class MutationQuery(object):
def __init__(self, url_information):
self.url_information = url_information
# print(url_information)
self.db = DBBase('Genes')
def resolve_url(self):
url_information_list = self.url_information.split(',')
url_information_dict = {}
for item in url_information_list:
try:
item_key, item_value = item.split('=')
url_information_dict[item_key] = item_value
except:
num = 0
for index, item_item in enumerate(item):
if item_item in "0123456789":
num = index
break
item_key = item[0:num]
item_value = item[num:-1]
url_information_dict[item_key] = item_value
return url_information_dict
def is_type(self, a):
lis = "1234567890"
for item in a:
if item not in lis:
return "symbol"
else:
return "ID"
def get_mutation(self):
mutation_dict = self.resolve_url()
if self.is_type(mutation_dict.get('gene')) == 'ID':
id_or_symbol = "ENTREZ_ID"
else:
id_or_symbol = "Symbol"
if mutation_dict.get('location') == 'cnv':
dnm_orcnv = 'CNV'
else:
dnm_orcnv = 'DNM'
all_mutation = self.db.find_one_by_one_condition(
id_or_symbol, mutation_dict.get('gene')).get(dnm_orcnv)
for item in all_mutation:
if dnm_orcnv == 'DNM':
if item.get('chr') == mutation_dict.get('chr') and item.get(
'Position') == mutation_dict.get('position'):
return item
else:
if item.get('start') == mutation_dict.get(
'start') and item.get('end') == mutation_dict.get(
'end'):
return item
class GetIsoformAndEnst(object):
def __init__(self, entrez_id):
self.entrez_id = entrez_id
self.db = DBBase("Uniprot_Information")
def get_isoform_and_enst(self):
query_id_result = self.db.find_one_by_one_condition(
"Entrez_ID", self.entrez_id)
isoform_and_enst_list = []
if query_id_result != None:
uniprot_ensmbel_information = query_id_result.get(
"Ensmbl_information")
if uniprot_ensmbel_information != None and uniprot_ensmbel_information != []:
for item in uniprot_ensmbel_information:
if item.get("uniprot_unique_flag") == True:
note = "No assemble annotation found in GRCH37."
else:
note = "-"
if note == '-':
if item.get("molecule") != None:
isoform = item.get("molecule").get("@id")
enst_id = item.get("@id")
if item.get('property') != None:
for item_item in item.get('property'):
if item_item.get(
"@type") == 'protein sequence ID':
ensp_id = item_item.get("@value")
if item_item.get("@type") == 'gene ID':
ensg_id = item_item.get("@value")
else:
if item.get("@id") != None:
enst_id = item.get("@id")
isoform = query_id_result.get("UniprotKB_AC")
if item.get("property") != None:
for item_item in item.get("property"):
if item_item.get(
"@type"
) == "protein sequence ID":
ensp_id = item_item.get("@value")
elif item_item.get(
"@type") == "gene ID":
ensg_id = item_item.get("@value")
item_list = [enst_id, ensp_id, ensg_id, isoform, note]
isoform_and_enst_list.append(item_list)
else:
pass
# print(isoform_and_enst_list)
return isoform_and_enst_list
def get_isoform(self, enst_id):
all_isoform = self.get_isoform_and_enst()
for item in all_isoform:
if item[0] == enst_id.split(".")[0]:
return item[3]
def __init__(self, ID, isoform="all"):
self.ID = ID
self.db = DBBase('ProteinExpress_Update')
# print(isoform)
self.isoform_list = isoform
def __init__(self, entrez_id):
self.entrez_id = entrez_id
self.db = DBBase("Uniprot_Information")
def __init__(self, ID, transcript, brain_express=0):
self.ID = ID
self.db = DBBase('Gtex_Trans_AllTissue_Pledged')
self.transcript_list = transcript
self.brain_express = brain_express
class GtexTranscriptExpressPlot(object):
def __init__(self, ID, transcript, brain_express=0):
self.ID = ID
self.db = DBBase('Gtex_Trans_AllTissue_Pledged')
self.transcript_list = transcript
self.brain_express = brain_express
def get_express_value_from_db(self):
