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Basically, this is an interactive heatmap but the twist is that the source is updated by reading values from a file that gets updated regularly.
dont bother about the class "generator", it is just for keeping data and it runs regularly threaded
make sure a file named "Server_dump.txt" exists in the same directory of the script with a single number greater than 0 inside before u execute the bokeh script.
what basically happens is i change a number inside the file named "Server_dump.txt" by using echo 4 > Server_dump.txt on bash,
u can put any number other than 4 and the script automatically checks the file and plots the new point.
if u don't use bash, u could use a text editor , replace the number and save, and all will be the same.
the run function inside the generator class is the one which checks if this file was modified , reads the number, transforms it into x& y coords and increments the number of taps associated with these coords and gives the source x,y,taps values based on that number.
well that function works fine and each time i echo a number , the correct rectangle is plotted but,
now I want to add the functionality of that clicking on a certain rectangle triggers a callback to plot a second graph based on the coords of the clicked rectangle but i can't even get it to trigger even though i have tried other examples with selected.on_change in them and they worked fine.
*if i increase self.taps for a certain rect by writing the number to the file multiple times, color gets updated but if i hover over the rect it shows me the past values and not the latest value only .
my bokeh version is 1.0.4
from functools import partial
from random import random,randint
import threading
import time
from tornado import gen
from os.path import getmtime
from math import pi
import pandas as pd
from random import randint, random
from bokeh.io import show
from bokeh.models import LinearColorMapper, BasicTicker, widgets, PrintfTickFormatter, ColorBar, ColumnDataSource, FactorRange
from bokeh.plotting import figure, curdoc
from bokeh.layouts import row, column, gridplot
source = ColumnDataSource(data=dict(x=[], y=[], taps=[]))
doc = curdoc()
#sloppy data receiving function to change data to a plottable shape
class generator(threading.Thread):
def __init__(self):
super(generator, self).__init__()
self.chart_coords = {'x':[],'y':[],'taps':[]}
self.Pi_coords = {}
self.coord = 0
self.pos = 0
self.col = 0
self.row = 0
self.s = 0
self.t = 0
def chart_dict_gen(self,row, col):
self.col = col
self.row = row+1
self.chart_coords['x'] = [i for i in range(1,cla.row)]
self.chart_coords['y'] = [i for i in range(cla.col, 0, -1)] #reversed list because chart requires that
self.chart_coords['taps']= [0]*(row * col)
self.taps = [[0 for y in range(col)] for x in range(row)]
def Pi_dict_gen(self,row,col):
key = 1
for x in range(1,row):
for y in range(1,col):
self.Pi_coords[key] = (x,y)
key = key + 1
def Pi_to_chart(self,N):
x,y = self.Pi_coords[N][0], self.Pi_coords[N][1]
return x,y
def run(self):
while True:
if(self.t == 0):
self.t=1
continue
time.sleep(0.1)
h = getmtime("Server_dump.txt")
if self.s != h:
self.s = h
with open('Server_dump.txt') as f:
m = next(f)
y,x = self.Pi_to_chart(int(m))
self.taps[x][y] += 1
# but update the document from callback
doc.add_next_tick_callback(partial(update, x=x, y=y, taps=self.taps[x][y]))
cla = generator()
cla.chart_dict_gen(15,15)
cla.Pi_dict_gen(15, 15)
x = cla.chart_coords['x']
y = cla.chart_coords['y']
taps = cla.chart_coords['taps']
#gen.coroutine
def update(x, y, taps):
taps += taps
print(x,y,taps)
source.stream(dict(x=[x], y=[y], taps=[taps]))
colors = ["#CCEBFF","#B2E0FF","#99D6FF","#80CCFF","#66c2FF","#4DB8FF","#33ADFF","#19A3FF", "#0099FF", "#008AE6", "#007ACC","#006BB2", "#005C99", "#004C80", "#003D66", "#002E4C", "#001F33", "#000F1A", "#000000"]
mapper = LinearColorMapper(palette=colors, low= 0, high= 15) #low = min(cla.chart_coords['taps']) high = max(cla.chart_coords['taps'])
