# load data into file
for line in input_data:
if delimiter: words = line.split(delimiter)
else: words = line.split()
if len(words) < column: # skip any lines that don't have enough values
continue
try: # skip any lines where value is not a float
data.append(float(words[column-1]))
except ValueError:
continue
input_data.close()
# Set up default ranges
if (binrange[0] == 0 and binrange[1] == 0):
xmaxminholder = utilities.minmax(data)
binrange[0] = math.floor(xmaxminholder[0])
binrange[1] = math.ceil(xmaxminholder[1])
# Set up default number of bins according to "Scott's Choice"
if bins == 0:
inttmp = 3.5 * utilities.stdev(data,'no') / float(len(data)) ** (1/3.0)
bins = int(math.ceil((binrange[1] - binrange[0])/inttmp))
if normalize:
pointweight /= float(len(data)) * (binrange[1] - binrange[0])/bins
interval = (binrange[1] - binrange[0])/bins
# create a large 1-D array with every bin (x1y1 x1y2 ... x1yN x2y1 x2y2 ... ... xNyN)
for x in range(bins):
words = line.split(opt.delimiter)
if len(words) < maxcol: # skip any lines that don't have enough values
continue
try: # skip any lines where at least 1 of first 2 columns are not floats
data1.append(float(words[col1].strip()))
data2.append(float(words[col2].strip()))
except ValueError:
print words
continue
input_data.close()
# Set up default ranges
if (binrange[0] == 0 and binrange[1] == 0) or \
(binrange[2] == 0 and binrange[3] == 0):
xmaxminholder = utilities.minmax(data1)
ymaxminholder = utilities.minmax(data2)
binrange[0] = math.floor(xmaxminholder[0])
binrange[1] = math.ceil(xmaxminholder[1])
binrange[2] = math.floor(ymaxminholder[0])
binrange[3] = math.ceil(ymaxminholder[1])
# Set up default number of bins according to "Scott's Choice"
if bins[0] == 0 or bins[1] == 0:
xinttmp = 3.5 * utilities.stdev(data1,'no') / float(len(data1)) ** (1/3)
yinttmp = 3.5 * utilities.stdev(data2,'no') / float(len(data2)) ** (1/3)
bins[0] = int(math.ceil((binrange[1] - binrange[0])/xinttmp))
bins[1] = int(math.ceil((binrange[3] - binrange[2])/yinttmp))
if opt.normalize:
pointweight /= float(len(data1)) * (
binrange[1]-binrange[0])*(binrange[3]-binrange[2])/(bins[0]*bins[1])
# load data into file
for line in input_data:
if delimiter: words = line.split(delimiter)
else: words = line.split()
if len(words) < column: # skip any lines that don't have enough values
continue
try: # skip any lines where value is not a float
data.append(float(words[column - 1]))
except ValueError:
continue
input_data.close()
# Set up default ranges
if (binrange[0] == 0 and binrange[1] == 0):
xmaxminholder = utilities.minmax(data)
binrange[0] = math.floor(xmaxminholder[0])
binrange[1] = math.ceil(xmaxminholder[1])
# Set up default number of bins according to "Scott's Choice"
if bins == 0:
inttmp = 3.5 * utilities.stdev(data, 'no') / float(len(data))**(1 / 3.0)
bins = int(math.ceil((binrange[1] - binrange[0]) / inttmp))
if normalize:
pointweight /= float(len(data)) * (binrange[1] - binrange[0]) / bins
interval = (binrange[1] - binrange[0]) / bins
# create a large 1-D array with every bin (x1y1 x1y2 ... x1yN x2y1 x2y2 ... ... xNyN)
for x in range(bins):