if self.transcript_list == "all":
return self.db.find_count_by_one_condition("Entrez_id", self.ID)
# .get('ProteinExpressData')
def get_columns(self):
columns = [
'Adipose - Subcutaneous', 'Adipose - Visceral (Omentum)',
'Adrenal Gland', 'Artery - Aorta', 'Artery - Coronary',
'Artery - Tibial', 'Bladder', 'Brain - Amygdala',
'Brain - Anterior cingulate cortex (BA24)',
'Brain - Caudate (basal ganglia)', 'Brain - Cerebellar Hemisphere',
'Brain - Cerebellum', 'Brain - Cortex',
'Brain - Frontal Cortex (BA9)', 'Brain - Hippocampus',
'Brain - Hypothalamus',
'Brain - Nucleus accumbens (basal ganglia)',
'Brain - Putamen (basal ganglia)',
'Brain - Spinal cord (cervical c-1)', 'Brain - Substantia nigra',
'Breast - Mammary Tissue', 'Cells - EBV-transformed lymphocytes',
'Cells - Transformed fibroblasts', 'Cervix - Ectocervix',
'Cervix - Endocervix', 'Colon - Sigmoid', 'Colon - Transverse',
'Esophagus - Gastroesophageal Junction', 'Esophagus - Mucosa',
'Esophagus - Muscularis', 'Fallopian Tube',
'Heart - Atrial Appendage', 'Heart - Left Ventricle',
'Kidney - Cortex', 'Liver', 'Lung', 'Minor Salivary Gland',
'Muscle - Skeletal', 'Nerve - Tibial', 'Ovary', 'Pancreas',
'Pituitary', 'Prostate', 'Skin - Not Sun Exposed (Suprapubic)',
'Skin - Sun Exposed (Lower leg)',
'Small Intestine - Terminal Ileum', 'Spleen', 'Stomach', 'Testis',
'Thyroid', 'Uterus', 'V****a', 'Whole Blood'
]
return columns
def get_brain_columns(self):
columns = [
'Brain - Amygdala', 'Brain - Anterior cingulate cortex (BA24)',
'Brain - Caudate (basal ganglia)', 'Brain - Cerebellar Hemisphere',
'Brain - Cerebellum', 'Brain - Cortex',
'Brain - Frontal Cortex (BA9)', 'Brain - Hippocampus',
'Brain - Hypothalamus',
'Brain - Nucleus accumbens (basal ganglia)',
'Brain - Putamen (basal ganglia)',
'Brain - Spinal cord (cervical c-1)', 'Brain - Substantia nigra'
]
return columns
def l2(self, x):
"""
对输入的x取log
:param x:
:return:
"""
x = float(x)
if x != 0:
return math.log2(x + 1)
else:
return 0
def r2(self, x):
"""
对输入的x取小数点后两位小数的方法
:param x:
:return:
"""
return round(x, 2)
def average(self, num_list):
# print(num_list)
num_list = [float(item) for item in num_list]
return (sum(num_list)) / len(num_list)
# return self.r2(self.l2(float(sum(num_list)) / len(num_list)))
def data_handling(self):
express_list = self.get_express_value_from_db()
if len(express_list) >= 1: #取出了多于等于一条转录本
express_average = []
if self.brain_express == 0:
for item_trans in express_list:
list_avg = [item_trans.get("transcript_id")]
for item_colunms in self.get_columns():
if item_trans.get(item_colunms) != None:
# list_avg.append(item_trans.get("transcript_id"))
# print(item_trans.get(item_colunms))
list_avg.append(
self.r2(self.l2(item_trans.get(item_colunms))))
else:
list_avg.append(None)
express_average.append(list_avg)
else:
for item_trans in express_list:
list_avg = [item_trans.get("transcript_id")]
for item_colunms in self.get_columns():
if item_trans.get(item_colunms) != None:
list_avg.append(
self.r2(self.l2(item_trans.get(item_colunms))))
else:
list_avg.append(None)
temp_flag = 0
for item_list_avg in list_avg[8:21]: #脑组织表达的列为8-21列,第一列为id
if item_list_avg >= 1:
temp_flag = 1
break
if temp_flag == 1:
express_average.append(list_avg)
else:
express_average = []
return express_average
def plot(self):
express_average = self.data_handling()
if express_average != []:
trace = []
x_columns = self.get_columns()
for item in express_average:
# print(item)
# print(item[1:-1])
trace.append(
go.Scatter(
x=x_columns,
y=item[1:-1],
hoverinfo='all',
# mode="line",
name=item[0],
ycalendar=180,
# line=dict(shape='spline'),
hoverlabel=dict(namelength=-1)))
layouts = go.Layout(