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
p = figure(title="Tou",
x_range=list(map(str,x)),
y_range=list(map(str,reversed(y))),
x_axis_location="above",
plot_width=900, plot_height=400,
tools=TOOLS, toolbar_location='below',
tooltips=[('coords', '#y #x'), ('taps', '#taps%')])
p.grid.grid_line_color = "#ffffff"
p.axis.axis_line_color = "#ef4723"
p.axis.major_tick_line_color = "#af0a36"
p.axis.major_label_text_font_size = "7pt"
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.rect(x="x", y="y",
width=0.9, height=0.9,
source=source,
fill_color={'field': 'taps', 'transform': mapper},
line_color = "#ffffff",
)
color_bar = ColorBar(color_mapper=mapper,
major_label_text_font_size="7pt",
ticker=BasicTicker(desired_num_ticks=len(colors)),
formatter=PrintfTickFormatter(format="%d%%"),
label_standoff=6, border_line_color=None, location=(0, 0))
curdoc().theme = 'dark_minimal'
def ck(attr, old, new):
print('here') #doesn't even print hi in the terminal if i click anywhere
source.selected.on_change('indices', ck)
p.add_layout(color_bar, 'right')
doc.add_root(p)
thread = cla
thread.start()
i wanted even to get a printed hi in the terminal but nothing
You have not actually added any selection tool at all to your plot, so no selection is ever made. You have specified:
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
Those are the only tools that will be added, and none of them make selections, there for nothing will cause source.selection.indices to ever be updated. If you are looking for selections based on tap, you must add a TapTool, e.g. with
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom,tap"
Note that there will not be repeated callbacks if you tap the same rect multiple times. The callback only fires when the selection changes and clicking the same glyph twice in a row results in an identical selection.
I'm trying to do something that I'd normally consider trivial but seems to be very difficult in bokeh: Adding a vertical colorbar to a plot and then having the title of the colorbar (a.k.a. the variable behind the colormapping) appear to one side of the colorbar but rotated 90 degrees clockwise from horizontal.
From what I can tell of the bokeh ColorBar() interface (looking at both documentation and using the python interpreter's help() function for this element), this is not possible. In desperation I have added my own Label()-based annotation. This works but is klunky and displays odd behavior when deployed in a bokeh serve situation--that the width of the data window on the plot varies inversely with the length of the title colorbar's title string.
Below I've included a modified version of the bokeh server mpg example. Apologies for its complexity, but I felt this was the best way to illustrate the problem using infrastructure/data that ships with bokeh. For those unfamiliar with bokeh serve, this code snippet needs to saved to a file named main.py that resides in a directory--for the sake of argument let's say CrossFilter2--and in the parent directory of CrossFilter2 one needs to invoke the command
bokeh serve --show CrossFilter2
this will then display in a browser window (localhost:5006/CrossFilter2) and if you play with the color selection widget you will see what I mean, namely that short variable names such as 'hp' or 'mpg' result in a wider data display windows than longer variable names such as 'accel' or 'weight'. I suspect that there may be a bug in how label elements are sized--that their x and y dimensions are swapped--and that bokeh has not understood that the label element has been rotated.
My questions are:
Must I really have to go to this kind of trouble to get a simple colorbar label feature that I can get with little-to-no trouble in matplotlib/plotly?
If I must go through the hassle you can see in my sample code, is there some other way I can do this that avoids the data window width problem?
