training loop speedup

.gitattributes

@@ -1,4 +1,5 @@
 data/**/* filter=lfs diff=lfs merge=lfs -text
+data/*.db filter=lfs diff=lfs merge=lfs -text
 data/*.ini filter=lfs diff=lfs merge=lfs text
 
 ## lfs setup

.gitignore

@@ -1,8 +1,5 @@
 src/**/*.ini
-.data
-
-# VSCode
-.vscode
+.*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

data/optuna_single_core_regen.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:72460af57347d35df91cd76982231bcf538a82fd7f1b8522795202fa298a2dcb
+size 696320

src/single-core-regen/regen.py

@@ -1,6 +1,6 @@
+import copy
 from dataclasses import dataclass
 from datetime import datetime
-import time
 import matplotlib.pyplot as plt
 
 import numpy as np
@@ -9,16 +9,21 @@ import warnings
 
 import torch
 import torch.nn as nn
-import torch.functional as F
+# import torch.nn.functional as F # mse_loss doesn't support complex numbers
 import torch.optim as optim
 import torch.utils.data
 
+from torch.utils.tensorboard import SummaryWriter
+
+from rich.progress import Progress, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn, MofNCompleteColumn
+from rich.console import Console
+
 import multiprocessing
 
 from util.datasets import FiberRegenerationDataset
+from util.complexNN import complex_sse_loss
+from util.optuna_helpers import optional_suggest_categorical, optional_suggest_float, optional_suggest_int
 import util
-
-
 # global settings
 @dataclass
 class GlobalSettings:
@@ -28,12 +33,14 @@ class GlobalSettings:
 # data settings
 @dataclass
 class DataSettings:
-    config_path: str = "data/*-128-16384-10000-0-0-17-0-PAM4-0.ini"
-    symbols_range: tuple = (1, 100)
-    data_size_range: tuple = (1, 20)
+    config_path: str = "data/*-128-16384-1000-0-0-17-0-PAM4-0.ini"
+    dtype: torch.dtype = torch.complex64
+    symbols_range: tuple|float|int = 16
+    data_size_range: tuple|float|int = 32
+    shuffle: bool = True
     target_delay: float = 0
-    xy_delay_range: tuple = (0, 1)
-    drop_first: int = 1000
+    xy_delay_range: tuple|float|int = 0
+    drop_first: int = 10
     train_split: float = 0.8
 
 
@@ -41,41 +48,46 @@ class DataSettings:
 @dataclass
 class PytorchSettings:
     device: str = "cuda"
-    batchsize: int = 128
-    epochs: int = 100
+    batchsize: int = 1024
+    epochs: int = 10
+    summary_dir: str = ".runs"
 
 
 # model settings
 @dataclass
 class ModelSettings:
     output_size: int = 2
-    n_layer_range: tuple = (1, 3)
-    n_units_range: tuple = (4, 128)
-    activation_func_range: tuple = ("ReLU",)
+    n_layer_range: tuple|float|int = (2,8)
+    n_units_range: tuple|float|int = (2,32)
+    # activation_func_range: tuple = ("ReLU",)
 
 
 @dataclass
 class OptimizerSettings:
-    optimizer_range: tuple = ("Adam", "RMSprop", "SGD")
-    lr_range: tuple = (1e-5, 1e-1)
+    # optimizer_range: tuple|str = ("Adam", "RMSprop", "SGD")
+    optimizer_range: tuple|str = "RMSprop"
+    # lr_range: tuple|float = (1e-5, 1e-1)
+    lr_range: tuple|float = 2e-5
 
 
 # optuna settings
 @dataclass
 class OptunaSettings:
     n_trials: int = 128
-    n_threads: int = 16
+    n_threads: int = 8
     timeout: int = 600
-    directions: tuple = ("maximize",)
+    directions: tuple = ("minimize",)
+    metrics_names: tuple = ("sse",)
 
     limit_examples: bool = True
-    n_train_examples: int = PytorchSettings.batchsize * 30
-    n_valid_examples: int = PytorchSettings.batchsize * 10
+    n_train_examples: int = PytorchSettings.batchsize * 50
+    # n_valid_examples: int = PytorchSettings.batchsize * 100
+    n_valid_examples: int = float("inf")
     storage: str = "sqlite:///optuna_single_core_regen.db"
     study_name: str = (
-        f"single_core_regen_{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
+        f"single_core_regen_{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}"
     )
-    metrics_names: tuple = ("accuracy",)
+
 
 class HyperTraining:
     def __init__(self):
@@ -86,9 +98,43 @@ class HyperTraining:
         self.optimizer_settings = OptimizerSettings()
         self.optuna_settings = OptunaSettings()
 
+        self.console = Console()
+
         # set some extra settings to make the code more readable
         self._extra_optuna_settings()
+
+    def setup_tb_writer(self, study_name=None, append=None):
+        log_dir = self.pytorch_settings.summary_dir + "/" + (study_name or self.optuna_settings.study_name)
+        if append is not None:
+            log_dir += "_" + str(append)
+
+        return SummaryWriter(log_dir)
+
+    def resume_latest_study(self, verbose=True):
+        study_name = hyper_training.get_latest_study()
     
+        if study_name:
+            print(f"Resuming study: {study_name}")
+            self.optuna_settings.study_name = study_name
+
+    def get_latest_study(self, verbose=True):
+        studies = self.get_studies()
+        for study in studies:
+            study.datetime_start = study.datetime_start or datetime.min
+        if studies:
+            study = sorted(studies, key = lambda x: x.datetime_start, reverse=True)[0]
+            if verbose:
+                print(f"Last study: {study.study_name}")
+            study_name = study.study_name
+        else:
+            if verbose:
+                print("No previous studies found")
+            study_name = None
+        return study_name
+
+    def get_studies(self):
+        return optuna.get_all_study_summaries(storage=self.optuna_settings.storage)
+
     def setup_study(self):
         self.study = optuna.create_study(
             study_name=self.optuna_settings.study_name,
@@ -100,29 +146,49 @@ class HyperTraining:
 
