Simple fast linear interpolation for Python
Project description
sinterp
Simple fast linear interpolation for Python
sinterp functions:
interp1d(x: float, xp: list, yp: list, make_checks: bool = CHECK_INPUT)
x - x-variable for interpolation, float
xp - list with x-values of function, list
yp - list with y-values of function, list
make_checks - bool-flag of enable/disable for check inputs. Default value is True.
interp2d(x: float, y: float, xp: list, yp: list, zp: list, make_checks: bool = CHECK_INPUT)
x, y - x- and y-variable for interpolation, float
xp - list with x-values of function, list
yp - list with y-values of function, list
zp - list with y-values of function, list
make_checks - bool-flag of enable/disable for check inputs. Default value is True.
Benchmarks
Simple benchmark for compare 1d-interpolation with Numpy:
import random
import time
from numpy import interp
from sinterp import interp1d
times = [] # list with time of calculation
ratios = [] # ratio of calc with interp to interp1d
deltas = [] # summary delta of difference results by iteration
size = []
for kk in range(2, 5):
x1 = 0
x2 = int(10 ** kk)
size.append(x2)
xp = [float(_) for _ in range(x1, x2 + 1)]
yp = [_ ** 3.0 for _ in xp]
x = [random.uniform(float(x1), float(x2)) for _ in range(10000)]
start_time = time.time()
v_1 = [interp(_, xp, yp) for _ in x]
time_1 = time.time() - start_time
start_time = time.time()
v_2 = [interp1d(_, xp, yp) for _ in x]
time_2 = time.time() - start_time
times.append([time_1, time_2])
ratios.append(time_1 / time_2)
deltas.append(sum(_[1] - _[0] for _ in zip(v_1, v_2)))
# Print benchmark ratios
print('--- Benchmark results ---')
print('List size : Ratio')
for r, v in zip(size, ratios):
print(' %i : %f' % (r, v))
print('Check convergence. Difference between interp and interp1d = %f' % max(deltas))
Results Python 3.6 Win10 (at my laptop):
--- Benchmark results ---
List size : Ratio
10 : 2.312361
100 : 1.810310
1000 : 7.835562
10000 : 54.542985
100000 : 514.559448
Check convergence. Delta between interp and interp1d = 0.000000
Results Python 3.7 Linux-Mint 19.3
--- Benchmark results ---
List size : Ratio
10 : 2.409009
100 : 3.836711
1000 : 19.986599
10000 : 141.633523
100000 : 1155.362543
Check convergence. Delta between interp and interp1d = 0.000000
Simple benchmark for compare interp2d from SciPy with sinterp
import random
import time
from numpy import meshgrid, array
from scipy.interpolate import interp2d as sc_interp2d
from sinterp import interp2d as si_interp2d
times = [] # list with time of calculation
ratios = [] # ratio of calc with interp to interp1d
deltas = [] # summary delta of difference results by iteration
size = []
for kk in range(2, 5):
x1 = 0
x2 = int(10 ** kk)
size.append(x2)
xp = [float(_) for _ in range(0, x2 + 1)]
yp = [float(_) for _ in range(0, x2 + 1)]
zp = [[x * y for y in yp] for x in xp]
XP_GRID, YP_GRID = meshgrid(xp, yp)
ZP_GRID = array(zp)
xv = [random.uniform(0.0, x2) for _ in range(1000)]
yv = [random.uniform(0.0, x2) for _ in range(1000)]
start_time = time.time()
sci_interp2d = sc_interp2d(xp, yp, zp)
v_1 = [sci_interp2d(x, y) for x, y in zip(xv, yv)]
time_1 = time.time() - start_time
start_time = time.time()
v_2 = [si_interp2d(x, y, xp, yp, zp) for x, y in zip(xv, yv)]
time_2 = time.time() - start_time
times.append([time_1, time_2])
ratios.append(time_1 / time_2)
deltas.append(sum(_[1] - _[0] for _ in zip(v_1, v_2)))
# Print benchmark ratios
print('--- Benchmark results ---')
print('List size : Ratio')
for r, v in zip(size, ratios):
print(' %i : %f' % (r, v))
print('Check convergence. Difference between interp2d (scipy) and interp2d (sinterp) = %f' % max(deltas))
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