import numpy as np
import pandas as pd
from bokeh.layouts import row, widgetbox
from bokeh.models import Select
from bokeh.models import HoverTool, ColorBar, LinearColorMapper, Label
from bokeh.palettes import Spectral5
from bokeh.plotting import curdoc, figure, ColumnDataSource
from bokeh.sampledata.autompg import autompg_clean as df
df = df.copy()
SIZES = list(range(6, 22, 3))
COLORS = Spectral5
# data cleanup
df.cyl = df.cyl.astype(str)
df.yr = df.yr.astype(str)
columns = sorted(df.columns)
discrete = [x for x in columns if df[x].dtype == object]
continuous = [x for x in columns if x not in discrete]
quantileable = [x for x in continuous if len(df[x].unique()) > 20]
def create_figure():
xs = df[x.value].tolist()
ys = df[y.value].tolist()
x_title = x.value.title()
y_title = y.value.title()
name = df['name'].tolist()
kw = dict()
if x.value in discrete:
kw['x_range'] = sorted(set(xs))
if y.value in discrete:
kw['y_range'] = sorted(set(ys))
kw['title'] = "%s vs %s" % (y_title, x_title)
p = figure(plot_height=600, plot_width=800,
tools='pan,box_zoom,wheel_zoom,lasso_select,reset,save',
toolbar_location='above', **kw)
p.xaxis.axis_label = x_title
p.yaxis.axis_label = y_title
if x.value in discrete:
p.xaxis.major_label_orientation = pd.np.pi / 4
if size.value != 'None':
groups = pd.qcut(df[size.value].values, len(SIZES))
sz = [SIZES[xx] for xx in groups.codes]
else:
sz = [9] * len(xs)
if color.value != 'None':
coloring = df[color.value].tolist()
cv_95 = np.percentile(np.asarray(coloring), 95)
mapper = LinearColorMapper(palette=Spectral5,
low=cv_min, high=cv_95)
mapper.low_color = 'blue'
mapper.high_color = 'red'
add_color_bar = True
ninety_degrees = pd.np.pi / 2.
color_bar = ColorBar(color_mapper=mapper, title='',
#title=color.value.title(),
title_text_font_style='bold',
title_text_font_size='20px',
title_text_align='center',
orientation='vertical',
major_label_text_font_size='16px',
major_label_text_font_style='bold',
label_standoff=8,
major_tick_line_color='black',
major_tick_line_width=3,
major_tick_in=12,
location=(0,0))
else:
c = ['#31AADE'] * len(xs)
add_color_bar = False
if add_color_bar:
source = ColumnDataSource(data=dict(x=xs, y=ys,
c=coloring, size=sz, name=name))
else:
source = ColumnDataSource(data=dict(x=xs, y=ys, color=c,
size=sz, name=name))
if add_color_bar:
p.circle('x', 'y', fill_color={'field': 'c',
'transform': mapper},
line_color=None, size='size', source=source)
else:
p.circle('x', 'y', color='color', size='size', source=source)
p.add_tools(HoverTool(tooltips=[('x', '#x'), ('y', '#y'),
('desc', '#name')]))
if add_color_bar:
color_bar_label = Label(text=color.value.title(),
angle=ninety_degrees,
text_color='black',
text_font_style='bold',
text_font_size='20px',
x=25, y=300,
x_units='screen', y_units='screen')
p.add_layout(color_bar, 'right')
p.add_layout(color_bar_label, 'right')
return p
def update(attr, old, new):
layout.children[1] = create_figure()
x = Select(title='X-Axis', value='mpg', options=columns)
x.on_change('value', update)
y = Select(title='Y-Axis', value='hp', options=columns)
y.on_change('value', update)
size = Select(title='Size', value='None',
options=['None'] + quantileable)
size.on_change('value', update)
color = Select(title='Color', value='None',
options=['None'] + quantileable)
color.on_change('value', update)
controls = widgetbox([x, y, color, size], width=200)
layout = row(controls, create_figure())
curdoc().add_root(layout)
curdoc().title = "Crossfilter"
You can add a vertical label to the Colorbar by plotting it on a separate axis and adding a title to this axis. To illustrate this, here's a modified version of Bokeh's standard Colorbar example (found here):
import numpy as np
from bokeh.plotting import figure, output_file, show
from bokeh.models import LogColorMapper, LogTicker, ColorBar
from bokeh.layouts import row
plot_height = 500
plot_width = 500
color_bar_height = plot_height + 11
color_bar_width = 180
output_file('color_bar.html')
def normal2d(X, Y, sigx=1.0, sigy=1.0, mux=0.0, muy=0.0):
z = (X-mux)**2 / sigx**2 + (Y-muy)**2 / sigy**2
return np.exp(-z/2) / (2 * np.pi * sigx * sigy)
X, Y = np.mgrid[-3:3:100j, -2:2:100j]
Z = normal2d(X, Y, 0.1, 0.2, 1.0, 1.0) + 0.1*normal2d(X, Y, 1.0, 1.0)
image = Z * 1e6
color_mapper = LogColorMapper(palette="Viridis256", low=1, high=1e7)
plot = figure(x_range=(0,1), y_range=(0,1), toolbar_location=None,
width=plot_width, height=plot_height)
plot.image(image=[image], color_mapper=color_mapper,
dh=[1.0], dw=[1.0], x=[0], y=[0])
Now, to make the Colorbar, create a separate dummy plot, add the Colorbar to the dummy plot and place it next to the main plot. Add the Colorbar label as the title of the dummy plot and center it appropriately.