# paper_bgcolor='rgb(249, 249, 249)',
# plot_bgcolor='rgb(249, 249, 249)',
height=400,
width=1200,
hovermode='closest',
margin=go.Margin( # x,y轴label距离图纸四周的距离
l=50, r=100, b=200, t=10, pad=0),
xaxis=dict(
showgrid=True,
zeroline=False,
showline=False,
showticklabels=True,
tickangle=90, # x轴刻度之间距离
),
yaxis=dict(autorange=True,
title='log<sub>2</sub> (TPM+1)',
titlefont=dict(family='Arial, sans-serif',
size=18,
color='lightgrey'),
showticklabels=True,
tickangle=90,
tickfont=dict(family='Old Standard TT, serif',
size=14,
color='black'),
exponentformat='e',
showexponent='All'))
figs = go.Figure(data=trace, layout=layouts)
# plotly.offline.plot(figs, show_link=False)
return plotly.offline.plot(figs,
show_link=False,
output_type="div",
include_plotlyjs=False)
else:
return '<div>There is no corresponding data published yet, we will update it when such data available. </div>'
def __init__(self, ID):
self.ID = ID
self.db = DBBase('ProteinExpression')
class ProteinExpressPlot(object):
def __init__(self, ID):
self.ID = ID
self.db = DBBase('ProteinExpression')
def get_express_value_from_db(self):
return self.db.find_one_by_one_condition("ENTREZ_ID", self.ID).get('ProteinExpressData')
def plot(self):
express_list = self.get_express_value_from_db()
if express_list != None:
x = []
y = []
array = []
for item in express_list:
x.append(item['TISSUE_NAME'])
y.append(item['NORMALIZED_INTENSITY'])
min = float(item['MIN_NORMALIZED_INTENSITY'])
max = float(item['MAX_NORMALIZED_INTENSITY'])
error = (max - min) / 2
array.append(error)
trace1 = go.Bar(
x=x,
y=y,
# name='NSD2',
error_y=dict(
type='data',
array=array,
visible=True
)
)
data = [trace1]
layout = go.Layout(
paper_bgcolor='rgb(249, 249, 249)',
plot_bgcolor='rgb(249, 249, 249)',
barmode='group',
height=500,
width=600,
title='<br>Median protein expression</br>',
# yaxis=dict(range=[0, 10]),
# titlefont=dict(size=25), plot_bgcolor='#EFECEA',
hovermode='closest',
margin=go.Margin( # x,y轴label距离图纸四周的距离
l=70,
r=30,
b=100,
t=10,
pad=0
),
xaxis=dict(
# autorange=True,
# title='Diffient Brain region',
# titlefont=dict(
# family='Arial, sans-serif',
# size=18,
# color='lightgrey'
# ),
showgrid=True,
zeroline=False,
showline=False,
showticklabels=True,
tickangle=20, # x轴刻度之间距离
# automargin=False,
# separatethousands=True,
), yaxis=dict(
# range=[0,],
autorange=True,
title='log <sub>10</sub> normalized iBAQ intensity',
titlefont=dict(
family='Arial, sans-serif',
size=18,
color='lightgrey'
),
showticklabels=True,
tickangle=90,
tickfont=dict(
family='Old Standard TT, serif',
size=14,
color='black'
),
exponentformat='e',
showexponent='All'
)
)
fig = go.Figure(data=data, layout=layout)
# plotly.offline.plot(fig, show_link=False)
return plotly.offline.plot(fig, show_link=False, output_type="div",include_plotlyjs=False)
else:
return '<div>There is no corresponding data published yet, we will update it when such data available. </div>'
def __init__(self, ID, transcript):
self.ID = ID
self.db = DBBase('Gtex_Trans_AllTissue_Pledged')
for index, item in enumerate(transcript):
transcript[index] = item.split(".")[0]
self.transcript_list = transcript
class GetBrainExpressTranscript(object):
def __init__(self, ID, transcript):
self.ID = ID
self.db = DBBase('Gtex_Trans_AllTissue_Pledged')
for index, item in enumerate(transcript):
transcript[index] = item.split(".")[0]
self.transcript_list = transcript
# print(self.transcript_list)
def get_express_value_from_db(self):
return self.db.find_count_by_one_condition("Entrez_id", self.ID)
def get_columns(self):
columns = ['Adipose - Subcutaneous', 'Adipose - Visceral (Omentum)', 'Adrenal Gland', 'Artery - Aorta',
