wassname

from rich.progress import (
    ProgressColumn,
    BarColumn,
    DownloadColumn,
    TextColumn,
    TransferSpeedColumn,
    TimeRemainingColumn,
    Progress,
    TaskID,
    TimeElapsedColumn,

wassname

# %%

import numpy as np
from functools import partial
from pykdtree.kdtree import KDTree


class LeftKDTree(KDTree):
    """
    KNN that only looks left.

wassname

import math
import torch
from torch.distributions import Normal
from torch.distributions.utils import broadcast_all, _standard_normal
from torch.distributions.kl import register_kl

class StableNormal(Normal):
    """Modified version that uses log_scale for stability of grad."""

    def __init__(self, loc, log_scale):

wassname

Exercise

Description:

1. first
2. second

wassname

For your IAM user you get a csv of credentials like this

User name,Password,Access key ID,Secret access key,Console login link
USERNAME,PASSWORD,ACCESS_KEY,SECRET_KEY,https://0123456.signin.aws.amazon.com/console

If your region is sydney (ap-southeast-2) in keepass you enter:

Title: USERNAME/COMPANY

wassname

name: azureml_py36_pytorch
channels:
  - pytorch
  - conda-forge
  - anaconda
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _py-xgboost-mutex=2.0=cpu_0
  - _pytorch_select=0.2=gpu_0

wassname

def row2date(row, tz="Australia/Perth"):
    """Parse time columns."""
    return pd.Timestamp(
        year=int(row.Year),
        month=int(row.Month),
        day=int(row.Day),
        hour=int(row.Hour),
        minute=int(row.Minute),
        second=int(row.Seconds),

wassname

def smooth_noise(shp, dim=1, smoothing=30):
    """
    Generate smoothed random noise that looks like a random walk.
    
    - shp: shape of noise
    - dim: dimension to smooth along
    - smooth_steps: smoothing steps
    

wassname

from torch import nn
import torch
import torch.nn.functional as F
import numpy as np



def radneg2radpos(f):
    """convert radians in range [-pi,pi] to [0,2*pi]"""
    return np.where(f < 0, f + np.pi * 2, f)

wassname

import pandas as pd
import numpy as np
import hashlib
import json

def default(o):
    """Sets are unordered so are no good for hasing"""
    if isinstance(o, set):
        try:
            o = sorted(o)