| %s | \" % str(data['url'].encode('utf-8'), 'utf-8'))\n # f.write(\"%s | \" % str(data['title'].encode('utf-8'), 'utf-8'))\n # f.write(\"%s | \" % str(data['summary'].encode('utf-8'), 'utf-8'))\n f.write(\"
'\n if hasattr(f, 'isDataValidForEntirePeriod'):\n if f.isDataValidForEntirePeriod == 'true':\n to_write += 'Valid for the entire period'\n else:\n to_write += 'Not valid for all period'\n table_content.append([Paragraph('Time period', header_style), Paragraph(to_write, large_table_style)])\n \n #synonyms\n if not utils.is_empty(f.get('synonym')):\n up_to = 3\n if len(f.synonym) == 1:\n table_content.append([Paragraph('Synonym(s)', header_style), Paragraph(f.synonym[0], large_table_style)])\n else:\n if len(f.synonym) > up_to:\n to_write = {0:{'system model': f.get('modellingAndValidation').get('systemModelName'), \n 'activityName': f.activityName, \n 'geography': f.geography, \n 'filename': f.filename, \n 'problem type': 'too many synonyms', \n 'details': 'only the first %s were written' % up_to}}\n #logs = concat([logs, DataFrame(to_write).transpose()])\n else:\n up_to = len(f.synonym)\n to_write = [Paragraph(f.synonym[0], large_table_style)]\n for i in range(1, up_to):\n to_write.append(Paragraph(f.synonym[i], large_table_style))\n table_content.append([Paragraph('Synonyms', header_style), to_write])\n else:\n table_content.append([Paragraph('Synonym', header_style), Paragraph('None', large_table_style)])\n \n #ISIC classification\n if not utils.is_empty(f.get('classifications')):\n for c in f.classifications:\n if c.classificationSystem == 'ISIC rev.4 ecoinvent':\n c = c.classificationValue.split(':')\n for i in range(len(c)):\n c[i] = c[i].strip()\n c = '%s: %s' % tuple(c)\n table_content.append([Paragraph('ISIC 4 classification', header_style), \n Paragraph(c, large_table_style)])\n break\n else:\n table_content.append([Paragraph('Classification', header_style), ''])\n \n #reference product\n ref = f.get_reference_product()\n table_content.append([Paragraph('Reference product', header_style), \n Paragraph(ref.name, large_table_style)])\n \n #CPC classification\n for c in ref.get('classifications'):\n if c.classificationSystem == 'CPC':\n table_content.append([Paragraph('CPC classification', header_style), \n Paragraph(c.classificationValue, large_table_style)])\n break\n \n #special activity type\n to_write = [Paragraph(spold2.meta_translator['specialActivityType'][f.get('specialActivityType')].capitalize(), large_table_style)]\n if type(ao_f) == pandas.core.series.Series and f.specialActivityType == 'market activity':\n if ao_f['constrained market'] == 'no':\n to_write.append(Paragraph('unconstrained market', large_table_style))\n else:\n to_write.append(Paragraph('constrained market', large_table_style))\n table_content.append([Paragraph('Dataset type', header_style), to_write])\n \n #Technology level\n table_content.append([Paragraph('Technology level', header_style), spold2.meta_translator['technologyLevel'][f.get('technologyLevel')]])\n \n #version\n if '2016-' in version:\n v = '3.3'\n else:\n v = version\n table_content.append([Paragraph('Version - system model', header_style), \n Paragraph('%s - %s' % (v, system_model), large_table_style)])\n \n #Table of content\n for i in range(1, len(table_content)):\n table_content[i].append('')\n table_style = deepcopy(v_header)\n table_style[0] = ('VALIGN', (0, 0), (-2, -1), 'MIDDLE')\n table_style[3] = ('BACKGROUND', (0, 1), (0, -1), colors.lightgrey)\n table_style.append(('SPAN',(0,0),(1,0)))\n table_style.append(('SPAN',(2,1),(2,-1)))\n table_style.append(('INNERGRID', (2, 0), (2, -1), 0.25, colors.white))\n table_style.append(('VALIGN', (-1, 0), (-1, -1), 'TOP'))\n inner_table = [[table_of_content(f)]]\n \n #Note\n if f.get('modellingAndValidation').get('systemModelName') == 'Undefined':\n p = Paragraph('Notes: This document contains only an extract of the information in the dataset. Additional data about properties of exchanges, mathematical relations, parameters, and contact information for authors and reviewers are available in the full dataset, e.g. through the ecoinvent website. \\nAmount and identity of the exchanges in an undefined dataset are independant of modeling choices in by the different system models. Linked dataset are available in separate documents. ', small_justified)\n else:\n p = Paragraph('Note: This document contains only an extract of the information in the dataset. Additional data about properties of exchanges, mathematical relations, parameters, and contact information for authors and reviewers are available in the full dataset, e.g. through the ecoinvent website. ', small_justified)\n inner_table.append([p])\n \n #Link to ecoQuery\n if type(serialization_mapping) == str:\n link = ''\n else:\n link = link_to_ecoQuery(serialization_mapping, f, system_model)\n inner_table.append([Paragraph('Link to the dataset on the ecoinvent website' % (\n link, ecoinvent_red), header_style)])\n table_content[1][2] = Table(inner_table)\n t = Table(table_content, colWidths = [1.2*inch, 3.2*inch, 2.8*inch])\n t.setStyle(TableStyle(table_style))\n Story.append(t)\n Story.append(Spacer(1, inter_par_dist))\n return Story, logs\ndef table_of_content(f):\n table_content = []\n table_content.append([Paragraph('Exchange summary' % ecoinvent_red, large_table_style)])\n table_content.append([Paragraph('Dataset description' % ecoinvent_red, large_table_style)])\n try:\n if f.dataGeneratorAndPublication.accessRestrictedTo in [0, 1]:\n table_content.append([Paragraph('Detailed information for exchanges' % ecoinvent_red, large_table_style)])\n except:\n pass\n if f.get('modellingAndValidation').get('systemModelName') != 'Undefined':\n table_content.append([Paragraph('Selected impact assessment results' % ecoinvent_red, large_table_style)])\n table_content.append([Paragraph('Sources' % ecoinvent_red, large_table_style)])\n table_style = [('LEFTPADDING', (0,0), (-1,-1), 0)]\n t = Table(table_content)\n t.setStyle(TableStyle(table_style))\n return t\ndef dataset_authorship(f, Story, MD):\n to_remove = [', NOENTRY']\n colWidths = [1.1*inch, .9*inch, 5.*inch]\n header = [['']*len(colWidths)]\n header[0][0] = Paragraph('Dataset authorship', title_style)\n header.append([Paragraph('Role', large_table_style), Paragraph('Date', large_table_style), \n Paragraph('Name, organisation', large_table_style)])\n header = Table(header, colWidths = colWidths)\n header.setStyle(TableStyle(table_header_style))\n table_content = [['']*len(colWidths)]\n table_content[0][0] = header\n try:\n name = f.personName\n except AttributeError:\n name = '[System]'\n if hasattr(f, 'personId') and f.personId in set(MD['persons'].index):\n sel = MD['persons'].loc[f.personId]\n if type(sel['companyId']) == str:\n name += ', ' + MD['Companies'].loc[sel['companyId']]['name']\n for t in to_remove:\n name = name.replace(t, '')\n name = [Paragraph(name, large_table_style)]\n if hasattr(f, 'isActiveAuthor') and f.isActiveAuthor == 'true':\n name.append(Paragraph('active author', large_table_style))\n table_content.append([Paragraph('Data generator', large_table_style), \n f.creationTimestamp.split('T')[0], name])\n name = f.dataGeneratorAndPublication.personName\n else:\n #new name not yet in the master data\n name = '[System]'\n if name != '[System]':\n try:\n sel = MD['persons'].loc[f.dataGeneratorAndPublication.personId]\n except:\n if type(sel['companyId']) == str:\n company = MD['Companies'].loc[sel['companyId']]['name']\n if company != 'NOENTRY':\n name += ', ' + company\n for t in to_remove:\n name = name.replace(t, '')\n table_content.append([Paragraph('Data entry', large_table_style), f.creationTimestamp.split('T')[0], \n Paragraph(name, large_table_style)])\n reviews = []\n for r in f.get('modellingAndValidation').get('reviews'):\n if not utils.is_empty(r.reviewDate):\n name = r.reviewerName\n if name != '[System]':\n sel = MD['Persons'].loc[r.get('reviewerId')]\n if type(sel['companyId']) == str:\n name += ', ' + MD['Companies'].loc[sel['companyId']]['name']\n for t in to_remove:\n name = name.replace(t, '')\n to_add = {'name': name, \n 'date': r.reviewDate, \n 'contact': sel['email']}\n reviews.append(to_add)\n if len(reviews) != 0:\n reviews = utils.list_to_df(reviews)\n reviews = reviews.sort('date').drop_duplicates('name', take_last = True)\n max_reviews = 3\n if len(reviews) > max_reviews:\n reviews = reviews.iloc[-max_reviews:]\n for i in range(len(reviews)):\n table_content.append([Paragraph('Review', large_table_style), \n Paragraph(reviews.iloc[i]['date'], large_table_style), \n Paragraph(reviews.iloc[i]['name'], large_table_style)])\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n t.setStyle(TableStyle(h_header2))\n Story.append(t)\n Story.append(Spacer(1, inter_par_dist))\n return Story\ndef TTextAndImage_print(f, Story, tag, field, logs, market_comments, \n image_counter, image_report):\n write = False\n if field == 'generalComment':\n write = True\n elif f.get(field) != None and len(f.get(field).comments) != 0:\n write = True\n if write:\n Story.append(Paragraph('%s' % tag, justified))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n something = False\n if field == 'generalComment' and f.specialActivityType in ['1']:\n sel = market_comments.loc[(f.specialActivityType, field), 'text']\n for c in sel.split('\\n'):\n Story.append(Paragraph(c, no_info_style))\n Story.append(Spacer(1, small_spacer))\n something = True\n if f.get(field) != None and len(f.get(field).comments) != 0:\n for comment_type, comment, comment_index in getattr(f, field).comments:\n something = True\n if comment_type == 'text':\n if comment not in [None, '']:\n if 'http' not in comment:\n comment = comment.split('//')\n else:\n comment = [comment]\n for c in comment:\n if len(c) > 0:\n c = text_corrections(c)\n if 'individually updated during' in c:\n link = 'http://www.ecoinvent.org/support/documents-and-files/documents-and-files.html#290'\n c += ' The original ecoinvent version 2 documentation can be consulted here. ' % (\n link, ecoinvent_red)\n Story.append(Paragraph(c, bullet))\n Story.append(Spacer(1, small_spacer))\n else:\n try:\n if f.activityName != 'lightweight concrete block production, expanded clay':\n I = Image(comment)\n aspect = I.imageHeight / float(I.imageWidth)\n width = min((PAGE_WIDTH-2.*MARGIN)*.8, I.imageWidth)\n I = Image(comment, width=width, height=width * aspect)\n I.hAlign = 'CENTER'\n t = Table([[I]], colWidths = [7.*inch])\n t.setStyle(TableStyle([('ALIGN',(0,0),(-1,-1),'CENTRE'),]))\n Story.append(t)\n image_counter +=1\n to_add = {'activityName': f.activityName, \n 'geography': f.geography, \n 'filename': f.filename, \n 'reference product': f.get_reference_product().name, \n 'image url': comment}\n image_report[len(image_report)] = copy(to_add)\n except IOError:\n to_write = {0:{'system model': f.get('modellingAndValidation').get('systemModelName'), \n 'activityName': f.activityName, \n 'geography': f.geography, \n 'filename': f.filename, \n 'problem type': 'image unavailable', \n 'details': comment}}\n logs = concat([logs, DataFrame(to_write).transpose()])\n if not something:\n Story.append(no_info)\n if Story[-1] != Spacer(1, small_spacer):\n Story.append(Spacer(1, small_spacer))\n return Story, logs, image_counter, image_report\ndef text_comments(f, Story, market_comments):\n Story.append(Paragraph('Included activities start', justified))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n something = False\n if f.specialActivityType in [1, 10]:\n sel = market_comments.loc[(f.specialActivityType, \n 'includedActivitiesStart'), 'text']\n for c in sel.split('\\n'):\n Story.append(Paragraph(c, no_info_style))\n something = True\n if f.get('includedActivitiesStart') != None:\n comment = text_corrections(f.includedActivitiesStart)\n Story.append(Paragraph(comment.split('- ')[0], bullet))\n something = True\n for c in comment.split('- ')[1:]:\n c = c.strip()\n c = ' - ' + c\n if len(c) > 1:\n if c[-1] not in ['.', ';', ':']:\n c += '.'\n Story.append(Paragraph(c, bullet))\n if not something:\n Story.append(no_info)\n Story.append(Spacer(1, small_spacer))\n Story.append(Paragraph('Included activities ends', justified))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n something = False\n if f.specialActivityType in ['1']: #no such comment for market groups\n sel = market_comments.loc[(f.specialActivityType, \n 'includedActivitiesEnd'), 'text']\n for c in sel.split('\\n'):\n Story.append(Paragraph(c, no_info_style))\n something = True\n if f.get('includedActivitiesEnd') != None:\n comment = text_corrections(f.includedActivitiesEnd)\n Story.append(Paragraph(comment.split('- ')[0], bullet))\n something = True\n for c in comment.split('- ')[1:]:\n c = text_corrections(c)\n c = ' - ' + c\n if len(c) > 1:\n Story.append(Paragraph(c, bullet))\n if not something:\n Story.append(no_info)\n Story.append(Spacer(1, small_spacer))\n if not utils.is_empty(f.get('modellingAndValidation').get('samplingProcedure')):\n Story.append(Paragraph('Sampling procedure', justified))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n comment = text_corrections(f.get('modellingAndValidation').get('samplingProcedure'))\n Story.append(Paragraph(comment, bullet))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n if not utils.is_empty(f.get('modellingAndValidation').get('extrapolations')):\n Story.append(Paragraph('Extrapolations', justified))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n comment = text_corrections(f.get('modellingAndValidation').get('extrapolations'))\n Story.append(Paragraph(comment, bullet))\n Story.append(Spacer(1, small_spacer))\n return Story\ndef build_exchange_df(f):\n exchange_df = []\n for exc in f.get_all_exchanges():\n to_add = exc.baseline(additional_fields = ['sourceId', 'productionVolumeAmount'])\n to_add['unitName'] = to_add['unitName'].replace('metric ton*km', 'ton*km')\n if exc.get('uncertainty') != None:\n to_add.update({'distribution': exc.uncertainty.type, \n 'pedigree': exc.uncertainty.pedigreeMatrix})\n else:\n to_add['distribution'] = to_add['pedigree'] = ''\n exchange_df.append(to_add)\n exchange_df = utils.list_to_df(exchange_df)\n classifications = ao[['name', 'By-product classification', 'mft']].reset_index()\n #fix me: classification could come from current MD. Would allow classification of\n #stuff not in the DB yet. \n if 'Activity UUID' in classifications.columns:\n del classifications['Activity UUID']\n classifications = classifications.drop_duplicates(subset = ['name']).set_index('name')\n exchange_df = exchange_df.set_index('name')\n exchange_df = exchange_df.join(classifications)\n exchange_df = exchange_df.reset_index()\n if 'name' not in exchange_df:\n exchange_df = exchange_df.rename(columns = {'index': 'name'})\n exchange_df = exchange_df.sort('name')\n exchange_df.loc[exchange_df['mft'] == 'not mft', 'mft'] = 'no'\n exchange_df.loc[exchange_df['mft'] == 'mft', 'mft'] = 'yes'\n exchange_df = exchange_df.replace(to_replace = {'compartment': {np.NaN: ''}}) \n return exchange_df\ndef exchange_summary_undefined(exchange_df, Story):\n table_style = [\n ('TOPPADDING', (0,0), (-1,-1), 1),\n ('BOTTOMPADDING', (0,0), (-1,-1), 1),\n ('VALIGN',(0,0),(-1,-1),'MIDDLE'), #valign middle for all\n ('HALIGN',(0,1),(-1,0),'MIDDLE'), #valign middle for all\n ('TEXTCOLOR',(0,0),(0,-1), colors.black), #black text for all\n ('BACKGROUND',(0,0),(-1,0), colors.lightgrey), \n ('INNERGRID', (0,0), (-1,0),thin_line, colors.white), #grid after the header\n ('INNERGRID', (0,1), (-1,-1),thin_line, colors.black), #grid after the header\n ]\n groups = ['ReferenceProduct', 'ByProduct']\n if f.dataGeneratorAndPublication.accessRestrictedTo in [0, 1]:\n groups.append('FromTechnosphere')\n else:\n Story.append(confidential)\n Story.append(Spacer(1, inter_par_dist))\n stars = 1\n footnotes = []\n for group in groups:\n table_style_ = deepcopy(table_style)\n sel = exchange_df[exchange_df['group'] == group]\n if len(sel) > 0:\n if group == 'ReferenceProduct':\n s = 'Reference products'\n if sel['amount'].min() < 0.:\n s += '*'*stars\n footnotes.append(('*'*stars, 'A negative reference product amount indicates a waste treatment activity, or a market linking to treatment activities.'))\n stars += 1\n elif group == 'ByProduct':\n s = 'By-products'\n if sel['amount'].min() < 0. and f.specialActivityType == '1':\n s += '*'*stars\n footnotes.append(('*'*stars, 'Negative by-products in a market dataset indicate a constrained market, a feature of the consequential system model. '))\n stars += 1\n elif sel['amount'].min() < 0.:\n 1/0\n else:\n s = 'Inputs from technosphere'\n if group != 'FromTechnosphere':\n colWidths = [3.9*inch, 0.9*inch, 1.*inch, amount_width]\n table_content = [[Paragraph('%s' % s, large_table_style), \n Paragraph('Material for treatment', large_table_style), \n Paragraph('Byproduct classif.', large_table_style), \n Paragraph('Amount', large_table_style)]]\n else:\n colWidths = [5.8*inch, amount_width]\n table_content = [[Paragraph('%s' % s, large_table_style), \n Paragraph('Amount', large_table_style)]]\n name = f.get_reference_product().name\n name_list = list(set(sel['name']))\n name_list.sort()\n if name in name_list:\n name_list.remove(name)\n name_list.insert(0, name)\n for name in name_list:\n sel1 = sel[sel['name'] == name]\n amount = sel1['amount'].sum()\n if group != 'FromTechnosphere':\n if utils.is_empty(sel1.iloc[0]['mft']):\n mft = ''\n else:\n mft = sel1.iloc[0]['mft']\n if utils.is_empty(sel1.iloc[0]['mft']):\n bpc = ''\n else:\n bpc = sel1.iloc[0]['By-product classification']\n table_content.append([Paragraph(name, large_table_style),\n Paragraph(mft, large_table_style), \n Paragraph(bpc, large_table_style), \n '%s %s' % (utils.arrange_float(amount), sel1.iloc[0]['unitName'])])\n else:\n table_content.append([Paragraph(name, large_table_style),\n '%s %s' % (utils.arrange_float(amount), sel1.iloc[0]['unitName'])])\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n t.setStyle(TableStyle(table_style_))\n Story.append(t)\n return Story, footnotes\ndef exchange_summary_cutoff(exchange_df, Story):\n sel = exchange_df[exchange_df['group'] == 'ReferenceProduct']\n colWidths = [4.9*inch, 0.9*inch, amount_width]\n table_style = [\n ('VALIGN',(0,0),(-1,-1),'MIDDLE'),\n ('TEXTCOLOR',(0,0),(0,-1), colors.black), \n ('ALIGN',(0,0),(-1,0),'CENTRE'),\n ('TOPPADDING', (0,0), (-1,-1), 1),\n ('BOTTOMPADDING', (0,0), (-1,-1), 1), \n ('INNERGRID', (0,0), (-1,0), thin_line, colors.white), \n ('INNERGRID', (0,1), (-1,-1), thin_line, colors.black), \n ('BACKGROUND', (0, 0), (-1, 0), colors.lightgrey), \n ('INNERGRID', (0,0), (-1,0), thin_line, colors.white), \n ('INNERGRID', (0,1), (-1,-1), thin_line, colors.black), \n ('BACKGROUND', (0, 0), (-1, 0), colors.lightgrey)]\n s = 'Reference product'\n stars = 1\n footnotes = []\n if sel['amount'].min() < 0.:\n s += '*'*stars\n footnotes.append(('*'*stars, 'A negative reference product amount indicates a waste treatment activity, or a market linking to treatment activities.'))\n stars += 1\n table_content = [[Paragraph('%s' % s, large_table_style), \n Paragraph('Byproduct classif.', large_table_style), \n Paragraph('Amount', large_table_style)]]\n table_content.append([Paragraph(sel.iloc[0]['name'], large_table_style),\n Paragraph(sel.iloc[0]['By-product classification'], large_table_style), \n '%s %s' % (utils.arrange_float(sel.iloc[0]['amount']), sel.iloc[0]['unitName'])])\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n t.setStyle(TableStyle(table_style))\n try:\n if f.dataGeneratorAndPublication.accessRestrictedTo not in [0, 1]:\n Story.append(confidential)\n Story.append(Spacer(1, inter_par_dist))\n except:\n pass\n Story.append(t)\n if (not hasattr(f, 'dataGeneratorAndPublication')) or f.dataGeneratorAndPublication.accessRestrictedTo in [0, 1]:\n table_style = [\n ('TOPPADDING', (0,0), (-1,-1), 1),\n ('BOTTOMPADDING', (0,0), (-1,-1), 1),\n ('VALIGN',(0,0),(-1,-1),'MIDDLE'), #valign middle for all\n ('HALIGN',(1,0),(1,0),'RIGHT'), #valign middle for all\n ('TEXTCOLOR',(0,0),(0,-1), colors.black), #black text for all\n ('BACKGROUND',(0,0),(-1,0), colors.lightgrey), \n ('INNERGRID', (1,0), (-1,0),thin_line, colors.white), #grid after the header\n ('INNERGRID', (0,1), (-1,-1),thin_line, colors.black), #grid after the header\n ]\n sel = exchange_df[exchange_df['group'] == 'FromTechnosphere']\n colWidths = [5.8*inch, amount_width]\n cats = ['allocatable product', 'Recyclable', 'Waste']\n for cat in cats:\n table_style_ = deepcopy(table_style)\n sel1 = sel[sel['By-product classification'] == cat]\n if len(sel1) > 0:\n if cat == 'allocatable product':\n s = 'Inputs from technosphere'\n elif cat == 'Waste':\n s = 'Inputs from technosphere, wastes' + '*'*stars\n footnotes.append(('*'*stars, 'A negative input indicates a by-product output of waste.'))\n stars += 1\n else:\n s = 'Inputs from technosphere, recyclable materials' + '*'*stars\n footnotes.append(('*'*stars, 'A negative input indicates a by-product output of recyclable material.'))\n stars += 1\n table_content = [[Paragraph('%s' % s, large_table_style), \n Paragraph('Amount', large_table_style)]]\n name = f.get_reference_product().name\n name_list = list(set(sel1['name']))\n name_list.sort()\n if name in name_list:\n name_list.remove(name)\n name_list.insert(0, name)\n for name in name_list:\n sel2 = sel1[sel1['name'] == name]\n amount = sel2['amount'].sum()\n table_content.append([Paragraph(name, large_table_style),\n '%s %s' % (utils.arrange_float(amount), sel2.iloc[0]['unitName'])])\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n t.setStyle(TableStyle(table_style_))\n Story.append(t)\n return Story, footnotes\ndef exchange_summary_consequential(exchange_df, Story):\n sel = exchange_df[exchange_df['group'] == 'ReferenceProduct']\n colWidths = [4.9*inch, 0.9*inch, amount_width]\n table_style = [\n ('VALIGN',(0,0),(-1,-1),'MIDDLE'),\n ('TEXTCOLOR',(0,0),(0,-1), colors.black), \n ('ALIGN',(0,0),(-1,0),'CENTRE'),\n ('TOPPADDING', (0,0), (-1,-1), 1),\n ('BOTTOMPADDING', (0,0), (-1,-1), 1), \n ('INNERGRID', (0,0), (-1,0), thin_line, colors.white), \n ('INNERGRID', (0,1), (-1,-1), thin_line, colors.black), \n ('BACKGROUND', (0, 0), (-1, 0), colors.lightgrey)]\n s = 'Reference product'\n stars = 1\n footnotes = []\n if sel['amount'].min() < 0.:\n s += '*'*stars\n footnotes.append(('*'*stars, 'A negative reference product amount indicates a waste treatment activity, or a market linking to treatment activities.'))\n stars += 1\n table_content = [[Paragraph('%s' % s, large_table_style), \n Paragraph('Material for treatment', large_table_style), \n Paragraph('Amount', large_table_style)]]\n table_content.append([Paragraph(sel.iloc[0]['name'], large_table_style),\n Paragraph(sel.iloc[0]['mft'], large_table_style), \n '%s %s' % (utils.arrange_float(sel.iloc[0]['amount']), sel.iloc[0]['unitName'])])\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n t.setStyle(TableStyle(table_style))\n if f.dataGeneratorAndPublication.accessRestrictedTo not in [0, 1]:\n Story.append(confidential)\n Story.append(Spacer(1, inter_par_dist))\n Story.append(t)\n if f.dataGeneratorAndPublication.accessRestrictedTo in [0, 1]:\n table_style = [\n ('TOPPADDING', (0,0), (-1,-1), 1),\n ('BOTTOMPADDING', (0,0), (-1,-1), 1),\n ('VALIGN',(0,0),(-1,-1),'MIDDLE'), #valign middle for all\n ('HALIGN',(0,1),(-1,0),'MIDDLE'), #valign middle for all\n ('TEXTCOLOR',(0,0),(0,-1), colors.black), #black text for all\n ('BACKGROUND',(0,0),(-1,0), colors.lightgrey), \n ('INNERGRID', (1,0), (-1,0),thin_line, colors.white), #grid after the header\n ('INNERGRID', (0,1), (-1,-1),thin_line, colors.black)\n ]\n sel = exchange_df[exchange_df['group'] == 'FromTechnosphere']\n sel = sel[sel['mft'] == 'no']\n colWidths = [5.8*inch, amount_width]\n l = [('normal', sel[sel['amount'] > 0], ''), \n ('credit', sel[sel['amount'] < 0], \n 'Following the consequential approach, these negative inputs act as credits for avoided production.'), \n ('wastes', exchange_df[exchange_df['mft'] == 'yes'], \n 'These exchanges listed as negative inputs are by-products requiring treatment. They carry the burdens of their treatments, and include credits for beneficial treatment in this system model.')\n ]\n for material, sel1, explanations in l:\n if len(sel1) > 0:\n if material == 'normal':\n s = 'Inputs from technosphere'\n if material == 'credit':\n s = 'Inputs from technosphere, credit for by-products'\n s += '*'*stars\n footnotes.append(('*'*stars, explanations))\n stars += 1\n if material == 'wastes':\n s = 'Inputs from technosphere, wastes'\n s += '*'*stars\n footnotes.append(('*'*stars, explanations))\n stars += 1\n table_content = [[Paragraph('%s' % s, large_table_style), \n Paragraph('Amount', large_table_style)]]\n table_style_ = deepcopy(table_style)\n name = f.get_reference_product().name\n name_list = list(set(sel1['name']))\n name_list.sort()\n if name in name_list:\n name_list.remove(name)\n name_list.insert(0, name)\n for name in name_list:\n sel2 = sel1[sel1['name'] == name]\n amount = sel2['amount'].sum()\n table_content.append([Paragraph(name, large_table_style),\n '%s %s' % (utils.arrange_float(amount), sel2.iloc[0]['unitName'])])\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n t.setStyle(TableStyle(table_style_))\n Story.append(t)\n return Story, footnotes\ndef exchange_summary_APOS(exchange_df, Story):\n sel = exchange_df[exchange_df['group'] == 'ReferenceProduct']\n colWidths = [4.9*inch, 0.9*inch, amount_width]\n table_style = [\n ('VALIGN',(0,0),(-1,-1),'MIDDLE'),\n ('TEXTCOLOR',(0,0),(0,-1), colors.black), \n ('ALIGN',(0,0),(-1,0),'CENTRE'),\n ('TOPPADDING', (0,0), (-1,-1), 1),\n ('BOTTOMPADDING', (0,0), (-1,-1), 1), \n ('INNERGRID', (0,0), (-1,0), thin_line, colors.white), \n ('INNERGRID', (0,1), (-1,-1), thin_line, colors.black), \n ('BACKGROUND', (0, 0), (-1, 0), colors.lightgrey)]\n s = 'Reference product'\n stars = 1\n footnotes = []\n if sel['amount'].min() < 0.:\n s += '*'*stars\n footnotes.append(('*'*stars, 'A negative reference product amount indicates a waste treatment activity or a market linking to treatment activities.'))