def draw_lag_plots(lmp_curves_list, hub_names, lag=24):
"""
Plots the lag plot to evaluate the time-series data's autocorrelation.
Parameters
----------
lmp_curves_list : list of arrays
A list comprised of historic price curves.
hub_names : list of strings
Names of the CAISO hubs.
lag : int
The difference in time of the two datapoints that are being evaluated for correlation.
"""
fig, axs = plt.subplots(nrows=1,
ncols=len(hub_names),
sharey=True,
figsize=(20, 6))
for i, curve, h in zip(range(len(hub_names)), lmp_curves_list, hub_names):
lag_plot(curve, ax=axs[i], c='green', alpha=0.5, lag=24)
axs[i].set_title(f"{h} Lag Plot - 24 Hours",
fontsize=16,
fontweight='bold')
plt.tight_layout()
plt.show()
def plot_lags_and_auto(df, size=250):
columns = len(df.columns)
for col in df.columns:
fig, ax = plt.subplots(1, 2, figsize=(20, 5))
fig.suptitle(col, fontsize=16)
lag_plot(df[col].tail(size), ax=ax[0])
autocorrelation_plot(df[col].tail(size), ax=ax[1])
def lag_plot(self, column=None, lag_list=None):
"""
:param column:
:param lag_list: receives lag_list if 9 ints
:return:
"""
if lag_list:
if column:
series = self.data[column]
else:
series = self.data[self.data_column]
f = plt.figure(figsize=(15, 15))
for index, lag in enumerate(lag_list):
f.add_subplot(3, 3, index + 1)
plt.title("lag {}".format(lag))
lag_plot(series, lag=lag)
plt.show()
else:
if column:
series = self.data[column]
else:
series = self.data[self.data_column]
lag_list = [int(i) for i in np.linspace(1, len(series), 9)]
f = plt.figure(figsize=(15, 15))
for index, lag in enumerate(lag_list):
f.add_subplot(3, 3, index + 1)
plt.title("lag {}".format(lag))
lag_plot(series, lag=lag)
plt.show()
def arma_fit(option, dataframes, TICK, dayly_returns, pplotacf, ppltopacf,
destranform_returns, freq_des, lag_acf, lag_pacf, def_seasonal, p,
q, arma_sintrans):
#ploteamos la descomposicion seasonal
#primero se ve si los datos son estacionarios sin formula
try:
print(f'p value {TICK} Price: ', adfuller(abs(dataframes[option]))[1])
test = adfuller(dataframes[option])[1]
except:
print(
"For the nature of the data it is not possible to predict these series with ARMA"
)
#se grafican las laf
lag_plot(dataframes[option])
if test > .05:
#Grid search
modelo_altranformarlo(dayly_returns, pplotacf, dataframes, option,
plot_lags, lag_acf, lag_pacf, p, q)
else:
arma_sintrans(dataframes, lag_acf, lag_pacf, ARMA, option, dataframes,
p, q)
def lag_plott(df, features, crypto_name, output_path):
for feature in features:
df = df.dropna(subset=[feature])
plt.figure(figsize=(5, 5))
plt.title("lag_plot_" + feature + "_" + crypto_name)
lag_plot(df[feature])
plt.savefig(output_path + crypto_name + "_" + feature + ".png",
dpi=120)
def autocorrelation(df, ticker):
plt.rcParams.update({'ytick.left' : False, 'axes.titlepad':10})
# Plot
fig, axes = plt.subplots(1, 7, figsize=(12,2), sharex=True, sharey=True, dpi=100)
for i, ax in enumerate(axes.flatten()[:7]):
lag_plot(df.Mean, lag=i+1, ax=ax, c='green')
ax.set_title('Lag ' + str(i+1))
fig.suptitle(f'The {ticker} stock', y=1.15)
plt.show()
def plot_Model_Identify(DataSet, frequency=1, acf_lag=12, pacf_lag=12):
"""
DataSet : dataframe with the type of first column either int()
or panda datetime
Frequency : int, Seasonal Component period (in time step)
"""
# Organize plot
fig, ax = plt.subplots(3, 4)
# Plot the Observed Data
DataSet.plot(ax=ax[0, 0])
ax[0, 0].set_title('Observed Value')
ax[0, 0].set_xlabel("")
# Plot the autocorrelation plot
autocorrelation_plot(DataSet.iloc[:, 0], ax=ax[1, 0])
ax[1, 0].set_title('Autocorrelation')
# Plot the QQ plot
qqplot(
DataSet.iloc[:, 0],
ax=ax[2, 0],
)
ax[2, 0].set_title('Q-Q Plot')
# Lag plot
lag_plot(DataSet.iloc[:, 0], ax=ax[0, 1])
ax[0, 1].set_title('Lag Plot')
ax[0, 1].set_ylabel("")
ax[0, 1].set_xlabel("")
# ACF Plot
tsa.plot_acf(DataSet.iloc[:, 0], ax=ax[1, 1], lags=acf_lag, alpha=0.05)
ax[1, 1].set_title('ACF')
# PACF Plot
tsa.plot_pacf(DataSet.iloc[:, 0], ax=ax[2, 1], lags=pacf_lag, alpha=0.05)
ax[2, 1].set_title('PACF')
# decomposition plot
decomposition = sm.tsa.seasonal_decompose(DataSet.iloc[:, 0],
freq=frequency)
decomposition.resid.plot(ax=ax[0, 2])
decomposition.resid.plot(ax=ax[0, 3], kind='kde')
decomposition.seasonal.plot(ax=ax[1, 2])
decomposition.trend.plot(ax=ax[2, 2])
ax[0, 2].set_title('Residual')
ax[1, 2].set_title('Seasonal')
ax[2, 2].set_title('Trend')