'Artery - Coronary', 'Artery - Tibial', 'Bladder', 'Brain - Amygdala',
'Brain - Anterior cingulate cortex (BA24)', 'Brain - Caudate (basal ganglia)',
'Brain - Cerebellar Hemisphere', 'Brain - Cerebellum', 'Brain - Cortex',
'Brain - Frontal Cortex (BA9)', 'Brain - Hippocampus', 'Brain - Hypothalamus',
'Brain - Nucleus accumbens (basal ganglia)', 'Brain - Putamen (basal ganglia)',
'Brain - Spinal cord (cervical c-1)', 'Brain - Substantia nigra', 'Breast - Mammary Tissue',
'Cells - EBV-transformed lymphocytes', 'Cells - Transformed fibroblasts', 'Cervix - Ectocervix',
'Cervix - Endocervix', 'Colon - Sigmoid', 'Colon - Transverse',
'Esophagus - Gastroesophageal Junction', 'Esophagus - Mucosa', 'Esophagus - Muscularis',
'Fallopian Tube', 'Heart - Atrial Appendage', 'Heart - Left Ventricle', 'Kidney - Cortex',
'Liver',
'Lung', 'Minor Salivary Gland', 'Muscle - Skeletal', 'Nerve - Tibial', 'Ovary', 'Pancreas',
'Pituitary', 'Prostate', 'Skin - Not Sun Exposed (Suprapubic)', 'Skin - Sun Exposed (Lower leg)',
'Small Intestine - Terminal Ileum', 'Spleen', 'Stomach', 'Testis', 'Thyroid', 'Uterus', 'V****a',
'Whole Blood']
return columns
def get_brain_columns(self):
columns = ['Brain - Amygdala', 'Brain - Anterior cingulate cortex (BA24)',
'Brain - Caudate (basal ganglia)',
'Brain - Cerebellar Hemisphere', 'Brain - Cerebellum', 'Brain - Cortex',
'Brain - Frontal Cortex (BA9)', 'Brain - Hippocampus', 'Brain - Hypothalamus',
'Brain - Nucleus accumbens (basal ganglia)', 'Brain - Putamen (basal ganglia)',
'Brain - Spinal cord (cervical c-1)', 'Brain - Substantia nigra']
return columns
def l2(self, x):
"""
对输入的x取log
:param x:
:return:
"""
if x != 0:
return math.log2(x + 1)
else:
return 0
def r2(self, x):
"""
对输入的x取小数点后两位小数的方法
:param x:
:return:
"""
return round(x, 2)
def average(self, num_list):
num_list = [float(item) for item in num_list]
return self.r2(self.l2(float(sum(num_list)) / len(num_list)))
def data_handling(self):
express_list = self.get_express_value_from_db()
express_average = []
final_transcript = []
if len(express_list) >= 1: # 取出了多于等于一条转录本
for item_trans in express_list:
list_avg = [item_trans.get("transcript_id")]
for item_colunms in self.get_columns():
if item_trans.get(item_colunms) != None:
list_avg.append(self.r2(self.l2(item_trans.get(item_colunms))))
else:
list_avg.append(None)
temp_flag = 0
for item_list_avg in list_avg[8:21]: # 脑组织表达的列为8-21列
if item_list_avg >= 1:
temp_flag = 1
if temp_flag == 1 and list_avg[0].split(".")[0] in self.transcript_list:
# if list_avg[0].split(".")[0] in self.transcript_list:
express_average.append(list_avg)
final_transcript.append(list_avg[0])
else:
express_average = []
return final_transcript, express_average
def __init__(self, url_information):
self.url_information = url_information
# print(url_information)
self.db = DBBase('Genes')
def __init__(self, ID, mutation):
self.id = ID
self.db = DBBase('Genes')
self.mutation_query = MutationQuery(mutation)
class MutationTrancript(object):
def __init__(self, ID, mutation):
self.id = ID
self.db = DBBase('Genes')
self.mutation_query = MutationQuery(mutation)
def get_all_tarncript(self):
gene_data = self.db.find_one_by_one_condition("ENTREZ_ID", self.id)
gene_genecode_trans = gene_data.get("Trans_Gencode")
# gene_refseq_data = gene_data.get("Trans_Ref")
all_tarncript = []
if gene_genecode_trans != None:
for item in gene_genecode_trans:
all_tarncript.append(item)
# if gene_refseq_data != None:
# for item in gene_refseq_data:
# all_tarncript.append(item)
return all_tarncript
def judge_mutation_position_on_trancript_exon(self,
position,
transcriot_exon,
type=None):
flag = False
if type == 'splice-site' or type.startswith("splice-site"):
transcriot_exon_temp_list = transcriot_exon.split(",")
for item in transcriot_exon_temp_list:
if int(position) >= int(item.split("_")[0].replace(
'"', '')) - 10 and int(position) <= int(
item.split("_")[1].replace('"', '')) + 10:
flag = True
return flag
else:
transcriot_exon_temp_list = transcriot_exon.split(",")
for item in transcriot_exon_temp_list:
if int(position) >= int(item.split("_")[0].replace(
'"', '')) and int(position) <= int(
item.split("_")[1].replace('"', '')):
flag = True
return flag
def get_mutation_impact_transcript(self):
all_transcript = self.get_all_tarncript()
mutation = self.mutation_query.get_mutation()
if mutation.get("Exonic Func") != None:
mutation_type = mutation.get("Exonic Func")
else:
mutation_type = None
mutation_position = mutation.get("Position")
mutation_chr = mutation.get("chr")
if mutation_position != None:
for item in all_transcript:
if item.get('coding_exon_region') != 'NA':
if mutation_chr == item.get("chrom").replace(
"chr", "") or mutation_chr == item.get("chrom"):
if self.judge_mutation_position_on_trancript_exon(
mutation_position,
item.get('coding_exon_region'), mutation_type):
item['flag'] = True
else:
item['flag'] = False
else:
item['flag'] = False
else:
item['flag'] = False
result = []
for item in all_transcript:
if item.get("flag") == True:
result.append(item.get("TranscriptID"))
return result
def get_mutation_impact_isoforms(self):
all_transcript = self.get_all_tarncript()
mutation = self.mutation_query.get_mutation()
# print(mutation.get("Exonic Func"))
if mutation.get("Exonic Func") != None:
mutation_type = mutation.get("Exonic Func")
else:
mutation_type = None
mutation_position = mutation.get("Position")
mutation_chr = mutation.get("chr")
if mutation_position != None:
for item in all_transcript:
if item.get('coding_exon_region') != 'NA':
if mutation_chr == item.get("chrom").replace(
"chr", "") or mutation_chr == item.get("chrom"):
if self.judge_mutation_position_on_trancript_exon(
mutation_position,
item.get('coding_exon_region'), mutation_type):
item['flag'] = True
else:
item['flag'] = False
else:
item['flag'] = False
else:
item['flag'] = False
transcript_result = []
for item in all_transcript:
if item.get("flag") == True:
transcript_result.append(item.get("TranscriptID"))
isoform_result = []
db = GetIsoformAndEnst(self.id)
for item in transcript_result:
if db.get_isoform(item) != None:
isoform_result.append(db.get_isoform(item))
return isoform_result
class ProteinExpressPlot(object):
def __init__(self, ID, isoform_list="all"):
self.ID = ID
self.db = DBBase('ProteinExpress_Update')
self.isoform_list = isoform_list
def get_express_value_from_db(self):
x_list = []
y_list = []
array_list = []
id_list = []
if self.isoform_list == "all":
results = self.db.find_count_by_one_condition("Entrez_ID", self.ID)
flag = [0] * len(results)
for index, item in enumerate(results):
# print()
if item.get('Express_Value') != {
'd': {
'results': []
}
} and '-' in item.get('Uniprot_ID'):
for item_item in item.get('Express_Value').get('d').get(
'results'):
if item_item.get('TISSUE_NAME') in [
"brain", 'arachnoid cyst', 'cerebral cortex',
'cerebrospinal fluid', 'prefrontal cortex',
'spinal cord'
] and float(
item_item.get('NORMALIZED_INTENSITY')) >= 1:
flag[index] = 1
for item, item_flag in zip(results, flag):
if item_flag != 0:
if item.get('Express_Value') != {'d': {'results': []}}:
x = []
y = []
array = []
for item_item in item.get('Express_Value').get(
'd').get('results'):
# print(item_item['TISSUE_NAME'])
x.append(item_item['TISSUE_NAME'])
y.append(item_item['NORMALIZED_INTENSITY'])
min = float(item_item['MIN_NORMALIZED_INTENSITY'])
