From e796c5b90a694b13b1782ac8dc0a9c4f06561db1 Mon Sep 17 00:00:00 2001 From: rpauly18 Date: Mon, 26 Oct 2020 14:54:41 -0600 Subject: [PATCH 1/3] updating qc example --- examples/qc_example.html | 1033 +++++++++++++++++++------------------- examples/qc_example.mlx | Bin 845871 -> 749723 bytes 2 files changed, 515 insertions(+), 518 deletions(-) diff --git a/examples/qc_example.html b/examples/qc_example.html index cf39ba551..5e2f3d973 100644 --- a/examples/qc_example.html +++ b/examples/qc_example.html @@ -1,11 +1,11 @@ -Example: MHKiT-MATLAB Quality Control Module

Example: MHKiT-MATLAB Quality Control Module

The following example runs a simple quality control analysis on wave elevation data using the MHKiT QC module. The data file used in this example is stored in the \MHKiT\examples\data directory.

Import Data

Load the data into a table. The data includes several issues, including timestamps that are out of order, corrupt data with values of -999, data outside expected range, and stagant data.
data=readtable('../examples/data/qc/wave_elevation_data.csv');
disp(data)
Time probe1 probe2 probe3 +.S12 { border-left: 1px solid rgb(233, 233, 233); border-right: 1px solid rgb(233, 233, 233); border-top: 0px none rgb(0, 0, 0); border-bottom: 1px solid rgb(233, 233, 233); border-radius: 0px 0px 4px 4px; padding: 0px 45px 4px 13px; line-height: 16.996px; min-height: 18px; white-space: nowrap; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; } +.S13 { border-left: 1px solid rgb(233, 233, 233); border-right: 1px solid rgb(233, 233, 233); border-top: 1px solid rgb(233, 233, 233); border-bottom: 1px solid rgb(233, 233, 233); border-radius: 4px 4px 0px 0px; padding: 6px 45px 4px 13px; line-height: 16.996px; min-height: 18px; white-space: nowrap; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; } +.S14 { border-left: 1px solid rgb(233, 233, 233); border-right: 1px solid rgb(233, 233, 233); border-top: 1px solid rgb(233, 233, 233); border-bottom: 1px solid rgb(233, 233, 233); border-radius: 0px; padding: 6px 45px 4px 13px; line-height: 16.996px; min-height: 18px; white-space: nowrap; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; }

Example: MHKiT-MATLAB Quality Control Module

The following example runs a simple quality control analysis on wave elevation data using the MHKiT QC module. The data file used in this example is stored in the \MHKiT\examples\data directory.

Import Data

Load the data into a table. The data includes several issues, including timestamps that are out of order, corrupt data with values of -999, data outside expected range, and stagant data.
data=readtable('../examples/data/qc/wave_elevation_data.csv');
disp(data)
Time probe1 probe2 probe3 ______ ______ ______ ______ 10 24.48 28.27 1.3 @@ -535,514 +535,514 @@ 10.994 -11.52 -10.73 2.3 10.996 -12.52 -16.73 -4.7 10.998 -19.52 -23.73 19.3 - 11 -14.52 -19.73 18.3
set(gcf, 'Position', [100, 100, 2000, 600]); % setting the size of the plots
plot(data.Time,[data.probe1,data.probe2,data.probe3]);
ylim([-60 60]);
xlabel('Time')
legend('probe 1', 'probe 2', 'probe 3');
The MHKiT-Matlab QC module utilizes structures. WE need to convert the table of data into a structure.
datast.time=data.Time;
datast.values = table2array(data(:,2:4));
datast
datast = struct with fields:
time: [500×1 double] + 11 -14.52 -19.73 18.3
set(gcf, 'Position', [100, 100, 2000, 600]); % setting the size of the plots
plot(data.Time,[data.probe1,data.probe2,data.probe3]);
ylim([-60 60]);
xlabel('Time')
legend('probe 1', 'probe 2', 'probe 3');
The MHKiT-Matlab QC module utilizes structures. WE need to convert the table of data into a structure.
datast.time=data.Time;
datast.values = table2array(data(:,2:4));
datast
datast = struct with fields:
time: [500×1 double] values: [500×3 double] -

Quality control tests

The following quality control tests are used to identify timestamp issues, corrupt data, data outside expected range, and stagnant data.
Each quality control tests results in the following information in a results structure:
  • Cleaned data that has NaN in place of data that did not pass the quality control test
  • Boolean mask with True/False that indicates if each data point passed the quality control test

Check timestamp

Quality control analysis generally starts by checking the timestamp index of the data.
The follow test checks to see if 1) the data contains duplicate timestamps, 2) timestamps are not monotonically increasing, and 3) timestamps occur at irregular intervals (an interval of 0.002s is expected for this data).
If duplicate timestamps are found, the resulting structure keeps the first occurrence. If timestamps are not monotonic, the timestamps in the resulting structure are reordered.
% define expected frequency of the data, in seconds
frequency = 0.002;
% run the timestamp quality control test
results = check_timestamp(datast,frequency);
The cleaned data and boolean mask are shown below.
disp(results)
values: [501×3 double] +

