Edited proportional flag to improve naming convention consistency

Edited proportional flag to improve naming convention consistency. Also removed duplicate code

Author: JAnns98

dabest/_dabest_object.py

@@ -54,7 +54,7 @@ def __init__(
         self.__is_paired = paired
         self.__resamples = resamples
         self.__random_seed = random_seed
-        self.__proportional = proportional
+        self.__is_proportional = proportional
         self.__mini_meta = mini_meta
 
         # after this call the attributes self.__experiment_label and self.__x1_level are updated
@@ -419,11 +419,11 @@ def _plot_data(self):
         return self.__plot_data
 
     @property
-    def proportional(self):
+    def is_proportional(self):
         """
         Returns the proportional parameter class.
         """
-        return self.__proportional
+        return self.__is_proportional
 
     @property
     def mini_meta(self):
@@ -448,7 +448,7 @@ def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):
         # Check if it is a valid mini_meta case
         if self.__mini_meta:
             # Only mini_meta calculation but not proportional and delta-delta function
-            if self.__proportional:
+            if self.__is_proportional:
                 err0 = "`proportional` and `mini_meta` cannot be True at the same time."
                 raise ValueError(err0)
             if self.__delta2:
@@ -501,7 +501,7 @@ def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):
                 error_msg = "If `delta2` is True. `x` parameter cannot be None. String or list expected"
                 raise ValueError(error_msg)
 
-            if self.__proportional:
+            if self.__is_proportional:
                 mes1 = "Only mean_diff is supported for proportional data when `delta2` is True"
                 warnings.warn(message=mes1, category=UserWarning)
 
@@ -683,7 +683,7 @@ def _compute_effectsize_dfs(self):
             is_paired=self.__is_paired,
             random_seed=self.__random_seed,
             resamples=self.__resamples,
-            proportional=self.__proportional,
+            proportional=self.__is_proportional,
             delta2=self.__delta2,
             experiment_label=self.__experiment_label,
             x1_level=self.__x1_level,

dabest/_effsize_objects.py

@@ -106,7 +106,7 @@ def __init__(
         self.__effect_size = effect_size
         self.__random_seed = random_seed
         self.__ci = ci
-        self.__proportional = proportional
+        self.__is_proportional = proportional
         self._check_errors(control, test)
 
         # Convert to numpy arrays for speed.
@@ -256,16 +256,16 @@ def _check_errors(self, control, test):
             err1 = "`paired` is not None; therefore Cliff's delta is not defined."
             raise ValueError(err1)
 
-        if self.__proportional and self.__effect_size not in ["mean_diff", "cohens_h"]:
-            err1 = "`proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined." + \
-                    "If you are calculating deltas' g, it's the same as delta-delta when `proportional` is True"
+        if self.__is_proportional and self.__effect_size not in ["mean_diff", "cohens_h"]:
+            err1 = "`is_proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined." + \
+                    "If you are calculating deltas' g, it's the same as delta-delta when `is_proportional` is True"
             raise ValueError(err1)
 
-        if self.__proportional and (
+        if self.__is_proportional and (
             isin(control, [0, 1]).all() == False or isin(test, [0, 1]).all() == False
         ):
             err1 = (
-                "`proportional` is True; Only accept binary data consisting of 0 and 1."
+                "`is_proportional` is True; Only accept binary data consisting of 0 and 1."
             )
             raise ValueError(err1)
 
@@ -333,7 +333,7 @@ def _perform_statistical_test(self):
             self.__permutation_count,
         )
 
-        if self.__is_paired and not self.__proportional:
+        if self.__is_paired and not self.__is_proportional:
             # Wilcoxon, a non-parametric version of the paired T-test.
             try:
                 wilcoxon = spstats.wilcoxon(self.__control, self.__test)
@@ -354,7 +354,7 @@ def _perform_statistical_test(self):
                 self.__pvalue_paired_students_t = paired_t.pvalue
                 self.__statistic_paired_students_t = paired_t.statistic
 
-        elif self.__is_paired and self.__proportional:
+        elif self.__is_paired and self.__is_proportional:
             # for binary paired data, use McNemar's test
             # References:
             # https://en.wikipedia.org/wiki/McNemar%27s_test
@@ -368,7 +368,7 @@ def _perform_statistical_test(self):
             self.__pvalue_mcnemar = _mcnemar.pvalue
             self.__statistic_mcnemar = _mcnemar.statistic
 
