From 6e9b13c0372e1d7eb0b8fcee7ecc62b86ca6b8ca Mon Sep 17 00:00:00 2001
From: Landung 'Don' Setiawan <landungs@uw.edu>
Date: Tue, 4 Apr 2023 12:12:09 -0700
Subject: [PATCH 01/11] Add function for computing harmonic mean

---
 src/seagap/configs/solver.py |  3 ++
 src/seagap/harmonic_mean.py  | 80 ++++++++++++++++++++++++++++++++++++
 2 files changed, 83 insertions(+)
 create mode 100644 src/seagap/harmonic_mean.py

diff --git a/src/seagap/configs/solver.py b/src/seagap/configs/solver.py
index a3b9a06..de029e9 100644
--- a/src/seagap/configs/solver.py
+++ b/src/seagap/configs/solver.py
@@ -148,6 +148,9 @@ class Solver(BaseModel):
             "Options: ship/SV3 = 0.0s, WG = 0.1s"
         ),
     )
+    harmonic_mean_start_depth: float = Field(
+        0.0, description="Start depth in meters for harmonic mean computation."
+    )
     input_files: SolverInputs = Field(
         ..., description="Input files data path specifications."
     )
diff --git a/src/seagap/harmonic_mean.py b/src/seagap/harmonic_mean.py
new file mode 100644
index 0000000..aee0657
--- /dev/null
+++ b/src/seagap/harmonic_mean.py
@@ -0,0 +1,80 @@
+import numba
+import math
+
+@numba.njit
+def _compute_hm(dd, sv, start_depth, end_depth, start_index):
+    """
+    Computes harmonic mean.
+    It's a direct translation from the original Fortran code found in
+    src/cal_sv_harmonic_mean/get_sv_harmonic_mean.F called
+    subroutine `sv_harmon_mean`
+    """
+    # TODO: Find a way to vectorize this computation
+    zs = start_depth
+    ze = end_depth
+    
+    z1=dd[start_index]
+    z2=dd[start_index+1]
+
+    c_z1 = sv[start_index]
+    c_z2 = sv[start_index+1]
+
+    zi = zs
+    if(z2 >= ze):
+        zf = ze
+    else:
+        zf = z2
+        
+    cumsum = 0.0
+    
+    for i in range(start_index+1, len(dd)):
+        b =  ( c_z2 - c_z1) / ( z2 - z1 )
+        wi = zi - z1 + c_z1/b
+        wf = zf - z1 + c_z1/b
+
+        wi = math.log( (zi - z1)*b + c_z1)/b
+        wf = math.log( (zf - z1)*b + c_z1)/b
+
+        delta = (wf-wi)
+        cumsum = cumsum + delta
+        z1=zf          
+        z2=dd[i+1]
+        c_z1 = c_z2
+        c_z2 = sv[i+1]
+        zi=zf
+
+        if ze > zi and ze < z2:
+            zf = ze
+        else:
+            zf = z2
+
+        if z1 >= ze: break
+    
+    return (ze-zs)/cumsum
+
+def sv_harmonic_mean(svdf, start_depth, end_depth):
+    """
+    Computes harmonic mean from a sound profile
+    containing depth (dd) and sound speed (sv)
+    
+    Parameters
+    ----------
+    svdf : pd.DataFrame
+        Sound speed profile data as dataframe
+    start_depth : int or float
+        The start depth for harmonic mean to be computed
+    end_depth : int or float
+        The end depth for harmonic mean to be computed
+        
+    Returns
+    -------
+    float
+        The sound speed harmonic mean value
+    """
+    abs_start = abs(start_depth)
+    abs_end = abs(end_depth)
+    abs_sv = abs(svdf)
+    # Get the index for the start of depth closest to specified start depth
+    start_index = abs_sv[(abs_sv['dd'].round() >= start_depth)].index[0]
+
+    return _compute_hm(abs_sv['dd'].values, abs_sv['sv'].values, abs_start, abs_end, start_index)

From 6a34aafbf6330d11522f0a8711792498940aceb4 Mon Sep 17 00:00:00 2001
From: Landung 'Don' Setiawan <landungs@uw.edu>
Date: Thu, 6 Apr 2023 12:50:28 -0700
Subject: [PATCH 02/11] Add function to compute harmonic mean from sound speed
 profile

---
 src/seagap/configs/solver.py | 12 +++++------
 src/seagap/harmonic_mean.py  | 42 ++++++++++++++++++++++++++++++++----
 2 files changed, 44 insertions(+), 10 deletions(-)

diff --git a/src/seagap/configs/solver.py b/src/seagap/configs/solver.py
index de029e9..376dfda 100644
--- a/src/seagap/configs/solver.py
+++ b/src/seagap/configs/solver.py
@@ -141,10 +141,10 @@ class Solver(BaseModel):
     travel_times_variance: float = Field(
         1e-10, description="VARIANCE (s**2) PXP two-way travel time measurement"
     )
-    transponder_wait_time: float = Field(
+    transducer_delay_time: float = Field(
         0.0,
         description=(
-            "Transponder Wait Time - delay at surface transducer (secs.). "
+            "Transducer Delay Time - delay at surface transducer (secs.). "
             "Options: ship/SV3 = 0.0s, WG = 0.1s"
         ),
     )
@@ -171,9 +171,9 @@ class Solver(BaseModel):
         ),
     )
 
-    @validator("transponder_wait_time")
-    def check_transponder_wait_time(cls, v):
-        """Validate transponder wait time value"""
+    @validator("transducer_delay_time")
+    def check_transducer_delay_time(cls, v):
+        """Validate transducer wait time value"""
         if v not in [0.0, 0.1]:
-            raise ValueError("Transponder wait time must either be 0.0s or 0.1s")
+            raise ValueError("Transducer wait time must either be 0.0s or 0.1s")
         return v
diff --git a/src/seagap/harmonic_mean.py b/src/seagap/harmonic_mean.py
index aee0657..f9ec5e6 100644
--- a/src/seagap/harmonic_mean.py
+++ b/src/seagap/harmonic_mean.py
@@ -1,8 +1,17 @@
+import pandas as pd
+import numpy as np
+from typing import Union
 import numba
 import math
 
 @numba.njit
-def _compute_hm(dd, sv, start_depth, end_depth, start_index):
+def _compute_hm(
+    dd: np.ndarray,
+    sv: np.ndarray,
+    start_depth: Union[int, float],
+    end_depth: Union[int, float],
+    start_index: int
+):
     """
     Computes harmonic mean.
     It's a direct translation from the original Fortran code found in
@@ -10,24 +19,42 @@ def _compute_hm(dd, sv, start_depth, end_depth, start_index):
     subroutine `sv_harmon_mean`
     """
     # TODO: Find a way to vectorize this computation
+    
+    # Assign start depth and end depth to zs and ze
     zs = start_depth
     ze = end_depth
     
+    # Extract the first and second depth values
     z1=dd[start_index]
     z2=dd[start_index+1]
 
+    # Extract the first and second sound speed values
     c_z1 = sv[start_index]
     c_z2 = sv[start_index+1]
 
+    # Set start depth to initial depth to keep track of it
     zi = zs
-    if(z2 >= ze):
+
+    # If the second depth value (z2) is greater than and equal to the 
+    # end depth value (ze) then the final depth (zf) should be assigned
+    # to the end depth value (ze), otherwise, the final depth
+    # should be assigned to the second depth values
+    if z2 >= ze:
         zf = ze
     else:
         zf = z2
-        
+    
+    # Start cumulative sum as 0.0
     cumsum = 0.0
     
+    # Loop over the whole array for depth and sound speed,
+    # starting from the index of the second value to the whole
+    # depth data, which is assumed to be the same exact number
+    # as the sound speed data
     for i in range(start_index+1, len(dd)):
+        # calculate the slope of the two points
+        # for sound speed and depth
+        # slope = (sv2 - sv1) / (d2 - d1)
         b =  ( c_z2 - c_z1) / ( z2 - z1 )
         wi = zi - z1 + c_z1/b
         wf = zf - z1 + c_z1/b
@@ -52,7 +79,11 @@ def _compute_hm(dd, sv, start_depth, end_depth, start_index):
     
     return (ze-zs)/cumsum
 
-def sv_harmonic_mean(svdf, start_depth, end_depth):
+def sv_harmonic_mean(
+    svdf: pd.DataFrame,
+    start_depth: Union[int, float],
+    end_depth: Union[int, float]
+):
     """
     Computes harmonic mean from a sound profile
     containing depth (dd) and sound speed (sv)
@@ -71,6 +102,9 @@ def sv_harmonic_mean(svdf, start_depth, end_depth):
     float
         The sound speed harmonic mean value
     """
+    # Clean up the sound speed value, ensuring that there's no negative value
+    svdf = svdf[svdf['sv'] < 0]
+    # Make all of the values absolute values, so we're only dealing with positives
     abs_start = abs(start_depth)
     abs_end = abs(end_depth)
     abs_sv = abs(svdf)

