diff --git a/.gitignore b/.gitignore index a48eb4435..d6fd59c29 100644 --- a/.gitignore +++ b/.gitignore @@ -86,6 +86,9 @@ ipython_config.py # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version +.conda +bootstrap_requirements.txt +environment.yml # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. diff --git a/docs/examples/Amplitude_LUT_Example.ipynb b/docs/examples/Amplitude_LUT_Example.ipynb new file mode 100644 index 000000000..b29225c4c --- /dev/null +++ b/docs/examples/Amplitude_LUT_Example.ipynb @@ -0,0 +1,417 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Amplitude Look-Up Table (LUT) Example\n", + "\n", + "This notebook demonstrates how to use an amplitude Look-Up Table (LUT) to map the output amplitude to required voltages. This is useful for compensating non-linearities in the signal chain, such as:\n", + "\n", + "- DAC non-linearity\n", + "- Amplifier response curves\n", + "- Cable/transmission line effects\n", + "\n", + "## The Mapping Function\n", + "\n", + "In this example, we use a sine function from 0 to π as our mapping function. This creates a non-linear transformation where:\n", + "\n", + "$$\n", + "V_{out} = A \\cdot \\sin\\left(\\frac{\\pi (V_{in} + 1)}{2}\\right)\n", + "$$\n", + "\n", + "This maps the input range $[-1, 1]$ through a sine curve, where:\n", + "- Input $-1$ → $\\sin(0) = 0$\n", + "- Input $0$ → $\\sin(\\pi/2) = 1$ (maximum)\n", + "- Input $1$ → $\\sin(\\pi) = 0$\n", + "\n", + "This type of transformation can be useful when you need to \"soften\" the output response or compensate for a system that has a similar inverse characteristic." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NoTagError: `git describe --long --dirty --always --tags '--match=v*'` could not find a tag\n" + ] + } + ], + "source": [ + "# Imports\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import broadbean as bb\n", + "from broadbean.plotting import plotter\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (10, 4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the LUT\n", + "\n", + "First, let's create and visualize our sine-based LUT mapping function." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create LUT input values (the \"x\" values)\n", + "# These represent the normalized amplitude values from the waveform\n", + "n_points = 101 # Number of points in the LUT\n", + "lut_input = np.linspace(-1, 1, n_points)\n", + "\n", + "# Create LUT output values using sine function from 0 to pi\n", + "# Map input range [-1, 1] to [-pi, pi]\n", + "theta = np.pi * lut_input / 2\n", + "lut_output = np.sin(theta)\n", + "\n", + "# Visualize the LUT\n", + "fig = plt.figure()\n", + "\n", + "# Plot 2: The scaled version\n", + "plt.plot(lut_input, lut_output, \"r-\", linewidth=2, label=\"Sine LUT\")\n", + "plt.plot(lut_input, lut_input, \"k--\", alpha=0.5, label=\"Linear (no transformation)\")\n", + "plt.xlabel(\"Input Amplitude\")\n", + "plt.ylabel(\"Output Amplitude\")\n", + "plt.title(\"Scaled Sine LUT Mapping\")\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "plt.xlim(-1, 1)\n", + "plt.ylim(-1.1, 1.1)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building a Simple Waveform Sequence\n", + "\n", + "Now let's create a simple sequence with a triangle wave to clearly demonstrate the effect of the LUT transformation." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original waveform (before LUT):\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get pulse atom functions\n", + "ramp = bb.PulseAtoms.ramp\n", + "\n", + "# Create a blueprint with a triangle wave pattern\n", + "bp = bb.BluePrint()\n", + "bp.setSR(100e6) # 100 MHz sample rate\n", + "\n", + "# Create triangle wave: ramp up, ramp down, ramp up (negative), ramp down (to zero)\n", + "bp.insertSegment(0, ramp, (0, 1), dur=1e-6) # Ramp from 0 to 1\n", + "bp.insertSegment(1, ramp, (1, -1), dur=2e-6) # Ramp from 1 to -1\n", + "bp.insertSegment(2, ramp, (-1, 