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fix np.NaN to np.nan #2

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fix np.NaN to np.nan #2

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8 changes: 4 additions & 4 deletions fastbarnes/interpolation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,7 +195,7 @@ def _interpolate_opt_convol(pts, val, sigma, x0, step, size, num_iter):
# set smaller weights to NaN with overall effect that corresponding quotient is NaN, too
for j in range(size[0]):
for i in range(size[1]):
if wg[j,i] < factor: wg[j,i] = np.NaN
if wg[j,i] < factor: wg[j,i] = np.nan

# yet to be considered:
# - add offset again to resulting quotient
Expand Down Expand Up @@ -325,7 +325,7 @@ def _interpolate_convol(pts, val, sigma, x0, step, size, num_iter):
# set smaller weights to NaN with overall effect that corresponding quotient is NaN, too
for j in range(size[0]):
for i in range(size[1]):
if wg[j,i] < factor: wg[j,i] = np.NaN
if wg[j,i] < factor: wg[j,i] = np.nan

# yet to be considered:
# - add offset again to resulting quotient
Expand Down Expand Up @@ -439,7 +439,7 @@ def _interpolate_radius(pts, val, sigma, x0, step, size, min_weight):
if weight_total >= 0.0022:
grid_value[j,i] = weighted_sum / weight_total + offset
else:
grid_value[j,i] = np.NaN
grid_value[j,i] = np.nan

return grid_value

Expand Down Expand Up @@ -470,6 +470,6 @@ def _interpolate_naive(pts, val, sigma, x0, step, size):
if weight_total > 0.0:
grid_value[j,i] = weighted_sum / weight_total + offset
else:
grid_value[j,i] = np.NaN
grid_value[j,i] = np.nan

return grid_value
2 changes: 1 addition & 1 deletion fastbarnes/interpolationS2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -229,7 +229,7 @@ def _interpolate_naive_S2(pts, val, sigma, x0, step, size):
if weight_total > 0.0:
grid_val[j,i] = weighted_sum / weight_total + offset
else:
grid_val[j,i] = np.NaN
grid_val[j,i] = np.nan

return grid_val

Expand Down