def run_report(table_data, figure_base_quality, figure_base_content,
figure_base_gc, tpl_docx, out_dir):
out_dir = mkdir(out_dir)
table_data = read_table_data(table_data)
r = {
"raw_data": table_data[2],
"clean_data": table_data[4],
"table_data": table_data
}
# docx report
tpl = DocxTemplate(tpl_docx)
tpl.render(r)
for i, j in zip(
["base_quality.png", "base_content.png", "gc_distribution.png"],
[figure_base_quality, figure_base_content, figure_base_gc]):
tpl.replace_pic(i, j)
tpl.save(os.path.join(out_dir, "report.docx"))
return r
def pdf_profil_student(data):
connect_to_bdd = sqlite3.connect('donnee.db')
cur = connect_to_bdd.cursor()
get_profil_student = cur.execute(
'''
SELECT matricule_etud, nom, prenom, date_naissance, tel, email, cin, adresse, niveau, pdp_name FROM ETUDIANT
WHERE matricule_etud=?
''', (data, ))
resu = get_profil_student.fetchall()
info = {
'num_matricule': RichText(resu[0][0],
font='Arial',
bold=False,
size=24),
'nom': RichText(resu[0][1], font='Arial', bold=False, size=24),
'prenom': RichText(resu[0][2], font='Arial', bold=False, size=24),
'date_naissance': RichText(resu[0][3],
font='Arial',
bold=False,
size=24),
'tel': RichText(resu[0][4], font='Arial', bold=False, size=24),
'email': RichText(resu[0][5], font='Arial', bold=False, size=24),
'cin': RichText(resu[0][6], font='Arial', bold=False, size=24),
'adresse': RichText(resu[0][7], font='Arial', bold=False, size=24),
'niveau': RichText(resu[0][8], font='Arial', bold=False, size=24)
}
#get tamplate
document = DocxTemplate(f"template/template_etudiant.docx")
#change image in the template
if resu[0][9] != '':
document.replace_pic(
'face0.png',
os.getcwd() + f"\\src\\dist\\img\\pdp\\{resu[0][9]}")
#creation template
document.render(info)
name = "Profil_" + resu[0][1] + "_" + resu[0][2]
#save doc created
document.save(f"{gettempdir()}\\{name}.docx")
#convertion template to pdf
dirpath = os.environ.get('USERPROFILE') + "\\Desktop"
pdf_got = f"{dirpath}\\{name}.pdf"
convert(f"{gettempdir()}\\{name}.docx", pdf_got)
return pdf_got
def save_doc(context, inline_imgs, file_dict, template_path, result_path):
tpl = DocxTemplate(template_path)
for cur_key in file_dict:
tpl.replace_pic(cur_key, file_dict[cur_key])
inline_image_context = dict()
for key in inline_imgs:
width = 170
width_key = key + '_width_percent'
if width_key in inline_imgs:
width = inline_imgs[width_key] * width
inline_image_context[key] = InlineImage(tpl,
inline_imgs[key],
width=Mm(width))
context = {**context, **inline_image_context}
tpl.render(context)
tpl.save(result_path)
def gen_docx(infile_path, outfile_path, data):
if infile_path[-5:] != '.docx':
raise Exception('input file must be a .docx')
if outfile_path[-5:] != '.docx':
raise Exception('output file must be a .docx')
doc = DocxTemplate(infile_path)
doc.replace_pic('dummy.jpg', f'{data["uid"]}.jpg')
context = {
'shareholder_name': data['name'],
'user_id': data['shareholder_id'],
'n_shares': data['n_shares'],
}
doc.render(context)
doc.save(outfile_path)
def fill_monitoring_card(animal_id, output_path):
conn = get_connection()
query = f"""select card_number pet_id, age pet_age, weight pet_weight, name pet_name,
s.value pet_sex, sp.value pet_type, col.value pet_color, e.value pet_ears,
t.value pet_tail, si.value pet_size, f.value pet_fur
FROM animal a
INNER JOIN dict_species sp ON sp.id=a.species_id
INNER JOIN dict_color col ON col.id=a.color_id
INNER JOIN dict_sex s ON s.id=a.sex_id
INNER JOIN dict_fur f ON f.id=a.fur_id
INNER JOIN dict_ear e ON e.id=a.ear_id
INNER JOIN dict_tail t ON t.id=a.tail_id
INNER JOIN dict_size si ON si.id=a.size_id
INNER JOIN file ON file.id=a.file_id
WHERE a.id = {animal_id}
"""
data_df = pd.read_sql_query(query, con=conn)
empty_row = ['__' for _ in range(len(data_df.columns))]
data_df.loc[data_df.shape[0]] = empty_row
# card_args = ['', '', '', '', 'adress', 'org_name', 'valier_id', '',
# '', '', '', '', 'pet_breed', '',
# '', '', '', '', 'pet_special_sign', 'pet_temper',
# 'mark_id',
# 'steril_date', 'steril_month', 'steril_year', 'steril_place', 'steril_doc', 'steril_flag',
# 'order_id', 'steril_date', 'steril_month', 'steril_year',
# 'trap_id', 'trap_date', 'trap_month', 'trap_year', 'trap_location', 'trap_video']
card_tables = ['tbl_treat_parasites', 'tbl_vaccine', 'tbl_health']
render_dict = data_df.loc[0].to_dict()
render_dict = _add_current_date(render_dict)
render_dict = _fill_empty_tables(card_tables, render_dict)
render_dict['tbl_health'] = [{'id': 1}, {'id': 2}]
tpl = DocxTemplate(_MONITORING_CARD_PATH)
tpl.replace_pic('pet_photo.png', test_img_path)
tpl.render(render_dict)
tpl.save(output_path)
conn.close()
from docxtpl import DocxTemplate
import os
image_dir = 'templates/images/'
files = [
f for f in os.listdir(image_dir)
if os.path.isfile(os.path.join(image_dir, f))
]
tpl = DocxTemplate('templates/replace_picture_tpl.docx')
for file in files:
DEST_FILE = f'output/auto_{file.rsplit(".",1)[0]}.docx'
context = {}
old_im = "fruit_logo.png"
new_im = f"templates/images/{file}"
tpl.replace_pic(old_im, new_im)
tpl.render(context)
tpl.save(DEST_FILE)
def main(searchTime1, searchTime2):
# 计算去年搜索起始时间
lastYear1 = datetime.strptime(searchTime1, '%Y-%m-%d %H:%M:%S').year - 1
lastMonth1 = datetime.strptime(searchTime1, '%Y-%m-%d %H:%M:%S').month
lastDay1 = datetime.strptime(searchTime1, '%Y-%m-%d %H:%M:%S').day
lastTime1 = datetime(lastYear1, lastMonth1, lastDay1, 0,
1).strftime('%Y-%m-%d %H:%M:%S')
# 计算去年搜索结束时间
lastYear2 = datetime.strptime(searchTime2, '%Y-%m-%d %H:%M:%S').year - 1
lastMonth2 = datetime.strptime(searchTime2, '%Y-%m-%d %H:%M:%S').month
lastDay2 = datetime.strptime(searchTime2, '%Y-%m-%d %H:%M:%S').day
lastTime2 = datetime(lastYear2, lastMonth2, lastDay2, 23,
59).strftime('%Y-%m-%d %H:%M:%S')
# 获取今年当月情况
monthInfo1 = thisYearCurrentMonth(searchTime1, searchTime2)
if monthInfo1 is None:
return
# 获取去年当月情况
monthInfo2 = lastYearCurrentMonth(lastTime1, lastTime2)
if monthInfo2 is None:
return
# 输出文件夹
outputRootDir = os.path.join(globalCfg['output_path'], 'Statistic')
timeStamp = time.strftime('%Y%m%d%H%M%S', time.localtime(time.time()))
outputDir = os.path.join(outputRootDir, timeStamp)
os.makedirs(outputDir)
# 当前月统计
outTif1 = os.path.join(outputDir, 'image1.tif')
outTif2 = os.path.join(outputDir, 'image2.tif')
outImage1 = os.path.join(outputDir, 'image1.jpg')
outImage2 = os.path.join(outputDir, 'image2.jpg')
para1 = paragraph(monthInfo1, monthInfo2, searchTime2)
exportImage(monthInfo1, monthInfo2, searchTime1, searchTime2, outTif1,
outTif2)
# ======================================================================================
# 搜索起始时间
MarchTime = '2020-03-01 00:01:00'
# 计算去年搜索起始时间
lastYear1 = datetime.strptime(MarchTime, '%Y-%m-%d %H:%M:%S').year - 1
lastMonth1 = datetime.strptime(MarchTime, '%Y-%m-%d %H:%M:%S').month
lastDay1 = datetime.strptime(MarchTime, '%Y-%m-%d %H:%M:%S').day