         with warnings.catch_warnings(action="ignore"):
             self.study.set_metric_names(self.optuna_settings.metrics_names)
-        
-        self.n_threads = min(self.optuna_settings.n_trials, self.optuna_settings.n_threads)
+
+        self.n_threads = min(
+            self.optuna_settings.n_trials, self.optuna_settings.n_threads
+        )
         self.processes = []
-        for _ in range(self.n_threads):
-            p = multiprocessing.Process(target=self._run_optimize)
-            self.processes.append(p)
-    
+        if self.n_threads > 1:
+            for _ in range(self.n_threads):
+                p = multiprocessing.Process(
+                    # target=lambda n_trials: self._run_optimize(self, n_trials),
+                    target = self._run_optimize,
+                    args = (self.optuna_settings.n_trials // self.n_threads,),
+                )
+                self.processes.append(p)
+
     def run_study(self):
-        for p in self.processes:
-            p.start()
-        
-        for p in self.processes:
-            p.join()
-        
-        remaining_trials = self.optuna_settings.n_trials - self.optuna_settings.n_trials % self.optuna_settings.n_threads
+        if self.processes:
+            for p in self.processes:
+                p.start()
+            for p in self.processes:
+                p.join()
+
+            remaining_trials = (
+                self.optuna_settings.n_trials
+                - self.optuna_settings.n_trials % self.optuna_settings.n_threads
+            )
+        else:
+            remaining_trials = self.optuna_settings.n_trials
+
         if remaining_trials:
             self._run_optimize(remaining_trials)
-    
-    def _run_optimize(self, n_trials):
-        self.study.optimize(self.objective, n_trials=n_trials, timeout=self.optuna_settings.timeout)
 
-    def eye(self, show=True):
-        util.plot.eye(self.data_settings.config_path, show=show)
+    def _run_optimize(self, n_trials):
+        self.study.optimize(
+            self.objective, n_trials=n_trials, timeout=self.optuna_settings.timeout
+        )
+
+    def plot_eye(self, show=True):
+        if not hasattr(self, "eye_data"):
+            data, config = util.datasets.load_data(
+                self.data_settings.config_path, skipfirst=10, symbols=1000
+            )
+            self.eye_data = {"data": data, "sps": int(config["glova"]["sps"])}
+        return util.plot.eye(**self.eye_data, show=show)
 
     def _extra_optuna_settings(self):
         self.optuna_settings.multi_objective = len(self.optuna_settings.directions) > 1
@@ -143,145 +209,256 @@ class HyperTraining:
             else float("inf")
         )
 
-    def define_model(self, trial: optuna.Trial):
-        n_layers = trial.suggest_int(
-            "model_n_layers", *self.model_settings.n_layer_range
+    def define_model(self, trial: optuna.Trial, writer=None):
+        n_layers = optional_suggest_int(trial, "model_n_layers", self.model_settings.n_layer_range)
+
+        in_features = 2 * trial.params.get(
+            "dataset_data_size",
+            optional_suggest_int(trial, "dataset_data_size", self.data_settings.data_size_range),
         )
+        trial.set_user_attr("input_dim", in_features)
+
         layers = []
-
-        # REVIEW does that work?
-        in_features = trial.params["dataset_data_size"] * 2
         for i in range(n_layers):
-            out_features = trial.suggest_int(
-                f"model_n_units_l{i}", *self.model_settings.n_units_range
-            )
-            activation_func = trial.suggest_categorical(
-                f"model_activation_func_l{i}", self.model_settings.activation_func_range
-            )
-
-            layers.append(nn.Linear(in_features, out_features))
-            layers.append(getattr(nn, activation_func))
+            out_features = optional_suggest_int(trial, f"model_n_units_l{i}", self.model_settings.n_units_range, log=True)
+            
+            layers.append(nn.Linear(in_features, out_features, dtype=self.data_settings.dtype))
+            # layers.append(getattr(nn, activation_func)())
             in_features = out_features
 
-        layers.append(nn.Linear(in_features, self.model_settings.output_size))
+        layers.append(nn.Linear(in_features, self.model_settings.output_size, dtype=self.data_settings.dtype))
+        
+        if writer is not None:
+            writer.add_graph(nn.Sequential(*layers), torch.zeros(1, trial.user_attrs["input_dim"], dtype=self.data_settings.dtype))
 
         return nn.Sequential(*layers)
 
     def get_sliced_data(self, trial: optuna.Trial):
-        assert ModelSettings.input_size % 2 == 0, "input_dim must be even"
-        symbols = trial.suggest_float(
-            "dataset_symbols", *self.data_settings.symbols_range, log=True
-        )
-        xy_delay = trial.suggest_float(
-            "dataset_xy_delay", *self.data_settings.xy_delay_range
-        )
-        data_size = trial.suggest_int(
-            "dataset_data_size", *self.data_settings.data_size_range
+        symbols = optional_suggest_float(trial, "dataset_symbols", self.data_settings.symbols_range)
+
+        xy_delay = optional_suggest_float(trial, "dataset_xy_delay", self.data_settings.xy_delay_range)
+
+        data_size = trial.params.get(
+            "dataset_data_size",
+            optional_suggest_int(trial, "dataset_data_size", self.data_settings.data_size_range)
         )
+
         # get dataset
         dataset = FiberRegenerationDataset(
             file_path=self.data_settings.config_path,
             symbols=symbols,
-            data_size=data_size,  # two channels (x,y)
+            data_size=data_size,
             target_delay=self.data_settings.target_delay,
             xy_delay=xy_delay,
             drop_first=self.data_settings.drop_first,
+            dtype=self.data_settings.dtype,
         )
 
         dataset_size = len(dataset)
         indices = list(range(dataset_size))
         split = int(np.floor(self.data_settings.train_split * dataset_size))
-        np.random.seed(self.global_settings.seed)
-        np.random.shuffle(indices)
+        if self.data_settings.shuffle:
+            np.random.seed(self.global_settings.seed)
+            np.random.shuffle(indices)
+        
         train_indices, valid_indices = indices[:split], indices[split:]
 
         train_sampler = torch.utils.data.SubsetRandomSampler(train_indices)
         valid_sampler = torch.utils.data.SubsetRandomSampler(valid_indices)
 
         train_loader = torch.utils.data.DataLoader(
-            dataset, batch_size=self.pytorch_settings.batchsize, sampler=train_sampler
+            dataset, batch_size=self.pytorch_settings.batchsize, sampler=train_sampler, drop_last=True
         )
         valid_loader = torch.utils.data.DataLoader(
-            dataset, batch_size=self.pytorch_settings.batchsize, sampler=valid_sampler
+            dataset, batch_size=self.pytorch_settings.batchsize, sampler=valid_sampler, drop_last=True
         )
 
         return train_loader, valid_loader
 
+    def train_model(self, model, optimizer, train_loader, epoch, writer=None, enable_progress=True):
+        if enable_progress:
+            progress = Progress(
+                TextColumn("[yellow]  Training..."),
+                TextColumn(" Loss: {task.description}"),
+                BarColumn(),
+                TaskProgressColumn(),
+                TextColumn("[green]Batch"),
+                MofNCompleteColumn(),
+                TimeRemainingColumn(),
+                # description="Training",
+                transient=False,
+                console=self.console,
+                refresh_per_second=10,
+            )
+            task = progress.add_task("-.---e--", total=len(train_loader))
 