From b56c1577b5f7ce013ba0e21d3822a85caf42daff Mon Sep 17 00:00:00 2001
From: Landung 'Don' Setiawan <landungs@uw.edu>
Date: Thu, 6 Apr 2023 14:02:50 -0700
Subject: [PATCH 03/11] Add harmonic mean tests

---
 tests/data/sound_profile_hm.csv | 985 ++++++++++++++++++++++++++++++++
 tests/test_harmonic_mean.py     |  50 ++
 2 files changed, 1035 insertions(+)
 create mode 100644 tests/data/sound_profile_hm.csv
 create mode 100644 tests/test_harmonic_mean.py

diff --git a/tests/data/sound_profile_hm.csv b/tests/data/sound_profile_hm.csv
new file mode 100644
index 0000000..8d24dd7
--- /dev/null
+++ b/tests/data/sound_profile_hm.csv
@@ -0,0 +1,985 @@
+dd,sv
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diff --git a/tests/test_harmonic_mean.py b/tests/test_harmonic_mean.py
new file mode 100644
index 0000000..a18dc23
--- /dev/null
+++ b/tests/test_harmonic_mean.py
@@ -0,0 +1,50 @@
+import pandas as pd
+import pytest
+import numpy as np
+
+from . import TEST_DATA_FOLDER
+from seagap.harmonic_mean import sv_harmonic_mean, _compute_hm
+
+@pytest.fixture()
+def sound_profile_data() -> pd.DataFrame:
+    sv_file = TEST_DATA_FOLDER / 'sound_profile_hm.csv'
+    return pd.read_csv(sv_file)
+
+
+@pytest.mark.parametrize(
+    "end_depth,expected_hm",
+    [
+        (-1176.5866, 1481.551),
+        (-1146.5881, 1481.521),
+        (-1133.7305, 1481.509)
+    ]
+)
+def test_sv_harmonic_mean(end_depth, expected_hm, sound_profile_data):
+    svdf = sound_profile_data
+    start_depth = -4
+    harmonic_mean = round(sv_harmonic_mean(svdf, start_depth, end_depth), 3)
+
+    assert harmonic_mean == expected_hm
+
+@pytest.mark.parametrize(
+    "test_idx,expected_hm",
+    [
+        (3, 1501.69),
+        (6, 1501.225)
+    ]
+)
+def test__compute_hm(test_idx, expected_hm):
+    dd = np.arange(7) * 10
+    sv = np.arange(1502, 1500, step=-0.31)
+
+    # Get partial of the data from test index
+    part_dd = dd[:test_idx]
+    part_sv = sv[:test_idx]
+    start_depth, end_depth = part_dd[0], part_dd[-1]
+    result_hm = round(_compute_hm(dd, sv, start_depth, end_depth, 0), 3)
+    
+    # Check for result to match expected
+    assert result_hm == expected_hm
+
+    # Result should be same as regular mean as the data is linear here
+    assert result_hm == round(sum(part_sv) / test_idx, 3)

From fe67dea8bc721360a4c6f7812aa311094c53046a Mon Sep 17 00:00:00 2001
From: Landung 'Don' Setiawan <landungs@uw.edu>
Date: Thu, 6 Apr 2023 14:07:22 -0700
Subject: [PATCH 04/11] Fix lint and add test folder path

---
 src/seagap/harmonic_mean.py | 76 +++++++++++++++++++++----------------
 tests/__init__.py           |  5 +++
 2 files changed, 48 insertions(+), 33 deletions(-)

diff --git a/src/seagap/harmonic_mean.py b/src/seagap/harmonic_mean.py
index f9ec5e6..b887618 100644
--- a/src/seagap/harmonic_mean.py
+++ b/src/seagap/harmonic_mean.py
@@ -1,8 +1,10 @@
-import pandas as pd
-import numpy as np
+import math
 from typing import Union
+
 import numba
-import math
+import numpy as np
+import pandas as pd
+
 
 @numba.njit
 def _compute_hm(
@@ -10,7 +12,7 @@ def _compute_hm(
     sv: np.ndarray,
     start_depth: Union[int, float],
     end_depth: Union[int, float],
-    start_index: int
+    start_index: int,
 ):
     """
     Computes harmonic mean.
@@ -19,23 +21,23 @@ def _compute_hm(
     subroutine `sv_harmon_mean`
     """
     # TODO: Find a way to vectorize this computation
-    
+
     # Assign start depth and end depth to zs and ze
     zs = start_depth
     ze = end_depth
-    
+
     # Extract the first and second depth values
-    z1=dd[start_index]
-    z2=dd[start_index+1]
+    z1 = dd[start_index]
+    z2 = dd[start_index + 1]
 
