Refactoring

.vscode/launch.json

@@ -6,7 +6,7 @@
     "configurations": [
         {
             "name": "Kedro: Default",
-            "type": "python",
+            "type": "debugpy",
             "request": "launch",
             "module": "kedro",
             "args": [
@@ -19,7 +19,7 @@
         },
         {
             "name": "Kedro: Slurm",
-            "type": "python",
+            "type": "debugpy",
             "request": "launch",
             "module": "kedro",
             "args": [

README.md

@@ -59,7 +59,19 @@ which will open Kedro`s dashboard in you browser.
 
 The following parameters can be adjusted:
 - Data Preprocessing Parameters: `conf/base/parameters/preprocessing.yml`
-  - Image sidelength
+  - Batch size: 
+    - a number >0 indicating the batch size
+    - -1 to use all of the data as batch
+  - Mode: Type of dataset
+    - "cosine": Fourier series
+    - "image": Cameraman image
+  - Domain: Range for the input data that goes into the encoding gates
+  - Scale Domain by Pi: If the range shall be multiplied by Pi
+  - Specific for 'Image' Dataset
+    - Sidelength: Image sidelength
+    - Nonlinear Coordinates: If an arcsin shall be applied on the coordinates
+  - Specific for 'Cosine' Dataset
+    - Omega: Frequencies for the Fourier series. List of lists where the "outer" list determines the dimensionality and the "inner" list the frequencies for that dimension
 - Training Parameters: `conf/base/parameters/data_science.yml`
   - Number of qubits
   - Number of layers

conf/base/parameters_postprocessing.yml

@@ -1,8 +1,2 @@
-# This is a boilerplate parameters config generated for pipeline 'postprocessing'
-# using Kedro 0.18.12.
-#
-# Documentation for this file format can be found in "Parameters"
-# Link: https://docs.kedro.org/en/0.18.12/kedro_project_setup/configuration.html#parameters
-
 postprocessing:
   upscale_factor: 2

conf/base/parameters_preprocessing.yml

@@ -1,9 +1,3 @@
-# This is a boilerplate parameters config generated for pipeline 'preprocessing'
-# using Kedro 0.18.12.
-#
-# Documentation for this file format can be found in "Parameters"
-# Link: https://docs.kedro.org/en/0.18.12/kedro_project_setup/configuration.html#parameters
-
 preprocessing:
   batch_size: -1
   mode: "cosine" # image, cosine

conf/base/parameters_training.yml

@@ -1,9 +1,3 @@
-# This is a boilerplate parameters config generated for pipeline 'training'
-# using Kedro 0.18.12.
-#
-# Documentation for this file format can be found in "Parameters"
-# Link: https://docs.kedro.org/en/0.18.12/kedro_project_setup/configuration.html#parameters
-
 training: #see globals.yml for adjustment of n_qubits, n_layers,...
   n_qubits: 4
   n_layers: 14

pyproject.toml

@@ -20,24 +20,24 @@ gpu=["torchvision"]
 
 [tool.poetry.dependencies]
 python = ">=3.9,<3.12"
-pennylane = "^0.33.1"
+pennylane = "^0.36.0"
 pillow = "^10.3.0"
 scikit-image = "^0.21.0"
 ipykernel = "^6.25.0"
-matplotlib = "^3.7.2"
 torch = [
 # Uncomment the following line once this issue is resolved: https://github.com/python-poetry/poetry/issues/7748 # noqa
 #    {version = "2.1.0", source = "torch_gpu", markers = "extra == 'gpu'"},
     {version = "2.1.0+cpu", source = "torch_cpu", markers = "extra != 'gpu'"}
 ]
 torchvision = "^0.16.0"
-kedro = "^0.19.5"
+kedro = "^0.19.6"
 torchsummary = "^1.5.1"
 torchmetrics = "^1.0.2"
 kedro-mlflow = "^0.12.2"
-plotly = "^5.15.0"
+plotly = "^5.22.0"
 black = "^24.4.2"
 kaleido = "0.2.1"
+mlflow = "^2.13.2"
 
 
 

src/quantum_siren/helpers/colors.py

@@ -0,0 +1,85 @@
+import numpy as np
+
+
+def hsv_to_rgb(hsv):
+    """
+    Convert HSV values to RGB.
+
+    Parameters
+    ----------
+    hsv : (..., 3) array-like
+       All values assumed to be in range [0, 1]
+
+    Returns
+    -------
+    (..., 3) `~numpy.ndarray`
+       Colors converted to RGB values in range [0, 1]
+    """
+    hsv = np.asarray(hsv)
+
+    # check length of the last dimension, should be _some_ sort of rgb
+    if hsv.shape[-1] != 3:
+        raise ValueError(
+            "Last dimension of input array must be 3; " f"shape {hsv.shape} was found."
+        )
+
+    in_shape = hsv.shape
+    hsv = np.array(
+        hsv,
+        copy=False,
+        dtype=np.promote_types(hsv.dtype, np.float32),  # Don't work on ints.
+        ndmin=2,  # In case input was 1D.
+    )
+
+    h = hsv[..., 0]
+    s = hsv[..., 1]
+    v = hsv[..., 2]
+
+    r = np.empty_like(h)
+    g = np.empty_like(h)
+    b = np.empty_like(h)
+
+    i = (h * 6.0).astype(int)
+    f = (h * 6.0) - i
+    p = v * (1.0 - s)
+    q = v * (1.0 - s * f)
+    t = v * (1.0 - s * (1.0 - f))
+
+    idx = i % 6 == 0
+    r[idx] = v[idx]
+    g[idx] = t[idx]
+    b[idx] = p[idx]
+
+    idx = i == 1
+    r[idx] = q[idx]
+    g[idx] = v[idx]
+    b[idx] = p[idx]
+
+    idx = i == 2
+    r[idx] = p[idx]
+    g[idx] = v[idx]
+    b[idx] = t[idx]
+
+    idx = i == 3
+    r[idx] = p[idx]
+    g[idx] = q[idx]
+    b[idx] = v[idx]
+
+    idx = i == 4
+    r[idx] = t[idx]
+    g[idx] = p[idx]
+    b[idx] = v[idx]
+
+    idx = i == 5
+    r[idx] = v[idx]
+    g[idx] = p[idx]
+    b[idx] = q[idx]
+
+    idx = s == 0
+    r[idx] = v[idx]
+    g[idx] = v[idx]
+    b[idx] = v[idx]
+
+    rgb = np.stack([r, g, b], axis=-1)
+
+    return rgb.reshape(in_shape)