How to use setvaropts for strangely-formatted text file

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Hello All,
Long time reader, first time asker - appreciate the great wealth of knowledge here!
I'm trying to use the "readtable" funciton to import data from a large (~600 MB) CSV file. The file contains separate columns for Date, Time, and a bunch of instrument channels. I've successfully used "detectImportOptions" to get the channel listing, eliminate unwanted columns using "SelectedVariableNames", and set the "VaribleTypes" for all of the desired columns. Note: by default, the function correclty flags the "Time" column as a duration, but the "Date" and individual data columns get flagged as "char" by default.
Here's an example line from the file, showing the header row, units row, and subsequent columns. I'm wanting to import "Date" (as a date/time), "Time" (as the default duration), and each of hte "CHU..." columns as a data channel.
Number,Date,Time,us,CHU1-1,CHU1-2,CHU1-3,CHU1-4,CHU1-5...
NO.,Date,Time,us,"uE","uE","uE","uE","uE",...
1,2014/10/30,17:56:41, 0,+ 194 ,+ 210 ,+ 196 ,- 180 ,+ 17 ,...
For the "Date" variable, I'm attempting to use the following code:
opts=setvaropts(opts,'Date','DatetimeFormat','yyyy/MM/dd');
However, despite the above, all of the dates end up importing as NaT. For the "CHU..." channels, I have changed the VariableTypes to double:
idx=find(contains(vnms,'CHU'));
ivars=[ivars idx];
opts.VariableTypes(idx)={'double'};
However, this also is not working, as I'm getting NaN for all values.
Can someone help me with the correct format/inputs to the "setvaropts" function to import the following?
  • "2014/10/30" as Date/Time
  • "+ 194 " as Signed Double.
Thanks much for any help!
Mark
------------------
Edit: attached a copy of a smaller-sized file (cannot upload the full 600 MB CSV that I'm actually trying to read, but I have verified that this one is formatted the same and returns similar results).
Here's the current version of my import code ('fpath' and 'fname' variables are identified from "uigetfile" call earlier in the script):
%determine import options and adjust
warning off;
opts=detectImportOptions(fullfile(fpath,fname));
ivars=[];
vnms=opts.VariableNames;
idx=find(ismember(vnms,'Date'));
ivars=[ivars idx];
opts.VariableTypes{idx}='datetime';
opts=setvaropts(opts,'Date','DatetimeFormat','yyyy/MM/dd');
idx=find(ismember(vnms,'Time'));
ivars=[ivars idx];
opts.VariableTypes{idx}='duration';
idx=find(contains(vnms,'CHU'));
ivars=[ivars idx];
opts.VariableTypes(idx)={'double'};
opts.SelectedVariableNames=vnms(ivars);
%read from CSV table
tt=readtable(fullfile(fpath,fname),opts);
  4 Comments
Stephen23
Stephen23 on 9 Feb 2024
Edited: Stephen23 on 9 Feb 2024
Just to confirm: the block of data you wish to import starts on line 120 (header) & 122 (data) to the EOF ?
Those are... rather strangely formatted numbers. Out of personal curiosity: what produced that number format? Do you know what language or formatting commands were used?
Mark Jaeger
Mark Jaeger on 9 Feb 2024
You are correct, headers on Line 120, data begins on Line 122.
I second your comment on strange formatting! This data file came from an old (likely defunct) piece of data acquisition hardware; it got passed to me as part of a proejct my company is investigating. I can usually make "readtable" work for just about anything, but this one really flummoxed me.
Mark

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Accepted Answer

Stephen23
Stephen23 on 9 Feb 2024
Edited: Stephen23 on 9 Feb 2024
First lets see if READTABLE et al can ignore those space characters on a simple test file:
type testformat.csv
blah,CH1,CH2,CH3,Alarm hello,+ 1,- 2,+ 3,LLLL world,+ 4,+ 5,- 6,LLLL blah,- 7,- 8,+ 9,LLLL
readtable('testformat.csv','Delimiter',',', 'TrimNonNumeric',true) % no minus :(
ans = 4×5 table
Var1 Var2 Var3 Var4 Var5 _________ ____ ____ ____ _________ {'blah' } 1 2 3 {'Alarm'} {'hello'} 1 2 3 {'LLLL' } {'world'} 4 5 6 {'LLLL' } {'blah' } 7 8 9 {'LLLL' }
Hmm, not quite. Nor do I see any other options that would obviously help with those oddly formatted numbers. I get the feeling, that you might need to import those values as text, do some (simple) text munging, and then convert to numeric.