     # Extract the first and second sound speed values
     c_z1 = sv[start_index]
-    c_z2 = sv[start_index+1]
+    c_z2 = sv[start_index + 1]
 
     # Set start depth to initial depth to keep track of it
     zi = zs
 
-    # If the second depth value (z2) is greater than and equal to the 
+    # If the second depth value (z2) is greater than and equal to the
     # end depth value (ze) then the final depth (zf) should be assigned
     # to the end depth value (ze), otherwise, the final depth
     # should be assigned to the second depth values
@@ -43,51 +45,54 @@ def _compute_hm(
         zf = ze
     else:
         zf = z2
-    
+
     # Start cumulative sum as 0.0
     cumsum = 0.0
-    
+
     # Loop over the whole array for depth and sound speed,
     # starting from the index of the second value to the whole
     # depth data, which is assumed to be the same exact number
     # as the sound speed data
-    for i in range(start_index+1, len(dd)):
+    for i in range(start_index + 1, len(dd)):
         # calculate the slope of the two points
         # for sound speed and depth
         # slope = (sv2 - sv1) / (d2 - d1)
-        b =  ( c_z2 - c_z1) / ( z2 - z1 )
-        wi = zi - z1 + c_z1/b
-        wf = zf - z1 + c_z1/b
+        b = (c_z2 - c_z1) / (z2 - z1)
+        wi = zi - z1 + c_z1 / b
+        wf = zf - z1 + c_z1 / b
 
-        wi = math.log( (zi - z1)*b + c_z1)/b
-        wf = math.log( (zf - z1)*b + c_z1)/b
+        wi = math.log((zi - z1) * b + c_z1) / b
+        wf = math.log((zf - z1) * b + c_z1) / b
 
-        delta = (wf-wi)
+        delta = wf - wi
         cumsum = cumsum + delta
-        z1=zf          
-        z2=dd[i+1]
+        z1 = zf
+        z2 = dd[i + 1]
         c_z1 = c_z2
-        c_z2 = sv[i+1]
-        zi=zf
+        c_z2 = sv[i + 1]
+        zi = zf
 
         if ze > zi and ze < z2:
             zf = ze
         else:
             zf = z2
 
-        if z1 >= ze: break
+        if z1 >= ze:
+            break
     
-    return (ze-zs)/cumsum
+    if cumsum == 0:
+        # If cumulative sum is 0, most likely only one value
+        return sv[start_index]
+    return (ze - zs) / cumsum
+
 
 def sv_harmonic_mean(
-    svdf: pd.DataFrame,
-    start_depth: Union[int, float],
-    end_depth: Union[int, float]
+    svdf: pd.DataFrame, start_depth: Union[int, float], end_depth: Union[int, float]
 ):
     """
     Computes harmonic mean from a sound profile
     containing depth (dd) and sound speed (sv)
-    
+
     Parameters
     ----------
     svdf : pd.DataFrame
@@ -96,19 +101,24 @@ def sv_harmonic_mean(
         The start depth for harmonic mean to be computed
     end_depth : int or float
         The end depth for harmonic mean to be computed
-        
+
     Returns
     -------
     float
         The sound speed harmonic mean value
     """
     # Clean up the sound speed value, ensuring that there's no negative value
-    svdf = svdf[svdf['sv'] < 0]
+    svdf = svdf[svdf["sv"] > 0].reset_index(drop=True)
     # Make all of the values absolute values, so we're only dealing with positives
     abs_start = abs(start_depth)
     abs_end = abs(end_depth)
     abs_sv = abs(svdf)
     # Get the index for the start of depth closest to specified start depth
-    start_index = abs_sv[(abs_sv['dd'].round() >= start_depth)].index[0]
+    try:
+        start_index = abs_sv[(abs_sv["dd"].round() >= abs_start)].index[0]
+    except IndexError:
+        raise ValueError("Dataframe is empty! Please check your data inputs.")
 
