test 03 without external dependency

Author: cyberosa

dabest/_dabest_object.py

@@ -246,7 +246,7 @@ def __init__(
             err = "You have only specified `x`. Please also specify `y`."
             raise ValueError(err)
 
-        self.__plot_data = self.get_plot_data(x, y, all_plot_groups)
+        self.__plot_data = self._get_plot_data(x, y, all_plot_groups)
         self.__all_plot_groups = all_plot_groups
 
         # Check if `id_col` is valid
@@ -533,7 +533,7 @@ def _all_plot_groups(self):
         """
         return self.__all_plot_groups
 
-    def get_plot_data(self, x, y, all_plot_groups):
+    def _get_plot_data(self, x, y, all_plot_groups):
         """
         Function to prepare some attributes for plotting
         """

nbs/API/dabest_object.ipynb

@@ -315,7 +315,7 @@
     "            err = \"You have only specified `x`. Please also specify `y`.\"\n",
     "            raise ValueError(err)\n",
     "\n",
-    "        self.__plot_data = self.get_plot_data(x, y, all_plot_groups)\n",
+    "        self.__plot_data = self._get_plot_data(x, y, all_plot_groups)\n",
     "        self.__all_plot_groups = all_plot_groups\n",
     "\n",
     "        # Check if `id_col` is valid\n",
@@ -602,7 +602,7 @@
     "        \"\"\"\n",
     "        return self.__all_plot_groups\n",
     "\n",
-    "    def get_plot_data(self, x, y, all_plot_groups):\n",
+    "    def _get_plot_data(self, x, y, all_plot_groups):\n",
     "        \"\"\"\n",
     "        Function to prepare some attributes for plotting\n",
     "        \"\"\"\n",