-    def train_model(self, model, optimizer, train_loader):
+        running_loss = 0.0
+        last_loss = 0.0
         model.train()
-        for batch_idx, (data, target) in enumerate(train_loader):
-            if (batch_idx * train_loader.batchsize
-                >= self.optuna_settings.n_train_examples):
+        for batch_idx, (x, y) in enumerate(train_loader):
+            if (
+                batch_idx * train_loader.batch_size
+                >= self.optuna_settings.n_train_examples
+            ):
                 break
             optimizer.zero_grad()
-            data, target = (
-                data.to(self.pytorch_settings.device),
-                target.to(self.pytorch_settings.device),
+            x, y = (
+                x.to(self.pytorch_settings.device),
+                y.to(self.pytorch_settings.device),
             )
-            target_pred = model(data)
-            loss = F.mean_squared_error(target_pred, target)
+            y_pred = model(x)
+            loss = complex_sse_loss(y_pred, y)
             loss.backward()
             optimizer.step()
 
+            # clamp weights to keep energy bounded
+            for p in model.parameters():
+                p.data.clamp_(-1.0, 1.0)
 
-    def eval_model(self, model, valid_loader):
+            last_loss = loss.item()
+
+            if enable_progress:
+                progress.update(task, advance=1, description=f"{last_loss:.3e}")
+
+            running_loss += loss.item()
+            if writer is not None:
+                if batch_idx % 10 == 0:
+                    writer.add_scalar("training loss", running_loss/10, epoch*min(len(train_loader), self.optuna_settings.n_train_examples/train_loader.batch_size) + batch_idx)
+                    running_loss = 0.0
+    
+        if enable_progress:
+            progress.update(task, description=f"{last_loss:.3e}")
+            progress.stop()
+
+
+    def eval_model(self, model, valid_loader, epoch, writer=None, enable_progress=True):
+        if enable_progress:
+            progress = Progress(
+                TextColumn("[green]Evaluating..."),
+                TextColumn("Error: {task.description}"),
+                BarColumn(),
+                TaskProgressColumn(),
+                TextColumn("[green]Batch"),
+                MofNCompleteColumn(),
+                TimeRemainingColumn(),
+                # description="Training",
+                transient=False,
+                console=self.console,
+                refresh_per_second=10,
+            )
+            task = progress.add_task("-.---e--", total=len(valid_loader))
+        
         model.eval()
-        correct = 0
+        running_error = 0
+        running_error_2 = 0
         with torch.no_grad():
-            for batch_idx, (data, target) in enumerate(valid_loader):
+            for batch_idx, (x, y) in enumerate(valid_loader):
                 if (
-                    batch_idx * valid_loader.batchsize
+                    batch_idx * valid_loader.batch_size
                     >= self.optuna_settings.n_valid_examples
                 ):
                     break
-                data, target = (
-                    data.to(self.pytorch_settings.device),
-                    target.to(self.pytorch_settings.device),
+                x, y = (
+                    x.to(self.pytorch_settings.device),
+                    y.to(self.pytorch_settings.device),
                 )
-                target_pred = model(data)
-                pred = target_pred.argmax(dim=1, keepdim=True)
-                correct += pred.eq(target.view_as(pred)).sum().item()
+                y_pred = model(x)
+                error = complex_sse_loss(y_pred, y)
+                running_error += error.item()
+                running_error_2 += error.item()
 
-        accuracy = correct / len(valid_loader.dataset)
-        # num_params = sum(p.numel() for p in model.parameters())
-        return accuracy
+                if enable_progress:
+                    progress.update(task, advance=1, description=f"{error.item():.3e}")
+
+                if writer is not None:
+                    if batch_idx % 10 == 0:
+                        writer.add_scalar("sse", running_error_2/10, epoch*min(len(valid_loader), self.optuna_settings.n_valid_examples/valid_loader.batch_size) + batch_idx)
+                        running_error_2 = 0.0
+        
+        running_error /= batch_idx + 1
+
+        if enable_progress:
+            progress.update(task, description=f"{running_error:.3e}")
+            progress.stop()
+
+        return running_error
+    
+    def run_model(self, model, loader):
+        model.eval()
+        y_preds = []
+        with torch.no_grad():
+            for x, y in loader:
+                x, y = (
+                    x.to(self.pytorch_settings.device),
+                    y.to(self.pytorch_settings.device),
+                )
+                y_preds.append(model(x))
+        return torch.stack(y_preds)
+        
 
     def objective(self, trial: optuna.Trial):
-        model = self.define_model(trial).to(self.pytorch_settings.device)
-
-        optimizer_name = trial.suggest_categorical(
-            "optimizer", self.optimizer_settings.optimizer_range
-        )
-        lr = trial.suggest_float("lr", *self.optimizer_settings.lr_range, log=True)
-        optimizer = getattr(optim, optimizer_name)(model.parameters(), lr=lr)
-
+        writer = self.setup_tb_writer(self.optuna_settings.study_name, f"{trial.number:0>len(str(self.optuna_settings.n_trials))}")
         train_loader, valid_loader = self.get_sliced_data(trial)
 
-        for epoch in range(self.pytorch_settings.epochs):
-            self.train_model(model, optimizer, train_loader)
-            accuracy = self.eval_model(model, valid_loader)
+        model = self.define_model(trial, writer).to(self.pytorch_settings.device)
 
-            if len(self.optuna_settings.directions) == 1:
-                trial.report(accuracy, epoch)
+        optimizer_name = optional_suggest_categorical(trial, "optimizer", self.optimizer_settings.optimizer_range)
+
+        lr = optional_suggest_float(trial, "lr", self.optimizer_settings.lr_range, log=True)
+
+        optimizer = getattr(optim, optimizer_name)(model.parameters(), lr=lr)
+
+        for epoch in range(self.pytorch_settings.epochs):
+            enable_progress = self.optuna_settings.n_threads == 1
+            if enable_progress:
+                print(f"Epoch {epoch+1}/{self.pytorch_settings.epochs}")
+            self.train_model(model, optimizer, train_loader, epoch, writer, enable_progress=enable_progress)
+            sse = self.eval_model(model, valid_loader, epoch, writer, enable_progress=enable_progress)
+
+            if not self.optuna_settings.multi_objective:
+                trial.report(sse, epoch)
                 if trial.should_prune():
                     raise optuna.exceptions.TrialPruned()
 
-        return accuracy
+        writer.close()
+
+        return sse
+    
+
 
 
 if __name__ == "__main__":
-    # plt.ion()
     hyper_training = HyperTraining()
-    hyper_training.eye()
 
