improve doc, bump to 0.0.5

Author: wkirgsn

README.md

@@ -10,7 +10,9 @@ This repository acts as a hub for selected power loss models that were elaborate
 Feel free to use these loss models for your power converter design as a complement to your datasheet.
 
 The loss models are designed such that you can request a certain frequency, temperature, material and $B$ wave (sequence), in order to be provided with a scalar power loss estimate and a corresponding $H$ wave estimate.
-Note that only steady-state and no varying DC-Bias is supported yet.
+
+__Disclaimer__: Only steady-state and no varying DC-Bias is supported yet. 
+Moreover, training data stemmed from measurements on toroid-shaped ferrites that had a fix size.
 
 Supported materials:
 - ML95S
@@ -42,6 +44,13 @@ Then install through pip:
 pip install mag-net-hub
 ```
 
+or, alternatively, clone this repo and execute
+
+```
+cd mag-net-hub
+pip install .
+```
+
 ### Matlab
 TBD
 
@@ -59,15 +68,15 @@ import magnethub as mh
 mdl = mh.loss.LossModel(material="3C92", team="paderborn")
 
 # dummy B field data (one trajectory with 1024 samples)
-b_wave = np.random.randn(1024)* 200e-3  # mT
+b_wave = np.random.randn(1024)* 200e-3  # in T
 freq = 124062  # Hz
 temp = 58  # °C
 
-# get power loss and estimated H wave
+# get power loss in W/m³ and estimated H wave in A/m
 p, h = mdl(b_wave, freq, temp)
 
 # batch execution for 100 trajectories
-b_waves = np.random.randn(100, 1024)* 200e-3  # mT
+b_waves = np.random.randn(100, 1024)* 200e-3  # in T
 freqs = np.random.randint(100e3, 750e3, size=100)
 temps = np.random.randint(20, 80, size=100)
 p, h = mdl(b_waves, freqs, temps)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "mag-net-hub"
-version = "0.0.4"
+version = "0.0.5"
 authors = [
     { name = "Wilhelm Kirchgässner" },
 ]

src_py/magnethub/loss.py

@@ -64,12 +64,17 @@ def __call__(self, b_field, frequency, temperature):
 
         Args
         ----
-        b_field: (B, T) array_like
-            The magnetic flux density array(s). First dimension describes the batch, the second
+        b_field: (X, Y) array_like
+            The magnetic flux density array(s) in T. First dimension X describes the batch size, the second Y
              the time length (will always be interpolated to 1024 samples)
         frequency: scalar or 1D array-like
-            The frequency operation point(s)
+            The frequency operation point(s) in Hz
         temperature: scalar or 1D array-like
-            The temperature operation point(s)
+            The temperature operation point(s) in °C
+        
+        Return
+        ------
+        p, h: (X,) np.array, (X, Y) np.ndarray
+            The estimated power loss (p) in W/m³ and the estimated magnetic field strength (h) in A/m.
         """
         return self.mdl(b_field, frequency, temperature)

src_py/magnethub/paderborn.py

@@ -504,13 +504,18 @@ def __call__(self, b_seq, frequency, temperature):
 
         Args
         ----
-        b_seq: (B, T) array_like
-            The magnetic flux density array(s). First dimension describes the batch, the second
+        b_seq: (X, Y) array_like
+            The magnetic flux density array(s) in T. First dimension X describes the batch, the second Y
              the time length (will always be interpolated to 1024 samples)
         frequency: scalar or 1D array-like
-            The frequency operation point(s)
+            The frequency operation point(s) in Hz
         temperature: scalar or 1D array-like
-            The temperature operation point(s)
+            The temperature operation point(s) in °C
+        
+        Return
+        ------
+        p, h: (X,) np.array, (X, Y) np.ndarray
+            The estimated power loss (p) in W/m³ and the estimated magnetic field strength (h) in A/m.
         """
         ds = engineer_features(b_seq, frequency, temperature, self.material)
         # construct tensors