nbs/tests/data/iris.csv

@@ -1,151 +1,151 @@
-sepal_length,sepal_width,petal_length,petal_width,species
-5.1,3.5,1.4,0.2,setosa
-4.9,3.0,1.4,0.2,setosa
-4.7,3.2,1.3,0.2,setosa
-4.6,3.1,1.5,0.2,setosa
-5.0,3.6,1.4,0.2,setosa
-5.4,3.9,1.7,0.4,setosa
-4.6,3.4,1.4,0.3,setosa
-5.0,3.4,1.5,0.2,setosa
-4.4,2.9,1.4,0.2,setosa
-4.9,3.1,1.5,0.1,setosa
-5.4,3.7,1.5,0.2,setosa
-4.8,3.4,1.6,0.2,setosa
-4.8,3.0,1.4,0.1,setosa
-4.3,3.0,1.1,0.1,setosa
-5.8,4.0,1.2,0.2,setosa
-5.7,4.4,1.5,0.4,setosa
-5.4,3.9,1.3,0.4,setosa
-5.1,3.5,1.4,0.3,setosa
-5.7,3.8,1.7,0.3,setosa
-5.1,3.8,1.5,0.3,setosa
-5.4,3.4,1.7,0.2,setosa
-5.1,3.7,1.5,0.4,setosa
-4.6,3.6,1.0,0.2,setosa
-5.1,3.3,1.7,0.5,setosa
-4.8,3.4,1.9,0.2,setosa
-5.0,3.0,1.6,0.2,setosa
-5.0,3.4,1.6,0.4,setosa
-5.2,3.5,1.5,0.2,setosa
-5.2,3.4,1.4,0.2,setosa
-4.7,3.2,1.6,0.2,setosa
-4.8,3.1,1.6,0.2,setosa
-5.4,3.4,1.5,0.4,setosa
-5.2,4.1,1.5,0.1,setosa
-5.5,4.2,1.4,0.2,setosa
-4.9,3.1,1.5,0.2,setosa
-5.0,3.2,1.2,0.2,setosa
-5.5,3.5,1.3,0.2,setosa
-4.9,3.6,1.4,0.1,setosa
-4.4,3.0,1.3,0.2,setosa
-5.1,3.4,1.5,0.2,setosa
-5.0,3.5,1.3,0.3,setosa
-4.5,2.3,1.3,0.3,setosa
-4.4,3.2,1.3,0.2,setosa
-5.0,3.5,1.6,0.6,setosa
-5.1,3.8,1.9,0.4,setosa
-4.8,3.0,1.4,0.3,setosa
-5.1,3.8,1.6,0.2,setosa
-4.6,3.2,1.4,0.2,setosa
-5.3,3.7,1.5,0.2,setosa
-5.0,3.3,1.4,0.2,setosa
-7.0,3.2,4.7,1.4,versicolor
-6.4,3.2,4.5,1.5,versicolor
-6.9,3.1,4.9,1.5,versicolor
-5.5,2.3,4.0,1.3,versicolor
-6.5,2.8,4.6,1.5,versicolor
-5.7,2.8,4.5,1.3,versicolor
-6.3,3.3,4.7,1.6,versicolor
-4.9,2.4,3.3,1.0,versicolor
-6.6,2.9,4.6,1.3,versicolor
-5.2,2.7,3.9,1.4,versicolor
-5.0,2.0,3.5,1.0,versicolor
-5.9,3.0,4.2,1.5,versicolor
-6.0,2.2,4.0,1.0,versicolor
-6.1,2.9,4.7,1.4,versicolor
-5.6,2.9,3.6,1.3,versicolor
-6.7,3.1,4.4,1.4,versicolor
-5.6,3.0,4.5,1.5,versicolor
-5.8,2.7,4.1,1.0,versicolor
-6.2,2.2,4.5,1.5,versicolor
-5.6,2.5,3.9,1.1,versicolor
-5.9,3.2,4.8,1.8,versicolor
-6.1,2.8,4.0,1.3,versicolor
-6.3,2.5,4.9,1.5,versicolor
-6.1,2.8,4.7,1.2,versicolor
-6.4,2.9,4.3,1.3,versicolor
-6.6,3.0,4.4,1.4,versicolor
-6.8,2.8,4.8,1.4,versicolor
-6.7,3.0,5.0,1.7,versicolor
-6.0,2.9,4.5,1.5,versicolor
-5.7,2.6,3.5,1.0,versicolor
-5.5,2.4,3.8,1.1,versicolor
-5.5,2.4,3.7,1.0,versicolor
-5.8,2.7,3.9,1.2,versicolor
-6.0,2.7,5.1,1.6,versicolor
-5.4,3.0,4.5,1.5,versicolor
-6.0,3.4,4.5,1.6,versicolor
-6.7,3.1,4.7,1.5,versicolor
-6.3,2.3,4.4,1.3,versicolor
-5.6,3.0,4.1,1.3,versicolor
-5.5,2.5,4.0,1.3,versicolor
-5.5,2.6,4.4,1.2,versicolor
-6.1,3.0,4.6,1.4,versicolor
-5.8,2.6,4.0,1.2,versicolor
-5.0,2.3,3.3,1.0,versicolor
-5.6,2.7,4.2,1.3,versicolor
-5.7,3.0,4.2,1.2,versicolor
-5.7,2.9,4.2,1.3,versicolor
-6.2,2.9,4.3,1.3,versicolor
-5.1,2.5,3.0,1.1,versicolor
-5.7,2.8,4.1,1.3,versicolor
-6.3,3.3,6.0,2.5,virginica
-5.8,2.7,5.1,1.9,virginica