ax[0, 3].set_title('Residual Prob. Distrib')
plt.show()
def noise_check(my_data):
Y1 = int(combo_Y1.get())
Y2 = int(combo_Y2.get())
M1 = int(combo_M1.get())
M2 = int(combo_M2.get())
D1 = int(combo_D1.get())
D2 = int(combo_D2.get())
h1 = int(combo_h1.get())
h2 = int(combo_h2.get())
m1 = int(combo_m1.get())
m2 = int(combo_m2.get())
s1 = int(combo_s1.get())
s2 = int(combo_s2.get())
start = pd.datetime(Y1, M1, D1, h1, m1, s1)
stop = pd.datetime(Y2, M2, D2, h2, m2, s2)
the_data = my_data[start:stop]
plt.figure(figsize=(10, 10))
plt.suptitle('LAG PLOTS')
ax1 = plt.subplot(211)
lag_plot(the_data['back_be'], c='r')
ax2 = plt.subplot(212)
lag_plot(the_data['pump_pr'])
plt.figure(figsize=(8, 8))
autocorrelation_plot(the_data.pump_pr)
x = the_data['pump_pr'].values
f_s = 1
X = fftpack.fft(x)
freqs = fftpack.fftfreq(len(x)) * f_s
fig, ax = plt.subplots()
ax.plot(freqs, np.abs(X))
ax.set_xlabel('Frequency in Hertz [Hz]')
ax.set_ylabel('Frequency Domain (Spectrum) Magnitude')
ax.set_xlim(0, f_s / 2)
## ax.set_ylim(-5, 110)
## fig, axes=plt.subplots(nrows=1, ncols=2,figsize=(8,8))
## axes[0]=lag_plot(the_data['pump_pr'])
## axes[0].set_title('Lag Plot')
## axes[0]=autocorrelation_plot(the_data.pump_pr)
plt.show()
def setup_arima(train_data, price_col, time_col, **kwargs):
plt.figure()
lag_plot(train_data[price_col])
plt.title('Amazon Stock (Dev Data) - Autocorrelation Plot')
plt.show()
fig, ax = plt.subplots()
ax.plot(train_data[time_col], train_data[price_col])
plt.xlabel('Time')
plt.ylabel('Stock Price')
ax.set_title('Amazon Stock (Dev Data) - Minute-by-Minute Closing Prices')
fig.autofmt_xdate()
plt.show()
def plot_lag(self, lag=1, ax=None):
"""
Plots a lag plot of power data
http://www.itl.nist.gov/div898/handbook/eda/section3/lagplot.htm
Returns
-------
matplotlib.axis
"""
if ax is None:
ax = plt.gca()
for power in self.power_series():
lag_plot(power, lag, ax=ax)
return ax
def plot_lag(self, lag=1, ax=None):
"""
Plots a lag plot of power data
http://www.itl.nist.gov/div898/handbook/eda/section3/lagplot.htm
Returns
-------
matplotlib.axis
"""
if ax is None:
ax = plt.gca()
for power in self.power_series():
lag_plot(power, lag, ax=ax)
return ax
def lag_plots(data_df):
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
lag_plot(data_df[data_df.columns[0]], ax=ax1)
ax1.set_title(data_df.columns[0]);
lag_plot(data_df[data_df.columns[1]], ax=ax2)
ax2.set_title(data_df.columns[1]);
ax1.set_ylabel('$y_{t+1}$');
ax1.set_xlabel('$y_t$');
ax2.set_ylabel('$y_{t+1}$');
ax2.set_xlabel('$y_t$');
plt.tight_layout()
def scatter_lag_plots(self, data, no_of_lags, col, district):
fig, axes = plt.subplots(2,
4,
figsize=(15, 8),
sharex=True,
sharey=True,
dpi=100)
for i, ax in enumerate(axes.flatten()[:no_of_lags]):
lag_plot(data[col], lag=i + 1, ax=ax, c='red')
ax.set_title('Lag ' + str(i + 1))
fig.suptitle('Lag Analysis for Sales with {} lags at {}'.format(
no_of_lags, district),
weight='bold')
plt.show()
return fig
def show_1d(s, log_scale=False):
fig, ax = plt.subplots(1, 3, figsize=FIG_SIZE)
tmp_s = s
data_name = ''
sns.swarmplot(y=tmp_s, ax=ax[0])
lag_plot(tmp_s, ax=ax[1], alpha=0.5)
autocorrelation_plot(tmp_s, ax[2], alpha=0.5)
if log_scale:
ax[0].set(yscale='symlog')
ax[1].set(yscale='symlog', xscale='symlog')
ax[0].set_title(f"{data_name}总体图")
ax[1].set_title(f"{data_name}时滞图")
ax[2].set_title(f"{data_name}自相关图")
def plot_lag_plots(df, column):
"""
Lag plots:
If points get wide and scattered with increasing lag,
this means lesser correlation
"""
fig, axes = plt.subplots(1,
4,
figsize=(10, 3),
sharex=True,
sharey=True,
dpi=100)
for i, ax in enumerate(axes.flatten()[:4]):
lag_plot(df[column], lag=i + 1, ax=ax)
ax.set_title("Lag " + str(i + 1))
plt.show()
def autocorrelation(file):
'''Checking for autocorrelation within mood variable'''
df = pd.read_csv(file)
df = df[["id", "time", "mood"]]
for id in df["id"].unique():
series = df[df["id"] == id].mood
autocorrelation_plot(series)
plt.title("Autocorrelation plot for user " + id)
plt.show()
lag_plot(series)
plt.xlabel("Mood at current timepoint")
plt.ylabel("Mood at next timepoint")
plt.title("Lag plot for user " + id)
plt.show()
def plot_autocorr(args: argparse.Namespace, column: str, df: pd.DataFrame):
fig = plt.figure(figsize=(8, 9), constrained_layout=True)
gs = fig.add_gridspec(3, 2)
ax1 = fig.add_subplot(gs[0, :])
autocorrelation_plot(df[column], c=colors[0], ax=ax1)
ax1.spines["left"].set_color("gray")
