Develop Reference

My plot is nested in another plot, and negative values are saturated using a diverging colormap

When making a plot using plt.subplot, my plot is nested within another plot, leading to an extra set of axes with ranges 0-1 and cutting off my colorbar.
Additionally, when using a diverging colorbar, all negative values are falsely saturated.
I have used this code format before without having an additional set of axes.
Is there another way to make this kind of plot without using plt.subplot if that is causing the issue?
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib
import cmocean
import h5py
import pandas as pd
import matplotlib.colors as colors
import cartopy
import matplotlib.colormaps as colormaps
july = h5py.File('data/CCS_Colour_Kahru/chl/1km/daily/2016/Daily_Chlorophyll_July.mat','r')
daily_chl_july = july.get('daily_chl')
lat_july = daily_chl_july.get('Latitude')
lon_july = daily_chl_july.get('Longitude')
chl_july = daily_chl_july.get('chl')
date_july = daily_chl_july.get('date')
data_july = daily_chl_july.get('data')
lat_july = np.array(lat_july)
lat_july = lat_july[0]
lon_july = np.array(lon_july)
lon_july = lon_july[0]
chl_july = np.array(chl_july)
inside_lat_july = np.where(lat_july>=(32.9))
lat_july = lat_july[inside_lat_july]
lon_july = lon_july[inside_lat_july]
chl_july = chl_july[inside_lat_july]
inside_lat_july = np.where(lat_july<=(34.05))
lat_july = lat_july[inside_lat_july]
lon_july = lon_july[inside_lat_july]
chl_july = chl_july[inside_lat_july]
inside_lon_july = np.where(lon_july>=(-118.96))
lon_july = lon_july[inside_lon_july]
lat_july = lat_july[inside_lon_july]
chl_july = chl_july[inside_lon_july]
inside_lon_july = np.where(lon_july<=(-117.54))
lon_july = lon_july[inside_lon_july]
lat_july = lat_july[inside_lon_july]
chl_july = chl_july[inside_lon_july]
mean_chl = np.nanmean(chl_july, axis=1)
remove_nan = ~np.isnan(mean_chl)
mean_chl = mean_chl[remove_nan]
chl_july_mean = chl_july[remove_nan]
lat_july_mean = lat_july[remove_nan]
lon_july_mean = lon_july[remove_nan]
for i in range (0,31,1):
day = i+1
chl_now = chl_july_mean[:,i]
remove_nan = ~np.isnan(chl_now)
chl_now_clean1 = chl_now[remove_nan]
lon_now_clean1 = lon_july_mean[remove_nan]
lat_now_clean1 = lat_july_mean[remove_nan]
mean_chl_clean = mean_chl[remove_nan]
if np.size(chl_now_clean1)==0:
continue
anomaly = chl_now_clean1 - mean_chl_clean
print('min')
print(anomaly.min())
print('max')
print(anomaly.max())
fig, ax = plt.subplots(1,1,figsize=(15,10))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([-118.96,-117.54,32.9,34.05])
im = ax.scatter(lon_now_clean1, lat_now_clean1, norm=colors.TwoSlopeNorm(vmin=-20, vcenter=0, vmax=20), c=anomaly, cmap='PiYG')
ax.add_feature(cfeature.LAND,zorder=4,color='gray')
ax.set_title("Chlorophyll Anomaly 2016 July "+str(day),fontsize=25)
ax.set_xlabel("Longitude",fontsize=20)
ax.set_ylabel("Latitude",fontsize=20)
xticks = np.linspace(-118.96,-117.54,5)
yticks = np.linspace(32.9,34.05,5)
ax.set_xticks(ticks=xticks)
ax.set_yticks(ticks=yticks)
fig.colorbar(im, ax=ax)
im.set_clim(0.1,1.5)
ax.coastlines()
plt.savefig('frankieleelopez/0124anomaly72016'+str(day))
plt.close()
enter image description here

How to show sums at nodes from the dataframe using Hovertool?

I have created a Sankeydiagram using a Holomap and I want to show the sums of the respective nodes by hovering over them but I don't know how I need to format my code or setup the dataframe to achieve this. Github
import holoviews as hv
from holoviews import opts, dim
import pandas as pd
from bokeh.palettes import Viridis
import bokeh.models
from bokeh.themes import built_in_themes
hv.extension('bokeh')
df = pd.read_excel("Refugees_V9.xlsx")
df = df.dropna()
df['year'] = df['year'].astype(int)
df['refugees'] = df['refugees'].astype(int)
hover = bokeh.models.HoverTool(tooltips=[('Refugees', '#refugees'),])
hv_ds = hv.Dataset(
data=df,
kdims=['source', 'target', 'year'],
vdims=['refugees'],
)
hv.renderer('bokeh').theme = built_in_themes['dark_minimal']
def hook(plot, element):
#plot.handles['text_1_glyph'].text_font = 'verdana'
#plot.handles['text_1_glyph'].text_font_size = '12pt'
plot.handles['text_1_glyph'].text_color = 'snow'
#plot.handles['text_2_glyph'].text_font = 'verdana'
#plot.handles['text_2_glyph'].text_font_size = '12pt'
plot.handles['text_2_glyph'].text_color = 'white'
graph = hv_ds.to(hv.Sankey)
graph.opts(
label_position='outer',
bgcolor = "#2f2f2f",
edge_color='target',
node_color='target',
show_values = False,
cmap= Viridis[10],
width=800,
height=800,
title = "Refugee migration into the Schengen-EU 2011-2022",
node_sort=False,
node_width = 20,
#tools= ['hover'],
default_tools = [hover],
show_frame=False,
edge_alpha = 0.8,
edge_hover_fill_alpha = 1,
node_alpha = 0.8,
node_hover_fill_alpha = 0.95,
label_text_font_size = "10pt",
hooks=[hook],
toolbar=None,
)
hv.output(graph, widget_location="bottom")
Data
Created Sankey
I want to show the sum of refugees over the nodes and the connecting line displaying the information how many are migrating from where to where.

Deploying bokeh dashboard via Jupyter notebook; plots not updating

I have been mainly working in VS code to create a bokeh dashboard and I now need to get it to run within a Jupyter notebook. I know that some transformations in the code are required to push the code to a Jupyter notebook and for it to update interactively with widgets.
I have referred to this documentation:-
https://docs.bokeh.org/en/latest/docs/user_guide/jupyter.html#userguide-jupyter-notebook
But it is either too simplistic for my code, or that I have not used the push_notebook commands properly (or both).
