I am trying to use async operators for bigquery; however,
from airflow.providers.google.cloud.operators.bigquery import BigQueryCheckAsyncOperator
gives the error:
ImportError: cannot import name 'BigQueryCheckOperatorAsync' from 'airflow.providers.google.cloud.operators.bigquery'
The documentation in https://airflow.apache.org/docs/apache-airflow-providers-google/stable/operators/cloud/bigquery.html mentions that BigQueryCheckAsyncOperator exists.
I am using airflow 2.4.
How to import it?
The operator you are trying to import was never released.
It was added in PR and removed in PR both were part of Google provider 8.4.0 release thus overall the BigQueryCheckAsyncOperator class was never part of the release.
You can use defer mode in the existed class BigQueryCheckOperator by setting the deferrable parameter to True.
federated_algorithm = tff.templates.IterativeProcess(
initialize_fn = initialize_fn, next_fn = next_fn)
TF=2.1.0
Tff=0.13.0
they are showing attribute error.
The tff.templates package was not available until TFF versions 0.14.0 and later. Prior to this, the IterativeProcess symbol existed in the tff.utils package.
See the 0.14.0 release notes for details about this change.
I am currently looking for a way to directly pass in a pandas dataframe or csv file to zipline for simple backtesting WITHOUT having to ingest a data bundle. The reason is that I am planning to generate new data outside of the existing bundle during a backtest and it seems very inefficient to ingest a new bundle for every handle_data call.
I have been looking for this everywhere, including the source codes of zipline. I found that an older version of zipline has a 'data' param in the run_algo function call where you could pass in a df directly, but I can't find that old version at the moment. Is anyone attempting the same thing? Is there any way other than ingesting data bundles in the command line everytime?
I'm using zipline 1.3.0 and it actually does have a data param. This comment is from run_algo.py file of zipline:
data : pd.DataFrame, pd.Panel, or DataPortal, optional
The ohlcv data to run the backtest with.
This argument is mutually exclusive with:
``bundle``
``bundle_timestamp``
Hope it helped
I use Julia 0.4.3 and I have updated all packages using Pkg.update().
From the "ControlSystems" documentation, it is explicitly stated that plotting requires extra care in that the user is free to choose plotting back-end (I guess back-end means which plotting-package is used by "ControlSystems"). I have installed and like to use pyplot - hence I try the following code:
using ControlSystems
Plots.pyplot()
s = tf("s");
G = 1/(s+1);
stepplot(G);
Gives the error-message
ERROR: UndefVarError: subplot not defined
in stepplot at C:\folder\.julia\v0.4\ControlSystems\src\plotting.jl81
in stepplot at C:\folder\.julia\v0.4\ControlSystems\src\plotting.jl103
I have also tried the same code without the "Plots.pyplot()"-command.
2018 Update: Be sure to check all the responses, as the answer to this question has changed multiple times over the years. At the time of this update, the Revise.jl answer is probably the best solution.
I have a file "/SomeAbsolutePath/ctbTestModule.jl", the contents of which are:
module ctbTestModule
export f1
f1(x) = x + 1
end
I fire up Julia in a terminal, which runs "~/.juliarc.jl". The startup code includes the line:
push!(LOAD_PATH, "/SomeAbsolutePath/")
Hence I can immediately type into the Julia console:
using ctbTestModule
to load my module. As expected f1(1) returns 2. Now I suddenly decide I want to edit f1. I open up "/SomeAbsolutePath/ctbTestModule.jl" in an editor, and change the contents to:
module ctbTestModule
export f1
f1(x) = x + 2
end
I now try to reload the module in my active Julia session. I try
using ctbTestModule
but f1(1) still returns 2. Next I try:
reload("ctbTestModule")
as suggested here, but f1(1) still returns 2. Finally, I try:
include("/SomeAbsolutePath/ctbTestModule.jl")
as suggested here, which is not ideal since I have to type out the full absolute path since the current directory might not be "/SomeAbsolutePath". I get the warning message Warning: replacing module ctbTestModule which sounds promising, but f1(1) still returns 2.
If I close the current Julia session, start a new one, and type in using ctbTestModule, I now get the desired behaviour, i.e. f1(1) returns 3. But obviously I want to do this without re-starting Julia.
So, what am I doing wrong?
Other details: Julia v0.2 on Ubuntu 14.04.
The basis of this problem is the confluence of reloading a module, but not being able to redefine a thing in the module Main (see the documentation here) -- that is at least until the new function workspace() was made available on July 13 2014. Recent versions of the 0.3 pre-release should have it.
Before workspace()
Consider the following simplistic module
module TstMod
export f
function f()
return 1
end
end
Then use it....
julia> using TstMod
julia> f()
1
If the function f() is changed to return 2 and the module is reloaded, f is in fact updated. But not redefined in module Main.
julia> reload("TstMod")
Warning: replacing module TstMod
julia> TstMod.f()
2
julia> f()
1
The following warnings make the problem clear
julia> using TstMod
Warning: using TstMod.f in module Main conflicts with an existing identifier.
julia> using TstMod.f
Warning: ignoring conflicting import of TstMod.f into Main
Using workspace()
However, the new function workspace() clears Main preparing it for reloading TstMod
julia> workspace()
julia> reload("TstMod")
julia> using TstMod
julia> f()
2
Also, the previous Main is stored as LastMain
julia> whos()
Base Module
Core Module
LastMain Module
Main Module
TstMod Module
ans Nothing
julia> LastMain.f()
1
Use the package Revise, e.g.