Quality control tests

The following quality control tests are used to identify timestamp issues, corrupt data, data outside expected range, and stagnant data.
Each quality control tests results in the following information in a results structure:
  • Cleaned data that has NaN in place of data that did not pass the quality control test
  • Boolean mask with True/False that indicates if each data point passed the quality control test

Check timestamp

Quality control analysis generally starts by checking the timestamp index of the data.
The follow test checks to see if 1) the data contains duplicate timestamps, 2) timestamps are not monotonically increasing, and 3) timestamps occur at irregular intervals (an interval of 0.002s is expected for this data).
If duplicate timestamps are found, the resulting structure keeps the first occurrence. If timestamps are not monotonic, the timestamps in the resulting structure are reordered.
% define expected frequency of the data, in seconds
frequency = 0.002;
% run the timestamp quality control test
results = check_timestamp(datast,frequency);
The cleaned data and boolean mask are shown below.
disp(results)
values: [501×3 double] mask: [501×3 int64] - time: [1×501 datetime]
plot(results.time,results.values);
ylim([-60 60]);
xlabel('Time')
legend('probe 1', 'probe 2', 'probe 3');

Check for corrupt data

In the following qualtiy control tests, the cleaned data from the previous test are used as input to the subsequent test. For each quality control test, a plot of the cleaned data is shown.
The quality control test below checks for corrupt data, indicated by a value of -999.
%define corrupt values
corrupt_values = {-999};
% run the corrupt data QC test
results = check_corrupt(results,corrupt_values);
% Plot the cleaned data
plot(results.time,results.values);
ylim([-60 60]);
xlabel('Time')
legend('probe 1', 'probe 2', 'probe 3');

Check for data ourside expected range

The next quality control test checks for data that is greater than 50 or less than -50. Note that expected range tests can also be used to compare measured values to a model, or analyze the expected relationships between data columns.
% define expected lower and upper bound
expected_bounds = [-50,50];
% run expected range QC test
results = check_range(results,expected_bounds);
% Plot the cleaned data
plot(results.time,results.values);
ylim([-60 60]);
xlabel('Time')
legend('probe 1', 'probe 2', 'probe 3');

Check for stagnant data

The final quality control test checks for stagnant data by looking for data that changes by less than 0.001 within a 0.02 second moving window.
% define the lower bound (no upper bound is specified in this example)
expected_bound = {0.001,py.None};
% define the moving window in seconds
window = 0.02;
% input the data, boundaries, py.None indicates apply to all columns, and window
results = check_delta(results,expected_bound,py.None,window);
% plot cleaned data
plot(results.time,results.values);
ylim([-60 60]);
xlabel('Time')
legend('probe 1', 'probe 2', 'probe 3');

Cleaned Data

The cleaned data can be used directly in MHKiT analysis, or the missing values can be replaced using various methods before analysis is run. Data replacement strategies are generally defined on a case by case basis. Pandas includes methods to interpolate, replace, and fill missing values.
% Extract final cleaned data for MHKiT analysis
results
results = struct with fields:
values: [501×3 double] + time: [1×501 datetime]
plot(results.time,results.values);
ylim([-60 60]);
xlabel('Time')
legend('probe 1', 'probe 2', 'probe 3');

Check for corrupt data

In the following qualtiy control tests, the cleaned data from the previous test are used as input to the subsequent test. For each quality control test, a plot of the cleaned data is shown.
The quality control test below checks for corrupt data, indicated by a value of -999.
%define corrupt values
corrupt_values = {-999};
% run the corrupt data QC test
results = check_corrupt(results,corrupt_values);
% Plot the cleaned data
plot(results.time,results.values);
ylim([-60 60]);
xlabel('Time')
legend('probe 1', 'probe 2', 'probe 3');

Check for data ourside expected range

The next quality control test checks for data that is greater than 50 or less than -50. Note that expected range tests can also be used to compare measured values to a model, or analyze the expected relationships between data columns.
% define expected lower and upper bound
expected_bounds = [-50,50];
% run expected range QC test
results = check_range(results,expected_bounds);
% Plot the cleaned data
plot(results.time,results.values);
ylim([-60 60]);
xlabel('Time')
legend('probe 1', 'probe 2', 'probe 3');

Check for stagnant data

The final quality control test checks for stagnant data by looking for data that changes by less than 0.001 within a 0.02 second moving window.
% define the lower bound (no upper bound is specified in this example)
expected_bound = {0.001,py.None};
% input the data, boundaries, py.None indicates apply to all columns, and window
results = check_delta(results,expected_bound);
% plot cleaned data
plot(results.time,results.values);
ylim([-60 60]);
xlabel('Time')
legend('probe 1', 'probe 2', 'probe 3');

