Dev/enable custom etas (#305)

- Allowing the users more flexibility to play around with custom eta
functions without touching the c++ code

- passing vector of eta values to ``transform_eta_values`` 

```
from aare import Interpolator, ClusterVector, Etai, Cluster
import numpy as np 

def custom_eta(cluster_pixel_coordinate_x, cluster_pixel_coordinate_y, cluster_data):
    # dummy custom eta function that just returns the sum of the cluster data
    eta = Etai()
    eta.x = 0.1 # dummy x value
    eta.y = 0.1 # dummy y value
    eta.sum = np.sum(cluster_data) # sum of the cluster data as the "energy
    return eta

# Create a dummy eta distribution and bins
eta_distribution = np.zeros((10, 10, 1)) # dummy eta distribution
etax_bins = np.linspace(0, 1.0, 11)
etay_bins = np.linspace(0, 1.0, 11)
e_bins = np.array([0., 10.]) # dummy energy bins

# Create the interpolator
interpolator = Interpolator(eta_distribution, etax_bins, etay_bins, e_bins)

# Create a dummy cluster vector
cluster_vector = ClusterVector()
cluster_vector.push_back(Cluster(10, 5, np.ones(shape=9, dtype = np.int32)))
cluster_vector.push_back(Cluster(20, 10, np.ones(shape=9, dtype = np.int32)))

# Create dummy etas for the clusters
cluster_array = np.array(cluster_vector)
etas = np.array([custom_eta(cluster["x"], cluster["y"], cluster["data"]) for cluster in cluster_array])

# transform eta values to uniform coordinates 
uniform_coordinates = interpolator.transform_eta_values(etas)

# Interpolate to get the photon coordinates e.g. apply interpolation logic 
photon_coordinates_x = cluster_array["x"] + uniform_coordinates["x"] # add to pixel coordinate 
photon_coordinates_y = cluster_array["y"] + uniform_coordinates["y"] # add to pixel coordinate 

```
advantage: full control over interpolation logic, 
downside: inefficient quite some loops in python
- passing pre computed eta values to interpolate function 
```
Interpolator.interpolate(cluster_vector, etas) 
```
downside: less flexibility in interpolation logic. 
downside: People might misuse it instead of using interpolate directly
with a pre compiled eta function implemented in c++

python/tests/test_InterpolationAPI.py

@@ -0,0 +1,31 @@
+import pytest 
+
+from aare import Interpolator, ClusterVector, Etai, Cluster
+
+import numpy as np 
+
+
+def test_interpolation_api():
+    eta_distribution = np.zeros((10, 10, 1)) # dummy eta distribution 
+    etax_bins = np.linspace(0, 1.0, 11) 
+    etay_bins = np.linspace(0, 1.0, 11)
+    e_bins = np.array([0., 10.]) # dummy energy bins
+    interpolator = Interpolator(eta_distribution, etax_bins, etay_bins, e_bins)
+
+    cluster_vector = ClusterVector()
+    cluster_vector.push_back(Cluster(10, 5, np.ones(shape=9, dtype=np.int32)))
+    cluster_vector.push_back(Cluster(20, 10, np.ones(shape=9, dtype=np.int32)))
+
+    eta1 = Etai()
+    eta1.x = 0.1
+    eta1.y = 0.1
+    eta1.sum = 5
+    eta2 = Etai()
+    eta2.x = 0.1
+    eta2.y = 0.9
+    eta2.sum = 6
+    etas = np.array([eta1, eta2]) # dummy etas for the clusters
+
+    photons = interpolator.interpolate(cluster_vector, etas)
+
+    assert photons.size == cluster_vector.size # should return one photon per cluster