def test_valid(self):
self.assertTrue(
Polynomial('1 1').approx_equal(interpolate([(1, 1), (2, 2)])))
self.assertTrue(
Polynomial('2 1').approx_equal(
interpolate([(-1, 1), (0, 0), (1, 1)])))
self.assertTrue(
Polynomial('3 1').approx_equal(
interpolate([(-1, -1), (0, 0), (1, 1), (2, 8)])))
self.assertTrue(
Polynomial('3 0.0154 2 -0.8939 1 7.1818 0 -8.9092').approx_equal(
interpolate([(-1, -17), (3, 5), (2, 2), (50, 8)]),
abs_tol=1e-3))
range_start = 2
range_end = 10
step_size = (range_end - range_start) / n
# X and Y values calculation using chebyshev polynomial
x_range_cheb = chebyshev_range(n, range_start, range_end)
y_range_cheb = [fn_to_eval(x) for x in x_range_cheb]
result = calculate_matrix(n, x_range_cheb, y_range_cheb)
# Shows f(x) and interpolation graphs
plt.plot(np.arange(range_start, range_end, 0.01),
[fn_to_eval(x) for x in np.arange(range_start, range_end, 0.01)],
'b',
label='f(x)')
plt.plot(
np.arange(range_start, range_end, 0.01),
[interpolate(result, x) for x in np.arange(range_start, range_end, 0.01)],
'r',
label=f'{n} points interpolation function')
plt.plot(np.arange(range_start, range_end, 0.01),
[loss(result, x) for x in np.arange(range_start, range_end, 0.01)],
'g',
label='Loss')
plt.scatter(x_range_cheb, y_range_cheb, color='g', label='Chebyshev points')
plt.title('f(x) interpolation using Chebyshev x')
plt.xlabel('x')
plt.ylabel('f(x)')
plt.ylim(top=8, bottom=-1)
plt.grid(True)
plt.legend()
from functions import interpolate
# Solution to PA1 (3)
if __name__ == '__main__':
try:
coord_list = input('Please specify a set of points: ').split(' ')
pts = []
for x, y in zip(coord_list[0::2], coord_list[1::2]):
pts.append((float(x), float(y)))
print('Interpolating over points: {{{}}}'.format(','.join(
map(lambda x: '({}, {})'.format(x[0], x[1]), pts))))
poly = interpolate(pts)
print('Result: ', str(poly))
except Exception as e:
print(e)
u'B4', u'B5', u'B7'], inplace = True)
####################
###### INTERPOLATION
######################
# fill missing rows with Nan Values
# to be interpolated over later
dfMaster.drop_duplicates(subset=['pixel', 'date'], keep='first', inplace=True)
time_range = pd.date_range(dfMaster.date.min().to_timestamp(),\
dfMaster.date.max().to_timestamp(), freq='M')\
.to_period('M')
dfMaster.index = dfMaster.date
dfMaster = dfMaster.groupby('pixel').apply(lambda x: x.reindex(time_range))
dfMaster = interpolate(df=dfMaster, columns=['NDVI', 'EVI', 'NDWI1', 'NDWI2'])
# fill other nan columns with appropriate stuff
#####################################
###################################
####### MORE DATA #######
###################################
#######################################
dfMaster2 = None
dfMaster2 = pd.DataFrame()
data_path = '../../Data/AgMet2/Monthly/'
print "Loading EM spectral dataframes..."
for f in glob.glob(data_path + 'monthlyLSclass' + crop_id + '*'):
dfMaster2 = pd.concat((dfMaster2, pd.read_csv(f)))
from functions import fn_to_eval, approximate, interpolate, loss, np
import matplotlib.pyplot as plt
x_range = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
y_range = [
-8.9003, -15.417, -0.6314, 1.93806, 7.63665, 13.1566, 15.3685, 15.025,
8.08982, 4.60416, -3.4688, -2.1801
]
n = len(x_range)
row = 12
result = approximate(n, x_range, y_range, row)
plt.scatter(x_range, y_range, label='Month`s temp. avg.')
plt.plot(x_range, [interpolate(result, x) for x in x_range],
'r',
label='Temp. interpolation')
plt.grid(True)
plt.legend()
plt.xlabel('Month')
plt.ylabel('Temp. avg.')
plt.title("2007 Finland temperature")
plt.show()
from functions import fn_to_eval, calculate_matrix, interpolate, loss, np
import matplotlib.pyplot as plt
x_range = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
y_range = [-8.9003,-15.417,-0.6314,1.93806,7.63665,13.1566,15.3685,15.025,8.08982,4.60416,-3.4688,-2.1801]
n = len(x_range)
result = calculate_matrix(n, x_range, y_range)
# Shows f(x) and interpolation graphs
plt.scatter(x_range, y_range, label='Month`s temp. avg.')
plt.plot(x_range, [interpolate(result,x) for x in x_range], 'r', label='Temp. interpolation')
plt.xticks(x_range)
plt.xlabel('Month')
plt.ylabel('Temp. avg.')
plt.title("2007 Finland temperature")
plt.grid(True)
plt.legend()
plt.show()