v0.0.1 release

.gitignore

@@ -50,6 +50,7 @@ docs/_build/
 
 # Jupyter
 .ipynb_checkpoints
+*.ipynb
 
 # Environments
 .env

README.md

@@ -9,4 +9,5 @@ pip install cavitometer-deconvolve
 ```
 
 ## Usage
-See docs/examples.rst for usage examples.
+See [docs/examples.rst](https://github.com/blebon/cavitometer-deconvolve/blob/master/docs/examples.rst) 
+for usage examples.

cavitometer_deconvolve/hardware/sensitivities.py

@@ -5,53 +5,60 @@
 
 """
 
-from numpy import genfromtxt
+from numpy import genfromtxt, ndarray
 
 
 class Probe:
     """Contains the probe sensitivity values.
 
     Probes are calibrated when resting vertically (i = 0) or at 45 deg (i = 1).
     """
-    def __init__(self, filename):
+    def __init__(self, filename: str) -> None:
         """Initializes the frequency and sensitivity arrays from file
 
-        Keyword arguments:
-        filename -- the name of the probe sensitivity values csv file
+        :param filename: the name of the probe sensitivity values csv file
+        :rtype: None
         """
-        self.filename = filename
+        self._filename = filename
         _file_array = genfromtxt(filename,
                                  delimiter=',',
                                  skip_header=1,
                                  usecols=None,
                                  unpack=False,
                                  )
-        self.frequencies = _file_array[:, 0]
-        self.sensitivities = _file_array[:, 1:]
-
-    def get_frequencies(self):
-        """Return frequencies"""
-        return self.frequencies
-
-    def get_sensitivities(self, i=0):
-        """Return sensitivities.
-
-        Keyword arguments:
-        i -- 0 = vertical probe, 1 = probe inclined at 45 deg.
+        self._frequencies = _file_array[:, 0]
+        self._sensitivities = _file_array[:, 1:]
+
+    @property
+    def frequencies(self) -> ndarray:
+        """Return frequencies
+
+        :rtype: ndarray
         """
-        return self.sensitivities[:, i]
+        return self._frequencies
+
+    def get_sensitivities(self, i: int = 0) -> ndarray:
+        """Return get_sensitivities.
+
+        :param i:  0 = vertical probe, 1 = probe inclined at 45 deg.
+        :rtype: ndarray
+        """
+        return self._sensitivities[:, i]
 
     def __repr__(self):
-        return self.filename
+        return self._filename
 
     def __unicode__(self):
-        return self.filename
+        return self._filename
 
     def __str__(self):
-        return self.filename
+        return self._filename
 
 
 class PreAmplifier(Probe):
-    def get_sensitivities(self):
-        """Return sensitivities. No orientation."""
-        return self.sensitivities[:, 0]
+    def get_sensitivities(self) -> ndarray:
+        """Return get_sensitivities. No orientation.
+
+        :rtype: ndarray
+        """
+        return self._sensitivities[:, 0]

cavitometer_deconvolve/math/FFT.py

@@ -7,17 +7,18 @@
 
 from __future__ import division
 
-from numpy import fft, empty
+from numpy import fft, empty, ndarray
 from pyfftw import interfaces
 
 
-def fast_fourier_transform(time, signal, units):
+def fast_fourier_transform(time: ndarray, signal: ndarray, units: list) -> tuple:
     """Performs the Fast Fourier Transform on the time signal.
 
-    Keyword arguments:
-    time -- the time numpy array
-    signal -- the signal numpy array
-    units -- the SI units for the time and signal arrays
+    :param time: the time numpy array
+    :param signal: the signal numpy array
+    :param units: the SI units for the time and signal arrays
+    :return: frequency, fourier transform
+    :rtype: tuple
     """
     # If units in ms, multiply step time by 1e-3
     step = (time[1] - time[0])
@@ -36,22 +37,28 @@ def fast_fourier_transform(time, signal, units):
     return freq, fourier
 
 
-def two_sided_to_one(two_sided):
+def two_sided_to_one(two_sided: ndarray) -> ndarray:
     """Two sided FFT to one.
 
     numpy/scipy FFT functions yield two sided spectra while calibration is on one
     sided spectra. Conversion is required before deconvolution process.
+
+    :param two_sided: two sided FFT
+    :rtype: ndarray
     """
     one_sided = empty(len(two_sided) // 2 + 1)
     one_sided[1:len(two_sided) // 2 + 1] = 2 * two_sided[0:len(two_sided) // 2]
     one_sided[0] = two_sided[len(two_sided) // 2]
     return one_sided
 
 
-def one_sided_to_two(one_sided):
+def one_sided_to_two(one_sided: ndarray) -> ndarray:
     """One sided FFT to two.
 
     Revert to two sided FFT before inverse fourier transform.
+
+    :param one_sided: one sided FFT
+    :rtype: ndarray
     """
     two_sided = empty(2 * len(one_sided) - 1)
     two_sided[0:len(one_sided) - 1] = 0.5 * one_sided[1:]

cavitometer_deconvolve/math/deconvolve.py

@@ -5,28 +5,30 @@
 
 """
 
-from numpy import vectorize, linspace, sqrt
+from numpy import vectorize, linspace, sqrt, ndarray
 # from scipy.signal import blackman
 from scipy.interpolate import interp1d
 
 from pyfftw import interfaces
 
 from cavitometer_deconvolve.math.FFT import fast_fourier_transform, two_sided_to_one, one_sided_to_two
 from cavitometer_deconvolve.math.convert import dB_to_V
+from cavitometer_deconvolve.hardware.sensitivities import Probe, PreAmplifier
 
 
-def deconvolution(time, signal, units, probe, pre_amp=None):
+def deconvolution(time: ndarray, signal: ndarray, units: list,
+                  probe: Probe, pre_amp: PreAmplifier = None) -> tuple:
     """Converts the voltage signal to pressures.
 
-    1. Interpolate the sensitivities and amplification factors in the FFT frequency range.
+    1. Interpolate the get_sensitivities and amplification factors in the FFT frequency range.
     2. Apply deconvolution formula
-        
-    Keyword arguments:
-    time -- the time numpy array
-    signal -- the signal numpy array
-    units -- the SI units for the time and signal arrays
-    probe -- Probe instance containing the sensitivity values
-    pre_amp -- PreAmplifier instance containing the pre-amp factors
+
+    :param time: the time numpy array
+    :param signal: the signal numpy array
+    :param units: the SI units for the time and signal arrays
+    :param probe: Probe instance containing the sensitivity values
+    :param pre_amp: PreAmplifier instance containing the pre-amp factors
+    :rtype: tuple
     """
     # For zero padding, uncomment concatenate lines if required
     # N0 = len(x1)
@@ -39,7 +41,7 @@ def deconvolution(time, signal, units, probe, pre_amp=None):
     # smooth = 0.0
 
     # 1. Interpolation
-    sensitivity_function = interp1d(probe.get_frequencies(),
+    sensitivity_function = interp1d(probe.frequencies,
                                     decibel_to_volts(probe.get_sensitivities()),
                                     kind='nearest',
                                     fill_value="extrapolate",
@@ -48,7 +50,7 @@ def deconvolution(time, signal, units, probe, pre_amp=None):
     # sensitivity_function.set_smoothing_factor(smooth)
 
     if pre_amp:
-        amplification_factor = interp1d(pre_amp.get_frequencies(),
+        amplification_factor = interp1d(pre_amp.frequencies,
                                         pre_amp.get_sensitivities(),
                                         kind='nearest',
                                         fill_value="extrapolate",

cavitometer_deconvolve/utils/read.py

@@ -8,12 +8,12 @@
 from numpy import genfromtxt, isnan, nan_to_num
 
 
-def get_units(filename, extension=".csv"):
+def get_units(filename: str, extension: str = ".csv") -> tuple:
     """ Read the units from the raw signal file.
 
-    Keyword arguments
-    filename -- name of raw signal file, including path
-    extension -- extension of raw files (default '.csv')
+    :param filename: name of raw signal file, including path
+    :param extension: extension of raw files (default '.csv')
+    :rtype: tuple
     """
     with open(filename, "r") as f:
         # Units are always in the second line
@@ -26,12 +26,12 @@ def get_units(filename, extension=".csv"):
     return units
 
 
-def read_signal(filename, extension='.csv'):
+def read_signal(filename: str, extension: str = ".csv") -> tuple:
     """ Read signals and remove Infs
 
-    Keyword arguments
-    filename -- name of raw signal file, including path
-    extension -- extension of raw files (default '.csv')
+    :param filename: name of raw signal file, including path
+    :param extension: extension of raw files (default '.csv')
+    :rtype: tuple
     """
     units = get_units(filename, extension=extension)
 

docs/examples.rst

@@ -37,6 +37,7 @@ Read signal
            [ 2.00019988e+00,  1.47723100e+01, -7.99658100e-01],
            [ 2.00039988e+00,  2.18776700e+01, -1.10487100e+00],
            [ 2.00059988e+00,  2.98376300e+01, -1.10487100e+00]])
+
 Deconvolve signal
 -------------------
 

setup.cfg

@@ -13,6 +13,6 @@ license_files = LICENSE
 universal=1
 
 [build_sphinx]
-project = 'cavitometer-deconvolve-blebon'
+project = 'cavitometer-deconvolve'
 version = 0.0.1
 source-dir = 'doc'

setup.py

@@ -7,7 +7,7 @@
     requirements = fh.read()
 
 setup(
-    name="cavitometer-deconvolve-blebon",
+    name="cavitometer-deconvolve",
     version="0.0.1",
     author="Bruno Lebon",
     author_email="Bruno.Lebon@brunel.ac.uk",