def get_line_x_axis(data, width, padding):
sam = next(iter(data))
minimum = min([x[0] for x in data[sam]]) - 1
maximum = max([x[0] for x in data[sam]]) + 1
return scale.axis([minimum, maximum], [padding, width - padding],
"Site Number",
tic_values=[x for x in range(minimum + 1, maximum)])
def get_x_axis(pop_data, width, padding):
minimum = 0
maximum = math.ceil(max(pop_data.keys())) + 1
return scale.axis([minimum, maximum], [padding, width - padding],
"Copy Number",
tics=8)
def get_normalised_y_axis(sample_data, cols, height, padding):
hap = sample_data["Median_depth"] / 2
all_data = sample_data[cols[0]] + sample_data[cols[1]]
mapped_data = [(x[1] / hap) for x in all_data]
max_val = math.ceil(max(mapped_data))
return scale.axis([0, max_val], [padding, height - padding], "Approx. CN", tics=4)
def get_bar_y_axis(norm_y_axis, sample_data):
hap = sample_data["Median_depth"] / 2
min_val = norm_y_axis["min"] * hap
max_val = norm_y_axis["max"] * hap
tics = [round(x * hap) for x in norm_y_axis["tics"]]
return scale.axis([min_val, max_val], norm_y_axis["domain"], "Read Count", tic_values=tics)
def get_bar_x_axis(sample_data, cols, width, padding):
length = len(sample_data[cols[0]])
for col in cols:
assert len(sample_data[col]) == length
data = [x[0] for x in sample_data[cols[0]]]
x_min = min(data) - 1
x_max = max(data) + 1
return scale.axis(
[x_min, x_max],
[padding, width - padding],
"Site Number",
tic_values=[x for x in range(x_min + 1, x_max)]
)
def get_line_y_axis(data, height, padding):
max_val = None
min_val = None
for sample_id in data:
sample_min = min([x[1] for x in data[sample_id]]) - 1
sample_max = max([x[1] for x in data[sample_id]]) + 1
if max_val is None or max_val < sample_max:
max_val = sample_max
if min_val is None or min_val > sample_min:
min_val = sample_min
if min_val < 0:
min_val = 0
return scale.axis(
[math.floor(min_val), math.ceil(max_val)], [padding, height - padding],
"Raw CN",
tics=6)
def get_y_axis(pop_data, height, padding):
minimum = 0
maximum = math.ceil(max(pop_data.values())) + 1
return scale.axis([minimum, maximum], [padding, height - padding],
"Sample Count")