Cleaned Data

The cleaned data can be used directly in MHKiT analysis, or the missing values can be replaced using various methods before analysis is run. Data replacement strategies are generally defined on a case by case basis. Pandas includes methods to interpolate, replace, and fill missing values.
% Extract final cleaned data for MHKiT analysis
results
results = struct with fields:
values: [501×3 double] mask: [501×3 int64] time: [1×501 datetime] -
disp(results.values)
24.4800 28.2700 1.3000 - 34.4800 40.2700 -8.7000 - 30.4800 38.2700 -13.7000 - 12.4800 24.2700 -32.7000 - 13.4800 22.2700 -21.7000 - 7.4800 15.2700 -13.7000 - 4.4800 13.2700 -19.7000 - -9.5200 -0.7300 -11.7000 - 8.4800 19.2700 -16.7000 - -4.5200 8.2700 -34.7000 - 16.4800 27.2700 5.3000 - -13.5200 -0.7300 -36.7000 - -13.5200 0.2700 -39.7000 - -15.5200 -3.7300 -32.7000 - -22.5300 -17.7300 -23.7000 - -22.5200 -16.7300 16.3000 - -30.5200 -23.7300 1.3000 - -30.5200 -16.7300 11.3000 - -36.5200 -24.7300 13.3000 - -26.5200 -13.7300 23.3000 - -28.5200 -14.7300 25.3000 - NaN NaN NaN - NaN NaN NaN - -14.5200 -11.7300 33.3000 - -10.5200 -1.7300 36.3000 - -19.5200 -10.7300 28.3000 - -3.5200 7.2700 39.3000 - 7.4800 20.2700 26.3000 - -2.5200 14.2700 11.3000 - 4.4800 19.2700 13.3000 - 2.4800 17.2700 16.3000 - 9.4800 22.2700 2.3000 - 17.4800 23.2700 19.3000 - 16.4800 27.2700 26.3000 - 33.4800 42.2700 5.3000 - 28.4800 36.2700 -6.7000 - 20.4800 36.2700 2.3000 - 17.4800 30.2700 -7.7000 - 11.4800 23.2700 -14.7000 - 16.4800 24.2700 2.3000 - 4.4800 12.2700 -8.7000 - 15.4800 22.2700 -9.7000 - 9.4800 21.2700 -15.7000 - -2.5200 7.2700 -14.7000 - -7.5200 9.2700 -31.7000 - 3.4800 17.2700 -40.7000 - -2.5200 8.2700 -46.7000 - -13.5200 -2.7300 -19.7000 - -4.5200 3.2700 -13.7000 - -14.5200 -7.7300 NaN - -18.5200 -11.7300 NaN - -40.5200 -22.7300 NaN - -33.5200 -18.7300 NaN - -40.5200 -23.7300 NaN - -34.5200 -18.7300 NaN - NaN -27.7300 NaN - NaN -19.7300 NaN - NaN -2.7300 NaN - NaN -21.7300 46.3000 - NaN -26.7300 21.3000 - NaN -14.7300 31.3000 - NaN -13.7300 30.3000 - NaN -16.7300 15.3000 - NaN -15.7300 34.3000 - NaN -18.7300 47.3000 - NaN -0.7300 31.3000 - NaN -1.7300 48.3000 - NaN 4.2700 44.3000 - 4.4800 13.2700 22.3000 - 15.4800 20.2700 10.3000 - 4.4800 20.2700 13.3000 - 18.4800 27.2700 21.3000 - 23.4800 34.2700 -1.7000 - 20.4800 26.2700 -7.7000 - 13.4800 21.2700 17.3000 - 27.4800 36.2700 -1.7000 - 20.4800 31.2700 -1.7000 - 16.4800 28.2700 -17.7000 - 12.4800 27.2700 -27.7000 - 24.4800 31.2700 -35.7000 - 10.4800 20.2700 -15.7000 - 10.4800 17.2700 -25.7000 - 11.4800 16.2700 -3.7000 - 3.4800 7.2700 -25.7000 - -14.5200 -7.7300 -24.7000 - -5.5200 6.2700 -24.7000 - -7.5200 1.2700 -24.7000 - -13.5200 -2.7300 -42.7000 - -13.5200 -3.7300 -19.7000 - -20.5200 -11.7300 -8.7000 - -11.5200 -10.7300 -21.7000 - -27.5200 -22.7300 2.3000 - -28.5200 -21.7300 17.3000 - -29.5200 -17.7300 11.3000 - -28.5200 -17.7300 0.3000 - -41.5200 -30.7300 30.3000 - -35.5200 -26.7300 18.3000 - -33.5200 -26.7300 10.3000 - -20.5200 -17.7300 33.3000 - -17.5200 -11.7300 29.3000 - -10.5200 -8.7300 49.3000 - 4.4800 13.2700 28.3000 - 2.4800 12.2700 25.3000 - 0.4800 14.2700 21.3000 - -7.5200 3.2700 NaN - -6.5200 1.2700 NaN - 16.4800 19.2700 18.3000 - 9.4800 16.2700 19.3000 - 12.4800 19.2700 26.3000 - 24.4800 31.2700 14.3000 - 20.4800 31.2700 -3.7000 - 18.4800 31.2700 -0.7000 - 15.4800 27.2700 13.3000 - 30.4800 34.2700 -11.7000 - 16.4800 23.2700 -15.7000 - 26.4800 23.2700 -0.7000 - 23.4800 28.2700 8.3000 - 13.4800 16.2700 -36.7000 - 1.4800 8.2700 -17.7000 - 16.4800 23.2700 -24.7000 - -7.5200 4.2700 NaN - -12.5200 -2.7300 -21.7000 - -2.5200 1.2700 -4.7000 - -6.5200 -9.7300 -19.7000 - -17.5200 -16.7300 -21.7000 - -12.5200 -13.7300 1.3000 - -22.5200 -17.7300 0.3000 - -24.5200 -21.7300 -20.7000 - -38.5200 -30.7300 21.3000 - -34.5200 -24.7300 10.3000 - -22.5200 -18.7300 30.3000 - -24.5200 -24.7300 21.3000 - -25.5200 -23.7300 42.3000 - -18.5200 -16.7300 15.3000 - -20.5200 -19.7300 17.3000 - -29.5200 -26.7300 23.3000 - -21.5200 -18.7300 26.3000 - -3.5200 5.2700 30.3000 - 4.4800 7.2700 34.3000 - 5.4800 8.2700 45.3000 - 7.4800 7.2700 44.3000 - 26.4800 24.2700 22.3000 - 17.4800 16.2700 9.3000 - 23.4800 24.2700 5.3000 - 28.4800 