Here is the code that I am trying to run in the notebook:-
################################### Code chunk 1##########################
from ipywidgets import interact
import pandas as pd
import numpy as np
import math
from bokeh.models import HoverTool
from bokeh.io import curdoc, output_notebook, push_notebook
from bokeh.plotting import figure, ColumnDataSource
from bokeh.layouts import layout, row, column, gridplot
from bokeh.models.widgets import RangeSlider
#https://discourse.bokeh.org/t/interactive-histograms-not-updating-with-sliders/3779/25
#clustering packages
from operator import index
from bokeh.models.widgets.markups import Div
import numpy as np
from numpy.lib import source
import pandas as pd
from bokeh.io import curdoc
from bokeh.layouts import column, row, gridplot, Column, Row
from bokeh.models import ColumnDataSource, Select, Slider, BoxSelectTool, LassoSelectTool, Tabs, Panel, LinearColorMapper, ColorBar, BasicTicker, PrintfTickFormatter, MultiSelect, DataTable, TableColumn
from bokeh.plotting import figure, curdoc, show
from bokeh.palettes import viridis, gray, cividis, Category20
from bokeh.transform import factor_cmap
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import classification_report, confusion_matrix, mean_squared_error, r2_score, recall_score, f1_score
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.cluster import KMeans
from sklearn.svm import SVC
from sklearn.decomposition import PCA
#tables
from time import time
output_notebook()
################################# Code chunk 2 ###########################################
#define the categorical variable
category_a = ['A','B','C']
category_b = ['X','Y','Z']
print("step 2")
df = pd.DataFrame({
'id': np.arange(0, 100),
'date': pd.date_range(start='1/1/2021', periods=100, freq='D'),
'month':np.random.randint(1, 12, 100),
'sensor_1': np.random.uniform(0, 1,100),
'sensor_2': np.random.uniform(10, 150, 100),
'sensor_3': np.random.randint(0, 90, 100),
'sensor_4': np.random.randint(0, 450, 100),
'sensor_5': np.random.randint(0, 352, 100),
'categorya': np.random.choice(category_a, 100, p=[0.2, 0.4, 0.4]),
'categoryb': np.random.choice(category_b, 100, p=[0.6, 0.2, 0.2]),
})
source = ColumnDataSource(data=df)
################################### Code chunk 3##############################
class hist_data:
def __init__(self, df, col, n_bins, bin_range):
self.sensor1_lwr = min(df['sensor_1'])#duration millisecond
self.sensor1_upr = max(df['sensor_1'])#duration millisecond
self.sensor2_lwr = min(df['sensor_2'])#count watch
self.sensor2_upr = max(df['sensor_2'])#count_watch
self.sensor3_lwr = min(df['sensor_3'])#count idle
self.sensor3_upr = max(df['sensor_3'])#count idle
self.sensor4_lwr = min(df['sensor_4'])#count inter and watch
self.sensor4_upr = max(df['sensor_4'])#count inter and watch
self.sensor5_lwr = min(df['sensor_5'])#count inter
self.sensor5_upr = max(df['sensor_5'])#count inter
self.col = col
self.n_bins = n_bins
self.bin_range = bin_range
self.original_df = df
self.source = ColumnDataSource(self.create_hist_data(df))
def filt_df(self):
filt = (pd.DataFrame(self.original_df[(self.original_df.sensor_1 >=self.sensor1_lwr) &
(self.original_df.sensor_1 <= self.sensor1_upr) &
(self.original_df.sensor_2 >= self.sensor2_lwr) &
(self.original_df.sensor_2 <= self.sensor2_upr) &
(self.original_df.sensor_3 >= self.sensor3_lwr) &
(self.original_df.sensor_3 <= self.sensor3_upr) &
(self.original_df.sensor_4 >= self.sensor4_lwr) &
(self.original_df.sensor_4 <= self.sensor4_upr) &
(self.original_df.sensor_5 >= self.sensor5_lwr) &
(self.original_df.sensor_5 <= self.sensor5_upr)]))
print(f'{self.sensor1_lwr} {self.sensor1_upr} {self.sensor2_lwr} {self.sensor2_upr} {self.sensor3_lwr} {self.sensor3_upr} {self.sensor4_lwr} {self.sensor4_upr} {self.sensor5_lwr} {self.sensor5_upr}')
filt.shape
return ColumnDataSource(self.create_hist_data(filt))
def create_hist_data(self,df):
arr_hist, edges = np.histogram(df[self.col],bins=self.n_bins, range=self.bin_range)
arr_df = pd.DataFrame({'count': arr_hist, 'left': edges[:-1], 'right': edges[1:]})
arr_df['f_count'] = ['%d' % count for count in arr_df['count']]
arr_df['f_interval'] = ['%d to %d ' % (left, right) for left, right in zip(arr_df['left'], arr_df['right'])]
return (arr_df)
df = df
########################histograms
hist_data_A = hist_data(df,'sensor_1',math.floor(math.sqrt(df['sensor_1'].nunique())),[min(df['sensor_1']),max(df['sensor_1'])])
hist_data_B = hist_data(df,'sensor_2',math.floor(math.sqrt(df['sensor_2'].nunique())),[min(df['sensor_2']),max(df['sensor_2'])])
hist_data_C = hist_data(df,'sensor_3',math.floor(math.sqrt(df['sensor_3'].nunique())),[min(df['sensor_3']),max(df['sensor_3'])])
hist_data_D = hist_data(df,'sensor_4',math.floor(math.sqrt(df['sensor_4'].nunique())),[min(df['sensor_4']),max(df['sensor_4'])])
hist_data_E = hist_data(df,'sensor_5',math.floor(math.sqrt(df['sensor_5'].nunique())),[min(df['sensor_5']),max(df['sensor_5'])])
############################slider
A_Slider= RangeSlider(start=min(df['sensor_1']), end=max(df['sensor_1']), value=(min(df['sensor_1']),max(df['sensor_1'])), step=1, title='sensor_1')
B_Slider = RangeSlider(start=min(df['sensor_2']), end=max(df['sensor_2']), value=(min(df['sensor_2']),max(df['sensor_2'])), step=1, title='sensor_2')
