How to Highlight Specific X-Value Ranges

How to highlight specific x-value ranges

Have a look at axvspan (and axhspan for highlighting a region of the y-axis).

import matplotlib.pyplot as plt

plt.plot(range(10))
plt.axvspan(3, 6, color='red', alpha=0.5)
plt.show()

If you're using dates, then you'll need to convert your min and max x values to matplotlib dates. Use matplotlib.dates.date2num for datetime objects or matplotlib.dates.datestr2num for various string timestamps.

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime as dt

t = mdates.drange(dt.datetime(2011, 10, 15), dt.datetime(2011, 11, 27),
                  dt.timedelta(hours=2))
y = np.sin(t)

fig, ax = plt.subplots()
ax.plot_date(t, y, 'b-')
ax.axvspan(*mdates.datestr2num(['10/27/2011', '11/2/2011']), color='red', alpha=0.5)
fig.autofmt_xdate()
plt.show()

Highlight a sub-sequence on matplotlib plot with specific x-value range

Here is a small demo based on @Thomas Kühn comment:

x = np.linspace(-10, 10, 100)
plt.plot(x, np.sin(x))
plt.plot(x[60:80], np.sin(x[60:80]), lw=10, c='yellow', zorder=-1)

Result:

Highlighting Date Range in matplotlib

import pandas as pd
from pandas import DataFrame as df
import matplotlib
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import datetime
import warnings
warnings.filterwarnings("ignore")

from matplotlib import dates as mdates

start = datetime.date(2020,1,1)
end = datetime.date.today()

stock =  'fb'

data = web.DataReader(stock, 'yahoo', start, end)
data.index = pd.to_datetime(data.index, format ='%Y-%m-%d')
data = data[~data.index.duplicated(keep='first')]
data['year'] = data.index.year
data['month'] = data.index.month
data['week'] = data.index.week
data['day'] = data.index.day
data.set_index('year', append=True, inplace =True)
data.set_index('month',append=True,inplace=True)
data.set_index('week',append=True,inplace=True)
data.set_index('day',append=True,inplace=True)

fig, ax = plt.subplots(dpi=300, figsize =(15,4))
ax.plot(data.index.get_level_values('Date'), data['Close'])
y0,y1 = ax.get_ylim()
offset = data['Close'].max()
new_max = (offset - y0) / (y1 - y0)
ax.axvspan((datetime.datetime(2020,3,12)), (datetime.datetime(2020,6,1)), 
            label="Labeled",color="green", alpha=0.3, ymin=0, ymax=new_max)

plt.show()

Highlight sequence of points in matplotlib

You can do what you need with plt.axvspan:

import matplotlib.pyplot as plt

# create some dummy data
data = {'a': np.arange(50),
        'c': np.random.randint(0, 50, 50),
        'd': np.random.randn(50)}
data['b'] = data['a'] + 10 * np.random.randn(50)
data['d'] = np.abs(data['d']) * 100

# plot data
plt.scatter('a', 'b', c='c', s='d', data=data)

# draw highlighted areas
plt.axvspan(10, 15, color='red', alpha=0.5)
plt.axvspan(30, 35, color='red', alpha=0.5)
plt.axvspan(40, 42, color='green', alpha=0.5)

plt.show()

result:

highlight areas within certain x range in ggplot2

Using diff to get regions to color rectangles, the rest is pretty straightforward.

## Example data
set.seed(0)
dat <- data.frame(dates=seq.Date(Sys.Date(), Sys.Date()+99, 1),
                  value=cumsum(rnorm(100)))

## Determine highlighted regions
v <- rep(0, 100)
v[c(5:20, 30:35, 90:100)] <- 1

## Get the start and end points for highlighted regions
inds <- diff(c(0, v))
start <- dat$dates[inds == 1]
end <- dat$dates[inds == -1]
if (length(start) > length(end)) end <- c(end, tail(dat$dates, 1))

## highlight region data
rects <- data.frame(start=start, end=end, group=seq_along(start))

library(ggplot2)
ggplot(data=dat, aes(dates, value)) +
  theme_minimal() +
  geom_line(lty=2, color="steelblue", lwd=1.1) +
  geom_point() +
  geom_rect(data=rects, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=min(dat$value),
                ymax=max(dat$value), group=group), color="transparent", fill="orange", alpha=0.3)

How to highlight regions in a pandas plot?

You can try something like this by using axvspan. You can avoid creating the dedicated c column.

ax = df.plot()

def highlight(indices,ax):
    i=0
    while i<len(indices):
        ax.axvspan(indices[i]-0.5, indices[i]+0.5, facecolor='pink', edgecolor='none', alpha=.2)
        i+=1

highlight(df[df['a'] < df['b']].index, ax)

Highlight matplotlib points that go over or under a threshold in colors based on the amount the boundaries are crossed

You can use boolean masks to select points that fulfill certain conditions,
and plot them:

import matplotlib.pyplot as plt
import numpy as np

std = 0.1
N = 100

x = np.linspace(0, 1, N)
expected_y = np.sin(2 * np.pi * x)
y = expected_y + np.random.normal(0, std, N)

dist = np.abs(y - expected_y) / std

mask1 = (1 < dist) & (dist <= 2)
mask2 = dist > 2

plt.fill_between(x, expected_y - 0.1, expected_y + 0.1, alpha=0.1)
plt.fill_between(x, expected_y - 0.2, expected_y + 0.2, alpha=0.1)

plt.plot(x, y)
plt.plot(x[mask1], y[mask1], 'x')
plt.plot(x[mask2], y[mask2], 'x')

plt.tight_layout()
plt.savefig('mp_points.png', dpi=300)

Result:

How can I automatically highlight multiple sections of the x axis in ggplot2?

This may be a bit hacky, but I think it gives the result you are looking for. Let me create some data first that roughly corresponds to yours:

df <- data.frame(step = rep(1:100, 3), group = rep(letters[1:3], each = 100),
                 value = c(cumsum(c(50, runif(99, -1, 1))), 
                           cumsum(c(50, runif(99, -1, 1))),
                           cumsum(c(50, runif(99, -1, 1)))))

df2 <- data.frame(step = 1:100, event = sample(c(TRUE, FALSE), 100, TRUE))

So the starting plot from df would look like this:

ggplot(df, aes(step, value, colour = group)) + geom_line()

and the event data frame looks like this:

head(df2)
#>   step event
#> 1    1 FALSE
#> 2    2 FALSE
#> 3    3 FALSE
#> 4    4  TRUE
#> 5    5 FALSE
#> 6    6  TRUE

The idea is that you add a semi-transparent red geom_area to the plot, making FALSE values way below the bottom of the range and TRUE values way above the top of the range, then just set coord_cartersian so that the y limits are near to the limits of your main data. This will give you red vertical bands whenever your event is TRUE:

ggplot(df, aes(step, value, colour = group)) + 
  geom_line() + 
  geom_area(data = df2, aes(x = step, y = 1000 * event), 
            inherit.aes = FALSE, fill = "red", alpha = 0.2) + 
  coord_cartesian(ylim = c(40, 60)

---

Related Topics