程式扎記

HowTo
I’ve recently been working on a project that uses a Cassandra database running in a Docker container, with this image. It works great, but it does not have authentication enabled by default. This is because unless you specify otherwise, the authenticator is set to AllowAllAuthenticator.

To enable authentication, just add this line to your Dockerfile:

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# Require user & pass for accessing Cassandra instance within container  
RUN echo "authenticator: PasswordAuthenticator" >> /etc/cassandra/cassandra.yaml  

All this does is add a line to the cassandra.yaml file (the main config file for Cassandra) indicating that you want Cassandra to require a username and password for authentication.

Now, when you start up your Cassandra Docker container, you’ll be required to specify a username and password to access the database. The default login’s username is cassandra, and the default password is also cassandra. So, if you want to start a cqlsh session, you’ll have to execute (after starting a shell in the container, of course - see this, unless you’ve mapped the container to a host port):

# cqlsh -u cassandra -p cassandra

Extra Steps
If you have enabled password authentication, you may also want to consider following the procedure outlined here:

* Increase the replication factor of your system_auth keyspace to avoid getting locked out if your lone replica node goes down.
* Create a new superuser to replace the default cassandra one.
* Use the new superuser to demote the default one to NOSUPERUSER status and change its password from the default cassandra to something more secure.

How-To
In this basic example, we check to see if a file exists before interacting with it (otherwise something’s not going to work as expected). We leverage the power of the os standard library and first use the Stat() function, which although it’s usually used to get information about a file, we’re only looking at the errors.

We can’t just check for err == nil because any number of errors could be returned so we pass it to IsNotExists() to confirm that it’s an error because the file does not exist:

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package main  
  
import (  
    "fmt"  
    "os"  
)  
  
func main() {  
    if fileExists("example.txt") {  
        fmt.Println("Example file exists")  
    } else {  
        fmt.Println("Example file does not exist (or is a directory)")  
    }  
}  
  
// fileExists checks if a file exists and is not a directory before we  
// try using it to prevent further errors.  
func fileExists(filename string) bool {  
    info, err := os.Stat(filename)  
    if os.IsNotExist(err) {  
        return false  
    }  
    return !info.IsDir()  
}  

Question
In a python script, I try to checkout a tag after cloning a git repository. I use GitPython 0.3.2.

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#!/usr/bin/env python  
import git  
g = git.Git()  
g.clone("user@host:repos")  
g = git.Git(repos)  
g.execute(["git", "checkout", "tag_name"])  

With this code I have an error:

g.execute(["git", "checkout", "tag_name"])
File "/usr/lib/python2.6/site-packages/git/cmd.py", line 377, in execute
raise GitCommandError(command, status, stderr_value)
GitCommandError: 'git checkout tag_name' returned exit status 1: error: pathspec 'tag_name' did not match any file(s) known to git.

If I replace the tag name with a branch name, I have no problem. I didn't find informations in GitPython documentation. And if I try to checkout the same tag in a shell, I have non problem. Do you know how can I checkout a git tag in python ?

HowTo
Assuming you cloned the repository in 'path/to/repo', just try this:

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from git import Git  
  
g = Git('path/to/repo')  
g.checkout('tag_name')  

Quesiton
Using the examples from seaborn.pydata.org and the Python DataScience Handbook, I'm able to produce a combined distribution plot with the following snippet:

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import pandas as pd  
import numpy as np  
import seaborn as sns  
import matplotlib.pyplot as plt  
  
# some settings  
sns.set_style("darkgrid")  
  
# Create some data  
data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)  
data = pd.DataFrame(data, columns=['x', 'y'])  
  
# Combined distributionplot  
sns.distplot(data['x'])  
sns.distplot(data['y'])  

How can I combine this setup with vertical lines so that I can illustrate thresholds like this:

I know I can do it with matplotlib like here Dynamic histogram subplots with line to mark target, but I really like the simplicity of seaborn plots and would like to know if it's possible to do it more elegantly (and yes, I know that seaborn builds on top of matplotlib).

How-To
Just use matplotlib.axes.Axes.axvline:

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plt.axvline(2.8, 0,0.17)  

And the same for the other line

Here instead of 0.17 you can put the maxima of your distribution using some variable such as maxx = max(data) or something similar. 2.8 is the position on the x-axis. Oh remember that the y-value has to be in between 0 and 1 where 1 is the top of the plot. You can rescale your values accordingly. Another obvious option is simply

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plt.plot([2.8, 2.8], [0, max(data)])  

程式扎記

標籤

2020年8月28日星期五

[ 文章收集 ] Turn on Cassandra Authentication in Docker Container

[ 常見問題 ] Check If a File Exists Before Using It

[ Python 常見問題 ] How to checkout a tag with GitPython

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[Linux 常見問題] Quickly create a large file on a Linux system

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[ 常見問題 ] Private registry push fail: server gave HTTP response to HTTPS client

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[ 常見問題 ] Seaborn: How to add vertical lines to a distribution plot (sns.distplot)

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[Git 常見問題] error: The following untracked working tree files would be overwritten by merge

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2020年8月28日星期五

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