keuv-grvl

@profile
def func1(i: float):
  import time
  time.sleep(i)


@profile
def func_mother():
  func1(1.2)
  a = 2 + 3

keuv-grvl

from fast_flask import Flask

app = Flask(__name__)

@app.get("/users/")  # equivalent to `@app.route("/users/")` from flask.Flask
def get_user_details(user_id: int = 123):
    return {"123": "bob"}


if __name__ == "__main__":

keuv-grvl

import gradio as gr
import numpy as np
import tensorflow as tf
import requests

# Load a classification model
model = tf.keras.applications.MobileNetV3Large(
    input_shape=None,
    alpha=1.0,
    minimalistic=False,

keuv-grvl

-- Learn Lua in 15 Minutes

-- Two dashes start a one-line comment.

--[[
     Adding two ['s and ]'s makes it a
     multi-line comment.
--]]

----------------------------------------------------

keuv-grvl

import tensorflow as tf

ReduceMean = lambda axis=1: tf.keras.layers.Lambda(lambda x: tf.reduce_mean(x, axis=axis))

i = tf.keras.layers.Input(shape=(5,15))  # shape: [BATCHSIZE, 5, 15]
m = ReduceMean(i)  # shape: [BATCHSIZE, 15]

# TODO would be nice to have a wrapping function for any TF op with arbitrary

keuv-grvl

# ln -s /path/to/this/.autoenv.sh /path/to/workdir/

if test -f .autoenv_conda_env_name ; then
    TARGET_CONDA_ENV="$(cat .autoenv_conda_env_name)"
    if [[ $TARGET_CONDA_ENV != $CONDA_DEFAULT_ENV ]]; then
        conda activate "$TARGET_CONDA_ENV" 2&>1 2> /dev/null
    fi
else
    CUR_DIR=$(basename $(pwd))
    conda activate "$CUR_DIR-dev" 2> /dev/null  \

keuv-grvl

def perfboxplot(
    setup,
    kernels,
    labels,
    n=3,
    xlabel="Performances",
    equality_check=None,
    save_path=None,
    log_scale=True,
):

keuv-grvl

import numpy as np
import pandas as pd
import seaborn as sbn
from matplotlib import pyplot as plt
from scipy.stats import anderson
from sklearn import datasets
from sklearn.cluster import MiniBatchKMeans
from sklearn.preprocessing import scale, LabelEncoder

# TODO doc, memoization

keuv-grvl

[user]
	name = Keuv Grvl
	email = k***@***m
[core]
    quotepath = false
    editor = vim
    pager = less -F -x2
[init]
    defaultBranch = main
[push]

keuv-grvl

class FItSNE_wrapper:
    """
    Wrapper class for FItSNE `fast_tsne.fast_tsne` v1.1.0.
    It is currently designed to work in a Conda environment with Python 3.7 and FFTW 3.
    
    Install and compile as follow:
    
      $ conda create -n YOUR-ENV python=3.7 fftw=3
      $ source activate YOUR-ENV