def create_color_mapper(mapper_type, c_min, c_max, cmap="YlGnBu", cmap_res=8, cmap_r=True):
"""
create the colormapper
mapper_type = "log" or "linear"
"""
# define the color palette
palette = brewer[cmap][cmap_res]
# reverese the color palette if desired
if cmap_r == True:
palette = palette[::-1]
#Instantiate LogColorMapper that maps numbers in a range, into a sequence of colors.
# Create the specified color mapper
if mapper_type == "log":
color_mapper = LogColorMapper(palette=palette, low=c_min, high=c_max)
elif mapper_type == "linear":
color_mapper = LinearColorMapper(palette=palette, low=c_min, high=c_max)
else:
raise ValueError("mapper_type must be specified as either 'log' or 'linear'")
# set the nan_color of the color mapper
color_mapper.nan_color = "#f0f0f0"
return color_mapper
def make_map(d, map_width):
rev_dict = {}
path = '/Users/a6002538/Downloads/results-20190429-111727.csv'
with open(path, 'r') as read_obj:
reader = csv.DictReader(read_obj)
for row in reader:
rev_dict[row['fips']] = float(row['rev_weighted'])
for key in rev_dict.keys():
if rev_dict[key] > 1:
print(key)
p = figure(title='Dollars Spent Per Person',
width=map_width,
height=int(round(4 / 5 * map_width)))
palette = Blues8
palette.reverse()
#color_mapper = LinearColorMapper(palette=palette)
color_mapper = LogColorMapper(palette=palette)
color_mapper.nan_color = 'gray'
_data = []
for key in d.keys():
_data.append((rev_dict.get(key, 0), d[key]['x'], d[key]['y']))
_data = sorted(_data)
data = [x[0] for x in _data]
xs = [x[1] for x in _data]
ys = [x[2] for x in _data]
#legends = make_legends(data, palette)
#fill_colors, legends = log_mapper(data, palette)
fill_colors, legends = linear_mapper(data, palette)
source = ColumnDataSource(data=dict(
x=xs, y=ys, data=data, legend=legends, fill_colors=fill_colors))
p.patches('x',
'y',
source=source,
fill_color='fill_colors',
fill_alpha=0.7,
line_color='black',
line_width=.2,
legend='legend')
p.legend.location = "bottom_right"
return p
def make_map(d, map_width):
rev_dict = {}
with open('/Users/a6002538/Downloads/results-20190426-163009.csv',
'r') as read_obj:
reader = csv.DictReader(read_obj)
for row in reader:
rev_dict[row['fips']] = float(row['rev'])
p = figure(width=map_width, height=int(round(4 / 5 * map_width)))
palette = Blues8
palette.reverse()
#color_mapper = LinearColorMapper(palette=palette)
color_mapper = LogColorMapper(palette=palette)
color_mapper.nan_color = 'gray'
_data = []
for key in d.keys():
_data.append((rev_dict.get(key, 0), d[key]['x'], d[key]['y']))
_data = sorted(_data)
data = [x[0] for x in _data]
xs = [x[1] for x in _data]
ys = [x[2] for x in _data]
#legends = make_legends(data, palette)
log_list = [math.log(x) if x > 0 else 0 for x in data]
perc = [1 / len(palette) * x for x in range(1, len(palette) + 1)]
q = np.quantile(log_list, perc)
ls = []
prev = 0
for i in q:
n = '{0:,}'.format(round(math.exp(i)))
ls.append('{p}:{n}'.format(p=prev, n=n))
prev = n
ls.append('>')
fill_colors = []
legends = []
for i in data:
if i == 0 or math.log(i) <= q[0]:
fill_colors.append(palette[0])
legends.append(ls[0])
elif math.log(i) <= q[1]:
fill_colors.append(palette[1])
legends.append(ls[1])
elif math.log(i) <= q[2]:
fill_colors.append(palette[2])
legends.append(ls[2])
elif math.log(i) <= q[3]:
fill_colors.append(palette[3])
legends.append(ls[3])
elif math.log(i) <= q[4]:
legends.append(ls[4])
fill_colors.append(palette[4])
elif math.log(i) <= q[5]:
fill_colors.append(palette[5])
legends.append(ls[5])
elif math.log(i) <= q[6]:
fill_colors.append('orange')
legends.append(ls[6])
elif math.log(i) <= q[7]:
fill_colors.append('red')
legends.append(ls[7])
else:
fill_colors.append('orange')
legends.append(ls[8])
source = ColumnDataSource(data=dict(
x=xs, y=ys, data=data, legend=legends, fill_colors=fill_colors))
p.patches('x',
'y',
source=source,
fill_color='fill_colors',
fill_alpha=0.7,
line_color='black',
line_width=.2,
legend='legend')
p.legend.location = "bottom_right"
return p