ax1.spines["bottom"].set_color("gray")
ax1.spines["right"].set_visible(False)
ax1.spines["top"].set_visible(False)
ax2 = fig.add_subplot(gs[1, 0])
lag_plot(df[column], lag=100, c=colors[1], ax=ax2, ec="k")
ax2.spines["left"].set_color("gray")
ax2.spines["bottom"].set_color("gray")
ax2.spines["right"].set_visible(False)
ax2.spines["top"].set_visible(False)
ax3 = fig.add_subplot(gs[1, 1])
lag_plot(df[column], lag=200, c=colors[2], ax=ax3, ec="k")
ax3.spines["left"].set_color("gray")
ax3.spines["bottom"].set_color("gray")
ax3.spines["right"].set_visible(False)
ax3.spines["top"].set_visible(False)
ax4 = fig.add_subplot(gs[2, 0])
lag_plot(df[column], lag=400, c=colors[3], ax=ax4, ec="k")
ax4.spines["left"].set_color("gray")
ax4.spines["bottom"].set_color("gray")
ax4.spines["right"].set_visible(False)
ax4.spines["top"].set_visible(False)
ax5 = fig.add_subplot(gs[2, 1])
lag_plot(df[column], lag=500, c=colors[4], ax=ax5, ec="k")
ax5.spines["left"].set_color("gray")
ax5.spines["bottom"].set_color("gray")
ax5.spines["right"].set_visible(False)
ax5.spines["top"].set_visible(False)
plt.suptitle(f"ELM ID:46, BES {column}", fontsize=18)
plt.tight_layout() # rect=[0, 0.03, 1, 0.95], pad=1.5, h_pad=1.5)
if not args.dry_run:
plt.savefig(
os.path.join(
args.output_dir,
f"auto_correlation_plots_elm_id_46_{column}.png",
),
dpi=150,
)
plt.show()
def plot(file):
'''
Vizualisation of the Temperature dataset(csv-file)
Parameters
----------
csv_file: str
The data to load
Returns
---------
plt.show()
'''
df = pd.read_csv(file, index_col=0, parse_dates=True)
plt.bar(x='Nan', height=df.isna().sum())
plt.show()
dates = df.index #assign x
temp = df['meanT']#assign y
plt.plot(dates,temp)
plt.xlabel('dates')
plt.ylabel('Mean Temp')
plt.title('Temperature profile in Berlin')
plt.show()
year_2021 = df.index[-700:]
temp_2021 = df['meanT'][-700:]
plt.plot(year_2021,temp_2021)
plt.xlabel('dates')
plt.ylabel('Mean Temperature')
plt.title('Temperature profile in Berlin 2021')
plt.show()
lag_plot(df)
plt.show()
autocorrelation_plot(df)
plt.show()
plot_acf(df, lags=30)
plt.show()
def showLagPlot(df, numero_hogar, fichero, titulo):
# Genera el gráfico y se establecen los ejes
seriesRange00_06 = df['Rango 00-06']
seriesRange06_12 = df['Rango 06-12']
seriesRange12_18 = df['Rango 12-18']
seriesRange18_00 = df['Rango 18-00']
lag_plot(seriesRange00_06, c="blue")
lag_plot(seriesRange06_12, c="orange")
lag_plot(seriesRange12_18, c="green")
lag_plot(seriesRange18_00, c="red")
pyplot.title(titulo + numero_hogar, fontsize=16)
pyplot.xlabel('y (t + 1)', fontsize=14)
pyplot.ylabel('y (t)', fontsize=14)
fichero = os.path.join(RUTA_ACTUAL, DIRECTORIO_GUARDADO_IMG,
fichero + '_dispersion.png')
pyplot.savefig(fichero)
pyplot.show()
fig = plotFigures(
df_tickername[[
"close", "MA for 10 days", "MA for 20 days", "MA for 50 days"
]],
f"{ticker_name} Moving Average",
"Time (in days)",
"Closing Price",
f"{ticker_name}_Moving_Average",
)
pdf.savefig(fig)
plt.show()
plt.close()
# Auto-Correlation
plt.figure(figsize=(10, 10))
lag_plot(df_tickername["open"], lag=5)
plt.title(f"{ticker_name} Autocorrelation Plot")
# plt.savefig(f_getFilePath(f'reports\\figures\\{ticker_name}_Autocorrelation_Plot.png'))
pdf.savefig()
plt.show()
plt.close()
# Volatility
fig = plotFigures(
df_tickername["Volatility"],
f"{ticker_name} Volatility",
"Time (in days)",
"Historical Volatility",
f"{ticker_name}_Volatility",
)
pdf.savefig(fig)
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import lag_plot
from pandas import datetime
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
import os
root_dir = "/home/charan/Documents/workspaces/python_workspaces/Data/BDA_Project"
data_path = "stocks_data/final_stock_consolidated.csv"
data_path = os.path.join(root_dir, data_path)
df = pd.read_csv(data_path)
plt.figure()
lag_plot(df['Open'], lag=3)
plt.title('IBM Stock - Autocorrelation plot with lag = 3')
plt.show()
train_data, test_data = df[0:int(len(df) * 0.7)], df[int(len(df) * 0.7):]
training_data = train_data['Close'].values
test_data = test_data['Close'].values
history = [x for x in training_data]
model_predictions = []
N_test_observations = len(test_data)
for time_point in range(N_test_observations):
model = ARIMA(history, order=(4, 1, 0))
model_fit = model.fit(disp=0)
output = model_fit.forecast()
yhat = output[0]
model_predictions.append(yhat)
from pandas.plotting import andrews_curves
from pydataset import data
iris = data('iris')
iris.head()
andrews_curves(iris, 'Species')
#Parallel Coordinates
from pandas.plotting import parallel_coordinates
from pydataset import data
iris = data('iris')
parallel_coordinates(iris, 'Species')
#Lag Plot
from pandas.plotting import lag_plot
spacing = np.linspace(-99 * np.pi, 99 * np.pi, num=1000)
spacing
data = pd.Series(0.1 * np.random.rand(1000) + 0.9 * np.sin(spacing))
data
lag_plot(data)
#Autocorrelation Plot
from pandas.plotting import autocorrelation_plot
spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
data = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
autocorrelation_plot(data)
#Bootstrap Plot
from pandas.plotting import bootstrap_plot
data = pd.Series(np.random.rand(1000))
bootstrap_plot(data, size=50, samples=500, color='grey')
stocks.head()
shelter_outcomes = pd.read_csv(
"../input/austin-animal-center-shelter-outcomes-and/aac_shelter_outcomes.csv",
parse_dates=['date_of_birth', 'datetime'])
shelter_outcomes = shelter_outcomes[[
'outcome_type', 'age_upon_outcome', 'datetime', 'animal_type', 'breed',
'color', 'sex_upon_outcome', 'date_of_birth'
]]
shelter_outcomes.head()
shelter_outcomes['date_of_birth'].value_counts().sort_values().plot.line()
shelter_outcomes['date_of_birth'].value_counts().resample(
'Y').sum().plot.line()
stocks['volume'].resample('Y').mean().plot.bar()
from pandas.plotting import lag_plot
lag_plot(stocks['volume'].tail(250))
from pandas.plotting import autocorrelation_plot
autocorrelation_plot(stocks['volume'])
import pandas as pd
crypto = pd.read_csv("../input/all-crypto-currencies/crypto-markets.csv")
crypto = crypto[crypto['name'] == 'Bitcoin']
crypto['date'] = pd.to_datetime(crypto['date'])
crypto.head()
from IPython.display import HTML
HTML("""
<ol>
<li>Time-series data is really a special case of interval data.</li>
<br/>
ax.plot_surface(x, y, z)
ax.set_xlabel('CRIM')
ax.set_ylabel('MEDV')
ax.set_zlabel('ZN')
ax.set_title("재광's")
plt.show()
# 1.5 boston_train.csv파일을 읽어와서 지연 plot을 출력하세요
df = pd.read_csv(
'C:/Users/CPB06GameN/PycharmProjects/GitHub/bigdata/bigdata/파이썬빅데이터분석/boston_train.csv'
)
# 1) 풀이
from pandas.plotting import lag_plot
lag_plot(np.log(df['MEDV']))
lag_plot(np.log(df['CRIM']))
plt.show()
# 2) 풀이
lag_plot(np.log(df['MEDV']))
plt.show()
# 1.6 boston_train.csv 파일을 읽어와서 자기 상관 plot을 출력하세요
df = pd.read_csv(
'C:/Users/CPB06GameN/PycharmProjects/GitHub/bigdata/bigdata/파이썬빅데이터분석/boston_train.csv'
)
from pandas.plotting import autocorrelation_plot
autocorrelation_plot(np.log(df['MEDV']))
plt.show()
years.boxplot(column=["2001", "2002", "2003"], ax=ax4)
ax4.set(xlabel="Years", ylabel="Sales", title="Box and whisker plot")
# Heatmap plot
img5 = ax5.matshow(years, interpolation=None, aspect='auto')
xaxis = [2000, 2001, 2002, 2003]
yaxis = range(-1, 13, 2)
ax5.set(xlabel="Year",
ylabel="Month",
xticklabels=xaxis,
yticklabels=yaxis,
title="Heatmap plot")
ax5.xaxis.tick_bottom()
fig.colorbar(img5, ax=ax5, aspect=5)
# Lag plot
lag_plot(series_shampoo, ax=ax6)
diagonal = range(int(series_shampoo.min()), int(series_shampoo.max()))
ax6.plot(diagonal, diagonal, '--k')
ax6.set(xlabel="Sales(t)", ylabel="Sales(t+1)", title="Lag plot")
# Autocorrelation plot
autocorrelation_plot(series_shampoo, ax=ax7)
ax7.set(title="Autocorrelation plot", ylim=(-1, 1))
ax8.remove()
fig.subplots_adjust(hspace=0.6)
# plt.tight_layout()
plt.show()
def plot_lagplot(df):
X = df['confirmed']
lag_plot(X)
plt.show()
def plot(input_ts='-',
columns=None,
start_date=None,
end_date=None,
clean=False,
skiprows=None,
index_type='datetime',
names=None,
ofilename='plot.png',
type='time',
xtitle='',
ytitle='',
title='',
figsize='10,6.0',
legend=None,
legend_names=None,
subplots=False,
sharex=True,
sharey=False,
colors='auto',
linestyles='auto',
markerstyles=' ',
style='auto',
logx=False,
logy=False,
xaxis='arithmetic',
yaxis='arithmetic',
xlim=None,
ylim=None,
secondary_y=False,
mark_right=True,
scatter_matrix_diagonal='kde',
bootstrap_size=50,
bootstrap_samples=500,
norm_xaxis=False,
norm_yaxis=False,
lognorm_xaxis=False,
lognorm_yaxis=False,
xy_match_line='',
grid=False,
label_rotation=None,
label_skip=1,
force_freq=None,
drawstyle='default',
por=False,
invert_xaxis=False,
invert_yaxis=False,
round_index=None,
plotting_position='weibull',
source_units=None,
target_units=None,
lag_plot_lag=1):
r"""Plot data."""