0), dur=1e-6) # Ramp from -1 to 0\n", + "\n", + "# Create an element and add the blueprint\n", + "elem = bb.Element()\n", + "elem.addBluePrint(1, bp)\n", + "\n", + "# Create a sequence\n", + "seq = bb.Sequence()\n", + "seq.addElement(1, elem)\n", + "seq.setSR(1e6)\n", + "\n", + "# Set channel amplitude and offset (required for output generation)\n", + "seq.setChannelAmplitude(1, 2.0) # 2V peak-to-peak\n", + "seq.setChannelOffset(1, 0)\n", + "\n", + "# Plot the original sequence\n", + "print(\"Original waveform (before LUT):\")\n", + "plotter(seq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Applying the Amplitude LUT\n", + "\n", + "Now we apply the sine LUT to the sequence. The LUT transformation is applied during the `forge()` method, which processes the waveforms for output." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Waveform with sine LUT applied:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a copy of the sequence to apply the LUT\n", + "seq_with_lut = seq.copy()\n", + "\n", + "# Apply the amplitude LUT\n", + "# Using the scaled version that maps [-1, 1] -> [-1, 1] through the sine function\n", + "seq_with_lut.setAmplitudeLUT(1, list(lut_input), list(lut_output))\n", + "\n", + "print(\"Waveform with sine LUT applied:\")\n", + "plotter(seq_with_lut)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing Before and After\n", + "\n", + "Let's extract the actual waveform data and compare the original vs LUT-transformed signals side by side." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Forge the sequences to get the actual waveform arrays\n", + "forged_original = seq.forge(apply_filters=True, includetime=True)\n", + "forged_with_lut = seq_with_lut.forge(apply_filters=True, includetime=True)\n", + "\n", + "# Extract waveform data\n", + "wfm_original = forged_original[1][\"content\"][1][\"data\"][1][\"wfm\"]\n", + "wfm_with_lut = forged_with_lut[1][\"content\"][1][\"data\"][1][\"wfm\"]\n", + "time = forged_original[1][\"content\"][1][\"data\"][1][\"time\"]\n", + "\n", + "# Plot comparison\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Time domain comparison\n", + "axes[0].plot(time * 1e6, wfm_original, \"b-\", linewidth=2, label=\"Original\")\n", + "axes[0].plot(time * 1e6, wfm_with_lut, \"r-\", linewidth=2, label=\"With Sine LUT\")\n", + "axes[0].set_xlabel(\"Time (µs)\")\n", + "axes[0].set_ylabel(\"Amplitude (V)\")\n", + "axes[0].set_title(\"Waveform Comparison: Original vs Sine LUT\")\n", + "axes[0].legend()\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Input vs Output relationship\n", + "axes[1].scatter(\n", + " wfm_original, wfm_with_lut, c=time * 1e6, cmap=\"viridis\", s=1, alpha=0.5\n", + ")\n", + "axes[1].plot(lut_input, lut_output, \"r-\", linewidth=2, label=\"LUT curve\")\n", + "axes[1].set_xlabel(\"Original Amplitude (V)\")\n", + "axes[1].set_ylabel(\"LUT-Transformed Amplitude (V)\")\n", + "axes[1].set_title(\"Transfer Function Applied by LUT\")\n", + "axes[1].legend()\n", + "axes[1].grid(True, alpha=0.3)\n", + "axes[1].set_aspect(\"equal\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Practical Example: Linearizing a Non-Linear Response\n", + "\n", + "A common use case for amplitude LUTs is to compensate for non-linear behavior in the signal chain. For example, if your amplifier has a response that follows $V_{out} = \\sin(\\frac{\\pi V_{in}}{2})$, you would apply the inverse function to pre-distort the signal.\n", + "\n", + "Let's demonstrate this by creating a LUT that would linearize such a response." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Waveform with linearization LUT (pre-distortion):\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Suppose the system has a sine-like response\n", + "# To compensate, we need the inverse: arcsin\n", + "\n", + "# Create linearization LUT\n", + "n_points = 1001\n", + "lut_input_linear = np.linspace(-1, 1, n_points)\n", + "\n", + "# The inverse of sin(pi*x/2) is (2/pi)*arcsin(x)\n", + "# We need to be careful with the domain of arcsin\n", + "lut_output_linear = (2 / np.pi) * np.arcsin(lut_input_linear)\n", + "\n", + "# Create sequence with linearization LUT\n", + "seq_linearized = seq.copy()\n", + "seq_linearized.setAmplitudeLUT(1, list(lut_input_linear), list(lut_output_linear))\n", + "\n", + "# Visualize the linearization LUT\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(\n", + " lut_input_linear,\n", + " lut_output_linear,\n", + " \"g-\",\n", + " linewidth=2,\n", + " label=\"Linearization LUT (arcsin)\",\n", + ")\n", + "plt.plot(\n", + " lut_input_linear,\n", + " lut_input_linear,\n", + " \"k--\",\n", + " alpha=0.5,\n", + " label=\"Identity (no transformation)\",\n", + ")\n", + "plt.plot(\n", + " lut_input, lut_output, \"r--\", alpha=0.5, label=\"Sine LUT (what we're compensating)\"\n", + ")\n", + "plt.xlabel(\"Input Amplitude\")\n", + "plt.ylabel(\"Output Amplitude\")\n", + "plt.title(\"Linearization LUT using Arcsin\")\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "plt.xlim(-1.1, 1.1)\n", + "plt.ylim(-1.1, 1.1)\n", + "plt.gca().set_aspect(\"equal\")\n", + "plt.show()\n", + "\n", + "print(\"\\nWaveform with linearization LUT (pre-distortion):\")\n", + "plotter(seq_linearized)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "This notebook demonstrated how to:\n", + "\n", + "1. **Create an amplitude LUT** using a sine function mapping from 0 to π\n", + "2. **Apply the LUT** to a sequence using `seq.setAmplitudeLUT(channel, lut_input, lut_output)`\n", + "3. **Visualize the effect** of the LUT on waveforms\n", + "4. **Use inverse functions** (like arcsin) to linearize non-linear system responses\n", + "\n", + "### Key Points\n", + "\n", + "- The LUT uses linear interpolation (`np.interp`) between specified points\n", + "- More points in the LUT provide smoother transformations\n", + "- The LUT is applied during the `forge()` method, after any filter compensation\n", + "- LUTs can be used for various purposes: linearization, compression, custom response shaping" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".conda", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/index.rst b/docs/examples/index.rst index 691f64a9a..35556a539 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -9,4 +9,5 @@ Broadbean Examples Making_output_for_Tektronix_AWG70000A.ipynb Example_Write_Read_JSON.ipynb Filter_compensation.ipynb + Amplitude_LUT_Example.ipynb Subsequences.ipynb diff --git a/src/broadbean/ripasso.py b/src/broadbean/ripasso.py index 7461ef357..c75bab144 100644 --- a/src/broadbean/ripasso.py +++ b/src/broadbean/ripasso.py @@ -6,9 +6,11 @@ # import logging +from typing import TypedDict import numpy as np from numpy.fft import fft, fftfreq, ifft +from numpy.typing import ArrayLike log = logging.getLogger(__name__) @@ -17,7 +19,7 @@ class MissingFrequenciesError(Exception): pass -def _rcFilter(SR, npts, f_cut, kind="HP", order=1, DCgain=0): +def _rcFilter(SR: int, npts: int, f_cut: float, kind="HP", order=1, DCgain=0): """ Nth order (RC circuit) filter made with frequencies matching the fft output @@ -44,7 +46,9 @@ def _rcFilter(SR, npts, f_cut, kind="HP", order=1, DCgain=0): return tf**order -def applyRCFilter(signal, SR, kind, f_cut, order, DCgain=0): +def applyRCFilter( + signal: np.ndarray, SR: int, kind: str, f_cut: float, order: int, DCgain: float = 0 +): """ Apply a simple RC-circuit filter to signal and return the filtered signal. @@ -80,7 +84,9 @@ def applyRCFilter(signal, SR, kind, f_cut, order, DCgain=0): return output -def applyInverseRCFilter(signal, SR, kind, f_cut, order, DCgain=1): +def applyInverseRCFilter( + signal: np.ndarray, SR: int, kind: str, f_cut: float, order: int, DCgain: float = 1 +): """ Apply the inverse of an RC-circuit filter to a signal and return the compensated signal. @@ -125,7 +131,13 @@ def