def plot_delay(self, file_name):
vol = decode_wave.read_wave_file(file_name)
num_range = 52
x = range(num_range)
goertzel_out = [np.zeros(num_range) for i in range(16)]
for i in range(num_range):
for ch in range(16):
#goertzel_out[ch][i] = goertzel.goertzel(vol[ch][i:64+i],2)
goertzel_out[ch][i] = goertzel.goertzel(
vol[ch][64 * 10 + i:64 * 10 + 64 + i], 2,
vol[ch][64 * 10 + i - 1])
print(goertzel_out[0])
plt.figure(figsize=(15, 9))
for ch in range(16):
plt.plot(x,
goertzel_out[ch],
linewidth=0.5,
marker='v',
markersize=1,
label=str(ch))
#plt.ylim(0,16*2**14)
plt.xlim(0, num_range)
plt.title(os.path.split(file_name)[1])
#plt.tight_layout()
plt.ylabel('ADC count')
plt.xlabel('Delay clk')
plt.grid()
file_name = file_name.replace('/wave_', '/process_')
mom = decode_process.read_process_file(file_name)
constant = 1 / 64e6
for ch in range(16):
print(mom[10][ch] * constant)
plt.show()
import matplotib.pyplot as plt
x = [1, 2, 3]
y = [1, 4, 9]
plt.plot(x, y)
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.title("My python first graph")
plt.show()
#Parsear
ser = pd.Series(['01 Jan 2010', '02-02-2011', '20120303', '2013/04/04', '2014-05-05', '2015-06-06T12:20'])
from dateutil.parser import parse
ser.map(lambda x: parse(x))
pd.to_datetime(ser)
#%%Un poco de todo
iris.SepalLength.rolling(2).sum()
#%%Imput Data
population.resample('A').first()interpolate('linear') #Imputar valores anualmente linealmente
#%% MATPLOTLIB
import matplotib.pyplot as plt
plt.plot(df['Mes'], df['data science'], label='data science')
plt.plot(df['Mes'], df['machine learning'], label='machine learning')
plt.plot(df['Mes'], df['deep learning'], label='deep learning')
plt.xlabel('Date')
plt.ylabel('Popularity')
plt.title('Popularity of AI terms by date')
plt.grid(True)
plt.legend()
plt.text(x='2010-01-01', y=80, s=r'$\lambda=1, r^2=0.8$') #Coordinates use the same units as the graph
plt.annotate('Notice something?', xy=('2014-01-01', 30), xytext=('2006-01-01', 50), arrowprops={'facecolor':'red', 'shrink':0.05})
fig, axes = plt.subplots(2,2)
axes[0, 0].hist(df['data science'])
axes[0, 1].scatter(df['Mes'], df['data science'])
axes[1, 0].plot(df['Mes'], df['machine learning'])
axes[1, 1].plot(df['Mes'], df['deep learning'])
import numpy as np K = 9100 Premium_Call = 179 Premium_Put = 185 Interval = 500 ST = np.arange(K - Interval, K + Interval) Payoff_LongCall = np.maximum(ST - K, 0) - Premium_Call Payoff_ShortCall = -Payoff_LongCall import matplotib.pyplot as plt plt.plot(ST, Payoff_LongCall) plt.plot(ST, Payoff_ShortCall) plt.show()