-        elif self.__proportional:
+        elif self.__is_proportional:
             # The Cohen's h calculation is for binary categorical data
             try:
                 self.__proportional_difference = es.cohens_h(
@@ -544,8 +544,8 @@ def is_paired(self):
         return self.__is_paired
 
     @property
-    def proportional(self):
-        return self.__proportional
+    def is_proportional(self):
+        return self.__is_proportional
 
     @property
     def ci(self):
@@ -842,7 +842,7 @@ def __init__(
         self.__resamples = resamples
         self.__permutation_count = permutation_count
         self.__random_seed = random_seed
-        self.__proportional = proportional
+        self.__is_proportional = proportional
         self.__x1_level = x1_level
         self.__experiment_label = experiment_label
         self.__x2 = x2
@@ -885,7 +885,7 @@ def __pre_calc(self):
                 self.__is_paired,
                 self.__resamples,
                 self.__random_seed,
-                self.__proportional,
+                self.__is_proportional,
             )
 
         for j, current_tuple in enumerate(idx):
@@ -903,7 +903,7 @@ def __pre_calc(self):
                     control,
                     test,
                     self.__effect_size,
-                    self.__proportional,
+                    self.__is_proportional,
                     self.__is_paired,
                     self.__ci,
                     self.__resamples,
@@ -1300,7 +1300,7 @@ def plot(
             passed to plot() : {'linewidth':1, 'alpha':0.5, 'jitter':0, 'jitter_seed':9876543210}.
         sankey_kwargs: dict, default None
             Whis will change the appearance of the sankey diagram used to depict
-            paired proportional data when `show_pairs=True` and `proportional=True`.
+            paired proportional data when `show_pairs=True` and `is_proportional=True`.
             Pass any keyword arguments accepted by plot_tools.sankeydiag() function
             here, as a dict. If None, the following keywords are passed to sankey diagram:
             {"width": 0.5, "align": "center", "alpha": 0.4, "bar_width": 0.1, "rightColor": False}
@@ -1437,9 +1437,6 @@ def plot(
         if self.__delta2 and not empty_circle:
             color_col = self.__x2
 
-        # if self.__proportional:
-        #     raw_marker_size = 0.01
-
         # Modification incurred due to update of Seaborn
         ci = ("ci", ci) if ci is not None else None
 
@@ -1450,12 +1447,12 @@ def plot(
         return out
 
     @property
-    def proportional(self):
+    def is_proportional(self):
         """
         Returns the proportional parameter
         class.
         """
-        return self.__proportional
+        return self.__is_proportional
 
     @property
     def results(self):
@@ -1568,13 +1565,6 @@ def dabest_obj(self):
         """
         return self.__dabest_obj
 
-    @property
-    def proportional(self):
-        """
-        Returns the proportional parameter
-        class.
-        """
-        return self.__proportional
 
     @property
     def lqrt(self):

dabest/misc_tools.py

@@ -115,7 +115,7 @@ def get_params(
     delta2 = effectsize_df.delta2
     mini_meta = effectsize_df.mini_meta
     effect_size = effectsize_df.effect_size
-    proportional = effectsize_df.proportional
+    proportional = effectsize_df.is_proportional
     results = effectsize_df.results
     dabest_obj = effectsize_df.dabest_obj
     all_plot_groups = dabest_obj._all_plot_groups

nbs/API/dabest_object.ipynb

@@ -130,7 +130,7 @@
     "        self.__is_paired = paired\n",
     "        self.__resamples = resamples\n",
     "        self.__random_seed = random_seed\n",
-    "        self.__proportional = proportional\n",
+    "        self.__is_proportional = proportional\n",
     "        self.__mini_meta = mini_meta\n",
     "\n",
     "        # after this call the attributes self.__experiment_label and self.__x1_level are updated\n",
@@ -495,11 +495,11 @@
     "        return self.__plot_data\n",
     "\n",
     "    @property\n",
-    "    def proportional(self):\n",
+    "    def is_proportional(self):\n",
     "        \"\"\"\n",
     "        Returns the proportional parameter class.\n",
     "        \"\"\"\n",
-    "        return self.__proportional\n",
+    "        return self.__is_proportional\n",
     "\n",
     "    @property\n",
     "    def mini_meta(self):\n",
@@ -524,7 +524,7 @@
     "        # Check if it is a valid mini_meta case\n",
     "        if self.__mini_meta:\n",
     "            # Only mini_meta calculation but not proportional and delta-delta function\n",
-    "            if self.__proportional:\n",
+    "            if self.__is_proportional:\n",
     "                err0 = \"`proportional` and `mini_meta` cannot be True at the same time.\"\n",
     "                raise ValueError(err0)\n",
     "            if self.__delta2:\n",
@@ -577,7 +577,7 @@
     "                error_msg = \"If `delta2` is True. `x` parameter cannot be None. String or list expected\"\n",
     "                raise ValueError(error_msg)\n",
     "            \n",
-    "            if self.__proportional:\n",
+    "            if self.__is_proportional:\n",
     "                mes1 = \"Only mean_diff is supported for proportional data when `delta2` is True\"\n",
     "                warnings.warn(message=mes1, category=UserWarning)\n",
     "\n",
@@ -759,7 +759,7 @@
     "            is_paired=self.__is_paired,\n",
     "            random_seed=self.__random_seed,\n",
     "            resamples=self.__resamples,\n",
-    "            proportional=self.__proportional,\n",
+    "            proportional=self.__is_proportional,\n",
     "            delta2=self.__delta2,\n",
     "            experiment_label=self.__experiment_label,\n",
     "            x1_level=self.__x1_level,\n",