-    # hyper_training.setup_study()
-    # hyper_training.run_study()
-    for i in range(10):
-        #simulate some work
-        print(i)
-        time.sleep(0.2)
+    # hyper_training.resume_latest_study()
+        
+    hyper_training.setup_study()
+    hyper_training.run_study()
 
+    best_model = hyper_training.define_model(hyper_training.study.best_trial).to(hyper_training.pytorch_settings.device)
+    data_settings_backup = copy.copy(hyper_training.data_settings)
+    hyper_training.data_settings.shuffle = False
+    hyper_training.data_settings.train_split = 0.01
+    plot_loader, _ = hyper_training.get_sliced_data(hyper_training.study.best_trial)
+    
+    regen = hyper_training.run_model(best_model, plot_loader)
+    regen = regen.view(-1, 2)
+    # [batch_no, batch_size, 2] -> [no, 2]
+
+    original, _ = util.datasets.load_data(hyper_training.data_settings.config_path, skipfirst=hyper_training.data_settings.drop_first)
+    original = original[:len(regen)]
+    
+    regen = regen.cpu().numpy()
+    _, axs = plt.subplots(2)
+    for i, ax in enumerate(axs):
+        ax.plot(np.abs(original[:, i])**2, label="original")
+        ax.plot(np.abs(regen[:, i])**2, label="regen")
+        ax.legend()
     plt.show()
-    ...
+    
 
+    print(f"Best model: {best_model}")
+
+
+    # eye_fig = hyper_training.plot_eye()
+    ...