# Need to work around some old option defaults with the implementation of
# mando
legend = bool(legend == '' or legend == 'True' or legend is None)
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.ticker import FixedLocator
tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts,
skiprows=skiprows,
names=names,
index_type=index_type),
start_date=start_date,
end_date=end_date,
pick=columns,
round_index=round_index,
dropna='all',
source_units=source_units,
target_units=target_units,
clean=clean)
if type in ['bootstrap',
'heatmap',
'autocorrelation',
'lag_plot']:
if len(tsd.columns) != 1:
raise ValueError("""
*
* The '{1}' plot can only work with 1 time-series in the DataFrame.
* The DataFrame that you supplied has {0} time-series.
*
""".format(len(tsd.columns), type))
if por is True:
tsd = tsutils.common_kwds(tsutils.read_iso_ts(tsd),
start_date=start_date,
end_date=end_date,
round_index=round_index,
dropna='no')
# This is to help pretty print the frequency
try:
try:
pltfreq = str(tsd.index.freq, 'utf-8').lower()
except TypeError:
pltfreq = str(tsd.index.freq).lower()
if pltfreq.split(' ')[0][1:] == '1':
beginstr = 3
else:
beginstr = 1
if pltfreq == 'none':
short_freq = ''
else:
# short freq string (day) OR (2 day)
short_freq = '({0})'.format(pltfreq[beginstr:-1])
except AttributeError:
short_freq = ''
if legend_names:
lnames = tsutils.make_list(legend_names)
if len(lnames) != len(set(lnames)):
raise ValueError("""
*
* Each name in legend_names must be unique.
*
""")
if len(tsd.columns) == len(lnames):
renamedict = dict(list(zip(tsd.columns, lnames)))
elif type == 'xy' and len(tsd.columns) // 2 == len(lnames):
renamedict = dict(list(zip(tsd.columns[2::2], lnames[1:])))
renamedict[tsd.columns[1]] = lnames[0]
else:
raise ValueError("""
*
* For 'legend_names' you must have the same number of comma
* separated names as columns in the input data. The input
* data has {0} where the number of 'legend_names' is {1}.
*
* If 'xy' type you need to have legend names as x,y1,y2,y3,...
*
""".format(len(tsd.columns), len(lnames)))
tsd.rename(columns=renamedict, inplace=True)
else:
lnames = tsd.columns
if colors == 'auto':
colors = color_list
else:
colors = tsutils.make_list(colors)
if linestyles == 'auto':
linestyles = line_list
else:
linestyles = tsutils.make_list(linestyles)
if markerstyles == 'auto':
markerstyles = marker_list
else:
markerstyles = tsutils.make_list(markerstyles)
if markerstyles is None:
markerstyles = ' '
if style != 'auto':
nstyle = tsutils.make_list(style)
if len(nstyle) != len(tsd.columns):
raise ValueError("""
*
* You have to have the same number of style strings as time-series to plot.
* You supplied '{0}' for style which has {1} style strings,
* but you have {2} time-series.
*
""".format(style, len(nstyle), len(tsd.columns)))
colors = []
markerstyles = []
linestyles = []
for st in nstyle:
colors.append(st[0])
if len(st) == 1:
markerstyles.append(' ')
linestyles.append('-')
continue
if st[1] in marker_list:
markerstyles.append(st[1])
try:
linestyles.append(st[2:])
except IndexError:
linestyles.append(' ')
else:
markerstyles.append(' ')
linestyles.append(st[1:])
if linestyles is None:
linestyles = [' ']
else:
linestyles = [' ' if i == ' ' else i for i in linestyles]
markerstyles = [' ' if i is None else i for i in markerstyles]
icolors = itertools.cycle(colors)
imarkerstyles = itertools.cycle(markerstyles)
ilinestyles = itertools.cycle(linestyles)
style = ['{0}{1}{2}'.format(next(icolors),
next(imarkerstyles),
next(ilinestyles))
for i in list(range(len(tsd.columns)))]
# reset to beginning of iterator
icolors = itertools.cycle(colors)
imarkerstyles = itertools.cycle(markerstyles)
ilinestyles = itertools.cycle(linestyles)
if (logx is True or
logy is True or
norm_xaxis is True or
norm_yaxis is True or
lognorm_xaxis is True or
lognorm_yaxis is True):
warnings.warn("""
*
* The --logx, --logy, --norm_xaxis, --norm_yaxis, --lognorm_xaxis, and
* --lognorm_yaxis options are deprecated.
*
* For --logx use --xaxis="log"
* For --logy use --yaxis="log"
* For --norm_xaxis use --type="norm_xaxis"
* For --norm_yaxis use --type="norm_yaxis"
* For --lognorm_xaxis use --type="lognorm_xaxis"
* For --lognorm_yaxis use --type="lognorm_yaxis"
*
""")
if xaxis == 'log':
logx = True
if yaxis == 'log':
logy = True
if type in ['norm_xaxis',
'lognorm_xaxis',
'weibull_xaxis']:
xaxis = 'normal'
if logx is True:
logx = False
warnings.warn("""
*
* The --type={1} cannot also have the xaxis set to {0}.
* The {0} setting for xaxis is ignored.
*
""".format(xaxis, type))
if type in ['norm_yaxis',
'lognorm_yaxis',
'weibull_yaxis']:
yaxis = 'normal'
if logy is True:
logy = False
warnings.warn("""
*
* The --type={1} cannot also have the yaxis set to {0}.
* The {0} setting for yaxis is ignored.