C_Slider = RangeSlider(start=min(df['sensor_3']), end=max(df['sensor_3']), value=(min(df['sensor_3']),max(df['sensor_3'])), step=1, title='sensor_3')
D_Slider = RangeSlider(start=min(df['sensor_4']), end=max(df['sensor_4']), value=(min(df['sensor_4']),max(df['sensor_4'])), step=1, title='sensor_4')
E_Slider = RangeSlider(start=min(df['sensor_5']), end=max(df['sensor_5']), value=(min(df['sensor_5']),max(df['sensor_5'])), step=1, title='sensor_5')
def callback_A(attr,new,old):
hist_data_A.sensor1_lwr = new[0]
hist_data_A.sensor1_upr = new[1]
hist_data_A.source = hist_data_A.filt_df()
Graphs1.children[0] = plot_data_A()
push_notebook(handle=grid)
def callback_B(attr,new,old):
hist_data_B.sensor2_lwr = new[0]
hist_data_B.sensor2_upr = new[1]
hist_data_B.source = hist_data_B.filt_df()
Graphs1.children[1] = plot_data_B()
push_notebook(handle=grid)
def callback_C(attr,new,old):
hist_data_C.sensor3_lwr = new[0]
hist_data_C.sensor3_upr = new[1]
hist_data_C.source = hist_data_C.filt_df()
Graphs1.children[2] = plot_data_C()
push_notebook(handle=grid)
def callback_D(attr,new,old):
hist_data_D.sensor4_lwr = new[0]
hist_data_D.sensor4_upr = new[1]
hist_data_D.source = hist_data_D.filt_df()
Graphs2.children[0] = plot_data_D()
push_notebook(handle=grid)
def callback_E(attr,new,old):
hist_data_E.sensor5_lwr = new[0]
hist_data_E.sensor5_upr = new[1]
hist_data_E.source = hist_data_E.filt_df()
Graphs2.children[1] = plot_data_E()
push_notebook(handle=grid)
A_Slider.on_change("value",callback_A)
B_Slider.on_change("value",callback_B)
C_Slider.on_change("value",callback_C)
D_Slider.on_change("value",callback_D)
E_Slider.on_change("value",callback_E)
(df,'sensor_1',df['sensor_1'].nunique(),[min(df['sensor_1']),max(df['sensor_1'])])
(df,'sensor_2',df['sensor_2'].nunique(),[min(df['sensor_2']),max(df['sensor_2'])])
(df,'sensor_3',df['sensor_3'].nunique(),[min(df['sensor_3']),max(df['sensor_3'])])
(df,'sensor_4',df['sensor_4'].nunique(),[min(df['sensor_4']),max(df['sensor_4'])])
(df,'sensor_5',df['sensor_5'].nunique(),[min(df['sensor_5']),max(df['sensor_5'])])
# Histogram
def interactive_histogram( hist_data, title,x_axis_label,x_tooltip):
source = hist_data
# Set up the figure same as before
toollist = ['lasso_select', 'tap', 'reset', 'save','crosshair','wheel_zoom','pan','hover','box_select']
p = figure(plot_width = 400,
plot_height = 400,
title = title,
x_axis_label = x_axis_label,
y_axis_label = 'Count',tools=toollist)
# Add a quad glyph with source this time
p.quad(bottom=0,
top='count',
left='left',
right='right',
source=source,
fill_color='red',
hover_fill_alpha=0.7,
hover_fill_color='blue',
line_color='black')
# Add style to the plot
p.title.align = 'center'
p.title.text_font_size = '18pt'
p.xaxis.axis_label_text_font_size = '12pt'
p.xaxis.major_label_text_font_size = '12pt'
p.yaxis.axis_label_text_font_size = '12pt'
p.yaxis.major_label_text_font_size = '12pt'
# Add a hover tool referring to the formatted columns
hover = HoverTool(tooltips = [(x_tooltip, '#f_interval'),
('Count', '#f_count')])
# Add the hover tool to the graph
p.add_tools(hover)
return p
push_notebook(handle=grid)
#binsize = 10
binzise = 100
def plot_data_A():
A_hist = interactive_histogram(hist_data_A.source, 'sensor_1','sensor_1','sensor_1')
return A_hist
def plot_data_B():
B_hist = interactive_histogram(hist_data_B.source, 'sensor_2','sensor_2','sensor_2')
return B_hist
#
def plot_data_C():
C_hist = interactive_histogram(hist_data_C.source, 'sensor_3','sensor_3','sensor_3')
return C_hist
#
def plot_data_D():
D_hist = interactive_histogram(hist_data_D.source, 'sensor_4','sensor_4','sensor_4')
return D_hist
#
def plot_data_E():
E_hist = interactive_histogram(hist_data_E.source, 'sensor_5','sensor_5','sensor_5')
return E_hist
#
Graphs1 = row([plot_data_A(), plot_data_B(), plot_data_C()])
Graphs2 = row([plot_data_D(), plot_data_E()])
Controls1= column([A_Slider,B_Slider,C_Slider,D_Slider,E_Slider])
#grid = gridplot([[Graphs1],
# [Controls1]])
grid = gridplot([[Controls1,Graphs1],[None,Graphs2]])
show(grid)
Now, it brings up the plots:-
But the widgets do not update the plots. Can someone kindly show me what I am missing?

Python matlibplot unable to build candlestick & ichimoku chart together

I have been trying to build a chart which has both candlestick and ichimoku (additionally I also want to draw support and resistance line, channels, harmonic patterns etc on the chart). I took help from various resources already...
I have built two scripts one which can build candlestick charts with RSI and MACD. Second one which can build ichimoku (Python/Pandas calculate Ichimoku chart components).
First script
# THIS VERSION IS FOR PYTHON 3 #
import urllib.request, urllib.error, urllib.parse
import time
import datetime
import numpy as np
from datetime import timedelta
import pandas as pd
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
#from matplotlib.finance import candlestick_ohlc
from mpl_finance import candlestick_ohlc
import matplotlib
import pylab
matplotlib.rcParams.update({'font.size': 9})
def rsiFunc(prices, n=14):
deltas = np.diff(prices)
seed = deltas[:n+1]
up = seed[seed>=0].sum()/n
down = -seed[seed<0].sum()/n
rs = up/down
rsi = np.zeros_like(prices)
rsi[:n] = 100. - 100./(1.+rs)
for i in range(n, len(prices)):
delta = deltas[i-1] # cause the diff is 1 shorter
if delta>0:
upval = delta
downval = 0.
else:
upval = 0.
downval = -delta
up = (up*(n-1) + upval)/n
down = (down*(n-1) + downval)/n
rs = up/down
rsi[i] = 100. - 100./(1.+rs)
return rsi
def movingaverage(values,window):
weigths = np.repeat(1.0, window)/window
smas = np.convolve(values, weigths, 'valid')
return smas # as a numpy array
def ExpMovingAverage(values, window):
weights = np.exp(np.linspace(-1., 0., window))
weights /= weights.sum()
a = np.convolve(values, weights, mode='full')[:len(values)]
a[:window] = a[window]
return a
def computeMACD(x, slow=26, fast=12):
"""
compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
return value is emaslow, emafast, macd which are len(x) arrays
"""
emaslow = ExpMovingAverage(x, slow)
emafast = ExpMovingAverage(x, fast)
return emaslow, emafast, emafast - emaslow
def bytespdate2num(fmt, encoding='utf-8'):
strconverter = mdates.strpdate2num(fmt)
def bytesconverter(b):
s = b.decode(encoding)
return strconverter(s)
return bytesconverter
def graphData(stock,MA1,MA2):
filepath = 'S:/Perl64/lambda/Litmus/data/daily/' + stock + '.csv'
print ('filepath = ' + filepath)
try:
with open(filepath) as f:
lines = (line for line in f if not line.startswith('D'))
date, openp, highp, lowp, closep, volume = np.loadtxt(lines,delimiter=',', unpack=True,
converters={ 0: bytespdate2num('%Y-%m-%d')})
x = 0
y = len(date)
newAr = []
while x < y:
appendLine = date[x],openp[x],highp[x],lowp[x],closep[x],volume[x]
print (appendLine)
newAr.append(appendLine)
x+=1
#Av1 = movingaverage(closep, MA1)
#Av2 = movingaverage(closep, MA2)
SP = len(date[MA2-1:])
fig = plt.figure(figsize=(14,8))
ax1 = plt.subplot2grid((6,4), (1,0), rowspan=4, colspan=4)
candlestick_ohlc(ax1, newAr[-SP:], width=.5, colorup='#ff1717', colordown='#53c156')
#Label1 = str(MA1)+' SMA'
#Label2 = str(MA2)+' SMA'
#ax1.plot(date[-SP:],Av1[-SP:],'blue',label=Label1, linewidth=1.5)
#ax1.plot(date[-SP:],Av2[-SP:],'orange',label=Label2, linewidth=1.5)
ax1.grid(True, color='#E8E8E8')
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.yaxis.label.set_color("w")
ax1.spines['bottom'].set_color("#E8E8E8")
ax1.spines['top'].set_color("#E8E8E8")
ax1.spines['left'].set_color("#E8E8E8")
ax1.spines['right'].set_color("#E8E8E8")
ax1.tick_params(axis='y', colors='black')
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax1.tick_params(axis='x', colors='black')
plt.ylabel('Stock price and Volume',color='black')
# --- legends
#maLeg = plt.legend(loc=9, ncol=2, prop={'size':7},
# fancybox=True, borderaxespad=0.)
#maLeg.get_frame().set_alpha(0.4)
#textEd = pylab.gca().get_legend().get_texts()
#pylab.setp(textEd[0:5], color = 'b')
volumeMin = 0
ax0 = plt.subplot2grid((6,4), (0,0), sharex=ax1, rowspan=1, colspan=4) #axisbg='#07000d'
rsi = rsiFunc(closep)
rsiCol = 'black'
posCol = '#386d13'
negCol = '#8f2020'
ax0.plot(date[-SP:], rsi[-SP:], rsiCol, linewidth=1.5)
ax0.axhline(70, color=negCol)
ax0.axhline(30, color=posCol)
ax0.fill_between(date[-SP:], rsi[-SP:], 70, where=(rsi[-SP:]>=70), facecolor=negCol, edgecolor=negCol, alpha=0.5)
ax0.fill_between(date[-SP:], rsi[-SP:], 30, where=(rsi[-SP:]<=30), facecolor=posCol, edgecolor=posCol, alpha=0.5)
ax0.set_yticks([30,70])
ax0.yaxis.label.set_color("w")
ax0.spines['bottom'].set_color("#7F7F7F")
ax0.spines['top'].set_color("#7F7F7F")
ax0.spines['left'].set_color("#7F7F7F")
ax0.spines['right'].set_color("#7F7F7F")
ax0.tick_params(axis='y', colors='black')
ax0.tick_params(axis='x', colors='black')
plt.ylabel('RSI', color='b')
ax1v = ax1.twinx()
ax1v.fill_between(date[-SP:],volumeMin, volume[-SP:], facecolor='#7F7F7F', alpha=.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.grid(False)
###Edit this to 3, so it's a bit larger
ax1v.set_ylim(0, 3*volume.max())
ax1v.spines['bottom'].set_color("#7F7F7F")
ax1v.spines['top'].set_color("#7F7F7F")
ax1v.spines['left'].set_color("#7F7F7F")
ax1v.spines['right'].set_color("#7F7F7F")
ax1v.tick_params(axis='x', colors='black')
ax1v.tick_params(axis='y', colors='black')
ax2 = plt.subplot2grid((6,4), (5,0), sharex=ax1, rowspan=1, colspan=4) #axisbg='#07000d'
fillcolor = '#7F7F7F'
nslow = 26
nfast = 12
nema = 9
emaslow, emafast, macd = computeMACD(closep)
ema9 = ExpMovingAverage(macd, nema)
ax2.plot(date[-SP:], macd[-SP:], color='blue', lw=1)
ax2.plot(date[-SP:], ema9[-SP:], color='red', lw=1)
ax2.fill_between(date[-SP:], macd[-SP:]-ema9[-SP:], 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax2.spines['bottom'].set_color("#7F7F7F")
ax2.spines['top'].set_color("#7F7F7F")
ax2.spines['left'].set_color("#7F7F7F")
ax2.spines['right'].set_color("#7F7F7F")
ax2.tick_params(axis='x', colors='black')
ax2.tick_params(axis='y', colors='black')
plt.ylabel('MACD', color='black')
ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='upper'))
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(30)
plt.suptitle(stock.upper(),color='black')
plt.setp(ax0.get_xticklabels(), visible=False)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.subplots_adjust(left=.09, bottom=.14, right=.94, top=.95, wspace=.20, hspace=0)
#plt.show()
print ("File saved")
fig.savefig('example.png',facecolor=fig.get_facecolor())
except Exception as e:
print('main loop',str(e))
graphData('RELIANCE',20,50)
Second script
import re
import time
import datetime