-    return _compute_hm(abs_sv['dd'].values, abs_sv['sv'].values, abs_start, abs_end, start_index)
+    return _compute_hm(
+        abs_sv["dd"].values, abs_sv["sv"].values, abs_start, abs_end, start_index
+    )
diff --git a/tests/__init__.py b/tests/__init__.py
index e69de29..c154d79 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -0,0 +1,5 @@
+from pathlib import Path
+
+HERE = Path(__file__).parent
+
+TEST_DATA_FOLDER = HERE / "data"

From 016030f0a8a60c819f3e456555c2f8a930c2207e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 6 Apr 2023 21:08:11 +0000
Subject: [PATCH 05/11] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 src/seagap/harmonic_mean.py |  2 +-
 tests/test_harmonic_mean.py | 25 +++++++++----------------
 2 files changed, 10 insertions(+), 17 deletions(-)

diff --git a/src/seagap/harmonic_mean.py b/src/seagap/harmonic_mean.py
index b887618..b4d16aa 100644
--- a/src/seagap/harmonic_mean.py
+++ b/src/seagap/harmonic_mean.py
@@ -79,7 +79,7 @@ def _compute_hm(
 
         if z1 >= ze:
             break
-    
+
     if cumsum == 0:
         # If cumulative sum is 0, most likely only one value
         return sv[start_index]
diff --git a/tests/test_harmonic_mean.py b/tests/test_harmonic_mean.py
index a18dc23..8fffe0c 100644
--- a/tests/test_harmonic_mean.py
+++ b/tests/test_harmonic_mean.py
@@ -1,23 +1,21 @@
+import numpy as np
 import pandas as pd
 import pytest
-import numpy as np
+
+from seagap.harmonic_mean import _compute_hm, sv_harmonic_mean
 
 from . import TEST_DATA_FOLDER
-from seagap.harmonic_mean import sv_harmonic_mean, _compute_hm
+
 
 @pytest.fixture()
 def sound_profile_data() -> pd.DataFrame:
-    sv_file = TEST_DATA_FOLDER / 'sound_profile_hm.csv'
+    sv_file = TEST_DATA_FOLDER / "sound_profile_hm.csv"
     return pd.read_csv(sv_file)
 
 
 @pytest.mark.parametrize(
     "end_depth,expected_hm",
-    [
-        (-1176.5866, 1481.551),
-        (-1146.5881, 1481.521),
-        (-1133.7305, 1481.509)
-    ]
+    [(-1176.5866, 1481.551), (-1146.5881, 1481.521), (-1133.7305, 1481.509)],
 )
 def test_sv_harmonic_mean(end_depth, expected_hm, sound_profile_data):
     svdf = sound_profile_data
@@ -26,13 +24,8 @@ def test_sv_harmonic_mean(end_depth, expected_hm, sound_profile_data):
 
     assert harmonic_mean == expected_hm
 
-@pytest.mark.parametrize(
-    "test_idx,expected_hm",
-    [
-        (3, 1501.69),
-        (6, 1501.225)
-    ]
-)
+
+@pytest.mark.parametrize("test_idx,expected_hm", [(3, 1501.69), (6, 1501.225)])
 def test__compute_hm(test_idx, expected_hm):
     dd = np.arange(7) * 10
     sv = np.arange(1502, 1500, step=-0.31)
@@ -42,7 +35,7 @@ def test__compute_hm(test_idx, expected_hm):
     part_sv = sv[:test_idx]
     start_depth, end_depth = part_dd[0], part_dd[-1]
     result_hm = round(_compute_hm(dd, sv, start_depth, end_depth, 0), 3)
-    
+
     # Check for result to match expected
     assert result_hm == expected_hm
 

From 766c131a77cf6b6d918fa7c5d67dec72d01f180e Mon Sep 17 00:00:00 2001
From: Landung 'Don' Setiawan <landungs@uw.edu>
Date: Thu, 6 Apr 2023 15:21:46 -0700
Subject: [PATCH 06/11] Remove catching single value

---
 src/seagap/harmonic_mean.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/seagap/harmonic_mean.py b/src/seagap/harmonic_mean.py
index b4d16aa..6e74a8a 100644
--- a/src/seagap/harmonic_mean.py
+++ b/src/seagap/harmonic_mean.py
@@ -80,9 +80,6 @@ def _compute_hm(
         if z1 >= ze:
             break
 
-    if cumsum == 0:
-        # If cumulative sum is 0, most likely only one value
-        return sv[start_index]
     return (ze - zs) / cumsum
 
 

From 418eb5de02826a7bffbfce571c926435edcde4d3 Mon Sep 17 00:00:00 2001
From: Landung 'Don' Setiawan <landungs@uw.edu>
Date: Thu, 6 Apr 2023 16:42:01 -0700
Subject: [PATCH 07/11] Add docs and return type annotations as suggested