*
""".format(yaxis, type))
xlim = _know_your_limits(xlim, axis=xaxis)
ylim = _know_your_limits(ylim, axis=yaxis)
figsize = tsutils.make_list(figsize)
if not isinstance(tsd.index, pd.DatetimeIndex):
tsd.insert(0, tsd.index.name, tsd.index)
if type in ['xy',
'double_mass']:
if tsd.shape[1] % 2 != 0:
raise AttributeError("""
*
* The 'xy' and 'double_mass' types must have an even number of columns
* arranged as x,y pairs. You supplied {0} columns.
*
""".format(tsd.shape[1]))
colcnt = tsd.shape[1] // 2
elif type in ['norm_xaxis',
'norm_yaxis',
'lognorm_xaxis',
'lognorm_yaxis',
'weibull_xaxis',
'weibull_yaxis']:
colcnt = tsd.shape[1]
if type in ['xy',
'double_mass',
'norm_xaxis',
'norm_yaxis',
'lognorm_xaxis',
'lognorm_yaxis',
'weibull_xaxis',
'weibull_yaxis',
'heatmap']:
_, ax = plt.subplots(figsize=figsize)
plotdict = {(False, True): ax.semilogy,
(True, False): ax.semilogx,
(True, True): ax.loglog,
(False, False): ax.plot}
if type == 'time':
ax = tsd.plot(legend=legend, subplots=subplots, sharex=sharex,
sharey=sharey, style=None, logx=logx, logy=logy,
xlim=xlim, ylim=ylim, secondary_y=secondary_y,
mark_right=mark_right, figsize=figsize,
drawstyle=drawstyle)
for index, line in enumerate(ax.lines):
plt.setp(line, color=style[index][0])
plt.setp(line, marker=style[index][1])
plt.setp(line, linestyle=style[index][2:])
xtitle = xtitle or 'Time'
if legend is True:
plt.legend(loc='best')
elif type in ['taylor']:
from .. skill_metrics import centered_rms_dev
from .. skill_metrics import taylor_diagram
ref = tsd.iloc[:, 0]
std = [pd.np.std(ref)]
ccoef = [1.0]
crmsd = [0.0]
for col in range(1, len(tsd.columns)):
std.append(pd.np.std(tsd.iloc[:, col]))
ccoef.append(pd.np.corrcoef(tsd.iloc[:, col],
ref)[0][1])
crmsd.append(centered_rms_dev(tsd.iloc[:, col].values,
ref.values))
taylor_diagram(pd.np.array(std),
pd.np.array(crmsd),
pd.np.array(ccoef))
elif type in ['target']:
from .. skill_metrics import centered_rms_dev
from .. skill_metrics import rmsd
from .. skill_metrics import bias
from .. skill_metrics import target_diagram
biases = []
rmsds = []
crmsds = []
ref = tsd.iloc[:, 0].values
for col in range(1, len(tsd.columns)):
biases.append(bias(tsd.iloc[:, col].values, ref))
crmsds.append(centered_rms_dev(tsd.iloc[:, col].values,
ref))
rmsds.append(rmsd(tsd.iloc[:, col].values,
ref))
target_diagram(pd.np.array(biases),
pd.np.array(crmsds),
pd.np.array(rmsds))
elif type in ['xy',
'double_mass']:
# PANDAS was not doing the right thing with xy plots
# if you wanted lines between markers.
# Fell back to using raw matplotlib.
# Boy I do not like matplotlib.
for colindex in range(colcnt):
ndf = tsd.iloc[:, colindex*2:colindex*2 + 2]
if type == 'double_mass':
ndf = ndf.dropna().cumsum()
oxdata = pd.np.array(ndf.iloc[:, 0])
oydata = pd.np.array(ndf.iloc[:, 1])
plotdict[(logx, logy)](oxdata,
oydata,
linestyle=next(ilinestyles),
color=next(icolors),
marker=next(imarkerstyles),
label=lnames[colindex],
drawstyle=drawstyle)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
if legend is True:
ax.legend(loc='best')
if type == 'double_mass':
xtitle = xtitle or 'Cumulative {0}'.format(tsd.columns[0])
ytitle = ytitle or 'Cumulative {0}'.format(tsd.columns[1])
elif type in ['norm_xaxis',
'norm_yaxis',
'lognorm_xaxis',
'lognorm_yaxis',
'weibull_xaxis',
'weibull_yaxis']:
ppf = tsutils.set_ppf(type.split('_')[0])
ys = tsd.iloc[:, :]
for colindex in range(colcnt):
oydata = pd.np.array(ys.iloc[:, colindex].dropna())
oydata = pd.np.sort(oydata)[::-1]
n = len(oydata)
norm_axis = ax.xaxis
oxdata = ppf(tsutils.set_plotting_position(n,
plotting_position))
if type in ['norm_yaxis',
'lognorm_yaxis',
'weibull_yaxis']:
oxdata, oydata = oydata, oxdata
norm_axis = ax.yaxis
plotdict[(logx, logy)](oxdata,
oydata,
linestyle=next(ilinestyles),
color=next(icolors),
marker=next(imarkerstyles),
label=lnames[colindex],
drawstyle=drawstyle)
# Make it pretty
xtmaj = pd.np.array([0.01, 0.1, 0.5, 0.9, 0.99])
xtmaj_str = ['1', '10', '50', '90', '99']
xtmin = pd.np.concatenate([pd.np.linspace(0.001, 0.01, 10),
pd.np.linspace(0.01, 0.1, 10),
pd.np.linspace(0.1, 0.9, 9),
pd.np.linspace(0.9, 0.99, 10),
pd.np.linspace(0.99, 0.999, 10)])
xtmaj = ppf(xtmaj)
xtmin = ppf(xtmin)
norm_axis.set_major_locator(FixedLocator(xtmaj))
norm_axis.set_minor_locator(FixedLocator(xtmin))
if type in ['norm_xaxis',
'lognorm_xaxis',
'weibull_xaxis']:
ax.set_xticklabels(xtmaj_str)
ax.set_ylim(ylim)
ax.set_xlim(ppf(xlim))
elif type in ['norm_yaxis',
'lognorm_yaxis',
'weibull_yaxis']:
ax.set_yticklabels(xtmaj_str)
ax.set_xlim(xlim)
ax.set_ylim(ppf(ylim))
if type in ['norm_xaxis',
'norm_yaxis']:
xtitle = xtitle or 'Normal Distribution'
ytitle = ytitle or tsd.columns[0]
elif type in ['lognorm_xaxis',
'lognorm_yaxis']:
