import pandas as pd
import numpy as np
import operator
import plotly
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
from plotly.graph_objs import *
init_notebook_mode()
def sliding_window(data_array, window=2):
length = len(data_array)
new_list = []
for i in range(length):
indices = range(max(i - window, 0),
min(i + window + 1, length))
avg = 0
for j in indices:
avg += data_array[j]
avg /= float(len(indices))
new_list.append(avg)
return np.array(new_list)
color=dict({'colors': ['rgb(31, 119, 180)', 'rgb(174, 199, 232)', 'rgb(255, 127, 14)', 'rgb(255, 187, 120)',
'rgb(44, 160, 44)', 'rgb(152, 223, 138)', 'rgb(214, 39, 40)', 'rgb(255, 152, 150)',
'rgb(148, 103, 189)', 'rgb(197, 176, 213)', 'rgb(140, 86, 75)', 'rgb(196, 156, 148)',
'rgb(227, 119, 194)', 'rgb(247, 182, 210)', 'rgb(127, 127, 127)', 'rgb(199, 199, 199)',
'rgb(188, 189, 34)', 'rgb(219, 219, 141)', 'rgb(23, 190, 207)', 'rgb(158, 218, 229)']})
data = pd.DataFrame.from_csv('EvolutionMajorRXNClassesOverTime.csv')
names=list(data)
years=np.array(data[names[0]])
d=[]
for i,k in enumerate(names[1:]):
occ=sliding_window(np.array(data[k]), window=2)
d.append(Scatter(x = years, y = occ, mode = 'lines', name = k, line=dict(color=color["colors"][i])))
layout = Layout(
height=400,
width=900,
margin=Margin(
l=150,
r=150,
b=50,
t=50,
pad=4
),
title='Evolution of major reaction classes over time .',
yaxis=dict(zeroline=False,title='Percentage per year'),
xaxis=dict(showline=True)
)
fig = Figure(data=d, layout=layout)
iplot(fig)
data = pd.DataFrame.from_csv('Heteroatom_alkylation_and_arylation_RXNTypesOverTime.csv')
names=list(data)
years=np.array(data[names[0]])
d=[]
for i,k in enumerate(names[1:]):
occ=sliding_window(np.array(data[k]), window=2)
d.append(Scatter(x = years, y = occ, mode = 'lines', name = k, line=dict(color=color["colors"][i])))
layout = Layout(
height=400,
width=900,
margin=Margin(
l=150,
r=150,
b=50,
t=50,
pad=4
),
title='Evolution of Heteroatom akylation and arylation over time .',
yaxis=dict(zeroline=False,title='Percentage per year'),
xaxis=dict(showline=True)
)
fig = Figure(data=d, layout=layout)
iplot(fig)
data = pd.DataFrame.from_csv('Acylation_and_related_processes_RXNTypesOverTime.csv')
names=list(data)
years=np.array(data[names[0]])
d=[]
for i,k in enumerate(names[1:]):
occ=sliding_window(np.array(data[k]), window=2)
d.append(Scatter(x = years, y = occ, mode = 'lines', name = k, line=dict(color=color["colors"][i])))
layout = Layout(
height=400,
width=900,
margin=Margin(
l=150,
r=150,
b=50,
t=50,
pad=4
),
title='Evolution of Acylation and related processes over time .',
yaxis=dict(zeroline=False,title='Percentage per year'),
xaxis=dict(showline=True)
)
fig = Figure(data=d, layout=layout)
iplot(fig)
data = pd.DataFrame.from_csv('Deprotections_RXNTypesOverTime.csv')
names=list(data)
years=np.array(data[names[0]])
d=[]
for i,k in enumerate(names[1:]):
occ=sliding_window(np.array(data[k]), window=2)
d.append(Scatter(x = years, y = occ, mode = 'lines', name = k, line=dict(color=color["colors"][i])))
layout = Layout(
height=400,
width=900,
margin=Margin(
l=150,
r=150,
b=50,
t=50,
pad=4
),
title='Evolution of Deprotections over time .',
yaxis=dict(zeroline=False,title='Percentage per year'),
xaxis=dict(showline=True)
)
fig = Figure(data=d, layout=layout)
iplot(fig)
data = pd.DataFrame.from_csv('C-C_bond_formation_RXNTypesOverTime.csv')
names=list(data)
years=np.array(data[names[0]])
d=[]
for i,k in enumerate(names[1:]):
occ=sliding_window(np.array(data[k]), window=2)
d.append(Scatter(x = years, y = occ, mode = 'lines', name = k, line=dict(color=color["colors"][i])))
layout = Layout(
height=400,
width=900,
margin=Margin(
l=150,
r=150,
b=50,
t=50,
