def excel(data, name): book = Workbook() title_info = [] for i in data: for k, v in i.items(): title_info.append(k) title = list(set(title_info)) if len(data) < 65535: excel = book.add_sheet(name, cell_overwrite_ok=True) for x in range(len(title)): excel.write(0, x, title[x].decode('UTF-8')) for i in range(len(data)): for j in range(len(data[i])): excel.write(i + 1, j, data[i][title[j]]) else: page = len(data) / 65535 for k in range(1, page + 2): excel = book.add_sheet(name + '_' + str(k), cell_overwrite_ok=True) for x in range(len(title)): excel.write(0, x, title[x].decode('UTF-8')) if k < page + 1: for i in range(65535 * (k - 1), 65535 * k): for j in title: excel.write(i - 65535 * (k - 1) + 1, j, data[i][title[j]]) else: for i in range(65535 * k, len(data)): for j in title: excel.write(i - 65535 * k + 1, j, data[i][title[j]]) date = datetime.datetime.now().strftime('%Y%m%d') book.save('../static/' + name + '.' + date + '.xls')
class ExcelWorkbook(object): def __init__(self, encoding='utf-8', style_compression=0): self.wb = Workbook(encoding, style_compression) self.sheets = [] def sheet(self, sheet_name=''): """get a sheet by name, if no name creates a new sheet""" if not self.sheets: return self.add_sheet(sheet_name) else: for sheet in self.sheets: if sheet.name == sheet_name: return sheet def add_sheet(self, sheet_name='Sheet 1', headers=[], header_line_no=0, use_borders=False, styleHeaders=None, styleDict={}, widths={}): ws = self.wb.add_sheet(sheet_name, cell_overwrite_ok=True) self.sheets.append(ExcelSheet(ws, headers, header_line_no, use_borders, styleHeaders, styleDict, widths)) return self.sheets[-1] def save(self, filename): """saves excel file""" if os.path.splitext(filename)[-1] not in ('.xls', '.xlsx'): filename = os.path.splitext(filename)[0] + '.xls' for sheet in iter(self): sheet.autoFit() self.wb.save(filename) return filename def __iter__(self): """generator to iterate through sheets""" for sheet in self.sheets: yield sheet
def make_group_connectome_lengths_excel_file(subject_names,cff_files,track_name,endpointsmm_name,labels_name, outfile_name): """ **NEEDS TESTING** write docstring """ import jg_DWI_util from xlwt import Workbook wb = Workbook() ws = wb.add_sheet('0') col_ind = 1 for c in range(0, len(cff_files)): print 'processing subject ' + str(subject_names[c]) flen, eud, fl = jg_DWI_util.analyze_connectome_lengths(cff_files[c], track_name,endpointsmm_name,labels_name,0,0) col_ind = col_ind+6 ws.write(0,col_ind,subject_names[c]) # for each subject, set out a block in the spreadsheet and write in the three variables for f in range(0, len(flen)): ws.write(f+1,col_ind+1,str(flen[f])) ws.write(f+1,col_ind+2,str(eud[f])) ws.write(f+1,col_ind+3,str(fl[f][0])) ws.write(f+1,col_ind+4,str(fl[f][1])) # (also get the mean, stdev, etc. fibre stats and put in on an edgewise,rather than fibrewise, basis) # write the excel file wb.save(outfile_name)
def render_to_response(self, context, **response_kwargs): from xlwt import Workbook book = Workbook() sheet1 = book.add_sheet(self.derive_title()) fields = self.derive_fields() # build up our header row for col in range(len(fields)): field = fields[col] sheet1.write(0, col, unicode(self.lookup_field_label(dict(), field))) # then our actual values for row in range(len(self.object_list)): obj = self.object_list[row] for col in range(len(fields)): field = fields[col] value = unicode(self.lookup_field_value(dict(), obj, field)) # skip the header sheet1.write(row + 1, col, value) # Create the HttpResponse object with the appropriate header. response = HttpResponse(mimetype='application/vnd.ms-excel') response['Content-Disposition'] = 'attachment; filename=%s' % self.derive_filename() book.save(response) return response
def save_excel(projects, path): ''' Сохраняет нащ список в файл xls :param projects: list :param path: str :return: file ''' wb = Workbook() ws = wb.add_sheet('Sheet 1') style1 = easyxf('pattern: pattern solid, fore_colour yellow;' + 'font: bold True, height 250') ws.col(0).width = 20000 ws.col(1).width = 11000 ws.col(2).width = 3000 ws.col(3).width = 4000 ws.col(4).width = 4000 headers = ('Название', 'Ссыдка', 'Размер', 'Раздатчики', 'Картинка') for j, header in enumerate(headers): ws.write(0, j, header, style1) for i, project in enumerate(projects, 1): ws.row(i).write(0, project['topic']) ws.row(i).write(1, project['link']) ws.row(i).write(2, project['size']) ws.row(i).write(3, project['seeders']) ws.row(i).write(4, project['img']) wb.save(path)
def test_unicode1(self): book = Workbook() ws1 = book.add_sheet(six.u('\N{GREEK SMALL LETTER ALPHA}\N{GREEK SMALL LETTER BETA}\N{GREEK SMALL LETTER GAMMA}')) ws1.write(0, 0, six.u('\N{GREEK SMALL LETTER ALPHA}\N{GREEK SMALL LETTER BETA}\N{GREEK SMALL LETTER GAMMA}')) ws1.write(1, 1, six.u('\N{GREEK SMALL LETTER DELTA}x = 1 + \N{GREEK SMALL LETTER DELTA}')) ws1.write(2, 0, six.u('A\u2262\u0391.')) # RFC2152 example ws1.write(3, 0, six.u('Hi Mom -\u263a-!')) # RFC2152 example ws1.write(4, 0, six.u('\u65E5\u672C\u8A9E')) # RFC2152 example ws1.write(5, 0, six.u('Item 3 is \u00a31.')) # RFC2152 example ws1.write(8, 0, six.u('\N{INTEGRAL}')) # RFC2152 example book.add_sheet(six.u('A\u2262\u0391.')) # RFC2152 example book.add_sheet(six.u('Hi Mom -\u263a-!')) # RFC2152 example one_more_ws = book.add_sheet(six.u('\u65E5\u672C\u8A9E')) # RFC2152 example book.add_sheet(six.u('Item 3 is \u00a31.')) # RFC2152 example one_more_ws.write(0, 0, six.u('\u2665\u2665')) book.add_sheet(six.u('\N{GREEK SMALL LETTER ETA WITH TONOS}')) stream = six.BytesIO() book.save(stream) md5 = hashlib.md5() md5.update(stream.getvalue()) self.assertEqual('0049fc8cdd164385c45198d2a75a4155', md5.hexdigest())
class ExcelWriter(object): def __init__(self): # grab excel file and columns self.wb_read = open_workbook('pids.xlsx') self.wb_write = Workbook() self.output = self.wb_write.add_sheet('PID and Names') def go(self): row_counter = 1 for sheet in self.wb_read.sheets(): for row in range(sheet.nrows): pid_to_query = sheet.cell(row,3).value.replace('-','') pid_to_query = str(pid_to_query) owner_name = pid_getter.get_pid(pid_to_query) print row_counter, "Query of PID", sheet.cell(row,0).value, "Owner name = ", owner_name # write to new file # self.output.write(row_counter,0,pid_to_query) # self.output.write(row_counter,1,owner_name) # writing to a new file by copying everything else for col in range(sheet.ncols): self.output.write(row_counter,col,sheet.cell(row,col).value) self.output.write(row_counter,sheet.ncols,owner_name) self.wb_write.save('names.xlsx') row_counter += 1
def write_excel(totals): """Write out the collected totals to an excel file """ workbook = Workbook() worksheet = workbook.add_sheet('New Sheet') # write the header for the first block total_hours = 0 total_tasks = 0 block_tasks, block_total = write_time_block(worksheet, totals) total_hours += block_total total_tasks += block_tasks block_tasks, block_total = write_time_block(worksheet, totals, block_tasks) total_hours += block_total total_tasks += block_tasks # write out the total hours worksheet.write(total_tasks + 6, 0, 'Monthly Total', summary_total_header) worksheet.write(total_tasks + 6, 1, total_hours, data_cell) # write out the user and date name = raw_input('Who is this report for? ') worksheet.write(total_tasks + 8, 0, 'Name: %s' % (name)) worksheet.write(total_tasks + 9, 0, 'Date: %s' % (datetime.strftime(datetime.today(), '%m/%d/%Y'))) # write the signature field worksheet.write(total_tasks + 8, 6, 'Signature:') # save the file to disk curpath = os.path.dirname(__file__) workbook.save(os.path.join(curpath, 'test.xls'))
def render(self, request, context, **response_kwargs): from xlwt import Workbook, XFStyle, easyxf w = Workbook(encoding='utf-8') ws = w.add_sheet('Report') style = XFStyle() row = 0 heading_xf = easyxf('font:height 200; font: bold on; align: wrap on, vert centre, horiz center') ws.write(row, 0, '#', style) for col, fieldname in enumerate(context['report'].headers, start=1): ws.write(row, col, str(fieldname), heading_xf) ws.col(col).width = 5000 ws.row(row).height = 500 # we have to prepare all the styles before going into the loop # to avoid the "More than 4094 XFs (styles)" Error styles = self._get_styles(context) for rownum, data in enumerate(context['report']): ws.write(rownum + 1, 0, rownum + 1) for idx, (fieldname, rowvalue) in enumerate(data.items()): style = styles[rowvalue.column.name] try: ws.write(rownum + 1, idx + 1, with_widget(rowvalue, format='xls'), style) except Exception: #logger.warning("TODO refine this exception: %s" % e) ws.write(rownum + 1, idx + 1, smart_str(with_widget(rowvalue)), style) f = StringIO.StringIO() w.save(f) f.seek(0) return f.read()
