Reduce log spam

examples/giese_lisa_fig2.py

@@ -82,7 +82,7 @@ def kappas_giese(
         multiple_params=True,
         # output_dtypes=(float, ),
         # max_workers=max_workers,
-        log_progress_percentage=True,
+        log_progress_percentage=20,
         kwargs={"model": model}
     )
     return kappas

pttools/analysis/parallel.py

@@ -88,7 +88,7 @@ def create_bubbles(
         v_walls: np.ndarray,
         alpha_ns: np.ndarray,
         func: callable = None,
-        log_progress_percentage: float = 5,
+        log_progress_percentage: float = 10,
         max_workers: int = options.MAX_WORKERS_DEFAULT,
         allow_bubble_failure: bool = False,
         kwargs: tp.Dict[str, any] = None,

pttools/bubble/fluid_reference.py

@@ -100,7 +100,7 @@ def create(
                     multiple_params=True,
                     unpack_params=True,
                     output_dtypes=(np.int_, np.float64, np.float64, np.float64, np.float64, np.float64, np.float64),
-                    log_progress_percentage=5
+                    log_progress_percentage=10
                 )
                 file.create_dataset("v_wall", data=v_walls)
                 file.create_dataset("alpha_n", data=alpha_ns)

pttools/models/analytic.py

@@ -45,8 +45,9 @@ def __init__(
             gen_cs2: bool = True,
             gen_cs2_neg: bool = True,
             allow_invalid: bool = False,
-            auto_potential: bool = False):
-        if V_b != 0:
+            auto_potential: bool = False,
+            log_info: bool = True):
+        if log_info and V_b != 0:
             logger.warning(
                 "Got V_b = %s != 0. This may result in inaccurate results with the GW spectrum computation, "
                 "as the GW spectrum equations presume V_b = 0.", V_b)
@@ -75,9 +76,9 @@ def __init__(
             T_min=T_min, T_max=T_max, T_crit_guess=T_crit_guess,
             name=name, label_latex=label_latex, label_unicode=label_unicode,
             gen_critical=gen_critical, gen_cs2=gen_cs2, gen_cs2_neg=gen_cs2_neg,
-            allow_invalid=allow_invalid
+            allow_invalid=allow_invalid, log_info=log_info
         )
-        if self.a_s <= self.a_b:
+        if log_info and self.a_s <= self.a_b:
             logger.warning(
                 f"The model \"{self.name}\" does not satisfy a_s > a_b. "
                 "Please check that the critical temperature is non-negative. "

pttools/models/const_cs.py

@@ -60,7 +60,8 @@ def __init__(
             name: str = None,
             label_latex: str = None,
             label_unicode: str = None,
-            allow_invalid: bool = False):
+            allow_invalid: bool = False,
+            log_info: bool = True):
         # Ensure that these descriptions correspond to those in the base class
         r"""
         :param a_s: prefactor of $p$ in the symmetric phase. The convention is as in :notes:`\ ` eq. 7.33.
@@ -72,7 +73,8 @@ def __init__(
         :param T_ref: reference temperature, usually 1 * unit of choice, e,g. 1 GeV
         :param name: custom name for the model
         """
-        logger.debug(f"Initialising ConstCSModel with css2={css2}, csb2={csb2}.")
+        if log_info:
+            logger.debug(f"Initialising ConstCSModel with css2={css2}, csb2={csb2}.")
         css2_flt, css2_label = cs2_to_float_and_label(css2)
         csb2_flt, csb2_label = cs2_to_float_and_label(csb2)
         self.css2 = self.validate_cs2(css2_flt, "css2")
@@ -83,7 +85,7 @@ def __init__(
                 "c_{s,s}^2 and c_{s,b}^2 have to be 0 < c_s <= 1."
                 f"Got: c_{{s,s}}^2={css2}, c_{{s,b}}^2={csb2}."
             )
-        if css2_flt > 1/3 or csb2_flt > 1/3:
+        if log_info and css2_flt > 1/3 or csb2_flt > 1/3:
             logger.warning(
                 "c_{s,s}^2 > 1/3 or c_{s,b}^2 > 1/3. "
                 "Please ensure that g_eff is monotonic in your model. "
@@ -122,7 +124,8 @@ def __init__(
             g_s=g_s, g_b=g_b,
             T_min=T_min, T_max=T_max, T_crit_guess=T_crit_guess,
             name=name, label_latex=label_latex, label_unicode=label_unicode,
-            allow_invalid=allow_invalid
+            allow_invalid=allow_invalid,
+            log_info=log_info
         )
         # This can only be set after self.a_s and self.a_b are set.
         # This seems to contain invalid assumptions and approximations.
@@ -304,7 +307,7 @@ def alpha_n_min_find_params(
         # ---
         # Solve numerically
         # ---
-        model = ConstCSModel(css2=self.css2, csb2=self.csb2, a_s=a_s_default, a_b=a_b, V_s=V_s_default)
+        model = ConstCSModel(css2=self.css2, csb2=self.csb2, a_s=a_s_default, a_b=a_b, V_s=V_s_default, log_info=False)
         # If we are already below the target
         if model.alpha_n_min < alpha_n_min_target:
             return a_s_default, a_b, V_s_default, V_b
@@ -380,7 +383,7 @@ def alpha_n_min_find_params_solvable(
             css2: float, csb2: float,
             alpha_n_target: float):
         try:
-            model = ConstCSModel(css2=css2, csb2=csb2, a_s=a_s, a_b=a_b, V_s=V_s, V_b=V_b)
+            model = ConstCSModel(css2=css2, csb2=csb2, a_s=a_s, a_b=a_b, V_s=V_s, V_b=V_b, log_info=False)
         except (ValueError, RuntimeError):
             return np.nan
         diff = model.alpha_n_min - alpha_n_target