Note that CONVERTVARS could probably do this for you in one call:
  • define the 2nd input as a PATTERN object that matches "CH" at the start of the variable name.
  • define the 3rd input as a function that accepts a text array (string or cell of char), removes those spaces, and then STR2DOUBLEs the whole thing.
This is a very useful approach that avoids the need to e.g. hardcode column numbers or the like:
T = readtable('testformat.csv','Delimiter',',') % backup option
T = 3×5 table
blah CH1 CH2 CH3 Alarm _________ _______ ________ _________ ________ {'hello'} {'+ 1'} {'- 2'} {'+ 3'} {'LLLL'} {'world'} {'+ 4'} {'+ 5'} {'- 6'} {'LLLL'} {'blah' } {'- 7'} {'- 8'} {'+ 9'} {'LLLL'}
P = "CH" + wildcardPattern;
F = @(txt)str2double(regexprep(txt,'\s+',''));
T = convertvars(T,P,F)
T = 3×5 table
blah CH1 CH2 CH3 Alarm _________ ___ ___ ___ ________ {'hello'} 1 -2 3 {'LLLL'} {'world'} 4 5 -6 {'LLLL'} {'blah' } -7 -8 9 {'LLLL'}
DATETIME: I think that as long as you set the delimiter READTABLE should be able to figure out that DATETIME format. If it doesn't, first use SETVARTYPE to set the "Date" variable to DATETIME, then use SETVAROPTS to set the format (as you showed).
I need to take a break now, but that should get you started. I will take a look later and see if you need support with the DATETIME.
  1 Comment
Mark Jaeger
Mark Jaeger on 9 Feb 2024
Thank you, this indeed did the trick. Note: I used portions of the answers from both Stephen23 and Cris LaPierre (Cris' answer addressed my DateTime question, which invovled using "InputFormat'). I found the P/F/T part of Stephen23's answer easiest for my feeble brain to follow, hence marking that the answer.
Here's the final code that I ended up with:
%determine import options and adjust
warning off;
opts=detectImportOptions(fullfile(fpath,fname));
ivars=[];
vnms=opts.VariableNames;
idx=find(ismember(vnms,'Date'));
ivars=[ivars idx];
opts.VariableTypes{idx}='datetime';
opts=setvaropts(opts,'Date','InputFormat','yyyy/MM/dd');
idx=find(ismember(vnms,'Time'));
ivars=[ivars idx];
opts.VariableTypes{idx}='duration';
idx=find(contains(vnms,'CHU'));
ivars=[ivars idx];
opts.SelectedVariableNames=vnms(ivars);
%read from CSV table
tt=readtable(fullfile(fpath,fname),opts);
%convert string variables to text
idx=find(contains(tt.Properties.VariableNames,'CHU'));
fmt=@(txt)str2double(regexprep(txt,'\s+',''));
tt=convertvars(tt,idx,fmt);
Thank you both for your help!
Mark

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More Answers (1)

Cris LaPierre
Cris LaPierre on 9 Feb 2024
You need to set the 'InputFormat' instead of the 'DatetimeFormat'.
opts = detectImportOptions('chuSampleFile.csv');
opts = setvartype(opts,'Date','datetime');
opts = setvaropts(opts,'Date','InputFormat','yyyy/MM/dd');
As for converting your numeric data to doubles, I think that is easiest done after the table has been imported. To capture the sign and number, read these in first as strings.
idx=contains(opts.VariableNames,'CHU');
opts.VariableTypes(idx)={'string'};
data = readtable('chuSampleFile.csv',opts)
Warning: Column headers from the file were modified to make them valid MATLAB identifiers before creating variable names for the table. The original column headers are saved in the VariableDescriptions property.