src/single-core-regen/regen_no_hyper.py

@@ -0,0 +1,429 @@
+import copy
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+import matplotlib.pyplot as plt
+
+import numpy as np
+
+import torch
+import torch.nn as nn
+
+# import torch.nn.functional as F # mse_loss doesn't support complex numbers
+import torch.optim as optim
+import torch.utils.data
+
+from torch.utils.tensorboard import SummaryWriter
+
+from rich.progress import (
+    Progress,
+    TextColumn,
+    BarColumn,
+    TaskProgressColumn,
+    TimeRemainingColumn,
+    MofNCompleteColumn,
+    TimeElapsedColumn,
+)
+from rich.console import Console
+from rich import print as rprint
+
+# from util.optuna_helpers import optional_suggest_categorical, optional_suggest_float, optional_suggest_int
+import util
+
+
+# global settings
+@dataclass
+class GlobalSettings:
+    seed: int = 42
+
+
+# data settings
+@dataclass
+class DataSettings:
+    config_path: str = "data/*-128-16384-100000-0-0-17-0-PAM4-0.ini"
+    dtype: torch.dtype = torch.complex64
+    symbols_range: float | int = 8
+    data_size_range: float | int = 64
+    shuffle: bool = True
+    target_delay: float = 0
+    xy_delay_range: float | int = 0
+    drop_first: int = 10
+    train_split: float = 0.8
+
+
+# pytorch settings
+@dataclass
+class PytorchSettings:
+    epochs: int = 1000
+    batchsize: int = 2**12
+    device: str = "cuda"
+    summary_dir: str = ".runs"
+    model_dir: str = ".models"
+
+
+# model settings
+@dataclass
+class ModelSettings:
+    output_size: int = 2
+    # n_layer_range: float|int = 2
+    # n_units_range: float|int = 32
+    n_layers: int = 3
+    n_units: int = 32
+    activation_func: tuple | str = "ModReLU"
+
+
+@dataclass
+class OptimizerSettings:
+    optimizer_range: str = "Adam"
+    lr_range: float = 2e-3
+
+
+class Training:
+    def __init__(self):
+        self.global_settings = GlobalSettings()
+        self.data_settings = DataSettings()
+        self.pytorch_settings = PytorchSettings()
+        self.model_settings = ModelSettings()
+        self.optimizer_settings = OptimizerSettings()
+        self.study_name = (
+            f"single_core_regen_{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}"
+        )
+
+        if not hasattr(self.pytorch_settings, "model_dir"):
+            self.pytorch_settings.model_dir = ".models"
+
+        self.writer = None
+        self.console = Console()
+
+    def setup_tb_writer(self, study_name=None):
+        log_dir = (
+            self.pytorch_settings.summary_dir + "/" + (study_name or self.study_name)
+        )
+        self.writer = SummaryWriter(log_dir)
+
+    def plot_eye(self, width=2, symbols=None, alpha=None, complex=False, show=True):
+        if not hasattr(self, "eye_data"):
+            data, config = util.datasets.load_data(
+                self.data_settings.config_path,
+                skipfirst=10,
+                symbols=symbols or 1000,
+                real=not self.data_settings.dtype.is_complex,
+                normalize=True,
+            )
+            self.eye_data = {"data": data, "sps": int(config["glova"]["sps"])}
+        return util.plot.eye(
+            **self.eye_data,
+            width=width,
+            show=show,
+            alpha=alpha,
+            complex=complex,
+            symbols=symbols or 1000,
+            skipfirst=0,
+        )
+
+    def define_model(self):
+        n_layers = self.model_settings.n_layers
+
+        in_features = 2 * self.data_settings.data_size_range
+
+        layers = []
+        for i in range(n_layers):
+            out_features = self.model_settings.n_units
+
+            layers.append(util.complexNN.UnitaryLayer(in_features, out_features))
+            # layers.append(getattr(nn, self.model_settings.activation_func)())
+            layers.append(
+                getattr(util.complexNN, self.model_settings.activation_func)()
+            )
+            in_features = out_features
+
+        layers.append(
+            util.complexNN.UnitaryLayer(in_features, self.model_settings.output_size)
+        )
+
+        if self.writer is not None:
+            self.writer.add_graph(
+                nn.Sequential(*layers),
+                torch.zeros(1, layers[0].in_features, dtype=self.data_settings.dtype),
+            )
+
+        return nn.Sequential(*layers)
+
+    def get_sliced_data(self):
+        symbols = self.data_settings.symbols_range
+
+        xy_delay = self.data_settings.xy_delay_range
+
+        data_size = self.data_settings.data_size_range
+
+        # get dataset
+        dataset = util.datasets.FiberRegenerationDataset(
+            file_path=self.data_settings.config_path,
+            symbols=symbols,
+            data_size=data_size,
+            target_delay=self.data_settings.target_delay,
+            xy_delay=xy_delay,
+            drop_first=self.data_settings.drop_first,
+            dtype=self.data_settings.dtype,
+            real=not self.data_settings.dtype.is_complex,
+            # device=self.pytorch_settings.device,
+        )
+
+        dataset_size = len(dataset)
+        indices = list(range(dataset_size))
+        split = int(np.floor(self.data_settings.train_split * dataset_size))
+        if self.data_settings.shuffle:
+            np.random.seed(self.global_settings.seed)
+            np.random.shuffle(indices)
+
+        train_indices, valid_indices = indices[:split], indices[split:]
+
+        if self.data_settings.shuffle:
+            train_sampler = torch.utils.data.SubsetRandomSampler(train_indices)
+            valid_sampler = torch.utils.data.SubsetRandomSampler(valid_indices)
+        else:
+            train_sampler = train_indices
+            valid_sampler = valid_indices
+
+
+        train_loader = torch.utils.data.DataLoader(
+            dataset,
+            batch_size=self.pytorch_settings.batchsize,
+            sampler=train_sampler,
+            drop_last=True,
+            pin_memory=True,
+            num_workers=24,
+            prefetch_factor=4,
+            # persistent_workers=True
+        )
+        valid_loader = torch.utils.data.DataLoader(
+            dataset,
+            batch_size=self.pytorch_settings.batchsize,
+            sampler=valid_sampler,
+            drop_last=True,
+            pin_memory=True,
+            num_workers=24,
+            prefetch_factor=4,
+            # persistent_workers=True
+        )
+
+        return train_loader, valid_loader
+
+    def train_model(self, model, optimizer, train_loader, epoch):
+        with Progress(
+            TextColumn("[yellow]  Training..."),
+            TextColumn("Loss: {task.description}"),
+            BarColumn(),
+            TaskProgressColumn(),
+            TextColumn("[green]Batch"),
+            MofNCompleteColumn(),
+            TimeRemainingColumn(),
+            TimeElapsedColumn(),
+            # description="Training",
+            transient=False,
+            console=self.console,
+            refresh_per_second=10,
+        ) as progress:
+            task = progress.add_task("-.---e--", total=len(train_loader))
+
+            running_loss = 0.0
+            model.train()
+            for batch_idx, (x, y) in enumerate(train_loader):
+                model.zero_grad(set_to_none=True)
+                x, y = (
+                    x.to(self.pytorch_settings.device),
+                    y.to(self.pytorch_settings.device),
+                )
+                y_pred = model(x)
+                loss = util.complexNN.complex_mse_loss(y_pred, y)
+                loss.backward()
+                optimizer.step()
+
+                progress.update(task, advance=1, description=f"{loss.item():.3e}")
+
+                running_loss += loss.item()
+                if self.writer is not None:
+                    if (batch_idx + 1) % 10 == 0:
+                        self.writer.add_scalar(
+                            "training loss",
+                            running_loss / 10,
+                            epoch * len(train_loader) + batch_idx,
+                        )
+                        running_loss = 0.0
+
+        return running_loss
+
+    def eval_model(self, model, valid_loader, epoch):
+        with Progress(
+            TextColumn("[green]Evaluating..."),
+            TextColumn("Loss: {task.description}"),
+            BarColumn(),
+            TaskProgressColumn(),
+            TextColumn("[green]Batch"),
+            MofNCompleteColumn(),
+            TimeRemainingColumn(),
+            TimeElapsedColumn(),
+            # description="Training",
+            transient=False,
+            console=self.console,
+            refresh_per_second=10,
+        ) as progress:
+            task = progress.add_task("-.---e--", total=len(valid_loader))
+
+            model.eval()
+            running_loss = 0
+            running_loss2 = 0
+            with torch.no_grad():
+                for batch_idx, (x, y) in enumerate(valid_loader):
+                    x, y = (
+                        x.to(self.pytorch_settings.device),
+                        y.to(self.pytorch_settings.device),
+                    )
+                    y_pred = model(x)
+                    loss = util.complexNN.complex_mse_loss(y_pred, y)
+                    running_loss += loss.item()
+                    running_loss2 += loss.item()
+
+                    progress.update(task, advance=1, description=f"{loss.item():.3e}")
+                    if self.writer is not None:
+                        if (batch_idx + 1) % 10 == 0:
+                            self.writer.add_scalar(
+                                "loss",
+                                running_loss / 10,
+                                epoch * len(valid_loader) + batch_idx,
+                            )
+                            running_loss = 0.0
+            
+            if self.writer is not None:
+                self.writer.add_figure("fiber response", self.plot_model_response(model, plot=False), epoch+1)
+
+        return running_loss2 / len(valid_loader)
+
+    def run_model(self, model, loader):
+        model.eval()
+        xs = []
+        ys = []
+        y_preds = []
+        with torch.no_grad():
+            model = model.to(self.pytorch_settings.device)
+            for x, y in loader:
+                x, y = (
+                    x.to(self.pytorch_settings.device),
+                    y.to(self.pytorch_settings.device),
+                )
+                y_pred = model(x).cpu()
+                # x = x.cpu()
+                # y = y.cpu()
+                y_pred = y_pred.view(y_pred.shape[0], -1, 2)
+                y = y.view(y.shape[0], -1, 2)
+                x = x.view(x.shape[0], -1, 2)
+                xs.append(x[:, 0, :].squeeze())
+                ys.append(y.squeeze())
+                y_preds.append(y_pred.squeeze())
+
+        xs = torch.vstack(xs).cpu()
+        ys = torch.vstack(ys).cpu()
+        y_preds = torch.vstack(y_preds).cpu()
+        return ys, xs, y_preds
+    
+    def dummy_model(self, loader):
+        xs = []
+        ys = []
+        for x, y in loader:
+            y = y.cpu().view(y.shape[0], -1, 2)
+            x = x.cpu().view(x.shape[0], -1, 2)
+            xs.append(x[:, 0, :].squeeze())
+            ys.append(y.squeeze())
+        xs = torch.vstack(xs)
+        ys = torch.vstack(ys)
+        return xs, ys
+
+    def objective(self, save=False, plot_before=False):
+        try:
+            rprint(*list(self.study_name.split("_")))
+
+            self.model = self.define_model().to(self.pytorch_settings.device)
+
+            if self.writer is not None:
+                self.writer.add_figure("fiber response", self.plot_model_response(plot=plot_before), 0)
+
+            train_loader, valid_loader = self.get_sliced_data()
+
+            optimizer_name = self.optimizer_settings.optimizer_range
+
+            lr = self.optimizer_settings.lr_range
+
+            optimizer = getattr(optim, optimizer_name)(self.model.parameters(), lr=lr)
+
+            for epoch in range(self.pytorch_settings.epochs):
+                self.console.rule(f"Epoch {epoch + 1}/{self.pytorch_settings.epochs}")
+                self.train_model(self.model, optimizer, train_loader, epoch)
+                eval_loss = self.eval_model(self.model, valid_loader, epoch)
+
+            if save:
+                save_path = (
+                    Path(self.pytorch_settings.model_dir) / f"{self.study_name}.pth"
+                )
+                save_path.parent.mkdir(parents=True, exist_ok=True)
+                torch.save(self.model, save_path)
+
+            return eval_loss
+        except KeyboardInterrupt:
+            pass
+        finally:
+            if hasattr(self, "model"):
+                except_save_path = Path(".models/exception") / f"{self.study_name}.pth"
+                except_save_path.parent.mkdir(parents=True, exist_ok=True)
+                torch.save(self.model, except_save_path)
+
+    def _plot_model_response_plotter(self, fiber_in, fiber_out, regen, plot=True):
+        fig, axs = plt.subplots(2)
+        for i, ax in enumerate(axs):
+            ax.plot(np.abs(fiber_in[:, i]) ** 2, label="fiber in")
+            ax.plot(np.abs(fiber_out[:, i]) ** 2, label="fiber out")
+            ax.plot(np.abs(regen[:, i]) ** 2, label="regenerated")
+            ax.legend()
+        if plot:
+            plt.show()
+        return fig
+
+    def plot_model_response(self, model=None, plot=True):
+        data_settings_backup = copy.copy(self.data_settings)
+        self.data_settings.shuffle = False
+        self.data_settings.train_split = 0.01
+        self.data_settings.drop_first = 100
+        plot_loader, _ = self.get_sliced_data()
+        self.data_settings = data_settings_backup
+
+        fiber_in, fiber_out, regen = self.run_model(model or self.model, plot_loader)
+        fiber_in = fiber_in.view(-1, 2)
+        fiber_out = fiber_out.view(-1, 2)
+        regen = regen.view(-1, 2)
+
+        fiber_in = fiber_in.numpy()
+        fiber_out = fiber_out.numpy()
+        regen = regen.numpy()
+
+        # https://github.com/matplotlib/matplotlib/issues/27713#issue-2104110987
+        # https://github.com/matplotlib/matplotlib/issues/27713#issuecomment-1915497463
+        import gc
+        fig = self._plot_model_response_plotter(fiber_in, fiber_out, regen, plot=plot)
+        gc.collect()
+        
+        return fig
+
+if __name__ == "__main__":
+    trainer = Training()
+
+    # trainer.plot_eye()
+    trainer.setup_tb_writer()
+    trainer.objective(save=True)
+
+    best_model = trainer.model
+
+    # best_model = trainer.define_model(trainer.study.best_trial).to(trainer.pytorch_settings.device)
+    trainer.plot_model_response(best_model)
+
+    # print(f"Best model: {best_model}")
+
+    ...

