text:
Add ability to remove close button

Description
I don't update my EdgeFrfox frequently so I was stuck with v24.2.4. In this version and before, Linux support for the hide tabs toolbar tweak wasn't there. My Firefox window looked like this:

In v24.2.13, the next version, where Linux support was properly added for the hide tabs toolbar tweak, my Firefox window now looks like this:

It would be nice to have a feature to hide the close button and the drag space after it. I don't want to delete the entire Linux/GTK part of hide-tabs.css just to remove my close button.

Now the window controls can be hidden using the uc.tweak.hide-tabs-bar.no-window-controls tweak (either enabled with the existing tweak or on its own):

repo: bmFtZQ/edge-frfox


text:
How to continue using this plugin,the official undo usage is poor

The official undo is too torturous. Ctrl+Z has not responded at all. Even the selection has been marked as a revocation step, and it seems to conflict with Efficientnode. I don't know why, but I used this plugin well before
According to what you said, "Optionally, if you wish to keep using the extension, you will have to remove the undoRedo. js from the web \ extensions \ core directory."
“
I found that there is still the same reaction as the official undo
In addition, when the official undo button presses Ctrl+Z, the preview image always disappears, which is very troublesome. It would be great if this problem could be solved

Hi,
I have to address your issue with some additional questions.
You mentioned the official is 'too torturous', did you disable this extension and delete the ZZZ-Bmad-DirtyUndoRedo folder prior to using it?
If so, and you prefer this extension behavior, but it seems broken now, my first guess is that one of the following (or both) apply:

You are updating Comfy and "undoRedo.js" is placed within the folder again, conflicting with this extension.
I've updated this extension near the time the official undo was added, and I might have broken some previously working behavior.

I doubt I can address any of these soon, but, with respect to the potential problem number 2, I would need to better understand the problems you mentioned.
Can you elaborate more (ignoring the last point about the previews)?
From what extension is the Efficient node (or is that the name of the extension???), what is the current and the desired behavior, etc...
Depending on the nature of the problem:

I could try to bypass (or delete) the undoRedo.js when the extension is active. Preferably, bypass; deleting could lead to confusion when disabling this extension.
I would have to verify older versions of this extension to check when it stopped behaving correctly and why.

Best Regards

source: gharchive/issue

license: MIT

license_type: permissive

license_source: github-api


text:
Fix data source values for mysqld-exporter

Description
As a title.

Fixed!

source: gharchive/pull-request

repo: bmf-san/gobel-example


text:
Add schemer version to schema

Adding the version number of schemer itself to the encoded schemas will allow even more compatibility as schemer evolves

I always thought adding a small frame to the schemer data make sense. such as:
[uniqueID][schemerVersion][payload length][schemer payload][checksum]
that would only add several bytes, and would allow us to verify that some bytes actually represent schemer data
it is just one idea that maybe makes sense, as long as we are adding the version number anyway?

I think we should discuss further. IMO payload length and checksum are not strictly needed.
The notion of an unique ID is interesting, but I think it's a separate issue.
The JSON document also needs the schemer version number. Should follow semver (i.e. major.minor.revision)

I was just thinking some way to verify that an arbitrary slice of bytes was actually schemer encoded data
My thinking was that common programmer off by one errors or similar could crash Schemer lib if incomplete data was passed to Schemer . Maybe it doesn't matter. I was thinking some way for schemer decode to verify if passed in data is OK.
A simple verification step [to make sure library users are passing correct byte slices by having simple checksum for schemer data] at our level can help prevent/identify buys for users of this library.
RIght now, according to my understanding, the library will crash and/or return garbage if passed in schemer schema / encoded data passed has issues.

So we will shelf the idea of adding a data frame to the encoded schemer data. (Reason for this is to keep the data as small as possible.) However, our discussions have decided that since schemas themselves are only transmitted over the wire occasionally, it is OK to add extra data to them.
For a JSON encoded scheme, we will need to modify the function DecodeSchemaJSON() and then MarshalJSON() in each datatype. The only change should be to add an additional field called: "schemerversion".
The only problem with this approach is that we support nested structures, so writing the version number in each MarshalJSON() would lead to redundancy. I think the way around this is to use a mutex.
The other type of schema we support is the binary format. For this, I think an easy way to implement this is to add a magic (64-bit) number to the beginning of the byte slice, which is followed by the Schema version number.
Like the JSON schemas, a similar use of a  mutex can be used to ensure nested data types do not also add these bytes.

I'd just call the additional field version. The version number will only appear at the root level, so I don't think we need a mutex.
For the binary format, I'm good with adding some framing, magic number, checksum, etc.... although I'm slightly worried it would add too much additional complexity to the encoding/decoding code. Similarly, the magic number, framing, and checksum will only appear at the root level. It should not be nested.

Ya, I might just be thinking wrong. But for example, we could have a schema for an integer [by itself]. So if we did, it seems like we would have to have the version number.
But if we had a struct with an integer in it, how would the integer know not to include the version number?
It seems like we would need a mutex [i.e. global variable] to prevent redundancy.
In any case,  I think I have enough to start work on this.
It doesn't seem like too big of a task, and we can double check the implementation to make sure it doesn't add to much complexity.
And if I am not thinking right about the struct fields, I will just leave that out.

The version number would not be stored in the Schema instance itself. In other words, there is no struct field for the schema version. It would only be produced when encoded. I think that the encoding function would have to know when it's encoding the root schema, so that it only produces the version number at the root level.
If, for example, you had a struct with an integer field, when the struct is encoded, the version number would only be added to the struct, not to the individual fields. To be honest, I'm not exactly sure how this would work. Perhaps, every type would encode the version, but then for nested types (i.e. object, array), I believe that the MarshalJSON function can unmarshal the field / element and remove its version field before re-marshalling.
For MarshalSchemer, if the first few bytes are reserved for the version number, these could be dropped before appending to the resulting byte slice.
Thoughts?

I think we are on the same page re: the JSON. But actually I think it will be clearer once I start working with the code and get schemer back into the front of my brain!
Re: your point about MarshalSchemer , yes I see what you mean I think.
But for sure we both are in agreement that somehow we want to make sure the schema version number is only encoded once into a JSON/binary schema.

authors: BenjaminPritchard bminer

repo: bminer/schemer


text:
Fix for problem with IERS workaround

I noticed a problem with the astropy/astroplan#71 approach: download_IERS_A wasn't working if you tried to use Time in the same session.  The underlying problem with how the table is applied at the end of download_IERS_A.  This PR fixes that, but more importantly adds a test to catch this.  Of couse it requires downloading, so it uses the remote_data decorator.

Wow, I can't believe I botched that.


text:
Have Tron download tools?

Would it be possible to have Tron download the external tools that it uses, rather than packaging them together with the bat files?

Frequently asked, see here

The closest pre-existing thing I can think would be something like  chocolatey.org but I don't think all the tools are available. Also involving something like chocolatey assumes that all machines support it (requires power shell), and the same would apply to pretty much anything that isn't custom built. I think building something specifically for tron would end up relying on either wget or curl, or possibly btsync, so those would be the minimum required to include.
In theory this is a neat goal for the project, but after a lot of thought I agree mostly with your FAQ note that the current setup is standalone and can be used offline. Having any kind of tools download would require you to maintain at least two versions of tron, one that downloads as needed, and the other that includes the tools and maybe downloads updates as needed (when a network connection is available, and if this behaviour is accepted by the user running from).
For now I'll close this issue, but I'll definitely help out if I think of something that might work for this.

Yeah, I like your thoughts on it. I kind of ended up at the standalone method after thinking for quite a while on it.
One possible option I thought of was to bundle the utilities like we're doing currently, then automatically try to grab the latest versions at launch based on the use of a flag (or use a flag to NOT try to get new versions). The problem I came to was back to the downloader code - I'd have to maintain an update script when various URL's change. Of course, it could failback to the individual local copy of whatever tool it couldn't find, but that's a decent-size undertaking to build.
If you want to build an update script that will handle failing back to the internal version of a tool and can put it together as a sub-script that Tron calls, I could test integrating it. It really would be ideal, especially for those times when I'm away for an extended period and can't push out a refresh release with the latest versions of everything.

I like the mentality of having the tools able to update and having the flag for it, and i agree if you're unavailable this would keep things a little more current and reduce the work you have to do to keep things updated between releases. If I can come up with some time and an idea about how to make something like this possible I'll certainly bring it to you. I will say don't hold your breath though because I don't consider myself nearly the level of guru that you are with putting together batch files (thus tron! ty!) but if there is something I can do and I find the time then I'll do what I can.

Sounds good

authors: nemchik vocatus

repo: bmrf/tron

license: mit

license_source: bigquery


text:
Problems about scalable offline map matching

Hello:
Recently, i am trying to move this wonderful procedure onto spark cluster. I mainly change your example code, to load roadmap from an existing file. for example, xx.bfmap. So i change this

"val matcher = sc.broadcast(new BroadcastMatcher(host, port, database, user, pass, config))"

into

"val matcher = sc.broadcast(new BroadcastMatcher(bfmappath))"

which bfmappath is the filepath of xx.bfmap. and i change the BoradcastMatcher object as follows

val map: RoadMap = Loader.roadmap(bfmapPath,true).construct

For example “root/data/xx.bfmap”, it test right on local model. which means i can get the roadmap object. however, when i use

spark-submit --master yarn-cluster --class com.bmwcarit.barefoot.taxi.PointMM  test.jar

it seems it cannot find the xx.bfmap path and return the null type roadmap. I consider put the xx.bfmap onto HDFS,but how should i write the bfmappath as parameters then? i am new to spark and hadoop,and please forgive me if i don't explain it clearly.
looking forward to your apply.
Thanks.

well, in fact, i wonder

"as an alternative map data can be stored/loaded as serialized object via HDFS"
as you write in this section, how to do for this?

Since you have the file serialized locally, you can just hadoop fs -put it into HDFS. Add the HDFS URI as a parameter to your Spark job.
https://github.com/jongiddy/barefoot-hdfs-reader.git has a class that you can add to your Spark map-matching code to read the map file from HDFS using:
val map = RoadMap.Load(new HdfsMapReader(new URI(map_uri))).construct()

where map_uri is the HDFS URI for the serialized map.

Thank you for your HdfsMapReader . I manage to get the read the map file from HDFS. But i get another problem now. The output on spark job is different from the procedure i run in my intellij idea. The first picture is log on spark job. The second is log on my pc (win10, intellij idea)

as you can see, on spark job it is full of HMM break, while on my local pc it works well. Since the the number and index of road is created the same, i think it is not caused by the bfmap. So what's the problem? Could you give me some advice?
Thanks a lot.

After you've read the road data, you should construct the map with map.construct() which kind of finalizes the read step and creates the spatial index and the routing graph.