\n stars += 1\n table_content = [[Paragraph('%s' % s, large_table_style), \n Paragraph('Material for treatment', large_table_style), \n Paragraph('Amount', large_table_style)]]\n table_content.append([Paragraph(sel.iloc[0]['name'], large_table_style),\n Paragraph(sel.iloc[0]['mft'], large_table_style), \n '%s %s' % (utils.arrange_float(sel.iloc[0]['amount']), sel.iloc[0]['unitName'])])\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n t.setStyle(TableStyle(table_style))\n if f.dataGeneratorAndPublication.accessRestrictedTo not in [0, 1]:\n Story.append(confidential)\n Story.append(Spacer(1, inter_par_dist))\n Story.append(t)\n if f.dataGeneratorAndPublication.accessRestrictedTo in [0, 1]:\n table_style = [\n ('TOPPADDING', (0,0), (-1,-1), 1),\n ('BOTTOMPADDING', (0,0), (-1,-1), 1),\n ('VALIGN',(0,0),(-1,-1),'MIDDLE'), #valign middle for all\n ('HALIGN',(0,1),(-1,0),'MIDDLE'), #valign middle for all\n ('TEXTCOLOR',(0,0),(0,-1), colors.black), #black text for all\n ('BACKGROUND',(0,0),(-1,0), colors.lightgrey), \n ('INNERGRID', (1,0), (-1,0),thin_line, colors.white), #grid after the header\n ('INNERGRID', (0,1), (-1,-1),thin_line, colors.black)\n ]\n sel = exchange_df[exchange_df['group'] == 'FromTechnosphere']\n colWidths = [5.8*inch, amount_width]\n l = [('no', sel[sel['mft'] == 'no'], ''), \n ('yes', sel[sel['mft'] == 'yes'], 'A negative input represents an output of by-product requiring treatment. It carries the burdens of its treatment, and a credit for beneficial treatment calculated by allocation in this system model.' \n )]\n for material, sel1, explanations in l:\n if len(sel1) > 0:\n if material == 'no':\n s = 'Inputs from technosphere'\n if material == 'yes':\n s = 'Inputs from technosphere, wastes'\n s += '*'*stars\n footnotes.append(('*'*stars, explanations))\n stars += 1\n table_content = [[Paragraph('%s' % s, large_table_style), \n Paragraph('Amount', large_table_style)]]\n table_style_ = deepcopy(table_style)\n name = f.get_reference_product().name\n name_list = list(set(sel1['name']))\n name_list.sort()\n if name in name_list:\n name_list.remove(name)\n name_list.insert(0, name)\n for name in name_list:\n sel2 = sel1[sel1['name'] == name]\n amount = sel2['amount'].sum()\n table_content.append([Paragraph(name, large_table_style),\n '%s %s' % (utils.arrange_float(amount), sel2.iloc[0]['unitName'])])\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n t.setStyle(TableStyle(table_style_))\n Story.append(t)\n return Story, footnotes\ndef exchange_summary_elementary(exchange_df, Story):\n table_style = [\n ('TOPPADDING', (0,0), (-1,-1), 1),\n ('BOTTOMPADDING', (0,0), (-1,-1), 1),\n ('VALIGN',(0,0),(-1,-1),'MIDDLE'), #valign middle for all\n ('HALIGN',(0,1),(-1,0),'MIDDLE'), #valign middle for all\n ('TEXTCOLOR',(0,0),(0,-1), colors.black), #black text for all\n ('BACKGROUND',(0,0),(-1,0), colors.lightgrey), \n ('INNERGRID', (0,0), (-1,0),thin_line, colors.white), #grid after the header\n ('INNERGRID', (0,1), (-1,-1),thin_line, colors.black), #grid after the header\n ]\n colWidths = [5.8*inch, amount_width]\n for compartment, s in [('natural resource', 'Inputs from environment'), \n ('air', 'Emissions to air'), ('water', 'Emissions to water'), \n ('soil', 'Emissions to soil')]:\n sel = exchange_df[exchange_df['compartment'] == compartment]\n if len(sel) > 0:\n table_content = [[Paragraph('%s' % s, large_table_style), \n Paragraph('Amount', large_table_style)]]\n l = list(set(sel['name']))\n l.sort()\n for name in l:\n sel2 = sel[sel['name'] == name]\n amount = sel2['amount'].sum()\n table_content.append([Paragraph(name, large_table_style),\n '%s %s' % (utils.arrange_float(amount), sel2.iloc[0]['unitName'])])\n if 'Transformation, from unspecified' in name:\n pass\n #1/0\n #print inner_table\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n t.setStyle(TableStyle(table_style))\n Story.append(t)\n return Story\ndef write_footnotes(Story, footnotes):\n if len(footnotes) > 0:\n #Story[-1].keepWithNext = True\n for footnote in footnotes:\n Story.append(Paragraph('%s%s' % footnote, small_justified_contrib))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n return Story\ndef amount_cell(f, sel, i, A):\n rel_contrib = get_relative_contributions(f, sel.iloc[i], A)\n contrib_arranged = []\n for k in range(len(rel_contrib)):\n contrib_arranged.append(rel_contrib[k] + '%')\n a = [[Paragraph('%s %s' % (utils.arrange_float(sel.iloc[i]['amount']), \n sel.iloc[i]['unitName']), small_table_style)],\n [Paragraph(' | '.join(contrib_arranged), small_justified_contrib)]]\n a = Table(a, colWidths = [amount_width])\n table_style_a = []\n table_style_a.append(('TOPPADDING', (0,0), (-1,-1), 0))\n table_style_a.append(('BOTTOMPADDING', (0,0), (-1,-1), 0))\n table_style_a.append(('LEFTPADDING', (0,0), (-1,-1), 0))\n table_style_a.append(('RIGHTPADDING', (0,0), (-1,-1), 0))\n a.setStyle(TableStyle(table_style_a))\n return a\ndef Technosphere_table(f, exchange_df, Story, ao, logs, A):\n tags = {'ReferenceProduct': 'Reference product', \n 'ReferenceProducts': 'Reference products', \n 'ByProduct': 'By-product', \n 'ByProducts': 'By-products', \n 'FromTechnosphere': 'Input from technosphere', \n 'FromTechnospheres': 'Inputs from technosphere', \n }\n Story.append(Paragraph('Detailed information for exchanges', title_style_TOC))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n Story.append(Paragraph('Back to table of content' % ecoinvent_red, \n small_justified_contrib))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n if f.get('modellingAndValidation').get('systemModelName') != 'Undefined':\n Story = contribution_to_impact_legend(Story)\n for group in ['ReferenceProduct', 'ByProduct', 'FromTechnosphere']:\n sel = exchange_df[exchange_df['group'] == group].sort(columns = 'name')\n if len(sel) > 0:\n if group == 'FromTechnosphere' and f.specialActivityType in [1, 10]:\n ref = f.get_reference_product()\n sel_ = sel[sel['name'] == ref.name]\n sel_ = sel_.sort('amount', ascending = False)\n sel = pandas.concat([sel_, sel[sel['name'] != ref.name]])\n #setting up the header\n if len(getattr(f, group)) > 1:\n tag = tags[group+'s']\n else:\n tag = tags[group]\n if group == 'FromTechnosphere':\n if f.get('modellingAndValidation').get('systemModelName') == 'Undefined':\n colWidths = [7.*inch - amount_width, amount_width]\n table_content = [[Paragraph('%s' % tag, large_table_style), \n Paragraph('Amount', large_table_style)]]\n else:\n colWidths = [7.*inch - amount_width, amount_width]\n if len(indicators_for_contrib_short) > 0:\n a = [[Paragraph('Amount', large_table_style)],\n [Paragraph('impact contributions', small_justified_contrib)]]\n else:\n a = [[Paragraph('Amount', large_table_style)]]\n a = Table(a, colWidths = [amount_width])\n table_style_a = []\n table_style_a.append(('TOPPADDING', (0,0), (-1,-1), 0))\n table_style_a.append(('BOTTOMPADDING', (0,0), (-1,-1), 0))\n a.setStyle(TableStyle(table_style_a))\n table_content = [[Paragraph('%s' % tag, large_table_style), a]]\n else:\n colWidths = [7.*inch - amount_width - prod_vol_width,\n prod_vol_width, amount_width]\n table_content = [[Paragraph('%s' % tag, large_table_style), \n Paragraph('Annual prod.vol.', large_table_style), \n Paragraph('Amount', large_table_style)\n ]]\n table_style = []\n for i in range(len(sel)):\n if group == 'FromTechnosphere':\n if f.get('modellingAndValidation').get('systemModelName') == 'Undefined':\n inner_table = [[Paragraph(sel.iloc[i]['name'], small_table_style), \n Paragraph('%s %s' % (utils.arrange_float(sel.iloc[i]['amount']), \n sel.iloc[i]['unitName']), small_table_style)]]\n inner_table[0][0].frags[0].textColor = colors.HexColor(ecoinvent_red)\n else:\n inner_table = [[Paragraph(sel.iloc[i]['name'], small_table_style), \n amount_cell(f, sel, i, A)]]\n inner_table[0][0].frags[0].textColor = colors.HexColor(ecoinvent_red)\n else: #byproduct or reference product\n if utils.is_empty(sel.iloc[i]['productionVolumeAmount']) or sel.iloc[i]['productionVolumeAmount'] == 0.:\n inner_table = [[Paragraph(sel.iloc[i]['name'], small_table_style), \n Paragraph('NA', small_table_style),\n Paragraph('%s %s' % (utils.arrange_float(sel.iloc[i]['amount']), \n sel.iloc[i]['unitName']), small_table_style)]]\n else:\n prod_vol = utils.arrange_float(sel.iloc[i]['productionVolumeAmount'])\n inner_table = [[Paragraph(sel.iloc[i]['name'], small_table_style), \n Paragraph('%s %s' % (prod_vol,sel.iloc[i]['unitName']), small_table_style),\n Paragraph('%s %s' % (utils.arrange_float(sel.iloc[i]['amount']), \n sel.iloc[i]['unitName']), small_table_style)]]\n inner_table[0][0].frags[0].textColor = colors.HexColor(ecoinvent_red)\n to_write, logs, length_to_write = arrange_comments(f, sel.iloc[i], ao, logs)\n line = ['']*len(colWidths)\n line[0] = to_write\n inner_table.append(line)\n inner_t = Table(inner_table, colWidths = colWidths)\n inner_t.setStyle(TableStyle(inner_table_style))\n line = ['']*len(colWidths)\n line[0] = inner_t\n if sel.iloc[i]['amount'] != 0.:\n table_content.append(line)\n table_style.append(('SPAN',(0,len(table_content)-1),(-1,len(table_content)-1)))\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n table_style.append(('LEFTPADDING', (0,1), (0,-1), 0))\n table_style.append(('TOPPADDING', (0,1), (0,-1), 0))\n table_style.extend(h_header)\n table_style.append(('BOTTOMPADDING', (0,0), (-1,1), 3))\n t.setStyle(TableStyle(table_style))\n Story.append(t)\n return Story, logs\ndef Technosphere_table_linked_market(f, exchange_df, Story, ao, logs, A):\n Story.append(Paragraph('Detailed information for exchanges', title_style_TOC))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n Story.append(Paragraph('Back to table of content' % ecoinvent_red, \n small_justified_contrib))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n p = 'To help better understand this market dataset, inputs from technosphere are displayed in distinct categories: supplies to the market as one category, then losses and other inputs if they occur within the market. '\n Story.append(Paragraph(p, no_info_style))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n Story = contribution_to_impact_legend(Story)\n to_write = []\n group = 'ReferenceProduct'\n sel = exchange_df[exchange_df['group'] == group].sort(columns = 'name')\n to_write.append(('Reference product', 'Reference product', sel.copy()))\n group = 'FromTechnosphere'\n sel = exchange_df[exchange_df['group'] == group].sort(columns = 'name')\n ref = f.get_reference_product()\n supply_of_reference_product = sel[sel['name'] == ref.name]\n supply_of_reference_product = supply_of_reference_product.sort('amount', ascending = False)\n loss = supply_of_reference_product[supply_of_reference_product[\n 'activityLinkId'] == f.id]\n if len(loss) > 0:\n supply_of_reference_product = supply_of_reference_product[supply_of_reference_product[\n 'activityLinkId'] != f.id]\n if len(supply_of_reference_product) == 1:\n to_write.append(('input of reference product', 'Input of %s' % ref.name, supply_of_reference_product.copy()))\n else:\n to_write.append(('input of reference product', 'Inputs of %s' % ref.name, supply_of_reference_product.copy()))\n if len(loss) > 0:\n to_write.append(('losses', 'Compensation for losses', loss.copy()))\n supply_of_others = sel[sel['name'] != ref.name]\n if len(supply_of_others) == 1:\n to_write.append(('Other', 'Other input from technosphere', supply_of_others.copy()))\n elif len(supply_of_others) > 1:\n to_write.append(('Other', 'Other inputs from technosphere', supply_of_others.copy()))\n for section, tag, sel in to_write:\n if section == 'Reference product':\n colWidths = [7.*inch - amount_width - prod_vol_width,\n prod_vol_width, amount_width]\n table_content = [[Paragraph('%s' % tag, large_table_style), \n Paragraph('Annual prod.vol.', large_table_style), \n Paragraph('Amount', large_table_style)\n ]]\n else:\n colWidths = [7.*inch - amount_width, amount_width]\n if len(indicators_for_contrib_short) > 0:\n a = [[Paragraph('Amount', large_table_style)],\n [Paragraph('impact contributions', small_justified_contrib)]]\n else:\n a = [[Paragraph('Amount', large_table_style)]]\n a = Table(a, colWidths = [amount_width])\n table_style_a = []\n table_style_a.append(('TOPPADDING', (0,0), (-1,-1), 0))\n table_style_a.append(('BOTTOMPADDING', (0,0), (-1,-1), 0))\n a.setStyle(TableStyle(table_style_a))\n table_content = [[Paragraph('%s' % tag, large_table_style), a]]\n table_style = []\n for i in range(len(sel)):\n if tag != 'Reference product':\n exc = find_exc(sel.iloc[i], f)\n if 'Compensation for ' in tag:\n name = exc.name\n else:\n name = '%s - %s' % find_activityLink(exc)\n if '<' in name:\n pieces = name.split('<')\n new_name = '%s' % (ecoinvent_red, pieces[0])\n for piece in pieces[1:]:\n new_name += '<' % ecoinvent_red\n new_name += '%s' % (ecoinvent_red, piece)\n name = copy(new_name)\n inner_table = [[Paragraph(name, small_table_style), \n amount_cell(f, sel, i, A)]]\n else: \n prod_vol = utils.arrange_float(sel.iloc[i]['productionVolumeAmount'])\n inner_table = [[Paragraph(sel.iloc[i]['name'], small_table_style), \n Paragraph('%s %s' % (prod_vol,sel.iloc[i]['unitName']), small_table_style),\n Paragraph('%s %s' % (utils.arrange_float(sel.iloc[i]['amount']), \n sel.iloc[i]['unitName']), small_table_style)]]\n inner_table[0][0].frags[0].textColor = colors.HexColor(ecoinvent_red)\n to_write, logs, length_to_write = arrange_comments(f, sel.iloc[i], ao, logs, \n AL = section in ['losses', 'Other'])\n line = ['']*len(colWidths)\n line[0] = to_write\n inner_table.append(line)\n inner_t = Table(inner_table, colWidths = colWidths)\n inner_t.setStyle(TableStyle(inner_table_style))\n line = ['']*len(colWidths)\n line[0] = inner_t\n if sel.iloc[i]['amount'] != 0.:\n table_content.append(line)\n table_style.append(('SPAN',(0,len(table_content)-1),(-1,len(table_content)-1)))\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n #table_style.append(('LEFTPADDING', (0,1), (0,-1), 0))\n #table_style.append(('TOPPADDING', (0,1), (0,-1), 0))\n table_style.extend(h_header)\n #table_style.append(('BOTTOMPADDING', (0,0), (-1,1), 3))\n t.setStyle(TableStyle(table_style))\n Story.append(t)\n return Story, logs\ndef Environment_table(f, exchange_df, Story, logs):\n colWidths = [7.*inch - amount_width - subcompartment_width, subcompartment_width, amount_width]\n for compartment in ['natural resource', 'air', 'water', 'soil']:\n sel = exchange_df[exchange_df['compartment'] == compartment].sort(columns = 'name')\n if len(sel) > 0:\n tag = compartment_header(compartment)\n table_content = [[Paragraph('%s' % tag, large_table_style), \n Paragraph('Subcompartment', large_table_style), \n Paragraph('Amount', large_table_style)]]\n if f.get('modellingAndValidation').get('systemModelName') != 'Undefined':\n if len(indicators_for_contrib_short) > 0:\n a = [[Paragraph('Amount', large_table_style)],\n [Paragraph('impact contributions', small_justified_contrib)]]\n else:\n a = [[Paragraph('Amount', large_table_style)]]\n a = Table(a, colWidths = [amount_width])\n table_style_a = []\n table_style_a.append(('TOPPADDING', (0,0), (-1,-1), 0))\n table_style_a.append(('BOTTOMPADDING', (0,0), (-1,-1), 3))\n a.setStyle(TableStyle(table_style_a))\n table_content[0][-1] = deepcopy(a)\n table_style = []\n for i in range(len(sel)):\n inner_table = [[Paragraph(sel.iloc[i]['name'], small_table_style), \n Paragraph(sel.iloc[i]['subcompartment'], small_table_style),\n Paragraph('%s %s' % (utils.arrange_float(sel.iloc[i]['amount']), \n sel.iloc[i]['unitName']), small_table_style)]]\n if f.get('modellingAndValidation').get('systemModelName') != 'Undefined':\n inner_table[0][-1] = amount_cell(f, sel, i, A)\n inner_table[0][0].frags[0].textColor = colors.HexColor(ecoinvent_red)\n to_write, logs, length_to_write = arrange_comments(f, sel.iloc[i], ao, logs)\n line = ['']*len(colWidths)\n line[0] = to_write\n inner_table.append(line)\n inner_t = Table(inner_table, colWidths = colWidths)\n inner_t.setStyle(TableStyle(inner_table_style))\n line = ['']*len(colWidths)\n line[0] = inner_t\n table_content.append(line)\n table_style.append(('SPAN',(0,len(table_content)-1),(-1,len(table_content)-1)))\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n table_style.append(('LEFTPADDING', (0,1), (0,-1), 0))\n table_style.append(('TOPPADDING', (0,1), (0,-1), 0))\n table_style.extend(h_header)\n table_style.append(('BOTTOMPADDING', (0,0), (-1,1), 3))\n t.setStyle(TableStyle(table_style))\n Story.append(t)\n return Story, logs\ndef impact_scores(Story, f):\n Story.append(Spacer(1, inter_par_dist))\n Story.append(Paragraph('Selected impact assessment results', title_style_TOC))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n Story.append(Paragraph('Back to table of content' % ecoinvent_red, \n small_justified_contrib))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n table_content = [[Paragraph('method', large_table_style), \n Paragraph('category', large_table_style), \n Paragraph('indicator', large_table_style), \n Paragraph('score', large_table_style)]]\n for exc in f.ReferenceProduct:\n if exc.amount != 0:\n break\n ie = (f.activityName, f.geography, exc.name)\n scores = utils.pkl_load(scores_path, str(indexes.toggle['ie'][ie]))\n for LCIA in indicators:\n method, category, indicator = LCIA\n try:\n s = scores[indexes.toggle['LCIA'][LCIA],0]\n except IndexError:\n s = scores[0,indexes.toggle['LCIA'][LCIA]]\n table_content.append([Paragraph(method, large_table_style), \n Paragraph(category, large_table_style), \n Paragraph(indicator, large_table_style), \n Paragraph('%s %s' % (utils.arrange_float(s, significative_digits = 4), \n indexes.units[LCIA]), large_table_style)])\n colWidths = [2.*inch, 1.75*inch, 1.75*inch, 1.5*inch]\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n t.setStyle(TableStyle(h_header))\n Story.append(t)\n Story.append(Spacer(1, inter_par_dist))\n return Story, ie\ndef print_source_list(Story, exchange_df, f, MD):\n if len(exchange_df) > 0:\n s = set(exchange_df['sourceId'])\n else:\n s = set()\n try:\n if (hasattr(f.dataGeneratorAndPublication, 'publishedSourceId') and \n f.dataGeneratorAndPublication.publishedSourceId != None):\n s.add(f.dataGeneratorAndPublication.publishedSourceId)\n except:\n pass\n source_list = set()\n for source in s:\n if not utils.is_empty(source) and type(source) == str:\n source_list.add(source)\n Story.append(Paragraph('Source', title_style))\n Story[-1].keepWithNext = True\n Story.append(Paragraph('Back to table of content' % ecoinvent_red, \n small_justified_contrib))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n source_list = list(source_list)\n if len(source_list) > 0:\n md = MD['Sources'].loc[source_list]\n if not (len(md) == 1 and source_list[0] not in MD['Sources'].index):\n headers = ['firstAuthor', 'additionalAuthors', 'title', 'year', 'journal', \n 'volumeNo', 'issueNo']\n tags = ['First author', 'Additional author(s)', 'Title', 'Year', 'Journal', \n 'Volume number', 'Issue number']\n header_to_tag = dict(zip(headers, tags))\n table_content = [[Paragraph('Source information', header_style)]]\n for i in range(len(md)):\n to_write = []\n for header in headers:\n if type(md.iloc[i][header]) in [str] and not utils.is_empty(md.iloc[i][header]):\n to_write.append(Paragraph('%s: %s' % (header_to_tag[header], md.iloc[i][header]), header_style))\n table_content.append([to_write])\n t = Table(table_content, colWidths = [7*inch], splitByRow = 1, repeatRows = 1)\n t.setStyle(TableStyle(h_header))\n Story.append(t)\n else:\n Story.append(Paragraph('No source cited in this dataset', no_info_style))\n return Story\ndef render(Story, f, doc, version):\n pieces = utils.break_pieces(f.activityName, 80)\n pieces[-1] += ' - ' + f.geography\n def myFirstPage(canvas, doc):\n canvas.saveState()\n canvas.setFont('Helvetica-Bold',16)\n header_text = 'Ecoinvent %s dataset documentation' % version\n canvas.drawCentredString(PAGE_WIDTH/2.0, PAGE_HEIGHT-35, header_text)\n canvas.setFont('Helvetica',9)\n height = 50\n for piece in pieces:\n canvas.drawCentredString(PAGE_WIDTH/2.0, PAGE_HEIGHT-height, piece)\n height += .15*inch\n canvas.drawImage(logo_path, MARGIN*1.3, 750, width = inch, preserveAspectRatio = True)\n canvas.drawCentredString(PAGE_WIDTH/2.0, 35, 'page %s' % canvas.getPageNumber())\n canvas.restoreState()\n def myLaterPages(canvas, doc):\n canvas.saveState()\n if canvas.getPageNumber() == 1:\n canvas.setFont('Helvetica-Bold',16)\n header_text = 'Ecoinvent %s dataset documentation' % version\n canvas.drawCentredString(PAGE_WIDTH/2.0, PAGE_HEIGHT-35, header_text)\n canvas.setFont('Helvetica',9)\n height = 50\n else:\n canvas.setFont('Helvetica', 9)\n header_text = 'Ecoinvent %s dataset documentation' % version\n canvas.drawCentredString(PAGE_WIDTH/2.0, PAGE_HEIGHT-35, header_text)\n height = 45\n for piece in pieces:\n canvas.drawCentredString(PAGE_WIDTH/2.0, PAGE_HEIGHT-height, piece)\n height += .15*inch\n canvas.drawImage(logo_path, MARGIN*1.3, 750, width = inch, preserveAspectRatio = True)\n canvas.drawCentredString(PAGE_WIDTH/2.0, 35, 'page %s' % canvas.getPageNumber())\n canvas.restoreState()\n doc.pageTemplates[0].beforeDrawPage = myFirstPage\n doc.pageTemplates[1].beforeDrawPage = myLaterPages\n doc.build(Story)\n return doc\ndef find_total_score(f):\n for exc in f.ReferenceProduct:\n if exc.amount != 0:\n break\n ie = (f.activityName, f.geography, exc.name)\n #gff.corrections(ie_name)\n scores = utils.pkl_load(scores_path, str(indexes.toggle['ie'][ie]))\n total_scores = []\n for LCIA_for_contribution in indicators_for_contrib:\n try:\n total_scores.append(scores[indexes.toggle['LCIA'][LCIA_for_contribution],0])\n except IndexError:\n total_scores.append(scores[0,indexes.toggle['LCIA'][LCIA_for_contribution]])\n return total_scores\ndef contribution_to_impact_legend(Story):\n if len(indicators_for_contrib_short) > 0:\n t = 'Impact contribution legend: %s, %s' % (\n ' | '.join(indicators_for_contrib_short), \n 'relative to dataset total score')\n Story.append(Paragraph(t, small_justified_contrib))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n return Story\ndef filelist_with_filtered_ao(ao):\n filelist = set()\n if 1:\n sel = ao[ao['activityName'].isin([\n 'market for methanol'])]\n sel = sel[sel['geography'].isin(['GLO'])]\n filelist.update(set(sel['filename']))\n elif 0:\n sel = ao[ao['activityName'] == 'electricity, high voltage, production mix']\n sel = sel[sel['geography'].isin(['ENTSO-E'])]\n filelist.update(set(sel['filename']))\n else:\n ao = ao.set_index(['activityName', 'geography']).sort_index(level=0)\n indexes = [('electricity production, photovoltaic, 3kWp slanted-roof installation, single-Si, panel, mounted', 'CH'), \n ('heat production, at heat pump 30kW, allocation exergy', 'CH'), \n('operation, solar collector system, Cu flat plate collector, multiple dwelling, for hot water', 'CH'), \n('heat production, natural gas, at boiler condensing modulating <100kW', 'CH'), \n('heat production, wood pellet, at furnace 300kW, state-of-the-art 2014', 'CH'), \n('tap water production, conventional treatment', 'CH'), \n('paper production, woodfree, coated, at non-integrated mill', 'RER'), \n('treatment of wastewater, average, capacity 1.6E8l/year', 'CH'), \n('treatment of waste paper, unsorted, sorting', 'CH'), \n('treatment of municipal solid waste, municipal incineration with fly ash extraction', 'CH'), \n('treatment of biowaste by anaerobic digestion', 'CH'), \n('treatment of used industrial electronic device, manual dismantling', 'CH'), \n('transport, passenger car', 'RER'), \n('transport, passenger train, regional', 'CH'), \n('transport, passenger, aircraft, intercontinental', 'RER'), \n('transport, passenger, aircraft, intracontinental', 'RER'), \n('transport, passenger, electric bicycle', 'CH'), \n ]\n sel = ao.loc[indexes]\n filelist.update(set(sel['filename']))\n return list(filelist)\ndef link_to_ecoQuery(serialization_mapping, f, system_model):\n if system_model == 'undefined':\n index = ('Undefined', f.activityName, f.geography, '')\n else:\n ref = f.get_reference_product()\n if system_model == 'cut-off':\n index = ('Allocation, cut-off', f.activityName, f.geography, ref.name)\n elif system_model == 'consequential':\n index = ('Consequential', f.activityName, f.geography, ref.name)\n elif system_model == 'APOS':\n index = ('Allocation, APOS', f.activityName, f.geography, ref.name)\n else:\n 1/0\n sel = serialization_mapping.loc[index]\n sel = sel.drop_duplicates(['STARTDATE', 'ENDDATE'])\n if type(sel) == pandas.core.frame.DataFrame:\n if len(sel) != 1:\n 1/0\n sel = sel.iloc[0]\n link = sel['UPR link']\n return link\ndef open_access(ao, dataset_folder):\n folder = os.path.join(utils.db_basepath, version, system_model, 'other')\n filename = '124 open access datasets.xlsx'\n df = read_excel(os.path.join(folder, filename), '124 open access datasets')\n ao_ = ao.set_index(['activityName', 'geography', 'Product']).sort_index(level=0)\n filelist = []\n for index in df.index:\n index1 = tuple(df.loc[index, ['Activity', 'Geography', 'Product']])\n if index1 in set(ao_.index):\n sel = ao_.loc[index1]\n if type(sel) == pandas.core.frame.DataFrame:\n sel = sel.iloc[0]\n filelist.append(sel['filename'])\n filename_option = 'cute'\n result_filename = os.path.join(os.path.dirname(dataset_folder), \n 'open access PDF documentation')\n if not os.path.exists(result_filename):\n os.makedirs(result_filename)\n return filelist, filename_option, result_filename\ndef LCI(Story, ee_index, ie_index, LCI_path):\n ie = (f.activityName, f.geography, f.get_reference_product().name)\n g = utils.pkl_load(LCI_path, str(ie_index.toggle['ie'][ie]))\n group_ee = {}\n for compartment in ['air', 'water', 'soil', 'natural resource']:\n group_ee[compartment] = []\n for ee in ee_index:\n group_ee[ee[1]].append(ee)\n Story.append(Spacer(1, inter_par_dist))\n Story.append(Paragraph('Life cycle inventory', title_style))\n Story.append(Spacer(1, inter_par_dist))\n table_content = [[Paragraph('name', large_table_style), \n Paragraph('compartment', large_table_style), \n Paragraph('subcompartment', large_table_style), \n Paragraph('unit', large_table_style), \n Paragraph('amount', large_table_style), \n ]]\n colWidths = [3.0*inch, 1.0*inch, 1.3*inch, 0.5*inch, 1.0*inch]\n for compartment in ['air', 'water', 'soil', 'natural resource']:\n for ee in group_ee[compartment]:\n table_row = [Paragraph(ee[0], large_table_style), \n Paragraph(ee[1], large_table_style), \n Paragraph(ee[2], large_table_style), \n Paragraph(indexes.units[ee], large_table_style), \n ]\n if g[ee_index[ee]] != 0.:\n c = utils.arrange_float(g[ee_index[ee], 0], \n switch_sci = 6, significative_digits = 3)\n table_row.append(Paragraph(c, large_table_style))\n table_content.append(table_row)\n t = Table(table_content, colWidths = colWidths, splitByRow = 1, repeatRows = 1)\n table_style = deepcopy(h_header)\n t.setStyle(TableStyle(table_style))\n Story.append(t)\n return Story\ndef restriction(Story):\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n Story.append(Paragraph('Restriction of use', title_style))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n Story.append(Paragraph('The restrictions of use stipulated in the EULA remain applicable for this pdf documentation.