---
 src/seagap/harmonic_mean.py | 26 ++++++++++++++++++++------
 1 file changed, 20 insertions(+), 6 deletions(-)

diff --git a/src/seagap/harmonic_mean.py b/src/seagap/harmonic_mean.py
index 6e74a8a..15bf622 100644
--- a/src/seagap/harmonic_mean.py
+++ b/src/seagap/harmonic_mean.py
@@ -1,5 +1,4 @@
 import math
-from typing import Union
 
 import numba
 import numpy as np
@@ -10,15 +9,30 @@
 def _compute_hm(
     dd: np.ndarray,
     sv: np.ndarray,
-    start_depth: Union[int, float],
-    end_depth: Union[int, float],
+    start_depth: float,
+    end_depth: float,
     start_index: int,
-):
+) -> float:
     """
     Computes harmonic mean.
     It's a direct translation from the original Fortran code found in
     src/cal_sv_harmonic_mean/get_sv_harmonic_mean.F called
     subroutine `sv_harmon_mean`
+
+    Parameters
+    ----------
+    dd : np.ndarray
+        Depth values array dd[n]
+    sv : np.ndarray
+        Sound speed values array sv[n]
+    start_depth : float
+        The start depth for calculation
+    end_depth : float
+        The end depth for calculation
+    start_index : int
+        The start index for the first value
+        computed by start depth
+
     """
     # TODO: Find a way to vectorize this computation
 
@@ -84,8 +98,8 @@ def _compute_hm(
 
 
 def sv_harmonic_mean(
-    svdf: pd.DataFrame, start_depth: Union[int, float], end_depth: Union[int, float]
-):
+    svdf: pd.DataFrame, start_depth: float, end_depth: float
+) -> float:
     """
     Computes harmonic mean from a sound profile
     containing depth (dd) and sound speed (sv)

From 03cda3adead05d47a4d1f26d8d558e77b0504b2a Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 6 Apr 2023 23:42:12 +0000
Subject: [PATCH 08/11] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 src/seagap/harmonic_mean.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/seagap/harmonic_mean.py b/src/seagap/harmonic_mean.py
index 15bf622..cf8e331 100644
--- a/src/seagap/harmonic_mean.py
+++ b/src/seagap/harmonic_mean.py
@@ -97,9 +97,7 @@ def _compute_hm(
     return (ze - zs) / cumsum
 
 
-def sv_harmonic_mean(
-    svdf: pd.DataFrame, start_depth: float, end_depth: float
-) -> float:
+def sv_harmonic_mean(svdf: pd.DataFrame, start_depth: float, end_depth: float) -> float:
     """
     Computes harmonic mean from a sound profile
     containing depth (dd) and sound speed (sv)

From 3c084cf12941f9afdcc84874c7c4851963b5ed26 Mon Sep 17 00:00:00 2001
From: Landung 'Don' Setiawan <landungs@uw.edu>
Date: Fri, 7 Apr 2023 16:49:47 -0700
Subject: [PATCH 09/11] Remove validation for transducer_delay_time

---
 src/seagap/configs/solver.py | 12 +-----------
 1 file changed, 1 insertion(+), 11 deletions(-)

diff --git a/src/seagap/configs/solver.py b/src/seagap/configs/solver.py
index 376dfda..0f3ff96 100644
--- a/src/seagap/configs/solver.py
+++ b/src/seagap/configs/solver.py
@@ -143,10 +143,7 @@ class Solver(BaseModel):
     )
     transducer_delay_time: float = Field(
         0.0,
-        description=(
-            "Transducer Delay Time - delay at surface transducer (secs.). "
-            "Options: ship/SV3 = 0.0s, WG = 0.1s"
-        ),
+        description="Transducer Delay Time - delay at surface transducer (secs). ",
     )
     harmonic_mean_start_depth: float = Field(
         0.0, description="Start depth in meters for harmonic mean computation."
@@ -170,10 +167,3 @@ class Solver(BaseModel):
             "which data points will be excluded from solution"
         ),
     )
-
-    @validator("transducer_delay_time")
-    def check_transducer_delay_time(cls, v):
-        """Validate transducer wait time value"""
-        if v not in [0.0, 0.1]:
-            raise ValueError("Transducer wait time must either be 0.0s or 0.1s")
-        return v

From cd6c6d5c2a41a385cede262b555dd3b5a1f4ee91 Mon Sep 17 00:00:00 2001
From: Landung 'Don' Setiawan <landungs@uw.edu>
Date: Fri, 7 Apr 2023 16:52:37 -0700
Subject: [PATCH 10/11] Remove unused module

---
 src/seagap/configs/solver.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/seagap/configs/solver.py b/src/seagap/configs/solver.py
index 0f3ff96..3eb36f9 100644
--- a/src/seagap/configs/solver.py
+++ b/src/seagap/configs/solver.py
@@ -6,7 +6,7 @@
 from typing import Any, List, Literal, Optional
 from uuid import uuid4
 