Set 'VariableNamingRule' to 'preserve' to use the original column headers as table variable names.
data = 1×9 table
Number Date Time us CHU1_1 CHU1_2 CHU1_3 CHU1_4 CHU1_5 ______ ___________ ________ __ ________ ________ ________ ________ ________ 1 30-Oct-2014 17:56:41 0 "+ 194" "+ 210" "+ 196" "- 180" "+ 17"
Then use varfun to remove the spaces, then convertvars to turn into doubles.
data(:,idx) = varfun(@(x)erase(x,' '),data,"InputVariables",idx)
data = 1×9 table
Number Date Time us CHU1_1 CHU1_2 CHU1_3 CHU1_4 CHU1_5 ______ ___________ ________ __ ______ ______ ______ ______ ______ 1 30-Oct-2014 17:56:41 0 "+194" "+210" "+196" "-180" "+17"
data = convertvars(data,idx,'double')
data = 1×9 table
Number Date Time us CHU1_1 CHU1_2 CHU1_3 CHU1_4 CHU1_5 ______ ___________ ________ __ ______ ______ ______ ______ ______ 1 30-Oct-2014 17:56:41 0 194 210 196 -180 17
  3 Comments
Mark Jaeger
Mark Jaeger on 9 Feb 2024
Thank you Cris, this was a very good answer and helped me immensely with the DateTime part. I could not get your version of "convertvars" to workas expected (kept receiving an error on unable to convert a cell to double), likely user error on my part. I combined this answer with the one from Stephen23, and that got me what I needed. Thanks again!
Mark
Cris LaPierre
Cris LaPierre on 9 Feb 2024
Edited: Cris LaPierre on 9 Feb 2024
The 'can't convert cell to double' happens if you do not set the numbers to import as strings.
idx=contains(opts.VariableNames,'CHU');
opts.VariableTypes(idx)={'string'};
You could instead just set the text type to be string
opts = detectImportOptions('DEFAULT_CP3.CSV','TextType','string');
opts = setvartype(opts,'Date','datetime');
opts = setvaropts(opts,'Date','InputFormat','yyyy/MM/dd');
data = readtable('DEFAULT_CP3.CSV',opts);
Warning: Column headers from the file were modified to make them valid MATLAB identifiers before creating variable names for the table. The original column headers are saved in the VariableDescriptions property.
Set 'VariableNamingRule' to 'preserve' to use the original column headers as table variable names.
idx = contains(data.Properties.VariableNames,'CHU');
data(:,idx) = varfun(@(x)erase(x,' '),data,"InputVariables",idx);
data = convertvars(data,idx,'double')
data = 3800×108 table
Number Date Time us CHU1_1 CHU1_2 CHU1_3 CHU1_4 CHU1_5 CHU1_6 CHU1_7 CHU1_8 CHU1_9 CHU1_10 CHU2_1 CHU2_2 CHU2_3 CHU2_4 CHU2_5 CHU2_6 CHU2_7 CHU2_8 CHU2_9 CHU2_10 CHU3_1 CHU3_2 CHU3_3 CHU3_4 CHU3_5 CHU3_6 CHU3_7 CHU3_8 CHU3_9 CHU3_10 CHU4_1 CHU4_2 CHU4_3 CHU4_4 CHU5_1 CHU5_2 CHU5_3 CHU5_4 CHU5_5 CHU5_6 CHU5_7 CHU5_8 CHU5_9 CHU5_10 CHU6_1 CHU6_2 CHU6_3 CHU6_4 CHU6_5 CHU6_6 CHU6_7 CHU6_8 CHU6_9 CHU6_10 CHU7_1 CHU7_2 CHU7_3 CHU7_4 CHU7_5 CHU7_6 CHU7_7 CHU7_8 CHU7_9 CHU7_10 CHU8_1 CHU8_2 CHU8_3 CHU8_4 CHU8_5 CHU8_6 CHU8_7 CHU8_8 CHU9_1 CHU9_2 CHU9_3 CHU9_4 CHU9_5 CHU9_6 CHU9_7 CHU9_8 CHU9_9 CHU9_10 CHU10_1 CHU10_2 CHU10_3 CHU10_4 CHU10_5 CHU10_6 CHU10_7 CHU10_8 CHU10_9 CHU10_10 AlarmU1 AlarmU2 AlarmU3 AlarmU4 AlarmU5 AlarmU6 AlarmU7 AlarmU8 AlarmU9 AlarmU10 AlarmOut MarkEvent ______ ___________ ________ __ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ ______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ ________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ ____________ _____________ 1 30-Oct-2014 16:51:34 0 131 141 105 -117 6 5 2 7 0 0 135.4 37.8 38.6 47.1 38.5 76.6 81.9 40.4 45.7 31.9 113.9 67.7 107.3 100.2 119.6 120.2 120.7 126 112.5 130.1 -2 NaN NaN NaN 223 164 3 169 36 25 11 1 8 -19 34.9 37.5 38 38.4 38.2 70.6 71.4 40.2 52.8 36.5 112.5 135.9 31.2 NaN 34.5 117.4 118.6 80.9 103.9 125.7 257 221 220 229 20 83 5 -7 134.9 39.9 40.7 40.7 41.9 76.2 77.4 42.7 53.2 32.7 99.6 84.9 79.5 79.4 79.9 119.3 124.6 131 116.2 128.3 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 2 30-Oct-2014 16:51:35 0 131 141 105 -118 6 5 2 7 0 0 135.4 37.8 38.6 47.1 38.4 76.6 81.9 40.3 45.7 31.8 113.7 67.5 107.4 100.1 119.6 120.2 120.7 126 112.4 130 -2 NaN NaN NaN 215 164 5 170 37 25 11 1 8 -19 34.9 37.5 38 38.4 38.2 70.6 71.3 40.2 53 36.5 112.5 136 31.4 NaN 34.5 117.5 118.6 81 103.9 125.7 258 222 220 229 20 83 5 -7 135 40 40.8 40.8 41.9 76.2 77.4 42.8 53.2 32.8 99.6 84.8 79.5 79.4 79.9 119.3 124.6 131 116.2 128.3 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 3 30-Oct-2014 16:51:36 0 131 141 105 -118 6 5 2 7 0 0 135.4 37.8 38.6 47.1 38.4 76.6 81.9 40.4 45.7 31.8 113.8 67.6 107.7 100.3 119.7 120.3 120.8 126.1 112.6 130.1 -2 NaN NaN NaN 229 165 5 170 37 25 11 1 8 -19 34.9 37.5 37.9 38.4 38.1 70.6 71.4 40.2 53.1 36.5 112.5 136.1 31.4 NaN 34.6 117.6 118.7 81.1 103.9 125.7 259 222 221 228 20 83 5 -7 134.9 39.8 40.7 40.7 41.8 76 77.3 42.7 53.2 32.7 99.6 84.9 79.5 79.4 79.9 119.3 124.7 131 116.2 128.3 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 4 30-Oct-2014 16:51:37 0 131 142 105 -117 7 5 2 7 0 0 135.3 37.8 38.6 47.1 38.5 76.6 81.9 40.4 45.7 31.8 113.8 67.6 107.9 100.5 119.7 120.2 120.8 126.1 112.6 130.1 -2 NaN NaN NaN 244 165 3 170 37 25 10 1 7 -19 34.9 37.5 37.9 38.4 38.1 70.5 71.4 40.2 53.1 36.5 112.5 136.1 31.4 NaN 34.6 117.6 118.6 81.1 103.8 125.8 259 222 221 228 21 83 5 -7 134.8 39.7 40.7 40.7 41.8 76 77.4 42.7 53.2 32.7 99.7 84.8 79.5 79.5 80 119.3 124.7 130.9 116.2 128.3 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 5 30-Oct-2014 16:51:38 0 131 142 104 -117 7 5 2 7 0 0 135.3 37.8 38.6 47.1 38.5 76.6 81.9 40.4 45.7 31.8 113.8 67.6 108 100.6 119.7 120.2 120.8 126.1 112.7 130.1 -3 NaN NaN NaN 245 165 3 170 37 25 10 1 7 -19 34.9 37.5 37.9 38.4 38.1 70.5 71.4 40.2 53.1 36.4 112.5 136.1 31.4 NaN 34.6 117.6 118.6 81.2 103.9 125.8 268 222 221 228 21 83 5 -7 134.9 39.7 40.6 40.6 41.8 76 