dyerrington

dyerrington

import time
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed

class ApiClient:
    def __init__(self):
        pass

    @RateLimiter(rate_limit=5, rate_unit="seconds")
    def make_request(self, data):

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import regex
import unicodedata

# Precompile the regex pattern for removing unwanted characters (do this outside of any iteration since it's an expensive operation)
remove_pattern = regex.compile(r'[\p{P}\p{S}\p{M}\p{C}\p{Z}]+', regex.UNICODE)

def clean_unicode_text(text):
    # Normalize the Unicode text
    normalized_text = unicodedata.normalize('NFKD', text)
    

dyerrington

dyerrington

Installation

It's recommended that you install the requirements for these t5 models in a new environment since they are known to conflict with common Python package requirements in the scientific Python Stack.

Conda ENV Setup

Create

conda create -n nlp-t5

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import finplot as fplt
import yfinance

df = yfinance.download('AAPL')
d = df[['Open', 'Close', 'High', 'Low']].reset_index(drop=True)
fplt.candlestick_ochl(d)
fplt.show()

dyerrington

dyerrington

--- eventloops.py-orig  2020-12-12 10:22:08.633178000 -0800
+++ eventloops.py       2020-12-12 10:22:56.335735100 -0800
@@ -20,7 +20,7 @@

     Checks if we are on OS X 10.9 or greater.
     """
-    return sys.platform == 'darwin' and V(platform.mac_ver()[0]) >= V('10.9')
+    return sys.platform == 'darwin' and V(platform.mac_ver()[0]) >= V('10.9') and platform.mac_ver()[2] != 'arm64'

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## import any sklearn models and collect predictions / probabilities beforehand

import matplotlib.pyplot as plt
from cycler import cycler

## Line color config -- rather than create a structure with a finite color palette, use your own to cycle through a list.
default_cycler = (cycler(color=['r', 'g', 'b', 'y']) + 
                  cycler(linestyle=['-', '--', ':', '-.']))

plt.rc('axes', prop_cycle = default_cycler)

dyerrington

def fetch_states():
    """
    Return all <option> values for the <select> element with all states.
    """
    data = {}
    states = tree.xpath('//select[@name="state"]')
    try:
        for state in states[0].xpath('option'):
            data[state.attrib['value']] = state.text_content()
    except: