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from sklearn.model_selection import train_test_split
import pandas as pd

def shuffle_df(df, random_seed=42):
    return df.sample(frac=1, random_state=random_seed, replace=False)


def split_by_unique_col(df, col='patient_id', stratify_cols=[], random_seed=42):
    """
    Make a dataframe of unique ids, with our stratification data

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"""
how to read only metadata from a dicom url to save bandwith

- note the server must support HTTP Range, e.g. s3 buckets or azure blobs.
- note that if you don't mind reading the whole thing, it's easier to just read the whole thing, then pass it into pydicom as io.BytesIO


url: https://gist.github.com/wassname/70106b2d66a7c6e83e4b0300c9d1d4d3
"""

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"""
If you want to split and sample at the same time use something else.

but in timeseries sometimes you want to split by time, then resample to get balanced weights

@url:https://gist.github.com/wassname/f34321d4797a356a82802bdfb935e6cd/edit
@author:wassname
@lic: meh
"""

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

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# %%

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


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

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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):

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Exercise

Description:

1. first
2. second

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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

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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

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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),