max = float(item_item['MAX_NORMALIZED_INTENSITY'])
error = (max - min) / 2
array.append(error)
id_list.append(item.get("Uniprot_ID"))
x_list.append(x)
y_list.append(y)
array_list.append(array)
# print(len(result_list))
# print(result_list)
return x_list, y_list, array_list, id_list
else:
results = self.db.find_count_by_one_condition("Entrez_ID", self.ID)
flag = [0] * len(results)
for index, item in enumerate(results):
# print(item)
if item.get('Express_Value') != {'d': {'results': []}}:
for item_item in item.get('Express_Value').get('d').get(
'results'):
if item_item.get('TISSUE_NAME') in [
"brain", 'arachnoid cyst', 'cerebral cortex',
'cerebrospinal fluid', 'prefrontal cortex',
'spinal cord'
] and float(
item_item.get('NORMALIZED_INTENSITY')) >= 0.5:
flag[index] = 1
for item, item_flag in zip(results, flag):
if item_flag != 0 and item.get(
'Uniprot_ID') in self.isoform_list:
if item.get('Express_Value') != {'d': {'results': []}}:
x = []
y = []
array = []
for item_item in item.get('Express_Value').get(
'd').get('results'):
# print(item_item['TISSUE_NAME'])
x.append(item_item['TISSUE_NAME'])
y.append(item_item['NORMALIZED_INTENSITY'])
min = float(item_item['MIN_NORMALIZED_INTENSITY'])
max = float(item_item['MAX_NORMALIZED_INTENSITY'])
error = (max - min) / 2
array.append(error)
if item.get("Uniprot_ID") != None and item.get(
"Uniprot_ID") in self.isoform_list:
id_list.append(item.get("Uniprot_ID"))
x_list.append(x)
y_list.append(y)
array_list.append(array)
# print(len(result_list))
# print(result_list)
return x_list, y_list, array_list, id_list
def get_weight(self, w):
if w <= 10:
return 200
elif w > 10 and w <= 30:
return w * 20 + 200
else:
return 800
def plot(self):
x_list, y_list, array_list, id_list = self.get_express_value_from_db()
if len(x_list) == len(y_list) and len(x_list) != 0:
trace = []
for x_item, y_item, array_item, id_item in zip(
x_list, y_list, array_list, id_list):
for i, item in enumerate(x_item):
if item in [
"brain", 'arachnoid cyst', 'cerebral cortex',
'cerebrospinal fluid', 'prefrontal cortex',
'spinal cord'
]:
x_item[i] = "<b>" + x_item[i] + " </b>"
else:
x_item[i] = x_item[i] + ' '
trace.append(
go.Bar(x=y_item,
y=x_item,
name=id_item,
orientation='h',
hoverinfo='all',
error_x=dict(type='data',
array=array_item,
visible=True)))
if len(x_list) >= 1:
l_height = self.get_weight(len(x_list[0]) * len(id_list))
else:
l_height = 400
layout = go.Layout(
paper_bgcolor='rgb(249, 249, 249)',
plot_bgcolor='rgb(249, 249, 249)',
barmode='stack',
height=l_height,
width=800,
title='<br>Median protein expression</br>',
# yaxis=dict(range=[0, 10]),
# titlefont=dict(size=25), plot_bgcolor='#EFECEA',
hovermode='closest',
margin=go.Margin( # x,y轴label距离图纸四周的距离
l=250, r=120, b=80, t=40, pad=0),
xaxis=dict(
# autorange=True,
# title='Diffient Brain region',
# titlefont=dict(
# family='Arial, sans-serif',
# size=18,
# color='lightgrey'
# ),
title='log <sub>10</sub> normalized iBAQ intensity',
titlefont=dict(
family='Arial, sans-serif',
size=18,
),
showgrid=True,
zeroline=True,
showline=True,
showticklabels=True,
# tickangle=40, # x轴刻度之间距离
# automargin=False,
# separatethousands=True,
),
yaxis=dict(
# range=[0,],
autorange=True,
# gridwidth=0.5,
showticklabels=True,
# tickangle=90,
# tickfont=dict(
# # family='Old Standard TT, serif',
# # size=14,
# # color='black'
# ),
exponentformat='e',
showexponent='All'))
fig = go.Figure(data=trace, layout=layout)
# plotly.offline.plot(fig, show_link=False)
return plotly.offline.plot(fig,
show_link=False,
output_type="div",
include_plotlyjs=False)
else:
return '<div>There is no corresponding data published yet, we will update it when such data available. </div>'