be implemented as different indentation levels, different background colors, different font weights and sizes, or perhaps some other mechanism — the important thing is that they be distinct in some easily-observable way.

For skip-levels in general, I agree that showing the highest 1–2 levels in the ToC seems reasonable.
I have a few other suggestions for that heuristic, but in my opinion none of these suggestions is as important as making the heading levels visually distinct. 🙂

If the document has only one of the highest headings and it appears only once and before all other headings, we could treat it as a de-facto page title and display it alongside (or instead of) the word “README” at the top of the page and the top of the ToC, rather that treat it as a section within the ToC.

We could choose the number of levels to retain based on the number of headings in the document so that, if possible, the resulting ToC will typically fit above the fold no matter how many sub-headings are used.

We could enclose the ToC and the links below it in some visually distinctive manner (such as a background color or an outline), so that if it does spill below the fold users will have a visual indication that they may need to scroll down to see some of the relevant links. Or, if the ToC doesn't fit above the fold perhaps we could collapse it into a <details> element.

These changes are now live.


Add gdo_singlesample pipeline with basic filtering.

Changelog
Add gdo_singlesample pipeline with basic filtering.
MERGE #668 first!
Issue ticket number and link
Closes #671 (Replace xxxx with the GitHub issue number)
Checklist before requesting a review

[x] I have performed a self-review of my code

[x] Conforms to the Contributor's guide

Check the correct box. Does this PR contain:

[ ] Breaking changes
[x] New functionality
[ ] Major changes
[ ] Minor changes
[ ] Documentation
[ ] Bug fixes

[ ] Proposed changes are described in the CHANGELOG.md

[x] CI tests succeed!

Integration tests here: https://github.com/openpipelines-bio/openpipeline/actions/runs/7642833258


Grid system example in modal does not use class as mentioned

Documentation says to use .container-fluid but the example code uses .row instead.
http://v4-alpha.getbootstrap.com/components/modal/#using-the-grid-system

X-Ref: #17204

Fixed by #17204.


Typescript support

Hi
In view of the fact that the module was written in TS I tweaked tsconfig to export declaration files and added a couple of interfaces for type safety
Thanks

Hi!
It's a bit difficult for me to understand what's going on in the constructor without refactoring the code, I suppose it should be like this..
export interface ErrorConfig {
  message: string;
  time_thrown?: string;
  data?: any;
  options?: {
    showPath?: boolean;
    showLocations?: boolean;
  };
}

export interface ErrorInfo {
  message: string;
  name: string;
  time_thrown: string;
  data?: {};
  path?: string;
  locations?: any;
}

Sorta.  Data should just be ?: any

@rasdaniil Hi, any update?


[wip: testing new doc-builds]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint.


remove note header from editor panel when in no-patient mode

pass patient to clinical editor component and use as trigger for whether to include patient header or not.

This looks good to me. Toggling between patient and no patient view removes the patient header


Correct copyright notices to reflect Copyright OpenSearch Contributors

Coming from https://github.com/opensearch-project/.github/issues/21.
The correct copyright for open-source projects in opensearch-project is "Copyright OpenSearch Contributors". Please correct any places that say otherwise, especially where it says copyright Amazon. Make sure NOTICE.txt and README match. See https://github.com/opensearch-project/.github/pull/24 for an example.

The correct header for files like this one is:
/*
 * Copyright OpenSearch Contributors
 * SPDX-License-Identifier: Apache-2.0
 */

For files that contain attribution to an external project, place this long version above those of the external project(s).
/*
 * Copyright OpenSearch Contributors
 * SPDX-License-Identifier: Apache-2.0
 *
 * The OpenSearch Contributors require contributions made to
 * this file be licensed under the Apache-2.0 license or a
 * compatible open source license.
 *
 * Modifications Copyright OpenSearch Contributors. See
 * GitHub history for details.
 */

Here is a rough list of files that need to change:
https://github.com/opensearch-project/notifications/search?q=copyright+amazon.com+reserved

@kavithacm do we need to reopen this issue? Is there remaining work according to Miki's comments

@kavithacm do we need to reopen this issue? Is there remaining work according to Miki's comments

It looks like we should reopen


Add license lines to all source code files

I'm applying the Kotlin language style of Apache 2.0 license notice
/*
 * Copyright 2020 thunderbiscuit and contributors.
 * Use of this source code is governed by the Apache 2.0 license that can be found in the ./LICENSE file.
 */

Done! See 81d0a1eb2aa3dcfc1a4f1ed15cde1876b2d3a797.

