From ab119c16537647295bd507e2f84f88b51bd481f8 Mon Sep 17 00:00:00 2001
From: Fadi <fadi.gattoussi@stud.th-deg.de>
Date: Mon, 25 Dec 2023 20:28:08 +0100
Subject: [PATCH] Cleanup git
---
.gitignore | 3 +-
.~lock.diamonds.csv# | 1 -
DisplayWindow.py => Main.py | 12 +-
__pycache__/RegressionModel.cpython-310.pyc | Bin 445 -> 0 bytes
__pycache__/RegressionModel.cpython-311.pyc | Bin 1084 -> 0 bytes
__pycache__/data_processing.cpython-310.pyc | Bin 1732 -> 0 bytes
__pycache__/modeling.cpython-310.pyc | Bin 2138 -> 0 bytes
data_processing.py | 29 -
diamonds_numerical.csv | 53921 ------------------
fadi.joblib | Bin 24812 -> 0 bytes
old/airlines.json | 1 -
old/angie.pkl | Bin 5026540 -> 0 bytes
old/destination_cities.json | 1 -
requirements.txt | 53 +
training.ipynb | 222 -
training2.ipynb | 988 -
training3 copy.ipynb | 35 -
17 files changed, 62 insertions(+), 55204 deletions(-)
delete mode 100644 .~lock.diamonds.csv#
rename DisplayWindow.py => Main.py (98%)
delete mode 100644 __pycache__/RegressionModel.cpython-310.pyc
delete mode 100644 __pycache__/RegressionModel.cpython-311.pyc
delete mode 100644 __pycache__/data_processing.cpython-310.pyc
delete mode 100644 __pycache__/modeling.cpython-310.pyc
delete mode 100644 data_processing.py
delete mode 100644 diamonds_numerical.csv
delete mode 100644 fadi.joblib
delete mode 100644 old/airlines.json
delete mode 100644 old/angie.pkl
delete mode 100644 old/destination_cities.json
create mode 100644 requirements.txt
delete mode 100644 training.ipynb
delete mode 100644 training2.ipynb
delete mode 100644 training3 copy.ipynb
diff --git a/.gitignore b/.gitignore
index e44f07c..309c056 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
# .gitignore
-sas2/
\ No newline at end of file
+sas2/
+.idea/
\ No newline at end of file
diff --git a/.~lock.diamonds.csv# b/.~lock.diamonds.csv#
deleted file mode 100644
index 9c971c0..0000000
--- a/.~lock.diamonds.csv#
+++ /dev/null
@@ -1 +0,0 @@
-,fadi,hp,21.12.2023 18:45,file:///home/fadi/.config/libreoffice/4;
\ No newline at end of file
diff --git a/DisplayWindow.py b/Main.py
similarity index 98%
rename from DisplayWindow.py
rename to Main.py
index 39459aa..a3e8719 100644
--- a/DisplayWindow.py
+++ b/Main.py
@@ -78,9 +78,6 @@ class MainWindow(QMainWindow):
self.GUI_selections = {}
# create new graphs
self.scatt = MplCanvas(self, width=6, height=5, dpi=100)
- self.scatt.axes.set_title("Carats vs Price")
- self.scatt.axes.scatter(diamonds['carat'], diamonds['price'], color='red', s=2)
-
# Create labels and input fields
self.input_fields = [QComboBox(self), QComboBox(self), QComboBox(self), QLineEdit(self),
QLineEdit(self), QLineEdit(self), QLineEdit(self), QLineEdit(self),
@@ -113,6 +110,11 @@ class MainWindow(QMainWindow):
self.setWindowTitle('Assistance Systems')
self.show()
+ def update_graph(self):
+ self.scatt.axes.set_title("Carats vs Price")
+ self.scatt.axes.scatter(diamonds['carat'], diamonds['price'], color='red', s=2)
+ # self.scatt.axes.scatter(self.GUI_selections['carat'], self.price, color='blue', s=50)
+
def disable_input_field(self, field_name):
for key, value in self.menu_items.items():
if value == field_name:
@@ -148,7 +150,7 @@ class MainWindow(QMainWindow):
self.price = model.predict_price(X_test)
# price = model.predict(X_test)[0]
self.display_price.setText(str(self.price))
-
+ self.update_graph()
def validate_inputs(self, val, attribute):
if Advanced.Advanced_selections[attribute]:
@@ -291,4 +293,4 @@ app = QApplication(sys.argv)
window = MyTabs()
window.show()
-app.exec()
+app.exec()
\ No newline at end of file
diff --git a/__pycache__/RegressionModel.cpython-310.pyc b/__pycache__/RegressionModel.cpython-310.pyc
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diff --git a/data_processing.py b/data_processing.py
deleted file mode 100644
index 4d27207..0000000
--- a/data_processing.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import pandas as pd
-from sklearn.model_selection import train_test_split
-
-class DataPreprocessor:
- def __init__(self, filepath):
- self.filepath = filepath
- self.data = pd.read_csv(filepath)
-
- def clean_data(self):
- self.data = self.data.dropna(inplace=True)
-
- @staticmethod
- def convert_to_numeric(self):
- self['cut'] = self['cut'].map({'Ideal': 5, 'Premium': 4, 'Very Good': 3, 'Good': 2, 'Fair': 1})
- self['color'] = self['color'].map({'D': 7, 'E': 6, 'F': 5, 'G': 4, 'H': 3, 'I': 2, 'J': 1})
- self['clarity'] = self['clarity'].map({'IF': 8, 'VVS1': 7, 'VVS2': 6, 'VS1': 5, 'VS2': 4, 'SI1': 3, 'SI2': 2,
- 'I1': 1})
-
- def get_X(self):
- return self.data.drop('price', axis=1)
-
- def get_y(self):
- return self.data['price']
-
- def split_data(X, y):
- X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
- return X_train, X_test, y_train, y_test
-
-
diff --git a/diamonds_numerical.csv b/diamonds_numerical.csv
deleted file mode 100644
index 98aabfd..0000000
--- a/diamonds_numerical.csv
+++ /dev/null
@@ -1,53921 +0,0 @@
-,carat,cut,color,clarity,depth,table,price,x,y,z
-0,0.23,0,0,0,61.5,55.0,326,3.95,3.98,2.43
-1,0.21,1,0,1,59.8,61.0,326,3.89,3.84,2.31
-2,0.23,2,0,2,56.9,65.0,327,4.05,4.07,2.31
-3,0.29,1,1,3,62.4,58.0,334,4.2,4.23,2.63
-4,0.31,2,2,0,63.3,58.0,335,4.34,4.35,2.75
-5,0.24,3,2,4,62.8,57.0,336,3.94,3.96,2.48
-6,0.24,3,1,5,62.3,57.0,336,3.95,3.98,2.47
-7,0.26,3,3,1,61.9,55.0,337,4.07,4.11,2.53
-8,0.22,4,0,3,65.1,61.0,337,3.87,3.78,2.49
-9,0.23,3,3,2,59.4,61.0,338,4.0,4.05,2.39
-10,0.3,2,2,1,64.0,55.0,339,4.25,4.28,2.73
-11,0.23,0,2,2,62.8,56.0,340,3.93,3.9,2.46
-12,0.22,1,4,1,60.4,61.0,342,3.88,3.84,2.33
-13,0.31,0,2,0,62.2,54.0,344,4.35,4.37,2.71
-14,0.2,1,0,0,60.2,62.0,345,3.79,3.75,2.27
-15,0.32,1,0,6,60.9,58.0,345,4.38,4.42,2.68
-16,0.3,0,1,0,62.0,54.0,348,4.31,4.34,2.68
-17,0.3,2,2,1,63.4,54.0,351,4.23,4.29,2.7
-18,0.3,2,2,1,63.8,56.0,351,4.23,4.26,2.71
-19,0.3,3,2,1,62.7,59.0,351,4.21,4.27,2.66
-20,0.3,2,1,0,63.3,56.0,351,4.26,4.3,2.71
-21,0.23,3,0,3,63.8,55.0,352,3.85,3.92,2.48
-22,0.23,3,3,2,61.0,57.0,353,3.94,3.96,2.41
-23,0.31,3,2,1,59.4,62.0,353,4.39,4.43,2.62
-24,0.31,3,2,1,58.1,62.0,353,4.44,4.47,2.59
-25,0.23,3,5,4,60.4,58.0,354,3.97,4.01,2.41
-26,0.24,1,1,2,62.5,57.0,355,3.97,3.94,2.47
-27,0.3,3,2,3,62.2,57.0,357,4.28,4.3,2.67
-28,0.23,3,6,3,60.5,61.0,357,3.96,3.97,2.4
-29,0.23,3,4,2,60.9,57.0,357,3.96,3.99,2.42
-30,0.23,3,4,2,60.0,57.0,402,4.0,4.03,2.41
-31,0.23,3,4,2,59.8,57.0,402,4.04,4.06,2.42
-32,0.23,3,0,2,60.7,59.0,402,3.97,4.01,2.42
-33,0.23,3,0,2,59.5,58.0,402,4.01,4.06,2.4
-34,0.23,3,6,2,61.9,58.0,402,3.92,3.96,2.44
-35,0.23,2,4,2,58.2,59.0,402,4.06,4.08,2.37
-36,0.23,2,0,2,64.1,59.0,402,3.83,3.85,2.46
-37,0.31,2,3,1,64.0,54.0,402,4.29,4.31,2.75
-38,0.26,3,6,3,60.8,59.0,403,4.13,4.16,2.52
-39,0.33,0,1,0,61.8,55.0,403,4.49,4.51,2.78
-40,0.33,0,1,0,61.2,56.0,403,4.49,4.5,2.75
-41,0.33,0,2,1,61.1,56.0,403,4.49,4.55,2.76
-42,0.26,2,6,3,65.2,56.0,403,3.99,4.02,2.61
-43,0.26,2,6,2,58.4,63.0,403,4.19,4.24,2.46
-44,0.32,2,3,0,63.1,56.0,403,4.34,4.37,2.75
-45,0.29,1,4,1,62.4,58.0,403,4.24,4.26,2.65
-46,0.32,3,3,0,61.8,55.0,403,4.35,4.42,2.71
-47,0.32,2,3,0,63.8,56.0,403,4.36,4.38,2.79
-48,0.25,3,0,3,63.3,60.0,404,4.0,4.03,2.54
-49,0.29,3,3,0,60.7,60.0,404,4.33,4.37,2.64
-50,0.24,3,4,1,60.9,61.0,404,4.02,4.03,2.45
-51,0.23,0,5,2,61.9,54.0,404,3.93,3.95,2.44
-52,0.32,0,1,1,60.9,55.0,404,4.45,4.48,2.72
-53,0.22,1,0,3,61.6,58.0,404,3.93,3.89,2.41
-54,0.22,1,6,3,59.3,62.0,404,3.91,3.88,2.31
-55,0.3,0,1,0,61.0,59.0,405,4.3,4.33,2.63
-56,0.3,1,2,0,59.3,61.0,405,4.43,4.38,2.61
-57,0.3,3,1,1,62.6,57.0,405,4.25,4.28,2.67
-58,0.3,3,1,1,63.0,57.0,405,4.28,4.32,2.71
-59,0.3,2,1,1,63.2,55.0,405,4.25,4.29,2.7
-60,0.35,0,1,2,60.9,57.0,552,4.54,4.59,2.78
-61,0.3,1,6,1,62.6,59.0,552,4.23,4.27,2.66
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-1796,0.7,3,6,3,62.6,58.0,3053,5.67,5.7,3.56
-1797,0.71,3,0,3,59.9,59.0,3053,5.79,5.83,3.48
-1798,0.7,3,4,2,62.8,59.0,3053,5.65,5.69,3.56
-1799,0.71,0,0,3,60.9,56.0,3053,5.77,5.83,3.53
-1800,0.81,0,4,0,62.3,54.0,3053,5.94,5.98,3.72
-1801,0.78,0,0,0,61.0,56.0,3053,5.92,5.98,3.63
-1802,0.75,0,0,1,61.3,57.0,3053,5.84,5.91,3.6
-1803,0.79,3,0,1,63.2,56.0,3053,5.91,5.83,3.71
-1804,0.79,1,5,2,62.3,56.0,3053,5.94,5.87,3.68
-1805,0.79,1,5,2,61.3,59.0,3053,5.97,5.91,3.64
-1806,0.79,0,0,1,62.1,54.0,3053,5.96,5.92,3.69
-1807,0.7,3,6,2,62.9,60.0,3054,5.62,5.67,3.55
-1808,1.01,1,3,0,61.1,60.0,3054,6.46,6.41,3.93
-1809,1.01,1,1,0,61.9,58.0,3054,6.45,6.41,3.98
-1810,1.01,4,1,0,66.1,58.0,3054,6.21,6.04,4.05
-1811,1.01,4,1,0,68.1,59.0,3054,6.14,6.11,4.17
-1812,0.78,3,0,1,60.9,57.0,3055,5.93,5.97,3.62
-1813,0.65,3,6,4,59.9,58.0,3056,5.63,5.69,3.39
-1814,0.61,0,0,4,60.8,56.0,3056,5.5,5.47,3.34
-1815,0.78,0,5,1,61.7,56.0,3056,5.9,5.93,3.65
-1816,0.76,0,0,1,62.4,54.0,3056,5.84,5.86,3.65
-1817,0.9,3,1,0,61.5,61.0,3057,6.13,6.16,3.78
-1818,0.92,3,6,0,60.2,61.0,3057,6.27,6.32,3.79
-1819,0.57,0,4,5,61.1,55.0,3057,5.36,5.44,3.3
-1820,0.76,2,4,2,59.9,61.0,3057,5.89,5.98,3.56
-1821,0.9,2,1,0,60.7,58.0,3057,6.17,6.21,3.76
-1822,0.82,4,3,1,64.9,58.0,3057,5.82,5.77,3.76
-1823,0.91,2,4,0,63.9,56.0,3058,6.11,6.06,3.89
-1824,0.79,1,6,1,61.4,59.0,3058,5.96,5.89,3.64
-1825,0.91,1,2,3,61.6,58.0,3058,6.28,6.23,3.85
-1826,0.72,3,4,2,62.1,59.0,3059,5.69,5.74,3.55
-1827,0.81,3,0,0,62.9,56.0,3059,5.87,5.9,3.7
-1828,0.71,3,0,2,61.8,56.0,3059,5.74,5.78,3.56
-1829,1.03,3,5,6,62.4,57.0,3060,6.41,6.45,4.01
-1830,0.9,2,5,0,63.6,57.0,3060,6.1,6.04,3.86
-1831,0.74,3,3,5,62.4,57.0,3061,5.76,5.81,3.61
-1832,0.7,3,0,2,61.1,55.0,3061,5.72,5.77,3.51
-1833,0.71,3,0,2,63.3,56.0,3061,5.64,5.68,3.58
-1834,0.9,3,2,1,63.8,56.0,3061,6.11,6.14,3.91
-1835,0.71,0,5,1,61.5,55.0,3061,5.75,5.79,3.55
-1836,0.91,2,1,0,57.9,60.0,3061,6.46,6.39,3.72
-1837,0.71,4,5,5,62.8,57.0,3062,5.67,5.57,3.53
-1838,0.7,1,4,4,58.7,60.0,3062,5.8,5.75,3.39
-1839,0.72,0,6,1,61.5,56.5,3062,5.76,5.79,3.55
-1840,0.9,4,4,0,65.7,63.0,3062,6.01,5.96,3.93
-1841,0.71,1,0,3,62.2,59.0,3062,5.71,5.61,3.52
-1842,0.71,1,0,3,62.0,61.0,3062,5.71,5.65,3.52
-1843,0.93,1,2,2,60.3,58.0,3062,6.37,6.31,3.82
-1844,0.7,3,0,2,62.2,57.0,3063,5.63,5.68,3.52
-1845,0.7,3,0,2,62.5,56.0,3063,5.64,5.68,3.54
-1846,0.7,2,0,2,59.4,61.0,3063,5.79,5.83,3.45
-1847,0.71,3,0,2,63.3,59.0,3064,5.64,5.68,3.58
-1848,0.76,1,0,3,61.7,62.0,3064,5.85,5.82,3.6
-1849,0.7,0,4,3,61.4,56.0,3064,5.72,5.75,3.52
-1850,0.7,0,4,3,61.6,55.0,3064,5.72,5.75,3.53
-1851,0.8,0,4,0,61.4,56.0,3064,5.99,5.93,3.66
-1852,0.72,3,0,3,63.0,58.0,3065,5.69,5.73,3.6
-1853,0.7,0,5,2,61.5,56.0,3065,5.7,5.75,3.52
-1854,0.65,0,3,7,61.5,55.0,3065,5.6,5.62,3.45
-1855,0.85,3,6,0,61.4,60.0,3066,6.05,6.13,3.74
-1856,0.77,0,1,2,61.4,56.0,3066,5.9,5.93,3.63
-1857,0.71,0,4,2,62.0,57.0,3066,5.7,5.75,3.55
-1858,0.71,0,4,2,62.1,57.0,3066,5.73,5.76,3.57
-1859,0.8,0,4,0,62.5,56.0,3066,5.94,5.99,3.73
-1860,0.8,0,4,0,61.8,59.0,3066,5.94,5.96,3.68
-1861,0.73,3,0,3,63.1,55.0,3066,5.77,5.71,3.62
-1862,0.9,2,5,0,63.7,62.0,3067,6.01,6.07,3.85
-1863,0.78,0,0,1,62.7,55.0,3067,5.87,5.9,3.69
-1864,0.7,3,0,2,63.4,60.0,3068,5.63,5.66,3.58
-1865,0.7,0,0,3,62.6,56.0,3068,5.65,5.69,3.55
-1866,0.85,3,1,3,60.0,57.0,3070,6.1,6.16,3.68
-1867,0.8,3,5,1,62.9,56.0,3070,5.88,5.95,3.72
-1868,0.8,3,0,0,62.6,59.0,3070,5.84,5.91,3.68
-1869,0.92,2,2,0,59.0,63.0,3070,6.3,6.35,3.73
-1870,0.8,3,5,1,62.1,56.0,3071,6.01,5.85,3.68
-1871,0.82,0,1,2,61.6,56.0,3071,6.05,6.01,3.72
-1872,0.71,2,5,5,62.7,61.0,3072,5.64,5.68,3.55
-1873,0.7,3,5,5,63.1,56.0,3073,5.64,5.67,3.57
-1874,0.79,3,4,1,58.5,61.0,3073,6.03,6.08,3.54
-1875,0.7,0,5,5,61.6,55.0,3073,5.72,5.75,3.53
-1876,0.75,0,5,3,61.6,55.0,3073,5.86,5.89,3.62
-1877,0.71,0,0,3,62.2,57.0,3073,5.69,5.73,3.55
-1878,0.62,1,0,5,61.9,59.0,3073,5.62,5.5,3.44
-1879,1.03,1,5,6,60.8,57.0,3073,6.55,6.51,3.97
-1880,0.7,2,6,3,58.0,65.0,3073,5.81,5.73,3.39
-1881,0.78,3,5,3,61.7,58.0,3074,5.87,5.92,3.64
-1882,0.9,4,1,4,67.0,56.0,3074,5.91,5.83,3.93
-1883,0.77,0,3,2,61.4,55.0,3074,5.89,5.93,3.63
-1884,0.9,2,5,1,63.7,56.0,3074,6.1,6.06,3.87
-1885,0.72,3,6,3,61.8,58.0,3075,5.73,5.76,3.55
-1886,0.72,3,6,3,62.6,59.0,3075,5.69,5.72,3.57
-1887,0.71,3,4,0,61.4,56.0,3075,5.71,5.73,3.51
-1888,0.7,3,6,1,62.4,57.0,3075,5.64,5.67,3.53
-1889,0.72,0,3,5,62.2,57.0,3075,5.72,5.75,3.57
-1890,0.76,0,1,3,61.7,56.0,3075,5.87,5.9,3.63