xtitle = xtitle or 'Log Normal Distribution'
ytitle = ytitle or tsd.columns[0]
elif type in ['weibull_xaxis',
'weibull_yaxis']:
xtitle = xtitle or 'Weibull Distribution'
ytitle = ytitle or tsd.columns[0]
if type in ['norm_yaxis',
'lognorm_yaxis',
'weibull_yaxis']:
xtitle, ytitle = ytitle, xtitle
if legend is True:
ax.legend(loc='best')
elif type in ['kde',
'probability_density']:
ax = tsd.plot(kind='kde', legend=legend, subplots=subplots,
sharex=sharex, sharey=sharey, style=None, logx=logx,
logy=logy, xlim=xlim, ylim=ylim, secondary_y=secondary_y,
figsize=figsize)
for index, line in enumerate(ax.lines):
plt.setp(line, color=style[index][0])
plt.setp(line, marker=style[index][1])
plt.setp(line, linestyle=style[index][2:])
ytitle = ytitle or 'Density'
if legend is True:
plt.legend(loc='best')
elif type == 'kde_time':
from scipy.stats.kde import gaussian_kde
_, (ax0, ax1) = plt.subplots(nrows=1,
ncols=2,
sharey=True,
figsize=figsize,
gridspec_kw={'width_ratios': [1, 4]})
tsd.plot(legend=legend, subplots=subplots, sharex=sharex,
sharey=sharey, style=None, logx=logx, logy=logy, xlim=xlim,
ylim=ylim, secondary_y=secondary_y, mark_right=mark_right,
figsize=figsize, drawstyle=drawstyle, ax=ax1)
for index, line in enumerate(ax1.lines):
plt.setp(line, color=style[index][0])
plt.setp(line, marker=style[index][1])
plt.setp(line, linestyle=style[index][2:])
xtitle = xtitle or 'Time'
ylimits = ax1.get_ylim()
ny = pd.np.linspace(ylimits[0], ylimits[1], 1000)
for col in range(len(tsd.columns)):
xvals = tsd.iloc[:, col].dropna().values
pdf = gaussian_kde(xvals)
ax0.plot(pdf(ny),
ny,
linestyle=style[col][2:],
color=style[col][0],
marker=style[col][1],
label=tsd.columns[col],
drawstyle=drawstyle)
ax0.set(xlabel='Probability Density', ylabel=ytitle)
elif type == 'boxplot':
tsd.boxplot(figsize=figsize)
elif type == 'scatter_matrix':
from pandas.plotting import scatter_matrix
if scatter_matrix_diagonal == 'probablity_density':
scatter_matrix_diagonal = 'kde'
scatter_matrix(tsd,
diagonal=scatter_matrix_diagonal,
figsize=figsize)
elif type == 'lag_plot':
from pandas.plotting import lag_plot
lag_plot(tsd,
lag=lag_plot_lag)
xtitle = xtitle or 'y(t)'
ytitle = ytitle or 'y(t+{0})'.format(short_freq or 1)
elif type == 'autocorrelation':
from pandas.plotting import autocorrelation_plot
autocorrelation_plot(tsd)
xtitle = xtitle or 'Time Lag {0}'.format(short_freq)
elif type == 'bootstrap':
from pandas.plotting import bootstrap_plot
bootstrap_plot(tsd,
size=bootstrap_size,
samples=bootstrap_samples,
color='gray')
elif type == 'heatmap':
# Find beginning and end years
byear = tsd.index[0].year
eyear = tsd.index[-1].year
tsd = tsutils.asbestfreq(tsd)
if tsd.index.freqstr != 'D':
raise ValueError("""
*
* The "heatmap" plot type can only work with daily time series.
*
""")
dr = pd.date_range('{0}-01-01'.format(byear),
'{0}-12-31'.format(eyear),
freq='D')
ntsd = tsd.reindex(index=dr)
groups = ntsd.iloc[:, 0].groupby(pd.TimeGrouper('A'))
years = pd.DataFrame()
for name, group in groups:
ngroup = group.values
if len(group.values) == 365:
ngroup = pd.np.append(group.values, [pd.np.nan])
years[name.year] = ngroup
years = years.T
plt.imshow(years,
interpolation=None,
aspect='auto')
plt.colorbar()
yticks = list(range(byear, eyear + 1))
skip = len(yticks)//20 + 1
plt.yticks(range(0, len(yticks), skip), yticks[::skip])
mnths = [0, 30, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334]
mnths_labels = ['Jan',
'Feb',
'Mar',
'Apr',
'May',
'Jun',
'Jul',
'Aug',
'Sep',
'Oct',
'Nov',
'Dec']
plt.xticks(mnths, mnths_labels)
grid = False
elif (type == 'bar' or
type == 'bar_stacked' or
type == 'barh' or
type == 'barh_stacked'):
stacked = False
if type[-7:] == 'stacked':
stacked = True
kind = 'bar'
if type[:4] == 'barh':
kind = 'barh'
ax = tsd.plot(kind=kind, legend=legend, stacked=stacked,
style=style, logx=logx, logy=logy, xlim=xlim,
ylim=ylim, figsize=figsize)
for index, line in enumerate(ax.lines):
plt.setp(line, color=style[index][0])
plt.setp(line, marker=style[index][1])
plt.setp(line, linestyle=style[index][2:])
freq = tsutils.asbestfreq(tsd, force_freq=force_freq).index.freqstr
if freq is not None:
if 'A' in freq:
endchar = 4
elif 'M' in freq:
endchar = 7
elif 'D' in freq:
endchar = 10
elif 'H' in freq:
endchar = 13
else:
endchar = None
nticklabels = []
if kind == 'bar':
taxis = ax.xaxis
else:
taxis = ax.yaxis
for index, i in enumerate(taxis.get_majorticklabels()):
if index % label_skip:
nticklabels.append(' ')
else:
nticklabels.append(i.get_text()[:endchar])
taxis.set_ticklabels(nticklabels)
plt.setp(taxis.get_majorticklabels(), rotation=label_rotation)
if legend is True:
plt.legend(loc='best')
elif type == 'histogram':
tsd.hist(figsize=figsize)
else:
raise ValueError("""
*
* Plot 'type' {0} is not supported.
