Voyz

def timeseries_vertices(ser:pd.Series, distance=15, coeff=3):

    name = ser.name

    # remove intermediate vertices on lines
    def no_lines(df):
        df.loc[:, 'gt_before'] = df.loc[:, name] > df.loc[:, name].shift(1)
        df.loc[:, 'gt_after'] = df.loc[:, name] > df.loc[:, name].shift(-1)
        df.loc[:, 'is_peak'] = df.loc[:, 'gt_before'] == df.loc[:, 'gt_after']
        df = df.loc[df.loc[:, 'is_peak'], :].copy()

Voyz

[
    {"side": "SELL", "tulips": 15, "tulip_price": 111.5, "transaction_date": "2022-02-01T00:00:00.250000"},
    {"side": "BUY", "tulips": 15, "tulip_price": 107.5, "transaction_date": "2022-02-01T00:30:00.250000"},
    {"side": "BUY", "tulips": 15, "tulip_price": 107, "transaction_date": "2022-02-01T01:30:00.250000"},
    {"side": "SELL", "tulips": 20, "tulip_price": 104.5, "transaction_date": "2022-02-01T02:00:00.250000"},
    {"side": "BUY", "tulips": 10, "tulip_price": 105, "transaction_date": "2022-02-01T02:00:00.250000"},
    {"side": "SELL", "tulips": 15, "tulip_price": 112.5, "transaction_date": "2022-02-01T03:00:00.250000"},
    {"side": "BUY", "tulips": 15, "tulip_price": 111.5, "transaction_date": "2022-02-01T03:30:00.250000"},
    {"side": "SELL", "tulips": 20, "tulip_price": 111.0, "transaction_date": "2022-02-01T04:00:00.250000"},
    {"side": "BUY", "tulips": 15, "tulip_price": 109.5, "transaction_date": "2022-02-01T04:30:00.250000"}

Voyz

FROM python:3.7.7-slim-buster

RUN python -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"

RUN mkdir -p /usr/share/man/man1

RUN apt-get update && \
    apt-get install -y default-jre && \
    apt-get -y install dbus-x11 xfonts-base xfonts-100dpi xfonts-75dpi xfonts-cyrillic xfonts-scalable && \

Voyz

TS_DATE_FORMAT_DB_ISO = '%Y-%m-%dT%H:%M:%S,%f'  # comma works for mongodb and is valid ISO

def datetimeindex_to_UTC(_dti):
    dti = _dti.copy(deep=True)
    if 'tz' not in dti or dti.tz is None:
        dti = dti.tz_localize('UTC')
    else:
        dti = dti.tz_convert('UTC')
    dti = dti.dt.strftime(TS_DATE_FORMAT_DB_ISO).astype(str).str.slice(0, -3)
    return dti

Voyz

import csv
import os
from pathlib import Path
from typing import List

from databay import Outlet, Update, Record, Link
from databay.inlets import RandomIntInlet
from databay.outlet import MetadataKey
from databay.outlets import CsvOutlet

Voyz

offset = [thisComp.width, thisComp.height]/2
p1 = thisComp.layer("P1").toComp([0,0]).slice(0,2) - offset
p2 = thisComp.layer("P2").toComp([0,0]).slice(0,2) - offset
t1 = thisComp.layer("T1").toComp([0,0]).slice(0,2) - offset - p1
t2 = thisComp.layer("T2").toComp([0,0]).slice(0,2) - offset - p2

ps = [p1, p2]
in_tangents = [[0,0], t2]
out_tangents = [t1, [0,0]]
createPath(ps, in_tangents, out_tangents, is_closed=false)

Voyz

*** Originally built for my mate Sam as part of our mentoring sessions. See: https://youtu.be/TXoDvTJuEb0 ***

####################
WEATHER FINDER BASIC
####################

Utilise an online weather API to fetch and display current weather information based on the city and country code provided.


###################

Voyz

-- Reboot --
Oct 28 08:46:32 gateway systemd[1]: Starting Google Compute Engine Startup Scripts...
Oct 28 08:46:33 gateway startup-script[483]: INFO Starting startup scripts.
Oct 28 08:46:33 gateway startup-script[483]: INFO Found startup-script in metadata.
Oct 28 08:46:38 gateway startup-script[483]: INFO startup-script: Unable to find image 'google/cloud-sdk:latest' locally
Oct 28 08:46:39 gateway startup-script[483]: INFO startup-script: latest: Pulling from google/cloud-sdk
Oct 28 08:46:39 gateway startup-script[483]: INFO startup-script: e4c3d3e4f7b0: Pulling fs layer
Oct 28 08:46:39 gateway startup-script[483]: INFO startup-script: 56c33728d4fb: Pulling fs layer
Oct 28 08:46:39 gateway startup-script[483]: INFO startup-script: bfce832607f8: Pulling fs layer
Oct 28 08:46:39 gateway startup-script[483]: INFO startup-script: f6a95ba73cf4: Pulling fs layer

Voyz

from keras import Input, Model
from keras.callbacks import Callback
from keras.layers import Dense, BatchNormalization, Lambda, Flatten, Dropout
import numpy as np
from sklearn.metrics import roc_auc_score
import tensorflow as tf
from keras import backend as K

# generate random test data
m = 10000

Voyz

; Usage:
; 1. Open Premiere
; 2. Select the bin you'd like to preload, even better - fullscreen it using tilde ` key. 
; 		(Make sure you're in Icon View, not List View)
; 3. Run this script
; 4. Manually input the number of items in the bin
; 5. You can grab a coffee or browse internet in the meantime (though be aware keyboard may not work 100% well)
; 6. Repeat for each bin you'd like to preload

#Persistent