-1891,0.76,0,6,1,62.8,57.0,3075,5.81,5.85,3.66
-1892,0.79,0,0,1,62.9,55.0,3075,5.93,5.86,3.71
-1893,0.79,3,0,1,60.4,59.0,3077,5.97,5.99,3.61
-1894,0.79,3,0,1,63.7,58.0,3077,5.79,5.85,3.71
-1895,0.73,0,0,3,62.7,56.0,3077,5.75,5.8,3.62
-1896,0.71,4,6,3,64.7,58.0,3077,5.61,5.58,3.62
-1897,0.71,1,6,3,60.3,62.0,3077,5.76,5.69,3.45
-1898,0.75,2,6,1,63.6,58.0,3078,5.65,5.77,3.63
-1899,0.72,1,0,3,62.5,59.0,3078,5.7,5.73,3.57
-1900,0.83,1,6,0,62.7,56.0,3078,6.01,5.98,3.76
-1901,0.76,0,0,3,61.3,56.0,3079,5.79,5.83,3.56
-1902,0.94,2,1,0,63.8,60.0,3079,6.21,6.14,3.94
-1903,0.91,4,6,0,62.5,66.0,3079,6.08,6.01,3.78
-1904,1.0,3,0,6,62.3,58.0,3080,6.33,6.38,3.96
-1905,1.0,0,3,6,61.3,57.0,3080,6.41,6.44,3.94
-1906,1.0,4,2,2,66.6,57.0,3080,6.07,6.03,4.03
-1907,1.0,1,3,0,59.4,60.0,3080,6.49,6.4,3.83
-1908,1.0,1,3,0,62.8,60.0,3080,6.46,6.34,4.02
-1909,0.82,1,4,1,61.1,60.0,3080,6.02,5.96,3.66
-1910,0.71,0,3,3,61.8,55.0,3080,5.75,5.78,3.56
-1911,0.71,0,3,3,61.8,55.0,3080,5.73,5.75,3.55
-1912,0.79,3,6,0,63.2,57.0,3081,5.81,5.86,3.69
-1913,0.75,0,4,3,62.1,57.0,3081,5.8,5.82,3.61
-1914,0.79,0,4,1,62.0,55.0,3081,5.93,5.95,3.68
-1915,0.71,0,0,2,62.2,57.0,3081,5.74,5.7,3.56
-1916,0.8,3,4,1,63.5,58.0,3082,5.89,5.85,3.73
-1917,0.91,3,3,1,63.4,55.0,3082,6.18,6.15,3.91
-1918,0.6,0,0,5,62.5,53.7,3082,5.35,5.43,3.38
-1919,0.6,0,0,5,62.0,53.7,3082,5.4,5.44,3.36
-1920,0.7,2,6,3,57.7,60.0,3082,5.83,5.86,3.37
-1921,0.91,3,2,2,63.5,55.0,3082,6.22,6.12,3.92
-1922,0.71,1,4,2,58.6,62.0,3082,5.88,5.82,3.43
-1923,0.86,1,3,3,61.3,59.0,3082,6.17,6.1,3.76
-1924,0.8,0,4,1,61.0,56.0,3082,6.05,5.98,3.67
-1925,0.8,1,4,1,60.1,58.0,3082,6.07,6.05,3.64
-1926,0.8,1,6,0,61.1,58.0,3082,5.94,5.9,3.62
-1927,0.92,1,4,0,62.1,60.0,3083,6.21,6.26,3.87
-1928,0.72,3,4,2,63.2,57.0,3083,5.69,5.73,3.61
-1929,1.01,1,3,0,59.4,62.0,3083,6.47,6.43,3.83
-1930,0.71,3,6,3,61.8,55.0,3084,5.72,5.74,3.54
-1931,0.72,3,0,0,61.0,59.0,3084,5.73,5.78,3.51
-1932,0.81,1,1,4,62.9,59.0,3084,5.95,5.84,3.71
-1933,0.53,0,0,5,61.8,55.0,3084,5.19,5.24,3.22
-1934,0.61,0,0,5,62.2,56.0,3084,5.43,5.47,3.39
-1935,0.85,0,5,1,62.0,54.0,3084,6.08,6.11,3.78
-1936,0.75,2,0,3,62.6,58.0,3084,5.73,5.78,3.6
-1937,0.9,2,0,0,64.1,57.0,3084,6.09,6.07,3.9
-1938,0.9,3,0,0,63.4,62.0,3084,6.02,6.0,3.81
-1939,0.9,1,0,0,60.7,61.0,3084,6.22,6.16,3.76
-1940,0.9,1,0,0,59.5,61.0,3084,6.3,6.24,3.73
-1941,0.9,4,0,0,64.5,55.0,3084,6.12,6.03,3.92
-1942,0.9,4,0,0,65.8,58.0,3084,6.02,5.98,3.95
-1943,0.9,1,0,0,63.0,57.0,3084,6.08,6.05,3.82
-1944,0.81,0,3,3,61.6,56.0,3084,6.03,5.98,3.7
-1945,0.81,1,5,3,61.2,60.0,3084,6.0,5.99,3.67
-1946,0.9,3,0,0,63.1,54.0,3084,6.2,6.17,3.9
-1947,1.02,4,1,0,64.5,56.0,3084,6.28,6.25,4.04
-1948,0.72,0,4,2,62.4,56.0,3084,5.76,5.72,3.58
-1949,0.75,3,6,1,63.9,56.0,3085,5.73,5.75,3.67
-1950,0.84,1,0,0,59.2,59.0,3086,6.19,6.14,3.65
-1951,0.75,0,0,2,61.8,55.0,3086,5.85,5.87,3.62
-1952,0.7,2,6,3,63.9,58.0,3087,5.58,5.62,3.58
-1953,0.7,2,6,3,63.2,60.0,3087,5.56,5.61,3.53
-1954,0.91,3,3,1,63.0,57.0,3087,6.1,6.19,3.87
-1955,0.7,2,6,3,64.1,59.0,3087,5.49,5.56,3.54
-1956,0.76,0,0,1,62.4,57.0,3087,5.83,5.87,3.65
-1957,0.74,0,6,3,61.9,56.0,3087,5.84,5.79,3.6
-1958,1.01,2,2,0,63.7,60.0,3088,6.4,6.29,4.05
-1959,1.01,3,2,0,63.3,53.0,3088,6.35,6.31,4.01
-1960,0.79,1,4,3,60.7,60.0,3089,5.95,5.97,3.62
-1961,0.77,0,3,1,61.3,56.0,3089,5.87,5.91,3.61
-1962,0.73,0,5,1,61.4,57.0,3089,5.74,5.79,3.54
-1963,0.73,0,5,1,60.9,57.0,3089,5.81,5.85,3.55
-1964,0.73,0,5,1,60.6,57.0,3089,5.84,5.87,3.55
-1965,0.73,0,5,1,61.9,55.0,3089,5.76,5.8,3.58
-1966,0.73,0,5,1,61.2,57.0,3089,5.79,5.86,3.56
-1967,0.73,0,5,1,61.7,57.0,3089,5.78,5.81,3.57
-1968,0.71,0,6,2,62.5,57.0,3090,5.71,5.68,3.56
-1969,0.58,0,4,5,61.7,55.0,3090,5.36,5.39,3.31
-1970,0.87,0,4,0,62.2,56.0,3090,6.03,6.13,3.78
-1971,0.71,1,6,2,58.1,62.0,3090,5.89,5.84,3.41
-1972,0.72,0,0,3,60.8,57.0,3091,5.79,5.82,3.53
-1973,0.92,1,2,3,60.9,62.0,3091,6.31,6.26,3.83
-1974,1.2,1,5,6,60.3,58.0,3091,6.82,6.77,4.1
-1975,0.8,1,0,1,62.9,60.0,3091,5.89,5.87,3.7
-1976,0.98,3,3,0,63.4,57.0,3091,6.41,6.32,4.04
-1977,0.7,3,6,3,60.7,58.0,3092,5.7,5.73,3.47
-1978,0.7,0,6,3,61.6,58.0,3092,5.66,5.7,3.5
-1979,0.7,0,6,3,62.3,55.0,3092,5.67,5.7,3.54
-1980,0.8,3,4,1,63.1,58.0,3093,5.89,5.93,3.73
-1981,0.7,0,0,3,62.0,57.0,3093,5.68,5.71,3.53
-1982,0.7,0,6,2,61.9,57.0,3093,5.69,5.72,3.53
-1983,0.72,0,0,4,62.1,57.0,3093,5.76,5.74,3.57
-1984,0.71,0,5,4,62.5,57.0,3095,5.7,5.73,3.57
-1985,0.7,3,0,2,63.4,57.0,3095,5.63,5.67,3.58
-1986,0.81,3,5,1,62.3,59.0,3095,5.93,5.98,3.71
-1987,0.94,1,4,0,62.3,59.0,3095,6.32,6.23,3.91
-1988,0.9,1,3,1,62.4,56.0,3095,6.13,6.05,3.8
-1989,0.71,3,6,3,63.1,59.0,3096,5.64,5.67,3.57
-1990,0.71,3,6,3,63.0,58.0,3096,5.65,5.68,3.57
-1991,0.77,2,3,5,63.1,57.0,3096,5.83,5.86,3.69
-1992,0.91,1,4,0,62.1,56.0,3096,6.26,6.21,3.87
-1993,0.81,0,1,1,61.3,56.0,3097,6.23,6.27,3.83
-1994,0.81,1,4,1,60.3,59.0,3097,5.98,6.05,3.63
-1995,0.52,0,0,5,61.8,55.0,3097,5.19,5.2,3.21
-1996,0.53,0,6,5,61.6,55.0,3097,5.21,5.27,3.23
-1997,1.29,2,1,6,64.2,54.0,3098,6.93,6.83,4.42
-1998,0.9,3,1,0,60.4,58.0,3099,6.25,6.31,3.79
-1999,0.72,0,6,3,61.8,57.0,3099,5.76,5.73,3.55
-2000,0.94,1,5,0,63.0,59.0,3099,6.16,6.1,3.86
-2001,0.93,0,1,0,62.3,57.0,3101,6.2,6.22,3.87
-2002,0.74,0,4,4,61.5,54.0,3101,5.84,5.86,3.6
-2003,0.81,0,0,0,62.1,55.0,3101,5.94,5.98,3.7
-2004,0.71,1,0,3,62.2,59.0,3101,5.79,5.76,3.59
-2005,0.72,3,6,1,60.5,57.0,3102,5.85,5.89,3.55
-2006,0.9,3,2,2,62.6,55.0,3102,6.11,6.13,3.83
-2007,0.72,0,5,2,61.3,56.0,3102,5.77,5.82,3.55
-2008,0.9,0,2,1,62.6,54.0,3102,6.16,6.23,3.88
-2009,0.83,0,0,0,61.9,53.0,3103,6.04,6.08,3.75
-2010,0.66,0,6,4,61.6,57.0,3103,5.57,5.64,3.45
-2011,0.71,0,0,3,62.2,56.0,3103,5.7,5.75,3.56
-2012,0.78,1,6,1,61.8,59.0,3103,5.93,5.88,3.65
-2013,0.81,3,3,2,63.6,57.0,3104,5.87,5.9,3.74
-2014,0.71,3,4,5,63.3,58.0,3104,5.67,5.64,3.58
-2015,0.9,4,5,0,55.9,62.0,3104,6.35,6.42,3.57
-2016,0.9,4,5,0,64.7,58.0,3104,5.94,6.03,3.87
-2017,0.9,0,1,0,62.8,55.0,3104,6.19,6.14,3.87
-2018,0.7,1,4,4,62.6,59.0,3105,5.66,5.62,3.53
-2019,0.78,0,1,2,61.9,56.0,3105,5.91,5.94,3.67
-2020,0.78,0,1,2,60.7,56.0,3105,5.97,6.03,3.64
-2021,0.9,3,3,0,59.5,63.0,3105,6.31,6.26,3.74
-2022,0.9,1,3,0,62.4,61.0,3105,6.11,6.03,3.79
-2023,0.9,3,3,0,61.1,63.0,3105,6.19,6.12,3.76
-2024,1.52,2,0,6,57.3,58.0,3105,7.53,7.42,4.28
-2025,1.52,2,0,6,57.3,58.0,3105,7.53,7.42,4.28
-2026,0.9,0,2,3,59.8,56.0,3105,6.27,6.24,3.74
-2027,0.9,4,3,0,65.6,58.0,3105,6.05,5.95,3.95
-2028,0.75,1,6,1,58.9,60.0,3105,5.99,5.97,3.52
-2029,0.7,1,5,5,61.2,56.0,3105,5.75,5.68,3.5
-2030,0.9,3,3,0,63.4,60.0,3105,6.08,6.03,3.84
-2031,0.72,0,4,2,62.4,55.0,3105,5.78,5.75,3.6
-2032,0.9,1,3,0,59.6,59.0,3105,6.38,6.24,3.76
-2033,0.9,2,3,0,64.1,58.0,3105,6.05,6.02,3.87
-2034,0.72,1,4,4,61.6,59.0,3105,5.78,5.72,3.54
-2035,0.79,0,0,1,62.1,54.0,3107,5.92,5.96,3.69
-2036,0.79,2,0,1,63.2,56.0,3107,5.83,5.91,3.71
-2037,0.7,3,0,2,63.4,56.0,3107,5.6,5.63,3.56
-2038,0.7,0,0,2,62.2,58.0,3107,5.62,5.69,3.52
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-2041,0.32,0,1,4,61.5,56.0,561,4.4,4.42,2.71
-2042,0.32,0,3,2,61.3,55.0,561,4.41,4.43,2.71
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-2046,0.32,0,1,4,62.6,54.0,561,4.37,4.41,2.75
-2047,0.32,3,0,1,61.6,56.0,561,4.39,4.41,2.71
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-2091,0.71,3,5,4,61.2,58.0,3113,5.68,5.72,3.49
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-2093,0.82,0,1,2,61.3,57.0,3113,6.02,6.07,3.7
-2094,0.9,2,3,0,60.4,61.0,3114,6.14,6.22,3.73
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-2117,1.02,1,2,0,62.3,58.0,3119,6.42,6.37,3.98
-2118,1.02,4,2,0,65.0,59.0,3119,6.34,6.24,4.08
-2119,0.91,4,0,0,64.5,60.0,3119,6.04,5.96,3.87
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-2121,0.9,3,1,1,63.2,56.0,3120,6.15,6.12,3.88
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-19750,1.56,3,3,0,59.9,59.0,8317,7.47,7.52,4.49
-19751,1.02,0,5,5,61.5,57.0,8317,6.47,6.44,3.97
-19752,1.01,1,4,4,61.8,59.0,8319,6.46,6.42,3.98
-19753,1.06,1,5,5,61.1,58.0,8319,6.57,6.53,4.0
-19754,1.2,0,5,3,59.9,61.0,8321,6.85,6.88,4.11
-19755,1.08,0,4,2,62.1,53.0,8321,6.6,6.64,4.11
-19756,1.72,1,2,1,61.7,58.0,8324,7.71,7.81,4.79
-19757,1.56,1,2,2,61.1,59.0,8324,7.49,7.52,4.58
-19758,1.59,0,2,1,62.4,55.0,8325,7.48,7.45,4.66
-19759,1.11,1,0,2,61.2,58.0,8329,6.69,6.65,4.08
-19760,1.01,3,4,3,62.0,60.0,8330,6.36,6.39,3.95
-19761,1.6,1,3,0,59.4,59.0,8333,7.6,7.55,4.5
-19762,1.5,1,3,0,62.5,60.0,8333,7.32,7.25,4.55
-19763,1.0,1,6,2,61.9,55.0,8333,6.4,6.36,3.95
-19764,1.0,1,6,2,62.2,55.0,8333,6.46,6.4,4.0
-19765,1.2,3,4,3,62.6,56.0,8334,6.73,6.78,4.23
-19766,1.2,0,4,3,62.3,55.0,8334,6.79,6.83,4.24
-19767,1.65,0,2,1,62.5,56.0,8334,7.59,7.54,4.73
-19768,1.01,2,6,2,64.1,55.0,8339,6.29,6.38,4.06
-19769,1.22,3,5,3,62.3,58.0,8340,6.79,6.82,4.24
-19770,1.04,1,6,2,62.3,59.0,8341,6.48,6.39,4.01
-19771,1.04,0,6,2,59.1,56.0,8341,6.65,6.59,3.91
-19772,1.51,0,1,1,61.1,59.0,8342,7.4,7.37,4.51
-19773,1.52,1,4,0,62.3,59.0,8342,7.37,7.33,4.58
-19774,1.06,0,4,2,61.7,57.0,8344,6.55,6.57,4.05
-19775,1.14,0,5,2,61.4,57.0,8346,6.75,6.81,4.16
-19776,1.16,1,0,2,58.7,58.0,8346,6.89,6.87,4.04
-19777,1.02,0,6,2,62.6,57.0,8347,6.4,6.44,4.02
-19778,1.08,0,5,5,61.6,56.0,8347,6.6,6.65,4.08
-19779,1.5,2,2,2,60.7,61.0,8347,7.27,7.3,4.42
-19780,1.01,3,0,2,59.4,59.0,8348,6.52,6.54,3.88
-19781,1.21,0,0,2,62.2,57.0,8348,6.83,6.8,4.24
-19782,1.21,0,4,3,61.5,56.0,8348,6.87,6.83,4.21
-19783,1.15,3,5,4,58.4,59.0,8349,6.84,6.92,4.02
-19784,1.13,1,0,2,61.1,59.0,8353,6.72,6.69,4.1
-19785,1.51,4,1,1,68.3,57.0,8355,6.95,6.87,4.72
-19786,1.64,3,4,0,63.2,57.0,8357,7.56,7.47,4.75
-19787,1.13,3,0,3,61.9,61.0,8358,6.61,6.64,4.1
-19788,1.06,0,5,4,61.7,56.0,8358,6.59,6.56,4.06
-19789,1.0,2,0,5,63.4,59.0,8359,6.27,6.24,4.0
-19790,1.07,1,6,2,62.0,60.0,8359,6.55,6.49,4.04
-19791,1.16,3,5,4,62.3,59.0,8361,6.69,6.72,4.18
-19792,1.81,4,2,1,56.2,62.0,8362,8.14,8.09,4.56
-19793,1.22,1,5,3,61.8,58.0,8362,6.91,6.84,4.25
-19794,1.22,0,5,3,61.4,56.0,8362,6.9,6.88,4.23
-19795,1.57,0,2,3,62.3,54.0,8363,7.5,7.49,4.67
-19796,1.74,1,2,3,61.9,62.0,8364,7.62,7.54,4.7
-19797,1.09,0,5,4,60.6,57.0,8364,6.62,6.7,4.05
-19798,1.25,0,3,2,62.1,57.0,8365,6.89,6.92,4.29
-19799,1.53,1,3,1,62.4,60.0,8365,7.34,7.31,4.57
-19800,1.71,1,1,0,59.0,60.0,8366,7.86,7.83,4.63
-19801,1.52,1,2,2,61.2,59.0,8366,7.39,7.36,4.51
-19802,1.0,0,5,5,61.9,57.0,8366,6.43,6.39,3.97
-19803,1.52,1,6,0,58.6,62.0,8366,7.56,7.45,4.41
-19804,1.73,2,0,6,63.6,59.0,8370,7.6,7.56,4.82
-19805,1.51,1,2,2,62.1,58.0,8371,7.36,7.4,4.58
-19806,1.5,0,1,1,62.3,56.0,8371,7.35,7.41,4.6
-19807,1.51,3,3,0,60.8,56.0,8372,7.41,7.46,4.52
-19808,1.01,3,5,5,61.8,59.0,8373,6.47,6.41,3.98
-19809,1.0,2,6,4,60.2,62.0,8374,6.42,6.46,3.88
-19810,1.03,3,4,4,61.3,57.0,8375,6.49,6.53,3.99
-19811,1.22,3,5,3,62.7,58.0,8377,6.8,6.72,4.24
-19812,1.6,0,1,0,61.9,57.0,8377,7.48,7.51,4.64
-19813,1.6,3,4,0,60.5,60.0,8377,7.52,7.55,4.56
-19814,1.2,2,0,3,63.6,57.0,8380,6.74,6.68,4.27
-19815,1.23,1,5,2,62.8,56.0,8381,6.86,6.81,4.29
-19816,1.23,1,5,2,61.8,58.0,8381,6.88,6.84,4.24
-19817,1.75,2,3,0,64.1,56.0,8385,7.59,7.55,4.85
-19818,1.03,0,5,4,60.7,57.0,8385,6.5,6.58,3.97
-19819,1.33,0,3,3,59.5,57.0,8385,7.18,7.23,4.29
-19820,1.2,2,5,2,63.6,58.0,8387,6.59,6.56,4.18
-19821,1.51,4,4,1,65.8,56.0,8387,7.16,7.12,4.7
-19822,1.51,1,3,0,62.4,58.0,8388,7.36,7.31,4.58
-19823,1.5,3,2,4,63.3,56.0,8391,7.18,7.29,4.58
-19824,1.31,0,3,2,62.4,56.0,8392,7.0,6.98,4.36
-19825,1.07,0,5,4,62.4,54.0,8393,6.51,6.56,4.08
-19826,1.12,0,5,2,62.5,54.0,8394,6.65,6.69,4.17
-19827,1.51,0,1,1,61.2,60.0,8396,7.39,7.37,4.52
-19828,1.51,0,1,1,59.4,61.0,8396,7.52,7.46,4.45
-19829,1.7,1,4,0,60.2,61.0,8397,7.79,7.68,4.66
-19830,1.03,0,5,5,62.0,56.0,8398,6.54,6.5,4.04
-19831,1.5,1,4,1,60.0,60.0,8400,7.48,7.42,4.47
-19832,1.51,0,1,1,62.7,57.0,8400,7.35,7.3,4.59
-19833,1.11,0,5,3,61.6,56.0,8400,6.62,6.65,4.09
-19834,1.0,1,4,4,61.2,60.0,8400,6.43,6.38,3.92
-19835,1.25,0,0,3,62.6,56.0,8400,6.89,6.84,4.3
-19836,1.01,3,0,4,61.6,58.0,8401,6.4,6.46,3.96
-19837,1.02,0,5,4,62.1,57.0,8401,6.43,6.45,4.0
-19838,1.02,0,5,4,61.6,57.0,8401,6.45,6.47,3.98
-19839,1.02,0,5,4,62.7,57.0,8401,6.41,6.45,4.03
-19840,1.21,1,5,3,62.8,59.0,8402,6.84,6.76,4.27
-19841,1.21,3,4,3,62.9,54.0,8403,6.78,6.82,4.28
-19842,1.01,3,0,2,60.4,60.0,8403,6.43,6.48,3.9