-from pydantic import BaseModel, Field, PrivateAttr, validator
+from pydantic import BaseModel, Field, PrivateAttr
 
 from .io import InputData
 

From 4b488e9c9fff552f02d51d00a975dce7bcf10cdd Mon Sep 17 00:00:00 2001
From: Landung 'Don' Setiawan <landungs@uw.edu>
Date: Mon, 10 Apr 2023 11:47:37 -0700
Subject: [PATCH 11/11] Update hmean computation to use scipy's hmean

---
 pyproject.toml              |   1 +
 src/seagap/harmonic_mean.py | 123 ++++++++++--------------------------
 tests/test_harmonic_mean.py |  32 +++++++---
 3 files changed, 56 insertions(+), 100 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 0f9c5b8..1c4d5da 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,6 +28,7 @@ dependencies = [
     "pandas>=1.5.3",
     "pydantic>=1.10.6",
     "pluggy>=1.0.0",
+    "scipy>=1.10.1",
     "typer>=0.7.0"
 ]
 
diff --git a/src/seagap/harmonic_mean.py b/src/seagap/harmonic_mean.py
index cf8e331..eeda430 100644
--- a/src/seagap/harmonic_mean.py
+++ b/src/seagap/harmonic_mean.py
@@ -1,100 +1,45 @@
-import math
-
-import numba
-import numpy as np
 import pandas as pd
+import scipy.stats
 
 
-@numba.njit
-def _compute_hm(
-    dd: np.ndarray,
-    sv: np.ndarray,
-    start_depth: float,
-    end_depth: float,
-    start_index: int,
-) -> float:
+def _compute_hm(svdf: pd.DataFrame, start_depth: float, end_depth: float) -> float:
     """
-    Computes harmonic mean.
-    It's a direct translation from the original Fortran code found in
-    src/cal_sv_harmonic_mean/get_sv_harmonic_mean.F called
-    subroutine `sv_harmon_mean`
+    Computes harmonic mean using `scipy's hmean <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.hmean.html>`_ method.
+    It takes the sound speed 'sv' as the input array and the depth 'dd' differences as the weights.
+
+    Note that this function assumes absolute values for depth array,
+    sound speed array, start depth, and end depth.
+
+    The underlying formula is
+
+    H = (w1+...+wn) / ((w1/x1)+...+(wn/xn))
+
+    H is the resulting harmonic mean
+    w is the weight value, in this case, the depth differences
+    x is the input value, in this case, the sound speed
 
     Parameters
     ----------
-    dd : np.ndarray
-        Depth values array dd[n]
-    sv : np.ndarray
-        Sound speed values array sv[n]
+    svdf : pd.DataFrame
+        Sound speed profile data as dataframe with columns 'dd' and 'sv'
     start_depth : float
         The start depth for calculation
     end_depth : float
         The end depth for calculation
-    start_index : int
-        The start index for the first value
-        computed by start depth
 
-    """
-    # TODO: Find a way to vectorize this computation
-
-    # Assign start depth and end depth to zs and ze
-    zs = start_depth
-    ze = end_depth
-
-    # Extract the first and second depth values
-    z1 = dd[start_index]
-    z2 = dd[start_index + 1]
-
-    # Extract the first and second sound speed values
-    c_z1 = sv[start_index]
-    c_z2 = sv[start_index + 1]
-
-    # Set start depth to initial depth to keep track of it
-    zi = zs
-
-    # If the second depth value (z2) is greater than and equal to the
-    # end depth value (ze) then the final depth (zf) should be assigned
-    # to the end depth value (ze), otherwise, the final depth
-    # should be assigned to the second depth values
-    if z2 >= ze:
-        zf = ze
-    else:
-        zf = z2
-
-    # Start cumulative sum as 0.0
-    cumsum = 0.0
-
-    # Loop over the whole array for depth and sound speed,
-    # starting from the index of the second value to the whole
-    # depth data, which is assumed to be the same exact number
-    # as the sound speed data
-    for i in range(start_index + 1, len(dd)):
-        # calculate the slope of the two points
-        # for sound speed and depth
-        # slope = (sv2 - sv1) / (d2 - d1)
-        b = (c_z2 - c_z1) / (z2 - z1)
-        wi = zi - z1 + c_z1 / b
-        wf = zf - z1 + c_z1 / b
-
-        wi = math.log((zi - z1) * b + c_z1) / b
-        wf = math.log((zf - z1) * b + c_z1) / b
-
-        delta = wf - wi
-        cumsum = cumsum + delta
-        z1 = zf
-        z2 = dd[i + 1]
-        c_z1 = c_z2
-        c_z2 = sv[i + 1]
-        zi = zf
-
-        if ze > zi and ze < z2:
-            zf = ze
-        else:
-            zf = z2
-
-        if z1 >= ze:
-            break
-
-    return (ze - zs) / cumsum
+    """  # noqa
+    for col in ["dd", "sv"]:
+        if col not in svdf.columns:
+            raise ValueError(f"{col} column must exist in the input dataframe!")
+
+    filtdf = svdf[
+        (svdf["dd"].round() >= start_depth) & (svdf["dd"].round() <= end_depth)
+    ]
+
+    # Get weights
+    weights = filtdf["dd"].diff()
+
+    return scipy.stats.hmean(filtdf["sv"], weights=weights, nan_policy="omit")
 