applyInverseRCFilter(signal, SR, kind, f_cut, order, DCgain=1): return output -def applyCustomTransferFunction(signal, SR, tf_freqs, tf_amp, invert=False): +def applyCustomTransferFunction( + signal: np.ndarray, + SR: int, + tf_freqs: np.ndarray, + tf_amp: np.ndarray, + invert: bool = False, +): """ Apply custom transfer function @@ -198,3 +210,26 @@ def applyCustomTransferFunction(signal, SR, tf_freqs, tf_amp, invert=False): signal_filtered = np.real(signal_filtered) return signal_filtered + + +class AmplitudeLUT(TypedDict): + LUT_input: ArrayLike + LUT_output: ArrayLike + + +def applyAmplitudeLUT(signal: np.ndarray, lut: AmplitudeLUT): + """ + Apply an amplitude LUT to the signal. + + Args: + signal (np.array): The input signal. + lut (AmplitudeLUT): A lookup table for amplitude compensation. The LUT_input + field should contain the input values and + the LUT_output field should contain the corresponding output values. + + Returns: + np.array: + The signal after applying the amplitude LUT. + """ + + return np.interp(signal, lut["LUT_input"], lut["LUT_output"]) diff --git a/src/broadbean/sequence.py b/src/broadbean/sequence.py index 3f2e7e2eb..1674dd8b6 100644 --- a/src/broadbean/sequence.py +++ b/src/broadbean/sequence.py @@ -11,7 +11,7 @@ from broadbean.blueprint import BluePrint from broadbean.element import Element # TODO: change import to element.py -from broadbean.ripasso import applyInverseRCFilter +from broadbean.ripasso import applyAmplitudeLUT, applyInverseRCFilter from .broadbean import ( PulseAtoms, @@ -385,6 +385,30 @@ def setChannelFilterCompensation( "tau": tau, } + def setAmplitudeLUT( + self, channel: int | str, lut_input: list[float], lut_output: list[float] + ) -> None: + """ + Set an amplitude lookup table for a channel. This is used when making + output for .awg files. The LUT is applied after all other waveform + processing. + + Args: + channel: The channel number/name + lut_input: The input levels for the LUT + lut_output: The output levels for the LUT + + Example: + >>> lut_input = [-1.0, -0.5, 0.0, 0.5, 1.0] + >>> lut_output = [-0.8, -0.3, 0.0, 0.3, 0.8] + >>> seq.setAmplitudeLUT(1, lut_input, lut_output) + """ + + self._awgspecs[f"channel{channel}_amplitude_LUT"] = { + "LUT_input": lut_input, + "LUT_output": lut_output, + } + def addElement(self, position: int, element: Element) -> None: """ Add an element to the sequence. Overwrites previous values. @@ -841,6 +865,19 @@ def forge( output[pos1]["content"][pos2]["data"][channame]["wfm"] ) = postfilter + # Apply amplitude LUT if present + lut_key = f"channel{channame}_amplitude_LUT" + if lut_key in self._awgspecs.keys(): + lut = self._awgspecs[lut_key] + output[pos1]["content"][pos2]["data"][channame]["wfm"] = ( + applyAmplitudeLUT( + output[pos1]["content"][pos2]["data"][channame][ + "wfm" + ], + lut, + ) + ) + return output def _prepareForOutputting(self) -> list[dict[int, Any]]: @@ -954,6 +991,15 @@ def _prepareForOutputting(self) -> list[dict[int, Any]]: ) elements[pos][chan]["wfm"] = postfilter + # Apply amplitude LUT if present + lut_key = f"channel{chan}_amplitude_LUT" + if lut_key in self._awgspecs.keys(): + lut = self._awgspecs[lut_key] + for pos in range(seqlen): + elements[pos][chan]["wfm"] = applyAmplitudeLUT( + elements[pos][chan]["wfm"], lut + ) + return elements def outputForSEQXFile( diff --git a/tests/test_sequence.py b/tests/test_sequence.py index 5895b6b67..7c46664ac 100644 --- a/tests/test_sequence.py +++ b/tests/test_sequence.py @@ -468,3 +468,242 @@ def test_write_read_sequence(protosequence1, protosequence2, tmp_path): seq.write_to_json(os.path.join(d, "Seq.json")) readbackseq = Sequence.init_from_json(os.path.join(d, "Seq.json")) assert seq == readbackseq + + +################################################## +# Amplitude LUT Tests + + +def test_setAmplitudeLUT(): + """Test the setAmplitudeLUT method and its application in _prepareForOutputting""" + + # Create