def output_mesg(company_lack): book = Workbook() sheet1 = book.add_sheet(u'1') i = 0 num = 1 for key, value in company_lack.items(): for s, d in value.items(): sheet1.write(i, 0, key) sheet1.write(i, num, s) sheet1.write(i, num+1, d) i = i + 1 num = 1 book.save('4.xls') # 存储excel book = xlrd.open_workbook('4.xls') print('----------------------------------------------------------------------------------------') print('----------------------------------------------------------------------------------------') print(u'计算完成') print('----------------------------------------------------------------------------------------') print('----------------------------------------------------------------------------------------') time.sleep(10)
def make_xls(sheetname='sheet_1', filename='filename.xls', columns=[], objs=[]): book = Workbook() sheet = book.add_sheet(sheetname) def index_of(key, list_2d): for i, one in enumerate(list_2d): if key == one[0]: return i attrs = [] for inner_list in columns: attrs.append(inner_list[0]) for i in xrange(len(columns)): sheet.write(0, i, columns[i][1]) sheet.col(i).width = columns[i][2] * 256 for i, obj in enumerate(objs, start=1): for attr in attrs: if isinstance(obj, dict): sheet.write(i, index_of(attr, columns), obj[attr]) else: sheet.write(i, index_of(attr, columns), obj.__getattribute__(attr)) book.save(filename)
def merge_count_data(): with open('data/text/summary/2014.json', 'r') as wh: press_briefings = json.load(wh) with open('data/text/summary/google.json', 'r') as g: google_trends = json.load(g) from xlwt import Workbook book = Workbook() for word in SEARCH_TERMS: sheet = book.add_sheet(word) header = sheet.row(0) for i, col in enumerate(['Week', 'Count']): header.write(i, col) for i, sunday in enumerate(all_sundays(2014)): row = sheet.row(i + 1) sunday = sunday.strftime('%Y-%m-%d') count = press_briefings[sunday].get(word, 0) #google = google_trends[sunday].get(word, 0) for i, col in enumerate([sunday, count]): row.write(i, col) book.save('data/text/summary/terms.xls')
def create_xls(data): work = Workbook(encoding='utf-8') work_sheet = work.add_sheet(u'账单') #head of table work_sheet.write(0, 0, 'ID') work_sheet.write(0, 1, u'名字') work_sheet.write(0, 2, u'编码') work_sheet.write(0, 3, u'数量') work_sheet.write(0, 4, u'单价') work_sheet.write(0, 5, u'合计') work_sheet.write(0, 6, u'备注') i = 1 total_price = 0 for row in data: work_sheet.write(i, 0, str(i)) work_sheet.write(i, 1, data[row]['name']) work_sheet.write(i, 2, data[row]['code']) work_sheet.write(i, 3, data[row]['number']) work_sheet.write(i, 4, data[row]['price']) work_sheet.write(i, 5, data[row]['total_price']) work_sheet.write(i, 6, data[row]['comment']) total_price += data[row]['number'] * data[row]['price'] i = i + 1 work_sheet.write(i, 4, u'总价:') work_sheet.write(i, 5, total_price) time_stamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S") file_name = "download_xls/bill_%s.xls" % time_stamp work.save(file_name) return file_name
def do_export(self): """ Does actual export. Called from a celery task. """ book = Workbook() self.render_book(book) temp = NamedTemporaryFile(delete=True) book.save(temp) temp.flush() org_root = getattr(settings, 'SITE_ORGS_STORAGE_ROOT', 'orgs') filename = '%s/%d/%s/%s.xls' % (org_root, self.org_id, self.directory, random_string(20)) default_storage.save(filename, File(temp)) self.filename = filename self.save(update_fields=('filename',)) subject = "Your export is ready" download_url = self.org.make_absolute_url(reverse(self.download_view, args=[self.pk])) send_email([self.created_by], subject, 'utils/email/export', {'download_url': download_url}) # force a gc import gc gc.collect()
def write_file(result_list, deal_date, company_name, filename): ''' given a list, put it into excel file. deal_date specifies a string which will be rendered as bold company_name and filename are self-explanatory ''' w = Workbook() sheet = w.add_sheet(company_name) row = 2 boldfont = easyxf(strg_to_parse='font: bold on') normalfont = easyxf(strg_to_parse='') sheet.write(0, 0, company_name) sheet.write(1, 0, 'Date') sheet.write(1, 1, 'Open') sheet.write(1, 2, 'Close') for line in result_list: elements = line.decode('utf8').split(',') date_string = elements[0] open_value = elements[1] close_value = elements[4] if datetime.strptime(date_string, '%Y-%m-%d') == deal_date: style = boldfont else: style = normalfont sheet.write(row, 0, date_string, style) sheet.write(row, 1, open_value, style) sheet.write(row, 2, close_value, style) row += 1 print(date_string, open_value, close_value) w.save(filename)
def main(): timestamp = strftime("%Y-%m-%d_%H-%M-%S") out_dir = 'outputs/'+timestamp+'_xl' os.makedirs(out_dir) out_file=out_dir+'/'+timestamp+'_test.xls' data_sets=[] data_sets.append(DataSet('dss1', [[Cell.from_link('one','http://www.google.com'),Cell.from_display('two')],[Cell.from_display('one2'),Cell.from_display('two2')]])) data_sets.append(DataSet('dss2', [[Cell.from_display('oneb'),Cell.from_display('twob')]])) book = Workbook() for ds in data_sets: # my_print_dataset(ds) add_data_set_sheet(ds, book) # sheet.write(1,0,Formula('HYPERLINK("http://www.google.com";"Python")'),style) # if len(row_data) > 0: book.save(out_file) os.system("start "+out_file)
def write_datatests(crisis_def_list, location = "./out", suffix = ""): book = Workbook() sheet1 = book.add_sheet('Sheet 1') result_crises = combine_crises(crisis_def_list) row_num = 0 for country_code in sort(result_crises.keys()): years = sort(list(result_crises[country_code])) try: len(years) except: print(years) print(country_code) sheet1.write(row_num, 0, country_code) crisis_row = sheet1.row(row_num) crisis_row.write(1, "crisis") for j in range(len(years)): crisis_row.write(j+2, years[j]) normal_row = sheet1.row(row_num+1) normal_row.write(1, "normal") normal_years = pick_normal_years(years) for j in range(len(normal_years)): normal_row.write(j+2, normal_years[j]) row_num+=2 saveloc = os.path.expanduser(location)+suffix+".xls" book.save(saveloc)
def create_excel_file(db, kurzus): book = Workbook(encoding='utf-8') #sheet = book.add_sheet('{0} - {1} - {2}'.format(kurzus['nev'],kurzus['nap'],kurzus['sav'])) sheet = book.add_sheet('Névsor') sheet = create_shit_head(sheet, kurzus_infok) kurzus_hallgatok = get_kurzus_hallgatok(db, kurzus['id']) sorszam = 1 for user in kurzus_hallgatok: sheet.write(sorszam+4,0,sorszam,easyxf( 'borders: left thick, right thick, top thick, bottom thick;' )) sheet.write(sorszam+4,1,user['neptun'],easyxf( 'borders: left thick, right thick, top thick, bottom thick;' )) sheet.write(sorszam+4,2,user['nev'],easyxf( 'borders: left thick, right thick, top thick, bottom thick;' )) for i in xrange(3,20): sheet.write(sorszam+4,i,'',easyxf( 'borders: left thick, right thick, top thick, bottom thick;' )) sorszam = sorszam + 1 book.save('{0}_{1}_{2}.xls'.format(ekezet_eltunteto(kurzus['nev'].lower()), ekezet_eltunteto(kurzus['nap'].lower()), kurzus['sav']))
def print_responseTime_persecond(self, folder = "." ): book = Workbook() sheet1 = book.add_sheet("response time") sheet2 = book.add_sheet("transactions") title = "Test Name: %s" % (self.name) sheet1.write(0,0,title) sheet2.write(0,0,title) column = 1 for id in self.test_ids: sheet1.write(1, column, "TEST" + id) sheet2.write(1, column, "TEST" + id) column += 1 results = self.perSecondResult rows = range(1, self.max_second /TIME_INTERVAL + 1) for row in rows: sheet1.write(row + 1, 0, row*TIME_INTERVAL ) sheet2.write(row + 1, 0, row*TIME_INTERVAL ) column = 1 for id in self.test_ids: key = id + "-" + str(row*TIME_INTERVAL) if results.has_key(key): result = results[key] sheet1.write(row + 1, column, result.getAvg()) sheet2.write(row + 1, column, result.transactions) column += 1 book.save(folder + "/" + self.name + "_bytime.xls")
def fog1270(): """ Create an xls with active evaluators emails by language """ from xlwt import Workbook rates = tst.EvaluatorRate.query.join('evaluator','service_provider','contact').\ filter_by(active=True).all() language_dict = {} for rate in rates: language_dict.setdefault(rate.language,[]).\ append(rate.evaluator.service_provider.contact.get_email_address()) distro = Workbook() row = 0 active = distro.add_sheet('Active Evaluators') for language in language_dict: emails = language_dict[language] emails = list(set(emails)) email_str = '' for email in emails: email_str = email_str + '%s;' % email email_str = email_str[:-1] active.write(row, 0, language.name) active.write(row, 2, email_str) row+=1 distro.save('evaluators.xls')
def tmz(): from xlwt import Workbook rates = tst.EvaluatorRate.query.join('evaluator','service_provider','contact').\ filter_by(active=True).all() language_dict = {} for rate in rates: language_dict.setdefault(rate.language,[]).\ append(rate.evaluator.service_provider.contact.time_zone) distro = Workbook() row = 0 active = distro.add_sheet('Active Evaluators') for language in language_dict: tmzs = language_dict[language] tmzs = list(set(tmzs)) tmz_str = '' for tmz in tmzs: tmz_str = tmz_str + '%s;' % tmz tmz_str = tmz_str[:-1] active.write(row, 0, language.name) active.write(row, 2, tmz_str) row+=1 distro.save('evaluators.xls')