color_bar = ColorBar(color_mapper=color_mapper, ticker=LogTicker(),
label_standoff=12, border_line_color=None, location=(0,0))
color_bar_plot = figure(title="My color bar title", title_location="right",
height=color_bar_height, width=color_bar_width,
toolbar_location=None, min_border=0,
outline_line_color=None)
color_bar_plot.add_layout(color_bar, 'right')
color_bar_plot.title.align="center"
color_bar_plot.title.text_font_size = '12pt'
layout = row(plot, color_bar_plot)
show(layout)
This gives the following output image:
One thing to look out for is that color_bar_width is set wide enough to incorporate both the Colorbar, its axes labels and the Colorbar label. If the width is set too small, you will get an error and the plot won't render.
As of Bokeh 0.12.10 there is no built in label available for colorbars. In addition to your approach or something like it, another possibility would be a custom extension, though that is similarly not trivial.
Offhand, a colobar label certainly seems like a reasonable thing to consider. Regarding the notion that it ought to be trivially available, if you polled all users about what they consider should be trivially available, there will be thousands of different suggestions for what to prioritize. As is very often the case in the OSS world, there are far more possible things to do, than there are people to do them (less than 3 in this case). So, would first suggest a GitHub Issue to request the feature, and second, if you have the ability, volunteering to help implement it. Your contribution would be valuable and appreciated by many.
is there a way to update xmin and xmax of Graph in kivy garden?
At the moment, my xmin is 0 and xmax is 10 and I am plotting a list containin (x,y) points. y starts from 0 and is incremented every second and x is just a counter that counts iterations. so once x is > 10 the graph is not updated in real time so the x axis does not move like e.g. 1-11 and next second 2-12 and next 3-13 etc etc so I always show 10 values but the grap is 'live'.
How to achieve it in kivy garden Graph?
graph = Graph(
xlabel='Iteration',
ylabel='Value',
x_ticks_minor=1,
x_ticks_major=5,
y_ticks_major=1,
y_grid_label=True,
x_grid_label=True,
padding=5,
xlog=False,
ylog=False,
x_grid=True,
y_grid=True,
xmin=0,
xmax=10,
ymin=0,
ymax=11,
**graph_theme)
Never used this flower, but try graph.xmin = <something>. If it doesn't work, the code probably has other name for that variable, because xmin in Graph(...) is only keyword argument. Try to look at its __init__() and after you see what has the value, try to access it via graph.<variable>. If it's not a local variable, then it should work.
However, this only says how to change it manually. If you have more of your code except graph = Graph(...), paste it, an issue may be there. Otherwise you could try to report a glitch at garden's repo for Graph.
According to the code found in "garden.graph" library you can call the instance of the graph to set properties of xmax, xmin, ymax and ymin.
As an example, I am seting the property of ymax to be maximum value of my data set:
Class Test(Widget):
data = #list of datapoints
graph_test = ObjectProperty(None)
def setgraph:
plot = MeshLinePlot(color=[1, 0, 0, 1])
self.graph_test.ymax=max(data[1])
plot.points = [(data[0][i], data[1][i]) for i in range(0,500)]
self.graph_test.add_plot(plot)
I also have a .kv file that contains the following
#:kivy 1.0.9
<Test>:
graph_test : graph_test
Graph:
id: graph_test
plot: MeshLinePlot
xlabel:'X'
ylabel:'Y'
x_ticks_minor:1000000000000
x_tics_major:5000000000000
y_ticks_major:1000
y_grid_label:True
x_grid_label:True
padding:5
x_grid:True
y_grid:True
xmin:123413241234
xmax:123412341234
ymin:2000
ymax:15000
pos: 0, 0
size: root.width , root.height
Old question, but I'm doing this with some Kivy Graphs and rather than resetting the XMin and XMax (which means those numbers would keep counting up for as long as the graph is running "live"), we shift the data points and leave the X range alone.
We built this simple LiveGraph class to wrap it, works nicely and is relatively performant.
class LiveGraph(Graph):
def __init__(self, num_data_points=50, **kwargs):
super(LiveGraph, self).__init__(**kwargs)
self._num_data_points = num_data_points
self.xmin = 0
self.xmax = num_data_points
build_chart()
def build_chart(self):
plot = MeshLinePlot()
# Initialize with empty Y values to start with
plot.points = [(x,0) for x in range(self._num_data_points)
# I tested a number of ways to do this - this ended up being
# the fastest.
def add_point(self, point_value):
new_points = self.plots[0].points
new_points = [(i, new_points[i+1][1]) for i in range(self._num_data_points - 1)]
new_points.append([len(new_points) - 1, new_value])
self.plots[0].points = new_points
I have modified the ImageView example by adding the statement data[:, ::10, :] = 0, which sets every tenth element of the middle dimension to 0. The program now shows horizontal lines. This is consistent with the documentation of the ImageView.setImage function: the default axes dictionary is {'t':0, 'x':1, 'y':2, 'c':3}. However, when I change this to {'t':0, 'x':2, 'y':1, 'c':3}, nothing changes where I would expect to get vertical rows.
So my question is: how can I give the row dimension a higher precedence in PyQtGraph? Of course I can transpose all my arrays myself before passing them to the setImage function but I prefer not to. Especially since both Numpy and Qt use the row/column convention and not X before Y. I don't see why PyQtGraph chooses the latter.
For completeness, find my modified ImageView example below.
import numpy as np
from pyqtgraph.Qt import QtCore, QtGui
import pyqtgraph as pg
app = QtGui.QApplication([])
## Create window with ImageView widget
win = QtGui.QMainWindow()
win.resize(800,800)
imv = pg.ImageView()
win.setCentralWidget(imv)
win.show()
win.setWindowTitle('pyqtgraph example: ImageView')
## Create random 3D data set with noisy signals
img = pg.gaussianFilter(np.random.normal(size=(200, 200)), (5, 5)) * 20 + 100
img = img[np.newaxis,:,:]
decay = np.exp(-np.linspace(0,0.3,100))[:,np.newaxis,np.newaxis]
data = np.random.normal(size=(100, 200, 200))
data += img * decay
data += 2
## Add time-varying signal
sig = np.zeros(data.shape[0])
sig[30:] += np.exp(-np.linspace(1,10, 70))
sig[40:] += np.exp(-np.linspace(1,10, 60))
sig[70:] += np.exp(-np.linspace(1,10, 30))
sig = sig[:,np.newaxis,np.newaxis] * 3
data[:,50:60,50:60] += sig
data[:, ::10, :] = 0 # Make image a-symmetrical
## Display the data and assign each frame a time value from 1.0 to 3.0
imv.setImage(data, xvals=np.linspace(1., 3., data.shape[0]),
axes={'t':0, 'x':2, 'y':1, 'c':3}) # doesn't help
## Start Qt event loop unless running in interactive mode.
if __name__ == '__main__':
import sys
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
QtGui.QApplication.instance().exec_()
Looking through ImageView.py, setImage() parses the axes dictionary and based on presence of 't' it builds the z-axis/frame slider, and that's it. Rearranging the axes seems unimplemented yet.
I have a data sample which looks like this:
a 10:15:22 10:15:30 OK
b 10:15:23 10:15:28 OK
c 10:16:00 10:17:10 FAILED
b 10:16:30 10:16:50 OK
What I want is to plot the above data in the following way:
captions ^
|
c | *------*
b | *---* *--*
a | *--*
|___________________
time >
With the color of lines depending on the OK/FAILED status of the data point. Labels (a/b/c/...) may or may not repeat.