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from mpl_finance import candlestick_ohlc
import pylab
def bytespdate2num(fmt, encoding='utf-8'):
strconverter = mdates.strpdate2num(fmt)
def bytesconverter(b):
s = b.decode(encoding)
return strconverter(s)
return bytesconverter
filepath = 'S:/Perl64/lambda/Litmus/data/daily/TATASTEEL.csv'
df = pd.read_csv(filepath, header=0)
df.columns = ["Date", "Open", "High", "Low", "Close", "Volume"]
df = df.reset_index(drop=True)
df = df.set_index("Date", inplace = False)
df.sort_index(inplace=True) ## Sort in chronological order or as earlier dates first
df = df.tail(250)
CL_period = 9 # length of Tenkan Sen or Conversion Line
BL_period = 26 # length of Kijun Sen or Base Line
Lead_span_B_period = 52 # length of Senkou Sen B or Leading Span B
Lag_span_period = 52 # length of Chikou Span or Lagging Span
# add to the dataframe, different components of the Ichimoku
# use shift function to shift a time series forward by the given value
high_9 = df['High'].rolling(window=CL_period).max()
low_9 = df['Low'].rolling(window=CL_period).min()
df['Conv_line'] = (high_9 + low_9) /2
high_26 = df['High'].rolling(window=BL_period).max()
low_26 = df['Low'].rolling(window=BL_period).min()
df['Base_line'] = (high_26 + low_26) /2
df['SpanA'] = ((df['Conv_line'] + df['Base_line'])/2)
#df['SpanA'] = df['SpanA'].shift(26)
high_52 = df['High'].rolling(window=Lead_span_B_period).max()
low_52 = df['Low'].rolling(window=Lead_span_B_period).min()
df['SpanB'] = (high_52 + low_52)/2
#df['SpanB'] = df['SpanB'].shift(26)
df['Lagging_span'] = df['Close']
#df.dropna(inplace=True) # drop NA values from Dataframe
# plot the data using matplotlib's functionality
#add figure and axis objects
fig,ax = plt.subplots(1,1,sharex=True,figsize = (14,8)) #share x axis and set a figure size
fn='S:/Perl64/lambda/scripts/reliance_coi.csv'
df.to_csv(fn, index=1, sep=',', mode='a')
df['Close'] = df['Close'].shift(-26)
ax.plot(df.index, df.Close,linewidth=2) # plot Close with index on x-axis with a line thickness of 4
try:
#candlestick_ohlc(ax, data.values, width=0.6, colorup='g', colordown='r')
candlestick_ohlc(ax=ax, opens=df['Open'], highs=df['High'],lows=df['Low'],closes=df['Close'],width=0.4, colorup='#ff1717', colordown='#53c156')
#candlestick_ohlc(ax, newAr[-SP:], width=.5, colorup='#ff1717', colordown='#53c156')
except Exception as e:
print('main loop',str(e))
df['Conv_line'] = df['Conv_line'].shift(-26)
ax.plot(df.index, df.Conv_line, color="blue") # Tenken
df['Base_line'] = df['Base_line'].shift(-26)
ax.plot(df.index, df.Base_line, color="red") # Kijun
df['Lagging_span'] = df['Lagging_span'].shift(-52)
ax.plot(df.index, df.Lagging_span, color="grey") # Tenken
ax.plot(df.index, df.SpanA) # plot Lead Span A with index on the shared x-axis
ax.plot(df.index, df.SpanB) # plot Lead Span B with index on the sahred x-axis
# use the fill_between call of ax object to specify where to fill the chosen color
# pay attention to the conditions specified in the fill_between call
ax.fill_between(df.index,df.SpanA,df.SpanB,where = df.SpanA >= df.SpanB, color = 'lightgreen')
ax.fill_between(df.index,df.SpanA,df.SpanB,where = df.SpanA < df.SpanB, color = 'lightcoral')
plt.legend(loc=0) #Let matplotlib choose best location for legend
plt.tight_layout()
#plt.grid() # display the major grid
fig.savefig('ichimoku.png')```
The stock prices csv file that I use is here
2019-01-01,1125.25,1127.30,1110.10,1121.00,4455850
2019-01-02,1114.50,1127.00,1101.00,1106.40,7144970
2019-01-03,1107.50,1114.60,1090.10,1092.75,7446457
2019-01-04,1097.40,1104.45,1081.10,1098.65,8465141
2019-01-07,1107.00,1118.45,1101.00,1104.75,5513559
2019-01-08,1105.10,1109.95,1096.00,1104.65,5625153
2019-01-09,1112.00,1117.00,1098.70,1110.75,5766805
2019-01-10,1107.75,1111.00,1103.00,1107.50,4080283
2019-01-11,1107.60,1113.80,1088.60,1098.05,6463903
2019-01-14,1095.00,1100.50,1086.40,1096.80,4111782
2019-01-15,1105.00,1132.00,1105.00,1129.65,10062875
2019-01-16,1135.00,1145.00,1130.35,1135.90,6382777
2019-01-17,1144.45,1147.90,1130.00,1134.45,7487963
2019-01-18,1148.80,1189.90,1135.25,1184.35,25684142
2019-01-21,1194.00,1239.95,1188.65,1237.70,22038534
2019-01-22,1232.85,1246.95,1219.60,1235.15,16552819
2019-01-23,1233.30,1244.20,1222.00,1226.30,8829502
2019-01-24,1225.00,1253.20,1220.10,1247.45,13155185
2019-01-25,1250.45,1264.70,1235.40,1246.00,8550836
2019-01-28,1250.50,1255.95,1222.40,1229.55,8569265
2019-01-29,1231.00,1231.65,1201.35,1210.65,9328866
2019-01-30,1215.00,1225.00,1191.10,1195.70,7846940
2019-01-31,1202.00,1229.70,1201.00,1227.15,10185347
2019-02-01,1234.00,1255.00,1227.05,1249.95,9228965
2019-02-04,1247.00,1296.95,1242.05,1290.90,11670570
2019-02-05,1292.00,1304.40,1278.60,1291.55,9362406
2019-02-06,1296.25,1317.65,1294.25,1310.25,9411585
2019-02-07,1310.25,1321.20,1286.10,1290.40,9212227
2019-02-08,1284.80,1300.50,1272.25,1277.70,6505502
2019-02-11,1275.90,1276.00,1251.00,1253.25,7523999
2019-02-12,1251.50,1272.35,1251.50,1256.40,6399485
2019-02-14,1241.00,1241.00,1218.00,1224.20,6627360
2019-02-15,1229.75,1249.90,1214.00,1244.45,9597961
2019-02-18,1250.00,1252.50,1215.00,1220.10,9649017
2019-02-19,1218.00,1239.70,1211.20,1216.10,6244189
2019-02-20,1223.85,1240.00,1219.00,1234.35,6298179