23.2700 5.3000 - 33.4800 36.2700 -9.7000 - 28.4800 35.2700 -0.7000 - 31.4800 30.2700 1.3000 - 19.4800 32.2700 -4.7000 - 18.4800 20.2700 -12.7000 - 7.4800 15.2700 -14.7000 - 8.4800 9.2700 -25.7000 - 10.4800 8.2700 -24.7000 - 12.4800 13.2700 -25.7000 - 8.4800 16.2700 -39.7000 - 4.4800 14.2700 -34.7000 - 17.4800 11.2700 -23.7000 - 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-32.5200 NaN 9.3000 - -21.5200 NaN 11.3000 - -17.5200 NaN 2.3000 - -21.5200 NaN 13.3000 - -22.5200 NaN 6.3000 - -11.5200 NaN 19.3000 - -9.5200 NaN 14.3000 - -29.5200 NaN 10.3000 - -17.5200 NaN 15.3000 - -4.5200 NaN 30.3000 - -6.5200 NaN 18.3000 - -6.5200 NaN 22.3000 - 17.4800 NaN 10.3000 - 18.4800 NaN 19.3000 - 9.4800 NaN -9.7000 - 18.4800 NaN 11.3000 - 16.4800 NaN -1.7000 - 28.4800 NaN -23.7000 - 17.4800 NaN -8.7000 - 25.4800 NaN -11.7000 - 25.4800 NaN -15.7000 - 31.4800 NaN -13.7000 - 15.4800 NaN -15.7000 - 11.4800 NaN -22.7000 - 21.4800 NaN -15.7000 - 17.4800 NaN -22.7000 - 10.4800 NaN -23.7000 - 9.4800 NaN -29.7000 - 17.4800 NaN -30.7000 - 0.4800 NaN -39.7000 - -4.5200 NaN -38.7000 - 0.4800 NaN -24.7000 - -10.5200 NaN -34.7000 - -7.5200 NaN -31.7000 - -12.5200 -18.7300 9.3000 - -10.5200 -18.7300 5.3000 - -21.5200 -27.7300 -16.7000 - -30.5200 -33.7300 1.3000 - -34.5200 -34.7300 -6.7000 - -24.5200 -23.7300 -20.7000 - -28.5200 -31.7300 9.3000 - -34.5200 -36.7300 8.3000 - -25.5200 -30.7300 23.3000 - 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z19|q(Z|hjrq4#Sq-X#GuJHBz~*fPWV>FMnL${9ZE18z<7cdMFlEsaJP+ll}n1^_^Y2>{TdzuK9(fK9*~QlbM(V^RWiE2;EiI zE7S?L2+XTY{FHdOcrhV4kCK_U5|Uu0bgKi27mxHI%O}@|WOd;l-YY4V(c$*S{ZU%C z9Do_aWGazV+toTs49$rj+$<@2gSFzDLchGy#^fWTtn3jiHwc z!cL5PNjspdxI1DaMgeS`X6qA^9CVy|8OfbhZh!*BX{lpC86M2#%Xichbl{I(GgqSfeNU-Zh{1_J_? zwDf|VN3H0>E~eD;rwfu5>BrU%Zyr4Yziuy!=l}F8PVR&(#;H`Ol^y2RjW;h>c*2}W z>n<#p*uJaC4b!V(R5WUx|br!Qr?INsSJ(4f35tJmJ87 zrqprpwM0`yjD@jtE~@!15nkj5|LJp59lP<`r_FXqz~A%do>xq6M(2G8oj>TG@|W{) zF|&8Jg4q8qEB)w3jZR)NIjCHY!K>Zr;a*j2A?y*zRN6F9=~#gP9~$G&F>x_3(DPv5 z5BxddnC|(HsEKs-Hj&|#`IC#fyQ5B@2!qH!-7DNQOb`_(8eZZBd8s(&X`1)jo&?RG z6eYNpX?BN6m~Lk)*Clgn8=qQZH?^fCOy+CEpfyhUQ-rzW??fq`GU=L&(g7=ez1xUeU`M z&fV9A*p+`Av|33&#BeG>ZA(E$ev#CopE{1v!(0r&A0>Gn+4Z=cR>*y#2VAb=rkFd| zI>NNyx@4%Pgn`zNzo^Qd6O0E^bTYGb=HdSR{HvppuC%&NJS0^V4*&>c{ Date: Mon, 26 Oct 2020 14:55:26 -0600 Subject: [PATCH 2/3] updating qc calls to work with pecos update --- mhkit/qc/check_corrupt.m | 28 +++++++++--------- mhkit/qc/check_delta.m | 53 ++++++++++++--------------------- mhkit/qc/check_increment.m | 42 ++++++++++++-------------- mhkit/qc/check_missing.m | 26 +++++++---------- mhkit/qc/check_outlier.m | 45 ++++++++++++++-------------- mhkit/qc/check_range.m | 31 +++++++++----------- mhkit/qc/check_timestamp.m | 60 ++++++++++++++++++++------------------ 7 files changed, 127 insertions(+), 158 deletions(-) diff --git a/mhkit/qc/check_corrupt.m b/mhkit/qc/check_corrupt.m index ae4755a92..da710a5b3 100755 --- a/mhkit/qc/check_corrupt.m +++ b/mhkit/qc/check_corrupt.m @@ -1,4 +1,4 @@ -function results = check_corrupt(data,vals,varargin) +function results = check_corrupt(data,vals,options) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check for data that is outside expected range @@ -27,13 +27,13 @@ % key: string (optional) % Data column name or translation dictionary key. If not specified % or set to py.None, all columns are used for test. +% to call: check_corrupt(data,vals,"key",key) % % min_failures: int (optional) % Minimum number of consecutive failures required for reporting % default = 1 +% to call: check_corrupt(data,vals,"min_failures",min_failures) % -% Must set previous arguments to use later optional arguments -% (i.e. must set key to use min_failures). % % Returns % --------- @@ -51,6 +51,14 @@ % Same as input times % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +arguments + data + vals + options.key = py.None; + options.min_failures = 1; +end + py.importlib.import_module('pecos'); % check to see if a pandas dataframe or not @@ -58,19 +66,9 @@ data=qc_data_to_dataframe(data); end vals = py.list(vals); - % Must use optional qc arguments in order, for now. - if nargin == 2 - - r = struct(py.pecos.monitoring.check_corrupt(data,vals)); - elseif