Python readCSVasFloat Examples

Example #1

File: processdata.py Project: siddsax/Motion_Prediction

def loadTrainData(subjects):
    trainData = {}
    completeData = []
    for subj in subjects:
        for action in actions:
            for subact in subactions:
                #filename = '../../../dataset'
                filename = '{0}/{1}/{2}_{3}.txt'.format(
                    path_to_dataset, subj, action, subact)
                action_sequence = readCSVasFloat(filename)

                T = action_sequence.shape[0]
                odd_list = range(1, T, 2)
                even_list = range(0, T, 2)

                trainData[(subj, action, subact)] = action_sequence
                trainData[(subj, action, subact,
                           'even')] = action_sequence[even_list, :]
                trainData[(subj, action, subact,
                           'odd')] = action_sequence[odd_list, :]
                if len(completeData) == 0:
                    completeData = copy.deepcopy(trainData[(subj, action,
                                                            subact)])
                else:
                    completeData = np.append(completeData,
                                             trainData[(subj, action, subact)],
                                             axis=0)
    return trainData, completeData

Example #2

File: generateMotionData.py Project: AndrewChiyz/RNNexp

def convertAndSave(fname):
	fpath = path_to_trajfiles + fname
	if not os.path.exists(fpath):
		return False
	normalizedData = readCSVasFloat(fpath)
	origData = unNormalizeData(normalizedData,data_mean,data_std,dimensions_to_ignore)
	if len(origData) > 0:
		fpath = path_to_trajfiles + fname
		writeMatToCSV(origData,fpath)
		return True

Example #3

def convertAndSave(fname):
    fpath = path_to_trajfiles + fname
    if not os.path.exists(fpath):
        return False
    normalizedData = readCSVasFloat(fpath)
    origData = unNormalizeData(normalizedData, data_mean, data_std,
                               dimensions_to_ignore)
    if len(origData) > 0:
        fpath = path_to_trajfiles + fname
        writeMatToCSV(origData, fpath)
        return True

Example #4

File: processdata.py Project: ZhouCS/RNNexp

def loadTrainData(subjects):
    trainData = {}
    completeData = []
    for subj in subjects:
        for action in actions:
            for subact in subactions:
                filename = "{0}/{1}/{2}_{3}.txt".format(path_to_dataset, subj, action, subact)
                action_sequence = readCSVasFloat(filename)

                T = action_sequence.shape[0]
                odd_list = range(1, T, 2)
                even_list = range(0, T, 2)

                trainData[(subj, action, subact)] = action_sequence
                trainData[(subj, action, subact, "even")] = action_sequence[even_list, :]
                trainData[(subj, action, subact, "odd")] = action_sequence[odd_list, :]
                if len(completeData) == 0:
                    completeData = copy.deepcopy(trainData[(subj, action, subact)])
                else:
                    completeData = np.append(completeData, trainData[(subj, action, subact)], axis=0)
    return trainData, completeData