src/single-core-regen/util/__init__.py

@@ -1,2 +1,17 @@
-from .datasets import FiberRegenerationDataset  # noqa: F401
-from .plot import eye # noqa: F401
+from . import datasets # noqa: F401
+# from .datasets import FiberRegenerationDataset  # noqa: F401
+# from .datasets import load_data  # noqa: F401
+
+
+from . import plot # noqa: F401
+# from .plot import eye # noqa: F401
+
+from . import optuna_helpers # noqa: F401
+# from .optuna_helpers import optional_suggest_categorical  # noqa: F401
+# from .optuna_helpers import optional_suggest_float  # noqa: F401
+# from .optuna_helpers import optional_suggest_int  # noqa: F401
+
+from . import complexNN # noqa: F401
+# from .complexNN import UnitaryLayer  # noqa: F401
+# from .complexNN import complex_mse_loss  # noqa: F401
+# from .complexNN import complex_sse_loss  # noqa: F401

src/single-core-regen/util/complexNN.py

@@ -0,0 +1,141 @@
+import torch
+import torch.nn as nn
+
+def complex_mse_loss(input, target):
+    """
+    Compute the mean squared error between two complex tensors.
+    """
+    return torch.mean(torch.square(input.real - target.real) + torch.square(input.imag - target.imag))
+
+def complex_sse_loss(input, target):
+    """
+    Compute the sum squared error between two complex tensors.
+    """
+    if input.is_complex():
+        return torch.sum(torch.square(input.real - target.real) + torch.square(input.imag - target.imag))
+    else:
+        return torch.sum(torch.square(input - target))
+
+
+
+
+class UnitaryLayer(nn.Module):
+    def __init__(self, in_features, out_features):
+        super(UnitaryLayer, self).__init__()
+        assert in_features >= out_features
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight = nn.Parameter(torch.randn(in_features, out_features, dtype=torch.cfloat))
+        self.reset_parameters()
+    
+    def reset_parameters(self):
+        q, _ = torch.linalg.qr(self.weight)
+        self.weight.data = q
+
+    @staticmethod
+    @torch.jit.script
+    def _unitary_forward(x, weight):
+        out = torch.matmul(x, weight)
+        return out
+
+    def forward(self, x):
+        return self._unitary_forward(x, self.weight)
+    
+
+#### as defined by zhang et al
+
+class Identity(nn.Module):
+    """
+    implements the "activation" function 
+    M(z) = z
+    """
+    def __init__(self):
+        super(Identity, self).__init__()
+
+    def forward(self, x):
+        return x
+
+class Mag(nn.Module):
+    """
+    implements the activation function 
+    M(z) = ||z||
+    """
+    def __init__(self):
+        super(Mag, self).__init__()
+
+    @torch.jit.script
+    def forward(self, x):
+        return torch.abs(x.real**2 + x.imag**2)
+    
+# class Tanh(nn.Module):
+#     """
+#     implements the activation function
+#     M(z) = tanh(z) = sinh(z)/cosh(z) = (exp(z)-exp(-z))/(exp(z)+exp(-z)) = (exp(2*z)-1)/(exp(2*z)+1)
+#     """
+#     def __init__(self):
+#         super(Tanh, self).__init__()
+
+#     def forward(self, x):
+#         return torch.tanh(x)
+    
+class ModReLU(nn.Module):
+    """
+    implements the activation function 
+    M(z) = ReLU(||z|| + b)*exp(j*theta_z)
+         = ReLU(||z|| + b)*z/||z||
+    """
+    def __init__(self, b=0):
+        super(ModReLU, self).__init__()
+        self.b = b
+        self.relu = nn.ReLU()
+
+    @staticmethod
+    # @torch.jit.script
+    def _mod_relu(x, b):
+        mod = torch.abs(x.real**2 + x.imag**2)
+        return torch.relu(mod + b) * x / mod
+
+    def forward(self, x):
+        return self._mod_relu(x, self.b)
+    
+class CReLU(nn.Module):
+    """
+    implements the activation function
+    M(z) = ReLU(Re(z)) + j*ReLU(Im(z))
+    """
+    def __init__(self):
+        super(CReLU, self).__init__()
+        self.relu = nn.ReLU()
+
+    @torch.jit.script
+    def forward(self, x):
+        return torch.relu(x.real) + 1j*torch.relu(x.imag)
+    
+class ZReLU(nn.Module):
+    """
+    implements the activation function
+
+    M(z) = z if 0 <= angle(z) <= pi/2
+         = 0 otherwise
+    """
+
+    def __init__(self):
+        super(ZReLU, self).__init__()
+
+    @torch.jit.script
+    def forward(self, x):
+        return x * (torch.angle(x) >= 0) * (torch.angle(x) <= torch.pi/2)
+    
+# class ComplexFeedForwardNN(nn.Module):
+#     def __init__(self, in_features, hidden_features, out_features):
+#         super(ComplexFeedForwardNN, self).__init__()
+#         self.in_features = in_features
+#         self.hidden_features = hidden_features
+#         self.out_features = out_features
+#         self.fc1 = UnitaryLayer(in_features, hidden_features)
+#         self.fc2 = UnitaryLayer(hidden_features, out_features)
+
+#     def forward(self, x):
+#         x = self.fc1(x)
+#         x = self.fc2(x)
+#         return x

