def clr_grad(color):
if color.startswith('rgba('):
r, g, b, a = rgba(color)
return '%d' % int(a*100000)
elif 'opacity' in keys:
return '%d' % int(float(e.get('opacity')) * 100000)
else:
return '%d' % 100000
def clr_grad(color):
if color.startswith('rgba('):
r, g, b, a = rgba(color)
return '%d' % int(a * 100000)
elif 'opacity' in keys:
return '%d' % int(float(e.get('opacity')) * 100000)
else:
return '%d' % 100000
def msclr(color):
r, g, b, a = rgba(color)
return '%02x%02x%02x' % (255*r, 255*g, 255*b)
def msclr(color):
r, g, b, a = rgba(color)
return '%02x%02x%02x' % (255 * r, 255 * g, 255 * b)
reader = csv.DictReader(open('daily-rainfall-data.csv'), delimiter='\t')
fields = reader.fieldnames[3:]
W = len(fields)
HI = 5.7 # 99% = 49.1, 95% = 14.3, 90% = 5.7, 80% = 0.6
STARTYEAR = 1971
ENDYEAR = 2005
YEARS = ENDYEAR - STARTYEAR + 1 # Number of years
ROWS = 65 # Number of latitude grids each day
im = Image.new('RGBA', (W * 366, YEARS * ROWS))
for key, rows in itertools.groupby(reader, lambda v: (v['Year'], v['Day'])):
print key
rows = list(rows)
X0 = W * (int(key[1]) - 1)
Y0 = ROWS * (int(key[0]) - STARTYEAR)
for y, row in enumerate(rows):
for x, field in enumerate(fields):
try:
cell = float(row[field])
except:
cell = -999
if cell < 0:
color = (0, 0, 0, 0)
else:
color = tuple(int(255 * v) for v in _color.rgba(
_color.gradient(cell / HI, _color.Greens)))
im.putpixel((x + X0, y + Y0), color)
im.save('rainfall.png')
