EXPNDI_BY_T - Ferret/PyFerret Documentation

Expand a 1-D variable in X, to an X-T variable

EXPNDI_BY_T(dat,mask,n_profiles,max_profile_len)

Use to split a variable into a set of time series (see also the EXPNDI_BY_Z function for Z profiles)

Arguments:	DAT	Variable to expand, a 1D variable on X axis
	MASK	Each valid point starts a new T column
	n_profiles	Number of time-series profiles: size in I of result
	max_profile_len	Max length of each profile: size in L of result
Result Axes:	X	Abstract: length is argument 3
	Y	Inherited from DAT and MASK
	Z	Inherited from DAT and MASK
	T	ABSTRACT: length is argument 4
	E	Inherited from DAT and MASK
	F	Inherited from DAT and MASK

Example:

Data read from ascii files or data organized as simple 1-D lists may represent sets of time series, but of different lengths. See how DAY starts with 5 for each station, but there are a different number of measurements for each time series.

STATION   DAY     LON      LAT     TEMP    SALINITY
1          5     12.45    45.5     24.25    36.42
1          6     12.45    45.5     24.46    36.42
1          7     12.45    45.5     24.23    36.43
2          5     15.08    51.3     24.74    36.06
2          6     15.08    51.3     24.65    36.05
2          7     15.08    51.3     24.74    36
2          8     15.08    51.3     24.75    35.98
2          9     15.08    51.3     24.75    35.95
2          10    15.08    51.3     24.75    35.95
2          11    15.08    51.3     24.75    35.96
3          5     17.11    41.3     24.79    36.34
3          6     17.11    41.3     24.8     36.34
3          7     17.11    41.3     24.79    36.35
3          8     17.11    41.3     24.78    36.34
3          9     17.11    41.3     24.78    36.31
3          10    17.11    41.3     24.78    36.34
3          11    17.11    41.3     24.77    36.31
3          12    17.11    41.3     24.78    36.32
3          13    17.11    41.3     25.1     36.27
3          14    17.11    41.3     24.56    36.24
3          15    17.11    41.3     24.81    36.3
3          16    17.11    41.3     24.8     36.31

Open this dataset. Read the data as single 1-D lists, then define a XT variable.


yes? define axis/x=1:22:1 xin
yes? define grid/x=xin input_grid
yes? file/skip=1/var="sta, day, lat, lon, temp, salt"/grid=input_grid tprofiles.dat
yes? list day, temp, salt 
             DATA SET: ./tprofiles.dat
             X: 0.5 to 22.5
 Column  1: DAY is day
 Column  2: TEMP is temp
 Column  3: SALT is salt
             DAY   TEMP   SALT
1    /  1:   5.00  24.25  36.42
2    /  2:   6.00  24.46  36.42
3    /  3:   7.00  24.23  36.43
4    /  4:   5.00  24.74  36.06
5    /  5:   6.00  24.65  36.05
6    /  6:   7.00  24.74  36.00
7    /  7:   8.00  24.75  35.98
8    /  8:   9.00  24.75  35.95
9    /  9:  10.00  24.75  35.95
10   / 10:  11.00  24.75  35.96
11   / 11:   5.00  24.79  36.34
12   / 12:   6.00  24.80  36.34
13   / 13:   7.00  24.79  36.35
14   / 14:   8.00  24.78  36.34
15   / 15:   9.00  24.78  36.31
16   / 16:  10.00  24.78  36.34
17   / 17:  11.00  24.77  36.31
18   / 18:  12.00  24.78  36.32
19   / 19:  13.00  25.10  36.27
20   / 20:  14.00  24.56  36.24
21   / 21:  15.00  24.81  36.30
22   / 22:  16.00  24.80  36.31

yes? let mask = if day[i=@ddb] LT 0 then 1
yes? list day, mask 
             DATA SET: ./tprofiles.dat
             X: 0.5 to 22.5
 Column  1: DAY is day
 Column  2: MASK is IF DAY[I=@DDB] LT 0 THEN 1
             DAY   MASK
1    /  1:   5.00   ...
2    /  2:   6.00   ...
3    /  3:   7.00   ...
4    /  4:   5.00  1.000
5    /  5:   6.00   ...
6    /  6:   7.00   ...
7    /  7:   8.00   ...
8    /  8:   9.00   ...
9    /  9:  10.00   ...
10   / 10:  11.00   ...
11   / 11:   5.00  1.000
12   / 12:   6.00   ...
13   / 13:   7.00   ...
14   / 14:   8.00   ...
15   / 15:   9.00   ...
16   / 16:  10.00   ...
17   / 17:  11.00   ...
18   / 18:  12.00   ...
19   / 19:  13.00   ...
20   / 20:  14.00   ...
21   / 21:  15.00   ...
22   / 22:  16.00   ...

yes? let n_profiles = `1 + mask[i=@ngd]`
 ! We may be able to define the size of the profile using the data, 
 ! Depending on the information available we might just give some upper bound.

yes? let max_profile_len = day[i=@max] - day[i=@min] + 1
yes? let sal_xt = expndi_by_t(salt, mask, n_profiles, max_profile_len)
yes? list sal_xt 
             VARIABLE : EXPNDI_BY_T(SALT, MASK, N_PROFILES, MAX_PROFILE_LEN)
             FILENAME : tprofiles.dat
             SUBSET   : 3 by 12 points (X-T)
               1      2      3    
               1      2      3
 1    /  1:  36.42  36.06  36.34
 2    /  2:  36.42  36.05  36.34
 3    /  3:  36.43  36.00  36.35
 4    /  4:   ...  35.98  36.34
 5    /  5:   ...  35.95  36.31
 6    /  6:   ...  35.95  36.34
 7    /  7:   ...  35.96  36.31
 8    /  8:   ...   ...  36.32
 9    /  9:   ...   ...  36.27
 10   / 10:   ...   ...  36.24
 11   / 11:   ...   ...  36.30
 12   / 12:   ...   ...  36.31

Now we could define a time axis in units of days, from the data in variable DEPTH, and regrid variable sal_xt to that axis using the @ASN transformation. We could also go on to use the information in the file to locate the profiles in X and Y.