 
 def sv_harmonic_mean(svdf: pd.DataFrame, start_depth: float, end_depth: float) -> float:
@@ -123,11 +68,7 @@ def sv_harmonic_mean(svdf: pd.DataFrame, start_depth: float, end_depth: float) -
     abs_end = abs(end_depth)
     abs_sv = abs(svdf)
     # Get the index for the start of depth closest to specified start depth
-    try:
-        start_index = abs_sv[(abs_sv["dd"].round() >= abs_start)].index[0]
-    except IndexError:
+    if len(abs_sv) == 0:
         raise ValueError("Dataframe is empty! Please check your data inputs.")
 
-    return _compute_hm(
-        abs_sv["dd"].values, abs_sv["sv"].values, abs_start, abs_end, start_index
-    )
+    return _compute_hm(abs_sv, abs_start, abs_end)
diff --git a/tests/test_harmonic_mean.py b/tests/test_harmonic_mean.py
index 8fffe0c..b0a4e02 100644
--- a/tests/test_harmonic_mean.py
+++ b/tests/test_harmonic_mean.py
@@ -15,7 +15,7 @@ def sound_profile_data() -> pd.DataFrame:
 
 @pytest.mark.parametrize(
     "end_depth,expected_hm",
-    [(-1176.5866, 1481.551), (-1146.5881, 1481.521), (-1133.7305, 1481.509)],
+    [(-1176.5866, 1481.542), (-1146.5881, 1481.513), (-1133.7305, 1481.5)],
 )
 def test_sv_harmonic_mean(end_depth, expected_hm, sound_profile_data):
     svdf = sound_profile_data
@@ -25,19 +25,33 @@ def test_sv_harmonic_mean(end_depth, expected_hm, sound_profile_data):
     assert harmonic_mean == expected_hm
 
 
-@pytest.mark.parametrize("test_idx,expected_hm", [(3, 1501.69), (6, 1501.225)])
-def test__compute_hm(test_idx, expected_hm):
+@pytest.mark.parametrize(
+    "start_idx,end_idx,expected_hm",
+    [(0, 3, 1501.535), (0, 6, 1501.07), (2, 5, 1500.915), (4, 7, 1500.295)],
+)
+def test__compute_hm(start_idx, end_idx, expected_hm):
     dd = np.arange(7) * 10
     sv = np.arange(1502, 1500, step=-0.31)
 
+    svdf = pd.DataFrame(dict(dd=dd, sv=sv))
+
     # Get partial of the data from test index
-    part_dd = dd[:test_idx]
-    part_sv = sv[:test_idx]
-    start_depth, end_depth = part_dd[0], part_dd[-1]
-    result_hm = round(_compute_hm(dd, sv, start_depth, end_depth, 0), 3)
+    partdf = svdf[start_idx:end_idx].copy()
+    start_depth, end_depth = partdf.iloc[0]["dd"], partdf.iloc[-1]["dd"]
+    result_hm = round(_compute_hm(svdf, start_depth, end_depth), 3)
 
     # Check for result to match expected
     assert result_hm == expected_hm
 
-    # Result should be same as regular mean as the data is linear here
-    assert result_hm == round(sum(part_sv) / test_idx, 3)
+    # Result should be same as manual computation of weighted harmonic mean
+    # https://en.wikipedia.org/wiki/Harmonic_mean#Weighted_harmonic_mean
+    #
+    # H = (w1+...+wn) / ((w1/x1)+...+(wn/xn))
+    #
+    # H is the resulting harmonic mean
+    # w is the weight value, in this case, the depth differences
+    # x is the input value, in this case, the sound speed
+    w = partdf["dd"].diff()
+    x = partdf["sv"]
+    H = w.dropna().sum() / (w / x).dropna().sum()
+    assert result_hm == round(H, 3)