2019-02-21,1236.00,1257.80,1229.35,1246.90,10580178
2019-02-22,1244.60,1245.30,1226.00,1232.35,8755865
2019-02-25,1236.00,1243.00,1220.65,1232.30,7852528
2019-02-26,1209.50,1234.80,1206.00,1220.25,10131050
2019-02-27,1228.05,1244.90,1209.00,1223.50,11113182
2019-02-28,1233.75,1239.85,1226.55,1231.05,11286916
2019-03-01,1237.00,1242.35,1222.25,1226.05,7922513
2019-03-05,1223.40,1239.80,1218.60,1237.65,7121509
2019-03-06,1239.80,1273.10,1235.10,1264.80,12038231
2019-03-07,1264.00,1279.80,1258.15,1270.25,8109259
2019-03-08,1266.05,1274.45,1262.00,1267.10,6040052
2019-03-11,1270.05,1312.00,1268.00,1304.10,9718840
2019-03-12,1316.90,1334.00,1314.25,1331.35,11228736
2019-03-13,1337.00,1360.00,1328.10,1347.30,11236048
2019-03-14,1349.75,1362.00,1336.10,1341.55,10402048
2019-03-15,1345.00,1358.80,1311.20,1321.65,15893093
2019-03-18,1331.00,1357.95,1329.00,1350.05,10105234
2019-03-19,1360.00,1380.00,1343.10,1376.55,9805318
2019-03-20,1377.80,1388.00,1364.00,1375.45,9892823
2019-03-22,1372.50,1380.90,1336.70,1341.75,11465112
2019-03-25,1330.60,1336.85,1316.70,1324.45,7951992
2019-03-26,1330.30,1371.60,1330.00,1367.25,9479288
2019-03-27,1377.95,1377.95,1344.25,1349.25,10094174
2019-03-28,1350.25,1369.80,1342.80,1360.00,10482938
2019-04-01,1370.00,1406.80,1362.55,1391.85,10098281
2019-04-02,1398.00,1403.10,1380.10,1389.70,8012636
2019-04-03,1392.75,1403.00,1372.00,1375.20,7849461
2019-04-04,1379.00,1383.70,1347.25,1353.05,8375674
2019-04-05,1360.95,1363.90,1343.00,1353.90,6728239
2019-04-08,1356.00,1357.50,1323.70,1329.25,8723577
2019-04-09,1328.90,1340.70,1321.00,1334.45,9497621
2019-04-10,1337.95,1348.00,1326.50,1331.40,7612711
2019-04-11,1332.95,1353.00,1329.00,1346.80,5741333
2019-04-12,1350.00,1356.90,1336.65,1343.10,5919742
2019-04-15,1345.00,1348.95,1335.00,1340.15,4245127
2019-04-16,1345.00,1360.00,1340.00,1343.75,7936553
2019-04-18,1375.00,1389.75,1365.00,1385.95,17960482
2019-04-22,1360.00,1367.00,1341.30,1345.35,10792748
2019-04-23,1348.00,1373.00,1346.00,1363.85,9055300
2019-04-24,1370.30,1394.80,1366.25,1389.50,7360887
2019-04-25,1389.10,1412.40,1362.60,1372.40,13929820
2019-04-26,1375.00,1395.95,1370.70,1392.80,6889444
2019-04-30,1396.40,1396.40,1366.80,1392.80,10217019
2019-05-02,1392.00,1413.90,1382.10,1405.05,8682505
2019-05-03,1407.95,1417.50,1402.65,1408.85,6510169
2019-05-06,1398.00,1402.80,1378.10,1384.90,7237910
2019-05-07,1394.80,1395.00,1340.20,1343.50,8876945
2019-05-08,1340.00,1340.00,1292.20,1299.45,14610543
2019-05-09,1288.80,1288.80,1251.75,1256.45,19507368
2019-05-10,1265.00,1277.70,1245.00,1251.15,11226831
2019-05-13,1247.90,1260.80,1227.50,1232.05,8047801
2019-05-14,1236.50,1269.35,1231.50,1260.45,13001004
2019-05-15,1273.00,1278.00,1250.60,1256.90,11163801
2019-05-16,1259.95,1271.90,1258.10,1265.35,6606652
2019-05-17,1267.00,1276.95,1252.00,1267.40,7898440
2019-05-20,1313.60,1337.70,1303.50,1325.90,12333937
2019-05-21,1332.20,1367.00,1330.05,1339.80,13872055
2019-05-22,1345.65,1359.70,1335.10,1340.40,11287400
2019-05-23,1372.00,1392.00,1325.00,1333.90,17722514
2019-05-24,1348.00,1353.80,1316.50,1336.85,10180759
2019-05-27,1337.10,1337.50,1307.00,1310.65,7349720
2019-05-28,1319.80,1334.80,1313.35,1323.75,19472659
2019-05-29,1321.00,1333.30,1304.15,1313.05,7112830
2019-05-30,1316.25,1342.00,1316.25,1329.75,10740841
2019-05-31,1337.90,1341.90,1320.20,1330.15,11760178
2019-06-03,1335.00,1367.25,1321.20,1360.20,8483610
2019-06-04,1357.45,1374.25,1348.10,1351.65,7059911
2019-06-06,1361.90,1361.90,1321.10,1327.35,7664319
2019-06-07,1325.95,1327.25,1305.60,1314.90,6730595
2019-06-10,1320.90,1327.00,1310.10,1319.15,5380148
2019-06-11,1321.85,1334.50,1318.00,1329.15,5253790
2019-06-12,1334.70,1338.40,1325.00,1332.15,4707716
2019-06-13,1330.00,1334.70,1308.65,1327.25,7171189
2019-06-14,1321.90,1325.00,1309.40,1317.55,6831331
2019-06-17,1320.00,1320.00,1278.50,1282.30,6815554
2019-06-18,1278.90,1287.95,1269.10,1281.00,7679193
2019-06-19,1286.90,1302.00,1262.60,1277.35,6625604
2019-06-20,1280.00,1300.00,1278.00,1296.75,4914012
2019-06-21,1295.95,1296.00,1275.55,1279.50,10623098
2019-06-24,1272.15,1276.45,1257.10,1262.40,5150998
2019-06-25,1258.90,1298.00,1254.25,1295.85,6842363
2019-06-26,1291.00,1304.60,1286.35,1294.15,5299942
2019-06-27,1293.15,1296.90,1271.00,1274.15,11385972
2019-06-28,1277.10,1282.85,1248.65,1253.10,8659721
2019-07-01,1258.05,1272.65,1246.45,1268.85,6162080
2019-07-02,1273.95,1281.00,1263.30,1278.50,4638751
2019-07-03,1282.90,1286.50,1275.05,1282.55,4026032
2019-07-04,1281.40,1291.00,1280.00,1284.00,4275148
2019-07-05,1285.10,1290.50,1260.00,1263.35,4995344
2019-07-08,1258.00,1268.10,1248.20,1252.05,6404544
2019-07-09,1248.95,1283.50,1245.50,1280.10,8016757
2019-07-10,1280.05,1289.35,1268.70,1278.85,5494315
2019-07-11,1287.00,1289.80,1279.30,1281.55,3935460
2019-07-12,1283.00,1300.00,1278.05,1280.50,7174054
2019-07-15,1285.00,1289.50,1270.35,1276.10,4873164
2019-07-16,1279.95,1294.90,1277.05,1293.00,4604019
2019-07-17,1294.30,1297.00,1280.00,1281.85,4334958
2019-07-18,1282.00,1286.40,1258.00,1261.85,5459896
2019-07-19,1268.20,1272.95,1242.70,1249.00,7468515
2019-07-22,1251.00,1284.50,1227.30,1280.50,13300153
2019-07-23,1285.00,1293.90,1260.40,1273.55,9287951
2019-07-24,1273.50,1278.80,1253.55,1259.10,6943982