Done! See 81d0a1eb2aa3dcfc1a4f1ed15cde1876b2d3a797.


Export Public _BeartypeConfReduceDecoratorExceptionToWarningDefault

Would you be open to exporting _BeartypeConfReduceDecoratorExceptionToWarningDefault as part of the beartype public API?
https://github.com/beartype/beartype/blob/7d9dc4efd2b14867d80f292cc328f204c1d4c888/beartype/_conf/confcls.py#L18-L19
mypy and pyright didn't like me using **kwargs to optionally set warning_cls_on_decorator_exception to None, so I'm using the private import in the interim:
https://github.com/KyleKing/calcipy_template/blob/23fa65c0400cac50c1cc1b3770526af669ce0159/package_template/{{ package_name_py }}/__init__.py.jinja#L39-L48

Gah! y u so face-numbingly dumb, mypy and pyright? </sigh>
The answer, of course, is: "Certainly!" I'd be happy to publicly export that to reduce the face-numbing pain you and your codebase have felt for over a week. That said:

I'll probably never publicly document BeartypeConfReduceDecoratorExceptionToWarningDefault – because nobody should ever explicitly pass that anywhere. The only reason that fake "warning type" exists is to enable beartype.claw to differentiate between someone passing the warning_cls_on_decorator_exception parameter and someone not passing that parameter.
Relatedly, thanks so much for supplying that working code snippet. Given that, you probably want to avoid violating privacy encapsulation by passing the public beartype.roar.BeartypeClawDecorWarning warning type rather than the fake beartype.roar._roarwarn._BeartypeConfReduceDecoratorExceptionToWarningDefault: e.g.,

    # You almost certainly want to do this rather than what you're currently doing.
    # I almost certainly want to be sleeping rather than what I am currently doing.
    # How could two devs have gone so wrong? I blame my shady docos for everything.
    if beartype_mode != _BeartypeModes.OFF:
        from beartype.roar import BeartypeClawDecorWarning
        beartype_warning_default = BeartypeClawDecorWarning

        beartype_this_package(conf=BeartypeConf(
            warning_cls_on_decorator_exception=(
                None if beartype_mode == _BeartypeModes.ERROR else beartype_warning_default
            ),
            is_color=getenv('BEARTYPE_NO_COLOR') is not None),
        )

Does that suffice to satisfy mypy and pyright or would you still like me to publicize BeartypeConfReduceDecoratorExceptionToWarningDefault? I'd be happy to if I have to – but it's probably best that I not do that unless I really need to. You decide, King Kyle. :+1: :prince: :crown:

Perfect, that works for me!


add ko-fi donation platform

Ko-fi is an alternative to Patreon without any fees on donations

Thanks @mirkoperillo 🙏


Melodic package is outdated

The pre-built package for ROS melodic is outdated / not on the kinetic-devel branch.

I just created a new release: ros/rosdistro#18719


Create a timeline

Add a list of all your activities, achievements, work experience. Add as much info as you can remember. If you don't have work experience, talk about your pet-projects or student projects.

Year
Experience
Achievements

2012-2018
Logistics Specialist
🚚 Validated technology implementation in logistics field

2019
Transition to Tech
💻 Started developing web applications and e-commerce stores

2020 - 2024
Freelance Web Designer
🎨 Designed various websites as a freelance designer

2024
Bundpac NPM Package
📦 Developed "Bundpac" NPM package for consolidating dependencies from different npm projects

2024
"Viewers Facebook profile" Chromium Extension
🔍 Created "Viewers Facebook profile" extension for Chromium browsers. Go to


Always loading largest size

We have implemented lazy sizes on the header image here: https://www.star-registration.com/pages/sympathy-gift-for-funeral
unfortunately, it always loads the 900px image no matter the device width.
I already tried to remove all css classes, set data-aspectratio and every other quickfix that can be found on the internet.
Any help would be appreciated

You need to either replace src with data-src (see https://github.com/aFarkas/lazysizes/tree/5.3.1#combine-data-srcset-with-data-src) or add an empty placeholder image in srcset (see https://github.com/aFarkas/lazysizes/tree/5.3.1#modern-transparent-srcset-pattern). Otherwise the browser will always load what it finds in src.


incorrect maven command in contributing guide

Duplicate of https://github.com/Azure/azure-sdk-for-java/pull/16307


Make ParsePushBroadcastReceiver subclassable once more.