a simple sequence with a known waveform + SR = 1e6 + + # Create a simple ramp blueprint + bp = bb.BluePrint() + bp.insertSegment(0, ramp, args=(-0.5, 0.5), name="test_ramp", dur=10e-6) + bp.setSR(SR) + + # Create element and sequence + elem = bb.Element() + elem.addBluePrint(1, bp) + + seq = Sequence() + seq.addElement(1, elem) + seq.setSR(SR) + + # Set required channel parameters for _prepareForOutputting + seq.setChannelAmplitude(1, 2.0) # 2V peak-to-peak + seq.setChannelOffset(1, 0.0) # 0V offset + + # Test that LUT is properly stored + lut_input = [-1.0, -0.5, 0.0, 0.5, 1.0] + lut_output = [-0.8, -0.3, 0.0, 0.4, 0.9] # Non-linear mapping + + seq.setAmplitudeLUT(1, lut_input, lut_output) + + # Verify the LUT is stored correctly + expected_lut = {"LUT_input": lut_input, "LUT_output": lut_output} + assert seq._awgspecs["channel1_amplitude_LUT"] == expected_lut + + # Test _prepareForOutputting applies the LUT + elements = seq._prepareForOutputting() + + # Get the original waveform without LUT for comparison + seq_no_lut = Sequence() + seq_no_lut.addElement(1, elem) + seq_no_lut.setSR(SR) + seq_no_lut.setChannelAmplitude(1, 2.0) + seq_no_lut.setChannelOffset(1, 0.0) + + elements_no_lut = seq_no_lut._prepareForOutputting() + + # Verify that the LUT was applied + original_wfm = elements_no_lut[0][1]["wfm"] + lut_applied_wfm = elements[0][1]["wfm"] + + # The waveforms should be different due to LUT application + assert not np.array_equal(original_wfm, lut_applied_wfm) + + # Verify the LUT transformation is correct by checking specific points + # For a ramp from -0.5 to 0.5, we can verify the interpolation + expected_transformed = np.interp(original_wfm, lut_input, lut_output) + assert np.allclose(lut_applied_wfm, expected_transformed) + + # Test edge cases: verify LUT works for boundary values + # The ramp goes from -0.5 to 0.4 (10 points, endpoint not included) + # so we should see the corresponding LUT output values at the boundaries + min_val = np.min(lut_applied_wfm) + max_val = np.max(lut_applied_wfm) + + # Check that the min/max values are approximately what we expect from LUT + # Original ramp: -0.5 to 0.4 -> LUT maps -0.5 to -0.3, and 0.4 to 0.32 + assert np.isclose(min_val, -0.3, rtol=1e-10) + assert np.isclose(max_val, 0.32, rtol=1e-10) + + # Test the forge() method with amplitude LUT + forged_output = seq.forge() + + # Verify structure of forged output + assert 1 in forged_output # Position 1 exists + assert forged_output[1]["type"] == "element" + assert "content" in forged_output[1] + assert 1 in forged_output[1]["content"] # Sub-position 1 exists + assert "data" in forged_output[1]["content"][1] + assert 1 in forged_output[1]["content"][1]["data"] # Channel 1 exists + assert "wfm" in forged_output[1]["content"][1]["data"][1] + + # Extract the LUT-applied waveform from forge() output + forged_wfm = forged_output[1]["content"][1]["data"][1]["wfm"] + + # The forged waveform should be identical to the _prepareForOutputting result + assert np.allclose(forged_wfm, lut_applied_wfm) + + # Verify the LUT was applied correctly in forge() as well + expected_forged = np.interp(original_wfm, lut_input, lut_output) + assert np.allclose(forged_wfm, expected_forged) + + +def test_setAmplitudeLUT_multiple_channels(): + """Test that amplitude LUT works correctly with multiple channels""" + + SR = 1e6 + + # Create blueprints for two channels + bp1 = bb.BluePrint() + bp1.insertSegment(0, ramp, args=(-1.0, 1.0), name="ramp_channel_a", dur=5e-6) + bp1.setSR(SR) + + bp2 = bb.BluePrint() + bp2.insertSegment(0, sine, args=(1e5, 0.5, 0, 0), name="sine_channel_b", dur=5e-6) + bp2.setSR(SR) + + # Create element and sequence + elem = bb.Element() + elem.addBluePrint(1, bp1) + elem.addBluePrint(2, bp2) + + seq = Sequence() + seq.addElement(1, elem) + seq.setSR(SR) + + # Set channel parameters + seq.setChannelAmplitude(1, 2.0) + seq.setChannelOffset(1, 