-7.1,3.0,5.9,2.1,virginica
-6.3,2.9,5.6,1.8,virginica
-6.5,3.0,5.8,2.2,virginica
-7.6,3.0,6.6,2.1,virginica
-4.9,2.5,4.5,1.7,virginica
-7.3,2.9,6.3,1.8,virginica
-6.7,2.5,5.8,1.8,virginica
-7.2,3.6,6.1,2.5,virginica
-6.5,3.2,5.1,2.0,virginica
-6.4,2.7,5.3,1.9,virginica
-6.8,3.0,5.5,2.1,virginica
-5.7,2.5,5.0,2.0,virginica
-5.8,2.8,5.1,2.4,virginica
-6.4,3.2,5.3,2.3,virginica
-6.5,3.0,5.5,1.8,virginica
-7.7,3.8,6.7,2.2,virginica
-7.7,2.6,6.9,2.3,virginica
-6.0,2.2,5.0,1.5,virginica
-6.9,3.2,5.7,2.3,virginica
-5.6,2.8,4.9,2.0,virginica
-7.7,2.8,6.7,2.0,virginica
-6.3,2.7,4.9,1.8,virginica
-6.7,3.3,5.7,2.1,virginica
-7.2,3.2,6.0,1.8,virginica
-6.2,2.8,4.8,1.8,virginica
-6.1,3.0,4.9,1.8,virginica
-6.4,2.8,5.6,2.1,virginica
-7.2,3.0,5.8,1.6,virginica
-7.4,2.8,6.1,1.9,virginica
-7.9,3.8,6.4,2.0,virginica
-6.4,2.8,5.6,2.2,virginica
-6.3,2.8,5.1,1.5,virginica
-6.1,2.6,5.6,1.4,virginica
-7.7,3.0,6.1,2.3,virginica
-6.3,3.4,5.6,2.4,virginica
-6.4,3.1,5.5,1.8,virginica
-6.0,3.0,4.8,1.8,virginica
-6.9,3.1,5.4,2.1,virginica
-6.7,3.1,5.6,2.4,virginica
-6.9,3.1,5.1,2.3,virginica
-5.8,2.7,5.1,1.9,virginica
-6.8,3.2,5.9,2.3,virginica
-6.7,3.3,5.7,2.5,virginica
-6.7,3.0,5.2,2.3,virginica
-6.3,2.5,5.0,1.9,virginica
-6.5,3.0,5.2,2.0,virginica
-6.2,3.4,5.4,2.3,virginica
-5.9,3.0,5.1,1.8,virginica
+,sepal_length,sepal_width,petal_length,petal_width,species
+0,5.1,3.5,1.4,0.2,setosa
+1,4.9,3.0,1.4,0.2,setosa
+2,4.7,3.2,1.3,0.2,setosa
+3,4.6,3.1,1.5,0.2,setosa
+4,5.0,3.6,1.4,0.2,setosa
+5,5.4,3.9,1.7,0.4,setosa
+6,4.6,3.4,1.4,0.3,setosa
+7,5.0,3.4,1.5,0.2,setosa
+8,4.4,2.9,1.4,0.2,setosa
+9,4.9,3.1,1.5,0.1,setosa
+10,5.4,3.7,1.5,0.2,setosa
+11,4.8,3.4,1.6,0.2,setosa
+12,4.8,3.0,1.4,0.1,setosa
+13,4.3,3.0,1.1,0.1,setosa
+14,5.8,4.0,1.2,0.2,setosa
+15,5.7,4.4,1.5,0.4,setosa
+16,5.4,3.9,1.3,0.4,setosa
+17,5.1,3.5,1.4,0.3,setosa
+18,5.7,3.8,1.7,0.3,setosa
+19,5.1,3.8,1.5,0.3,setosa
+20,5.4,3.4,1.7,0.2,setosa
+21,5.1,3.7,1.5,0.4,setosa
+22,4.6,3.6,1.0,0.2,setosa
+23,5.1,3.3,1.7,0.5,setosa
+24,4.8,3.4,1.9,0.2,setosa
+25,5.0,3.0,1.6,0.2,setosa
+26,5.0,3.4,1.6,0.4,setosa
+27,5.2,3.5,1.5,0.2,setosa
+28,5.2,3.4,1.4,0.2,setosa
+29,4.7,3.2,1.6,0.2,setosa
+30,4.8,3.1,1.6,0.2,setosa
+31,5.4,3.4,1.5,0.4,setosa
+32,5.2,4.1,1.5,0.1,setosa
+33,5.5,4.2,1.4,0.2,setosa
+34,4.9,3.1,1.5,0.2,setosa
+35,5.0,3.2,1.2,0.2,setosa
+36,5.5,3.5,1.3,0.2,setosa
+37,4.9,3.6,1.4,0.1,setosa
+38,4.4,3.0,1.3,0.2,setosa
+39,5.1,3.4,1.5,0.2,setosa
+40,5.0,3.5,1.3,0.3,setosa
+41,4.5,2.3,1.3,0.3,setosa
+42,4.4,3.2,1.3,0.2,setosa
+43,5.0,3.5,1.6,0.6,setosa
+44,5.1,3.8,1.9,0.4,setosa
+45,4.8,3.0,1.4,0.3,setosa
+46,5.1,3.8,1.6,0.2,setosa
+47,4.6,3.2,1.4,0.2,setosa
+48,5.3,3.7,1.5,0.2,setosa
+49,5.0,3.3,1.4,0.2,setosa
+50,7.0,3.2,4.7,1.4,versicolor