77.4 42.7 53.2 32.7 99.8 84.8 79.5 79.5 80 119.3 124.7 130.9 116.3 128.3 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 6 30-Oct-2014 16:51:39 0 131 143 101 -117 7 5 2 7 0 0 135.4 37.8 38.6 47.1 38.5 76.6 81.9 40.4 45.7 31.9 113.8 67.6 108.1 100.7 119.7 120.3 120.8 126.1 112.7 130.1 -3 NaN NaN NaN 245 166 3 170 37 25 10 1 8 -19 34.9 37.5 38 38.4 38.1 70.5 71.4 40.2 52.9 36.4 112.5 136.1 31.4 NaN 34.6 117.7 118.6 81.1 103.9 125.8 279 222 221 227 21 83 5 -7 134.9 39.7 40.7 40.6 41.8 76 77.4 42.8 53.3 32.7 99.8 84.9 79.5 79.5 80 119.2 124.7 130.9 116.3 128.3 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 7 30-Oct-2014 16:51:40 0 131 143 99 -117 7 5 2 7 0 0 135.4 37.8 38.6 47.1 38.5 76.6 81.9 40.4 45.7 31.9 113.7 67.6 108.1 100.7 119.7 120.3 120.7 126 112.6 130.1 -3 NaN NaN NaN 238 166 3 170 37 25 10 1 8 -19 35 37.5 37.9 38.4 38.1 70.5 71.4 40.2 52.8 36.4 112.5 136.1 31.4 NaN 34.6 117.7 118.7 81 103.8 125.9 283 222 221 228 21 83 5 -7 134.8 39.7 40.7 40.6 41.8 76 77.4 42.8 53.3 32.7 99.9 84.8 79.5 79.5 80 119.2 124.7 130.9 116.3 128.3 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 8 30-Oct-2014 16:51:41 0 130 143 97 -116 7 5 2 6 0 0 135.4 37.8 38.6 47.2 38.5 76.6 81.9 40.4 45.7 31.9 113.6 67.5 108.1 100.6 119.6 120.2 120.7 125.9 112.5 130 -4 NaN NaN NaN 235 167 2 170 37 25 10 1 7 -20 35 37.5 37.9 38.3 38.1 70.6 71.4 40.2 52.8 36.4 112.5 136.1 31.4 NaN 34.6 117.7 118.7 81.1 103.8 125.9 285 222 221 228 21 83 5 -7 134.8 39.8 40.6 40.6 41.8 76.1 77.4 42.7 53.2 32.7 99.9 84.5 79.5 79.5 80 119.2 124.7 130.9 116.3 128.3 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 9 30-Oct-2014 16:51:42 0 130 144 95 -116 7 5 2 6 0 0 135.4 37.8 38.6 47.2 38.5 76.6 81.9 40.4 45.7 31.8 113.6 67.5 108.1 100.5 119.6 120.2 120.7 125.9 112.6 129.9 -4 NaN NaN NaN 233 167 2 169 37 25 10 1 7 -20 34.9 37.5 37.9 38.3 38.1 70.6 71.4 40.2 52.8 36.4 112.5 136.1 31.4 NaN 34.6 117.7 118.6 81.1 103.8 126 286 222 221 228 21 83 5 -7 134.9 39.8 40.7 40.6 41.8 76.1 77.4 42.7 53.2 32.7 100 84.4 79.5 79.5 80 119.2 124.8 130.9 116.3 128.3 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 10 30-Oct-2014 16:51:43 0 131 145 97 -115 7 5 2 7 0 0 135.4 37.8 38.6 47.1 38.5 76.6 82 40.4 45.7 31.8 113.6 67.4 108.1 100.4 119.6 120.2 120.7 125.9 112.6 129.9 -4 NaN NaN NaN 230 167 3 169 37 25 10 1 7 -20 34.9 37.5 37.9 38.3 38.1 70.5 71.4 40.2 52.8 36.4 112.5 136.1 31.4 NaN 34.6 117.7 118.6 81.1 103.8 126 286 222 221 228 20 83 5 -7 134.8 39.6 40.6 40.6 41.8 75.9 77.3 42.6 53.1 32.6 100.1 84.5 79.4 79.5 79.9 119.2 124.8 130.9 116.4 128.2 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 11 30-Oct-2014 16:51:44 0 132 145 97 -115 6 5 2 7 0 0 135.4 37.8 38.6 47.1 38.5 76.6 82 40.4 45.7 31.9 113.6 67.4 108.2 100.3 119.6 120.2 120.7 125.9 112.5 129.9 -4 NaN NaN NaN 232 167 4 169 37 25 10 1 7 -19 34.9 37.5 