ReadTheDocs | Documentation for diyHue | Animation library

In my opinion, the CORE_VM_DOCKER_HOSTCONFIG_NETWORKMODE setting seems to be wrong.
you can check docker-compose.yaml or core.yaml

• I will explain fabric-samples/test-network as targeting according to your current situation.
• You can check in CORE_VM_DOCKER_HOSTCONFIG_NETWORKMODE in docker-compose.yaml
• Perhaps in your case(fabric-samples/test-network), the value of ${COMPOSE_PROJECT_NAME} was not set properly, so it was set to _test.
• Make sure the value is set correctly and change it to your network name.

• You can visit This website for Installing pip in centos 7 Pip Install In Centos 7

for maybe some errors in installing pip.

• reinstall python.

• check that the module name is correctly typed

• install stockstats in pip like "pip install stockstats" (getten from pypi.com)

Thank You

Security Coding.

Typo.

In the base.html, you've named the block "content". In index.html, you've called it "contend".

It would be nice if Django threw an error when this sort of thing happens - but I think the main reason it doesn't is for adaptability. At a glance it seem you're doing everything else correctly though.

I found the answer too this.

In order to do this I ended up using the wtforms_json from json methodas below:

Extensions are files that are loaded, think of them as modules that the discord.py library imports when you call load_extension. After the import happens, a setup function is called, and is passed the Bot instance that it was loaded into....essentially module.setup(bot). That is it, there is no more to extensions...typically this setup function calls add_cog which will be described next, however there is no requirement that they do so.

A Cog is a class that inherits from commands.Cog and is added to the bot's list of cogs through add_cog, these classes typically house commands and act as a "category" for these commands. It can also house listeners for events such as on_message or on_member_join, etc.

TL;DR - Extensions are imported modules, cogs are classes

It isn't that the Fake's configuration is being changed. In the first test, Resolve is returning different Fakes each time it's called. (Check them for reference equality; I did.)

Provide creates a new scope and pushes it on a stack. The topmost scope is used by Resolve when it finds an object to return. I think this is why you're getting different Fakes in ACallTo_before_Provide.

Is this a bug? Or is this the expected behavior? If this is the expected behavior, can someone explain why it works like this?

It's not clear to me. I'm not an Autofac user, and don't understand why an additional scope is introduced by Provide. The stacked scope behaviour was introduced in PR 18. Perhaps the author can explain why.

In the meantime, if possible, I'd Provide all you need to before Resolveing, if you can manage it.

The module fractions is part of the Python standard library. There used to be a function gcd, which, as the linked documentation says, is:

Deprecated since version 3.5: Use math.gcd() instead.

Since the function gcd was removed from the module fractions in Python 3.9, it seems that the question uses Python 3.9, not Python 3.7.6 as the question notes, because that Python version still had fractions.gcd.

The error is raised by networkx. Upgrading to the latest version of networkx is expected to avoid this issue:

The docs are pretty clear (emphasis added):

The Python types int, long, and float are not available for static typing and instead interpreted as C int, long, and float respectively, as statically typing variables with these Python types has zero advantages.

There are a number of concrete C APIs associated with stuff like list, tuple, etc., where static typing provides a meaningful benefit, allowing Cython to save time by compiling code that works with them on a more intrusive level (directly accessing the underlying array for list and tuple for example). For Python 3's int (and Python 2's long), that advantage largely doesn't exist; at best Cython could skip a tiny amount of type-checking work, in exchange for needing to reproduce all the rest of the code involved in the operations without those prechecks (Python 2's int is a C long under the hood, so you may as well declare it as such and benefit from using the raw C value directly). And it's quite a lot of code, given the complexities of working with arbitrary precision integers, and entirely unnecessary. If your values are small enough, you can use them as C types, but for anything larger, the cost of specializing for them would be greater than the gains.

In short: Declare it as nothing, or object if you want to be explicit. There's no benefit to be had from what you're trying to do, and you can't do it in any event.

Great question. As you say, there are no hard and fast rules here and opinions do vary, but let me share my perspective as a QB data scientist and kedro maintainer who has used the layering convention you referred to several times.

For a start, let me emphasise that there's absolutely no reason to stick to the data engineering convention suggested by kedro if it's not suitable for your needs. 99% of users don't change the folder structure in data. This is not because the kedro default is the right structure for them but because they just don't think of changing it. You should absolutely add/remove/rename layers to suit yourself. The most important thing is to choose a set of layers (or even a non-layered structure) that works for your project rather than trying to shoehorn your datasets to fit the kedro default suggestion.

Now, assuming you are following kedro's suggested structure - onto your questions:

When is a dataset a feature rather than a primary dataset? The distinction seems vague...

In the case of simple features, a feature dataset can be very similar to a primary one. The distinction is maybe clearest if you think about more complex features, e.g. formed by aggregating over time windows. A primary dataset would have a column that gives a cleaned version of the original data, but without doing any complex calculations on it, just simple transformations. Say the raw data is the colour of all cars driving past your house over a week. By the time the data is in primary, it will be clean (e.g. correcting "rde" to "red", maybe mapping "crimson" and "red" to the same colour). Between primary and the feature layer, we will have done some less trivial calculations on it, e.g. to find one-hot encoded most common car colour each day.

Is it OK for a primary dataset to consume data from another primary dataset?

In my opinion, yes. This might be necessary if you want to join multiple primary tables together. In general if you are building complex pipelines it will become very difficult if you don't allow this. e.g. in the feature layer I might want to form a dataset containing composite_feature = feature_1 * feature_2 from the two inputs feature_1 and feature_2. There's no way of doing this without having multiple sub-layers within the feature layer.

However, something that is generally worth avoiding is a node that consumes data from many different layers. e.g. a node that takes in one dataset from the feature layer and one from the intermediate layer. This seems a bit strange (why has the latter dataset not passed through the feature layer?).

Is it good practice to build a feature dataset from the INT layer? or should it always pass through Primary?

Building features from the intermediate layer isn't unheard of, but it seems a bit weird. The primary layer is typically an important one which forms the basis for all feature engineering. If your data is in a shape that you can build features then that means it's probably primary layer already. In this case, maybe you don't need an intermediate layer.

The above points might be summarised by the following rules (which should no doubt be broken when required):

1. The input datasets for a node in layer L should all be in the same layer, which can be either L or L-1
2. The output datasets for a node in layer L should all be in the same layer L, which can be either L or L+1

If anyone can offer any further advice or blogs\docs talking about Kedro Data Modelling that would be awesome!

I'm also interested in seeing what others think here! One possibly useful thing to note is that kedro was inspired by cookiecutter data science, and the kedro layer structure is an extended version of what's suggested there. Maybe other projects have taken this directory structure and adapted it in different ways.