lastTime1 = datetime(lastYear1, lastMonth1, lastDay1, 0,
1).strftime('%Y-%m-%d %H:%M:%S')
# 计算去年搜索结束时间
lastYear2 = datetime.strptime(searchTime2, '%Y-%m-%d %H:%M:%S').year - 1
lastMonth2 = datetime.strptime(searchTime2, '%Y-%m-%d %H:%M:%S').month
lastDay2 = datetime.strptime(searchTime2, '%Y-%m-%d %H:%M:%S').day
lastTime2 = datetime(lastYear2, lastMonth2, lastDay2, 23,
59).strftime('%Y-%m-%d %H:%M:%S')
# 3月到当前月统计
# 获取今年当月情况
monthInfo3 = thisYearCurrentMonth(MarchTime, searchTime2)
# 获取去年当月情况
monthInfo4 = lastYearCurrentMonth(lastTime1, lastTime2)
outTif3 = os.path.join(outputDir, 'image3.tif')
outTif4 = os.path.join(outputDir, 'image4.tif')
outImage3 = os.path.join(outputDir, 'image3.jpg')
outImage4 = os.path.join(outputDir, 'image4.jpg')
para2 = paragraph(monthInfo3, monthInfo4, searchTime2)
exportImage(monthInfo3, monthInfo4, MarchTime, searchTime2, outTif3,
outTif4)
# 月报word写出
# 数据获取
this_year = str(datetime.strptime(searchTime1, '%Y-%m-%d %H:%M:%S').year)
this_month = str(datetime.strptime(searchTime1, '%Y-%m-%d %H:%M:%S').month)
last_year = str(datetime.strptime(MarchTime, '%Y-%m-%d %H:%M:%S').year - 1)
time_range = '3~%s' % this_month
paragraph1 = para1
paragraph2 = para2
# [count, maxRegionStr, maxArea, maxAreaDateStr, meanArea, imageUuidList]
count1 = monthInfo1[0]
count2 = monthInfo2[0]
count3 = monthInfo3[0]
count4 = monthInfo4[0]
max_area1 = monthInfo1[2]
max_area2 = monthInfo2[2]
max_area3 = monthInfo3[2]
max_area4 = monthInfo4[2]
mean_area1 = monthInfo1[4]
mean_area2 = monthInfo2[4]
mean_area3 = monthInfo3[4]
mean_area4 = monthInfo4[4]
countCal1 = count1 - count2
if countCal1 > 0:
count_cal1 = '+%d' % countCal1
elif countCal1 < 0:
count_cal1 = '-%d' % abs(countCal1)
else:
count_cal1 = '0'
countCal2 = count3 - count4
if countCal2 > 0:
count_cal2 = '+%d' % countCal2
elif countCal2 < 0:
count_cal2 = '-%d' % abs(countCal2)
else:
count_cal2 = '0'
maxAreaCal1 = (max_area1 - max_area2) / max_area2 * 100
if maxAreaCal1 > 0:
max_area_cal1 = '+%.1f%%' % maxAreaCal1
elif maxAreaCal1 < 0:
max_area_cal1 = '-%.1f%%' % abs(maxAreaCal1)
else:
max_area_cal1 = '0%'
maxAreaCal2 = (max_area3 - max_area4) / max_area4 * 100
if maxAreaCal2 > 0:
max_area_cal2 = '+%.1f%%' % maxAreaCal2
elif maxAreaCal2 < 0:
max_area_cal2 = '-%.1f%%' % abs(maxAreaCal2)
else:
max_area_cal2 = '0%'
meanAreaCal1 = (mean_area1 - mean_area2) / mean_area2 * 100
if meanAreaCal1 > 0:
mean_area_cal1 = '+%.1f%%' % meanAreaCal1
elif meanAreaCal1 < 0:
mean_area_cal1 = '-%.1f%%' % abs(meanAreaCal1)
else:
mean_area_cal1 = '0%'
meanAreaCal2 = (mean_area3 - mean_area4) / mean_area4 * 100
if meanAreaCal2 > 0:
mean_area_cal2 = '+%.1f%%' % meanAreaCal2
elif meanAreaCal2 < 0:
mean_area_cal2 = '-%.1f%%' % abs(meanAreaCal2)
else:
mean_area_cal2 = '0%'
replaceText = {
'this_year': this_year,
'this_month': this_month,
'last_year': last_year,
'time_range': time_range,
'paragraph1': paragraph1,
'paragraph2': paragraph2,
'count1': count1,
'count2': count2,
'count3': count3,
'count4': count4,
'max_area1': max_area1,
'max_area2': max_area2,
'max_area3': max_area3,
'max_area4': max_area4,
'mean_area1': mean_area1,
'mean_area2': mean_area2,
'mean_area3': mean_area3,
'mean_area4': mean_area4,
'count_cal1': count_cal1,
'count_cal2': count_cal2,
'max_area_cal1': max_area_cal1,
'max_area_cal2': max_area_cal2,
'mean_area_cal1': mean_area_cal1,
'mean_area_cal2': mean_area_cal2
}
dependDir = globalCfg['depend_path']
templateDir = os.path.join(dependDir, 'word')
templatePath = os.path.join(templateDir, 'report_month.docx')
tpl = DocxTemplate(templatePath)
tpl.render(replaceText)
reportName = '%s年%s月太湖蓝藻遥感监测月报' % (this_year, this_month)
docxName = reportName + '.docx'
outWordPath = os.path.join(outputDir, docxName)
replacePic = {
"template_picture1.jpg": outImage1,
"template_picture2.jpg": outImage2,
"template_picture3.jpg": outImage3,
"template_picture4.jpg": outImage4
}
for key in replacePic.keys():
tpl.replace_pic(key, replacePic[key])
if os.path.exists(outWordPath):
os.remove(outWordPath)
tpl.save(outWordPath)
monitorTime = '%s年%s月' % (this_year, this_month)
processTime = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
db_info = [
reportName, monitorTime, '月报', processTime, outWordPath, 0, '太湖',
'EOS', 'MODIS', '6ce5de13-da13-11ea-871a-0242ac110003'
]
executeSql(db_info)
def run_report(cfg, table_data, table_species, table_findgse, table_scope,
table_kmer, table_assembly, figure_base_quality,
figure_base_content, figure_base_gc, figure_species,
figure_findgse, figure_genomescope, figure_kmer,
figure_gc_depth, tpl_docx, tpl_html, out_dir):
out_dir = mkdir(out_dir)
now = datetime.now()
config = read_config(cfg)
table_data = read_table_data(table_data)
table_species = read_tsv(table_species, '\t')
if config["general"]["assembly"].lower() == "true":
if table_assembly.lower == "no":
LOG.debug("Please enter the assembly result statistics file")
assembly = True
table_assembly = read_table_assembly(table_assembly)
else:
assembly = False
table_assembly = [[0, 0, 0, 0], [0], [0], [0], [0], [0],
[0, 0, 0, 0, 0]]
if config["general"]["homogeneous"].lower() == "true":
if table_findgse.lower == "no" or table_scope.lower == "no":
LOG.debug(
"Please output the genomic prediction results of FindGSE and GenomeScope"
)
homogeneous = True
table_findgse = read_table_findgse(table_findgse)
table_scope = read_table_scope(table_scope)
findgse_genome = table_findgse[0]
scope_genome = table_scope[0]
max_genome = max(findgse_genome, scope_genome)
min_genome = min(findgse_genome, scope_genome)
average_genome = (max_genome + min_genome) * 1.0 / 2
software = "FindGSE和GenomeScope"
estimate_size = '{0:,}-{1:,}'.format(min_genome, max_genome)
heterozygosity = table_scope[1]
else:
if table_kmer.lower == "no":
LOG.debug(
"Please output the genomic prediction results of Kmerfreq")
homogeneous = False
table_kmer = read_table_kmer(table_kmer)
software = "Kmerfreq"
estimate_size = '{:,}'.format(int(table_kmer[4].replace(",", "")))
heterozygosity = float(table_kmer[5].replace("%", ""))
findgse_genome = 0
scope_genome = 0
max_genome = 0
min_genome = 0
average_genome = 0
r = {
"project": "",
"id": "",
"name": "",
"species": "",
"strain": "",
"sequencer": "Illumina",
"author": "",
"reviewer": "",
"year": now.year,
"month": now.month,
"day": now.day,
"kmer": "",
"raw_data": table_data[2],
"clean_data": table_data[4],
"estimate_size": estimate_size,
"heterozygosity": '{}%'.format(heterozygosity),
"software": software,
"homogeneous": homogeneous,
"assembly_size": '{:,}'.format(int(table_assembly[6][3])),
"findgse_genome": '{:,}'.format(findgse_genome),
"scope_genome": '{:,}'.format(scope_genome),
"average_genome": '{:,}'.format(average_genome),
"max_genome": '{:,}'.format(max_genome),
"min_genome": '{:,}'.format(min_genome),
"table_data": table_data,
"table_species": table_species,
"pollution_description": "",
"table_findgse": table_findgse,
