Hi I am new to use obspy.
I want to plot two streams to one plot.
I made code as below.
st1=read('/path/1.SAC')
st1+=read('/path/2.SAC')
st1.plot()
I succeed to plot two plots but what I want to do is plotting them as two colors.
When I put the option of 'color', then both colors are changed.
How can I set colors seperately?
Currently it is not possible to change the color of individual waveforms, and changing color will change all waveforms as you mentioned. I suggest you make your own graph from the ObsPy Stream using Matplotlib:
from obspy import read
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
st1=read('/path/1.SAC')
st1+=read('/path/2.SAC')
# Start figure
fig, ax = plt.subplots(nrows=2, sharex='col')
ax[0].plot(st1[0].times("matplotlib"), st1[0].data, color='red')
ax[1].plot(st1[1].times("matplotlib"), st1[1].data, color='blue')
# Format xaxis
xfmt_day = mdates.DateFormatter('%H:%M')
ax[0].xaxis.set_major_formatter(xfmt_day)
ax[0].xaxis.set_major_locator(mdates.MinuteLocator(interval=1))
plt.show()
Related
I am unable to plot candlestick in Julia properly.
Here is a sample code:
using Plots
using Dates
using MarketData
using TimeSeries
gr()
ta = yahoo(:GOOG, YahooOpt(period1 = now() - Month(1)))
display(plot(ta, seriestype= :candlestick))
The output I get is below
Candlestick Plot with one colour
How to ensure that I get red & green candles?
I would update your installation. I get two colors with Julia 1.8.0-beta3 and Plots version 1.29:
https://i.stack.imgur.com/q1p7J.png
I am trying to plot simple histogram in R. I have an integer vector and I want to draw a histogram with one column per each value.
test_data = c(1,1,1,2,2,3,3,4)
hist(test_data)
But I get this
Please tell me whether it is possible to get the same result as I have in Python?
import matplotlib.pyplot as plt
test_data = [1,1,1,2,2,3,3,4]
plt.hist(test_data)
plt.show()
You could us the barplot and table functions
barplot(table(test_data))
You can use the nclass or breaks argument to adjust the number of bins.
test_data = c(1,1,1,2,2,3,3,4)
hist(test_data,breaks=5)
hist(test_data,nclass=5)
In fact it is the same thing for python. The argument is bins. The default value is 10 (according to this page)
So if you modify it, we will get a different plot
import matplotlib.pyplot as plt
test_data = [1,1,1,2,2,3,3,4]
plt.hist(test_data,bins=4)
plt.show()
you get
Simple question - in bokeh you can plot circles with a radius rather than a size,such that the circles adjust when zooming in or out. Is it possible to do this with a holoviews based scatter - it doesn't have an option currently that I can see for setting the radius and I couldn't work out how to provide it in another manner (eg renders). Likely user error so apologies in advance, many thanks.
import holoviews as hv
hv.extension('bokeh')
from bokeh.plotting import figure, show
x=(1,2,3)
y=(1,2,3)
p=figure()
p.scatter(x, y, radius=0.2)
show(p) # bokeh plot working as expected
scatter=hv.Scatter((x,y)).opts(marker="circle", size=20)
scatter # holoviews plot, cannot code "radius" for code above - causes error.
All hv.Scatter plots are based around Bokeh's Scatter marker which has this section in its docstring:
Note that circles drawn with `Scatter` conform to the standard Marker
interface, and can only vary by size (in screen units) and *not* by radius
(in data units). If you need to control circles by radius in data units,
you should use the Circle glyph directly.
This means that you cannot use hv.Scatter, you have to use something else:
import holoviews as hv
import param
from holoviews.element.chart import Chart
from holoviews.plotting.bokeh import PointPlot
hv.extension('bokeh')
x = (1, 2, 3)
y = (1, 2, 3)
class Circle(Chart):
group = param.String(default='Circle', constant=True)
size = param.Integer()
class CirclePlot(PointPlot):
_plot_methods = dict(single='circle', batched='circle')
style_opts = ['radius' if so == 'size' else so for so in PointPlot.style_opts if so != 'marker']
hv.Store.register({Circle: CirclePlot}, 'bokeh')
scatter = Circle((x, y)).opts(radius=0.5)
I have a barplot using the bokeh library, and I would like to show only the first 5 barplot and then if I want to see the rest of the bars I have to move the x axis to the right or left. I am struggling in find the information that would allow me to do so.
An example would be this:
from bokeh.plotting import figure
from bokeh.io import output_file, show
import calendar
values = [2,3,4,5,6,7,8]
days = [calendar.day_name[i-1] for i in range(1,8)]
p = figure(x_range=days,plot_height=500)
p.vbar(x=days, width=0.5, top=values, color = "#ff1200")
output_file('foo.html')
show(p)
and what I would like it would be something like this:
and then if I want to see the resting of the days I have to click on the figure and move the mouse.
Any idea?
I wasn't able to find a solution for limiting the x axis while using categorical data. Instead I made a workaround where the x axis labels are overridden by days of the week. This makes it possible to use x_range to limit the x axis.
#!/usr/bin/python3
from bokeh.plotting import figure
from bokeh.io import output_file, show
values = [2,3,4,5,6,7,8]
days = [0,1,2,3,4,5,6]
p = figure(x_range=(-0.3,4.3),plot_height=500)
p.xaxis.major_label_overrides = {0:'Monday', 1:'Tuesday', 2:'Wednesday', 3:'Thursday', 4:'Friday', 5:'Saturday', 6:'Sunday'}
p.vbar(x=days, width=0.5, top=values, color = "#ff1200")
output_file('foo.html')
show(p)
Using ggplot2 under R (and the code below uses rpy2, I don't actually know straight R, sorry).
I'm trying to add an aspect_ratio to my plot (so it'll fit nicely on a slide). The below example is pretty minimal. Aspect_ratio does the right thing EXCEPT the title and x-axis label are placed way above and below the plot, respectively, with a giant white space between the title and plot and between the plot and x-axis label, as in (plot here):
TITLE TITLE
PLOT PLOT PLOT PLOT
PLOT PLOT PLOT PLOT
PLOT PLOT PLOT PLOT
PLOT PLOT PLOT PLOT
x-axis label
Not cool. What can I do to squeeze them together?
#!/usr/bin/env python2.6
import rpy2.robjects.lib.ggplot2 as ggplot2
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
df = ro.DataFrame({'x': ro.IntVector((1,2)), 'y': ro.IntVector((3,4))})
pp = ggplot2.ggplot(df) + \
ggplot2.aes_string(x='x', y='y') + \
ggplot2.opts(**{'title' : 'Title',
'aspect.ratio' : 0.618033989} ) + \
ggplot2.geom_line()
grdevices = importr('grDevices')
grdevices.pdf(file="aspect.pdf")
pp.plot()
grdevices.dev_off()
This is a limitation in the current layout system, and something I hope to work on soon.