pad=4
),
title='Evolution of C-C bond formation over time .',
yaxis=dict(zeroline=False,title='Percentage per year'),
xaxis=dict(showline=True)
)
fig = Figure(data=d, layout=layout)
iplot(fig)
data = pd.DataFrame.from_csv('Heterocycle_formation_RXNTypesOverTime.csv')
names=list(data)
years=np.array(data[names[0]])
d=[]
for i,k in enumerate(names[1:]):
occ=sliding_window(np.array(data[k]), window=2)
d.append(Scatter(x = years, y = occ, mode = 'lines', name = k, line=dict(color=color["colors"][i])))
layout = Layout(
height=400,
width=900,
margin=Margin(
l=150,
r=150,
b=50,
t=50,
pad=4
),
title='Heterocycle formation over time .',
yaxis=dict(zeroline=False,title='Percentage per year'),
xaxis=dict(showline=True)
)
fig = Figure(data=d, layout=layout)
iplot(fig)
data = pd.DataFrame.from_csv('Protections_RXNTypesOverTime.csv')
names=list(data)
years=np.array(data[names[0]])
d=[]
for i,k in enumerate(names[1:]):
occ=sliding_window(np.array(data[k]), window=2)
d.append(Scatter(x = years, y = occ, mode = 'lines', name = k, line=dict(color=color["colors"][i])))
layout = Layout(
height=400,
width=900,
margin=Margin(
l=150,
r=150,
b=50,
t=50,
pad=4
),
title='Protections over time .',
yaxis=dict(zeroline=False,title='Percentage per year'),
xaxis=dict(showline=True)
)
fig = Figure(data=d, layout=layout)
iplot(fig)
data = pd.DataFrame.from_csv('Oxidations_RXNTypesOverTime.csv')
names=list(data)
years=np.array(data[names[0]])
d=[]
for i,k in enumerate(names[1:]):
occ=sliding_window(np.array(data[k]), window=2)
d.append(Scatter(x = years, y = occ, mode = 'lines', name = k, line=dict(color=color["colors"][i])))
layout = Layout(
height=400,
width=900,
margin=Margin(
l=150,
r=150,
b=50,
t=50,
pad=4
),
title='Oxidations over time .',
yaxis=dict(zeroline=False,title='Percentage per year'),
xaxis=dict(showline=True)
)
fig = Figure(data=d, layout=layout)
iplot(fig)
data = pd.DataFrame.from_csv('Reductions_RXNTypesOverTime.csv')
names=list(data)
years=np.array(data[names[0]])
d=[]
for i,k in enumerate(names[1:]):
occ=sliding_window(np.array(data[k]), window=2)
d.append(Scatter(x = years, y = occ, mode = 'lines', name = k, line=dict(color=color["colors"][i])))
layout = Layout(
height=400,
width=900,
margin=Margin(
l=150,
r=150,
b=50,
t=50,
pad=4
),
title='Reductions over time .',
yaxis=dict(zeroline=False,title='Percentage per year'),
xaxis=dict(showline=True)
)
fig = Figure(data=d, layout=layout)
iplot(fig)
data = pd.DataFrame.from_csv('Functional_group_interconversion_(FGI)_RXNTypesOverTime.csv')
names=list(data)
years=np.array(data[names[0]])
d=[]
for i,k in enumerate(names[1:]):
occ=sliding_window(np.array(data[k]), window=2)
d.append(Scatter(x = years, y = occ, mode = 'lines', name = k, line=dict(color=color["colors"][i])))
layout = Layout(
height=400,
width=900,
margin=Margin(
l=150,
r=150,
b=50,
t=50,
pad=4
),
legend=dict(
x=1000,
y=1
),
title='Functional group interconversion over time .',
yaxis=dict(zeroline=False,title='Percentage per year'),
xaxis=dict(showline=True)
)
fig = Figure(data=d, layout=layout)
iplot(fig)
data = pd.DataFrame.from_csv('Functional_group_addition_(FGA)_RXNTypesOverTime.csv')
names=list(data)
years=np.array(data[names[0]])
d=[]
for i,k in enumerate(names[1:]):
occ=sliding_window(np.array(data[k]), window=2)
d.append(Scatter(x = years, y = occ, mode = 'lines', name = k, line=dict(color=color["colors"][i])))
layout = Layout(
height=400,
width=900,
margin=Margin(
l=150,
r=150,
b=50,
t=50,
pad=4
),
legend=dict(
x=1000,
y=1,
xanchor="left"
),
title='Functional group addition over time .',
yaxis=dict(zeroline=False,title='Percentage per year'),
xaxis=dict(showline=True)
)
fig = Figure(data=d, layout=layout)
iplot(fig)