nargin == 3 - r = struct(py.pecos.monitoring.check_corrupt(data,vals,varargin{1})); - elseif nargin == 4 - r = struct(py.pecos.monitoring.check_corrupt(data,vals,varargin{1},varargin{2})); - else - ME = MException('MATLAB:qc_corrupt','incorrect number of arguments (2 to 4)'); - throw(ME); - end +r = struct(py.pecos.monitoring.check_corrupt(data,vals,pyargs("key",options.key,... + "min_failures",int32(options.min_failures)))); % Convert to qcdata structure results.values=double(r.cleaned_data.values); results.mask=int64(r.mask.values); diff --git a/mhkit/qc/check_delta.m b/mhkit/qc/check_delta.m index f2a8d6a8c..093959657 100755 --- a/mhkit/qc/check_delta.m +++ b/mhkit/qc/check_delta.m @@ -1,4 +1,4 @@ -function results = check_delta(data,bound,varargin) +function results = check_delta(data,bound,options) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check for stagnant data and/or abrupt changes in the data using @@ -26,10 +26,7 @@ % key: string (optional) % Data column name or translation dictionary key. If not specified % or set to py.None, all columns are used for test. -% -% window: int (optional) -% Size of rolling window (in seconds) used to compute delta -% default = 3600 +% to call: check_delta(data,bound,"key",key) % % direction: string (optional) % Options: 'positive', 'negative', or py.None (default) @@ -37,14 +34,13 @@ % (the min occurs before the max) % If direction is negative, then only identify negative deltas (the max occurs before the min) % If direction is py.None, then identify both positive and negative deltas +% to call: check_delta(data,bound,"direction",direction) % % min_failures: int (optional) -% % Minimum number of consecutive failures required for reporting, % default = 1 +% to call: check_delta(data,bound,"min_failures",min_failures) % -% Must set previous arguments to use later optional arguments -% (i.e. must set key to use min_failures). % % Returns % --------- @@ -63,40 +59,27 @@ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +arguments + data + bound + options.key = py.None; + options.direction = py.None; + options.min_failures = 1; +end + py.importlib.import_module('pecos'); + % check to see if a pandas dataframe or not if (isa(data,'py.pandas.core.frame.DataFrame')~=1) data=qc_data_to_dataframe(data); end bound = py.list(bound); - if nargin == 2 - - r = struct(py.pecos.monitoring.check_delta(data,bound)); - elseif nargin == 3 - - r = struct(py.pecos.monitoring.check_delta(data,bound,... - varargin{1})); - elseif nargin == 4 - - r = struct(py.pecos.monitoring.check_delta(data,bound,... - varargin{1},varargin{2})); - elseif nargin == 5 - - r = struct(py.pecos.monitoring.check_delta(data,bound,... - varargin{1},varargin{2},varargin{3})); - elseif nargin == 6 - - r = struct(py.pecos.monitoring.check_delta(data,bound,... - varargin{1},varargin{2},varargin{3},varargin{4})); - elseif nargin == 7 - - r = struct(py.pecos.monitoring.check_delta(data,bound,... - varargin{1},varargin{2},varargin{3},varargin{4},varargin{5})); - else - ME = MException('MATLAB:qc_delta','incorrect number of arguments (2 to 7)'); - throw(ME); - end + + +r = struct(py.pecos.monitoring.check_delta(data,bound,... + pyargs("key",options.key,"direction",options.direction,... + "min_failures",int32(options.min_failures)))); % Convert to qcdata structure results.values=double(r.cleaned_data.values); diff --git a/mhkit/qc/check_increment.m b/mhkit/qc/check_increment.m index ba6b9f122..bd10a2740 100755 --- a/mhkit/qc/check_increment.m +++ b/mhkit/qc/check_increment.m @@ -1,4 +1,4 @@ -function results = check_increment(data,bound,varargin) +function results = check_increment(data,bound,options) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check data increments using the difference between values @@ -25,19 +25,21 @@ % key: string (optional) % Data column name or translation dictionary key. If not specified % or set to py.None, all columns are used for test. +% to call: check_increment(data,bound,"key",key) % % increment: int (optional) % Time step shift used to compute difference, default = 1 +% to call: check_increment(data,bound,"increment",increment) % % absolute_value: logical (optional) -% Use