This is required to do custom push notification handling as outlined in our official guide:
https://parse.com/tutorials/android-push-notifications.
Fixes #152.

I don't think  this is truly necessary anymore, now that we're going to modularize and open up push.


Possible error with carriage returns in body

I had an issue that needed to be edited and re-submitted before running through the invite process again.  I haven't been able to test and confirm, but somehow it seems that carriage returns were introduced during the editing process.   When I looked at jobs that succeeded versus mine, I noticed none of the other ones had carriage returns.  I only edited in the GitHub Issues editor, but I wonder if that act alone introduced the carriage returns.
https://github.com/lindluni/warden-invite/blob/main/index.js#L37-L42
This is the action output error:
Run lindluni/warden-invite@main
(node:1555) UnhandledPromiseRejectionWarning: TypeError: Cannot read property 'trim' of undefined
    at /home/runner/work/_actions/lindluni/warden-invite/main/dist/index.js:2:121816
    at /home/runner/work/_actions/lindluni/warden-invite/main/dist/index.js:2:124125
    at /home/runner/work/_actions/lindluni/warden-invite/main/dist/index.js:2:124129
    at Object.<anonymous> (/home/runner/work/_actions/lindluni/warden-invite/main/dist/index.js:2:124133)
    at Module._compile (internal/modules/cjs/loader.js:959:30)
    at Object.Module._extensions..js (internal/modules/cjs/loader.js:995:10)
    at Module.load (internal/modules/cjs/loader.js:815:32)
    at Function.Module._load (internal/modules/cjs/loader.js:727:14)
    at Function.Module.runMain (internal/modules/cjs/loader.js:1047:10)
    at internal/main/run_main_module.js:17:11
(node:1555) UnhandledPromiseRejectionWarning: Unhandled promise rejection. This error originated either by throwing inside of an async function without a catch block, or by rejecting a promise which was not handled with .catch(). (rejection id: 1)
(node:1555) [DEP0018] DeprecationWarning: Unhandled promise rejections are deprecated. In the future, promise rejections that are not handled will terminate the Node.js process with a non-zero exit code.

The [sanitized] body from my edit came out as seen in the GitHub Actions log as:
body: "### Acknowledge Instructions\r\n\r\n- [X] I acknowledge that I have read the above instructions and will provide the user I am requesting to add with instructions on how to accept their invite via email or by visiting the NAME page\r\n\r\n\r\n### Full Name\r\n\r\nX Service Account\r\n\r\n###<EMAIL_ADDRESS>GitHub Username\r\n\r\n@test-svc\r\n\r\n### PM/COR Email<EMAIL_ADDRESS>PM/COR GitHub Username\r\n\r\n@pm\r\n\r\n### Assigned Contract\r\n\r\nTEST"

Hey @ebram-va, that is the issue 😭
When you edit an issue GitHub replaces all \n characters with \r\n, I was aware of this, but hadn't gotten around to fixing it, your best bet in that instance is to just create a new issue, until I can fix it. We are also looking to just write a standalone replacement for this Action as an app, so we can actually validate data on submit and reduce friction.

Cool!  I'm not sure what your build process is, if you want PRs to include a pre-built dist/index.js or not, but I did create my own fork and test issue repo to test out the fix:
Fix:  https://github.com/ebram-va/warden-invite/blob/main/index.js#L37
Output to show start of flow working properly:  https://github.com/ebram-va/test-warden-invite/runs/4312904221?check_suite_focus=true
Forked repo:  https://github.com/ebram-va/warden-invite
Test calling repo: https://github.com/ebram-va/test-warden-invite/blob/main/.github/workflows/test.yml
Test Issue:  https://github.com/ebram-va/test-warden-invite/issues/3
If you'd like me to put in a PR, just let me know if you want just the ./index.js or ./index.js and ./dist/index.js.

