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visualizations.py
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visualizations.py
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"""
# -- --------------------------------------------------------------------------------------------------- -- #
# -- project: Applications of Genetic Methods for Feature Engineering and Hyperparameter Optimization -- #
# -- -------- for Neural Networks. -- #
# -- script: visualizations.py : python script with functions for plots and tables -- #
# -- author: IFFranciscoME - [email protected] -- #
# -- license: GPL-3.0 License -- #
# -- repository: https://github.com/IFFranciscoME/GeneticMethods -- #
# -- --------------------------------------------------------------------------------------------------- -- #
"""
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from functools import reduce
from itertools import product
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
# -- -------------------------------------------------------- PLOT: OHLC Price Chart with Vertical Lines -- #
# -- --------------------------------------------------------------------------------------------------- -- #
def g_ohlc(p_ohlc, p_theme=None, p_vlines=None):
"""
Timeseries Candlestick with OHLC prices and figures for trades indicator
Requirements
------------
numpy
pandas
plotly
Parameters
----------
p_ohlc: pd.DataFrame
that contains the following float or int columns: 'timestamp', 'open', 'high', 'low', 'close'
p_theme: dict
with the theme for the visualizations
p_vlines: list
with the dates where to visualize the vertical lines, format = pd.to_datetime('2020-01-01 22:15:00')
Returns
-------
fig_g_ohlc: plotly
objet/dictionary to .show() and plot in the browser
References
----------
https://plotly.com/python/candlestick-charts/
"""
# default value for lables to use in main title, and both x and y axisp_fonts
if p_theme is not None:
p_labels = p_theme['p_labels']
else:
p_theme = dict(p_colors={'color_1': '#6b6b6b', 'color_2': '#ABABAB', 'color_3': '#ABABAB'},
p_fonts={'font_title': 18, 'font_axis': 10, 'font_ticks': 10},
p_dims={'width': 900, 'height': 400},
p_labels={'title': 'OHLC Prices',
'x_title': 'Dates', 'y_title': 'Historical Prices'})
# tick values calculation for simetry in y axes
y0_ticks_vals = np.arange(min(p_ohlc['low']), max(p_ohlc['high']),
(max(p_ohlc['high']) - min(p_ohlc['low'])) / 10)
y0_ticks_vals = np.append(y0_ticks_vals, max(p_ohlc['high']))
y0_ticks_vals = np.round(y0_ticks_vals, 4)
# Instantiate a figure object
fig_g_ohlc = go.Figure()
# Add layer for OHLC candlestick chart
fig_g_ohlc.add_trace(go.Candlestick(name='ohlc', x=p_ohlc['timestamp'], open=p_ohlc['open'],
high=p_ohlc['high'], low=p_ohlc['low'], close=p_ohlc['close'],
opacity=0.7))
# Layout for margin, and both x and y axes
fig_g_ohlc.update_layout(margin=go.layout.Margin(l=50, r=50, b=20, t=60, pad=20),
xaxis=dict(title_text=p_theme['p_labels']['x_title']),
yaxis=dict(title_text=p_theme['p_labels']['y_title']))
# Color and font type for text in axes
fig_g_ohlc.update_layout(xaxis=dict(titlefont=dict(color=p_theme['p_colors']['color_1']),
tickfont=dict(color=p_theme['p_colors']['color_1'],
size=p_theme['p_fonts']['font_axis']), showgrid=False),
yaxis=dict(zeroline=False, automargin=True, tickformat='.4f',
titlefont=dict(color=p_theme['p_colors']['color_1']),
tickfont=dict(color=p_theme['p_colors']['color_1'],
size=p_theme['p_fonts']['font_axis']),
showgrid=True, gridcolor='lightgrey', gridwidth=.05))
# If parameter vlines is used
if p_vlines is not None:
# Dynamically add vertical lines according to the provided list of x dates.
shapes_list = list()
for i in p_vlines:
shapes_list.append({'type': 'line', 'fillcolor': p_theme['p_colors']['color_1'],
'line': {'color': p_theme['p_colors']['color_1'],
'dash': 'dashdot', 'width': 3},
'x0': i, 'x1': i, 'xref': 'x',
'y0': min(p_ohlc['low']), 'y1': max(p_ohlc['high']), 'yref': 'y'})
# add v_lines to the layout
fig_g_ohlc.update_layout(shapes=shapes_list)
# Update layout for the background
fig_g_ohlc.update_layout(yaxis=dict(tickfont=dict(color='grey', size=p_theme['p_fonts']['font_axis']),
tickvals=y0_ticks_vals),
xaxis=dict(tickfont=dict(color='grey', size=p_theme['p_fonts']['font_axis'])))
# Update layout for the y axis
fig_g_ohlc.update_xaxes(rangebreaks=[dict(pattern="day of week", bounds=['sat', 'sun'])])
# Update layout for the background
fig_g_ohlc.update_layout(title_font_size=p_theme['p_fonts']['font_title'],
title=dict(x=0.5, text='<b> ' + p_theme['p_labels']['title'] + ' </b>'),
yaxis=dict(title=p_theme['p_labels']['y_title'],
titlefont=dict(size=p_theme['p_fonts']['font_axis'] + 4)),
xaxis=dict(title=p_theme['p_labels']['x_title'], rangeslider=dict(visible=False),
titlefont=dict(size=p_theme['p_fonts']['font_axis'] + 4)))
# Final plot dimensions
fig_g_ohlc.layout.autosize = True
fig_g_ohlc.layout.width = p_theme['p_dims']['width']
fig_g_ohlc.layout.height = p_theme['p_dims']['height']
return fig_g_ohlc
# -- -------------------------------------------------------------------- PLOT: HeatMap Correlation Plot -- #
# -- --------------------------------------------------------------------------------------------------- -- #
def g_heat_corr(p_data, p_double):
"""
Generates a heatmap correlation matrix with seaborn library
Parameters
----------
p_data: pd.DataFrame
With correlation matrix
p_data = pd.DataFrame(np.random.randn(10, 10))
p_double: bool
True: To generate 2 plots (horizontal axis)
False: To generate 1 centered plot
p_double = False
p_annot: bool
True: To include annotations in the plot
False: Not to include annotations in the plot
p_annot = True
Returns
-------
plt = matplotlib plot object
References
----------
https://seaborn.pydata.org/generated/seaborn.heatmap.html
"""
# copy of original data
g_data = p_data.copy()
# mask = np.triu(np.ones_like(g_data, dtype=bool))
rLT = g_data.where(np.tril(np.ones(g_data.shape)).astype(np.bool_))
# mask = np.triu(np.ones_like(g_data, dtype=bool))
nrLT = g_data.where(np.triu(np.ones(g_data.shape)).astype(np.bool_))
g_heat = go.Figure()
title = 'Correlation Matrix'
g_heat = g_heat.add_trace(go.Heatmap(showscale=False,
z = nrLT*1,
x = nrLT.columns.values,
y = nrLT.columns.values,
xgap = 0, # Sets the horizontal gap (in pixels) between bricks
ygap = 0,
zmin = -1, # Sets the lower bound of the color domain
zmax = +1,
colorscale = ['#FFFFFF', '#FFFFFF']))
g_heat = g_heat.add_trace(go.Heatmap(showscale=False,
z = rLT,
x = rLT.columns.values,
y = rLT.columns.values,
zmin = -1, # Sets the lower bound of the color domain
zmax = +1,
xgap = +1, # Sets the horizontal gap (in pixels) between bricks
ygap = +1,
colorscale = 'Blues'))
g_heat = g_heat.update_layout(
title_text=title,
title_x = 0.5,
title_y = 0.90,
width = 900,
height = 900,
xaxis_showgrid = False,
yaxis_showgrid = False,
yaxis_autorange = 'reversed')
z = np.array(rLT.values).tolist()
def get_att(Mx):
Mx = z
att=[]
Mx = Mx
a, b = len(Mx), len(Mx[0])
flat_z = reduce(lambda x, y: x + y, Mx) # Mx.flat if you deal with numpy
flat_z = [1 if str(i) == 'nan' else i for i in flat_z]
colors_z = ['#FAFAFA' if i > 0 else '#6E6E6E' for i in flat_z]
coords = product(range(a), range(b))
for pos, elem, color in zip(coords, flat_z, colors_z):
att.append({'font': {'color': color, 'size':9},
'text': str(np.round(elem, 2)), 'showarrow': False,
'x': pos[1],
'y': pos[0]})
return att
g_heat.update_layout(annotations=get_att(z))
return g_heat.show()