Copyright ecoinvent Centre, 2016', large_table_style))\n return Story\nif 0:\n indicators = [('IPCC 2007', 'climate change', 'GWP 100a'), \n ('IPCC 2013', 'climate change', 'GWP 100a'), \n ('ecological scarcity 2013', 'total', 'total'),\n ('USEtox', 'human toxicity', 'carcinogenic'), \n ('USEtox', 'human toxicity', 'non-carcinogenic'), \n ('USEtox', 'ecotoxicity', 'total'), \n ('ReCiPe Endpoint (H,A)', 'human health', 'total'), \n ('ReCiPe Endpoint (H,A)', 'ecosystem quality', 'total'), \n ('ReCiPe Endpoint (H,A)', 'resources', 'total'), \n ('ReCiPe Endpoint (H,A)', 'total', 'total'), \n ]\n indicators_for_contrib = [('IPCC 2007', 'climate change', 'GWP 100a'), \n ('ecological scarcity 2013', 'total', 'total'),\n ('ReCiPe Endpoint (H,A)', 'total', 'total')]\n indicators_for_contrib_short = ['IPCC 2007 GWP100', 'ecoscarcity 2013 total', 'ReCiPe H total']\nelif 1:\n indicators = [\n ('IPCC 2007', 'climate change', 'GWP 100a'), \n ('IPCC 2013', 'climate change', 'GWP 100a')\n ]\n indicators_for_contrib = [('IPCC 2007', 'climate change', 'GWP 100a'), \n ('IPCC 2013', 'climate change', 'GWP 100a'), \n ]\n indicators_for_contrib_short = ['IPCC 2007 GWP100', \n 'IPCC 2013 GWP100'\n ]\nelse:\n indicators = [\n #('IPCC 2007', 'climate change', 'GWP 100a'), \n ('IPCC 2013', 'climate change', 'GWP 100a')\n ]\n indicators_for_contrib = [#('IPCC 2007', 'climate change', 'GWP 100a'), \n ('IPCC 2013', 'climate change', 'GWP 100a')\n ]\n indicators_for_contrib_short = [#'IPCC 2007 GWP100', \n 'IPCC 2013 GWP100'\n ]\n#pr = cProfile.Profile()\n#pr.enable()\nlogs = DataFrame()\n#version = '3.2'\nversion = '3.3'\nsystem_model = 'Allocation, APOS'\n#dataset_folder = r'C:\\python\\DB_versions\\%s\\Undefined\\datasets' % version\n#dataset_folder = r'C:\\python\\DB_versions\\%s\\Allocation, cut-off\\datasets' % version\n#dataset_folder = r'C:\\python\\DB_versions\\%s\\consequential\\datasets' % version\ndataset_folder = os.path.join(utils.db_basepath, version, system_model, 'datasets')\n#dataset_folder = r'C:\\Users\\Guillaume\\Desktop\\emilia'\n#dataset_folder = r'C:\\python\\documentation_generator\\test_datasets_spold'\n#filename_option = 'official'\nfilename_option = 'cute'\n#filename_option = 'official'\nprint_LCI = False\n#manual_system_model = 'undefined'\n#manual_version = '3.2'\nsystem_model_found = False\nfor system_model in ['Undefined', 'Allocation, cut-off', 'Consequential', 'Allocation, APOS']:\n if system_model in dataset_folder:\n system_model_found = True\n break\nif not system_model_found:\n #system_model = manual_system_model\n pass\nversion_found = False\n#for version in ['3.1', '3.2']:\n# if version in dataset_folder:\n# version_found = True\n# break\nfolder = os.path.join(utils.db_basepath, version, system_model, 'pkl')\nao = utils.pkl_load(folder, 'ao')\nao_for_AL = ao.set_index(['id', 'name']).sort_index(level=0)\nMD = utils.pkl_load(folder, 'MD')\nif 'undefined' != system_model:\n folder = os.path.join(utils.db_basepath, version, system_model)\n A, B, C, indexes, Z = utils.load_matrices(folder)\n \n new = {}\n #for ie in ie_index:\n # if ie != gff.corrections(ie):\n # new[gff.corrections(ie)] = ie_index[ie]\n #ie_index.update(new)\n scores_path = os.path.join(utils.db_basepath, version, system_model, 'pkl', 'LCIA')\n LCI_path = os.path.join(utils.db_basepath, version, system_model, 'pkl', 'LCI')\n LCIA_for_contribution = ('IPCC 2013', 'climate change', 'GWP 100a')\nfolder = os.path.join(utils.db_basepath, version, 'pkl')\nfilename = 'version_%s_url' % version\ntry:\n serialization_mapping = utils.pkl_load(folder, filename)\n fields = ['system model', 'activity name', 'geography', 'reference product']\n serialization_mapping = serialization_mapping.set_index(\n fields).sort_index(level=0)\nexcept IOError:\n serialization_mapping = ''\nexcept TypeError:\n serialization_mapping = ''\nfolder = os.path.join(utils.db_basepath, version, 'Undefined', 'pkl')\nao_undefined = utils.pkl_load(folder, 'ao')\nao_undefined = ao_undefined.set_index(['activityName', 'geography'], drop = False).sort_index(level=0)\nundefined_dataset_folder = os.path.join(utils.db_basepath, version, 'undefined', 'datasets')\nao = ao.set_index('id', drop = False)\nMD['Persons'] = MD['Persons'].set_index('id', drop = False)\nMD['Companies'] = MD['Companies'].set_index('id', drop = False)\nMD['Geographies'] = MD['Geographies'].set_index('shortname', drop = False)\nMD['Sources'] = MD['Sources'].set_index('id', drop = False)\nif 'test_dataset' in dataset_folder:\n result_folder = r'C:\\python\\documentation_generator\\test_datasets_PDF'\nelse:\n result_folder = os.path.join(utils.db_basepath, version, system_model, 'pdf_documentation')\n if not os.path.exists(result_folder):\n os.makedirs(result_folder)\n#result_folder = r'C:\\python\\documentation_generator\\test_datasets_PDF'\nif 0:\n folder = r'C:\\python\\DB_versions\\3.3\\Allocation, cut-off\\excel'\n filename = 'pdf_to_produce.xlsx'\n tab = 'Sheet1'\n filelist = pandas.read_excel(os.path.join(folder, filename), tab)\n filelist = [tuple(sel) for i, sel in filelist.iterrows()]\n ao_ = ao.set_index(['activityName', 'geography', 'name']).sort_index(level=0)\n filelist = list(ao_.loc[filelist, 'filename'])\nelse:\n #filelist = utils.build_file_list(dataset_folder, extension = 'spold')\n #filelist = ['018a92b5-f344-4feb-b024-42e1b501a4fe_f593c5c2-0980-43e9-8f70-617142b07bd6.spold']\n filelist = filelist_with_filtered_ao(ao)\n def filelist_with_other_filter():\n date = '2016-07-01'\n folder = r'C:\\python\\DB_versions\\%s\\Undefined\\pkl' % date\n filtered_filelist = utils.pkl_load(folder, 'filelist')\n return list(filtered_filelist)\n #filelist = filelist_with_other_filter()\n #filelist, filename_option, result_folder = open_access(ao, dataset_folder)\n#adjust code so that datasets can be from mixed system models\nstart = time.time()\ncounter = 0\n#start_at = 12491\nstart_at = 0\nstop_at = len(filelist)\n#stop_at = start_at +1\nexchange_summary = {'Undefined': exchange_summary_undefined, \n 'Allocation, cut-off by classification': exchange_summary_cutoff, \n 'Allocation at the point of substitution': exchange_summary_APOS, \n 'Allocation, ecoinvent default': exchange_summary_APOS, \n 'Substitution, consequential, long-term': exchange_summary_consequential}\nfolder = os.path.join(utils.script_folder, 'modules', 'release', 'documentation')\nfilename = 'market_general_comment.xlsx'\nmarket_comments = read_excel(os.path.join(folder, filename), 'Sheet1')\nmarket_comments['specialActivityType'] = market_comments['specialActivityType']\nmarket_comments = market_comments.set_index(['systemModelName', \n 'specialActivityType', 'field']).sort_index(level=0)\nmarket_comments = market_comments.loc[system_model]\nreport = {}\nimage_report = {}\nalready_present = utils.build_file_list(result_folder)\nalready_present = []\nfor i in pyprind.prog_bar(range(start_at, stop_at), title = 'iterating datasets'):\n image_counter = 0\n start2 = time.time()\n counter += 1\n print(system_model, i, 'of', len(filelist))\n filename = filelist[i]\n #filename = '06e8913f-3e2c-4243-bc62-9282e6de8587_7fe99768-d571-4bc2-a272-7df585bd0d48.spold'\n f = spold2.Dataset(dataset_folder, filename, ao_for_AL = ao_for_AL, all_fields = True)\n if filename_option == 'cute':\n exc = f.get_reference_product()\n if system_model == 'undefined':\n result_filename = 'v%s - %s - %s - %s.pdf' % (version, \n system_model, f.activityName, f.geography)\n else:\n result_filename = 'v%s - %s - %s - %s - %s.pdf' % (version, \n system_model, f.activityName, f.geography, exc.name)\n result_filename = result_filename.replace('/', '_')\n result_filename = utils.replace_HTML_entities(result_filename)\n elif filename_option == 'official':\n if system_model == 'undefined':\n result_filename = '%s.pdf' % f.id\n else:\n result_filename = '%s_%s.pdf' % (f.id, f.get_reference_product().intermediateExchangeId)\n else:\n result_filename = f.filename.split('.')[0] + '.pdf'\n if result_filename not in already_present:\n Story = []\n Story, logs = dataset_id(f, Story, ao, logs, \n serialization_mapping, system_model)\n Story.append(Spacer(1, small_spacer))\n Story = dataset_authorship(f, Story, MD)\n exchange_df = build_exchange_df(f)\n #exchange_df = exchange_df[exchange_df['group'] == 'ReferenceProduct']\n exchange_df = exchange_df[exchange_df['amount'] != 0.]\n Story.append(Paragraph('Exchange summary', title_style_TOC))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n Story.append(Paragraph('Back to table of content' % ecoinvent_red, \n small_justified_contrib))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n Story, footnotes = exchange_summary[f.get('modellingAndValidation').get('systemModelName')](exchange_df, Story)\n if not hasattr(f, 'dataGeneratorAndPublication') or f.dataGeneratorAndPublication.accessRestrictedTo in [0, 1]:\n Story = exchange_summary_elementary(exchange_df, Story)\n Story = write_footnotes(Story, footnotes)\n Story.append(Spacer(1, inter_par_dist))\n Story.append(Paragraph('Dataset description', \n title_style_TOC))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n Story.append(Paragraph(\n 'Back to table of content' % ecoinvent_red, \n small_justified_contrib))\n Story[-1].keepWithNext = True\n Story.append(Spacer(1, small_spacer))\n Story[-1].keepWithNext = True\n Story, logs, image_counter, image_report = TTextAndImage_print(f, Story, 'General comments', \n 'generalComment', logs, market_comments, image_counter, image_report)\n Story = text_comments(f, Story, market_comments)\n for field, tag in [['Technology comments', 'technologyComment'], \n ['Allocation comments', 'allocationComment'], \n ['Geography comments', 'geographyComment'], \n ['Time period comments', 'timePeriodComment']]:\n Story, logs, image_counter, image_report = TTextAndImage_print(f, Story, field, \n tag, logs, market_comments, image_counter, image_report)\n Story.append(Spacer(1, small_spacer))\n if not hasattr(f, 'dataGeneratorAndPublication') or f.dataGeneratorAndPublication.accessRestrictedTo in [0, 1]:\n if f.specialActivityType != 0 and system_model != 'Undefined':\n Story, logs = Technosphere_table_linked_market(f, exchange_df, Story, ao, logs, A)\n else:\n Story, logs = Technosphere_table(f, exchange_df, Story, ao, logs, A)\n Story, logs = Environment_table(f, exchange_df, Story, logs)\n if f.get('modellingAndValidation').get('systemModelName') != 'Undefined':\n Story, ie = impact_scores(Story, f)\n Story = print_source_list(Story, exchange_df, f, MD)\n if print_LCI:\n Story = LCI(Story, indexes, LCI_path)\n Story = restriction(Story)\n for j in range(len(Story)):\n Story[j].hAlign = 'LEFT'\n start3 = time.time()\n doc = MyDocTemplate(os.path.join(result_folder, result_filename))\n if '2016' in version:\n version = '3.3'\n try:\n doc = render(Story, f, doc, version)\n success = True\n except PermissionError:\n print('')\n print('close the damn file!')\n print('')\n print(result_filename)\n print('')\n 1/0\n except:\n success = False\n if 'spold' in result_filename:\n 1/0\n to_add = {'filename': result_filename, \n 'activityName': f.activityName, \n 'geography': f.geography, \n 'reference product': f.get_reference_product().name, \n 'nb exchanges': len(exchange_df), \n 'nb pages': doc.page, \n 'time': time.time() - start2,\n 'results only': f.dataGeneratorAndPublication.accessRestrictedTo not in [0, 1], \n 'image counter': image_counter, \n 'success': success\n }\n if success:\n to_add['file size (KB)'] = float(os.stat(os.path.join(result_folder, result_filename)\n ).st_size)/1000.\n report[len(report)] = copy(to_add)\n for key in to_add:\n print(key, ':', to_add[key])\n print('')\n if len(report) == 50:\n #break\n pass\nprint('files in', result_folder)\nreport = DataFrame(report).transpose()\nfilename = '%s_%s_report.xlsx' % (version, system_model)\ncols = ['activityName', 'geography', 'reference product', 'filename', \n 'time', 'file size (KB)', 'nb exchanges', 'nb pages', 'results only', 'image counter', 'success']\nreport.to_excel(os.path.join(result_folder, filename), cols = cols, \n merge_cells = False, index = False)\nfilename = '%s_%s_image_report.xlsx' % (version, system_model)\ncols = ['activityName', 'geography', 'reference product', 'filename', \n 'image url']\nimage_report = DataFrame(image_report).transpose()\nimage_report.to_excel(os.path.join(result_folder, filename), cols = cols, \n merge_cells = False, index = False)\nfor exc in f.get_all_exchanges():\n print(exc.name, exc.activityLinkId, exc.amount)","sub_path":"modules/release/pdf_generator_old.py","file_name":"pdf_generator_old.py","file_ext":"py","file_size_in_byte":101127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"405321739","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport sys\nimport seaborn as sns\nsys.path.append('../../../../scripts/')\nfrom fig_settings import configure_fig_settings\nsys.path.append('../../modules_gammak24/')\nfrom plotobservables import PlotObservables\n\n\n\n# see user_inputs.md for details on what typically goes in these inputs.\nc_1 = input(\"please type the first gamma k24 coordinate, with a comma delimiter: \")\n\n#c_2 = input(\"please type the second gamma k24 coordinate, with a comma delimiter: \")\n\n#c_3 = input(\"please type the third gamma k24 coordinate, with a comma delimiter: \")\n\n#c_list = [c_1,c_2,c_3]\n\nc_list = [c_1]\n\ncoordinates = np.array([[float(s) for s in arg.split(',')] for arg in c_list],float)\n\n\nwidth = 3.487\nheight = width\n\nobservable_list = ['E','R','eta','delta','surfacetwist']\n\nconfigure_fig_settings()\n\nfig = {}\nax = {}\n\nfor observable in observable_list:\n \n fig[observable],ax[observable] = plt.subplots()\n\n fig[observable].set_size_inches(width,height)\n\n\ncolors = sns.color_palette()\n\nfor scan_dir in [\"scanforward\",\"scanbackward\"]:\n for i,coordinate in enumerate(coordinates):\n \n gamma,k24 = coordinate\n obs = PlotObservables(gamma,k24,scan_dir=scan_dir)\n obs.sort_observables()\n \n for j,observable in enumerate(observable_list):\n if scan_dir == \"scanforward\":\n obs.plot_observable_omega_eq_Lambda(ax[observable],observable,\n label=fr'$\\gamma_s,k_{{24}}={gamma},{k24}$',\n color=colors[j])\n else:\n obs.plot_observable_omega_eq_Lambda(ax[observable],observable,\n color=colors[j])\nxlabel = r'$\\omega=\\Lambda$'\n\nfor observable in observable_list:\n\n if observable == 'surfacetwist':\n ylabel = r'$\\psi(R)$'\n elif len(observable) > 1:\n ylabel = fr'$\\{observable}$'\n else:\n ylabel = fr'${observable}$'\n\n ax[observable].set_xlabel(xlabel)\n ax[observable].set_ylabel(ylabel)\n ax[observable].legend(frameon=False)\n fig[observable].tight_layout()\n fig[observable].savefig(obs.observable_sname(observable))\n\n\n\nplt.show()\n","sub_path":"gradient_descent_cos_in_denom/experiments/2018-12-05/gamma-k24-space-runs/observables_plot.py","file_name":"observables_plot.py","file_ext":"py","file_size_in_byte":2248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"49159843","text":"import math\nimport torch\n\nclass Quantizer:\n def __init__(self, mask) -> None:\n super().__init__()\n self.max = 32 # use no more than 5 bit (2^5 -> 32) for storage\n self.mask = mask # less than 5\n\n def quantify(self, activation_value):\n with torch.no_grad():\n rand = torch.rand(activation_value.shape).cuda() - 0.5\n activation = activation_value * self.max / 2\n compression_code = (activation / math.pow(2, self.mask) + rand).ceil()\n compression_code = torch.clamp(compression_code, 1 - math.pow(2, 4 - self.mask), math.pow(2, 4 - self.mask))\n eps = compression_code - activation_value\n compression = activation_value + eps\n return compression\n\n def restore(self, compression_code):\n with torch.no_grad():\n compression = compression_code * math.pow(2, self.mask)\n activation_value = compression / (self.max / 2)\n eps = activation_value - compression_code\n activation = compression_code + eps\n return activation","sub_path":"Data-Driven E2E Latency Estimation/1.Latency Sample Generation/1)Profiling and Modeling/(2)Services/b.Defect Detection/Train/3.ensemble/utils/quantization.py","file_name":"quantization.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"391696846","text":"from .diarization.diarization import diarize_all\n# from .EmotionsRecognizer.predict_mixed import prognoze_data\nimport sys\n\n\ndef pipeline(path_folder, gpu=False):\n name_folder = path_folder.split('/')[-2]\n diarize_all(name_folder, gpu)\n # prognoze_data(name_folder)\n\n\nif __name__ == '__main__':\n pipeline(sys.argv[1], True)","sub_path":"src/pipeline_1.py","file_name":"pipeline_1.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"212691357","text":"#!/usr/bin/python3.7\n\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output\nimport plotly.graph_objs as go\n#from plotly.subplots import make_subplots\nimport pandas as pd\nimport dash_table\nimport os\nimport dash_bootstrap_components as dbc\nfrom datetime import datetime as dt\n\napp = dash.Dash(external_stylesheets=[dbc.themes.FLATLY])\n\nmy_path = os.path.abspath(os.path.dirname(__file__))\npath = f'{my_path}/df_data/df_clean.pkl'\ndf_daily = pd.read_pickle(f'{path}')\n#df_daily = df_daily.fillna(0)\ndf = df_daily['2020-05-29':]\ndf['date'] = df.index.strftime('%x, %X')\ndf['avg_score'] = round(df['avg_score'], 2)\n\napp.layout = html.Div([\n dbc.Row([\n dbc.Col([\n html.Div([\n html.H1('CryptoNews'),\n dcc.Markdown('An interactive dashboard by [Manohar Sitapati](https://www.linkedin.com/in/manoharsitapati/).'),\n dcc.Markdown('Detailed documentation and code are available on [GitHub](https://github.com/TheManohar/CryptoNews#readme).')\n ], style={'padding':10}),\n html.Div([\n html.H3('Pick a Keyword'),\n dcc.Dropdown(\n id='coin-selector',\n options=[\n {'label': 'Crypto', 'value': 'crypto'},\n {'label': 'Bitcoin', 'value': 'bitcoin'},\n {'label': 'Ethereum', 'value': 'ethereum'}\n ],\n value='bitcoin',\n clearable=False\n )\n ], style={'padding':10}),\n html.Div([\n html.H3('Pick a Date Range'),\n dcc.DatePickerRange(\n id='date-picker',\n min_date_allowed=dt(2020, 5, 29),\n initial_visible_month=dt.today(),\n first_day_of_week=1\n )\n ], style={'padding':10})\n ], width=3),\n dbc.Col([\n html.Div([\n dcc.Graph(\n id='graph-1',\n config={'displayModeBar':False}\n )\n ],\n style={'padding':10}\n )\n ])\n ]),\n dbc.Row([\n dbc.Col([\n html.Div([\n dash_table.DataTable(\n id='table-1',\n columns=[\n {'name': 'Date', 'id': 'date'},\n {'name': 'Source', 'id': 'source'},\n {'name': 'Title', 'id': 'title'},\n {'name': 'Description', 'id': 'description'},\n {'name': 'Score', 'id': 'avg_score'}\n ],\n style_cell={\n 'textAlign': 'left',\n 'padding': '5px',\n 'whiteSpace': 'normal',\n 'height': 'auto',\n 'textOverflow': 'ellipsis',\n 'overflow': 'hidden',\n 'maxWidth': '800px'\n },\n style_cell_conditional=[\n {'if': {'column_id': 'title'},\n 'maxWidth': '600px'},\n {'if': {'column_id': 'description'},\n 'maxWidth': '800px'},\n ],\n sort_by=[{'column_id':'date', 'direction':'desc'}],\n filter_action=\"native\",\n sort_action=\"native\",\n sort_mode=\"multi\",\n page_action=\"native\",\n page_current= 0,\n page_size= 10\n )\n ])\n ])\n ], style={'padding-left':40, 'padding-right':40})\n])\n\n@app.callback(\n Output('graph-1', 'figure'),\n [Input('coin-selector', 'value'),\n Input('date-picker', 'start_date'),\n Input('date-picker', 'end_date')\n ])\ndef update_graph(coin, start_date, end_date):\n dff = df[df[str(coin)] == True]\n dff = dff[start_date:end_date]\n dff_as = pd.DataFrame()\n dff_as['avg_score'] = dff['avg_score'].resample('H').mean()\n dff_as['mvn_avg'] = dff_as['avg_score'].rolling('12h').mean()\n dff_as.fillna(0, inplace=True)\n dff_as = round(dff_as,2)\n dff_ac = pd.DataFrame()\n dff_ac['count'] = dff[str(coin)].resample('H').sum()\n dff_ac.fillna(0, inplace=True)\n bins=[0,1,2,3,99]\n colors=['tomato', 'orange', 'gold', 'lightgreen']\n dff_ac['color'] = pd.cut(dff_ac['count'], bins=bins, labels=colors)\n\n return {\n 'data': [\n go.Scatter(\n name=' Hourly Score',\n x=dff_as.index,\n y=dff_as['avg_score'],\n mode='lines',\n opacity=0.8,\n line=dict(\n color='midnightblue',\n width=1\n )\n ),\n go.Scatter(\n name='Moving Average',\n x=dff_as.index,\n y=dff_as['mvn_avg'],\n mode='lines',\n fill='tozeroy',\n opacity=0.8,\n line=dict(\n color='darkorange',\n width=2\n )\n ),\n go.Bar(\n name = 'Article Count',\n x=dff_ac.index,\n y=dff_ac['count'],\n yaxis='y2',\n opacity=0.5,\n marker=dict(\n color=dff_ac['color']\n )\n )\n ],\n 'layout': go.Layout(\n legend=dict(\n orientation='h',\n x=-0,\n y=1.1\n ),\n yaxis = dict(\n #tickformat=\".2%\",\n showticklabels=False,\n rangemode='tozero',\n range=[-1, 1],\n showgrid=False\n ),\n yaxis2 = dict(\n overlaying='y',\n side='right',\n rangemode='tozero',\n range=[0, 50],\n showgrid=False,\n ),\n xaxis = dict(\n showgrid=True,\n rangeselector=dict(\n buttons=list([\n dict(\n count=24,\n label=\"24h\",\n step=\"hour\",\n stepmode=\"backward\"\n ),\n dict(\n count=7,\n label=\"1w\",\n step=\"day\",\n stepmode=\"backward\"\n ),\n dict(\n count=1,\n label=\"1m\",\n step=\"month\",\n stepmode=\"backward\"\n ),\n dict(\n step=\"all\"\n )\n ])\n ),\n rangeslider = dict(\n visible=True,\n borderwidth=1,\n thickness=0.01\n ),\n tickformat = '%a %d-%m
