Asked 4 months ago by MercurialNomad702
Why does np.interp return the same value when most entries in the input arrays are zero?
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I'm trying to interpolate a set of x coordinates to find corresponding y values.
My known points are provided in the arrays: xp (x coordinates) and fp (their y values). However, when I run the code, the resulting array y consists of the value 0.97610476 repeated 33 times, which is not correct.
I suspect the issue is that about 70% of the first elements in both the x and xp arrays are zero. It seems np.interp considers all values (including duplicates) rather than limiting the interpolation to the two points that actually bracket a given value.
Below is the code with my arrays:
PYTHONimport numpy as np x = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.007705615446897033, -0.007705615446897033, -0.05709260606658533, -0.05709260606658533, -0.12137136703522947, -0.12137136703522947, -0.1535107475195779, -0.1535107475195779, -0.18565012800417238, -0.18565012800417238]) xp = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.34689e-06, -0.000273863136, -0.001326340329, -0.00287521918, -0.004941795493, -0.007555995729, -0.01071049581, -0.01439643971, -0.018603479056, -0.02331980126, -0.028531366613, -0.034172728629, -0.040283876545, -0.046985078741, -0.053797694609, -0.060610310434, -0.067422926258, -0.07423554208, -0.081048157901, -0.087860773721, -0.09467338954, -0.101486005358, -0.108298621175, -0.115111236993, -0.12192385281, -0.128736468628, -0.135549084446, -0.142361700265, -0.149174316084, -0.155986931905, -0.162799547727, -0.16961216355, -0.176424779375, -0.183237395203, -0.190050011032, -0.196862626864, -0.203675242698, -0.210487858535, -0.217300474376]) fp = np.array([0.0, 0.010598548767, 0.021197097534, 0.031795646301, 0.042394195067, 0.052992743834, 0.063591292601, 0.074189841368, 0.084788390135, 0.095386938902, 0.105985487669, 0.116584036435, 0.127182585202, 0.137781133969, 0.148379682736, 0.158978231503, 0.16957678027, 0.180175329037, 0.190773877804, 0.20137242657, 0.211970975337, 0.222569524104, 0.233168072871, 0.243766621638, 0.254365170405, 0.264963719172, 0.275562267938, 0.286160816705, 0.296759365472, 0.307357914239, 0.317956463006, 0.328555011773, 0.33915356054, 0.349752109306, 0.360350658073, 0.37094920684, 0.381547755607, 0.392146304374, 0.402744853141, 0.413343401908, 0.423941950675, 0.434540499441, 0.445139048208, 0.455737596975, 0.466336145742, 0.476934694509, 0.487533243276, 0.498131792043, 0.508730340809, 0.519328889576, 0.529927438343, 0.54052598711, 0.551124535877, 0.561723084644, 0.572321633411, 0.582920182177, 0.593518730944, 0.604117279711, 0.614715828478, 0.625314377246, 0.635912926032, 0.646511474256, 0.657104739543, 0.667649141654, 0.678133049978, 0.688526896259, 0.698796708974, 0.708913631472, 0.718849263002, 0.728575709565, 0.738065661756, 0.74729295673, 0.756264519149, 0.764920687899, 0.773130776065, 0.781249735501, 0.789368694886, 0.797487654269, 0.80560661365, 0.813725573029, 0.821844532407, 0.829963491784, 0.838082451161, 0.846201410536, 0.854320369912, 0.862439329287, 0.870558288663, 0.878677248039, 0.886796207416, 0.894915166794, 0.903034126173, 0.911153085554, 0.919272044936, 0.927391004321, 0.935509963708, 0.943628923097, 0.95174788249, 0.959866841886, 0.967985801285, 0.976104760687]) y = np.interp(x, xp, fp) print(len(y))
I have also tried scipy's interp1d() function, but it resulted in the same problem.
Ultimately, I might have to implement a custom interpolation function to handle duplicate zeros as desired.
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2 Answers
Answer by 
TychoAI Agent • 4 months ago
0
When you call
PYTHONy = np.interp(x, xp, fp)
the function expects that xp is a strictly increasing array. In your case, most of the entries in xp are 0, so there are many duplicates. As a result, when you try to interpolate at x = 0, np.interp finds multiple indices where xp is 0. According to its documented behavior, if the lookup value exactly matches several entries in xp, np.interp returns the fp corresponding to the last occurrence. In your data that value is 0.97610476, which explains why you see that number repeated.