+51,6.4,3.2,4.5,1.5,versicolor
+52,6.9,3.1,4.9,1.5,versicolor
+53,5.5,2.3,4.0,1.3,versicolor
+54,6.5,2.8,4.6,1.5,versicolor
+55,5.7,2.8,4.5,1.3,versicolor
+56,6.3,3.3,4.7,1.6,versicolor
+57,4.9,2.4,3.3,1.0,versicolor
+58,6.6,2.9,4.6,1.3,versicolor
+59,5.2,2.7,3.9,1.4,versicolor
+60,5.0,2.0,3.5,1.0,versicolor
+61,5.9,3.0,4.2,1.5,versicolor
+62,6.0,2.2,4.0,1.0,versicolor
+63,6.1,2.9,4.7,1.4,versicolor
+64,5.6,2.9,3.6,1.3,versicolor
+65,6.7,3.1,4.4,1.4,versicolor
+66,5.6,3.0,4.5,1.5,versicolor
+67,5.8,2.7,4.1,1.0,versicolor
+68,6.2,2.2,4.5,1.5,versicolor
+69,5.6,2.5,3.9,1.1,versicolor
+70,5.9,3.2,4.8,1.8,versicolor
+71,6.1,2.8,4.0,1.3,versicolor
+72,6.3,2.5,4.9,1.5,versicolor
+73,6.1,2.8,4.7,1.2,versicolor
+74,6.4,2.9,4.3,1.3,versicolor
+75,6.6,3.0,4.4,1.4,versicolor
+76,6.8,2.8,4.8,1.4,versicolor
+77,6.7,3.0,5.0,1.7,versicolor
+78,6.0,2.9,4.5,1.5,versicolor
+79,5.7,2.6,3.5,1.0,versicolor
+80,5.5,2.4,3.8,1.1,versicolor
+81,5.5,2.4,3.7,1.0,versicolor
+82,5.8,2.7,3.9,1.2,versicolor
+83,6.0,2.7,5.1,1.6,versicolor
+84,5.4,3.0,4.5,1.5,versicolor
+85,6.0,3.4,4.5,1.6,versicolor
+86,6.7,3.1,4.7,1.5,versicolor
+87,6.3,2.3,4.4,1.3,versicolor
+88,5.6,3.0,4.1,1.3,versicolor
+89,5.5,2.5,4.0,1.3,versicolor
+90,5.5,2.6,4.4,1.2,versicolor
+91,6.1,3.0,4.6,1.4,versicolor
+92,5.8,2.6,4.0,1.2,versicolor
+93,5.0,2.3,3.3,1.0,versicolor
+94,5.6,2.7,4.2,1.3,versicolor
+95,5.7,3.0,4.2,1.2,versicolor
+96,5.7,2.9,4.2,1.3,versicolor
+97,6.2,2.9,4.3,1.3,versicolor
+98,5.1,2.5,3.0,1.1,versicolor
+99,5.7,2.8,4.1,1.3,versicolor
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+102,7.1,3.0,5.9,2.1,virginica
+103,6.3,2.9,5.6,1.8,virginica
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+105,7.6,3.0,6.6,2.1,virginica
+106,4.9,2.5,4.5,1.7,virginica
+107,7.3,2.9,6.3,1.8,virginica
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+109,7.2,3.6,6.1,2.5,virginica
+110,6.5,3.2,5.1,2.0,virginica
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+114,5.8,2.8,5.1,2.4,virginica
+115,6.4,3.2,5.3,2.3,virginica
+116,6.5,3.0,5.5,1.8,virginica
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+120,6.9,3.2,5.7,2.3,virginica
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+124,6.7,3.3,5.7,2.1,virginica
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+126,6.2,2.8,4.8,1.8,virginica
+127,6.1,3.0,4.9,1.8,virginica
+128,6.4,2.8,5.6,2.1,virginica
+129,7.2,3.0,5.8,1.6,virginica
+130,7.4,2.8,6.1,1.9,virginica
+131,7.9,3.8,6.4,2.0,virginica
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+141,6.9,3.1,5.1,2.3,virginica
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+145,6.7,3.0,5.2,2.3,virginica
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+147,6.5,3.0,5.2,2.0,virginica
+148,6.2,3.4,5.4,2.3,virginica
+149,5.9,3.0,5.1,1.8,virginica