37.9 38.3 38.1 70.6 71.4 40.2 52.8 36.3 112.6 136.1 31.4 NaN 34.6 117.8 118.7 81.1 103.9 126 286 222 221 228 21 83 5 -7 134.7 39.5 40.4 40.5 41.7 75.9 77.2 42.5 53 32.5 100 84.5 79.4 79.5 79.9 119.2 124.7 131 116.5 128.2 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 12 30-Oct-2014 16:51:45 0 131 144 102 -115 6 5 2 7 0 0 135.4 37.8 38.6 47.1 38.5 76.6 81.9 40.4 45.7 31.8 113.6 67.4 108.3 100.3 119.6 120.2 120.7 125.9 112.5 129.9 -3 NaN NaN NaN 224 168 4 169 37 25 10 1 7 -19 34.9 37.5 37.9 38.3 38.1 70.6 71.4 40.2 52.8 36.3 112.5 136.1 31.4 NaN 34.5 117.7 118.6 81 103.8 126 270 222 221 228 21 83 5 -7 134.7 39.5 40.5 40.4 41.7 75.9 77.2 42.6 52.9 32.4 100 84.3 79.5 79.5 79.9 119.2 124.7 131 116.5 128.3 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 13 30-Oct-2014 16:51:46 0 131 144 106 -115 6 5 2 7 0 0 135.4 37.8 38.6 47.1 38.5 76.6 81.9 40.4 45.7 31.8 113.6 67.4 108.4 100.4 119.6 120.2 120.7 125.9 112.5 129.9 -2 NaN NaN NaN 216 168 5 169 37 26 10 1 8 -19 34.9 37.5 37.9 38.3 38.1 70.6 71.4 40.2 52.8 36.3 112.5 136 31.3 NaN 34.5 117.7 118.6 80.8 103.8 125.9 255 222 222 228 20 83 5 -7 134.7 39.5 40.6 40.4 41.7 75.8 77.2 42.6 52.9 32.4 100 84.3 79.5 79.5 79.9 119.2 124.7 131 116.4 128.2 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 14 30-Oct-2014 16:51:47 0 131 144 106 -115 6 5 2 7 0 0 135.4 37.8 38.6 47.1 38.5 76.6 82 40.4 45.7 31.9 113.6 67.4 108.6 100.4 119.6 120.2 120.7 125.9 112.5 129.9 -3 NaN NaN NaN 216 168 6 169 37 25 10 1 8 -19 34.9 37.5 37.9 38.3 38.1 70.6 71.4 40.2 52.8 36.3 112.5 136 31.3 NaN 34.5 117.8 118.6 80.9 103.8 125.9 260 222 222 228 20 83 5 -7 134.7 39.6 40.5 40.4 41.7 75.8 77.2 42.6 52.9 32.4 99.9 84.4 79.5 79.4 79.9 119.2 124.8 131 116.3 128.1 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 15 30-Oct-2014 16:51:48 0 131 145 106 -116 6 5 3 7 0 0 135.4 37.8 38.6 47.1 38.5 76.6 82 40.4 45.7 31.9 113.6 67.3 108.6 100.6 119.6 120.2 120.8 125.9 112.5 129.9 -3 NaN NaN NaN 217 168 6 170 37 26 10 1 7 -19 35 37.5 37.9 38.3 38.1 70.6 71.4 40.2 52.8 36.3 112.5 136 31.3 NaN 34.5 117.7 118.6 81.1 103.8 126 267 222 222 228 20 83 5 -7 134.7 39.6 40.5 40.4 41.7 75.9 77.2 42.6 52.9 32.4 99.9 84.4 79.5 79.4 79.9 119.2 124.8 131 116.2 128 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL" 16 30-Oct-2014 16:51:49 0 130 146 106 -116 6 5 2 7 0 0 135.5 37.8 38.6 47.2 38.5 76.6 82 40.4 45.7 31.9 113.7 67.3 108.4 100.8 119.6 120.2 120.8 125.9 112.5 130 -3 NaN NaN NaN 219 168 7 169 37 26 10 1 8 -19 35 37.5 37.9 38.3 38.1 70.6 71.4 40.2 52.8 36.3 112.5 136 31.3 NaN 34.5 117.7 118.6 81.1 103.8 126 268 222 221 228 20 83 5 -7 134.7 39.6 40.5 40.4 41.6 75.9 77.3 42.6 52.8 32.5 99.8 84.6 79.5 79.4 79.9 119.2 124.8 131 116.2 128 "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLL" "LLLLLLLLLLL"

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