diff --git a/blowback/LICENSE.md b/backwash/LICENSE.md
similarity index 100%
rename from blowback/LICENSE.md
rename to backwash/LICENSE.md
diff --git a/blowback/README.md b/backwash/README.md
similarity index 100%
rename from blowback/README.md
rename to backwash/README.md
diff --git a/blowback/__init__.py b/backwash/__init__.py
similarity index 100%
rename from blowback/__init__.py
rename to backwash/__init__.py
diff --git a/blowback/obr.py b/backwash/obr.py
similarity index 98%
rename from blowback/obr.py
rename to backwash/obr.py
index 181e9c4..27725b4 100644
--- a/blowback/obr.py
+++ b/backwash/obr.py
@@ -79,7 +79,7 @@ class OBRData:
         metadata = fromfile(dtype=numpy.float64, offset=0x0c, count=4)
         ng = fromfile(dtype=numpy.float64, offset=0x2e, count=1)
         gain_dB = fromfile(dtype=numpy.int16, offset=0x36, count=1)
-        dates = fromfile(dtype=numpy.int16, offset=0x46, count=2)
+        dates = fromfile(dtype=numpy.int16, offset=0x46, count=2 * 8)
         t0half = fromfile(dtype=numpy.float64, offset=0x96, count=1)
         freq_windowed = fromfile(dtype=numpy.int8, offset=0xb6, count=1).any()
         arr = fromfile(dtype=numpy.float64, offset=0x800)
@@ -92,7 +92,6 @@ class OBRData:
         tm = arrs[2] + 1j * arrs[3]
 
         meas_date, calib_date = numpy.split(dates, 2)
-        tt = numpy.arange(arrs[0].size) * dt_ns + t0half * 2
 