As I've gathered from documentation for gnuplot and matplotlib, this type of a plot should be easier to do in the latter as it's not a standard plot and would require some preprocessing.
The question is:
Is there a standard way to do plots like this in any of the tools?
If not, how should I go about plotting this data (pointers to relevant tools/documentation/functions/examples which do something-kinda-like the thing described here)?
Updated: Now includes handling the data sample and uses mpl dates functionality.
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, MinuteLocator, SecondLocator
import numpy as np
from StringIO import StringIO
import datetime as dt
### The example data
a=StringIO("""a 10:15:22 10:15:30 OK
b 10:15:23 10:15:28 OK
c 10:16:00 10:17:10 FAILED
b 10:16:30 10:16:50 OK
""")
#Converts str into a datetime object.
conv = lambda s: dt.datetime.strptime(s, '%H:%M:%S')
#Use numpy to read the data in.
data = np.genfromtxt(a, converters={1: conv, 2: conv},
names=['caption', 'start', 'stop', 'state'], dtype=None)
cap, start, stop = data['caption'], data['start'], data['stop']
#Check the status, because we paint all lines with the same color
#together
is_ok = (data['state'] == 'OK')
not_ok = np.logical_not(is_ok)
#Get unique captions and there indices and the inverse mapping
captions, unique_idx, caption_inv = np.unique(cap, 1, 1)
#Build y values from the number of unique captions.
y = (caption_inv + 1) / float(len(captions) + 1)
#Plot function
def timelines(y, xstart, xstop, color='b'):
"""Plot timelines at y from xstart to xstop with given color."""
plt.hlines(y, xstart, xstop, color, lw=4)
plt.vlines(xstart, y+0.03, y-0.03, color, lw=2)
plt.vlines(xstop, y+0.03, y-0.03, color, lw=2)
#Plot ok tl black
timelines(y[is_ok], start[is_ok], stop[is_ok], 'k')
#Plot fail tl red
timelines(y[not_ok], start[not_ok], stop[not_ok], 'r')
#Setup the plot
ax = plt.gca()
ax.xaxis_date()
myFmt = DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(myFmt)
ax.xaxis.set_major_locator(SecondLocator(interval=20)) # used to be SecondLocator(0, interval=20)
#To adjust the xlimits a timedelta is needed.
delta = (stop.max() - start.min())/10
plt.yticks(y[unique_idx], captions)
plt.ylim(0,1)
plt.xlim(start.min()-delta, stop.max()+delta)
plt.xlabel('Time')
plt.show()
the answer for #tillsten is not working for Python3 any more I did some modification I hope it will helps.
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, MinuteLocator, SecondLocator
import numpy as np
import pandas as pd
import datetime as dt
import io
### The example data
a=io.StringIO("""
caption start stop state
a 10:15:22 10:15:30 OK
b 10:15:23 10:15:28 OK
c 10:16:00 10:17:10 FAILED
b 10:16:30 10:16:50 OK""")
data = pd.read_table(a, delimiter=" ")
data["start"] = pd.to_datetime(data["start"])
data["stop"] = pd.to_datetime(data["stop"])
cap, start, stop = data['caption'], data['start'], data['stop']
#Check the status, because we paint all lines with the same color
#together
is_ok = (data['state'] == 'OK')
not_ok = np.logical_not(is_ok)
#Get unique captions and there indices and the inverse mapping
captions, unique_idx, caption_inv = np.unique(cap, 1, 1)
#Build y values from the number of unique captions.
y = (caption_inv + 1) / float(len(captions) + 1)
#Plot function
def timelines(y, xstart, xstop, color='b'):
"""Plot timelines at y from xstart to xstop with given color."""
plt.hlines(y, xstart, xstop, color, lw=4)
plt.vlines(xstart, y+0.03, y-0.03, color, lw=2)
plt.vlines(xstop, y+0.03, y-0.03, color, lw=2)
#Plot ok tl black
timelines(y[is_ok], start[is_ok], stop[is_ok], 'k')
#Plot fail tl red
timelines(y[not_ok], start[not_ok], stop[not_ok], 'r')
#Setup the plot
ax = plt.gca()
ax.xaxis_date()
myFmt = DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(myFmt)
ax.xaxis.set_major_locator(SecondLocator(interval=20)) # used to be SecondLocator(0, interval=20)
#To adjust the xlimits a timedelta is needed.
