From 796e63c22f0d8d7c532f3d66cb278a1c0b3c0c97 Mon Sep 17 00:00:00 2001
From: Fruzsina Agocs <agocs.fruzsina@gmail.com>
Date: Wed, 4 Dec 2024 15:10:45 -0700
Subject: [PATCH] BIE example for merge conflict, updates reqs

---
 quadutils/bie.py    | 41 +++++++++++++++++++++++++++++++++++++++++
 requirements.txt    |  1 +
 tests/test_utils.py |  4 ++++
 3 files changed, 46 insertions(+)
 create mode 100644 quadutils/bie.py

diff --git a/quadutils/bie.py b/quadutils/bie.py
new file mode 100644
index 0000000..97f38b4
--- /dev/null
+++ b/quadutils/bie.py
@@ -0,0 +1,41 @@
+import numpy as np
+import scipt as sp
+
+def Phi(x, y, k = 0.0):
+    """
+    What does this function do?
+
+    Parameters
+    ----------
+    x: np.array<float>
+        Can have shape (2, n) or (2,). In the latter case, it'll be converted
+        to (2, 1).
+        What is this variable?
+    y: ?
+        Can have shape (2, m) or (2,). In the latter case, it'll be converted
+        to (2, 1).
+        What is this variable?       
+    k: float, optional
+        Default k = 0. What is this variable?
+
+    Returns
+    -------
+    phi: ? 
+        What's this variable? What's its shape?
+    """
+    # Reshape input vectors if given in the shape (2,). Skip if (2, n).
+    if x.ndim < 2: 
+        x = x.reshape((2, -1))
+    if y.ndim < 2:
+        y = y.reshape((2, -1))
+    # Distance vector(s) between pairs of (x, y). 
+    # If x is (2, n), and y is (2, m), d will still be of shape (2, n, m).
+    d = x[..., None] - y[:, None]
+    # What are these two cases?
+    if k == 0.0:
+        phi = -1/(2*np.pi)*np.log(np.linalg.norm(d, axis = 0))
+    else:
+        r = np.linalg.norm(d, axis = 0)
+        phi = 1j/4*sp.hankel1(0, k*r)
+    return phi
+    
diff --git a/requirements.txt b/requirements.txt
index 1cc18fc..f651ca5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,3 @@
 numpy
 pytest
+scipy
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 547d725..3c60ce0 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -14,6 +14,7 @@ def test_quadroots_1():
 def test_chebD_1():
     n = 16
     D, x = ch.chebD(n) 
+    print(x)
     f = lambda x: x**2 # Test function should be differentiated exactly
     interval = np.array([0, 0.5])
     h = interval[1] - interval[0]
@@ -22,3 +23,6 @@ def test_chebD_1():
     df_num = 2/h*D@f(x_scaled)
     df_err = max(np.abs(df_ana(x_scaled) - df_num))
     assert df_err < 1e-13
+
+def test_phi_1():
+    pass