Thanks for your reply. In fact, I did map.construct()  after read this road data. In details, the code i use for spark is as follows. the version of spark i use is 1.6.2
in the main class, i change your example code on spark like this:
val matcher = sc.broadcast(new BroadcastMatcher(bfmapPath)).value
... some code to read trace data to tracerdd
val matches = tracerdd. groupBy(x=>x._1).map(x=>{
        val trip = x._2.map({
          x => new MatcherSample(x._1,x._2,x._3,x._4)
        }).toList
        matcher.mmatch(trip,10,500)
      })

in the BroadcastMatcher , the code like this:
object BroadcastMatcher {
  private var instance = null: Matcher

  private def initialize(bfmappath:String) {
    if (instance != null) return
    this.synchronized {
      if (instance == null) { // initialize map matcher once per Executor (JVM process/cluster node)

        val map = RoadMap.Load(new HdfsMapReader(new URI(bfmappath))).construct

        val router = new Dijkstra[Road, RoadPoint]()
        val cost = new TimePriority()
        val spatial = new Geography()

        instance = new Matcher(map, router, cost, spatial)
      }
    }
  }
}

@SerialVersionUID(1L)
class BroadcastMatcher(bfmappath:String) extends Serializable {

  def mmatch(samples: List[MatcherSample]): MatcherKState = {
    mmatch(samples, 0, 0)
  }

  def mmatch(samples: List[MatcherSample], minDistance: Double, minInterval: Int): MatcherKState = {
    BroadcastMatcher.initialize(bfmappath)
    BroadcastMatcher.instance.mmatch(new ArrayList[MatcherSample](samples.asJava), minDistance, minInterval)
  }
}

As you can see, i did the map.construct(). and it works well when i run locally in my intellij idea. But when i transfer to spark (yarn cluster). It return HMM breaks.
If you can think of other reasons I might be getting the HMM breaks, I'd really appreciate your thoughts. If there's further debug information I can enable to help find the problem, I'm happy to do that.
Thanks a lot

Sorry, I haven't seen that map construction is actually shown in your log. Anyways, from remote it's difficult to see what could be the problem. With regard, to "IntelliJ" vs "Spark" execution, do you use the same reading/parsing of your trace data? (Usually, it's different because in Spark you also read your data from HDFS.) Have you checked if there is some (typical) error, e.g., swapped lat/lon or cut off decimals of floating point values due to errorneous implicit number types (integer vs. float)?

Thank you.Finally I manage to run it on spark, in fact ,the "IntelliJ" and "Spark" execution is intrinsically the same judging from the result it generates. The only different is about the logs which,in fact,doesn't matter .
Thanks again. It is really nice of you to provide this wonderful program.

谢谢。最后我设法在spark上运行它，事实上，“IntelliJ”和“Spark”执行从它产生的结果来看本质上是相同的。唯一不同的是日志，实际上并不重要。
再次感谢。提供这个精彩的节目真是太好了。

Hello:
Recently, i am trying to move this wonderful procedure onto spark cluster. I mainly change your example code, to load roadmap from an existing file. for example, xx.bfmap. So i change this

"val matcher = sc.broadcast(new BroadcastMatcher(host, port, database, user, pass, config))"

into

"val matcher = sc.broadcast(new BroadcastMatcher(bfmappath))"

which bfmappath is the filepath of xx.bfmap. and i change the BoradcastMatcher object as follows

val map: RoadMap = Loader.roadmap(bfmapPath,true).construct

For example “root/data/xx.bfmap”, it test right on local model. which means i can get the roadmap object. however, when i use

spark-submit --master yarn-cluster --class com.bmwcarit.barefoot.taxi.PointMM  test.jar

it seems it cannot find the xx.bfmap path and return the null type roadmap. I consider put the xx.bfmap onto HDFS,but how should i write the bfmappath as parameters then? i am new to spark and hadoop,and please forgive me if i don't explain it clearly.
looking forward to your apply.
Thanks.

hey
can i ask you some question ？ i dowload about spark some code but in idea  have some error
,how to solve this question?

thanks

authors: XiaoXiCanNotFly biallen jongiddy smattheis wxlreis

repo: bmwcarit/barefoot


text:
Better nomenclature.

For the glory.

Nice, thanks!

:+1:

authors: bnjbvr jankeromnes

repo: bnjbvr/kresus


text:
Issue with date Conversion

Hi there,
I think I discovered a serious issue with date conversion here is an example:
console.log(new DateOnly(20160101).toDate())
//Mon Feb 01 2016 00:00:00 GMT+1300 (NZDT) - Correct
Now we change year to 2017
console.log(new DateOnly(20170101).toDate())
//Wed Mar 01 2017 00:00:00 GMT+1300 (NZDT) - Not correct
So I'm not sure if you aware of this but if you could shine some light on why it works this way would be much appreciated.
Thanks

Thanks, this is now fixed and published on npm @1.1.1.

repo: boblauer/dateonly


text:
Fix #6

I haven't actually tested this (sorry, one of those days), but it should prevent the unnecessary adding of a listener and it's really simple.

Ok, tested and works great!

Thanks!

That's strange... I don't see these changes in master. Master still has the delaying binding issue in that setTimeout will run the next tick and leave a listener behind.

see my comment here - https://github.com/boblauer/react-onclickout/issues/6#issuecomment-158823487

authors: boblauer grassick

repo: boblauer/react-onclickout


text:
Craft\EntryModel.date is not defined.

Hello Sir.
I am using the 0.5.7 version of craft export plugin.
When I tick the fields Post Date or Expiry date of entries to export.
After clicking the export it gives error >> Craft\EntryModel.date is not defined.

Is this still an issue?

repo: boboldehampsink/export


text:
[p5.js KO] Create lerpColor.mdx

Changes: KO translation

[] npm run lint passes
[] [Inline documentation] is included / updated

@yinhwa @designerSejinOH 깃허브로 돌아와서 다시 작업시작했습니다. 이렇게 진행하면 될까요?

Hi @yunyoungJang yes this is the right place to PR! But kindly close down this PR and be back instead with multiple commits ;) it should be 1 PR per 1 contributor

@yinhwa 다시 반영할게요~!

authors: yinhwa yunyoungJang


text:
Bug:  save prompt when switching language without changes

I encountered an issue when using the field on a multilingual page. If I have a translated table field on the page and switch languages, the panel always prompts me to save the page, even when no changes have been made. I tested this on a clean Kirby starter kit with version 4.5.0 and table field version 2.4.0. Functionally, everything still works as expected.

@saltandbits Thanks for reporting this, I'm currently facing some reactive issues since the introduction of the writer-input in which it detects changes even if there's none of them. I don't have a solution atm but would investigate this further.

authors: bogdancondorachi saltandbits

repo: bogdancondorachi/kirby-table-field


text:
./clsrv: symbol lookup error: ./clsrv: undefined symbol: free_mem_coffer

zeromq version: tags/v2.0.6^0=ae35a1 (installed locally in my home, so i had to add -I$(HOME)/include and -L$(HOME)/include to Makefile-s below)
gonewrong version: 754648
coffer version: 19c834
gozero version: 7b78b4
$ ./clsrv
./clsrv: symbol lookup error: ./clsrv: undefined symbol: free_mem_coffer
$ ldd -r clsrv
linux-vdso.so.1 =>  (0x00007fffcc7ff000)
cgo_zmq.so => /home/temoto/src/go/pkg/linux_amd64/cgo_zmq.so (0x00007f208f296000)
libcgo.so => /home/temoto/src/go/pkg/linux_amd64/libcgo.so (0x00007f208f093000)
cgo_unsafe_coffer.so => /home/temoto/src/go/pkg/linux_amd64/cgo_unsafe_coffer.so (0x00007f208ee90000)
libzmq.so.0 => /home/temoto/lib/libzmq.so.0 (0x00007f208ec52000)
libpthread.so.0 => /lib/libpthread.so.0 (0x00007f208ea11000)
libm.so.6 => /lib/libm.so.6 (0x00007f208e78d000)
libc.so.6 => /lib/libc.so.6 (0x00007f208e40b000)
/lib64/ld-linux-x86-64.so.2 (0x00007f208f49b000)
libuuid.so.1 => /lib/libuuid.so.1 (0x00007f208e206000)
libstdc++.so.6 => /usr/lib/libstdc++.so.6 (0x00007f208def1000)
libgcc_s.so.1 => /lib/libgcc_s.so.1 (0x00007f208dcda000)
undefined symbol: free_mem_coffer   (./clsrv)
What could be the reason of this?
Looking at the code, i suppose zmq.go:410 tries to use coffer.FreeMemCoffer by it's "direct C name", so maybe it's about that coffer exports that function with different name on my Linux amd64?

likely irrelevant now

repo: boggle/gozero


text:
Criar assets personalizados

Esta tarefa é opcional. Além disso, poderá ser desmembrada, de acordo com o volume de trabalho escolhido pela equipe, em outras:

[ ] Tiles;
[ ] Sprites;
[ ] Sons de efeito;
[ ] Músicas.

Depende de #16, #18 e #20.

Esta tarefa é usada pelas equipes para documentar a criação de assets. Neste repositório, em particular, ela não é realizada - apenas fechada para registro.


text:
datashader Anacoder demo code: 2 errors numpy.core.multiarray failed to import, cannot import name config

Hi - Making progress getting the datashader demo code to run so far :) , except...  ;(
Ref # https://github.com/bokeh/datashader/issues/72#issuecomment-185412152
Now at "10-million-point datashaded plots: auto-ranging, but not perceptually calibrated" step @
https://anaconda.org/jbednar/nyc_taxi/notebook
import datashader as ds
from datashader import transfer_functions as tf
from functools import partial
def create_image(x_range, y_range, w, h, color_fn=tf.interpolate):
cvs = ds.Canvas(plot_width=w, plot_height=h, x_range=x_range, y_range=y_range)
agg = cvs.points(df, 'dropoff_x',  'dropoff_y',  ds.count('passenger_count'))
image = color_fn(agg)
return image
I am receiving these two errors.  Any guidance on this please? I ran "pip install numpy"  just in case and received the message: " Requirement already satisfied"
Many Thanks.
Error#1 of 2:
RuntimeError                              Traceback (most recent call last)
RuntimeError: module compiled against API version a but this version of numpy is 9

ImportError                               Traceback (most recent call last)
 in ()
----> 1 import datashader as ds
2 from datashader import transfer_functions as tf
3 from functools import partial
4
5 def create_image(x_range, y_range, w, h, color_fn=tf.interpolate):
anaconda/lib/python2.7/site-packages/datashader/init.py in ()
3 version = '0.1.0'
4
----> 5 from .core import Canvas
6 from .reductions import (count, sum, min, max, mean, std, var, count_cat,
7                          summary)
anaconda/lib/python2.7/site-packages/datashader/core.py in ()
5 from odo import discover
6
----> 7 from .utils import Dispatcher, ngjit
8
9
anaconda/lib/python2.7/site-packages/datashader/utils.py in ()
3 from inspect import getmro
4
----> 5 import numba as nb
6 from datashape import Unit
7 from datashape.predicates import launder
anaconda/lib/python2.7/site-packages/numba/init.py in ()
5 import re
6
----> 7 from . import testing, decorators
8 from . import errors, special, types, config
9
anaconda/lib/python2.7/site-packages/numba/decorators.py in ()
8 from . import config, sigutils
9 from .errors import DeprecationError
---> 10 from .targets import registry
11 from . import cuda
12
anaconda/lib/python2.7/site-packages/numba/targets/registry.py in ()
1 from future import print_function, division, absolute_import
2
----> 3 from . import cpu
4 from .descriptors import TargetDescriptor
5 from .. import dispatcher, utils, typing
anaconda/lib/python2.7/site-packages/numba/targets/cpu.py in ()
9 from numba import _dynfunc, config
10 from numba.callwrapper import PyCallWrapper
---> 11 from .base import BaseContext, PYOBJECT
12 from numba import utils, cgutils, types
13 from numba.utils import cached_property
anaconda/lib/python2.7/site-packages/numba/targets/base.py in ()
13
14 from numba import types, utils, cgutils, typing
---> 15 from numba import _dynfunc, _helperlib
16 from numba.pythonapi import PythonAPI
17 from numba.targets.imputils import (user_function, user_generator,
ImportError: numpy.core.multiarray failed to import
Error#2 of 2:
Then after running the same code/cell again in jupyter I receive this error message:

ImportError                               Traceback (most recent call last)
 in ()
----> 1 import datashader as ds
2 from datashader import transfer_functions as tf
3 from functools import partial
4
5 def create_image(x_range, y_range, w, h, color_fn=tf.interpolate):
anaconda/lib/python2.7/site-packages/datashader/init.py in ()
3 version = '0.1.0'
4
----> 5 from .core import Canvas
6 from .reductions import (count, sum, min, max, mean, std, var, count_cat,
7                          summary)
anaconda/lib/python2.7/site-packages/datashader/core.py in ()
5 from odo import discover
6
----> 7 from .utils import Dispatcher, ngjit
8
9
anaconda/lib/python2.7/site-packages/datashader/utils.py in ()
3 from inspect import getmro
4
----> 5 import numba as nb
6 from datashape import Unit
7 from datashape.predicates import launder
anaconda/lib/python2.7/site-packages/numba/init.py in ()
5 import re
6
----> 7 from . import testing, decorators
8 from . import errors, special, types, config
9
anaconda/lib/python2.7/site-packages/numba/decorators.py in ()
6 import warnings
7
----> 8 from . import config, sigutils
9 from .errors import DeprecationError
10 from .targets import registry
ImportError: cannot import name config

I suspect you've got some different versions of numpy installed in different places.  E.g. if you do python -c "import numpy;print numpy.version;print numpy.file";, I think it needs to be at least 1.8 for multiarray to be available.  Perhaps if you're combining pip and conda, one of them has gotten confused?

Thanks for your reply I typed print numpy.version.version in jupyter where I am running the code, and got back 1.9.2
I also have 1.10 and 1.10.4 installed?

Also not sure I understand what you mean by pip and conda being confused?

By pip and conda being confused, I just meant that you mentioned installing something using pip, while the datashader instructions say to use conda, and I was theorizing (with no data! :-) that perhaps something got mixed up between those two package-management systems.  I myself tend to stick to conda for everything that's available on conda (like numpy), which works very smoothly.  I think multiarray should be available in both numpy 1.9 and 1.10, so I can't see what the problem might be.  But it looks like a problem with numpy or numba, not datashader per se.   If nothing else worked, I'd try a fresh conda install on a different machine or at least in a different environment.

Okay I'll give that a shot. Thanks.

I'll assume that worked and close this, but if it's still an issue, please re-open this and provide additional details.

I reinstalled OSX and am going to try it again.  Hopefully there won't be any conflicts  with my Python installs this time. Thx
Sent from my iPhone

On Mar 24, 2016, at 7:16 PM, James A. Bednar notifications@github.com wrote:
I'll assume that worked and close this, but if it's still an issue, please re-open this and provide additional details.
—
You are receiving this because you authored the thread.
Reply to this email directly or view it on GitHub

repo: bokeh/datashader


text:
Add grid property

This makes the grid property from draggable available.

@calculon0 thanks!!

repo: bokuweb/react-resizable-and-movable


text:
🛑 Geomark is down

In 1f98de0, Geomark (https://apps.gov.bc.ca/pub/geomark/overview) was down:

HTTP code: 502
Response time: 420 ms

Resolved: Geomark is back up in 057a3b1.

repo: bolyachevets/upptime


text:
Calendar Events

Contains the implementation of the Calendar events

Calendar Event implemention


text:
Update documentation

Update links (see https://github.com/bonej-org/BoneJ2/issues/295)
Legacy/bonej/src/main/java/org/bonej/DebugLauncher.java was not update because https://imagej.net/images/bat-cochlea-volume.zip might be already in the system.

Update links (see #295)
Legacy/bonej/src/main/java/org/bonej/DebugLauncher.java was not update because https://imagej.net/images/bat-cochlea-volume.zip might be already in the system.

I uploaded bat-cochlea-volume.zip to https://imagej.github.io/images/bat-cochlea-volume.zip so you could update the link there.

authors: mdoube rgaiacs


text:
Last layer's weights when changing the number of classes + expected labels

Thank you for sharing your code
In my case , i want to train on my own dataset, should the labels (mask) be a 2-d image (with one chanel eventually)  where each pixel has as value the class label (for example pixel belonging to a person has value 15,ect, which will requires later on a colormap) ? or what format the labels (mask) should have.......may i know what software do you use to create your labels ?
again thank you for sharing your effort

For labels check original impl https://github.com/tensorflow/models/blob/master/research/deeplab/utils/input_generator.py#L133-L134

thank you, will check it

authors: IhamdiS bhack


text:
🛑 Radarr4K is down

In fb249cb, Radarr4K ($URLS_RADARR4K) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Radarr4K is back up in 0effeea.


text:
🛑 Prowlarr is down

In 90654bb, Prowlarr ($URLS_PROWLARR) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Prowlarr is back up in 2f57728.


text:
🛑 Deluge is down

In 10cd635, Deluge ($URLS_DELUGE) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Deluge is back up in c927a64.


text:
Command built-in controllers via events or another keymap agnostic way.

As implemented, the controls are hardcoded. This is not a practical design for any serious game.

The controllers do currently work based on events, but the plugins will add a default_input_map system that hardcodes the bindings.
As a workaround, you could make your own Plugin that uses all the same systems, except for the default_input_map.
But I could also try to make the plugins generic over an input bindings system.

Not urgent for my use-case, just something I noticed. I'm planning on implementing my own controller as of now, but if I make a PR with it, I would like there to be a standard for generic bindings.
Perhaps a solution could be to expose an events enum with more "input"-y variants -- something you could map one to one with a button press. As of now the events used aren't much more abstract as using the the LookTransform itself.

Perhaps a solution could be to expose an events enum with more "input"-y variants -- something you could map one to one with a button press.

I assume you mean something like the amethyst Bindings type:
https://github.com/amethyst/amethyst/blob/a4d87713f873d59bf5bad37b4c5f9074dd978daf/amethyst_input/src/bindings.rs#L17-L56
Basically a mapping of bevy's Input and Axis types.
Consider the FPS controller event:
pub enum ControlEvent {
    Rotate(Vec2),
    TranslateEye(Vec3),
}

I don't think abstracting over Bindings works very well here because in some cases (mouse + keyboard) you want a Input to control the TranslateEye and in other cases (gamepad) you want an Axis to control TranslateEye. The amethyst solution is to have "emulated" axes using buttons. But that seems like a feature best left for bevy proper.
So that's why I think it makes sense for controller to define their own event type to be as abstract as possible. Another crate could provide the abstraction you want in order to generate the events from Bindings.

As of now the events used aren't much more abstract as using the the LookTransform itself.

I disagree. The controller implementations require a fair bit of complexity that is hidden by the event interface. Many different input schemes could be implemented by "simply" generating those events.
So that's why I like my idea of making the plugins generic over an entire input system. And in Bevy, that's as simple as not implementing the input system at all. The API can be "you just need to run a system that generates ControlEvents." That gives an enormous amount of flexibility and control, while still offering a specific way of controlling the camera.

After looking at the current controllers in more detail, I think the current ControlEvents are sufficient, but making the generic over an input system is still a goal for sure.

authors: bonsairobo scrblue

repo: bonsairobo/smooth-bevy-cameras


text:
[FEATURE] RGB COLORS

What is the feature request for?
Add RGB colors support
Relevant links
No response
Additional Information
like #C00000
Feature requests are not going to be implemented by booksaw, instead you will have to find another developer to make a pull request to implement the feature.

[X] I understand

Duplicate of https://github.com/booksaw/BetterTeams/issues/353

authors: HonzasikCZ booksaw

repo: booksaw/BetterTeams


text:
Update database to version 1.3.9

Integrate the new version of the boolder database.
Resolves #84

FYI I have an uncommitted file appearing in /Users/nico/Dev/boolder-android/app/schemas/com.boolder.boolder.data.database.BoolderAppDatabase/16.json
Content:
https://gist.github.com/nmondollot/d0f4c1dd138351202f82deb0e05104fe
First time I'm seeing this, is it normal? Should it be committed inside the repo?

@nmondollot Yes, this is normal. It is the database schema that is generated by Room upon compilation, in case we want to perform migrations across the different versions of the database. This is useful for the user's database (which contains the ticked problems and the projects, and you'll see that the generated .json schema has been commited), but not for the boulders one, as this database is always initialized from the .db file.
In short, the schema under app/schemas/com.boolder.boolder.data.userdatabase.UserDatabase have to be versioned, but not the ones under app/schemas/com.boolder.boolder.data.database.BoolderAppDatabase (at least until we continue to initialize from a .db file)

authors: nmondollot wang-li

repo: boolder-org/boolder-android


text:
Avoid using deprecated header boost/detail/iterator.hpp

This header is deprecated in favor of <iterator> and will be removed in a future release. This silences deprecation warnings.

Please, merge to master.

Done.

authors: Lastique mclow

repo: boostorg/algorithm


text:
Building with g++ in C++03 mode fails

Building the library with f.ex. msvc-8.0, or 10.0, works; and so does building with g++ in C++11 mode. However, building with g++ in C++03 mode fails:
C:\Projects\boost-git\boost\libs\contract>b2 build toolset=gcc cxxstd=03
Performing configuration checks

    - 32-bit                   : yes (cached)
    - arm                      : no  (cached)
    - mips1                    : no  (cached)
    - power                    : no  (cached)
    - sparc                    : no  (cached)
    - x86                      : yes (cached)
    - symlinks supported       : no  (cached)
    - junctions supported      : yes (cached)
    - hardlinks supported      : yes (cached)
...patience...
...patience...
...found 2483 targets...
...updating 8 targets...
gcc.compile.c++ ..\..\bin.v2\libs\contract\build\gcc-gnu-6.4.0\debug\cxxstd-03-i
so\threadapi-win32\contract.o
In file included from ..\../boost/contract/detail/checking.hpp:11:0,
                 from ..\../boost/contract/old.hpp:17,
                 from ..\../boost/contract/detail/inlined/old.hpp:13,
                 from ..\../boost/contract/detail/inlined.hpp:10,
                 from ..\..\libs\contract\src\contract.cpp:11:
..\../boost/contract/detail/static_local_var.hpp:19:71: error: a cast to a type
other than an integral or enumeration type cannot appear in a constant-expressio
n
 template<typename Tag, typename T, typename Init = none*, Init init = Init()>
                                                                       ^~~~~~
..\../boost/contract/detail/static_local_var.hpp:28:31: error: template argument
 4 is invalid
 struct static_local_var<Tag, T> {
                               ^

and so on.

Yes, thanks for reporting this. I actually noted it too just other day. GCC/Clang C++03 get confused on a template specialization that I recently introduced. I removed the specialization fixing this on my local copy.
I'm running some other tests now. I'll be checking the fix in the next few days.

Fix this in the last commit (split template specializations into 2 separate templates so that works on GCC/Clang C++03).

authors: lcaminiti pdimov

repo: boostorg/contract


text:
std::vector<boost::dynamic_bitset<>>unable to free memory

Hi，
I am using boost 1.66.0 version,  gcc/g++ -7, and I found when using std::vector<boost::dynamic_bitset<>>, it is unable to free memory when destruction, even using function swap(), resize(), reset(). it doesn't work.
The test program is show below
int main()
{
cout << "[ memory start] "  << getMemoryUsage();
{
std::vector<boost::dynamic_bitset<>> m_nodes;
m_nodes.resize(1000000 + 1);
uint64_t descriptor[4] = {1, 2, 3, 4};
for(size_t i = 0; i < m_nodes.size(); i++)
{
m_nodes[i] = boost::dynamic_bitset<>(descriptor, descriptor + 4);
}
}
cout << "[ memory end] "  << getMemoryUsage() << endl;
}
result is:
[ memory start ] 13
[ memory end ] 59
As the result show:
The memory of boost::dynamic_bitset can not release, so it is the problem.
Could you please confirm it, and tell me how to solve it?

I'm not seeing a memory leak here. What is getMemoryUsage, and what system are you running this on?
My test program (attached) reports that every allocation is paired with a deallocation. (Boost trunk, on Mac OS 10.14)
#include <vector>
#include <iostream>
#include <cstdio>

#include <boost/dynamic_bitset.hpp>

void* operator new (std::size_t count)
{
    void *ptr = malloc(count);
    ::printf ("Allocating %ld bytes at %p\n", count, ptr);

    return ptr;
}

void* operator new[]( std::size_t count)
{
    void *ptr = malloc(count);
    ::printf ("Allocating %ld bytes[] at %p\n", count, ptr);

    return ptr;
}

void operator delete(void *ptr) throw()
{
    ::printf ("deleting %p\n", ptr);
	free(ptr);
}

void operator delete[](void *ptr) throw()
{
    ::printf ("deleting[] %p\n", ptr);
    free(ptr);
}

int main() {
	{
	std::vector<boost::dynamic_bitset<>> m_nodes;
// 	m_nodes.resize(1000000 + 1);
	m_nodes.resize(10 + 1);
	uint64_t descriptor[4] = {1, 2, 3, 4};
	for(size_t i = 0; i < m_nodes.size(); i++)
		{
		m_nodes[i] = boost::dynamic_bitset<>(descriptor, descriptor + 4);
		}
	::printf("Exiting scope\n");
	}
}

Hi, thanks for reply.

system: ubuntu 18.04, boost 1.66.0 release version, gcc/g++ -7
getMemoryUsage() is code of get the program memory, it like command "busybox top"
here is the linux code of it:
unsigned long getMemoryUsage()
{
unsigned long MEM = 0;
FILE *f = ::fopen ("/proc/self/stat", "r");
if (!f) return 0;
if (!::fscanf(f,
"%*d %*s %*c %*d %*d %*d %*d %*d%*u %*u %*u %*u %*u %*u %*u%*d %*d %*d %*d %*d %*d %*u %lu", &MEM))
{
// Error parsing:
MEM=0;
}
::fclose (f);
return MEM/1024/1024;
}
For my test program, you can see after destruct of "std::vector<boost::dynamic_bitset<>>", the memory can not restore "13M",  that why I mean "std::vector<boost::dynamic_bitset<>>" has memory leak.

Hi, I also test std::bitset, the memory of this has not meomory leak, here is the test program
int main()
{
cout << "[ memory start ] "  << getMemoryUsage() << std::endl;
{
    std::vector<std::bitset <256> > m_nodes;
    m_nodes.resize(1000000 + 1);
    uint64_t descriptor[4] = {1, 2, 3, 4};
    std::bitset <256> tmp(ULONG_MAX);
    for(size_t i = 0; i < m_nodes.size(); i++)
    {
        m_nodes[i] = tmp;
    }
}
cout << "[ memory end ] "  << getMemoryUsage() << endl;

}
The result is:
[ memory start ] 13
[ memory end ] 13

AMDG
This is not a bug.  It's the normal behavior of memory allocation.
Allocators do not return memory to the system when it is deallocated,
as that would be horribly inefficient for multiple reasons.  Instead,
the freed memory will be reused on the next allocation.
In Christ,
Steven Watanabe

Ok, got it, thank you very much.

authors: mclow robot-kimbo swatanabe


text:
docs: modify logo variable

When building the docs with Sphinx 8.1.3 there is an error about 'logo'.
Where is the variable originally defined?
The modification in this PR seems to solve the problem.

Where is the variable originally defined?

Possibly a bug!
Thank you!

Thanks. RemovedInSphinx90Warning warnings (not errors) also appeared. I have not researched them.
writing output... [  2%] design/basics
/opt/venvboostdocs/lib/python3.12/site-packages/sphinx/builders/html/init.py:1076: RemovedInSphinx90Warning: The str interface for _JavaScript objects is deprecated. Use js.filename instead.
if resource and '://' in otheruri:
/opt/venvboostdocs/lib/python3.12/site-packages/sphinx/util/osutil.py:48: RemovedInSphinx90Warning: The str interface for _JavaScript objects is deprecated. Use js.filename instead.
if to.startswith(SEP):
/opt/venvboostdocs/lib/python3.12/site-packages/sphinx/util/osutil.py:51: RemovedInSphinx90Warning: The str interface for _JavaScript objects is deprecated. Use js.filename instead.
t2 = to.split('#')[0].split(SEP)

authors: mloskot sdarwin

repo: boostorg/gil


text:
Segmentation fault when a JSON string with whitespaces is passed to boost::json::parse() function

Description
As described in the title.
Version of Boost: 1.80.0
Operating system: Ubuntu 22.04.3 LTS (64-bit)
Compiler: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
CMake: 3.22.1
Link to the ready-to-build project with reproduction example (just run ./run.sh):
https://github.com/Elnee/minimal-reproduction-for-boost-issue
Minimal reproduction example:
#include <bits/stdc++.h>
#include <boost/json.hpp>

int main() {

  const std::string json = R"({"example": "foo"})"; // NOTE: The whitespace here between colon and quotation mark.
  const auto &object = boost::json::parse(json).as_object();

  try {
    // Crash appears on the next line while executing the `at()` function.
    std::cout << object.at("example").as_string().c_str() << std::endl;
  } catch (const std::exception &ex) {
    std::cout << "Exception: " << ex.what() << std::endl;
  }

  return 0;
}

Output:
Segmentation fault (core dumped)

json::parse returns a json::value, which you don't store anywhere. You instead get a reference to its subobject of type json::object. At the end of that statement the value goes out of scope and destroys its internal object. After that you use a dangling reference. In order to fix this you should store either the value or the object:
const auto value = boost::json::parse(json);
const auto& object = value.as_object();
// or
const auto object = boost::json::parse(json).as_object();

@grisumbras Thank you, it works. But I don't understand why using a const-ref doesn't expand the lifetime of that object.
As I see boost::json::value has a union returned by as_object(). Is it because its lifetime is bound to the instance of boost::json::value?

And also, isn't it a bit misleading that I should think about as_object() method in such a way? I thought about it as getting the same object but in a different representation (due to the as prefix). It turned out to return some internals to me.
Doesn't it force me to know some internal library implementation details aside from the interface as a user?

@Elnee Per [class.temporary] p4, temporary objects are destroyed after evaluating the full-expression in which they appear (which in this case is the initialization of object). When a temporary object is bound to a reference, it's lifetime will be extended to that of the reference if, and only if, one of the conditions specified by  [class.temporary] p6 are met. Binding a reference to the result of calling a non-static member function of a temporary object does not satisfy any of these conditions, so the lifetime is not extended.

@sdkrystian Many thanks for the detailed explanation.

authors: Elnee grisumbras sdkrystian

repo: boostorg/json


text:
Access VPN from Docker containers

It seems that Docker containers are unable to access host machine VPN resources. Unfortunately I don't know much about networking, but I suspect it's because boot2docker VM does not use the same routing tables as the host (OS X) machine.
Is there perhaps an easy way how to make boot2docker use the same network settings?
boot2docker:
docker@boot2docker:~$ route -n
Kernel IP routing table
Destination     Gateway         Genmask         Flags Metric Ref    Use Iface
0.0.0.0         10.0.2.2        0.0.0.0         UG    1      0        0 eth0
10.0.2.0        0.0.0.0         255.255.255.0   U     0      0        0 eth0
127.0.0.1       0.0.0.0         255.255.255.255 UH    0      0        0 lo
172.17.0.0      0.0.0.0         255.255.0.0     U     0      0        0 docker0
192.168.59.0    0.0.0.0         255.255.255.0   U     0      0        0 eth1

host:
Routing tables

Internet:
Destination        Gateway            Flags        Refs      Use   Netif Expire
default            192.168.1.1        UGSc           40        1     en1
default            link#7             UCSI            0        0   utun0
10.1/16            172.27.249.211     UGSc            0        0   utun0
10.56/16           172.27.249.211     UGSc            0        0   utun0
10.66/16           172.27.249.211     UGSc            0        0   utun0
10.201.104.224/28  172.27.249.211     UGSc            0        0   utun0
10.202.104.224/28  172.27.249.211     UGSc            0        0   utun0
64.41.140.228/32   192.168.1.1        UGSc            1        0     en1
127                localhost          UCS             0        0     lo0
localhost          localhost          UH              9  2473956     lo0
169.254            link#4             UCS             0        0     en1
172.16/24          172.27.249.211     UGSc            0        0   utun0
172.16.1/27        172.27.249.211     UGSc            0        0   utun0
172.16.1.32/32     172.27.249.211     UGSc            0        0   utun0
172.16.1.33/32     172.27.249.211     UGSc            0        0   utun0
172.16.1.34/32     172.27.249.211     UGSc            0        0   utun0
172.16.1.37/32     172.27.249.211     UGSc            0        0   utun0
172.16.1.38/32     172.27.249.211     UGSc            0        0   utun0
172.16.1.39/32     172.27.249.211     UGSc            0        0   utun0
172.16.1.40/30     172.27.249.211     UGSc            0        0   utun0
172.16.1.44/30     172.27.249.211     UGSc            0        0   utun0
172.16.1.48/28     172.27.249.211     UGSc            0        0   utun0
172.16.1.64/26     172.27.249.211     UGSc            0        0   utun0
172.16.1.128/25    172.27.249.211     UGSc            0        0   utun0
172.16.2/23        172.27.249.211     UGSc            0        0   utun0
172.16.4/24        172.27.249.211     UGSc            0        0   utun0
172.16.8/21        172.27.249.211     UGSc            0        0   utun0
172.17.36/24       172.27.249.211     UGSc            0        0   utun0
172.18             172.27.249.211     UGSc            0        0   utun0
172.19             172.27.249.211     UGSc            0        0   utun0
172.27             172.27.249.211     UGSc           16       54   utun0
172.27.240/20      link#7             UCS             0        0   utun0
172.27.249.211/32  localhost          UGSc           26        0     lo0
172.31             172.27.249.211     UGSc            0        0   utun0
192.168.1          link#4             UCS             2        0     en1
192.168.1.1        4c:60:de:97:ce:2b  UHLS            0        0     en1
192.168.1.1        4c:60:de:97:ce:2b  UHLWIir        42       11     en1   1190
192.168.1.8/32     link#4             UCS             0        0     en1
192.168.1.255      ff:ff:ff:ff:ff:ff  UHLWbI          0        3     en1

can you tell us more about your VPN? so far, we've have very little success getting VM's and VPNs to play nice.

authors: Elijen SvenDowideit


text:
bootstrap_for_tag and collection_select giving bad name

Hello, I have a form that isn't associated with an object and therefore I am using bootstrap_form_tag. On this form I have a button where I can add different search filters which will add an an additional dropdown box when clicked. This means I can have an indefinite amount of dropdowns. My code abbreviated looks like the following:

= bootstrap_form_tag(bsf_opts(url: "/recorded_calls", method: :post)) do |f|
...
  %td= f.collection_select "durations_greater_less[]", @duration_options, :last, :first, hide_label: true

The html that the f.collection_select generates is found and cloned and added to the page with jQuery when the user clicks a button to add a new filter. The problem is the html that is generated has what I think is an incorrect name. Below is the html that comes out. the name is "[durations_greater_less[]]". This is a problem because lets says a user makes 5 of these dropdowns, I expect to receive as params an array for durations_greater_less, but since the name is enclosed with "[]" the params is not an array, it is a single value. I expect the name to be just durations_greater_less[].
<select class=form-control name=[durations_greater_less[]] id=_durations_greater_less[]>
  <option value=gt>Greater Than</option>
  <option value=lt>Less Than</option>
</select>

I tried to trace this out in the code, but I got stuck in the form_group method on form_builder.rb on the line

control = capture(█).to_s

I see that capture is delegated to @template, but I do not know where @template comes from. Before  the aforementioned line I did capture(1,2) just so it would raise an exception that I had the wrong number of args. In the backtrace I saw capture was going to code in another gem, so I'm not sure if it is the other gem where the issue lies or maybe there is just some data in bootstrap-forms I need to change to get this working.
In the meantime I can hack a solution with using rails select_tag and I'll set the classes manually to have it bootstrap styled.

If this was too complicated, allow me to clarify with a easier to understand example. Imagine I have a form where there is a button that says "Add Fruit". When clicked it will inset a text_field named amounts[] and a dropdown a fuits named fruits[]. So you can do something like

Hit "Add Fruit"
Enter "5" in the text field
Select "Banana "from the drop down
Hit "Add Fruit"
Enter "2" in the text field
Enter "Pineapple" from the drop down
Hit Submit

The params will be {amounts => ["5","2"], fruits => nil}

this might be coming from rails framework...

repo: bootstrap-ruby/rails-bootstrap-forms


text:
Fixes as per GTSAM 4.2

Updates to get GTD to compile with GTSAM 4.2.

Looks like we need a PPA of the 4.2 release. @dellaert @ProfFan do we have that?

Looks like we need a PPA of the 4.2 release. @dellaert @ProfFan do we have that?

Not finalized yet. When I finalize (actually make a tag and verify the wrappers) I'll need help cutting a PPA. 4.2a8 is the latest "official" release at this point. The other is a release branch.

I'll add the const when updating the CI for the new PPA.

authors: dellaert varunagrawal

repo: borglab/GTDynamics


text:
🛑 clans.wowsgame.cn is down

In dc353d3, clans.wowsgame.cn (https://clans.wowsgame.cn) was down:

HTTP code: 0
Response time: 0 ms

Resolved: clans.wowsgame.cn is back up in e51be45 after 27 minutes.

repo: boriskhodok/wowsuptime


text:
Add definitions for markerclustererplus

Hello, I would like to propose a definition file for the library MarkerClustererPlus for Google Maps V3.
Project home: mahnunchik/markerclustererplus
This *.d.ts file correspond to the 2.1.1 version.
I'm not sure about how to provide a meaningful demo without the heavy JSON data I put in the -tests file, so please feel free to correct me if this is wrong.
Cheers!

markerclusterer/markerclusterer-2.1.1.d.ts
check list

[ ] is collect naming convention?

https://www.npmjs.com/package/markerclusterer
http://bower.io/search/?q=markerclusterer
others?

[X] has a test file? (markerclusterer/markerclusterer-2.1.1-tests.ts or others)
[ ] pass the Travic-CI test?

@enanox thank you for your contributions.
please rename markerclusterer/markerclusterer-2.1.1.d.ts to markerclustererplus/markerclustererplus.d.ts

Hi @vvakame, thanks for your feedback! Please find the changes on fa3b6b5.
About the issue about the full dummy data on markerclustererplus-tests.ts, don't you think it will be fine to provide an external file named markerclustererplus-tests.json? Otherwise, please let me know your thoughts.
Thanks in advance!

@enanox don't mind! thanks mate.

(Y)

authors: enanox vvakame

repo: borisyankov/DefinitelyTyped


text:
You might have unsuporrted firmware version None

Describe the bug
After the upgrade to HA Core 2022.2.8 unsupported firmware error
Version

HA version? 2022.2.8
HA Bosch component version? 0.17.3

** Debug SCAN **
** IMPORTANT **
Go to Developer tools in Home Assistant, choose Service tab and choose bosch.debug_scan
Download file to your computer and upload it somewhere eg. https://jsonblob.com/
bosch_scan.zip

Restarting HA fix the issue


text:
scollector: enabling json or toml config

After some conversation in stack chat, we decided we don't like toml so much. This allows toml or json files to be passed in.
At some point in the future we may depricate the toml file format.
This also removes the totoml functionality. If that is needed, the 0.4.0 release of scollector should have it availible.

Closing this after talking to Craig. The frustrations with toml are coming in when we do more complex data structures, in particular for SNMP definitions.
I think since this is the problem, we are better keeping the SNMP defs out of the config and treating them as loadable object definitions. This also makes them more sharable (other people may have the same or similar PDUs for example).
Those definitions could be in JSON, Craig would rather have them go in the scollector code. I'm okay with either as long as keep the main conf clean and avoid switching conf formats yet again unless there is a strong need to.

authors: captncraig kylebrandt

repo: bosun-monitor/bosun


text:
upgrade isort

Upgrade isort to resolve current action failures. See https://github.com/PyCQA/isort/issues/2083 for details

Codecov Report
Base: 93.55% // Head: 93.55% // No change to project coverage :thumbsup:

Coverage data is based on head (ff18ef4) compared to base (be50153).
Patch has no changes to coverable lines.

:mega: This organization is not using Codecov’s GitHub App Integration. We recommend you install it so Codecov can continue to function properly for your repositories. Learn more
Additional details and impacted files
@@           Coverage Diff            @@
##           develop    #2877   +/-   ##
========================================
  Coverage    93.55%   93.55%
========================================
  Files           63       63
  Lines        13398    13398
========================================
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  Misses         864      864

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:loudspeaker: Do you have feedback about the report comment? Let us know in this issue.

authors: codecov-commenter nateprewitt

repo: boto/botocore


text:
Label has incorrect "for" tag

Label references the name of the input, this is incorrect, should use ID

Larger issue is ID is being used for the wrapper, this should be changed so ID is always used on the field, and the wrapper has a different ID

repo: boughtbymany/mutt-forms


text:
training error

2018-03-04 13:19:20.290777: W tensorflow/core/framework/op_kernel.cc:1192] Resource exhausted: OOM when allocating tensor with shape[1,1024,51,38]
INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.ResourceExhaustedError'>, OOM when allocating tensor with shape[1,1024,51,38]
[[Node: FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/FusedBatchNorm = FusedBatchNorm[T=DT_FLOAT, data_format="NHWC", epsilon=1.001e-05, is_training=false, _device="/job:localhost/replica:0/task:0/device:GPU:0"](FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/Conv2D, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/gamma/read/_683, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/beta/read/_685, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/moving_mean/read/_687, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/moving_variance/read/_689)]]
[[Node: Reshape_24/_1255 = _HostRecvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4073_Reshape_24", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
Caused by op u'FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/FusedBatchNorm', defined at:
File "object_detection/train.py", line 198, in
tf.app.run()
File "~/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "object_detection/train.py", line 194, in main
worker_job_name, is_chief, FLAGS.train_dir)
File "~/Documents/Custom-Object-Detection-master/object_detection/trainer.py", line 192, in train
clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])
File "~/Documents/Custom-Object-Detection-master/slim/deployment/model_deploy.py", line 193, in create_clones
outputs = model_fn(*args, **kwargs)
File "~/Documents/Custom-Object-Detection-master/object_detection/trainer.py", line 131, in _create_losses
prediction_dict = detection_model.predict(images)
File "~/Documents/Custom-Object-Detection-master/object_detection/meta_architectures/faster_rcnn_meta_arch.py", line 513, in predict
image_shape) = self._extract_rpn_feature_maps(preprocessed_inputs)
File "~/Documents/Custom-Object-Detection-master/object_detection/meta_architectures/faster_rcnn_meta_arch.py", line 652, in _extract_rpn_feature_maps
preprocessed_inputs, scope=self.first_stage_feature_extractor_scope)
File "~/Documents/Custom-Object-Detection-master/object_detection/meta_architectures/faster_rcnn_meta_arch.py", line 131, in extract_proposal_features
return self._extract_proposal_features(preprocessed_inputs, scope)
File "~/Documents/Custom-Object-Detection-master/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py", line 126, in _extract_proposal_features
scope=var_scope)
File "~/Documents/Custom-Object-Detection-master/slim/nets/resnet_v1.py", line 298, in resnet_v1_101
reuse=reuse, scope=scope)
File "~/Documents/Custom-Object-Detection-master/slim/nets/resnet_v1.py", line 216, in resnet_v1
net = resnet_utils.stack_blocks_dense(net, blocks, output_stride)
File "~/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "~/Documents/Custom-Object-Detection-master/slim/nets/resnet_utils.py", line 185, in stack_blocks_dense
net = block.unit_fn(net, rate=rate, **dict(unit, stride=1))
File "~/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "~/Documents/Custom-Object-Detection-master/slim/nets/resnet_v1.py", line 118, in bottleneck
activation_fn=None, scope='conv3')
File "~/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "~/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1042, in convolution
outputs = normalizer_fn(outputs, **normalizer_params)
File "~/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "~/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 643, in batch_norm
outputs = layer.apply(inputs, training=is_training)
File "~/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 671, in apply
return self.call(inputs, *args, **kwargs)
File "~/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 575, in call
outputs = self.call(inputs, *args, **kwargs)
File "~/lib/python2.7/site-packages/tensorflow/python/layers/normalization.py", line 395, in call
return self._fused_batch_norm(inputs, training=training)
File "~/lib/python2.7/site-packages/tensorflow/python/layers/normalization.py", line 302, in _fused_batch_norm
training, _fused_batch_norm_training, _fused_batch_norm_inference)
File "~/lib/python2.7/site-packages/tensorflow/python/layers/utils.py", line 208, in smart_cond
return fn2()
File "~/lib/python2.7/site-packages/tensorflow/python/layers/normalization.py", line 299, in _fused_batch_norm_inference
data_format=self._data_format)
File "~/lib/python2.7/site-packages/tensorflow/python/ops/nn_impl.py", line 831, in fused_batch_norm
name=name)
File "~/lib/python2.7/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 2034, in _fused_batch_norm
is_training=is_training, name=name)
File "~/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "~/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
op_def=op_def)
File "~/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1470, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[1,1024,51,38]
[[Node: FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/FusedBatchNorm = FusedBatchNorm[T=DT_FLOAT, data_format="NHWC", epsilon=1.001e-05, is_training=false, _device="/job:localhost/replica:0/task:0/device:GPU:0"](FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/Conv2D, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/gamma/read/_683, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/beta/read/_685, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/moving_mean/read/_687, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/moving_variance/read/_689)]]
[[Node: Reshape_24/_1255 = _HostRecvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4073_Reshape_24", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
Traceback (most recent call last):
File "object_detection/train.py", line 198, in
tf.app.run()
File "~/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "object_detection/train.py", line 194, in main
worker_job_name, is_chief, FLAGS.train_dir)
File "~/Documents/Custom-Object-Detection-master/object_detection/trainer.py", line 296, in train
saver=saver)
File "~/lib/python2.7/site-packages/tensorflow/contrib/slim/python/slim/learning.py", line 775, in train
sv.stop(threads, close_summary_writer=True)
File "~/lib/python2.7/contextlib.py", line 35, in exit
self.gen.throw(type, value, traceback)
File "~/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 964, in managed_session
self.stop(close_summary_writer=close_summary_writer)
File "~/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 792, in stop
stop_grace_period_secs=self._stop_grace_secs)
File "~/lib/python2.7/site-packages/tensorflow/python/training/coordinator.py", line 389, in join
six.reraise(*self._exc_info_to_raise)
File "~/lib/python2.7/site-packages/tensorflow/python/training/coordinator.py", line 296, in stop_on_exception
yield
File "~/lib/python2.7/site-packages/tensorflow/python/training/coordinator.py", line 494, in run
self.run_loop()
File "~/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 994, in run_loop
self._sv.global_step])
File "~/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 889, in run
run_metadata_ptr)
File "~/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1120, in _run
feed_dict_tensor, options, run_metadata)
File "~/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1317, in _do_run
options, run_metadata)
File "~/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1336, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[1,1024,51,38]
[[Node: FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/FusedBatchNorm = FusedBatchNorm[T=DT_FLOAT, data_format="NHWC", epsilon=1.001e-05, is_training=false, _device="/job:localhost/replica:0/task:0/device:GPU:0"](FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/Conv2D, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/gamma/read/_683, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/beta/read/_685, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/moving_mean/read/_687, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/moving_variance/read/_689)]]
[[Node: Reshape_24/_1255 = _HostRecvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4073_Reshape_24", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
Caused by op u'FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/FusedBatchNorm', defined at:
File "object_detection/train.py", line 198, in
tf.app.run()
File "~/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "object_detection/train.py", line 194, in main
worker_job_name, is_chief, FLAGS.train_dir)
File "~/Documents/Custom-Object-Detection-master/object_detection/trainer.py", line 192, in train
clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])
File "~/Documents/Custom-Object-Detection-master/slim/deployment/model_deploy.py", line 193, in create_clones
outputs = model_fn(*args, **kwargs)
File "~/Documents/Custom-Object-Detection-master/object_detection/trainer.py", line 131, in _create_losses
prediction_dict = detection_model.predict(images)
File "~/Documents/Custom-Object-Detection-master/object_detection/meta_architectures/faster_rcnn_meta_arch.py", line 513, in predict
image_shape) = self._extract_rpn_feature_maps(preprocessed_inputs)
File "~/Documents/Custom-Object-Detection-master/object_detection/meta_architectures/faster_rcnn_meta_arch.py", line 652, in _extract_rpn_feature_maps
preprocessed_inputs, scope=self.first_stage_feature_extractor_scope)
File "~/Documents/Custom-Object-Detection-master/object_detection/meta_architectures/faster_rcnn_meta_arch.py", line 131, in extract_proposal_features
return self._extract_proposal_features(preprocessed_inputs, scope)
File "~/Documents/Custom-Object-Detection-master/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py", line 126, in _extract_proposal_features
scope=var_scope)
File "~/Documents/Custom-Object-Detection-master/slim/nets/resnet_v1.py", line 298, in resnet_v1_101
reuse=reuse, scope=scope)
File "~/Documents/Custom-Object-Detection-master/slim/nets/resnet_v1.py", line 216, in resnet_v1
net = resnet_utils.stack_blocks_dense(net, blocks, output_stride)
File "~/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "~/Documents/Custom-Object-Detection-master/slim/nets/resnet_utils.py", line 185, in stack_blocks_dense
net = block.unit_fn(net, rate=rate, **dict(unit, stride=1))
File "~/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "~/Documents/Custom-Object-Detection-master/slim/nets/resnet_v1.py", line 118, in bottleneck
activation_fn=None, scope='conv3')
File "~/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "~/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1042, in convolution
outputs = normalizer_fn(outputs, **normalizer_params)
File "~/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "~/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 643, in batch_norm
outputs = layer.apply(inputs, training=is_training)
File "~/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 671, in apply
return self.call(inputs, *args, **kwargs)
File "~/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 575, in call
outputs = self.call(inputs, *args, **kwargs)
File "~/lib/python2.7/site-packages/tensorflow/python/layers/normalization.py", line 395, in call
return self._fused_batch_norm(inputs, training=training)
File "~/lib/python2.7/site-packages/tensorflow/python/layers/normalization.py", line 302, in _fused_batch_norm
training, _fused_batch_norm_training, _fused_batch_norm_inference)
File "~/lib/python2.7/site-packages/tensorflow/python/layers/utils.py", line 208, in smart_cond
return fn2()
File "~/lib/python2.7/site-packages/tensorflow/python/layers/normalization.py", line 299, in _fused_batch_norm_inference
data_format=self._data_format)
File "~/lib/python2.7/site-packages/tensorflow/python/ops/nn_impl.py", line 831, in fused_batch_norm
name=name)
File "~/lib/python2.7/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 2034, in _fused_batch_norm
is_training=is_training, name=name)
File "~/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "~/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
op_def=op_def)
File "~/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1470, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[1,1024,51,38]
[[Node: FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/FusedBatchNorm = FusedBatchNorm[T=DT_FLOAT, data_format="NHWC", epsilon=1.001e-05, is_training=false, _device="/job:localhost/replica:0/task:0/device:GPU:0"](FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/Conv2D, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/gamma/read/_683, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/beta/read/_685, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/moving_mean/read/_687, FirstStageFeatureExtractor/resnet_v1_101/block3/unit_12/bottleneck_v1/conv3/BatchNorm/moving_variance/read/_689)]]
[[Node: Reshape_24/_1255 = _HostRecvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4073_Reshape_24", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

What might cause error, Why this error?

Whenever I saw the "ResourceExhaustedError" I restarted my computer and it helped. Usually the memory is full and the computer cannot function anymore. Restarting is helping with the memory. Try that.

