Delete several elements from array

Question

I have this data :

Temperature = [1054.36258, 1092.96819, 1051.45602, 1058.94926, 1083.66115, 1080.66721
, 1046.96163, 1018.42441, 1049.44307, 1068.91551, 1018.44764, 1035.11906
, 1020.44599,  993.275818 1037.41013, 1005.4047,  1025.73646,  975.301913
, 1000.45743, 1033.21562, 1037.31104,  968.730834, 907.474634, 930.465587
, 998.967526, 911.791887, 915.951873, 829.331306, 931.702088, 890.075633
, 830.659093, 878.715978, 866.238768, 897.958014, 940.495055, 841.990924
, 875.391469, 898.393043, 925.048353, 931.445104, 904.151363, 965.550728
, 916.348809, 936.315168, 900.445995, 887.76832,  875.064126, 881.480871
, 878.240278, 862.958271, 893.813659, 883.678318, 923.593998, 915.52458
, 877.919073, 891.754242, 919.274917, 862.223914, 881.275387, 862.33147
, 869.461632, 890.014577, 902.656117, 874.446393, 876.284046, 866.751916
, 854.095049, 844.540741, 870.263794, 866.687327, 818.019291, 821.875267
, 813.385138, 843.198211, 870.558259, 794.039978, 813.497634, 812.217789
, 801.361143, 800.263045, 747.101493, 735.923635, 732.930255, 775.930026
, 783.786631, 775.255742, 774.938671, 704.186773, 747.612911, 729.315237
, 694.021293, 742.723487, 709.890191, 760.674339, 751.491228, 723.875166
, 741.451471, 749.69441,  743.337883, 700.286359, 720.250078, 732.189596
, 693.097572, 782.342462, 711.995854, 684.432159, 761.195087, 746.725427
, 744.614939, 648.985204, 676.023106, 689.141056, 627.855922, 707.298358
, 652.207871, 652.609278, 680.52524,  689.328581, 678.148423, 728.229663
, 691.857497, 743.998987, 696.885527, 733.249599, 722.833678, 734.832942
, 719.049095, 703.573908, 711.15146,  689.345427]

and :