def fwriteinexcel(xlsname, results_gen, results_load_lambda, results_branch): book = Workbook() Sheet1 = book.add_sheet('Sheet1') Sheet1.write(0, 0, 'Bus') Sheet1.write(0, 1, 'Generation (MW)') Sheet1.write(0, 2, 'Load (MW)') Sheet1.write(0, 3, 'Lambda (€/MWh)') Sheet1.write(0, 5, 'From Bus') Sheet1.write(0, 6, 'To Bus') Sheet1.write(0, 7, 'P (MW)') for i in range(len(results_gen)): Sheet1.write(i+1, 0, results_gen[i, 0]) Sheet1.write(i+1, 1, results_gen[i, 1]) compteur = 0 for j in range(len(results_load_lambda)): if j != 15 and j != 16 and j != 32 and j != 38 and j != 39: Sheet1.write(compteur+1, 2, results_load_lambda[j, 1]) Sheet1.write(compteur+1, 3, results_load_lambda[j, 2]) compteur += 1 for k in range(len(results_branch)): Sheet1.write(k+1, 5, results_branch[k, 0]) Sheet1.write(k+1, 6, results_branch[k, 1]) Sheet1.write(k+1, 7, results_branch[k, 2]) book.save(xlsname)
def configErrorReporting(headers): """ Configure import exception log, which is an Excel spreadsheet in the same format as the input format, but with an extra column added - "Error", which contains the error message. Can only be called after first row of input Excel spreadsheet is read to initialize the global, "headers" """ dateFmt = easyxf( 'font: name Arial, bold True, height 200;', #'borders: left thick, right thick, top thick, bottom thick;', num_format_str='MM-DD-YYYY' ) headerFmt = easyxf( 'font: name Arial, bold True, height 200;', ) global errorsWorkbook, erroutSheet, erroutRow errorsWorkbook = Workbook() erroutSheet = errorsWorkbook.add_sheet('Import Errors') for colnum in range(0, len(headers)): erroutSheet.write(0, colnum, headers[colnum][0], tern(headers[colnum][0]==xlrd.XL_CELL_DATE, dateFmt, headerFmt)) # Add extra column for error message erroutSheet.write(0, len(headers), "Error", headerFmt) erroutSheet.flush_row_data() erroutRow = 1 errorsWorkbook.save('errors.xls')
def write(self, collection, filename=None, fail_silently=False): # create new book book = Workbook() # write dataset for name, dataset in collection.items(): self._write_dataset(dataset, book) # write peakset if there are more than single dataset if len(dataset) > 1: sheet = book.add_sheet('peakset') offsets = [0, 1] for name, dataset in collection.items(): # write classify name # Note: +1 for heading line sheet.write(offsets[0]+1, 0, get_sheet_name(name)) # write peakset self._write_peakset(dataset, offsets, sheet, self.peakset_basecolumn, self.peakset_method, self.peakset_where_function) # update offsets offsets[0] += len(dataset) + 1 # save book.save(filename or self.default_filename)
class XLWriter(BookWriter): """ xls, xlsx and xlsm writer """ def __init__(self, file, encoding='ascii', style_compression=2, **keywords): """Initialize a xlwt work book :param encoding: content encoding, defaults to 'ascii' :param style_compression: undocumented, but 2 is magically better reference: `style_compression <https://groups.google.com/ forum/#!topic/python-excel/tUZkMRi8ITw>`_ """ BookWriter.__init__(self, file, **keywords) self.wb = Workbook(style_compression=style_compression, encoding=encoding) def create_sheet(self, name): """Create a xlwt writer""" return XLSheetWriter(self.wb, None, name) def close(self): """ This call actually save the file """ self.wb.save(self.file)
def write_report_xl(self, itfs): headers = ("S/N", "LC NUMBER", "CUSTOMER NAME", "CCY", "AMOUNT", 'RELATIONSHIP MANAGER') xl = Workbook() self.xlsh = xl.add_sheet("ITF-REPORT") row_in_xlsh = 0 self.write_xl_row(("", "", "ITF INTEREST REPORT",), row_in_xlsh) row_in_xlsh += 1 self.write_xl_row(headers, row_in_xlsh) row_in_xlsh += 1 sequence = 1 for itf in itfs: self.write_xl_row( (sequence, itf.lc_number, itf.customer.name, itf.currency(), itf.amount, itf.rm_name(),), row_in_xlsh) row_in_xlsh += 1 sequence += 1 xl.save(open(itf_report_xl_path, 'wb')) return '%d Itfs reported for the given period' % len(itfs)
def process_test(request): start_date=request.GET.get('start','') end_date=request.GET.get('end','') now = dt.datetime.now().isocalendar() this_week_start,this_week_end = get_week_days(now[0],now[1]) if start_date == '': start_date=this_week_start.strftime("%Y-%m-%d") if end_date == '': end_date=this_week_end.strftime("%Y-%m-%d") start_date=time.strptime(start_date,'%Y-%m-%d') start_date=dt.datetime.fromtimestamp(time.mktime(start_date)) end_date=time.strptime(end_date,'%Y-%m-%d') end_date=dt.datetime.fromtimestamp(time.mktime(end_date)) end_date=end_date+dt.timedelta(1) wb = Workbook() ws = wb.add_sheet('Sheetname') ws.write(0, 0, 'Firstname') ws.write(0, 1, 'Surname') ws.write(1, 0, 'Hans') ws.write(1, 1, 'Muster') fname = 'process_excel-testfile.xls' response = HttpResponse(mimetype="application/ms-excel") response['Content-Disposition'] = 'attachment; filename=%s' % fname wb.save(response) return response
def write(self, dirname=None): if dirname is None: dirname = self.description self.create_workbooks() dir = self.safe_mkdir(dirname) print 'Saving annotation in directory %s' % dir for workbook_name, sheets in self.workbooks.items(): workbook_name = self.escape_name(workbook_name) workbook = Workbook() for sheet_name, sentences in sorted(sheets.items()): sheet_name = self.escape_name(sheet_name) sheet = workbook.add_sheet(sheet_name) sheet.col(1).width = 0x3000 sheet.col(3).width = 0x3000 for index, sentence in enumerate(sentences): self.write_splitted(sentence, sheet, index) meta_sheet = workbook.add_sheet('meta') meta_sheet.write(0, 0, self.description) meta_sheet.write(1, 0, str(datetime.now())) outfile = os.path.join(dir, '%s.xls' % workbook_name) workbook.save(outfile) sentence_file_name = os.path.join(dir, 'sentences.json') write_sentence_file(self.sentences, sentence_file_name)
def merge_synonym_counts(): """ Merge counts for synonyms. """ with open('data/text/summary/2014.json', 'r') as wh: press_briefings = json.load(wh) from xlwt import Workbook book = Workbook() for synonyms in SYNONYMS: sheet = book.add_sheet('%s (+%i)' % (synonyms[0], len(synonyms))) header = sheet.row(0) for i, col in enumerate(['Week', 'Count']): header.write(i, col) for i, sunday in enumerate(all_sundays(2014)): row = sheet.row(i + 1) sunday = sunday.strftime('%Y-%m-%d') count = 0 for word in synonyms: count += press_briefings[sunday].get(word, 0) for i, col in enumerate([sunday, count]): row.write(i, col) book.save('data/text/summary/synonyms.xls')
def exportToExcel(self,objectProject): book = Workbook(); sheet1 = book.add_sheet('Sheet 1') if( objectProject): i=0; row1 = sheet1.row(i) ; row1.write(0, ('ประเภท').decode('UTF8') ); row1.write(1, ('ชื่อโครงการ').decode('UTF8')); row1.write(2, ('รายละเอืยด').decode('UTF8') ); row1.write(3, ('งบประมาณรวม').decode('UTF8') ); row1.write(4, ('งบประมาณ').decode('UTF8') ); row1.write(5, ('เงินบำรุง').decode('UTF8') ); row1.write(6, ('งบประมาณอื่น').decode('UTF8') ); row1.write(7, ('งบประมาณอื่นจาก').decode('UTF8') ); row1.write(8, ('ผู้รับผิดชอบ').decode('UTF8') ); row1.write(9, ('กลุ่ม').decode('UTF8') ); row1.write(10, ('หน่วย/งาน').decode('UTF8') ); i=i+1; style = XFStyle(); style.num_format_str = '#,##0.00'; for value in objectProject: row1 = sheet1.row(i) ; row1.write(0, value.get('project_type').decode('UTF8') ); row1.write(1, value.get('project_name').decode('UTF8') ); row1.write(2, value.get('detail').decode('UTF8') ); row1.write(3, value.get('allBudget') ,style ); row1.write(4, value.get('project_budget' ) ,style ); row1.write(5, value.get('maintenance_funds_budget'),style ); row1.write(6, value.get('budget_other') ,style ); if(value.get('budget_other_from')): row1.write(7, value.get('budget_other_from').decode('UTF8') ); if(value.get('user_name')): row1.write(8, value.get('user_name').decode('UTF8') ); row1.write(9, value.get('division').decode('UTF8') ); row1.write(10, value.get('section').decode('UTF8') ); i=i+1; dirTempFile = gettempdir() + _os.sep + str('simple.xls'); book.save(dirTempFile); #book.save(TemporaryFile()); return dirTempFile;
texts=stemmed_all #from gensim.corpora import Dictionary dictionary = Dictionary(stemmed_all) dictionary .filter_extremes(no_below=pr, keep_n=None) # Workbook is created wb = Workbook() # add_sheet is used to create sheet. sheet1 = wb.add_sheet('dict') for i in range(len(dictionary)): sheet1.write(i+1, 0, dictionary[i]) wb.save('Dictionary_after_2%_words_removal.xls') # remov% #all_tokens = sum(texts, []) #tokens_once = set(word for word in set(all_tokens) if all_tokens.count(word) < (len(dictionary))*0.02) #texts = [[word for word in text if word not in tokens_once] # for text in texts] #stemmed_all=texts csv.register_dialect('myDialect', delimiter=',', quoting=csv.QUOTE_ALL) with open('Stemmed_documents.csv', 'w', newline='') as file: writer = csv.writer(file, dialect='myDialect') writer.writerows(stemmed_all) mydict = corpora.Dictionary() #dtm