         result = OBRData(
             te = te,
diff --git a/backwash/ova_bin.py b/backwash/ova_bin.py
new file mode 100644
index 0000000..caeae6a
--- /dev/null
+++ b/backwash/ova_bin.py
@@ -0,0 +1,135 @@
+from typing import IO, Self
+from dataclasses import dataclass
+from pathlib import Path
+import logging
+import numpy
+from numpy.typing import NDArray
+from numpy.fft import fftshift, fftfreq
+
+from .utils import C0
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class OVABINData:
+    te: NDArray[numpy.complex128]       # time domain complex amplitude, polarization 1
+    tm: NDArray[numpy.complex128]       # time domain complex amplitude, polarization 2
+    dt_ns: float                        # delta between time points
+    max_freq_GHz: float                 # sweep's max frequency (shortest wavelength)
+    ng_setting: float | None            # set by user (for info only)
+    meas_date: NDArray[numpy.int16]     # 8 items: year month [week?] day hour min sec ms
+    _wip_metadata: NDArray[numpy.float64] | None  # unknown metadata values
+
+    @property
+    def time_ns(self) -> NDArray[numpy.float64]:
+        """
+        Time delay (x-axis) for `te` and `tm` data
+        """
+        tt = numpy.arange(self.tee.size) * self.dt_ns
+        return tt
+
+    @property
+    def wl_range_nm(self) -> NDArray[numpy.float64]:
+        freq_range = numpy.asarray([
+            self.max_freq_GHz - 1 / (2 * self.dt_ns),
+            welf.max_freq_GHz,
+            ])
+        return C0 / freq_range
+
+    @property
+    def ctr_freq_GHz(self) -> float:
+        return self.max_freq_GHz - 1 / (4 * self.dt_ns)
+
+    def freqs(self, size: int) -> NDArray[numpy.float64]:
+        # To be used with spectrum generated with e.g. fft(fftshift(data.te))
+        frq_GHz = fftshift(fftfreq(size, d=self.dt_ns)) + self.ctr_freq_GHz
+        return frq_GHz
+
+    def wls(self, size: int) -> NDArray[numpy.float64]:
+        # To be used with spectrum generated with e.g. fft(fftshift(data.te))
+        return C0 / self.freqs(size)
+
+    def window(self, size: int) -> NDArray[numpy.float64]:
+        # Approximation of the generalized Hamming window used when freq_windowed==True
+        nn = numpy.linspace(0, 1, size)
+        alpha = 0.572
+        scale = 1.5         # likely related to "incoherent power gain compensation" for Hamming window (=1.54)
+                            # 1.50 seems to fit the data better though
+        ham = alpha - (1 - alpha) * numpy.cos(2 * numpy.pi * nn)
+        return scale * ham
+
+    @staticmethod
+    def read(file: str | IO[bytes] | Path) -> Self:
+        raise NotImplementedError('Still WIP')
+        def fromfile(*args, **kwargs) -> NDArray:
+            if not isinstance(file, str | Path):
+                file.seek(0)
+            return numpy.fromfile(file, *args, **kwargs)
+
+        magic = fromfile(dtype=numpy.uint8, offset=0x10, count=7)
+        if (magic != [0x40] + [ord(cc) for cc in 'S33OVA']).any():
+            logger.warning(f'Unexpected magic bytes: {magic}')
+
+        metadata = fromfile(dtype=numpy.float64, offset=0x00, count=2)
+        meas_date = fromfile(dtype=numpy.int16, offset=0x49, count=8)
+
+        wl_range = fromfile(dtype=numpy.int32, offset=0x17, count=1)
+        data_len = 4 * wl_range
+        arr = fromfile(dtype=numpy.float64, offset=0x59, count=data_len)
+        ng = fromfile(dtype=numpy.float32, offset=0x21, count=1)
+
+        max_freq_GHz = metadata[0]
+        #dt_ns = metadata[3] / 2
+
+        arrs = numpy.split(arr, 4)
+        te = arrs[0] + 1j * arrs[1]
+        tm = arrs[2] + 1j * arrs[3]
+
+        result = OVABINData(
+            te = te,
+            tm = tm,
+            #dt_ns = dt_ns,
+            max_freq_GHz = max_freq_GHz,
+            ng_setting = ng,
+            meas_date = meas_date,
+            _wip_metadata = metadata[1:],
+            )
+
+
+"""
+Notes on .bin file format (OVA)
+================================
+
+0x00: 9D A8 5A 6B  C8 D0 0C 41  13 23 63 4D  22 74 C4 3F
+     |max_freq_GHZ (f64)------ | f64 ??----------------- |
+      C0/freq = wl_nm           5.848e6 ~> 51.3nm if GHz
+                                could be dt in ps?
+
+0x10: 40 53 33 33  4F 56 41 00  00 00 01 00  00 00 00 00
+     |   S  3  3  | O V  A | wl range   |
+                             1 = biggest range (58.97nm), others are exponents of 2 smaller (resolution)
+                             effectively length of each sub-array (x 4 arrays)
+
+0x20: 00 00 80 20  40 01 00 00  00 00 00 00  00 00 00 00
+        | ng (f32)   |----........|
+           ???        average count
+
+0x30: 00 00 00 00  00 00 38 98  40 00 00 00  00 00 00 00
+                         ???????????
+
+0x40: 00 00 00 00  00 00 00 00  00 E7 07 0A  00 05 00 06
+                                  |year  month  week? day-
+                                    acquisition timestamp
+
+0x50: 00 12 00 0F  00 0F 00 23  00 F7 27 6F  E5 17 A1 A7
+     ---  hr   minute  sec    ms  | f64 data starts----
+                                    (offset 0x59)
+
+0x400040:
+      3F C8 E2 3E  89 8A A4 8C  BF A9 4D A8  8A D6 9B 94
+0x400050:
+      3F 8E BB 79  0C C2 84 92  3F 4E 4F 4E  45 40
+                                  |N  O  N   E |
+"""
diff --git a/blowback/py.typed b/backwash/py.typed
similarity index 100%
rename from blowback/py.typed
rename to backwash/py.typed
diff --git a/blowback/utils.py b/backwash/utils.py
similarity index 100%
rename from blowback/utils.py
rename to backwash/utils.py
diff --git a/pyproject.toml b/pyproject.toml
index 4082f1e..6e2a4ab 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,5 +1,5 @@
 [project]
-name = "blowback"
+name = "backwash"
 description = "Optical reflectometry file format reader"
 readme = "README.md"
 license = { file = "LICENSE.md" }
@@ -39,7 +39,7 @@ dependencies = [
     "numpy>=1.26",
     ]
 [tool.hatch.version]
-path = "blowback/__init__.py"
+path = "backwash/__init__.py"
 
 
 [tool.ruff]
diff --git a/uv.lock b/uv.lock
index 510cc94..8771fe8 100644
--- a/uv.lock
+++ b/uv.lock
@@ -3,7 +3,7 @@ revision = 3
 requires-python = ">=3.13"
 
 [[package]]
-name = "blowback"
+name = "backwash"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [