def df_to_html_table(
dataframe: DataFrame,
data_only: bool = False,
n_mod: Optional[int] = None,
formats: Optional[Dict[Any, str]] = None,
) -> Table:
"""Converts pandas data frame to html table
"""
formats = formats or {}
def cast_type(val):
for dtype, cast in formats.items():
if isinstance(val, dtype):
try:
val = cast(val)
break
except ValueError:
break
return val
index_name = dataframe.index.name
index_name = index_name or "#"
tmp = dataframe.copy()
if n_mod is not None:
tmp = tmp[mod(tmp.index, n_mod) == 0].copy()
data = [
Thead([Tr([Th(index_name)] + [Th(col) for col in tmp.columns])]),
Tbody([
Tr([Th(cast_type(idx))] + [Td(cast_type(col)) for col in row])
for idx, row in tmp.iterrows()
]),
]
return data if data_only else Table(data)
def make_options_table(self, data, header_row):
return Table(
# Makes the header row
className="table",
children=[
Thead([Tr([Th(col, scope="col") for col in header_row])]),
Tbody([
Tr((Th(prop, scope="row"), Td(data[prop])))
for prop in data
])
])
def df_to_html_table(
dataframe: DataFrame,
data_only: bool = False,
n_mod: Optional[int] = None,
) -> Table:
"""Converts pandas data frame to html table
"""
index_name = dataframe.index.name
tmp = dataframe.reset_index()
tmp = tmp[mod(tmp.index, n_mod) == 0].copy()
tmp = tmp.set_index(index_name)
data = [
Thead([Tr([Th(tmp.index.name)] + [Th(col) for col in tmp.columns])]),
Tbody([
Tr([Th(idx)] + [Td(col) for col in row])
for idx, row in tmp.iterrows()
]),
]
return data if data_only else Table(data)
def generate_layout(headers, reports, models):
"""Generates report table and returns it
headers - texts in headers in format {metric_internal_name: metric_ui_name}
reports - DB data in format [(id, {metric: value})
models - information about models in format [(id, name, type)]"""
if not reports: return Div()
return Div([
H2("Метрики"),
Table(
[Tr(
[Th("Name")] +
[Th(headers[metric]) for metric in reports[0][1]]
)] +
[Tr(
[Td([m[1] for m in models if m[0] == report[0]][0])] +
[Td(round(value,4)) for value in report[1].values()]
)
for report in reports]
)
])
def get_logs(max_rows=20):
p = api_request_to_json('logs')['posts']
headers = ['Post', 'Author', 'Date']
d = sorted(p, key=itemgetter('date'), reverse=True)
link = 'https://hvointernal.wr.usgs.gov/hvo_logs/read?id={}'
return [[Tr([Th(col) for col in headers])] + [
Tr([
Td(
A(href=link.format(d[i]['id']),
children='%s' % d[i]['subject'],
target='_blank')),
Td(children='%s' % d[i]['user']),
Td(children='%s' % d[i]['date'])
]) for i in range(0, max_rows)
]]
'q': lambda q: q**2.0,
'i': lambda q, i: log2(i), # in ln scale
},
'kratky': {
'q': lambda q: q,
'i': lambda q, i: i * q**2.0,
},
'porod': {
'q': lambda q: q**4.0,
'i': lambda q, i: i * q**4.0,
},
}
_line_style_table = html.Table(children=[
html.Thead([
Th('Curves'),
Th('Marker'),
Th('Linestyle'),
Th('Removed'),
]),
Tr([
Td('curve 1'),
Td('2'),
Td('3'),
Td('4'),
]),
Tr([
Td('curve 2'),
Td('6'),
Td('7'),
Td('8'),
def monthly_html_table() -> Div:
"""
Build pretty monthly transaction table with names as indices and months as columns
"""
df = transactions_monthly()
df.index = [s.strftime('%b %y') for s in df.index]
expenses = [n for n in NAMES if n in df.columns and df[n].sum() <= 0]
income = [n for n in NAMES if n in df.columns and df[n].sum() > 0]
df = df[expenses + income].T
curr = ' {:,.2f}'.format
perc = '{:,.2%}'.format
def circle(color):
css = {
'width': '14px',
'height': '14px',
'background': color,
'display': 'inline-block',
'margin': '0 8px 0 0',
'padding': 0
}
return Div(style=css)
header_row = Thead([Th('Name', scope='col')] +
[Th(col, scope='col') for col in df.columns],
className='thead-light')
# name rows
name_rows = []
for cat, row in df.iterrows():
cells = [
Th([circle(color(cat)), cat],
scope='row',
style={'background': '#fafafa'})
]
for cell in row:
style = {
'color': color('INC') if cell >= 0 else color('EXP'),
'text-align': 'right'
}
cells.append(Td(curr(cell or '') if cell != 0 else '',
style=style))
name_rows.append(Tr(cells))
# growth row
balances = list(df.sum().cumsum() + BALANCE)
saving_rel = np.divide(balances, [BALANCE] + balances[:-1]) - 1
saving_abs = np.array(balances) - np.array([BALANCE] + balances[:-1])
growth_row = [
Th('GROWTH',
style={
'background': '#f5f5f5',
'border-top': 'solid 2px #CCC'
})
]
for sr, sa in zip(saving_rel, saving_abs):
sa = Span(curr(sa),
style={
'color': color('INC') if sr >= 0 else color('EXP'),
'font-weight': 'bold'
})
sr = Span(
f'{perc(sr)}',
style={
'color':
color('INC', alpha=0.8) if sr >= 0 else color('EXP', alpha=0.8)
})
sep = Span(' | ', style={'color': '#CCC'})
growth_row.append(
Td([sr, sep, sa],
style={
'border-top': 'solid 2px #CCC',
'text-align': 'right'
}))
growth_row = Tr(growth_row)
# total row
total_row = [Th('TOTAL', style={'background': '#f5f5f5'})]
for exp, inc in zip(df.loc[expenses].sum(), df.loc[income].sum()):
sexp = Span(curr(exp),
style={
'color': color('EXP'),
'font-weight': 'bold'
})
sinc = Span(curr(inc),
style={
'color': color('INC'),
'font-weight': 'bold'
})
sep = Span(' | ', style={'color': '#CCC'})
total_row.append(Td([sexp, sep, sinc], style={'text-align': 'right'}))
total_row = Tr(total_row)
# balance row
balance_row = [Th('BALANCE', style={'background': '#f5f5f5'})]
for diff in df.sum().cumsum() + BALANCE:
balance_row.append(
Td(curr(diff),
style={
'font-weight': 'bold',
'text-align': 'right'
}))
balance_row = Tr(balance_row)
all_rows = [header_row] + name_rows + [growth_row] + [total_row
] + [balance_row]
return Div(Table(all_rows, className='table table-bordered table-sm'),
className='table-responsive-sm')