I Have the same error:
INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framewo
rk.errors_impl.InvalidArgumentError'>, Incompatible shapes: [1,63,4] vs. [1,64,4
]
[[Node: gradients/Loss/BoxClassifierLoss/Loss/sub_grad/BroadcastGradien
tArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task
:0/device:CPU:0"](gradients/Loss/BoxClassifierLoss/Loss/sub_grad/Shape, gradient
s/Loss/BoxClassifierLoss/Loss/sub_1_grad/Shape)]]
Caused by op 'gradients/Loss/BoxClassifierLoss/Loss/sub_grad/BroadcastGradientAr
gs', defined at:
File "object_detection/train.py", line 198, in
tf.app.run()
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\platform\app.py", line 126, in run
_sys.exit(main(argv))
File "object_detection/train.py", line 194, in main
worker_job_name, is_chief, FLAGS.train_dir)
File "C:\Users\sukhovva\Conv1\object_detection\trainer.py", line 226, in train
clones, training_optimizer, regularization_losses=None)

File "C:\Users\sukhovva\Conv1\slim\deployment\model_deploy.py", line 297, in o
ptimize_clones
optimizer, clone, num_clones, regularization_losses, **kwargs)
File "C:\Users\sukhovva\Conv1\slim\deployment\model_deploy.py", line 261, in _
optimize_clone
clone_grad = optimizer.compute_gradients(sum_loss, **kwargs)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\training\optimizer.py", line 526, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gradients_impl.py", line 494, in gradients
gate_gradients, aggregation_method, stop_gradients)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gradients_impl.py", line 636, in _GradientsHelper
lambda: grad_fn(op, *out_grads))
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gradients_impl.py", line 385, in _MaybeCompile
return grad_fn()  # Exit early
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gradients_impl.py", line 636, in
lambda: grad_fn(op, *out_grads))
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\math_grad.py", line 857, in _SubGrad
rx, ry = gen_array_ops.broadcast_gradient_args(sx, sy)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gen_array_ops.py", line 812, in broadcast_gradient_args
"BroadcastGradientArgs", s0=s0, s1=s1, name=name)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\ops.py", line 3392, in create_op
op_def=op_def)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\ops.py", line 1718, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-
access
...which was originally created as op 'Loss/BoxClassifierLoss/Loss/sub', defined
at:
File "object_detection/train.py", line 198, in
tf.app.run()
[elided 1 identical lines from previous traceback]
File "object_detection/train.py", line 194, in main
worker_job_name, is_chief, FLAGS.train_dir)
File "C:\Users\sukhovva\Conv1\object_detection\trainer.py", line 192, in train
clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])

File "C:\Users\sukhovva\Conv1\slim\deployment\model_deploy.py", line 193, in c
reate_clones
outputs = model_fn(*args, **kwargs)
File "C:\Users\sukhovva\Conv1\object_detection\trainer.py", line 133, in crea
te_losses
losses_dict = detection_model.loss(prediction_dict)
File "C:\Users\sukhovva\Conv1\object_detection\meta_architectures\faster_rcnn
meta_arch.py", line 1265, in loss
groundtruth_classes_with_background_list))
File "C:\Users\sukhovva\Conv1\object_detection\meta_architectures\faster_rcnn_
meta_arch.py", line 1421, in loss_box_classifier
batch_reg_targets, weights=batch_reg_weights) / normalizer
File "C:\Users\sukhovva\Conv1\object_detection\core\losses.py", line 71, in __
call_
return self._compute_loss(prediction_tensor, target_tensor, **params)
File "C:\Users\sukhovva\Conv1\object_detection\core\losses.py", line 157, in _
compute_loss
diff = prediction_tensor - target_tensor
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\math_ops.py", line 979, in binary_op_wrapper
return func(x, y, name=name)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gen_math_ops.py", line 8582, in sub
"Sub", x=x, y=y, name=name)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\ops.py", line 3392, in create_op
op_def=op_def)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\ops.py", line 1718, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-
access
InvalidArgumentError (see above for traceback): Incompatible shapes: [1,63,4] vs
. [1,64,4]
[[Node: gradients/Loss/BoxClassifierLoss/Loss/sub_grad/BroadcastGradien
tArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task
:0/device:CPU:0"](gradients/Loss/BoxClassifierLoss/Loss/sub_grad/Shape, gradient
s/Loss/BoxClassifierLoss/Loss/sub_1_grad/Shape)]]
Traceback (most recent call last):
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\client\session.py", line 1322, in _do_call
return fn(*args)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\client\session.py", line 1307, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\client\session.py", line 1409, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shape
s: [1,63,4] vs. [1,64,4]
[[Node: gradients/Loss/BoxClassifierLoss/Loss/sub_grad/BroadcastGradien
tArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task
:0/device:CPU:0"](gradients/Loss/BoxClassifierLoss/Loss/sub_grad/Shape, gradient
s/Loss/BoxClassifierLoss/Loss/sub_1_grad/Shape)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "object_detection/train.py", line 198, in
tf.app.run()
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\platform\app.py", line 126, in run
_sys.exit(main(argv))
File "object_detection/train.py", line 194, in main
worker_job_name, is_chief, FLAGS.train_dir)
File "C:\Users\sukhovva\Conv1\object_detection\trainer.py", line 296, in train
saver=saver)

File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\co
ntrib\slim\python\slim\learning.py", line 769, in train
sess, train_op, global_step, train_step_kwargs)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\co
ntrib\slim\python\slim\learning.py", line 487, in train_step
run_metadata=run_metadata)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\client\session.py", line 900, in run
run_metadata_ptr)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\client\session.py", line 1135, in _run
feed_dict_tensor, options, run_metadata)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\client\session.py", line 1316, in _do_run
run_metadata)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\client\session.py", line 1335, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shape
s: [1,63,4] vs. [1,64,4]
[[Node: gradients/Loss/BoxClassifierLoss/Loss/sub_grad/BroadcastGradien
tArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task
:0/device:CPU:0"](gradients/Loss/BoxClassifierLoss/Loss/sub_grad/Shape, gradient
s/Loss/BoxClassifierLoss/Loss/sub_1_grad/Shape)]]
Caused by op 'gradients/Loss/BoxClassifierLoss/Loss/sub_grad/BroadcastGradientAr
gs', defined at:
File "object_detection/train.py", line 198, in
tf.app.run()
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\platform\app.py", line 126, in run
_sys.exit(main(argv))
File "object_detection/train.py", line 194, in main
worker_job_name, is_chief, FLAGS.train_dir)
File "C:\Users\sukhovva\Conv1\object_detection\trainer.py", line 226, in train
clones, training_optimizer, regularization_losses=None)

File "C:\Users\sukhovva\Conv1\slim\deployment\model_deploy.py", line 297, in o
ptimize_clones
optimizer, clone, num_clones, regularization_losses, **kwargs)
File "C:\Users\sukhovva\Conv1\slim\deployment\model_deploy.py", line 261, in _
optimize_clone
clone_grad = optimizer.compute_gradients(sum_loss, **kwargs)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\training\optimizer.py", line 526, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gradients_impl.py", line 494, in gradients
gate_gradients, aggregation_method, stop_gradients)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gradients_impl.py", line 636, in _GradientsHelper
lambda: grad_fn(op, *out_grads))
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gradients_impl.py", line 385, in _MaybeCompile
return grad_fn()  # Exit early
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gradients_impl.py", line 636, in
lambda: grad_fn(op, *out_grads))
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\math_grad.py", line 857, in _SubGrad
rx, ry = gen_array_ops.broadcast_gradient_args(sx, sy)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gen_array_ops.py", line 812, in broadcast_gradient_args
"BroadcastGradientArgs", s0=s0, s1=s1, name=name)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\ops.py", line 3392, in create_op
op_def=op_def)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\ops.py", line 1718, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-
access
...which was originally created as op 'Loss/BoxClassifierLoss/Loss/sub', defined
at:
File "object_detection/train.py", line 198, in
tf.app.run()
[elided 1 identical lines from previous traceback]
File "object_detection/train.py", line 194, in main
worker_job_name, is_chief, FLAGS.train_dir)
File "C:\Users\sukhovva\Conv1\object_detection\trainer.py", line 192, in train
clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])

File "C:\Users\sukhovva\Conv1\slim\deployment\model_deploy.py", line 193, in c
reate_clones
outputs = model_fn(*args, **kwargs)
File "C:\Users\sukhovva\Conv1\object_detection\trainer.py", line 133, in crea
te_losses
losses_dict = detection_model.loss(prediction_dict)
File "C:\Users\sukhovva\Conv1\object_detection\meta_architectures\faster_rcnn
meta_arch.py", line 1265, in loss
groundtruth_classes_with_background_list))
File "C:\Users\sukhovva\Conv1\object_detection\meta_architectures\faster_rcnn_
meta_arch.py", line 1421, in loss_box_classifier
batch_reg_targets, weights=batch_reg_weights) / normalizer
File "C:\Users\sukhovva\Conv1\object_detection\core\losses.py", line 71, in __
call_
return self._compute_loss(prediction_tensor, target_tensor, **params)
File "C:\Users\sukhovva\Conv1\object_detection\core\losses.py", line 157, in _
compute_loss
diff = prediction_tensor - target_tensor
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\math_ops.py", line 979, in binary_op_wrapper
return func(x, y, name=name)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\ops\gen_math_ops.py", line 8582, in sub
"Sub", x=x, y=y, name=name)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\ops.py", line 3392, in create_op
op_def=op_def)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\py
thon\framework\ops.py", line 1718, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-
access
InvalidArgumentError (see above for traceback): Incompatible shapes: [1,63,4] vs
. [1,64,4]
[[Node: gradients/Loss/BoxClassifierLoss/Loss/sub_grad/BroadcastGradien
tArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task
:0/device:CPU:0"](gradients/Loss/BoxClassifierLoss/Loss/sub_grad/Shape, gradient
s/Loss/BoxClassifierLoss/Loss/sub_1_grad/Shape)]]


text:
formula to calculate accuracy

I saw the following formula to calculate accuracy in your scGPT paper:
Accuracy = tp / (tp + fp + tn + fn)
Should the numerator be: (tp + tn)? The correct formula should be:
Accuracy = (tp + tn) / (tp + fp + tn + fn)

Thank you so much! This indeed is a typo. The actual calculation should be correct since we used the sklearn interface. We will update this.

authors: subercui yueming-ding

repo: bowang-lab/scGPT


text:
FileNotFoundError

Dear author:
Thank you for sharing the project. During the replication process, I couldn't find two files (as shown in the figure below), which are two pre trained models that I couldn't find in the checkpoints download section. I hope you can add a download link.
I would greatly appreciate it if you could reply to me.

Hi Yayalel. I am currently traveling for work and will be back office at the beginning of July. Sorry for the inconvenience.

Okay, thank you for your reply. If you have time to upload it after you come back, I would greatly appreciate it.

May I ask if your business trip has ended now? If it's convenient for you, could you please upload the file? I would greatly appreciate it.

Hello. May we know how urgent you need these two checkpoints? We find that these two checkpoints, which are relatively old, were not preserved during recent server updates, so we would need to retrain them to get their weights. However, these two models are only two of the intermediate ablation studies, rather than the final best-performing models we would recommend for either downstream applications or comparative studies in your own work.

Thank you for your reply. I understand what you mean. Currently, I am in the experimental stage and need to know the training results of the model with only hierarchical relationship classification. I am deeply sorry for the inconvenience caused to you and sincerely hope that you can upload two pre trained models after retraining. Regarding the urgency level, I would like to complete the entire experiment within a month, but everything will be based on your schedule.

I think the model with a flat classification head + commonsense validation has already been uploaded, and you can find it on README: https://drive.google.com/file/d/1nwN8ToqfcRfabtf5PcJLAzd0J-Ky-6s3/view?usp=sharing. The saved model name might be different, so please try with this one first and rename it if needed.
Currently, we are busy with several upcoming submissions and there is no free GPU resource left in our group, but you can follow our README to train your baseline model with a flat classification head and without the commonsense validation step.
Apologize for the inconvenience and thank you so much for your understanding.