from xlwt import Workbook book = Workbook() sheet1 = book.add_sheet('result 1') for row_index in range(sheet0.nrows): keyword = sheet0.cell(row_index, 0).value params = {'query': keyword} enc_params = urllib.urlencode(params) request = urllib2.Request('http://search.naver.com/' + 'search.naver' + '?' + enc_params) request.add_header('User-agent', 'Mozilla/5.0') request.add_header('Accept-encoding', 'gzip') response = urllib2.urlopen(request) compressedstream = StringIO.StringIO(response.read()) gzipper = gzip.GzipFile(fileobj=compressedstream) data = gzipper.read().encode('utf-8') if (data.find('people_info section') != -1): sheet1.write(row_index, 6, 'yes') book.save('result.xls') book.save(TemporaryFile()) print 'yes' else: print 'no'
c1=10, r2=8, c2=10, label='Giá trị còn lại', style=style) sheet1.write_merge(r1=7, c1=11, r2=8, c2=11, label='TL còn lại (%)', style=style) sheet1.write_merge(r1=6, c1=12, r2=6, c2=14, label='Chênh lệch', style=style) sheet1.write_merge(r1=7, c1=12, r2=8, c2=12, label='Số lượng', style=style) sheet1.write_merge(r1=7, c1=13, r2=8, c2=13, label='Nguyên giá', style=style) sheet1.write_merge(r1=7, c1=14, r2=8, c2=14, label='Giá trị còn lại', style=style) sheet1.write_merge(r1=6, c1=15, r2=8, c2=15, label='Ghi chú', style=style) j = 0 for x in range(9, 11, 1): for i in range(1, 16, 1): sheet1.write(x, i, "x" + "i") j = x sheet1.write_merge(r1=j + 1, c1=1, r2=j + 1, c2=2, label='Cộng', style=style) wb.save('example4.xlsx')
header = worksheet.row_values(0) data.append(header) for row_index in range(1,worksheet.nrows): row_list = [] sale_amount = worksheet.cell_value(row_index, sale_amount_column_index) if sale_amount > 1400.0: for column_index in range(worksheet.ncols): cell_value = worksheet.cell_value(row_index,column_index) cell_type = worksheet.cell_type(row_index, column_index) if cell_type == 3: date_cell = xldate_as_tuple(cell_value,workbook.datemode) date_cell = date(*date_cell[0:3]).strftime('%m/%d/%Y') row_list.append(date_cell) else: row_list.append(cell_value) if row_list: data.append(row_list) for list_index, output_list in enumerate(data): for element_index, element in enumerate(output_list): output_worksheet.write(list_index, element_index, element) output_workbook.save(output_file)
key = b"Jsp3nd762MAO283N" #iv=b"This is an IV456" iv = Random.new().read(ARC2.block_size) print("Block Size: ", ARC2.block_size) #message=b'A really secret message. Not for prying eyes.' for i in range(8): f = open('ciphertext_1mb.txt', encoding="ANSI") inp = f.read() message = 'null' for j in range(2**i): message += inp output.write(i + 1, 0, round(len(message) / (1024 * 1024))) print("Message size(mb): ", len(message) / (1024 * 1024)) message = str.encode(message) cipher_text, time_enc = encryption(message) plain_text, time_dec = decryption(cipher_text) print(message == plain_text) #print("Message size(mb): ", len(message)/(1024*1024)) print("Cipher text size(mb): ", len(cipher_text) / (1024 * 1024)) print("\n\n") output.write(i + 1, 1, time_enc) output.write(i + 1, 2, time_dec) output.write(i + 1, 3, len(cipher_text) / (1024 * 1024)) wb.save('ARC2.xls') #print(plain_text) #print(timeit.timeit(encryption))
try: odds = driver.find_element_by_xpath( '/html/body/div[1]/div/div[2]/div[6]/div[1]/div/div[1]/div[2]/div[1]/div[8]/div[' + str(i) + ']/div/strong/a') #/html/body/div[1]/div/div[2]/div[6]/div[1]/div/div[1]/div[2]/div[1]/div[8]/div[2]/div/strong/a print(odds.text) odd = odds.text.split(' ')[1] i += 1 sheet1.write(row, col, odd) sheet1.write(row - 1, col, 'over-under') col += 1 print(i) except: break wb.save('databse.xls') #over_under_button = driver.find_element_by_xpath('/html/body/div[1]/div/div[2]/div[6]/div[1]/div/div[1]/div[2]/div[1]/div[6]/table/tbody/tr[4]/td[2]/a'+'#over-under;') # .click() to mimic button click #over_under_button.click() #time.sleep(1) ### locate email form by_class_name ##username = driver.find_element_by_xpath('/html/body/nav/section[2]/form/div[1]/div[1]/input') ### send_keys() to simulate key strokes ##username.send_keys('*****@*****.**') ### sleep for 0.5 seconds ###sleep(0.5) ### locate password form by_class_name ##password = driver.find_element_by_xpath('/html/body/nav/section[2]/form/div[1]/div[2]/input') ### send_keys() to simulate key strokes
count = 0 i = i + 1 img = cv2.imread(f) for x in range(0, len(img) - 1): for y in range(0, len(img[x]) - 1): if (list(img[x][y]) == [0, 0, 0]): count += 1 print(img[x][y]) sheet1.write(i, 0, f) sheet1.write(i, 1, count / 60.0) if (count / 60.0 > 0): sheet1.write(i, 1, 1) else: sheet1.write(i, 1, 0) image_list = [] wb.save('Pixel_Density_' + k[0:1] + "_" + k[2:3] + '.xls') x_list = [] for g in glob.glob("*.xls"): x_list.append(g) x_list.sort() print x_list kkk = 0 for z in x_list: xls_file = pd.ExcelFile(z) df = xls_file.parse('Sheet 1') df = df.sort_values(by='IMAGE NAME') df = df.drop('IMAGE NAME', 1) df = df.T df = df.reset_index(drop=True) df.to_csv('file' + str(kkk) + '.csv') df = read_csv('file' + str(kkk) + '.csv')
sheet1.write(count + 2, i, statusCodeDict[int(splitline[i], 10)]) # getting the status code to write vs the number. else: splitline = line.split() for i in range(len(splitline)): # taking advantage of the dictionary created earlier if i != 1: sheet1.write(count + 2, i, splitline[i]) #Writing of the line to the excel sheet else: print(splitline[i]) sheet1.write(count + 2, i, statusCodeDict[int(splitline[i], 10)]) # getting the status code to write vs the number. count += 1 return line #call get event will get us to "<event>" line = getEvent(line) line = writeEvent(line, wb, "1") line = getEvent(line) line = writeEvent(line, wb, "2") end = True count = 1 # establish a count to keep track of what line in the event we are at #increment the count by one after iterating through once wb.save("event.xls")
def Q_Learning(Pr_des, eps_unc, eps_unc_learning, N_EPISODES, SHOW_EVERY, LEARN_RATE, DISCOUNT, EPS_DECAY, epsilon, i_s, pa, energy_pa, pa2ts, pa_s, pa_t, act_num, possible_acts_not_pruned, possible_acts_pruned, possible_next_states_not_pruned,possible_next_states_pruned, pick_up, delivery, pick_ups, deliveries, test_n, n_samples, ts_size): wb = Workbook() sheet_name = 'Simulation' + str(test_n+1) s1 = wb.add_sheet(sheet_name) s1.write(1,0,'Task-1') s1.write(1+N_EPISODES/SHOW_EVERY,0,'Task-2') s1.write(1+2*N_EPISODES/SHOW_EVERY,0,'Task-3') s1.write(1+3*N_EPISODES/SHOW_EVERY,0,'Task-4') s1.write(0,1,'# of Hit') s1.write(0,2,' Avg. Reward') s1.write(0,3,' Discounted Avg. Reward') s1.write(0,11,' Discounted Episode Reward - Task 1') s1.write(0,12,' Discounted Episode Reward - Task 2') s1.write(0,13,' Discounted Episode Reward - Task 3') s1.write(0,14,' Discounted Episode Reward - Task 4') s1.write(0,6,'Total Run Time') s1.write(0,7,'Total Avg. Reward') inx = 0 QL_start_time = timeit.default_timer() EVERY_PATH = [] episode_rewards = [] # Initialize the Q - table (Between -0.01 and 0) pa_size = [] q_table = [] agent_s = [] hit_count = [] mission_tracker = [] ep_per_task = [] disc_ep_per_task = [] old_q_tables = [] all_samples = [] for i in range(len(energy_pa)): pa_size.append(len(pa[i].g.nodes())) agent_s.append(i_s[i]) # Initialize the agent's location hit_count.append(0) mission_tracker.append(0) ep_per_task.append([]) disc_ep_per_task.append([]) all_samples.append([]) q_table.append([]) old_q_tables.append([]) for t in range(ep_len+1): q_table[i].append(np.random.rand(pa_size[i],9) * 0.001 - 0.001) # of states x # of actions old_q_tables[i].append(q_table[i][t]) ep_rewards = [] ep_trajectories_pa = [] agent_upt_i = [] agent_upt = [] for j in range(len(energy_pa)): for i in range(len(pa[j].g.nodes())): if pa[j].g.nodes()[i][1] == 0 or str(pa[j].g.nodes()[i][0]) == 'r'+str(pick_up[j]) :#or str(pa[j].g.nodes()[i][0]) == 'r'+str(delivery[j]): # If the mission changes check here agent_upt_i.append(pa2ts[j][i]) else: agent_upt_i.append([]) agent_upt.append(agent_upt_i) for episode in range(N_EPISODES): # if episode > 900000: # can be switch to only exploitation after some episode # epsilon = 0 which_pd = np.random.randint(len(energy_pa)) # randomly chosing the pick_up delivery states mission_tracker[which_pd] = mission_tracker[which_pd] + 1 hit = [] ep_rew = [] for i in range(len(energy_pa)): hit.append(0) ep_traj_pa = [agent_s[which_pd]] # Initialize the episode trajectory ep_rew = 0 # Initialize the total episode reward disc_ep_rew = 0 for t_ep in range(ep_len): old_q_tables[which_pd][t_ep] = q_table[which_pd][t_ep] possible_acts = possible_acts_not_pruned[which_pd] possible_next_states = possible_next_states_not_pruned[which_pd] if hit[which_pd] == 0: if energy_pa[which_pd][agent_s[which_pd]] == 0: # Raise the 'hit flag' if the mission is achieved hit[which_pd] = 1 # agent_s[which_pd] = agent_upt[which_pd].index(pa2ts[which_pd][agent_s[which_pd]]) # hit_count[which_pd] = hit_count[which_pd] + 1 else: possible_acts = possible_acts_pruned[which_pd] possible_next_states = possible_next_states_pruned[which_pd] if len(possible_acts[t_ep][agent_s[which_pd]]) == 0: agent_s[which_pd] = agent_upt[which_pd].index(pa2ts[which_pd][agent_s[which_pd]]) if