General refactor

estimage/persistence/event/ini.py

@@ -21,12 +21,8 @@ def _event_to_string_dict(self, event):
             task_name=event.task_name
         )
         if (val := event.value_before) is not None:
-            if event.quantity == "state":
-                val = val
             to_save["value_before"] = str(val)
         if (val := event.value_after) is not None:
-            if event.quantity == "state":
-                val = val
             to_save["value_after"] = str(val)
 
         return to_save

estimage/statops/func.py

@@ -133,14 +133,8 @@ def get_time_to_completion(velocity_mean, velocity_stdev, distance, confidence=0
 
 
 def get_prob_of_completion(velocity_mean, velocity_stdev, distance, time):
-    if distance == 0:
-        return 1
-    if velocity_stdev == 0:
-        return 0 if velocity_mean * time < distance else 1
-    if time == 0:
-        return 0
-    dist = sp.stats.norm(loc=velocity_mean * time, scale=np.sqrt(velocity_stdev ** 2 * time))
-    return 1 - dist.cdf(distance)
+    times = np.ones(1) * time
+    return get_prob_of_completion_vector(velocity_mean, velocity_stdev, distance, times)[0]
 
 
 def get_prob_of_completion_vector(velocity_mean, velocity_stdev, distance, times):
@@ -212,17 +206,20 @@ def estimate_lognorm(grids, samples):
     return result
 
 
-def autoestimate_lognorm(samples):
-    grids = get_1d_lognorm_grid(0.01, 5.0, samples.mean(), 10)
-    res = estimate_lognorm(grids, samples)
 
-    grids = get_1d_lognorm_grid(res[1] - 0.5, res[1] + 0.5, samples.mean(), 10)
-    res2 = estimate_lognorm(grids, samples)
+def autoestimage_lognorm_general(samples, first_grid, radii, counts):
+    mean = samples.mean()
+    res = estimate_lognorm(first_grid, samples)
+    for (radius, count) in zip(radii, counts):
+        grid = get_1d_lognorm_grid(res[1] - radius, res[1] + radius, mean, count)
+        res = estimate_lognorm(grid, samples)
+    return res
 
-    grids = get_1d_lognorm_grid(res2[1] - 0.2, res2[1] + 0.2, samples.mean(), 20)
-    res3 = estimate_lognorm(grids, samples)
 
-    return res3
+def autoestimate_lognorm(samples):
+    grids = get_1d_lognorm_grid(0.01, 5.0, samples.mean(), 10)
+    res = autoestimage_lognorm_general(samples, grids, (0.5, 0.2), (10, 20))
+    return res
 
 
 def get_nonzero_velocity(velocity):

estimage/webapp/main/routes.py

@@ -101,6 +101,16 @@ def move_consensus_estimate_to_authoritative(task_name):
     return view_projective_task(task_name, dict(authoritative=form))
 
 
+def _attempt_record_of_estimate(task_name, form, pollster):
+    if form.submit.data:
+        tell_pollster_about_obtained_data(pollster, task_name, form)
+    elif form.delete.data:
+        if pollster.knows_points(task_name):
+            pollster.forget_points(task_name)
+        else:
+            flask.flash("Told to forget something that we don't know")
+
+
 @bp.route('/estimate/<task_name>', methods=['POST'])
 @flask_login.login_required
 def estimate(task_name):
@@ -109,13 +119,7 @@ def estimate(task_name):
     form = forms.NumberEstimationForm()
 
     if form.validate_on_submit():
-        if form.submit.data:
-            tell_pollster_about_obtained_data(pollster, task_name, form)
-        elif form.delete.data:
-            if pollster.knows_points(task_name):
-                pollster.forget_points(task_name)
-            else:
-                flask.flash("Told to forget something that we don't know")
+        _attempt_record_of_estimate(task_name, form, pollster)
     else:
         msg = "There were following errors: "
         msg += ", ".join(form.get_all_errors())