*
""".format(type))
if xy_match_line:
if isinstance(xy_match_line, str):
xymsty = xy_match_line
else:
xymsty = 'g--'
nxlim = ax.get_xlim()
nylim = ax.get_ylim()
maxt = max(nxlim[1], nylim[1])
mint = min(nxlim[0], nylim[0])
ax.plot([mint, maxt], [mint, maxt], xymsty, zorder=1)
ax.set_ylim(nylim)
ax.set_xlim(nxlim)
plt.xlabel(xtitle)
plt.ylabel(ytitle)
if invert_xaxis is True:
plt.gca().invert_xaxis()
if invert_yaxis is True:
plt.gca().invert_yaxis()
plt.grid(grid)
plt.title(title)
plt.tight_layout()
if ofilename is None:
return plt
plt.savefig(ofilename)
st.write(
'We will transform our Data because it is not stacionary, we will apply de following transformation:'
)
st.image(Image.open('./imag/trans.png'), caption='Continuous returns')
st.write(
'After the tranformation we will apply de adfuller test again.')
dayly_returns(dataframes, option)
st.write('P-Value ', TICK, 'Price: ',
adfuller(abs(dataframes['trans']))[1])
U = arma1_fortrans(adfuller, dataframes)
st.write(U)
st.write(
'Based on the following plots choose the p and q for the ARMA model'
)
lag_plot(dataframes[option])
lag_acf = st.slider(
'Would you choose the lags for the autocorrlation ', 0, 10, 50)
lag_pacf = st.slider(
'Would you choose the lags for the partial autocallation', 0, 10,
50)
#plot_lags(lag_acf,lag_pacf,dataframes)
o = 'trans'
try:
m11 = pplotacf(dataframes, lag_acf, o)
plt.savefig('./imag/acf.png')
image11 = Image.open('./imag/acf.png')
st.image(image11, caption='')
if valid['Predictions'][i] < valid['Predictions'][i + 1]:
valid['Predicted'][i + 1] = "Increase"
else:
valid['Predicted'][i + 1] = "Decrease"
for i in range(1, len(valid)):
if valid['Actual'][i] == valid['Predicted'][i]:
valid['Recommendation'][i] = "Correct"
else:
valid['Recommendation'][i] = "Incorrect"
print(valid.tail(10))
# # Autocorrelation
plt.figure(figsize=(10, 10))
lag_plot(df['Close'], lag=5)
plt.title(f'{ticker} Autocorrelation plot')
train_data, test_data = df[0:int(len(df) * 0.8)], df[int(len(df) * 0.8):]
plt.figure(figsize=(16, 8))
plt.title(f'{ticker} Stock Price')
plt.xlabel('Dates')
plt.ylabel('Prices')
plt.plot(df['Close'], 'blue', label='Training Data')
plt.plot(test_data['Close'], 'green', label='Testing Data')
plt.legend()
# # Arima
plt.figure(figsize=(24, 16))
ax = plt.axes()
ax = sns.heatmap(corr, ax = ax)
ax.set_title('Heatmap - Granger Causality Test Results')
plt.xlabel("Time Series")
plt.ylabel("Time Series")
plt.show()
# In[23]:
## lag plot
plt.figure()
lag_plot(datat)
# In[24]:
print(datat.shape)
# In[25]:
i = 10
datatp = datat.iloc[:, 0:i]
print(df.head())
df = df.set_index(df.columns[0])
print(df.head())
df['hh_sp'].plot(figsize = (14,6), grid=True)
plt.title('Henry Hub Spot Price')
plt.ylabel('Price(Dollars per Million Btu)')
plt.show()
"""We are dealing with discrete parameter process of time series."""
# Plot
fig, axes = plt.subplots(1, 4, figsize=(10,3), sharex=True, sharey=True, dpi=100)
for i, ax in enumerate(axes.flatten()[:4]):
lag_plot(df, lag=i+1, ax=ax)
ax.set_title('Lag ' + str(i+1))
fig.suptitle('Lag Plots of Natural Gas \n(Points get wide and scattered with increasing lag -> lesser correlation)\n', y=1.15)
plt.show()
lag_plot(df)
plt.show()
"""Running the example plots the data (t) on the x-axis against the data on the previous day (t-1) on the y-axis. We can see a large ball of observations along a diagonal line of the plot. It clearly shows a relationship or some correlation."""
from pandas.plotting import autocorrelation_plot
autocorrelation_plot(df.hh_sp)
plt.show()
def do_lag_plot(X):
for i in range(4):
plt.clf()
lag_plot(X, lag=i + 1, c='firebrick')
plt.savefig(folder_out + 'lag_plot_%02d.png' % i)
return