def makeData(self, data_from_db, factors):
val_factor_idx_list \
, cat_factor_idx_list \
, grp_factor_idx_list = self.classifyFactors(factors)
if len(cat_factor_idx_list) <= 0:
raise Exception('')
self.cat_idx = cat_factor_idx_list[0]
pivot_data_from_db = map(list, zip(*data_from_db))
grp_idx_legend_list = [
(idx, uniqList(pivot_data_from_db[idx])) \
for idx in grp_factor_idx_list
]
self.data, self.pivot_data = data_from_db, pivot_data_from_db
series = []
if len(val_factor_idx_list) < 1:
raise Exception('')
elif len(val_factor_idx_list) == 1:
i = val_factor_idx_list[0]
series = self.yieldSeries(data_from_db, grp_idx_legend_list, '', i)
else:
for i in val_factor_idx_list:
copyed_grp_idx_legend_list = grp_idx_legend_list[:]
column_name = factors[i].getProperty(Protocol.Attr)
series.extend(
self.yieldSeries(data_from_db, copyed_grp_idx_legend_list, column_name, i)
)
result = self.constructOption(factors, series)
return result
def makeData(self, data_from_db, factors):
'''
为雷达图做图表数据
至少有3个value列，1个cat列，没有grp列
'''
val_factor_idx_list \
, cat_factor_idx_list \
, grp_factor_idx_list = self.classifyFactors(factors)
if len(cat_factor_idx_list) != 1 \
or len(val_factor_idx_list) < 3 \
or len(grp_factor_idx_list) > 0:
raise Exception('')
cat_idx = cat_factor_idx_list[0]
pivot_data_from_db = map(list, zip(*data_from_db))
cat_data_list = uniqList(pivot_data_from_db[cat_idx])
series_one, polar_one = {
'type': self.type
, 'data': []
}, {
'indicator': [{
'text': factors[i].getProperty(Protocol.Attr)
} for i in val_factor_idx_list]
}
for row in data_from_db:
name = row[cat_idx]
value = [row[i] for i in val_factor_idx_list]
series_one['data'].append({
'name': name
, 'value': value
})
legends = [item['name'] for item in series_one['data']]
series = [series_one]
polars = [polar_one]
return {
'legend': {
'data': legends
}
, 'polar': polars
, 'series': series
}
def makeData(self, data_from_db, factors):
'''
给成分图做绘图数据
ps: facotrs里面只能有一个value，最少1一个cat，最多一个group
'''
val_factor_idx_list \
, cat_factor_idx_list \
, grp_factor_idx_list = self.classifyFactors(factors)
if len(val_factor_idx_list) != 1 \
or len(cat_factor_idx_list) < 1 \
or len(grp_factor_idx_list) > 1:
raise Exception('')
val_idx = val_factor_idx_list[0]
# 建立系列名-系列内容的字典
if len(grp_factor_idx_list) < 1:
grp_series_dict = {Protocol.Default_Legend: {
'name': Protocol.Default_Legend
, 'type': self.type
, 'data': []
}}
else:
grp_idx = grp_factor_idx_list[0]
pivot_data_from_db = map(list, zip(*data_from_db))
grp_list = uniqList(pivot_data_from_db[grp_idx])
grp_series_dict = {}
for g in grp_list:
grp_series_dict[g] = {
'name': g
, 'type': self.type
, 'data': []
}
grp_idx = grp_factor_idx_list[0] \
if len(grp_factor_idx_list) > 0 \
else -1
for row in data_from_db:
grp = row[grp_idx] if grp_idx >= 0 else Protocol.Default_Legend
cat_data_list = [row[i] for i in cat_factor_idx_list]
name = (Protocol.Legend_Link).join(cat_data_list)
value = row[val_idx]
grp_series_dict[grp]['data'].append({
'name': name
, 'value': value
})
legends, series = [], grp_series_dict.values()
map(lambda x: legends.extend(
[d['name'] for d in x['data']]
), series)
legends = uniqList(legends)
return {
'legend': {
'data': legends
}
, 'series': series
}
def cat_data(self):
'''
挑拣出全体cat数据
'''
cat_column = self.pivot_data[self.cat_idx]
return uniqList(cat_column)