np.random.uniform() > epsilon: # Exploit possible_qs = q_table[which_pd][t_ep][agent_s[which_pd], possible_acts[t_ep][agent_s[which_pd]]] # Possible Q values for each action next_ind = np.argmax(possible_qs) # Pick the action with max Q value else: # Explore next_ind = np.random.randint(len(possible_acts[t_ep][agent_s[which_pd]])) # Picking a random action # Taking the action prev_state = agent_s[which_pd] intended_action = possible_acts[t_ep][prev_state][next_ind] if np.random.uniform() < eps_unc_learning: [chosen_act, next_state] = action_uncertainity(intended_action, pa_s[which_pd], pa_t[which_pd], act_num[which_pd], agent_s[which_pd]) action = chosen_act s_a = (agent_s[which_pd], action) # State & Action pair agent_s[which_pd] = next_state # possible_next_states else: action = intended_action s_a = (agent_s[which_pd], action) # State & Action pair agent_s[which_pd] = possible_next_states[t_ep][agent_s[which_pd]][next_ind] # moving to next state (s,a) ep_traj_pa.append(agent_s[which_pd]) current_q = q_table[which_pd][t_ep][prev_state, intended_action] max_future_q = np.amax(q_table[which_pd][t_ep+1][agent_s[which_pd], :]) # Find the max future q rew_obs = rewards_pa[which_pd][agent_s[which_pd]] * np.random.binomial(1, 1-rew_uncertainity) # Observe the rewards of the next state new_q = (1 - LEARN_RATE) * current_q + LEARN_RATE * (rew_obs + DISCOUNT * max_future_q) q_table[which_pd][t_ep][prev_state, intended_action] = new_q disc_ep_rew += rew_obs * (DISCOUNT ** t_ep) ep_rew += rew_obs # Adding sample to the memory all_samples[which_pd].append([prev_state, intended_action, rew_obs, agent_s[which_pd], t_ep]) # S,A,R,S' and time # Sample n times for i in range(n_samples): random_sample_index = np.random.choice(len(all_samples[which_pd])) sample_s = all_samples[which_pd][random_sample_index][0] sample_action = all_samples[which_pd][random_sample_index][1] sample_r = all_samples[which_pd][random_sample_index][2] sample_s_prime = all_samples[which_pd][random_sample_index][3] sample_t = all_samples[which_pd][random_sample_index][4] current_q = q_table[which_pd][sample_t][sample_s, sample_action] max_future_q = np.amax(q_table[which_pd][sample_t+1][sample_s_prime, :]) new_q = (1 - LEARN_RATE) * current_q + LEARN_RATE * (sample_r + DISCOUNT * max_future_q) q_table[which_pd][sample_t][sample_s, sample_action] = new_q agent_s[which_pd] = agent_upt[which_pd].index(pa2ts[which_pd][agent_s[which_pd]]) # Re-initialize after the episode is finished ep_rewards.append(ep_rew) ep_trajectories_pa.append(ep_traj_pa) epsilon = epsilon * EPS_DECAY disc_ep_per_task[which_pd].append(disc_ep_rew) ep_per_task[which_pd].append(ep_rew) if (episode+1) % SHOW_EVERY == 0: inx = inx + 1 for ind in range(len(energy_pa)): avg_per_task = np.mean(ep_per_task[ind]) disc_avg_per_task = np.mean(disc_ep_per_task[ind]) print('Episode # ' + str(episode+1) + ' : Task-' + str(ind) + ' # of Hit=' + str(len(ep_per_task[ind])) + ' Avg.=' + str(avg_per_task)) s1.write(ind*N_EPISODES/SHOW_EVERY+inx,1,len(ep_per_task[ind])) s1.write(ind*N_EPISODES/SHOW_EVERY+inx,2,avg_per_task) s1.write(ind*N_EPISODES/SHOW_EVERY+inx,3,disc_avg_per_task) if (episode+1) % SHOW_EVERY == 0: avg_rewards = np.mean(ep_rewards[episode-SHOW_EVERY +1: episode]) print('Episode # ' + str(episode+1) + ' : Epsilon=' + str(round(epsilon, 4)) + ' Avg. reward in the last ' + str(SHOW_EVERY) + ' episodes=' + str(round(avg_rewards,2))) best_episode_index = ep_rewards.index(max(ep_rewards)) optimal_policy_pa = ep_trajectories_pa[N_EPISODES-1]#ep_trajectories_pa[best_episode_index] # Optimal policy in pa ep_trajectories_pa[N_EPISODES-1]# optimal_policy_ts = [] # optimal policy in ts opt_pol = [] # optimal policy in (m, n, h) format for visualization for ind, val in enumerate(optimal_policy_pa): optimal_policy_ts.append(pa2ts[which_pd][val]) opt_pol.append((math.floor(optimal_policy_ts[ind]/n), optimal_policy_ts[ind]%n, 0)) print('Tajectory at the last episode : ' + str(optimal_policy_ts)) indices=[0,1,2]#, 50000,50001,50001,100000,100001,100002,299997,299998,299999,N_EPISODES-3,N_EPISODES-2,N_EPISODES-1 optimal_policy_pas = [] for i in range(len(indices)): optimal_policy_pas.append(ep_trajectories_pa[indices[i]]) optimal_policy_ts = [] for ind, val in enumerate(optimal_policy_pas[i]): optimal_policy_ts.append(pa2ts[which_pd][val]) #print('Tajectory at the episode ' + str(indices[i]) + ' : '+str(optimal_policy_ts)) QL_timecost = timeit.default_timer() - QL_start_time success_ratio = [] for i in range(len(energy_pa)): success_ratio.append(100*hit_count[i]/mission_tracker[i]) print("Successful Mission Ratio[%] = " + str(success_ratio[i])) print("Successful Missions = " + str(hit_count[i]) + " out of " + str(mission_tracker[i])) d_maxs = [] for i in range(len(energy_pa)): d_maxs.append(max(energy_pa[i])) max_energy = max(d_maxs) for i in range(len(energy_pa)): for j in range(ep_len): #name_diff = "q_table_diff_perc_" + str(i) + ".npy" name_q = "Env3_Converged_Q_TABLE_GNC" + str(n_samples) + '_task'+ str(i) + '_t' + str(j) + ".npy" #np.save(name_diff,q_table_diff_perc) np.save(os.path.join('Q_TABLES',name_q) ,q_table[i][j]) print('Total time for Q-Learning : ' + str(QL_timecost) + ' seconds') print('Action uncertainity[%] = ' + str(eps_unc*100)) print('# of Samples = ' + str(n_samples)) print("Desired Minimum Success Ratio[%] = " + str(100*Pr_des)) print("Episode Length = " + str(ep_len) + " and Max. Energy of the System = " + str(max_energy)) print('Reward at last episode = '+str(ep_rewards[-1])) # for task in range(len(energy_pa)): # for ind in range(len(disc_ep_per_task[task])): # s1.write(1+ind,11+task,disc_ep_per_task[task][ind]) s1.write(1,6,QL_timecost) s1.write(1,7,np.mean(ep_rewards)) filename = 'model_based_GNC_' +str(n_samples) +'.xls' filename = filename wb.save(filename) return opt_pol
def Traitement(duree = 60, facteur = 10): duree_traitement_video = datetime.datetime.now() command = ['ffmpeg.exe', '-i', chemin, '-f', 'image2pipe', '-pix_fmt','rgb24','-vcodec','rawvideo','-'] vid = sp.Popen(command, stdout = sp.PIPE, bufsize=10**8) Namefile = str(name)+'.xls' book = Workbook() feuil1 = book.add_sheet('Vitesse(temps)') feuil1.write(0,0,'Secondes de la video') feuil1.write(1,0,'Vitesses mesurees') mn09=-2.9335 mn1=-1.9446 mn15=-1 mn2=-0.72440310 mn3=-0.463642477347 mn4=-0.269393042370 mn5=-0.192672237 mn6=0.1379402202341 xabs1 = [mn09,mn1,mn15,mn2,mn3,mn4,mn5,mn6] yord1=[0.9,1,1.5,2,3,4,5,6] Mod = interpolate.interp1d(xabs1, yord1, fill_value='extrapolate') # Add taux d'erreur et vitesse myenne # Supp elements seuillage # Finir durée X = [] Y = [] for t in range(duree): #boucle sur chaque seconde de la video duree_traitement_par_sec = datetime.datetime.now() #compteur de temps pour chaque seconde de traitement regression_moy_sec = 0 #(ré)initialisation de la valeur de regression moyenne sur le nombre d'image par seconde for i in range(25*t,25*(1+t),facteur): #boucle sur x image par seconde de video (x entre 1 et 25) image = vid.stdout.read(1080*1920*3) # Extraction de l'ensemble des données d'une image image = np.fromstring(image, dtype='uint8') # Normalisation des données en int 8 bits image = image.reshape((1080,1920,3)) # Mise en forme des donnée en une image en RGB de taille 1080x1920 vid.stdout.flush() # Suppression du buffer image = image[xmin:ymin,xmax:ymax] image = image[xmin:xmax,ymin:ymax] #Image recadrée autour du drapeau image1 = image[:,:,0] #Image en niveau de rouge list_pt_x = [] #Liste de regression des pixels d'absice pour la ie image de la te seconde list_pt_y = [] #Liste de regression des pixels d'ordonnées pour la ie image de la te seconde for p in range(len(image)): #On parcourt l'image for pp in range(len(image[0])): if image1[p][pp] > 175: #On rajoute les coordonnées des pixels appartenant au drapeau dans les listes list_pt_x.append(p) list_pt_y.append(pp) reg = np.polyfit(list_pt_y, list_pt_x, 1) #Liste des valeurs de regression a et b (y=ax+b) if -0.0973 >= reg[0] >= -2.7864: #si la valeurs correspond a un vent dans le cadre du modele alors X.append(i/25) #on ajoute le temps dans la liste des absices Y.append(float(Mod(reg[0]))) #On ajoue la valeur du vent dans la liste des ordonnées if i == 25*t: #Affichage images seuillées + droites de reg.(une image par sec affichée) for p in range(len(image1)): for pp in range(len(image1[0])): if image1[p][pp] > 175: image[p,pp,0] = 255 image[p,pp,1] = 255 image[p,pp,2] = 255 else : image[p,pp,0] = 0 image[p,pp,1] = 0 image[p,pp,2] = 0 y = int(reg[0]*pp + reg[1]) if y > 0 and y < len(image1): image[y,pp,0] = 127 image[y,pp,1] = 127 image[y,pp,2] = 127 image = Image.fromarray(image) global img img = ImageTk.PhotoImage(image) canvas_img.create_image(0, 0, anchor=NW, image=img) canvas_img.update_idletasks() fenetre.update() PourcExe(t+1,duree) TempsExe((datetime.datetime.now()-duree_traitement_par_sec).total_seconds(), duree) canvas_pourc.itemconfigure(text_pourc, text=pourc) canvas_temps.itemconfigure(text_temps, text=time) regression_moy_sec = -sum(Y)/len(Y) if regression_moy_sec<-2.7865: feuil1.write(0,t+1,t) feuil1.write(1,t+1,'Vent trop faible') canvas_vit.itemconfigure(text_vit, text='faible') elif regression_moy_sec > -0.0973: feuil1.write(0,t+1,t) feuil1.write(1,t+1,'Vent trop fort') canvas_vit.itemconfigure(text_vit, text='faible') else: feuil1.write(0,t+1,t) feuil1.write(1,t+1,float(Mod(regression_moy_sec))) vall=int(Mod(regression_moy_sec)*1000) val=str(float(vall)/1000)+" m/s" canvas_vit.itemconfigure(text_vit, text=val) fenetre.update print("Temps video :",t,"s") print((datetime.datetime.now()-duree_traitement_par_sec).total_seconds(),"secondes pour traiter une seconde de video") pl.plot(X,Y) #affichage graphique pl.show() book.save(Namefile) #sauvegarde du fichier excel temps_traitement_total=(datetime.datetime.now()-duree_traitement_video).total_seconds() print("Temps traitement total :",temps_traitement_total,"s") #temps tratement total print(sum(Y)/len(Y)) showinfo("Boite de dialogue", "Le traitement de la video est terminé")
