def get_timeline(assignment_id, current_date, name = 'timeline'):
mask_1 = df['Assignment'] == assignment_id
mask_2 = df['Entry Date'] <= current_date
max_date = df.loc[mask_1, 'Entry Date'].max()
mask = mask_1 & mask_2
df_small = df.loc[mask, ['Transaction', 'status_date']]
tmp = df_small.groupby(['status_date'], as_index=True).apply(aggregator).reset_index()
# tmp['status_date'] = tmp['status_date'].astype(str)
tmp = tmp.rename(
columns={
0: 'status'
}
)
# mask = df['Assignment'] == assignment_id
# df_small = df.loc[mask, ['Transaction', 'status_date']]
#
# ([rec for rec in df_small.iterrows()][0][1]['status_date'])
items = [
{'time': rec[1]['status_date'], 'text': rec[1]['status']} for rec in tmp.iterrows()
]
# print(f'max_date is {max_date}, current_date is {current_date}')
if current_date < max_date:
# print('True')
today_dict = {
'time': datetime.strptime(current_date, DATE_FORMAT),
'text': 'Today',
}
items.append(today_dict)
if len(items) <= 1:
print('Only one sample')
return None
# return tmp.loc[0, 'status_date'], tmp.loc[0, 'status']
# return items
try:
tl = TimelineTex(items, options=options)
tl.export(f'{name}.tex')
# tl.export(f'timeline_{assignment_id}.tex')
# else:
# tl.export(f'{folder}/timeline_{assignment_id}.tex')
pdf_filename = f'{name}.pdf'
command = 'convert -verbose -density 500 "{}" -quality 100 "{}"'.format(pdf_filename,
pdf_filename.replace('.pdf',
'.png'))
p = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True)
(out, err) = p.communicate()
except:
pass
def make_rank_plot(
results,
output_file,
keep_methods=None,
higher_better=True,
return_ranks=False,
):
methods = keep_methods[:]
avg_ranks, all_ranks = compute_ranks(results,
keep_methods=keep_methods,
higher_better=higher_better)
plot_data = [{
"time": rank,
"text": method_name(method)
} for method, rank in avg_ranks.items()]
color = "#000"
options = {
"scale": LinearScale(),
"direction": "up",
"domain": [1, len(methods)],
"layerGap": 20,
"borderColor": "#000",
"showBorder": False,
"labelBgColor": "#fff",
"linkColor": color,
"labelTextColor": color,
"dotColor": color,
"initialWidth": 600,
"initialHeight": 75,
"latex": {
"linkThickness": "thin",
"reproducible": True
},
"dotRadius": 2,
"margin": {
"left": 0,
"bottom": 0,
"right": 0,
"top": 0
},
}
tl = TimelineTex(plot_data, options=options)
texlines = tl.export()
n_datasets = len(all_ranks)
ref_method, CD, _ = reference_difference(avg_ranks, n_datasets)
# we're going to insert the critical difference line after the dots
# scope,·so we first have to figure out where that is.
lines = texlines.split("\n")
idx = None
find_scope = False
for i, line in enumerate(lines):
if line.strip() == "% dots":
find_scope = True
if find_scope and "\\end{scope}" in line:
idx = i + 1
break
before = lines[:idx]
after = lines[idx:]
nodes, _ = tl.compute()
bestnode = next(
(n for n in nodes if n.data.text == method_name(ref_method)), None)
# idealPos is the position on the axis
posBest = bestnode.getRoot().idealPos
posCD = tl.options["scale"](bestnode.data.time + CD)
CDlines = [
"% Critical difference",
"\\def\\posBest{%.16f}" % posBest,
"\\def\\posCD{%.16f}" % posCD,
"\\begin{scope}",
"\\draw (\\posBest, 30) -- (\\posBest, 20);",
"\\draw (\\posBest, 25) --node[below] {CD} (\\posCD, 25);",
"\\draw (\\posCD, 30) -- (\\posCD, 20);",
"\\end{scope}",
]
all_lines = before + [""] + CDlines + after
with open(output_file, "w") as fp:
fp.write("\n".join(all_lines))
# werkervaring.py
import pandas as pd
from labella.scale import LinearScale
from labella.timeline import TimelineTex
def makeInput(_df):
length = len(_df.year_start)
out = []
print(_df)
for i in range(length):
_dict = {}
_dict['time'] = _df.year_start[i]
#dict['year_end'] = _df.year_end[i]
_dict['text'] = _df.employer[i]
#dict['job_title'] = _df.job_title[i]
out = out + [_dict]
return out
header_list = ['year_start', 'year_end', 'employer', 'job_title']
df = pd.read_csv('werkervaring.csv', names=header_list)
xtimeline = makeInput(df)
ttimeline = TimelineTex(xtimeline, options={'scale': LinearScale()})
ttimeline.export('werkervaring.tex')
def test_labella(struct, name):
#tl = TimelineTex(struct, options={'scale': LinearScale()})
tl = TimelineTex(struct, options={'scale': TimeScale()})
tl.export(f"{name}.tex")
