def getPredictedPriceNormalized(self):
# get model first
self.getModelFromFilePath(self, self.file)
input_features = self.df.iloc[:, [2, 3]].values
input_data = input_features
predicted_value = self.model.predict(self.X_test)
plt.figure(figsize=(100, 40))
plt.plot(predicted_value, color='red')
plt.plot(input_data[self.lookback:self.test_size + (2 * self.lookback),
1],
color='green')
plt.title("Opening price of stocks sold")
plt.xlabel("Time (latest-> oldest)")
plt.ylabel("Stock Opening Price")
plt.show()
self.sc.inverse_transform(input_features[self.lookback:self.test_size +
(2 * self.lookback)])
return predicted_value
def plot_square_piece_simple():
"""simple demo - makes more sense for a 'cut' to be the next
level of abstraction after an 'edge' though"""
pset = get_default_nub_parameters()
pset.randomize()
bxy, _ = create_puzzle_piece_edge(pset)
pset.randomize()
txy, _ = create_puzzle_piece_edge(pset)
pset.randomize()
lxy, _ = create_puzzle_piece_edge(pset)
pset.randomize()
rxy, _ = create_puzzle_piece_edge(pset)
fig = plt.figure()
fig.add_subplot(111, aspect='equal')
plt.plot(bxy[:, 0], random_sign() * bxy[:, 1], 'k-')
plt.plot(txy[:, 0], random_sign() * txy[:, 1] + 1, 'k-')
plt.plot(random_sign() * lxy[:, 1], lxy[:, 0], 'k-')
plt.plot(random_sign() * rxy[:, 1] + 1, rxy[:, 0], 'k-')
plt.show()
class GuyMagen():
def __init__(self):
self.penisSize = 0
self.favoriteRabi = 'kanievski'
def __repr__(self):
print('<=====3')
def didGuyKillRabin(self):
return True
guyMagen = 0
import matplotlib.pyploy as plt
x = [i for i in range(guyMagen,20)]
y = [a**2 fro a in x]
plt.plot(x,y)
import csv
open_file = open("sitka_weather_07-2018_simple.csv", "r")
csv_file = csv.reader(open_file, delimiter=",")
header_row = next(csv_file)
'''
print(header_row)
for index, column_header in enumerate(header_row):
print(index,column_header)
'''
highs = []
for row in csv_file:
highs.append(int(row[5]))
print(highs)
import matplotlib.pyploy as plt
plt.plot(highs, c="red")
plt.title("Daily High Temp, July 2018", fontsize=16)
plt.xlabel("")
plt.ylabel("Temperature (F)", fontsize=16)
plt.tick_params(axis="both", which="major", labelsize=16)
plt.show()
high = int(row[4])
low = int(row[5])
current_date= datetime.strptime(row[2],'%Y-%m-%d')
except ValueError:
print(f"Missing data for {current_date}")
else:
lows.append(int(row[5]))
highs.append(int(row[4]))
dates.append(current_date)
import matplotlib.pyploy as plt
fig= plt.figure()
plt.plot(dates, highs, c="red",alpha=0.5)
plt.plot(dates, lows, c="blue", aplha=0.5)
plt.title("Daily high and low temperatures- 2018\nDeath Valley", fontsize=16)
plt.xlabel("", fontsize=12)
plt.fill_between(dates, highs, lows, facecolor= 'blue', alpha=0.1)
fig.autofmt_xdate()
plt.ylabel("Temperature (F)", fontsize=16)
plt.tick_params(axis="both", labelsize=16)
plt.show()
import csv
import matplotlib.pyploy as plt
games = []
record = []
wins = 0
f = open('cardinals34.csv')
for row in csv.reader(f):
if not row[0].isdigit():
continue
if row[6].startswith('W') and row[13] == "Dean":
wins += 1
games.append(int(row[0]))
record.append(wins)
plt.title('Dean Brothers progress toward 49 wins')
plt.xlabel('Game number')
plt.ylabel('Win count')
plt.plot(games, record, 'r+')
plt.savefig(games.pdf)
import numpy as np
import matplotlib.pyploy as plt
import time
import Save as sv
#Creo el objetos:
generador = gf.GeneradorFunciones()
osciloscopio = osc.Osciloscopio()
frec = []
vpp = []
for i in range(10):
generador.SetFrequency(str(i)) #No se si esto lo va a leer porque no se bien los parametros de la clase.
vpp = vpp.append(osciloscopio.ReadVoltage()) #hay que ver que mide ymult para hacer la cuenta ahi y que devuelva Vpp.
frec = frec.append(i)
time.sleep(1)
#Guardo los datos en un txt
f = save('frecuencia.txt', frec)
f.open()
v = save.('vpp.txt', vpp)
v.open()
#Plot
plt.plot(f,v, 'ro')
plt.show()
x= datetime.strptime('2018-07-01','%Y-%m-%d')
print(x)
for row in csv_file:
highs.append(int(row[5]))
the_date= datetime.strptime(row[2],'%Y-%m-%d')
dates.append(the_date)
import matplotlib.pyploy as plt
fig= plt.figure()
plt.plot(dates, highs, c="red")
plt.title("Daily High Temp, July 2018", fontsize=16)
plt.xlabel("")
plt.ylabel("Temperature (F)", fontsize=16)
plt.tick_params(axis="both", labelsize=16)
fig.autofmt_xdate()
plt.show()