-19843,1.13,0,0,3,62.0,54.0,8404,6.7,6.66,4.14
-19844,1.23,3,3,5,62.5,58.0,8405,6.77,6.82,4.25
-19845,1.08,0,4,2,61.4,55.0,8405,6.61,6.69,4.08
-19846,1.12,3,5,5,60.9,57.0,8408,6.67,6.72,4.08
-19847,1.52,3,3,0,62.8,58.0,8408,7.25,7.3,4.57
-19848,2.04,1,1,6,59.3,61.0,8408,8.3,8.25,4.91
-19849,1.5,3,1,1,63.1,54.0,8408,7.34,7.31,4.62
-19850,1.54,1,0,0,59.9,60.0,8408,7.49,7.38,4.46
-19851,1.18,3,5,3,62.7,54.7,8411,6.74,6.77,4.23
-19852,1.22,0,3,2,61.9,54.0,8411,6.88,6.9,4.26
-19853,1.06,0,0,2,62.1,57.0,8413,6.49,6.52,4.04
-19854,1.2,0,5,3,62.8,54.0,8414,6.75,6.79,4.25
-19855,1.18,0,5,2,61.3,56.0,8414,6.81,6.83,4.17
-19856,1.01,2,0,4,63.9,58.0,8415,6.31,6.37,4.05
-19857,1.5,3,2,5,62.3,62.0,8415,7.16,7.23,4.48
-19858,1.06,3,5,7,61.2,57.0,8415,6.52,6.56,4.0
-19859,1.04,0,4,3,61.6,56.0,8415,6.53,6.55,4.03
-19860,1.19,0,4,3,62.0,57.0,8415,6.78,6.76,4.2
-19861,1.3,0,5,1,61.6,55.0,8416,7.01,7.05,4.33
-19862,1.01,1,6,2,62.4,58.0,8416,6.41,6.38,3.99
-19863,1.22,1,4,3,59.8,59.0,8417,6.96,6.92,4.15
-19864,1.6,0,2,1,62.8,55.0,8418,7.48,7.42,4.68
-19865,1.57,3,5,0,61.1,61.0,8419,7.43,7.46,4.55
-19866,2.68,1,5,6,58.6,60.0,8419,9.11,9.07,5.33
-19867,1.4,1,6,0,61.8,54.0,8419,7.21,7.16,4.44
-19868,1.46,0,0,0,62.3,55.0,8421,7.29,7.25,4.53
-19869,1.09,0,4,2,60.6,57.0,8422,6.62,6.64,4.02
-19870,1.37,1,3,2,60.9,59.0,8424,7.17,7.24,4.39
-19871,1.44,1,1,2,62.6,59.0,8426,7.08,7.14,4.45
-19872,1.52,1,2,2,62.2,59.0,8426,7.32,7.38,4.57
-19873,1.52,1,2,4,58.3,62.0,8427,7.61,7.49,4.4
-19874,1.5,3,2,4,63.3,57.0,8427,7.28,7.25,4.6
-19875,1.31,0,3,2,61.5,56.0,8429,7.0,7.06,4.32
-19876,1.31,0,3,2,61.5,55.0,8429,7.02,7.05,4.33
-19877,1.13,0,0,3,61.4,58.0,8430,6.71,6.74,4.13
-19878,1.12,2,4,4,57.9,57.0,8430,6.86,6.83,3.96
-19879,1.12,0,6,3,62.0,55.0,8430,6.66,6.63,4.12
-19880,1.23,0,3,7,61.3,57.0,8431,6.87,6.92,4.23
-19881,1.5,3,4,0,62.3,60.0,8431,7.29,7.34,4.56
-19882,1.5,1,1,3,62.3,59.0,8431,7.24,7.3,4.53
-19883,1.23,1,5,3,62.0,59.0,8431,6.87,6.8,4.24
-19884,1.2,3,4,3,61.1,59.0,8436,6.81,6.87,4.18
-19885,1.2,0,4,3,62.3,56.0,8436,6.79,6.82,4.24
-19886,1.03,3,6,2,60.5,60.0,8437,6.52,6.57,3.96
-19887,1.24,3,3,4,61.3,55.0,8438,6.94,6.96,4.26
-19888,1.59,2,5,1,63.8,56.0,8441,7.41,7.34,4.71
-19889,1.2,2,5,2,63.4,56.0,8442,6.7,6.74,4.26
-19890,1.2,1,5,2,60.2,60.0,8442,6.82,6.87,4.12
-19891,1.2,0,5,2,62.3,56.0,8442,6.77,6.82,4.23
-19892,1.2,3,5,2,60.2,60.0,8442,6.91,7.0,4.19
-19893,1.2,2,5,2,62.8,59.0,8442,6.64,6.7,4.19
-19894,2.25,3,2,0,60.6,63.0,8442,8.45,8.38,5.1
-19895,1.51,3,3,0,60.2,59.0,8444,7.33,7.43,4.44
-19896,1.02,1,4,4,61.5,60.0,8445,6.5,6.45,3.98
-19897,1.21,1,3,7,62.2,58.0,8446,6.83,6.8,4.24
-19898,1.0,1,4,4,59.9,60.0,8448,6.46,6.5,3.88
-19899,1.24,1,5,2,62.0,55.0,8449,6.95,6.89,4.29
-19900,1.24,0,5,2,61.1,57.0,8449,6.98,6.91,4.24
-19901,1.05,0,6,3,61.0,56.0,8451,6.54,6.61,4.01
-19902,1.06,0,5,2,62.3,55.0,8451,6.5,6.53,4.06
-19903,1.21,0,4,3,61.2,57.0,8452,6.87,6.82,4.19
-19904,1.2,3,4,3,62.9,55.0,8454,6.75,6.8,4.26
-19905,1.03,0,5,5,62.4,57.0,8454,6.39,6.47,4.01
-19906,1.09,1,5,4,59.5,61.0,8454,6.74,6.7,4.0
-19907,1.53,3,5,0,62.1,61.0,8455,7.28,7.3,4.53
-19908,1.65,0,0,0,58.7,56.0,8455,7.76,7.73,4.55
-19909,1.21,1,5,2,59.5,58.0,8456,7.01,6.97,4.16
-19910,1.21,1,5,2,62.7,58.0,8456,6.79,6.74,4.24
-19911,1.24,0,6,1,60.4,57.0,8457,6.95,7.02,4.22
-19912,1.8,1,1,0,61.8,59.0,8457,7.9,7.8,4.85
-19913,1.28,1,5,3,61.4,58.0,8458,7.0,6.92,4.27
-19914,1.51,3,4,0,63.2,60.0,8462,7.2,7.27,4.57
-19915,1.27,1,6,1,62.5,58.0,8463,6.92,6.87,4.31
-19916,1.51,2,1,1,63.7,58.0,8464,7.26,7.22,4.61
-19917,1.23,0,5,2,61.8,57.0,8465,6.89,6.86,4.25
-19918,1.4,1,1,4,62.0,58.0,8467,7.22,7.16,4.46
-19919,1.5,2,2,2,64.1,55.0,8467,7.23,7.13,4.6
-19920,1.0,3,4,4,63.2,56.0,8467,6.42,6.37,4.04
-19921,2.5,1,3,6,59.9,60.0,8467,8.75,8.67,5.22
-19922,1.03,1,6,4,61.7,59.0,8469,6.43,6.48,3.98
-19923,1.56,3,1,1,60.2,61.0,8470,7.5,7.59,4.54
-19924,1.01,1,4,4,61.8,59.0,8470,6.46,6.42,3.98
-19925,1.5,0,3,0,62.4,58.0,8471,7.29,7.26,4.54
-19926,1.5,3,1,1,60.7,58.0,8475,7.34,7.4,4.47
-19927,1.56,1,2,2,61.1,59.0,8476,7.52,7.49,4.58
-19928,1.39,1,4,1,62.1,58.0,8476,7.1,7.16,4.43
-19929,1.52,1,1,1,61.9,58.0,8477,7.37,7.42,4.58
-19930,1.54,0,2,3,62.7,56.0,8479,7.32,7.36,4.6
-19931,1.34,1,0,3,58.2,60.0,8480,7.26,7.21,4.21
-19932,1.08,0,6,3,61.9,58.0,8481,6.54,6.57,4.06
-19933,1.03,0,5,4,61.2,57.0,8483,6.48,6.52,3.98
-19934,1.22,0,5,3,59.9,56.0,8485,7.01,6.97,4.19
-19935,1.02,0,5,5,62.6,56.0,8485,6.39,6.42,4.01
-19936,1.23,1,4,3,62.9,58.0,8486,6.82,6.76,4.27
-19937,1.2,0,4,3,62.3,55.0,8486,6.83,6.79,4.24
-19938,1.2,0,4,3,62.6,56.0,8486,6.78,6.73,4.23
-19939,1.66,1,2,1,60.7,58.0,8487,7.72,7.63,4.66
-19940,1.06,0,5,5,61.6,57.0,8488,6.53,6.55,4.03
-19941,1.02,1,5,7,61.8,59.0,8489,6.41,6.46,3.98
-19942,1.24,0,3,5,62.3,54.0,8489,6.88,6.9,4.29
-19943,1.5,1,3,0,62.3,60.0,8490,7.22,7.3,4.52
-19944,1.2,3,5,3,63.2,56.0,8491,6.82,6.75,4.29
-19945,1.07,0,5,4,61.8,55.0,8491,6.57,6.61,4.07
-19946,2.34,1,1,6,61.5,60.0,8491,8.47,8.43,5.2
-19947,1.5,2,0,0,64.5,58.0,8492,7.06,7.11,4.57
-19948,1.66,2,2,3,64.4,54.0,8496,7.45,7.54,4.83
-19949,1.55,0,2,3,61.4,55.0,8498,7.41,7.49,4.58
-19950,1.25,0,3,3,61.1,55.0,8498,6.94,6.97,4.25
-19951,1.53,1,3,0,62.5,59.0,8499,7.4,7.36,4.61
-19952,1.53,0,3,0,61.9,57.0,8499,7.42,7.34,4.57
-19953,1.15,1,5,4,58.4,59.0,8501,6.92,6.84,4.02
-19954,1.32,0,1,5,61.0,56.0,8501,7.09,7.07,4.32
-19955,1.21,1,5,3,61.0,58.0,8504,6.88,6.82,4.18
-19956,1.24,0,5,3,62.1,56.0,8504,6.87,6.91,4.28
-19957,1.21,3,4,3,61.6,58.0,8505,6.79,6.88,4.21
-19958,1.7,0,2,0,60.5,57.0,8505,7.74,7.84,4.71
-19959,1.11,0,3,5,60.8,56.0,8508,6.68,6.76,4.08
-19960,1.23,0,3,4,61.6,57.0,8509,6.87,6.92,4.25
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-48145,0.7,0,2,3,61.6,56.0,1940,5.71,5.65,3.5
-48146,0.7,1,2,3,61.2,60.0,1940,5.73,5.7,3.5
-48147,0.53,1,6,2,62.4,61.0,1940,5.25,5.14,3.24
-48148,0.55,3,0,2,62.8,56.0,1941,5.2,5.24,3.28
-48149,0.53,0,5,4,61.8,58.0,1941,5.18,5.2,3.21
-48150,0.72,1,2,3,61.7,58.0,1941,5.67,5.77,3.53
-48151,0.56,0,3,5,61.7,55.0,1941,5.3,5.33,3.28
-48152,0.56,0,3,5,61.4,56.0,1941,5.3,5.32,3.26
-48153,0.59,0,0,3,60.2,59.0,1941,5.47,5.49,3.3
-48154,0.59,0,0,3,60.2,61.0,1941,5.43,5.47,3.28
-48155,0.54,0,6,2,62.0,56.0,1941,5.21,5.25,3.24
-48156,0.57,3,0,2,62.9,56.0,1942,5.31,5.35,3.35
-48157,0.72,3,3,0,63.5,58.0,1942,5.65,5.68,3.6
-48158,0.59,3,5,2,62.5,53.0,1942,5.46,5.35,3.38
-48159,0.71,3,5,1,61.1,61.0,1942,5.71,5.72,3.49
-48160,0.5,3,4,4,62.7,56.0,1942,5.08,5.03,3.17
-48161,0.7,1,3,0,62.9,61.0,1942,5.57,5.53,3.49
-48162,0.57,0,5,2,61.2,55.0,1942,5.35,5.38,3.29
-48163,0.57,0,5,2,59.3,57.0,1942,5.42,5.45,3.23
-48164,0.57,0,0,2,62.1,53.0,1942,5.36,5.41,3.34
-48165,0.51,2,4,4,60.2,62.0,1942,5.16,5.21,3.12
-48166,0.62,1,4,2,60.1,58.0,1942,5.56,5.48,3.32
-48167,0.53,0,4,4,62.3,56.0,1943,5.2,5.24,3.25
-48168,0.57,0,5,2,62.5,54.2,1943,5.29,5.31,3.31
-48169,0.79,2,4,0,64.3,60.0,1943,5.82,5.75,3.72
-48170,0.54,4,4,1,64.8,59.0,1944,5.11,5.05,3.29
-48171,0.54,3,4,1,63.5,58.0,1944,5.21,5.15,3.29
-48172,0.54,1,1,4,60.9,59.0,1944,5.28,5.26,3.21
-48173,0.57,1,6,3,61.9,58.0,1944,5.32,5.35,3.3
-48174,0.57,1,6,3,61.7,60.0,1944,5.28,5.32,3.27
-48175,0.72,1,2,3,61.4,58.0,1944,5.76,5.74,3.53
-48176,0.54,0,3,5,60.4,55.0,1944,5.32,5.27,3.2
-48177,0.54,0,3,5,60.3,56.0,1944,5.33,5.28,3.2
-48178,0.54,0,3,5,61.7,57.0,1944,5.24,5.23,3.23
-48179,0.54,0,3,5,61.1,57.0,1944,5.26,5.22,3.2
-48180,0.54,0,3,5,61.8,55.0,1944,5.24,5.21,3.23
-48181,0.54,1,3,5,62.0,57.0,1944,5.25,5.2,3.24
-48182,0.75,4,2,3,59.1,66.0,1944,5.99,5.82,3.49
-48183,0.75,0,2,0,60.8,57.0,1944,5.91,5.87,3.58
-48184,0.6,2,6,1,63.8,57.0,1944,5.32,5.27,3.38
-48185,0.72,1,2,3,60.2,59.0,1944,5.85,5.81,3.51
-48186,0.65,3,6,1,62.4,58.0,1945,5.52,5.57,3.46
-48187,0.91,2,2,6,63.1,57.0,1945,6.06,6.12,3.84
-48188,0.7,0,2,5,61.6,58.0,1945,5.69,5.71,3.51
-48189,0.72,2,3,0,65.8,59.0,1945,5.51,5.58,3.65
-48190,0.53,0,5,4,60.4,59.0,1946,5.26,5.3,3.19
-48191,0.53,0,5,4,61.0,56.0,1946,5.21,5.25,3.19
-48192,0.69,1,4,1,61.2,58.0,1946,5.73,5.7,3.5
-48193,0.53,1,0,3,59.9,61.0,1946,5.3,5.25,3.16
-48194,0.7,4,4,0,64.6,57.0,1947,5.6,5.57,3.61
-48195,0.71,0,1,0,61.6,56.0,1947,5.72,5.74,3.53
-48196,0.52,0,0,3,61.2,56.0,1947,5.2,5.16,3.17
-48197,0.46,1,6,5,60.0,59.0,1947,5.04,5.0,3.01
-48198,0.53,3,5,5,61.7,57.0,1948,5.19,5.22,3.21
-48199,0.59,3,6,3,63.2,57.0,1948,5.28,5.32,3.35
-48200,0.55,0,5,4,60.5,56.0,1948,5.36,5.37,3.24
-48201,0.53,0,5,4,61.4,57.0,1948,5.19,5.26,3.21
-48202,0.53,0,5,4,62.2,54.0,1948,5.17,5.22,3.23
-48203,0.53,0,5,4,61.7,56.0,1948,5.2,5.23,3.22
-48204,0.53,0,5,4,60.4,56.0,1948,5.24,5.29,3.18
-48205,0.7,1,3,1,61.1,58.0,1948,5.74,5.79,3.52
-48206,0.53,0,0,3,61.5,56.0,1948,5.22,5.25,3.22
-48207,0.7,2,5,0,59.4,61.0,1948,5.77,5.81,3.44
-48208,0.58,1,5,1,62.4,60.0,1949,5.34,5.3,3.32
-48209,0.62,3,6,1,61.3,58.0,1949,5.47,5.49,3.36
-48210,0.56,0,5,4,62.0,54.0,1949,5.28,5.32,3.29
-48211,0.57,0,4,2,62.6,56.0,1949,5.27,5.33,3.32
-48212,0.76,3,2,3,59.9,59.0,1949,5.92,5.94,3.55
-48213,0.57,0,4,2,61.9,56.0,1949,5.3,5.39,3.31
-48214,0.57,0,3,5,60.9,56.0,1949,5.36,5.38,3.27
-48215,0.57,0,0,3,60.4,57.0,1949,5.4,5.35,3.25
-48216,0.7,4,5,1,66.0,53.0,1949,5.53,5.47,3.63
-48217,0.56,0,3,2,61.6,56.0,1949,5.31,5.34,3.28
-48218,0.53,0,3,7,62.0,54.0,1949,5.22,5.27,3.25
-48219,0.57,0,5,2,62.0,55.0,1949,5.35,5.29,3.3
-48220,0.57,0,5,2,62.1,56.0,1949,5.33,5.3,3.3
-48221,0.57,0,5,2,61.3,56.0,1949,5.36,5.34,3.28
-48222,0.57,1,5,2,62.4,58.0,1949,5.32,5.29,3.31
-48223,0.57,0,0,3,61.7,56.0,1949,5.36,5.34,3.3
-48224,0.57,0,0,3,61.8,55.0,1949,5.38,5.34,3.31
-48225,0.57,0,0,3,61.9,57.0,1949,5.31,5.29,3.28
-48226,0.57,0,0,3,61.8,56.0,1949,5.33,5.29,3.28
-48227,0.57,1,0,3,61.0,58.0,1949,5.34,5.31,3.25
-48228,0.57,1,0,3,61.4,58.0,1949,5.37,5.32,3.28
-48229,0.57,0,0,3,61.6,56.0,1949,5.38,5.33,3.3
-48230,0.57,0,0,3,62.9,57.0,1949,5.29,5.26,3.32
-48231,0.57,1,6,1,58.9,59.0,1949,5.46,5.41,3.2
-48232,0.58,1,0,1,60.6,56.0,1949,5.42,5.38,3.27
-48233,0.57,1,6,1,63.0,59.0,1949,5.27,5.23,3.31
-48234,0.7,0,2,1,60.1,59.0,1949,5.79,5.75,3.47
-48235,0.56,0,4,3,61.8,55.0,1950,5.3,5.34,3.29
-48236,0.46,0,4,7,62.0,55.0,1950,4.96,4.98,3.08
-48237,0.7,2,3,0,60.2,63.0,1950,5.7,5.73,3.44
-48238,0.53,1,0,2,58.5,61.0,1950,5.39,5.28,3.12
-48239,0.54,0,5,4,60.8,56.0,1950,5.31,5.28,3.22
-48240,0.86,3,2,0,63.2,61.0,1950,6.05,5.97,3.79
-48241,0.5,4,0,2,56.1,64.0,1950,5.31,5.28,2.97
-48242,0.69,3,4,1,62.6,59.6,1951,5.59,5.66,3.52
-48243,0.58,0,5,3,61.1,55.0,1951,5.42,5.38,3.3
-48244,0.56,2,0,2,59.8,61.0,1951,5.35,5.36,3.2
-48245,0.69,2,4,1,58.4,61.7,1951,5.75,5.82,3.38
-48246,0.49,3,4,5,62.2,56.0,1952,5.04,5.08,3.14
-48247,0.73,3,2,3,60.6,61.0,1952,5.77,5.81,3.51
-48248,0.7,3,2,3,61.1,61.0,1952,5.64,5.69,3.46
-48249,0.63,3,0,1,61.0,57.6,1952,5.48,5.51,3.36
-48250,0.71,1,1,3,60.0,58.0,1952,5.79,5.85,3.49
-48251,0.53,0,6,2,61.3,56.0,1952,5.21,5.23,3.2
-48252,0.63,4,0,1,64.6,57.7,1952,5.36,5.41,3.47
-48253,0.51,1,4,4,62.3,58.0,1953,5.08,5.1,3.17
-48254,0.72,3,3,0,61.0,56.0,1953,5.75,5.8,3.52
-48255,0.7,0,2,3,62.0,58.0,1953,5.66,5.69,3.52
-48256,0.58,0,4,2,61.4,56.0,1953,5.38,5.4,3.31
-48257,0.7,1,2,2,59.6,62.0,1953,5.77,5.7,3.42
-48258,0.78,1,2,1,63.0,60.0,1954,5.88,5.82,3.66
-48259,1.0,4,5,6,67.3,57.0,1954,6.26,6.12,4.18
-48260,0.54,3,0,4,63.3,60.0,1955,5.1,5.17,3.25
-48261,0.53,0,5,4,60.8,57.0,1955,5.24,5.28,3.2
-48262,0.7,2,4,1,64.3,63.0,1955,5.55,5.58,3.58
-48263,0.46,0,0,5,62.5,53.0,1955,4.95,5.0,3.11
-48264,0.57,0,6,3,61.8,57.0,1955,5.29,5.32,3.28
-48265,0.54,0,6,2,61.4,53.0,1955,5.28,5.3,3.25
-48266,0.53,0,5,5,61.8,56.0,1956,5.19,5.24,3.22
-48267,0.52,0,3,7,62.1,57.0,1956,5.13,5.16,3.19
-48268,0.53,1,6,3,60.4,58.0,1956,5.26,5.23,3.17
-48269,0.53,0,6,3,60.2,57.0,1956,5.26,5.24,3.16
-48270,0.53,0,6,3,60.6,57.0,1956,5.28,5.24,3.19
-48271,0.53,1,6,3,61.5,58.0,1956,5.24,5.2,3.21
-48272,0.53,0,6,3,61.2,57.0,1956,5.26,5.2,3.2
-48273,0.53,0,6,3,61.7,55.0,1956,5.23,5.21,3.22
-48274,0.53,0,6,3,61.4,55.0,1956,5.25,5.21,3.21
-48275,0.53,0,6,3,61.2,55.0,1956,5.25,5.21,3.2
-48276,0.53,0,6,3,62.6,56.0,1956,5.18,5.14,3.23
-48277,0.53,0,6,3,62.2,56.0,1956,5.19,5.16,3.22
-48278,0.53,0,6,3,62.1,55.0,1956,5.2,5.17,3.22
-48279,0.53,0,6,3,61.5,56.0,1956,5.23,5.17,3.2
-48280,0.53,0,6,3,61.2,56.0,1956,5.23,5.2,3.19
-48281,0.53,0,6,3,61.4,56.0,1956,5.23,5.2,3.2
