Merge branch 'machine_learning' of git.suuppl.dev:seppl/optical-regeneration into machine_learning

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src/single-core-data-gen/generate_signal.py

@@ -89,6 +89,25 @@ def get_config(config_file=None):
     return conf
 
 
+class PDM_IM_IPM:
+    def __init__(
+        self,
+        glova,
+        mod_order=8,
+        seed=None,
+    ):
+        assert np.cbrt(mod_order) == int(np.cbrt(mod_order)) and mod_order > 1, "mod_order must be a cube of an integer greater than 1"
+        self.glova = glova
+        self.mod_order = mod_order
+        self.symbols_per_dim = int(np.cbrt(mod_order))
+        self.seed = seed
+
+    def generate_symbols(self, n):
+        rs = np.random.RandomState(self.seed)
+        symbols = rs.randint(0, self.mod_order, n)
+        return symbols
+    
+
 class pam_generator:
     def __init__(
         self,
@@ -616,16 +635,21 @@ if __name__ == "__main__":
     add_pypho.show_log()
     config = get_config()
 
-    ranges = (10000,)
+    # ranges = (1000,10000)
     # scales = tuple(range(1, 10))
-    scales = (1,)
-    lengths = [range_ * scale for range_ in ranges for scale in scales]
+    # scales = (1,)
+    # lengths = [range_ * scale for range_ in ranges for scale in scales]
     # lengths.append(10*max(ranges))
-    lengths = [*lengths, *lengths]
+    # lengths = [*lengths, *lengths]
+    lengths = (
+        # 8000, 9000, 
+        10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 
+        95000, 100000, 105000, 110000, 115000, 120000
+    )
     lengths = sorted(lengths)
 
-    # length_loop(config, lengths, incremental=False, bireflength=None, save=True)
+    length_loop(config, lengths, incremental=False, bireflength=1000, save=True)
     # birefringence is constant over coupling length -> several 100m -> bireflength=1000 (m)
 
-    single_run_with_plot(config, save=True)
+    # single_run_with_plot(config, save=True)