def test_no_wells():
with raises(ConfigError, match="No wells defined"):
wellmap.load(DIR / "empty.toml")
# The following examples actually trigger a different (and more specific)
# exception, but it still has "No wells defined" in the message.
with raises(ConfigError, match="No wells defined"):
wellmap.load(DIR / "row_without_col.toml")
with raises(ConfigError, match="No wells defined"):
wellmap.load(DIR / "irow_without_col.toml")
with raises(ConfigError, match="No wells defined"):
wellmap.load(DIR / "col_without_row.toml")
with raises(ConfigError, match="No wells defined"):
wellmap.load(DIR / "icol_without_row.toml")
def test_two_plates():
df = wellmap.load(
DIR / 'two_plates.toml',
data_loader=pd.read_csv,
merge_cols={'well': 'Well'},
)
assert row(df, 'plate == "a"') == dict(
path=DIR / 'two_plates_a.csv',
plate='a',
well='A1',
Well='A1',
well0='A01',
row='A',
col='1',
row_i=0,
col_j=0,
x=1,
Data=0,
)
assert row(df, 'plate == "b"') == dict(
path=DIR / 'two_plates_b.csv',
plate='b',
well='A1',
Well='A1',
well0='A01',
row='A',
col='1',
row_i=0,
col_j=0,
x=2,
Data=1,
)
def run(self):
rel_paths = shlex.split(self.arguments[0])
example_rst = ""
if not self.content:
contents = [None] * len(rel_paths)
else:
contents = '\n'.join(self.content).split('--EOF--')
if len(contents) != len(rel_paths):
raise self.error(
f"found {len(content)} TOML snippets, but {len(rel_paths)} paths."
)
for rel_path, content in zip(rel_paths, contents):
toml_path, toml_abs_path = self.env.relfn2path(rel_path)
name = os.path.basename(toml_path)
if content:
update_toml_file(toml_abs_path, content)
if not os.path.exists(toml_abs_path):
raise self.error(f"no such file: {toml_path}")
example_rst += f'''\
.. literalinclude:: /{toml_path}
:language: toml
:caption: :download:`{name} </{toml_path}>`
'''
# Only make a figure for the last snippet.
if 'no-figure' not in self.options:
svg_path = change_ext(toml_path, '.svg')
svg_abs_path = change_ext(toml_abs_path, '.svg')
df, deps = wellmap.load(toml_abs_path, report_dependencies=True)
if any_deps_stale(svg_abs_path, deps):
logger.info(f"[example] rendering: {svg_path}")
attrs = self.options.get('attrs', [])
cmap = wellmap.plot.get_colormap(
self.options.get('color', 'rainbow'))
fig = wellmap.plot.plot_layout(df, attrs, cmap)
fig.savefig(svg_abs_path, bbox_inches='tight')
example_rst += f'''\
.. figure:: /{svg_path}
'''
example_str_list = StringList(example_rst.splitlines())
wrapper = nodes.container(classes=['wellmap-example'])
self.state.nested_parse(example_str_list, 0, wrapper)
return [wrapper]
def test_one_plate():
labels = wellmap.load(DIR / 'one_plate.toml')
assert row(labels, 'well == "A1"') == dict(
path=DIR / 'one_plate.csv',
plate='a',
well='A1',
well0='A01',
row='A',
col='1',
row_i=0,
col_j=0,
x=1,
)
def load(self):
df = wellmap.load(
self.layout_toml,
data_loader=load_cq,
merge_cols=True,
path_guess='{0.stem}',
)
# Fill in optional columns:
if 'template' not in df:
df['template'] = np.nan
if 'primers' not in df:
df['primers'] = np.nan
return df, {}
def load(self):
df, extras = wellmap.load(
self.layout_toml,
data_loader=load_cq,
merge_cols=True,
path_guess='{0.stem}',
extras=['qpcr'],
)
def fill_default(k, default=pd.NA):
df[k] = df[k].fillna(pd.NA) if k in df else default
fill_default('control')
fill_default('template')
fill_default('primers')
fill_default('date')
df['is_control'] = df['control'].fillna(False).astype(bool)
return df, extras
def test_concat():
labels = wellmap.load(DIR / 'one_concat.toml')
assert len(labels) == 2
with raises(ConfigError, match="Did you mean to set `meta.path`?"):
wellmap.load(DIR / 'one_concat.toml', path_required=True)
labels = wellmap.load(DIR / 'two_concats_list.toml')
assert len(labels) == 3
with raises(ConfigError, match="Did you mean to set `meta.path`?"):
wellmap.load(DIR / 'two_concats_list.toml', path_required=True)
# Should not raise. It's ok that `just_concat.csv` doesn't exist, because
# `just_concat.toml` doesn't specify any wells.
labels = wellmap.load(
DIR / 'just_concat.toml',
path_guess='{0.stem}.csv',
path_required=True,
)
assert len(labels) == 1
def test_bad_args():
# Doesn't make sense to specify `merge_cols` without `data_loader`:
with raises(ValueError):
wellmap.load(DIR / 'two_plates.toml', merge_cols={})
# Non-existent merge columns.
with raises(ValueError, match='xxx'):
wellmap.load(
DIR / 'two_plates.toml',
data_loader=pd.read_csv,
merge_cols={'xxx': 'Well'},
)
with raises(ValueError, match='xxx'):
wellmap.load(
DIR / 'two_plates.toml',
data_loader=pd.read_csv,
merge_cols={'well': 'xxx'},
)
import matplotlib.pyplot as plt
import color_me
import itertools
from dbp.plate_reader import BiotekExperiment
from scipy.stats import linregress
def load_plate_reader(p):
expt = BiotekExperiment(p)
return expt.kinetic['450,521']
df = wellmap.load(
toml_path='20201211_meaure_o42_o213.toml',
data_loader=load_plate_reader,
merge_cols=True,
)
fig, ax = plt.subplots()
x_fit = np.linspace(min(df['minutes']), max(df['minutes']))
iter_colors = lambda it: zip(itertools.cycle(color_me.ucsf.cycle), it)
for color, ((beacon, dnazyme),
g) in iter_colors(df.groupby(['beacon', 'dnazyme'])):
x, y = g['minutes'], g['read']
m, b, r, p, err = linregress(x, y)
y_fit = m * x_fit + b
ax.plot(
def load_cq(path):
csv_dir = path.parent / path.stem
csv_path = csv_dir / 'Quantification Cq Results.csv'
return pd.read_csv(csv_path)[['Well', 'Cq']]
def format_label(slug):
low, high = slug.split('_')
return f'{low}–{high}°C'
path = Path('20200803_optimize_ta.toml')
df = wellmap.load(
path,
data_loader=load_cq,
merge_cols={'well0': 'Well'},
)
for label, df_plate in df.groupby('plate'):
plt.plot(
df_plate['temperature_C'],
df_plate['Cq'] - min(df_plate['Cq']),
marker='+',
linestyle='none',
label=format_label(label),
)
plt.xlabel('Ta (°C)')
plt.ylabel('ΔCq')
plt.legend(loc='best')
if ylim:
ax.set_ylim(*ylim)
else:
ax.set_ylim(0, ax.get_ylim()[1])
return fig, ax
if __name__ == '__main__':
args = docopt.docopt(__doc__)
toml_path = Path(args['<toml>'])
df, extras = wellmap.load(
toml_path=toml_path,
data_loader=load_plate_reader,
merge_cols=True,
path_guess='{0.stem}.xlsx',
extras='style',
)
if 'fit_start_min' not in df:
df['fit_start_min'] = 0
if 'fit_stop_min' not in df:
df['fit_stop_min'] = max(df['minutes'])
df['control'] = df['control'].fillna('')
style = Style.from_extras(df, extras)
fits = calc_linear_fits(df)
if args['--y-lim']:
ylim = map(float, args['--y-lim'].split(','))
else:
from scipy.stats import linregress
def load_cq(path):
return (pd.read_csv(path).rename(columns={
'Cq': 'row'
}).melt(
id_vars=['row'],
var_name='col',
value_name='Cq',
))
df = wellmap.load(
'std_curve.toml',
data_loader=load_cq,
merge_cols=True,
path_guess='{0.stem}.csv',
)
x = df['dilution']
y = df['Cq']
m, b, r, p, err = linregress(np.log10(x), y)
x_fit = np.logspace(0, 5)
y_fit = np.polyval((m, b), np.log10(x_fit))
r2 = r**2
eff = 100 * (10**(1 / m) - 1)
label = 'R²={:.5f}\neff={:.2f}%'.format(r2, eff)
plt.plot(x_fit, y_fit, '--', label=label)
#!/usr/bin/env python3
import wellmap
df = wellmap.load('simple_1.toml')
print(df)
def test_reasonably_complex():
df = wellmap.load(DIR / 'reasonably_complex.toml')
assert len(df) == 32
def test_one_well():
labels = wellmap.load(DIR / 'one_well_xy.toml')
assert row(labels, 'well == "A1"') == dict(
well='A1',
well0='A01',
row='A',
col='1',
row_i=0,
col_j=0,
x=1,
y=1,
)
labels = wellmap.load(DIR / 'one_well_xy.toml', path_guess='{0.stem}.csv')
assert row(labels, 'well == "A1"') == dict(
path=DIR / 'one_well_xy.csv',
well='A1',
well0='A01',
row='A',
col='1',
row_i=0,
col_j=0,
x=1,
y=1,
)
_, deps = wellmap.load(DIR / 'one_well_xy.toml', report_dependencies=True)
assert deps == {DIR / 'one_well_xy.toml'}
with raises(ConfigError, match='one_well_xy.toml'):
wellmap.load(DIR / 'one_well_xy.toml', path_required=True)
with raises(ConfigError, match='one_well_xy.toml'):
wellmap.load(DIR / 'one_well_xy.toml', data_loader=pd.read_csv)
labels, data = wellmap.load(
DIR / 'one_well_xy.toml',
data_loader=pd.read_csv,
path_guess='{0.stem}.csv',
)
assert row(labels, 'well == "A1"') == dict(
path=DIR / 'one_well_xy.csv',
well='A1',
well0='A01',
row='A',
col='1',
row_i=0,
col_j=0,
x=1,
y=1,
)
assert row(data, 'Well == "A1"') == dict(
Well='A1',
path=DIR / 'one_well_xy.csv',
Data='xy',
)
df = wellmap.load(
DIR / 'one_well_xy.toml',
data_loader=pd.read_csv,
merge_cols={'well': 'Well'},
path_guess='{0.stem}.csv',
)
assert row(df, 'well == "A1"') == dict(
path=DIR / 'one_well_xy.csv',
well='A1',
Well='A1',
well0='A01',
row='A',
col='1',
row_i=0,
col_j=0,
x=1,
y=1,
Data='xy',
)
df = wellmap.load(
DIR / 'one_well_xy.toml',
data_loader=read_csv_and_rename,
merge_cols=True,
path_guess='{0.stem}.csv',
)
assert row(df, 'well == "A1"') == dict(
path=DIR / 'one_well_xy.csv',
well='A1',
Well='A1',
well0='A01',
row='A',
col='1',
row_i=0,
col_j=0,
x=1,
y=1,
Data='xy',
)
def test_one_well_with_extras():
expected = {'extras': {'a': 1, 'b': 1}}
def data_loader(path, extras):
assert extras == expected
return pd.read_csv(path)
# No data:
a1_expected = dict(
well='A1',
well0='A01',
row='A',
col='1',
row_i=0,
col_j=0,
x=1,
y=1,
)
labels, extras = wellmap.load(
DIR / 'one_well_xy_extras.toml',
extras=True,
)
assert row(labels, 'well == "A1"') == a1_expected
assert extras == expected
# No data, with extras present but not requested:
labels = wellmap.load(DIR / 'one_well_xy_extras.toml', )
assert row(labels, 'well == "A1"') == a1_expected
# No data, with extras and dependencies requested:
labels, extras, deps = wellmap.load(
DIR / 'one_well_xy_extras.toml',
extras=True,
report_dependencies=True,
)
assert row(labels, 'well == "A1"') == a1_expected
assert extras == expected
assert deps == {
DIR / 'one_well_xy_extras.toml',
}
# Load labels and data, but don't merge:
labels, data, extras = wellmap.load(
DIR / 'one_well_xy_extras.toml',
data_loader=data_loader,
path_guess='{0.stem}.csv',
extras=True,
)
assert row(labels, 'well == "A1"') == dict(
path=DIR / 'one_well_xy_extras.csv',
well='A1',
well0='A01',
row='A',
col='1',
row_i=0,
col_j=0,
x=1,
y=1,
)
assert row(data, 'Well == "A1"') == dict(
Well='A1',
path=DIR / 'one_well_xy_extras.csv',
Data='xy',
)
assert extras == expected
# Automatic merge:
a1_expected = dict(
path=DIR / 'one_well_xy_extras.csv',
well='A1',
Well='A1',
well0='A01',
row='A',
col='1',
row_i=0,
col_j=0,
x=1,
y=1,
Data='xy',
)
df, extras = wellmap.load(
DIR / 'one_well_xy_extras.toml',
data_loader=data_loader,
merge_cols={'well': 'Well'},
path_guess='{0.stem}.csv',
extras=True,
)
assert row(df, 'well == "A1"') == a1_expected
assert extras == expected
df, extras = wellmap.load(
DIR / 'one_well_xy_extras.toml',
data_loader=read_csv_and_rename,
merge_cols=True,
path_guess='{0.stem}.csv',
extras=True,
)
assert row(df, 'well == "A1"') == a1_expected
assert extras == expected