0.0) + seq.setChannelAmplitude(2, 1.0) + seq.setChannelOffset(2, 0.0) + + # Set different LUTs for each channel + lut1_input = [-1.0, 0.0, 1.0] + lut1_output = [-0.5, 0.0, 0.8] # Asymmetric compression + + lut2_input = [-1.0, 0.0, 1.0] + lut2_output = [-1.2, 0.0, 1.2] # Expansion + + seq.setAmplitudeLUT(1, lut1_input, lut1_output) + seq.setAmplitudeLUT(2, lut2_input, lut2_output) + + # Test _prepareForOutputting + elements = seq._prepareForOutputting() + + # Verify both LUTs are applied correctly + wfm1 = elements[0][1]["wfm"] + wfm2 = elements[0][2]["wfm"] + + # Channel 1 should have compressed range + assert np.max(wfm1) <= 0.8 + assert np.min(wfm1) >= -0.5 + + # Channel 2 should have expanded range (beyond original sine amplitude) + # The original sine only goes from 0 to ~0.48, so with expansion LUT it should exceed that + original_max = 0.48 # Approximate max from our sine wave + assert np.max(wfm2) > original_max # Should be expanded + + # Verify LUT is actually applied by checking that values are different from input range + # The LUT should map values according to the interpolation table + assert ( + np.min(wfm2) >= 0.0 + ) # Should still be non-negative since original sine is non-negative + + # Test the forge() method with multiple channel LUTs + forged_output = seq.forge() + + # Verify structure of forged output for multiple channels + assert 1 in forged_output # Position 1 exists + assert forged_output[1]["type"] == "element" + forged_data = forged_output[1]["content"][1]["data"] + assert 1 in forged_data # Channel 1 exists + assert 2 in forged_data # Channel 2 exists + + # Extract the LUT-applied waveforms from forge() output + forged_wfm1 = forged_data[1]["wfm"] + forged_wfm2 = forged_data[2]["wfm"] + + # The forged waveforms should be identical to the _prepareForOutputting results + assert np.allclose(forged_wfm1, wfm1) + assert np.allclose(forged_wfm2, wfm2) + + # Verify the LUTs were applied correctly in forge() as well + # Channel 1 should have compressed range + assert np.max(forged_wfm1) <= 0.8 + assert np.min(forged_wfm1) >= -0.5 + + # Channel 2 should have expanded range + original_max = 0.48 # Approximate max from our sine wave + assert np.max(forged_wfm2) > original_max # Should be expanded + assert np.min(forged_wfm2) >= 0.0 # Should still be non-negative + + +def test_setAmplitudeLUT_no_lut(): + """Test that _prepareForOutputting works correctly when no LUT is set""" + + SR = 1e6 + + bp = bb.BluePrint() + bp.insertSegment(0, ramp, args=(0.0, 1.0), name="test_ramp", dur=5e-6) + bp.setSR(SR) + + elem = bb.Element() + elem.addBluePrint(1, bp) + + seq = Sequence() + seq.addElement(1, elem) + seq.setSR(SR) + seq.setChannelAmplitude(1, 2.0) + seq.setChannelOffset(1, 0.0) + + # Don't set any LUT + elements = seq._prepareForOutputting() + + # Should work without errors + assert len(elements) == 1 + assert 1 in elements[0] + assert "wfm" in elements[0][1] + + # Waveform should be the original ramp + wfm = elements[0][1]["wfm"] + assert np.min(wfm) >= 0.0 + assert np.max(wfm) <= 1.0 + + # Test the forge() method when no LUT is set + forged_output = seq.forge() + + # Verify structure of forged output + assert 1 in forged_output # Position 1 exists + assert forged_output[1]["type"] == "element" + assert "content" in forged_output[1] + assert 1 in forged_output[1]["content"] # Sub-position 1 exists + assert "data" in forged_output[1]["content"][1] + assert 1 in forged_output[1]["content"][1]["data"] # Channel 1 exists + assert "wfm" in forged_output[1]["content"][1]["data"][1] + + # Extract the waveform from forge() output + forged_wfm = forged_output[1]["content"][1]["data"][1]["wfm"] + + # The forged waveform should be identical to the _prepareForOutputting result + assert np.allclose(forged_wfm, wfm) + + # Waveform should still be the original ramp (no LUT applied) + assert np.min(forged_wfm) >= 0.0 + assert np.max(forged_wfm) <= 1.0