"table_scope": table_scope,
"table_kmer": table_kmer,
"assembly": assembly,
"table_assembly": table_assembly,
"scaffold_length": '{:,}'.format(int(table_assembly[6][3])),
"scaffold_n50": '{:,}'.format(int(table_assembly[0][3])),
"scaffold_number": '{:,}'.format(int(table_assembly[6][4])),
"contig_length": '{:,}'.format(int(table_assembly[6][1])),
"contig_n50": '{:,}'.format(int(table_assembly[0][1])),
"contig_number": '{:,}'.format(int(table_assembly[6][2])),
"depth_description": ""
}
r.update(config["general"])
tpl = DocxTemplate(tpl_docx)
tpl.render(r)
if homogeneous:
if assembly:
figure_name = [
"base_quality.png", "base_content.png", "base_gc.png",
"top10_species.png", "findgse.png", "genomescope.png",
"gc_depth.png"
]
figure_variable = [
figure_base_quality, figure_base_content, figure_base_gc,
figure_species, figure_findgse, figure_genomescope,
figure_gc_depth
]
else:
figure_name = [
"base_quality.png", "base_content.png", "base_gc.png",
"top10_species.png", "findgse.png", "genomescope.png",
"gc_depth.png"
]
figure_variable = [
figure_base_quality, figure_base_content, figure_base_gc,
figure_species, figure_findgse, figure_genomescope
]
else:
if assembly:
figure_name = [
"base_quality.png", "base_content.png", "base_gc.png",
"top10_species.png", "heterozygosity.png", "gc_depth.png"
]
figure_variable = [
figure_base_quality, figure_base_content, figure_base_gc,
figure_species, figure_kmer, figure_gc_depth
]
else:
figure_name = [
"base_quality.png", "base_content.png", "base_gc.png",
"top10_species.png", "heterozygosity.png"
]
figure_variable = [
figure_base_quality, figure_base_content, figure_base_gc,
figure_species, figure_kmer
]
for i, j in zip(figure_name, figure_variable):
tpl.replace_pic(i, j)
tpl.save(os.path.join(out_dir, "report.docx"))
# html_report
for i in ["images", "static"]:
temp = os.path.join(out_dir, i)
if os.path.exists(temp):
shutil.rmtree(temp)
shutil.copytree(os.path.join(tpl_html, i), temp)
for i in figure_variable:
shutil.copy(i, os.path.join(out_dir, "images/"))
for j in ["index.html", "main.html"]:
tpl = Template(open(os.path.join(tpl_html, j)).read().decode("utf-8"))
with open(os.path.join(out_dir, j), "w") as fh:
fh.write(tpl.render(r).encode("utf-8"))
# html_report
return r
def generate(data):
try:
domaine = data['metier']['domaine'].strip()
nbp = len(data['parcours'])
if nbp == 5: flimit = 288
else: flimit = 403
# Try to make a space between aspet&contrainte
if len(data['metier']['aspectPositif']) < 45:
data['metier']['aspectPositif'] += '\n'
context = {
'nom' : RichText(data['profil']['nom'], font='Arial', bold=True,\
size = 22 if len(data['profil']['nom'])>50 else 24),
'poste' : data['profil']['poste'],
'biographie' : RichText(data['profil']['biographie'], font='Arial', \
size = 22 if len(data['profil']['biographie'])>399 else 24),
'competenceQualite' : RichText(data['metier']['competenceQualite'], font='Arial', color='#000000', \
size = 22 if len(data['metier']['competenceQualite'])>185 else 24),
'accessMetier' : RichText(data['metier']['accessMetier'], font='Arial', color='#000000',\
size = 22 if len(data['metier']['accessMetier'])>160 else 24),
'aspectPositif' : RichText(data['metier']['aspectPositif'], font='Arial', color='#000000',\
size = 22 if len(data['metier']['aspectPositif'])>72 else 24),
'contrainte' : RichText(data['metier']['contrainte'], font='Arial', color='#000000',\
size = 22 if len(data['metier']['contrainte'])>72 else 24),
'domaine' : domaine,
'formation' : RichText(data['etudes']['formation'], font='Arial', color='#000000',\
size = 22 if (len(data['etudes']['formation'])>flimit) else 24),
'etablissement' : RichText(data['etudes']['etablissement'], font='Arial', color='#000000',\
size = 28 if len(data['etudes']['etablissement'])>32 else 32),
'insertionProfessionnel' : RichText(data['etudes']['insertionProfessionnel'], font='Arial', color='#000000',\
size = 22 if len(data['etudes']['insertionProfessionnel'])>111 else 24)
}
for i in range(nbp):
if i == nbp - 1: color = '#ffffff'
else: color = '#000000'
context[f'p{i}'] = RichText(data['parcours'][i][0], font='Arial', color=color,\
size = 22 if len(data['parcours'][i][0])>11 else 24)
context[f'v{i}'] = RichText(data[ 'parcours'][i][1], font='Arial', color=color, \
size = 22 if len(data['parcours'][i][1])>34 else 24)
col = None
for kdom, vdom in verifJSON().items():
if kdom == domaine:
col = vdom
break
# Si aucune correspondance
if not col: return
# Get the template
doc = DocxTemplate(f"template/Trame-vierge{nbp}-{col}.docx")
# Change image on template if image set
if data['profil']['profilImage'] != '':
data['profil']['profilImage'] = forceJPG(
data['profil']['profilImage'])
doc.replace_pic('test.jpg', data['profil']['profilImage'])
# Generate template
doc.render(context)
name = "FicheMetier_" + data['profil']['poste'].replace(' ', '_')
# Save the doc generated
doc.save(f"{gettempdir()}\\{name}.docx")
# Convert to pdf
convert(f"{gettempdir()}\\{name}.docx")
return f"{gettempdir()}\\{name}.pdf"
except Exception as err:
print(err)
return
# -*- coding: utf-8 -*-
'''
Created : 2017-09-03
@author: Eric Lapouyade
'''
from docxtpl import DocxTemplate
DEST_FILE = 'output/replace_picture.docx'
tpl = DocxTemplate('templates/replace_picture_tpl.docx')
context = {}
tpl.replace_pic('python_logo.png', 'templates/python.png')
tpl.render(context)
tpl.save(DEST_FILE)
def exportWord(jsonPath, productUuid):
# 解析当前json中信息
with open(jsonPath, 'r') as f:
jsonData = json.load(f)
cloud = jsonData['cloud'] # 云量信息
totalArea = jsonData['totalArea'] # 蓝藻总面积
# 生成文字所需信息===============================================================
issue = os.path.basename(jsonPath).split('_')[3]
year = int(issue[0:4])
mm = int(issue[4:6])
dd = int(issue[6:8])
hour = int(issue[8:10])
minute = int(issue[10:12])
timeStr = '%d月%d日%d时%d分' % (mm, dd, hour, minute)
totalPercent = jsonData['totalPercent'] # 蓝藻总百分比
lakeStat = jsonData['lakeStat'] # 蓝藻面积分布区域
algaeThreshold = jsonData['algaeThreshold']
lakeRegionList = []
for key in lakeStat.keys():
if lakeStat[key] == 1:
lakeRegionList.append(LAKE_REGION_NAME[key])
if len(lakeRegionList) == 0:
lakeRegionStr = ''
elif len(lakeRegionList) == 1:
lakeRegionStr = lakeRegionList[0]
else:
tempList = lakeRegionList[0:-1]
lakeRegionStr = '、'.join(tempList) + '和' + lakeRegionList[-1]
areaWX = jsonData['adminArea']['wuxi']
areaCZ = jsonData['adminArea']['changzhou']
areaSZ = jsonData['adminArea']['suzhou']
percentWX = jsonData['adminPercent']['wuxi']
percentCZ = jsonData['adminPercent']['changzhou']
percentSZ = jsonData['adminPercent']['suzhou']
areaH = jsonData['highArea']
areaM = jsonData['midArea']
areaL = jsonData['lowArea']
percentH = jsonData['highPercent']
percentM = jsonData['midPercent']
percentL = jsonData['lowPercent']
# 计算期号
nowDatetime = datetime.datetime.strptime(issue[0:8] + '0000', '%Y%m%d%H%M')
# 3月以前算上一年期号
if mm < 3:
startDatetime = datetime.datetime.strptime(str(year) + '01010000', '%Y%m%d%H%M')
else:
startDatetime = datetime.datetime.strptime(str(year) + '03010000', '%Y%m%d%H%M')
num = (nowDatetime - startDatetime).days + 1 # 期号
label2 = ''
label3 = ''
# 蓝藻日报文字部分===================================================
# 1.全云
if cloud >= 95:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖全部被云层覆盖，无法判断蓝藻聚集情况。' % timeStr
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖全部被云层覆盖，无法判断蓝藻聚集情况。' % timeStr
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
templateID = 1
typeID = 1
# 2.有云无藻
elif 5 < cloud < 95 and totalArea == 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖部分湖区被云层覆盖，无云区域内未发现蓝藻聚集现象。' % timeStr
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内未发现蓝藻聚集现象。' % timeStr
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
templateID = 1
typeID = 2
# 3.无云无藻
elif cloud <= 5 and totalArea == 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖未发现蓝藻聚集现象。' % timeStr
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖未发现蓝藻聚集现象。' % timeStr
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
templateID = 1
typeID = 3
# 4.有云有藻 面积不大于300
elif 5 < cloud < 95 and 0 < totalArea <= 300:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米（图2），' \
'占全湖总面积的%.1f%%，主要分布在%s。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米，' \
'占全湖总面积的%.1f%%，主要分布在%s。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
templateID = 2
typeID = 4
# 5.无云有藻 面积不大于300
elif cloud <= 5 and 0 < totalArea <= 300:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖发现蓝藻聚集面积约%d平方千米（图2），占全湖总面积的%.1f%%，' \
'主要分布在%s。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，占%d%%；苏州水域%d平方千米，' \
'占%d%%。'\
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖发现蓝藻聚集面积约%d平方千米，占全湖总面积的%.1f%%，' \
'主要分布在%s。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，占%d%%；苏州水域%d平方千米，' \
'占%d%%。'\
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
templateID = 2
typeID = 5
# 6.无云有藻 面积大于300 有高中低聚集区
elif cloud <= 5 and totalArea > 300 and areaH > 0 and areaM > 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖发现蓝藻聚集面积约%d平方千米（图2），占全湖总面积的%.1f%%，' \
'主要分布在%s。其中，高、中、低聚集区面积分别约为%d平方千米、%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%、%d%%和%d%%（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaH, areaM, areaL, percentH, percentM,
percentL, areaWX, percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖发现蓝藻聚集面积约%d平方千米，占全湖总面积的%.1f%%，' \
'主要分布在%s。其中，高、中、低聚集区面积分别约为%d平方千米、%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%、%d%%和%d%%。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaH, areaM, areaL, percentH, percentM,
percentL, areaWX, percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 5
# 7.无云有藻 面积大于300 无高聚集区 有中低聚集区
elif cloud <= 5 and totalArea > 300 and areaH == 0 and areaM > 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖发现蓝藻聚集面积约%d平方千米（图2），占全湖总面积的%.1f%%，' \
'主要分布在%s。其中，无高聚集区，中、低聚集区面积分别约为%d平方千米和%d平方千米，占蓝藻总聚集面积的%d%%和%d%%' \
'（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，占%d%%；苏州水域%d平方千米，' \
'占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaM, areaL, percentM, percentL, areaWX,
percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖发现蓝藻聚集面积约%d平方千米，占全湖总面积的%.1f%%，' \
'主要分布在%s。其中，无高聚集区，中、低聚集区面积分别约为%d平方千米和%d平方千米，占蓝藻总聚集面积的%d%%和%d%%' \
'。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，占%d%%；苏州水域%d平方千米，' \
'占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaM, areaL, percentM, percentL, areaWX,
percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 5
# 8.无云有藻 面积大于300 无高中聚集区 有低聚集区
elif cloud <= 5 and totalArea > 300 and areaH == 0 and areaM == 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖发现蓝藻聚集面积约%d平方千米（图2），占全湖总面积的%.1f%%，' \
'主要分布在%s，全部为低聚集区（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖发现蓝藻聚集面积约%d平方千米，占全湖总面积的%.1f%%，' \
'主要分布在%s，全部为低聚集区。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 5
# 9.有云有藻 面积大于300 有高中低聚集区
elif 5 < cloud < 95 and totalArea > 300 and areaH > 0 and areaM > 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米（图2），' \
'占全湖总面积的%.1f%%，主要分布在%s。其中，高、中、低聚集区面积分别约为%d平方千米、%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%、%d%%和%d%%（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaH, areaM, areaL, percentH, percentM,
percentL, areaWX, percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米，' \
'占全湖总面积的%.1f%%，主要分布在%s。其中，高、中、低聚集区面积分别约为%d平方千米、%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%、%d%%和%d%%。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaH, areaM, areaL, percentH, percentM,
percentL, areaWX, percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 4
# 10.有云有藻 面积大于300 无高聚集区 有中低聚集区
elif 5 < cloud < 95 and totalArea > 300 and areaH == 0 and areaM > 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米（图2），' \
'占全湖总面积的%.1f%%，主要分布在%s。其中，无高聚集区，中、低聚集区面积约分别约为%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%和%d%%（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaM, areaL, percentM, percentL, areaWX,
percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米，' \
'占全湖总面积的%.1f%%，主要分布在%s。其中，无高聚集区，中、低聚集区面积约分别约为%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%和%d%%。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaM, areaL, percentM, percentL, areaWX,
percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 4
# 11.有云有藻 面积大于300 无高中聚集区 有低聚集区
elif 5 < cloud < 95 and totalArea > 300 and areaH == 0 and areaM == 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米（图2），' \
'占全湖总面积的%.1f%%，主要分布在%s，全部为低聚集区（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米，' \
'占全湖总面积的%.1f%%，主要分布在%s，全部为低聚集区。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 4
else:
print('No Match Found!!!')
return
print(description)
# 生成文件====================================================
# 1.生成日报
replaceText = {'year': year, 'num': num, 'mm': mm, 'dd': dd, 'description': description, 'label1': label1,
'label2': label2, 'label3': label3, 'areaH': areaH, 'areaM': areaM, 'areaL': areaL,
'totalArea': totalArea, 'percentH': percentH, 'percentM': percentM, 'percentL': percentL}
dependDir = globalCfg['depend_path']
templateDir = os.path.join(dependDir, 'word')
templatePath = os.path.join(templateDir, 'report_daily' + str(templateID) + '.docx')
tpl = DocxTemplate(templatePath)
tpl.render(replaceText)
jsonBaseName = os.path.basename(jsonPath)
outputDir = os.path.dirname(jsonPath)
outWordName = jsonBaseName.replace('.json', '.docx')
outWordPath = os.path.join(outputDir, outWordName)
picturePath1 = os.path.join(outputDir, jsonBaseName.replace('.json', '_reportImg1_noPoints.jpg'))
picturePath2 = os.path.join(outputDir, jsonBaseName.replace('.json', '_reportImg2.jpg'))
picturePath3 = os.path.join(outputDir, jsonBaseName.replace('.json', '_reportImg3.jpg'))
if not (os.path.exists(picturePath1) and os.path.exists(picturePath2) and os.path.exists(picturePath2)):
print('Cannot Find JPG File!!!')