delta = (stop.max() - start.min())/10
plt.yticks(y[unique_idx], captions)
plt.ylim(0,1)
plt.xlim(start.min()-delta, stop.max()+delta)
plt.xlabel('Time')
plt.show()
gnuplot 5.2 version with creating a unique key list
The main difference to #CiroSantilli's solution is that a list of unique keys is created automatically from column 1 and the index can be accessed via the defined function Lookup(). The referenced gnuplot demo already uses a list of unique items, however, in the OP's case there are duplicates.
Creating such a list of unique items does not exist in gnuplot right away, so you have to implement it yourself.
The code requires gnuplot >=5.2. It is probably difficult to get a solution which works under gnuplot 4.4 (the time of OP's question) because a few useful features were not implemented at that time: do for-loops, summation, datablocks, ... (a version for gnuplot 4.6 might be possible with some workarounds).
Edit: the earlier version used with vectors and linewidth 20 to plot the bars, however, linewidth 20 also extends in x-direction which is not desired here. Therefore, with boxxyerror is now used.
Yes, it can be done shorter and clearer.
Script:
### Time chart with gnuplot (requires gnuplot>=5.0)
reset session
$Data <<EOD
# category start end status
"event 1" 10:15:22 10:15:30 OK
"event 2" 10:15:23 10:15:28 OK
pause 10:16:00 10:17:10 FAILED
"something else" 10:16:30 10:17:50 OK
unknown 10:17:30 10:18:50 OK
"event 3" 10:18:30 10:19:50 FAILED
pause 10:19:30 10:20:50 OK
"event 1" 10:17:30 10:19:20 FAILED
EOD
# create list of unique items
uniqueList = ''
item(col) = ' "'.strcol(col).'"'
isInList(list,col) = strstrt(uniqueList,item(col)) # returns a number >0 if found
addToList(list,col) = list.item(col)
stats $Data u (!isInList(uniqueList,1) ? uniqueList = addToList(uniqueList,1) : 0) nooutput
timeCenter(col1,col2) = (timecolumn(col1,myTimeFmt)+timecolumn(col2,myTimeFmt))*0.5
timeDeltaT(col1,col2) = (timecolumn(col1,myTimeFmt)-timecolumn(col2,myTimeFmt))*0.5
Lookup(col) = int(sum [i=1:words(uniqueList)] (strcol(col) eq word(uniqueList,i)) ? i : 0)
myColor(col) = strcol(col) eq "OK" ? 0x00cc00 : 0xff0000
myBoxWidth = 0.6
myTimeFmt = "%H:%M:%S"
set format x "%M:%S" timedate
set yrange [0.5:words(uniqueList)+0.5]
set grid x,y
plot $Data u (timeCenter(2,3)):(Lookup(1)):(timeDeltaT(2,3)):(0.5*myBoxWidth): \
(myColor(4)):ytic(1) w boxxyerror fill solid 1.0 lc rgb var notitle
### end of script
Result:
gnuplot with vector solution
Minimized from: http://gnuplot.sourceforge.net/demo_5.2/gantt.html
main.gnuplot
#!/usr/bin/env gnuplot
$DATA << EOD
1 1 5
1 11 13
2 3 10
3 4 8
4 7 13
5 6 15
EOD
set terminal png size 512,512
set output "main.png"
set xrange [-1:]
set yrange [0:]
unset key
set border 3
set xtics nomirror
set ytics nomirror
set style arrow 1 nohead linewidth 3
plot $DATA using 2 : 1 : ($3-$2) : (0.0) with vector as 1, \
$DATA using 2 : 1 : 1 with labels right offset -2
GitHub upstream.
Output:
You can remove the labels by removing the second plot command line, I added them because they are useful in many applications to more easily identify the intervals.
The Gantt example I linked to shows how to handle date formats instead of integers.
Tested in gnuplot 5.2 patchlevel 2, Ubuntu 18.04.