How to use setvaropts for strangely-formatted text file

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);

[Stephen23]

"Can someone help me with the correct format/inputs to the "setvaropts" function to import the following?"

Yes, once you upload a sample data file by clicking the paperclip button.

As a "Long time reader" you should know that not providing a representative data file to work with just delays you getting a working solution. Showing part of a file here is not sufficient: real files have strange newline characters, unexpected hidden characters, special characters, trailing stuff, formatting that changes halfway through the file...

If I had ten cents for every time someone wrote "my file looks exactly like this..."

[Mark Jaeger]

Thanks Stephen23, sample file and code snipped provided per your suggestion. Let me know where to mail that dime.

Mark

[Stephen23]

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]

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

Answer 1

[Stephen23]

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.

[Mark Jaeger]

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

Answer 2

[Cris LaPierre]

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  

[Cris LaPierre]

Testing on the sample file provided.

opts = detectImportOptions('DEFAULT_CP3.CSV');
opts = setvartype(opts,'Date','datetime');
opts = setvaropts(opts,'Date','InputFormat','yyyy/MM/dd');
idx=contains(opts.VariableNames,'CHU');
opts.VariableTypes(idx)={'string'};
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.

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'}

[Mark Jaeger]

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]

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"