Remove p and np conformant limits [#25]

Author: jchester

sql/02-spc-intermediates-schema.sql

@@ -208,7 +208,6 @@ create view spc_intermediates.fraction_conforming_sample_statistics as
 -- every sample has been reduced to a single number, being the fraction.
 create view spc_intermediates.conformant_limit_establishment_statistics as
   select w.id                              as limit_establishment_window_id
-       , avg(mean_fraction_conforming)     as grand_mean_conforming
        , avg(mean_fraction_non_conforming) as grand_mean_non_conforming
        , avg(sample_size)                  as mean_sample_size
   from spc_intermediates.fraction_conforming_sample_statistics fcss
@@ -217,21 +216,6 @@ create view spc_intermediates.conformant_limit_establishment_statistics as
     and fcss.include_in_limit_calculations
   group by w.id;
 
--- For each limit establishment window, this view derives the p chart upper control limit, center line and lower control
--- limit for fraction conforming (aka a yield chart). The limits are based on a function of the grand mean of fractions
--- conforming.
---
--- This chart is not very commonly used; it is more traditional to use the fraction non-conforming for control. This is
--- included mostly for completeness.
-create view spc_intermediates.p_limits_conformant as
-  select limit_establishment_window_id
-       , grand_mean_conforming + (3 * (sqrt((grand_mean_conforming * (1.0 - grand_mean_conforming)) /
-                                            mean_sample_size)))                as upper_limit
-       , grand_mean_conforming                                                 as center_line
-       , greatest(0.0, grand_mean_conforming - (3 * (sqrt((grand_mean_conforming * (1.0 - grand_mean_conforming)) /
-                                                          mean_sample_size)))) as lower_limit
-  from spc_intermediates.conformant_limit_establishment_statistics;
-
 -- For each limit establishment window, this view derives the p chart upper control limit, center line and lower control
 -- limit for fraction non-conforming (aka a fallout chart). The limits are based on a function of the grand mean of
 -- fractions non-conforming.
@@ -249,20 +233,6 @@ create view spc_intermediates.p_limits_non_conformant as
 
 -- np charts
 
--- For each limit establishment window, this view derives the np chart (number conforming) upper control limit, center
--- line and lower control limit. Note that the p chart and np chart can disagree on whether a sample is in-control or
--- not, because the limits are calculated as decimals but samples are composed of an integer number of inspected items.
---
--- This is the conforming version of an np chart, rarely used.
-create view spc_intermediates.np_limits_conformant as
-  select limit_establishment_window_id
-       , (grand_mean_conforming * mean_sample_size) +
-         (3 * (sqrt((grand_mean_conforming * mean_sample_size) * (1.0 - grand_mean_conforming)))) as upper_limit
-       , grand_mean_conforming * mean_sample_size                                                 as center_line
-       , greatest(0.0, (3 * (sqrt((grand_mean_conforming * mean_sample_size) *
-                                  (1.0 - grand_mean_conforming)))))                               as lower_limit
-  from spc_intermediates.conformant_limit_establishment_statistics;
-
 -- For each limit establishment window, this view derives the np chart (number non-conforming) upper control limit,
 -- center line and lower control limit. Note that the p chart and np chart can disagree whether a sample is in-control
 -- or not, because the limits are calculated as decimals but samples are composed of an integer number of inspected

sql/03-spc-reports-schema.sql

@@ -127,34 +127,6 @@ create view spc_reports.s_rules as
        join spc_intermediates.s_limits on limits_w.id = s_limits.limit_establishment_window_id
   where include_in_limit_calculations;
 
--- This view applies the limits derived in p_limits_conformant to matching control windows, showing which sample
--- fractions conforming were in-control and out-of-control according to the limits on the fraction conforming.
---
--- This is a non-traditional application, the typical approach is to set rules on fraction non-conforming. This is
--- included for completeness.
-create view spc_reports.p_conformant_rules as
-  select ss.sample_id
-       , control_w.id             as control_window_id
-       , limits_w.id              as limit_establishment_window_id
-       , i.id                     as instrument_id
-       , center_line
-       , mean_fraction_conforming as controlled_value
-       , lower_limit
-       , upper_limit
-       , case
-           when mean_fraction_conforming > upper_limit then 'out_of_control_upper'
-           when mean_fraction_conforming < lower_limit then 'out_of_control_lower'
-           else 'in_control'
-         end                      as control_status
-  from spc_intermediates.fraction_conforming_sample_statistics ss
-       join spc_data.windows                                   control_w on ss.window_id = control_w.id
-       join spc_data.window_relationships                      wr on control_w.id = wr.control_window_id
-       join spc_data.windows                                   limits_w
-            on limits_w.id = wr.limit_establishment_window_id
-       join spc_data.instruments                               i on control_w.instrument_id = i.id
-       join spc_intermediates.p_limits_conformant on limits_w.id = p_limits_conformant.limit_establishment_window_id
-  where include_in_limit_calculations;
-
 -- This view applies the limits derived in p_limits_non_conformant to matching control windows, showing which sample
 -- fractions non-conforming were in-control and out-of-control according to the limits on the fraction non-conforming.
 create view spc_reports.p_non_conformant_rules as
@@ -181,34 +153,6 @@ create view spc_reports.p_non_conformant_rules as
             on limits_w.id = p_limits_non_conformant.limit_establishment_window_id
   where include_in_limit_calculations;
 
--- This view applies the limits derived in np_limits_conformant to matching control windows, showing which sample counts
--- conforming were in-control and out-of-control according to the limits on the count conforming.
---
--- This is a non-traditional application, the typical approach is to set rules on fraction non-conforming. This is
--- included for completeness.
-create view spc_reports.np_conformant_rules as
-  select ss.sample_id
-       , control_w.id                           as control_window_id
-       , limits_w.id                            as limit_establishment_window_id
-       , i.id                                   as instrument_id
-       , center_line
-       , mean_fraction_conforming * sample_size as controlled_value
-       , lower_limit
-       , upper_limit
-       , case
-           when (mean_fraction_conforming * sample_size) > upper_limit then 'out_of_control_upper'
-           when (mean_fraction_conforming * sample_size) < lower_limit then 'out_of_control_lower'
-           else 'in_control'
-         end                                    as control_status
-  from spc_intermediates.fraction_conforming_sample_statistics ss
-       join spc_data.windows                                   control_w on ss.window_id = control_w.id
-       join spc_data.window_relationships                      wr on control_w.id = wr.control_window_id
-       join spc_data.windows                                   limits_w
-            on limits_w.id = wr.limit_establishment_window_id
-       join spc_data.instruments                               i on control_w.instrument_id = i.id
-       join spc_intermediates.np_limits_conformant on limits_w.id = np_limits_conformant.limit_establishment_window_id
-  where include_in_limit_calculations;
-
 -- This view applies the limits derived in np_limits_non_conformant to matching control windows, showing which sample
 -- counts non-conforming were in-control and out-of-control according to the limits on the counts non-conforming.
 create view spc_reports.np_non_conformant_rules as