ritchie46

import polars as pl
import numpy as np

df = (pl.DataFrame({
    "a": np.random.randint(0, 100, int(1e8))
}).groupby("a").agg(
    pl.col("a").alias("list")
))

# prints

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size = 10 ** 2

df = pl.DataFrame({
    "groupid": [floor(i*0.1)for i in range(size)],
    "vectors": [[i,i+1,i-1] for i in range(size)],
    "numbers": [i for i in range(size)]
})

print(df)

ritchie46

ritchie46

# $ pip install requests
import requests
username = "my-username"
password = "my-little-secret"

r = requests.post(
    "https://crux-nuclei.com/login",
    json={"username": username, "password": password},
)
ACCESS_TOKEN = r.json()["access_token"]

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nodename,batch,timestamp,task,data,in_rows,question,out_rows,out_cols,solution,version,git,fun,run,time_sec,mem_gb,cache,chk,chk_time_sec,comment,on_disk
instance-2,,1610130993.9517767,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1,100,2,polars,0.4.0,unknown,.groupby,1,0.296,0.855,TRUE,30001016,0.0,,FALSE
instance-2,,1610130994.2619028,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1,100,2,polars,0.4.0,unknown,.groupby,2,0.287,0.847,TRUE,30001016,0.0,,FALSE
instance-2,,1610130994.8431137,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1:id2,10000,3,polars,0.4.0,unknown,.groupby,1,0.575,0.92,TRUE,30001016,0.0,,FALSE
instance-2,,1610130995.4009216,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1:id2,10000,3,polars,0.4.0,unknown,.groupby,2,0.552,0.924,TRUE,30001016,0.0,,FALSE
instance-2,,1610130995.930853,groupby,G1_1e7_1e2_0_0,10000000,sum v1 mean v3 by id3,100000,3,polars,0.4.0,unknown,.groupby,1,0.522,0.832,TRUE,[30001016.          5001212.36305564],0.001,,FALSE
instance-2,,1610130996.471266,groupby,G1_1e7_1e2_0_0,10000

ritchie46

git filter-branch --force --index-filter \
  "git rm --cached --ignore-unmatch PATH-TO-YOUR-FILE-WITH-SENSITIVE-DATA" \
  --prune-empty --tag-name-filter cat -- --all

ritchie46

"""
How to do minibatches for RNNs in pytorch

Assume we feed characters to the model and predict the language of the words.
"""


def prepare_batch(x, y):
    # determine the maximum word length per batch and zero pad the tensors
    n_max = max([a.shape[0] for a in x])

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<navigator :navs="[
        {name: 'page 1', id: 'link-page1'},
        {name: 'page 2', id: 'link-page2'},
        {name: 'page 3', id: 'link-page3'},
        ]" active="link-page1">
</navigator>
        
<div v-show="activePage === 'link-page1'">
<p>Some content</p>
</div>

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import navigator from './components/navigator.vue'

let vm = new Vue({
    el: "#app",
    components: {
        navigator
    },
    data: {
        activePage: 'link-page1',
    },

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<template>
    <nav>
        <ul class="pagination">
            <li v-for="page in navs" class="page-item"
                 v-bind:class="{active: activeId === page.id}" v-bind:id="page.id">
                <a class="page-link" v-on:click="toggle">
                    {{ page.name }}
                </a>
            </li>
        </ul>