-48282,0.53,0,6,3,62.3,56.0,1956,5.17,5.14,3.21
-48283,0.77,4,2,1,60.2,66.0,1956,5.97,5.81,3.55
-48284,0.53,0,0,3,62.0,56.0,1956,5.29,5.2,3.25
-48285,0.53,0,0,3,60.8,56.0,1956,5.28,5.25,3.21
-48286,0.53,0,0,3,62.3,57.0,1956,5.23,5.19,3.25
-48287,0.73,0,2,1,61.3,57.0,1956,5.82,5.8,3.56
-48288,0.52,0,5,4,61.4,55.3,1957,5.18,5.21,3.19
-48289,0.52,0,5,4,61.6,55.0,1957,5.14,5.18,3.19
-48290,0.53,1,3,7,62.5,59.0,1957,5.15,5.18,3.23
-48291,0.54,0,5,2,61.6,56.0,1957,5.24,5.28,3.24
-48292,0.54,0,5,2,60.6,56.0,1957,5.28,5.31,3.21
-48293,0.54,0,5,2,61.0,57.0,1957,5.25,5.28,3.21
-48294,0.54,0,5,2,60.1,57.0,1957,5.29,5.31,3.18
-48295,0.52,0,5,4,61.6,54.0,1958,5.17,5.22,3.2
-48296,0.53,0,5,5,62.1,56.0,1958,5.19,5.21,3.23
-48297,0.75,1,2,1,61.7,56.0,1958,5.92,5.81,3.62
-48298,0.75,3,2,1,63.1,57.0,1958,5.79,5.74,3.64
-48299,0.51,3,6,2,62.2,59.0,1959,5.1,5.13,3.18
-48300,0.31,0,6,1,61.8,57.0,533,4.33,4.37,2.69
-48301,0.31,0,6,1,61.2,57.0,533,4.34,4.38,2.67
-48302,0.31,0,6,1,62.0,56.0,533,4.35,4.39,2.71
-48303,0.31,2,6,1,63.8,57.0,533,4.29,4.33,2.75
-48304,0.27,0,1,3,62.1,56.0,534,4.14,4.18,2.58
-48305,0.32,0,3,3,61.7,56.0,534,4.39,4.42,2.72
-48306,0.32,0,3,3,61.5,57.0,534,4.41,4.45,2.72
-48307,0.33,0,1,2,62.1,53.9,534,4.43,4.45,2.76
-48308,0.39,0,1,1,62.4,54.0,534,4.67,4.69,2.92
-48309,0.33,3,3,0,63.5,57.0,534,4.39,4.36,2.78
-48310,0.25,0,4,3,61.6,55.0,535,4.06,4.08,2.51
-48311,0.25,0,4,3,62.9,55.0,535,4.01,4.05,2.53
-48312,0.25,0,4,3,61.7,56.0,535,4.04,4.07,2.5
-48313,0.25,0,0,3,62.1,57.0,535,4.01,4.05,2.5
-48314,0.25,0,0,3,60.6,57.0,535,4.08,4.11,2.48
-48315,0.25,0,0,3,62.1,57.0,535,4.02,4.06,2.5
-48316,0.28,3,4,2,62.3,52.0,536,4.2,4.26,2.62
-48317,0.34,3,5,1,61.2,54.0,536,4.53,4.56,2.78
-48318,0.31,0,1,5,61.2,55.6,536,4.33,4.38,2.67
-48319,0.23,0,5,5,61.9,58.0,536,3.93,3.96,2.44
-48320,0.23,0,5,5,61.5,57.0,536,3.94,3.97,2.43
-48321,0.23,0,5,5,60.8,57.0,536,3.96,3.99,2.41
-48322,0.23,0,5,7,61.8,56.0,536,3.92,3.94,2.43
-48323,0.23,0,5,7,62.8,55.0,536,3.93,3.96,2.48
-48324,0.23,0,5,7,61.7,55.0,536,3.94,3.98,2.44
-48325,0.33,2,4,1,61.8,62.0,536,4.4,4.45,2.74
-48326,0.24,0,4,2,62.0,56.0,536,3.99,3.94,2.46
-48327,0.24,0,4,3,61.8,57.0,536,4.02,3.97,2.47
-48328,0.24,1,0,3,62.6,58.0,536,4.03,3.99,2.51
-48329,0.34,1,3,0,58.6,62.0,536,4.62,4.6,2.7
-48330,0.7,3,2,0,63.2,60.0,1959,5.58,5.62,3.54
-48331,0.7,3,2,1,61.1,60.0,1959,5.66,5.7,3.47
-48332,0.62,0,6,1,62.2,55.0,1959,5.45,5.51,3.41
-48333,0.72,2,2,2,63.7,54.0,1959,5.68,5.71,3.63
-48334,0.51,0,6,2,62.1,55.0,1959,5.14,5.2,3.21
-48335,0.63,3,5,2,61.5,56.0,1960,5.53,5.49,3.39
-48336,0.54,1,0,2,61.3,56.0,1960,5.27,5.23,3.22
-48337,0.63,1,4,3,58.2,61.0,1960,5.68,5.62,3.29
-48338,0.5,0,4,4,62.1,58.0,1960,5.07,5.11,3.16
-48339,0.71,1,3,0,61.5,57.0,1960,5.76,5.69,3.52
-48340,0.71,2,5,0,63.8,56.0,1960,5.65,5.6,3.59
-48341,0.51,3,4,2,60.8,59.0,1961,5.15,5.18,3.14
-48342,0.58,0,3,5,62.1,53.6,1961,5.35,5.38,3.34
-48343,0.56,0,3,5,62.1,53.8,1961,5.27,5.33,3.29
-48344,0.57,0,3,5,61.6,56.0,1961,5.32,5.35,3.28
-48345,0.58,0,3,5,62.4,55.0,1961,5.32,5.36,3.33
-48346,0.57,0,4,2,62.3,57.0,1961,5.29,5.32,3.3
-48347,0.7,0,1,0,61.3,57.0,1961,5.7,5.73,3.5
-48348,0.74,1,3,0,60.9,61.0,1962,5.84,5.75,3.53
-48349,0.54,0,5,4,62.4,53.4,1962,5.22,5.25,3.26
-48350,0.51,0,6,2,61.6,57.0,1962,5.13,5.16,3.17
-48351,0.54,0,0,2,61.2,57.0,1962,5.25,5.27,3.22
-48352,0.74,4,3,0,62.0,53.0,1962,5.85,5.74,3.6
-48353,0.56,0,6,3,61.4,57.0,1963,5.32,5.36,3.28
-48354,0.56,0,6,3,61.9,57.0,1963,5.26,5.3,3.27
-48355,0.56,0,6,3,60.9,56.0,1963,5.32,5.35,3.25
-48356,0.56,0,6,3,61.6,56.0,1963,5.27,5.31,3.26
-48357,0.56,0,6,3,60.9,56.0,1963,5.31,5.36,3.25
-48358,0.56,0,6,3,61.2,56.0,1963,5.3,5.35,3.26
-48359,0.56,0,6,3,62.2,56.0,1963,5.24,5.28,3.27
-48360,0.56,3,0,3,61.1,57.0,1963,5.33,5.37,3.27
-48361,0.56,0,6,3,61.1,56.0,1963,5.3,5.33,3.25
-48362,0.56,0,6,3,61.4,55.0,1963,5.33,5.35,3.28
-48363,0.56,0,6,3,60.6,56.0,1963,5.33,5.37,3.24
-48364,0.56,0,6,3,61.0,57.0,1963,5.28,5.35,3.24
-48365,0.56,1,6,3,61.1,58.0,1963,5.3,5.34,3.25
-48366,0.56,3,6,3,62.8,57.0,1963,5.25,5.29,3.31
-48367,0.56,0,6,3,61.5,57.0,1963,5.28,5.33,3.26
-48368,0.56,0,6,3,62.1,57.0,1963,5.25,5.28,3.27
-48369,0.71,1,5,0,62.4,59.0,1963,5.67,5.61,3.52
-48370,0.51,0,5,5,61.4,56.0,1963,5.14,5.15,3.16
-48371,0.74,0,2,1,61.7,56.0,1963,5.81,5.85,3.6
-48372,0.57,0,6,3,62.1,56.0,1963,5.34,5.29,3.3
-48373,0.57,0,6,3,62.4,57.0,1963,5.32,5.26,3.3
-48374,0.68,3,4,1,60.1,57.9,1964,5.69,5.74,3.43
-48375,0.7,3,0,0,60.2,64.0,1964,5.67,5.7,3.42
-48376,0.63,0,6,1,61.5,56.0,1964,5.52,5.53,3.4
-48377,0.88,2,2,2,64.0,56.0,1964,5.98,5.9,3.8
-48378,0.57,0,0,2,61.4,57.0,1965,5.32,5.37,3.28
-48379,0.58,0,0,2,60.1,57.0,1965,5.41,5.47,3.27
-48380,0.75,2,2,1,61.0,59.0,1965,5.79,5.85,3.55
-48381,0.59,1,1,7,61.2,58.0,1965,5.41,5.38,3.3
-48382,0.7,0,5,0,61.8,56.0,1966,5.66,5.6,3.48
-48383,0.7,1,5,0,62.0,59.0,1966,5.62,5.58,3.47
-48384,0.72,3,2,1,63.0,57.0,1966,5.69,5.74,3.6
-48385,0.5,0,5,5,62.2,56.0,1966,5.1,5.12,3.18
-48386,0.5,1,5,5,61.0,60.0,1966,5.08,5.12,3.11
-48387,0.59,0,5,4,62.2,56.0,1966,5.37,5.4,3.35
-48388,0.5,2,5,5,63.3,56.0,1966,5.07,5.1,3.22
-48389,0.5,3,5,5,62.3,59.0,1966,5.01,5.04,3.13
-48390,0.5,0,5,5,62.7,57.0,1966,5.07,5.1,3.19
-48391,0.7,3,5,0,58.2,63.0,1966,5.82,5.79,3.38
-48392,0.72,0,2,1,62.6,56.0,1966,5.67,5.74,3.57
-48393,0.5,2,5,5,60.0,62.0,1966,5.12,5.18,3.09
-48394,0.7,1,1,3,63.0,60.0,1966,5.62,5.59,3.53
-48395,0.7,1,4,0,58.4,61.0,1966,5.86,5.79,3.4
-48396,0.64,3,6,1,62.2,55.0,1967,5.51,5.55,3.44
-48397,0.64,0,6,1,62.1,56.0,1967,5.5,5.55,3.43
-48398,0.52,0,3,7,59.2,58.0,1967,5.23,5.31,3.12
-48399,0.59,0,0,3,62.3,55.0,1968,5.35,5.38,3.34
-48400,0.71,1,2,3,60.1,61.0,1968,5.82,5.77,3.48
-48401,0.83,2,4,0,63.7,59.0,1968,5.91,5.86,3.75
-48402,0.7,3,2,4,63.2,55.0,1968,5.63,5.57,3.54
-48403,0.64,3,6,1,62.3,58.0,1969,5.49,5.53,3.43
-48404,0.72,2,5,1,64.0,59.0,1969,5.59,5.63,3.59
-48405,0.52,0,5,5,61.0,57.0,1969,5.18,5.2,3.17
-48406,0.61,3,3,4,60.5,57.0,1970,5.52,5.54,3.35
-48407,0.61,3,3,4,61.4,57.0,1970,5.46,5.5,3.36
-48408,0.72,3,3,0,62.6,53.0,1970,5.75,5.78,3.61
-48409,0.72,3,3,0,62.9,57.0,1970,5.64,5.68,3.56
-48410,0.51,3,0,2,61.8,54.7,1970,5.12,5.15,31.8
-48411,0.51,3,0,2,62.8,57.1,1970,5.07,5.1,3.2
-48412,0.6,0,4,3,61.9,57.0,1970,5.39,5.43,3.35
-48413,0.76,1,5,0,62.6,55.0,1970,5.87,5.82,3.66
-48414,0.65,1,4,3,59.6,58.0,1970,5.65,5.62,3.36
-48415,0.73,1,4,0,60.2,59.0,1971,5.87,5.82,3.52
-48416,0.61,3,4,3,62.9,58.0,1971,5.33,5.39,3.37
-48417,0.7,3,1,2,62.4,58.0,1971,5.62,5.67,3.52
-48418,0.61,3,4,3,59.6,61.0,1971,5.5,5.58,3.3
-48419,0.51,1,5,5,61.6,59.0,1971,5.12,5.08,3.14
-48420,0.7,0,2,3,61.5,57.0,1971,5.7,5.74,3.52
-48421,0.61,2,4,3,63.0,64.2,1971,5.29,5.32,3.34
-48422,0.51,3,5,5,63.2,58.0,1971,5.11,5.08,3.22
-48423,0.7,1,0,1,61.1,60.0,1971,5.69,5.56,3.44
-48424,0.5,1,3,7,61.4,61.0,1971,5.14,5.03,3.12
-48425,0.51,1,4,2,61.7,58.0,1971,5.11,5.07,3.14
-48426,0.71,3,3,0,63.7,56.0,1972,5.62,5.65,3.59
-48427,0.57,0,5,2,61.4,55.0,1972,5.34,5.38,3.29
-48428,0.7,3,1,1,63.8,56.0,1973,5.58,5.62,3.57
-48429,0.7,0,3,0,63.1,54.0,1973,5.64,5.68,3.57
-48430,0.7,0,2,3,61.9,57.0,1974,5.65,5.69,3.51
-48431,0.7,0,2,1,62.3,57.0,1974,5.69,5.71,3.55
-48432,0.51,1,5,4,59.1,59.0,1974,5.21,5.18,3.07
-48433,0.51,0,5,4,60.1,56.0,1974,5.22,5.19,3.13
-48434,0.51,1,5,4,61.7,58.0,1974,5.18,5.13,3.18
-48435,0.51,0,5,4,61.2,55.0,1974,5.18,5.15,3.16
-48436,0.51,0,5,4,61.1,56.0,1974,5.19,5.15,3.16
-48437,0.51,0,5,4,61.9,55.0,1974,5.19,5.15,3.2
-48438,0.51,0,5,4,61.5,57.0,1974,5.18,5.13,3.17
-48439,0.51,0,5,4,61.3,57.0,1974,5.15,5.12,3.15
-48440,0.51,1,5,4,61.5,56.0,1974,5.15,5.12,3.16
-48441,0.51,0,5,4,61.6,56.0,1974,5.17,5.12,3.17
-48442,0.51,0,5,4,62.9,57.0,1974,5.12,5.09,3.21
-48443,0.51,0,5,4,62.6,57.0,1974,5.12,5.1,3.2
-48444,0.51,0,5,4,61.8,56.0,1974,5.16,5.1,3.17
-48445,0.51,1,5,4,61.7,58.0,1974,5.14,5.11,3.16
-48446,0.51,3,5,4,63.1,58.0,1974,5.11,5.07,3.21
-48447,0.7,1,3,0,61.0,59.0,1974,5.76,5.71,3.5
-48448,0.7,4,4,1,65.3,54.0,1974,5.58,5.54,3.63
-48449,0.55,0,0,2,61.7,55.0,1975,5.27,5.29,3.26
-48450,0.55,3,6,3,58.5,58.4,1975,5.39,5.42,3.16
-48451,0.55,0,0,2,62.2,55.0,1975,5.22,5.26,3.26
-48452,0.55,0,0,2,61.1,55.0,1975,5.3,5.33,3.25
-48453,0.55,1,6,2,62.0,58.0,1975,5.26,5.28,3.27
-48454,0.55,3,4,2,61.2,56.4,1975,5.28,5.33,3.25
-48455,0.58,1,5,4,61.4,61.0,1975,5.43,5.38,3.32
-48456,0.58,1,5,4,61.5,60.0,1975,5.37,5.33,3.29
-48457,0.73,3,0,0,63.1,59.0,1975,5.76,5.72,3.62
-48458,0.51,0,5,4,62.7,55.0,1976,5.08,5.1,3.19
-48459,0.57,0,5,4,60.5,56.0,1976,5.36,5.38,3.25
-48460,0.5,2,5,7,64.0,55.0,1976,5.05,5.08,3.24
-48461,0.56,0,5,4,61.6,56.0,1976,5.29,5.32,3.27
-48462,0.61,2,3,4,58.7,64.0,1976,5.53,5.5,3.24
-48463,0.61,1,6,1,62.3,58.0,1976,5.45,5.4,3.38
-48464,0.58,3,6,3,60.2,57.0,1977,5.41,5.45,3.27
-48465,0.7,3,1,0,61.8,58.0,1977,5.65,5.71,3.51
-48466,0.71,1,5,0,62.3,55.0,1977,5.72,5.68,3.55
-48467,0.51,1,6,3,60.1,59.0,1977,5.19,5.16,3.11
-48468,0.5,3,4,5,63.5,60.0,1978,4.98,5.04,3.18
-48469,0.74,3,2,3,62.3,55.0,1978,5.79,5.83,3.62
-48470,0.66,3,3,2,61.6,58.1,1978,5.54,5.58,3.42
-48471,0.52,0,4,4,62.1,54.0,1978,5.17,5.2,3.22
-48472,0.59,0,6,3,61.4,57.0,1978,5.39,5.4,3.31
-48473,0.56,0,0,2,61.0,57.0,1978,5.32,5.34,3.25
-48474,0.66,2,3,2,59.8,60.9,1978,5.62,5.68,3.38
-48475,0.7,2,4,0,63.3,58.0,1978,5.57,5.61,3.54
-48476,0.58,3,3,5,63.3,55.0,1979,5.27,5.31,3.35
-48477,0.71,3,2,1,59.7,57.0,1979,5.84,5.88,3.5
-48478,0.7,3,2,1,61.7,57.0,1979,5.65,5.69,3.5
-48479,0.56,0,5,4,61.2,55.0,1979,5.35,5.37,3.28
-48480,0.58,0,4,2,61.2,57.0,1979,5.36,5.39,3.29
-48481,0.58,0,4,2,59.9,59.0,1979,5.42,5.47,3.26
-48482,0.51,2,0,4,62.0,60.5,1980,5.05,5.08,3.15
-48483,0.55,1,3,5,61.1,57.0,1980,5.35,5.26,3.24
-48484,0.55,0,4,2,62.0,55.0,1980,5.29,5.26,3.27
-48485,0.55,0,4,2,61.6,57.0,1980,5.29,5.23,3.24
-48486,0.55,0,4,2,61.9,56.0,1980,5.29,5.25,3.26
-48487,0.5,1,6,3,61.3,61.0,1980,5.1,5.04,3.11
-48488,0.5,4,6,4,67.6,57.0,1980,4.95,4.84,3.31
-48489,0.75,1,5,1,59.4,62.0,1980,5.95,5.91,3.52
-48490,0.67,3,3,2,63.4,59.0,1981,5.53,5.57,3.52
-48491,0.55,0,5,4,62.0,56.0,1981,5.26,5.28,3.27
-48492,0.54,0,5,4,61.9,57.0,1981,5.22,5.25,3.24
-48493,0.66,0,6,3,61.8,56.0,1981,5.6,5.57,3.45
-48494,0.52,0,4,3,61.5,56.0,1981,5.19,5.21,3.2
-48495,0.57,0,6,3,61.8,55.0,1981,5.32,5.34,3.29
-48496,0.56,0,4,2,61.7,55.1,1981,5.29,5.33,3.27
-48497,0.54,0,3,7,61.5,54.0,1981,5.27,5.3,3.25
-48498,0.7,4,3,3,57.0,58.0,1981,5.87,5.89,3.35
-48499,0.7,4,3,3,64.9,56.0,1981,5.49,5.51,3.57
-48500,0.71,3,2,2,63.3,55.0,1981,5.66,5.62,3.57
-48501,0.57,0,0,2,62.9,57.0,1982,5.29,5.27,3.32
-48502,0.54,0,5,5,61.3,56.0,1982,5.23,5.28,3.22
-48503,0.57,0,6,2,61.8,55.0,1982,5.31,5.37,3.3
-48504,0.59,1,3,5,62.0,58.0,1982,5.38,5.36,3.33
-48505,0.74,4,2,2,58.8,68.0,1982,5.91,5.8,3.44
-48506,0.7,1,3,0,61.7,58.0,1982,5.72,5.69,3.52
-48507,0.7,4,5,1,65.0,59.0,1982,5.57,5.5,3.6
-48508,0.54,3,5,5,60.6,55.0,1983,5.28,5.31,3.21
-48509,0.61,3,0,1,62.2,60.0,1983,5.44,5.4,3.37
-48510,0.58,0,4,2,62.1,56.0,1983,5.32,5.38,3.32
-48511,0.54,0,3,4,60.9,57.0,1983,5.26,5.22,3.19
-48512,0.6,3,6,3,61.5,59.0,1984,5.4,5.43,3.33
-48513,0.58,0,0,3,61.1,57.0,1984,5.41,5.39,3.3
-48514,0.58,0,5,2,62.6,54.0,1984,5.34,5.3,3.33
-48515,0.58,1,5,2,61.5,58.0,1984,5.41,5.35,3.31
-48516,0.58,0,0,3,61.9,57.0,1984,5.37,5.33,3.31
-48517,0.58,0,0,3,61.6,55.0,1984,5.38,5.33,3.3
-48518,0.58,0,0,3,61.9,56.0,1984,5.38,5.35,3.32
-48519,0.76,1,3,0,59.0,59.0,1985,6.05,6.02,3.56
-48520,0.54,0,5,4,61.4,55.0,1985,5.22,5.3,3.23
-48521,0.54,0,5,4,60.6,56.0,1985,5.26,5.27,3.19
-48522,0.54,0,5,4,61.0,56.0,1985,5.25,5.3,3.22
-48523,0.54,0,5,4,61.8,56.0,1985,5.23,5.25,3.24
-48524,0.54,0,5,4,62.1,56.0,1985,5.19,5.22,3.23
-48525,0.54,0,5,4,61.5,54.0,1985,5.24,5.26,3.23
-48526,0.51,0,0,3,61.6,55.0,1985,5.15,5.2,3.19
-48527,0.74,0,2,2,61.2,56.0,1985,5.82,5.87,3.58
-48528,0.59,3,6,3,59.2,56.0,1986,5.47,5.54,3.26
-48529,0.55,3,0,2,61.6,55.0,1986,5.26,5.28,3.25
-48530,0.71,3,2,0,60.9,55.0,1986,5.76,5.87,3.54
-48531,0.73,3,5,0,62.6,56.0,1986,5.77,5.79,3.62
-48532,0.51,0,6,3,61.1,56.0,1986,5.16,5.2,3.17
-48533,0.71,3,3,1,62.5,63.0,1986,5.66,5.63,3.53
-48534,0.54,0,6,2,61.6,54.0,1987,5.24,5.25,3.23
-48535,0.46,0,5,7,61.7,56.0,1987,4.97,5.01,3.08
-48536,0.46,0,5,7,61.1,56.0,1987,5.0,5.02,3.06
-48537,0.7,1,6,0,60.8,61.0,1987,5.69,5.64,3.45