%H:%M',\n type = \"date\",\n range=[df.index[-1]-pd.DateOffset(30), df.index[-1]]\n ),\n margin={'l': 40, 'b': 40, 't': 10, 'r': 0},\n dragmode= 'pan'\n )\n }\n\n@app.callback(\n Output('table-1', 'data'),\n [Input('coin-selector', 'value'),\n Input('date-picker', 'start_date'),\n Input('date-picker', 'end_date')\n ])\ndef update_table(coin, start_date, end_date):\n dff = df[df[str(coin)] == True]\n dff = dff[start_date:end_date]\n return dff.to_dict('records')","sub_path":"dash_app.py","file_name":"dash_app.py","file_ext":"py","file_size_in_byte":7833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"509211071","text":"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport re\r\nimport os\r\nfrom datetime import datetime\r\nfrom textwrap import wrap\r\nfrom langdetect import detect\r\nimport pandas as pd\r\nimport numpy as np\r\nimport re\r\nimport os\r\nfrom datetime import datetime\r\nfrom textwrap import wrap\r\nfrom langdetect import detect\r\n\r\nclass Preprocessing_Otodom:\r\n \"\"\"\r\n A class used to preprocess offers information from Otodom.pl.\r\n ...\r\n Attributes\r\n ----------\r\n apartment_details: name of apartments table\r\n information_types: columns of apartments table\r\n Methods\r\n -------\r\n remove_quotation_marks() -> pd.DataFrame:\r\n Remove quotation marks from columns.\r\n numeric_information() -> pd.DataFrame:\r\n Replace numeric information to float.\r\n remove_new_line_marks() -> pd.DataFrame:\r\n Remove new line marks from columns.\r\n prepare_table_information(table) -> pd.DataFrame:\r\n Change table information from list to dictionary and create external table from it.\r\n get_number(price_information: str) -> str:\r\n Get only numeric information of price from string.\r\n extract_price(apartment_details_price_table) -> pd.DataFrame:\r\n Extract price and currency from a string and convert price to float.\r\n prepare_additional_info(apartment_details_add_info_table, apartment_details_details_table) -> pd.DataFrame:\r\n Join additional information and details to additional information.\r\n prepare_description_table(apartment_details_description_table: pd.DataFrame) -> pd.DataFrame:\r\n Split description of 4000 characters to several columns and if record has more than 16000 characters check the language and get only polish description.\r\n create_table() -> pd.DataFrame:\r\n Create final preprocessing table.\r\n \"\"\"\r\n def __init__(self, apartment_details, information_types):\r\n \"\"\"\r\n Parameters\r\n ----------\r\n apartment_details : PandasDataFrame\r\n name of apartments table.\r\n information_types : str\r\n columns of apartments table.\r\n \"\"\"\r\n self.apartment_details = apartment_details\r\n self.information_types = information_types\r\n\r\n def remove_quotation_marks(self) -> pd.DataFrame:\r\n \"\"\"Remove quotation marks from columns.\r\n Parameters\r\n ----------\r\n information_types: str\r\n names of columns from which quotation marks need to be removed.\r\n apartment_details : pd.DataFrame\r\n data frame with information about apartments.\r\n Returns\r\n ------\r\n apartment_details: pd.DataFrame\r\n data frame with information about apartments.\r\n \"\"\"\r\n information_types = self.information_types\r\n apartment_details = self.apartment_details\r\n for information_type in information_types:\r\n for index in range(len(apartment_details)):\r\n if type(apartment_details.loc[:, information_type][index])==list:\r\n apartment_details.loc[:, information_type][index] = list(filter(lambda x: x != \"\", apartment_details.loc[:, information_type][index]))\r\n try:\r\n apartment_details.loc[:, information_type][index] = ', '.join(apartment_details.loc[:, information_type][index])\r\n except:\r\n continue\r\n else:\r\n continue\r\n return apartment_details\r\n\r\n\r\n def numeric_information(self) -> pd.DataFrame:\r\n \"\"\"Change numeric information to float.\r\n Parameters\r\n ----------\r\n information_types: str\r\n names of columns from which quotation marks need to be removed\r\n apartment_details: pd.DataFrame\r\n data frame with information about apartments.\r\n Returns\r\n ------\r\n apartment_details: pd.DataFrame\r\n data frame where numeric information type was changed to float.\r\n \"\"\"\r\n information_types = self.information_types\r\n apartment_details = self.remove_quotation_marks()\r\n for position, information_type in enumerate(information_types):\r\n try:\r\n [float(apartment_details.loc[:, information_type][index]) for index in range(len(apartment_details))]\r\n except:\r\n continue\r\n return apartment_details\r\n\r\n\r\n def remove_new_line_marks(self) -> pd.DataFrame:\r\n \"\"\"Remove new line marks from columns.\r\n Parameters\r\n ----------\r\n information_types: str\r\n names of columns from which new line marks need to be removed.\r\n apartment_details: pd.DataFrame\r\n data frame with information about apartments.\r\n Returns\r\n ------\r\n apartment_details: pd.DataFrame\r\n data frame where new line marks was removed.\r\n \"\"\"\r\n information_types = self.information_types\r\n apartment_details = self.numeric_information()\r\n for information_type in information_types:\r\n for index in range(len(apartment_details)):\r\n try:\r\n apartment_details.loc[:, information_type][index] = apartment_details.loc[:, information_type][\r\n index].replace('\\n\\n', ', ').replace(', ,', ',').replace('\\\\xa0',' ')\r\n except:\r\n continue\r\n return apartment_details\r\n\r\n def prepare_table_information(self, table: pd.DataFrame) -> pd.DataFrame:\r\n \"\"\"Change table information from list to dictionary and create external table from it.\r\n Parameters\r\n ----------\r\n table: pd.DataFrame\r\n column with table information.\r\n Returns\r\n ------\r\n prepared_tables: pd.DataFrame\r\n data frame with table information.\r\n \"\"\"\r\n prepared_table = []\r\n params_table = pd.DataFrame()\r\n for index,row in enumerate(table):\r\n\r\n try:\r\n list_info = row.replace(\":\", \", \").replace(\" ,\",\"\").split(\", \")\r\n to_append = dict([x for x in zip(*[iter(list_info)]*2)])\r\n prepared_table.append(to_append)\r\n except:\r\n prepared_table.append(None)\r\n\r\n for i in range(len(prepared_table)):\r\n column = []\r\n row = []\r\n try:\r\n for key, value in prepared_table[i].items():\r\n column.append(key.strip(':'))\r\n row.append(value)\r\n except:\r\n row.append(None)\r\n df_temp = pd.DataFrame([row], columns=column)\r\n params_table = pd.concat([params_table, df_temp], ignore_index=True)\r\n\r\n return params_table.where(pd.notnull(params_table),None)\r\n\r\n def get_number(self, price_information: str) -> str:\r\n \"\"\"Get only numeric information of price from string.\r\n Parameters\r\n ----------\r\n price_information: str\r\n string with numeric and string price information.\r\n Returns\r\n ------\r\n price_number: str\r\n string with numeric price information.\r\n \"\"\"\r\n signs = [',','.']\r\n if price_information in signs:\r\n price_number = price_information\r\n else:\r\n price_number = str.isdigit(price_information)\r\n return price_number\r\n\r\n def extract_price(self, apartment_details_price_table: pd.DataFrame) -> pd.DataFrame:\r\n \"\"\"Extract price and currency from a string and convert price to float.\r\n Parameters\r\n ----------\r\n apartment_details_price_table: pd.DataFrame\r\n column with information about price.\r\n Returns\r\n ------\r\n apartment_details_price_table: pd.DataFrame\r\n data frame where price information type was changed to float.\r\n \"\"\"\r\n\r\n currency = []\r\n for i in range(len(apartment_details_price_table)):\r\n try:\r\n filtered_str = filter(self.get_number, ''.join(apartment_details_price_table[i]))\r\n only_digit = \"\".join(filtered_str)\r\n if only_digit == \"\" or only_digit == None:\r\n apartment_details_price_table[i] = None\r\n currency.append(None)\r\n else:\r\n currency.append(''.join(apartment_details_price_table[i]).split()[-1])\r\n apartment_details_price_table[i] = float(only_digit.replace(\",\", \".\"))\r\n except:\r\n apartment_details_price_table[i] = None\r\n currency.append(None)\r\n \r\n self.apartment_details['currency'] = currency\r\n return apartment_details_price_table\r\n\r\n def prepare_additional_info(self, apartment_details_add_info_table: pd.DataFrame, apartment_details_details_table: pd.DataFrame) -> pd.DataFrame:\r\n \"\"\"Join additional information and details to additional information.\r\n Parameters\r\n ----------\r\n apartment_details_add_info_table: pd.DataFrame\r\n column with additional information.\r\n apartment_details_details_table: pd.DataFrame\r\n column with details information.\r\n Returns\r\n ------\r\n apartment_details_add_info_table: pd.DataFrame\r\n data frame with additional information.\r\n \"\"\"\r\n for i in range(len(apartment_details_add_info_table)):\r\n try:\r\n apartment_details_add_info_table[i] += (', ' + apartment_details_details_table[i].replace(\":\",\": \"))\r\n except:\r\n continue\r\n return apartment_details_add_info_table\r\n\r\n def prepare_description_table(self, apartment_details_description_table: pd.DataFrame) -> pd.DataFrame:\r\n \"\"\"Split description of 4000 characters to several columns and if record has more than 16000 characters check the language and get only polish description.\r\n Parameters\r\n ----------\r\n apartment_details_description_table: pd.DataFrame\r\n column with description.\r\n Returns\r\n ------\r\n description_1: List\r\n list with tha first part of description.\r\n \"\"\"\r\n description_1 = []\r\n description_2 = []\r\n description_3 = []\r\n description_4 = []\r\n\r\n\r\n for i in range(len(apartment_details_description_table)):\r\n desc_list = [None, None, None, None]\r\n if apartment_details_description_table[i]==None:\r\n description_splitted = None\r\n elif len(apartment_details_description_table[i]) > 16000:\r\n description = apartment_details_description_table[i]\r\n text = ' '.join(description.replace(\",\",\"\").replace(\"-\",\"\").split(\" \")).split()\r\n elements = [text[x:x+6] for x in range(0, len(text),6)]\r\n\r\n pl = []\r\n for index, element in enumerate(elements):\r\n element = list(map(str.lower,element))\r\n try:\r\n language = detect(\" \".join(element))\r\n except:\r\n language = 'pl'\r\n if language =='pl':\r\n pl.append(\" \".join(element))\r\n\r\n description_splitted = wrap(\" \".join(pl), 4000)\r\n\r\n else:\r\n try:\r\n description_splitted = wrap(apartment_details_description_table[i], 4000)\r\n except:\r\n description_splitted = wrap(''.join(apartment_details_description_table[i]), 4000)\r\n\r\n\r\n try:\r\n for element in range(len(description_splitted)):\r\n desc_list[element] = description_splitted[element]\r\n except:\r\n desc_list[element] = None\r\n\r\n description_1.append(desc_list[0])\r\n description_2.append(desc_list[1])\r\n description_3.append(desc_list[2])\r\n description_4.append(desc_list[3])\r\n\r\n self.apartment_details['description_2'] = description_2\r\n self.apartment_details['description_3'] = description_3\r\n self.apartment_details['description_4'] = description_4\r\n\r\n return description_1\r\n\r\n def create_table(self):\r\n \"\"\"Create final preprocessing table.\r\n Returns\r\n ------\r\n otodom_table: pd.DataFrame\r\n final preprocessing table with None instead of empty strings.\r\n \"\"\"\r\n otodom_table = pd.DataFrame()\r\n params_tables_otodom = self.prepare_table_information(table=self.remove_new_line_marks()['details'])\r\n otodom_table['area'] = self.apartment_details['Area']\r\n otodom_table['latitude'] = self.apartment_details['lat']\r\n otodom_table['longitude'] = self.apartment_details['lng']\r\n otodom_table['link'] = self.apartment_details['link']\r\n otodom_table['price'] = self.extract_price(self.apartment_details['price'])\r\n otodom_table['currency'] = self.apartment_details['currency']\r\n try:\r\n otodom_table['rooms'] = params_tables_otodom['Liczba pokoi']\r\n except:\r\n otodom_table['rooms'] = None\r\n try:\r\n otodom_table['floors_number'] = params_tables_otodom['Liczba pięter']\r\n except:\r\n otodom_table['floors_number'] = None\r\n try:\r\n otodom_table['floor'] = params_tables_otodom['Piętro']\r\n except:\r\n otodom_table['floor'] = None\r\n try:\r\n otodom_table['type_building'] = params_tables_otodom['Rodzaj zabudowy'].str.lower()\r\n except:\r\n otodom_table['type_building']=None\r\n try:\r\n otodom_table['material_building'] = params_tables_otodom['Materiał budynku'].str.lower()\r\n except:\r\n otodom_table['material_building'] = None\r\n try:\r\n otodom_table['year'] = params_tables_otodom['Rok budowy']\r\n except:\r\n otodom_table['year'] = None\r\n otodom_table['headers'] = self.apartment_details['additional_info_headers']\r\n otodom_table['additional_info'] = self.prepare_additional_info(apartment_details_add_info_table=self.apartment_details['additional_info'], apartment_details_details_table = self.apartment_details['details'])\r\n otodom_table['city'] = self.apartment_details['city']\r\n otodom_table['address'] = self.apartment_details['address']\r\n otodom_table['district'] = self.apartment_details['district']\r\n otodom_table['voivodeship'] = self.apartment_details['voivodeship']\r\n otodom_table['active'] = 'Yes'\r\n otodom_table['scrape_date'] = str(datetime.now().date())\r\n otodom_table['inactive_date'] = '-'\r\n otodom_table['page_name'] = 'Otodom'\r\n otodom_table['offer_title'] = self.apartment_details['title']\r\n otodom_table['description_1'] = self.prepare_description_table(self.apartment_details['description'])\r\n otodom_table['description_2'] = self.apartment_details['description_2']\r\n otodom_table['description_3'] = self.apartment_details['description_3']\r\n otodom_table['description_4'] = self.apartment_details['description_4']\r\n return otodom_table.replace({\"\": None})\r\n\r\nif \"__name__\" == \"__main__\":\r\n otodom_preprocess = Preprocessing_Otodom(apartment_details=apartments.where(pd.notnull(apartments),None), information_types=apartments.columns)\r\n\r\n start_time = datetime.now()\r\n otodom_table = otodom_preprocess.create_table()\r\n end_time = datetime.now()\r\n print('Duration: {}'.format(end_time - start_time))\r\n\r\n","sub_path":"Preprocessing_scripts/otodom.py","file_name":"otodom.py","file_ext":"py","file_size_in_byte":15496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"54121393","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nA couple of tests for the Exporter class and subclasses\n\"\"\"\n\nfrom spectraplotpy import Exporter\nfrom spectraplotpy import BaseTextExporter\nfrom spectraplotpy import CSVExporter\nfrom spectraplotpy import AvivExporter\n#from spectraplotpy import GraphicExporter\n\nfrom spectraplotpy import Dataset\n\ndef test_construction():\n \"\"\"Test the constructor of the class Exporter\"\"\"\n assert Exporter([])\n\ndef test_txt_construction():\n \"\"\"Test construction of the class BaseTextExporter\"\"\"\n assert BaseTextExporter([])\n\ndef test_default_function():\n \"\"\"Test for basic functionality\"\"\"\n ds = Dataset()\n bte = BaseTextExporter(ds)\n \n with open('.testfile.txt', 'w') as f:\n assert bte.write(f) is None\n\n assert bte.text()\n assert bte.save('.testfile.txt') is None\n\ndef test_aviv_exporter():\n \"\"\"Test for specific functionality of the AvivExporter\"\"\"\n ds = Dataset(x=[1, 2, 3], y=[1, 2, 3],\n metadata={'temp': 10, 'date': 'today'})\n ave = AvivExporter(ds)\n \n with open('.testfile.cd', 'w') as f:\n assert ave.write(f) is None\n\n assert ave.text()\n assert ave.save('.testfile.cd') is None\n\n\ndef test_csv_construction():\n ds = Dataset()\n assert CSVExporter(ds)\n\n\n\n\n","sub_path":"test/test_exporter.py","file_name":"test_exporter.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"314863374","text":"import anapioficeandfire\r\nimport discord\r\nimport STATICS\r\nfrom commands import cmd_gify\r\nimport configparser\r\nimport random\r\n\r\napi = anapioficeandfire.API()\r\n\r\nconfig = configparser.ConfigParser()\r\nconfig.read(\"SAVES/got_aliases.ini\")\r\n\r\n#aliases_houses = {}\r\n#\r\n#try:\r\n# for i in range(426, 450):\r\n# alias = api.get_house(id=i).name\r\n# if alias.startswith(\"House\"):\r\n# alias_splitted = alias.split(\" \")[1:]\r\n# alias = str(alias_splitted)[2:][:-2].replace(\",\", \"\").replace(\"'\", \"\")\r\n# if alias.__contains__(\" of \"):\r\n# alias = alias.split(\" of \")[0]\r\n# try:\r\n# aliases_houses[alias] = aliases_houses[alias] + \";\" + str(i)\r\n# except:\r\n# aliases_houses[alias] = str(i)\r\n# print(alias + \" : \" + aliases_houses[alias])\r\n#except:\r\n# print(\"error\")\r\n#config['Houses'] = aliases_houses\r\n#with open('../SAVES/got_aliases.ini', 'w') as configfile:\r\n# config.write(configfile)\r\n\r\nasync def executor(cmd, args, client, msg):\r\n try:\r\n if len(args) == 0 or len(args) == 1:\r\n await help(client, msg)\r\n return True\r\n embed = discord.Embed(\r\n title=\"<:null:397485751358455808>\",\r\n color=0x2ecc71,\r\n description=\" \"\r\n )\r\n embed.set_author(\r\n name=\"GAME OF THRONES\"\r\n )\r\n embed.set_footer(\r\n text=\"Hodor version \" + STATICS.version,\r\n icon_url=client.user.avatar_url\r\n )\r\n sended_msg = await client.send_message(msg.channel, \"Loading Data ... :inbox_tray:\")\r\n search_arg = \"\"\r\n for i in range(1, len(args)):\r\n search_arg += args[i] + \" \"\r\n if args[0] == \"character\":\r\n await get_character(search_arg, embed, client, sended_msg)\r\n elif args[0] == \"house\":\r\n await get_house(search_arg, embed, client, sended_msg)\r\n else:\r\n await help(client, msg)\r\n client.delete_message(sended_msg)\r\n return True\r\n return True\r\n except Exception as e:\r\n return e\r\n\r\n#----------------------------------------------Character--------------------------------------------------\r\nasync def get_character(name, embed, client, sended_msg):\r\n print(name)\r\n characters = api.get_characters(name=name)\r\n if len(characters) == 0:\r\n embed.add_field(\r\n name=\"No Character found with the name:\",\r\n value=str(name),\r\n )\r\n await client.edit_message(sended_msg, \" \", embed=embed)\r\n if len(characters) == 2:\r\n list = \"\"\r\n for i in range(len(characters)):\r\n list += characters[i].name + \"\\n\"\r\n embed.add_field(\r\n name=\"Found %s Characters:\" % str(len(characters)),\r\n value=list,\r\n inline=False\r\n )\r\n if len(characters) == 1:\r\n character = characters[0]\r\n print(\"Found one Character \" + character.name)\r\n\r\n embed.set_author(\r\n name=character.name.upper()\r\n )\r\n\r\n if not character.name == \"\":\r\n embed.add_field(\r\n name=\"Name:\",\r\n value=str(character.name),\r\n )\r\n\r\n if not character.gender == \"\":\r\n embed.add_field(\r\n name=\"Gender:\",\r\n value=str(character.gender),\r\n )\r\n\r\n if not character.father == \"\":\r\n father = character.father\r\n if father.startswith(\"http\"):\r\n id = father.split(\"/\")[-1]\r\n father = api.get_character(id=id).name\r\n embed.add_field(\r\n name=\"Father:\",\r\n value=father,\r\n )\r\n\r\n if not character.mother == \"\":\r\n mother = character.mother\r\n if mother.startswith(\"http\"):\r\n id = mother.split(\"/\")[-1]\r\n mother = api.get_character(id=id).name\r\n embed.add_field(\r\n name=\"Mother:\",\r\n value=mother,\r\n )\r\n\r\n if not character.spouse == \"\":\r\n spouse = character.spouse\r\n if spouse.startswith(\"http\"):\r\n id = spouse.split(\"/\")[-1]\r\n spouse = api.get_character(id=id).name\r\n embed.add_field(\r\n name=\"Spouse:\",\r\n value=spouse,\r\n )\r\n\r\n if not character.culture == \"\":\r\n embed.add_field(\r\n name=\"Culture:\",\r\n value=str(character.culture),\r\n )\r\n\r\n if not character.born == \"\":\r\n embed.add_field(\r\n name=\"Born:\",\r\n value=str(character.born),\r\n )\r\n\r\n if not character.died == \"\":\r\n embed.add_field(\r\n name=\"Died:\",\r\n value=str(character.died),\r\n )\r\n\r\n actors = \"\"\r\n for actor in character.playedBy:\r\n actors += actor + \"\\n\"\r\n if not actors == \"\":\r\n embed.add_field(\r\n name=\"Played By:\",\r\n value=actors,\r\n )\r\n\r\n titles = \"\"\r\n for title in character.titles:\r\n titles += title + \"\\n\"\r\n if not titles == \"\":\r\n embed.add_field(\r\n name=\"Titles:\",\r\n value=titles,\r\n )\r\n\r\n aliases = \"\"\r\n for alias in character.aliases:\r\n aliases += alias + \"\\n\"\r\n if not aliases == \"\":\r\n embed.add_field(\r\n name=\"Aliases:\",\r\n value=aliases,\r\n )\r\n\r\n allegiances = \"\"\r\n for allegiance in character.allegiances:\r\n if allegiance.startswith(\"http\"):\r\n id = allegiance.split(\"/\")[-1]\r\n allegiances += api.get_house(id=id).name + \"\\n\"\r\n else:\r\n allegiances += allegiance + \"\\n\"\r\n if not allegiances == \"\":\r\n embed.add_field(\r\n name=\"Allegiances:\",\r\n value=allegiances,\r\n )\r\n books = \"\"\r\n\r\n for book in character.povBooks:\r\n if book.startswith(\"http\"):\r\n id = book.split(\"/\")[-1]\r\n books += api.get_book(id=id).name + \"\\n\"\r\n else:\r\n books += book + \"\\n\"\r\n if not books == \"\":\r\n embed.add_field(\r\n name=\"Books:\",\r\n value=books,\r\n )\r\n\r\n seasons = \"\"\r\n for season in character.tvSeries:\r\n seasons += season + \"\\n\"\r\n if not seasons == \"\":\r\n embed.add_field(\r\n name=\"Series:\",\r\n value=seasons,\r\n )\r\n\r\n embed.add_field(\r\n name=\"<:null:397485751358455808>\",\r\n value=\"More Informations: http://awoiaf.westeros.org/index.php/\" + character.name.replace(\" \", \"_\")\r\n )\r\n\r\n embed.set_image(url=cmd_gify.get_gif_url(character.name))\r\n\r\n await client.edit_message(sended_msg, \" \", embed=embed)\r\n\r\n\r\n#-------------------------------------------------------------House----------------------------------------------------------------\r\nasync def get_house(name, embed, client, sended_msg):\r\n ids=None\r\n houses=None\r\n try:\r\n if not name.lower().__contains__(\"of\"):\r\n alias = name.lower()\r\n if alias.startswith(\"house \"):\r\n alias = alias.split(\" \")[1]\r\n config_get = config['Houses'][alias.replace(\" \", \"\")]\r\n ids = config_get.split(\";\")\r\n print(\"Found alias \" + alias + \" : \" + str(ids))\r\n if len(ids) == 1:\r\n houses = api.get_houses(name=api.get_house(id=ids[0]).name)\r\n else:\r\n houses = []\r\n for id in ids:\r\n houses.append(api.get_house(id=id))\r\n else:\r\n i = \"1\"+1\r\n except:\r\n print(\"Dont found in aliases ...