@lindluni ^

Hey @ebram-va
You only need to edit index.js, ncc builds the dist
You can open a pull request, this action is now built with ncc you will need to do a git fetch && git rebase origin/main
and then you can do an npm run build which will generate the dist directory for you

If you open a PR, I can always commit on top of it to do the rebase and add the dist dir

Got it, no problem!  The confusion for me before was that dist was being generated as minified, and I felt like it was a security risk that someone other than you or your automation was generating and committing it.  Now that it appears to not be minified, it's easy for you to verify that the dist/index.js file is valid.  I put in a PR, thanks!


Ignore function code collisions

"make update" is broken.
Before this fix:
EC function code 300 collision at EC_F_OSSL_ECDSA_SIGN_SETUP
Makefile:582: recipe for target 'errors' failed
make: *** [errors] Error 1

Since function codes aren't used any more in master, just ignore errors.

Merged, thanks.


There is no memorised next action

Rasa Core version:
0.12.3
Python version:
Python 3.6.5
Operating system (windows, osx, ...):
Linux 98d9549c495f 4.9.93-linuxkit-aufs #1 SMP Wed Jun 6 16:55:56 UTC 2018 x86_64 GNU/Linux
Issue:
On certain inputs, the model predicts 'action_listen' with  'no memorised next action'.
2018-07-30 03:09:37 DEBUG    rasa_core.policies.memoization  - There is no memorised next action
2018-07-30 03:09:37 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_1_KerasPolicy

Subsequently, for any other inputs (including previously working inputs), it no longer responds but gives the same 'action_listen' prediction.
Content of domain file (if used & relevant):
config:
  store_entities_as_slots: true
slots:
  (service_entity):
    type: text
  (action_entity):
    type: text
entities:
- (service_entity)
- (action_entity)
intents:
- How_To_Intent
- General_Intent
- intent_greet
- intent_bye
templates:
  how_to_action:
    - "how to triggered: action={(action_entity)} for service={(service_entity)}"
  general_action:
    - "General enquiry of service={(service_entity)}"
  utter_default:
    - "default message"
  utter_greet:
    - "Hey, how can I help you?"
  utter_bye:
    - "Goodbye"
actions:
  - general_action
  - how_to_action
  - utter_greet
  - utter_bye

Input of 'what is wireless?' before entering 'what is?'
2018-07-30 03:09:34 DEBUG    rasa_core.processor  - Received user message 'what is wireless?' with intent '{'name': 'General_Intent', 'confidence': 0.7479798097904102}' and entities '[{'start': 8, 'end': 19, 'value': 'wireless', 'entity': '(service_entity)', 'confidence': 0.6078668333884136, 'extractor': 'ner_crf'}]'
2018-07-30 03:09:34 DEBUG    rasa_core.processor  - Logged UserUtterance - tracker now has 33 events
2018-07-30 03:09:34 DEBUG    rasa_core.processor  - Current slot values:
        (action_entity): None
        (service_entity): wireless
2018-07-30 03:09:34 DEBUG    rasa_core.policies.memoization  - Current tracker state [{'intent_General_Intent': 1.0, 'slot_(service_entity)_0': 1.0, 'entity_(service_entity)': 1.0, 'prev_action_listen': 1.0}, {'intent_General_Intent': 1.0, 'prev_general_action': 1.0, 'slot_(service_entity)_0': 1.0, 'entity_(service_entity)': 1.0}, {'intent_General_Intent': 1.0, 'slot_(service_entity)_0': 1.0, 'entity_(service_entity)': 1.0, 'prev_action_listen': 1.0}]
2018-07-30 03:09:34 DEBUG    rasa_core.policies.memoization  - There is a memorised next action '2'
2018-07-30 03:09:34 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_0_MemoizationPolicy
2018-07-30 03:09:34 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'general_action' with prob 1.00.
General enquiry of service=wireless
2018-07-30 03:09:34 DEBUG    rasa_core.processor  - Bot utterance 'BotUttered(text: General enquiry of service=wireless, data: null)'
2018-07-30 03:09:34 DEBUG    rasa_core.processor  - Action 'general_action' ended with events '[]'
2018-07-30 03:09:34 DEBUG    rasa_core.policies.memoization  - Current tracker state [{'intent_General_Intent': 1.0, 'prev_general_action': 1.0, 'slot_(service_entity)_0': 1.0, 'entity_(service_entity)': 1.0}, {'intent_General_Intent': 1.0, 'slot_(service_entity)_0': 1.0, 'entity_(service_entity)': 1.0, 'prev_action_listen': 1.0}, {'intent_General_Intent': 1.0, 'prev_general_action': 1.0, 'slot_(service_entity)_0': 1.0, 'entity_(service_entity)': 1.0}]
2018-07-30 03:09:34 DEBUG    rasa_core.policies.memoization  - There is a memorised next action '0'
2018-07-30 03:09:34 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_0_MemoizationPolicy
2018-07-30 03:09:34 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'action_listen' with prob 1.00.
2018-07-30 03:09:34 DEBUG    rasa_core.processor  - Action 'action_listen' ended with events '[]'
2018-07-30 03:09:34 DEBUG    rasa_core.processor  - Current topic: None
what is?
2018-07-30 03:09:37 DEBUG    rasa_core.tracker_store  - Recreating tracker for id 'default'