the absolute value of increment data, default = 1 (True) +% Use the absolute value of increment data, default = py.True +% to call: check_increment(data,bound,"absolute_value",absolute_value) % % min_failures: int (optional) % Minimum number of consecutive failures required for reporting % default = 1 +% to call: check_increment(data,bound,"min_failures",min_failures) % -% Must set previous arguments to use later optional arguments -% (i.e. must set key to use min_failures). % % Returns % --------- @@ -55,6 +57,16 @@ % Same as input times % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +arguments + data + bound + options.key = py.None; + options.increment = 1; + options.absolute_value = py.True; + options.min_failures = 1; +end + py.importlib.import_module('pecos'); % check to see if a pandas dataframe or not @@ -62,26 +74,10 @@ data=qc_data_to_dataframe(data); end bound=py.list(bound); - if nargin == 2 - - r = struct(py.pecos.monitoring.check_increment(data,bound)); - elseif nargin == 3 - r = struct(py.pecos.monitoring.check_increment(data,bound,... - varargin{1})); - elseif nargin == 4 - r = struct(py.pecos.monitoring.check_increment(data,bound,... - varargin{1},varargin{2})); - elseif nargin == 5 - r = struct(py.pecos.monitoring.check_increment(data,bound,... - varargin{1},varargin{2},varargin{3})); - elseif nargin == 6 - r = struct(py.pecos.monitoring.check_increment(data,bound,... - varargin{1},varargin{2},varargin{3},varargin{4})); - else - ME = MException('MATLAB:qc_increment','incorrect number of arguments (2 to 6)'); - throw(ME); - end +r = struct(py.pecos.monitoring.check_increment(data,bound,... + pyargs("key",options.key,"increment",int32(options.increment),... + "absolute_value",options.absolute_value,"min_failures",int32(options.min_failures)))); % Convert to qcdata structure results.values=double(r.cleaned_data.values); results.mask=int64(r.mask.values); diff --git a/mhkit/qc/check_missing.m b/mhkit/qc/check_missing.m index e64afa774..420c47e31 100755 --- a/mhkit/qc/check_missing.m +++ b/mhkit/qc/check_missing.m @@ -1,4 +1,4 @@ -function results = check_missing(data) +function results = check_missing(data,options) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check for missing data % @@ -20,13 +20,13 @@ % key: string (optional) % Data column name or translation dictionary key. If not specified % or set to py.None, all columns are used for test. +% to call: check_missing(data,"key",key) % % min_failures: int (optional) % Minimum number of consecutive failures required for reporting, % default = 1 +% to call: check_missing(data,"min_failures",min_failures) % -% Must set previous arguments to use later optional arguments -% (i.e. must set key to use min_failures). % % Returns % --------- @@ -45,6 +45,12 @@ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +arguments + data + options.key = py.None; + options.min_failures = 1; +end + py.importlib.import_module('pecos'); % check to see if a pandas dataframe or not @@ -52,18 +58,8 @@ data=qc_data_to_dataframe(data); end - if nargin == 1 - r = struct(py.pecos.monitoring.check_missing(data)); - elseif nargin == 2 - r = struct(py.pecos.monitoring.check_missing(data,... - varargin{1})); - elseif nargin == 3 - r = struct(py.pecos.monitoring.check_missing(data,... - varargin{1},varargin{2})); - else - ME = MException('MATLAB:qc_missing','incorrect number of arguments (1 to 3)'); - throw(ME); - end +r = struct(py.pecos.monitoring.check_missing(data,... + pyargs("key",options.key,"min_failures",int32(options.min_failures)))); % Convert to qcdata structure results.values=double(r.cleaned_data.values); diff --git a/mhkit/qc/check_outlier.m b/mhkit/qc/check_outlier.m index 2ccb269ef..6b2e1bdf5 100755 --- a/mhkit/qc/check_outlier.m +++ b/mhkit/qc/check_outlier.m @@ -1,4 +1,4 @@ -function results = check_outlier(data,bound,varargin) +function results = check_outlier(data,bound,options) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check or outliers using normalized data within a rolling window @@ -27,22 +27,26 @@ % key: string (optional) % Data column name or translation dictionary key. If not specified % or set to py.None, all columns are used for