\")\r\n try:\r\n if name.lower().startswith(\"house \"):\r\n houses = api.get_houses(name=name)\r\n else:\r\n houses = api.get_houses(name=\"House \" + name)\r\n except:\r\n print(\"Dont found\")\r\n embed.add_field(\r\n name=\"No House found with the name:\",\r\n value=str(name),\r\n )\r\n await client.edit_message(sended_msg, \" \", embed=embed)\r\n\r\n if len(houses) == 0:\r\n embed.add_field(\r\n name=\"No House found with the name:\",\r\n value=str(name),\r\n )\r\n await client.edit_message(sended_msg, \" \", embed=embed)\r\n if len(houses) >= 2:\r\n list = \"\"\r\n for i in range(len(houses)):\r\n list += houses[i].name + \"\\n\"\r\n embed.add_field(\r\n name=\"Found %s Houses:\" % str(len(houses)),\r\n value=list,\r\n inline=False\r\n )\r\n if len(houses) == 1:\r\n house = houses[0]\r\n\r\n embed.set_author(\r\n name=house.name.upper()\r\n )\r\n\r\n if not house.name == \"\":\r\n embed.add_field(\r\n name=\"Name:\",\r\n value=str(house.name),\r\n )\r\n\r\n if not house.region == \"\":\r\n embed.add_field(\r\n name=\"Region:\",\r\n value=str(house.region),\r\n )\r\n\r\n if not house.currentLord == \"\":\r\n lord = house.currentLord\r\n if lord.startswith(\"http\"):\r\n id = lord.split(\"/\")[-1]\r\n lord = api.get_character(id=id).name\r\n embed.add_field(\r\n name=\"Lord:\",\r\n value=lord,\r\n )\r\n\r\n if not house.overlord == \"\":\r\n overlord = house.overlord\r\n if overlord.startswith(\"http\"):\r\n id = overlord.split(\"/\")[-1]\r\n overlord = api.get_character(id=id).name\r\n embed.add_field(\r\n name=\"OverLord:\",\r\n value=overlord,\r\n )\r\n\r\n if not house.heir == \"\":\r\n heir = house.heir\r\n if heir.startswith(\"http\"):\r\n id = heir.split(\"/\")[-1]\r\n heir = api.get_character(id=id).name\r\n embed.add_field(\r\n name=\"Heir:\",\r\n value=heir,\r\n )\r\n\r\n if not house.founded == \"\":\r\n embed.add_field(\r\n name=\"Founded:\",\r\n value=str(house.founded),\r\n )\r\n\r\n if not house.founder == \"\":\r\n founder = house.founder\r\n if founder.startswith(\"http\"):\r\n id = founder.split(\"/\")[-1]\r\n founder = api.get_character(id=id).name\r\n embed.add_field(\r\n name=\"Founder:\",\r\n value=founder,\r\n )\r\n\r\n if not house.coatOfArms == \"\":\r\n embed.add_field(\r\n name=\"Coat Of Arms:\",\r\n value=str(house.coatOfArms),\r\n )\r\n\r\n if not house.words == \"\":\r\n embed.add_field(\r\n name=\"Words:\",\r\n value=str(house.words),\r\n )\r\n\r\n seats = \"\"\r\n for seat in house.seats:\r\n seats += seat + \"\\n\"\r\n if not seats == \"\":\r\n embed.add_field(\r\n name=\"Seats:\",\r\n value=seats,\r\n )\r\n\r\n titles = \"\"\r\n for title in house.titles:\r\n titles += title + \"\\n\"\r\n if not titles == \"\":\r\n embed.add_field(\r\n name=\"Titles:\",\r\n value=titles,\r\n )\r\n\r\n if not house.diedOut == \"\":\r\n embed.add_field(\r\n name=\"Died Out:\",\r\n value=str(house.diedOut),\r\n )\r\n\r\n cadetbranches = \"\"\r\n for cadetbranch in house.cadetBranches:\r\n if cadetbranch.startswith(\"http\"):\r\n id = cadetbranch.split(\"/\")[-1]\r\n cadetbranches += api.get_house(id=id).name + \"\\n\"\r\n else:\r\n cadetbranches += cadetbranch + \"\\n\"\r\n if not cadetbranches == \"\":\r\n embed.add_field(\r\n name=\"Cadet Branches:\",\r\n value=cadetbranches,\r\n )\r\n\r\n embed.add_field(\r\n name=\"<:null:397485751358455808>\",\r\n value=\"More Informations: http://awoiaf.westeros.org/index.php/\" + house.name.split(\" of \")[0].replace(\" \", \"_\")\r\n )\r\n await client.add_reaction(sended_msg, \"🙏\")\r\n\r\n #embed.set_image(url=cmd_gify.get_gif_url(house.name))\r\n await client.edit_message(sended_msg, \" \", embed=embed)\r\n\r\n\r\nasync def help(client, msg):\r\n embed = discord.Embed(\r\n title=\"<:null:397485751358455808>\",\r\n color=0x2ecc71,\r\n description=\"\\n\"\r\n \"\"\r\n )\r\n embed.add_field(\r\n name=\"Commands:\",\r\n value=\"got house [name]\\n<:null:397485751358455808>\\n\"\r\n \"got character [name]\",\r\n )\r\n embed.add_field(\r\n name=\"Description:\",\r\n value=\"Shows informations\\nabout a House in GoT\\n\"\r\n \"Shows informations\\nabout a Character in GoT\",\r\n )\r\n embed.set_footer(\r\n text=\"Hodor version %s\" % STATICS.version,\r\n icon_url=client.user.avatar_url\r\n )\r\n embed.set_thumbnail(\r\n url=\"https://emojipedia-us.s3.amazonaws.com/thumbs/160/google/110/scroll_1f4dc.png\"\r\n )\r\n embed.set_author(\r\n name=\"HELP\"\r\n )\r\n\r\n await client.send_message(msg.channel, embed=embed)\r\n\r\n\r\nasync def send_sworn_private(house, client, user):\r\n house = api.get_houses(name=house)[0]\r\n print(\"Loading Sworns for \" + house.name + \" | Requested by \" + user.name)\r\n embed = discord.Embed(\r\n title=\"<:null:397485751358455808>\",\r\n color=0x2ecc71,\r\n description=\" \"\r\n )\r\n\r\n sworns = \"\"\r\n n = 0\r\n if len(house.swornMembers) > 20:\r\n n = 20\r\n else:\r\n n = len(house.swornMembers)\r\n for i in range(1, n):\r\n sworn = random.choice(house.swornMembers)\r\n if sworns.__contains__(sworn):\r\n sworn = random.choice(house.swornMembers)\r\n if sworn.startswith(\"http\"):\r\n try:\r\n id = sworn.split(\"/\")[-1]\r\n sworns += api.get_character(id=id).name + \", \"\r\n except:\r\n i = 0\r\n else:\r\n sworns += sworn + \"\\n\"\r\n if not sworns == \"\":\r\n embed.add_field(\r\n name=\"%s of the Sworn Members:\" % str(n),\r\n value=sworns,\r\n )\r\n\r\n embed.set_footer(\r\n text=\"Hodor version %s\" % STATICS.version,\r\n icon_url=client.user.avatar_url\r\n )\r\n\r\n embed.set_author(\r\n name=house.name.upper()\r\n )\r\n\r\n await client.send_message(user, embed=embed)\r\n","sub_path":"commands/got_old.py","file_name":"got_old.py","file_ext":"py","file_size_in_byte":14747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"546022609","text":"from AgentState import AgentState\nfrom random import randint\n\nclass Agent:\n\tdef __init__(self,id, vertex):\n\t\tself.id = id\n\t\tself.state = AgentState()\n\t\tself.location = vertex\n\n\tdef get_coords_from_movement(self,direction, row, col):\n\t\tif direction == \"L\":\n\t\t\treturn row, col-1\n\t\telif direction == \"R\":\n\t\t\treturn row, col+1\n\t\telif direction == \"U\":\n\t\t\treturn row-1, col\n\t\telif direction == \"D\":\n\t\t\treturn row+1, col\n\t\telse:\n\t\t\treturn row, col\n\n\tdef transition(self,nearby_agents):\n\t\t# Depends on the application\n\t\tdirection_map = {0:\"S\", 1:\"L\", 2:\"R\", 3:\"U\", 4:\"D\"}\n\n\t\t# Try to cluster\n\t\tif len(nearby_agents) == 0:\n\t\t\treturn self.state, \"S\"\n\n\t\ttotal_dist = 0\n\t\tfor agent in nearby_agents:\n\t\t\ttotal_dist += abs(self.location.row-agent.location.row)\n\t\t\ttotal_dist += abs(self.location.col-agent.location.col)\n\n\t\tbest_move = \"S\"\n\t\tbest_distance = 0\n\n\t\tfor direction in direction_map.values():\n\t\t\tnew_loc = self.get_coords_from_movement(direction, self.location.row, self.location.col)\n\n\t\t\tdist = 0\n\t\t\tfor agent in nearby_agents:\n\t\t\t\tdist += abs(new_loc[0]-agent.location.row)+abs(new_loc[1]-agent.location.row)\n\t\t\tif dist > best_distance:\n\t\t\t\tbest_distance = dist\n\t\t\t\tbest_move = direction\n\t\treturn self.state, best_move\n\n\t\t# Random direction movement\n\t\t# return self.state, direction[randint(0,4)] \n\n\n","sub_path":"Agent.py","file_name":"Agent.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"66124952","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\nurlpatterns = patterns('', \n url(r'^ator/(?P
参与活动即有机会获得大奖。活动为概率中奖，奖品数量有限，祝君好运。
惊喜一：1000元现金
惊喜二：500元现金
惊喜三：200元现金
惊喜四：100元现金
惊喜五：50元现金
惊喜六：幸运奖
重要声明：
1、实物类奖品将在活动结束后5-10个工作日内安排发货，请耐心等待
2、卡券类奖品使用规则详见卡券介绍页
3、通过非法途径获得奖品的，主办方有权不提供奖品
\",\r\n \"isPopupAd\":\"1\",\r\n \"popupTemplateId\":\"17\",\r\n \"isRelevance\":\"1\",\r\n \"customAct\":\"0\",\r\n \"actStyleId\":\"109\"\r\n }\r\n json_body['actName'] = self.act_name\r\n json_body['templateId'] = template_id\r\n json_body['freeNum'] = free_num\r\n re = self.s.post(post_url, data =json_body)\r\n return re\r\n # act_sql=\"\"\"\r\n # INSERT INTO voyager.base_act_info (act_type,act_name,banner_image_url,cover_image_url,award_num,free_num,begin_time,end_time,act_rule_info,`STATUS`,update_time,create_time,template_id,expand1,expand2,expand3,expand4,expand5,expand6,expand7,expand8,expand9,change_times\r\n # )VALUES(1,{0},'https://img4.adhudong.com/award/201802/22/089c376e9519c85ab8ce5fced7c9ea49.jpg',\r\n # NULL,{1},{2},NULL,NULL,\r\n # '参与活动即有机会获得大奖。活动为概率中奖，奖品数量有限，祝君好运。
惊喜一：1000元现金
惊喜二：500元现金
惊喜三：200元现金
惊喜四：100元现金
惊喜五：50元现金
惊喜六：幸运奖
重要声明：
1、实物类奖品将在活动结束后5-10个工作日内安排发货，请耐心等待
2、卡券类奖品使用规则详见卡券介绍页
3、通过非法途径获得奖品的，主办方有权不提供奖品1
',\r\n # 1,now(),now(),{3},1,NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL\r\n # );\"\"\".format(\"'\"+self.act_name+\"'\", self.award_num, free_num, templateId)\r\n # print(act_sql)\r\n # try:\r\n # self.db.execute_sql(act_sql)\r\n # self.db.mycommit()\r\n # if self.get_actId():\r\n # return ('create_act,成功了')\r\n # else:\r\n # try:\r\n # raise myException('create_act, 失败了')\r\n # except Exception as e:\r\n # return e.message\r\n # except:\r\n # self.db.myrollback()\r\n\r\n\r\n def create_awards(self):\r\n act_id = str(self.get_actId())\r\n # act_id = str(self.get_actId('淘粉吧转盘0228'))\r\n #创建奖品的sql\r\n award_ths = ''',0,15,1,now(),now(),'谢谢参与','https://img0.adhudong.com/association/201802/22/10bf5888ea77ea198db1dbadc663b05f.jpg',\r\n 6,NULL,NULL,NULL,'商品详情的信息
','1.实物类奖品将在活动结束后5-10个工作日内安��发货，请耐心等待；
2.卡券类奖品使用规则详见卡券介绍页
')'''\r\n award_onemore= ''',0,15,2,now(),now(),'再抽一次奖品','https://img4.adhudong.com/association/201802/22/49b99fca05649598059cc2dc733509f4.jpg',\r\n 5,NULL,NULL,NULL,'商品详情的信息
','1.实物类奖品将在活动结束后5-10个工作日内安排发货，请耐心等待；
2.卡券类奖品使用规则详见卡券介绍页
')'''\r\n award_lucky1= ''',0,70,3,now(),now(),'百元现金红包','https://img3.adhudong.com/association/201802/22/2c6f4700db7982447348db4d0960e3ad.png',\r\n 7,NULL,NULL,NULL,'商品详情的信息
','1.实物类奖品将在活动结束后5-10个工作日内安排发货，请耐心等待；
2.卡券类奖品使用规则详见卡券介绍页
')'''\r\n award_lucky2= ''',0,0,4,now(),now(),'20G流量','https://img2.adhudong.com/association/201802/22/d1c54c0eb8259f992f500015f67f8907.png',\r\n 7,NULL,NULL,NULL,'商品详情的信息
','1.实物类奖品将在活动结束后5-10个工作日内安排发货，请耐心等待；
2.卡券类奖品使用规则详见卡券介绍页
')'''\r\n award_lucky3 = ''',0,0,5,now(),now(),'第3个大奖品','https://img0.adhudong.com/association/201802/22/a4a40a28a4b21c1b50011102f07801a0.png',\r\n 7,NULL,NULL,NULL,'商品详情的信息
','1.实物类奖品将在活动结束后5-10个工作日内安排发货，请耐心等待；
2.卡券类奖品使用规则详见卡券介绍页
')'''\r\n award_lucky4 = ''',0,0,6,now(),now(),'第4个奖品','https://img0.adhudong.com/association/201802/22/a4a40a28a4b21c1b50011102f07801a0.png',\r\n 7,NULL,NULL,NULL,'商品详情的信息
','1.实物类奖品将在活动结束后5-10个工作日内安排发货，请耐心等待；
2.卡券类奖品使用规则详见卡券介绍页
')'''\r\n award_lucky5 = ''',0,0,7,now(),now(),'第5个奖品','https://img3.adhudong.com/association/201801/09/7f7b527213eccb7e2279b428379bf193.png',\r\n 7,NULL,NULL,NULL,'商品详情的信息
','1.实物类奖品将在活动结束后5-10个工作日内安排发货，请耐心等待；
2.卡券类奖品使用规则详见卡券介绍页
')'''\r\n award_lucky6 = ''',0,0,8,now(),now(),'第6个奖品','https://img2.adhudong.com/association/201801/09/8f3e1ae44f5f69d1f7d9730243f5a2ec.png',\r\n 7,NULL,NULL,NULL,'商品详情的信息
','1.实物类奖品将在活动结束后5-10个工作日内安排发货，请耐心等待；
2.卡券类奖品使用规则详见卡券介绍页
')'''\r\n\r\n award_6_list = [award_ths, award_onemore, award_lucky1, award_lucky2, award_lucky3, award_lucky4]\r\n award_8_list = [award_ths, award_onemore, award_lucky1, award_lucky2, award_lucky3, award_lucky4, award_lucky5, award_lucky6]\r\n if int(self.award_num) == 6:\r\n award_list = award_6_list\r\n else:\r\n award_list = award_8_list\r\n for award in award_list:\r\n award_sql = '''INSERT INTO act_award (act_id,award_id,award_rate,\r\n priority,update_time,create_time,show_copy,award_icon,act_award_type,begin_time,end_time,award_num,\r\n award_details,award_get_instructions)\r\n VALUES (''' + act_id + award\r\n print(award_sql)\r\n try:\r\n self.db.execute_sql(award_sql)\r\n self.db.mycommit()\r\n except:\r\n self.db.myrollback()\r\n if self.get_awards_count() == self.award_num:\r\n return '奖品添加成功,奖品个数为' + str(self.award_num)\r\n else:\r\n return '奖品添加失败'\r\n\r\n def __del__(self):\r\n self.db.close_cursor()\r\n self.db.close_db()\r\n\r\n\r\n def create_volidate_info(self):\r\n sql = \"select id from voyager.advertiser where type=2 and state in (2,3,5) limit 15\"\r\n adv_ids = self.db.execute_sql(sql)\r\n adv_id = ''\r\n post_url = \"http://api.admin.adhudong.com/adver/violationInsert.htm\"\r\n\r\n adv_count = len(adv_ids)\r\n for i in range(adv_count):\r\n adv_id = str(adv_ids[i][0])\r\n remark = '测试违规记录分页数据'+str(i)\r\n params = {'violationType': '1',\r\n 'violationRemark': remark,\r\n 'violationLink': 'http://music.baidu.com/songlist/tag/%E7%BA%AF%E9%9F%B3%E4%B9%90?orderType=1',\r\n 'advertiserId': adv_id\r\n }\r\n print(params)\r\n # re = self.s.post(post_url, data=params)\r\n # print(re.url)\r\n\r\n def get_list(self):\r\n url = \"http://api.admin.adhudong.com/advertiser/creativelist.htm?page=1&page_size=200\"\r\n re = self.s.get(url).json()['data']['data']\r\n re_len = len(re)\r\n # print(re)\r\n for i in range(re_len):\r\n print(re[i]['id'],re[i]['state'], re[i]['lastSubReviewTime'], re[i]['createTime'])\r\n # print(re[i]['state'])\r\n\r\n def adzone_act(self):\r\n dsql=r\"delete from voyager.adzone_act where adzone_id in ({});\".format(self.adzoneId)\r\n self.db.execute_sql(dsql)\r\n isql=r'''INSERT INTO voyager.adzone_act (adzone_id,act_id,act_time,priority,act_begin_time,act_end_time ) VALUES\r\n ( '{}','{}','1', '1','{}','{}' );'''.format(self.adzoneId, self.get_actId(), self.begin_time, self.end_time)\r\n self.db.execute_sql(isql)\r\n keysql = '''select app_key from voyager.base_adzone_info where id in ({})'''.format(self.adzoneId)\r\n key_re = self.db.execute_sql(keysql)\r\n return self.appkey + str(key_re[0][0])\r\n\r\nif __name__=='__main__':\r\n #为模板类型名称\r\n #__init__(self, templateTypeName, act_name, award_num):\r\n ct = TemplateActCreation('九宫格ce2',8,15,1810)\r\n #创建模板类型，create_template_type(self, classifi, locationAdress, preview=\"https://img0.adhudong.com/template/201802/24/999337a35a1a9169450685cc66560a05.png\",prizesNum=6)\r\n # ct.create_template_type('https://display.adhudong.com/new/nineBlockBox.html')\r\n\r\n ct.create_template('testTemp','https://display.adhudong.com/new/nineBlockBox.html')\r\n # ct.get_list()\r\n #创建模板 ct.create_template(templateName, templateStyleUrl)\r\n # ct.create_template('九宫格ce2',\"https://display.adhudong.com/new/nineBlockBox.html\",template_conf_items=r'''背景(750*1206)|bg|image||1|\\n\r\n# 全屏底色|bodyBgColor|string||0|\\n\r\n# 剩余次数文字颜色|leftTimesColor|string||0|/n\r\n# 剩余次数数字颜色|leftTimesNumColor|string||0|)''')\r\n # #创建活动， create_act(self, act_name,free_num=20, award_num=6)\r\n # ct.create_act()\r\n # #创建活动关联的奖品，\r\n # ct.create_awards()\r\n","sub_path":"business_modle/querytool/create_template.py","file_name":"create_template.py","file_ext":"py","file_size_in_byte":16467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"529783187","text":"#!/usr/bin/env python\n\nimport RPi.GPIO as GPIO\nimport time\n\nGPIO.setmode(GPIO.BCM) # use GPIO Number\n\nBZ1 = 13 # BZ1 --> GPIO13\nGPIO.setup(BZ1, GPIO.OUT) # set GPIO13 output\n\nbuzzer = GPIO.PWM(BZ1, 1000) # set BZ1 1000 Hz\n\nbuzzer.start(50) # start BZ1 duty 50%\nprint(\" buzzer 1000 Hz ,duty 50 %\")\ntime.sleep(3)\n\nbuzzer.ChangeFrequency(500) # change frequency 500 Hz\nprint(\" change 500 Hz \")\ntime.sleep(3)\n\nbuzzer.ChangeDutyCycle(10) # change duty cycle 10 %\nprint(\" duty 10 % \")\ntime.sleep(3)\n\nbuzzer.stop() # stop buzzer\nGPIO.cleanup()\n\n","sub_path":"python/buzzur/bz_sample1.py","file_name":"bz_sample1.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"619182044","text":"#!/usr/bin/python3\n\nfrom brownie import *\nfrom scripts.deployment import main\n\n\ndef setup():\n config['test']['always_transact'] = False\n main(NFToken)\n global token, issuer\n token = NFToken[0]\n issuer = IssuingEntity[0]\n\n\ndef mint_issuer():\n '''mint to issuer'''\n token.mint(issuer, 1000, 0, \"0x00\", {'from': a[0]})\n _check_countries()\n token.mint(issuer, 9000, 0, \"0x00\", {'from': a[0]})\n _check_countries()\n\ndef burn_issuer():\n '''burn from issuer'''\n token.mint(issuer, 10000, 0, \"0x00\", {'from': a[0]})\n token.burn(1, 2001, {'from': a[0]})\n _check_countries()\n token.burn(2001, 10001, {'from': a[0]})\n _check_countries()\n\n\ndef mint_investors():\n '''mint to investors'''\n _check_countries()\n token.mint(a[1], 1000, 0, \"0x00\", {'from': a[0]})\n _check_countries(one=(1, 1, 0))\n token.mint(a[1], 1000, 0, \"0x00\", {'from': a[0]})\n token.mint(a[2], 1000, 0, \"0x00\", {'from': a[0]})\n token.mint(a[3], 1000, 0, \"0x00\", {'from': a[0]})\n _check_countries(one=(2, 1, 1), two=(1, 1, 0))\n token.mint(a[1], 996000, 0, \"0x00\", {'from': a[0]})\n _check_countries(one=(2, 1, 1), two=(1, 1, 0))\n\ndef burn_investors():\n '''burn from investors'''\n token.mint(a[1], 5000, 0, \"0x00\", {'from': a[0]})\n token.mint(a[2], 3000, 0, \"0x00\", {'from': a[0]})\n token.mint(a[3], 2000, 0, \"0x00\", {'from': a[0]})\n token.burn(1, 1001, {'from': a[0]})\n _check_countries(one=(2, 1, 1), two=(1, 1, 0))\n token.burn(1001, 5001, {'from': a[0]})\n _check_countries(one=(1, 0, 1), two=(1, 1, 0))\n token.burn(5001, 8001, {'from': a[0]})\n token.burn(8001, 10001, {'from': a[0]})\n _check_countries()\n\ndef _check_countries(one=(0,0,0),two=(0,0,0),three=(0,0,0)):\n check.equal(\n issuer.getInvestorCounts()[0][:3],\n (\n one[0]+two[0]+three[0],\n one[1]+two[1]+three[1],\n one[2]+two[2]+three[2]\n )\n )\n check.equal(issuer.getCountry(1)[1][:3], one)\n check.equal(issuer.getCountry(2)[1][:3], two)\n check.equal(issuer.getCountry(3)[1][:3], three)","sub_path":"tests/nft/mint_burn/investor_counts.py","file_name":"investor_counts.py","file_ext":"py","file_size_in_byte":2075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"317447446","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec 6 19:11:40 2019\n\n@author: rhari\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndiabetes=pd.read_csv('Diabetes.csv')\ndiabetes.columns\ndiabetes.head\ndiabetes.describe()\nfeature_names=['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']\nX=diabetes[feature_names]\ny=diabetes.Outcome\n\nprint(\"Diabetes data set dimensions:{}\".format(diabetes.shape))\n\n#Visualising the dataset\ncorr=diabetes.corr()\nprint(corr)\nsns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns)\n\nsns.countplot(x=diabetes['Age'])\nsns.countplot(x=diabetes['Age'], hue=diabetes['Outcome'])\nsns.countplot(diabetes['Outcome'])\n\nplt.scatter(diabetes['Age'], diabetes['BMI'])\nplt.xlabel('Age')\nplt.ylabel('BMI')\nplt.show()\n\ndiabetes.groupby('Outcome').size()\ndiabetes.hist(bins=50, figsize=(20,15))\nplt.show()\n\n#Data cleaning\ndiabetes.isnull().sum()\ndiabetes.isna().sum()\n\nmedian_bmi=diabetes['BMI'].median()\ndiabetes['BMI']=diabetes['BMI'].replace(to_replace=0, value=median_bmi)\n\nmedian_bloodp=diabetes['BloodPressure'].median()\ndiabetes['BloodPressure']=diabetes['BloodPressure'].replace(to_replace=0, value=median_bloodp)\n\nmedian_gluc=diabetes['Glucose'].median()\ndiabetes['Glucose']=diabetes['Glucose'].replace(to_replace=0, value=median_gluc)\n\nmedian_skin=diabetes['SkinThickness'].median()\ndiabetes['SkinThickness']=diabetes['SkinThickness'].replace(to_replace=0, value=median_skin)\n\nmedian_ins=diabetes['Insulin'].median()\ndiabetes['Insulin']=diabetes['Insulin'].replace(to_replace=0, value=median_ins)\n\n#Feature selection\nfrom sklearn.feature_selection import SelectKBest, chi2\nfeature_ranking=SelectKBest(chi2, k=5)\nfit=feature_ranking.fit(X,y)\nfmt = '%-8s%-20s%s'\n\nprint(fmt % ('', 'Scores', 'Features'))\nfor i, (score, feature) in enumerate(zip(feature_ranking.scores_, X.columns)):\n print(fmt % (i, score, feature))\n \nplt.bar(feature_names ,feature_ranking.scores_)\nplt.xlabel('Features', color='red')\nplt.ylabel('Feature Score', color='red')\nplt.show()\n\n#Splitting\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test= train_test_split(X,y, stratify=diabetes.Outcome, random_state=0)\nnames=[]\nscores=[]\n\n#Model Selection\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import accuracy_score\n\n#Applying K-Nearest Neighbors Model\nfrom sklearn.neighbors import KNeighborsClassifier\nclassifier1=KNeighborsClassifier()\nclassifier1.fit(X_train, y_train)\ny_pred=classifier1.predict(X_test)\n\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import f1_score\nconf_matrix = confusion_matrix(y_test,y_pred)\nprint(conf_matrix)\nprint(f1_score(y_test,y_pred))\n\nscores.append(accuracy_score(y_test, y_pred))\nnames.append(\"K-Nearest Neighbor\")\n\n#Applying Naive Bayes Model\nfrom sklearn.naive_bayes import GaussianNB\nclassifier2=GaussianNB()\nclassifier2.fit(X_train,y_train)\ny_pred=classifier2.predict(X_test)\n\nconf_matrix = confusion_matrix(y_test,y_pred)\nprint(conf_matrix)\nprint(f1_score(y_test,y_pred))\n\nscores.append(accuracy_score(y_test,y_pred))\nnames.append(\"Naive Bayes\")\n\n#Applying Logistic Regression Model\nfrom sklearn.linear_model import LogisticRegression\nclassifier3=LogisticRegression()\nclassifier3.fit(X_train, y_train)\ny_pred=classifier3.predict(X_test)\n\nconf_matrix = confusion_matrix(y_test,y_pred)\nprint(conf_matrix)\nprint(f1_score(y_test,y_pred))\n\nscores.append(accuracy_score(y_test, y_pred))\nnames.append(\"Logistic Regression\")\n\n#Applying Decision Tree Model\nfrom sklearn.tree import DecisionTreeClassifier\nclassifier4=DecisionTreeClassifier(random_state=0)\nclassifier4.fit(X_train, y_train)\ny_pred=classifier4.predict(X_test)\nconf_matrix = confusion_matrix(y_test,y_pred)\nprint(conf_matrix)\nprint(f1_score(y_test,y_pred))\n\nscores.append(accuracy_score(y_test, y_pred))\nnames.append('Decision Tree')\n\n#Applying Random Forest Model\nfrom sklearn.ensemble import RandomForestClassifier\nrf=RandomForestClassifier(n_estimators=100, random_state=0)\nrf.fit(X_train, y_train)\ny_pred=rf.predict(X_test)\nconf_matrix = confusion_matrix(y_test,y_pred)\nprint(conf_matrix)\nprint(f1_score(y_test,y_pred))\n\nscores.append(accuracy_score(y_test, y_pred))\nnames.append('Random Forest')\n\nFinal=pd.DataFrame({'Name':names,'Score':scores})\nprint(Final)\n\naxis=sns.barplot(x='Name', y='Score', data=Final)\naxis.set(xlabel='Model',ylabel='Accuracy')\n\nfor p in axis.patches:\n height=p.get_height()\n axis.text(p.get_x()+p.get_width()/2,height+0.005,'{:1.4f}'.format(height), ha=\"center\")\nplt.show()\n\n\n\n\n\n","sub_path":"Machine_Learning/Project/Report_18BCE1010/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":4609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"259690710","text":"from flask import Flask , render_template, request, session\n# the above sessin is the native session that store cookies in client side\n\n# the session below is the flask flask session, extension in flask that store info in the server side\nfrom flask_session import Session\n\n\napp = Flask(__name__)\n\napp.config[\"SESSION_PERMANENT\"] = False\napp.config[\"SESSION_TYPE\"] = \"filesystem\"\nSession (app)\n\n# notes = []\n\n\n@app.route('/', methods=[\"GET\",\"POST\"])\ndef index():\n\t# return \"Hello, World!\n\n\t# headline = \"My page!\"\n\n\t# return render_template('index.html', headline=headline)\n\t# names = ['Alice','Bob', 'Chalie', 'David', 'Hung Om']\n\n\t# return render_template('index.html')\n\t# session[\"notes\"] = []\n\n\tif session.get(\"notes\") is None:\n\t\t# if there is no session stored in the note session the empty list is created to begin.\n\t\tsession[\"notes\"] = []\n\n\tif request.method == \"POST\":\n\t\t# if the session is post(new note is added)\n\t\tnote = request.form.get(\"note\")\n\t\t# this line will get the data(note in the form)\n\t\t# add in the session by appending it in the list\n\t\tsession[\"notes\"].append(note)\n\n\treturn render_template(\"index.html\", notes=session[\"notes\"])\n\n@app.route('/more')\ndef more():\n\treturn render_template('more.html')\n\n\n\n# @app.route('/hello', methods=[\"GET\",\"POST\"])\n# def hello():\n# \tif request.method==\"GET\":\n# \t\treturn \"Please submit the form intead.\"\n# \telse:\n# \t\tname = request.form.get(\"name\")\n# \t\treturn render_template(\"hello.html\", name=name)\n\n\n\n\n# @app.route('/david')\n\n# def david():\n# \treturn \"Hello , David!\"\n\n# @app.route('/maria')\n# def maria():\n# # \treturn \"Hello Maria!\"\n\n# @app.route('/![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n\n# # Data Wrangling - La cirugía de los datos\n\n# In[1]:\n\n\nfrom google.colab import drive\ndrive.mount('/content/drive')\n\n\n# El **data wrangling**, a veces denominada **data munging**, es el proceso de transformar y mapear datos de un dataset *raw* (en bruto) en otro formato con la intención de hacerlo más apropiado y valioso para una variedad de propósitos posteriores, como el análisis. Un **data wrangler** es una persona que realiza estas operaciones de transformación.\n# \n# Esto puede incluir munging, visualización de datos, agregación de datos, entrenamiento de un modelo estadístico, así como muchos otros usos potenciales. La oscilación de datos como proceso generalmente sigue un conjunto de pasos generales que comienzan extrayendo los datos en forma cruda del origen de datos, dividiendo los datos en bruto usando algoritmos (por ejemplo, clasificación) o analizando los datos en estructuras de datos predefinidas, y finalmente depositando el contenido resultante en un sistema de almacenamiento (o silo) para su uso futuro.\n\n# In[ ]:\n\n\nimport pandas as pd\n\n\n# In[ ]:\n\n\ndata = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/customer-churn-model/Customer Churn Model.txt\")\n\n\n# In[5]:\n\n\ndata.head()\n\n\n# ### Crear un subconjunto de datos\n\n# #### Subconjunto de columna o columnas\n\n# In[ ]:\n\n\naccount_length = data[\"Account Length\"]\n\n\n# In[7]:\n\n\naccount_length.head()\n\n\n# In[8]:\n\n\ntype(account_length)\n\n\n# In[ ]:\n\n\nsubset = data[[\"Account Length\", \"Phone\", \"Eve Charge\", \"Day Calls\"]]\n\n\n# In[10]:\n\n\nsubset.head()\n\n\n# In[11]:\n\n\ntype(subset)\n\n\n# In[12]:\n\n\ndesired_columns = [\"Account Length\", \"Phone\", \"Eve Charge\", \"Night Calls\"]\nsubset = data[desired_columns]\nsubset.head()\n\n\n# In[13]:\n\n\ndesired_columns = [\"Account Length\", \"VMail Message\", \"Day Calls\"]\ndesired_columns\n\n\n# In[14]:\n\n\nall_columns_list = data.columns.values.tolist()\nall_columns_list\n\n\n# In[15]:\n\n\nsublist = [x for x in all_columns_list if x not in desired_columns]\nsublist\n\n\n# In[16]:\n\n\nsubset = data[sublist]\nsubset.head()\n\n\n# #### Subconjunto de filas\n\n# In[17]:\n\n\ndata[1:25]\n\n\n# In[18]:\n\n\ndata[10:35]\n\n\n# In[19]:\n\n\ndata[:8] # CORRECCIÓN: es lo mismo que data[0:8]\n\n\n# In[20]:\n\n\ndata[3320:]\n\n\n# #### Subconjuntos de filas con condiciones booleanas\n\n# In[21]:\n\n\n##Usuarios con Day Mins > 300\ndata1 = data[data[\"Day Mins\"]>300]\ndata1.shape\n\n\n# In[22]:\n\n\n##Usuarios de Nueva York (State = \"NY\")\ndata2 = data[data[\"State\"]==\"NY\"]\ndata2.shape\n\n\n# In[23]:\n\n\n##AND -> &\ndata3 = data[(data[\"Day Mins\"]>300) & (data[\"State\"]==\"NY\")]\ndata3.shape\n\n\n# In[24]:\n\n\n##OR -> |\ndata4 = data[(data[\"Day Mins\"]>300) | (data[\"State\"]==\"NY\")]\ndata4.shape\n\n\n# In[25]:\n\n\ndata5 = data[data[\"Day Calls\"]' + html + '')\n return response\n\n@authenticated_user\ndef printDonationReceipt(request, id):\n user = request.user\n dn = Donation.objects.get(uniqueID=id)\n\n context = {\n 'dn': dn,\n 'user':user,\n }\n\n template_path = 'changemaker/donateReceipt.html'\n response = HttpResponse(content_type='application/pdf')\n response['Content-Disposition'] = 'filename=\"Donation-Receipt.pdf'\n template = get_template(template_path)\n html = template.render(context)\n pisa_status = pisa.CreatePDF(\n html, dest=response, link_callback=link_callback)\n if pisa_status.err:\n return HttpResponse('We had some errors