Entering 'what is?'
2018-07-30 03:09:37 DEBUG    rasa_core.processor  - Received user message 'what is?' with intent '{'name': 'General_Intent', 'confidence': 0.7316421186069496}' and entities '[]'
2018-07-30 03:09:37 DEBUG    rasa_core.processor  - Logged UserUtterance - tracker now has 37 events
2018-07-30 03:09:37 DEBUG    rasa_core.processor  - Current slot values:
        (action_entity): None
        (service_entity): wireless@sg
2018-07-30 03:09:37 DEBUG    rasa_core.policies.memoization  - Current tracker state [{'intent_General_Intent': 1.0, 'slot_(service_entity)_0': 1.0, 'entity_(service_entity)': 1.0, 'prev_action_listen': 1.0}, {'intent_General_Intent': 1.0, 'prev_general_action': 1.0, 'slot_(service_entity)_0': 1.0, 'entity_(service_entity)': 1.0}, {'intent_General_Intent': 1.0, 'slot_(service_entity)_0': 1.0, 'prev_action_listen': 1.0}]
2018-07-30 03:09:37 DEBUG    rasa_core.policies.memoization  - There is no memorised next action
2018-07-30 03:09:37 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_1_KerasPolicy
2018-07-30 03:09:37 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'action_listen' with prob 0.93.
2018-07-30 03:09:37 DEBUG    rasa_core.processor  - Action 'action_listen' ended with events '[]'
2018-07-30 03:09:37 DEBUG    rasa_core.processor  - Current topic: None
what is wireless@sg?
2018-07-30 03:09:41 DEBUG    rasa_core.tracker_store  - Recreating tracker for id 'default'

Input of 'what is wireless?' after entering 'what is?'
2018-07-30 03:09:41 DEBUG    rasa_core.processor  - Received user message 'what is wireless?' with intent '{'name': 'General_Intent', 'confidence': 0.7479798097904102}' and entities '[{'start': 8, 'end': 19, 'value': 'wireless', 'entity': '(service_entity)', 'confidence': 0.6078668333884136, 'extractor': 'ner_crf'}]'
2018-07-30 03:09:41 DEBUG    rasa_core.processor  - Logged UserUtterance - tracker now has 40 events
2018-07-30 03:09:41 DEBUG    rasa_core.processor  - Current slot values:
        (action_entity): None
        (service_entity): wireless
2018-07-30 03:09:41 DEBUG    rasa_core.policies.memoization  - Current tracker state [{'intent_General_Intent': 1.0, 'prev_general_action': 1.0, 'slot_(service_entity)_0': 1.0, 'entity_(service_entity)': 1.0}, {'intent_General_Intent': 1.0, 'slot_(service_entity)_0': 1.0, 'prev_action_listen': 1.0}, {'intent_General_Intent': 1.0, 'slot_(service_entity)_0': 1.0, 'entity_(service_entity)': 1.0, 'prev_action_listen': 1.0}]
2018-07-30 03:09:41 DEBUG    rasa_core.policies.memoization  - There is no memorised next action
2018-07-30 03:09:41 DEBUG    rasa_core.policies.ensemble  - Predicted next action using policy_1_KerasPolicy
2018-07-30 03:09:41 DEBUG    rasa_core.policies.ensemble  - Predicted next action 'action_listen' with prob 0.89.
2018-07-30 03:09:41 DEBUG    rasa_core.processor  - Action 'action_listen' ended with events '[]'
2018-07-30 03:09:41 DEBUG    rasa_core.processor  - Current topic: None
wireless
2018-07-30 03:15:16 DEBUG    rasa_core.tracker_store  - Recreating tracker for id 'default'