return
if templateID == 1:
replacePic = {"template_picture1.jpg": picturePath1}
elif templateID == 2:
replacePic = {"template_picture1.jpg": picturePath1, "template_picture2.jpg": picturePath2}
elif templateID == 3:
replacePic = {"template_picture1.jpg": picturePath1, "template_picture2.jpg": picturePath2,
"template_picture3.jpg": picturePath3}
else:
replacePic = {}
for key in replacePic.keys():
tpl.replace_pic(key, replacePic[key])
if os.path.exists(outWordPath):
os.remove(outWordPath)
tpl.save(outWordPath)
# 2.生成推送所需txt第一段文字，剩余两段文字后续添加
outTxtName = jsonBaseName.replace('.json', '.txt')
outTxtPath = os.path.join(outputDir, outTxtName)
with open(outTxtPath, 'w') as f:
f.write(description2)
# 3.生成EXCEL
xls_num = num
xls_date = str(year) + '/' + str(mm) + '/' + str(dd)
xls_time = '%s时%s分' % (str(hour), str(minute))
xls_threshold = str(algaeThreshold)
xls_ndviMax = str(jsonData['ndviMax'])
xls_ndviMin = str(jsonData['ndviMin'])
xls_ndviMean = str(jsonData['ndviMean'])
xls_boundary = str(jsonData['boundaryThreshold'])
xls_area = ''
if typeID == 4 or typeID == 5:
xls_area = str(totalArea)
xls_algae_area = ''
if typeID == 2 or typeID == 3:
xls_algae_area = '0'
elif typeID == 4 or typeID == 5:
xls_algae_area = str(totalArea)
xls_high = ''
if totalArea >= 300 and areaH > 0:
xls_high = str(areaH)
xls_mid = ''
if totalArea >= 300 and areaM > 0:
xls_mid = str(areaM)
xls_low = ''
if totalArea >= 300 and areaL > 0:
xls_low = str(areaL)
xls_region = lakeRegionStr
xls_cloud = str(cloud)
if cloud > 50 and totalArea == 0:
xls_activate = '0'
else:
xls_activate = '1'
xls_explain = str(typeID)
xls_weather = ''
if cloud <= 5:
xls_cloud_cover = '无覆盖'
elif cloud >= 95:
xls_cloud_cover = '全部覆盖'
else:
xls_cloud_cover = '部分覆盖'
xls_total_percent = '%.2f%%' % totalPercent
xls_intensity_threshold = ''
if totalArea >= 300:
xls_intensity_threshold = '%.3f-%.3f,%.3f-%.3f,%.3f-%.3f' \
% (jsonData['ndviMin'], jsonData['insThreshold1'], jsonData['insThreshold1'],
jsonData['insThreshold2'], jsonData['insThreshold2'], jsonData['ndviMax'])
outXlsxName = jsonBaseName.replace('.json', '.xlsx')
outXlsxPath = os.path.join(outputDir, outXlsxName)
if os.path.exists(outXlsxPath):
os.remove(outXlsxPath)
workBook = xlsxwriter.Workbook(outXlsxPath)
sheet = workBook.add_worksheet()
writeTable = {'A1': '报告期数', 'A2': xls_num,
'B1': '日期', 'B2': xls_date,
'C1': '时间', 'C2': xls_time,
'D1': 'NDVI阈值', 'D2': xls_threshold,
'E1': 'NDVI最大值（蓝藻区域）', 'E2': xls_ndviMax,
'F1': 'NDVI最小值（蓝藻区域）', 'F2': xls_ndviMin,
'G1': 'NDVI均值（蓝藻区域）', 'G2': xls_ndviMean,
'H1': '边界缩放', 'H2': xls_boundary,
'I1': '面积(km2)无云无藻不填', 'I2': xls_area,
'J1': '蓝藻面积（无云无藻填0，全云不填，其他按面积填）', 'J2': xls_algae_area,
'K1': '高聚区面积', 'K2': xls_high,
'L1': '中聚区面积', 'L2': xls_mid,
'M1': '低聚区面积', 'M2': xls_low,
'N1': '分布范围（竺山湖、梅梁湖、贡湖、西部沿岸、南部沿岸、东部沿岸和湖心区）', 'N2': xls_region,
'O1': '云量', 'O2': xls_cloud,
'P1': '是否为有效监测（云量超过50%并且没有监测到蓝藻算无效,1为有效，0为无效）', 'P2': xls_activate,
'Q1': '说明（1全云；2有云无藻；3无云无藻；4有云有藻；5无云有藻）', 'Q2': xls_explain,
'R1': '天气', 'R2': xls_weather,
'S1': '是否被云覆盖（无覆盖、全部覆盖、部分覆盖）', 'S2': xls_cloud_cover,
'T1': '水华面积百分比', 'T2': xls_total_percent,
'U1': 'NDVI分级阈值', 'U2': xls_intensity_threshold
}
format1 = workBook.add_format({'align': 'center', 'font_size': 10,
'valign': 'vcenter', 'text_wrap': 1})
for key in writeTable.keys():
sheet.write(key, writeTable[key], format1)
sheet.set_row(0, 60)
sheet.set_column('A:M', 8.85)
sheet.set_column('N:N', 73)
sheet.set_column('P:P', 73)
sheet.set_column('Q:Q', 60)
sheet.set_column('S:S', 44)
sheet.set_column('T:T', 15)
sheet.set_column('U:U', 40)
workBook.close()
# 4.生成EXCEL_WX
writeTable2 = {'A1': '报告期数', 'A2': xls_num,
'B1': '日期', 'B2': xls_date,
'C1': '时间', 'C2': xls_time,
'D1': 'NDVI阈值', 'D2': xls_threshold,
'E1': '边界缩放', 'E2': xls_boundary,
'F1': '面积(km2)无云无藻不填', 'F2': xls_area,
}
outXlsxWxName = jsonBaseName.replace('.json', '_wx.xlsx')
outXlsxWxPath = os.path.join(outputDir, outXlsxWxName)
if os.path.exists(outXlsxWxPath):
os.remove(outXlsxWxPath)
workBook2 = xlsxwriter.Workbook(outXlsxWxPath)
sheet2 = workBook2.add_worksheet()
format2 = workBook2.add_format({'align': 'center', 'font_size': 10,
'valign': 'vcenter', 'text_wrap': 1})
for key in writeTable2.keys():
sheet2.write(key, writeTable2[key], format2)
sheet.set_row(0, 60)
sheet.set_column('A:F', 9)
workBook2.close()
# 转pdf供前端查看 Windows无法测试===================================================
outPdfDir = os.path.join(globalCfg['taihu_report_remote'], issue[0:8])
if not os.path.exists(outPdfDir):
os.makedirs(outPdfDir)
cmdStr = 'libreoffice6.3 --headless --convert-to pdf:writer_pdf_Export ' + outWordPath + ' --outdir ' + outPdfDir
print(cmdStr)
try:
os.system(cmdStr)
print('Convert PDF Success.')
except Exception as e:
print(e)
# 信息入库=========================================================
conn = pymysql.connect(
db=globalCfg['database'],
user=globalCfg['database_user'],
password=globalCfg['database_passwd'],
host=globalCfg['database_host'],
port=globalCfg['database_port']
)
# t_water_report_taihu
cursor = conn.cursor()
algaeTifName = os.path.basename(jsonPath).replace('.json', '.tif')
db_uuid = str(uuid.uuid4())
db_date = issue[0:4] + '-' + issue[4:6] + '-' + issue[6:8]
db_number = str(num)
db_description = description
db_label1 = ''
db_label2 = ''
db_label3 = ''
db_image1 = ''
db_image2 = ''
db_image3 = ''
db_high_area = ''
db_mid_area = ''
db_low_area = ''
db_total_area = ''
db_high_percent = ''
db_mid_percent = ''
db_low_percent = ''
db_total_percent = ''
db_image = algaeTifName
db_title = '太湖蓝藻水华卫星遥感监测日报'
db_time_modify = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
if templateID == 1:
db_label1 = label1
db_image1 = picturePath1.replace('\\', '/').replace('/mnt/resource/', '')
elif templateID == 2:
db_label1 = label1
db_image1 = picturePath1.replace('\\', '/').replace('/mnt/resource/', '')
db_label2 = label2
db_image2 = picturePath2.replace('\\', '/').replace('/mnt/resource/', '')
elif templateID == 3:
db_label1 = label1
db_image1 = picturePath1.replace('\\', '/').replace('/mnt/resource/', '')
db_label2 = label2
db_image2 = picturePath2.replace('\\', '/').replace('/mnt/resource/', '')
db_label3 = label3
db_image3 = picturePath3.replace('\\', '/').replace('/mnt/resource/', '')
db_high_area = str(areaH)
db_mid_area = str(areaM)
db_low_area = str(areaL)
db_total_area = str(totalArea)
db_total_percent = '100.0'
db_high_percent = str(percentH)
db_mid_percent = str(percentM)
db_low_percent = str(percentL)
else:
pass
# 查找是否已存在
sqlStr = 'SELECT * FROM ' + globalCfg['database_table_report_taihu'] + \
' WHERE image=%s and is_deleted=0;'
cursor.execute(sqlStr, algaeTifName)
sqlRes = cursor.fetchall()
if len(sqlRes) > 0:
