def test_readSC():
"""Test the readSC function."""
df, plot_names = readSC()
assert isinstance(df, pd.DataFrame)
assert isinstance(plot_names, list)
for name in plot_names:
assert isinstance(name, str)
def inventory_plot_filter():
field = request.args.get('field')
value = request.args.get('value')
# print(", ".join((field, value)))
df, columns = readSC()
filterdf = df[df[str(field)] == value]
# filterdfs = filterdf.sort_values('impound_no', ascending=False) # [:50] # TODO: Remove the slicing!
return plot_page_inventory(filterdf, columns, request, field=field, value=value)
def los_plot():
"""Plot inventory over time"""
df, columns = readSC()
dfs = df.sort_values('animal_id', ascending=False)
# cols = ['animal_id','animal_type','impound_no','intake_type','intake_subtype','intake_cond','outcome_type','outcome_subtype','outcome_cond','weight','intake_date','outcome_date','dob','primary_bree','secondary_','kennel_no','s_n_date','weight_1_week','due_out_date','location_1','location_1_date','location_2','location_2_date','los','days_old','los_1','los_2','age_s_n_date','weight_difference','in_to_due_out_date_diff','due_out_to_outcome_date_diff']
decimals = dict.fromkeys(columns, 2)
dfs = dfs.round(decimals=decimals)
dfs = dfs.to_dict('records')
return plot_page_los(df, columns, request, rows=dfs)
def test_readSC_with_nrows():
"""Test shortened readSC function."""
for nrows in [2, 5]:
df, plot_names = readSC(nrows=nrows)
assert isinstance(df, pd.DataFrame)
assert isinstance(plot_names, list)
for name in plot_names:
assert isinstance(name, str)
assert len(df) == nrows
def counts_plot_filter():
field = request.args.get('field')
value = request.args.get('value')
df, columns = readSC()
filterdf = df[df[str(field)] == value]
dfs = filterdf.sort_values('animal_id', ascending=False)
decimals = dict.fromkeys(columns, 2)
dfs = dfs.round(decimals=decimals)
dfs = dfs.to_dict('records')
return plot_page_counts(filterdf, columns, request, field=field, value=value, rows=dfs)
def los_plot_filter():
field = request.args.get('field')
value = request.args.get('value')
df, columns = readSC()
filterdf = df[df[str(field)] == value]
dfs = filterdf.sort_values('animal_id', ascending=False)
# columns = ['animal_id','animal_type','impound_no','intake_type','intake_subtype','intake_cond','outcome_type','outcome_subtype','outcome_cond','weight','intake_date','outcome_date','dob','primary_bree','secondary_','kennel_no','s_n_date','weight_1_week','due_out_date','location_1','location_1_date','location_2','location_2_date','los','days_old','los_1','los_2','age_s_n_date','weight_difference','in_to_due_out_date_diff','due_out_to_outcome_date_diff']
decimals = dict.fromkeys(columns, 2)
dfs = dfs.round(decimals=decimals)
dfs = dfs.to_dict('records')
return plot_page_los(filterdf, columns, request, field=field, value=value, rows=dfs)
def filter():
field = request.args.get('field')
value = request.args.get('value')
df, cols = readSC()
filterdf = df[df[str(field)] == value]
filterdfs = filterdf.sort_values('animal_id', ascending=False) # [:50] # TODO: Remove the slicing!
# cols = ['animal_id','animal_type','impound_no','intake_type','intake_subtype','intake_cond','outcome_type','outcome_subtype','outcome_cond','weight','intake_date','outcome_date','dob','primary_bree','secondary_','kennel_no','s_n_date','weight_1_week','due_out_date','location_1','location_1_date','location_2','location_2_date']
decimals = dict.fromkeys(cols, 2)
filterdfs = filterdfs.round(decimals=decimals)
# df = df[(df['intake_type']!="DEAD")&(df['intake_subtype']!="DEAD")&(df['intake_cond']!="DEAD")&(df['intake_type']!="DISPOSAL")&(df['intake_subtype']!="DISPOSAL")&(df['intake_cond']!="DISPOSAL")&(df['intake_type']!="DECEASED")&(df['intake_subtype']!="DECEASED")&(df['intake_cond']!="DECEASED")]
filterdfs = filterdfs.to_dict('records')
return render_template('main.html', rows=filterdfs)
def homepage():
"""Home page for SWEETer-Cat with updated table"""
df, _ = readSC()
df['alink'] = list(map(author_html, df['Author'], df['link']))
dfs = df.sort_values('updated', ascending=False) # [:50] # TODO: Remove the slicing!
dfs.drop(['Author', 'link'], axis=1, inplace=True)
cols = ['par', 'parerr', 'logg', 'loggerr', 'logglc', 'logglcerr', 'vterr',
'mass', 'masserr', 'Vmag', 'Vmagerr', 'feh', 'feherr']
decimals = dict.fromkeys(cols, 2)
dfs = dfs.round(decimals=decimals)
dfs['HD'].fillna('...', inplace=True)
dfs['Comment'].fillna('...', inplace=True)
dfs['source'].fillna('...', inplace=True)
dfs = dfs.to_dict('records')
return render_template('main.html', rows=dfs)
def test_hz():
"""Test the hz function."""
df, _ = readSC()
for (teff, logg, mass) in df.loc[:, ['teff', 'logg', 'mass']].values:
lum = (teff / 5777)**4 * (mass / ((10**logg) / (10**4.44)))**2
assert isinstance(hz(teff, lum, model=2), float)
assert isinstance(hz(teff, lum, model=4), float)
teff = 5777
lum = 1
invalids = [{teff: lum}, [teff, lum], (teff, lum), "..."]
for model in range(1, 6):
assert isinstance(hz(teff, lum, model), float)
results = [0.75, 0.98, 0.99, 1.71, 1.77]
for model, result in enumerate(results, start=1):
assert round(hz(teff, lum, model), 2) == result
for invalid in invalids:
assert np.isnan(hz(invalid, lum, model))
assert np.isnan(hz(teff, invalid, model))
assert hz(teff, lum, 2) < hz(teff, lum, 4) # hz1 < hz2
def animal_iddetail(animal_id=None):
"""Page with details on the individual animal"""
if animal_id:
df, cols = readSC()
# t1, t2 = min(df['intake_date']), max(df['outcome_date'])
t1, t2 = min(df['los']), max(df['los'])
index = df['animal_id'] == animal_id
d = df.loc[index, :].copy()
if len(d):
show_intake = bool(~d['los'].isnull().values[0])
if len(d) == sum(d['datetime'].isnull()):
plot = None
else:
plot = detail_plot(d, t1, t2)
d.fillna('...', inplace=True)
info = d.to_dict('records')
dfs = d.sort_values('animal_id', ascending=False)
# cols = ['animal_id','animal_type','impound_no','intake_type','intake_subtype','intake_cond','outcome_type','outcome_subtype','outcome_cond','weight','intake_date','outcome_date','dob','primary_bree','secondary_','kennel_no','s_n_date','weight_1_week','due_out_date','location_1','location_1_date','location_2','location_2_date','los','days_old','los_1','los_2','age_s_n_date','weight_difference','in_to_due_out_date_diff','due_out_to_outcome_date_diff']
decimals = dict.fromkeys(cols, 2)
dfs = dfs.round(decimals=decimals)
dfs = dfs.to_dict('records')
return render_template('detail.html', info=info, show_intake=show_intake, plot=plot, rows=dfs)
return redirect(url_for('homepage'))
def SCdata():
"""SWEET-Cat database fixture."""
return readSC()
def inventory_plot():
"""Plot inventory over time"""
df, columns = readSC()
return plot_page_inventory(df, columns, request)
def linked_plot_filter():
field = request.args.get('field')
value = request.args.get('value')
df, columns = readSC()
filterdf = df[df[str(field)] == value]
return plot_page_mpld3(filterdf, columns, request, field=field, value=value)
def linked_plot():
"""Plot inventory over time"""
df, columns = readSC()
# print(", ".join(((df, columns))))
return plot_page_mpld3(df, columns, request)
def plot():
"""Plot stellar parameters"""
df, columns = readSC()
return plot_page(df, columns, request, page="plot")