I am a new user of MatlabR2021b and I have a table where the last column (with name loadings) spans multiple sub-columns (all sub-columns were added under the same variable/column and are threated as one column). I wanto to create a For loop that goes through each separate loading column and iterates through them, prior to creating a tbl that I will input into a model. The sub-columns contain numbers with rows corresponding to the number of participants.
Previously, I had a similar analogy where the loop was iterating through the names of different regions of interest, whereas now the loop has to iterate through columns that have numbers in them. First, the numbers in the first sub-column, then in the second, and so on.
I am not sure whether I should split the last column with T1 = splitvars(T1, 'loadings') first or whether I am not indexing into the table correctly or performing the right transformations. I would appreciate any help.
roi.ic = T.loadings;
roinames = roi.ic(:,1);
roinames = [num2str(roinames)];
for iroi = 1:numel(roinames)
f_roiname = roinames{iroi};
tbl = T1;
tbl.(roinames) = T1.loadings(:,roiname);
**tbl.(roinames) = T1.loadings_rsfa(:,roiname)
Unable to use a value of type cell as an index.
Error in tabular/dotParenReference (line 120)
b = b(rowIndices,colIndices)**
I tried
myTable.MyField{:}='AAA'
myTable.MyField(:)='AAA'
myTable.MyField{:}={'AAA'}
myTable.MyField{:}=deal('AAA')
but all failed.
Is there any way?
MATLAB requires:
To assign to or create a variable in a table, the number of rows must match the height of the table.
So it would be:
myTable.MyField = repmat('AAA', length(myTable.MyField), 1);
or if you know the column number of MyField, you can do:
myTable(:,colnum) = {'AAA'}; %where colnum is the column number
or otherwise if you don't know the column number, you can directly use the column name as well:
myTable(:,'MyField') = {'AAA'};
I have a pinned row that acts as a column's total, which works fine using gridOptions.pinnedBottomRowData. I need to update this value when the columns' cells' values change but I cannot find a means of accessing the column values to do so. What's the best means of updating a column total when it's columns' cells' values change?
You can calculate the total on cell value changed or value setter function.
this.gridOptions.onCellValueChanged = function (event) {
if (event.colDef.colId == "ColumnName") {
//Logic to update the total row value
total = total + event.data[event.colDef.field]
}
}
I have a postgresql database and in one particular table, with many rows. One column in this table, called data, is a float array, REAL[], and gets filled with an array of ~4500 elements. I want to access this table through some query via SQLAlchemy and the ORM.
How do I select all rows in the table where a subset of this column satisfies some condition, e.g.contains a range of values? Like I want to select all rows where the data contains values >= 10, or values between >=10 and <=20.
Can I do this with a straight session query like
rows = session.query(Table).filter(Table.data.(some conditional)).all()
where my conditional is something like "VALUES >= 10 and VALUES <= 20"?
Or do I need to define some special methods, or setup, when I'm defining my SQLAlchemy table class. For example, I have my table set up as
class Table(Base):
__tablename__ = 'table'
__table_args__ = {'autoload' : True, 'schema' : 'testdb', 'extend_existing':True}
data = deferred(Column(ARRAY(Float)))
def __repr__(self):
return '<Table (pk={0})>'.format(self.pk)
Ideally I'd like to set it up so I can just do simple filtering in my session.query calls. Is this possible? I'm not super familiar with the ORM, so maybe it is?
I've had a look at the ARRAY Comparator sqlalchemy docs but those only seem to work on exact values. My data is precise to 6 sigfigs, and I don't know the exact values ahead of time.
What's the best way to do this? Thanks.
EDIT:
Based on the below comment, here is the code I'm using in attempting to select all rows (out of 1000) that have data (from 1 column) >= 1.0. There should be 537 rows.
rows = session.query(datadb.Table).filter(datadb.Table.data.any(1.0,operator=operators.le)).all()
This gives the correct subset number. len(rows) = 537. However, I don't understand the logic of with this operator, where to select data >=1.0 , I use the le operator? Also, along those same lines, there should be 234 rows that have data between the values >=1.0 and <1.0, but this statement fails to give the correct subset..
rows = session.query(datadb.Table).filter(datadb.Table.data.any(1.0,operator=operators.le)).filter(datadb.Table.data.any(1.2,operator=operators.ge)).all()
* EDIT 2 *
Here's an example of my database Table with a few rows. pk is an integer, and data is a real[].
db datadb
schema Table
pk data
0 [0.0,0.0,0.5,0.3,1.3,1.9,0.3,0.0,0.0]
1 [0.1,0.0,1.0,0.7,1.1,1.5,1.2,0.3,1.4]
2 [0.0,0.6,0.4,0.3,1.6,1.7,0.4,1.3,0.0]
3 [0.0,0.1,0.2,0.4,1.0,1.1,1.2,0.9,0.0]
4 [0.0,0.0,0.5,0.3,0.2,0.1,0.7,0.3,0.1]
I have 5 rows, 4 of them have data with values >= 1.0, while just 2 have values in the range >= 1.0 and <= 1.2. The query I would do to grab the rows is in the first case
rows = session.query(datadb.Table).filter(datadb.Table.data.any(1.0,operator=operators.le)).all()
This should return the 4 rows, at pk=0,1,2,3. This query does what I expect. The second case
rows = session.query(datadb.Table).filter(datadb.Table.data.any(1.0,operator=operators.le)).filter(datadb.Table.data.any(1.2,operator=operators.ge)).all()
and should return the 2 rows at pk=1,3. However this query just returns the 4 rows from the first query. For the second query, I also tried
rows = session.query(datadb.Table).filter(datadb.Table.data.any(1.0,operator=operators.le),datadb.Table.data.any(1.2,operator=operators.ge)).all()
which also didn't work.
Please read documentation on ARRAY.Comparator, according to which you should be able to do the following:
rows = (session.query(Table)
.filter(Table.data.any(10, operator=operators.le))
.filter(Table.data.any(20, operator=operators.ge)
).all()
EDIT:
# combined filter does not work,
# but applying one or the other is still useful as it reduces the result set
q = (session.query(MyTable)
.filter(MyTable.data.any(1.0, operator=operators.le))
# .filter(MyTable.data.any(1.2, operator=operators.ge))
)
# filter in memory
items = [_row for _row in q.all()
if any(1.0 <= item <= 1.2 for item in _row.data)]
for item in items:
print(item)
I have a dataset of stock prices called 'stocks'. Each column is a different stock. Each row is the date of the stock prices.
How can I rank the stock price of a given date?
I tried
tiedrank(stocks.yhoo)
And it successfully ranked the prices of YHOO stock. However, I would like to rank by row, not column.
Also, when I tried
tiedrank(stocks(1,:))
or to delete the date column in column 1
tiedrank(stocks(1,2:3))
I got the error message: Dataset array subscripts must be two-dimensional.
Am I doing something wrong? Or am I better off using matrices?
If I understand correctly, you want to rank the stocks according to price at a given date, where dates are rows, and stocks are columns. To use tiedrank across a row, you need to convert that part of the dataset to double, and then use the output index list to sort:
%# create index for sorting
idx = tiedrank( double( stocks(1,:) ));
%# reorder columns with index
sortedStocks = stocks(:,idx);