Pkg.add("Revise") # do this only once
include("src/my_module.jl")
using Revise
import my_module
You may need to start this in a new REPL session. Notice the use of import instead of using, because using does not redefine the function in the Main module (as explained by #Maciek Leks and #waTeim).
Other solutions: Two advantages of Revise.jl compared to workspace() are that (1) it is much faster, and (2) it is future-proof, as workspace() was deprecated in 0.7, as discussed in this GitHub issue:
julia> VERSION
v"0.7.0-DEV.3089"
julia> workspace()
ERROR: UndefVarError: workspace not defined
and a GitHub contributor recommended Revise.jl:
Should we add some mesage like "workspace is deprecated, check out Revise.jl instead"?
Even in Julia 0.6.3, the three previous solutions of workspace(), import, and reload fail when a module called other modules, such as DataFrames. With all three methods, I got the same error when I called that module the second time in the same REPL:
ERROR: LoadError: MethodError: all(::DataFrames.##58#59, ::Array{Any,1}) is ambiguous. Candidates: ...
I also got many warning messages such as:
WARNING: Method definition macroexpand(Module, ANY) in module Compat at /Users/mmorin/.julia/v0.6/Compat/src/Compat.jl:87 overwritten in module Compat at /Users/mmorin/.julia/v0.6/Compat/src/Compat.jl:87.
Restarting the Julia session worked, but it was cumbersome. I found this issue in the Reexport package, with a similar error message:
MethodError: all(::Reexport.##2#6, ::Array{Any,1}) is ambiguous.
and followed the suggestion of one contributor:
Does this happen without using workspace()? That function is notorious for interacting poorly with packages, which is partly why it was deprecated in 0.7.
In my humble opinion, the better way is to use import from the very beginning instead of using for the reported issue.
Consider the module:
module ModuleX1
export produce_text
produce_text() = begin
println("v1.0")
end
println("v1.0 loaded")
end
Then in REPL:
julia> import ModuleX1
v1.0 loaded
julia> ModuleX1.produce_text()
v1.0
Update the code of the module and save it:
module ModuleX1
export produce_text
produce_text() = begin
println("v2.0")
end
println("v2.0 loaded")
end
Next, in the REPL:
julia> reload("ModuleX1")
Warning: replacing module ModuleX1
v2.0 loaded
julia> ModuleX1.produce_text()
v2.0
Advantages of using import over using:
avoiding ambiguity in function calls (What to call: ModuleX1.produce_text() or produce_text() after reloading?)
do not have to call workspace() in order to get rid of ambiguity
Disadvantages of using import over using:
a fully qualified name in every call for every exported name is needed
Edited: Discarded "full access to the module, even to the not-exported names" from "Disadvantages..." according to the conversation below.
workspace() has been deprecated.
You can reload("MyModule") in an active REPL session, and it works as expected: changes made to the source file that contains MyModule are reflected in the active REPL session.
This applies to modules that have been brought into scope by either import MyModule or using MyModule
I wanted to create a new module from scratch, and tried the different answers with 1.0 and didn’t get a satisfactory result, but I found the following worked for me:
From the Julia REPL in the directory I want to use for my project I run
pkg> generate MyModule
This creates a subdirectory like the following structure:
MyModule
├── Project.toml
└── src
└── MyModule.jl
I put my module code in MyModule.jl. I change to the directory MyModule (or open it in my IDE) and add a file Scratch.jl with the following code:
using Pkg
Pkg.activate(".")
using Revise
import MyModule # or using MyModule
Then I can add my code to test below and everything updates without reloading the REPL.
I battled to get Revise.jl to work for me, probably because I also use code generation with OOPMacro #class . I had to also call revise(...) See https://timholy.github.io/Revise.jl/stable/limitations/#Limitations-1.
I have been struggling with this problem for up to 5 years and finally got something that works for me that don't involve manually running my main module in the IDE repl after EVERY SMALL CHANGE :'(
Here is some of my code I use to force a revise and exit early when running tests when there were revise errors:
# Common code I include into my test files now:
using Pkg
using Revise
# force include module
Pkg.activate("MyModule")
include("../src/MyModule.jl")
Pkg.activate("MyModule/test")
using Revise
# if there were any revise errors which means some compilation error or
# some change that requires a manual rerun or restart.
# then force you to fix it, rather that running lying tests..
for (k, e) in Revise.queue_errors
if !isnothing(e)
warn(logger, "Something went wrong while revising, you probably have a compile error in this file:")
throw(e)
end
end
module TestSomethingModule
using Revise
using MyModule
revise(MyModule)
...
# then you can call MyModule.doSomithing in a test and it actually updates
end