test. +% to call: check_outlier(data,bound,"key",key) % % window: int (optional) % Size of rolling window (in seconds) used to normalize data % default = 3600. If window is set to py.None, data is normalized % using mean and stddev of entire data set (column by column) +% to call: check_outlier(data,bound,"window",window) % % absolute_value: logical (optional) -% Use the absolute value of the normalized data, default = 1 (True) +% Use the absolute value of the normalized data, default = py.True +% to call: check_outlier(data,bound,"absolute_value",absolute_value) % % min_failures: int (optional) -% % Minimum number of consecutive failures required for reporting, % default = 1 +% to call: check_outlier(data,bound,"min_failures",min_failures) % -% Must set previous arguments to use later optional arguments -% (i.e. must set key to use min_failures). +% streaming: logical (optional) +% Indicates if streaming analysis should be used, default = py.False +% to call: check_outlier(data,bound,"streaming",streaming) % % Returns % --------- @@ -60,6 +64,15 @@ % Same as input times % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +arguments + data + bound + options.key = py.None; + options.window = 3600; + options.absolute_value = py.True; + options.min_failures = 1; + options.streaming = py.False; +end py.importlib.import_module('pecos'); @@ -68,25 +81,11 @@ data=qc_data_to_dataframe(data); end bound = py.list(bound); - if nargin == 2 - r = struct(py.pecos.monitoring.check_outlier(data,bound)); - elseif nargin == 3 - r = struct(py.pecos.monitoring.check_outlier(data,bound,... - varargin{1})); - elseif nargin == 4 - r = struct(py.pecos.monitoring.check_outlier(data,bound,... - varargin{1},varargin{2})); - elseif nargin == 5 - r = struct(py.pecos.monitoring.check_outlier(data,bound,... - varargin{1},varargin{2},varargin{3})); - elseif nargin == 6 - r = struct(py.pecos.monitoring.check_outlier(data,bound,... - varargin{1},varargin{2},varargin{3},varargin{4})); - else - ME = MException('MATLAB:qc_outlier','incorrect number of arguments (2 to 7)'); - throw(ME); - end +r = struct(py.pecos.monitoring.check_outlier(data,bound,... + pyargs("key",options.key,"window",options.window,... + "absolute_value",options.absolute_value,"min_failures",int32(options.min_failures),... + "streaming",options.streaming))); % Convert to qcdata structure results.values=double(r.cleaned_data.values); results.mask=int64(r.mask.values); diff --git a/mhkit/qc/check_range.m b/mhkit/qc/check_range.m index 66c67f373..122807ba7 100755 --- a/mhkit/qc/check_range.m +++ b/mhkit/qc/check_range.m @@ -1,4 +1,4 @@ -function results = check_range(data, bound, varargin) +function results = check_range(data, bound, options) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check for data that is outside expected range @@ -25,13 +25,13 @@ % key: string (optional) % Data column name or translation dictionary key. If not specified % or set to py.None, all columns are used for test. +% to call: check_range(data,bound,"key",key) % % min_failures: int (optional) % Minimum number of consecutive failures required for reporting % default = 1 +% to call: check_range(data,bound,"min_failures",min_failures) % -% Must set previous arguments to use later optional arguments -% (i.e. must set key to use min_failures). % % Returns % --------- @@ -49,28 +49,23 @@ % Same as input times % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +arguments + data + bound + options.key = py.None; + options.min_failures = 1; +end + py.importlib.import_module('pecos'); % check to see if a pandas dataframe or not if (isa(data,'py.pandas.core.frame.DataFrame')~=1) data=qc_data_to_dataframe(data); end - - % Must use optional qc arguments in order, for now. - if nargin == 2 - bound = py.list(bound); - r = struct(py.pecos.monitoring.check_range(data, bound)); - elseif nargin == 3 - bound = py.list(bound); - r = struct(py.pecos.monitoring.check_range(data,bound,varargin{1})); - elseif nargin == 4 - bound = py.list(bound); - r = struct(py.pecos.monitoring.check_range(data,bound,varargin{1},varargin{2})); - else - ME = MException('MATLAB:qc_range','incorrect number of arguments (2 to 