# 更新
sqlStr = 'UPDATE ' + globalCfg['database_table_report_taihu'] + \
' SET date=%s,number=%s,description=%s,image1=%s,image2=%s,image3=%s,label1=%s,label2=%s,label3=%s,' \
'high_area=%s,mid_area=%s,low_area=%s,total_area=%s,high_percent=%s,mid_percent=%s,low_percent=%s,' \
'total_percent=%s,title=%s,time_modify=%s WHERE image=%s;'
sqlData = (db_date, db_number, db_description, db_image1, db_image2, db_image3, db_label1, db_label2, db_label3,
db_high_area, db_mid_area, db_low_area, db_total_area, db_high_percent, db_mid_percent,
db_low_percent, db_total_percent, db_title, db_time_modify, db_image)
cursor.execute(sqlStr, sqlData)
conn.commit()
else:
# 插入
sqlStr = 'INSERT INTO ' + globalCfg['database_table_report_taihu'] + \
' (uuid,date,number,description,image1,image2,image3,label1,label2,label3,high_area,' \
'mid_area,low_area,total_area,high_percent,mid_percent,low_percent,total_percent,is_deleted,' \
'is_default,image,title,time_modify) VALUES ' \
'(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);'
sqlData = (db_uuid, db_date, db_number, db_description, db_image1, db_image2, db_image3, db_label1, db_label2,
db_label3, db_high_area, db_mid_area, db_low_area, db_total_area, db_high_percent,
db_mid_percent, db_low_percent, db_total_percent, 0, 0, db_image, db_title, db_time_modify)
cursor.execute(sqlStr, sqlData)
conn.commit()
# t_water_taihu_modis
# 查找是否已存在
sqlStr = 'SELECT * FROM ' + globalCfg['database_table_report_taihu_info'] + \
' WHERE image_uuid=%s;'
cursor.execute(sqlStr, productUuid)
sqlRes = cursor.fetchall()
db_date = '%s-%s-%s %s:%s' % (issue[0:4], issue[4:6], issue[6:8], issue[8:10], issue[10:12])
regionArea = jsonData['regionArea']
area_zsh = str(regionArea['zhushanhu'])
area_mlh = str(regionArea['meilianghu'])
area_gh = str(regionArea['gonghu'])
area_xbya = str(regionArea['westCoast'])
area_nbya = str(regionArea['southCoast'])
area_hxq = str(regionArea['centerLake'])
area_dbya = str(regionArea['eastCoast'])
area_dth = str(regionArea['eastTaihu'])
db_region_area = ','.join([area_zsh, area_mlh, area_gh, area_xbya, area_nbya, area_hxq, area_dbya, area_dth])
if len(sqlRes) > 0:
# 更新
sqlStr = 'UPDATE ' + globalCfg['database_table_report_taihu_info'] + \
' SET number=%s,date=%s,ndvi_threshold=%s,ndvi_max=%s,ndvi_min=%s,ndvi_mean=%s,boundary=%s,area=%s,' \
'region_area=%s,high_area=%s,mid_area=%s,low_area=%s,cloud=%s,type=%s,is_activate=%s,ndvi_grade=%s ' \
'WHERE image_uuid=%s;'
sqlData = (xls_num, db_date, xls_threshold, xls_ndviMax, xls_ndviMin, xls_ndviMean, xls_boundary,
str(totalArea), db_region_area, str(areaH), str(areaM), str(areaL), xls_cloud, xls_explain,
xls_activate, xls_intensity_threshold, productUuid)
cursor.execute(sqlStr, sqlData)
conn.commit()
else:
sqlStr = 'INSERT INTO ' + globalCfg['database_table_report_taihu_info'] + \
' (number,date,ndvi_threshold,ndvi_max,ndvi_min,ndvi_mean,boundary,area,region_area,high_area,' \
'mid_area,low_area,cloud,type,is_activate,ndvi_grade,image_uuid) ' \
'VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);'
sqlData = (xls_num, db_date, xls_threshold, xls_ndviMax, xls_ndviMin, xls_ndviMean, xls_boundary,
str(totalArea), db_region_area, str(areaH), str(areaM), str(areaL), xls_cloud, xls_explain,
xls_activate, xls_intensity_threshold, productUuid)
cursor.execute(sqlStr, sqlData)
conn.commit()
# 更新t_export_image信息
sqlStr = 'SELECT * FROM ' + globalCfg['database_table_export_image'] + \
' WHERE uuid=%s and is_deleted=0;'
cursor.execute(sqlStr, productUuid)
sqlRes = cursor.fetchall()
if len(sqlRes) > 0:
sqlStr = 'UPDATE ' + globalCfg['database_table_export_image'] + \
' SET area=%s,threshold=%s WHERE uuid=%s;'
sqlData = (totalArea, algaeThreshold, productUuid)
cursor.execute(sqlStr, sqlData)
conn.commit()
else:
pass
cursor.close()
conn.close()
# 更新切片==============================================================
tileDict = {}
basename = '_'.join(jsonBaseName.split('_')[0:7])
# 1.蓝藻产品切片
algaeTifPath = os.path.join(outputDir, jsonBaseName.replace('.json', '.tif'))
algaeTifRender = os.path.join(outputDir, jsonBaseName.replace('.json', '_render.tif'))
colorTable = {1: (255, 251, 0)}
UniqueValues.Render(algaeTifPath, colorTable, returnMode='GEOTIFF', outputPath=algaeTifRender, isAlpha=True)
tileDict['taihu_algae_ndvi'] = {'tif': algaeTifRender, 'name': basename + '_taihu_algae_ndvi',
'legendType': '1', 'legendColor': [(255, 251, 0)], 'legendName': ['水华']}
# 2.蓝藻强度产品切片
intensityTifPath = os.path.join(outputDir, jsonBaseName.replace('_ndvi.json', '_intensity.tif'))
intensityTifRender = os.path.join(outputDir, jsonBaseName.replace('_ndvi.json', '_intensity_render.tif'))
colorTable = {1: (0, 255, 102), 2: (255, 255, 0), 3: (255, 153, 0)}
UniqueValues.Render(intensityTifPath, colorTable, returnMode='GEOTIFF', outputPath=intensityTifRender, isAlpha=True)
tileDict['algaeClassify'] = {'tif': intensityTifRender, 'name': basename + '_classify', 'legendType': '1',
'legendColor': [(0, 255, 102), (255, 255, 0), (255, 153, 0)],
'legendName': ['轻度', '中度', '重度']}
# 调用gdal2tiles工具进行切片
pythonPath = globalCfg['python_path']
gdal2tilesPath = globalCfg['gdal2tiles_path']
tileOutRootDir = globalCfg['tile_server_path']
for key in tileDict.keys():
tileTif = tileDict[key]['tif']
tileOutDir = os.path.join(tileOutRootDir, tileDict[key]['name'])
if os.path.exists(tileOutDir):
shutil.rmtree(tileOutDir)
cmd = '%s %s -z %s -w all %s %s' % (pythonPath, gdal2tilesPath, TILE_LEVEL, tileTif, tileOutDir)
os.system(cmd)
os.remove(tileTif)
tileDict[key]['path'] = tileOutDir
class Report(object):
"""生成报告"""
def __init__(self, proj_id, sample_id, customer_institute,
bioinfo_engineer, supervisor, proj_path, template_file,
output_path):
"""初始化结果
:proj_id: TODO
:proj_name: TODO
:proj_path: TODO
"""
self._proj_id = proj_id
self._sample_id = sample_id
self._customer_institute = customer_institute
self._bioinfo_engineer = bioinfo_engineer
self._supervisor = supervisor
self._proj_path = proj_path
self._template_file = template_file
self._output_path = output_path
self.tpl = DocxTemplate(self._template_file)
def format_cover_table_string(self, string):
return "{}".format(string)
def get_proj_info(self):
"""获取项目信息
:returns: TODO
"""
proj_info = {
'customer_institute':
self.format_cover_table_string(self._customer_institute),
'proj_id':
self.format_cover_table_string(self._proj_id),
'sample_id':
self._sample_id,
'report_date':
self.format_cover_table_string(
datetime.now().strftime("%Y-%m-%d")),
'bioinfo_engineer':
self.format_cover_table_string(self._bioinfo_engineer),
'supervisor':
self.format_cover_table_string(self._supervisor)
}
return proj_info
'''
def get_img(self, file_name, width=110.1, flag=True):
"""TODO: Docstring for proc_img.