Okay, thank you very much for your patient reply.

authors: Yayalel bowen-upenn


text:
No AX.25 port data configured

Hi,
I'm not sure that bower is the problem, but each time I try to launch it, i get this message in the syslog.
for information :
npm -v
1.3.5
nodejs -v
v0.10.15
regards,

You're probably installing and running the node package which is an amateur radio related tool - I had this issue installing node-red.
Try this to fix it:

sudo ln -s /usr/bin/nodejs /usr/bin/node

authors: CoStiC yaleman

repo: bower/bower


text:
SDK fails with "Module checksum failed: Could not find '/app/'." in a Linux container on .NET 8

[x] I have checked that the SDK documentation doesn't solve my issue.
[x] I have checked that the API documentation doesn't solve my issue.
[x] I have searched the Box Developer Forums and my issue isn't already reported (or if it has been reported, I have attached a link to it, for reference).
[x] I have searched Issues in this repo and my issue isn't already reported.

Description of the Issue
SDK fails with "Module checksum failed: Could not find '/app/'." when deployed in a Linux container on .NET 8
I'm in the process of upgrading some of my organization's .NET Framework apps to .NET Core. I updated the SDK from the deprecated one to Box.SDK.Gen 1.4.0.
My organization's C# architect notes that Bouncy Castle BC-FNA 1.0.2 is certified for use with .NET applications running on CLR 4.
I thought this issue might be similar to the AWS PublishReadyToRun issue, but I wanted to confirm they're related. It seems like in my case, Bouncy Castle is attempting to use Win32 calls in the Linux container.
Steps to Reproduce
I'm working with other teams at my organization to get a minimal example that reproduces the issue. These are the steps that are visible to me so far from our build process:

Base image OS Ubuntu 22.04 jammy-20240911.1
dotnet build --no-incremental -r linux-x64 --no-self-contained --no-restore --configuration Release AppService.sln
dotnet publish --no-build -c Release -r linux-x64 AppService/src/AppService.csproj -o ./Target --no-self-contained -p:PublishSingleFile=true
COPY Target/AppService app-bin
Call GetFolderItemsAsync from the Box client's FoldersManager

Expected Behavior
A list of folders is returned.
Error Message, Including Stack Trace
unable to read encrypted data: Module checksum failed: Could not find file '/app/<Unknown>'.
 ---> Org.BouncyCastle.Pkcs.PkcsException: unable to read encrypted data: Module checksum failed: Could not find file '/app/<Unknown>'.
 ---> Org.BouncyCastle.Crypto.CryptoOperationError: Module checksum failed: Could not find file '/app/<Unknown>'.
 ---> System.IO.FileNotFoundException: Could not find file '/app/<Unknown>'.
File name: '/app/<Unknown>'
   at Interop.ThrowExceptionForIoErrno(ErrorInfo errorInfo, String path, Boolean isDirError)
   at Microsoft.Win32.SafeHandles.SafeFileHandle.Open(String fullPath, FileMode mode, FileAccess access, FileShare share, FileOptions options, Int64 preallocationSize, UnixFileMode openPermissions, Int64& fileLength, UnixFileMode& filePermissions, Boolean failForSymlink, Boolean& wasSymlink, Func`4 createOpenException)
   at Microsoft.Win32.SafeHandles.SafeFileHandle.Open(String fullPath, FileMode mode, FileAccess access, FileShare share, FileOptions options, Int64 preallocationSize, Nullable`1 unixCreateMode, Func`4 createOpenException)
   at System.IO.File.OpenHandle(String path, FileMode mode, FileAccess access, FileShare share, FileOptions options, Int64 preallocationSize)
   at System.IO.File.ReadAllBytes(String path)
   at Org.BouncyCastle.Crypto.CryptoStatus.CalculateAssemblyHMac()
   at Org.BouncyCastle.Crypto.CryptoStatus.ChecksumValidate()
   --- End of inner exception stack trace ---
   at Org.BouncyCastle.Crypto.CryptoStatus.MoveToErrorStatus(CryptoOperationError error)
   at Org.BouncyCastle.Crypto.CryptoStatus.ChecksumValidate()
   at Org.BouncyCastle.Crypto.CryptoStatus.IsReady()
   at Org.BouncyCastle.Security.SecurityContext.CreateBuilder[A](IBuilderServiceType`1 type)
   at Org.BouncyCastle.Crypto.CryptoServicesRegistrar.CreateService[A](IBuilderServiceType`1 type)
   at Org.BouncyCastle.Operators.PkixPbeDecryptorProviderBuilder.MyDecryptorBuilderProvider.CreateDecryptorBuilder(AlgorithmIdentifier algorithmDetails)
   at Org.BouncyCastle.Pkcs.Pkcs8EncryptedPrivateKeyInfo.DecryptPrivateKeyInfo(IDecryptorBuilderProvider`1 inputDecryptorProvider)
   --- End of inner exception stack trace ---
   at Org.BouncyCastle.Pkcs.Pkcs8EncryptedPrivateKeyInfo.DecryptPrivateKeyInfo(IDecryptorBuilderProvider`1 inputDecryptorProvider)
   at Box.Sdk.Gen.Internal.JwtUtils.GetSigningCredentials(JwtKey key)
   at Box.Sdk.Gen.Internal.JwtUtils.CreateJwtAssertion(Dictionary`2 claims, JwtKey key, JwtSignOptions options)
   at Box.Sdk.Gen.BoxJwtAuth.RefreshTokenAsync(NetworkSession networkSession)
   at Box.Sdk.Gen.BoxJwtAuth.RetrieveTokenAsync(NetworkSession networkSession)
   at Box.Sdk.Gen.BoxJwtAuth.RetrieveAuthorizationHeaderAsync(NetworkSession networkSession)
   at Box.Sdk.Gen.Internal.HttpClientAdapter.BuildHttpRequest(FetchOptions options, Stream stream)
   at Box.Sdk.Gen.Internal.HttpClientAdapter.FetchAsync(FetchOptions options)
   at Box.Sdk.Gen.Managers.FoldersManager.GetFolderItemsAsync(String folderId, GetFolderItemsQueryParams queryParams, GetFolderItemsHeaders headers, Nullable`1 cancellationToken)

System.IO.FileNotFoundException: Could not find file '/app/<Unknown>'.
File name: '/app/<Unknown>'
   at Interop.ThrowExceptionForIoErrno(ErrorInfo errorInfo, String path, Boolean isDirError)
   at Microsoft.Win32.SafeHandles.SafeFileHandle.Open(String fullPath, FileMode mode, FileAccess access, FileShare share, FileOptions options, Int64 preallocationSize, UnixFileMode openPermissions, Int64& fileLength, UnixFileMode& filePermissions, Boolean failForSymlink, Boolean& wasSymlink, Func`4 createOpenException)
   at Microsoft.Win32.SafeHandles.SafeFileHandle.Open(String fullPath, FileMode mode, FileAccess access, FileShare share, FileOptions options, Int64 preallocationSize, Nullable`1 unixCreateMode, Func`4 createOpenException)
   at System.IO.File.OpenHandle(String path, FileMode mode, FileAccess access, FileShare share, FileOptions options, Int64 preallocationSize)
   at System.IO.File.ReadAllBytes(String path)
   at Org.BouncyCastle.Crypto.CryptoStatus.CalculateAssemblyHMac()
   at Org.BouncyCastle.Crypto.CryptoStatus.ChecksumValidate()

Versions Used
.NET SDK: 1.4.0
.NET: 8.0.10

Hi @tmoore8RJ
After investigation, I found out this issue can from the feature PublishSingleFile during the publish process, from the docs of MS, we can ExcludeFromSingleFile but it's not really working from my side.
I found a workaround for this issue from this comment, with some modification from my side, so you can try this:
<Target Name="ExplicitRemoveFromFilesToBundle" BeforeTargets="GenerateSingleFileBundle" DependsOnTargets="PrepareForBundle">
  <ItemGroup>
	  <FilesToRemoveFromBundle Include="@(FilesToBundle)" Condition="$([System.String]::new('%(Filename)').ToLower().Contains('bc-fips')) OR $([System.String]::new('%(Filename)').ToLower().Contains('bcpkix'))" />
  </ItemGroup>
  <Message Text="FilesToRemoveFromBundle '@(FilesToRemoveFromBundle)'" Importance="high" />
  <ItemGroup>
	  <FilesToBundle Remove="@(FilesToRemoveFromBundle)" />
  </ItemGroup>
</Target>
<Target Name="CopyFilesToRemoveFromBundle" AfterTargets="Publish">
  <Copy SourceFiles="@(FilesToRemoveFromBundle)" DestinationFolder="$(PublishDir)" />
  <Message Text="Copied files to remove from bundle to '$(PublishDir)'" Importance="high" />
</Target>

Please put this into the bottom of your .csproj file and let me know if it resolves the issue.
Bests,
Minh

Awesome, makes sense. I'll test these changes out and report back. Thanks!

I got it working. Thank you so much for your help!
In case others encounter something similar, in addition to the snippet provided that makes sure the BouncyCastle DLLs are in the Target folder after I restore, build, and publish, I also modified my Docker file to use the same pattern as the arguments to FilesToRemoveFromBundle to match the DLLs and copy them into the container in the same folder as the single file produced by the publish.
My csproj file with unrelated entries omitted
<Project Sdk="Microsoft.NET.Sdk.Web">
  <PropertyGroup>
    <AssemblyName>AppService</AssemblyName>
    <RootNamespace>AppService</RootNamespace>
    <TargetFramework>net8.0</TargetFramework>
  </PropertyGroup>
  <ItemGroup>
    <PackageReference Include="Box.Sdk.Gen" Version="1.4.0" />
  </ItemGroup>
  <Target Name="ExplicitRemoveFromFilesToBundle" BeforeTargets="GenerateSingleFileBundle" DependsOnTargets="PrepareForBundle">
    <ItemGroup>
      <FilesToRemoveFromBundle Include="@(FilesToBundle)" Condition="$([System.String]::new('%(Filename)').ToLower().Contains('bc-fips')) OR $([System.String]::new('%(Filename)').ToLower().Contains('bcpkix'))" />
    </ItemGroup>
    <Message Text="FilesToRemoveFromBundle '@(FilesToRemoveFromBundle)'" Importance="high" />
    <ItemGroup>
      <FilesToBundle Remove="@(FilesToRemoveFromBundle)" />
    </ItemGroup>
  </Target>
  <Target Name="CopyFilesToRemoveFromBundle" AfterTargets="Publish">
    <Copy SourceFiles="@(FilesToRemoveFromBundle)" DestinationFolder="$(PublishDir)" />
    <Message Text="Copied files to remove from bundle to '$(PublishDir)'" Importance="high" />
  </Target>
</Project>

The additional commands in my Docker file
COPY Target/*bc-fips* .
COPY Target/*bcpkix-fips* .

repo: box/box-dotnet-sdk-gen