The story file
## general_story_datalocker
* General_Intent{"(service_entity)": "datalocker"}
  - general_action

## general_story_wireless
* General_Intent{"(service_entity)": "wireless"}
  - general_action

## how_to_story
* How_To_Intent
  - how_to_action

## greet
* intent_greet
    - utter_greet

## bye
* intent_bye
    - utter_bye

It does not have memorized action for what is? because it is General_Intent with no entities, and you do not have a corresponding story for this.
The same goes for what is wireless? after what is?

The same goes for what is wireless? after what is? - you do not have corresponding story for this, so there is no memorized action

It is defined!
general_story_wireless

General_Intent{"(service_entity)": "wireless"}

general_action

In any case I think the issue is solved now. In another project with MaxHistory set to 1 for  MemoizationPolicy, the outcomes are actually better now. It was likely using the failed state for its prediction causing what is wireless? which worked the first time to fail the second time.

 It was likely using the failed state for its prediction causing what is wireless? which worked the first time to fail the second time. - yes, because you used default max_history=3 from train.py the failed state included what is?, so it could not recall anything for the second what is wireless?


Add documentation on sorting chapters

We decided we will adopt the general practice in Nuxt to sort files using numbering using the format of 1.filename.md etc.
https://content.nuxt.com/usage/content-directory#ordering

There is a change in how we order the chapters. As of #36 , the chapters are sorted using a .md header keyword order. THe documentation should state that every .md file should have the following three keywords: title, type, order. For example:
---
title: FAIR in practice
type: exercise
order: 4
---


Derive :namespace only when key is present

Currently namespace will be overridden by any extended model regardless of whether it includes a :namespace field or not

The use case?
I don't understand why do you need to share namespace with other models.

I think the use case is when you use umi, you don't need to write namespace in the model.
If this library override namespace whether it includes namespace field or not, the output model from this library, which namespace field will be underfined and that will cause dva crash on the runtime.

I expected modelExtend to be commutative in the no key collision case, and when there is a collision, it should take the last argument to define that property.  This is consistent with Object.assigns()
This just seems weird/buggy/bad rationale
const { namespace } = modelExtend({}, {namespace: "users"})
// namespace => "users"

const { namespace } = modelExtend({namespace: "users"}, {})
// namespace => undefined ????? huh ???

My original use case was composing common tasks (crud / socket listeners / forms) via functions and folding them into the model.

@sitch how did you manage to use umi with dva-model-extend? Will greatly appreciate your response.


[2704] Add validation for custom age ranges

Context
We need a validation on the course that checks that custom age ranges are valid for the new and create actions.
It must do three things:

Check if the age ranges are 4 or more years apart
Check that the from value is valid (so in 3_to_7 that 3 is an integer)
Check that the to value is valid (so in 3_to_7 that 7 is an integer)

Changes proposed in this pull request

Add a validation that is triggered on the new and create course endpoints that does the above.

Checklist

[x] Make sure all information from the Trello card is in here
[x] Attach to Trello card
[x] Rebased master
[x] Cleaned commit history
[x] Tested by running locally

 Add validation for custom age range for new courses (MCBE)

It'd be good to confirm with Paul G what the content should be for the error messages so we don't need to do work after this to correct them :tada:

Have checked with Paul on the wording of the error messages. He signed off on the updated values.


Make insertion of type parameter more generic

I noticed a minor issue while performing some experiments on methods with annotations. PatchGeneration threw a type-cast exception while processing the test data.

Can you add a test to reproduce this behavior? I can't tell if this is a worthwhile change just from looking at it, and in the future someone could change it back if there's no regression test to cover the "bad" case.

Codecov just magically worked :tada:

Codecov just magically worked tada

Yeah, it's sporadic -.-. Hopefully stabilizing, this week has been terrible.