NO_OF_ROWS = 38 # Give the location of the file loc = ("/home/rahul/Desktop/FormsToBeDeleted.xlsx") # To open Workbook wb = xlrd.open_workbook(loc) sheet = wb.sheet_by_index(0) # For row 0 and column 0 for i in range(0, NO_OF_ROWS): row = [] for j in range(0, NO_OF_COLUMNS): #print(type(sheet.cell_value(i,j))) row.append(sheet.cell_value(i, j)) print(row) data = sheet.cell_value(0, 3) print(type(data)) print(datetime.datetime.fromtimestamp(data)) #data = sheet.cell_value(0, 0) #print(data) loc = ("/home/rahul/Desktop/FormsToBeDeleted.xlsx") # To open Workbook wb = Workbook() sheet1 = wb.add_sheet('Sheet 1') wb.save('xlwt example.xls')
generalSheet = wb.add_sheet('general', cell_overwrite_ok=True) bronzeSheet = wb.add_sheet('bronze', cell_overwrite_ok=True) silverSheet = wb.add_sheet('silver', cell_overwrite_ok=True) goldSheet = wb.add_sheet('gold', cell_overwrite_ok=True) platinumSheet = wb.add_sheet('platinum', cell_overwrite_ok=True) advancedSheet = wb.add_sheet('advanced', cell_overwrite_ok=True) links = [("https://usaco.guide/general/using-this-guide", generalSheet), ("https://usaco.guide/bronze/time-comp", bronzeSheet), ('https://usaco.guide/silver/binary-search-sorted', silverSheet), ('https://usaco.guide/gold/divis', goldSheet), ('https://usaco.guide/plat/seg-ext', platinumSheet), ('https://usaco.guide/adv/springboards', advancedSheet)] tcount = 70 for link, sheet in links: tcount += 1 for i in get_all_links(link, d): px = px + get_all_unique_problems(i, d) cu = 1 for i in px: write_problem_at_row(i, sheet, cu) cu += 1 px.clear() wb.save(f'{tcount}.xls') d.close() wb.save('final.xls')
#time function def time(): now = datetime.datetime.today() return now #write txt file class txt_files(Thread): def run(self): txt_file = open("user input.txt", "a") txt_file.write((user_input + "\n" + str(time()) + "\n")) #write excel file class xlsx_files(Thread): def run(self): sheet.write(i, 0, user_input) sheet.write(i, 1, str(time())) #user input name for i in range(2): user_input = input("enter your name: ") txt_files2 = txt_files() xlsx_files2 = xlsx_files() txt_files2.start() xlsx_files2.start() xlsx_file.save("user input.xls")
from bs4 import BeautifulSoup from xlwt import Workbook from fake_useragent import UserAgent excel_name = u'douban_hot_review.xls' sheet_name = u'豆瓣影评' column = [u'标题', u'作者', u'影片', u'影评'] douban_excel = Workbook(excel_name) douban_excel = Workbook(encoding='utf-8') douban_sheet = douban_excel.add_sheet(sheet_name, cell_overwrite_ok=True) douban_sheet.write(0, 0, u'标题') douban_sheet.write(0, 1, u'作者') douban_sheet.write(0, 2, u'影片') douban_sheet.write(0, 3, u'影评') douban_excel.save(excel_name) def get_movie_review(): #html = get_html(url, 1, 3) # titles = soup.select('.main-bd h2') # i = 0 # for row in (range(1+page*10,11+page*10)): # douban_sheet.write(row,0,titles[i].text) # i = i + 1 #print(titles[i].text) # names = soup.select('.name') # i = 0 # for row in (range(1+page*10,11+page*10)): # douban_sheet.write(row,1,names[i].text)
today_date, 5) ws_index.write_merge(row_no, row_no + 1, 0, 0, each_index, style_name) col_no = 1 temp_series = temp_result[u"涨跌幅"] Xls_Writer_pctchg(ws_index, temp_series, all_data, row_no, col_no, u"涨跌幅") col_no = col_no + 2 for each_field in [u"振幅", u"日内波动率", u"成交额", u"换手率"]: temp_series = temp_result[each_field] Xls_Writer(ws_index, temp_series, row_no, col_no, each_field) col_no = col_no + 2 row_no = row_no + 2 row_no = row_no + 1 for each_index in bond_index_list: temp_result, all_data = Bondindex_Performance(each_index, daily_backtest_start_date, today_date) ws_index.write_merge(row_no, row_no + 1, 0, 0, each_index, style_name) col_no = 1 for each_field in [u"涨跌幅", u"振幅", u"日内波动率", u"成交额", u"换手率"]: try: temp_series = temp_result[each_field] Xls_Writer(ws_index, temp_series, row_no, col_no, each_field) except: pass col_no = col_no + 2 row_no = row_no + 2 ws.save("D:\\test.xls")
next_page = tree.xpath('//div[@class="unified pagination js_pageLinks"]/a/@href') max_length = max(len(res_name), len(res_comments), len(comment_hrefs), len(res_scores), len(res_rank)) res_name = res_name + ['NULL' for i in range(max_length-len(res_name))] res_comments = res_comments + ['NULL' for i in range(max_length-len(res_comments))] comment_hrefs = comment_hrefs + ['NULL' for i in range(max_length-len(comment_hrefs))] res_scores = res_scores + ['NULL' for i in range(max_length-len(res_scores))] res_rank = res_rank + ['NULL' for i in range(max_length-len(res_rank))] for i in range(max_length): res_info.append([res_name[i], '厦门', res_scores[i], res_rank[i], res_comments[i], herf+comment_hrefs[i]]) if len(res_info) >= 3000: for i in range(len(res_info)): for j in range(len(res_info[i])): sheet.write(row_count, j, unicode(res_info[i][j])) row_count += 1 print('Generate file count:' + str(file_count)) workbook.save('Xiamen_Maotuying_Restaurant_Information%d.xls' % file_count) del res_info[:] workbook = Workbook() sheet = workbook.add_sheet('Restaurant food information') write_excel_title(sheet, title_name) row_count = 1 file_count += 1 if len(next_page) > 0: next_url = herf + next_page[-1] url_queue.put(next_url) for i in range(len(res_info)): for j in range(res_info[i]): sheet.write(row_count, j, res_info[i][j]) row_count += 1 workbook.save('Maotuying_Restaurant_Information%d.xls' % file_count)
def teste(args, semente=None): numJogadores = 101 numJogadas = 10000 memoria = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] jogadores = [] vuns = [] vzeros = [] somaTotal = [] random.seed(semente) for i in range(numJogadores): nome = "PerceptronSimples-" + str(i) p = Perceptron(numInputs=13, taxaAprendizado=args, semente=random.randint(0, 1000)) jogador = Jogador(nome, p) jogadores.append(jogador) #Essa parte e usada para escrever a planilha wb = Workbook() sheet1 = wb.add_sheet('tabela') for i in range(1, numJogadas + 1): sheet1.write(i, 0, "jogada " + str(i)) sheet1.write(0, 1, "Uns") sheet1.write(0, 2, "Zeros") sheet1.write(0, 3, "Soma") sheet1.write(0, 4, "Diferença") #[fim] planilha for i in range(numJogadas): soma = 0 jogadas = [] for j in range(numJogadores): jogada = jogadores[j].jogar(memoria[-13:]) jogadas.append(jogada) soma += jogada minoria = -np.sign(soma) for j in range(numJogadores): if jogadas[j] == minoria: jogadores[j].addVitorias(i) else: jogadores[j].treinar(minoria, memoria[-13:]) if (minoria < 0): vuns.append(abs(math.floor(soma / 2) - (numJogadores - 1) / 2)) vzeros.append( abs(math.floor(soma / 2) + ((numJogadores - 1) / 2) + 1)) else: vuns.append( abs(math.ceil(soma / 2) - ((numJogadores - 1) / 2) - 1)) vzeros.append(abs(math.ceil(soma / 2) + ((numJogadores - 1) / 2))) #Usado para planilha sheet1.write(i + 1, 1, vuns[i]) sheet1.write(i + 1, 2, vzeros[i]) sheet1.write(i + 1, 3, vuns[i] + vzeros[i]) sheet1.write(i + 1, 4, int(abs(soma))) #[fim] planilha memoria.append(minoria) somaTotal.append(abs(soma)) print(soma) #os.system('clear') #salva a planilha wb.save('resultados/' + str(numJogadas) + ' - 101jogadores - 13inpts - ' + str(args) + 'eta - 02.ods') return somaTotal
}).find_all('tr') try: for corredor in corredores_pagina: if corredor.find_all('a'): for link in corredor.find_all('a'): row += 1 link_corredor = link_base + link.get('href') vista_corredor = urlopen(link_corredor) soup_corredor = BeautifulSoup(vista_corredor, 'html.parser') empresa = soup_corredor.find('h1').text campos = soup_corredor.find_all('td', attrs={'class': 'Campo'}) valores = soup_corredor.find_all('td', attrs={'class': 'Valor'}) sheet1.write(row, 0, empresa) for i, campo in enumerate(campos): valor = re.sub(' +', ' ', valores[i].text) if campo.text == 'Teléfonos': sheet1.write(row, 1, valor) elif campo.text == 'Email': sheet1.write(row, 2, valor) elif campo.text == 'Contacto': sheet1.write(row, 3, valor) elif campo.text == 'Dirección': sheet1.write(row, 4, valor) except: pass wb.save('corredores.xls')