:file_name: TODO
:returns: TODO
"""
if 0:
return InlineImage(self.tpl, file_name, width=Mm(width))
return file_name
'''
def N50(self, file_name):
with open(file_name, 'r') as io:
for line in io:
line = line.strip()
fields = line.split("\t")
if fields[0] == "N50":
return fields[1]
return 0
def basic_reads_stats(self, file_name):
stats_list = []
with open(file_name, 'r') as io:
for line in io:
line = line.strip()
if line == '':
continue
if not line[0] == "#":
fields = line.split("\t")
stats_list.append(fields[1])
stats_row_name = [
"file_num", "num_of_reads", "num_bases", "average_length",
"max_length"
]
stats_dict = dict(i for i in zip(stats_row_name, stats_list))
return stats_dict
def get_first_match(self, end_with):
file_list = [
i for i in os.listdir(self._proj_path) if i.endswith(end_with)
]
num_of_matchs = len(file_list)
if num_of_matchs > 1:
raise RuntimeError(
"Only one {} file is supported now".format(end_with))
elif num_of_matchs < 1:
raise RuntimeError(
"Cannot find {} file in proj_path".format(end_with))
else:
first = os.path.join(self._proj_path, file_list[0])
return first
def fastq_qc(self):
read_distribute = self.get_first_match("reads.distribute.xls")
read_stats = self.get_first_match("reads.stat.xls")
fastq_qc = {}
if self.N50(read_distribute):
fastq_qc["N50"] = self.N50(read_distribute)
else:
raise RuntimeError("Cannot find N50 in read_distribute file")
fastq_qc.update(self.basic_reads_stats(read_stats))
# depth = num_bases/3000000000
fastq_qc['depth'] = "{:4.2f}".format(
int(fastq_qc['num_bases'].replace(',', '')) / 3000000000)
# length distribut
read_length_distribute = self.get_first_match(
"Reads_length_histogram.png")
self.tpl.replace_pic('sample_id.Reads_length_histogram.png',
read_length_distribute)
# fastq_qc['length_histogram'] = self.get_img(read_length_distribute)
return fastq_qc
def basic_bam_stats(self):
bam_stat_file = self.get_first_match("bam.bc")
stats_row_name = [
"raw_total_sequences", "reads_mapped", "total_length",
"bases_mapped", "error_rate"
]
stats_list = []
with open(bam_stat_file, "r") as io:
bam_stat_dict = {}
for line in io:
line = line.strip()
if line[0:2] == "SN":
fields = line.split("\t")
key = fields[1].strip(":").replace(" ", "_")
value = fields[2]
if key == "error_rate":
value = "{0:.2f}%".format(10 * float(value[0:6]))
if not key == "error_rate":
value = "{:,}".format(int(value))
if key in stats_row_name:
stats_list.append(value)
if len(stats_list) == len(stats_row_name):
break
bam_stat_dict = dict(i for i in zip(stats_row_name, stats_list))
bam_stat_dict["reads_map_rate"] = "{0:.2f}%".format(
100 * float(bam_stat_dict["reads_mapped"].replace(',', '')) /
float(bam_stat_dict["raw_total_sequences"].replace(',', '')))
bam_stat_dict["bases_map_rate"] = "{0:.2f}%".format(
100 * float(bam_stat_dict["bases_mapped"].replace(',', '')) /
float(bam_stat_dict["total_length"].replace(',', '')))
return bam_stat_dict
def sv_num(self):
read_svnum = self.get_first_match("re4.svnum")
with open(read_svnum, "r") as io:
sv_num_dict = {}
for line in io:
line = line.strip()
if not line[0] == "#":
fields = line.split("\t")
sv_num_dict[fields[0]] = "{:,}".format(int(fields[1]))
total_sv_num = sum(
[int(sv_num_dict[k].replace(',', '')) for k in sv_num_dict])
sv_num_dict["Total"] = "{:,}".format(total_sv_num)
# sv_num bar
sv_num_barplot = self.get_first_match("re4.svnum.png")
self.tpl.replace_pic('sample_id.re4.svnum.png', sv_num_barplot)
return sv_num_dict
def sv_len(self):
sv_len_densityplot = self.get_first_match("re4.svlen.png")
self.tpl.replace_pic('sample_id.re4.svlen.png', sv_len_densityplot)
def create_report(self):
"""生成报告
"""
# sv len pic
self.sv_len()
content = {
'proj_info': self.get_proj_info(),
'fastq_qc': self.fastq_qc(),
'bam_stat': self.basic_bam_stats(),
'sv_num': self.sv_num()
}
self.tpl.render(content)
output_file = '{proj_id}_{sample_id}_sv_report_{date}.docx'.format(
proj_id=self._proj_id,
sample_id=self._sample_id,
date=time.strftime('%Y-%m-%d', time.localtime(time.time())))
self.tpl.save(os.path.join(self._output_path, output_file))
# outPicPath = r'C:\Users\Administrator\Desktop\NHS\TH_AD.jpg'
# barChartInfo = {'legend': ('2020', '2021'), 'yMax': 2000, 'yLineStep': 200, 'labelOffsetY': 10, 'labelOffsetX': 0,
# 'labelSize': 20, 'barWidth': 0.3, 'barGap': 0.1, 'yAxisName': '藻类密度（万个/升）',
# 'paperSize': (25, 15), 'adjust': [0.07, 0.15, 0.98, 0.95]}
# df = pd.DataFrame(
# {'StationName': ['傀儡湖三水厂取水口', '阳澄湖二水厂', '水源地均值'],
# '2020': [Decimal('3.9'), Decimal('11.4'), Decimal('7.6')],
# '2021': [Decimal('10.0'), Decimal('8.1'), Decimal('9.0')]})
# outPicPath = r'C:\Users\Administrator\Desktop\NHS\YCH_CHLA.jpg'
# barChartInfo = {'legend': ('2020', '2021'), 'yMax': 30, 'yLineStep': 5, 'labelOffsetY': 0.5, 'labelOffsetX': 0.03,
# 'labelSize': 30, 'barWidth': 0.15, 'barGap': 0.1, 'yAxisName': '叶绿素a浓度（微克/升）',
# 'paperSize': (20, 15), 'adjust': [0.07, 0.2, 0.98, 0.95]}
#
# df = pd.DataFrame(
# {'StationName': ['傀儡湖三水厂取水口', '阳澄湖二水厂', '水源地均值'],
# '2020': [Decimal('925'), Decimal('751'), Decimal('838')],
# '2021': [Decimal('190'), Decimal('262'), Decimal('226')]})
# outPicPath = r'C:\Users\Administrator\Desktop\NHS\YCH_AD.jpg'
# barChartInfo = {'legend': ('2020', '2021'), 'yMax': 2000, 'yLineStep': 200, 'labelOffsetY': 30,
# 'labelOffsetX': 0.03, 'labelSize': 30, 'barWidth': 0.15, 'barGap': 0.1,
# 'yAxisName': '藻类密度（万个/升）', 'paperSize': (20, 15), 'adjust': [0.15, 0.2, 0.98, 0.95]}
drawBarChart(df, outPicPath, barChartInfo)
tpl = DocxTemplate(r'C:\Users\Administrator\Desktop\NHS\template.docx')
tpl.replace_pic('standard.png', outPicPath)
outWordPath = r'C:\Users\Administrator\Desktop\NHS\1.docx'
tpl.save(outWordPath)
print('F')
# from docxtpl import DocxTemplate
# doc = DocxTemplate("LetterHead.docx")
# tpl=DocxTemplate('LetterHead.docx')
# #rt = RichText('You can add an hyperlink, here to ')
# #rt.add('google',url_id=tpl.build_url_id('http://google.com'))
# tpl.replace_pic('logo.png','sbfc.png')
# #context = { 'company_name' : "World company" }
# #doc.render(context)
# tpl.save("generated_doc.docx")
from docxtpl import DocxTemplate
headerImageUrl = './header.png'
footerImageUrl = './footer.png'
templateUrl = './LetterHeadTemplate.docx'
docx_template = DocxTemplate('./LetterHeadTemplate.docx')
docx_template.replace_pic('header_sample.png',headerImageUrl)
docx_template.replace_pic('footer_sample.png',footerImageUrl)
docx_template.save("generated_doc.docx")
def exportWord(jsonPath, productUuid):
# 解析当前json中信息
with open(jsonPath, 'r') as f:
jsonData = json.load(f)
cloud = jsonData['cloud'] # 云量信息
totalArea = jsonData['totalArea'] # 蓝藻总面积
# 生成文字所需信息===============================================================
issue = os.path.basename(jsonPath).split('_')[3]
year = int(issue[0:4])
month = int(issue[4:6])
day = int(issue[6:8])
hour = int(issue[8:10])
minute = int(issue[10:12])
if hour < 12:
apStr = '上午'
else:
apStr = '下午'
totalPercent = jsonData['totalPercent'] # 蓝藻总占比
lakeStat = jsonData['lakeStat'] # 蓝藻面积分布区域
algaeThreshold = jsonData['algaeThreshold']
lakeRegionList = []
lakeRegionStr = '' # 区域描述1
for key in lakeStat.keys():
if lakeStat[key] == 1:
lakeRegionList.append(LAKE_REGION_NAME[key])
if len(lakeStat) > 0:
lakeRegionStr = '、'.join(lakeRegionList)
# 区域描述2
if len(lakeRegionList) == 0:
lakeRegionStr2 = ''
elif len(lakeRegionList) == 1:
lakeRegionStr2 = lakeRegionList[0]
else:
tempList = lakeRegionList[0:-1]
lakeRegionStr2 = '、'.join(tempList) + '和' + lakeRegionList[-1]
description = '' # 未会商报告文字部分
description2 = '' # 监测中心每日一报文字部分
# 1.全云
if cloud >= 95:
description = '%d月%d日遥感监测结果显示，太湖%s全部被云层覆盖，无法判断蓝藻聚集情况，未会商。' % (
month, day, apStr)
description2 = '%d月%d日%d时%d分EOS/MODIS卫星遥感影像显示，太湖全部被云层覆盖，无法判断蓝藻聚集情况。' \
% (month, day, hour, minute)
# 2.有云无藻
elif 5 < cloud < 95 and totalArea == 0:
description = '%d月%d日遥感监测结果显示，太湖%s部分被云层覆盖，无云区域内未发现蓝藻聚集现象，未会商。' % (
month, day, apStr)
description2 = '%d月%d日%d时%d分EOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内未发现蓝藻聚集现象。' \
% (month, day, hour, minute)
# 3.无云无藻
elif cloud <= 5 and totalArea == 0:
description = '%d月%d日遥感监测结果显示，太湖%s未发现蓝藻聚集现象，未会商。' % (month, day, apStr)
description2 = '%d月%d日%d时%d分EOS/MODIS卫星遥感影像显示，太湖未发现蓝藻聚集现象。' % (
month, day, hour, minute)
# 4.有云有藻
elif 5 < cloud < 95 and totalArea > 0:
description = '%d月%d日遥感监测结果显示，太湖%s部分被云层覆盖，在%s发现蓝藻聚集现象，面积约%d平方千米，占太湖总面积的%.1f%%，' \
'未会商。' % (month, day, apStr, lakeRegionStr, totalArea, totalPercent)
description2 = '%d月%d日%d时%d分EOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米，' \
'主要分布在%s。' % (month, day, hour, minute, totalArea, lakeRegionStr2)
# 5.无云有藻
elif cloud <= 5 and totalArea > 0:
description = '%d月%d日遥感监测结果显示，太湖%s在%s发现蓝藻聚集现象，面积约%d平方千米，占太湖总面积的%.1f%%，未会商。' \
% (month, day, apStr, lakeRegionStr, totalArea, totalPercent)
description2 = '%d月%d日%d时%d分EOS/MODIS卫星遥感影像显示，太湖发现蓝藻聚集面积约%d平方千米，' \
'主要分布%s。' % (month, day, hour, minute, totalArea, lakeRegionStr2)
else:
pass
print(description)
print(description2)
# 生成word====================================================
# 简报word生成
dependDir = globalCfg['depend_path']
templateDir = os.path.join(dependDir, 'word')
templatePath = os.path.join(templateDir, 'report_quick.docx')
tpl = DocxTemplate(templatePath)
replaceText = {"content": description}
tpl.render(replaceText)
outputDir = os.path.dirname(jsonPath)
jsonBaseName = os.path.basename(jsonPath)
outWordName = jsonBaseName.replace('.json', '_quick.docx')
outWordPath = os.path.join(outputDir, outWordName)
picturePath1 = os.path.join(
outputDir, jsonBaseName.replace('.json', '_reportImg1_noPoints.jpg'))
picturePath2 = os.path.join(
outputDir, jsonBaseName.replace('.json', '_reportImg2.jpg'))
if not (os.path.exists(picturePath1) and os.path.exists(picturePath2)):
print('Cannot Find JPG File!!!')
