On Sat, May 15, 2010 at 12:24 AM, Jimmie Houchin
Hello, I am really liking Numpy a lot. It is wonderful to be able to do the things that it does in a language as friendly as Python, and with the performance Numpy delivers over standard Python. Thanks.
I am having a problem with creation of Numpy arrays with my generated dtypes. I am creating a dataset of a weeks worth of financial instruments data, in order to explore and test relationships with various Technical Analysis functions.
My basic data is simply a list (or tuple) of lists (or tuples). ((startdate, bidopen, bidhigh, bidlow, bidclose, askopen, askhigh, asklow, askclose), ...)
Nothing unusual. However I am creating arrays which have many, many more columns to allow storing the data generated from applying the functions to the original data.
I have created two functions. One to dynamically create the dtype based on data I want to create for the exploration. And another to create the array and populate it with the initial data from a database.
Code slightly modified, not tested.
#examples taFunctions = (smva, wmva) inputColumns = (bidclose, ohlcavg)
def createDType(): """Will create a dtype based on the pattern for naming and the parameters of those items being stored in the array. """ dttypes = [('startdate','object'), ('bidopen','f8'), ('bidhigh','f8'), ('bidlow','f8'), ('bidclose','f8'), ('askopen','f8'), ('askhigh','f8'), ('asklow','f8'), ('askclose','f8'), ('ocavg','f8'), ('hlavg','f8'), ('ohlavg','f8'), ('ohlcavg','f8'), ('direction','i1'), ('volatility', 'f8'), ('spread', 'f8'), ('pivot', 'S4')] for f in taFunctions: for i in inputColumns: dttypes.append((f+"-"+i,'f8')) dtminute = np.dtype(dttypes) return dtminute, dttypes
def getArray(instrument, weekString=None): ... cur.execute(sql) weekData = cur.fetchall() wdata = [] lst = [] dtminute, dttypes = createDType() for i in dttypes: if i[1] == 'f8': lst.append(0.0) elif i[1] == 'i1': lst.append(0) else: lst.append('') for m in weekData: data = list(m)+lst[9:] wdata.append(data)
I think "data" here should be a tuple, i.e. tuple(data) structured arrays expect tuples for each element/row If this is not it, then you could provide a mini example of wdata with just a few rows.
return np.array(wdata,dtype=dtminute)
The createDType() function works fine. The getArray() function fails with: ValueError: Setting void-array with object members using buffer.
cryptic exceptions messages in array construction usually means there is some structure in the argument data that numpy doesn't understand, I usually work with trial and error for a specific example Josef
However changing the getArray() function to this works just fine.
def getArray(instrument, weekString=None): ... cur.execute(sql) weekData = cur.fetchall() arrayLength = len(weekData) lst = [] dtminute, dttypes = createDType() for i in dttypes: if i[1] == 'f8': lst.append(0.0) elif i[1] == 'i1': lst.append(0) else: lst.append('') listLength = len(lst) weekArray = np.zeros(arrayLength, dtype=dtminute) for i in range(arrayLength): for j in range(listLength): if j < 9: weekArray[i][j] = weekData[i][j] else: weekArray[i][j] = lst[j] return weekArray
After I finally worked out getArray number two I am back in business writing the rest of my app. But I banged my head on version number one for quite some time trying to figure out what I am doing wrong. I still don't know.
I find no errors in my data length or types. I would thing that either would cause version two to fail also.
In help in understanding is greatly appreciated.
This is using Numpy 1.4.1.
Thanks.
Jimmie
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion