bug fix in data management; allow log scale parameter swipes

data_management.py

@@ -79,7 +79,11 @@ class KondoExport:
         else:
             method = 'mu'
         solver_flags = 0
-        # TODO: use solver_flags
+        try:
+            if self.kondo.simplified_initial_conditions:
+                solver_flags |= DataManager.SOLVER_FLAGS["simplified_initial_conditions"]
+        except AttributeError:
+            pass
         return dict(
                 hash = self.hash,
                 omega = self.kondo.omega,

gen_data.py

@@ -17,6 +17,7 @@ import numpy as np
 
 def gen_option_iter(options, verbose=False):
     steps = options.pop("steps")
+    scales = options.pop("scale")
     iter_variables = {}
     if steps is None:
         max_length = 1
@@ -35,17 +36,31 @@ def gen_option_iter(options, verbose=False):
             options.pop(key)
         steps = [max_length]
     else:
+        if isinstance(scales, str):
+            scales = len(steps)*[scales]
+        if len(scales) == 1:
+            scales *= len(steps)
         for key, value in options.items():
             if type(value) != list or key == "fourier_coef":
                 continue
             if len(value) == 1:
                 options[key], = value
             elif len(value) == 2:
+                if scales[0] in ("lin", "linear"):
                     iter_variables[key] = (0, np.linspace(value[0], value[1], steps[0]))
+                elif scales[0] == "log":
+                    iter_variables[key] = (0, np.logspace(np.log10(value[0]), np.log10(value[1]), steps[0]))
+                else:
+                    raise ValueError("Unexpected value for parameters \"scales\": %s"%scales[0])
             elif len(value) == 3:
                 dim = round(value[2])
                 assert 0 <= dim < len(steps)
+                if scales[dim] in ("lin", "linear"):
                     iter_variables[key] = (dim, np.linspace(value[0], value[1], steps[dim]))
+                elif scales[dim] == "log":
+                    iter_variables[key] = (dim, np.logspace(np.log10(value[0]), np.log10(value[1]), steps[dim]))
+                else:
+                    raise ValueError("Unexpected value for parameters \"scales\": %s"%scales[dim])
             else:
                 raise ValueError("Array parameters must be of the form (start, stop, dim)")
         for key in iter_variables.keys():
@@ -64,6 +79,7 @@ def main():
     """
     parser = argparse.ArgumentParser(description=main.__doc__)
     parser.add_argument("--steps", type=int, nargs='+', help="Number of steps")
+    parser.add_argument("--scale", type=str, nargs='+', default="linear", choices=("linear", "log"), help="Scale used for swipes")
     parser.add_argument("--save", type=str, default="all", choices=("all", "reduced", "observables", "minimal"), help="What to save")
     parser.add_argument("--threads", type=int, default=4, help="Number parallel processes")
     parser.add_argument("--method", type=str, required=True, choices=('J', 'mu'), help="method: J or mu")