pttools/models/model.py

@@ -44,9 +44,10 @@ def __init__(
             gen_cs2_neg: bool = True,
             implicit_V: bool = False,
             temperature_is_physical: bool = None,
-            allow_invalid: bool = False):
+            allow_invalid: bool = False,
+            log_info: bool = True):
 
-        if implicit_V:
+        if log_info and implicit_V:
             if V_s != 0 or V_b != 0:
                 logger.warning(
                     "Potentials have been specified for the implicit model: %s. "
@@ -98,7 +99,7 @@ def __init__(
 
         if gen_critical:
             # w_crit = wn_max
-            self.T_crit, self.w_crit = self.criticals(T_crit_guess, allow_invalid)
+            self.T_crit, self.w_crit = self.criticals(T_crit_guess, allow_fail=allow_invalid, log_info=log_info)
             self.w_at_alpha_n_min, self.alpha_n_min = self.alpha_n_min_find()
 
     # Concrete methods
@@ -451,12 +452,13 @@ def criticals(self, t_crit_guess: float, allow_fail: bool = False, log_info: boo
         wn_min = self.w(t_crit, Phase.SYMMETRIC)
         alpha_n_at_wn_min = self.alpha_n(wn_min)
 
-        logger.info(
-            f"Initialized model with name={self.name}, T_crit={t_crit}, alpha_n_at_wn_min={alpha_n_at_wn_min}. "
-            f"At T_crit: w_s={wn_min}, w_b={self.w(t_crit, Phase.BROKEN)}, "
-            f"e_s={self.e_temp(t_crit, Phase.SYMMETRIC)}, e_b={self.e_temp(t_crit, Phase.BROKEN)}, "
-            f"p_s={self.p_temp(t_crit, Phase.SYMMETRIC)}, p_b={self.p_temp(t_crit, Phase.BROKEN)}"
-        )
+        if log_info:
+            logger.info(
+                f"Initialised model with name={self.name}, T_crit={t_crit}, alpha_n_at_wn_min={alpha_n_at_wn_min}. "
+                f"At T_crit: w_s={wn_min}, w_b={self.w(t_crit, Phase.BROKEN)}, "
+                f"e_s={self.e_temp(t_crit, Phase.SYMMETRIC)}, e_b={self.e_temp(t_crit, Phase.BROKEN)}, "
+                f"p_s={self.p_temp(t_crit, Phase.SYMMETRIC)}, p_b={self.p_temp(t_crit, Phase.BROKEN)}"
+            )
         return t_crit, wn_min
 
     def critical_temp(

pttools/speedup/parallel.py

@@ -51,9 +51,9 @@ def run(self, param, index: int = None, multi_index: tp.Iterable = None):
                     np.floor(percentage / self.log_progress_percentage) != np.floor(
                     percentage_prev / self.log_progress_percentage)):
                 if multi_index is None:
-                    logger.debug("Processed item %s/%s, %s %%", index, self.arr_size, percentage)
+                    logger.debug("Processed item %d/%d, %.2f %%", index, self.arr_size, percentage)
                 else:
-                    logger.debug("Processed item %s, %s/%s, %s %%", multi_index, index, self.arr_size, percentage)
+                    logger.debug("Processed item %s, %d/%d, %.2f %%", multi_index, index, self.arr_size, percentage)
 
         return ret