4)'); - throw(ME); - end +bound = py.list(bound); +r = struct(py.pecos.monitoring.check_range(data,bound,pyargs("key",options.key,... + "min_failures",int32(options.min_failures)))); % Convert to qcdata structure results.values=double(r.cleaned_data.values); results.mask=int64(r.mask.values); diff --git a/mhkit/qc/check_timestamp.m b/mhkit/qc/check_timestamp.m index 27fae9271..4eff8d573 100755 --- a/mhkit/qc/check_timestamp.m +++ b/mhkit/qc/check_timestamp.m @@ -1,4 +1,4 @@ -function results = check_timestamp(data, freq, varargin) +function results = check_timestamp(data, freq, options) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Check time series for missing, non-monotonic, and duplicate timestamps % @@ -21,23 +21,24 @@ % Expected time series frequency, in seconds % % expected_start_time: Timestamp (optional) -% Expected start time. Default: min(data.time) +% Expected start time in datetime format. Default: None +% to call: check_timestamp(data,freq,"expected_start_time",expected_start_time) % % expected_end_time: Timestamp (optional) -% Expected end time. Default: max(data.time) +% Expected end time in datetime format. Default: None +% to call: check_timestamp(data,freq,"expected_end_time",expected_end_time) % % min_failures: int (optional) % Minimum number of consecutive failures required for reporting, % default = 1 +% to call: check_timestamp(data,freq,"min_failures",min_failures) % % exact_times: logical (optional) -% If 1 (True), times are expected to occur at regular intervals +% If py.True, times are expected to occur at regular intervals % (specified by freq) and data is reindexed to match expected frequency -% If 0 (False), times only need to occur once or more within each interval +% If py.False, times only need to occur once or more within each interval % (specified by freq) and data is not reindexed -% -% Must set previous arguments to use later optional arguments -% (i.e. must set expected_start_time and expected_end_time to use min_failures). +% to call: check_timestamp(data,freq,"exact_times",exact_times) % % Returns % --------- @@ -55,34 +56,35 @@ % Same as input times (possibly reindexed by exact_times) % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - +arguments + data + freq + options.expected_start_time = py.None; + options.expected_end_time = py.None; + options.min_failures = 1; + options.exact_times = py.True; +end py.importlib.import_module('pecos'); + py.importlib.import_module('pandas'); % check to see if a pandas dataframe or not if (isa(data,'py.pandas.core.frame.DataFrame')~=1) data=qc_data_to_dataframe(data); end - - if nargin == 2 - r = struct(py.pecos.monitoring.check_timestamp(data,freq)); - elseif nargin == 3 - % Unlike the others, this might need a little work to convert - % start and end times into something python can use - r = struct(py.pecos.monitoring.check_timestamp(data,freq,... - varargin{1})); - elseif nargin == 4 - r = struct(py.pecos.monitoring.check_timestamp(data,freq,... - varargin{1},varargin{2})); - elseif nargin == 5 - r = struct(py.pecos.monitoring.check_timestamp(data,freq,... - varargin{1},varargin{2},varargin{3})); - elseif nargin == 6 - r = struct(py.pecos.monitoring.check_timestamp(data,freq,... - varargin{1},varargin{2},varargin{3},varargin{4})); - else - ME = MException('MATLAB:qc_timestamp','incorrect number of arguments (2 to 6)'); - throw(ME); + + if options.expected_start_time ~= py.None + options.expected_start_time = py.pandas.to_datetime(options.expected_start_time); end + + if options.expected_end_time ~= py.None + options.expected_end_time = py.pandas.to_datetime(options.expected_end_time); + end + + +r = struct(py.pecos.monitoring.check_timestamp(data,freq,... + pyargs("expected_start_time",options.expected_start_time,... + "expected_end_time",options.expected_end_time,"min_failures",... + int32(options.min_failures),"exact_times",options.exact_times))); % Convert to qcdata structure results.values=double(r.cleaned_data.values); From 4924a874b15a6bc8ec688027ec9c4e6d821edd35 Mon Sep 17 00:00:00 2001 From: rpauly18 Date: Mon, 26 Oct 2020 14:56:43 -0600 Subject: [PATCH 3/3] updating toolbox 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