[FIXED] matplotlib - removing time gaps in time-series data plots?

Issue

I have a set of data that matches time values against another value (for example, elevation). I'm currently plotting this in matplotlib, and it looks something like this:

As can be seen, there are big 'gaps' in the data, with lines connecting between them, because the times plotted are not right next to each other.

My code looks like this (I do not have have pandas):

time_sorted_list = sorted(
    unsorted_value_list, key=lambda x: x.time
)

elev = [i.elev for i in time_sorted_list]

time = [i.time for i in time_sorted_list]

...(creating elev_plot figure)

elev_plot.plot(time, elev)

elev_plot.grid()
plot.show()

How can I set up matplotlib to remove these large gaps in time, such that all values sit close to each other? I don't want to remove time altogether - knowing when each elevation value occurs is important.

Example data:

Elevations:

[7.061637017210896, 8.62634035986128, 9.449231409579046, 9.449245213599722, 11.183401391828983, 11.183478912151985, 12.097695062804538, 14.032121063226736, 19.53103255309029, 20.132430448781705, 22.61562154333468, 23.892538058003574, 25.174568988146742, 25.81347252259264, 27.07766665010065, 28.301824218809962, 29.4560748154805, 30.51425894250495, 31.44003996067941, 32.19935454662037, 32.75797351858856, 33.09230892539046, 33.185638377860386, 32.64289077682021, 32.64282073446187, 32.03439364065985, 32.03432718356379, 31.235743890788736, 30.278072995085186, 29.198208966807904, 28.02762496428912, 25.534718034319297, 24.259335234095236, 22.987561974637945, 21.733969026630948, 20.50551578656278, 19.30698140187512, 18.145822000390414, 17.021157410685678, 14.89032761900031, 13.881534452146786, 12.910228441720443, 11.9735858799619, 10.20078824064575, 8.548230021876677, 7.7622314951825935, 7.002108526108933, 6.2652418436101245, 5.550265750342097, 4.180538242033181, 3.523953356314147, 10.468976986513358, 10.826799614265274, 11.548804997129018, 15.198784031309774, 15.913277577899912, 16.609161706884624, 18.52422058507705, 19.077032064883326, 19.57286148977654, 20.002244208317894, 20.91143576667658, 20.91127829131031, 20.911272488234292, 20.817089892791472, 20.630717861747748, 20.630698531303153, 20.35705893695184, 20.357030796826844, 20.001375885553323, 19.571700419702164, 19.075697249423904, 17.921110661414083, 15.911217262744842, 15.197579232135709, 14.472858366158526, 13.740784521720999, 13.007405649336956]

Time (stored as datetime):