-48538,0.5,1,5,5,61.3,57.0,1987,5.15,5.07,3.13
-48539,0.5,1,4,4,62.7,56.0,1987,5.08,5.03,3.17
-48540,0.72,1,2,1,62.1,59.0,1987,5.75,5.71,3.56
-48541,0.5,3,5,5,59.6,63.0,1987,5.16,5.11,3.06
-48542,0.72,3,2,2,62.7,58.0,1988,5.67,5.72,3.57
-48543,0.54,3,6,2,59.9,57.0,1988,5.38,5.3,3.2
-48544,0.52,0,1,7,60.2,56.0,1988,5.23,5.27,3.16
-48545,0.7,2,4,1,63.5,59.0,1988,5.58,5.64,3.56
-48546,0.61,3,4,3,61.8,56.0,1989,5.42,5.46,3.36
-48547,0.72,3,2,1,63.0,58.0,1989,5.67,5.7,3.58
-48548,0.74,2,3,3,59.2,64.0,1989,5.79,5.66,3.4
-48549,0.65,0,0,1,62.7,54.0,1989,5.58,5.56,3.49
-48550,0.54,1,4,3,62.0,59.0,1989,5.22,5.16,3.22
-48551,0.51,1,0,2,61.6,57.0,1989,5.13,5.1,3.15
-48552,0.55,0,6,2,61.7,55.0,1990,5.26,5.25,3.24
-48553,0.7,3,6,0,60.9,60.0,1990,5.68,5.72,3.47
-48554,0.58,1,5,4,59.7,59.0,1990,5.4,5.45,3.24
-48555,0.67,2,5,3,63.8,60.0,1990,5.5,5.47,3.5
-48556,0.53,0,5,5,62.1,54.0,1990,5.2,5.23,3.24
-48557,0.7,4,4,0,57.1,64.0,1990,5.81,5.86,3.33
-48558,0.63,3,3,2,61.1,57.0,1991,5.5,5.54,3.37
-48559,0.71,0,0,0,62.6,55.0,1991,5.72,5.68,3.57
-48560,0.64,0,6,1,62.1,57.0,1991,5.52,5.49,3.42
-48561,0.56,0,6,3,61.2,56.0,1991,5.31,5.38,3.27
-48562,0.55,0,5,2,60.9,55.0,1991,5.34,5.37,3.26
-48563,0.52,0,6,2,60.9,56.0,1991,5.19,5.22,3.17
-48564,0.64,0,1,7,62.0,54.0,1991,5.53,5.56,3.44
-48565,0.5,2,5,4,64.4,57.0,1991,4.97,5.05,3.22
-48566,0.72,1,1,3,61.1,61.0,1991,5.79,5.74,3.52
-48567,0.59,1,5,2,62.5,53.0,1991,5.46,5.35,3.38
-48568,0.38,0,6,5,61.6,55.0,1992,4.66,4.69,2.88
-48569,0.63,0,6,1,62.0,56.0,1992,5.55,5.48,3.42
-48570,0.46,3,6,5,61.6,59.0,1993,4.92,4.97,3.05
-48571,0.71,3,2,2,64.1,58.0,1993,5.61,5.65,3.61
-48572,0.59,3,0,2,61.6,61.0,1993,5.34,5.37,3.3
-48573,0.71,3,5,0,63.0,56.0,1993,5.62,5.65,3.55
-48574,0.53,0,5,4,61.7,54.5,1993,5.2,5.26,3.23
-48575,0.56,0,5,5,61.7,57.0,1993,5.29,5.31,3.27
-48576,0.54,1,0,3,61.3,59.0,1993,5.26,5.21,3.21
-48577,0.54,1,6,3,61.1,55.0,1993,5.37,5.24,3.24
-48578,0.54,0,6,3,61.1,56.0,1993,5.29,5.25,3.22
-48579,0.54,1,6,3,60.6,58.0,1993,5.3,5.26,3.2
-48580,0.54,1,6,3,60.9,58.0,1993,5.31,5.27,3.22
-48581,0.54,0,6,3,61.1,57.0,1993,5.25,5.22,3.2
-48582,0.54,0,6,3,61.3,56.0,1993,5.26,5.22,3.21
-48583,0.54,0,6,3,61.8,57.0,1993,5.26,5.22,3.24
-48584,0.54,0,6,3,62.6,57.0,1993,5.24,5.18,3.26
-48585,0.54,0,6,3,61.1,57.0,1993,5.29,5.19,3.2
-48586,0.54,0,6,3,61.8,56.0,1993,5.24,5.21,3.23
-48587,0.54,1,6,3,61.5,58.0,1993,5.26,5.21,3.22
-48588,0.54,1,6,3,61.7,58.0,1993,5.24,5.16,3.21
-48589,0.54,1,6,2,61.1,58.0,1994,5.24,5.3,3.22
-48590,0.59,0,0,1,61.6,55.0,1994,5.41,5.43,3.34
-48591,0.71,4,5,0,65.6,61.0,1994,5.46,5.37,3.55
-48592,0.52,3,6,2,59.6,57.0,1995,5.24,5.3,3.14
-48593,0.7,2,1,1,63.6,55.0,1995,5.62,5.58,3.56
-48594,0.7,4,3,1,61.0,66.0,1995,5.68,5.64,3.45
-48595,0.7,3,3,0,63.1,55.0,1995,5.71,5.66,3.59
-48596,0.7,3,1,2,63.3,55.0,1995,5.64,5.6,3.56
-48597,0.7,4,5,1,61.6,66.0,1995,5.62,5.57,3.45
-48598,0.7,4,1,3,64.4,55.0,1996,5.6,5.55,3.59
-48599,0.55,0,0,2,61.2,54.0,1996,5.3,5.32,3.25
-48600,0.6,0,6,1,62.2,56.0,1996,5.44,5.4,3.37
-48601,0.6,1,6,1,60.4,60.0,1996,5.48,5.44,3.3
-48602,0.72,0,2,4,61.6,56.0,1996,5.76,5.73,3.54
-48603,0.63,1,3,2,61.7,59.0,1996,5.53,5.49,3.4
-48604,0.77,1,5,0,61.2,58.0,1996,5.99,5.96,3.64
-48605,0.7,3,2,3,63.2,57.0,1997,5.63,5.66,3.57
-48606,1.0,4,1,6,66.9,57.0,1997,6.15,6.03,4.09
-48607,0.57,3,6,3,61.4,56.0,1998,5.34,5.37,3.29
-48608,0.57,0,6,3,61.8,56.0,1998,5.31,5.34,3.29
-48609,0.7,4,0,0,64.4,58.0,1998,5.58,5.51,3.57
-48610,0.7,4,0,0,65.3,56.0,1998,5.57,5.54,3.63
-48611,0.51,0,4,4,62.2,55.0,1998,5.12,5.11,3.18
-48612,0.51,0,4,4,61.9,56.0,1998,5.13,5.15,3.18
-48613,0.51,0,4,4,61.7,56.0,1998,5.12,5.15,3.17
-48614,0.79,1,3,0,61.6,58.0,1998,5.98,5.94,3.67
-48615,0.75,1,2,2,61.6,57.0,1998,5.88,5.84,3.61
-48616,0.54,0,5,1,60.0,57.0,1999,5.31,5.36,3.2
-48617,0.7,4,5,0,61.5,66.0,1999,5.55,5.6,3.43
-48618,0.7,3,3,0,61.5,63.0,1999,5.63,5.59,3.45
-48619,0.7,3,1,1,63.5,59.0,1999,5.65,5.62,3.58
-48620,0.7,1,3,0,62.1,62.0,1999,5.67,5.63,3.51
-48621,0.7,2,3,0,63.6,54.0,1999,5.67,5.62,3.59
-48622,0.56,1,5,2,61.0,60.0,1999,5.39,5.32,3.27
-48623,0.59,3,4,1,63.4,54.0,2000,5.35,5.4,3.41
-48624,0.7,0,2,2,62.8,56.0,2000,5.64,5.66,3.55
-48625,0.7,2,0,1,64.4,58.0,2000,5.48,5.52,3.54
-48626,0.7,4,0,1,57.2,66.0,2000,5.76,5.74,3.29
-48627,0.58,3,4,2,61.5,60.0,2001,5.32,5.38,3.29
-48628,0.77,3,2,0,61.2,56.0,2001,5.91,5.96,3.63
-48629,0.77,3,2,0,61.3,58.0,2001,5.84,5.91,3.6
-48630,0.3,4,6,0,64.6,54.0,536,4.29,4.25,2.76
-48631,0.34,1,2,3,62.3,60.0,536,4.49,4.46,2.79
-48632,0.34,2,4,0,63.9,56.0,536,4.43,4.4,2.82
-48633,0.3,3,5,1,63.2,55.0,536,4.29,4.26,2.7
-48634,0.3,2,6,0,60.8,65.0,536,4.29,4.26,2.6
-48635,0.37,3,3,3,61.2,54.0,537,4.62,4.7,2.85
-48636,0.37,0,1,3,61.5,56.0,537,4.6,4.63,2.84
-48637,0.34,0,4,1,61.1,56.0,537,4.52,4.55,2.77
-48638,0.31,1,1,1,62.3,58.0,537,4.38,4.35,2.72
-48639,0.31,1,1,1,60.1,59.0,537,4.41,4.38,2.64
-48640,0.31,1,1,1,59.6,58.0,537,4.44,4.39,2.63
-48641,0.31,1,1,1,62.8,60.0,537,4.31,4.29,2.7
-48642,0.31,0,1,1,62.5,56.0,537,4.35,4.29,2.7
-48643,0.31,0,1,1,62.2,56.0,537,4.37,4.34,2.71
-48644,0.27,0,0,3,60.8,56.0,537,4.25,4.23,2.58
-48645,0.31,1,6,1,58.5,59.0,537,4.49,4.44,2.61
-48646,0.23,2,5,4,63.8,56.0,537,3.94,3.9,2.5
-48647,0.34,0,3,3,61.7,57.0,537,4.46,4.48,2.76
-48648,0.34,1,1,2,61.0,60.0,537,4.48,4.51,2.74
-48649,0.34,2,3,3,63.7,57.0,537,4.42,4.44,2.82
-48650,0.34,1,3,3,61.1,58.0,537,4.47,4.5,2.74
-48651,0.36,1,5,1,61.8,59.0,537,4.55,4.57,2.82
-48652,0.36,1,6,0,61.2,59.0,537,4.56,4.59,2.8
-48653,0.36,2,5,1,63.3,56.0,537,4.5,4.53,2.86
-48654,0.34,1,3,3,61.7,60.0,537,4.47,4.51,2.77
-48655,0.34,3,3,3,62.4,60.0,537,4.41,4.44,2.76
-48656,0.34,1,1,2,59.5,59.0,537,4.55,4.59,2.72
-48657,0.34,1,3,3,60.2,60.0,537,4.51,4.56,2.73
-48658,0.34,2,3,3,63.1,56.0,537,4.41,4.46,2.8
-48659,0.34,2,3,3,63.2,55.0,537,4.46,4.5,2.83
-48660,0.72,3,5,0,63.4,57.0,2001,5.67,5.71,3.61
-48661,0.73,3,3,1,62.2,57.0,2001,5.77,5.8,3.6
-48662,0.55,0,0,2,61.2,57.0,2001,5.28,5.3,3.24
-48663,0.55,0,0,2,62.0,56.0,2001,5.24,5.27,3.26
-48664,0.53,0,5,5,61.4,57.0,2001,5.18,5.21,3.19
-48665,0.75,0,2,3,61.5,54.0,2001,5.87,5.9,3.62
-48666,0.71,0,2,1,60.7,61.0,2001,5.72,5.77,3.49
-48667,0.57,1,6,1,62.6,56.0,2001,5.4,5.34,3.36
-48668,0.61,0,4,3,62.4,55.0,2002,5.42,5.45,3.39
-48669,0.71,4,0,0,64.8,63.0,2002,5.63,5.51,3.61
-48670,0.74,1,0,0,62.2,57.0,2002,5.79,5.77,3.59
-48671,0.5,3,4,4,61.9,56.0,2003,5.09,5.12,3.16
-48672,0.5,3,0,4,60.9,59.0,2003,5.07,5.11,3.1
-48673,0.74,3,2,3,60.8,58.0,2003,5.79,5.85,3.54
-48674,0.71,3,1,0,60.4,59.0,2003,5.76,5.79,3.49
-48675,0.54,3,6,2,62.1,59.0,2004,5.18,5.19,3.22
-48676,0.56,0,5,4,61.4,54.0,2004,5.33,5.35,3.28
-48677,0.54,0,6,2,62.1,57.0,2004,5.19,5.22,3.23
-48678,0.51,0,5,5,62.6,57.0,2005,5.08,5.14,3.2
-48679,0.51,3,5,5,60.1,56.0,2005,5.16,5.35,3.16
-48680,0.51,0,5,5,62.3,55.0,2005,5.1,5.14,3.19
-48681,0.53,0,5,5,60.8,56.0,2005,5.22,5.24,3.18
-48682,0.53,0,5,5,61.8,56.0,2005,5.18,5.2,3.21
-48683,0.53,0,5,5,61.2,55.0,2005,5.19,5.23,3.19
-48684,0.7,1,2,2,60.9,58.0,2005,5.73,5.66,3.47
-48685,0.77,1,2,2,62.2,61.0,2005,5.85,5.82,3.63
-48686,0.77,4,2,2,61.2,66.0,2005,5.92,5.83,3.6
-48687,0.77,2,0,0,64.1,56.0,2005,5.84,5.77,3.72
-48688,0.71,0,3,1,60.8,57.0,2006,5.77,5.81,3.52
-48689,0.72,0,2,2,62.7,57.0,2006,5.71,5.74,3.59
-48690,0.71,1,3,1,61.0,58.0,2006,5.73,5.77,3.51
-48691,0.58,0,3,5,61.6,56.0,2006,5.35,5.39,3.31
-48692,0.76,3,6,0,59.1,63.0,2006,5.91,5.84,3.47
-48693,0.67,3,6,1,60.2,60.0,2007,5.61,5.68,3.4
-48694,0.71,2,1,2,64.2,57.0,2007,5.62,5.57,3.59
-48695,0.54,0,5,5,62.2,53.9,2007,5.22,5.25,3.26
-48696,0.57,3,0,2,59.1,57.0,2008,5.43,5.5,3.23
-48697,0.71,3,1,1,62.9,59.0,2008,5.66,5.7,3.57
-48698,0.5,2,0,5,58.3,62.0,2008,5.16,5.23,3.03
-48699,0.65,3,5,3,63.2,57.0,2009,5.54,5.47,3.48
-48700,0.54,1,6,3,62.2,56.0,2009,5.27,5.21,3.26
-48701,0.62,1,4,3,62.3,60.0,2009,5.46,5.39,3.38
-48702,0.56,3,5,4,61.4,57.0,2010,5.26,5.32,3.25
-48703,0.56,3,5,4,61.6,56.0,2010,5.29,5.31,3.27
-48704,0.6,3,6,3,63.7,56.0,2010,5.34,5.36,3.41
-48705,0.7,2,1,3,57.1,61.0,2010,5.9,5.93,3.38
-48706,0.56,0,0,2,61.1,56.0,2010,5.29,5.34,3.25
-48707,0.7,2,1,3,63.6,58.0,2010,5.57,5.59,3.55
-48708,0.7,3,5,1,61.7,57.0,2010,5.65,5.8,3.53
-48709,0.56,0,0,2,61.8,55.0,2010,5.28,5.33,3.28
-48710,0.7,1,5,1,61.5,58.0,2010,5.69,5.76,3.52
-48711,0.56,0,0,2,61.8,56.0,2010,5.28,5.33,3.28
-48712,0.7,1,3,3,61.7,59.0,2010,5.66,5.68,3.5
-48713,0.67,0,5,3,62.5,56.0,2010,5.64,5.56,3.5
-48714,0.52,0,6,2,61.0,56.0,2010,5.2,5.23,3.18
-48715,0.77,1,2,1,61.8,59.0,2010,5.86,5.82,3.61
-48716,0.77,3,2,1,63.3,57.0,2010,5.84,5.79,3.68
-48717,0.7,0,4,0,62.6,53.0,2011,5.72,5.69,3.57
-48718,0.56,1,6,3,61.5,58.0,2011,5.33,5.28,3.26
-48719,0.63,4,0,3,65.7,60.0,2011,5.41,5.31,3.52
-48720,0.52,1,5,4,61.5,62.0,2012,5.17,5.14,3.17
-48721,0.52,0,5,4,60.8,57.0,2012,5.21,5.16,3.15
-48722,0.52,0,5,4,61.8,57.0,2012,5.19,5.14,3.19
-48723,0.52,1,5,4,61.1,59.0,2012,5.2,5.15,3.16
-48724,0.52,0,5,4,62.4,55.0,2012,5.19,5.17,3.23
-48725,0.52,0,5,4,61.5,56.0,2012,5.2,5.18,3.19
-48726,0.52,0,5,4,61.0,56.0,2012,5.24,5.18,3.18
-48727,0.52,0,5,4,59.8,57.0,2012,5.26,5.24,3.14
-48728,0.52,0,5,4,62.1,56.0,2012,5.17,5.14,3.2
-48729,0.52,0,5,4,61.9,55.0,2012,5.16,5.14,3.19
-48730,0.52,0,5,4,61.7,56.0,2012,5.16,5.14,3.18
-48731,0.52,1,5,4,61.2,59.0,2012,5.16,5.14,3.15
-48732,0.52,0,5,4,62.1,57.0,2012,5.17,5.13,3.2
-48733,0.52,0,5,4,62.3,55.0,2012,5.16,5.12,3.2
-48734,0.52,1,5,4,61.8,59.0,2012,5.16,5.13,3.18
-48735,0.78,0,1,3,62.0,56.0,2012,5.93,5.87,3.66
-48736,0.78,0,3,0,61.9,54.0,2012,5.89,5.85,3.63
-48737,0.5,3,4,4,61.0,61.0,2013,5.09,5.11,3.11
-48738,0.63,0,6,3,61.5,54.0,2013,5.54,5.58,3.42
-48739,0.56,1,5,2,59.9,60.0,2013,5.41,5.37,3.23
-48740,0.72,3,5,0,62.1,59.0,2015,5.66,5.73,3.54
-48741,0.53,0,5,5,61.3,56.0,2015,5.22,5.26,3.21
-48742,0.57,2,6,3,58.3,61.0,2015,5.42,5.45,3.17
-48743,0.76,2,2,2,62.6,61.9,2015,5.7,5.75,3.58
-48744,0.71,2,6,0,56.6,63.0,2015,5.9,5.76,3.3
-48745,0.7,1,5,1,59.9,58.0,2016,5.8,5.68,3.44
-48746,0.56,1,4,1,62.7,59.0,2016,5.27,5.22,3.29
-48747,0.8,1,2,1,58.4,59.0,2016,6.12,6.04,3.55
-48748,0.54,3,6,2,63.0,54.0,2016,5.19,5.23,3.28
-48749,0.55,0,5,4,60.5,58.0,2016,5.3,5.34,3.22
-48750,0.56,0,4,2,61.7,54.0,2016,5.34,5.3,3.28
-48751,0.56,0,4,2,61.9,55.0,2016,5.36,5.32,3.3
-48752,0.5,0,4,4,59.9,58.0,2016,5.14,5.17,3.09
-48753,0.76,2,1,1,64.1,59.0,2016,5.75,5.73,3.68
-48754,0.7,2,5,1,64.0,55.0,2016,5.62,5.59,3.59
-48755,0.7,4,5,1,64.8,59.0,2016,5.65,5.56,3.63
-48756,0.56,0,4,2,61.7,56.0,2016,5.32,5.28,3.27
-48757,0.56,0,4,2,61.7,54.0,2016,5.32,5.28,3.27
-48758,0.56,0,4,2,61.7,56.0,2016,5.33,5.3,3.28
-48759,0.5,1,4,4,60.9,58.0,2016,5.13,5.09,3.11
-48760,0.5,0,4,4,62.0,56.0,2016,5.08,5.05,3.14
-48761,0.56,1,4,2,58.2,60.0,2016,5.41,5.38,3.14
-48762,0.6,1,0,1,59.6,59.0,2016,5.52,5.48,3.28
-48763,0.66,2,4,1,60.4,60.1,2017,5.59,5.65,3.39
-48764,1.01,1,1,6,59.2,57.0,2017,6.58,6.44,3.85
-48765,0.7,3,3,1,59.2,59.0,2018,5.79,5.86,3.45
-48766,0.5,0,4,5,62.0,58.0,2018,5.06,5.07,3.14
-48767,0.55,0,6,1,60.9,56.0,2018,5.31,5.34,3.24
-48768,0.59,0,5,2,62.1,55.0,2018,5.4,5.35,3.34
-48769,0.6,0,6,3,60.4,57.0,2019,5.49,5.53,3.33
-48770,0.53,0,0,2,61.6,55.6,2019,5.19,5.24,3.22
-48771,0.57,1,6,3,60.8,59.0,2019,5.38,5.34,3.26
-48772,0.72,1,0,0,61.6,59.0,2019,5.83,5.68,3.55
-48773,0.7,3,4,0,58.8,57.0,2020,5.85,5.89,3.45
-48774,0.7,1,3,1,62.3,58.0,2020,5.63,5.57,3.49
-48775,0.7,2,4,0,58.1,61.0,2020,5.76,5.8,3.36
-48776,0.55,0,6,2,61.5,56.0,2020,5.28,5.26,3.24
-48777,0.51,2,0,5,58.6,65.0,2020,5.32,5.24,3.1
-48778,0.7,1,3,1,62.2,59.0,2020,5.7,5.65,3.53
-48779,0.7,1,3,1,62.7,59.0,2020,5.68,5.63,3.55
-48780,0.51,1,0,2,58.2,58.0,2020,5.33,5.29,3.09
-48781,0.58,3,5,4,59.1,59.6,2021,5.39,5.42,3.19
-48782,0.52,3,0,2,61.8,60.0,2021,5.14,5.18,3.19
-48783,0.59,3,0,2,60.0,56.0,2021,5.46,5.5,3.29
-48784,0.7,3,1,1,61.1,56.0,2021,5.74,5.75,3.51
-48785,0.53,0,6,1,60.4,57.0,2021,5.28,5.31,3.2
-48786,0.53,0,6,1,61.5,55.0,2021,5.19,5.22,3.2
-48787,0.62,3,6,3,59.0,59.0,2022,5.59,5.66,3.32
-48788,0.55,0,5,4,61.9,56.0,2022,5.23,5.27,3.25
-48789,0.55,0,5,4,62.0,57.0,2022,5.22,5.26,3.25
-48790,0.55,0,5,4,62.5,56.0,2022,5.22,5.25,3.27
-48791,0.72,1,5,0,62.8,61.0,2022,5.68,5.59,3.54
-48792,0.8,1,2,0,60.2,59.0,2022,6.07,5.95,3.62
-48793,0.83,1,2,1,59.3,59.0,2022,6.17,6.1,3.64
-48794,0.78,1,2,0,60.6,58.0,2022,5.94,5.91,3.59
