gautierdag

import datetime
from typing import Optional, List
from pydantic import BaseModel


class PlaidBalance(BaseModel):
    current: float
    available: Optional[float]
    iso_currency_code: Optional[str]
    limit: Optional[float]

gautierdag

from random import randint
import pandas as pd

num_observations = 500
dates = pd.to_datetime([f"{randint(2018, 2020)}-{randint(1, 12)}-{randint(1, 27)}" 
                        for _ in range(num_observations)]).unique()
df = pd.DataFrame({"date": dates, 
                   "balance": [randint(0, 10000) 
                               for _ in range(len(dates))]
                  })

gautierdag

# set first (oldest balance as index entry)
new_indexes = [0]
# choose columns that are not the date column
cols = [d for d in df.columns if d != "date"]
# iterate over rows
for i, row in df.iterrows():
    if i == 0: #skip 0 index since it has no previous balance
        continue
    # if not all values of the previous row/date is not equal to current - then we have new observation
    if not (df.iloc[i-1][cols] ==row[cols]).all():

gautierdag

from functools import reduce

values = df.values
new_values = []
cols = [df.columns.get_loc(d) for d in df.columns if d != "date"]
new_values.append(values[0])

def compress_helper(x, y):
    if (x[cols] == y[cols]).all():
        return x

gautierdag

cols = [d for d in df.columns if d != "date"]
compressed_df = df.loc[~(df[cols].shift() == df[cols]).all(axis=1)].reset_index(drop=True)

gautierdag

import numpy as np

#cast as object to be able to set np.nan and handle different types
arr = df[cols].values.astype(object)

# build shifted array
shifted = np.roll(arr, 1)
shifted[0] = np.nan

# choose indexes based on shift comparaison

gautierdag

# https://apple.stackexchange.com/questions/175215/how-do-i-assign-a-keyboard-shortcut-to-an-applescript-i-wrote

on run {input, parameters}
	
	tell application "System Events"
		key code 123 using command down
		keystroke "print("
		key code 124 using command down
		keystroke ")"
	end tell

gautierdag

import torch
import pytorch_lightning as pl

class MyTransformer(pl.LightningModule):
    def __init__(
        self,
        learning_rate=0.001,
        warmup=4000,
    ):
      self.learning_rate = learning_rate

gautierdag

import torch
import torch.nn as nn
import pytorch_lightning as pl

class NCE(pl.LightningModule):
    """
    This implementation is taken from https://github.com/Spijkervet/SimCLR/blob/master/simclr/modules/nt_xent.py
    The mask_correlated_samples funtion has been modified to be much faster to compute
    and therefore be able to be called at train time without a predifined batch size.
    """

gautierdag

import torch
import torch.nn as nn
import pytorch_lightning as pl

class BoringModel(pl.LightningModule):
    def __init__(
        self
    ):
        super(BoringModel, self).__init__()
        self.automatic_optimization = False