def transformBack(obj):
if type(obj) == dict:
out = {}
for k, v in obj.items():
out[str(k)] = transformBack(v)
if "type" in out:
if out['type'] == "Plot" \
or out['type'] == "TimePlot" \
or out['type'] == "NanoPlot" \
or out['type'] == "SimpleTimePlot" \
or out['type'] == "CombinedPlot":
return chart.transformBack(out)
if out['type'] == 'EasyForm':
return easyform.transformBack(out)
if out['type'] == "BeakerCodeCell":
c = BeakerCodeCell(out['cellId'], out['evaluatorId'])
if 'code' in out:
c.code = out['code']
if 'outputtype' in out:
c.outputtype = out['outputtype']
if 'output' in out:
c.output = transformBack(out['output'])
if 'tags' in out:
c.tags = out['tags']
return c
if out['type'] == "OutputContainer":
c = OutputContainer()
if 'items' in out:
for i in out['items']:
c.addItem(i)
return c
if out['type'] == "Date":
return datetime.fromtimestamp(out["timestamp"] / 1000)
if out['type'] == "TableDisplay":
if 'subtype' in out:
if out['subtype'] == "Dictionary":
out2 = {}
for r in out['values']:
out2[r[0]] = fixNaNBack(r[1])
if out['columnNames'][0] == "Index":
return pandas.Series(out2)
return out2
if out['subtype'] == "Matrix":
vals = out['values']
fixNaNsBack(vals)
return numpy.matrix(vals)
if out['subtype'] == "ListOfMaps":
out2 = []
cnames = out['columnNames']
for r in out['values']:
out3 = {}
for i in range(len(cnames)):
if r[i] != '':
out3[cnames[i]] = r[i]
out2.append(out3)
return out2
# transform to dataframe
if ('hasIndex' in out) and (out['hasIndex'] == "true"):
# first column becomes the index
vals = out['values']
is_standard_index = ('indexName' in out) and (len(out['indexName']) == 1) and (out['indexName'][0] == 'index')
cnames = out['columnNames'][1:]
index = []
for x in range(0, len(vals)):
index.append(transformBack(vals[x][0]))
v = vals[x][1:]
fixNaNsBack(v)
vals[x] = v
if len(out['indexName']) > 1:
index = pandas.MultiIndex.from_tuples(index, names=out['indexName'])
else:
index = pandas.Index(index, name=out['indexName'])
frame = pandas.DataFrame(data=vals, columns=cnames, index=index)
return frame
else:
vals = out['values']
cnames = out['columnNames']
for x in range(0, len(vals)):
v = vals[x]
fixNaNsBack(v)
vals[x] = v
return pandas.DataFrame(data=vals, columns=cnames)
return out
if type(obj) == list:
out = []
for v in obj:
out.append(transformBack(v))
return out
try:
if type(obj) == bytes:
obj = str(obj)
except Exception as e:
return obj
return obj
def transformBack(obj):
if type(obj) == dict:
out = {}
for k, v in obj.items():
out[str(k)] = transformBack(v)
if "type" in out:
if out['type'] == "Plot" \
or out['type'] == "TimePlot" \
or out['type'] == "NanoPlot" \
or out['type'] == "SimpleTimePlot" \
or out['type'] == "CombinedPlot":
return chart.transformBack(out)
if out['type'] == 'EasyForm':
return easyform.transformBack(out)
if out['type'] == "BeakerCodeCell":
c = BeakerCodeCell(out['cellId'], out['evaluatorId'])
if 'code' in out:
c.code = out['code']
if 'outputtype' in out:
c.outputtype = out['outputtype']
if 'output' in out:
c.output = transformBack(out['output'])
if 'tags' in out:
c.tags = out['tags']
return c
if out['type'] == "OutputContainer":
c = OutputContainer()
if 'items' in out:
for i in out['items']:
c.addItem(i)
return c
if out['type'] == "Date":
return datetime.datetime.fromtimestamp(out["timestamp"] / 1000)
if out['type'] == "TableDisplay":
if 'subtype' in out:
if out['subtype'] == "Dictionary":
out2 = {}
for r in out['values']:
out2[r[0]] = fixNaNBack(r[1])
if out['columnNames'][0] == "Index":
return pandas.Series(out2)
return out2
if out['subtype'] == "Matrix":
vals = out['values']
fixNaNsBack(vals)
return numpy.matrix(vals)
if out['subtype'] == "ListOfMaps":
out2 = []
cnames = out['columnNames']
for r in out['values']:
out3 = {}
for i in range(len(cnames)):
if r[i] != '':
out3[cnames[i]] = r[i]
out2.append(out3)
return out2
# transform to dataframe
if ('hasIndex' in out) and (out['hasIndex'] == "true"):
# first column becomes the index
vals = out['values']
cnames = out['columnNames'][1:]
index = []
for x in range(0, len(vals)):
index.append(transformBack(vals[x][0]))
v = vals[x][1:]
fixNaNsBack(v)
vals[x] = v
return pandas.DataFrame(data=vals,
columns=cnames,
index=index)
else:
vals = out['values']
cnames = out['columnNames']
for x in range(0, len(vals)):
v = vals[x]
fixNaNsBack(v)
vals[x] = v
return pandas.DataFrame(data=vals, columns=cnames)
return out
if type(obj) == list:
out = []
for v in obj:
out.append(transformBack(v))
return out
try:
if type(obj) == bytes:
obj = str(obj)
except Exception as e:
return obj
return obj