['08/26/2021, 08:28:28', '08/26/2021, 08:28:48', '08/26/2021, 08:28:58', '08/26/2021, 08:28:58', '08/26/2021, 08:29:18', '08/26/2021, 08:29:18', '08/26/2021, 08:29:28', '08/26/2021, 08:29:48', '08/26/2021, 08:30:38', '08/26/2021, 08:30:43', '08/26/2021, 08:31:03', '08/26/2021, 08:31:13', '08/26/2021, 08:31:23', '08/26/2021, 08:31:28', '08/26/2021, 08:31:38', '08/26/2021, 08:31:48', '08/26/2021, 08:31:58', '08/26/2021, 08:32:08', '08/26/2021, 08:32:18', '08/26/2021, 08:32:28', '08/26/2021, 08:32:38', '08/26/2021, 08:32:48', '08/26/2021, 08:32:58', '08/26/2021, 08:33:18', '08/26/2021, 08:33:18', '08/26/2021, 08:33:28', '08/26/2021, 08:33:28', '08/26/2021, 08:33:38', '08/26/2021, 08:33:48', '08/26/2021, 08:33:58', '08/26/2021, 08:34:08', '08/26/2021, 08:34:28', '08/26/2021, 08:34:38', '08/26/2021, 08:34:48', '08/26/2021, 08:34:58', '08/26/2021, 08:35:08', '08/26/2021, 08:35:18', '08/26/2021, 08:35:28', '08/26/2021, 08:35:38', '08/26/2021, 08:35:58', '08/26/2021, 08:36:08', '08/26/2021, 08:36:18', '08/26/2021, 08:36:28', '08/26/2021, 08:36:48', '08/26/2021, 08:37:08', '08/26/2021, 08:37:18', '08/26/2021, 08:37:28', '08/26/2021, 08:37:38', '08/26/2021, 08:37:48', '08/26/2021, 08:38:08', '08/26/2021, 08:38:18', '08/26/2021, 10:11:00', '08/26/2021, 10:11:05', '08/26/2021, 10:11:15', '08/26/2021, 10:12:05', '08/26/2021, 10:12:15', '08/26/2021, 10:12:25', '08/26/2021, 10:12:55', '08/26/2021, 10:13:05', '08/26/2021, 10:13:15', '08/26/2021, 10:13:25', '08/26/2021, 10:14:05', '08/26/2021, 10:14:15', '08/26/2021, 10:14:15', '08/26/2021, 10:14:25', '08/26/2021, 10:14:35', '08/26/2021, 10:14:35', '08/26/2021, 10:14:45', '08/26/2021, 10:14:45', '08/26/2021, 10:14:55', '08/26/2021, 10:15:05', '08/26/2021, 10:15:15', '08/26/2021, 10:15:35', '08/26/2021, 10:16:05', '08/26/2021, 10:16:15', '08/26/2021, 10:16:25', '08/26/2021, 10:16:35', '08/26/2021, 10:16:45']

Solution

IIUC, you want to remove the large gaps between the peaks?

updated answer using pandas

I broke down the steps in independent columns for you to see the logic

df = pd.DataFrame({'time': time, 'elev': elev})
df['time']   = pd.to_datetime(df['time'])         
df['delta']  = df['time'].diff()              # diff from previous time
df['gap']    = df['delta'].dt.seconds.gt(100) # gap = diff > 100 seconds
df['group']  = df['gap'].cumsum()             # make groups


groups = df.groupby('group')
f, ax = plt.subplots(ncols=len(groups), sharey=True)
for i, g in groups:
    ax[i].plot(g['time'], g['elev'])
    start = g['time'].iloc[0].time()
    stop = g['time'].iloc[-1].time()
    ax[i].set_title(f'group {i+1}\n({start}--{stop})')

output:

old answer

Original data:

