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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)

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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:

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import requests, re

def test_station_data_availability(station_id):
    for year in range(1960, 2020 + 1):
        r = requests.get(f"https://www.ncei.noaa.gov/data/local-climatological-data/access/{year}/")
        matches = re.search(r"href=\"([0-9]{6}" +str(station_id) + ".csv)", r.text)
        if matches:
            print(station_id, " data exists for ", year)
        else:
            print(station_id, " data not found for ", year)

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import pandas as pd
import numpy as np
from sklearn.datasets import load_wine

# Load example wine dataset from sklearn
data = load_wine()

# Create a basic DataFrame
df = pd.DataFrame(data['data'], columns = data['feature_names'])

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import pandas as pd
df = pd.read_clipboard()

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import numpy as np
import sys

# lines = input.split("\n")
lines = sys.stdin.readlines()

train_header = lines[0].split()
n_train_features, n_train_observations = int(train_header[0]), int(train_header[1])
training = np.array([row.split() for row in lines[1:n_train_observations]], dtype = float)
X_train, y_train = training[:, 0:n_train_features], training[:, n_train_features:]

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import pandas as pd
import math

lines = [line.split() for line in input.split("\n") if len(line)]

X, y = [[int(score) for score in scores] for index, (variable, _, *scores) in enumerate(lines)]

n = len(X)
sum_X, sum_y = sum(X), sum(y)
sum_Xy = sum([X[i] * y[i] for i in range(len(X))])