-48795,0.73,2,4,0,63.4,55.0,2023,5.71,5.75,3.63
-48796,0.7,1,4,1,60.8,59.0,2023,5.73,5.71,3.48
-48797,0.71,0,5,0,62.2,56.0,2023,5.7,5.74,3.56
-48798,0.71,0,3,0,61.8,55.0,2024,5.75,5.7,3.54
-48799,0.59,0,5,4,61.0,57.0,2024,5.41,5.44,3.31
-48800,0.7,3,1,3,60.7,59.0,2025,5.75,5.79,3.5
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diff --git a/fadi.joblib b/fadi.joblib
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diff --git a/old/airlines.json b/old/airlines.json
deleted file mode 100644
index 453f772..0000000
--- a/old/airlines.json
+++ /dev/null
@@ -1 +0,0 @@
-["Endeavor Air Inc.", "JetBlue Airways", "ExpressJet Airlines Inc.", "Allegiant Air", "Hawaiian Airlines Inc.", "Spirit Air Lines", "SkyWest Airlines Inc.", "Frontier Airlines Inc.", "Empire Airlines Inc.", "Southwest Airlines Co.", "Mesa Airlines Inc.", "Republic Airlines", "Delta Air Lines Inc.", "United Air Lines Inc.", "Alaska Airlines Inc.", "Horizon Air", "Commutair Aka Champlain Enterprises, Inc.", "Trans States Airlines", "GoJet Airlines, LLC d/b/a United Express", "Air Wisconsin Airlines Corp", "Cape Air", "Compass Airlines", "Virgin America", "Peninsula Airways Inc.", "Envoy Air", "American Airlines Inc.", "Comair Inc.", "Capital Cargo International"]
\ No newline at end of file
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diff --git a/old/destination_cities.json b/old/destination_cities.json
deleted file mode 100644
index 545c68e..0000000
--- a/old/destination_cities.json
+++ /dev/null
@@ -1 +0,0 @@
-["Atlanta, GA", "Albany, GA", "Mobile, AL", "Evansville, IN", "Detroit, MI", "Jacksonville/Camp Lejeune, NC", "Boston, MA", "Syracuse, NY", "New Bern/Morehead/Beaufort, NC", "Montgomery, AL", "New York, NY", "Minneapolis, MN", "Cleveland, OH", "Charlotte, NC", "Fayetteville, NC", "St. Louis, MO", "Columbus, OH", "Indianapolis, IN", "Knoxville, TN", "Mosinee, WI", "Alexandria, LA", "Killeen, TX", "San Antonio, TX", "Peoria, IL", "Houston, TX", "Dallas/Fort Worth, TX", "Richmond, VA", "Portland, ME", "Kalamazoo, MI", "Cincinnati, OH", "Bristol/Johnson City/Kingsport, TN", "Washington, DC", "Asheville, NC", "Baton Rouge, LA", "Buffalo, NY", "Grand Rapids, MI", "Bismarck/Mandan, ND", "Chattanooga, TN", "Traverse City, MI", "Fort Smith, AR", "Burlington, VT", "Kansas City, MO", "Green Bay, WI", "Tulsa, OK", "Valdosta, GA", "Charleston/Dunbar, WV", "Fayetteville, AR", "Rochester, NY", "Savannah, GA", "Roanoke, VA", "Harrisburg, PA", "New Orleans, LA", "Nashville, TN", "Raleigh/Durham, NC", "Milwaukee, WI", "Gainesville, FL", "Tampa, FL", "Greensboro/High Point, NC", "Charlottesville, VA", "Bangor, ME", "Grand Forks, ND", "Brunswick, GA", "Memphis, TN", "Wilmington, NC", "Bloomington/Normal, IL", "Norfolk, VA", "Manchester, NH", "Dayton, OH", "Greer, SC", "Lexington, KY", "Louisville, KY", "Hartford, CT", "Saginaw/Bay City/Midland, MI", "Minot, ND", "Columbia, SC", "Columbus, GA", "Des Moines, IA", "Dothan, AL", "Elmira/Corning, NY", "Pittsburgh, PA", "Jacksonville, FL", "Chicago, IL", "Springfield, MO", "Shreveport, LA", "Huntsville, AL", "Omaha, NE", "Tallahassee, FL", "Madison, WI", "Lansing, MI", "Baltimore, MD", "Charleston, SC", "Little Rock, AR", "Fort Myers, FL", "Orlando, FL", "Fargo, ND", "Appleton, WI", "Akron, OH", "Harlingen/San Benito, TX", "White Plains, NY", "Philadelphia, PA", "Moline, IL", "Aguadilla, PR", "San Francisco, CA", "Newark, NJ", "Fort Lauderdale, FL", "Salt Lake City, UT", "Ponce, PR", "San Jose, CA", "Long Beach, CA", "Las Vegas, NV", "Los Angeles, CA", "West Palm Beach/Palm Beach, FL", "San Juan, PR", "Sarasota/Bradenton, FL", "Austin, TX", "Newburgh/Poughkeepsie, NY", "Denver, CO", "Seattle, WA", "Providence, RI", "Sacramento, CA", "Charlotte Amalie, VI", "Portland, OR", "Worcester, MA", "San Diego, CA", "Reno, NV", "Albany, NY", "Phoenix, AZ", "Oakland, CA", "Palm Springs, CA", "Christiansted, VI", "Burbank, CA", "Daytona Beach, FL", "Albuquerque, NM", "Brownsville, TX", "El Paso, TX", "Oklahoma City, OK", "Gulfport/Biloxi, MS", "South Bend, IN", "Mission/McAllen/Edinburg, TX", "Lake Charles, LA", "Lafayette, LA", "Myrtle Beach, SC", "Rochester, MN", "Lincoln, NE", "Birmingham, AL", "Augusta, GA", "Jackson/Vicksburg, MS", "Cedar Rapids/Iowa City, IA", "Panama City, FL", "Valparaiso, FL", "Key West, FL", "Allentown/Bethlehem/Easton, PA", "Sanford, FL", "Billings, MT", "Flint, MI", "Missoula, MT", "Idaho Falls, ID", "Stockton, CA", "Toledo, OH", "Sioux Falls, SD", "Ashland, WV", "Fresno, CA", "Eugene, OR", "Bellingham, WA", "Punta Gorda, FL", "St. Cloud, MN", "St. Petersburg, FL", "Plattsburgh, NY", "Ogdensburg, NY", "Concord, NC", "Springfield, IL", "Provo, UT", "Montrose/Delta, CO", "Fort Wayne, IN", "Portsmouth, NH", "Trenton, NJ", "Niagara Falls, NY", "Belleville, IL", "Rockford, IL", "Ogden, UT", "Boise, ID", "Medford, OR", "Great Falls, MT", "Pasco/Kennewick/Richland, WA", "Wichita, KS", "Grand Island, NE", "Monterey, CA", "Grand Junction, CO", "Laredo, TX", "Rapid City, SD", "Kalispell, MT", "Santa Maria, CA", "Bozeman, MT", "Hagerstown, MD", "Clarksburg/Fairmont, WV", "Colorado Springs, CO", "Owensboro, KY", "Honolulu, HI", "Kahului, HI", "Kona, HI", "Lihue, HI", "Hilo, HI", "Columbia, MO", "Casper, WY", "Scranton/Wilkes-Barre, PA", "Tucson, AZ", "Bend/Redmond, OR", "Santa Barbara, CA", "Aspen, CO", "Ontario, CA", "Helena, MT", "Sun Valley/Hailey/Ketchum, ID", "Durango, CO", "Williston, ND", "Arcata/Eureka, CA", "San Luis Obispo, CA", "Mammoth Lakes, CA", "Spokane, WA", "Jackson, WY", "Santa Rosa, CA", "Santa Ana, CA", "Redding, CA", "Bakersfield, CA", "Hayden, CO", "Midland/Odessa, TX", "Ithaca/Cortland, NY", "Lewiston, ID", "Pocatello, ID", "Aberdeen, SD", "Cody, WY", "Alpena, MI", "Escanaba, MI", "Pellston, MI", "Bemidji, MN", "Brainerd, MN", "Butte, MT", "Cedar City, UT", "St. George, UT", "Sault Ste. Marie, MI", "Marquette, MI", "Elko, NV", "Hibbing, MN", "Binghamton, NY", "Twin Falls, ID", "Rhinelander, WI", "International Falls, MN", "Iron Mountain/Kingsfd, MI", "Dallas, TX", "Columbus, MS", "Monroe, LA", "State College, PA", "Erie, PA", "La Crosse, WI", "Duluth, MN", "Muskegon, MI", "Pueblo, CO", "Hancock/Houghton, MI", "Paducah, KY", "Quincy, IL", "Rock Springs, WY", "Jamestown, ND", "Devils Lake, ND", "Laramie, WY", "Gillette, WY", "Eau Claire, WI", "Hays, KS", "Eagle, CO", "Cape Girardeau, MO", "North Bend/Coos Bay, OR", "Amarillo, TX", "Miami, FL", "Islip, NY", "Pago Pago, TT", "Hoolehua, HI", "Lanai, HI", "Kapalua, HI", "Atlantic City, NJ", "Latrobe, PA", "Lubbock, TX", "Pensacola, FL", "Corpus Christi, TX", "Melbourne, FL", "Fairbanks, AK", "Anchorage, AK", "Newport News/Williamsburg, VA", "Guam, TT", "Gunnison, CO", "Bethel, AK", "Kodiak, AK", "Deadhorse, AK", "Barrow, AK", "Ketchikan, AK", "Juneau, AK", "Sitka, AK", "Petersburg, AK", "Wrangell, AK", "Nome, AK", "Kotzebue, AK", "Yakutat, AK", "Cordova, AK", "Adak Island, AK", "Yakima, WA", "Santa Fe, NM", "Champaign/Urbana, IL", "Dickinson, ND", "Saipan, TT", "Rota, TT", "Walla Walla, WA", "Wenatchee, WA", "Pullman, WA", "College Station/Bryan, TX", "Hobbs, NM", "Youngstown/Warren, OH", "Unalaska, AK", "Scottsbluff, NE", "Nantucket, MA", "Martha's Vineyard, MA", "Branson, MO", "Tyler, TX", "San Angelo, TX", "Wichita Falls, TX", "Beaumont/Port Arthur, TX", "Lawton/Fort Sill, OK", "Waco, TX", "Flagstaff, AZ", "Yuma, AZ", "Meridian, MS", "Hattiesburg/Laurel, MS", "Roswell, NM", "Kearney, NE", "Moab, UT", "Vernal, UT", "Lewisburg, WV", "Staunton, VA", "Salina, KS", "Liberal, KS", "North Platte, NE", "Prescott, AZ", "Abilene, TX", "Manhattan/Ft. Riley, KS", "Texarkana, AR", "Lynchburg, VA", "Greenville, NC", "New Haven, CT", "Presque Isle/Houlton, ME", "Hilton Head, SC", "Longview, TX", "Waterloo, IA", "Sioux City, IA", "Dubuque, IA", "Garden City, KS", "Salisbury, MD", "Williamsport, PA", "Florence, SC", "Stillwater, OK", "Joplin, MO", "Watertown, NY", "Cheyenne, WY", "Del Rio, TX", "Hyannis, MA", "Dillingham, AK", "King Salmon, AK", "Gustavus, AK", "West Yellowstone, MT"]
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..a0f1d70
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,53 @@
+asttokens==2.4.1
+comm==0.2.0
+contourpy==1.2.0
+cycler==0.12.1
+debugpy==1.8.0
+decorator==5.1.1
+exceptiongroup==1.2.0
+executing==2.0.1
+fonttools==4.46.0
+ipykernel==6.27.1
+ipython==8.18.1
+jedi==0.19.1
+joblib==1.3.2
+jupyter_client==8.6.0
+jupyter_core==5.5.0
+kiwisolver==1.4.5
+lightgbm==4.1.0
+Markdown==3.5.1
+matplotlib==3.8.2
+matplotlib-inline==0.1.6
+nest-asyncio==1.5.8
+numpy==1.26.2
+packaging==23.2
+pandas==2.1.4
+parso==0.8.3
+pexpect==4.9.0
+Pillow==10.1.0
+platformdirs==4.1.0
+prompt-toolkit==3.0.41
+psutil==5.9.6
+ptyprocess==0.7.0
+pure-eval==0.2.2
+Pygments==2.17.2
+pyparsing==3.1.1
+PyQt6==6.6.1
+PyQt6-Qt6==6.6.1
+PyQt6-sip==13.6.0
+pyqtgraph==0.13.3
+python-dateutil==2.8.2
+pytz==2023.3.post1
+pyzmq==25.1.2
+qtgui==0.0.1
+qtwidgets==1.1
+scikit-learn==1.3.2
+scipy==1.11.4
+six==1.16.0
+stack-data==0.6.3
+threadpoolctl==3.2.0
+tornado==6.4
+traitlets==5.14.0
+tzdata==2023.3
+wcwidth==0.2.12
+xgboost==2.0.3
diff --git a/training.ipynb b/training.ipynb
deleted file mode 100644
index ee224ed..0000000
--- a/training.ipynb
+++ /dev/null
@@ -1,222 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-11T17:07:26.744932200Z",
- "start_time": "2023-12-11T17:07:26.740214500Z"
- }
- },
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import sklearn as sk\n",
- "import matplotlib.pyplot as plt\n",
- "import json\n",
- "import math"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-11T17:07:55.590798700Z",
- "start_time": "2023-12-11T17:07:26.744932200Z"
- }
- },
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'pd' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m cf_2018 \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mflight_data/Combined_Flights_2019.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 2\u001b[0m cf_2018\u001b[38;5;241m.\u001b[39mhead()\n",
- "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
- ]
- }
- ],
- "source": [
- "cf_2018 = pd.read_csv('flight_data/Combined_Flights_2018.csv')\n",
- "cf_2019 = pd.read_csv('flight_data/Combined_Flights_2019.csv')\n",
- "cf_2018.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-11T17:07:56.493778700Z",
- "start_time": "2023-12-11T17:07:55.953264900Z"
- }
- },
- "outputs": [],
- "source": [
- "# Filter the dataframe to include only the delays from JFK\n",
- "import RegressionModel\n",
- "\n",
- "filtered_df = cf_2018[(cf_2018['Origin'] == 'JFK')].copy()\n",
- "\n",
- "RegressionModel.destinations = list(cf_2018['DestCityName'].unique())\n",
- "\n",
- "filtered_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-11T17:07:56.527530200Z",
- "start_time": "2023-12-11T17:07:56.498869700Z"
- }
- },
- "outputs": [],
- "source": [
- "relevant_columns = ['FlightDate', 'DOT_ID_Operating_Airline', 'DestAirportID', 'DepDelayMinutes', 'ArrDelayMinutes']\n",
- "jfk_flights_2018 = filtered_df[relevant_columns].copy()\n",
- "jfk_flights_2018.dropna(inplace=True)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-11T17:07:56.542402200Z",
- "start_time": "2023-12-11T17:07:56.526530600Z"
- }
- },
- "outputs": [],
- "source": [
- "jfk_flights_2018"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-11T17:07:56.666500700Z",
- "start_time": "2023-12-11T17:07:56.546471Z"
- }
- },
- "outputs": [],
- "source": [
- "# Convert 'FlightDate' into numerical components\n",
- "jfk_flights_2018['FlightDate'] = pd.to_datetime(jfk_flights_2018['FlightDate'])\n",
- "jfk_flights_2018['Year'] = jfk_flights_2018['FlightDate'].dt.year\n",
- "jfk_flights_2018['Month'] = jfk_flights_2018['FlightDate'].dt.month\n",
- "jfk_flights_2018['Day'] = jfk_flights_2018['FlightDate'].dt.day\n",
- "\n",
- "jfk_flights_2018 = jfk_flights_2018.dropna()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-11T17:07:56.737639800Z",
- "start_time": "2023-12-11T17:07:56.666500700Z"
- }
- },
- "outputs": [],
- "source": [
- "from sklearn.model_selection import train_test_split\n",
- "\n",
- "# Choose your target variable, e.g., 'DepDelayMinutes'\n",
- "X = jfk_flights_2018.drop('DepDelayMinutes', axis=1)\n",
- "y = jfk_flights_2018['DepDelayMinutes']\n",
- "\n",
- "jfk_flights_2018"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "\n",
- "# Splitting the dataset into training and testing sets\n",
- "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
- "\n",
- "X_train = X_train.apply(pd.to_numeric, errors='coerce')\n",
- "y_train = y_train.apply(pd.to_numeric, errors='coerce')\n",
- "X_test = X_test.apply(pd.to_numeric, errors='coerce')\n",
- "y_test = y_test.apply(pd.to_numeric, errors='coerce')\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-11T17:07:57.060977300Z",
- "start_time": "2023-12-11T17:07:56.738640200Z"
- }
- },
- "outputs": [],
- "source": [
- "from sklearn.linear_model import LinearRegression\n",
- "\n",
- "# Initialize the model\n",
- "model = LinearRegression()\n",
- "\n",
- "# Train the model\n",
- "model.fit(X_train, y_train)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-11T17:08:11.323526900Z",
- "start_time": "2023-12-11T17:08:10.881732300Z"
- }
- },
- "outputs": [],
- "source": [
- "from sklearn.metrics import mean_squared_error, r2_score\n",
- "\n",
- "# Predict on the test set\n",
- "y_pred = model.predict(X_test)\n",
- "\n",
- "# Evaluate the model\n",
- "mse = mean_squared_error(y_test, y_pred)\n",
- "r2 = r2_score(y_test, y_pred)\n",
- "print(\"Mean Squared Error:\", mse)\n",
- "print(\"R^2 Score:\", r2)\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "sas2",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/training2.ipynb b/training2.ipynb
deleted file mode 100644
index 45cc563..0000000
--- a/training2.ipynb
+++ /dev/null
@@ -1,988 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:07:42.460196100Z",
- "start_time": "2023-12-19T19:07:37.812448500Z"
- }
- },
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import sklearn as sk\n",
- "import matplotlib.pyplot as plt\n",
- "import json\n",
- "import math"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:12:53.871500800Z",
- "start_time": "2023-12-19T19:12:31.371636500Z"
- }
- },
- "outputs": [],
- "source": [
- "cf_2018 = pd.read_csv('flight_data/Combined_Flights_2018.csv')\n",
- "# cf_2019 = pd.read_csv('flight_data/Combined_Flights_2019.csv')\n",
- "# combined_data = pd.concat([cf_2018, cf_2019])\n",
- "# cf_2018 = combined_data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:12:58.721917100Z",
- "start_time": "2023-12-19T19:12:58.100801800Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['FlightDate', 'Airline', 'Origin', 'Dest', 'Cancelled', 'Diverted',\n",
- " 'CRSDepTime', 'DepTime', 'DepDelayMinutes', 'DepDelay', 'ArrTime',\n",
- " 'ArrDelayMinutes', 'AirTime', 'CRSElapsedTime', 'ActualElapsedTime',\n",
- " 'Distance', 'Year', 'Quarter', 'Month', 'DayofMonth', 'DayOfWeek',\n",
- " 'Marketing_Airline_Network', 'Operated_or_Branded_Code_Share_Partners',\n",
- " 'DOT_ID_Marketing_Airline', 'IATA_Code_Marketing_Airline',\n",
- " 'Flight_Number_Marketing_Airline', 'Operating_Airline',\n",
- " 'DOT_ID_Operating_Airline', 'IATA_Code_Operating_Airline',\n",
- " 'Tail_Number', 'Flight_Number_Operating_Airline', 'OriginAirportID',\n",
- " 'OriginAirportSeqID', 'OriginCityMarketID', 'OriginCityName',\n",
- " 'OriginState', 'OriginStateFips', 'OriginStateName', 'OriginWac',\n",
- " 'DestAirportID', 'DestAirportSeqID', 'DestCityMarketID', 'DestCityName',\n",
- " 'DestState', 'DestStateFips', 'DestStateName', 'DestWac', 'DepDel15',\n",
- " 'DepartureDelayGroups', 'DepTimeBlk', 'TaxiOut', 'WheelsOff',\n",
- " 'WheelsOn', 'TaxiIn', 'CRSArrTime', 'ArrDelay', 'ArrDel15',\n",
- " 'ArrivalDelayGroups', 'ArrTimeBlk', 'DistanceGroup',\n",
- " 'DivAirportLandings'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Filter the dataframe to include only the delays from JFK\n",
- "import RegressionModel\n",
- "\n",
- "filtered_df = cf_2018 # [(cf_2018['Origin'] == 'JFK')].copy()\n",
- "\n",
- "RegressionModel.destinations = list(cf_2018['DestCityName'].unique())\n",
- "\n",
- "filtered_df.columns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:28:41.065489500Z",
- "start_time": "2023-12-19T19:28:41.051898700Z"
- }
- },
- "outputs": [],
- "source": [
- "relevant_columns = ['ArrivalDelayGroups', 'DepartureDelayGroups', 'Quarter', 'Month', 'DayofMonth', 'DayOfWeek', 'DestAirportID', 'OriginAirportID']\n",
- "jfk_flights_2018 = filtered_df[relevant_columns].copy()\n",
- "jfk_flights_2018.dropna(inplace=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:28:42.540461600Z",
- "start_time": "2023-12-19T19:28:42.523178400Z"
- }
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- "outputs": [
- {
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- "<div>\n",
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>ArrivalDelayGroups</th>\n",
- " <th>DepartureDelayGroups</th>\n",
- " <th>Quarter</th>\n",
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- " <th>DayofMonth</th>\n",
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- " <td>13930</td>\n",
- " <td>11612</td>\n",
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- " <th>5689510</th>\n",
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- " <td>2</td>\n",
- " <td>13930</td>\n",
- " <td>12197</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5585544 rows × 8 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " ArrivalDelayGroups DepartureDelayGroups Quarter Month DayofMonth \\\n",
- "0 -1.0 -1.0 1 1 23 \n",
- "1 -1.0 -1.0 1 1 24 \n",
- "2 -1.0 -1.0 1 1 25 \n",
- "3 -1.0 -1.0 1 1 26 \n",
- "4 -1.0 -1.0 1 1 27 \n",
- "... ... ... ... ... ... \n",
- "5689507 -2.0 -1.0 3 9 11 \n",
- "5689508 -1.0 -1.0 3 9 11 \n",
- "5689509 -2.0 -1.0 3 9 11 \n",
- "5689510 -1.0 -1.0 3 9 11 \n",
- "5689511 -1.0 -1.0 3 9 11 \n",
- "\n",
- " DayOfWeek DestAirportID OriginAirportID \n",
- "0 2 10397 10146 \n",
- "1 3 10397 10146 \n",
- "2 4 10397 10146 \n",
- "3 5 10397 10146 \n",
- "4 6 10397 10146 \n",
- "... ... ... ... \n",
- "5689507 2 12264 14711 \n",
- "5689508 2 11995 12264 \n",
- "5689509 2 13930 11612 \n",
- "5689510 2 12197 13930 \n",
- "5689511 2 13930 12197 \n",
- "\n",
- "[5585544 rows x 8 columns]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Convert 'FlightDate' into numerical components\n",
- "#jfk_flights_2018['FlightDate'] = pd.to_datetime(jfk_flights_2018['FlightDate'])\n",
- "# jfk_flights_2018['Year'] = jfk_flights_2018['FlightDate'].dt.year\n",
- "#jfk_flights_2018['Month'] = jfk_flights_2018['FlightDate'].dt.month\n",
- "#jfk_flights_2018['Day'] = jfk_flights_2018['FlightDate'].dt.day\n",
- "#jfk_flights_2018['Weekday'] = jfk_flights_2018['FlightDate'].dt.weekday\n",
- "\n",
- "jfk_flights_2018 = jfk_flights_2018.dropna()\n",
- "jfk_flights_2018\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:28:43.294133400Z",
- "start_time": "2023-12-19T19:28:43.277070800Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Quarter</th>\n",
- " <th>Month</th>\n",
- " <th>DayofMonth</th>\n",
- " <th>DayOfWeek</th>\n",
- " <th>DestAirportID</th>\n",
- " <th>OriginAirportID</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
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- " <th>1</th>\n",
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- " <tr>\n",
- " <th>2</th>\n",
- " <td>1</td>\n",
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- " <td>4</td>\n",
- " <td>10397</td>\n",
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- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1</td>\n",
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- " <td>26</td>\n",
- " <td>5</td>\n",
- " <td>10397</td>\n",
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- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>27</td>\n",
- " <td>6</td>\n",
- " <td>10397</td>\n",
- " <td>10146</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Quarter Month DayofMonth DayOfWeek DestAirportID OriginAirportID\n",
- "0 1 1 23 2 10397 10146\n",
- "1 1 1 24 3 10397 10146\n",
- "2 1 1 25 4 10397 10146\n",
- "3 1 1 26 5 10397 10146\n",
- "4 1 1 27 6 10397 10146"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from sklearn.model_selection import train_test_split\n",
- "\n",
- "# Choose your target variable, e.g., 'DepDelayMinutes'\n",
- "X = jfk_flights_2018.drop('DepartureDelayGroups', axis=1)\n",
- "X = X.drop('ArrivalDelayGroups', axis=1)\n",
- "# y = jfk_flights_2018[['DepDelayMinutes', 'ArrDelayMinutes']]\n",
- "y = jfk_flights_2018['ArrivalDelayGroups']\n",
- "\n",
- "X.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:28:44.615901600Z",
- "start_time": "2023-12-19T19:28:44.499070300Z"
- }
- },
- "outputs": [
- {
- "data": {
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- "<div>\n",
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- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Quarter</th>\n",
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- " <th>DayOfWeek</th>\n",
- " <th>DestAirportID</th>\n",
- " <th>OriginAirportID</th>\n",
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- " <tr>\n",
- " <th>1995552</th>\n",
- " <td>4</td>\n",
- " <td>12</td>\n",
- " <td>1</td>\n",
- " <td>6</td>\n",
- " <td>14570</td>\n",
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- " <tr>\n",
- " <th>2532533</th>\n",
- " <td>1</td>\n",
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- " <td>21</td>\n",
- " <td>3</td>\n",
- " <td>13495</td>\n",
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- " <th>3596784</th>\n",
- " <td>2</td>\n",
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- " <tr>\n",
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- " <td>4</td>\n",
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- " <td>11433</td>\n",
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- " <tr>\n",
- " <th>4935672</th>\n",
- " <td>3</td>\n",
- " <td>8</td>\n",
- " <td>20</td>\n",
- " <td>1</td>\n",
- " <td>11986</td>\n",
- " <td>11618</td>\n",
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- " <tr>\n",
- " <th>...</th>\n",
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- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1598056</th>\n",
- " <td>4</td>\n",
- " <td>11</td>\n",
- " <td>16</td>\n",
- " <td>5</td>\n",
- " <td>11292</td>\n",
- " <td>12889</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2271686</th>\n",
- " <td>4</td>\n",
- " <td>12</td>\n",
- " <td>29</td>\n",
- " <td>6</td>\n",
- " <td>11298</td>\n",
- " <td>10721</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5018751</th>\n",
- " <td>3</td>\n",
- " <td>8</td>\n",
- " <td>3</td>\n",
- " <td>5</td>\n",
- " <td>13367</td>\n",
- " <td>13930</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4380944</th>\n",
- " <td>3</td>\n",
- " <td>7</td>\n",
- " <td>30</td>\n",
- " <td>1</td>\n",
- " <td>12982</td>\n",
- " <td>12173</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1722157</th>\n",
- " <td>4</td>\n",
- " <td>11</td>\n",
- " <td>27</td>\n",
- " <td>2</td>\n",
- " <td>10849</td>\n",
- " <td>11292</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5026989 rows × 6 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " Quarter Month DayofMonth DayOfWeek DestAirportID OriginAirportID\n",
- "1995552 4 12 1 6 14570 11298\n",
- "2532533 1 2 21 3 13495 12889\n",
- "3596784 2 5 28 1 12982 14771\n",
- "1404941 4 11 28 3 11066 11433\n",
- "4935672 3 8 20 1 11986 11618\n",
- "... ... ... ... ... ... ...\n",
- "1598056 4 11 16 5 11292 12889\n",
- "2271686 4 12 29 6 11298 10721\n",
- "5018751 3 8 3 5 13367 13930\n",
- "4380944 3 7 30 1 12982 12173\n",
- "1722157 4 11 27 2 10849 11292\n",
- "\n",
- "[5026989 rows x 6 columns]"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "\n",
- "\n",
- "# Splitting the dataset into training and testing sets\n",
- "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
- "\n",
- "X_train = X_train.apply(pd.to_numeric, errors='coerce')\n",
- "y_train = y_train.apply(pd.to_numeric, errors='coerce')\n",
- "X_test = X_test.apply(pd.to_numeric, errors='coerce')\n",
- "y_test = y_test.apply(pd.to_numeric, errors='coerce')\n",
- "\n",
- "X_train\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- ],
- "text/plain": [