return
replacePic = {
"template_picture1.jpg": picturePath1,
"template_picture2.jpg": picturePath2
}
for key in replacePic.keys():
tpl.replace_pic(key, replacePic[key])
if os.path.exists(outWordPath):
os.remove(outWordPath)
tpl.save(outWordPath)
# 每日一报word生成
templatePath2 = os.path.join(templateDir, 'report_jsem.docx')
tpl2 = DocxTemplate(templatePath2)
replaceText2 = {
"content": description2,
"year": str(year),
"mm": str(month),
"dd": str(day)
}
tpl2.render(replaceText2)
outWordName2 = jsonBaseName.replace('.json', '_jsem.docx')
outWordPath2 = os.path.join(outputDir, outWordName2)
replacePic2 = {
"template_picture1.jpg": picturePath1,
}
for key in replacePic2.keys():
tpl2.replace_pic(key, replacePic2[key])
if os.path.exists(outWordPath2):
os.remove(outWordPath2)
tpl2.save(outWordPath2)
# 信息入库t_water_report_taihu_quick====================================================
conn = pymysql.connect(db=globalCfg['database'],
user=globalCfg['database_user'],
password=globalCfg['database_passwd'],
host=globalCfg['database_host'],
port=globalCfg['database_port'])
# 先查询数据库是否有这期数据
cursor = conn.cursor()
sqlStr = 'SELECT * FROM ' + globalCfg['database_table_report_taihu_quick'] + \
' WHERE image=%s and is_deleted=0;'
algaeTifName = os.path.basename(jsonPath).replace('.json', '.tif')
cursor.execute(sqlStr, algaeTifName)
sqlRes = cursor.fetchall()
if len(sqlRes) > 0:
# 更新
db_image = algaeTifName
db_description = description
db_image1 = picturePath1.replace('\\',
'/').replace('/mnt/resource/', '')
db_image2 = picturePath2.replace('\\',
'/').replace('/mnt/resource/', '')
db_time_modify = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
sqlStr = 'UPDATE ' + globalCfg['database_table_report_taihu_quick'] + \
' SET description=%s,image1=%s,image2=%s,time_modify=%s WHERE image=%s;'
sqlData = (db_description, db_image1, db_image2, db_time_modify,
db_image)
cursor.execute(sqlStr, sqlData)
conn.commit()
else:
# 插入
db_uuid = str(uuid.uuid4())
db_description = description
db_image1 = picturePath1.replace('\\',
'/').replace('/mnt/resource/', '')
db_image2 = picturePath2.replace('\\',
'/').replace('/mnt/resource/', '')
db_image = algaeTifName
sqlStr = 'INSERT INTO ' + globalCfg['database_table_report_taihu_quick'] + \
' (uuid,description,image1,image2,image) VALUES (%s,%s,%s,%s,%s);'
sqlData = (db_uuid, db_description, db_image1, db_image2, db_image)
cursor.execute(sqlStr, sqlData)
conn.commit()
# 更新t_export_image表信息
sqlStr = 'SELECT * FROM ' + globalCfg['database_table_export_image'] + \
' WHERE uuid=%s and is_deleted=0;'
cursor.execute(sqlStr, productUuid)
sqlRes = cursor.fetchall()
if len(sqlRes) > 0:
sqlStr = 'UPDATE ' + globalCfg['database_table_export_image'] + \
' SET area=%s,threshold=%s WHERE uuid=%s;'
sqlData = (totalArea, algaeThreshold, productUuid)
cursor.execute(sqlStr, sqlData)
conn.commit()
else:
pass
cursor.close()
conn.close()
# 更新切片==============================================================
tileDict = {}
basename = '_'.join(jsonBaseName.split('_')[0:7])
# 1.蓝藻产品切片
algaeTifPath = os.path.join(outputDir,
jsonBaseName.replace('.json', '.tif'))
algaeTifRender = os.path.join(outputDir,
jsonBaseName.replace('.json', '_render.tif'))
colorTable = {1: (255, 251, 0)}
UniqueValues.Render(algaeTifPath,
colorTable,
returnMode='GEOTIFF',
outputPath=algaeTifRender,
isAlpha=True)
tileDict['taihu_algae_ndvi'] = {
'tif': algaeTifRender,
'name': basename + '_taihu_algae_ndvi',
'legendType': '1',
'legendColor': [(255, 251, 0)],
'legendName': ['水华']
}
# 2.蓝藻强度产品切片
intensityTifPath = os.path.join(
outputDir, jsonBaseName.replace('_ndvi.json', '_intensity.tif'))
intensityTifRender = os.path.join(
outputDir, jsonBaseName.replace('_ndvi.json', '_intensity_render.tif'))
colorTable = {1: (0, 255, 102), 2: (255, 255, 0), 3: (255, 153, 0)}
UniqueValues.Render(intensityTifPath,
colorTable,
returnMode='GEOTIFF',
outputPath=intensityTifRender,
isAlpha=True)
tileDict['algaeClassify'] = {
'tif': intensityTifRender,
'name': basename + '_classify',
'legendType': '1',
'legendColor': [(0, 255, 102), (255, 255, 0), (255, 153, 0)],
'legendName': ['轻度', '中度', '重度']
}
# 调用gdal2tiles工具进行切片
pythonPath = globalCfg['python_path']
gdal2tilesPath = globalCfg['gdal2tiles_path']
tileOutRootDir = globalCfg['tile_server_path']
for key in tileDict.keys():
tileTif = tileDict[key]['tif']
tileOutDir = os.path.join(tileOutRootDir, tileDict[key]['name'])
if os.path.exists(tileOutDir):
shutil.rmtree(tileOutDir)
cmd = '%s %s -z %s -w all %s %s' % (pythonPath, gdal2tilesPath,
TILE_LEVEL, tileTif, tileOutDir)
os.system(cmd)
os.remove(tileTif)
tileDict[key]['path'] = tileOutDir
# -*- coding: utf-8 -*-
'''
Created : 2017-09-03
@author: Eric Lapouyade
'''
from docxtpl import DocxTemplate
DEST_FILE = 'test_files/replace_picture.docx'
tpl = DocxTemplate('test_files/replace_picture_tpl.docx')
context = {}
tpl.replace_pic('python_logo.png', 'test_files/python.png')
tpl.render(context)
tpl.save(DEST_FILE)