nbs/API/effsize_objects.ipynb

@@ -166,7 +166,7 @@
     "        self.__effect_size = effect_size\n",
     "        self.__random_seed = random_seed\n",
     "        self.__ci = ci\n",
-    "        self.__proportional = proportional\n",
+    "        self.__is_proportional = proportional\n",
     "        self._check_errors(control, test)\n",
     "\n",
     "        # Convert to numpy arrays for speed.\n",
@@ -316,16 +316,16 @@
     "            err1 = \"`paired` is not None; therefore Cliff's delta is not defined.\"\n",
     "            raise ValueError(err1)\n",
     "\n",
-    "        if self.__proportional and self.__effect_size not in [\"mean_diff\", \"cohens_h\"]:\n",
-    "            err1 = \"`proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined.\" + \\\n",
-    "                    \"If you are calculating deltas' g, it's the same as delta-delta when `proportional` is True\"\n",
+    "        if self.__is_proportional and self.__effect_size not in [\"mean_diff\", \"cohens_h\"]:\n",
+    "            err1 = \"`is_proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined.\" + \\\n",
+    "                    \"If you are calculating deltas' g, it's the same as delta-delta when `is_proportional` is True\"\n",
     "            raise ValueError(err1)\n",
     "\n",
-    "        if self.__proportional and (\n",
+    "        if self.__is_proportional and (\n",
     "            isin(control, [0, 1]).all() == False or isin(test, [0, 1]).all() == False\n",
     "        ):\n",
     "            err1 = (\n",
-    "                \"`proportional` is True; Only accept binary data consisting of 0 and 1.\"\n",
+    "                \"`is_proportional` is True; Only accept binary data consisting of 0 and 1.\"\n",
     "            )\n",
     "            raise ValueError(err1)\n",
     "\n",
@@ -393,7 +393,7 @@
     "            self.__permutation_count,\n",
     "        )\n",
     "\n",
-    "        if self.__is_paired and not self.__proportional:\n",
+    "        if self.__is_paired and not self.__is_proportional:\n",
     "            # Wilcoxon, a non-parametric version of the paired T-test.\n",
     "            try:\n",
     "                wilcoxon = spstats.wilcoxon(self.__control, self.__test)\n",
@@ -414,7 +414,7 @@
     "                self.__pvalue_paired_students_t = paired_t.pvalue\n",
     "                self.__statistic_paired_students_t = paired_t.statistic\n",
     "\n",
-    "        elif self.__is_paired and self.__proportional:\n",
+    "        elif self.__is_paired and self.__is_proportional:\n",
     "            # for binary paired data, use McNemar's test\n",
     "            # References:\n",
     "            # https://en.wikipedia.org/wiki/McNemar%27s_test\n",
@@ -428,7 +428,7 @@
     "            self.__pvalue_mcnemar = _mcnemar.pvalue\n",
     "            self.__statistic_mcnemar = _mcnemar.statistic\n",
     "\n",
-    "        elif self.__proportional:\n",
+    "        elif self.__is_proportional:\n",
     "            # The Cohen's h calculation is for binary categorical data\n",
     "            try:\n",
     "                self.__proportional_difference = es.cohens_h(\n",
@@ -604,8 +604,8 @@
     "        return self.__is_paired\n",
     "\n",
     "    @property\n",
-    "    def proportional(self):\n",
-    "        return self.__proportional\n",
+    "    def is_proportional(self):\n",
+    "        return self.__is_proportional\n",
     "\n",
     "    @property\n",
     "    def ci(self):\n",
@@ -1001,7 +1001,7 @@
     "        self.__resamples = resamples\n",
     "        self.__permutation_count = permutation_count\n",
     "        self.__random_seed = random_seed\n",
-    "        self.__proportional = proportional\n",
+    "        self.__is_proportional = proportional\n",
     "        self.__x1_level = x1_level\n",
     "        self.__experiment_label = experiment_label\n",
     "        self.__x2 = x2\n",
@@ -1044,7 +1044,7 @@
     "                self.__is_paired,\n",
     "                self.__resamples,\n",
     "                self.__random_seed,\n",
-    "                self.__proportional,\n",
+    "                self.__is_proportional,\n",
     "            )\n",
     "\n",
     "        for j, current_tuple in enumerate(idx):\n",
@@ -1062,7 +1062,7 @@
     "                    control,\n",
     "                    test,\n",
     "                    self.__effect_size,\n",
-    "                    self.__proportional,\n",
+    "                    self.__is_proportional,\n",
     "                    self.__is_paired,\n",
     "                    self.__ci,\n",
     "                    self.__resamples,\n",
@@ -1459,7 +1459,7 @@
     "            passed to plot() : {'linewidth':1, 'alpha':0.5, 'jitter':0, 'jitter_seed':9876543210}.\n",
     "        sankey_kwargs: dict, default None\n",
     "            Whis will change the appearance of the sankey diagram used to depict\n",
-    "            paired proportional data when `show_pairs=True` and `proportional=True`.\n",
+    "            paired proportional data when `show_pairs=True` and `is_proportional=True`.\n",
     "            Pass any keyword arguments accepted by plot_tools.sankeydiag() function\n",
     "            here, as a dict. If None, the following keywords are passed to sankey diagram:\n",
     "            {\"width\": 0.5, \"align\": \"center\", \"alpha\": 0.4, \"bar_width\": 0.1, \"rightColor\": False}\n",
@@ -1596,9 +1596,6 @@
     "        if self.__delta2 and not empty_circle:\n",
     "            color_col = self.__x2\n",
     "\n",
-    "        # if self.__proportional:\n",
-    "        #     raw_marker_size = 0.01\n",
-    "\n",
     "        # Modification incurred due to update of Seaborn\n",
     "        ci = (\"ci\", ci) if ci is not None else None\n",
     "\n",
@@ -1609,12 +1606,12 @@
     "        return out\n",
     "\n",
     "    @property\n",
-    "    def proportional(self):\n",
+    "    def is_proportional(self):\n",
     "        \"\"\"\n",
     "        Returns the proportional parameter\n",
     "        class.\n",
     "        \"\"\"\n",
-    "        return self.__proportional\n",
+    "        return self.__is_proportional\n",
     "\n",
     "    @property\n",
     "    def results(self):\n",
@@ -1727,13 +1724,6 @@
     "        \"\"\"\n",
     "        return self.__dabest_obj\n",
     "\n",
-    "    @property\n",
-    "    def proportional(self):\n",
-    "        \"\"\"\n",
-    "        Returns the proportional parameter\n",
-    "        class.\n",
-    "        \"\"\"\n",
-    "        return self.__proportional\n",
     "\n",
     "    @property\n",
     "    def lqrt(self):\n",