tokens = nltk.word_tokenize(raw) # print(tokens) # text = nltk.Text(tokens) # print(text) tagged = nltk.pos_tag(tokens) # print(tagged) for i in range(0, len(tagged)): label = str(tagged[i][1]) if (label in nounList): numNouns += 1 elif (label in verbList): numVerbs += 1 elif (label in modList): numMods += 1 else: others += 1 total = numNouns + numVerbs + numMods # write to Excel sheet1.write(row, 0, emot) sheet1.write(row, 1, str(numNouns / total)) sheet1.write(row, 2, str(numVerbs / total)) sheet1.write(row, 3, str(numMods / total)) row += 1 print(row) statsWB.save(title + '.xls')
worddic = word_dic(tierslist, tiernames) #print(worddic, '\n') #writes worddic to sheet wb = Workbook() sheet1 = wb.add_sheet('Textgrid') numtiers = len(tiernames) #for every interval tier for wordi, elem in enumerate(worddic): #write interval tier name, elem, starttime,endtime sheet1.write(numtiers * wordi * 2, 0, tiernames[0]) j = 1 for tup in elem: sheet1.write(numtiers * wordi * 2, j, tup) j += 1 #for each point tier, write landmark and time row for namei, name in enumerate(tiernames): if namei != 0: sheet1.write(numtiers * wordi * 2 + 2 * namei - 1, 0, name) sheet1.write(numtiers * wordi * 2 + 2 * namei, 0, 'Time') j = 1 for lmtime in worddic[elem][name]: sheet1.write(numtiers * wordi * 2 + 2 * namei - 1, j, lmtime[1]) sheet1.write(numtiers * wordi * 2 + 2 * namei, j, lmtime[0]) j += 1 wb.save(savexlsas)
from tempfile import TemporaryFile from xlwt import Workbook book = Workbook() sheet1 = book.add_sheet('Wangzi') sheet1.write(0, 0, '0,0') sheet1.write(0, 1, '0,1') book.save('D:/Desktop/simple.xls') #book.save(TemporaryFile())
def render_excel(filename, title_list, data_list, file_extension='.xls'): if file_extension == '.csv': response = HttpResponse(mimetype='text/csv') response['Content-Disposition'] = 'attachment; filename=' + filename csv_writer = csv.writer(response) csv_writer.writerow(title_list) for row_item_list in data_list: for i in xrange(0, len(row_item_list)): if row_item_list[i]: if isinstance(row_item_list[i], datetime.datetime): row_item_list[i] = row_item_list[i].strftime( '%Y-%m-%d %H:%M:%S') elif isinstance(row_item_list[i], datetime.date): row_item_list[i] = row_item_list[i].strftime( '%Y-%m-%d') elif isinstance(row_item_list[i], datetime.time): row_item_list[i] = row_item_list[i].strftime( '%H:%M:%S') if isinstance(row_item_list[i], basestring): row_item_list[i] = row_item_list[i].encode("utf-8") csv_writer.writerow(row_item_list) else: import StringIO output = StringIO.StringIO() export_wb = Workbook() export_sheet = export_wb.add_sheet('Sheet1') col_idx = 0 for col_title in title_list: export_sheet.write(0, col_idx, "%s" % col_title) col_idx += 1 row_idx = 1 for row_item_list in data_list: col_idx = 0 for cell_value in row_item_list: if cell_value: cell_value_is_date = False if isinstance(cell_value, datetime.datetime): cell_value = xlrd.xldate.xldate_from_datetime_tuple( (cell_value.year, cell_value.month, cell_value.day, cell_value.hour, cell_value.minute, cell_value.second), 0) cell_value_is_date = True elif isinstance(cell_value, datetime.date): cell_value = xlrd.xldate.xldate_from_date_tuple( (cell_value.year, cell_value.month, cell_value.day), 0) cell_value_is_date = True elif isinstance(cell_value, datetime.time): cell_value = xlrd.xldate.xldate_from_time_tuple( (cell_value.hour, cell_value.minute, cell_value.second)) cell_value_is_date = True elif isinstance(cell_value, models.Model): cell_value = str(cell_value) if cell_value_is_date: s = XFStyle() s.num_format_str = 'M/D/YY' export_sheet.write(row_idx, col_idx, cell_value, s) else: export_sheet.write(row_idx, col_idx, cell_value) col_idx += 1 row_idx += 1 export_wb.save(output) output.seek(0) str_out = output.getvalue() response = HttpResponse(str_out) response['Content-Type'] = 'application/vnd.ms-excel' response['Content-Disposition'] = 'attachment; filename=' + filename return response
def write_excel(data): file_w = Workbook() sheet1 = file_w.add_sheet(u'Data', cell_overwrite_ok=True) # 创建sheet write_data(data, sheet1) file_w.save('data.xls') return 0
def main(): usdinr = [] usdinr_change = [] dates = [] diffrates = [] ratesD = [] ratesF = [] # expiry in 12 months T = 12 wb = excel.open_workbook(filename='India_data.xlsx') wb1 = Workbook() ws1 = wb1.add_sheet('Prices') # code outputs tons of data to an excel spreadsheet including prices using # 1. Vanilla monte carlo # 2. Antithetic monte carlo # 3. Black Scholes Model # and also outputs the standard error of these price estimations # We simulate 4 different paths with strike prices a few std's away from the actual prevailing price on the date. # The labels below aren't accurate. We settled on different sigma values in the final simulation. ws1.write(0, 0, 'Date') ws1.write(0, 1, 'USDINR') ws1.write(0, 2, 'Vanilla K=-1sigma') ws1.write(0, 3, 'Vanilla K=-0.5sigma') ws1.write(0, 4, 'Vanilla K=+0.5sigma') ws1.write(0, 5, 'Vanilla K=+1sigma') ws1.write(0, 6, 'Antithetic K=-1sigma') ws1.write(0, 7, 'Antithetic K=-0.5sigma') ws1.write(0, 8, 'Antithetic K=+0.5sigma') ws1.write(0, 9, 'Antithetic K=+1sigma') ws1.write(0, 10, 'BS K=-1sigma') ws1.write(0, 11, 'BS K=-0.5sigma') ws1.write(0, 12, 'BS K=+0.5sigma') ws1.write(0, 13, 'BS K=+1sigma') ws1.write(0, 14, 'Strike Price K=-1sigma') ws1.write(0, 15, 'Strike Price K=-0.5sigma') ws1.write(0, 16, 'Strike Price K=+0.5sigma') ws1.write(0, 17, 'Strike Price K=+1sigma') ws1.write(0, 18, 'Std Error Vanilla K=-1sigma') ws1.write(0, 19, 'Std Error Vanilla K=-0.5sigma') ws1.write(0, 20, 'Std Error Vanilla K=+0.5sigma') ws1.write(0, 21, 'Std Error Vanilla K=+1sigma') ws1.write(0, 22, 'Std Error Antithetic K=-1sigma') ws1.write(0, 23, 'Std Error Antithetic K=-0.5sigma') ws1.write(0, 24, 'Std Error Antithetic K=+0.5sigma') ws1.write(0, 25, 'Std Error Antithetic K=+1sigma') # cell co-ordinates work like array indexes within the spreadsheet for i in range(2, 121): single_usd_inr = wb.sheet_by_name("Sheet1").cell_value(i, 3) usdinr.append(single_usd_inr) usdinr_change.append( math.log(1 + wb.sheet_by_name("Sheet1").cell_value(i, 4))) single_date = wb.sheet_by_name("Sheet1").cell_value(i, 0) ws1.write(i, 0, single_date) ws1.write(i, 1, single_usd_inr) dates.append( datetime.fromordinal( datetime(1900, 1, 1).toordinal() + int(single_date) - 2)) ratesD.append( math.log(1 + wb.sheet_by_name("Sheet1").cell_value(i, 1) / 100) / 12) ratesF.append( math.log(1 + wb.sheet_by_name("Sheet1").cell_value(i, 9) / 100) / 12) diffrates.append(ratesD[i - 2] - ratesF[i - 2]) optionPricesVanilla = [] optionPricesAntithetic = [] optionPricesBS = [] for i in range(12, len(dates) - T): sigma1 = np.std(usdinr_change[i - 12:i - 1]) sigma = np.std(usdinr[i - 12:i - 1]) mean = np.average(usdinr_change[i - 12:i - 1]) # Strike prices are decided as +/- [0.75, 0.25] sigma from spot price. # Since this is a simulation, we calculate the option price through simulated paths and compare it against the # actual strike price during the selected expiry date to decide if the option is exercised or not. K = [ usdinr[i] - 0.75 * sigma * math.sqrt(T), usdinr[i] - 0.25 * sigma * math.sqrt(T), usdinr[i] + 0.25 * sigma * math.sqrt(T), usdinr[i] + 0.75 * sigma * math.sqrt(T) ] ws1.write(i, 14, K[0]) ws1.write(i, 15, K[1]) ws1.write(i, 16, K[2]) ws1.write(i, 17, K[3]) optionPrice = [] stderr = [] for k in K: optionPrice_single, stderr_single = price_asian_option_vanilla( usdinr[i], ratesD[i], ratesF[i], diffrates[i], sigma1, T, 100, 120, k) optionPrice.append(optionPrice_single) stderr.append(stderr_single) optionPricesVanilla.append(optionPrice) ws1.write(i, 2, optionPrice[0]) ws1.write(i, 3, optionPrice[1]) ws1.write(i, 4, optionPrice[2]) ws1.write(i, 5, optionPrice[3]) ws1.write(i, 18, stderr[0]) ws1.write(i, 19, stderr[1]) ws1.write(i, 20, stderr[2]) ws1.write(i, 21, stderr[3]) optionPrice = [] stderr = [] for k in K: optionPrice_single, stderr_single = price_asian_option_antithetic( usdinr[i], ratesD[i], ratesF[i], diffrates[i], sigma1, T, 100, 120, k) optionPrice.append(optionPrice_single) stderr.append(stderr_single) optionPricesAntithetic.append(optionPrice) ws1.write(i, 6, optionPrice[0]) ws1.write(i, 7, optionPrice[1]) ws1.write(i, 8, optionPrice[2]) ws1.write(i, 9, optionPrice[3]) ws1.write(i, 22, stderr[0]) ws1.write(i, 23, stderr[1]) ws1.write(i, 24, stderr[2]) ws1.write(i, 25, stderr[3]) optionPrice = [ price_option_BS(usdinr[i], ratesD[i], ratesF[i], diffrates[i], sigma1, T, 100, k) for k in K ] optionPricesBS.append(optionPrice) ws1.write(i, 10, optionPrice[0]) ws1.write(i, 11, optionPrice[1]) ws1.write(i, 12, optionPrice[2]) ws1.write(i, 13, optionPrice[3]) optionPricesVanilla = np.array(optionPricesVanilla) optionPricesAntithetic = np.array(optionPricesAntithetic) optionPricesBS = np.array(optionPricesBS) wb1.save('Option Prices.xls') # finally, we plot all the different option prices from our simulations. axis1 = plotter.subplot(2, 2, 1) axis1.plot_date(dates[12:len(dates) - T], optionPricesVanilla[:, 0], label="-2sigma", linestyle='solid', marker=',') axis1.plot_date(dates[12:len(dates) - T], optionPricesVanilla[:, 1], label="-1sigma", linestyle='solid', marker=',') axis1.plot_date(dates[12:len(dates) - T], optionPricesVanilla[:, 2], label="+1sigma", linestyle='solid', marker=',') axis1.plot_date(dates[12:len(dates) - T], optionPricesVanilla[:, 3], label="+2sigma", linestyle='solid', marker=',') axis1.set_ylabel('Option Prices') axis1.legend() axis2 = axis1.twinx() axis2.plot_date(dates, usdinr, label='USDINR', linestyle='solid', marker=',') axis2.set_ylabel('USD / INR Prices') axis2.legend() plotter.title('Vanilla Monte Carlo Simulation') axis1 = plotter.subplot(2, 2, 2) axis1.plot_date(dates[12:len(dates) - T], optionPricesBS[:, 0], label="-2sigma", linestyle='solid', marker=',') axis1.plot_date(dates[12:len(dates) - T], optionPricesBS[:, 1], label="-1sigma", linestyle='solid', marker=',') axis1.plot_date(dates[12:len(dates) - T], optionPricesBS[:, 2], label="+1sigma", linestyle='solid', marker=',') axis1.plot_date(dates[12:len(dates) - T], optionPricesBS[:, 3], label="+2sigma", linestyle='solid', marker=',') axis1.set_ylabel('Option Prices') axis1.legend() # axis2 = axis1.twinx() # axis2.plot_date(dates, usdinr, label='USDINR', linestyle='solid', marker=',') # , secondary_y=True) # axis2.set_ylabel('USD / INR Prices') # axis2.legend() plotter.title('Black Scholes Calculation') plotter.subplot(2, 2, 3) plotter.plot_date(dates[12:len(dates) - T], optionPricesAntithetic[:, 0], label="-2sigma", linestyle='solid', marker=',') plotter.plot_date(dates[12:len(dates) - T], optionPricesAntithetic[:, 1], label="-1sigma", linestyle='solid', marker=',') plotter.plot_date(dates[12:len(dates) - T], optionPricesAntithetic[:, 2], label="+1sigma", linestyle='solid', marker=',') plotter.plot_date(dates[12:len(dates) - T], optionPricesAntithetic[:, 3], label="+2sigma", linestyle='solid', marker=',') plotter.legend() plotter.title('Antithetic Monte Carlo Simulation') plotter.ylabel('Option Prices') plotter.show()
if __name__ == "__main__": w = Workbook() today = datetime.date.today() yesterday = datetime.date.today() - datetime.timedelta(days=1) all_city = gansu + xinjiang + qinghai + ningxia + shanxi dict_all = {} for city in all_city: dict_all[city] = 0 get_all_wenshu() dict_b = OrderedDict() dict_b["甘肃"] = gansu dict_b["新疆"] = xinjiang dict_b["青海"] = qinghai dict_b["宁夏"] = ningxia dict_b["陕西"] = shanxi mysql_db = get_conn() mysql_cur = mysql_db.cursor() for a, b in dict_b.items(): logger.info(a) ktgg_ = ktgg(a) wenshu_ = wenshu(b) main(a, wenshu_, ktgg_) w.save("5province_increase-{}.xls".format(yesterday)) my_email.hz_send( "兰州项目西北五省涉诉站点统计-{}".format(yesterday), "5province_increase-{}.xls".decode("utf8").format(yesterday), "*****@*****.**") mysql_db.close()
valid = validators.url(webs) if valid: print(webs) sheet1.write(count1, 1, webs) else: print('no website') sheet1.write(count1, 1, 'NONE') element.click() else: driver.back() time.sleep(10) else: count2 = count2 + 1 name1 = code1.surgeon(1, dau) sheet2.write(count2, 0, name1) ph = code1.surgeon(2, dau) sheet2.write(count2, 1, ph) email = code1.surgeon(3, dau) sheet2.write(count2, 2, email) driver.execute_script("window.scrollBy(0,110)", "") driver.close() url = "TYPE IN REQUIRED EMAIL" count = 0 begin(url) wb.save('doctors.xls') wb1.save('surgeons.xls')
from datetime import date from xlwt import Workbook, XFStyle, Borders, Pattern, Font fnt = Font() fnt.name = 'Arial' borders = Borders() borders.left = Borders.THICK borders.right = Borders.THICK borders.top = Borders.THICK borders.bottom = Borders.THICK pattern = Pattern() pattern.pattern = Pattern.SOLID_PATTERN pattern.pattern_fore_colour = 0x0A style = XFStyle() style.num_format_str = 'YYYY-MM-DD' style.font = fnt style.borders = borders style.pattern = pattern book = Workbook() sheet = book.add_sheet('A Date') sheet.write(1, 1, date(2009, 3, 18), style) book.save('date.xls')
sheet.cell_value(vallist.index(response), numCol)) averageVal = respCounter / counter robject = word, averageVal resultList.append(robject) # Writing value to another excel sheet openWB(KeyDeiverName, resultList) # Creating the lookup table analyzeInitSurvey("Employee Development", 5, 6) analyzeInitSurvey("Culture", 11, 12) analyzeInitSurvey("Work-Life Balance", 17, 18) analyzeInitSurvey("Leadership", 23, 24) wb.save(saveFileName) def getLookupTable(sheet): wb = xlrd.open_workbook(saveFileName) qSheet = wb.sheet_by_name(sheet) # print(qSheet.cell_value(0, 0)) qlist = [] for i in range(qSheet.nrows): qlist.append(qSheet.row_values(i)) return qlist def isImportant(comment):
class MakeExcel(object): def __init__(self,excelinfo=None): self.STARTLINE=1 engine=Engine() timemanager=TimeManager() self.workbook = Workbook() self.sheet = self.workbook.add_sheet(u'公示信息',cell_overwrite_ok=False) self.inforsheet=self.workbook.add_sheet(u'文档信息',cell_overwrite_ok=False) _tableTitle=[u"一卡通",u"学号",u"姓名",u"明细",u"说明",u"得分",u"总分"] #设置列宽(固定宽度) self.sheet.col(0).width=4000 self.sheet.col(1).width=4000 self.sheet.col(2).width=3000 self.sheet.col(3).width=20000 self.sheet.col(4).width=20000 self.sheet.col(5).width=2000 self.sheet.col(6).width=2000 #定义info栏的字体 # (<element>:(<attribute> <value>,)+;)+ xls_title =easyxf( 'font: name Arial,height 400,colour black;' 'pattern: pattern solid, fore_colour pale_blue;' 'alignment: horizontal center,vertical center;' ) xls_info=easyxf( 'font: name Arial,height 250,colour black;' 'pattern: pattern solid, fore_colour white;' 'alignment: horizontal center,vertical center;' 'borders:top medium,bottom medium,left medium,right medium;' ) self.xls_detail=easyxf( 'font: name Arial,height 250,colour black;' 'pattern: pattern solid, fore_colour white;' 'alignment: horizontal center,vertical center;' 'borders:top medium,bottom medium,left medium,right medium;' ) self.sumary=easyxf( 'font: name Arial,height 250,colour black;' 'pattern: pattern solid, fore_colour white;' 'alignment: horizontal center,vertical center;' 'borders:top medium,bottom medium,left medium,right medium;' ) self.details=easyxf( 'font: name Arial,height 250,colour black;' 'pattern: pattern solid, fore_colour yellow;' 'alignment: horizontal center,vertical center;' 'borders:top medium,bottom medium,left medium,right medium,bottom_colour violet;' ) self.inforsheet.write_merge(0,1,0,6,excelinfo["filename"], xls_title) self.inforsheet.write_merge(2,3,2,6,excelinfo["admin"],xls_info) self.inforsheet.write_merge(2,3,0,1,u"创建者:",xls_info) self.inforsheet.write_merge(4,5,2,6,excelinfo["grade"],xls_info) self.inforsheet.write_merge(4,5,0,1,u"公示年级:",xls_info) #self.inforsheet.write_merge(6,7,2,6,timemanager.strTime(excelinfo["maketime"]),xls_info) self.inforsheet.write_merge(6,7,0,1,u"创建时间:",xls_info) self.inforsheet.write_merge(8,9,2,6,excelinfo["start"]+u"至"+excelinfo["end"],xls_info) self.inforsheet.write_merge(8,9,0,1,u"统计区间:",xls_info) self.inforsheet.write_merge(10,11,2,6,excelinfo["note"],self.xls_detail) self.inforsheet.write_merge(10,11,0,1,u"备注:",xls_info) for i in range(len(_tableTitle)):#Make table title self.sheet.write(0,i,_tableTitle[i],self.xls_detail) def _writeuser(self,rowNo,infobuf,lines): """ 写用户总体信息 """ _info=[infobuf["campID"],infobuf["studentID"],infobuf["name"],u"无",u"无",u"0",float(infobuf["sum"])] if lines==0:#如果无加分 for i in range(len(_info)): self.sheet.write_merge(rowNo,rowNo+lines,i,i,_info[i],self.sumary) return lines+1 else: for i in range(len(_info)): if i!=3 and i!=4 and i!=5:#明细,分值留空 self.sheet.write_merge(rowNo,rowNo+lines-1,i,i,_info[i],self.sumary) return lines def _writedetail(self,rowNo,items,lines): """ 写具体得分细则 """ i=0 for item in items: _info=[item["item_name"],item["note"],item["add"]] for s in range(len(_info)): self.sheet.write(rowNo+i,3+s,_info[s],self.details) i+=1 def saveAs(self,filename): self.workbook.save(filename) def run(self,userlist,starttime,endtime): i=self.STARTLINE _count=0 for user in userlist:#写用户总体信息 engine=Engine() result=engine.getUserDetail(user,start_time=starttime,end_time=endtime,is_jsonify=False) lines=len(result["items"]) if result is not None: _count+=1#增加一条记录 self._writedetail(i,result["items"],lines) i+=self._writeuser(i,result,lines) if _count>0: return True #至少有一个条目 else: return False #没有任何条目 return True
mytree = ET.parse('data.xml.xml') myroot = mytree.getroot() #print(myroot.tag) [for head node of data] z = 1 ct = 0 for x in myroot[1].findall('Stroke'): ct = ct + 1 #print no. of strokes print(ct) sheet1.write(0, 0, "X_value") sheet1.write(0, 1, "Y_value") sheet1.write(0, 2, "Time") #excessing each child for x in myroot[1]: for y in x: sheet1.write(z, 0, y.get('x')) sheet1.write(z, 1, y.get('y')) sheet1.write(z, 2, y.get('time')) z = z + 1 sheet1.write(z, 0, '\n') sheet1.write(z, 1, '\n') sheet1.write(z, 2, '\n') z = z + 1 #saving data from sheet to xls file wb.save('datafile.xls')