Plots for info on PP

bazilinskyy · Dec 29, 2024 · a0503db · a0503db
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diff --git a/figures/corr_matrix.jpg b/figures/corr_matrix.jpg
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diff --git a/figures/map_age.html b/figures/map_age.html
diff --git a/figures/map_age.png b/figures/map_age.png
diff --git a/figures/map_counts.html b/figures/map_counts.html
diff --git a/figures/map_counts.png b/figures/map_counts.png
diff --git a/figures/map_gender.html b/figures/map_gender.html
diff --git a/figures/map_gender.png b/figures/map_gender.png
diff --git a/figures/map_year_ad.html b/figures/map_year_ad.html
diff --git a/figures/map_year_ad.png b/figures/map_year_ad.png
diff --git a/figures/map_year_license.html b/figures/map_year_license.html
diff --git a/figures/map_year_license.png b/figures/map_year_license.png
diff --git a/trust/analysis/appen.py b/trust/analysis/appen.py
@@ -52,12 +52,8 @@ class Appen:
                        'what_is_your_age': 'age',
                        'what_is_your_gender': 'gender',
                        'what_is_the_highest_level_of_education_you_have_completed': 'education',  # noqa: E501
-                       'what_is_your_primary_mode_of_transportation': 'mode_transportation',  # noqa: E501
                        'which_input_device_are_you_using_now': 'device',
                        'if_you_answered_other_in_the_previous_question_please_describe_your_input_device_below': 'device_other',  # noqa: E501
-                       'as_a_driver_what_does_it_mean_to_you_when_a_pedestrian_makes_eye_contact_with_you': 'ec_driver',  # noqa: E501
-                       'as_a_pedestrian_what_does_it_mean_to_you_when_a_driver_makes_eye_contact_with_you': 'ec_pedestrian',  # noqa: E501
-                       'how_do_you_feel_about_the_following_communication_between_driver_and_pedestrian_is_important_for_road_safety': 'communication_importance',  # noqa: E501
                        'i_would_like_to_communicate_with_other_road_users_while_driving_for_instance_using_eye_contact_gestures_verbal_communication_etc': 'communcation_others',  # noqa: E501
                        'i_am_worried_about_where_all_this_technology_is_leading': 'technology_worried',  # noqa: E501
                        'i_enjoy_making_use_of_the_latest_technological_products_and_services_when_i_have_the_opportunity': 'technology_enjoyment',  # noqa: E501

diff --git a/trust/run.py b/trust/run.py
@@ -34,10 +34,10 @@
 CLEAN_DATA = False  # clean Appen data
 REJECT_CHEATERS = False  # reject cheaters on Appen
 CALC_COORDS = False  # extract points from heroku data
-UPDATE_MAPPING = True  # update mapping with keypress data
+UPDATE_MAPPING = False  # update mapping with keypress data
 SHOW_OUTPUT = True  # should figures be plotted
-SHOW_OUTPUT_KP = True  # should figures with keypress data be plotted
-SHOW_OUTPUT_ST = True  # should figures with stimulus data be plotted
+SHOW_OUTPUT_KP = False  # should figures with keypress data be plotted
+SHOW_OUTPUT_ST = False  # should figures with stimulus data be plotted
 SHOW_OUTPUT_PP = True  # should figures with info about participants be plotted
 SHOW_OUTPUT_ET = False  # should figures for eye tracking be plotted
 
@@ -352,16 +352,86 @@
                           color='country',
                           pretty_text=True,
                           save_file=True)
-            # driving with AVs
+            # histogram of driving frequency
+            analysis.hist(appen_data,
+                          x=['driving_freq'],
+                          pretty_text=True,
+                          save_file=True)
+            # grouped barchart of DBQ data
+            analysis.hist(appen_data,
+                          x=['dbq1_anger',
+                             'dbq2_speed_motorway',
+                             'dbq3_speed_residential',
+                             'dbq4_headway',
+                             'dbq5_traffic_lights',
+                             'dbq6_horn',
+                             'dbq7_mobile'],
+                          marginal='violin',
+                          pretty_text=True,
+                          save_file=True)
+            # histogram of the year of license
+            analysis.hist(appen_data,
+                          x=['year_license'],
+                          pretty_text=True,
+                          save_file=True)
+            # histogram of the highest level of education
+            analysis.hist(appen_data,
+                          x=['education'],
+                          pretty_text=True,
+                          save_file=True)
+            # histogram of communication with other
+            analysis.hist(appen_data,
+                          x=['communcation_others'],
+                          marginal='violin',
+                          pretty_text=True,
+                          save_file=True)
+            # grouped barchart of technology questions
+            analysis.hist(appen_data,
+                          x=['technology_worried',
+                             'technology_enjoyment',
+                             'technology_lives_easier',
+                             'technology_lives_change',
+                             'technology_not_interested'],
+                          marginal='violin',
+                          pretty_text=True,
+                          save_file=True)
+            # scatter plot of machines questions
+            analysis.scatter(appen_data,
+                             x='machines_roles',
+                             y='machines_profit',
+                             color='year_license',
+                             pretty_text=True,
+                             save_file=True)
+            # histogram of attitude towards AD
+            analysis.hist(appen_data,
+                          x=['attitude_ad'],
+                          pretty_text=True,
+                          save_file=True)
+            # scatter plot of driving with AVs
             analysis.scatter(appen_data,
                              x='driving_in_ad',
                              y='driving_alongside_ad',
                              color='year_license',
                              pretty_text=True,
                              save_file=True)
-            # histogram for driving frequency
+            # histogram of the capability of AD
             analysis.hist(appen_data,
-                          x=['driving_freq'],
+                          x=['capability_ad'],
+                          pretty_text=True,
+                          save_file=True)
+            # histogram of the experience of AD
+            analysis.hist(appen_data,
+                          x=['experience_ad'],
+                          pretty_text=True,
+                          save_file=True)
+            # histogram of the input device
+            analysis.hist(appen_data,
+                          x=['device'],
+                          pretty_text=True,
+                          save_file=True)
+            # histogram of milage
+            analysis.hist(appen_data,
+                          x=['milage'],
                           pretty_text=True,
                           save_file=True)
             # map of participants