Update unclear formulation

Author: luseverin

climada/engine/unsequa/input_var.py

@@ -246,7 +246,7 @@ def haz(haz_list, n_ev=None, bounds_int=None, bounds_frac=None, bounds_freq=None
             The frequency of all events is multiplied by a number
             sampled uniformly from a distribution with (min, max) = bounds_freq
         HL: sample uniformly from hazard list
-            Uniformly sample one element from the provided list of hazards.
+            For each sample, one element is drawn uniformly from the provided list of hazards.
             For example, Hazards outputs from dynamical models for different input factors.
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
@@ -309,7 +309,7 @@ def exp(exp_list, bounds_totval=None, bounds_noise=None):
             with (min, max) = bounds_noise. EN is the value of the seed
             for  the uniform random number generator.
         EL: sample uniformly from exposure list
-            Uniformly sample one element from the provided list of exposures.
+            For each sample, one element is drawn uniformly from the provided list of exposures.
             For example, LitPop instances with different exponents.
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
@@ -375,7 +375,7 @@ def impfset(
             sampled uniformly from a distribution with
             (min, max) = bounds_int
         IL: sample uniformly from impact function set list
-            Uniformly sample one element from the provided list of impact function sets.
+            For each sample, one element is drawn uniformly from the provided list of impact function sets.
             For example, impact functions obtained from different calibration methods.
 
 
@@ -466,7 +466,7 @@ def ent(
             with (min, max) = bounds_noise. EN is the value of the seed
             for  the uniform random number generator.
         EL: sample uniformly from exposure list
-            Uniformly sample one element from the provided list of exposures.
+            For each sample, one element is drawn uniformly from the provided list of exposures.
             For example, LitPop instances with different exponents.
         MDD: scale the mdd (homogeneously)
             The value of mdd at each intensity is multiplied by a number
@@ -481,7 +481,7 @@ def ent(
             sampled uniformly from a distribution with
             (min, max) = bounds_int
         IL: sample uniformly from impact function set list
-            Uniformly sample one element from the provided list of impact function sets.
+            For each sample, one element is drawn uniformly from the provided list of impact function sets.
             For example, impact functions obtained from different calibration methods.
 
         If a bounds is None, this parameter is assumed to have no uncertainty.
@@ -613,7 +613,7 @@ def entfut(
             with (min, max) = bounds_noise. EN is the value of the seed
             for  the uniform random number generator.
         EL: sample uniformly from exposure list
-            Uniformly sample one element from the provided list of exposures.
+            For each sample, one element is drawn uniformly from the provided list of exposures.
             For example, LitPop instances with different exponents.
         MDD: scale the mdd (homogeneously)
             The value of mdd at each intensity is multiplied by a number
@@ -628,7 +628,7 @@ def entfut(
             sampled uniformly from a distribution with
             (min, max) = bounds_impfi
         IL: sample uniformly from impact function set list
-            Uniformly sample one element from the provided list of impact function sets.
+            For each sample, one element is drawn uniformly from the provided list of impact function sets.
             For example, impact functions obtained from different calibration methods.
 
         If a bounds is None, this parameter is assumed to have no uncertainty.

doc/user-guide/climada_engine_unsequa_helper.ipynb

@@ -67,8 +67,7 @@
     "- EN: mutliplicative noise (inhomogeneous)\n",
     "> The value of each exposure point is independently multiplied by a random number sampled uniformly from a distribution with (min, max) = bounds_noise. EN is the value of the seed for  the uniform random number generator.\n",
     "- EL: sample uniformly from exposure list\n",
-    "> Uniformly sample one element from the provided list of exposures. For example, LitPop instances with different exponents.\n",
-    "\n",
+    "> For each sample, one element is drawn uniformly from the provided list of exposures. For example, LitPop instances with different exponents.\n",
     "\n",
     "If a bounds is None, this parameter is assumed to have no uncertainty."
    ]
@@ -1153,8 +1152,7 @@
     "- IFi: shift the intensity (homogeneously)\n",
     "> The value intensity are all summed with a random number sampled uniformly from a distribution with (min, max) = bounds_int\n",
     "- IL: sample uniformly from impact function set list\n",
-    "> Uniformly sample one element from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n",
-    "\n",
+    "> For each sample, one element is drawn uniformly from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n",
     "\n",
     "If a bounds is None, this parameter is assumed to have no uncertainty."
    ]
@@ -1268,15 +1266,15 @@
     "- EN: mutliplicative noise (inhomogeneous)\n",
     "> The value of each exposure point is independently multiplied by a random number sampled uniformly from a distribution with (min, max) = bounds_noise. EN is the value of the seed for  the uniform random number generator.\n",
     "- EL: sample uniformly from exposure list\n",
-    "> Uniformly sample one element from the provided list of exposures. For example, LitPop instances with different exponents.\n",
+    "> For each sample, one element is drawn uniformly from the provided list of exposures. For example, LitPop instances with different exponents.\n",
     "- MDD: scale the mdd (homogeneously)\n",
     "> The value of mdd at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_mdd\n",
     "- PAA: scale the paa (homogeneously)\n",
     "> The value of paa at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_paa\n",
     "- IFi: shift the intensity (homogeneously)\n",
     "> The value intensity are all summed with a random number sampled uniformly from a distribution with (min, max) = bounds_int\n",
     "- IL: sample uniformly from impact function set list\n",
-    "> Uniformly sample one element from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n",
+    "> For each sample, one element is drawn uniformly from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n",
     "\n",
     "\n",
     "If a bounds is None, this parameter is assumed to have no uncertainty."
@@ -1821,16 +1819,15 @@
     "- EN: mutliplicative noise (inhomogeneous)\n",
     "> The value of each exposure point is independently multiplied by a random number sampled uniformly from a distribution with (min, max) = bounds_noise. EN is the value of the seed for  the uniform random number generator.\n",
     "- EL: sample uniformly from exposure list\n",
-    "> Uniformly sample one element from the provided list of exposures. For example, LitPop instances with different exponents.\n",
+    "> For each sample, one element is drawn uniformly from the provided list of exposures. For example, LitPop instances with different exponents.\n",
     "- MDD: scale the mdd (homogeneously)\n",
     "> The value of mdd at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_mdd\n",
     "- PAA: scale the paa (homogeneously)\n",
     "> The value of paa at each intensity is multiplied by a number sampled uniformly from a distribution with (min, max) = bounds_paa\n",
     "- IFi: shift the impact function intensity (homogeneously)\n",
     "> The value intensity are all summed with a random number sampled uniformly from a distribution with (min, max) = bounds_impfi\n",
     "- IL: sample uniformly from impact function set list\n",
-    "> Uniformly sample one element from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n",
-    "\n",
+    "> For each sample, one element is drawn uniformly from the provided list of impact function sets. For example, impact functions obtained from different calibration methods.\n",
     "\n",
     "If a bounds is None, this parameter is assumed to have no uncertainty."
    ]