def iterate(self, nested_dict=None):
"""http://stackoverflow.com/questions/10756427/loop-through-all-nested-dictionary-values"""
d = self if nested_dict is None else nested_dict
if nested_dict is None:
self.level = 0
for key in list(d.keys()):
value = d[key]
if isinstance(value, _Mapping):
if value.get('@class') == 'Structure':
from pymatgen import Structure
yield key, Structure.from_dict(value)
continue
yield (self.level, key), None
if value.get('@class') == 'Table':
from mpcontribs.io.core.components.tdata import Table
yield key, Table.from_dict(value)
continue
if Quantity is not None and value.get('@class') == 'Quantity':
quantity = Quantity.from_dict(value)
yield key, quantity
continue
self.level += 1
for inner_key, inner_value in self.iterate(nested_dict=value):
yield inner_key, inner_value
self.level -= 1
else:
yield (self.level, key), value
def get_context(project, columns=None):
ctx = {'project': project}
client = load_client()
prov = client.projects.get_entry(project=project).response().result
for k in ['id', 'project', 'other']:
prov.pop(k)
ctx['title'] = prov.pop('title')
ctx['provenance'] = RecursiveDict(prov).render()
data = client.projects.get_table(project=project,
columns=columns,
per_page=3).response().result
if data['items']:
columns = list(data['items'][0].keys())
table = Table(data['items'], columns=columns)
ctx['table'] = table.render(project=project)
return ctx
def read_csv(body, is_data_section=True, **kwargs):
"""run pandas.read_csv on (sub)section body"""
import pandas
body = body.strip()
if not body:
return None
from mpcontribs.io.core.components.tdata import Table
if is_data_section:
cur_line = 1
while 1:
body_split = body.split('\n', cur_line)
first_line = body_split[cur_line - 1].strip()
cur_line += 1
if first_line and not first_line.startswith(csv_comment_char):
break
sep = kwargs.get('sep', ',')
options = {'sep': sep, 'header': 0}
header = [col.strip() for col in first_line.split(sep)]
body = '\n'.join([sep.join(header), body_split[1]])
if first_line.startswith('level_'):
options.update({'index_col': [0, 1]})
ncols = len(header)
else:
options = {'sep': ':', 'header': None, 'index_col': 0}
ncols = 2
options.update(**kwargs)
converters = dict((col, strip_converter) for col in range(ncols))
return Table(
pandas.read_csv(StringIO(body),
comment=csv_comment_char,
skipinitialspace=True,
squeeze=True,
converters=converters,
encoding='utf8',
**options).dropna(how='all'))
def index(request):
ctx = RequestContext(request)
try:
ctx['project'] = project
prov = client.projects.get_entry(project=project).response().result
prov.pop('id')
ctx['title'] = prov.pop('title')
ctx['provenance'] = RecursiveDict(prov).render()
columns = ['phase', 'ΔH', 'ΔH|hyd', 'GS?', 'CIF']
data = client.contributions.get_table(
project=project, columns=columns, per_page=3
).response().result
columns = list(data['items'][0].keys())
table = Table(data['items'], columns=columns)
ctx['table'] = table.render(project=project)
except Exception as ex:
ctx['alert'] = str(ex)
return render(request, "explorer_index.html", ctx.flatten())
def index(request):
ctx = RequestContext(request)
try:
ctx['project'] = project
prov = client.projects.get_entry(project=project).response().result
prov.pop('id')
ctx['title'] = prov.pop('title')
ctx['provenance'] = RecursiveDict(prov).render()
columns = ['phase', 'ΔH', 'ΔH|hyd', 'GS?', 'CIF']
data = client.contributions.get_table(project=project,
columns=columns,
per_page=3).response().result
columns = list(data['items'][0].keys())
table = Table(data['items'], columns=columns)
ctx['table'] = table.render(project=project)
except Exception as ex:
ctx['alert'] = str(ex)
return render(request, "explorer_index.html", ctx.flatten())
def index(request):
ctx = RequestContext(request)
try:
ctx['project'] = project
prov = client.projects.get_entry(project=project).response().result
prov.pop('id')
ctx['title'] = prov.pop('title')
ctx['provenance'] = RecursiveDict(prov).render()
keys, subkeys = ['NUS', 'JARVIS'], ['id', 'Eₓ', 'CIF']
columns = ['##'.join([k, sk]) for k in keys for sk in subkeys]
# columns_jarvis = ['id', 'E', 'ΔE|optB88vdW', 'ΔE|mbj', 'CIF']
data = client.contributions.get_table(
project=project, columns=columns, per_page=3
).response().result
columns = list(data['items'][0].keys())
table = Table(data['items'], columns=columns)
ctx['table'] = table.render(project=project)
except Exception as ex:
ctx['alert'] = str(ex)
return render(request, "explorer_index.html", ctx.flatten())
def index(request):
ctx = RequestContext(request)
try:
ctx['project'] = project
prov = client.projects.get_entry(project=project).response().result
prov.pop('id')
ctx['title'] = prov.pop('title')
ctx['provenance'] = RecursiveDict(prov).render()
keys, subkeys = ['NUS', 'JARVIS'], ['id', 'Eₓ', 'CIF']
columns = ['##'.join([k, sk]) for k in keys for sk in subkeys]
# columns_jarvis = ['id', 'E', 'ΔE|optB88vdW', 'ΔE|mbj', 'CIF']
data = client.contributions.get_table(project=project,
columns=columns,
per_page=3).response().result
columns = list(data['items'][0].keys())
table = Table(data['items'], columns=columns)
ctx['table'] = table.render(project=project)
except Exception as ex:
ctx['alert'] = str(ex)
return render(request, "explorer_index.html", ctx.flatten())
def test_components():
d = {k: {} for k in mp_level01_titles}
d["data"] = {"a": 3.5, "b": {"display": "6 eV", "value": 6, "unit": "eV"}}
exp = HierarchicalData([("data", RecursiveDict([("a", 3.5),
("b", "6 eV")]))])
assert HierarchicalData(d) == exp
fn = os.path.join(os.path.dirname(__file__), "test_table.json")
with open(fn, "r") as f:
d = json.load(f)
table = Table.from_dict(d)
assert table.render()
plot = Plot.from_dict(d)
assert plot.get_figure()
def add_data_table(self, identifier, dataframe, name, plot_options=None):
"""add a datatable to the root-level section
Args:
identifier (str): MP category ID (`mp_cat_id`)
dataframe (pandas.DataFrame): tabular data as Pandas DataFrame
name (str): table name, optional if only one table in section
plot_options (dict): options for according plotly graph
"""
# TODO: optional table name, required if multiple tables per root-level section
name = "".join([replacements.get(c, c) for c in name])
self.document.rec_update(
nest_dict(Table(dataframe).to_dict(), [identifier, name]))
self.document[identifier].insert_default_plot_options(
dataframe, name, update_plot_options=plot_options)
def read_csv(body, is_data_section=True, **kwargs):
"""run pandas.read_csv on (sub)section body"""
csv_comment_char = "#"
import pandas
body = body.strip()
if not body:
return None
from mpcontribs.io.core.components.tdata import Table
if is_data_section:
cur_line = 1
while 1:
body_split = body.split("\n", cur_line)
first_line = body_split[cur_line - 1].strip()
cur_line += 1
if first_line and not first_line.startswith(csv_comment_char):
break
sep = kwargs.get("sep", ",")
options = {"sep": sep, "header": 0}
header = [col.strip() for col in first_line.split(sep)]
body = "\n".join([sep.join(header), body_split[1]])
if first_line.startswith("level_"):
options.update({"index_col": [0, 1]})
ncols = len(header)
else:
options = {"sep": ":", "header": None, "index_col": 0}
ncols = 2
options.update(**kwargs)
converters = dict((col, strip_converter) for col in range(ncols))
return Table(
pandas.read_csv(
StringIO(body),
comment=csv_comment_char,
skipinitialspace=True,
squeeze=True,
converters=converters,
encoding="utf8",
**options
).dropna(how="all")
)
def from_dict(cls, d):
return cls(Table.from_dict(d), config=d.get("config"))
def from_dict(cls, d):
return cls(Table.from_dict(d), config=d.get('config'), tid=d['id'])