from datetime import datetime
elev = [7.061637017210896, 8.62634035986128, 9.449231409579046, 9.449245213599722, 11.183401391828983, 11.183478912151985, 12.097695062804538, 14.032121063226736, 19.53103255309029, 20.132430448781705, 22.61562154333468, 23.892538058003574, 25.174568988146742, 25.81347252259264, 27.07766665010065, 28.301824218809962, 29.4560748154805, 30.51425894250495, 31.44003996067941, 32.19935454662037, 32.75797351858856, 33.09230892539046, 33.185638377860386, 32.64289077682021, 32.64282073446187, 32.03439364065985, 32.03432718356379, 31.235743890788736, 30.278072995085186, 29.198208966807904, 28.02762496428912, 25.534718034319297, 24.259335234095236, 22.987561974637945, 21.733969026630948, 20.50551578656278, 19.30698140187512, 18.145822000390414, 17.021157410685678, 14.89032761900031, 13.881534452146786, 12.910228441720443, 11.9735858799619, 10.20078824064575, 8.548230021876677, 7.7622314951825935, 7.002108526108933, 6.2652418436101245, 5.550265750342097, 4.180538242033181, 3.523953356314147, 10.468976986513358, 10.826799614265274, 11.548804997129018, 15.198784031309774, 15.913277577899912, 16.609161706884624, 18.52422058507705, 19.077032064883326, 19.57286148977654, 20.002244208317894, 20.91143576667658, 20.91127829131031, 20.911272488234292, 20.817089892791472, 20.630717861747748, 20.630698531303153, 20.35705893695184, 20.357030796826844, 20.001375885553323, 19.571700419702164, 19.075697249423904, 17.921110661414083, 15.911217262744842, 15.197579232135709, 14.472858366158526, 13.740784521720999, 13.007405649336956]
time = ['08/26/2021, 08:28:28', '08/26/2021, 08:28:48', '08/26/2021, 08:28:58', '08/26/2021, 08:28:58', '08/26/2021, 08:29:18', '08/26/2021, 08:29:18', '08/26/2021, 08:29:28', '08/26/2021, 08:29:48', '08/26/2021, 08:30:38', '08/26/2021, 08:30:43', '08/26/2021, 08:31:03', '08/26/2021, 08:31:13', '08/26/2021, 08:31:23', '08/26/2021, 08:31:28', '08/26/2021, 08:31:38', '08/26/2021, 08:31:48', '08/26/2021, 08:31:58', '08/26/2021, 08:32:08', '08/26/2021, 08:32:18', '08/26/2021, 08:32:28', '08/26/2021, 08:32:38', '08/26/2021, 08:32:48', '08/26/2021, 08:32:58', '08/26/2021, 08:33:18', '08/26/2021, 08:33:18', '08/26/2021, 08:33:28', '08/26/2021, 08:33:28', '08/26/2021, 08:33:38', '08/26/2021, 08:33:48', '08/26/2021, 08:33:58', '08/26/2021, 08:34:08', '08/26/2021, 08:34:28', '08/26/2021, 08:34:38', '08/26/2021, 08:34:48', '08/26/2021, 08:34:58', '08/26/2021, 08:35:08', '08/26/2021, 08:35:18', '08/26/2021, 08:35:28', '08/26/2021, 08:35:38', '08/26/2021, 08:35:58', '08/26/2021, 08:36:08', '08/26/2021, 08:36:18', '08/26/2021, 08:36:28', '08/26/2021, 08:36:48', '08/26/2021, 08:37:08', '08/26/2021, 08:37:18', '08/26/2021, 08:37:28', '08/26/2021, 08:37:38', '08/26/2021, 08:37:48', '08/26/2021, 08:38:08', '08/26/2021, 08:38:18', '08/26/2021, 10:11:00', '08/26/2021, 10:11:05', '08/26/2021, 10:11:15', '08/26/2021, 10:12:05', '08/26/2021, 10:12:15', '08/26/2021, 10:12:25', '08/26/2021, 10:12:55', '08/26/2021, 10:13:05', '08/26/2021, 10:13:15', '08/26/2021, 10:13:25', '08/26/2021, 10:14:05', '08/26/2021, 10:14:15', '08/26/2021, 10:14:15', '08/26/2021, 10:14:25', '08/26/2021, 10:14:35', '08/26/2021, 10:14:35', '08/26/2021, 10:14:45', '08/26/2021, 10:14:45', '08/26/2021, 10:14:55', '08/26/2021, 10:15:05', '08/26/2021, 10:15:15', '08/26/2021, 10:15:35', '08/26/2021, 10:16:05', '08/26/2021, 10:16:15', '08/26/2021, 10:16:25', '08/26/2021, 10:16:35', '08/26/2021, 10:16:45']
time = [datetime.strptime(t, '%m/%d/%Y, %H:%M:%S') for t in time]
ax = plt.subplot()
ax.plot(time, elev, marker='.')

The issue is that the points are not evenly spaced:

   interval  count
0      10.0     50
1      20.0     11
2       0.0      7
3       5.0      3
4      50.0      2
5      30.0      2
6      40.0      1
7    5562.0      1

A simple solution is to plot without keeping the time information, with a fixed step:

ax = plt.subplot()
ax.plot(range(len(elev)), elev, marker='.')

There are other ways, but it is more complicated, so make your requirements explicit if the quick solution does not work for you.

Answered By - mozway