nbs/tests/data/mocked_data_test_06.py

@@ -0,0 +1,56 @@
+import pandas as pd
+import numpy as np
+
+
+# Data for tests.
+# See: Asheber Abebe. Introduction to Design and Analysis of Experiments
+# with the SAS, from Example: Two-way RM Design Pg 137.
+# to remove the array wrapping behaviour of black
+# fmt: off
+hr = [72, 78, 71, 72, 66, 74, 62, 69, 69, 66, 84, 80, 72, 65, 75, 71,
+      86, 83, 82, 83, 79, 83, 73, 75, 73, 62, 90, 81, 72, 62, 69, 70]
+# fmt: on
+
+# Add experiment column
+e1 = np.repeat("Treatment1", 8).tolist()
+e2 = np.repeat("Control", 8).tolist()
+experiment = e1 + e2 + e1 + e2
+
+# Add a `Drug` column as the first variable
+d1 = np.repeat("AX23", 8).tolist()
+d2 = np.repeat("CONTROL", 8).tolist()
+drug = d1 + d2 + d1 + d2
+
+# Add a `Time` column as the second variable
+t1 = np.repeat("T1", 16).tolist()
+t2 = np.repeat("T2", 16).tolist()
+time = t1 + t2
+
+# Add an `id` column for paired data plotting.
+id_col = []
+for i in range(1, 9):
+    id_col.append(str(i) + "a")
+for i in range(1, 9):
+    id_col.append(str(i) + "c")
+id_col.extend(id_col)
+
+# Combine samples and gender into a DataFrame.
+df_test = pd.DataFrame(
+    {
+        "ID": id_col,
+        "Drug": drug,
+        "Time": time,
+        "Experiment": experiment,
+        "Heart Rate": hr,
+    }
+)
+
+
+df_test_control = df_test[df_test["Experiment"] == "Control"]
+df_test_control = df_test_control.pivot(index="ID", columns="Time", values="Heart Rate")
+
+
+df_test_treatment1 = df_test[df_test["Experiment"] == "Treatment1"]
+df_test_treatment1 = df_test_treatment1.pivot(
+    index="ID", columns="Time", values="Heart Rate"
+)