' + html + '')\n return response\n","sub_path":"changemaker/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"500858416","text":"#ashwin\nn,q=map(int,input().split())\nprint(\"Prime numbers between\",n,\"and\",q,\"are:\")\nfor num in range(n,q + 1):\n if num > 1:\n for i in range(2,num):\n if (num % i) == 0:\n break\n else:\n print(num)\n","sub_path":"prime_range.py","file_name":"prime_range.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"232658869","text":"import csv\nimport os\nfrom outputBuilder import OutputBuilder\n\n\ndef main():\n output = OutputBuilder()\n cur_path = os.path.dirname(__file__)\n input_path = os.path.relpath(\"../inputs/input.csv\", cur_path)\n\n try:\n # Assuming \"input.csv\" is simulating a real-time buffer\n stream = open(input_path, \"r\", newline=\"\") \n ireader = csv.reader(stream)\n while True:\n try:\n line = next(ireader)\n output.process_line(line)\n except StopIteration:\n print(\"task completed\")\n break\n except:\n print(\"input file error\")\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"559631004","text":"from pyretic.lib.corelib import *\nfrom pyretic.lib.std import *\nfrom pyretic.lib.query import *\n\nclass VlanFilter(DynamicPolicy):\n def __init__(self, vlans, ports):\n super(VlanFilter, self).__init__()\n self.vlans = vlans\n self.ports = ports\n self.update_filter()\n\n def update_filter(self):\n self.filter_policy = drop\n for key in self.ports.keys():\n switch = int(key.split('-')[0])\n port = int(key.split('-')[1])\n vid = self.ports[key]\n self.filter_policy = self.filter_policy + match(switch=switch, outport=port, vlan_id=vid)\n self.policy = if_(egress_network(), self.filter_policy >> modify(vlan_id=None), identity)\n","sub_path":"controller/app/vlan_filter.py","file_name":"vlan_filter.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"209041445","text":"# William McLaughlin\r\n# Evan LeValley\r\n\r\n\r\nclass Graph:\r\n\r\n def __init__(self, vertices):\r\n self.V = vertices\r\n self.graph = []\r\n\r\n def addEdge(self, u, v, w):\r\n self.graph.append([u, v, w])\r\n\r\n def find(self, parent, i):\r\n if parent[i] == i:\r\n return i\r\n return self.find(parent, parent[i])\r\n\r\n def union(self, parent, rank, x, y):\r\n xroot = self.find(parent, x)\r\n yroot = self.find(parent, y)\r\n\r\n if rank[xroot] < rank[yroot]:\r\n parent[xroot] = yroot\r\n elif rank[xroot] > rank[yroot]:\r\n parent[yroot] = xroot\r\n\r\n else:\r\n parent[yroot] = xroot\r\n rank[xroot] += 1\r\n\r\n def KruskalMST(self):\r\n result = []\r\n i = 0\r\n e = 0\r\n\r\n self.graph = sorted(self.graph, key=lambda item: item[2])\r\n\r\n parent = []\r\n rank = []\r\n\r\n for node in range(self.V):\r\n parent.append(node)\r\n rank.append(0)\r\n\r\n while e < self.V - 1:\r\n\r\n u, v, w = self.graph[i]\r\n i = i + 1\r\n x = self.find(parent, u)\r\n y = self.find(parent, v)\r\n\r\n if x != y:\r\n e = e + 1\r\n result.append([u, v, w])\r\n self.union(parent, rank, x, y)\r\n\r\n edge = []\r\n for u, v, weight in result:\r\n\r\n newList = [u, v,weight]\r\n edge.append(newList)\r\n newList = [v, u, weight]\r\n edge.append(newList)\r\n\r\n return find_eulerian_tour(edge)\r\n\r\n\r\ndef next_node(edge, current):\r\n return edge[0] if current == edge[1] else edge[1]\r\n\r\n\r\ndef remove_edge(raw_list, discard):\r\n return [item for item in raw_list if item != discard]\r\n\r\n\r\ndef find_eulerian_tour(graph):\r\n search = [[[], graph[0][0], graph]]\r\n while search:\r\n path, node, unexplore = search.pop()\r\n path += [node]\r\n\r\n if not unexplore:\r\n return path\r\n\r\n for edge in unexplore:\r\n if node in edge:\r\n search += [[path, next_node(edge, node), remove_edge(unexplore, edge)]]\r\n\r\n\r\ndef trimPath(eucPath):\r\n unique = []\r\n\r\n for i in eucPath:\r\n if i not in unique:\r\n unique.append(i)\r\n\r\n unique.append(unique[0])\r\n return unique\r\n\r\n\r\ndef getWeight(eucPath, matrix):\r\n sum = 0\r\n for i in range(len(eucPath)-2):\r\n j = eucPath[i]\r\n k = eucPath[i+1]\r\n sum += matrix[j][k]\r\n return sum\r\n\r\n\r\ndef main():\r\n matrix = [[]]\r\n count = 0\r\n\r\n with open('in.txt', 'r') as f:\r\n for line in f:\r\n matrix.append([])\r\n for word in line.split():\r\n string = word.strip()\r\n integer = int(string, 10)\r\n matrix[count].append(integer)\r\n count += 1\r\n\r\n g = Graph(len(matrix)-1)\r\n for i in range(len(matrix)-1):\r\n for j in range(len(matrix)-1):\r\n g.addEdge(i, j, matrix[i][j])\r\n\r\n\r\n eucPath = g.KruskalMST()\r\n eucPath = trimPath(eucPath)\r\n weight = getWeight(eucPath, matrix)\r\n print(\"sum: \", weight)\r\n\r\n for u in eucPath:\r\n # if u == 0:\r\n # u = 'Aa'\r\n # if u == 1:\r\n # u = 'Ba'\r\n # if u == 2:\r\n # u = 'Be'\r\n # if u == 3:\r\n # u = 'Du'\r\n # if u == 4:\r\n # u = 'Fr'\r\n # if u == 5:\r\n # u = 'Ha'\r\n # if u == 6:\r\n # u = 'Mu'\r\n # if u == 7:\r\n # u = 'Nu'\r\n # if u == 8:\r\n # u = 'St'\r\n print(u, end=' -- ')\r\n\r\n\r\nmain()\r\n","sub_path":"TSP.py","file_name":"TSP.py","file_ext":"py","file_size_in_byte":3581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"269385199","text":"\"\"\"\nCopyright (c) 2014-2016 Ehsan Iran-Nejad\nPython scripts for Autodesk Revit\n\nThis file is part of pyRevit repository at https://github.com/eirannejad/pyRevit\n\npyRevit is a free set of scripts for Autodesk Revit: you can redistribute it and/or modify\nit under the terms of the GNU General Public License version 3, as published by\nthe Free Software Foundation.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\n\nSee this link for a copy of the GNU General Public License protecting this package.\nhttps://github.com/eirannejad/pyRevit/blob/master/LICENSE\n\"\"\"\n\n__doc__ = 'Merges the selected text note elements into one.'\n\n__window__.Close()\nfrom Autodesk.Revit.DB import Transaction\n\ndoc = __revit__.ActiveUIDocument.Document\nuidoc = __revit__.ActiveUIDocument\n\nt = Transaction(doc, 'Merge Single-Line Text')\nt.Start()\n\nselection = [doc.GetElement(elId) for elId in __revit__.ActiveUIDocument.Selection.GetElementIds()]\ntnotes = sorted(selection, key=lambda txnote: 0 - txnote.Coord.Y)\n\nmtxt = tnotes[0]\nmtxtwidth = mtxt.Width\nfor txt in tnotes[1:]:\n if txt.Text[0] == ' ':\n mtxt.Text = mtxt.Text + txt.Text\n else:\n mtxt.Text = mtxt.Text + ' ' + txt.Text\n doc.Delete(txt.Id)\n\nmtxt.Width = mtxtwidth\nt.Commit()\n","sub_path":"Text_mergeTextNotes.py","file_name":"Text_mergeTextNotes.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"328111104","text":"from copy import deepcopy\nimport re\nimport sys\nfrom django.shortcuts import render\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom django.conf import settings\nfrom django.db.models import Q\nfrom django.http import JsonResponse, HttpResponseNotFound\nfrom django.conf import settings\nfrom django.db import connection\nfrom django.core.urlresolvers import reverse\nfrom data_upload.models import UserUpload, UserUploadedFile\nfrom projects.models import User_Feature_Definitions, User_Feature_Counts, Project\nfrom sharing.service import create_share\nfrom google.appengine.api.mail import send_mail\n\nimport json\nimport requests\n\n@login_required\ndef public_project_list(request):\n return project_list(request, is_public=True)\n\n@login_required\ndef project_list(request, is_public=False):\n template = 'projects/project_list.html'\n\n ownedProjects = request.user.project_set.all().filter(active=True)\n sharedProjects = Project.objects.filter(shared__matched_user=request.user, shared__active=True, active=True)\n\n projects = ownedProjects | sharedProjects\n projects = projects.distinct()\n\n context = {\n 'projects': projects,\n 'public_projects': Project.objects.all().filter(is_public=True,active=True),\n 'is_public': is_public\n }\n return render(request, template, context)\n\n@login_required\ndef project_detail(request, project_id=0):\n # \"\"\" if debug: print >> sys.stderr,'Called '+sys._getframe().f_code.co_name \"\"\"\n template = 'projects/project_detail.html'\n\n ownedProjects = request.user.project_set.all().filter(active=True)\n sharedProjects = Project.objects.filter(shared__matched_user=request.user, shared__active=True, active=True)\n publicProjects = Project.objects.all().filter(is_public=True,active=True)\n\n projects = ownedProjects | sharedProjects | publicProjects\n projects = projects.distinct()\n\n proj = projects.get(id=project_id)\n\n shared = None\n if proj.owner.id != request.user.id and not proj.is_public:\n shared = request.user.shared_resource_set.get(project__id=project_id)\n\n proj.mark_viewed(request)\n context = {\n 'project': proj,\n 'studies': proj.study_set.all().filter(active=True),\n 'shared': shared\n }\n return render(request, template, context)\n\n@login_required\ndef request_project(request):\n send_mail(\n 'request@' + settings.PROJECT_NAME + '.appspotmail.com',\n settings.REQUEST_PROJECT_EMAIL,\n 'User has requested a Google Project',\n '''\nThe user %s has requested a new Google Project be created. Here is their message:\n\n%s\n ''' % (request.user.email, request.POST['message']))\n\n template = 'projects/project_request.html'\n context = {\n 'requested': True\n }\n return render(request, template, context)\n\n@login_required\ndef project_upload(request):\n if not hasattr(request.user, 'googleproject'):\n template = 'projects/project_request.html'\n else:\n template = 'projects/project_upload.html'\n\n projects = request.user.project_set.all().filter(active=True)\n\n context = {\n 'requested': False,\n 'projects': projects,\n }\n return render(request, template, context)\n\ndef filter_column_name(original):\n return re.sub(r\"[^a-zA-Z0-9]+\", \"_\", original.lower())\n\ndef create_metadata_tables(user, study, columns, skipSamples=False):\n with connection.cursor() as cursor:\n cursor.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS user_metadata_%s_%s (\n id INTEGER UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n study_id INTEGER UNSIGNED,\n sample_barcode VARCHAR(200),\n file_path VARCHAR(200),\n file_name VARCHAR(200),\n data_type VARCHAR(200),\n pipeline VARCHAR(200),\n platform VARCHAR(200)\n )\n \"\"\", [user.id, study.id])\n\n if not skipSamples:\n feature_table_sql = \"\"\"\n CREATE TABLE IF NOT EXISTS user_metadata_samples_%s_%s (\n id INTEGER UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n participant_barcode VARCHAR(200),\n sample_barcode VARCHAR(200) UNIQUE,\n has_mrna BOOLEAN,\n has_mirna BOOLEAN,\n has_protein BOOLEAN,\n has_meth BOOLEAN\n \"\"\"\n feature_table_args = [user.id, study.id]\n\n for column in columns:\n feature_table_sql += \", \" + filter_column_name(column['name']) + \" \" + column['type']\n\n feature_table_sql += \")\"\n cursor.execute(feature_table_sql, feature_table_args)\n\n@login_required\ndef upload_files(request):\n status = 'success'\n message = None\n proj = None\n study = None\n\n # TODO: Validation\n\n if request.POST['project-type'] == 'new':\n proj = request.user.project_set.create(name=request.POST['project-name'], description=request.POST['project-description'])\n proj.save()\n else:\n proj = request.user.project_set.all().filter(id=request.POST['project-id'])[0]\n\n if proj is None:\n status = 'error'\n message = 'Unable to create project'\n else:\n study = proj.study_set.create(\n name=request.POST['study-name'],\n description=request.POST['study-description'],\n owner=request.user\n )\n\n if request.POST['data-type'] == 'extend':\n # TODO Does this need a share check??\n study.extends_id = request.POST['extend-study-id']\n\n study.save()\n\n upload = UserUpload(owner=request.user)\n upload.save()\n\n config = {\n \"USER_PROJECT\": proj.id,\n \"USER_ID\": request.user.id,\n \"STUDY\": study.id,\n \"BUCKET\": request.user.bucket_set.all()[0].bucket_name,\n \"GOOGLE_PROJECT\": request.user.googleproject.project_name,\n \"BIGQUERY_DATASET\": request.user.googleproject.big_query_dataset,\n \"FILES\": [],\n \"USER_METADATA_TABLES\": {\n \"METADATA_DATA\" : \"user_metadata_\" + str(request.user.id) + \"_\" + str(study.id),\n \"METADATA_SAMPLES\" : \"user_metadata_samples_\" + str(request.user.id) + \"_\" + str(study.id),\n \"FEATURE_DEFS\": User_Feature_Definitions._meta.db_table\n }\n }\n all_columns = []\n\n for formfield in request.FILES:\n file = request.FILES[formfield]\n file_upload = UserUploadedFile(upload=upload, file=file, bucket=config['BUCKET'])\n try :\n file_upload.save()\n except Exception :\n study.delete()\n proj.delete()\n upload.delete()\n\n resp = {\n 'status': \"error\",\n 'error' : \"bad_file\",\n 'message': \"There is a problem with the format of your file. Check for empty lines at the end of your file\"\n }\n return JsonResponse(resp)\n\n descriptor = json.loads(request.POST[formfield + '_desc'])\n datatype = request.POST[formfield + '_type']\n fileJSON = {\n \"FILENAME\": file_upload.file.name,\n \"PLATFORM\": descriptor['platform'],\n \"PIPELINE\": descriptor['pipeline'],\n \"BIGQUERY_TABLE_NAME\": \"cgc_\" + (\"user\" if datatype == 'user_gen' else datatype) +\n \"_\" + str(proj.id) + \"_\" + str(study.id),\n \"DATATYPE\": datatype,\n \"COLUMNS\": []\n }\n\n if datatype == \"user_gen\":\n for column in descriptor['columns']:\n if column['ignored']:\n continue\n\n type = column['type']\n if type == 'string' or type == 'url' or type == 'file':\n type = 'VARCHAR(200)'\n else:\n type = filter_column_name(type)\n\n controlled = None\n shared_id = None\n if 'controlled' in column and column['controlled'] is not None:\n controlled = column['controlled']['key']\n shared_id = \"CLIN:\" + controlled # All shared IDs at the moment are clinical TCGA\n else:\n controlled = filter_column_name(column['name'])\n\n fileJSON['COLUMNS'].append({\n \"NAME\" : column['name'],\n \"TYPE\" : type,\n \"INDEX\" : column['index'],\n \"MAP_TO\" : controlled,\n \"SHARED_ID\" : shared_id\n })\n\n all_columns.append({\n \"name\": column['name'],\n \"type\": type\n })\n\n config['FILES'].append(fileJSON)\n\n # Skip *_samples table for low level data\n create_metadata_tables(request.user, study, all_columns, request.POST['data-type'] == 'low')\n\n dataset = request.user.user_data_tables_set.create(\n study=study,\n metadata_data_table=config['USER_METADATA_TABLES']['METADATA_DATA'],\n metadata_samples_table=config['USER_METADATA_TABLES']['METADATA_SAMPLES'],\n data_upload=upload,\n google_project=request.user.googleproject,\n google_bucket=request.user.bucket_set.all()[0]\n )\n\n if settings.PROCESSING_ENABLED:\n files = {'config.json': ('config.json', json.dumps(config))}\n post_args = {\n 'project_id':proj.id,\n 'study_id':study.id,\n 'dataset_id':dataset.id\n }\n success_url = reverse('study_data_success', kwargs=post_args) + '?key=' + upload.key\n failure_url = reverse('study_data_error', kwargs=post_args) + '?key=' + upload.key\n parameters = {\n 'SUCCESS_POST_URL': request.build_absolute_uri( success_url ),\n 'FAILURE_POST_URL': request.build_absolute_uri( failure_url )\n }\n r = requests.post(settings.PROCESSING_JENKINS_URL + '/job/' + settings.PROCESSING_JENKINS_PROJECT + '/buildWithParameters',\n files=files, params=parameters,\n auth=(settings.PROCESSING_JENKINS_USER, settings.PROCESSING_JENKINS_PASSWORD))\n\n if r.status_code < 400:\n upload.status = 'Processing'\n upload.jobURL = r.headers['Location']\n else:\n upload.status = 'Error Initializing'\n\n upload.save()\n\n resp = {\n 'status': status,\n 'message': message\n }\n if status is \"success\":\n resp['redirect_url'] = '/projects/' + str(proj.id) + '/'\n\n return JsonResponse(resp)\n\n@login_required\ndef project_delete(request, project_id=0):\n proj = request.user.project_set.get(id=project_id)\n proj.active = False\n proj.save()\n\n return JsonResponse({\n 'status': 'success'\n })\n\n@login_required\ndef project_edit(request, project_id=0):\n name = request.POST['name']\n description = request.POST['description']\n\n if not name:\n raise Exception(\"Projects cannot have an empty name\")\n\n proj = request.user.project_set.get(id=project_id)\n proj.name = name\n proj.description = description\n proj.save()\n\n return JsonResponse({\n 'status': 'success'\n })\n\n@login_required\ndef project_share(request, project_id=0):\n proj = request.user.project_set.get(id=project_id)\n emails = re.split('\\s*,\\s*', request.POST['share_users'].strip())\n\n create_share(request, proj, emails, 'Project')\n\n return JsonResponse({\n 'status': 'success'\n })\n\n@login_required\ndef study_delete(request, project_id=0, study_id=0):\n proj = request.user.project_set.get(id=project_id)\n study = proj.study_set.get(id=study_id)\n study.active = False\n study.save()\n\n return JsonResponse({\n 'status': 'success'\n })\n\n@login_required\ndef study_edit(request, project_id=0, study_id=0):\n name = request.POST['name']\n description = request.POST['description']\n\n if not name:\n raise Exception(\"Projects cannot have an empty name\")\n\n proj = request.user.project_set.get(id=project_id)\n study = proj.study_set.get(id=study_id)\n study.name = name\n study.description = description\n study.save()\n\n return JsonResponse({\n 'status': 'success'\n })\n\ndef study_data_success(request, project_id=0, study_id=0, dataset_id=0):\n proj = Project.objects.get(id=project_id)\n study = proj.study_set.get(id=study_id)\n datatables = study.user_data_tables_set.get(id=dataset_id)\n\n if not datatables.data_upload.key == request.GET.get('key'):\n raise Exception(\"Invalid data key when marking data success\")\n\n ufds = User_Feature_Definitions.objects.filter(study_id=study.id)\n cursor = connection.cursor()\n\n for user_feature in ufds:\n if ' ' in user_feature.feature_name:\n # Molecular data will not be column names but rather names of features\n continue\n col_name = filter_column_name(user_feature.feature_name)\n\n cursor.execute('SELECT COUNT(1) AS \"count\", '+ col_name +' AS \"val\" FROM ' + datatables.metadata_samples_table)\n values = cursor.fetchall()\n\n for value in values:\n ufc = User_Feature_Counts.objects.create(feature=user_feature, value=value[1], count=value[0])\n ufc.save()\n\n cursor.close()\n\n datatables.data_upload.status = 'Complete'\n datatables.data_upload.save()\n\n return JsonResponse({\n 'status': 'success'\n })\n\ndef study_data_error(request, project_id=0, study_id=0, dataset_id=0):\n proj = Project.objects.get(id=project_id)\n study = proj.study_set.get(id=study_id)\n datatables = study.user_data_tables_set.get(id=dataset_id)\n\n if not datatables.data_upload.key == request.GET.get('key'):\n raise Exception(\"Invalid data key when marking data success\")\n\n datatables.data_upload.status = 'Error'\n datatables.data_upload.save()\n\n return JsonResponse({\n 'status': 'success'\n })\n","sub_path":"projects/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":14312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"59545443","text":"#########\n# Copyright (c) 2016 GigaSpaces Technologies Ltd. All rights reserved\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# * See the License for the specific language governing permissions and\n# * limitations under the License.\n\nimport os\n\nfrom mock import patch\nfrom nose.plugins.attrib import attr\n\nfrom manager_rest import models\nfrom manager_rest.utils import read_json_file, write_dict_to_json_file\nfrom manager_rest.utils import plugin_installable_on_current_platform\nfrom manager_rest.test import base_test\n\n\n@attr(client_min_version=1, client_max_version=base_test.LATEST_API_VERSION)\nclass TestUtils(base_test.BaseServerTestCase):\n\n def test_read_write_json_file(self):\n test_dict = {'test': 1, 'dict': 2}\n tmp_file_path = os.path.join(self.tmpdir, 'tmp_dict.json')\n write_dict_to_json_file(tmp_file_path, test_dict)\n read_dict = read_json_file(tmp_file_path)\n self.assertEqual(test_dict, read_dict)\n\n test_dict = {'test': 3, 'new': 2}\n write_dict_to_json_file(tmp_file_path, test_dict)\n read_dict = read_json_file(tmp_file_path)\n self.assertEqual(3, read_dict['test'])\n self.assertEqual(test_dict, read_dict)\n\n @attr(client_min_version=2,\n client_max_version=base_test.LATEST_API_VERSION)\n def test_plugin_installable_on_current_platform(self):\n def create_plugin(supported_platform=None,\n distribution=None,\n distribution_release=None):\n mock_data = {k: 'stub' for k in models.Plugin.fields}\n if supported_platform:\n mock_data['supported_platform'] = supported_platform\n if distribution:\n mock_data['distribution'] = distribution\n if distribution_release:\n mock_data['distribution_release'] = distribution_release\n return models.Plugin(**mock_data)\n\n plugin = create_plugin()\n self.assertFalse(plugin_installable_on_current_platform(plugin))\n\n plugin = create_plugin(supported_platform='any')\n self.assertTrue(plugin_installable_on_current_platform(plugin))\n\n platform = 'platform1'\n dist = 'dist1'\n rel = 'rel1'\n\n def mock_linux_dist(full_distribution_name):\n return dist, '', rel\n\n def mock_get_platform():\n return platform\n\n with patch('platform.linux_distribution', mock_linux_dist):\n with patch('wagon.utils.get_platform', mock_get_platform):\n plugin = create_plugin(supported_platform=platform)\n self.assertFalse(\n plugin_installable_on_current_platform(plugin))\n\n plugin = create_plugin(distribution=dist,\n distribution_release=rel)\n self.assertFalse(\n plugin_installable_on_current_platform(plugin))\n\n plugin = create_plugin(supported_platform=platform,\n distribution=dist,\n distribution_release=rel)\n self.assertTrue(\n plugin_installable_on_current_platform(plugin))\n","sub_path":"rest-service/manager_rest/test/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":3605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"529474381","text":"from math import ceil\nfrom flask import render_template, request\nfrom sqlalchemy import desc\nfrom app import app, db\nfrom app.database import Post, Category, Keyword\nfrom app.helpers import urlize, unurlize\n\n@app.route('/')\ndef index():\n stories = Post.query.order_by(desc(Post.date)).limit(5).all()\n return render_template('index.html', stories=stories)\n\n@app.route('/blog/', defaults={'path': ''})\n@app.route('/blog/