src/single-core-regen/util/datasets.py

@@ -1,11 +1,28 @@
 from pathlib import Path
 import torch
 from torch.utils.data import Dataset
+# from torch.utils.data import Sampler
 import numpy as np
 import configparser
 
+# class SubsetSampler(Sampler[int]):
+#     """
+#     Samples elements from a given list of indices.
 
-def load_data(config_path, skipfirst=0, num_symbols=None):
+#     :param indices: List of indices to sample from.
+#     :type indices: list[int]
+#     """
+
+#     def __init__(self, indices):
+#         self.indices = indices
+
+#     def __iter__(self):
+#         return iter(self.indices)
+
+#     def __len__(self):
+#         return len(self.indices)
+
+def load_data(config_path, skipfirst=0, symbols=None, real=False, normalize=False, device=None, dtype=None):
     filepath = Path(config_path)
     filepath = filepath.parent.glob(filepath.name)
     config = configparser.ConfigParser()
@@ -18,15 +35,25 @@ def load_data(config_path, skipfirst=0, num_symbols=None):
     datapath = Path("/".join(path_elements).replace('"', ""))
     sps = int(config["glova"]["sps"])
 
-    if num_symbols is None:
-        num_symbols = int(config["glova"]["nos"]) - skipfirst
+    if symbols is None:
+        symbols = int(config["glova"]["nos"]) - skipfirst
 
-    data = np.load(datapath)[skipfirst * sps : num_symbols * sps + skipfirst * sps]
-    config["glova"]["nos"] = str(num_symbols)
+    data = np.load(datapath)[skipfirst * sps : symbols * sps + skipfirst * sps]
+
+    if normalize:
+        a, b, c, d = data.T
+        a, b, c, d = a/np.max(np.abs(a)), b/np.max(np.abs(b)), c/np.max(np.abs(c)), d/np.max(np.abs(d))
+        data = np.array([a, b, c, d]).T
+
+    if real:
+        data = np.abs(data)
+    
+    config["glova"]["nos"] = str(symbols)
+
+    data = torch.tensor(data, device=device, dtype=dtype)
 
     return data, config
 
-
 def roll_along(arr, shifts, dim):
     # https://stackoverflow.com/a/76920720
     # (c) Mateen Ulhaq, 2023
@@ -39,7 +66,6 @@ def roll_along(arr, shifts, dim):
     indices = (dim_indices - shifts.unsqueeze(dim)) % arr.shape[dim]
     return torch.gather(arr, dim, indices)
 
-
 class FiberRegenerationDataset(Dataset):
     """
     Dataset for fiber regeneration training.
@@ -76,6 +102,9 @@ class FiberRegenerationDataset(Dataset):
         target_delay: float | int = 0,
         xy_delay: float | int = 0,
         drop_first: float | int = 0,
+        dtype: torch.dtype = None,
+        real: bool = False,
+        device = None,
         **kwargs,
     ):
         """
@@ -123,13 +152,16 @@ class FiberRegenerationDataset(Dataset):
                 [[i + 0.1j, i + 0.2j, i + 1.1j, i + 1.2j] for i in range(12800)],
                 dtype=np.complex128,
             )
+            data_raw = torch.tensor(data_raw, device=device, dtype=dtype)
             self.config = {
                 "data": {"dir": '"."', "npy_dir": '"."', "file": "faux"},
                 "glova": {"sps": 128},
             }
         else:
-            data_raw, self.config = load_data(file_path)
+            data_raw, self.config = load_data(file_path, skipfirst=drop_first, real=real, normalize=True, device=device, dtype=dtype)
 
+        self.device = data_raw.device
+        
         self.samples_per_symbol = int(self.config["glova"]["sps"])
         self.samples_per_slice = int(symbols * self.samples_per_symbol)
         self.symbols_per_slice = self.samples_per_slice / self.samples_per_symbol
@@ -140,7 +172,6 @@ class FiberRegenerationDataset(Dataset):
 
         ovrd_target_delay_samples = kwargs.pop("ovrd_target_delay_samples", None)
         ovrd_xy_delay_samples = kwargs.pop("ovrd_xy_delay_samples", None)
-        ovrd_drop_first_samples = kwargs.pop("ovrd_drop_first_samples", None)
 
         self.target_delay_samples = (
             ovrd_target_delay_samples
@@ -152,14 +183,8 @@ class FiberRegenerationDataset(Dataset):
             if ovrd_xy_delay_samples is not None
             else int(self.xy_delay * self.samples_per_symbol)
         )
-        drop_first_samples = (
-            ovrd_drop_first_samples
-            if ovrd_drop_first_samples is not None
-            else int(drop_first * self.samples_per_symbol)
-        )
 
-        # drop samples from the beginning
-        data_raw = data_raw[drop_first_samples:]
+        # data_raw = torch.tensor(data_raw, dtype=dtype)
 
         # data layout
         # [ [E_in_x0, E_in_y0, E_out_x0, E_out_y0],
@@ -240,10 +265,10 @@ class FiberRegenerationDataset(Dataset):
             data = data.view(data.shape[0], self.data_size, -1)
             data = data[:, :, 0]
 