time =[datetime.datetime(2015, 11, 7, 18, 14, 24),
 datetime.datetime(2015, 11, 7, 18, 19, 12),
 datetime.datetime(2015, 11, 7, 18, 23, 9),
 datetime.datetime(2015, 11, 7, 18, 26, 38),
 datetime.datetime(2015, 11, 7, 18, 29, 55),
 datetime.datetime(2015, 11, 7, 18, 32, 52),
 datetime.datetime(2015, 11, 7, 18, 35, 36),
 datetime.datetime(2015, 11, 7, 18, 38, 26),
 datetime.datetime(2015, 11, 7, 18, 41, 13),
 datetime.datetime(2015, 11, 7, 18, 44, 16),
 datetime.datetime(2015, 11, 7, 18, 47, 12),
 datetime.datetime(2015, 11, 7, 18, 50, 1),
 datetime.datetime(2015, 11, 7, 18, 53, 2),
 datetime.datetime(2015, 11, 7, 18, 56, 17),
 datetime.datetime(2015, 11, 7, 18, 59, 45),
 datetime.datetime(2015, 11, 7, 19, 3, 14),
 datetime.datetime(2015, 11, 7, 19, 6, 28),
 datetime.datetime(2015, 11, 7, 19, 10, 4),
 datetime.datetime(2015, 11, 7, 19, 13, 46),
 datetime.datetime(2015, 11, 7, 19, 17, 47),
 datetime.datetime(2015, 11, 7, 19, 21, 35),
 datetime.datetime(2015, 11, 7, 19, 25, 15),
 datetime.datetime(2015, 11, 7, 19, 29, 22),
 datetime.datetime(2015, 11, 7, 19, 33, 41),
 datetime.datetime(2015, 11, 7, 19, 38, 38),
 datetime.datetime(2015, 11, 7, 19, 43, 16),
 datetime.datetime(2015, 11, 7, 19, 47, 53),
 datetime.datetime(2015, 11, 7, 19, 53, 21),
 datetime.datetime(2015, 11, 7, 19, 59, 4),
 datetime.datetime(2015, 11, 7, 20, 5, 14),
 datetime.datetime(2015, 11, 7, 20, 11, 6),
 datetime.datetime(2015, 11, 7, 20, 17, 7),
 datetime.datetime(2015, 11, 7, 20, 24, 11),
 datetime.datetime(2015, 11, 7, 20, 31, 5),
 datetime.datetime(2015, 11, 7, 20, 50, 8),
 datetime.datetime(2015, 11, 7, 20, 54, 31),
 datetime.datetime(2015, 11, 7, 20, 59, 28),
 datetime.datetime(2015, 11, 7, 21, 4, 54),
 datetime.datetime(2015, 11, 7, 21, 10, 24),
 datetime.datetime(2015, 11, 7, 21, 15, 56),
 datetime.datetime(2015, 11, 7, 21, 21, 50),
 datetime.datetime(2015, 11, 7, 21, 33, 24),
 datetime.datetime(2015, 11, 7, 21, 37, 54),
 datetime.datetime(2015, 11, 7, 21, 42, 24),
 datetime.datetime(2015, 11, 7, 21, 47, 20),
 datetime.datetime(2015, 11, 7, 21, 52, 12),
 datetime.datetime(2015, 11, 7, 21, 57, 3),
 datetime.datetime(2015, 11, 7, 22, 1, 41),
 datetime.datetime(2015, 11, 7, 22, 6, 21),
 datetime.datetime(2015, 11, 7, 22, 11, 30),
 datetime.datetime(2015, 11, 7, 22, 16, 44),
 datetime.datetime(2015, 11, 7, 22, 21, 59),
 datetime.datetime(2015, 11, 7, 22, 26, 56),
 datetime.datetime(2015, 11, 7, 22, 32),
 datetime.datetime(2015, 11, 7, 22, 37, 43),
 datetime.datetime(2015, 11, 7, 22, 43, 21),
 datetime.datetime(2015, 11, 7, 22, 48, 45),
 datetime.datetime(2015, 11, 7, 22, 53, 49),
 datetime.datetime(2015, 11, 7, 22, 58, 49),
 datetime.datetime(2015, 11, 7, 23, 4, 4),
 datetime.datetime(2015, 11, 7, 23, 9, 8),
 datetime.datetime(2015, 11, 7, 23, 14, 3),
 datetime.datetime(2015, 11, 7, 23, 18, 34),
 datetime.datetime(2015, 11, 7, 23, 22, 58),
 datetime.datetime(2015, 11, 7, 23, 27, 43),
 datetime.datetime(2015, 11, 7, 23, 32, 22),
 datetime.datetime(2015, 11, 7, 23, 36, 48),
 datetime.datetime(2015, 11, 7, 23, 41, 9),
 datetime.datetime(2015, 11, 7, 23, 45, 29),
 datetime.datetime(2015, 11, 7, 23, 49, 59),
 datetime.datetime(2015, 11, 7, 23, 54, 34),
 datetime.datetime(2015, 11, 7, 23, 59, 6),
 datetime.datetime(2015, 11, 8, 0, 3, 37),
 datetime.datetime(2015, 11, 8, 0, 8, 17),
 datetime.datetime(2015, 11, 8, 0, 13, 15),
 datetime.datetime(2015, 11, 8, 0, 18, 22),
 datetime.datetime(2015, 11, 8, 0, 23, 23),
 datetime.datetime(2015, 11, 8, 0, 28, 28),
 datetime.datetime(2015, 11, 8, 0, 33, 59),
 datetime.datetime(2015, 11, 8, 0, 39, 51),
 datetime.datetime(2015, 11, 8, 0, 45, 56),
 datetime.datetime(2015, 11, 8, 0, 51, 57),
 datetime.datetime(2015, 11, 8, 0, 57, 48),
 datetime.datetime(2015, 11, 8, 1, 4, 2),
 datetime.datetime(2015, 11, 8, 1, 10, 47),
 datetime.datetime(2015, 11, 8, 1, 17, 43),
 datetime.datetime(2015, 11, 8, 1, 24, 22),
 datetime.datetime(2015, 11, 8, 1, 30, 39),
 datetime.datetime(2015, 11, 8, 1, 37, 2),
 datetime.datetime(2015, 11, 8, 1, 43, 51),
 datetime.datetime(2015, 11, 8, 1, 50, 38),
 datetime.datetime(2015, 11, 8, 1, 57, 23),
 datetime.datetime(2015, 11, 8, 2, 4, 3),
 datetime.datetime(2015, 11, 8, 2, 10, 46),
 datetime.datetime(2015, 11, 8, 2, 18, 6),
 datetime.datetime(2015, 11, 8, 2, 25, 14),
 datetime.datetime(2015, 11, 8, 2, 32, 30),
 datetime.datetime(2015, 11, 8, 2, 39, 35),
 datetime.datetime(2015, 11, 8, 2, 46, 49),
 datetime.datetime(2015, 11, 8, 2, 54, 43),
 datetime.datetime(2015, 11, 8, 3, 2, 33),
 datetime.datetime(2015, 11, 8, 3, 10, 15),
 datetime.datetime(2015, 11, 8, 3, 17, 28),
 datetime.datetime(2015, 11, 8, 3, 24, 30),
 datetime.datetime(2015, 11, 8, 3, 32, 8),
 datetime.datetime(2015, 11, 8, 3, 39, 13),
 datetime.datetime(2015, 11, 8, 3, 46, 10),
 datetime.datetime(2015, 11, 8, 3, 52, 48),
 datetime.datetime(2015, 11, 8, 3, 59, 1),
 datetime.datetime(2015, 11, 8, 4, 5, 39),
 datetime.datetime(2015, 11, 8, 4, 11, 59),
 datetime.datetime(2015, 11, 8, 4, 18, 27),
 datetime.datetime(2015, 11, 8, 4, 24, 49),
 datetime.datetime(2015, 11, 8, 4, 31, 7),
 datetime.datetime(2015, 11, 8, 4, 39, 18),
 datetime.datetime(2015, 11, 8, 4, 46, 26),
 datetime.datetime(2015, 11, 8, 4, 53, 13),
 datetime.datetime(2015, 11, 8, 5, 0, 11),
 datetime.datetime(2015, 11, 8, 5, 8, 57),
 datetime.datetime(2015, 11, 8, 5, 15, 45),
 datetime.datetime(2015, 11, 8, 5, 22, 6),
 datetime.datetime(2015, 11, 8, 5, 28, 5),
 datetime.datetime(2015, 11, 8, 5, 34, 57),
 datetime.datetime(2015, 11, 8, 5, 40, 4),
 datetime.datetime(2015, 11, 8, 5, 44, 45),
 datetime.datetime(2015, 11, 8, 5, 49, 8),
 datetime.datetime(2015, 11, 8, 5, 54, 41),
 datetime.datetime(2015, 11, 8, 5, 58, 46),
 datetime.datetime(2015, 11, 8, 6, 2, 35),
 datetime.datetime(2015, 11, 8, 6, 6, 18)]