- "LinearRegression()"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Initialize the model\n",
- "from sklearn.linear_model import LinearRegression\n",
- "model = LinearRegression()\n",
- "# Train the models\n",
- "model.fit(X_train, y_train)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.031053 seconds.\n",
- "You can set `force_row_wise=true` to remove the overhead.\n",
- "And if memory is not enough, you can set `force_col_wise=true`.\n",
- "[LightGBM] [Info] Total Bins 450\n",
- "[LightGBM] [Info] Number of data points in the train set: 5026989, number of used features: 6\n",
- "[LightGBM] [Info] Start training from score -0.163231\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(n_estimators=10, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(n_estimators=10, random_state=42)</pre></div></div></div></div></div>"
- ],
- "text/plain": [
- "LGBMRegressor(n_estimators=10, random_state=42)"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from lightgbm import LGBMRegressor\n",
- "model2 = LGBMRegressor(n_estimators=10,random_state=42)\n",
- "model2.fit(X_train, y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/fadi/Desktop/dev/sas2/ws-23-sas-02/sas2/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [01:39:17] WARNING: /workspace/src/learner.cc:742: \n",
- "Parameters: { \"force_col_wise\" } are not used.\n",
- "\n",
- " warnings.warn(smsg, UserWarning)\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
- " colsample_bylevel=None, colsample_bynode=None,\n",
- " colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None, feature_types=None,\n",
- " force_col_wise='true', gamma=None, grow_policy=None,\n",
- " importance_type=None, interaction_constraints=None,\n",
- " learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
- " max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
- " max_leaves=None, min_child_weight=None, missing=nan,\n",
- " monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
- " n_jobs=None, num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
- " colsample_bylevel=None, colsample_bynode=None,\n",
- " colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None, feature_types=None,\n",
- " force_col_wise='true', gamma=None, grow_policy=None,\n",
- " importance_type=None, interaction_constraints=None,\n",
- " learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
- " max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
- " max_leaves=None, min_child_weight=None, missing=nan,\n",
- " monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
- " n_jobs=None, num_parallel_tree=None, ...)</pre></div></div></div></div></div>"
- ],
- "text/plain": [
- "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
- " colsample_bylevel=None, colsample_bynode=None,\n",
- " colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
- " enable_categorical=False, eval_metric=None, feature_types=None,\n",
- " force_col_wise='true', gamma=None, grow_policy=None,\n",
- " importance_type=None, interaction_constraints=None,\n",
- " learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
- " max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
- " max_leaves=None, min_child_weight=None, missing=nan,\n",
- " monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
- " n_jobs=None, num_parallel_tree=None, ...)"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from xgboost import XGBRegressor\n",
- "model3 = XGBRegressor(n_estimators=100,random_state=42, force_col_wise='true')\n",
- "model3.fit(X_train, y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:28:56.633803400Z",
- "start_time": "2023-12-19T19:28:56.233809100Z"
- }
- },
- "outputs": [],
- "source": [
- "from sklearn.neural_network import MLPRegressor\n",
- "model4 = MLPRegressor(random_state=42, max_iter=500)\n",
- "# model4.fit(X_train, y_train)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Mean Squared Error: 5.519213622114545\n",
- "MSE 2 5.4075592894907905\n",
- "MSE 3 5.108867387628766\n",
- "MSE 4 5.519213622114545\n",
- "R^2 Score: 0.0003778816697866949\n",
- "R^2 Score: 0.020600353228215518\n",
- "R^2 Score: 0.07469846431981053\n",
- "R^2 Score: 0.0003778816697866949\n"
- ]
- }
- ],
- "source": [
- "from sklearn.metrics import mean_squared_error, r2_score\n",
- "\n",
- "# Predict on the test set\n",
- "y_pred = model.predict(X_test)\n",
- "y_pred2 = model2.predict(X_test)\n",
- "y_pred3 = model3.predict(X_test)\n",
- "y_pred4 = model.predict(X_test)\n",
- "# Evaluate the model\n",
- "mse = mean_squared_error(y_test, y_pred)\n",
- "mse2 = mean_squared_error(y_test, y_pred2)\n",
- "mse3 = mean_squared_error(y_test, y_pred3)\n",
- "mse4 = mean_squared_error(y_test, y_pred4)\n",
- "r2 = r2_score(y_test, y_pred)\n",
- "r22 = r2_score(y_test, y_pred2)\n",
- "r23 = r2_score(y_test, y_pred3)\n",
- "r24 = r2_score(y_test, y_pred4)\n",
- "print(\"Mean Squared Error:\", mse)\n",
- "print(\"MSE 2 \", mse2)\n",
- "print(\"MSE 3 \", mse3)\n",
- "print(\"MSE 4 \", mse4)\n",
- "print(\"R^2 Score:\", r2)\n",
- "print(\"R^2 Score:\", r22)\n",
- "print(\"R^2 Score:\", r23)\n",
- "print(\"R^2 Score:\", r24)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "\n",
- "# Plot the predictions vs. actual values the first 100 flights\n",
- "fig, ax = plt.subplots()\n",
- "\n",
- "ax.plot(y_test[:100],y_pred[:100], 'o', color='black')\n",
- "plt.xlabel('Actual Delay')\n",
- "plt.ylabel('Predicted Delay')\n",
- "plt.title('Predicted vs. Actual Delay')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 135,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:28:59.088599400Z",
- "start_time": "2023-12-19T19:28:59.083322200Z"
- },
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['fadi.joblib']"
- ]
- },
- "execution_count": 135,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from joblib import dump\n",
- "# dump(model4, 'fadi.joblib')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 83,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:28:59.878613100Z",
- "start_time": "2023-12-19T19:28:59.869948100Z"
- },
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from joblib import load\n",
- "model = load('fadi.joblib')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 84,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:29:00.609582Z",
- "start_time": "2023-12-19T19:29:00.601911100Z"
- },
- "collapsed": false
- },
- "outputs": [
- {
- "ename": "OSError",
- "evalue": "Cannot save file into a non-existent directory: 'C:/Users/s2080/PycharmProjects/ws-23-sas-02'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[84], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mjfk_flights_2018\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_pickle\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mC:/Users/s2080/PycharmProjects/ws-23-sas-02/angie.pkl\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Desktop/dev/sas2/ws-23-sas-02/sas2/lib/python3.10/site-packages/pandas/core/generic.py:3085\u001b[0m, in \u001b[0;36mNDFrame.to_pickle\u001b[0;34m(self, path, compression, protocol, storage_options)\u001b[0m\n\u001b[1;32m 3033\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3034\u001b[0m \u001b[38;5;124;03mPickle (serialize) object to file.\u001b[39;00m\n\u001b[1;32m 3035\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3081\u001b[0m \u001b[38;5;124;03m4 4 9\u001b[39;00m\n\u001b[1;32m 3082\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m \u001b[38;5;66;03m# noqa: E501\u001b[39;00m\n\u001b[1;32m 3083\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpickle\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m to_pickle\n\u001b[0;32m-> 3085\u001b[0m \u001b[43mto_pickle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3086\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3087\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3088\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3089\u001b[0m \u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprotocol\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3090\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3091\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Desktop/dev/sas2/ws-23-sas-02/sas2/lib/python3.10/site-packages/pandas/io/pickle.py:105\u001b[0m, in \u001b[0;36mto_pickle\u001b[0;34m(obj, filepath_or_buffer, compression, protocol, storage_options)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m protocol \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 103\u001b[0m protocol \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mHIGHEST_PROTOCOL\n\u001b[0;32m--> 105\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 107\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 110\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 111\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[1;32m 112\u001b[0m \u001b[38;5;66;03m# letting pickle write directly to the buffer is more memory-efficient\u001b[39;00m\n\u001b[1;32m 113\u001b[0m pickle\u001b[38;5;241m.\u001b[39mdump(obj, handles\u001b[38;5;241m.\u001b[39mhandle, protocol\u001b[38;5;241m=\u001b[39mprotocol)\n",
- "File \u001b[0;32m~/Desktop/dev/sas2/ws-23-sas-02/sas2/lib/python3.10/site-packages/pandas/io/common.py:739\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[38;5;66;03m# Only for write methods\u001b[39;00m\n\u001b[1;32m 738\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode \u001b[38;5;129;01mand\u001b[39;00m is_path:\n\u001b[0;32m--> 739\u001b[0m \u001b[43mcheck_parent_directory\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 741\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression:\n\u001b[1;32m 742\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzstd\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 743\u001b[0m \u001b[38;5;66;03m# compression libraries do not like an explicit text-mode\u001b[39;00m\n",
- "File \u001b[0;32m~/Desktop/dev/sas2/ws-23-sas-02/sas2/lib/python3.10/site-packages/pandas/io/common.py:604\u001b[0m, in \u001b[0;36mcheck_parent_directory\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 602\u001b[0m parent \u001b[38;5;241m=\u001b[39m Path(path)\u001b[38;5;241m.\u001b[39mparent\n\u001b[1;32m 603\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m parent\u001b[38;5;241m.\u001b[39mis_dir():\n\u001b[0;32m--> 604\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot save file into a non-existent directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparent\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
- "\u001b[0;31mOSError\u001b[0m: Cannot save file into a non-existent directory: 'C:/Users/s2080/PycharmProjects/ws-23-sas-02'"
- ]
- }
- ],
- "source": [
- "jfk_flights_2018.to_pickle('C:/Users/s2080/PycharmProjects/ws-23-sas-02/angie.pkl')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:23:51.598644400Z",
- "start_time": "2023-12-19T19:23:51.582771600Z"
- },
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "jfk_flights_2018 = pd.read_pickle(\"angie.pkl\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# jfk_unpickle.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-12-19T19:28:41.793062300Z",
- "start_time": "2023-12-19T19:28:41.766357100Z"
- }
- },
- "outputs": [],
- "source": [
- "# make the arrivel delay column logaritmic and in brackets of 15 minutes\n",
- "def delay_to_bracket(delay):\n",
- " if delay == 0:\n",
- " return 0\n",
- " else:\n",
- " return math.ceil(math.log(delay, 2))\n",
- "jfk_flights_2018['ArrDelayMinutes'] = jfk_flights_2018['ArrDelayMinutes'].apply(delay_to_bracket)\n",
- "jfk_flights_2018['DepDelayMinutes'] = jfk_flights_2018['DepDelayMinutes'].apply(delay_to_bracket)\n",
- "# drop the rows with delay more than 2 hours\n",
- "jfk_flights_2018 = jfk_flights_2018[jfk_flights_2018['ArrDelayMinutes'] < 8]\n",
- "jfk_flights_2018 = jfk_flights_2018[jfk_flights_2018['DepDelayMinutes'] < 8]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "sas2",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/training3 copy.ipynb b/training3 copy.ipynb
deleted file mode 100644
index 29cd743..0000000
--- a/training3 copy.ipynb
+++ /dev/null
@@ -1,35 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "initial_id",
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--
GitLab