-            # target is corresponding to the latest data point -> try to regenerate that
+            # target is corresponding to the middle of the data as the output sample is influenced by the data before and after it
             target = target[:, : target.shape[1] // self.data_size * self.data_size]
             target = target.view(target.shape[0], self.data_size, -1)
-            target = target[:, 0, 0]
+            target = target[:, 0, target.shape[2] // 2]
 
             data = data.transpose(0, 1).flatten().squeeze()
             target = target.flatten().squeeze()

src/single-core-regen/util/optuna_helpers.py

@@ -0,0 +1,30 @@
+def _optional_suggest(trial, name, range_or_value, log=False, step=None, type='int'):
+    # not a range
+    if not hasattr(range_or_value, '__iter__') or isinstance(range_or_value, str):
+        return range_or_value
+    
+    # range with only one value
+    if len(range_or_value) == 1:
+        return range_or_value[0]
+    
+    if type == 'int':
+        step = step or 1
+        return trial.suggest_int(name, *range_or_value, step=step, log=log)
+
+    if type == 'float':
+        return trial.suggest_float(name, *range_or_value, step=step, log=log)
+    
+    if type == 'categorical':
+        return trial.suggest_categorical(name, range_or_value)
+    
+    raise ValueError(f"Unknown type: {type}")
+    
+
+def optional_suggest_categorical(trial, name, choices_or_value):
+    return _optional_suggest(trial, name, choices_or_value, type='categorical')
+
+def optional_suggest_int(trial, name, range_or_value, step=None, log=False):
+    return _optional_suggest(trial, name, range_or_value, step=step, log=log, type='int')
+
+def optional_suggest_float(trial, name, range_or_value, step=None, log=False):
+    return _optional_suggest(trial, name, range_or_value, step=step, log=log, type='float')

src/single-core-regen/util/optuna_vis.py

@@ -0,0 +1,18 @@
+from dash import Dash, dcc, html
+import logging
+import dash_bootstrap_components as dbc
+
+
+def show_figures(*figures):
+    for figure in figures:
+        figure.layout.template = 'plotly_dark'
+
+    app = Dash(external_stylesheets=[dbc.themes.DARKLY])
+    app.layout = html.Div([
+        dcc.Graph(figure=figure) for figure in figures
+    ])
+    log = logging.getLogger('werkzeug')
+    log.setLevel(logging.ERROR)
+
+    app.show = lambda *args, **kwargs: app.run_server(*args, **kwargs, debug=False)
+    return app

src/single-core-regen/util/plot.py

@@ -2,33 +2,72 @@ import matplotlib.pyplot as plt
 import numpy as np
 from .datasets import load_data
 
-def eye(path, title=None, head=1000, skipfirst=1000, show=True):
+def eye(*, path=None, data=None, sps=None, title=None, symbols=1000, skipfirst=0, width=2, alpha=None, complex=False, show=True):
     """Plot an eye diagram for the data given by filepath.
+
+    Either path or data and sps must be given.
     
     Args:
         path (str): Path to the data description file.
+        data (np.ndarray): Data to plot.
+        sps (int): Samples per symbol.
         title (str): Title of the plot.
         head (int): Number of symbols to plot.
         skipfirst (int): Number of symbols to skip.
         show (bool): Whether to call plt.show().
     """
-    data, config = load_data(path, skipfirst, head)
-    sps = int(config["glova"]["sps"])
+    if path is None and data is None:
+        raise ValueError("Either path or data and sps must be given.")
+    if path is not None:
+        data, config = load_data(path, skipfirst, symbols)
+        sps = int(config["glova"]["sps"])
+    if sps is None:
+        raise ValueError("sps not set.")
 
-    xaxis = np.linspace(0, 2, 2*sps, endpoint=False)
+    xaxis = np.linspace(0, width, width*sps, endpoint=False)
     fig, axs = plt.subplots(2, 2, figsize=(10, 10), sharex=True, sharey=True)
-    for i in range(head-1):
-        inx, iny, outx, outy = data[i*sps:(i+2)*sps].T
-        axs[0, 0].plot(xaxis, np.abs(inx)**2, color="C0", alpha=0.1)
-        axs[0, 1].plot(xaxis, np.abs(outx)**2, color="C0", alpha=0.1)
-        axs[1, 0].plot(xaxis, np.abs(iny)**2, color="C0", alpha=0.1)
-        axs[1, 1].plot(xaxis, np.abs(outy)**2, color="C0", alpha=0.1)
+    if complex:
+        # create secondary axis for phase
+        axs2 = axs[0, 0].twinx(), axs[0, 1].twinx(), axs[1, 0].twinx(), axs[1, 1].twinx()
+        axs2 = np.reshape(axs2, (2, 2))
+
+    for i in range(symbols-(width-1)):
+        inx, iny, outx, outy = data[i*sps:(i+width)*sps].T
+        if complex:
+            axs[0, 0].plot(xaxis, np.abs(inx), color="C0", alpha=alpha or 0.1)
+            axs[0, 1].plot(xaxis, np.abs(outx), color="C0", alpha=alpha or 0.1)
+            axs[1, 0].plot(xaxis, np.abs(iny), color="C0", alpha=alpha or 0.1)
+            axs[1, 1].plot(xaxis, np.abs(outy), color="C0", alpha=alpha or 0.1)
+            axs[0,0].set_ylim(0, 1.1*np.max(np.abs(data)))
+            
+            axs2[0, 0].plot(xaxis, np.angle(inx), color="C1", alpha=alpha or 0.1)
+            axs2[0, 1].plot(xaxis, np.angle(outx), color="C1", alpha=alpha or 0.1)
+            axs2[1, 0].plot(xaxis, np.angle(iny), color="C1", alpha=alpha or 0.1)
+            axs2[1, 1].plot(xaxis, np.angle(outy), color="C1", alpha=alpha or 0.1)
+        else:
+            axs[0, 0].plot(xaxis, np.abs(inx)**2, color="C0", alpha=alpha or 0.1)
+            axs[0, 1].plot(xaxis, np.abs(outx)**2, color="C0", alpha=alpha or 0.1)
+            axs[1, 0].plot(xaxis, np.abs(iny)**2, color="C0", alpha=alpha or 0.1)
+            axs[1, 1].plot(xaxis, np.abs(outy)**2, color="C0", alpha=alpha or 0.1)
+    
+    if complex:
+        axs2[0, 0].sharey(axs2[0, 1])
+        axs2[0, 1].sharey(axs2[1, 0])
+        axs2[1, 0].sharey(axs2[1, 1])
+        # make y axis symmetric
+        ylim = np.max(np.abs(np.angle(data)))*1.1
+        if ylim != 0:
+            axs2[0, 0].set_ylim(-ylim, ylim)
+    else:
+        axs[0,0].set_ylim(0, 1.1*np.max(np.abs(data))**2)
     
     axs[0, 0].set_title("Input x")
     axs[0, 1].set_title("Output x")
     axs[1, 0].set_title("Input y")
     axs[1, 1].set_title("Output y")
-    fig.suptitle(title)
+    fig.suptitle(title or "Eye diagram")
 
     if show:
-        plt.show(block=False)
+        plt.show()
+    
+    return fig