2019-07-25,1264.00,1269.05,1227.00,1231.50,9968545
2019-07-26,1231.50,1242.50,1210.00,1213.80,9320481
2019-07-29,1216.90,1222.00,1205.10,1210.95,8058035
2019-07-30,1213.95,1220.00,1175.95,1180.90,9533344
2019-07-31,1175.75,1185.00,1162.40,1166.25,9705619
2019-08-01,1163.40,1188.20,1150.35,1180.25,10344862
2019-08-02,1175.70,1198.25,1162.10,1184.35,10865385
2019-08-05,1167.00,1167.00,1128.00,1143.35,13897461
2019-08-06,1134.75,1149.60,1122.00,1128.30,13288909
2019-08-07,1126.50,1138.55,1103.10,1109.40,11588879
2019-08-08,1108.35,1158.00,1095.30,1152.35,14262936
2019-08-09,1161.85,1175.50,1152.30,1162.10,10043949
2019-08-13,1233.15,1302.80,1226.00,1274.75,47923444
2019-08-14,1304.00,1304.45,1280.35,1288.25,14487137
2019-08-16,1291.20,1291.80,1273.00,1278.00,10047496
2019-08-19,1281.05,1296.80,1280.00,1292.60,7459859
2019-08-20,1289.80,1292.60,1272.60,1275.95,6843460
2019-08-21,1275.65,1278.65,1266.50,1270.95,4881553
2019-08-22,1270.95,1271.00,1238.90,1246.75,6414937
2019-08-23,1239.00,1284.00,1226.50,1275.85,9741262
2019-08-26,1294.00,1294.00,1259.00,1266.80,8778739
2019-08-27,1285.00,1285.00,1261.20,1274.85,12984396
2019-08-28,1273.75,1281.00,1256.05,1263.30,5305639
2019-08-29,1256.45,1260.25,1235.30,1241.75,8635974
2019-08-30,1245.50,1254.40,1221.00,1248.55,11308120
2019-09-03,1242.25,1243.00,1200.00,1206.40,8563009
2019-09-04,1200.55,1205.25,1186.05,1201.15,15063355
2019-09-05,1206.80,1213.20,1193.30,1198.60,10512763
2019-09-06,1203.00,1229.00,1195.25,1222.50,10600234
2019-09-09,1220.65,1233.00,1213.15,1222.20,5370758
2019-09-11,1222.50,1240.00,1222.50,1234.40,5544468
2019-09-12,1235.00,1240.45,1205.70,1210.35,5431139
2019-09-13,1212.00,1228.50,1206.90,1225.60,5919260
2019-09-16,1189.00,1219.10,1186.10,1210.75,9393731
2019-09-17,1211.00,1211.00,1193.50,1197.45,7150435
2019-09-18,1204.95,1216.30,1197.20,1205.70,6827281
2019-09-19,1207.85,1209.70,1172.65,1179.05,6293454
2019-09-20,1187.95,1269.90,1174.30,1254.35,22019674
2019-09-23,1274.15,1281.00,1235.00,1239.20,9879751
2019-09-24,1243.60,1298.80,1242.75,1278.70,15982067
2019-09-25,1284.00,1295.00,1268.85,1279.55,8316894
2019-09-26,1292.00,1298.80,1283.50,1296.80,8389212
2019-09-27,1292.50,1315.00,1284.00,1309.05,8712980
2019-09-30,1310.00,1335.75,1305.55,1332.25,11549746
2019-10-01,1337.00,1342.00,1293.30,1304.90,8192597
2019-10-03,1286.00,1314.70,1281.30,1311.05,6183107
2019-10-04,1319.90,1328.60,1303.85,1308.10,6853954
2019-10-07,1308.10,1320.80,1301.70,1310.10,4599818
2019-10-09,1308.70,1329.95,1292.50,1324.75,8040938
2019-10-10,1325.00,1369.00,1321.00,1362.75,16003744
2019-10-11,1363.70,1365.60,1336.55,1352.60,7587648
2019-10-14,1364.95,1364.95,1350.85,1358.00,6123412
2019-10-15,1362.50,1370.00,1354.30,1364.15,4422075
2019-10-16,1369.90,1379.65,1363.70,1372.35,8870701
2019-10-17,1375.00,1399.00,1372.00,1396.50,7332464
2019-10-18,1404.00,1427.90,1398.70,1416.35,12856410
2019-10-22,1425.00,1436.85,1403.35,1414.15,12703054
2019-10-23,1416.30,1425.95,1383.15,1392.40,8432964
2019-10-24,1401.00,1441.40,1386.55,1436.45,10351384
2019-10-25,1441.10,1441.45,1411.25,1431.20,6190013
2019-10-29,1445.50,1480.00,1442.10,1467.05,11780494
2019-10-30,1480.00,1484.55,1460.30,1479.10,7470723
2019-10-31,1484.00,1489.65,1461.70,1464.35,8898168
2019-11-01,1455.00,1461.80,1441.00,1456.90,6356579
2019-11-04,1465.90,1471.00,1445.10,1457.65,6429329
2019-11-05,1463.10,1468.95,1441.00,1447.30,5799318
2019-11-06,1442.70,1446.45,1428.50,1434.90,6686289
2019-11-07,1435.00,1463.00,1432.20,1458.60,6438749
2019-11-08,1449.00,1459.65,1441.30,1445.50,5494844
2019-11-11,1439.10,1444.25,1422.55,1427.80,5192423
2019-11-13,1430.00,1475.90,1430.00,1472.30,11532364
2019-11-14,1476.00,1481.60,1455.80,1462.75,6518339
2019-11-15,1465.65,1486.80,1463.15,1470.85,7173674
2019-11-18,1472.65,1486.00,1455.40,1459.20,6435097
2019-11-19,1467.00,1514.90,1465.00,1509.75,13795569
2019-11-20,1555.05,1572.40,1543.20,1547.65,19904164
2019-11-21,1545.00,1556.00,1528.55,1537.60,6810577
2019-11-22,1542.10,1569.50,1537.60,1546.50,10218978
2019-11-25,1551.15,1564.80,1551.00,1561.55,6924313
2019-11-26,1568.10,1576.35,1556.00,1560.25,16152137
2019-11-27,1559.95,1575.50,1556.10,1569.85,4408336
2019-11-28,1572.65,1584.15,1563.95,1580.30,6284885
2019-11-29,1581.95,1581.95,1547.85,1551.15,8484822
2019-12-02,1600.00,1614.45,1577.00,1586.50,14275186
2019-12-03,1592.75,1594.00,1572.60,1578.90,5938786
2019-12-04,1573.00,1577.50,1533.75,1552.70,9594828
2019-12-05,1574.00,1579.75,1544.00,1550.85,9117022
2019-12-06,1553.00,1568.00,1541.10,1554.90,5982129
2019-12-09,1556.15,1577.40,1546.50,1572.60,5779807
2019-12-10,1572.05,1573.60,1554.15,1561.95,4650906
2019-12-11,1555.60,1574.50,1550.60,1562.40,5652698
2019-12-12,1570.25,1573.85,1556.65,1568.20,4720977
2019-12-13,1580.00,1590.00,1572.40,1582.90,5791522
2019-12-16,1592.00,1593.90,1564.35,1566.60,5436951
2019-12-17,1566.75,1579.00,1555.55,1562.70,9291724
2019-12-18,1563.00,1580.00,1562.00,1575.85,6739582
2019-12-19,1573.70,1614.90,1571.80,1609.95,9375484
2019-12-20,1615.00,1617.55,1596.10,1599.10,9724619
2019-12-23,1560.10,1577.55,1557.80,1571.40,11478429
2019-12-24,1568.90,1572.05,1542.50,1546.45,8251144
2019-12-26,1541.65,1552.95,1510.15,1515.40,13605737
2019-12-27,1527.00,1546.20,1521.30,1542.35,8081591
2019-12-30,1545.95,1547.65,1528.05,1544.20,7828402
2019-12-31,1542.00,1543.70,1508.05,1514.05,10150467
2020-01-01,1518.00,1527.10,1505.50,1509.60,6402372
2020-01-02,1512.00,1540.95,1512.00,1535.30,8096561
2020-01-03,1533.00,1541.65,1523.00,1537.15,9593498
2020-01-06,1520.00,1527.90,1498.00,1501.50,11209343
2020-01-07,1519.00,1534.50,1513.50,1524.60,7627191
2020-01-08,1515.00,1534.45,1510.00,1513.15,7336561
2020-01-09,1538.60,1550.00,1531.25,1548.00,6849606
2020-01-10,1551.90,1557.95,1539.65,1547.65,5704686
2020-01-13,1545.05,1558.70,1538.40,1543.70,8358090
2020-01-14,1540.00,1550.00,1521.85,1529.40,7230788
2020-01-15,1535.85,1539.90,1518.25,1523.85,7231393
2020-01-16,1529.00,1543.35,1528.00,1537.90,5873662
2020-01-17,1553.50,1584.95,1553.20,1581.00,13469708
2020-01-20,1609.00,1609.00,1526.40,1532.35,14878868
2020-01-21,1528.60,1545.85,1522.00,1533.90,8650831
2020-01-22,1544.00,1546.75,1531.10,1533.35,4719245
2020-01-23,1536.50,1541.95,1520.70,1526.85,5142037
2020-01-24,1527.00,1536.35,1518.55,1521.55,6687633
2020-01-27,1514.90,1524.45,1505.00,1506.55,6120429
2020-01-28,1508.60,1510.00,1463.60,1471.75,11215313
2020-01-29,1474.05,1494.40,1464.05,1479.85,11313297
2020-01-30,1479.00,1479.70,1440.00,1443.75,10241756
2020-01-31,1453.00,1453.25,1407.20,1411.65,15886673
2020-02-01,1405.30,1426.95,1317.10,1383.35,10718667
The problem with second script above is that I am getting error at line
candlestick_ohlc(ax=ax, opens=df['Open'], highs=df['High'],lows=df['Low'],closes=df['Close'],width=0.4, colorup='#ff1717', colordown='#53c156')
It gives me error (you can see I have tried various version of code to build candlestick_ohlc charts)
main loop candlestick_ohlc() got an unexpected keyword argument 'opens'
I have the following questions to ask:
What is wrong with second script?
How can draw support/resistance (line), harmonic-pattern using matplotlib. The support lines and harmonic-patterns are stored in a pandas dataframe which needs to be plotted on the chart
Note: Support and resistance are some points of stock charts on which you can draw line and they act as support or resistance for stock

matplotlib.use('agg')
This is non-interactive backend. Use interactive backends like -GTK3Agg, GTK3Cairo, MacOSX, nbAgg,Qt4Agg, Qt4Cairo, Qt5Agg, Qt5Cairo, TkAgg, TkCairo, WebAgg, WX, WXAgg, WXCairo.
Try using above. May be this is helpful

Optimisation of plot animation for large data with dynamic grid

I am trying to generate an animation for a large data with a dynamic grid (ocean waves). I have managed to write a script that is functional but it is time and resource consuming. I was hoping if anyone could see what i can improve in my code to help speed it up.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
from matplotlib import animation as anim
import xarray as xr
import PySimpleGUI as sg
import sys
#file importing mechanism
jan = xr.open_dataset('Model/Bar_mig/xboutput_equi.nc')
########################## labeling the variables in the data-set ##############
nx = jan.variables['globalx']
globaltime = jan.variables['globaltime']
zb = jan.variables['zb'][:,0,:]
zs = jan.variables['zs'][:,0,:]
ccz = jan.variables['ccz'][:,:,0,:]
uz = jan.variables['uz'][:,:,0,:]
nz = np.array(range(0,100))
nx = (np.array(range(0,nx.size)))
globaltime_ar = np.array(globaltime)
conc = np.vstack(globaltime_ar)
newdf = pd.DataFrame(conc)
itr = len(newdf.index)
uz1=np.flip(uz,0)
uz2=np.flip(np.flip(uz1,1),axis=0)
depth1 = (zb)
a = np.array(depth1)
b = pd.DataFrame(a)
depth = b.dropna(axis=1, how='all')
zba1 = (np.array(zb))
zsa1 = (np.array(-zs))
zba = pd.DataFrame(zba1)
zsa = pd.DataFrame(zsa1)
This is how i am setting up the dynamic grid. ( example of the the output)
#dynamic grid
for w in range(0,itr):
AA=[]
sizer = depth.iloc[w,]
sizer1 = sizer.dropna(axis=0, how='all')
for j in range(0,sizer1.size):
maxi = -zsa.iloc[w,j]
mini = depth.iloc[w,j]
step = mini/nz.shape[-1]
globals()['col_{}'.format(j)] = pd.DataFrame(np.linspace(maxi,mini,nz.shape[-1],endpoint=True))
globals()['col_{}'.format(j)] = globals()['col_{}'.format(j)].reset_index(drop=True)
AA.append(globals()['col_{}'.format(j)])
globals()['df_{}'.format(w)] = pd.concat(AA, axis=1).iloc[:nz.size]
globals()['df_{}'.format(w)].columns = range(globals()['df_{}'.format(w)].shape[1])
AA.clear()
sg.OneLineProgressMeter('My meter title', w, itr-1, 'key')
from matplotlib import animation as anim
fig = plt.figure(figsize=(15,7.5)) # Create a dummy figure
ax = plt.axes() # Set the axis rigid
mywriter = anim.FFMpegWriter()
scale=1
def animate(w):
w = w*scale
plt.clf()
plt.title(str(w) + 'hr')
y = globals()['df_{}'.format(w)]
x = np.array([nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx,nx])
data2 = np.flip(ccz[w*1,:,:],0)
cont = plt.pcolor(x,y,np.array(data2), cmap = 'jet',
vmin = 0, vmax = 0.03
)
plt.colorbar(label='Concentration Profile ($m^3/m^3$)')
plt.fill_between(nx,min(zb[0])-1,zb[w],color = 'yellow')
point = 350
plt.xlim(point,nx.shape[-1])
plt.ylim(min(zb[0,point:point+1]),max(zb[0,point:]))
plt.xlabel('Cross shore distance (m)')
plt.ylabel('Depth (m)')
fig.tight_layout()
return cont,
ani = anim.FuncAnimation(fig, animate, interval = 1, frames=itr)
ani.save('Sed_Con.mp4', writer=mywriter)