\\n\"\n cur=1\n prev=1\n for tok in lexer:\n line = tok.lineno\n if prev != line :\n cur=1\n code_out += \"\\n\"*(line-prev)\n prev=line\n col = find_column(code, tok)\n bold = bold_token.get(tok.value, \"\")\n if bold != \"\":\n bold = \"; font-weight: bold\"\n keyword_def= color_dict.get(\"KEYWORDS\", \"rgb(170,170,170)\")\n color = color_dict.get(str(tok.type), keyword_def)\n else:\n color = color_dict.get(str(tok.type), \"rgb(170,170,170)\")\n \n span = \"\"\n code_out = code_out + \" \"*(col-cur) + span + tok.value + \"\"\n cur=col + len(str(tok.value))\n code_out = code_out + \"\\n2014-2018
\n\n# In[ ]:\n\n\nyear_list = np.arange(2014, 2018).tolist()\nfor year in year_list:\n #Importing Data\n filename = \"/project/lindner/moving/summer2018/Pollutant_Data/{}_data_ibh.csv\".format(year)\n original_data = pd.read_csv(filename, low_memory=False, nrows=10)\n #Below use when ready \n #original_data = pd.read_csv(filename, low_memory=False)\n try:\n original_data = original_data.drop('Unnamed: 0', axis = 1)\n except:\n pass\n data=pd.DataFrame()\n data['time'] = pd.to_datetime(original_data['epoch'], unit='s').dt.time\n data['date'] = pd.to_datetime(original_data['epoch'], unit='s').dt.date\n data['day'] = pd.to_datetime(original_data['epoch'], unit='s').dt.day\n data['month'] = pd.to_datetime(original_data['epoch'], unit='s').dt.month\n data['year'] = pd.to_datetime(original_data['epoch'], unit='s').dt.year\n data['hour'] = pd.to_datetime(original_data['epoch'], unit='s').dt.hour\n\n #List of epoch times\n epoch_start = original_data['epoch'].unique().min()\n epoch_end = original_data['epoch'].unique().max()\n epoch_list = list(np.arange(epoch_start, epoch_end+300, 300))\n\n #import available sites data\n site_data = pd.read_csv(\"Valid_sites.csv\")\n site_list = list(site_data['O3'].dropna())\n\n try:\n site_data = site_data.drop('Unnamed: 0', axis = 1)\n except:\n pass\n\n #conversion of data\n sample_data = original_data\n data['epoch'] = epoch_list\n data.set_index('epoch', inplace=True)\n data['site'] = original_data['siteID']\n data['o3']=original_data['o3']\n data['temp']=original_data['temp']\n data['wind_x_dir'] = original_data['windspd'] * np.cos(original_data['winddir']*(np.pi/180))\n data['wind_y_dir'] = original_data['windspd'] * np.sin(original_data['winddir']*(np.pi/180))\n data_hour = data.groupby(['year','month','day', 'hour'], as_index=False).mean()\n data_hour = data.groupby(['year','month','day', 'hour'], as_index=False).mean()\n data_hour.to_csv('{}_ozone_hourly_ds3.csv'.format(year))\n\n","sub_path":"python-scripts/Ozone Formatting (Valid Data Conversion).py","file_name":"Ozone Formatting (Valid Data Conversion).py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"598608094","text":"import unittest\nimport unit\n\nclass TestUnitRubyApplication(unit.TestUnitApplicationRuby):\n\n def setUpClass():\n unit.TestUnit().check_modules('ruby')\n\n def test_ruby_application(self):\n self.load('variables')\n\n body = 'Test body string.'\n\n resp = self.post(headers={\n 'Host': 'localhost',\n 'Content-Type': 'text/html',\n 'Custom-Header': 'blah'\n }, body=body)\n\n self.assertEqual(resp['status'], 200, 'status')\n headers = resp['headers']\n header_server = headers.pop('Server')\n self.assertRegex(header_server, r'Unit/[\\d\\.]+', 'server header')\n self.assertEqual(headers.pop('Server-Software'), header_server,\n 'server software header')\n\n date = headers.pop('Date')\n self.assertEqual(date[-4:], ' GMT', 'date header timezone')\n self.assertLess(abs(self.date_to_sec_epoch(date) - self.sec_epoch()), 5,\n 'date header')\n\n self.assertDictEqual(headers, {\n 'Content-Length': str(len(body)),\n 'Content-Type': 'text/html',\n 'Request-Method': 'POST',\n 'Request-Uri': '/',\n 'Http-Host': 'localhost',\n 'Server-Protocol': 'HTTP/1.1',\n 'Custom-Header': 'blah',\n 'Rack-Version': '13',\n 'Rack-Url-Scheme': 'http',\n 'Rack-Multithread': 'false',\n 'Rack-Multiprocess': 'true',\n 'Rack-Run-Once': 'false',\n 'Rack-Hijack-Q': 'false',\n 'Rack-Hijack': '',\n 'Rack-Hijack-IO': ''\n }, 'headers')\n self.assertEqual(resp['body'], body, 'body')\n\n def test_ruby_application_query_string(self):\n self.load('query_string')\n\n resp = self.get(url='/?var1=val1&var2=val2')\n\n self.assertEqual(resp['headers']['Query-String'], 'var1=val1&var2=val2',\n 'Query-String header')\n\n @unittest.expectedFailure\n def test_ruby_application_server_port(self):\n self.load('server_port')\n\n self.assertEqual(self.get()['headers']['Server-Port'], '7080',\n 'Server-Port header')\n\n def test_ruby_application_status_int(self):\n self.load('status_int')\n\n self.assertEqual(self.get()['status'], 200, 'status int')\n\n def test_ruby_application_input_read_empty(self):\n self.load('input_read_empty')\n\n self.assertEqual(self.get()['body'], '', 'read empty')\n\n def test_ruby_application_input_read_parts(self):\n self.load('input_read_parts')\n\n self.assertEqual(self.post(body='0123456789')['body'], '012345678',\n 'input read parts')\n\n def test_ruby_application_input_read_buffer(self):\n self.load('input_read_buffer')\n\n self.assertEqual(self.post(body='0123456789')['body'], '0123456789',\n 'input read buffer')\n\n def test_ruby_application_input_read_buffer_not_empty(self):\n self.load('input_read_buffer_not_empty')\n\n self.assertEqual(self.post(body='0123456789')['body'], '0123456789',\n 'input read buffer not empty')\n\n def test_ruby_application_input_gets(self):\n self.load('input_gets')\n\n body = '0123456789'\n\n self.assertEqual(self.post(body=body)['body'], body, 'input gets')\n\n def test_ruby_application_input_gets_2(self):\n self.load('input_gets')\n\n self.assertEqual(self.post(body='01234\\n56789\\n')['body'], '01234\\n',\n 'input gets 2')\n\n def test_ruby_application_input_gets_all(self):\n self.load('input_gets_all')\n\n body = '\\n01234\\n56789\\n\\n'\n\n self.assertEqual(self.post(body=body)['body'], body, 'input gets all')\n\n def test_ruby_application_input_each(self):\n self.load('input_each')\n\n body = '\\n01234\\n56789\\n\\n'\n\n self.assertEqual(self.post(body=body)['body'], body, 'input each')\n\n @unittest.expectedFailure\n def test_ruby_application_input_rewind(self):\n self.load('input_rewind')\n\n body = '0123456789'\n\n self.assertEqual(self.post(body=body)['body'], body, 'input rewind')\n\n @unittest.expectedFailure\n def test_ruby_application_syntax_error(self):\n self.skip_alerts.extend([\n r'Failed to parse rack script',\n r'syntax error',\n r'new_from_string',\n r'parse_file'\n ])\n self.load('syntax_error')\n\n self.assertEqual(self.get()['status'], 500, 'syntax error')\n\n def test_ruby_application_errors_puts(self):\n self.load('errors_puts')\n\n self.get()\n\n self.stop()\n\n self.assertIsNotNone(\n self.search_in_log(r'\\[error\\].+Error in application'),\n 'errors puts')\n\n def test_ruby_application_errors_puts_int(self):\n self.load('errors_puts_int')\n\n self.get()\n\n self.stop()\n\n self.assertIsNotNone(\n self.search_in_log(r'\\[error\\].+1234567890'),\n 'errors puts int')\n\n def test_ruby_application_errors_write(self):\n self.load('errors_write')\n\n self.get()\n\n self.stop()\n\n self.assertIsNotNone(\n self.search_in_log(r'\\[error\\].+Error in application'),\n 'errors write')\n\n def test_ruby_application_errors_write_to_s_custom(self):\n self.load('errors_write_to_s_custom')\n\n self.assertEqual(self.get()['status'], 200,\n 'errors write to_s custom')\n\n def test_ruby_application_errors_write_int(self):\n self.load('errors_write_int')\n\n self.get()\n\n self.stop()\n\n self.assertIsNotNone(\n self.search_in_log(r'\\[error\\].+1234567890'),\n 'errors write int')\n\n def test_ruby_application_at_exit(self):\n self.skip_alerts.append(r'sendmsg.+failed')\n self.load('at_exit')\n\n self.get()\n\n self.conf({\n \"listeners\": {},\n \"applications\": {}\n })\n\n self.stop()\n\n self.assertIsNotNone(\n self.search_in_log(r'\\[error\\].+At exit called\\.'), 'at exit')\n\n def test_ruby_application_header_custom(self):\n self.load('header_custom')\n\n resp = self.post(body=\"\\ntc=one,two\\ntc=three,four,\\n\\n\")\n\n self.assertEqual(resp['headers']['Custom-Header'],\n ['', 'tc=one,two', 'tc=three,four,', '', ''], 'header custom')\n\n @unittest.expectedFailure\n def test_ruby_application_header_custom_non_printable(self):\n self.load('header_custom')\n\n self.assertEqual(self.post(body='\\b')['status'], 500,\n 'header custom non printable')\n\n def test_ruby_application_header_status(self):\n self.load('header_status')\n\n self.assertEqual(self.get()['status'], 200, 'header status')\n\n @unittest.expectedFailure\n def test_ruby_application_header_rack(self):\n self.load('header_rack')\n\n self.assertEqual(self.get()['status'], 500, 'header rack')\n\n def test_ruby_application_body_empty(self):\n self.load('body_empty')\n\n self.assertEqual(self.get()['body'], '0\\r\\n\\r\\n', 'body empty')\n\n def test_ruby_application_body_array(self):\n self.load('body_array')\n\n self.assertEqual(self.get()['body'], '0123456789', 'body array')\n\n def test_ruby_application_body_large(self):\n self.load('mirror')\n\n body = '0123456789' * 1000\n\n self.assertEqual(self.post(body=body)['body'], body, 'body large')\n\n @unittest.expectedFailure\n def test_ruby_application_body_each_error(self):\n self.load('body_each_error')\n\n self.assertEqual(self.get()['status'], 500, 'body each error status')\n\n self.stop()\n\n self.assertIsNotNone(\n self.search_in_log(r'\\[error\\].+Failed to run ruby script'),\n 'body each error')\n\n def test_ruby_application_body_file(self):\n self.load('body_file')\n\n self.assertEqual(self.get()['body'], 'body\\n', 'body file')\n\n def test_ruby_keepalive_body(self):\n self.load('mirror')\n\n (resp, sock) = self.post(headers={\n 'Connection': 'keep-alive',\n 'Content-Type': 'text/html',\n 'Host': 'localhost'\n }, start=True, body='0123456789' * 500)\n\n self.assertEqual(resp['body'], '0123456789' * 500, 'keep-alive 1')\n\n resp = self.post(headers={\n 'Connection': 'close',\n 'Content-Type': 'text/html',\n 'Host': 'localhost'\n }, sock=sock, body='0123456789')\n\n self.assertEqual(resp['body'], '0123456789', 'keep-alive 2')\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"test/test_ruby_application.py","file_name":"test_ruby_application.py","file_ext":"py","file_size_in_byte":8530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"515866223","text":"import sys\n\nfrom setuptools import setup\nfrom setuptools import find_packages\n\nversion = '0.0.1'\n\ninstall_requires = [\n 'f5-icontrol-rest',\n]\n\ndef read_file(filename, encoding='utf8'):\n \"\"\"Read unicode from given file.\"\"\"\n with codecs.open(filename, encoding=encoding) as fd:\n return fd.read()\n\nhere = os.path.abspath(os.path.dirname(__file__))\nreadme = read_file(os.path.join(here, 'README.rst'))\n\ninstall_requires = [\n 'certbot-bigip',\n 'letsencrypt=={0}'.format(version),\n]\n\nsetup(\n name='certbot-bigip',\n version=version,\n description='F5 BIG-IP plugin for Certbot',\n url='https://github.com/colin-stubbs/certbot-bigip',\n author='Colin Stubbs',\n author_email='cstubbs@gmail.com',\n license='Apache License 2.0',\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Environment :: Plugins',\n 'Intended Audience :: System Administrators',\n 'License :: OSI Approved :: Apache Software License',\n 'Operating System :: POSIX :: Linux',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n 'Topic :: Internet :: WWW/HTTP',\n 'Topic :: Security',\n 'Topic :: System :: Installation/Setup',\n 'Topic :: System :: Networking',\n 'Topic :: System :: Systems Administration',\n 'Topic :: Utilities',\n ],\n\n packages=find_packages(),\n include_package_data=True,\n install_requires=install_requires,\n)\n","sub_path":"letsencrypt-bigip/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"68714311","text":"# CleanDXF v3.0\n\n# RGB to ACI table here: https://gohtx.com/acadcolors.php\n\nfrom collections import namedtuple\nimport ezdxf, sys\n\nfrom ezdxf.layouts.layout import Modelspace\n\nif len(sys.argv) < 2: exit() # Exit when there is no file\n\n\ndef main():\n\n for file in sys.argv[1:]:\n\n doc = ezdxf.readfile(file)\n msp = doc.modelspace()\n\n entities = Entities(msp)\n query = entities.query()\n\n Entities.delete(query.bend)\n Entities.delete(query.dims)\n\n doc.save()\n\n\nclass Entities:\n\n def __init__(self, msp) -> None: \n self.msp = msp\n\n def query(self):\n \n Query = namedtuple(\"Entities\", [\"bend\", \"dims\"]) \n\n # Query all entities on a layer named BEND\n bend_lines = self.msp.query('*[layer==\"BEND\"]')\n\n # Add entities on a layer named BEND_EXTENT\n bend_lines.extend('*[layer==\"BEND_EXTENT\"]')\n\n # Query all pink entities (255,0,255)\n dimensions = self.msp.query('*[color==6]')\n\n return Query(\n bend_lines,\n dimensions,\n )\n\n\n def delete(self, entities):\n\n for entity in entities: self.msp.delete_entity(entity)\n\nif __name__ == '__main__':\n main()","sub_path":"CleanDXF_v3.py","file_name":"CleanDXF_v3.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"440129233","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib\n\n# Look pretty...\n# matplotlib.style.use('ggplot')\nplt.style.use('ggplot')\n\nwheat = pd.read_csv(\"C:/Users/mckinns/Documents/GitHub/DAT210x/Module3/Datasets/wheat.data\", \n index_col=0, header=0)\n\ndf_wh = pd.DataFrame(wheat)\ns1 = df_wh[['perimeter', 'area']]\ns2 = df_wh[['groove','asymmetry' ]]\n\ns1.plot.hist(alpha=0.75)\ns2.plot.hist(alpha=0.75)\n\nplt.show()\n\n","sub_path":"LAB3_assignment1.py","file_name":"LAB3_assignment1.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"601926660","text":"import sqlite3\nimport json\nimport urllib.parse as urlparse\nimport sys\nimport datetime\n\n# you know, I should post this in a gist\n\njsonexpt={\n\t\"bookmarks\":[],\n\t\"folders\":[],\n}\n\nif len(sys.argv) == 2:\n\twith sqlite3.connect(sys.argv[1]) as db:\n\t\tdb.row_factory = sqlite3.Row\n\t\tc = db.cursor()\n\t\tget_parent_c = db.cursor()\n\n\t\tfor row in c.execute('SELECT * from bookmarks'):\n\t\t\t#print(row, type(row))\n\t\t\tif row['is_folder'] == 0:\n\t\t\t\tbkobj={}\n\t\t\t\tbkobj[\"visitedDate\"]=int(row['date'] / 1000)\n\t\t\t\tbkobj[\"creationDate\"]=int(row['created'] / 1000)\n\t\t\t\tbkobj[\"visits\"]=row['visits']\n\t\t\t\tbkobj[\"url\"]=urlparse.quote(row['url'])\n\t\t\t\tbkobj[\"title\"]=urlparse.quote(row['title'])\n\t\t\t\tif row['parent_crc_id'] == 0:\n\t\t\t\t\tbkobj[\"folderId\"]=-1\n\t\t\t\telse:\n\t\t\t\t\t#print(row['parent_crc_id'])\n\t\t\t\t\tget_parent_c.execute('SELECT * from bookmarks WHERE crc_id=?', (row['parent_crc_id'],))\n\t\t\t\t\tprow = get_parent_c.fetchone()\n\t\t\t\t\tbkobj[\"folderId\"]=int(prow['_id'])\n\t\t\t\tjsonexpt[\"bookmarks\"].append(bkobj)\n\n\t\t\telse:\n\t\t\t\tbkobj={}\n\t\t\t\tbkobj[\"title\"]=urlparse.quote(row['title'])\n\t\t\t\tif row['parent_crc_id'] == 0:\n\t\t\t\t\tbkobj[\"parentId\"]=-1\n\t\t\t\telse:\n\t\t\t\t\tget_parent_c.execute('SELECT * from bookmarks WHERE crc_id=?', (row['parent_crc_id'],))\n\t\t\t\t\tprow = get_parent_c.fetchone()\n\t\t\t\t\tbkobj[\"parentId\"]=int(prow['_id'])\n\t\t\t\tbkobj[\"title\"]=urlparse.quote(row['title'])\n\t\t\t\tbkobj[\"id\"]=int(row['_id'])\n\t\t\t\tjsonexpt[\"folders\"].append(bkobj)\n\n\twith open(sys.argv[1].replace(\"db\",\"json\"), 'w', encoding='utf-8') as wf:\n\t\tjson.dump(jsonexpt, wf)\n","sub_path":"converters/nextbrowser2tint.py","file_name":"nextbrowser2tint.py","file_ext":"py","file_size_in_byte":1509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"590079137","text":"\"\"\"\nhttps://leetcode.com/problems/making-a-large-island/\n\n1. Check all island, compute the area and index them.\n2. Try to change 0 to 1 one-by-one, check the neighbor islands, compute the area.\n\nTime complexity: O(n^2)\n\"\"\"\nclass Solution:\n def largestIsland(self, grid) -> int:\n N = len(grid)\n def move(x, y):\n for i, j in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n if 0 <= x + i < N and 0 <= y + j < N:\n yield x + i, y + j\n\n def dfs(x, y, index):\n area = 0\n grid[x][y] = index\n for new_x, new_y in move(x, y):\n if grid[new_x][new_y] == 1:\n area += dfs(new_x, new_y, index)\n return area + 1\n\n index = 2\n area = {}\n for x in range(N):\n for y in range(N):\n if grid[x][y] == 1:\n area[index] = dfs(x, y, index)\n index += 1\n\n res = max(area.values() or [0])\n for x in range(N):\n for y in range(N):\n if grid[x][y] == 0:\n possible_idx = set()\n for new_x, new_y in move(x, y):\n if grid[new_x][new_y] > 1:\n possible_idx.add(grid[new_x][new_y])\n res = max(res, sum(area[idx] for idx in possible_idx) + 1)\n\n return res\n\nsol = Solution()\ngrid = [[1, 0], [0, 1]]\nassert sol.largestIsland(grid) == 3\ngrid = [[1, 1], [1, 0]]\nassert sol.largestIsland(grid) == 4\ngrid = [[1, 1], [1, 1]]\nassert sol.largestIsland(grid) == 4\n","sub_path":"0827_MakingALargeIsland.py","file_name":"0827_MakingALargeIsland.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"540003112","text":"# --- Internal Imports ---\r\nfrom .solver import solveLinearSystem\r\n\r\n# --- Python Imports ---\r\nimport numpy as np\r\nimport scipy.sparse as sparse\r\n\r\n# ---------------------------------------------------------\r\ndef newtonIteration( targetSystem,\r\n initialSolution,\r\n tolerance=1e-5,\r\n maxIterations=10,\r\n solverKWArgs={},\r\n verbose=False,\r\n convergencePlot=None ):\r\n '''\r\n Attempts to find a root of a target function, using 1st order linearization.\r\n The target function must return a 1D numpy array, while the target derivative\r\n must return a matching nonsingular square numpy ndarray. Both the function and\r\n derivative must expect the initial state (1D numpy array) as argument (and no other\r\n positional arguments).\r\n '''\r\n # Initialize\r\n solution = initialSolution.copy()\r\n functionValue, derivative = targetSystem(solution)\r\n iterationCounter = 1\r\n targetNorm = np.linalg.norm(functionValue)\r\n\r\n if verbose:\r\n print( \"Initial target\\t: %.3E\" % targetNorm )\r\n if convergencePlot is not None:\r\n convergencePlot(targetNorm)\r\n\r\n # Loop\r\n while True:\r\n # Check if iteration limit exceeded\r\n if iterationCounter >= maxIterations:\r\n print( \"Newton iteration failed to converge after %i iterations\" % iterationCounter )\r\n raise StopIteration\r\n\r\n # Step\r\n solution -= solveLinearSystem( derivative, functionValue, **solverKWArgs )\r\n functionValue, derivative = targetSystem(solution)\r\n\r\n # Check convergence\r\n targetNorm = np.linalg.norm(functionValue)\r\n if verbose:\r\n print( \"Target norm\\t: %.3E\" % targetNorm )\r\n if convergencePlot is not None:\r\n convergencePlot(targetNorm)\r\n if targetNorm < tolerance:\r\n break\r\n\r\n iterationCounter += 1\r\n \r\n return solution\r\n\r\n\r\n\r\ndef reintegrate( model, \r\n loadFactor, \r\n solution,\r\n stiffness=True,\r\n load=True,\r\n mass=True,\r\n geometricStiffness=True ):\r\n '''\r\n Function that does all the necessary operations for updating \r\n the model to the current load factor and solution\r\n '''\r\n # Set up arguments for stationary or transient models\r\n keywordArgs = { \"stiffness\" : stiffness,\r\n \"load\" : load,\r\n \"geometricStiffness\" : geometricStiffness }\r\n\r\n if hasattr( model, \"mass\" ):\r\n keywordArgs[\"mass\"] = mass\r\n\r\n # Wipe matrices and set solution function\r\n model.resetMatrices( **keywordArgs )\r\n solutionFunction = lambda x: model.sample(solution, x)\r\n\r\n # Compute structural matrices\r\n model.integrate( solutionFunction, \r\n solution, \r\n **keywordArgs )\r\n\r\n # Apply boundaries\r\n for boundary in model.boundaries:\r\n model.applyBoundaryCondition( boundary )\r\n\r\n\r\n\r\ndef stationaryLoadControl( model,\r\n initialSolution,\r\n baseIncrement=0.5,\r\n minIncrement=0.05,\r\n maxIncrement=1.0,\r\n maxIncrements=50,\r\n maxCorrections=10,\r\n tolerance=1e-5,\r\n verbose=True,\r\n axes=None,\r\n convergencePlot=None ):\r\n '''\r\n Solves a nonlinear FEModel using a load controlled predictor and a constant load corrector\r\n '''\r\n # ---------------------------------------------------------\r\n # Initialize arguments\r\n u = initialSolution.copy()\r\n loadFactors = []\r\n\r\n if convergencePlot is not None:\r\n convergencePlot.start()\r\n\r\n # Define inputs for the Newton iteration\r\n def updateSystem(solution, loadFactor):\r\n reintegrate( model,\r\n loadFactor,\r\n solution,\r\n mass=False)\r\n residual = model.stiffness.dot(solution) - loadFactor*model.load\r\n tangentStiffness = model.stiffness + model.geometricStiffness\r\n return residual, tangentStiffness\r\n\r\n # Initialize structural matrices\r\n reintegrate( model,\r\n 0.0,\r\n initialSolution,\r\n mass=False)\r\n \r\n # ---------------------------------------------------------\r\n # Increment loop\r\n increment = maxIncrement\r\n lastControl = 0.0\r\n control = lastControl + increment\r\n incrementIndex = -1\r\n\r\n while control < 1.0:\r\n\r\n # Update control\r\n incrementIndex += 1\r\n control = lastControl + increment\r\n\r\n if control > 1.0:\r\n control = 1.0\r\n\r\n # Print iteration header\r\n if verbose:\r\n print( \"Increment# \" + str(incrementIndex) + \" \" + \"-\"*(35-11-len(str(incrementIndex))-1) )\r\n\r\n # Predict\r\n controlIncrement = control-lastControl\r\n #u += solveLinearSystem( model.stiffness + model.geometricStiffness, controlIncrement * model.load )\r\n uMid = u + 0.5 * solveLinearSystem( model.stiffness + model.geometricStiffness, controlIncrement * model.load )\r\n reintegrate( model, \r\n control, \r\n uMid,\r\n mass=False )\r\n uNew = u + solveLinearSystem( model.stiffness + model.geometricStiffness, controlIncrement * model.load )\r\n\r\n # Correct\r\n try:\r\n uNew = newtonIteration( lambda solution: updateSystem(solution, control),\r\n uNew,\r\n tolerance=tolerance,\r\n maxIterations=maxCorrections,\r\n verbose=True,\r\n convergencePlot=convergencePlot)\r\n except StopIteration:\r\n increment /= 2.0\r\n if increment < minIncrement:\r\n raise RuntimeError( \"Nonlinear iteration failed to converge\" )\r\n continue\r\n\r\n # Update\r\n lastControl = control\r\n u = uNew.copy()\r\n loadFactors.append(control)\r\n\r\n if 2.0*increment < maxIncrement:\r\n increment *= 2.0\r\n\r\n # Plot if requested\r\n if axes is not None:\r\n axes.plot( np.linspace( 0, 1, num=100 ), model.sample( u, np.linspace( 0, 1, num=100 ) ) )\r\n\r\n # ---------------------------------------------------------\r\n # Decorate plot if requested\r\n if axes is not None:\r\n axes.legend( [ \"Control parameter = %.2f\" % l for l in loadFactors ] )\r\n axes.set_xlabel( \"x [m]\" )\r\n axes.set_ylabel( \"T [C]\" )\r\n axes.set_title( \"Temperature Field\" )\r\n\r\n if convergencePlot is not None:\r\n convergencePlot.finish()\r\n \r\n return u\r\n\r\n\r\n\r\n\r\ndef stationaryFixedPointIteration( model,\r\n initialSolution,\r\n loadFactors=None,\r\n maxCorrections=10,\r\n tolerance=1e-5,\r\n verbose=True,\r\n axes=None,\r\n convergencePlot=None ):\r\n '''\r\n Solves a nonlinear FEModel using fixed point iterations\r\n '''\r\n # ---------------------------------------------------------\r\n # Initialize arguments\r\n u = initialSolution\r\n\r\n if loadFactors is None:\r\n loadFactors = np.linspace( 0.0, 1.0, num=5+1 )\r\n\r\n if convergencePlot is not None:\r\n convergencePlot.start()\r\n \r\n # ---------------------------------------------------------\r\n # Increment loop\r\n for incrementIndex, control in enumerate(loadFactors):\r\n\r\n # Print iteration header\r\n if verbose:\r\n print( \"\\nIncrement# \" + str(incrementIndex) + \" \" + \"-\"*(35-11-len(str(incrementIndex))-1) )\r\n\r\n # Check if first run (initialization)\r\n if incrementIndex == 0:\r\n reintegrate( model, \r\n loadFactors[0], \r\n u,\r\n mass=False,\r\n geometricStiffness=False )\r\n continue\r\n\r\n # Correction loop\r\n for correctionIndex in range(maxCorrections):\r\n # Correct\r\n u = solveLinearSystem( model.stiffness, control * model.load )\r\n\r\n # Update residual and check termination criterion\r\n reintegrate( model, \r\n control, \r\n u,\r\n mass=False,\r\n geometricStiffness=False )\r\n residual = model.stiffness.dot(u) - control*model.load\r\n resNorm = np.linalg.norm(residual)\r\n if verbose:\r\n print( \"Current residual\\t: %.3E\" % resNorm )\r\n if convergencePlot is not None:\r\n convergencePlot( resNorm )\r\n if resNorm < tolerance:\r\n break\r\n elif correctionIndex == maxCorrections-1:\r\n print( \"Warning: corrector failed to converge within the specified tolerance\" )\r\n\r\n # Plot if requested\r\n if axes is not None:\r\n axes.plot( np.linspace( 0, 1, num=100 ), model.sample( u, np.linspace( 0, 1, num=100 ) ) )\r\n\r\n # ---------------------------------------------------------\r\n # Decorate plot if requested\r\n if axes is not None:\r\n axes.legend( [ \"Control parameter = %.2f\" % l for l in loadFactors[1:] ] )\r\n axes.set_xlabel( \"x [m]\" )\r\n axes.set_ylabel( \"T [C]\" )\r\n axes.set_title( \"Temperature Field\" )\r\n\r\n if convergencePlot is not None:\r\n convergencePlot.finish()\r\n \r\n return u\r\n\r\n\r\n\r\n\r\ndef transientResidual( model, \r\n solution, \r\n loadFactor, \r\n massInverse,\r\n theta,\r\n dt,\r\n previousState ):\r\n temp = theta*massInverse.dot(model.stiffness.todense()) + 1.0/dt*np.eye(model.size)\r\n return np.ravel( temp.dot(solution) - theta*massInverse.dot(loadFactor*model.load) + previousState )\r\n\r\n\r\n\r\ndef transientResidualDerivative( model, \r\n solution, \r\n loadFactor, \r\n massInverse, \r\n theta, \r\n dt ):\r\n '''\r\n Compute the derivative of the residual with respect to the solution coefficients.\r\n See the readme for a better view: eq.(23)\r\n '''\r\n # Preallocate\r\n temp = np.zeros(massInverse.shape)\r\n MKu = massInverse.dot( model.stiffness.dot(solution) )\r\n Mq = massInverse.dot(model.load)\r\n temperature = lambda x: model.sample(solution, x)\r\n\r\n # Integrate\r\n for e in model.elements:\r\n dCapacityCache = e.integrator.createCache( lambda xi: e.capacityDerivative( temperature(e.toGlobalCoordinates(xi)) ),\r\n e.basisFunctions.domain )\r\n for i in range(len(e.basisFunctions)):\r\n cache0 = dCapacityCache * e._basisCache[i]\r\n for l in range(len(e.basisFunctions)):\r\n cache1 = cache0 * e._basisCache[l]\r\n for j in range(len(e.basisFunctions)):\r\n integral = e._jacobian * e.integrator.integrateCached(\r\n lambda xi: 1.0,\r\n cache1 * e._basisCache[j],\r\n e.basisFunctions.domain\r\n )\r\n temp[e.DoFs[i], e.DoFs[l]] += integral * (loadFactor*Mq[e.DoFs[j]] - MKu[e.DoFs[j]])\r\n \r\n # Compute derivative\r\n return theta * massInverse.dot( \\\r\n model.stiffness.todense() \\\r\n + model.geometricStiffness.todense() \\\r\n + temp ) \\\r\n + np.eye(model.size)/dt\r\n\r\n\r\n\r\ndef transientLoadControl( model,\r\n initialSolution,\r\n initialStiffness,\r\n initialMass,\r\n initialLoad,\r\n dt,\r\n baseIncrement=0.5,\r\n minIncrement=0.05,\r\n maxIncrement=1.0,\r\n maxIncrements=50,\r\n maxCorrections=10,\r\n tolerance=1e-5,\r\n theta=0.5,\r\n verbose=True,\r\n axes=None,\r\n convergencePlot=None ):\r\n '''\r\n Solves a nonlinear transient FEModel using a load controlled predictor and a constant load corrector\r\n '''\r\n # ---------------------------------------------------------\r\n # Initialize arguments\r\n u = initialSolution.copy()\r\n loadFactors = []\r\n\r\n if convergencePlot is not None:\r\n convergencePlot.start()\r\n\r\n DT = 1.0/dt * sparse.eye(model.size)\r\n previousState = (1.0-theta) * sparse.linalg.inv(initialMass).dot( initialLoad - initialStiffness.dot(u) ) \\\r\n + DT.dot(u)\r\n\r\n # Define inputs for the Newton iteration\r\n def updateSystem(solution, loadFactor):\r\n reintegrate( model,\r\n loadFactor,\r\n solution)\r\n\r\n mInv = np.zeros( model.mass.shape, dtype=model.mass.dtype )\r\n sparse.linalg.inv(model.mass).todense( out=mInv )\r\n\r\n residual = transientResidual( model,\r\n solution,\r\n loadFactor,\r\n mInv,\r\n theta,\r\n dt,\r\n -1.0*previousState)\r\n tangentStiffness = transientResidualDerivative( model,\r\n solution,\r\n loadFactor,\r\n mInv,\r\n theta,\r\n dt)\r\n return residual, tangentStiffness\r\n\r\n # Initialize structural matrices\r\n reintegrate( model,\r\n 0.0,\r\n initialSolution)\r\n \r\n # ---------------------------------------------------------\r\n # Increment loop\r\n increment = maxIncrement\r\n lastControl = 0.0\r\n control = lastControl + increment\r\n incrementIndex = -1\r\n\r\n while control < 1.0 or incrementIndex < 0:\r\n\r\n # Update control\r\n incrementIndex += 1\r\n control = lastControl + increment\r\n\r\n if control > 1.0:\r\n control = 1.0\r\n\r\n # Print iteration header\r\n if verbose:\r\n print( \"Increment# \" + str(incrementIndex) + \" \" + \"-\"*(35-11-len(str(incrementIndex))-1) )\r\n\r\n # Predict\r\n controlIncrement = control-lastControl\r\n tangentStiffness = updateSystem( u, control )[1]\r\n uMid = u + 0.5 * solveLinearSystem( tangentStiffness, controlIncrement * model.load, sparse=False )\r\n\r\n tangentStiffness = updateSystem( uMid, control )[1]\r\n uNew = u + solveLinearSystem( tangentStiffness, controlIncrement * model.load, sparse=False )\r\n\r\n # Correct\r\n try:\r\n uNew = newtonIteration( lambda solution: updateSystem(solution, control),\r\n uNew,\r\n tolerance=tolerance,\r\n maxIterations=maxCorrections,\r\n solverKWArgs={\"sparse\" : False},\r\n verbose=verbose,\r\n convergencePlot=convergencePlot)\r\n except StopIteration:\r\n increment /= 2.0\r\n if increment < minIncrement:\r\n raise RuntimeError( \"Nonlinear iteration failed to converge\" )\r\n continue\r\n\r\n # Update\r\n lastControl = control\r\n u = uNew.copy()\r\n loadFactors.append(control)\r\n \r\n if 2.0*increment < maxIncrement:\r\n increment *= 2.0\r\n\r\n # Plot if requested\r\n if axes is not None:\r\n axes.plot( np.linspace( 0, 1, num=100 ), model.sample( u, np.linspace( 0, 1, num=100 ) ) )\r\n\r\n # ---------------------------------------------------------\r\n # Decorate plot if requested\r\n if axes is not None:\r\n axes.legend( [ \"Control parameter = %.2f\" % l for l in loadFactors ] )\r\n axes.set_xlabel( \"x [m]\" )\r\n axes.set_ylabel( \"T [C]\" )\r\n axes.set_title( \"Temperature Field\" )\r\n\r\n if convergencePlot is not None:\r\n convergencePlot.finish()\r\n \r\n return u\r\n\r\n\r\n\r\n\r\ndef transientFixedPointIteration( model,\r\n initialSolution,\r\n initialStiffness,\r\n initialMass,\r\n initialLoad,\r\n dt,\r\n loadFactors=None,\r\n maxCorrections=10,\r\n tolerance=1e-5,\r\n theta=0.5,\r\n verbose=True,\r\n convergencePlot=None ):\r\n '''\r\n Solves a transient nonlinear FEModel using fixed point iterations\r\n ( Arguments are copied internally )\r\n '''\r\n # ---------------------------------------------------------\r\n # Initialize arguments\r\n u = initialSolution.copy()\r\n DT = 1.0/dt * sparse.eye(model.size)\r\n resNorm = None\r\n\r\n if loadFactors is None:\r\n loadFactors = [1.0]\r\n\r\n if convergencePlot is not None:\r\n convergencePlot.start()\r\n\r\n # Initialize system components\r\n initialState = (1.0-theta) * sparse.linalg.inv(initialMass).dot( initialLoad - initialStiffness.dot(u) ) \\\r\n + DT.dot(u)\r\n \r\n # ---------------------------------------------------------\r\n # Increment loop\r\n for incrementIndex, control in enumerate(loadFactors):\r\n\r\n # Print iteration header\r\n if verbose:\r\n print( \"\\nIncrement# \" + str(incrementIndex) + \" \" + \"-\"*(35-11-len(str(incrementIndex))-1) )\r\n\r\n # Correction loop\r\n for correctionIndex in range(maxCorrections):\r\n # Update residual\r\n reintegrate( model, \r\n control, \r\n u,\r\n geometricStiffness=False )\r\n\r\n massInverse = sparse.linalg.inv(model.mass)\r\n currentState = theta * massInverse.dot(model.stiffness) + DT\r\n currentLoad = theta * massInverse.dot(control * model.load) + initialState\r\n \r\n residual = currentState.dot(u) - currentLoad\r\n resNorm = np.linalg.norm(residual)\r\n \r\n # Check termination criterion and update output streams\r\n if verbose:\r\n print( \"Current residual\\t: %.3E\" % resNorm )\r\n if convergencePlot is not None:\r\n convergencePlot( resNorm )\r\n if resNorm < tolerance:\r\n break\r\n\r\n # Correct\r\n u = solveLinearSystem( currentState,\r\n currentLoad )\r\n\r\n # Compute final residual\r\n if resNorm > tolerance:\r\n reintegrate( model, \r\n loadFactors[-1], \r\n u,\r\n geometricStiffness=False )\r\n massInverse = sparse.linalg.inv(model.mass)\r\n currentState = theta * massInverse.dot(model.stiffness) + DT\r\n currentLoad = theta * massInverse.dot(loadFactors[-1] * model.load) + initialState\r\n residual = currentState.dot(u) - currentLoad\r\n resNorm = np.linalg.norm(residual)\r\n\r\n if verbose:\r\n print( \"Final residual\\t\\t: %.3E\" % resNorm )\r\n\r\n if resNorm > tolerance:\r\n print( \"Nonlinear solver failed to correct increment\" )\r\n\r\n # ---------------------------------------------------------\r\n #uDot = (u-initialSolution)/dt\r\n #eq = model.mass.dot(uDot) + model.stiffness.dot(u) - model.load\r\n #print( \"Eq residual\\t\\t: %.3E\" % np.linalg.norm(eq) )\r\n\r\n if resNorm > tolerance:\r\n raise RuntimeError( \"Nonlinear solver failed to converge\" )\r\n\r\n if convergencePlot is not None:\r\n convergencePlot.finish()\r\n \r\n return u","sub_path":"libraries/fem/python/modules/fem/numeric/nonlinearsolver.py","file_name":"nonlinearsolver.py","file_ext":"py","file_size_in_byte":21951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"591650601","text":"# Credit to Colin Skow\n# Adapted code from https://github.com/colinskow/move37/blob/master/ars/ars.py\n\nimport gym\nfrom gym import wrappers\nimport numpy as np\nimport os\n\n\nclass Normalizer():\n \"\"\"Normalize input values.\n Args:\n n_inputs: Number of input values\n \"\"\"\n\n def __init__(self, n_inputs):\n self.n = np.zeros(n_inputs)\n self.mean = np.zeros(n_inputs)\n self.mean_diff = np.zeros(n_inputs)\n self.var = np.zeros(n_inputs)\n\n def observe(self, x):\n \"\"\"\n Update mean and variance values based on observation values.\n\n Args:\n x(Numpy array): Observed value\n \"\"\"\n self.n += 1.0\n last_mean = self.mean.copy()\n self.mean += (x - self.mean) / self.n\n self.mean_diff += (x - last_mean) * (x - self.mean)\n self.var = (self.mean_diff / self.n).clip(min = 1e-2)\n\n def normalize(self, inputs):\n \"\"\"\n Normalize input values.\n\n Args:\n inputs(list of floats): Input values to normalize.\n\n Returns:\n Normalized input values with same shape and `inputs`.\n \"\"\"\n obs_mean = self.mean\n obs_std = np.sqrt(self.var)\n return (inputs - obs_mean) / obs_std\n \n\nclass Policy():\n \"\"\"Policy Agent should follow\n \n Args: \n input_size(int): Number of weights in input layer.\n output_size(int): Number of weights in output layer.\n \"\"\"\n\n def __init__(self, input_size, output_size):\n self.theta = np.zeros((output_size, input_size))\n\n def evaluate(self, input, noise, delta=None, direction=None):\n \"\"\"\n Add noise to weights based on gradient decent direction \n and return new theta values dot product (with input).\n\n Args:\n input(list of floats): Normalized observed input values.\n delta(list of floats): Relative step size (multiplied with noise) during gradient descent\n noise(float): Amount of delta distortion to add to weights.\n \n Returns:\n N-dimensional array of dot product of input and \n new theta values.\n \"\"\"\n\n if direction is None:\n return self.theta.dot(input)\n elif direction == \"+\":\n return (self.theta + noise * delta).dot(input)\n elif direction == \"-\":\n return (self.theta - noise * delta).dot(input)\n \n def sample_deltas(self, n_deltas):\n \"\"\"Sample random delta values from normal distribution for each input weight.\n\n Args:\n n_deltas(int): Number of deltas to sample for each input weight.\n \n Returns:\n List of lists of floats.\n \"\"\"\n\n return [np.random.randn(*self.theta.shape) for _ in range(n_deltas)]\n\n def update(self, rollouts, lr, sigma_rewards):\n \"\"\"\n Update theta values / weights based on learning rate and step size.\n\n Args:\n lr(float): Learning Rate\n rollouts(list of tuples): Positive rewards, Negative rewards, Delta values.\n sigma_rewards(float): Standard deviation of rewards\n \"\"\"\n \n # step for every theta (direction)\n step = np.zeros(self.theta.shape)\n n_best_deltas = len(rollouts)\n for r_pos, r_neg, delta in rollouts:\n step += (r_pos - r_neg) * delta\n self.theta += lr / (n_best_deltas * sigma_rewards) * step\n\nclass Model():\n \"\"\"Training Model for agent.\"\"\"\n def __init__(self):\n pass\n\n def make_env(self, env, video_freq, monitor_dir):\n \"\"\"Set Gym Environment\n \n Args:\n env(str): OpenAI Gym environment name.\n video_freq(int): How often (step frequency) to record video.\n monitor_dir(str): Directory to write log and video files to.\n \"\"\"\n\n self.env = gym.make(env)\n self.video_freq = video_freq\n should_record = lambda i: self.record_video\n self.record_video = False\n\n self.env = wrappers.Monitor(self.env, monitor_dir, \n video_callable=should_record, force=True)\n\n def set_normalizer(self, normalizer):\n \"\"\"Set Normalizer class to use for input normalizing.\"\"\"\n self.normalizer = normalizer\n\n # Explore the policy on one specific direction and over one episode\n def explore(self, noise, direction=None, delta=None):\n \"\"\"\n Run episode as exploration.\n\n Args:\n noise(float): Amount of delta distortion to add to weights.\n direction(str in ('-', '+') or None): Direction of next exploration.\n delta(list of floats): Relative step size (multiplied with noise) during gradient descent.\n\n Returns:\n Sum of rewards collected during episode.\n \"\"\"\n state = self.env.reset()\n done = False\n num_plays = 0\n sum_rewards = 0.0\n while not done and num_plays < self.episode_length:\n self.normalizer.observe(state)\n state = self.normalizer.normalize(state)\n action = self.policy.evaluate(state, noise, delta, direction)\n state, reward, done, _ = self.env.step(action)\n reward = max(min(reward, 1), -1)\n sum_rewards += reward\n num_plays += 1\n return sum_rewards\n\n def train(self, n_steps, n_deltas, n_best_deltas, noise, episode_length, lr):\n \"\"\"\n Train agent.\n\n Args:\n n_steps(int): Number of episodes.\n n_deltas(int): Number of deltas to use per episode.\n n_best_deltas(int): Number of best deltas to use for policy update.\n noise(float): Amount of delta distortion to add to weights.\n episode_length(int): Maximum number of actions to perform during an episode.\n lr(float): Learning rate of model.\n \"\"\"\n self.episode_length = episode_length\n self.noise = noise\n for step in range(n_steps):\n # initialize the random noise deltas and the positive/negative rewards\n deltas = self.policy.sample_deltas(n_deltas=n_deltas)\n positive_rewards = [0] * n_deltas\n negative_rewards = [0] * n_deltas\n\n # play an episode each with positive deltas and negative deltas, collect rewards\n for k in range(n_deltas):\n positive_rewards[k] = self.explore(noise, direction=\"+\", delta=deltas[k])\n negative_rewards[k] = self.explore(noise, direction=\"-\", delta=deltas[k])\n \n # Compute the standard deviation of all rewards\n sigma_rewards = np.array(positive_rewards + negative_rewards).std()\n\n # Sort the rollouts by the max(r_pos, r_neg) and select the deltas with best rewards\n scores = {k:max(r_pos, r_neg) for k,(r_pos,r_neg) in enumerate(zip(positive_rewards, negative_rewards))}\n order = sorted(scores.keys(), key = lambda x:scores[x], reverse = True)[:n_best_deltas]\n rollouts = [(positive_rewards[k], negative_rewards[k], deltas[k]) for k in order]\n\n # Update the policy\n self.policy.update(rollouts, lr, sigma_rewards)\n\n # Only record video during evaluation, every n steps\n if step % self.video_freq == 0:\n self.record_video = True\n # Play an episode with the new weights and print the score\n reward_evaluation = self.explore(noise)\n print('Step: ', step, 'Reward: ', reward_evaluation)\n self.record_video = False\n\nif __name__ == '__main__':\n # openAI gym environment\n ENV_NAME = 'BipedalWalker-v2'\n \n # video directories\n video_dir = '../additional/videos'\n monitor_dir = os.path.join(video_dir, ENV_NAME)\n\n # create video directories\n if not os.path.exists(video_dir):\n os.makedirs(video_dir)\n if not os.path.exists(monitor_dir):\n os.makedirs(monitor_dir)\n\n # hyperparameters for training\n hyperparams = {\n 'n_steps': 1000, \n 'episode_length': 2000, \n 'lr': 0.01, \n 'n_deltas': 16, \n 'n_best_deltas': 16, \n 'noise': 0.03, \n 'video_freq': 50\n }\n\n # init model\n model = Model()\n\n # set environment and record params\n model.make_env(ENV_NAME, \n video_freq=hyperparams['video_freq'], \n monitor_dir=monitor_dir)\n \n # define input and output layer sizes\n input_size = model.env.observation_space.shape[0]\n output_size=model.env.action_space.shape[0]\n\n # initialize policy to use for training\n model.policy = Policy(input_size=input_size, output_size=output_size)\n\n # specify normalizing method to use\n model.set_normalizer(Normalizer(n_inputs=input_size))\n\n # train model\n model.train(n_steps=hyperparams['n_steps'], \n n_deltas=hyperparams['n_deltas'], \n n_best_deltas=hyperparams['n_best_deltas'], \n noise=hyperparams['noise'], \n episode_length=hyperparams['episode_length'], \n lr=hyperparams['lr'])\n\n ","sub_path":"src/bipedal.py","file_name":"bipedal.py","file_ext":"py","file_size_in_byte":9014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"107283005","text":"import os\nimport sys\nimport argparse\nfrom egcg_core.app_logging import logging_default as log_cfg\n\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfrom config import load_config\nfrom upload_to_gel.deliver_data_to_gel import GelDataDelivery, check_all_deliveries, report_all\n\n\ndef main():\n p = argparse.ArgumentParser()\n p.add_argument('--dry_run', action='store_true')\n p.add_argument('--debug', action='store_true')\n p.add_argument('--work_dir', type=str)\n group = p.add_mutually_exclusive_group()\n group.add_argument('--sample_id', type=str)\n group.add_argument('--user_sample_id', type=str)\n p.add_argument('--force_new_delivery', action='store_true')\n p.add_argument('--no_cleanup', action='store_true')\n p.add_argument('--check_all_deliveries', action='store_true')\n p.add_argument('--report', action='store_true')\n\n args = p.parse_args()\n load_config()\n log_cfg.add_stdout_handler()\n\n if args.debug:\n log_cfg.set_log_level(10)\n\n if args.check_all_deliveries:\n check_all_deliveries()\n elif args.report:\n report_all()\n else:\n gel_delivery = GelDataDelivery(args.sample_id, user_sample_id=args.user_sample_id, work_dir=args.work_dir,\n dry_run=args.dry_run, no_cleanup=args.no_cleanup,\n force_new_delivery=args.force_new_delivery)\n gel_delivery.deliver_data()\n\n\nif __name__ == '__main__':\n sys.exit(main())\n","sub_path":"bin/deliver_data_to_gel.py","file_name":"deliver_data_to_gel.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"422222193","text":"import requests,bs4,os\n\nurl = 'http://docs.python-requests.org/zh_CN/latest/index.html#'\nos.makedirs('xkcd',exist_ok=True)\nwhile not url.endswith('#'):\n print('Downloading page %s...'%url)\n res =requests.get(url)\n res.raise_for_status()\n soup =bs4.BeautifulSoup(res.text)\n comicElem =soup.select('img [class = \"logo\"]')\n if comicElem ==[]:\n print('Could you find comic image...')\n else:\n comicURL = 'http:'+comicElem[0].get('src')\n print('Downloading image %s...'%(comicURL))\n res =requests.get(comicURL)\n res.raise_for_status()\n imageFile =open(os.path.join('xkcd',os.path.basename(comicURL)),'wb')\n for chunk in res.iter_content(100000):\n imageFile.write(chunk)\n imageFile.close()\n preLink =soup.select('a[rel=\"prev\"]')[0]\n url ='http://www.xkcd.com'+preLink.get('href')\n\nprint('Done.')\n\n","sub_path":"getcomdic.py","file_name":"getcomdic.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"64"} +{"seq_id":"638711470","text":"import cv2\nimport sys\n\nif len(sys.argv) != 2:\n sys.exit(\"usage: python2 SIFT.py![\"你与星河,皆可收藏-唯美女生\"](\"//t.cdn.ink/image/2020/01/2020020916481695-scaled.jpeg\")
\n\n# urls=re.findall('',html)\nurls = re.findall('