Detect features instead of checking Django version

Merhaba!
First of all, thank you for your work on nece, it's a very elegant design, my teammates and myself are looking forward to using it in our projects instead of other, more cumbersome content translation engines ;)
I just ran into a little problem though. An edge case, really.
At the moment we have to work with the master branch of Django, in which get_version returns something like 1.11.dev20160811092707. That does not play well with StrictVersion.
Instead of relying on this mechanism, I suggest to use a try/except block to import what we need and fallback, if necessary, on what's actually available :)

Coverage decreased (-33.9%) to 53.659% when pulling caa78eb33ea23067adba2effc09fdac58d71863c on Polyconseil:meta/dj_master into 1907aa603e2b744a3b094fc6abe9e90632394cfb on tatterdemalion:master.

Also, I see there haven't been any commits on your side since April, I hope everything is okay and you are not caught in the current political troubles :-/

@metamatik Thank you for your nice comments. We are fine here. Thanks for asking that as well. We are also using nece on production environment in my company. It seems to work so seamlessly that we currently have no need to change anything. But I will definitely take a look at your  Pull Request in depth and create a new version as soon as possible.

Thank you for your quick feedback and integration!
And happy to know you're okay :)


[CARBONDATA-2227] Added support to show partition details in describe formatted

Added Detailed information in describe formatted command like partition location and partition values.
To see detailed partition information use the DDL below
descsribe formatted partition(partition_col_name=partition_value)

Be sure to do all of the following checklist to help us incorporate
your contribution quickly and easily:

[x] Any interfaces changed?

[x] Any backward compatibility impacted?

[x] Document update required?

[x] Testing done
Please provide details on
- Whether new unit test cases have been added or why no new tests are required?
- How it is tested? Please attach test report.
- Is it a performance related change? Please attach the performance test report.
- Any additional information to help reviewers in testing this change.

[x] For large changes, please consider breaking it into sub-tasks under an umbrella JIRA.

Build Failed with Spark 2.2.1, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder/2836/

retest this please

SDV Build Success , Please check CI http://<IP_ADDRESS>:8080/job/ApacheSDVTests/3779/

Build Failed  with Spark 2.1.0, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder1/4081/

SDV Build Success , Please check CI http://<IP_ADDRESS>:8080/job/ApacheSDVTests/3780/

Build Failed  with Spark 2.1.0, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder1/4084/

Build Failed with Spark 2.2.1, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder/2839/

Build Success with Spark 2.2.1, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder/2842/

Build Success with Spark 2.2.1, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder/3027/

@ravipesala Please review

Build Success with Spark 2.1.0, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder1/4271/

retest this please

Build Failed with Spark 2.2.1, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder/3876/

Build Success with Spark 2.1.0, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder1/5098/

SDV Build Fail , Please check CI http://<IP_ADDRESS>:8080/job/ApacheSDVTests/4455/

Build Failed with Spark 2.2.1, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder/4838/

retest this please

Build Success with Spark 2.1.0, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder1/6006/

Build Success with Spark 2.2.1, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder/4847/

SDV Build Success , Please check CI http://<IP_ADDRESS>:8080/job/ApacheSDVTests/5022/

Build Success with Spark 2.1.0, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder1/6018/

Build Success with Spark 2.2.1, Please check CI http://<IP_ADDRESS>:8080/job/ApacheCarbonPRBuilder/4860/

@kunal642 ...If IDG is not updated then raise a jira for IDG updation and tracking

LGTM


Improve Vue tests using selenium

Related to #1875, don't know if it will fix it
Changes

Bump test dep versions
Use a fresh browser per test
Add fixes to the TestWebBrowser test class

Considering

Use: https://bonigarcia.dev/selenium-jupiter/

After close to 100 CI runs, 0 failures. It might work... We can always come back when it fails.

After close to 100 CI runs, 0 failures. It might work... We can always come back when it fails.

Thanks ! I'll merge it once it's marked as non-draft :)

Thanks @zugazagoitia !


Remove unused forward declaration of AveragePoolPadMode enum

AveragePoolPadMode was no longer used in device.h.

Jenkins, test this please

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Jenkins, test this please

Jenkins CI test (for commit e9206863baaef23fbc2d1097992cdcf3762939b1, target branch master) succeeded!

CI unrelated

pfnCIs, test this please

Jenkins CI test (for commit e9206863baaef23fbc2d1097992cdcf3762939b1, target branch master) failed with status FAILURE.

Jenkins, test this please

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