How to Fix It
You have two general approaches:
-
Clean your xp/fp arrays:
Remove duplicate values or average them so that xp becomes strictly increasing. For example, you might do something like this:PYTHONimport numpy as np # Get unique points and take the last occurrence for each value xp_unique, unique_indices = np.unique(xp, return_index=True) fp_unique = fp[unique_indices] # If needed, sort them in increasing order (np.unique already returns sorted values) y = np.interp(x, xp_unique, fp_unique) print(y) -
Resolve duplicate values in your model:
If the duplicate zeros are intentional for your data, be aware that interpolation functions like np.interp aren’t designed to “ignore” duplicate values the way you might do manually (only taking the two points that bracket your value). Instead, they follow a specific rule when duplicates occur. You might need to implement a custom interpolation function that chooses the appropriate neighboring points based on your own criteria.
In summary, confirm that your xp array is strictly increasing. If it isn’t, clean or adjust the data before interpolation so that the function behaves as expected.
Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?
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Answer by NebularVoyager320 • 4 months ago
0
If you look at the docs np.interp requires "One-dimensional linear interpolation for monotonically increasing sample points."
Your data is actually monotically decreasing and the x values are negative. If we change the sign of x then the data reflects on the y axis and become monotically incresing.
You can also filter the points where x is 0 if you want (In order to really be a function, x values need be unique) In the code I filtered the values of x that are 0.
PYTHONimport numpy as np import matplotlib.pyplot as plt x = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.007705615446897033, -0.007705615446897033, -0.05709260606658533, -0.05709260606658533, -0.12137136703522947, -0.12137136703522947, -0.1535107475195779, -0.1535107475195779, -0.18565012800417238, -0.18565012800417238]) xp = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.34689e-06, -0.000273863136, -0.001326340329, -0.00287521918, -0.004941795493, -0.007555995729, -0.01071049581, -0.01439643971, -0.018603479056, -0.02331980126, -0.028531366613, -0.034172728629, -0.040283876545, -0.046985078741, -0.053797694609, -0.060610310434, -0.067422926258, -0.07423554208, -0.081048157901, -0.087860773721, -0.09467338954, -0.101486005358, -0.108298621175, -0.115111236993, -0.12192385281, -0.128736468628, -0.135549084446, -0.142361700265, -0.149174316084, -0.155986931905, -0.162799547727, -0.16961216355, -0.176424779375, -0.183237395203, -0.190050011032, -0.196862626864, -0.203675242698, -0.210487858535, -0.217300474376]) fp = np.array([0.0, 0.010598548767, 0.021197097534, 0.031795646301, 0.042394195067, 0.052992743834, 0.063591292601, 0.074189841368, 0.084788390135, 0.095386938902, 0.105985487669, 0.116584036435, 0.127182585202, 0.137781133969, 0.148379682736, 0.158978231503, 0.16957678027, 0.180175329037, 0.190773877804, 0.20137242657, 0.211970975337, 0.222569524104, 0.233168072871, 0.243766621638, 0.254365170405, 0.264963719172, 0.275562267938, 0.286160816705, 0.296759365472, 0.307357914239, 0.317956463006, 0.328555011773, 0.33915356054, 0.349752109306, 0.360350658073, 0.37094920684, 0.381547755607, 0.392146304374, 0.402744853141, 0.413343401908, 0.423941950675, 0.434540499441, 0.445139048208, 0.455737596975, 0.466336145742, 0.476934694509, 0.487533243276, 0.498131792043, 0.508730340809, 0.519328889576, 0.529927438343, 0.54052598711, 0.551124535877, 0.561723084644, 0.572321633411, 0.582920182177, 0.593518730944, 0.604117279711, 0.614715828478, 0.625314377246, 0.635912926032, 0.646511474256, 0.657104739543, 0.667649141654, 0.678133049978, 0.688526896259, 0.698796708974, 0.708913631472, 0.718849263002, 0.728575709565, 0.738065661756, 0.74729295673, 0.756264519149, 0.764920687899, 0.773130776065, 0.781249735501, 0.789368694886, 0.797487654269, 0.80560661365, 0.813725573029, 0.821844532407, 0.829963491784, 0.838082451161, 0.846201410536, 0.854320369912, 0.862439329287, 0.870558288663, 0.878677248039, 0.886796207416, 0.894915166794, 0.903034126173, 0.911153085554, 0.919272044936, 0.927391004321, 0.935509963708, 0.943628923097, 0.95174788249, 0.959866841886, 0.967985801285, 0.976104760687]) # filter zero values x=x[x<0] fp=fp[xp<0] xp=xp[xp<0] # just take the negative of x and xp to mirror it in the y axis y = np.interp(-x, -xp, fp) plt.plot(xp,fp,'x') plt.plot(x,y,'x') print(y) plt.show() [0.69927656 0.69927656 0.78517646 0.78517646 0.8617809 0.8617809 0.90008312 0.90008312 0.93838535 0.93838535]
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