ploted like this :

My objectif is to delete the elements between index 27 and 90, in the red region below :

By using this code :

time_2 = np.delete(time, slice(27, 90+1))
Temperature_2 = np.delete(Temperature, slice(27, 90+1))

But by ploting, I found a figure like this :

I want to delete the red region below ?

I want to delete this red region in order to be able to found an appropriate fuction for fitting.

Just plot the dataset twice in the same figure, once with the left selection, once with the right selection. — 9769953
– 9769953, Commented Jan 3, 2022 at 23:47

user7864386 · Accepted Answer · 2022-01-04 00:10:08Z

1

You can set the color of the unwanted range to white:

import matplotlib.pyplot as plt
plt.plot(time[:27], Temperature[:27], 'g--')
plt.plot(time[91:], Temperature[91:], 'g--')
plt.plot(time[27:91], Temperature[27:91], 'w');

Output:

answered Jan 4, 2022 at 0:10

user7864386

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1 Comment

Mad Physicist Over a year ago

Why bother plotting the part that you don't want to see?

ZXYNINE · Accepted Answer · 2022-01-03 23:55:50Z

The responce given in the comments "Just plot the dataset twice in the same figure, once with the left selection, once with the right selection. – 9769953"

Is correct. Two separate plots would work quite well. With that said, it all depends on what you truly mean by delete. What num py is doing i believe is trying to draw a line from each point to the next. So when you remove a bunch of entries, numpy is still trying to connect the dots and will just draw a line from the last point to the start of the new one. its purely visual and you should be able to add in data where you deleted the old ones and itll appear as you would expect.

Thank you sir, I forgot that along the line there is no values, it is just a connecting line between the first and the last point

Mad Physicist · Accepted Answer · 2022-01-04 06:26:07Z

You have figured out how to delete elements from the array, which is what you want if you need to do numerical fitting.

Regardless of your data, plt.plot draws connected line segments. Let's say you have a simpler dataset:

t = np.arange(5)
y = np.array([0, 1, 0, 1, 0])

The data is show in the leftmost plot below.

Let's say you wanted to make the middle point and its two neighboring segments segments disappear. There are a couple of solutions.

The first is to only plot the segments you care about. Matplotlib will only connect dots within each call to plot:

plt.plot(t[:2], y[:2])
plt.plot(t[3:], y[3:])

The chart for this option is shown in the middle of the three plots below. Notice that the color cycler will choose different colors for each segment unless you manually override the settings.

A different approach is to insert NaNs into the data, which tells matplotlib not to connect to the missing points:

ynan = y.astype(float)
ynan[3] = np.nan
plt.plot(t, ynan)

This chart is shown on the right below.

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